From 431a5218a06e8cf3e2f8f6ef58053575a8dbb0fc Mon Sep 17 00:00:00 2001 From: Sattar Gyulmamedov Date: Thu, 10 Apr 2025 12:44:28 +0300 Subject: [PATCH 01/22] init docs transform to md --- .gitignore | 3 + mddocs/README.md | 657 ++++++++++++++++ mddocs/_sphinx_design_static/design-tabs.js | 101 +++ .../sphinx-design.min.css | 1 + mddocs/_static/autodoc_pydantic.css | 11 + mddocs/_static/icon.svg | 11 + mddocs/_static/logo.svg | 214 +++++ mddocs/_static/logo_wide.svg | 329 ++++++++ mddocs/changelog.md | 31 + mddocs/changelog/0.10.0.md | 359 +++++++++ mddocs/changelog/0.10.1.md | 25 + mddocs/changelog/0.10.2.md | 34 + mddocs/changelog/0.11.0.md | 202 +++++ mddocs/changelog/0.11.1.md | 9 + mddocs/changelog/0.11.2.md | 5 + mddocs/changelog/0.12.0.md | 48 ++ mddocs/changelog/0.12.1.md | 17 + mddocs/changelog/0.12.2.md | 18 + mddocs/changelog/0.12.3.md | 5 + mddocs/changelog/0.12.4.md | 5 + mddocs/changelog/0.12.5.md | 13 + mddocs/changelog/0.13.0.md | 197 +++++ mddocs/changelog/0.13.1.md | 9 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mddocs/troubleshooting/spark.md diff --git a/.gitignore b/.gitignore index 31223eb62..ea2d3b6d9 100644 --- a/.gitignore +++ b/.gitignore @@ -78,6 +78,9 @@ docs/_build/ docs/build docs/*.tar.gz +# Sphinx mddocs +mddocs/_build/ + # PyBuilder target/ diff --git a/mddocs/README.md b/mddocs/README.md new file mode 100644 index 000000000..ffb6fc0c5 --- /dev/null +++ b/mddocs/README.md @@ -0,0 +1,657 @@ +# onETL + +[![Repo status - Active](https://www.repostatus.org/badges/latest/active.svg)](https://github.com/MobileTeleSystems/onetl) [![PyPI - Latest Release](https://img.shields.io/pypi/v/onetl)](https://pypi.org/project/onetl/) [![PyPI - License](https://img.shields.io/pypi/l/onetl.svg)](https://github.com/MobileTeleSystems/onetl/blob/develop/LICENSE.txt) [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/onetl.svg)](https://pypi.org/project/onetl/) [![PyPI - Downloads](https://img.shields.io/pypi/dm/onetl)](https://pypi.org/project/onetl/) +[![Documentation - ReadTheDocs](https://readthedocs.org/projects/onetl/badge/?version=stable)](https://onetl.readthedocs.io/) [![Github Actions - latest CI build status](https://github.com/MobileTeleSystems/onetl/workflows/Tests/badge.svg)](https://github.com/MobileTeleSystems/onetl/actions) [![Test coverage - percent](https://codecov.io/gh/MobileTeleSystems/onetl/branch/develop/graph/badge.svg?token=RIO8URKNZJ)](https://codecov.io/gh/MobileTeleSystems/onetl) [![pre-commit.ci - status](https://results.pre-commit.ci/badge/github/MobileTeleSystems/onetl/develop.svg)](https://results.pre-commit.ci/latest/github/MobileTeleSystems/onetl/develop) + + + +![onETL logo](_static/logo_wide.svg) + + + +## What is onETL? + +Python ETL/ELT library powered by [Apache Spark](https://spark.apache.org/) & other open-source tools. + +## Goals + +* Provide unified classes to extract data from (**E**) & load data to (**L**) various stores. +* Provides [Spark DataFrame API](https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.DataFrame.html) for performing transformations (**T**) in terms of *ETL*. +* Provide direct assess to database, allowing to execute SQL queries, as well as DDL, DML, and call functions/procedures. This can be used for building up *ELT* pipelines. +* Support different [read strategies](https://onetl.readthedocs.io/en/stable/strategy/index.html) for incremental and batch data fetching. +* Provide [hooks](https://onetl.readthedocs.io/en/stable/hooks/index.html) & [plugins](https://onetl.readthedocs.io/en/stable/plugins.html) mechanism for altering behavior of internal classes. + +## Non-goals + +* onETL is not a Spark replacement. It just provides additional functionality that Spark does not have, and improves UX for end users. +* onETL is not a framework, as it does not have requirements to project structure, naming, the way of running ETL/ELT processes, configuration, etc. All of that should be implemented in some other tool. +* onETL is deliberately developed without any integration with scheduling software like Apache Airflow. All integrations should be implemented as separated tools. +* Only batch operations, no streaming. For streaming prefer [Apache Flink](https://flink.apache.org/). + +## Requirements + +* **Python 3.7 - 3.13** +* PySpark 2.3.x - 3.5.x (depends on used connector) +* Java 8+ (required by Spark, see below) +* Kerberos libs & GCC (required by `Hive`, `HDFS` and `SparkHDFS` connectors) + +## Supported storages + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TypeStoragePowered by
DatabaseClickhouseApache Spark JDBC Data Source
MSSQL
MySQL
Postgres
Oracle
Teradata
HiveApache Spark Hive integration
KafkaApache Spark Kafka integration
GreenplumVMware Greenplum Spark connector
MongoDBMongoDB Spark connector
FileHDFSHDFS Python client
S3minio-py client
SFTPParamiko library
FTPFTPUtil library
FTPS
WebDAVWebdavClient3 library
Sambapysmb library
Files as DataFrameSparkLocalFSApache Spark File Data Source
SparkHDFS
SparkS3Hadoop AWS library
+ + + + +## Documentation + +See [https://onetl.readthedocs.io/](https://onetl.readthedocs.io/) + +## How to install + + + +### Minimal installation + + + +Base `onetl` package contains: + +* `DBReader`, `DBWriter` and related classes +* `FileDownloader`, `FileUploader`, `FileMover` and related classes, like file filters & limits +* `FileDFReader`, `FileDFWriter` and related classes, like file formats +* Read Strategies & HWM classes +* Plugins support + +It can be installed via: + +```bash +pip install onetl +``` + +```{admonition} WARNING +:class: warning + +This method does NOT include any connections. + +This method is recommended for use in third-party libraries which require for `onetl` to be installed, but do not use its connection classes. +``` + +### With DB and FileDF connections + + + +All DB connection classes (`Clickhouse`, `Greenplum`, `Hive` and others) +and all FileDF connection classes (`SparkHDFS`, `SparkLocalFS`, `SparkS3`) +require Spark to be installed. + + + +Firstly, you should install JDK. The exact installation instruction depends on your OS, here are some examples: + +```bash +yum install java-1.8.0-openjdk-devel # CentOS 7 + Spark 2 +dnf install java-11-openjdk-devel # CentOS 8 + Spark 3 +apt-get install openjdk-11-jdk # Debian-based + Spark 3 +``` + + + +#### Compatibility matrix + +| Spark | Python | Java | Scala | +|-----------------------------------------------------------|------------|------------|---------| +| [2.3.x](https://spark.apache.org/docs/2.3.1/#downloading) | 3.7 only | 8 only | 2.11 | +| [2.4.x](https://spark.apache.org/docs/2.4.8/#downloading) | 3.7 only | 8 only | 2.11 | +| [3.2.x](https://spark.apache.org/docs/3.2.4/#downloading) | 3.7 - 3.10 | 8u201 - 11 | 2.12 | +| [3.3.x](https://spark.apache.org/docs/3.3.4/#downloading) | 3.7 - 3.12 | 8u201 - 17 | 2.12 | +| [3.4.x](https://spark.apache.org/docs/3.4.4/#downloading) | 3.7 - 3.12 | 8u362 - 20 | 2.12 | +| [3.5.x](https://spark.apache.org/docs/3.5.5/#downloading) | 3.8 - 3.13 | 8u371 - 20 | 2.12 | + + + +Then you should install PySpark via passing `spark` to `extras`: + +```bash +pip install onetl[spark] # install latest PySpark +``` + +or install PySpark explicitly: + +```bash +pip install onetl pyspark==3.5.5 # install a specific PySpark version +``` + +or inject PySpark to `sys.path` in some other way BEFORE creating a class instance. +**Otherwise connection object cannot be created.** + +### With File connections + + + +All File (but not *FileDF*) connection classes (`FTP`, `SFTP`, `HDFS` and so on) requires specific Python clients to be installed. + +Each client can be installed explicitly by passing connector name (in lowercase) to `extras`: + +```bash +pip install onetl[ftp] # specific connector +pip install onetl[ftp,ftps,sftp,hdfs,s3,webdav,samba] # multiple connectors +``` + +To install all file connectors at once you can pass `files` to `extras`: + +```bash +pip install onetl[files] +``` + +**Otherwise class import will fail.** + +### With Kerberos support + + + +Most of Hadoop instances set up with Kerberos support, +so some connections require additional setup to work properly. + +* `HDFS` + Uses [requests-kerberos](https://pypi.org/project/requests-kerberos/) and + [GSSApi](https://pypi.org/project/gssapi/) for authentication. + It also uses `kinit` executable to generate Kerberos ticket. +* `Hive` and `SparkHDFS` + require Kerberos ticket to exist before creating Spark session. + +So you need to install OS packages with: + +* `krb5` libs +* Headers for `krb5` +* `gcc` or other compiler for C sources + +The exact installation instruction depends on your OS, here are some examples: + +```bash +apt install libkrb5-dev krb5-user gcc # Debian-based +dnf install krb5-devel krb5-libs krb5-workstation gcc # CentOS, OracleLinux +``` + +Also you should pass `kerberos` to `extras` to install required Python packages: + +```bash +pip install onetl[kerberos] +``` + +### Full bundle + + + +To install all connectors and dependencies, you can pass `all` into `extras`: + +```bash +pip install onetl[all] + +# this is just the same as +pip install onetl[spark,files,kerberos] +``` + + +```{admonition} WARNING +:class: warning + +This method consumes a lot of disk space, and requires for Java & Kerberos libraries to be installed into your OS. +``` + + + +## Quick start + +### MSSQL → Hive + +Read data from MSSQL, transform & write to Hive. + +```bash +# install onETL and PySpark +pip install onetl[spark] +``` + +```python +# Import pyspark to initialize the SparkSession +from pyspark.sql import SparkSession + +# import function to setup onETL logging +from onetl.log import setup_logging + +# Import required connections +from onetl.connection import MSSQL, Hive + +# Import onETL classes to read & write data +from onetl.db import DBReader, DBWriter + +# change logging level to INFO, and set up default logging format and handler +setup_logging() + +# Initialize new SparkSession with MSSQL driver loaded +maven_packages = MSSQL.get_packages() +spark = ( + SparkSession.builder.appName("spark_app_onetl_demo") + .config("spark.jars.packages", ",".join(maven_packages)) + .enableHiveSupport() # for Hive + .getOrCreate() +) + +# Initialize MSSQL connection and check if database is accessible +mssql = MSSQL( + host="mssqldb.demo.com", + user="onetl", + password="onetl", + database="Telecom", + spark=spark, + # These options are passed to MSSQL JDBC Driver: + extra={"applicationIntent": "ReadOnly"}, +).check() + +# >>> INFO:|MSSQL| Connection is available + +# Initialize DBReader +reader = DBReader( + connection=mssql, + source="dbo.demo_table", + columns=["on", "etl"], + # Set some MSSQL read options: + options=MSSQL.ReadOptions(fetchsize=10000), +) + +# checks that there is data in the table, otherwise raises exception +reader.raise_if_no_data() + +# Read data to DataFrame +df = reader.run() +df.printSchema() +# root +# |-- id: integer (nullable = true) +# |-- phone_number: string (nullable = true) +# |-- region: string (nullable = true) +# |-- birth_date: date (nullable = true) +# |-- registered_at: timestamp (nullable = true) +# |-- account_balance: double (nullable = true) + +# Apply any PySpark transformations +from pyspark.sql.functions import lit + +df_to_write = df.withColumn("engine", lit("onetl")) +df_to_write.printSchema() +# root +# |-- id: integer (nullable = true) +# |-- phone_number: string (nullable = true) +# |-- region: string (nullable = true) +# |-- birth_date: date (nullable = true) +# |-- registered_at: timestamp (nullable = true) +# |-- account_balance: double (nullable = true) +# |-- engine: string (nullable = false) + +# Initialize Hive connection +hive = Hive(cluster="rnd-dwh", spark=spark) + +# Initialize DBWriter +db_writer = DBWriter( + connection=hive, + target="dl_sb.demo_table", + # Set some Hive write options: + options=Hive.WriteOptions(if_exists="replace_entire_table"), +) + +# Write data from DataFrame to Hive +db_writer.run(df_to_write) + +# Success! +``` + +### SFTP → HDFS + +Download files from SFTP & upload them to HDFS. + +```bash +# install onETL with SFTP and HDFS clients, and Kerberos support +pip install onetl[hdfs,sftp,kerberos] +``` + +```python +# import function to setup onETL logging +from onetl.log import setup_logging + +# Import required connections +from onetl.connection import SFTP, HDFS + +# Import onETL classes to download & upload files +from onetl.file import FileDownloader, FileUploader + +# import filter & limit classes +from onetl.file.filter import Glob, ExcludeDir +from onetl.file.limit import MaxFilesCount + +# change logging level to INFO, and set up default logging format and handler +setup_logging() + +# Initialize SFTP connection and check it +sftp = SFTP( + host="sftp.test.com", + user="someuser", + password="somepassword", +).check() + +# >>> INFO:|SFTP| Connection is available + +# Initialize downloader +file_downloader = FileDownloader( + connection=sftp, + source_path="/remote/tests/Report", # path on SFTP + local_path="/local/onetl/Report", # local fs path + filters=[ + # download only files matching the glob + Glob("*.csv"), + # exclude files from this directory + ExcludeDir("/remote/tests/Report/exclude_dir/"), + ], + limits=[ + # download max 1000 files per run + MaxFilesCount(1000), + ], + options=FileDownloader.Options( + # delete files from SFTP after successful download + delete_source=True, + # mark file as failed if it already exist in local_path + if_exists="error", + ), +) + +# Download files to local filesystem +download_result = downloader.run() + +# Method run returns a DownloadResult object, +# which contains collection of downloaded files, divided to 4 categories +download_result + +# DownloadResult( +# successful=[ +# LocalPath('/local/onetl/Report/file_1.json'), +# LocalPath('/local/onetl/Report/file_2.json'), +# ], +# failed=[FailedRemoteFile('/remote/onetl/Report/file_3.json')], +# ignored=[RemoteFile('/remote/onetl/Report/file_4.json')], +# missing=[], +# ) + +# Raise exception if there are failed files, or there were no files in the remote filesystem +download_result.raise_if_failed() or download_result.raise_if_empty() + +# Do any kind of magic with files: rename files, remove header for csv files, ... +renamed_files = my_rename_function(download_result.success) + +# function removed "_" from file names +# [ +# LocalPath('/home/onetl/Report/file1.json'), +# LocalPath('/home/onetl/Report/file2.json'), +# ] + +# Initialize HDFS connection +hdfs = HDFS( + host="my.name.node", + user="someuser", + password="somepassword", # or keytab +) + +# Initialize uploader +file_uploader = FileUploader( + connection=hdfs, + target_path="/user/onetl/Report/", # hdfs path +) + +# Upload files from local fs to HDFS +upload_result = file_uploader.run(renamed_files) + +# Method run returns a UploadResult object, +# which contains collection of uploaded files, divided to 4 categories +upload_result + +# UploadResult( +# successful=[RemoteFile('/user/onetl/Report/file1.json')], +# failed=[FailedLocalFile('/local/onetl/Report/file2.json')], +# ignored=[], +# missing=[], +# ) + +# Raise exception if there are failed files, or there were no files in the local filesystem, or some input file is missing +upload_result.raise_if_failed() or upload_result.raise_if_empty() or upload_result.raise_if_missing() + +# Success! +``` + +### S3 → Postgres + +Read files directly from S3 path, convert them to dataframe, transform it and then write to a database. + +```bash +# install onETL and PySpark +pip install onetl[spark] +``` + +```python +# Import pyspark to initialize the SparkSession +from pyspark.sql import SparkSession + +# import function to setup onETL logging +from onetl.log import setup_logging + +# Import required connections +from onetl.connection import Postgres, SparkS3 + +# Import onETL classes to read files +from onetl.file import FileDFReader +from onetl.file.format import CSV + +# Import onETL classes to write data +from onetl.db import DBWriter + +# change logging level to INFO, and set up default logging format and handler +setup_logging() + +# Initialize new SparkSession with Hadoop AWS libraries and Postgres driver loaded +maven_packages = SparkS3.get_packages(spark_version="3.5.5") + Postgres.get_packages() +exclude_packages = SparkS3.get_exclude_packages() +spark = ( + SparkSession.builder.appName("spark_app_onetl_demo") + .config("spark.jars.packages", ",".join(maven_packages)) + .config("spark.jars.excludes", ",".join(exclude_packages)) + .getOrCreate() +) + +# Initialize S3 connection and check it +spark_s3 = SparkS3( + host="s3.test.com", + protocol="https", + bucket="my-bucket", + access_key="somekey", + secret_key="somesecret", + # Access bucket as s3.test.com/my-bucket + extra={"path.style.access": True}, + spark=spark, +).check() + +# >>> INFO:|SparkS3| Connection is available + +# Describe file format and parsing options +csv = CSV( + delimiter=";", + header=True, + encoding="utf-8", +) + +# Describe DataFrame schema of files +from pyspark.sql.types import ( + DateType, + DoubleType, + IntegerType, + StringType, + StructField, + StructType, + TimestampType, +) + +df_schema = StructType( + [ + StructField("id", IntegerType()), + StructField("phone_number", StringType()), + StructField("region", StringType()), + StructField("birth_date", DateType()), + StructField("registered_at", TimestampType()), + StructField("account_balance", DoubleType()), + ], +) + +# Initialize file df reader +reader = FileDFReader( + connection=spark_s3, + source_path="/remote/tests/Report", # path on S3 there *.csv files are located + format=csv, # file format with specific parsing options + df_schema=df_schema, # columns & types +) + +# Read files directly from S3 as Spark DataFrame +df = reader.run() + +# Check that DataFrame schema is same as expected +df.printSchema() +# root +# |-- id: integer (nullable = true) +# |-- phone_number: string (nullable = true) +# |-- region: string (nullable = true) +# |-- birth_date: date (nullable = true) +# |-- registered_at: timestamp (nullable = true) +# |-- account_balance: double (nullable = true) + +# Apply any PySpark transformations +from pyspark.sql.functions import lit + +df_to_write = df.withColumn("engine", lit("onetl")) +df_to_write.printSchema() +# root +# |-- id: integer (nullable = true) +# |-- phone_number: string (nullable = true) +# |-- region: string (nullable = true) +# |-- birth_date: date (nullable = true) +# |-- registered_at: timestamp (nullable = true) +# |-- account_balance: double (nullable = true) +# |-- engine: string (nullable = false) + +# Initialize Postgres connection +postgres = Postgres( + host="192.169.11.23", + user="onetl", + password="somepassword", + database="mydb", + spark=spark, +) + +# Initialize DBWriter +db_writer = DBWriter( + connection=postgres, + # write to specific table + target="public.my_table", + # with some writing options + options=Postgres.WriteOptions(if_exists="append"), +) + +# Write DataFrame to Postgres table +db_writer.run(df_to_write) + +# Success! +``` diff --git a/mddocs/_sphinx_design_static/design-tabs.js b/mddocs/_sphinx_design_static/design-tabs.js new file mode 100644 index 000000000..b25bd6a4f --- /dev/null +++ b/mddocs/_sphinx_design_static/design-tabs.js @@ -0,0 +1,101 @@ +// @ts-check + +// Extra JS capability for selected tabs to be synced +// The selection is stored in local storage so that it persists across page loads. + +/** + * @type {Record} + */ +let sd_id_to_elements = {}; +const storageKeyPrefix = "sphinx-design-tab-id-"; + +/** + * Create a key for a tab element. + * @param {HTMLElement} el - The tab element. + * @returns {[string, string, string] | null} - The key. + * + */ +function create_key(el) { + let syncId = el.getAttribute("data-sync-id"); + let syncGroup = el.getAttribute("data-sync-group"); + if (!syncId || !syncGroup) return null; + return [syncGroup, syncId, syncGroup + "--" + syncId]; +} + +/** + * Initialize the tab selection. + * + */ +function ready() { + // Find all tabs with sync data + + /** @type {string[]} */ + let groups = []; + + document.querySelectorAll(".sd-tab-label").forEach((label) => { + if (label instanceof HTMLElement) { + let data = create_key(label); + if (data) { + let [group, id, key] = data; + + // add click event listener + // @ts-ignore + label.onclick = onSDLabelClick; + + // store map of key to elements + if (!sd_id_to_elements[key]) { + sd_id_to_elements[key] = []; + } + sd_id_to_elements[key].push(label); + + if (groups.indexOf(group) === -1) { + groups.push(group); + // Check if a specific tab has been selected via URL parameter + const tabParam = new URLSearchParams(window.location.search).get( + group + ); + if (tabParam) { + console.log( + "sphinx-design: Selecting tab id for group '" + + group + + "' from URL parameter: " + + tabParam + ); + window.sessionStorage.setItem(storageKeyPrefix + group, tabParam); + } + } + + // Check is a specific tab has been selected previously + let previousId = window.sessionStorage.getItem( + storageKeyPrefix + group + ); + if (previousId === id) { + // console.log( + // "sphinx-design: Selecting tab from session storage: " + id + // ); + // @ts-ignore + label.previousElementSibling.checked = true; + } + } + } + }); +} + +/** + * Activate other tabs with the same sync id. + * + * @this {HTMLElement} - The element that was clicked. + */ +function onSDLabelClick() { + let data = create_key(this); + if (!data) return; + let [group, id, key] = data; + for (const label of sd_id_to_elements[key]) { + if (label === this) continue; + // @ts-ignore + label.previousElementSibling.checked = true; + } + window.sessionStorage.setItem(storageKeyPrefix + group, id); +} + +document.addEventListener("DOMContentLoaded", ready, false); diff --git a/mddocs/_sphinx_design_static/sphinx-design.min.css b/mddocs/_sphinx_design_static/sphinx-design.min.css new file mode 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-0,0 +1,31 @@ +# Changelog + +# Changelog + +* [0.13.4 (2025-03-20)](changelog/0.13.4.md) +* [0.13.3 (2025-03-11)](changelog/0.13.3.md) +* [0.13.1 (2025-03-06)](changelog/0.13.1.md) +* [0.13.0 (2025-02-24)](changelog/0.13.0.md) +* [0.12.5 (2024-12-03)](changelog/0.12.5.md) +* [0.12.4 (2024-11-27)](changelog/0.12.4.md) +* [0.12.3 (2024-11-22)](changelog/0.12.3.md) +* [0.12.2 (2024-11-12)](changelog/0.12.2.md) +* [0.12.1 (2024-10-28)](changelog/0.12.1.md) +* [0.12.0 (2024-09-03)](changelog/0.12.0.md) +* [0.11.2 (2024-09-02)](changelog/0.11.2.md) +* [0.11.1 (2024-05-29)](changelog/0.11.1.md) +* [0.11.0 (2024-05-27)](changelog/0.11.0.md) +* [0.10.2 (2024-03-21)](changelog/0.10.2.md) +* [0.10.1 (2024-02-05)](changelog/0.10.1.md) +* [0.10.0 (2023-12-18)](changelog/0.10.0.md) +* [0.9.5 (2023-10-10)](changelog/0.9.5.md) +* [0.9.4 (2023-09-26)](changelog/0.9.4.md) +* [0.9.3 (2023-09-06)](changelog/0.9.3.md) +* [0.9.2 (2023-09-06)](changelog/0.9.2.md) +* [0.9.1 (2023-08-17)](changelog/0.9.1.md) +* [0.9.0 (2023-08-17)](changelog/0.9.0.md) +* [0.8.1 (2023-07-10)](changelog/0.8.1.md) +* [0.8.0 (2023-05-31)](changelog/0.8.0.md) +* [0.7.2 (2023-05-24)](changelog/0.7.2.md) +* [0.7.1 (2023-05-23)](changelog/0.7.1.md) +* [0.7.0 (2023-05-15)](changelog/0.7.0.md) diff --git a/mddocs/changelog/0.10.0.md b/mddocs/changelog/0.10.0.md new file mode 100644 index 000000000..ff0fe78bc --- /dev/null +++ b/mddocs/changelog/0.10.0.md @@ -0,0 +1,359 @@ +# 0.10.0 (2023-12-18) + +## Breaking Changes + +- Upgrade `etl-entities` from v1 to v2 ([#172](https://github.com/MobileTeleSystems/onetl/pull/172)). + + This implies that `HWM` classes are now have different internal structure than they used to. + + Before: + ```python + from etl_entities.old_hwm import IntHWM as OldIntHWM + from etl_entities.source import Column, Table + from etl_entities.process import Process + + hwm = OldIntHWM( + process=Process(name="myprocess", task="abc", dag="cde", host="myhost"), + source=Table(name="schema.table", instance="postgres://host:5432/db"), + column=Column(name="col1"), + value=123, + ) + ``` + + After: + ```python + from etl_entities.hwm import ColumnIntHWM + + hwm = ColumnIntHWM( + name="some_unique_name", + description="any value you want", + source="schema.table", + expression="col1", + value=123, + ) + ``` + + **Breaking change:** If you used HWM classes from `etl_entities` module, you should rewrite your code to make it compatible with new version. + + ### More details + + - `HWM` classes used by previous onETL versions were moved from `etl_entities` to `etl_entities.old_hwm` submodule. They are here for compatibility reasons, but are planned to be removed in `etl-entities` v3 release. + - New `HWM` classes have flat structure instead of nested. + - New `HWM` classes have mandatory `name` attribute (it was known as `qualified_name` before). + - Type aliases used while serializing and deserializing `HWM` objects to `dict` representation were changed too: `int` → `column_int`. + + To make migration simpler, you can use new method: + ```python + old_hwm = OldIntHWM(...) + new_hwm = old_hwm.as_new_hwm() + ``` + + Which automatically converts all fields from old structure to new one, including `qualified_name` → `name`. +- **Breaking changes:** + * Methods `BaseHWMStore.get()` and `BaseHWMStore.save()` were renamed to `get_hwm()` and `set_hwm()`. + * They now can be used only with new HWM classes from `etl_entities.hwm`, **old HWM classes are not supported**. + + If you used them in your code, please update it accordingly. +- YAMLHWMStore **CANNOT read files created by older onETL versions** (0.9.x or older). + + ### Update procedure + + ```python + # pip install onetl==0.9.5 + + # Get qualified_name for HWM + + + # Option 1. HWM is built manually + from etl_entities import IntHWM, FileListHWM + from etl_entities.source import Column, Table, RemoteFolder + from etl_entities.process import Process + + # for column HWM + old_column_hwm = IntHWM( + process=Process(name="myprocess", task="abc", dag="cde", host="myhost"), + source=Table(name="schema.table", instance="postgres://host:5432/db"), + column=Column(name="col1"), + ) + qualified_name = old_column_hwm.qualified_name + # "col1#schema.table@postgres://host:5432/db#cde.abc.myprocess@myhost" + + # for file HWM + old_file_hwm = FileListHWM( + process=Process(name="myprocess", task="abc", dag="cde", host="myhost"), + source=RemoteFolder(name="/absolute/path", instance="ftp://ftp.server:21"), + ) + qualified_name = old_file_hwm.qualified_name + # "file_list#/absolute/path@ftp://ftp.server:21#cde.abc.myprocess@myhost" + + + # Option 2. HWM is generated automatically (by DBReader/FileDownloader) + # See onETL logs and search for string like qualified_name = '...' + + qualified_name = "col1#schema.table@postgres://host:5432/db#cde.abc.myprocess@myhost" + + + # Get .yml file path by qualified_name + + import os + from pathlib import PurePosixPath + from onetl.hwm.store import YAMLHWMStore + + # here you should pass the same arguments as used on production, if any + yaml_hwm_store = YAMLHWMStore() + hwm_path = yaml_hwm_store.get_file_path(qualified_name) + print(hwm_path) + + # for column HWM + # LocalPosixPath('/home/maxim/.local/share/onETL/yml_hwm_store/col1__schema.table__postgres_host_5432_db__cde.abc.myprocess__myhost.yml') + + # for file HWM + # LocalPosixPath('/home/maxim/.local/share/onETL/yml_hwm_store/file_list__absolute_path__ftp_ftp.server_21__cde.abc.myprocess__myhost.yml') + + + # Read raw .yml file content + + from yaml import safe_load, dump + + raw_old_hwm_items = safe_load(hwm_path.read_text()) + print(raw_old_hwm_items) + + # for column HWM + # [ + # { + # "column": { "name": "col1", "partition": {} }, + # "modified_time": "2023-12-18T10: 39: 47.377378", + # "process": { "dag": "cde", "host": "myhost", "name": "myprocess", "task": "abc" }, + # "source": { "instance": "postgres: //host:5432/db", "name": "schema.table" }, + # "type": "int", + # "value": "123", + # }, + # ] + + # for file HWM + # [ + # { + # "modified_time": "2023-12-18T11:15:36.478462", + # "process": { "dag": "cde", "host": "myhost", "name": "myprocess", "task": "abc" }, + # "source": { "instance": "ftp://ftp.server:21", "name": "/absolute/path" }, + # "type": "file_list", + # "value": ["file1.txt", "file2.txt"], + # }, + # ] + + + # Convert file content to new structure, compatible with onETL 0.10.x + raw_new_hwm_items = [] + for old_hwm in raw_old_hwm_items: + new_hwm = {"name": qualified_name, "modified_time": old_hwm["modified_time"]} + + if "column" in old_hwm: + new_hwm["expression"] = old_hwm["column"]["name"] + new_hwm["entity"] = old_hwm["source"]["name"] + old_hwm.pop("process", None) + + if old_hwm["type"] == "int": + new_hwm["type"] = "column_int" + new_hwm["value"] = old_hwm["value"] + + elif old_hwm["type"] == "date": + new_hwm["type"] = "column_date" + new_hwm["value"] = old_hwm["value"] + + elif old_hwm["type"] == "datetime": + new_hwm["type"] = "column_datetime" + new_hwm["value"] = old_hwm["value"] + + elif old_hwm["type"] == "file_list": + new_hwm["type"] = "file_list" + new_hwm["value"] = [ + os.fspath(PurePosixPath(old_hwm["source"]["name"]).joinpath(path)) + for path in old_hwm["value"] + ] + + else: + raise ValueError("WAT?") + + raw_new_hwm_items.append(new_hwm) + + + print(raw_new_hwm_items) + # for column HWM + # [ + # { + # "name": "col1#schema.table@postgres://host:5432/db#cde.abc.myprocess@myhost", + # "modified_time": "2023-12-18T10:39:47.377378", + # "expression": "col1", + # "source": "schema.table", + # "type": "column_int", + # "value": 123, + # }, + # ] + + # for file HWM + # [ + # { + # "name": "file_list#/absolute/path@ftp://ftp.server:21#cde.abc.myprocess@myhost", + # "modified_time": "2023-12-18T11:15:36.478462", + # "entity": "/absolute/path", + # "type": "file_list", + # "value": ["/absolute/path/file1.txt", "/absolute/path/file2.txt"], + # }, + # ] + + + # Save file with new content + with open(hwm_path, "w") as file: + dump(raw_new_hwm_items, file) + + + # Stop Python interpreter and update onETL + # pip install onetl==0.10.0 + # Check that new .yml file can be read + + from onetl.hwm.store import YAMLHWMStore + + qualified_name = ... + + # here you should pass the same arguments as used on production, if any + yaml_hwm_store = YAMLHWMStore() + yaml_hwm_store.get_hwm(qualified_name) + + # for column HWM + # ColumnIntHWM( + # name='col1#schema.table@postgres://host:5432/db#cde.abc.myprocess@myhost', + # description='', + # entity='schema.table', + # value=123, + # expression='col1', + # modified_time=datetime.datetime(2023, 12, 18, 10, 39, 47, 377378), + # ) + + # for file HWM + # FileListHWM( + # name='file_list#/absolute/path@ftp://ftp.server:21#cde.abc.myprocess@myhost', + # description='', + # entity=AbsolutePath('/absolute/path'), + # value=frozenset({AbsolutePath('/absolute/path/file1.txt'), AbsolutePath('/absolute/path/file2.txt')}), + # expression=None, + # modified_time=datetime.datetime(2023, 12, 18, 11, 15, 36, 478462) + # ) + + + # That's all! + ``` + + But most of users use other HWM store implementations which do not have such issues. +- Several classes and functions were moved from `onetl` to `etl_entities`: + + | onETL `0.9.x` and older | onETL `0.10.x` and newer | + |-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| + | ```python
from onetl.hwm.store import (
detect_hwm_store,
BaseHWMStore,
HWMStoreClassRegistry,
register_hwm_store_class,
HWMStoreManager,
MemoryHWMStore,
)
``` | ```python
from etl_entities.hwm_store import (
detect_hwm_store,
BaseHWMStore,
HWMStoreClassRegistry,
register_hwm_store_class,
HWMStoreManager,
MemoryHWMStore,
)
``` | + + They still can be imported from old module, but this is deprecated and will be removed in v1.0.0 release. +- Change the way of passing `HWM` to `DBReader` and `FileDownloader` classes: + + | onETL `0.9.x` and older | onETL `0.10.x` and newer | + |----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| + | ```python
reader = DBReader(
connection=...,
source=...,
hwm_column="col1",
)
``` | ```python
reader = DBReader(
connection=...,
source=...,
hwm=DBReader.AutoDetectHWM(
# name is mandatory now!
name="my_unique_hwm_name",
expression="col1",
),
)
``` | + | ```python
reader = DBReader(
connection=...,
source=...,
hwm_column=(
"col1",
"cast(col1 as date)",
),
)
``` | ```python
reader = DBReader(
connection=...,
source=...,
hwm=DBReader.AutoDetectHWM(
# name is mandatory now!
name="my_unique_hwm_name",
expression="cast(col1 as date)",
),
)
``` | + | ```python
downloader = FileDownloader(
connection=...,
source_path=...,
target_path=...,
hwm_type="file_list",
)
``` | ```python
downloader = FileDownloader(
connection=...,
source_path=...,
target_path=...,
hwm=FileListHWM(
# name is mandatory now!
name="another_unique_hwm_name",
),
)
``` | + + New HWM classes have **mandatory** `name` attribute which should be passed explicitly, + instead of generating if automatically under the hood. + + Automatic `name` generation using the old `DBReader.hwm_column` / `FileDownloader.hwm_type` + syntax is still supported, but will be removed in v1.0.0 release. ([#179](https://github.com/MobileTeleSystems/onetl/pull/179)) +- Performance of read Incremental and Batch strategies has been drastically improved. ([#182](https://github.com/MobileTeleSystems/onetl/pull/182)). + + ### Before and after in details + + `DBReader.run()` + incremental/batch strategy behavior in versions 0.9.x and older: + - Get table schema by making query `SELECT * FROM table WHERE 1=0` (if `DBReader.columns` has `*`) + - Expand `*` to real column names from table, add here `hwm_column`, remove duplicates (as some RDBMS does not allow that). + - Create dataframe from query like `SELECT hwm_expression AS hwm_column, ...other table columns... FROM table WHERE hwm_expression > prev_hwm.value`. + - Determine HWM class using dataframe schema: `df.schema[hwm_column].dataType`. + - Determine x HWM column value using Spark: `df.select(max(hwm_column)).collect()`. + - Use `max(hwm_column)` as next HWM value, and save it to HWM Store. + - Return dataframe to user. + + This was far from ideal: + - Dataframe content (all rows or just changed ones) was loaded from the source to Spark only to get min/max values of specific column. + - Step of fetching table schema and then substituting column names in the next query caused some unexpected errors. + > For example, source contains columns with mixed name case, like `"CamelColumn"` or `"spaced column"`. + + > Column names were *not* escaped during query generation, leading to queries that cannot be executed by database. + + > So users have to *explicitly* pass column names `DBReader`, wrapping columns with mixed naming with `"`: + > ```python + > reader = DBReader( + > connection=..., + > source=..., + > columns=[ # passing '*' here leads to wrong SQL query generation + > "normal_column", + > '"CamelColumn"', + > '"spaced column"', + > ..., + > ], + > ) + > ``` + - Using `DBReader` with `IncrementalStrategy` could lead to reading rows already read before. + > Dataframe was created from query with WHERE clause like `hwm.expression > prev_hwm.value`, + > not `hwm.expression > prev_hwm.value AND hwm.expression <= current_hwm.value`. + + > So if new rows appeared in the source **after** HWM value is determined, + > they can be read by accessing dataframe content (because Spark dataframes are lazy), + > leading to inconsistencies between HWM value and dataframe content. + + > This may lead to issues then `DBReader.run()` read some data, updated HWM value, and next call of `DBReader.run()` + > will read rows that were already read in previous run. + + `DBReader.run()` + incremental/batch strategy behavior in versions 0.10.x and newer: + - Detect type of HWM expression: `SELECT hwm.expression FROM table WHERE 1=0`. + - Determine corresponding Spark type `df.schema[0]` and when determine matching HWM class (if `DReader.AutoDetectHWM` is used). + - Get min/max values by querying the source: `SELECT MAX(hwm.expression) FROM table WHERE hwm.expression >= prev_hwm.value`. + - Use `max(hwm.expression)` as next HWM value, and save it to HWM Store. + - Create dataframe from query `SELECT ... table columns ... FROM table WHERE hwm.expression > prev_hwm.value AND hwm.expression <= current_hwm.value`, baking new HWM value into the query. + - Return dataframe to user. + + Improvements: + - Allow source to calculate min/max instead of loading everything to Spark. This should be **faster** on large amounts of data (**up to x2**), because we do not transfer all the data from the source to Spark. This can be even faster if source have indexes for HWM column. + - Columns list is passed to source as-is, without any resolving on `DBReader` side. So you can pass `DBReader(columns=["*"])` to read tables with mixed columns naming. + - Restrict dataframe content to always match HWM values, which leads to never reading the same row twice. + + **Breaking change**: HWM column is not being implicitly added to dataframe. It was a part of `SELECT` clause, but now it is mentioned only in `WHERE` clause. + + So if you had code like this, you have to rewrite it: + + | onETL `0.9.x` and older | onETL `0.10.x` and newer | + |-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| + | ```python
reader = DBReader(
connection=...,
source=...,
columns=[
"col1",
"col2",
],
hwm_column="hwm_col",
)

df = reader.run()
# hwm_column value is in the dataframe
assert df.columns == ["col1", "col2", "hwm_col"]
``` | ```python
reader = DBReader(
connection=...,
source=...,
columns=[
"col1",
"col2",
# add hwm_column explicitly
"hwm_col",
],
hwm_column="hwm_col",
)

df = reader.run()
# if columns list is not updated,
# this fill fail
assert df.columns == ["col1", "col2", "hwm_col"]
``` | + | ```python
reader = DBReader(
connection=...,
source=...,
columns=[
"col1",
"col2",
],
hwm_column=(
"hwm_col",
"cast(hwm_col as int)",
),
)

df = reader.run()
# hwm_expression value is in the dataframe
assert df.columns == ["col1", "col2", "hwm_col"]
``` | ```python
reader = DBReader(
connection=...,
source=...,
columns=[
"col1",
"col2",
# add hwm_expression explicitly
"cast(hwm_col as int) as hwm_col",
],
hwm_column=(
"hwm_col",
"cast(hwm_col as int)",
),
)

df = reader.run()
# if columns list is not updated,
# this fill fail
assert df.columns == ["col1", "col2", "hwm_col"]
``` | + + But most users just use `columns=["*"]` anyway, they won’t see any changes. +- `FileDownloader.run()` now updates HWM in HWM Store not after each file is being successfully downloaded, + but after all files were handled. + + This is because: + * FileDownloader can be used with `DownloadOptions(workers=N)`, which could lead to race condition - one thread can save to HWM store one HWM value when another thread will save different value. + * FileDownloader can download hundreds and thousands of files, and issuing a request to HWM Store for each file could potentially DDoS HWM Store. ([#189](https://github.com/MobileTeleSystems/onetl/pull/189)) + + There is a exception handler which tries to save HWM to HWM store if download process was interrupted. But if it was interrupted by force, like sending `SIGKILL` event, + HWM will not be saved to HWM store, so some already downloaded files may be downloaded again next time. + + But unexpected process kill may produce other negative impact, like some file will be downloaded partially, so this is an expected behavior. + +## Features + +- Add Python 3.12 compatibility. ([#167](https://github.com/MobileTeleSystems/onetl/pull/167)) +- `Excel` file format now can be used with Spark 3.5.0. ([#187](https://github.com/MobileTeleSystems/onetl/pull/187)) +- `SnapshotBatchStagy` and `IncrementalBatchStrategy` does no raise exceptions if source does not contain any data. + Instead they stop at first iteration and return empty dataframe. ([#188](https://github.com/MobileTeleSystems/onetl/pull/188)) +- Cache result of `connection.check()` in high-level classes like `DBReader`, `FileDownloader` and so on. This makes logs less verbose. ([#190](https://github.com/MobileTeleSystems/onetl/pull/190)) + +## Bug Fixes + +- Fix `@slot` and `@hook` decorators returning methods with missing arguments in signature (Pylance, VS Code). ([#183](https://github.com/MobileTeleSystems/onetl/pull/183)) +- Kafka connector documentation said that it does support reading topic data incrementally by passing `group.id` or `groupIdPrefix`. + Actually, this is not true, because Spark does not send information to Kafka which messages were consumed. + So currently users can only read the whole topic, no incremental reads are supported. diff --git a/mddocs/changelog/0.10.1.md b/mddocs/changelog/0.10.1.md new file mode 100644 index 000000000..1bc703cb4 --- /dev/null +++ b/mddocs/changelog/0.10.1.md @@ -0,0 +1,25 @@ +# 0.10.1 (2024-02-05) + +## Features + +- Add support of `Incremental Strategies` for `Kafka` connection: + ```python + reader = DBReader( + connection=Kafka(...), + source="topic_name", + hwm=DBReader.AutoDetectHWM(name="some_hwm_name", expression="offset"), + ) + + with IncrementalStrategy(): + df = reader.run() + ``` + + This lets you resume reading data from a Kafka topic starting at the last committed offset from your previous run. ([#202](https://github.com/MobileTeleSystems/onetl/pull/202)) +- Add `has_data`, `raise_if_no_data` methods to `DBReader` class. ([#203](https://github.com/MobileTeleSystems/onetl/pull/203)) +- Updare VMware Greenplum connector from `2.1.4` to `2.3.0`. This implies: + : * Greenplum 7.x support + * [Kubernetes support](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/configure.html#k8scfg) + * New read option [gpdb.matchDistributionPolicy](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/options.html#distpolmotion) + which allows to match each Spark executor with specific Greenplum segment, avoiding redundant data transfer between Greenplum segments + * Allows overriding [Greenplum optimizer parameters](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/options.html#greenplum-gucs) in read/write operations ([#208](https://github.com/MobileTeleSystems/onetl/pull/208)) +- `Greenplum.get_packages()` method now accepts optional arg `package_version` which allows to override version of Greenplum connector package. ([#208](https://github.com/MobileTeleSystems/onetl/pull/208)) diff --git a/mddocs/changelog/0.10.2.md b/mddocs/changelog/0.10.2.md new file mode 100644 index 000000000..3ca2693c2 --- /dev/null +++ b/mddocs/changelog/0.10.2.md @@ -0,0 +1,34 @@ +# 0.10.2 (2024-03-21) + +## Features + +- Add support of Pydantic v2. ([#230](https://github.com/MobileTeleSystems/onetl/pull/230)) + +## Improvements + +- Improve database connections documentation: + : * Add “Types” section describing mapping between Clickhouse and Spark types + * Add “Prerequisites” section describing different aspects of connecting to Clickhouse + * Separate documentation of `DBReader` and `.sql()` / `.pipeline(...)` + * Add examples for `.fetch()` and `.execute()` ([#211](https://github.com/MobileTeleSystems/onetl/pull/211), [#228](https://github.com/MobileTeleSystems/onetl/pull/228), [#229](https://github.com/MobileTeleSystems/onetl/pull/229), [#233](https://github.com/MobileTeleSystems/onetl/pull/233), [#234](https://github.com/MobileTeleSystems/onetl/pull/234), [#235](https://github.com/MobileTeleSystems/onetl/pull/235), [#236](https://github.com/MobileTeleSystems/onetl/pull/236), [#240](https://github.com/MobileTeleSystems/onetl/pull/240)) +- Add notes to Greenplum documentation about issues with IP resolution and building `gpfdist` URL ([#228](https://github.com/MobileTeleSystems/onetl/pull/228)) +- Allow calling `MongoDB.pipeline(...)` with passing just collection name, without explicit aggregation pipeline. ([#237](https://github.com/MobileTeleSystems/onetl/pull/237)) +- Update default `Postgres(extra={...})` to include `{"stringtype": "unspecified"}` option. + This allows to write text data to non-text column (or vice versa), relying to Postgres cast capabilities. + + For example, now it is possible to read column of type `money` as Spark’s `StringType()`, and write it back to the same column, + without using intermediate columns or tables. ([#229](https://github.com/MobileTeleSystems/onetl/pull/229)) + +## Bug Fixes + +- Return back handling of `DBReader(columns="string")`. This was a valid syntax up to v0.10 release, but it was removed because + most of users neved used it. It looks that we were wrong, returning this behavior back, but with deprecation warning. ([#238](https://github.com/MobileTeleSystems/onetl/pull/238)) +- Downgrade Greenplum package version from `2.3.0` to `2.2.0`. ([#239](https://github.com/MobileTeleSystems/onetl/pull/239)) + + This is because version 2.3.0 introduced issues with writing data to Greenplum 6.x. + Connector can open transaction with `SELECT * FROM table LIMIT 0` query, but does not close it, which leads to deadlocks. + + For using this connector with Greenplum 7.x, please pass package version explicitly: + ```python + maven_packages = Greenplum.get_packages(package_version="2.3.0", ...) + ``` diff --git a/mddocs/changelog/0.11.0.md b/mddocs/changelog/0.11.0.md new file mode 100644 index 000000000..2069d081f --- /dev/null +++ b/mddocs/changelog/0.11.0.md @@ -0,0 +1,202 @@ +# 0.11.0 (2024-05-27) + +## Breaking Changes + +There can be some changes in connection behavior, related to version upgrades. So we mark these changes as **breaking** although +most of users will not see any differences. + +- Update Clickhouse JDBC driver to latest version ([#249](https://github.com/MobileTeleSystems/onetl/pull/249)): + : * Package was renamed `ru.yandex.clickhouse:clickhouse-jdbc` → `com.clickhouse:clickhouse-jdbc`. + * Package version changed `0.3.2` → `0.6.0-patch5`. + * Driver name changed `ru.yandex.clickhouse.ClickHouseDriver` → `com.clickhouse.jdbc.ClickHouseDriver`. + + This brings up several fixes for Spark <-> Clickhouse type compatibility, and also Clickhouse clusters support. +- Update other JDBC drivers to latest versions: + : * MSSQL `12.2.0` → `12.6.2` ([#254](https://github.com/MobileTeleSystems/onetl/pull/254)). + * MySQL `8.0.33` → `8.4.0` ([#253](https://github.com/MobileTeleSystems/onetl/pull/253), [#285](https://github.com/MobileTeleSystems/onetl/pull/285)). + * Oracle `23.2.0.0` → `23.4.0.24.05` ([#252](https://github.com/MobileTeleSystems/onetl/pull/252), [#284](https://github.com/MobileTeleSystems/onetl/pull/284)). + * Postgres `42.6.0` → `42.7.3` ([#251](https://github.com/MobileTeleSystems/onetl/pull/251)). +- Update MongoDB connector to latest version: `10.1.1` → `10.3.0` ([#255](https://github.com/MobileTeleSystems/onetl/pull/255), [#283](https://github.com/MobileTeleSystems/onetl/pull/283)). + + This brings up Spark 3.5 support. +- Update `XML` package to latest version: `0.17.0` → `0.18.0` ([#259](https://github.com/MobileTeleSystems/onetl/pull/259)). + + This brings few bugfixes with datetime format handling. +- For JDBC connections add new `SQLOptions` class for `DB.sql(query, options=...)` method ([#272](https://github.com/MobileTeleSystems/onetl/pull/272)). + + Firsly, to keep naming more consistent. + + Secondly, some of options are not supported by `DB.sql(...)` method, but supported by `DBReader`. + For example, `SQLOptions` do not support `partitioning_mode` and require explicit definition of `lower_bound` and `upper_bound` when `num_partitions` is greater than 1. + `ReadOptions` does support `partitioning_mode` and allows skipping `lower_bound` and `upper_bound` values. + + This require some code changes. Before: + ```python + from onetl.connection import Postgres + + postgres = Postgres(...) + df = postgres.sql( + """ + SELECT * + FROM some.mytable + WHERE key = 'something' + """, + options=Postgres.ReadOptions( + partitioning_mode="range", + partition_column="id", + num_partitions=10, + ), + ) + ``` + + After: + ```python + from onetl.connection import Postgres + + postgres = Postgres(...) + df = postgres.sql( + """ + SELECT * + FROM some.mytable + WHERE key = 'something' + """, + options=Postgres.SQLOptions( + # partitioning_mode is not supported! + partition_column="id", + num_partitions=10, + lower_bound=0, # <-- set explicitly + upper_bound=1000, # <-- set explicitly + ), + ) + ``` + + For now, `DB.sql(query, options=...)` can accept `ReadOptions` to keep backward compatibility, but emits deprecation warning. + The support will be removed in `v1.0.0`. +- Split up `JDBCOptions` class into `FetchOptions` and `ExecuteOptions` ([#274](https://github.com/MobileTeleSystems/onetl/pull/274)). + + New classes are used by `DB.fetch(query, options=...)` and `DB.execute(query, options=...)` methods respectively. + This is mostly to keep naming more consistent. + + This require some code changes. Before: + ```python + from onetl.connection import Postgres + + postgres = Postgres(...) + df = postgres.fetch( + "SELECT * FROM some.mytable WHERE key = 'something'", + options=Postgres.JDBCOptions( + fetchsize=1000, + query_timeout=30, + ), + ) + + postgres.execute( + "UPDATE some.mytable SET value = 'new' WHERE key = 'something'", + options=Postgres.JDBCOptions(query_timeout=30), + ) + ``` + + After: + ```python + from onetl.connection import Postgres + + # Using FetchOptions for fetching data + postgres = Postgres(...) + df = postgres.fetch( + "SELECT * FROM some.mytable WHERE key = 'something'", + options=Postgres.FetchOptions( # <-- change class name + fetchsize=1000, + query_timeout=30, + ), + ) + + # Using ExecuteOptions for executing statements + postgres.execute( + "UPDATE some.mytable SET value = 'new' WHERE key = 'something'", + options=Postgres.ExecuteOptions(query_timeout=30), # <-- change class name + ) + ``` + + For now, `DB.fetch(query, options=...)` and `DB.execute(query, options=...)` can accept `JDBCOptions`, to keep backward compatibility, + but emit a deprecation warning. The old class will be removed in `v1.0.0`. +- Serialize `ColumnDatetimeHWM` to Clickhouse’s `DateTime64(6)` (precision up to microseconds) instead of `DateTime` (precision up to seconds) ([#267](https://github.com/MobileTeleSystems/onetl/pull/267)). + + In previous onETL versions, `ColumnDatetimeHWM` value was rounded to the second, and thus reading some rows that were read in previous runs, + producing duplicates. + + For Clickhouse versions below 21.1 comparing column of type `DateTime` with a value of type `DateTime64` is not supported, returning an empty dataframe. + To avoid this, replace: + ```python + DBReader( + ..., + hwm=DBReader.AutoDetectHWM( + name="my_hwm", + expression="hwm_column", # <-- + ), + ) + ``` + + with: + ```python + DBReader( + ..., + hwm=DBReader.AutoDetectHWM( + name="my_hwm", + expression="CAST(hwm_column AS DateTime64)", # <-- add explicit CAST + ), + ) + ``` +- Pass JDBC connection extra params as `properties` dict instead of URL with query part ([#268](https://github.com/MobileTeleSystems/onetl/pull/268)). + + This allows passing custom connection parameters like `Clickhouse(extra={"custom_http_options": "option1=value1,option2=value2"})` + without need to apply urlencode to parameter value, like `option1%3Dvalue1%2Coption2%3Dvalue2`. + +## Features + +Improve user experience with Kafka messages and Database tables with serialized columns, like JSON/XML. + +- Allow passing custom package version as argument for `DB.get_packages(...)` method of several DB connectors: + : * `Clickhouse.get_packages(package_version=..., apache_http_client_version=...)` ([#249](https://github.com/MobileTeleSystems/onetl/pull/249)). + * `MongoDB.get_packages(scala_version=..., spark_version=..., package_version=...)` ([#255](https://github.com/MobileTeleSystems/onetl/pull/255)). + * `MySQL.get_packages(package_version=...)` ([#253](https://github.com/MobileTeleSystems/onetl/pull/253)). + * `MSSQL.get_packages(java_version=..., package_version=...)` ([#254](https://github.com/MobileTeleSystems/onetl/pull/254)). + * `Oracle.get_packages(java_version=..., package_version=...)` ([#252](https://github.com/MobileTeleSystems/onetl/pull/252)). + * `Postgres.get_packages(package_version=...)` ([#251](https://github.com/MobileTeleSystems/onetl/pull/251)). + * `Teradata.get_packages(package_version=...)` ([#256](https://github.com/MobileTeleSystems/onetl/pull/256)). + + Now users can downgrade or upgrade connection without waiting for next onETL release. Previously only `Kafka` and `Greenplum` supported this feature. +- Add `FileFormat.parse_column(...)` method to several classes: + : * `Avro.parse_column(col)` ([#265](https://github.com/MobileTeleSystems/onetl/pull/265)). + * `JSON.parse_column(col, schema=...)` ([#257](https://github.com/MobileTeleSystems/onetl/pull/257)). + * `CSV.parse_column(col, schema=...)` ([#258](https://github.com/MobileTeleSystems/onetl/pull/258)). + * `XML.parse_column(col, schema=...)` ([#269](https://github.com/MobileTeleSystems/onetl/pull/269)). + + This allows parsing data in `value` field of Kafka message or string/binary column of some table as a nested Spark structure. +- Add `FileFormat.serialize_column(...)` method to several classes: + : * `Avro.serialize_column(col)` ([#265](https://github.com/MobileTeleSystems/onetl/pull/265)). + * `JSON.serialize_column(col)` ([#257](https://github.com/MobileTeleSystems/onetl/pull/257)). + * `CSV.serialize_column(col)` ([#258](https://github.com/MobileTeleSystems/onetl/pull/258)). + + This allows saving Spark nested structures or arrays to `value` field of Kafka message or string/binary column of some table. + +## Improvements + +Few documentation improvements. + +- Replace all `assert` in documentation with doctest syntax. This should make documentation more readable ([#273](https://github.com/MobileTeleSystems/onetl/pull/273)). +- Add generic `Troubleshooting` guide ([#275](https://github.com/MobileTeleSystems/onetl/pull/275)). +- Improve Kafka documentation: + : * Add “Prerequisites” page describing different aspects of connecting to Kafka. + * Improve “Reading from” and “Writing to” page of Kafka documentation, add more examples and usage notes. + * Add “Troubleshooting” page ([#276](https://github.com/MobileTeleSystems/onetl/pull/276)). +- Improve Hive documentation: + : * Add “Prerequisites” page describing different aspects of connecting to Hive. + * Improve “Reading from” and “Writing to” page of Hive documentation, add more examples and recommendations. + * Improve “Executing statements in Hive” page of Hive documentation. ([#278](https://github.com/MobileTeleSystems/onetl/pull/278)). +- Add “Prerequisites” page describing different aspects of using SparkHDFS and SparkS3 connectors. ([#279](https://github.com/MobileTeleSystems/onetl/pull/279)). +- Add note about connecting to Clickhouse cluster. ([#280](https://github.com/MobileTeleSystems/onetl/pull/280)). +- Add notes about versions when specific class/method/attribute/argument was added, renamed or changed behavior ([#282](https://github.com/MobileTeleSystems/onetl/pull/282)). + +## Bug Fixes + +- Fix missing `pysmb` package after installing `pip install onetl[files]` . diff --git a/mddocs/changelog/0.11.1.md b/mddocs/changelog/0.11.1.md new file mode 100644 index 000000000..823afe3be --- /dev/null +++ b/mddocs/changelog/0.11.1.md @@ -0,0 +1,9 @@ +# 0.11.1 (2024-05-29) + +## Features + +- Change `MSSQL.port` default from `1433` to `None`, allowing use of `instanceName` to detect port number. ([#287](https://github.com/MobileTeleSystems/onetl/pull/287)) + +## Bug Fixes + +- Remove `fetchsize` from `JDBC.WriteOptions`. ([#288](https://github.com/MobileTeleSystems/onetl/pull/288)) diff --git a/mddocs/changelog/0.11.2.md b/mddocs/changelog/0.11.2.md new file mode 100644 index 000000000..9278d22f8 --- /dev/null +++ b/mddocs/changelog/0.11.2.md @@ -0,0 +1,5 @@ +# 0.11.2 (2024-09-02) + +## Bug Fixes + +- Fix passing `Greenplum(extra={"options": ...})` during read/write operations. ([#308](https://github.com/MobileTeleSystems/onetl/pull/308)) diff --git a/mddocs/changelog/0.12.0.md b/mddocs/changelog/0.12.0.md new file mode 100644 index 000000000..d212c0062 --- /dev/null +++ b/mddocs/changelog/0.12.0.md @@ -0,0 +1,48 @@ +# 0.12.0 (2024-09-03) + +## Breaking Changes + +- Change connection URL used for generating HWM names of S3 and Samba sources: + : * `smb://host:port` -> `smb://host:port/share` + * `s3://host:port` -> `s3://host:port/bucket` ([#304](https://github.com/MobileTeleSystems/onetl/pull/304)) +- Update DB connectors/drivers to latest versions: + : * Clickhouse `0.6.0-patch5` → `0.6.5` + * MongoDB `10.3.0` → `10.4.0` + * MSSQL `12.6.2` → `12.8.1` + * MySQL `8.4.0` → `9.0.0` + * Oracle `23.4.0.24.05` → `23.5.0.24.07` + * Postgres `42.7.3` → `42.7.4` +- Update `Excel` package from `0.20.3` to `0.20.4`, to include Spark 3.5.1 support. ([#306](https://github.com/MobileTeleSystems/onetl/pull/306)) + +## Features + +- Add support for specifying file formats (`ORC`, `Parquet`, `CSV`, etc.) in `HiveWriteOptions.format` ([#292](https://github.com/MobileTeleSystems/onetl/pull/292)): + ```python + Hive.WriteOptions(format=ORC(compression="snappy")) + ``` +- Collect Spark execution metrics in following methods, and log then in DEBUG mode: + : * `DBWriter.run()` + * `FileDFWriter.run()` + * `Hive.sql()` + * `Hive.execute()` + + This is implemented using custom `SparkListener` which wraps the entire method call, and + then report collected metrics. But these metrics sometimes may be missing due to Spark architecture, + so they are not reliable source of information. That’s why logs are printed only in DEBUG mode, and + are not returned as method call result. ([#303](https://github.com/MobileTeleSystems/onetl/pull/303)) +- Generate default `jobDescription` based on currently executed method. Examples: + : * `DBWriter.run(schema.table) -> Postgres[host:5432/database]` + * `MongoDB[localhost:27017/admin] -> DBReader.has_data(mycollection)` + * `Hive[cluster].execute()` + + If user already set custom `jobDescription`, it will left intact. ([#304](https://github.com/MobileTeleSystems/onetl/pull/304)) +- Add log.info about JDBC dialect usage ([#305](https://github.com/MobileTeleSystems/onetl/pull/305)): + ```text + |MySQL| Detected dialect: 'org.apache.spark.sql.jdbc.MySQLDialect' + ``` +- Log estimated size of in-memory dataframe created by `JDBC.fetch` and `JDBC.execute` methods. ([#303](https://github.com/MobileTeleSystems/onetl/pull/303)) + +## Bug Fixes + +- Fix passing `Greenplum(extra={"options": ...})` during read/write operations. ([#308](https://github.com/MobileTeleSystems/onetl/pull/308)) +- Do not raise exception if yield-based hook whas something past (and only one) `yield`. diff --git a/mddocs/changelog/0.12.1.md b/mddocs/changelog/0.12.1.md new file mode 100644 index 000000000..3575634d5 --- /dev/null +++ b/mddocs/changelog/0.12.1.md @@ -0,0 +1,17 @@ +# 0.12.1 (2024-10-28) + +## Features + +- Log detected JDBC dialect while using `DBWriter`. + +## Bug Fixes + +- Fix `SparkMetricsRecorder` failing when receiving `SparkListenerTaskEnd` without `taskMetrics` (e.g. executor was killed by OOM). ([#313](https://github.com/MobileTeleSystems/onetl/pull/313)) +- Call `kinit` before checking for HDFS active namenode. +- Wrap `kinit` with `threading.Lock` to avoid multithreading issues. +- Immediately show `kinit` errors to user, instead of hiding them. +- Use `AttributeError` instead of `ImportError` in module’s `__getattr__` method, to make code compliant with Python spec. + +## Doc only Changes + +- Add note about [spark-dialect-extension](https://github.com/MobileTeleSystems/spark-dialect-extension) package to Clickhouse connector documentation. ([#310](https://github.com/MobileTeleSystems/onetl/pull/310)) diff --git a/mddocs/changelog/0.12.2.md b/mddocs/changelog/0.12.2.md new file mode 100644 index 000000000..23a8d383d --- /dev/null +++ b/mddocs/changelog/0.12.2.md @@ -0,0 +1,18 @@ +# 0.12.2 (2024-11-12) + +## Improvements + +- Change Spark `jobDescription` for DBReader & FileDFReader from `DBReader.run() -> Connection` to `Connection -> DBReader.run()`. + +## Bug Fixes + +- Fix `log_hwm` result for `KeyValueIntHWM` (used by Kafka). ([#316](https://github.com/MobileTeleSystems/onetl/pull/316)) +- Fix `log_collection` hiding values of `Kafka.addresses` in logs with `INFO` level. ([#316](https://github.com/MobileTeleSystems/onetl/pull/316)) + +## Dependencies + +- Allow using [etl-entities==2.4.0](https://github.com/MobileTeleSystems/etl-entities/releases/tag/2.4.0). + +## Doc only Changes + +- Fix links to MSSQL date & time type documentation. diff --git a/mddocs/changelog/0.12.3.md b/mddocs/changelog/0.12.3.md new file mode 100644 index 000000000..741c74e1f --- /dev/null +++ b/mddocs/changelog/0.12.3.md @@ -0,0 +1,5 @@ +# 0.12.3 (2024-11-22) + +## Bug Fixes + +- Allow passing table names in format `schema."table.with.dots"` to `DBReader(source=...)` and `DBWriter(target=...)`. diff --git a/mddocs/changelog/0.12.4.md b/mddocs/changelog/0.12.4.md new file mode 100644 index 000000000..3ebc57a87 --- /dev/null +++ b/mddocs/changelog/0.12.4.md @@ -0,0 +1,5 @@ +# 0.12.4 (2024-11-27) + +## Bug Fixes + +- Fix `DBReader(conn=oracle, options={"partitioning_mode": "hash"})` lead to data skew in last partition due to wrong `ora_hash` usage. ([#319](https://github.com/MobileTeleSystems/onetl/pull/319)) diff --git a/mddocs/changelog/0.12.5.md b/mddocs/changelog/0.12.5.md new file mode 100644 index 000000000..c542a50fd --- /dev/null +++ b/mddocs/changelog/0.12.5.md @@ -0,0 +1,13 @@ +# 0.12.5 (2024-12-03) + +## Improvements + +- Use `sipHash64` instead of `md5` in Clickhouse for reading data with `{"partitioning_mode": "hash"}`, as it is 5 times faster. +- Use `hashtext` instead of `md5` in Postgres for reading data with `{"partitioning_mode": "hash"}`, as it is 3-5 times faster. +- Use `BINARY_CHECKSUM` instead of `HASHBYTES` in MSSQL for reading data with `{"partitioning_mode": "hash"}`, as it is 5 times faster. + +## Big fixes + +- In JDBC sources wrap `MOD(partitionColumn, numPartitions)` with `ABS(...)` to make al returned values positive. This prevents data skew. +- Fix reading table data from MSSQL using `{"partitioning_mode": "hash"}` with `partitionColumn` of integer type. +- Fix reading table data from Postgres using `{"partitioning_mode": "hash"}` lead to data skew (all the data was read into one Spark partition). diff --git a/mddocs/changelog/0.13.0.md b/mddocs/changelog/0.13.0.md new file mode 100644 index 000000000..0b7256299 --- /dev/null +++ b/mddocs/changelog/0.13.0.md @@ -0,0 +1,197 @@ +# 0.13.0 (2025-02-24) + +🎉 3 years since first release 0.1.0 🎉 + +## Breaking Changes + +- Add Python 3.13. support. ([#298](https://github.com/MobileTeleSystems/onetl/pull/298)) +- Change the logic of `FileConnection.walk` and `FileConnection.list_dir`. ([#327](https://github.com/MobileTeleSystems/onetl/pull/327)) + + Previously `limits.stops_at(path) == True` considered as “return current file and stop”, and could lead to exceeding some limit. + Not it means “stop immediately”. +- Change default value for `FileDFWriter.Options(if_exists=...)` from `error` to `append`, + to make it consistent with other `.Options()` classes within onETL. ([#343](https://github.com/MobileTeleSystems/onetl/pull/343)) + +## Features + +- Add support for `FileModifiedTimeHWM` HWM class (see [etl-entities 2.5.0](https://github.com/MobileTeleSystems/etl-entities/releases/tag/2.5.0)): + ```python + from etl_entitites.hwm import FileModifiedTimeHWM + from onetl.file import FileDownloader + from onetl.strategy import IncrementalStrategy + + downloader = FileDownloader( + ..., + hwm=FileModifiedTimeHWM(name="somename"), + ) + + with IncrementalStrategy(): + downloader.run() + ``` +- Introduce `FileSizeRange(min=..., max=...)` filter class. ([#325](https://github.com/MobileTeleSystems/onetl/pull/325)) + + Now users can set `FileDownloader` / `FileMover` to download/move only files with specific file size range: + ```python + from onetl.file import FileDownloader + from onetl.file.filter import FileSizeRange + + downloader = FileDownloader( + ..., + filters=[FileSizeRange(min="10KiB", max="1GiB")], + ) + ``` +- Introduce `TotalFilesSize(...)` limit class. ([#326](https://github.com/MobileTeleSystems/onetl/pull/326)) + + Now users can set `FileDownloader` / `FileMover` to stop downloading/moving files after reaching a certain amount of data: + ```python + from datetime import datetime, timedelta + from onetl.file import FileDownloader + from onetl.file.limit import TotalFilesSize + + downloader = FileDownloader( + ..., + limits=[TotalFilesSize("1GiB")], + ) + ``` +- Implement `FileModifiedTime(since=..., until=...)` file filter. ([#330](https://github.com/MobileTeleSystems/onetl/pull/330)) + + Now users can set `FileDownloader` / `FileMover` to download/move only files with specific file modification time: + ```python + from datetime import datetime, timedelta + from onetl.file import FileDownloader + from onetl.file.filter import FileModifiedTime + + downloader = FileDownloader( + ..., + filters=[FileModifiedTime(before=datetime.now() - timedelta(hours=1))], + ) + ``` +- Add `SparkS3.get_exclude_packages()` and `Kafka.get_exclude_packages()` methods. ([#341](https://github.com/MobileTeleSystems/onetl/pull/341)) + + Using them allows to skip downloading dependencies not required by this specific connector, or which are already a part of Spark/PySpark: + ```python + from onetl.connection import SparkS3, Kafka + + maven_packages = [ + *SparkS3.get_packages(spark_version="3.5.4"), + *Kafka.get_packages(spark_version="3.5.4"), + ] + exclude_packages = SparkS3.get_exclude_packages() + Kafka.get_exclude_packages() + spark = ( + SparkSession.builder.appName("spark_app_onetl_demo") + .config("spark.jars.packages", ",".join(maven_packages)) + .config("spark.jars.excludes", ",".join(exclude_packages)) + .getOrCreate() + ) + ``` + +## Improvements + +- All DB connections opened by `JDBC.fetch(...)`, `JDBC.execute(...)` or `JDBC.check()` + are immediately closed after the statements is executed. ([#334](https://github.com/MobileTeleSystems/onetl/pull/334)) + + Previously Spark session with `master=local[3]` actually opened up to 5 connections to target DB - one for `JDBC.check()`, + another for Spark driver interaction with DB to create tables, and one for each Spark executor. Now only max 4 connections are opened, + as `JDBC.check()` does not hold opened connection. + + This is important for RDBMS like Postgres or Greenplum where number of connections is strictly limited and limit is usually quite low. +- Set up `ApplicationName` (client info) for Clickhouse, MongoDB, MSSQL, MySQL and Oracle. ([#339](https://github.com/MobileTeleSystems/onetl/pull/339), [#248](https://github.com/MobileTeleSystems/onetl/pull/248)) + + Also update `ApplicationName` format for Greenplum, Postgres, Kafka and SparkS3. + Now all connectors have the same `ApplicationName` format: `${spark.applicationId} ${spark.appName} onETL/${onetl.version} Spark/${spark.version}` + + The only connections not sending `ApplicationName` are Teradata and FileConnection implementations. +- Now `DB.check()` will test connection availability not only on Spark driver, but also from some Spark executor. ([#346](https://github.com/MobileTeleSystems/onetl/pull/346)) + + This allows to fail immediately if Spark driver host has network access to target DB, but Spark executors have not. + + #### NOTE + Now `Greenplum.check()` requires the same user grants as `DBReader(connection=greenplum)`: + ```sql + -- yes, "writable" for reading data from GP, it's not a mistake + ALTER USER username CREATEEXTTABLE(type = 'writable', protocol = 'gpfdist'); + + -- for both reading and writing to GP + -- ALTER USER username CREATEEXTTABLE(type = 'readable', protocol = 'gpfdist') CREATEEXTTABLE(type = 'writable', protocol = 'gpfdist'); + ``` + + Please ask your Greenplum administrators to provide these grants. + +## Bug Fixes + +- Avoid suppressing Hive Metastore errors while using `DBWriter`. ([#329](https://github.com/MobileTeleSystems/onetl/pull/329)) + + Previously this was implemented as: + ```python + try: + spark.sql(f"SELECT * FROM {table}") + table_exists = True + except Exception: + table_exists = False + ``` + + If Hive Metastore was overloaded and responded with an exception, it was considered as non-existing table, resulting + to full table override instead of append or override only partitions subset. +- Fix using onETL to write data to PostgreSQL or Greenplum instances behind *pgbouncer* with `pool_mode=transaction`. ([#336](https://github.com/MobileTeleSystems/onetl/pull/336)) + + Previously `Postgres.check()` opened a read-only transaction, pgbouncer changed the entire connection type from read-write to read-only, + and when `DBWriter.run(df)` executed in read-only connection, producing errors like: + ```default + org.postgresql.util.PSQLException: ERROR: cannot execute INSERT in a read-only transaction + org.postgresql.util.PSQLException: ERROR: cannot execute TRUNCATE TABLE in a read-only transaction + ``` + + Added a workaround by passing `readOnly=True` to JDBC params for read-only connections, so pgbouncer may differ read-only and read-write connections properly. + + After upgrading onETL 0.13.x or higher the same error still may appear of pgbouncer still holds read-only connections and returns them for DBWriter. + To this this, user can manually convert read-only connection to read-write: + ```python + postgres.execute("BEGIN READ WRITE;") # <-- add this line + DBWriter(...).run() + ``` + + After all connections in pgbouncer pool were converted from read-only to read-write, and error fixed, this additional line could be removed. + + See [Postgres JDBC driver documentation](https://jdbc.postgresql.org/documentation/use/). +- Fix `MSSQL.fetch(...)` and `MySQL.fetch(...)` opened a read-write connection instead of read-only. ([#337](https://github.com/MobileTeleSystems/onetl/pull/337)) + + Now this is fixed: + : * `MSSQL.fetch(...)` establishes connection with `ApplicationIntent=ReadOnly`. + * `MySQL.fetch(...)` calls `SET SESSION TRANSACTION READ ONLY` statement. +- Fixed passing multiple filters to `FileDownloader` and `FileMover`. ([#338](https://github.com/MobileTeleSystems/onetl/pull/338)) + If was caused by sorting filters list in internal logging method, but `FileFilter` subclasses are not sortable. +- Fix a false warning about a lof of parallel connections to Grenplum. ([#342](https://github.com/MobileTeleSystems/onetl/pull/342)) + + Creating Spark session with `.master("local[5]")` may open up to 6 connections to Greenplum (=number of Spark executors + 1 for driver), + but onETL instead used number of *CPU cores* on the host as a number of parallel connections. + + This lead to showing a false warning that number of Greenplum connections is too high, + which actually should be the case only if number of executors is higher than 30. +- Fix MongoDB trying to use current database name as `authSource`. ([#347](https://github.com/MobileTeleSystems/onetl/pull/347)) + + Use default connector value which is `admin` database. Previous onETL versions could be fixed by: + ```python + from onetl.connection import MongoDB + + mongodb = MongoDB( + ..., + database="mydb", + extra={ + "authSource": "admin", + }, + ) + ``` + +## Dependencies + +- Minimal `etl-entities` version is now [2.5.0](https://github.com/MobileTeleSystems/etl-entities/releases/tag/2.5.0). ([#331](https://github.com/MobileTeleSystems/onetl/pull/331)) +- Update DB connectors/drivers to latest versions: ([#345](https://github.com/MobileTeleSystems/onetl/pull/345)) + : * Clickhouse `0.6.5` → `0.7.2` + * MongoDB `10.4.0` → `10.4.1` + * MySQL `9.0.0` → `9.2.0` + * Oracle `23.5.0.24.07` → `23.7.0.25.01` + * Postgres `42.7.4` → `42.7.5` + +## Doc only Changes + +- Split large code examples to tabs. ([#344](https://github.com/MobileTeleSystems/onetl/pull/344)) diff --git a/mddocs/changelog/0.13.1.md b/mddocs/changelog/0.13.1.md new file mode 100644 index 000000000..e8eb120d2 --- /dev/null +++ b/mddocs/changelog/0.13.1.md @@ -0,0 +1,9 @@ +# 0.13.1 (2025-03-06) + +## Bug Fixes + +In 0.13.0, using `DBWriter(connection=hive, target="SOMEDB.SOMETABLE")` lead to executing `df.write.saveAsTable()` +instead of `df.write.insertInto()` if target table `somedb.sometable` already exist. + +This is caused by table name normalization (Hive uses lower-case names), which wasn’t properly handled by method used for checking table existence. +([#350](https://github.com/MobileTeleSystems/onetl/pull/350)) diff --git a/mddocs/changelog/0.13.3.md b/mddocs/changelog/0.13.3.md new file mode 100644 index 000000000..1aa289b49 --- /dev/null +++ b/mddocs/changelog/0.13.3.md @@ -0,0 +1,5 @@ +# 0.13.3 (2025-03-11) + +## Dependencies + +Allow using [etl-entities 2.6.0](https://github.com/MobileTeleSystems/etl-entities/releases/tag/2.6.0). diff --git a/mddocs/changelog/0.13.4.md b/mddocs/changelog/0.13.4.md new file mode 100644 index 000000000..10f695e0c --- /dev/null +++ b/mddocs/changelog/0.13.4.md @@ -0,0 +1,10 @@ +# 0.13.4 (2025-03-20) + +## Doc only Changes + +- Prefer `ReadOptions(partitionColumn=..., numPartitions=..., queryTimeout=...)` + instead of `ReadOptions(partition_column=..., num_partitions=..., query_timeout=...)`, + to match Spark documentation. ([#352](https://github.com/MobileTeleSystems/onetl/pull/352)) +- Prefer `WriteOptions(if_exists=...)` instead of `WriteOptions(mode=...)` for IDE suggestions. ([#354](https://github.com/MobileTeleSystems/onetl/pull/354)) +- Document all options of supported file formats. + ([#355](https://github.com/MobileTeleSystems/onetl/pull/355), [#356](https://github.com/MobileTeleSystems/onetl/pull/356), [#357](https://github.com/MobileTeleSystems/onetl/pull/357), [#358](https://github.com/MobileTeleSystems/onetl/pull/358), [#359](https://github.com/MobileTeleSystems/onetl/pull/359), [#360](https://github.com/MobileTeleSystems/onetl/pull/360), [#361](https://github.com/MobileTeleSystems/onetl/pull/361), [#362](https://github.com/MobileTeleSystems/onetl/pull/362)) diff --git a/mddocs/changelog/0.7.0.md b/mddocs/changelog/0.7.0.md new file mode 100644 index 000000000..3f896b82b --- /dev/null +++ b/mddocs/changelog/0.7.0.md @@ -0,0 +1,211 @@ +# 0.7.0 (2023-05-15) + +## 🎉 onETL is now open source 🎉 + +That was long road, but we finally did it! + +## Breaking Changes + +* Changed installation method. + + **TL;DR What should I change to restore previous behavior** + + Simple way: + + | onETL < 0.7.0 | onETL >= 0.7.0 | + |-------------------|-----------------------------------| + | pip install onetl | pip install onetl[files,kerberos] | + + Right way - enumerate connectors should be installed: + ```bash + pip install onetl[hdfs,ftp,kerberos] # except DB connections + ``` + + **Details** + + In onetl<0.7 the package installation looks like: + ```bash + pip install onetl + ``` + + But this includes all dependencies for all connectors, even if user does not use them. + This caused some issues, for example user had to install Kerberos libraries to be able to install onETL, even if user uses only S3 (without Kerberos support). + + Since 0.7.0 installation process was changed: + ```bash + pip install onetl # minimal installation, only onETL core + # there is no extras for DB connections because they are using Java packages which are installed in runtime + + pip install onetl[ftp,ftps,hdfs,sftp,s3,webdav] # install dependencies for specified file connections + pip install onetl[files] # install dependencies for all file connections + + pip install onetl[kerberos] # Kerberos auth support + pip install onetl[spark] # install PySpark to use DB connections + + pip install onetl[spark,kerberos,files] # all file connections + Kerberos + PySpark + pip install onetl[all] # alias for previous case + ``` + + There are corresponding documentation items for each extras. + + Also onETL checks that some requirements are missing, and raises exception with recommendation how to install them: + ```text + Cannot import module "pyspark". + + Since onETL v0.7.0 you should install package as follows: + pip install onetl[spark] + + or inject PySpark to sys.path in some other way BEFORE creating MongoDB instance. + ``` + + ```text + Cannot import module "ftputil". + + Since onETL v0.7.0 you should install package as follows: + pip install onetl[ftp] + + or + pip install onetl[files] + ``` +* Added new `cluster` argument to `Hive` and `HDFS` connections. + + `Hive` qualified name (used in HWM) contains cluster name. But in onETL<0.7.0 cluster name had hard coded value `rnd-dwh` which was not OK for some users. + + `HDFS` connection qualified name contains host (active namenode of Hadoop cluster), but its value can change over time, leading to creating of new HWM. + + Since onETL 0.7.0 both `Hive` and `HDFS` connections have `cluster` attribute which can be set to a specific cluster name. + For `Hive` it is mandatory, for `HDFS` it can be omitted (using host as a fallback). + + But passing cluster name every time could lead to errors. + + Now `Hive` and `HDFS` have nested class named `slots` with methods: + * `normalize_cluster_name` + * `get_known_clusters` + * `get_current_cluster` + * `normalize_namenode_host` (only `HDFS`) + * `get_cluster_namenodes` (only `HDFS`) + * `get_webhdfs_port` (only `HDFS`) + * `is_namenode_active` (only `HDFS`) + + And new method `HDFS.get_current` / `Hive.get_current`. + + Developers can implement hooks validating user input or substituting values for automatic cluster detection. + This should improve user experience while using these connectors. + + See slots documentation. +* Update JDBC connection drivers. + * Greenplum `2.1.3` → `2.1.4`. + * MSSQL `10.2.1.jre8` → `12.2.0.jre8`. Minimal supported version of MSSQL is now 2014 instead 2021. + * MySQL `8.0.30` → `8.0.33`: + \* Package was renamed `mysql:mysql-connector-java` → `com.mysql:mysql-connector-j`. + \* Driver class was renamed `com.mysql.jdbc.Driver` → `com.mysql.cj.jdbc.Driver`. + * Oracle `21.6.0.0.1` → `23.2.0.0`. + * Postgres `42.4.0` → `42.6.0`. + * Teradata `17.20.00.08` → `17.20.00.15`: + \* Package was renamed `com.teradata.jdbc:terajdbc4` → `com.teradata.jdbc:terajdbc`. + \* Teradata driver is now published to Maven. + + See [#31](https://github.com/MobileTeleSystems/onetl/pull/31). + +## Features + +* Added MongoDB connection. + + Using official [MongoDB connector for Spark v10](https://www.mongodb.com/docs/spark-connector/current/). Only Spark 3.2+ is supported. + + There are some differences between MongoDB and other database sources: + * Instead of `mongodb.sql` method there is `mongodb.pipeline`. + * No methods `mongodb.fetch` and `mongodb.execute`. + * `DBReader.hint` and `DBReader.where` have different types than in SQL databases: + + ```python + where = { + "col1": { + "$eq": 10, + }, + } + + hint = { + "col1": 1, + } + ``` + + * Because MongoDB does not have schemas of collections, but Spark cannot create dataframe with dynamic schema, new option `DBReader.df_schema` was introduced. + It is mandatory for MongoDB, but optional for other sources. + * Currently DBReader cannot be used with MongoDB and hwm expression, e.g. `hwm_column=("mycolumn", {"$cast": {"col1": "date"}})` + + Because there are no tables in MongoDB, some options were renamed in core classes: + * `DBReader(table=...)` → `DBReader(source=...)` + * `DBWriter(table=...)` → `DBWriter(target=...)` + + Old names can be used too, they are not deprecated ([#30](https://github.com/MobileTeleSystems/onetl/pull/30)). +* Added option for disabling some plugins during import. + + Previously if some plugin were failing during the import, the only way to import onETL would be to disable all plugins + using environment variable. + + Now there are several variables with different behavior: + * `ONETL_PLUGINS_ENABLED=false` - disable all plugins autoimport. Previously it was named `ONETL_ENABLE_PLUGINS`. + * `ONETL_PLUGINS_BLACKLIST=plugin-name,another-plugin` - set list of plugins which should NOT be imported automatically. + * `ONETL_PLUGINS_WHITELIST=plugin-name,another-plugin` - set list of plugins which should ONLY be imported automatically. + + Also we improved exception message with recommendation how to disable a failing plugin: + ```text + Error while importing plugin 'mtspark' from package 'mtspark' v4.0.0. + + Statement: + import mtspark.onetl + + Check if plugin is compatible with current onETL version 0.7.0. + + You can disable loading this plugin by setting environment variable: + ONETL_PLUGINS_BLACKLIST='mtspark,failing-plugin' + + You can also define a whitelist of packages which can be loaded by onETL: + ONETL_PLUGINS_WHITELIST='not-failing-plugin1,not-failing-plugin2' + + Please take into account that plugin name may differ from package or module name. + See package metadata for more details + ``` + +## Improvements + +* Added compatibility with Python 3.11 and PySpark 3.4.0. + + File connections were OK, but `jdbc.fetch` and `jdbc.execute` were failing. Fixed in [#28](https://github.com/MobileTeleSystems/onetl/pull/28). +* Added check for missing Java packages. + + Previously if DB connection tried to use some Java class which were not loaded into Spark version, it raised an exception + with long Java stacktrace. Most users failed to interpret this trace. + + Now onETL shows the following error message: + ```text + |Spark| Cannot import Java class 'com.mongodb.spark.sql.connector.MongoTableProvider'. + + It looks like you've created Spark session without this option: + SparkSession.builder.config("spark.jars.packages", MongoDB.package_spark_3_2) + + Please call `spark.stop()`, restart the interpreter, + and then create new SparkSession with proper options. + ``` +* Documentation improvements. + * Changed documentation site theme - using [furo](https://github.com/pradyunsg/furo) + instead of default [ReadTheDocs](https://github.com/readthedocs/sphinx_rtd_theme). + + New theme supports wide screens and dark mode. + See [#10](https://github.com/MobileTeleSystems/onetl/pull/10). + * Now each connection class have compatibility table for Spark + Java + Python. + * Added global compatibility table for Spark + Java + Python + Scala. + +## Bug Fixes + +* Fixed several SFTP issues. + * If SSH config file `~/.ssh/config` contains some options not recognized by Paramiko (unknown syntax, unknown option name), + previous versions were raising exception until fixing or removing this file. Since 0.7.0 exception is replaced with warning. + * If user passed `host_key_check=False` but server changed SSH keys, previous versions raised exception until new key is accepted. + Since 0.7.0 exception is replaced with warning if option value is `False`. + + Fixed in [#19](https://github.com/MobileTeleSystems/onetl/pull/19). +* Fixed several S3 issues. + + There was a bug in S3 connection which prevented handling files in the root of a bucket - they were invisible for the connector. Fixed in [#29](https://github.com/MobileTeleSystems/onetl/pull/29). diff --git a/mddocs/changelog/0.7.1.md b/mddocs/changelog/0.7.1.md new file mode 100644 index 000000000..fc423f756 --- /dev/null +++ b/mddocs/changelog/0.7.1.md @@ -0,0 +1,31 @@ +# 0.7.1 (2023-05-23) + +## Bug Fixes + +* Fixed `setup_logging` function. + + In onETL==0.7.0 calling `onetl.log.setup_logging()` broke the logging: + ```text + Traceback (most recent call last): + File "/opt/anaconda/envs/py39/lib/python3.9/logging/__init__.py", line 434, in format + return self._format(record) + File "/opt/anaconda/envs/py39/lib/python3.9/logging/__init__.py", line 430, in _format + return self._fmt % record.dict + KeyError: 'levelname:8s' + ``` +* Fixed installation examples. + + In onETL==0.7.0 there are examples of installing onETL with extras: + ```bash + pip install onetl[files, kerberos, spark] + ``` + + But pip fails to install such package: + ```text + ERROR: Invalid requirement: 'onet[files,' + ``` + + This is because of spaces in extras clause. Fixed: + ```bash + pip install onetl[files,kerberos,spark] + ``` diff --git a/mddocs/changelog/0.7.2.md b/mddocs/changelog/0.7.2.md new file mode 100644 index 000000000..80bb2cfc5 --- /dev/null +++ b/mddocs/changelog/0.7.2.md @@ -0,0 +1,33 @@ +# 0.7.2 (2023-05-24) + +## Dependencies + +* Limited `typing-extensions` version. + + `typing-extensions==4.6.0` release contains some breaking changes causing errors like: + ```text + Traceback (most recent call last): + File "/Users/project/lib/python3.9/typing.py", line 852, in __subclasscheck__ + return issubclass(cls, self.__origin__) + TypeError: issubclass() arg 1 must be a class + ``` + + `typing-extensions==4.6.1` was causing another error: + ```text + Traceback (most recent call last): + File "/home/maxim/Repo/typing_extensions/1.py", line 33, in + isinstance(file, ContainsException) + File "/home/maxim/Repo/typing_extensions/src/typing_extensions.py", line 599, in __instancecheck__ + if super().__instancecheck__(instance): + File "/home/maxim/.pyenv/versions/3.7.8/lib/python3.7/abc.py", line 139, in __instancecheck__ + return _abc_instancecheck(cls, instance) + File "/home/maxim/Repo/typing_extensions/src/typing_extensions.py", line 583, in __subclasscheck__ + return super().__subclasscheck__(other) + File "/home/maxim/.pyenv/versions/3.7.8/lib/python3.7/abc.py", line 143, in __subclasscheck__ + return _abc_subclasscheck(cls, subclass) + File "/home/maxim/Repo/typing_extensions/src/typing_extensions.py", line 661, in _proto_hook + and other._is_protocol + AttributeError: type object 'PathWithFailure' has no attribute '_is_protocol' + ``` + + We updated requirements with `typing-extensions<4.6` until fixing compatibility issues. diff --git a/mddocs/changelog/0.8.0.md b/mddocs/changelog/0.8.0.md new file mode 100644 index 000000000..3fe438549 --- /dev/null +++ b/mddocs/changelog/0.8.0.md @@ -0,0 +1,137 @@ +# 0.8.0 (2023-05-31) + +## Breaking Changes + +- Rename methods of `FileConnection` classes: + * `get_directory` → `resolve_dir` + * `get_file` → `resolve_file` + * `listdir` → `list_dir` + * `mkdir` → `create_dir` + * `rmdir` → `remove_dir` + + New naming should be more consistent. + + They were undocumented in previous versions, but someone could use these methods, so this is a breaking change. ([#36](https://github.com/MobileTeleSystems/onetl/pull/36)) +- Deprecate `onetl.core.FileFilter` class, replace it with new classes: + * `onetl.file.filter.Glob` + * `onetl.file.filter.Regexp` + * `onetl.file.filter.ExcludeDir` + + Old class will be removed in v1.0.0. ([#43](https://github.com/MobileTeleSystems/onetl/pull/43)) +- Deprecate `onetl.core.FileLimit` class, replace it with new class `onetl.file.limit.MaxFilesCount`. + + Old class will be removed in v1.0.0. ([#44](https://github.com/MobileTeleSystems/onetl/pull/44)) +- Change behavior of `BaseFileLimit.reset` method. + + This method should now return `self` instead of `None`. + Return value could be the same limit object or a copy, this is an implementation detail. ([#44](https://github.com/MobileTeleSystems/onetl/pull/44)) +- Replaced `FileDownloader.filter` and `.limit` with new options `.filters` and `.limits`: + ```python + FileDownloader( + ..., + filter=FileFilter(glob="*.txt", exclude_dir="/path"), + limit=FileLimit(count_limit=10), + ) + ``` + + ```python + FileDownloader( + ..., + filters=[Glob("*.txt"), ExcludeDir("/path")], + limits=[MaxFilesCount(10)], + ) + ``` + + This allows to developers to implement their own filter and limit classes, and combine them with existing ones. + + Old behavior still supported, but it will be removed in v1.0.0. ([#45](https://github.com/MobileTeleSystems/onetl/pull/45)) +- Removed default value for `FileDownloader.limits`, user should pass limits list explicitly. ([#45](https://github.com/MobileTeleSystems/onetl/pull/45)) +- Move classes from module `onetl.core`: + ```python + from onetl.core import DBReader + from onetl.core import DBWriter + from onetl.core import FileDownloader + from onetl.core import FileUploader + ``` + + with new modules `onetl.db` and `onetl.file`: + ```python + from onetl.db import DBReader + from onetl.db import DBWriter + + from onetl.file import FileDownloader + from onetl.file import FileUploader + ``` + + Imports from old module `onetl.core` still can be used, but marked as deprecated. Module will be removed in v1.0.0. ([#46](https://github.com/MobileTeleSystems/onetl/pull/46)) + +## Features + +- Add `rename_dir` method. + + Method was added to following connections: + * `FTP` + * `FTPS` + * `HDFS` + * `SFTP` + * `WebDAV` + + It allows to rename/move directory to new path with all its content. + + `S3` does not have directories, so there is no such method in that class. ([#40](https://github.com/MobileTeleSystems/onetl/pull/40)) +- Add `onetl.file.FileMover` class. + + It allows to move files between directories of remote file system. + Signature is almost the same as in `FileDownloader`, but without HWM support. ([#42](https://github.com/MobileTeleSystems/onetl/pull/42)) + +## Improvements + +- Document all public methods in `FileConnection` classes: + * `download_file` + * `resolve_dir` + * `resolve_file` + * `get_stat` + * `is_dir` + * `is_file` + * `list_dir` + * `create_dir` + * `path_exists` + * `remove_file` + * `rename_file` + * `remove_dir` + * `upload_file` + * `walk` ([#39](https://github.com/MobileTeleSystems/onetl/pull/39)) +- Update documentation of `check` method of all connections - add usage example and document result type. ([#39](https://github.com/MobileTeleSystems/onetl/pull/39)) +- Add new exception type `FileSizeMismatchError`. + + Methods `connection.download_file` and `connection.upload_file` now raise new exception type instead of `RuntimeError`, + if target file after download/upload has different size than source. ([#39](https://github.com/MobileTeleSystems/onetl/pull/39)) +- Add new exception type `DirectoryExistsError` - it is raised if target directory already exists. ([#40](https://github.com/MobileTeleSystems/onetl/pull/40)) +- Improved `FileDownloader` / `FileUploader` exception logging. + + If `DEBUG` logging is enabled, print exception with stacktrace instead of + printing only exception message. ([#42](https://github.com/MobileTeleSystems/onetl/pull/42)) +- Updated documentation of `FileUploader`. + * Class does not support read strategies, added note to documentation. + * Added examples of using `run` method with explicit files list passing, both absolute and relative paths. + * Fix outdated imports and class names in examples. ([#42](https://github.com/MobileTeleSystems/onetl/pull/42)) +- Updated documentation of `DownloadResult` class - fix outdated imports and class names. ([#42](https://github.com/MobileTeleSystems/onetl/pull/42)) +- Improved file filters documentation section. + + Document interface class `onetl.base.BaseFileFilter` and function `match_all_filters`. ([#43](https://github.com/MobileTeleSystems/onetl/pull/43)) +- Improved file limits documentation section. + + Document interface class `onetl.base.BaseFileLimit` and functions `limits_stop_at` / `limits_reached` / `reset_limits`. ([#44](https://github.com/MobileTeleSystems/onetl/pull/44)) +- Added changelog. + + Changelog is generated from separated news files using [towncrier](https://pypi.org/project/towncrier/). ([#47](https://github.com/MobileTeleSystems/onetl/pull/47)) + +## Misc + +- Improved CI workflow for tests. + * If developer haven’t changed source core of a specific connector or its dependencies, + run tests only against maximum supported versions of Spark, Python, Java and db/file server. + * If developed made some changes in a specific connector, or in core classes, or in dependencies, + run tests for both minimal and maximum versions. + * Once a week run all aganst for minimal and latest versions to detect breaking changes in dependencies + * Minimal tested Spark version is 2.3.1 instead on 2.4.8. ([#32](https://github.com/MobileTeleSystems/onetl/pull/32)) diff --git a/mddocs/changelog/0.8.1.md b/mddocs/changelog/0.8.1.md new file mode 100644 index 000000000..d3bd32564 --- /dev/null +++ b/mddocs/changelog/0.8.1.md @@ -0,0 +1,34 @@ +# 0.8.1 (2023-07-10) + +## Features + +- Add `@slot` decorator to public methods of: + * `DBConnection` + * `FileConnection` + * `DBReader` + * `DBWriter` + * `FileDownloader` + * `FileUploader` + * `FileMover` ([#49](https://github.com/MobileTeleSystems/onetl/pull/49)) +- Add `workers` field to `FileDownloader` / `FileUploader` / `FileMover`. `Options` classes. + + This allows to speed up all file operations using parallel threads. ([#57](https://github.com/MobileTeleSystems/onetl/pull/57)) + +## Improvements + +- Add documentation for HWM store `.get` and `.save` methods. ([#49](https://github.com/MobileTeleSystems/onetl/pull/49)) +- Improve Readme: + * Move `Quick start` section from documentation + * Add `Non-goals` section + * Fix code blocks indentation ([#50](https://github.com/MobileTeleSystems/onetl/pull/50)) +- Improve Contributing guide: + * Move `Develop` section from Readme + * Move `docs/changelog/README.rst` content + * Add `Limitations` section + * Add instruction of creating a fork and building documentation ([#50](https://github.com/MobileTeleSystems/onetl/pull/50)) +- Remove duplicated checks for source file existence in `FileDownloader` / `FileMover`. ([#57](https://github.com/MobileTeleSystems/onetl/pull/57)) +- Update default logging format to include thread name. ([#57](https://github.com/MobileTeleSystems/onetl/pull/57)) + +## Bug Fixes + +- Fix `S3.list_dir('/')` returns empty list on latest Minio version. ([#58](https://github.com/MobileTeleSystems/onetl/pull/58)) diff --git a/mddocs/changelog/0.9.0.md b/mddocs/changelog/0.9.0.md new file mode 100644 index 000000000..b2d10f257 --- /dev/null +++ b/mddocs/changelog/0.9.0.md @@ -0,0 +1,107 @@ +# 0.9.0 (2023-08-17) + +## Breaking Changes + +- Rename methods: + * `DBConnection.read_df` → `DBConnection.read_source_as_df` + * `DBConnection.write_df` → `DBConnection.write_df_to_target` ([#66](https://github.com/MobileTeleSystems/onetl/pull/66)) +- Rename classes: + * `HDFS.slots` → `HDFS.Slots` + * `Hive.slots` → `Hive.Slots` + + Old names are left intact, but will be removed in v1.0.0 ([#103](https://github.com/MobileTeleSystems/onetl/pull/103)) +- Rename options to make them self-explanatory: + * `Hive.WriteOptions(mode="append")` → `Hive.WriteOptions(if_exists="append")` + * `Hive.WriteOptions(mode="overwrite_table")` → `Hive.WriteOptions(if_exists="replace_entire_table")` + * `Hive.WriteOptions(mode="overwrite_partitions")` → `Hive.WriteOptions(if_exists="replace_overlapping_partitions")` + * `JDBC.WriteOptions(mode="append")` → `JDBC.WriteOptions(if_exists="append")` + * `JDBC.WriteOptions(mode="overwrite")` → `JDBC.WriteOptions(if_exists="replace_entire_table")` + * `Greenplum.WriteOptions(mode="append")` → `Greenplum.WriteOptions(if_exists="append")` + * `Greenplum.WriteOptions(mode="overwrite")` → `Greenplum.WriteOptions(if_exists="replace_entire_table")` + * `MongoDB.WriteOptions(mode="append")` → `Greenplum.WriteOptions(if_exists="append")` + * `MongoDB.WriteOptions(mode="overwrite")` → `Greenplum.WriteOptions(if_exists="replace_entire_collection")` + * `FileDownloader.Options(mode="error")` → `FileDownloader.Options(if_exists="error")` + * `FileDownloader.Options(mode="ignore")` → `FileDownloader.Options(if_exists="ignore")` + * `FileDownloader.Options(mode="overwrite")` → `FileDownloader.Options(if_exists="replace_file")` + * `FileDownloader.Options(mode="delete_all")` → `FileDownloader.Options(if_exists="replace_entire_directory")` + * `FileUploader.Options(mode="error")` → `FileUploader.Options(if_exists="error")` + * `FileUploader.Options(mode="ignore")` → `FileUploader.Options(if_exists="ignore")` + * `FileUploader.Options(mode="overwrite")` → `FileUploader.Options(if_exists="replace_file")` + * `FileUploader.Options(mode="delete_all")` → `FileUploader.Options(if_exists="replace_entire_directory")` + * `FileMover.Options(mode="error")` → `FileMover.Options(if_exists="error")` + * `FileMover.Options(mode="ignore")` → `FileMover.Options(if_exists="ignore")` + * `FileMover.Options(mode="overwrite")` → `FileMover.Options(if_exists="replace_file")` + * `FileMover.Options(mode="delete_all")` → `FileMover.Options(if_exists="replace_entire_directory")` + + Old names are left intact, but will be removed in v1.0.0 ([#108](https://github.com/MobileTeleSystems/onetl/pull/108)) +- Rename `onetl.log.disable_clients_logging()` to `onetl.log.setup_clients_logging()`. ([#120](https://github.com/MobileTeleSystems/onetl/pull/120)) + +## Features + +- Add new methods returning Maven packages for specific connection class: + * `Clickhouse.get_packages()` + * `MySQL.get_packages()` + * `Postgres.get_packages()` + * `Teradata.get_packages()` + * `MSSQL.get_packages(java_version="8")` + * `Oracle.get_packages(java_version="8")` + * `Greenplum.get_packages(scala_version="2.12")` + * `MongoDB.get_packages(scala_version="2.12")` + * `Kafka.get_packages(spark_version="3.4.1", scala_version="2.12")` + + Deprecate old syntax: + * `Clickhouse.package` + * `MySQL.package` + * `Postgres.package` + * `Teradata.package` + * `MSSQL.package` + * `Oracle.package` + * `Greenplum.package_spark_2_3` + * `Greenplum.package_spark_2_4` + * `Greenplum.package_spark_3_2` + * `MongoDB.package_spark_3_2` + * `MongoDB.package_spark_3_3` + * `MongoDB.package_spark_3_4` ([#87](https://github.com/MobileTeleSystems/onetl/pull/87)) +- Allow to set client modules log level in `onetl.log.setup_clients_logging()`. + + Allow to enable underlying client modules logging in `onetl.log.setup_logging()` by providing additional argument `enable_clients=True`. + This is useful for debug. ([#120](https://github.com/MobileTeleSystems/onetl/pull/120)) +- Added support for reading and writing data to Kafka topics. + + For these operations, new classes were added. + * `Kafka` ([#54](https://github.com/MobileTeleSystems/onetl/pull/54), [#60](https://github.com/MobileTeleSystems/onetl/pull/60), [#72](https://github.com/MobileTeleSystems/onetl/pull/72), [#84](https://github.com/MobileTeleSystems/onetl/pull/84), [#87](https://github.com/MobileTeleSystems/onetl/pull/87), [#89](https://github.com/MobileTeleSystems/onetl/pull/89), [#93](https://github.com/MobileTeleSystems/onetl/pull/93), [#96](https://github.com/MobileTeleSystems/onetl/pull/96), [#102](https://github.com/MobileTeleSystems/onetl/pull/102), [#104](https://github.com/MobileTeleSystems/onetl/pull/104)) + * `Kafka.PlaintextProtocol` ([#79](https://github.com/MobileTeleSystems/onetl/pull/79)) + * `Kafka.SSLProtocol` ([#118](https://github.com/MobileTeleSystems/onetl/pull/118)) + * `Kafka.BasicAuth` ([#63](https://github.com/MobileTeleSystems/onetl/pull/63), [#77](https://github.com/MobileTeleSystems/onetl/pull/77)) + * `Kafka.KerberosAuth` ([#63](https://github.com/MobileTeleSystems/onetl/pull/63), [#77](https://github.com/MobileTeleSystems/onetl/pull/77), [#110](https://github.com/MobileTeleSystems/onetl/pull/110)) + * `Kafka.ScramAuth` ([#115](https://github.com/MobileTeleSystems/onetl/pull/115)) + * `Kafka.Slots` ([#109](https://github.com/MobileTeleSystems/onetl/pull/109)) + * `Kafka.ReadOptions` ([#68](https://github.com/MobileTeleSystems/onetl/pull/68)) + * `Kafka.WriteOptions` ([#68](https://github.com/MobileTeleSystems/onetl/pull/68)) + + Currently, Kafka does not support incremental read strategies, this will be implemented in future releases. +- Added support for reading files as Spark DataFrame and saving DataFrame as Files. + + For these operations, new classes were added. + + FileDFConnections: + * `SparkHDFS` ([#98](https://github.com/MobileTeleSystems/onetl/pull/98)) + * `SparkS3` ([#94](https://github.com/MobileTeleSystems/onetl/pull/94), [#100](https://github.com/MobileTeleSystems/onetl/pull/100), [#124](https://github.com/MobileTeleSystems/onetl/pull/124)) + * `SparkLocalFS` ([#67](https://github.com/MobileTeleSystems/onetl/pull/67)) + + High-level classes: + * `FileDFReader` ([#73](https://github.com/MobileTeleSystems/onetl/pull/73)) + * `FileDFWriter` ([#81](https://github.com/MobileTeleSystems/onetl/pull/81)) + + File formats: + * `Avro` ([#69](https://github.com/MobileTeleSystems/onetl/pull/69)) + * `CSV` ([#92](https://github.com/MobileTeleSystems/onetl/pull/92)) + * `JSON` ([#83](https://github.com/MobileTeleSystems/onetl/pull/83)) + * `JSONLine` ([#83](https://github.com/MobileTeleSystems/onetl/pull/83)) + * `ORC` ([#86](https://github.com/MobileTeleSystems/onetl/pull/86)) + * `Parquet` ([#88](https://github.com/MobileTeleSystems/onetl/pull/88)) + +## Improvements + +- Remove redundant checks for driver availability in Greenplum and MongoDB connections. ([#67](https://github.com/MobileTeleSystems/onetl/pull/67)) +- Check of Java class availability moved from `.check()` method to connection constructor. ([#97](https://github.com/MobileTeleSystems/onetl/pull/97)) diff --git a/mddocs/changelog/0.9.1.md b/mddocs/changelog/0.9.1.md new file mode 100644 index 000000000..1779274b1 --- /dev/null +++ b/mddocs/changelog/0.9.1.md @@ -0,0 +1,7 @@ +# 0.9.1 (2023-08-17) + +## Bug Fixes + +- Fixed bug then number of threads created by `FileDownloader` / `FileUploader` / `FileMover` was + not `min(workers, len(files))`, but `max(workers, len(files))`. leading to create too much workers + on large files list. diff --git a/mddocs/changelog/0.9.2.md b/mddocs/changelog/0.9.2.md new file mode 100644 index 000000000..4ca4fcff9 --- /dev/null +++ b/mddocs/changelog/0.9.2.md @@ -0,0 +1,21 @@ +# 0.9.2 (2023-09-06) + +## Features + +- Add `if_exists="ignore"` and `error` to `Greenplum.WriteOptions` ([#142](https://github.com/MobileTeleSystems/onetl/pull/142)) + +## Improvements + +- Improve validation messages while writing dataframe to Kafka. ([#131](https://github.com/MobileTeleSystems/onetl/pull/131)) +- Improve documentation: + * Add notes about reading and writing to database connections documentation + * Add notes about executing statements in JDBC and Greenplum connections + +## Bug Fixes + +- Fixed validation of `headers` column is written to Kafka with default `Kafka.WriteOptions()` - default value was `False`, + but instead of raising an exception, column value was just ignored. ([#131](https://github.com/MobileTeleSystems/onetl/pull/131)) +- Fix reading data from Oracle with `partitioningMode="range"` without explicitly set `lowerBound` / `upperBound`. ([#133](https://github.com/MobileTeleSystems/onetl/pull/133)) +- Update Kafka documentation with SSLProtocol usage. ([#136](https://github.com/MobileTeleSystems/onetl/pull/136)) +- Raise exception if someone tries to read data from Kafka topic which does not exist. ([#138](https://github.com/MobileTeleSystems/onetl/pull/138)) +- Allow to pass Kafka topics with name like `some.topic.name` to DBReader. Same for MongoDB collections. ([#139](https://github.com/MobileTeleSystems/onetl/pull/139)) diff --git a/mddocs/changelog/0.9.3.md b/mddocs/changelog/0.9.3.md new file mode 100644 index 000000000..1a8c25d4d --- /dev/null +++ b/mddocs/changelog/0.9.3.md @@ -0,0 +1,5 @@ +# 0.9.3 (2023-09-06) + +## Bug Fixes + +- Fix documentation build diff --git a/mddocs/changelog/0.9.4.md b/mddocs/changelog/0.9.4.md new file mode 100644 index 000000000..cf9288760 --- /dev/null +++ b/mddocs/changelog/0.9.4.md @@ -0,0 +1,24 @@ +# 0.9.4 (2023-09-26) + +## Features + +- Add `Excel` file format support. ([#148](https://github.com/MobileTeleSystems/onetl/pull/148)) +- Add `Samba` file connection. + It is now possible to download and upload files to Samba shared folders using `FileDownloader`/`FileUploader`. ([#150](https://github.com/MobileTeleSystems/onetl/pull/150)) +- Add `if_exists="ignore"` and `error` to `Hive.WriteOptions` ([#143](https://github.com/MobileTeleSystems/onetl/pull/143)) +- Add `if_exists="ignore"` and `error` to `JDBC.WriteOptions` ([#144](https://github.com/MobileTeleSystems/onetl/pull/144)) +- Add `if_exists="ignore"` and `error` to `MongoDB.WriteOptions` ([#145](https://github.com/MobileTeleSystems/onetl/pull/145)) + +## Improvements + +- Add documentation about different ways of passing packages to Spark session. ([#151](https://github.com/MobileTeleSystems/onetl/pull/151)) +- Drastically improve `Greenplum` documentation: + : * Added information about network ports, grants, `pg_hba.conf` and so on. + * Added interaction schemas for reading, writing and executing statements in Greenplum. + * Added recommendations about reading data from views and `JOIN` results from Greenplum. ([#154](https://github.com/MobileTeleSystems/onetl/pull/154)) +- Make `.fetch` and `.execute` methods of DB connections thread-safe. Each thread works with its own connection. ([#156](https://github.com/MobileTeleSystems/onetl/pull/156)) +- Call `.close()` on `FileConnection` then it is removed by garbage collector. ([#156](https://github.com/MobileTeleSystems/onetl/pull/156)) + +## Bug Fixes + +- Fix issue when stopping Python interpreter calls `JDBCMixin.close()`, but it is finished with exceptions. ([#156](https://github.com/MobileTeleSystems/onetl/pull/156)) diff --git a/mddocs/changelog/0.9.5.md b/mddocs/changelog/0.9.5.md new file mode 100644 index 000000000..1d7358c0b --- /dev/null +++ b/mddocs/changelog/0.9.5.md @@ -0,0 +1,14 @@ +# 0.9.5 (2023-10-10) + +## Features + +- Add `XML` file format support. ([#163](https://github.com/MobileTeleSystems/onetl/pull/163)) +- Tested compatibility with Spark 3.5.0. `MongoDB` and `Excel` are not supported yet, but other packages do. ([#159](https://github.com/MobileTeleSystems/onetl/pull/159)) + +## Improvements + +- Add check to all DB and FileDF connections that Spark session is alive. ([#164](https://github.com/MobileTeleSystems/onetl/pull/164)) + +## Bug Fixes + +- Fix `Hive.check()` behavior when Hive Metastore is not available. ([#164](https://github.com/MobileTeleSystems/onetl/pull/164)) diff --git a/mddocs/changelog/DRAFT.md b/mddocs/changelog/DRAFT.md new file mode 100644 index 000000000..e69de29bb diff --git a/mddocs/changelog/NEXT_RELEASE.md b/mddocs/changelog/NEXT_RELEASE.md new file mode 100644 index 000000000..8cf66aa33 --- /dev/null +++ b/mddocs/changelog/NEXT_RELEASE.md @@ -0,0 +1 @@ + diff --git a/mddocs/changelog/index.md b/mddocs/changelog/index.md new file mode 100644 index 000000000..37d5d85ae --- /dev/null +++ b/mddocs/changelog/index.md @@ -0,0 +1,29 @@ +Changelog + +* [0.13.4 (2025-03-20)](0.13.4.md) +* [0.13.3 (2025-03-11)](0.13.3.md) +* [0.13.1 (2025-03-06)](0.13.1.md) +* [0.13.0 (2025-02-24)](0.13.0.md) +* [0.12.5 (2024-12-03)](0.12.5.md) +* [0.12.4 (2024-11-27)](0.12.4.md) +* [0.12.3 (2024-11-22)](0.12.3.md) +* [0.12.2 (2024-11-12)](0.12.2.md) +* [0.12.1 (2024-10-28)](0.12.1.md) +* [0.12.0 (2024-09-03)](0.12.0.md) +* [0.11.2 (2024-09-02)](0.11.2.md) +* [0.11.1 (2024-05-29)](0.11.1.md) +* [0.11.0 (2024-05-27)](0.11.0.md) +* [0.10.2 (2024-03-21)](0.10.2.md) +* [0.10.1 (2024-02-05)](0.10.1.md) +* [0.10.0 (2023-12-18)](0.10.0.md) +* [0.9.5 (2023-10-10)](0.9.5.md) +* [0.9.4 (2023-09-26)](0.9.4.md) +* [0.9.3 (2023-09-06)](0.9.3.md) +* [0.9.2 (2023-09-06)](0.9.2.md) +* [0.9.1 (2023-08-17)](0.9.1.md) +* [0.9.0 (2023-08-17)](0.9.0.md) +* [0.8.1 (2023-07-10)](0.8.1.md) +* [0.8.0 (2023-05-31)](0.8.0.md) +* [0.7.2 (2023-05-24)](0.7.2.md) +* [0.7.1 (2023-05-23)](0.7.1.md) +* [0.7.0 (2023-05-15)](0.7.0.md) diff --git a/mddocs/concepts.md b/mddocs/concepts.md new file mode 100644 index 000000000..cf74e9eaa --- /dev/null +++ b/mddocs/concepts.md @@ -0,0 +1,340 @@ +# Concepts + +Here you can find detailed documentation about each one of the onETL concepts and how to use them. + +## Connection + +### Basics + +onETL is used to pull and push data into other systems, and so it has a first-class `Connection` concept for storing credentials that are used to communicate with external systems. + +A `Connection` is essentially a set of parameters, such as username, password, hostname. + +To create a connection to a specific storage type, you must use a class that matches the storage type. The class name is the same as the storage type name (`Oracle`, `MSSQL`, `SFTP`, etc): + +```python +from onetl.connection import SFTP + +sftp = SFTP( + host="sftp.test.com", + user="onetl", + password="onetl", +) +``` + +All connection types are inherited from the parent class `BaseConnection`. + +### Class diagram + +### DBConnection + +Classes inherited from `DBConnection` could be used for accessing databases. + +A `DBConnection` could be instantiated as follows: + +```python +from onetl.connection import MSSQL + +mssql = MSSQL( + host="mssqldb.demo.com", + user="onetl", + password="onetl", + database="Telecom", + spark=spark, +) +``` + +where **spark** is the current SparkSession. +`onETL` uses `Spark` and specific Java connectors under the hood to work with databases. + +For a description of other parameters, see the documentation for the [available DBConnections](connection/db_connection/index.md#db-connections). + +### FileConnection + +Classes inherited from `FileConnection` could be used to access files stored on the different file systems/file servers + +A `FileConnection` could be instantiated as follows: + +```python +from onetl.connection import SFTP + +sftp = SFTP( + host="sftp.test.com", + user="onetl", + password="onetl", +) +``` + +For a description of other parameters, see the documentation for the [available FileConnections](connection/file_connection/index.md#file-connections). + +### FileDFConnection + +Classes inherited from `FileDFConnection` could be used for accessing files as Spark DataFrames. + +A `FileDFConnection` could be instantiated as follows: + +```python +from onetl.connection import SparkHDFS + +spark_hdfs = SparkHDFS( + host="namenode1.domain.com", + cluster="mycluster", + spark=spark, +) +``` + +where **spark** is the current SparkSession. +`onETL` uses `Spark` and specific Java connectors under the hood to work with DataFrames. + +For a description of other parameters, see the documentation for the [available FileDFConnections](connection/file_df_connection/index.md#file-df-connections). + +### Checking connection availability + +Once you have created a connection, you can check the database/filesystem availability using the method `check()`: + +```python +mssql.check() +sftp.check() +spark_hdfs.check() +``` + +It will raise an exception if database/filesystem cannot be accessed. + +This method returns connection itself, so you can create connection and immediately check its availability: + +```Python +mssql = MSSQL( + host="mssqldb.demo.com", + user="onetl", + password="onetl", + database="Telecom", + spark=spark, +).check() # <-- +``` + +## Extract/Load data + +### Basics + +As we said above, onETL is used to extract data from and load data into remote systems. + +onETL provides several classes for this: + +> * [DBReader](db/db_reader.md#db-reader) +> * [DBWriter](db/db_writer.md#db-writer) +> * [FileDFReader](file_df/file_df_reader/file_df_reader.md#file-df-reader) +> * [FileDFWriter](file_df/file_df_writer/file_df_writer.md#file-df-writer) +> * [FileDownloader](file/file_downloader/file_downloader.md#file-downloader) +> * [FileUploader](file/file_uploader/file_uploader.md#file-uploader) +> * [FileMover](file/file_mover/file_mover.md#file-mover) + +All of these classes have a method `run()` that starts extracting/loading the data: + +```python +from onetl.db import DBReader, DBWriter + +reader = DBReader( + connection=mssql, + source="dbo.demo_table", + columns=["column_1", "column_2"], +) + +# Read data as Spark DataFrame +df = reader.run() + +db_writer = DBWriter( + connection=hive, + target="dl_sb.demo_table", +) + +# Save Spark DataFrame to Hive table +writer.run(df) +``` + +### Extract data + +To extract data you can use classes: + +| | Use case | Connection | `run()` gets | `run()` returns | +|-------------------------------------------------------------------------|-------------------------------------------|------------------------------------------------------------------------------------|---------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------| +| [DBReader](db/db_reader.md#db-reader) | Reading data from a database | Any [DBConnection](connection/db_connection/index.md#db-connections) | - | [Spark DataFrame](https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.sql.DataFrame.html#pyspark.sql.DataFrame) | +| [FileDFReader](file_df/file_df_reader/file_df_reader.md#file-df-reader) | Read data from a file or set of files | Any [FileDFConnection](connection/file_df_connection/index.md#file-df-connections) | No input, or List[File path on FileSystem] | [Spark DataFrame](https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.sql.DataFrame.html#pyspark.sql.DataFrame) | +| [FileDownloader](db/db_reader.md#db-reader) | Download files from remote FS to local FS | Any [FileConnection](connection/file_connection/index.md#file-connections) | No input, or List[File path on remote FileSystem] | [DownloadResult](file/file_downloader/result.md#file-downloader-result) | + +### Load data + +To load data you can use classes: + +| | Use case | Connection | `run()` gets | `run()` returns | +|-------------------------------------------------------------------|----------------------------------------------|------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------| +| [DBWriter](db/db_writer.md#db-writer) | Writing data from a DataFrame to a database | Any [DBConnection](connection/db_connection/index.md#db-connections) | [Spark DataFrame](https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.sql.DataFrame.html#pyspark.sql.DataFrame) | None | +| [FileDFWriter](db/db_writer.md#db-writer) | Writing data from a DataFrame to a folder | Any [FileDFConnection](connection/file_df_connection/index.md#file-df-connections) | [Spark DataFrame](https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.sql.DataFrame.html#pyspark.sql.DataFrame) | None | +| [FileUploader](file/file_uploader/file_uploader.md#file-uploader) | Uploading files from a local FS to remote FS | Any [FileConnection](connection/file_connection/index.md#file-connections) | List[File path on local FileSystem] | [UploadResult](file/file_uploader/result.md#file-uploader-result) | + +### Manipulate data + +To manipulate data you can use classes: + +| | Use case | Connection | `run()` gets | `run()` returns | +|-------------------------------------------------------|---------------------------------------------|----------------------------------------------------------------------------|--------------------------------------|-----------------------------------------------------------| +| [FileMover](file/file_mover/file_mover.md#file-mover) | Move files between directories in remote FS | Any [FileConnection](connection/file_connection/index.md#file-connections) | List[File path on remote FileSystem] | [MoveResult](file/file_mover/result.md#file-mover-result) | + +### Options + +Extract and load classes have a `options` parameter, which has a special meaning: + +> * all other parameters - *WHAT* we extract / *WHERE* we load to +> * `options` parameter - *HOW* we extract/load data +```python +db_reader = DBReader( + # WHAT do we read: + connection=mssql, + source="dbo.demo_table", # some table from MSSQL + columns=["column_1", "column_2"], # but only specific set of columns + where="column_2 > 1000", # only rows matching the clause + # HOW do we read: + options=MSSQL.ReadOptions( + numPartitions=10, # read in 10 parallel jobs + partitionColumn="id", # balance data read by assigning each job a part of data using `hash(id) mod N` expression + partitioningMode="hash", + fetchsize=1000, # each job will fetch block of 1000 rows each on every read attempt + ), +) + +db_writer = DBWriter( + # WHERE do we write to - to some table in Hive + connection=hive, + target="dl_sb.demo_table", + # HOW do we write - overwrite all the data in the existing table + options=Hive.WriteOptions(if_exists="replace_entire_table"), +) + +file_downloader = FileDownloader( + # WHAT do we download - files from some dir in SFTP + connection=sftp, + source_path="/source", + filters=[Glob("*.csv")], # only CSV files + limits=[MaxFilesCount(1000)], # 1000 files max + # WHERE do we download to - a specific dir on local FS + local_path="/some", + # HOW do we download: + options=FileDownloader.Options( + delete_source=True, # after downloading each file remove it from source_path + if_exists="replace_file", # replace existing files in the local_path + ), +) + +file_uploader = FileUploader( + # WHAT do we upload - files from some local dir + local_path="/source", + # WHERE do we upload to- specific remote dir in HDFS + connection=hdfs, + target_path="/some", + # HOW do we upload: + options=FileUploader.Options( + delete_local=True, # after uploading each file remove it from local_path + if_exists="replace_file", # replace existing files in the target_path + ), +) + +file_mover = FileMover( + # WHAT do we move - files in some remote dir in HDFS + source_path="/source", + connection=hdfs, + # WHERE do we move files to + target_path="/some", # a specific remote dir within the same HDFS connection + # HOW do we load - replace existing files in the target_path + options=FileMover.Options(if_exists="replace_file"), +) + +file_df_reader = FileDFReader( + # WHAT do we read - *.csv files from some dir in S3 + connection=s3, + source_path="/source", + file_format=CSV(), + # HOW do we read - load files from /source/*.csv, not from /source/nested/*.csv + options=FileDFReader.Options(recursive=False), +) + +file_df_writer = FileDFWriter( + # WHERE do we write to - as .csv files in some dir in S3 + connection=s3, + target_path="/target", + file_format=CSV(), + # HOW do we write - replace all existing files in /target, if exists + options=FileDFWriter.Options(if_exists="replace_entire_directory"), +) +``` + +More information about `options` could be found on [DB connection](connection/db_connection/index.md#db-connections). and +[File Downloader](file/file_downloader/file_downloader.md#file-downloader) / [File Uploader](file/file_uploader/file_uploader.md#file-uploader) / [File Mover](file/file_mover/file_mover.md#file-mover) / [FileDF Reader](file_df/file_df_reader/file_df_reader.md#file-df-reader) / [FileDF Writer](file_df/file_df_writer/file_df_writer.md#file-df-writer) documentation + +### Read Strategies + +onETL have several builtin strategies for reading data: + +1. [Snapshot strategy](strategy/snapshot_strategy.html) (default strategy) +2. [Incremental strategy](strategy/incremental_strategy.html) +3. [Snapshot batch strategy](strategy/snapshot_batch_strategy.html) +4. [Incremental batch strategy](strategy/incremental_batch_strategy.html) + +For example, an incremental strategy allows you to get only new data from the table: + +```python +from onetl.strategy import IncrementalStrategy + +reader = DBReader( + connection=mssql, + source="dbo.demo_table", + hwm_column="id", # detect new data based on value of "id" column +) + +# first run +with IncrementalStrategy(): + df = reader.run() + +sleep(3600) + +# second run +with IncrementalStrategy(): + # only rows, that appeared in the source since previous run + df = reader.run() +``` + +or get only files which were not downloaded before: + +```python +from onetl.strategy import IncrementalStrategy + +file_downloader = FileDownloader( + connection=sftp, + source_path="/remote", + local_path="/local", + hwm_type="file_list", # save all downloaded files to a list, and exclude files already present in this list +) + +# first run +with IncrementalStrategy(): + files = file_downloader.run() + +sleep(3600) + +# second run +with IncrementalStrategy(): + # only files, that appeared in the source since previous run + files = file_downloader.run() +``` + +Most of strategies are based on [HWM](hwm_store/index.md#hwm), Please check each strategy documentation for more details + +### Why just not use Connection class for extract/load? + +Connections are very simple, they have only a set of some basic operations, +like `mkdir`, `remove_file`, `get_table_schema`, and so on. + +High-level operations, like +: * [Read Strategies](strategy/index.md#strategy) support + * Handling metadata push/pull + * Handling different options, like `if_exists="replace_file"` in case of file download/upload + +is moved to a separate class which calls the connection object methods to perform some complex logic. diff --git a/mddocs/conf.py b/mddocs/conf.py new file mode 100644 index 000000000..c69e54521 --- /dev/null +++ b/mddocs/conf.py @@ -0,0 +1,150 @@ +# Configuration file for the Sphinx documentation builder. +# +# This file only contains a selection of the most common options. For a full +# list see the documentation: +# https://www.sphinx-doc.org/en/master/usage/configuration.html + +# -- Path setup -------------------------------------------------------------- + +# If extensions (or modules to document with autodoc) are in another directory, +# add these directories to sys.path here. If the directory is relative to the +# documentation root, use os.path.abspath to make it absolute, like shown here. + + +import os +import subprocess +import sys +from pathlib import Path + +from packaging.version import Version + +PROJECT_ROOT_DIR = Path(__file__).parent.parent.resolve() + +sys.path.insert(0, os.fspath(PROJECT_ROOT_DIR)) + +# -- Project information ----------------------------------------------------- + +project = "onETL" +copyright = "2021-2024 MTS PJSC" +author = "DataOps.ETL" + +# The version info for the project you're documenting, acts as replacement for +# |version| and |release|, also used in various other places throughout the +# built documents. +# +# The short X.Y version. + +ver = Version(subprocess.check_output("python ../setup.py --version", shell=True, text=True).strip()) +version = ver.base_version +# The full version, including alpha/beta/rc tags. +release = ver.public + +# -- General configuration --------------------------------------------------- + +# Add any Sphinx extension module names here, as strings. They can be +# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom +# ones. +extensions = [ + "numpydoc", + "sphinx_design", + "sphinx_substitution_extensions", + "sphinx_tabs.tabs", + "sphinx_toolbox.more_autodoc.autoprotocol", + "sphinx_toolbox.github", + "sphinx_copybutton", + "sphinx.ext.autodoc", + "sphinx.ext.autosummary", + "sphinxcontrib.towncrier", # provides `towncrier-draft-entries` directive + "sphinxcontrib.plantuml", + "sphinx.ext.extlinks", + "sphinx_favicon", + "sphinxcontrib.autodoc_pydantic", + "sphinx_last_updated_by_git", + "sphinx_markdown_builder", + "myst_parser", +] +numpydoc_show_class_members = False +autodoc_pydantic_model_show_config = False +autodoc_pydantic_model_show_config_summary = False +autodoc_pydantic_model_show_config_member = False +autodoc_pydantic_model_show_json = False +autodoc_pydantic_model_show_validator_summary = False +autodoc_pydantic_model_show_validator_members = False +autodoc_pydantic_field_list_validators = False +sphinx_tabs_disable_tab_closing = True + +# prevent >>>, ... and doctest outputs from copying +copybutton_prompt_text = r">>> |\.\.\. |\$ |In \[\d*\]: | {2,5}\.\.\.: | {5,8}: " +copybutton_prompt_is_regexp = True +copybutton_copy_empty_lines = False +copybutton_only_copy_prompt_lines = True + +towncrier_draft_autoversion_mode = "draft" +towncrier_draft_include_empty = False +towncrier_draft_working_directory = PROJECT_ROOT_DIR + +github_username = "MobileTeleSystems" +github_repository = "onetl" + +rst_prolog = f""" +.. |support_hooks| image:: https://img.shields.io/badge/%20-support%20hooks-blue + :target: https://onetl.readthedocs.io/en/{ver}/hooks/index.html +""" + +# Add any paths that contain templates here, relative to this directory. +templates_path = ["_templates"] + +# List of patterns, relative to source directory, that match files and +# directories to ignore when looking for source files. +# This pattern also affects html_static_path and html_extra_path. +exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"] + + +# -- Options for HTML output ------------------------------------------------- + +# The theme to use for HTML and HTML Help pages. See the documentation for +# a list of builtin themes. + +html_theme = "furo" + +# Theme options are theme-specific and customize the look and feel of a theme +# further. For a list of options available for each theme, see the +# documentation. +html_theme_options = {} + +# Add any paths that contain custom static files (such as style sheets) here, +# relative to this directory. They are copied after the builtin static files, +# so a file named "default.css" will overwrite the builtin "default.css". +html_static_path = ["_static"] +html_extra_path = ["robots.txt"] +html_logo = "./_static/logo.svg" +favicons = [ + {"rel": "icon", "href": "icon.svg", "type": "image/svg+xml"}, +] + +# The master toctree document. +master_doc = "index" + +# The language for content autogenerated by Sphinx. Refer to documentation +# for a list of supported languages. +# +# This is also used if you do content translation via gettext catalogs. +# Usually you set "language" from the command line for these cases. +language = "en" + +# The name of the Pygments (syntax highlighting) style to use. +pygments_style = "sphinx" + +# Create an alias for etl-entities lib in onetl documentation +extlinks = { + "etl-entities": ("https://etl-entities.readthedocs.io/en/stable/%s", None), +} + + +# If true, `todo` and `todoList` produce output, else they produce nothing. +todo_include_todos = False + +# -- Options for HTMLHelp output ------------------------------------------ + +# Output file base name for HTML help builder. +htmlhelp_basename = "onetl-doc" diff --git a/mddocs/connection/db_connection/clickhouse/connection.md b/mddocs/connection/db_connection/clickhouse/connection.md new file mode 100644 index 000000000..09365e6a8 --- /dev/null +++ b/mddocs/connection/db_connection/clickhouse/connection.md @@ -0,0 +1,128 @@ + + +# Clickhouse connection + +### *class* onetl.connection.db_connection.clickhouse.connection.Clickhouse(\*, spark: SparkSession, user: str, password: SecretStr, host: Host, port: int = 8123, database: str | None = None, extra: ClickhouseExtra = ClickhouseExtra()) + +Clickhouse JDBC connection. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +Based on Maven package [com.clickhouse:clickhouse-jdbc:0.7.2](https://mvnrepository.com/artifact/com.clickhouse/clickhouse-jdbc/0.7.2) +([official Clickhouse JDBC driver](https://github.com/ClickHouse/clickhouse-jdbc)). + +#### SEE ALSO +Before using this connector please take into account [Prerequisites](prerequisites.md#clickhouse-prerequisites) + +#### Versionadded +Added in version 0.1.0. + +* **Parameters:** + **host** + : Host of Clickhouse database. For example: `test.clickhouse.domain.com` or `193.168.1.11` + + **port** + : Port of Clickhouse database + + **user** + : User, which have proper access to the database. For example: `some_user` + + **password** + : Password for database connection + + **database** + : Database in RDBMS, NOT schema. +
+ See [this page](https://www.educba.com/postgresql-database-vs-schema/) for more details + + **spark** + : Spark session. + + **extra** + : Specifies one or more extra parameters by which clients can connect to the instance. +
+ For example: `{"continueBatchOnError": "false"}`. +
+ See: + : * [Clickhouse JDBC driver properties documentation](https://clickhouse.com/docs/en/integrations/java#configuration) + * [Clickhouse core settings documentation](https://clickhouse.com/docs/en/operations/settings/settings) + * [Clickhouse query complexity documentation](https://clickhouse.com/docs/en/operations/settings/query-complexity) + * [Clickhouse query level settings](https://clickhouse.com/docs/en/operations/settings/query-level) + +### Examples + +Create and check Clickhouse connection: + +```python +from onetl.connection import Clickhouse +from pyspark.sql import SparkSession + +# Create Spark session with Clickhouse driver loaded +maven_packages = Clickhouse.get_packages() +spark = ( + SparkSession.builder.appName("spark-app-name") + .config("spark.jars.packages", ",".join(maven_packages)) + .getOrCreate() +) + +# Create connection +clickhouse = Clickhouse( + host="database.host.or.ip", + user="user", + password="*****", + extra={"continueBatchOnError": "false"}, + spark=spark, +).check() +``` + + + +#### check() + +Check source availability. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +If not, an exception will be raised. + +* **Returns:** + Connection itself +* **Raises:** + RuntimeError + : If the connection is not available + +### Examples + +```python +connection.check() +``` + + + +#### *classmethod* get_packages(package_version: str | None = None, apache_http_client_version: str | None = None) → list[str] + +Get package names to be downloaded by Spark. Allows specifying custom JDBC and Apache HTTP Client versions. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### Versionadded +Added in version 0.9.0. + +* **Parameters:** + **package_version** + : ClickHouse JDBC version client packages. Defaults to `0.7.2`. +
+ #### Versionadded + Added in version 0.11.0. + + **apache_http_client_version** + : Apache HTTP Client version package. Defaults to `5.4.2`. +
+ Used only if `package_version` is in range `0.5.0-0.7.0`. +
+ #### Versionadded + Added in version 0.11.0. + +### Examples + +```python +from onetl.connection import Clickhouse + +Clickhouse.get_packages(package_version="0.6.0", apache_http_client_version="5.4.2") +``` + + diff --git a/mddocs/connection/db_connection/clickhouse/execute.md b/mddocs/connection/db_connection/clickhouse/execute.md new file mode 100644 index 000000000..c65c26a67 --- /dev/null +++ b/mddocs/connection/db_connection/clickhouse/execute.md @@ -0,0 +1,191 @@ + + +# Executing statements in Clickhouse + +#### WARNING +Methods below **read all the rows** returned from DB **to Spark driver memory**, and then convert them to DataFrame. + +Do **NOT** use them to read large amounts of data. Use [DBReader](read.md#clickhouse-read) or [Clickhouse.sql](sql.md#clickhouse-sql) instead. + +## How to + +There are 2 ways to execute some statement in Clickhouse + +### Use `Clickhouse.fetch` + +Use this method to perform some `SELECT` query which returns **small number or rows**, like reading +Clickhouse config, or reading data from some reference table. Method returns Spark DataFrame. + +Method accepts [`Clickhouse.FetchOptions`](#onetl.connection.db_connection.clickhouse.options.ClickhouseFetchOptions). + +Connection opened using this method should be then closed with `connection.close()` or `with connection:`. + +#### WARNING +Please take into account [Clickhouse <-> Spark type mapping](types.md#clickhouse-types). + +#### Syntax support + +This method supports **any** query syntax supported by Clickhouse, like: + +* ✅︎ `SELECT ... FROM ...` +* ✅︎ `WITH alias AS (...) SELECT ...` +* ✅︎ `SELECT func(arg1, arg2)` - call function +* ✅︎ `SHOW ...` +* ❌ `SET ...; SELECT ...;` - multiple statements not supported + +#### Examples + +```python +from onetl.connection import Clickhouse + +clickhouse = Clickhouse(...) + +df = clickhouse.fetch( + "SELECT value FROM some.reference_table WHERE key = 'some_constant'", + options=Clickhouse.FetchOptions(queryTimeout=10), +) +clickhouse.close() +value = df.collect()[0][0] # get value from first row and first column +``` + +### Use `Clickhouse.execute` + +Use this method to execute DDL and DML operations. Each method call runs operation in a separated transaction, and then commits it. + +Method accepts [`Clickhouse.ExecuteOptions`](#onetl.connection.db_connection.clickhouse.options.ClickhouseExecuteOptions). + +Connection opened using this method should be then closed with `connection.close()` or `with connection:`. + +#### Syntax support + +This method supports **any** query syntax supported by Clickhouse, like: + +* ✅︎ `CREATE TABLE ...`, `CREATE VIEW ...`, and so on +* ✅︎ `ALTER ...` +* ✅︎ `INSERT INTO ... SELECT ...`, `UPDATE ...`, `DELETE ...`, and so on +* ✅︎ `DROP TABLE ...`, `DROP VIEW ...`, `TRUNCATE TABLE`, and so on +* ✅︎ other statements not mentioned here +* ❌ `SET ...; SELECT ...;` - multiple statements not supported + +#### Examples + +```python +from onetl.connection import Clickhouse + +clickhouse = Clickhouse(...) + +clickhouse.execute("DROP TABLE schema.table") +clickhouse.execute( + """ + CREATE TABLE schema.table ( + id UInt8, + key String, + value Float32 + ) + ENGINE = MergeTree() + ORDER BY id + """, + options=Clickhouse.ExecuteOptions(queryTimeout=10), +) +``` + +## Notes + +These methods **read all the rows** returned from DB **to Spark driver memory**, and then convert them to DataFrame. + +So it should **NOT** be used to read large amounts of data. Use [DBReader](read.md#clickhouse-read) or [Clickhouse.sql](sql.md#clickhouse-sql) instead. + +## Options + +### *pydantic model* onetl.connection.db_connection.clickhouse.options.ClickhouseFetchOptions + +Options related to fetching data from databases via JDBC. + +#### Versionadded +Added in version 0.11.0: Replace `Clickhouse.JDBCOptions` → `Clickhouse.FetchOptions` + +### Examples + +#### NOTE +You can pass any value supported by underlying JDBC driver class, +even if it is not mentioned in this documentation. + +```python +from onetl.connection import Clickhouse + +options = Clickhouse.FetchOptions( + queryTimeout=60_000, + fetchsize=100_000, + customSparkOption="value", +) +``` + + + +#### *field* query_timeout *: int | None* *= None* *(alias 'queryTimeout')* + +The number of seconds the driver will wait for a statement to execute. +Zero means there is no limit. + +This option depends on driver implementation, +some drivers can check the timeout of each query instead of an entire JDBC batch. + + + +#### *field* fetchsize *: int | None* *= None* + +How many rows to fetch per round trip. + +Tuning this option can influence performance of reading. + +#### WARNING +Default value depends on driver. For example, Oracle has +default `fetchsize=10`. + + + +### *pydantic model* onetl.connection.db_connection.clickhouse.options.ClickhouseExecuteOptions + +Options related to executing statements in databases via JDBC. + +#### Versionadded +Added in version 0.11.0: Replace `Clickhouse.JDBCOptions` → `Clickhouse.ExecuteOptions` + +### Examples + +#### NOTE +You can pass any value supported by underlying JDBC driver class, +even if it is not mentioned in this documentation. + +```python +from onetl.connection import Clickhouse + +options = Clickhouse.ExecuteOptions( + queryTimeout=60_000, + customSparkOption="value", +) +``` + + + +#### *field* query_timeout *: int | None* *= None* *(alias 'queryTimeout')* + +The number of seconds the driver will wait for a statement to execute. +Zero means there is no limit. + +This option depends on driver implementation, +some drivers can check the timeout of each query instead of an entire JDBC batch. + + + +#### *field* fetchsize *: int | None* *= None* + +How many rows to fetch per round trip. + +Tuning this option can influence performance of reading. + +#### WARNING +Default value depends on driver. For example, Oracle has +default `fetchsize=10`. + + diff --git a/mddocs/connection/db_connection/clickhouse/index.md b/mddocs/connection/db_connection/clickhouse/index.md new file mode 100644 index 000000000..3b226910d --- /dev/null +++ b/mddocs/connection/db_connection/clickhouse/index.md @@ -0,0 +1,19 @@ + + +# Clickhouse + +# Connection + +* [Prerequisites](prerequisites.md) +* [Clickhouse connection](connection.md) + +# Operations + +* [Reading from Clickhouse using `DBReader`](read.md) +* [Reading from Clickhouse using `Clickhouse.sql`](sql.md) +* [Writing to Clickhouse using `DBWriter`](write.md) +* [Executing statements in Clickhouse](execute.md) + +# Troubleshooting + +* [Clickhouse <-> Spark type mapping](types.md) diff --git a/mddocs/connection/db_connection/clickhouse/prerequisites.md b/mddocs/connection/db_connection/clickhouse/prerequisites.md new file mode 100644 index 000000000..86551b41a --- /dev/null +++ b/mddocs/connection/db_connection/clickhouse/prerequisites.md @@ -0,0 +1,73 @@ + + +# Prerequisites + +## Version Compatibility + +* Clickhouse server versions: + : * Officially declared: 22.8 or higher + * Actually tested: 21.1, 25.1 +* Spark versions: 2.3.x - 3.5.x +* Java versions: 8 - 20 + +See [official documentation](https://clickhouse.com/docs/en/integrations/java#jdbc-driver). + +## Installing PySpark + +To use Clickhouse connector you should have PySpark installed (or injected to `sys.path`) +BEFORE creating the connector instance. + +See [Spark](../../../install/spark.md#install-spark) installation instruction for more details. + +## Connecting to Clickhouse + +### Connection port + +Connector can only use **HTTP** (usually `8123` port) or **HTTPS** (usually `8443` port) protocol. + +TCP and GRPC protocols are NOT supported. + +### Connecting to cluster + +It is possible to connect to Clickhouse cluster, and use it’s load balancing capabilities to read or write data in parallel. +Each Spark executor can connect to random Clickhouse nodes, instead of sending all the data to a node specified in connection params. + +This requires all Clickhouse servers to run on different hosts, and **listen the same HTTP port**. +Set `auto_discovery=True` to enable this feature (disabled by default): + +```python +Clickhouse( + host="node1.of.cluster", + port=8123, + extra={ + "auto_discovery": True, + "load_balancing_policy": "roundRobin", + }, +) +``` + +See [official documentation](https://clickhouse.com/docs/en/integrations/java#configuring-node-discovery-load-balancing-and-failover). + +### Required grants + +Ask your Clickhouse cluster administrator to set following grants for a user, +used for creating a connection: + +Read + Write + +```sql +-- allow creating tables in the target schema +GRANT CREATE TABLE ON myschema.* TO username; + +-- allow read & write access to specific table +GRANT SELECT, INSERT ON myschema.mytable TO username; +``` + +Read only + +```sql +-- allow read access to specific table +GRANT SELECT ON myschema.mytable TO username; +``` + +More details can be found in [official documentation](https://clickhouse.com/docs/en/sql-reference/statements/grant). diff --git a/mddocs/connection/db_connection/clickhouse/read.md b/mddocs/connection/db_connection/clickhouse/read.md new file mode 100644 index 000000000..611930634 --- /dev/null +++ b/mddocs/connection/db_connection/clickhouse/read.md @@ -0,0 +1,339 @@ + + +# Reading from Clickhouse using `DBReader` + +[`DBReader`](../../../db/db_reader.md#onetl.db.db_reader.db_reader.DBReader) supports [Read Strategies](../../../strategy/index.md#strategy) for incremental data reading, +but does not support custom queries, like `JOIN`. + +#### WARNING +Please take into account [Clickhouse <-> Spark type mapping](types.md#clickhouse-types) + +## Supported DBReader features + +* ✅︎ `columns` +* ✅︎ `where` +* ✅︎ `hwm`, supported strategies: +* * ✅︎ [Snapshot Strategy](../../../strategy/snapshot_strategy.md#snapshot-strategy) +* * ✅︎ [Incremental Strategy](../../../strategy/incremental_strategy.md#incremental-strategy) +* * ✅︎ [Snapshot Batch Strategy](../../../strategy/snapshot_batch_strategy.md#snapshot-batch-strategy) +* * ✅︎ [Incremental Batch Strategy](../../../strategy/incremental_batch_strategy.md#incremental-batch-strategy) +* ❌ `hint` (is not supported by Clickhouse) +* ❌ `df_schema` +* ✅︎ `options` (see [`Clickhouse.ReadOptions`](#onetl.connection.db_connection.clickhouse.options.ClickhouseReadOptions)) + +## Examples + +Snapshot strategy: + +```python +from onetl.connection import Clickhouse +from onetl.db import DBReader + +clickhouse = Clickhouse(...) + +reader = DBReader( + connection=clickhouse, + source="schema.table", + columns=["id", "key", "CAST(value AS String) value", "updated_dt"], + where="key = 'something'", + options=Clickhouse.ReadOptions(partitionColumn="id", numPartitions=10), +) +df = reader.run() +``` + +Incremental strategy: + +```python +from onetl.connection import Clickhouse +from onetl.db import DBReader +from onetl.strategy import IncrementalStrategy + +clickhouse = Clickhouse(...) + +reader = DBReader( + connection=clickhouse, + source="schema.table", + columns=["id", "key", "CAST(value AS String) value", "updated_dt"], + where="key = 'something'", + hwm=DBReader.AutoDetectHWM(name="clickhouse_hwm", expression="updated_dt"), + options=Clickhouse.ReadOptions(partitionColumn="id", numPartitions=10), +) + +with IncrementalStrategy(): + df = reader.run() +``` + +## Recommendations + +### Select only required columns + +Instead of passing `"*"` in `DBReader(columns=[...])` prefer passing exact column names. This reduces the amount of data passed from Clickhouse to Spark. + +### Pay attention to `where` value + +Instead of filtering data on Spark side using `df.filter(df.column == 'value')` pass proper `DBReader(where="column = 'value'")` clause. +This both reduces the amount of data send from Clickhouse to Spark, and may also improve performance of the query. +Especially if there are indexes or partitions for columns used in `where` clause. + +## Options + +### *pydantic model* onetl.connection.db_connection.clickhouse.options.ClickhouseReadOptions + +Spark JDBC reading options. + +#### Versionadded +Added in version 0.5.0: Replace `Clickhouse.Options` → `Clickhouse.ReadOptions` + +### Examples + +#### NOTE +You can pass any value +[supported by Spark](https://spark.apache.org/docs/latest/sql-data-sources-jdbc.html), +even if it is not mentioned in this documentation. **Option names should be in** `camelCase`! + +The set of supported options depends on Spark version. + +```python +from onetl.connection import Clickhouse + +options = Clickhouse.ReadOptions( + partitioning_mode="range", + partitionColumn="reg_id", + numPartitions=10, + customSparkOption="value", +) +``` + + + +#### *field* partition_column *: str | None* *= None* *(alias 'partitionColumn')* + +Column used to parallelize reading from a table. + +#### WARNING +It is highly recommended to use primary key, or column with an index +to avoid performance issues. + +#### NOTE +Column type depends on [`partitioning_mode`](#onetl.connection.db_connection.clickhouse.options.ClickhouseReadOptions.partitioning_mode). + +* `partitioning_mode="range"` requires column to be an integer, date or timestamp (can be NULL, but not recommended). +* `partitioning_mode="hash"` accepts any column type (NOT NULL). +* `partitioning_mode="mod"` requires column to be an integer (NOT NULL). + +See documentation for [`partitioning_mode`](#onetl.connection.db_connection.clickhouse.options.ClickhouseReadOptions.partitioning_mode) for more details + + + +#### *field* num_partitions *: PositiveInt* *= 1* *(alias 'numPartitions')* + +Number of jobs created by Spark to read the table content in parallel. +See documentation for [`partitioning_mode`](#onetl.connection.db_connection.clickhouse.options.ClickhouseReadOptions.partitioning_mode) for more details + + + +#### *field* lower_bound *: int | None* *= None* *(alias 'lowerBound')* + +See documentation for [`partitioning_mode`](#onetl.connection.db_connection.clickhouse.options.ClickhouseReadOptions.partitioning_mode) for more details + + + +#### *field* upper_bound *: int | None* *= None* *(alias 'upperBound')* + +See documentation for [`partitioning_mode`](#onetl.connection.db_connection.clickhouse.options.ClickhouseReadOptions.partitioning_mode) for more details + + + +#### *field* session_init_statement *: str | None* *= None* *(alias 'sessionInitStatement')* + +After each database session is opened to the remote DB and before starting to read data, +this option executes a custom SQL statement (or a PL/SQL block). + +Use this to implement session initialization code. + +Example: + +```python +sessionInitStatement = """ + BEGIN + execute immediate + 'alter session set "_serial_direct_read"=true'; + END; +""" +``` + + + +#### *field* query_timeout *: int | None* *= None* *(alias 'queryTimeout')* + +The number of seconds the driver will wait for a statement to execute. +Zero means there is no limit. + +This option depends on driver implementation, +some drivers can check the timeout of each query instead of an entire JDBC batch. + + + +#### *field* fetchsize *: int* *= 100000* + +Fetch N rows from an opened cursor per one read round. + +Tuning this option can influence performance of reading. + +#### WARNING +Default value is different from Spark. + +Spark uses driver’s own value, and it may be different in different drivers, +and even versions of the same driver. For example, Oracle has +default `fetchsize=10`, which is absolutely not usable. + +Thus we’ve overridden default value with `100_000`, which should increase reading performance. + +#### Versionchanged +Changed in version 0.2.0: Set explicit default value to `100_000` + + + +#### *field* partitioning_mode *: JDBCPartitioningMode* *= JDBCPartitioningMode.RANGE* + +Defines how Spark will parallelize reading from table. + +Possible values: + +* `range` (default) + : Allocate each executor a range of values from column passed into [`partition_column`](#onetl.connection.db_connection.clickhouse.options.ClickhouseReadOptions.partition_column). +
+ ### Spark generates for each executor an SQL query +
+ Executor 1: + ```sql + SELECT ... FROM table + WHERE (partition_column >= lowerBound + OR partition_column IS NULL) + AND partition_column < (lower_bound + stride) + ``` +
+ Executor 2: + ```sql + SELECT ... FROM table + WHERE partition_column >= (lower_bound + stride) + AND partition_column < (lower_bound + 2 * stride) + ``` +
+ … +
+ Executor N: + ```sql + SELECT ... FROM table + WHERE partition_column >= (lower_bound + (N-1) * stride) + AND partition_column <= upper_bound + ``` +
+ Where `stride=(upper_bound - lower_bound) / num_partitions`. +
+ #### NOTE + Can be used only with columns of integer, date or timestamp types. +
+ #### NOTE + [`lower_bound`](#onetl.connection.db_connection.clickhouse.options.ClickhouseReadOptions.lower_bound), [`upper_bound`](#onetl.connection.db_connection.clickhouse.options.ClickhouseReadOptions.upper_bound) and [`num_partitions`](#onetl.connection.db_connection.clickhouse.options.ClickhouseReadOptions.num_partitions) are used just to + calculate the partition stride, **NOT** for filtering the rows in table. + So all rows in the table will be returned (unlike *Incremental* [Read Strategies](../../../strategy/index.md#strategy)). +
+ #### NOTE + All queries are executed in parallel. To execute them sequentially, use *Batch* [Read Strategies](../../../strategy/index.md#strategy). +* `hash` + : Allocate each executor a set of values based on hash of the [`partition_column`](#onetl.connection.db_connection.clickhouse.options.ClickhouseReadOptions.partition_column) column. +
+ ### Spark generates for each executor an SQL query +
+ Executor 1: + ```sql + SELECT ... FROM table + WHERE (some_hash(partition_column) mod num_partitions) = 0 -- lower_bound + ``` +
+ Executor 2: + ```sql + SELECT ... FROM table + WHERE (some_hash(partition_column) mod num_partitions) = 1 -- lower_bound + 1 + ``` +
+ … +
+ Executor N: + ```sql + SELECT ... FROM table + WHERE (some_hash(partition_column) mod num_partitions) = num_partitions-1 -- upper_bound + ``` +
+ #### NOTE + The hash function implementation depends on RDBMS. It can be `MD5` or any other fast hash function, + or expression based on this function call. Usually such functions accepts any column type as an input. +* `mod` + : Allocate each executor a set of values based on modulus of the [`partition_column`](#onetl.connection.db_connection.clickhouse.options.ClickhouseReadOptions.partition_column) column. +
+ ### Spark generates for each executor an SQL query +
+ Executor 1: + ```sql + SELECT ... FROM table + WHERE (partition_column mod num_partitions) = 0 -- lower_bound + ``` +
+ Executor 2: + ```sql + SELECT ... FROM table + WHERE (partition_column mod num_partitions) = 1 -- lower_bound + 1 + ``` +
+ Executor N: + ```sql + SELECT ... FROM table + WHERE (partition_column mod num_partitions) = num_partitions-1 -- upper_bound + ``` +
+ #### NOTE + Can be used only with columns of integer type. + +#### Versionadded +Added in version 0.5.0. + +### Examples + +Read data in 10 parallel jobs by range of values in `id_column` column: + +```python +ReadOptions( + partitioning_mode="range", # default mode, can be omitted + partitionColumn="id_column", + numPartitions=10, + # Options below can be discarded because they are + # calculated automatically as MIN and MAX values of `partitionColumn` + lowerBound=0, + upperBound=100_000, +) +``` + +Read data in 10 parallel jobs by hash of values in `some_column` column: + +```python +ReadOptions( + partitioning_mode="hash", + partitionColumn="some_column", + numPartitions=10, + # lowerBound and upperBound are automatically set to `0` and `9` +) +``` + +Read data in 10 parallel jobs by modulus of values in `id_column` column: + +```python +ReadOptions( + partitioning_mode="mod", + partitionColumn="id_column", + numPartitions=10, + # lowerBound and upperBound are automatically set to `0` and `9` +) +``` + + diff --git a/mddocs/connection/db_connection/clickhouse/sql.md b/mddocs/connection/db_connection/clickhouse/sql.md new file mode 100644 index 000000000..e2ce4add5 --- /dev/null +++ b/mddocs/connection/db_connection/clickhouse/sql.md @@ -0,0 +1,191 @@ + + +# Reading from Clickhouse using `Clickhouse.sql` + +`Clickhouse.sql` allows passing custom SQL query, but does not support incremental strategies. + +#### WARNING +Please take into account [Clickhouse <-> Spark type mapping](types.md#clickhouse-types) + +#### WARNING +Statement is executed in **read-write** connection, so if you’re calling some functions/procedures with DDL/DML statements inside, +they can change data in your database. + +## Syntax support + +Only queries with the following syntax are supported: + +* ✅︎ `SELECT ... FROM ...` +* ✅︎ `WITH alias AS (...) SELECT ...` +* ❌ `SET ...; SELECT ...;` - multiple statements not supported + +## Examples + +```python +from onetl.connection import Clickhouse + +clickhouse = Clickhouse(...) +df = clickhouse.sql( + """ + SELECT + id, + key, + CAST(value AS String) value, + updated_at + FROM + some.mytable + WHERE + key = 'something' + """, + options=Clickhouse.SQLOptions( + partitionColumn="id", + numPartitions=10, + lowerBound=0, + upperBound=1000, + ), +) +``` + +## Recommendations + +### Select only required columns + +Instead of passing `SELECT * FROM ...` prefer passing exact column names `SELECT col1, col2, ...`. +This reduces the amount of data passed from Clickhouse to Spark. + +### Pay attention to `where` value + +Instead of filtering data on Spark side using `df.filter(df.column == 'value')` pass proper `WHERE column = 'value'` clause. +This both reduces the amount of data send from Clickhouse to Spark, and may also improve performance of the query. +Especially if there are indexes or partitions for columns used in `where` clause. + +## Options + +### *pydantic model* onetl.connection.db_connection.clickhouse.options.ClickhouseSQLOptions + +Options specifically for SQL queries + +These options allow you to specify configurations for executing SQL queries +without relying on Spark’s partitioning mechanisms. + +#### Versionadded +Added in version 0.11.0: Split up `Clickhouse.ReadOptions` to `Clickhouse.SQLOptions` + +### Examples + +#### NOTE +You can pass any JDBC configuration +[supported by Spark](https://spark.apache.org/docs/latest/sql-data-sources-jdbc.html), +tailored to optimize SQL query execution. **Option names should be in** `camelCase`! + +```python +from onetl.connection import Clickhouse + +options = Clickhouse.SQLOptions( + partitionColumn="reg_id", + numPartitions=10, + lowerBound=0, + upperBound=1000, + customSparkOption="value", +) +``` + + + +#### *field* partition_column *: str | None* *= None* *(alias 'partitionColumn')* + +Column used to partition data across multiple executors for parallel query processing. + +#### WARNING +It is highly recommended to use primary key, or column with an index +to avoid performance issues. + +### Example of using `partitionColumn="id"` with `partitioning_mode="range"` + +```sql +-- If partition_column is 'id', with numPartitions=4, lowerBound=1, and upperBound=100: +-- Executor 1 processes IDs from 1 to 25 +SELECT ... FROM table WHERE id >= 1 AND id < 26 +-- Executor 2 processes IDs from 26 to 50 +SELECT ... FROM table WHERE id >= 26 AND id < 51 +-- Executor 3 processes IDs from 51 to 75 +SELECT ... FROM table WHERE id >= 51 AND id < 76 +-- Executor 4 processes IDs from 76 to 100 +SELECT ... FROM table WHERE id >= 76 AND id <= 100 + +-- General case for Executor N +SELECT ... FROM table +WHERE partition_column >= (lowerBound + (N-1) * stride) +AND partition_column <= upperBound +-- Where ``stride`` is calculated as ``(upperBound - lowerBound) / numPartitions``. +``` + + + +#### *field* num_partitions *: int | None* *= None* *(alias 'numPartitions')* + +Number of jobs created by Spark to read the table content in parallel. + + + +#### *field* lower_bound *: int | None* *= None* *(alias 'lowerBound')* + +Defines the starting boundary for partitioning the query’s data. Mandatory if [`partition_column`](#onetl.connection.db_connection.clickhouse.options.ClickhouseSQLOptions.partition_column) is set + + + +#### *field* upper_bound *: int | None* *= None* *(alias 'upperBound')* + +Sets the ending boundary for data partitioning. Mandatory if [`partition_column`](#onetl.connection.db_connection.clickhouse.options.ClickhouseSQLOptions.partition_column) is set + + + +#### *field* session_init_statement *: str | None* *= None* *(alias 'sessionInitStatement')* + +After each database session is opened to the remote DB and before starting to read data, +this option executes a custom SQL statement (or a PL/SQL block). + +Use this to implement session initialization code. + +Example: + +```python +sessionInitStatement = """ + BEGIN + execute immediate + 'alter session set "_serial_direct_read"=true'; + END; +""" +``` + + + +#### *field* query_timeout *: int | None* *= None* *(alias 'queryTimeout')* + +The number of seconds the driver will wait for a statement to execute. +Zero means there is no limit. + +This option depends on driver implementation, +some drivers can check the timeout of each query instead of an entire JDBC batch. + + + +#### *field* fetchsize *: int* *= 100000* + +Fetch N rows from an opened cursor per one read round. + +Tuning this option can influence performance of reading. + +#### WARNING +Default value is different from Spark. + +Spark uses driver’s own value, and it may be different in different drivers, +and even versions of the same driver. For example, Oracle has +default `fetchsize=10`, which is absolutely not usable. + +Thus we’ve overridden default value with `100_000`, which should increase reading performance. + +#### Versionchanged +Changed in version 0.2.0: Set explicit default value to `100_000` + + diff --git a/mddocs/connection/db_connection/clickhouse/types.md b/mddocs/connection/db_connection/clickhouse/types.md new file mode 100644 index 000000000..447a8ae26 --- /dev/null +++ b/mddocs/connection/db_connection/clickhouse/types.md @@ -0,0 +1,347 @@ + + +# Clickhouse <-> Spark type mapping + +#### NOTE +The results below are valid for Spark 3.5.5, and may differ on other Spark versions. + +#### NOTE +It is recommended to use [spark-dialect-extension](https://github.com/MobileTeleSystems/spark-dialect-extension) package, +which implements writing Arrays from Spark to Clickhouse, fixes dropping fractions of seconds in `TimestampType`, +and fixes other type conversion issues. + +## Type detection & casting + +Spark’s DataFrames always have a `schema` which is a list of columns with corresponding Spark types. All operations on a column are performed using column type. + +### Reading from Clickhouse + +This is how Clickhouse connector performs this: + +* For each column in query result (`SELECT column1, column2, ... FROM table ...`) get column name and Clickhouse type. +* Find corresponding `Clickhouse type (read)` → `Spark type` combination (see below) for each DataFrame column. If no combination is found, raise exception. +* Create DataFrame from query with specific column names and Spark types. + +### Writing to some existing Clickhouse table + +This is how Clickhouse connector performs this: + +* Get names of columns in DataFrame. [1](#id3) +* Perform `SELECT * FROM table LIMIT 0` query. +* Take only columns present in DataFrame (by name, case insensitive). For each found column get Clickhouse type. +* **Find corresponding** `Clickhouse type (read)` → `Spark type` **combination** (see below) for each DataFrame column. If no combination is found, raise exception. [2](#id4) +* Find corresponding `Spark type` → `Clickhousetype (write)` combination (see below) for each DataFrame column. If no combination is found, raise exception. +* If `Clickhousetype (write)` match `Clickhouse type (read)`, no additional casts will be performed, DataFrame column will be written to Clickhouse as is. +* If `Clickhousetype (write)` does not match `Clickhouse type (read)`, DataFrame column will be casted to target column type **on Clickhouse side**. For example, you can write column with text data to `Int32` column, if column contains valid integer values within supported value range and precision. + +* **[1]** This allows to write data to tables with `DEFAULT` columns - if DataFrame has no such column, it will be populated by Clickhouse. +* **[2]** Yes, this is weird. + +### Create new table using Spark + +#### WARNING +ABSOLUTELY NOT RECOMMENDED! + +This is how Clickhouse connector performs this: + +* Find corresponding `Spark type` → `Clickhouse type (create)` combination (see below) for each DataFrame column. If no combination is found, raise exception. +* Generate DDL for creating table in Clickhouse, like `CREATE TABLE (col1 ...)`, and run it. +* Write DataFrame to created table as is. + +But Spark does not have specific dialect for Clickhouse, so Generic JDBC dialect is used. +Generic dialect is using SQL ANSI type names while creating tables in target database, not database-specific types. + +If some cases this may lead to using wrong column type. For example, Spark creates column of type `TIMESTAMP` +which corresponds to Clickhouse type `DateTime32` (precision up to seconds) +instead of more precise `DateTime64` (precision up to nanoseconds). +This may lead to incidental precision loss, or sometimes data cannot be written to created table at all. + +So instead of relying on Spark to create tables: + +### See example + +```python +writer = DBWriter( + connection=clickhouse, + target="default.target_tbl", + options=Clickhouse.WriteOptions( + if_exists="append", + # ENGINE is required by Clickhouse + createTableOptions="ENGINE = MergeTree() ORDER BY id", + ), +) +writer.run(df) +``` + +Always prefer creating tables with specific types **BEFORE WRITING DATA**: + +### See example + +```python +clickhouse.execute( + """ + CREATE TABLE default.target_tbl ( + id UInt8, + value DateTime64(6) -- specific type and precision + ) + ENGINE = MergeTree() + ORDER BY id + """, +) + +writer = DBWriter( + connection=clickhouse, + target="default.target_tbl", + options=Clickhouse.WriteOptions(if_exists="append"), +) +writer.run(df) +``` + +### References + +Here you can find source code with type conversions: + +* [Clickhouse -> JDBC](https://github.com/ClickHouse/clickhouse-java/blob/0.3.2/clickhouse-jdbc/src/main/java/com/clickhouse/jdbc/JdbcTypeMapping.java#L39-L176) +* [JDBC -> Spark](https://github.com/apache/spark/blob/v3.5.5/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JdbcUtils.scala#L307) +* [Spark -> JDBC](https://github.com/apache/spark/blob/v3.5.5/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JdbcUtils.scala#L141-L164) +* [JDBC -> Clickhouse](https://github.com/ClickHouse/clickhouse-java/blob/0.3.2/clickhouse-jdbc/src/main/java/com/clickhouse/jdbc/JdbcTypeMapping.java#L185-L311) + +## Supported types + +See [official documentation](https://clickhouse.com/docs/en/sql-reference/data-types) + +### Generic types + +* `LowCardinality(T)` is same as `T` +* `Nullable(T)` is same as `T`, but Spark column is inferred as `nullable=True` + +### Numeric types + +| Clickhouse type (read) | Spark type | Clickhouse type (write) | Clickhouse type (create) | +|------------------------------|----------------------------------|-----------------------------|-----------------------------| +| `Bool` | `BooleanType()` | `Bool` | `UInt64` | +| `Decimal` | `DecimalType(P=10, S=0)` | `Decimal(P=10, S=0)` | `Decimal(P=10, S=0)` | +| `Decimal(P=0..38)` | `DecimalType(P=0..38, S=0)` | `Decimal(P=0..38, S=0)` | `Decimal(P=0..38, S=0)` | +| `Decimal(P=0..38, S=0..38)` | `DecimalType(P=0..38, S=0..38)` | `Decimal(P=0..38, S=0..38)` | `Decimal(P=0..38, S=0..38)` | +| `Decimal(P=39..76, S=0..76)` | unsupported [3](#id9) | | | +| `Decimal32(P=0..9)` | `DecimalType(P=9, S=0..9)` | `Decimal(P=9, S=0..9)` | `Decimal(P=9, S=0..9)` | +| `Decimal64(S=0..18)` | `DecimalType(P=18, S=0..18)` | `Decimal(P=18, S=0..18)` | `Decimal(P=18, S=0..18)` | +| `Decimal128(S=0..38)` | `DecimalType(P=38, S=0..38)` | `Decimal(P=38, S=0..38)` | `Decimal(P=38, S=0..38)` | +| `Decimal256(S=0..76)` | unsupported [3](#id9) | | | +| `Float32` | `FloatType()` | `Float32` | `Float32` | +| `Float64` | `DoubleType()` | `Float64` | `Float64` | +| `Int8` | `IntegerType()` | `Int32` | `Int32` | +| `Int16` | | | | +| `Int32` | | | | +| `Int64` | `LongType()` | `Int64` | `Int64` | +| `Int128` | unsupported [3](#id9) | | | +| `Int256` | | | | +| `-` | `ByteType()` | `Int8` | `Int8` | +| `-` | `ShortType()` | `Int32` | `Int32` | +| `UInt8` | `IntegerType()` | `Int32` | `Int32` | +| `UInt16` | `LongType()` | `Int64` | `Int64` | +| `UInt32` | `DecimalType(20,0)` | `Decimal(20,0)` | `Decimal(20,0)` | +| `UInt64` | | | | +| `UInt128` | unsupported [3](#id9) | | | +| `UInt256` | | | | +* **[3]** Clickhouse support numeric types up to 256 bit - `Int256`, `UInt256`, `Decimal256(S)`, `Decimal(P=39..76, S=0..76)`. But Spark’s `DecimalType(P, S)` supports maximum `P=38` (128 bit). It is impossible to read, write or operate with values of larger precision, this leads to an exception. + +### Temporal types + +Notes: +: * Datetime with timezone has the same precision as without timezone + * `DateTime` is alias for `DateTime32` + * `TIMESTAMP` is alias for `DateTime32`, but `TIMESTAMP(N)` is alias for `DateTime64(N)` + +| Clickhouse type (read) | Spark type | Clickhouse type (write) | Clickhouse type (create) | +|---------------------------------|-------------------------------------------------------------------------------|-------------------------------|---------------------------------------------------------------------| +| `Date` | `DateType()` | `Date` | `Date` | +| `Date32` | `DateType()` | `Date` | `Date`,
**cannot insert data** [4](#id15) | +| `DateTime32`, seconds | `TimestampType()`, microseconds | `DateTime64(6)`, microseconds | `DateTime32`, seconds | +| `DateTime64(3)`, milliseconds | `TimestampType()`, microseconds | `DateTime64(6)`, microseconds | `DateTime32`, seconds,
**precision loss** [5](#id16) | +| `DateTime64(6)`, microseconds | `TimestampType()`, microseconds | | `DateTime32`, seconds,
**precision loss** [7](#id18) | +| `DateTime64(7..9)`, nanoseconds | `TimestampType()`, microseconds,
**precision loss** [6](#id17) | | | +| `-` | `TimestampNTZType()`, microseconds | | | +| `DateTime32(TZ)` | unsupported [7](#id18) | | | +| `DateTime64(P, TZ)` | | | | +| `IntervalNanosecond` | `LongType()` | `Int64` | `Int64` | +| `IntervalMicrosecond` | | | | +| `IntervalMillisecond` | | | | +| `IntervalSecond` | | | | +| `IntervalMinute` | | | | +| `IntervalHour` | | | | +| `IntervalDay` | | | | +| `IntervalMonth` | | | | +| `IntervalQuarter` | | | | +| `IntervalWeek` | | | | +| `IntervalYear` | | | | + +#### WARNING +Note that types in Clickhouse and Spark have different value ranges: + +| Clickhouse type | Min value | Max value | Spark type | Min value | Max value | +|----------------------|---------------------------------|---------------------------------|-------------------|------------------------------|------------------------------| +| `Date` | `1970-01-01` | `2149-06-06` | `DateType()` | `0001-01-01` | `9999-12-31` | +| `DateTime32` | `1970-01-01 00:00:00` | `2106-02-07 06:28:15` | `TimestampType()` | `0001-01-01 00:00:00.000000` | `9999-12-31 23:59:59.999999` | +| `DateTime64(P=0..8)` | `1900-01-01 00:00:00.00000000` | `2299-12-31 23:59:59.99999999` | | | | +| `DateTime64(P=9)` | `1900-01-01 00:00:00.000000000` | `2262-04-11 23:47:16.999999999` | | | | + +So not all of values in Spark DataFrame can be written to Clickhouse. + +References: +: * [Clickhouse Date documentation](https://clickhouse.com/docs/en/sql-reference/data-types/date) + * [Clickhouse Datetime32 documentation](https://clickhouse.com/docs/en/sql-reference/data-types/datetime) + * [Clickhouse Datetime64 documentation](https://clickhouse.com/docs/en/sql-reference/data-types/datetime64) + * [Spark DateType documentation](https://spark.apache.org/docs/latest/api/java/org/apache/spark/sql/types/DateType.html) + * [Spark TimestampType documentation](https://spark.apache.org/docs/latest/api/java/org/apache/spark/sql/types/TimestampType.html) + +* **[4]** `Date32` has different bytes representation than `Date`, and inserting value of type `Date32` to `Date` column leads to errors on Clickhouse side, e.g. `Date(106617) should be between 0 and 65535 inclusive of both values`. Although Spark does properly read the `Date32` column as `DateType()`, and there should be no difference at all. Probably this is some bug in Clickhouse driver. +* **[5]** Generic JDBC dialect generates DDL with Clickhouse type `TIMESTAMP` which is alias for `DateTime32` with precision up to seconds (`23:59:59`). Inserting data with milliseconds precision (`23:59:59.999`) will lead to **throwing away milliseconds**. Solution: create table manually, with proper column type. +* **[6]** Clickhouse support datetime up to nanoseconds precision (`23:59:59.999999999`), but Spark `TimestampType()` supports datetime up to microseconds precision (`23:59:59.999999`). Nanoseconds will be lost during read or write operations. Solution: create table manually, with proper column type. +* **[7]** Clickhouse will raise an exception that data in format `2001-01-01 23:59:59.999999` has data `.999999` which does not match format `YYYY-MM-DD hh:mm:ss` of `DateTime32` column type (see [5](#id16)). So Spark can create Clickhouse table, but cannot write data to column of this type. Solution: create table manually, with proper column type. + +### String types + +| Clickhouse type (read) | Spark type | Clickhousetype (write) | Clickhouse type (create) | +|--------------------------|----------------|--------------------------|----------------------------| +| `FixedString(N)` | `StringType()` | `String` | `String` | +| `String` | | | | +| `Enum8` | | | | +| `Enum16` | | | | +| `IPv4` | | | | +| `IPv6` | | | | +| `UUID` | | | | +| `-` | `BinaryType()` | | | + +## Unsupported types + +Columns of these Clickhouse types cannot be read by Spark: +: * `AggregateFunction(func, T)` + * `Array(T)` + * `JSON` + * `Map(K, V)` + * `MultiPolygon` + * `Nested(field1 T1, ...)` + * `Nothing` + * `Point` + * `Polygon` + * `Ring` + * `SimpleAggregateFunction(func, T)` + * `Tuple(T1, T2, ...)` + +Dataframe with these Spark types cannot be written to Clickhouse: +: * `ArrayType(T)` + * `BinaryType()` + * `CharType(N)` + * `DayTimeIntervalType(P, S)` + * `MapType(K, V)` + * `NullType()` + * `StructType([...])` + * `TimestampNTZType()` + * `VarcharType(N)` + +This is because Spark does not have dedicated Clickhouse dialect, and uses Generic JDBC dialect instead. +This dialect does not have type conversion between some types, like Clickhouse `Array` -> Spark `ArrayType()`, and vice versa. + +The is a way to avoid this - just cast everything to `String`. + +## Explicit type cast + +### `DBReader` + +Use `CAST` or `toJSONString` to get column data as string in JSON format, + +For parsing JSON columns in ClickHouse, [`JSON.parse_column`](../../../file_df/file_formats/json.md#onetl.file.format.json.JSON.parse_column) method. + +```python +from pyspark.sql.types import ArrayType, IntegerType + +from onetl.file.format import JSON +from onetl.connection import ClickHouse +from onetl.db import DBReader + +reader = DBReader( + connection=clickhouse, + target="default.source_tbl", + columns=[ + "id", + "toJSONString(array_column) array_column", + ], +) +df = reader.run() + +# Spark requires all columns to have some specific type, describe it +column_type = ArrayType(IntegerType()) + +json = JSON() +df = df.select( + df.id, + json.parse_column("array_column", column_type), +) +``` + +### `DBWriter` + +For writing JSON data to ClickHouse, use the [`JSON.serialize_column`](../../../file_df/file_formats/json.md#onetl.file.format.json.JSON.serialize_column) method to convert a DataFrame column to JSON format efficiently and write it as a `String` column in Clickhouse. + +```python +from onetl.file.format import JSON +from onetl.connection import ClickHouse +from onetl.db import DBWriter + +clickhouse = ClickHouse(...) + +clickhouse.execute( + """ + CREATE TABLE default.target_tbl ( + id Int32, + array_column_json String, + ) + ENGINE = MergeTree() + ORDER BY id + """, +) + +json = JSON() +df = df.select( + df.id, + json.serialize_column(df.array_column).alias("array_column_json"), +) + +writer.run(df) +``` + +Then you can parse this column on Clickhouse side - for example, by creating a view: + +```sql +SELECT + id, + JSONExtract(json_column, 'Array(String)') AS array_column +FROM target_tbl +``` + +You can also use [ALIAS](https://clickhouse.com/docs/en/sql-reference/statements/create/table#alias) +or [MATERIALIZED](https://clickhouse.com/docs/en/sql-reference/statements/create/table#materialized) columns +to avoid writing such expression in every `SELECT` clause all the time: + +```sql +CREATE TABLE default.target_tbl ( + id Int32, + array_column_json String, + -- computed column + array_column Array(String) ALIAS JSONExtract(json_column, 'Array(String)') + -- or materialized column + -- array_column Array(String) MATERIALIZED JSONExtract(json_column, 'Array(String)') +) +ENGINE = MergeTree() +ORDER BY id +``` + +Downsides: + +* Using `SELECT JSONExtract(...)` or `ALIAS` column can be expensive, because value is calculated on every row access. This can be especially harmful if such column is used in `WHERE` clause. +* `ALIAS` and `MATERIALIZED` columns are not included in `SELECT *` clause, they should be added explicitly: `SELECT *, calculated_column FROM table`. + +#### WARNING +[EPHEMERAL](https://clickhouse.com/docs/en/sql-reference/statements/create/table#ephemeral) columns are not supported by Spark +because they cannot be selected to determine target column type. diff --git a/mddocs/connection/db_connection/clickhouse/write.md b/mddocs/connection/db_connection/clickhouse/write.md new file mode 100644 index 000000000..dd8ca31cc --- /dev/null +++ b/mddocs/connection/db_connection/clickhouse/write.md @@ -0,0 +1,187 @@ + + +# Writing to Clickhouse using `DBWriter` + +For writing data to Clickhouse, use [`DBWriter`](../../../db/db_writer.md#onetl.db.db_writer.db_writer.DBWriter). + +#### WARNING +Please take into account [Clickhouse <-> Spark type mapping](types.md#clickhouse-types) + +#### WARNING +It is always recommended to create table explicitly using [Clickhouse.execute](execute.md#clickhouse-execute) +instead of relying on Spark’s table DDL generation. + +This is because Spark’s DDL generator can create columns with different precision and types than it is expected, +causing precision loss or other issues. + +## Examples + +```python +from onetl.connection import Clickhouse +from onetl.db import DBWriter + +clickhouse = Clickhouse(...) + +df = ... # data is here + +writer = DBWriter( + connection=clickhouse, + target="schema.table", + options=Clickhouse.WriteOptions( + if_exists="append", + # ENGINE is required by Clickhouse + createTableOptions="ENGINE = MergeTree() ORDER BY id", + ), +) + +writer.run(df) +``` + +## Options + +Method above accepts [`Clickhouse.WriteOptions`](#onetl.connection.db_connection.clickhouse.options.ClickhouseWriteOptions) + +### *pydantic model* onetl.connection.db_connection.clickhouse.options.ClickhouseWriteOptions + +Spark JDBC writing options. + +#### Versionadded +Added in version 0.5.0: Replace `Clickhouse.Options` → `Clickhouse.WriteOptions` + +### Examples + +#### NOTE +You can pass any value +[supported by Spark](https://spark.apache.org/docs/latest/sql-data-sources-jdbc.html), +even if it is not mentioned in this documentation. **Option names should be in** `camelCase`! + +The set of supported options depends on Spark version. + +```python +from onetl.connection import Clickhouse + +options = Clickhouse.WriteOptions( + if_exists="append", + batchsize=20_000, + customSparkOption="value", +) +``` + + + +#### *field* if_exists *: JDBCTableExistBehavior* *= JDBCTableExistBehavior.APPEND* *(alias 'mode')* + +Behavior of writing data into existing table. + +Possible values: +: * `append` (default) + : Adds new rows into existing table. +
+ ### Behavior in details +
+ * Table does not exist + : Table is created using options provided by user + (`createTableOptions`, `createTableColumnTypes`, etc). + * Table exists + : Data is appended to a table. Table has the same DDL as before writing data +
+ #### WARNING + This mode does not check whether table already contains + rows from dataframe, so duplicated rows can be created. +
+ Also Spark does not support passing custom options to + insert statement, like `ON CONFLICT`, so don’t try to + implement deduplication using unique indexes or constraints. +
+ Instead, write to staging table and perform deduplication + using `execute` method. + * `replace_entire_table` + : **Table is dropped and then created, or truncated**. +
+ ### Behavior in details +
+ * Table does not exist + : Table is created using options provided by user + (`createTableOptions`, `createTableColumnTypes`, etc). + * Table exists + : Table content is replaced with dataframe content. +
+ After writing completed, target table could either have the same DDL as + before writing data (`truncate=True`), or can be recreated (`truncate=False` + or source does not support truncation). + * `ignore` + : Ignores the write operation if the table already exists. +
+ ### Behavior in details +
+ * Table does not exist + : Table is created using options provided by user + (`createTableOptions`, `createTableColumnTypes`, etc). + * Table exists + : The write operation is ignored, and no data is written to the table. + * `error` + : Raises an error if the table already exists. +
+ ### Behavior in details +
+ * Table does not exist + : Table is created using options provided by user + (`createTableOptions`, `createTableColumnTypes`, etc). + * Table exists + : An error is raised, and no data is written to the table. + +#### Versionchanged +Changed in version 0.9.0: Renamed `mode` → `if_exists` + + + +#### *field* query_timeout *: int | None* *= None* *(alias 'queryTimeout')* + +The number of seconds the driver will wait for a statement to execute. +Zero means there is no limit. + +This option depends on driver implementation, +some drivers can check the timeout of each query instead of an entire JDBC batch. + + + +#### *field* batchsize *: int* *= 20000* + +How many rows can be inserted per round trip. + +Tuning this option can influence performance of writing. + +#### WARNING +Default value is different from Spark. + +Spark uses quite small value `1000`, which is absolutely not usable +in BigData world. + +Thus we’ve overridden default value with `20_000`, +which should increase writing performance. + +You can increase it even more, up to `50_000`, +but it depends on your database load and number of columns in the row. +Higher values does not increase performance. + +#### Versionchanged +Changed in version 0.4.0: Changed default value from 1000 to 20_000 + + + +#### *field* isolation_level *: str* *= 'READ_UNCOMMITTED'* *(alias 'isolationLevel')* + +The transaction isolation level, which applies to current connection. + +Possible values: +: * `NONE` (as string, not Python’s `None`) + * `READ_COMMITTED` + * `READ_UNCOMMITTED` + * `REPEATABLE_READ` + * `SERIALIZABLE` + +Values correspond to transaction isolation levels defined by JDBC standard. +Please refer the documentation for +[java.sql.Connection](https://docs.oracle.com/javase/8/docs/api/java/sql/Connection.html). + + diff --git a/mddocs/connection/db_connection/greenplum/connection.md b/mddocs/connection/db_connection/greenplum/connection.md new file mode 100644 index 000000000..b180a5fc7 --- /dev/null +++ b/mddocs/connection/db_connection/greenplum/connection.md @@ -0,0 +1,144 @@ + + +# Greenplum connection + +### *class* onetl.connection.db_connection.greenplum.connection.Greenplum(\*, spark: SparkSession, host: Host, user: str, password: SecretStr, database: str, port: int = 5432, extra: GreenplumExtra = GreenplumExtra(tcpKeepAlive='true')) + +Greenplum connection. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +Based on package `io.pivotal:greenplum-spark:2.2.0` +([VMware Greenplum connector for Spark](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/index.html)). + +#### SEE ALSO +Before using this connector please take into account [Prerequisites](prerequisites.md#greenplum-prerequisites) + +#### Versionadded +Added in version 0.5.0. + +* **Parameters:** + **host** + : Host of Greenplum master. For example: `test.greenplum.domain.com` or `193.168.1.17` + + **port** + : Port of Greenplum master + + **user** + : User, which have proper access to the database. For example: `some_user` + + **password** + : Password for database connection + + **database** + : Database in RDBMS, NOT schema. +
+ See [this page](https://www.educba.com/postgresql-database-vs-schema/) for more details + + **spark** + : Spark session. + + **extra** + : Specifies one or more extra parameters by which clients can connect to the instance. +
+ For example: `{"tcpKeepAlive": "true", "server.port": "50000-65535"}` +
+ Supported options are: + : * All [Postgres JDBC driver properties](https://jdbc.postgresql.org/documentation/use/) + * Properties from [Greenplum connector for Spark documentation](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/options.html) page, but only starting with `server.` or `pool.` + +### Examples + +Create and check Greenplum connection: + +```python +from onetl.connection import Greenplum +from pyspark.sql import SparkSession + +# Create Spark session with Greenplum connector loaded +maven_packages = Greenplum.get_packages(spark_version="3.2") +spark = ( + SparkSession.builder.appName("spark-app-name") + .config("spark.jars.packages", ",".join(maven_packages)) + .config("spark.executor.allowSparkContext", "true") + # IMPORTANT!!! + # Set number of executors according to "Prerequisites" -> "Number of executors" + .config("spark.dynamicAllocation.maxExecutors", 10) + .config("spark.executor.cores", 1) + .getOrCreate() +) + +# IMPORTANT!!! +# Set port range of executors according to "Prerequisites" -> "Network ports" +extra = { + "server.port": "41000-42000", +} + +# Create connection +greenplum = Greenplum( + host="master.host.or.ip", + user="user", + password="*****", + database="target_database", + extra=extra, + spark=spark, +).check() +``` + + + +#### check() + +Check source availability. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +If not, an exception will be raised. + +* **Returns:** + Connection itself +* **Raises:** + RuntimeError + : If the connection is not available + +### Examples + +```python +connection.check() +``` + + + +#### *classmethod* get_packages(\*, scala_version: str | None = None, spark_version: str | None = None, package_version: str | None = None) → list[str] + +Get package names to be downloaded by Spark. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### WARNING +You should pass either `scala_version` or `spark_version`. + +#### Versionadded +Added in version 0.9.0. + +* **Parameters:** + **scala_version** + : Scala version in format `major.minor`. +
+ If `None`, `spark_version` is used to determine Scala version. + + **spark_version** + : Spark version in format `major.minor`. +
+ Used only if `scala_version=None`. + + **package_version** + : Package version in format `major.minor.patch` +
+ #### Versionadded + Added in version 0.10.1. + +### Examples + +```python +from onetl.connection import Greenplum + +Greenplum.get_packages(scala_version="2.12") +Greenplum.get_packages(spark_version="3.2", package_version="2.3.0") +``` + + diff --git a/mddocs/connection/db_connection/greenplum/execute.md b/mddocs/connection/db_connection/greenplum/execute.md new file mode 100644 index 000000000..c923512a3 --- /dev/null +++ b/mddocs/connection/db_connection/greenplum/execute.md @@ -0,0 +1,195 @@ + + +# Executing statements in Greenplum + +#### WARNING +Methods below **read all the rows** returned from DB **to Spark driver memory**, and then convert them to DataFrame. + +Do **NOT** use them to read large amounts of data. Use [DBReader](read.md#greenplum-read) instead. + +## How to + +There are 2 ways to execute some statement in Greenplum + +### Use `Greenplum.fetch` + +Use this method to perform some `SELECT` query which returns **small number or rows**, like reading +Greenplum config, or reading data from some reference table. Method returns Spark DataFrame. + +Method accepts [`Greenplum.FetchOptions`](#onetl.connection.db_connection.greenplum.options.GreenplumFetchOptions). + +Connection opened using this method should be then closed with `connection.close()` or `with connection:`. + +#### WARNING +`Greenplum.fetch` is implemented using Postgres JDBC connection, +so types are handled a bit differently than in `DBReader`. See [Postgres <-> Spark type mapping](../postgres/types.md#postgres-types). + +#### Syntax support + +This method supports **any** query syntax supported by Greenplum, like: + +* ✅︎ `SELECT ... FROM ...` +* ✅︎ `WITH alias AS (...) SELECT ...` +* ✅︎ `SELECT func(arg1, arg2)` or `{call func(arg1, arg2)}` - special syntax for calling functions +* ❌ `SET ...; SELECT ...;` - multiple statements not supported + +#### Examples + +```python +from onetl.connection import Greenplum + +greenplum = Greenplum(...) + +df = greenplum.fetch( + "SELECT value FROM some.reference_table WHERE key = 'some_constant'", + options=Greenplum.FetchOptions(queryTimeout=10), +) +greenplum.close() +value = df.collect()[0][0] # get value from first row and first column +``` + +### Use `Greenplum.execute` + +Use this method to execute DDL and DML operations. Each method call runs operation in a separated transaction, and then commits it. + +Method accepts [`Greenplum.ExecuteOptions`](#onetl.connection.db_connection.greenplum.options.GreenplumExecuteOptions). + +Connection opened using this method should be then closed with `connection.close()` or `with connection:`. + +#### Syntax support + +This method supports **any** query syntax supported by Greenplum, like: + +* ✅︎ `CREATE TABLE ...`, `CREATE VIEW ...`, and so on +* ✅︎ `ALTER ...` +* ✅︎ `INSERT INTO ... SELECT ...`, `UPDATE ...`, `DELETE ...`, and so on +* ✅︎ `DROP TABLE ...`, `DROP VIEW ...`, `TRUNCATE TABLE`, and so on +* ✅︎ `CALL procedure(arg1, arg2) ...` +* ✅︎ `SELECT func(arg1, arg2)` or `{call func(arg1, arg2)}` - special syntax for calling functions +* ✅︎ other statements not mentioned here +* ❌ `SET ...; SELECT ...;` - multiple statements not supported + +#### Examples + +```python +from onetl.connection import Greenplum + +greenplum = Greenplum(...) + +greenplum.execute("DROP TABLE schema.table") +greenplum.execute( + """ + CREATE TABLE schema.table ( + id int, + key text, + value real + ) + DISTRIBUTED BY id + """, + options=Greenplum.ExecuteOptions(queryTimeout=10), +) +``` + +## Interaction schema + +Unlike reading & writing, executing statements in Greenplum is done **only** through Greenplum master node, +without any interaction between Greenplum segments and Spark executors. More than that, Spark executors are not used in this case. + +The only port used while interacting with Greenplum in this case is `5432` (Greenplum master port). + +### Spark <-> Greenplum interaction during Greenplum.execute()/Greenplum.fetch() + +## Options + +### *pydantic model* onetl.connection.db_connection.greenplum.options.GreenplumFetchOptions + +Options related to fetching data from databases via JDBC. + +#### Versionadded +Added in version 0.11.0: Replace `Greenplum.JDBCOptions` → `Greenplum.FetchOptions` + +### Examples + +#### NOTE +You can pass any value supported by underlying JDBC driver class, +even if it is not mentioned in this documentation. + +```python +from onetl.connection import Greenplum + +options = Greenplum.FetchOptions( + queryTimeout=60_000, + fetchsize=100_000, + customSparkOption="value", +) +``` + + + +#### *field* query_timeout *: int | None* *= None* *(alias 'queryTimeout')* + +The number of seconds the driver will wait for a statement to execute. +Zero means there is no limit. + +This option depends on driver implementation, +some drivers can check the timeout of each query instead of an entire JDBC batch. + + + +#### *field* fetchsize *: int | None* *= None* + +How many rows to fetch per round trip. + +Tuning this option can influence performance of reading. + +#### WARNING +Default value depends on driver. For example, Oracle has +default `fetchsize=10`. + + + +### *pydantic model* onetl.connection.db_connection.greenplum.options.GreenplumExecuteOptions + +Options related to executing statements in databases via JDBC. + +#### Versionadded +Added in version 0.11.0: Replace `Greenplum.JDBCOptions` → `Greenplum.ExecuteOptions` + +### Examples + +#### NOTE +You can pass any value supported by underlying JDBC driver class, +even if it is not mentioned in this documentation. + +```python +from onetl.connection import Greenplum + +options = Greenplum.ExecuteOptions( + queryTimeout=60_000, + customSparkOption="value", +) +``` + + + +#### *field* query_timeout *: int | None* *= None* *(alias 'queryTimeout')* + +The number of seconds the driver will wait for a statement to execute. +Zero means there is no limit. + +This option depends on driver implementation, +some drivers can check the timeout of each query instead of an entire JDBC batch. + + + +#### *field* fetchsize *: int | None* *= None* + +How many rows to fetch per round trip. + +Tuning this option can influence performance of reading. + +#### WARNING +Default value depends on driver. For example, Oracle has +default `fetchsize=10`. + + diff --git a/mddocs/connection/db_connection/greenplum/index.md b/mddocs/connection/db_connection/greenplum/index.md new file mode 100644 index 000000000..9fcc68f66 --- /dev/null +++ b/mddocs/connection/db_connection/greenplum/index.md @@ -0,0 +1,18 @@ + + +# Greenplum + +# Connection + +* [Prerequisites](prerequisites.md) +* [Greenplum connection](connection.md) + +# Operations + +* [Reading from Greenplum using `DBReader`](read.md) +* [Writing to Greenplum using `DBWriter`](write.md) +* [Executing statements in Greenplum](execute.md) + +# Troubleshooting + +* [Greenplum <-> Spark type mapping](types.md) diff --git a/mddocs/connection/db_connection/greenplum/prerequisites.md b/mddocs/connection/db_connection/greenplum/prerequisites.md new file mode 100644 index 000000000..15c06300b --- /dev/null +++ b/mddocs/connection/db_connection/greenplum/prerequisites.md @@ -0,0 +1,358 @@ + + +# Prerequisites + +## Version Compatibility + +* Greenplum server versions: + : * Officially declared: 5.x, 6.x, and 7.x (which requires `Greenplum.get_packages(package_version="2.3.0")` or higher) + * Actually tested: 6.23, 7.0 +* Spark versions: 2.3.x - 3.2.x (Spark 3.3+ is not supported yet) +* Java versions: 8 - 11 + +See [official documentation](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.2/greenplum-connector-spark/release_notes.html). + +## Installing PySpark + +To use Greenplum connector you should have PySpark installed (or injected to `sys.path`) +BEFORE creating the connector instance. + +See [Spark](../../../install/spark.md#install-spark) installation instruction for more details. + +## Downloading VMware package + +To use Greenplum connector you should download connector `.jar` file from +[VMware website](https://network.tanzu.vmware.com/products/vmware-greenplum#/releases/1413479/file_groups/16966) +and then pass it to Spark session. + +#### WARNING +Please pay attention to [Spark & Scala version compatibility](../../../install/spark.md#spark-compatibility-matrix). + +#### WARNING +There are issues with using package of version 2.3.0/2.3.1 with Greenplum 6.x - connector can +open transaction with `SELECT * FROM table LIMIT 0` query, but does not close it, which leads to deadlocks +during write. + +There are several ways to do that. See [Injecting Java packages](../../../install/spark.md#java-packages) for details. + +#### NOTE +If you’re uploading package to private package repo, use `groupId=io.pivotal` and `artifactoryId=greenplum-spark_2.12` +(`2.12` is Scala version) to give uploaded package a proper name. + +## Connecting to Greenplum + +### Interaction schema + +Spark executors open ports to listen incoming requests. +Greenplum segments are initiating connections to Spark executors using [EXTERNAL TABLE](https://docs.vmware.com/en/VMware-Greenplum/7/greenplum-database/ref_guide-sql_commands-CREATE_EXTERNAL_TABLE.html) +functionality, and send/read data using [gpfdist protocol](https://docs.vmware.com/en/VMware-Greenplum/7/greenplum-database/admin_guide-external-g-using-the-greenplum-parallel-file-server--gpfdist-.html#about-gpfdist-setup-and-performance-1). + +Data is **not** send through Greenplum master. +Greenplum master only receives commands to start reading/writing process, and manages all the metadata (external table location, schema and so on). + +More details can be found in [official documentation](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/overview.html). + +### Set number of connections + +#### WARNING +This is very important!!! + +If you don’t limit number of connections, you can exceed the [max_connections](https://docs.vmware.com/en/VMware-Greenplum/7/greenplum-database/admin_guide-client_auth.html#limiting-concurrent-connections#limiting-concurrent-connections-2) +limit set on the Greenplum side. It’s usually not so high, e.g. 500-1000 connections max, +depending on your Greenplum instance settings and using connection balancers like `pgbouncer`. + +Consuming all available connections means **nobody** (even admin users) can connect to Greenplum. + +Each job on the Spark executor makes its own connection to Greenplum master node, +so you need to limit number of connections to avoid opening too many of them. + +* Reading about `5-10Gb` of data requires about `3-5` parallel connections. +* Reading about `20-30Gb` of data requires about `5-10` parallel connections. +* Reading about `50Gb` of data requires ~ `10-20` parallel connections. +* Reading about `100+Gb` of data requires `20-30` parallel connections. +* Opening more than `30-50` connections is not recommended. + +Number of connections can be limited by 2 ways: + +* By limiting number of Spark executors and number of cores per-executor. Max number of parallel jobs is `executors * cores`. + +Spark with master=local + +```py +spark = ( + SparkSession.builder + # Spark will run with 5 threads in local mode, allowing up to 5 parallel tasks + .config("spark.master", "local[5]") + .config("spark.executor.cores", 1) +).getOrCreate() +``` + +Spark with master=yarn or master=k8s, dynamic allocation + +```py +spark = ( + SparkSession.builder + .config("spark.master", "yarn") + # Spark will start MAX 10 executors with 1 core each (dynamically), so max number of parallel jobs is 10 + .config("spark.dynamicAllocation.maxExecutors", 10) + .config("spark.executor.cores", 1) +).getOrCreate() +``` + +Spark with master=yarn or master=k8s, static allocation + +```py +spark = ( + SparkSession.builder + .config("spark.master", "yarn") + # Spark will start EXACTLY 10 executors with 1 core each, so max number of parallel jobs is 10 + .config("spark.executor.instances", 10) + .config("spark.executor.cores", 1) +).getOrCreate() +``` + +* By limiting connection pool size user by Spark (**only** for Spark with `master=local`): + +```python +spark = SparkSession.builder.config("spark.master", "local[*]").getOrCreate() + +# No matter how many executors are started and how many cores they have, +# number of connections cannot exceed pool size: +Greenplum( + ..., + extra={ + "pool.maxSize": 10, + }, +) +``` + +See [connection pooling](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/using_the_connector.html#jdbcconnpool) +documentation. + +* By setting [`num_partitions`](read.md#onetl.connection.db_connection.greenplum.options.GreenplumReadOptions.num_partitions) + and [`partition_column`](read.md#onetl.connection.db_connection.greenplum.options.GreenplumReadOptions.partition_column) (not recommended). + +### Allowing connection to Greenplum master + +Ask your Greenplum cluster administrator to allow your user to connect to Greenplum master node, +e.g. by updating `pg_hba.conf` file. + +More details can be found in [official documentation](https://docs.vmware.com/en/VMware-Greenplum/7/greenplum-database/admin_guide-client_auth.html#limiting-concurrent-connections#allowing-connections-to-greenplum-database-0). + +### Set connection port + +#### Spark with `master=k8s` + +Please follow [the official documentation](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/configure.html#k8scfg) + +#### Spark with `master=yarn` or `master=local` + +To read data from Greenplum using Spark, following ports should be opened in firewall between Spark and Greenplum: + +* Spark driver and all Spark executors -> port `5432` on Greenplum master node. + + This port number should be set while connecting to Greenplum: + ```python + greenplum = Greenplum(host="master.host", port=5432, ...) + ``` +* Greenplum segments -> some port range (e.g. `41000-42000`) **listened by Spark executors**. + + This range should be set in `extra` option: + ```python + greenplum = Greenplum( + ..., + extra={ + "server.port": "41000-42000", + }, + ) + ``` + + Number of ports in this range is `number of parallel running Spark sessions` \* `number of parallel connections per session`. + + Number of connections per session (see below) is usually less than `30` (see above). + + Number of session depends on your environment: + : * For `master=local` only few ones-tens sessions can be started on the same host, depends on available RAM and CPU. + * For `master=yarn` hundreds or thousands of sessions can be started simultaneously, + but they are executing on different cluster nodes, so one port can be opened on different nodes at the same time. + +More details can be found in official documentation: +: * [port requirements](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/sys_reqs.html#network-port-requirements) + * [format of server.port value](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/options.html#server.port) + * [port troubleshooting](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/troubleshooting.html#port-errors) + +### Set connection host + +#### Spark with `master=k8s` + +Please follow [the official documentation](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/configure.html#k8scfg) + +#### Spark with `master=local` + +By default, Greenplum connector tries to resolve IP of current host, and then pass it as `gpfdist` URL to Greenplum segment. +This may fail in some cases. + +For example, IP can be resolved using `/etc/hosts` content like this: + +```text +127.0.0.1 localhost real-host-name +``` + +```bash +$ hostname -f +localhost + +$ hostname -i +127.0.0.1 +``` + +Reading/writing data to Greenplum will fail with following exception: + +```text +org.postgresql.util.PSQLException: ERROR: connection with gpfdist failed for +"gpfdist://127.0.0.1:49152/local-1709739764667/exec/driver", +effective url: "http://127.0.0.1:49152/local-1709739764667/exec/driver": +error code = 111 (Connection refused); (seg3 slice1 12.34.56.78:10003 pid=123456) +``` + +There are 2 ways to fix that: + +* Explicitly pass your host IP address to connector, like this + ```python + import os + + # pass here real host IP (accessible from GP segments) + os.environ["HOST_IP"] = "192.168.1.1" + + greenplum = Greenplum( + ..., + extra={ + # connector will read IP from this environment variable + "server.hostEnv": "env.HOST_IP", + }, + spark=spark, + ) + ``` + + More details can be found in [official documentation](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/options.html#server.hostenv). +* Update `/etc/hosts` file to include real host IP: + ```text + 127.0.0.1 localhost + # this IP should be accessible from GP segments + 192.168.1.1 driver-host-name + ``` + + So Greenplum connector will properly resolve host IP. + +#### Spark with `master=yarn` + +The same issue with resolving IP address can occur on Hadoop cluster node, but it’s tricky to fix, because each node has a different IP. + +There are 3 ways to fix that: + +* Pass node hostname to `gpfdist` URL. So IP will be resolved on segment side: + ```python + greenplum = Greenplum( + ..., + extra={ + "server.useHostname": "true", + }, + ) + ``` + + But this may fail if Hadoop cluster node hostname cannot be resolved from Greenplum segment side. + + More details can be found in [official documentation](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/options.html#server.usehostname). +* Set specific network interface to get IP address from: + ```python + greenplum = Greenplum( + ..., + extra={ + "server.nic": "eth0", + }, + ) + ``` + + You can get list of network interfaces using this command. + + #### NOTE + This command should be executed on Hadoop cluster node, **not** Spark driver host! + + ```bash + $ ip address + 1: lo: mtu 65536 qdisc noqueue state UNKNOWN group default qlen 1000 + inet 127.0.0.1/8 scope host lo + valid_lft forever preferred_lft forever + 2: eth0: mtu 1500 qdisc fq_codel state UP group default qlen 1000 + inet 192.168.1.1/24 brd 192.168.1.255 scope global dynamic noprefixroute eth0 + valid_lft 83457sec preferred_lft 83457sec + ``` + + Note that in this case **each** Hadoop cluster node node should have network interface with name `eth0`. + + More details can be found in [official documentation](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/options.html#server.nic). +* Update `/etc/hosts` on each Hadoop cluster node to include real node IP: + ```text + 127.0.0.1 localhost + # this IP should be accessible from GP segments + 192.168.1.1 cluster-node-name + ``` + + So Greenplum connector will properly resolve node IP. + +### Set required grants + +Ask your Greenplum cluster administrator to set following grants for a user, +used for creating a connection: + +Read + Write + +```sql +-- get access to get tables metadata & cluster information +GRANT SELECT ON information_schema.tables TO username; +GRANT SELECT ON pg_attribute TO username; +GRANT SELECT ON pg_class TO username; +GRANT SELECT ON pg_namespace TO username; +GRANT SELECT ON pg_settings TO username; +GRANT SELECT ON pg_stats TO username; +GRANT SELECT ON gp_distributed_xacts TO username; +GRANT SELECT ON gp_segment_configuration TO username; +-- Greenplum 5.x only +GRANT SELECT ON gp_distribution_policy TO username; + +-- allow creating external tables in the same schema as source/target table +GRANT USAGE ON SCHEMA myschema TO username; +GRANT CREATE ON SCHEMA myschema TO username; +ALTER USER username CREATEEXTTABLE(type = 'readable', protocol = 'gpfdist') CREATEEXTTABLE(type = 'writable', protocol = 'gpfdist'); + +-- allow read access to specific table (to get column types) +-- allow write access to specific table +GRANT SELECT, INSERT ON myschema.mytable TO username; +``` + +Read only + +```sql +-- get access to get tables metadata & cluster information +GRANT SELECT ON information_schema.tables TO username; +GRANT SELECT ON pg_attribute TO username; +GRANT SELECT ON pg_class TO username; +GRANT SELECT ON pg_namespace TO username; +GRANT SELECT ON pg_settings TO username; +GRANT SELECT ON pg_stats TO username; +GRANT SELECT ON gp_distributed_xacts TO username; +GRANT SELECT ON gp_segment_configuration TO username; +-- Greenplum 5.x only +GRANT SELECT ON gp_distribution_policy TO username; + +-- allow creating external tables in the same schema as source table +GRANT USAGE ON SCHEMA schema_to_read TO username; +GRANT CREATE ON SCHEMA schema_to_read TO username; +-- yes, ``writable`` for reading from GP, because data is written from Greenplum to Spark executor. +ALTER USER username CREATEEXTTABLE(type = 'writable', protocol = 'gpfdist'); + +-- allow read access to specific table +GRANT SELECT ON schema_to_read.table_to_read TO username; +``` + +More details can be found in [official documentation](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/install_cfg.html#role-privileges). diff --git a/mddocs/connection/db_connection/greenplum/read.md b/mddocs/connection/db_connection/greenplum/read.md new file mode 100644 index 000000000..476f6f0db --- /dev/null +++ b/mddocs/connection/db_connection/greenplum/read.md @@ -0,0 +1,405 @@ + + +# Reading from Greenplum using `DBReader` + +Data can be read from Greenplum to Spark using [`DBReader`](../../../db/db_reader.md#onetl.db.db_reader.db_reader.DBReader). +It also supports [Read Strategies](../../../strategy/index.md#strategy) for incremental data reading. + +#### WARNING +Please take into account [Greenplum <-> Spark type mapping](types.md#greenplum-types). + +#### NOTE +Unlike JDBC connectors, *Greenplum connector for Spark* does not support +executing **custom** SQL queries using `.sql` method. Connector can be used to only read data from a table or view. + +## Supported DBReader features + +* ✅︎ `columns` (see note below) +* ✅︎ `where` (see note below) +* ✅︎ `hwm` (see note below), supported strategies: +* * ✅︎ [Snapshot Strategy](../../../strategy/snapshot_strategy.md#snapshot-strategy) +* * ✅︎ [Incremental Strategy](../../../strategy/incremental_strategy.md#incremental-strategy) +* * ✅︎ [Snapshot Batch Strategy](../../../strategy/snapshot_batch_strategy.md#snapshot-batch-strategy) +* * ✅︎ [Incremental Batch Strategy](../../../strategy/incremental_batch_strategy.md#incremental-batch-strategy) +* ❌ `hint` (is not supported by Greenplum) +* ❌ `df_schema` +* ✅︎ `options` (see [`Greenplum.ReadOptions`](#onetl.connection.db_connection.greenplum.options.GreenplumReadOptions)) + +#### WARNING +In case of Greenplum connector, `DBReader` does not generate raw `SELECT` query. Instead it relies on Spark SQL syntax +which in some cases (using column projection and predicate pushdown) can be converted to Greenplum SQL. + +So `columns`, `where` and `hwm.expression` should be specified in [Spark SQL](https://spark.apache.org/docs/latest/sql-ref-syntax.html) syntax, +not Greenplum SQL. + +This is OK: + +```python +DBReader( + columns=[ + "some_column", + # this cast is executed on Spark side + "CAST(another_column AS STRING)", + ], + # this predicate is parsed by Spark, and can be pushed down to Greenplum + where="some_column LIKE 'val1%'", +) +``` + +This is will fail: + +```python +DBReader( + columns=[ + "some_column", + # Spark does not have `text` type + "CAST(another_column AS text)", + ], + # Spark does not support ~ syntax for regexp matching + where="some_column ~ 'val1.*'", +) +``` + +## Examples + +Snapshot strategy: + +```python +from onetl.connection import Greenplum +from onetl.db import DBReader + +greenplum = Greenplum(...) + +reader = DBReader( + connection=greenplum, + source="schema.table", + columns=["id", "key", "CAST(value AS string) value", "updated_dt"], + where="key = 'something'", +) +df = reader.run() +``` + +Incremental strategy: + +```python +from onetl.connection import Greenplum +from onetl.db import DBReader +from onetl.strategy import IncrementalStrategy + +greenplum = Greenplum(...) + +reader = DBReader( + connection=greenplum, + source="schema.table", + columns=["id", "key", "CAST(value AS string) value", "updated_dt"], + where="key = 'something'", + hwm=DBReader.AutoDetectHWM(name="greenplum_hwm", expression="updated_dt"), +) + +with IncrementalStrategy(): + df = reader.run() +``` + +## Interaction schema + +High-level schema is described in [Prerequisites](prerequisites.md#greenplum-prerequisites). You can find detailed interaction schema below. + +### Spark <-> Greenplum interaction during DBReader.run() + +## Recommendations + +### Select only required columns + +Instead of passing `"*"` in `DBReader(columns=[...])` prefer passing exact column names. This reduces the amount of data passed from Greenplum to Spark. + +### Pay attention to `where` value + +Instead of filtering data on Spark side using `df.filter(df.column == 'value')` pass proper `DBReader(where="column = 'value'")` clause. +This both reduces the amount of data send from Greenplum to Spark, and may also improve performance of the query. +Especially if there are indexes or partitions for columns used in `where` clause. + +### Read data in parallel + +`DBReader` in case of Greenplum connector requires view or table to have a column which is used by Spark +for parallel reads. + +Choosing proper column allows each Spark executor to read only part of data stored in the specified segment, +avoiding moving large amounts of data between segments, which improves reading performance. + +#### Using `gp_segment_id` + +By default, `DBReader` will use [gp_segment_id](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/troubleshooting.html#reading-from-a-view) +column for parallel data reading. Each DataFrame partition will contain data of a specific Greenplum segment. + +This allows each Spark executor read only data from specific Greenplum segment, avoiding moving large amounts of data between segments. + +If view is used, it is recommended to include `gp_segment_id` column to this view: + +### Reading from view with gp_segment_id column + +```python +from onetl.connection import Greenplum +from onetl.db import DBReader + +greenplum = Greenplum(...) + +greenplum.execute( + """ + CREATE VIEW schema.view_with_gp_segment_id AS + SELECT + id, + some_column, + another_column, + gp_segment_id -- IMPORTANT + FROM schema.some_table + """, +) + +reader = DBReader( + connection=greenplum, + source="schema.view_with_gp_segment_id", +) +df = reader.run() +``` + +#### Using custom `partition_column` + +Sometimes table or view is lack of `gp_segment_id` column, but there is some column +with value range correlated with Greenplum segment distribution. + +In this case, custom column can be used instead: + +### Reading from view with custom partition_column + +```python +from onetl.connection import Greenplum +from onetl.db import DBReader + +greenplum = Greenplum(...) + +greenplum.execute( + """ + CREATE VIEW schema.view_with_partition_column AS + SELECT + id, + some_column, + part_column -- correlated to greenplum segment ID + FROM schema.some_table + """, +) + +reader = DBReader( + connection=greenplum, + source="schema.view_with_partition_column", + options=Greenplum.ReadOptions( + # parallelize data using specified column + partitionColumn="part_column", + # create 10 Spark tasks, each will read only part of table data + partitions=10, + ), +) +df = reader.run() +``` + +#### Reading `DISTRIBUTED REPLICATED` tables + +Replicated tables do not have `gp_segment_id` column at all, so you need to set `partition_column` to some column name +of type integer/bigint/smallint. + +### Parallel `JOIN` execution + +In case of using views which require some data motion between Greenplum segments, like `JOIN` queries, another approach should be used. + +Each Spark executor N will run the same query, so each of N query will start its own JOIN process, leading to really heavy load on Greenplum segments. +**This should be avoided**. + +Instead is recommended to run `JOIN` query on Greenplum side, save the result to an intermediate table, +and then read this table using `DBReader`: + +### Reading from view using intermediate table + +```python +from onetl.connection import Greenplum +from onetl.db import DBReader + +greenplum = Greenplum(...) + +greenplum.execute( + """ + CREATE UNLOGGED TABLE schema.intermediate_table AS + SELECT + id, + tbl1.col1, + tbl1.data, + tbl2.another_data + FROM + schema.table1 as tbl1 + JOIN + schema.table2 as tbl2 + ON + tbl1.col1 = tbl2.col2 + WHERE ... + """, +) + +reader = DBReader( + connection=greenplum, + source="schema.intermediate_table", +) +df = reader.run() + +# write dataframe somethere + +greenplum.execute( + """ + DROP TABLE schema.intermediate_table + """, +) +``` + +#### WARNING +**NEVER** do that: + +```python +df1 = DBReader(connection=greenplum, target="public.table1", ...).run() +df2 = DBReader(connection=greenplum, target="public.table2", ...).run() + +joined_df = df1.join(df2, on="col") +``` + +This will lead to sending all the data from both `table1` and `table2` to Spark executor memory, and then `JOIN` +will be performed on Spark side, not inside Greenplum. This is **VERY** inefficient. + +#### `TEMPORARY` tables notice + +Someone could think that writing data from view or result of `JOIN` to `TEMPORARY` table, +and then passing it to `DBReader`, is an efficient way to read data from Greenplum. This is because temp tables are not generating WAL files, +and are automatically deleted after finishing the transaction. + +That will **NOT** work. Each Spark executor establishes its own connection to Greenplum. +And each connection starts its own transaction which means that every executor will read empty temporary table. + +You should use [UNLOGGED](https://docs.vmware.com/en/VMware-Greenplum/7/greenplum-database/ref_guide-sql_commands-CREATE_TABLE.html) tables +to write data to intermediate table without generating WAL logs. + +## Options + +### *pydantic model* onetl.connection.db_connection.greenplum.options.GreenplumReadOptions + +VMware’s Greenplum Spark connector reading options. + +#### WARNING +Some options, like `url`, `dbtable`, `server.*`, `pool.*`, +etc are populated from connection attributes, and cannot be overridden by the user in `ReadOptions` to avoid issues. + +### Examples + +#### NOTE +You can pass any value +[supported by connector](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/read_from_gpdb.html), +even if it is not mentioned in this documentation. **Option names should be in** `camelCase`! + +The set of supported options depends on connector version. + +```python +from onetl.connection import Greenplum + +options = Greenplum.ReadOptions( + partitionColumn="reg_id", + partitions=10, +) +``` + + + +#### *field* partition_column *: str | None* *= None* *(alias 'partitionColumn')* + +Column used to parallelize reading from a table. + +#### WARNING +You should not change this option, unless you know what you’re doing. + +It’s preferable to use default values to read data parallel by number of segments in Greenplum cluster. + +Possible values: +: * `None` (default): + : Spark generates N jobs (where N == number of segments in Greenplum cluster), + each job is reading only data from a specific segment + (filtering data by `gp_segment_id` column). +
+ This is very effective way to fetch the data from a cluster. + * table column + : Allocate each executor a range of values from a specific column. +
+ Spark generates for each executor an SQL query: +
+ Executor 1: + ```sql + SELECT ... FROM table + WHERE (partition_column >= lowerBound + OR partition_column IS NULL) + AND partition_column < (lower_bound + stride) + ``` +
+ Executor 2: + ```sql + SELECT ... FROM table + WHERE partition_column >= (lower_bound + stride) + AND partition_column < (lower_bound + 2 * stride) + ``` +
+ … +
+ Executor N: + ```sql + SELECT ... FROM table + WHERE partition_column >= (lower_bound + (N-1) * stride) + AND partition_column <= upper_bound + ``` +
+ Where `stride=(upper_bound - lower_bound) / num_partitions`, + `lower_bound=MIN(partition_column)`, `upper_bound=MAX(partition_column)`. +
+ #### NOTE + Column type must be numeric. Other types are not supported. +
+ #### NOTE + [`num_partitions`](#onetl.connection.db_connection.greenplum.options.GreenplumReadOptions.num_partitions) is used just to + calculate the partition stride, **NOT** for filtering the rows in table. + So all rows in the table will be returned (unlike *Incremental* [Read Strategies](../../../strategy/index.md#strategy)). +
+ #### NOTE + All queries are executed in parallel. To execute them sequentially, use *Batch* [Read Strategies](../../../strategy/index.md#strategy). + +#### WARNING +Both options [`partition_column`](#onetl.connection.db_connection.greenplum.options.GreenplumReadOptions.partition_column) and [`num_partitions`](#onetl.connection.db_connection.greenplum.options.GreenplumReadOptions.num_partitions) should have a value, +or both should be `None` + +### Examples + +Read data in 10 parallel jobs by range of values in `id_column` column: + +```python +Greenplum.ReadOptions( + partitionColumn="id_column", + partitions=10, +) +``` + + + +#### *field* num_partitions *: int | None* *= None* *(alias 'partitions')* + +Number of jobs created by Spark to read the table content in parallel. + +See documentation for [`partition_column`](#onetl.connection.db_connection.greenplum.options.GreenplumReadOptions.partition_column) for more details + +#### WARNING +By default connector uses number of segments in the Greenplum cluster. +You should not change this option, unless you know what you’re doing + +#### WARNING +Both options [`partition_column`](#onetl.connection.db_connection.greenplum.options.GreenplumReadOptions.partition_column) and [`num_partitions`](#onetl.connection.db_connection.greenplum.options.GreenplumReadOptions.num_partitions) should have a value, +or both should be `None` + + diff --git a/mddocs/connection/db_connection/greenplum/types.md b/mddocs/connection/db_connection/greenplum/types.md new file mode 100644 index 000000000..6a6c0effb --- /dev/null +++ b/mddocs/connection/db_connection/greenplum/types.md @@ -0,0 +1,318 @@ + + +# Greenplum <-> Spark type mapping + +#### NOTE +The results below are valid for Spark 3.2.4, and may differ on other Spark versions. + +## Type detection & casting + +Spark’s DataFrames always have a `schema` which is a list of columns with corresponding Spark types. All operations on a column are performed using column type. + +### Reading from Greenplum + +This is how Greenplum connector performs this: + +* Execute query `SELECT * FROM table LIMIT 0` [1](#id2). +* For each column in query result get column name and Greenplum type. +* Find corresponding `Greenplum type (read)` → `Spark type` combination (see below) for each DataFrame column. If no combination is found, raise exception. +* Use Spark column projection and predicate pushdown features to build a final query. +* Create DataFrame from generated query with inferred schema. + +* **[1]** Yes, **all columns of a table**, not just selected ones. This means that if source table **contains** columns with unsupported type, the entire table cannot be read. + +### Writing to some existing Greenplum table + +This is how Greenplum connector performs this: + +* Get names of columns in DataFrame. +* Perform `SELECT * FROM table LIMIT 0` query. +* For each column in query result get column name and Greenplum type. +* Match table columns with DataFrame columns (by name, case insensitive). + If some column is present only in target table, but not in DataFrame (like `DEFAULT` or `SERIAL` column), and vice versa, raise an exception. + See [Explicit type cast](). +* Find corresponding `Spark type` → `Greenplumtype (write)` combination (see below) for each DataFrame column. If no combination is found, raise exception. +* If `Greenplumtype (write)` match `Greenplum type (read)`, no additional casts will be performed, DataFrame column will be written to Greenplum as is. +* If `Greenplumtype (write)` does not match `Greenplum type (read)`, DataFrame column will be casted to target column type **on Greenplum side**. For example, you can write column with text data to `json` column which Greenplum connector currently does not support. + +### Create new table using Spark + +#### WARNING +ABSOLUTELY NOT RECOMMENDED! + +This is how Greenplum connector performs this: + +* Find corresponding `Spark type` → `Greenplum type (create)` combination (see below) for each DataFrame column. If no combination is found, raise exception. +* Generate DDL for creating table in Greenplum, like `CREATE TABLE (col1 ...)`, and run it. +* Write DataFrame to created table as is. + +More details [can be found here](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/write_to_gpdb.html). + +But Greenplum connector support only limited number of types and almost no custom clauses (like `PARTITION BY`). +So instead of relying on Spark to create tables: + +### See example + +```python +writer = DBWriter( + connection=greenplum, + target="public.table", + options=Greenplum.WriteOptions( + if_exists="append", + # by default distribution is random + distributedBy="id", + # partitionBy is not supported + ), +) +writer.run(df) +``` + +Always prefer creating table with desired DDL **BEFORE WRITING DATA**: + +### See example + +```python +greenplum.execute( + """ + CREATE TABLE public.table ( + id int32, + business_dt timestamp(6), + value json + ) + PARTITION BY RANGE (business_dt) + DISTRIBUTED BY id + """, +) + +writer = DBWriter( + connection=greenplum, + target="public.table", + options=Greenplum.WriteOptions(if_exists="append"), +) +writer.run(df) +``` + +See Greenplum [CREATE TABLE](https://docs.vmware.com/en/VMware-Greenplum/7/greenplum-database/ref_guide-sql_commands-CREATE_TABLE.html) documentation. + +## Supported types + +See: +: * [official connector documentation](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/reference-datatype_mapping.html) + * [list of Greenplum types](https://docs.vmware.com/en/VMware-Greenplum/7/greenplum-database/ref_guide-data_types.html) + +### Numeric types + +| Greenplum type (read) | Spark type | Greenplumtype (write) | Greenplum type (create) | +|-----------------------------|----------------------------------|-----------------------------|---------------------------| +| `decimal` | `DecimalType(P=38, S=18)` | `decimal(P=38, S=18)` | `decimal` (unbounded) | +| `decimal(P=0..38)` | `DecimalType(P=0..38, S=0)` | `decimal(P=0..38, S=0)` | | +| `decimal(P=0..38, S=0..38)` | `DecimalType(P=0..38, S=0..38)` | `decimal(P=0..38, S=0..38)` | | +| `decimal(P=39.., S=0..)` | unsupported [2](#id4) | | | +| `real` | `FloatType()` | `real` | `real` | +| `double precision` | `DoubleType()` | `double precision` | `double precision` | +| `-` | `ByteType()` | unsupported | unsupported | +| `smallint` | `ShortType()` | `smallint` | `smallint` | +| `integer` | `IntegerType()` | `integer` | `integer` | +| `bigint` | `LongType()` | `bigint` | `bigint` | +| `money` | unsupported | | | +| `int4range` | | | | +| `int8range` | | | | +| `numrange` | | | | +| `int2vector` | | | | +* **[2]** Greenplum support decimal types with unlimited precision. But Spark’s `DecimalType(P, S)` supports maximum `P=38` (128 bit). It is impossible to read, write or operate with values of larger precision, this leads to an exception. + +### Temporal types + +| Greenplum type (read) | Spark type | Greenplumtype (write) | Greenplum type (create) | +|----------------------------------|----------------------------------------------------------------|-------------------------|---------------------------| +| `date` | `DateType()` | `date` | `date` | +| `time` | `TimestampType()`,
time format quirks [3](#id6) | `timestamp` | `timestamp` | +| `time(0..6)` | | | | +| `time with time zone` | | | | +| `time(0..6) with time zone` | | | | +| `timestamp` | `TimestampType()` | `timestamp` | `timestamp` | +| `timestamp(0..6)` | | | | +| `timestamp with time zone` | | | | +| `timestamp(0..6) with time zone` | | | | +| `interval` or any precision | unsupported | | | +| `daterange` | | | | +| `tsrange` | | | | +| `tstzrange` | | | | + +#### WARNING +Note that types in Greenplum and Spark have different value ranges: + +| Greenplum type | Min value | Max value | Spark type | Min value | Max value | +|------------------|-------------------------------|--------------------------------|-------------------|------------------------------|------------------------------| +| `date` | `-4713-01-01` | `5874897-01-01` | `DateType()` | `0001-01-01` | `9999-12-31` | +| `timestamp` | `-4713-01-01 00:00:00.000000` | `294276-12-31 23:59:59.999999` | `TimestampType()` | `0001-01-01 00:00:00.000000` | `9999-12-31 23:59:59.999999` | +| `time` | `00:00:00.000000` | `24:00:00.000000` | | | | + +So not all of values can be read from Greenplum to Spark. + +References: +: * [Greenplum types documentation](https://docs.vmware.com/en/VMware-Greenplum/7/greenplum-database/ref_guide-data_types.html) + * [Spark DateType documentation](https://spark.apache.org/docs/latest/api/java/org/apache/spark/sql/types/DateType.html) + * [Spark TimestampType documentation](https://spark.apache.org/docs/latest/api/java/org/apache/spark/sql/types/TimestampType.html) + +* **[3]** `time` type is the same as `timestamp` with date `1970-01-01`. So instead of reading data from Postgres like `23:59:59` it is actually read `1970-01-01 23:59:59`, and vice versa. + +### String types + +| Greenplum type (read) | Spark type | Greenplumtype (write) | Greenplum type (create) | +|---------------------------|----------------|-------------------------|---------------------------| +| `character` | `StringType()` | `text` | `text` | +| `character(N)` | | | | +| `character varying` | | | | +| `character varying(N)` | | | | +| `text` | | | | +| `xml` | | | | +| `CREATE TYPE ... AS ENUM` | | | | +| `json` | unsupported | | | +| `jsonb` | | | | + +### Binary types + +| Greenplum type (read) | Spark type | Greenplumtype (write) | Greenplum type (create) | +|-------------------------|----------------------------------|-------------------------|---------------------------| +| `boolean` | `BooleanType()` | `boolean` | `boolean` | +| `bit` | unsupported | | | +| `bit(N)` | | | | +| `bit varying` | | | | +| `bit varying(N)` | | | | +| `bytea` | unsupported [4](#id8) | | | +| `-` | `BinaryType()` | `bytea` | `bytea` | +* **[4]** Yes, that’s weird. + +### Struct types + +| Greenplum type (read) | Spark type | Greenplumtype (write) | Greenplum type (create) | +|------------------------------|----------------|-------------------------|---------------------------| +| `T[]` | unsupported | | | +| `-` | `ArrayType()` | unsupported | | +| `CREATE TYPE sometype (...)` | `StringType()` | `text` | `text` | +| `-` | `StructType()` | unsupported | | +| `-` | `MapType()` | | | + +## Unsupported types + +Columns of these types cannot be read/written by Spark: +: * `cidr` + * `inet` + * `macaddr` + * `macaddr8` + * `circle` + * `box` + * `line` + * `lseg` + * `path` + * `point` + * `polygon` + * `tsvector` + * `tsquery` + * `uuid` + +The is a way to avoid this - just cast unsupported types to `text`. But the way this can be done is not a straightforward. + +## Explicit type cast + +### `DBReader` + +Direct casting of Greenplum types is not supported by DBReader due to the connector’s implementation specifics. + +```python +reader = DBReader( + connection=greenplum, + # will fail + columns=["CAST(unsupported_column AS text)"], +) +``` + +But there is a workaround - create a view with casting unsupported column to text (or any other supported type). +For example, you can use [to_json](https://www.postgresql.org/docs/current/functions-json.html) Postgres function to convert column of any type to string representation and then parse this column on Spark side using [`JSON.parse_column`](../../../file_df/file_formats/json.md#onetl.file.format.json.JSON.parse_column) method. + +```python +from pyspark.sql.types import ArrayType, IntegerType + +from onetl.connection import Greenplum +from onetl.db import DBReader +from onetl.file.format import JSON + +greenplum = Greenplum(...) + +greenplum.execute( + """ + CREATE VIEW schema.view_with_json_column AS + SELECT + id, + supported_column, + to_json(array_column) array_column_as_json, + gp_segment_id -- ! important ! + FROM + schema.table_with_unsupported_columns + """, +) + +# create dataframe using this view +reader = DBReader( + connection=greenplum, + source="schema.view_with_json_column", +) +df = reader.run() + +# Define the schema for the JSON data +json_scheme = ArrayType(IntegerType()) + +df = df.select( + df.id, + df.supported_column, + JSON().parse_column(df.array_column_as_json, json_scheme).alias("array_column"), +) +``` + +### `DBWriter` + +To write data to a `text` or `json` column in a Greenplum table, use [`JSON.serialize_column`](../../../file_df/file_formats/json.md#onetl.file.format.json.JSON.serialize_column) method. + +```python +from onetl.connection import Greenplum +from onetl.db import DBWriter +from onetl.file.format import JSON + +greenplum = Greenplum(...) + +greenplum.execute( + """ + CREATE TABLE schema.target_table ( + id int, + supported_column timestamp, + array_column_as_json jsonb, -- or text + ) + DISTRIBUTED BY id + """, +) + +write_df = df.select( + df.id, + df.supported_column, + JSON().serialize_column(df.array_column).alias("array_column_json"), +) + +writer = DBWriter( + connection=greenplum, + target="schema.target_table", +) +writer.run(write_df) +``` + +Then you can parse this column on Greenplum side: + +```sql +SELECT + id, + supported_column, + -- access first item of an array + array_column_as_json->0 +FROM + schema.target_table +``` diff --git a/mddocs/connection/db_connection/greenplum/write.md b/mddocs/connection/db_connection/greenplum/write.md new file mode 100644 index 000000000..0815e0760 --- /dev/null +++ b/mddocs/connection/db_connection/greenplum/write.md @@ -0,0 +1,141 @@ + + +# Writing to Greenplum using `DBWriter` + +For writing data to Greenplum, use [`DBWriter`](../../../db/db_writer.md#onetl.db.db_writer.db_writer.DBWriter) +with [`GreenplumWriteOptions`](#onetl.connection.db_connection.greenplum.options.GreenplumWriteOptions). + +#### WARNING +Please take into account [Greenplum <-> Spark type mapping](types.md#greenplum-types). + +#### WARNING +It is always recommended to create table explicitly using [Greenplum.execute](execute.md#greenplum-execute) +instead of relying on Spark’s table DDL generation. + +This is because Spark’s DDL generator can create columns with different types than it is expected. + +## Examples + +```python +from onetl.connection import Greenplum +from onetl.db import DBWriter + +greenplum = Greenplum(...) + +df = ... # data is here + +writer = DBWriter( + connection=greenplum, + target="schema.table", + options=Greenplum.WriteOptions( + if_exists="append", + # by default distribution is random + distributedBy="id", + # partitionBy is not supported + ), +) + +writer.run(df) +``` + +## Interaction schema + +High-level schema is described in [Prerequisites](prerequisites.md#greenplum-prerequisites). You can find detailed interaction schema below. + +### Spark <-> Greenplum interaction during DBWriter.run() + +## Options + +### *pydantic model* onetl.connection.db_connection.greenplum.options.GreenplumWriteOptions + +VMware’s Greenplum Spark connector writing options. + +#### WARNING +Some options, like `url`, `dbtable`, `server.*`, `pool.*`, etc +are populated from connection attributes, and cannot be overridden by the user in `WriteOptions` to avoid issues. + +### Examples + +#### NOTE +You can pass any value +[supported by connector](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/write_to_gpdb.html), +even if it is not mentioned in this documentation. **Option names should be in** `camelCase`! + +The set of supported options depends on connector version. + +```python +from onetl.connection import Greenplum + +options = Greenplum.WriteOptions( + if_exists="append", + truncate="false", + distributedBy="mycolumn", +) +``` + + + +#### *field* if_exists *: GreenplumTableExistBehavior* *= GreenplumTableExistBehavior.APPEND* *(alias 'mode')* + +Behavior of writing data into existing table. + +Possible values: +: * `append` (default) + : Adds new rows into existing table. +
+ ### Behavior in details +
+ * Table does not exist + : Table is created using options provided by user + (`distributedBy` and others). + * Table exists + : Data is appended to a table. Table has the same DDL as before writing data. +
+ #### WARNING + This mode does not check whether table already contains + rows from dataframe, so duplicated rows can be created. +
+ Also Spark does not support passing custom options to + insert statement, like `ON CONFLICT`, so don’t try to + implement deduplication using unique indexes or constraints. +
+ Instead, write to staging table and perform deduplication + using `execute` method. + * `replace_entire_table` + : **Table is dropped and then created**. +
+ ### Behavior in details +
+ * Table does not exist + : Table is created using options provided by user + (`distributedBy` and others). + * Table exists + : Table content is replaced with dataframe content. +
+ After writing completed, target table could either have the same DDL as + before writing data (`truncate=True`), or can be recreated (`truncate=False`). + * `ignore` + : Ignores the write operation if the table already exists. +
+ ### Behavior in details +
+ * Table does not exist + : Table is created using options provided by user + (`distributedBy` and others). + * Table exists + : The write operation is ignored, and no data is written to the table. + * `error` + : Raises an error if the table already exists. +
+ ### Behavior in details +
+ * Table does not exist + : Table is created using options provided by user + (`distributedBy` and others). + * Table exists + : An error is raised, and no data is written to the table. + +#### Versionchanged +Changed in version 0.9.0: Renamed `mode` → `if_exists` + + diff --git a/mddocs/connection/db_connection/hive/connection.md b/mddocs/connection/db_connection/hive/connection.md new file mode 100644 index 000000000..7a8470de1 --- /dev/null +++ b/mddocs/connection/db_connection/hive/connection.md @@ -0,0 +1,118 @@ + + +# Hive Connection + +### *class* onetl.connection.db_connection.hive.connection.Hive(\*, spark: SparkSession, cluster: Cluster) + +Spark connection with Hive MetaStore support. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### SEE ALSO +Before using this connector please take into account [Prerequisites](prerequisites.md#hive-prerequisites) + +#### Versionadded +Added in version 0.1.0. + +* **Parameters:** + **cluster** + : Cluster name. Used for HWM and lineage. +
+ #### Versionadded + Added in version 0.7.0. + + **spark** + : Spark session with Hive metastore support enabled + +### Examples + +Create Hive connection with Kerberos auth + +Execute `kinit` consome command before creating Spark Session + +```bash +$ kinit -kt /path/to/keytab user +``` + +```python +from onetl.connection import Hive +from pyspark.sql import SparkSession + +# Create Spark session +# Use names "spark.yarn.access.hadoopFileSystems", "spark.yarn.principal" +# and "spark.yarn.keytab" for Spark 2 + +spark = ( + SparkSession.builder.appName("spark-app-name") + .option("spark.kerberos.access.hadoopFileSystems", "hdfs://cluster.name.node:8020") + .option("spark.kerberos.principal", "user") + .option("spark.kerberos.keytab", "/path/to/keytab") + .enableHiveSupport() + .getOrCreate() +) + +# Create connection +hive = Hive(cluster="rnd-dwh", spark=spark).check() +``` + +Create Hive connection with anonymous auth + +```py +from onetl.connection import Hive +from pyspark.sql import SparkSession + +# Create Spark session +spark = SparkSession.builder.appName("spark-app-name").enableHiveSupport().getOrCreate() + +# Create connection +hive = Hive(cluster="rnd-dwh", spark=spark).check() +``` + + + +#### *classmethod* get_current(spark: SparkSession) + +Create connection for current cluster. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### NOTE +Can be used only if there are some hooks bound to +[`Slots.get_current_cluster`](slots.md#onetl.connection.db_connection.hive.slots.HiveSlots.get_current_cluster) slot. + +#### Versionadded +Added in version 0.7.0. + +* **Parameters:** + **spark** + : Spark session + +### Examples + +```python +from onetl.connection import Hive +from pyspark.sql import SparkSession + +spark = SparkSession.builder.appName("spark-app-name").enableHiveSupport().getOrCreate() + +# injecting current cluster name via hooks mechanism +hive = Hive.get_current(spark=spark) +``` + + + +#### check() + +Check source availability. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +If not, an exception will be raised. + +* **Returns:** + Connection itself +* **Raises:** + RuntimeError + : If the connection is not available + +### Examples + +```python +connection.check() +``` + + diff --git a/mddocs/connection/db_connection/hive/execute.md b/mddocs/connection/db_connection/hive/execute.md new file mode 100644 index 000000000..c1d77874c --- /dev/null +++ b/mddocs/connection/db_connection/hive/execute.md @@ -0,0 +1,57 @@ + + +# Executing statements in Hive + +Use `Hive.execute(...)` to execute DDL and DML operations. + +## Syntax support + +This method supports **any** query syntax supported by Hive, like: + +* ✅︎ `CREATE TABLE ...`, `CREATE VIEW ...`, and so on +* ✅︎ `LOAD DATA ...`, and so on +* ✅︎ `ALTER ...` +* ✅︎ `INSERT INTO ... SELECT ...`, and so on +* ✅︎ `DROP TABLE ...`, `DROP VIEW ...`, and so on +* ✅︎ `MSCK REPAIR TABLE ...`, and so on +* ✅︎ other statements not mentioned here +* ❌ `SET ...; SELECT ...;` - multiple statements not supported + +#### WARNING +Actually, query should be written using [SparkSQL](https://spark.apache.org/docs/latest/sql-ref-syntax.html#ddl-statements) syntax, not HiveQL. + +## Examples + +```python +from onetl.connection import Hive + +hive = Hive(...) + +hive.execute("DROP TABLE schema.table") +hive.execute( + """ + CREATE TABLE schema.table ( + id NUMBER, + key VARCHAR, + value DOUBLE + ) + PARTITION BY (business_date DATE) + STORED AS orc + """ +) +``` + +### Details + +#### Hive.execute(statement: str) → None + +Execute DDL or DML statement. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### Versionadded +Added in version 0.2.0. + +* **Parameters:** + **statement** + : Statement to be executed. + + diff --git a/mddocs/connection/db_connection/hive/index.md b/mddocs/connection/db_connection/hive/index.md new file mode 100644 index 000000000..bc551b745 --- /dev/null +++ b/mddocs/connection/db_connection/hive/index.md @@ -0,0 +1,19 @@ + + +# Hive + +# Connection + +* [Prerequisites](prerequisites.md) +* [Hive Connection](connection.md) + +# Operations + +* [Reading from Hive using `DBReader`](read.md) +* [Reading from Hive using `Hive.sql`](sql.md) +* [Writing to Hive using `DBWriter`](write.md) +* [Executing statements in Hive](execute.md) + +# For developers + +* [Hive Slots](slots.md) diff --git a/mddocs/connection/db_connection/hive/prerequisites.md b/mddocs/connection/db_connection/hive/prerequisites.md new file mode 100644 index 000000000..aa0fb49c7 --- /dev/null +++ b/mddocs/connection/db_connection/hive/prerequisites.md @@ -0,0 +1,128 @@ + + +# Prerequisites + +#### NOTE +onETL’s Hive connection is actually SparkSession with access to [Hive Thrift Metastore](https://docs.cloudera.com/cdw-runtime/1.5.0/hive-hms-overview/topics/hive-hms-introduction.html) +and HDFS/S3. +All data motion is made using Spark. Hive Metastore is used only to store tables and partitions metadata. + +This connector does **NOT** require Hive server. It also does **NOT** use Hive JDBC connector. + +## Version Compatibility + +* Hive Metastore version: + : * Officially declared: 0.12 - 3.1.3 (may require to add proper .jar file explicitly) + * Actually tested: 1.2.100, 2.3.10, 3.1.3 +* Spark versions: 2.3.x - 3.5.x +* Java versions: 8 - 20 + +See [official documentation](https://spark.apache.org/docs/latest/sql-data-sources-hive-tables.html). + +## Installing PySpark + +To use Hive connector you should have PySpark installed (or injected to `sys.path`) +BEFORE creating the connector instance. + +See [Spark](../../../install/spark.md#install-spark) installation instruction for more details. + +## Connecting to Hive Metastore + +#### NOTE +If you’re using managed Hadoop cluster, skip this step, as all Spark configs are should already present on the host. + +Create `$SPARK_CONF_DIR/hive-site.xml` with Hive Metastore URL: + +```xml + + + + + hive.metastore.uris + thrift://metastore.host.name:9083 + + +``` + +Create `$SPARK_CONF_DIR/core-site.xml` with warehouse location ,e.g. HDFS IPC port of Hadoop namenode, or S3 bucket address & credentials: + +HDFS + +```xml + + + + + fs.defaultFS + hdfs://myhadoopcluster:9820 + + +``` + +S3 + +```xml + + + + !-- See https://hadoop.apache.org/docs/current/hadoop-aws/tools/hadoop-aws/index.html#General_S3A_Client_configuration + + fs.defaultFS + s3a://mys3bucket/ + + + fs.s3a.bucket.mybucket.endpoint + http://s3.somain + + + fs.s3a.bucket.mybucket.connection.ssl.enabled + false + + + fs.s3a.bucket.mybucket.path.style.access + true + + + fs.s3a.bucket.mybucket.aws.credentials.provider + org.apache.hadoop.fs.s3a.SimpleAWSCredentialsProvider + + + fs.s3a.bucket.mybucket.access.key + some-user + + + fs.s3a.bucket.mybucket.secret.key + mysecrettoken + + +``` + +## Using Kerberos + +Some of Hadoop managed clusters use Kerberos authentication. In this case, you should call [kinit](https://web.mit.edu/kerberos/krb5-1.12/doc/user/user_commands/kinit.html) command +**BEFORE** starting Spark session to generate Kerberos ticket. See [Kerberos support](../../../install/kerberos.md#install-kerberos). + +Sometimes it is also required to pass keytab file to Spark config, allowing Spark executors to generate own Kerberos tickets: + +Spark 3 + +```python +SparkSession.builder + .option("spark.kerberos.access.hadoopFileSystems", "hdfs://namenode1.domain.com:9820,hdfs://namenode2.domain.com:9820") + .option("spark.kerberos.principal", "user") + .option("spark.kerberos.keytab", "/path/to/keytab") + .gerOrCreate() +``` + +Spark 2 + +```python +SparkSession.builder + .option("spark.yarn.access.hadoopFileSystems", "hdfs://namenode1.domain.com:9820,hdfs://namenode2.domain.com:9820") + .option("spark.yarn.principal", "user") + .option("spark.yarn.keytab", "/path/to/keytab") + .gerOrCreate() +``` + +See [Spark security documentation](https://spark.apache.org/docs/latest/security.html#kerberos) +for more details. diff --git a/mddocs/connection/db_connection/hive/read.md b/mddocs/connection/db_connection/hive/read.md new file mode 100644 index 000000000..cbe3469d9 --- /dev/null +++ b/mddocs/connection/db_connection/hive/read.md @@ -0,0 +1,92 @@ + + +# Reading from Hive using `DBReader` + +[`DBReader`](../../../db/db_reader.md#onetl.db.db_reader.db_reader.DBReader) supports [Read Strategies](../../../strategy/index.md#strategy) for incremental data reading, +but does not support custom queries, like `JOIN`. + +## Supported DBReader features + +* ✅︎ `columns` +* ✅︎ `where` +* ✅︎ `hwm`, supported strategies: +* * ✅︎ [Snapshot Strategy](../../../strategy/snapshot_strategy.md#snapshot-strategy) +* * ✅︎ [Incremental Strategy](../../../strategy/incremental_strategy.md#incremental-strategy) +* * ✅︎ [Snapshot Batch Strategy](../../../strategy/snapshot_batch_strategy.md#snapshot-batch-strategy) +* * ✅︎ [Incremental Batch Strategy](../../../strategy/incremental_batch_strategy.md#incremental-batch-strategy) +* ❌ `hint` (is not supported by Hive) +* ❌ `df_schema` +* ❌ `options` (only Spark config params are used) + +#### WARNING +Actually, `columns`, `where` and `hwm.expression` should be written using [SparkSQL](https://spark.apache.org/docs/latest/sql-ref-syntax.html#data-retrieval-statements) syntax, +not HiveQL. + +## Examples + +Snapshot strategy: + +```python +from onetl.connection import Hive +from onetl.db import DBReader + +hive = Hive(...) + +reader = DBReader( + connection=hive, + source="schema.table", + columns=["id", "key", "CAST(value AS text) value", "updated_dt"], + where="key = 'something'", +) +df = reader.run() +``` + +Incremental strategy: + +```python +from onetl.connection import Hive +from onetl.db import DBReader +from onetl.strategy import IncrementalStrategy + +hive = Hive(...) + +reader = DBReader( + connection=hive, + source="schema.table", + columns=["id", "key", "CAST(value AS text) value", "updated_dt"], + where="key = 'something'", + hwm=DBReader.AutoDetectHWM(name="hive_hwm", expression="updated_dt"), +) + +with IncrementalStrategy(): + df = reader.run() +``` + +## Recommendations + +### Use column-based write formats + +Prefer these write formats: +: * [ORC](https://spark.apache.org/docs/latest/sql-data-sources-orc.html) + * [Parquet](https://spark.apache.org/docs/latest/sql-data-sources-parquet.html) + * [Iceberg](https://iceberg.apache.org/spark-quickstart/) + * [Hudi](https://hudi.apache.org/docs/quick-start-guide/) + * [Delta](https://docs.delta.io/latest/quick-start.html#set-up-apache-spark-with-delta-lake) + +For colum-based write formats, each file contains separated sections there column data is stored. The file footer contains +location of each column section/group. Spark can use this information to load only sections required by specific query, e.g. only selected columns, +to drastically speed up the query. + +Another advantage is high compression ratio, e.g. 10x-100x in comparison to JSON or CSV. + +### Select only required columns + +Instead of passing `"*"` in `DBReader(columns=[...])` prefer passing exact column names. +This drastically reduces the amount of data read by Spark, **if column-based file formats are used**. + +### Use partition columns in `where` clause + +Queries should include `WHERE` clause with filters on Hive partitioning columns. +This allows Spark to read only small set of files (*partition pruning*) instead of scanning the entire table, so this drastically increases performance. + +Supported operators are: `=`, `>`, `<` and `BETWEEN`, and only against some **static** value. diff --git a/mddocs/connection/db_connection/hive/slots.md b/mddocs/connection/db_connection/hive/slots.md new file mode 100644 index 000000000..75bad02ff --- /dev/null +++ b/mddocs/connection/db_connection/hive/slots.md @@ -0,0 +1,105 @@ + + +# Hive Slots + +### *class* onetl.connection.db_connection.hive.slots.HiveSlots + +[Slots](../../../hooks/slot.md#slot-decorator) that could be implemented by third-party plugins. + +#### Versionadded +Added in version 0.7.0. + + + +#### *static* normalize_cluster_name(cluster: str) → str | None + +Normalize cluster name passed into Hive constructor. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +If hooks didn’t return anything, cluster name is left intact. + +#### Versionadded +Added in version 0.7.0. + +* **Parameters:** + **cluster** + : Cluster name (raw) +* **Returns:** + str | None + : Normalized cluster name. +
+ If hook cannot be applied to a specific cluster, it should return `None`. + +### Examples + +```python +from onetl.connection import Hive +from onetl.hooks import hook + +@Hive.Slots.normalize_cluster_name.bind +@hook +def normalize_cluster_name(cluster: str) -> str: + return cluster.lower() +``` + + + +#### *static* get_known_clusters() → set[str] | None + +Return collection of known clusters. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +Cluster passed into Hive constructor should be present in this list. +If hooks didn’t return anything, no validation will be performed. + +#### Versionadded +Added in version 0.7.0. + +* **Returns:** + set[str] | None + : Collection of cluster names (normalized). +
+ If hook cannot be applied, it should return `None`. + +### Examples + +```python +from onetl.connection import Hive +from onetl.hooks import hook + +@Hive.Slots.get_known_clusters.bind +@hook +def get_known_clusters() -> str[str]: + return {"rnd-dwh", "rnd-prod"} +``` + + + +#### *static* get_current_cluster() → str | None + +Get current cluster name. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +Used in `check` method to verify that connection is created only from the same cluster. +If hooks didn’t return anything, no validation will be performed. + +#### Versionadded +Added in version 0.7.0. + +* **Returns:** + str | None + : Current cluster name (normalized). +
+ If hook cannot be applied, it should return `None`. + +### Examples + +```python +from onetl.connection import Hive +from onetl.hooks import hook + +@Hive.Slots.get_current_cluster.bind +@hook +def get_current_cluster() -> str: + # some magic here + return "rnd-dwh" +``` + + diff --git a/mddocs/connection/db_connection/hive/sql.md b/mddocs/connection/db_connection/hive/sql.md new file mode 100644 index 000000000..9e6d15f1c --- /dev/null +++ b/mddocs/connection/db_connection/hive/sql.md @@ -0,0 +1,86 @@ + + +# Reading from Hive using `Hive.sql` + +`Hive.sql` allows passing custom SQL query, but does not support incremental strategies. + +## Syntax support + +Only queries with the following syntax are supported: + +* ✅︎ `SELECT ... FROM ...` +* ✅︎ `WITH alias AS (...) SELECT ...` +* ❌ `SET ...; SELECT ...;` - multiple statements not supported + +#### WARNING +Actually, query should be written using [SparkSQL](https://spark.apache.org/docs/latest/sql-ref-syntax.html#data-retrieval-statements) syntax, not HiveQL. + +## Examples + +```python +from onetl.connection import Hive + +hive = Hive(...) +df = hive.sql( + """ + SELECT + id, + key, + CAST(value AS text) value, + updated_at + FROM + some.mytable + WHERE + key = 'something' + """ +) +``` + +## Recommendations + +### Use column-based write formats + +Prefer these write formats: +: * [ORC](https://spark.apache.org/docs/latest/sql-data-sources-orc.html) + * [Parquet](https://spark.apache.org/docs/latest/sql-data-sources-parquet.html) + * [Iceberg](https://iceberg.apache.org/spark-quickstart/) + * [Hudi](https://hudi.apache.org/docs/quick-start-guide/) + * [Delta](https://docs.delta.io/latest/quick-start.html#set-up-apache-spark-with-delta-lake) + +For colum-based write formats, each file contains separated sections there column data is stored. The file footer contains +location of each column section/group. Spark can use this information to load only sections required by specific query, e.g. only selected columns, +to drastically speed up the query. + +Another advantage is high compression ratio, e.g. 10x-100x in comparison to JSON or CSV. + +### Select only required columns + +Instead of passing `SELECT * FROM ...` prefer passing exact column names `SELECT col1, col2, ...`. +This drastically reduces the amount of data read by Spark, **if column-based file formats are used**. + +### Use partition columns in `where` clause + +Queries should include `WHERE` clause with filters on Hive partitioning columns. +This allows Spark to read only small set of files (*partition pruning*) instead of scanning the entire table, so this drastically increases performance. + +Supported operators are: `=`, `>`, `<` and `BETWEEN`, and only against some **static** value. + +## Details + +#### Hive.sql(query: str) → DataFrame + +Lazily execute SELECT statement and return DataFrame. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +Same as `spark.sql(query)`. + +#### Versionadded +Added in version 0.2.0. + +* **Parameters:** + **query** + : SQL query to be executed. +* **Returns:** + **df** + : Spark dataframe + + diff --git a/mddocs/connection/db_connection/hive/write.md b/mddocs/connection/db_connection/hive/write.md new file mode 100644 index 000000000..1c61846df --- /dev/null +++ b/mddocs/connection/db_connection/hive/write.md @@ -0,0 +1,394 @@ + + +# Writing to Hive using `DBWriter` + +For writing data to Hive, use [`DBWriter`](../../../db/db_writer.md#onetl.db.db_writer.db_writer.DBWriter). + +## Examples + +```python +from onetl.connection import Hive +from onetl.db import DBWriter + +hive = Hive(...) + +df = ... # data is here + +# Create dataframe with specific number of Spark partitions. +# Use the Hive partitioning columns to group the data. Create max 20 files per Hive partition. +# Also sort the data by column which most data is correlated with (e.g. user_id), reducing files size. + +num_files_per_partition = 20 +partition_columns = ["country", "business_date"] +sort_columns = ["user_id"] +write_df = df.repartition( + num_files_per_partition, + *partition_columns, + *sort_columns, +).sortWithinPartitions(*partition_columns, *sort_columns) + +writer = DBWriter( + connection=hive, + target="schema.table", + options=Hive.WriteOptions( + if_exists="append", + # Hive partitioning columns. + partitionBy=partition_columns, + ), +) + +writer.run(write_df) +``` + +## Recommendations + +### Use column-based write formats + +Prefer these write formats: +: * [ORC](https://spark.apache.org/docs/latest/sql-data-sources-orc.html) (**default**) + * [Parquet](https://spark.apache.org/docs/latest/sql-data-sources-parquet.html) + * [Iceberg](https://iceberg.apache.org/spark-quickstart/) + * [Hudi](https://hudi.apache.org/docs/quick-start-guide/) + * [Delta](https://docs.delta.io/latest/quick-start.html#set-up-apache-spark-with-delta-lake) + +#### WARNING +When using `DBWriter`, the default spark data format configured in `spark.sql.sources.default` is ignored, as `Hive.WriteOptions(format=...)` default value is explicitly set to `orc`. + +For column-based write formats, each file contains separated sections where column data is stored. The file footer contains +location of each column section/group. Spark can use this information to load only sections required by specific query, e.g. only selected columns, +to drastically speed up the query. + +Another advantage is high compression ratio, e.g. 10x-100x in comparison to JSON or CSV. + +### Use partitioning + +#### How does it work + +Hive support splitting data to partitions, which are different directories in filesystem with names like `some_col=value1/another_col=value2`. + +For example, dataframe with content like this: + +| country: string | business_date: date | user_id: int | bytes: long | +|-------------------|-----------------------|----------------|---------------| +| RU | 2024-01-01 | 1234 | 25325253525 | +| RU | 2024-01-01 | 2345 | 23234535243 | +| RU | 2024-01-02 | 1234 | 62346634564 | +| US | 2024-01-01 | 5678 | 4252345354 | +| US | 2024-01-02 | 5678 | 5474575745 | +| US | 2024-01-03 | 5678 | 3464574567 | + +With `partitionBy=["country", "business_dt"]` data will be stored as files in the following subfolders: +: * `/country=RU/business_date=2024-01-01/` + * `/country=RU/business_date=2024-01-02/` + * `/country=US/business_date=2024-01-01/` + * `/country=US/business_date=2024-01-02/` + * `/country=US/business_date=2024-01-03/` + +A separated subdirectory is created for each distinct combination of column values in the dataframe. + +Please do not confuse Spark dataframe partitions (a.k.a batches of data handled by Spark executors, usually in parallel) +and Hive partitioning (store data in different subdirectories). +Number of Spark dataframe partitions is correlated the number of files created in **each** Hive partition. +For example, Spark dataframe with 10 partitions and 5 distinct values of Hive partition columns will be saved as 5 subfolders with 10 files each = 50 files in total. +Without Hive partitioning, all the files are placed into one flat directory. + +#### But why? + +Queries which has `WHERE` clause with filters on Hive partitioning columns, like `WHERE country = 'RU' AND business_date='2024-01-01'`, will +read only files from this exact partitions, like `/country=RU/business_date=2024-01-01/`, and skip files from other partitions. + +This drastically increases performance and reduces the amount of memory used by Spark. +Consider using Hive partitioning in all tables. + +#### Which columns should I use? + +Usually Hive partitioning columns are based on event date or location, like `country: string`, `business_date: date`, `run_date: date` and so on. + +**Partition columns should contain data with low cardinality.** +Dates, small integers, strings with low number of possible values are OK. +But timestamp, float, decimals, longs (like user id), strings with lots oj unique values (like user name or email) should **NOT** be used as Hive partitioning columns. +Unlike some other databases, range and hash-based partitions are not supported. + +Partition column should be a part of a dataframe. If you want to partition values by date component of `business_dt: timestamp` column, +add a new column to dataframe like this: `df.withColumn("business_date", date(df.business_dt))`. + +### Use compression + +Using compression algorithms like `snappy`, `lz4` or `zstd` can reduce the size of files (up to 10x). + +### Prefer creating large files + +Storing millions of small files is not that HDFS and S3 are designed for. Minimal file size should be at least 10Mb, but usually it is like 128Mb+ or 256Mb+ (HDFS block size). +**NEVER** create files with few Kbytes in size. + +Number of files can be different in different cases. +On one hand, Spark Adaptive Query Execution (AQE) can merge small Spark dataframe partitions into one larger. +On the other hand, dataframes with skewed data can produce a larger number of files than expected. + +To create small amount of large files, you can reduce number of Spark dataframe partitions. +Use [df.repartition(N, columns…)](https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.DataFrame.repartition.html) function, +like this: `df.repartition(20, "col1", "col2")`. +This creates new Spark dataframe with partitions using `hash(df.col1 + df.col2) mod 20` expression, avoiding data skew. + +Note: larger dataframe partitions requires more resources (CPU, RAM) on Spark executor. The exact number of partitions +should be determined empirically, as it depends on the amount of data and available resources. + +### Sort data before writing + +Dataframe with sorted content: + +| country: string | business_date: date | user_id: int | business_dt: timestamp | bytes: long | +|-------------------|-----------------------|----------------|--------------------------|---------------| +| RU | 2024-01-01 | 1234 | 2024-01-01T11:22:33.456 | 25325253525 | +| RU | 2024-01-01 | 1234 | 2024-01-01T12:23:44.567 | 25325253525 | +| RU | 2024-01-02 | 1234 | 2024-01-01T13:25:56.789 | 34335645635 | +| US | 2024-01-01 | 2345 | 2024-01-01T10:00:00.000 | 12341 | +| US | 2024-01-02 | 2345 | 2024-01-01T15:11:22.345 | 13435 | +| US | 2024-01-03 | 2345 | 2024-01-01T20:22:33.567 | 14564 | + +Has a much better compression rate than unsorted one, e.g. 2x or even higher: + +| country: string | business_date: date | user_id: int | business_dt: timestamp | bytes: long | +|-------------------|-----------------------|----------------|--------------------------|---------------| +| RU | 2024-01-01 | 1234 | 2024-01-01T11:22:33.456 | 25325253525 | +| RU | 2024-01-01 | 6345 | 2024-12-01T23:03:44.567 | 25365 | +| RU | 2024-01-02 | 5234 | 2024-07-01T06:10:56.789 | 45643456747 | +| US | 2024-01-01 | 4582 | 2024-04-01T17:59:00.000 | 362546475 | +| US | 2024-01-02 | 2345 | 2024-09-01T04:24:22.345 | 3235 | +| US | 2024-01-03 | 3575 | 2024-03-01T21:37:33.567 | 346345764 | + +Choosing columns to sort data by is really depends on the data. If data is correlated with some specific +column, like in example above the amount of traffic is correlated with both `user_id` and `timestamp`, +use `df.sortWithinPartitions("user_id", "timestamp")` before writing the data. + +If `df.repartition(N, repartition_columns...)` is used in combination with `df.sortWithinPartitions(sort_columns...)`, +then `sort_columns` should start with `repartition_columns` or be equal to it. + +## Options + +### *pydantic model* onetl.connection.db_connection.hive.options.HiveWriteOptions + +Hive source writing options. + +You can pass here key-value items which then will be converted to calls +of `pyspark.sql.readwriter.DataFrameWriter` methods. + +For example, `Hive.WriteOptions(if_exists="append", partitionBy="reg_id")` will +be converted to `df.write.mode("append").partitionBy("reg_id")` call, and so on. + +### Examples + +#### NOTE +You can pass any method name and its value +[supported by Spark](https://spark.apache.org/docs/latest/sql-data-sources-load-save-functions.html), +even if it is not mentioned in this documentation. **Option names should be in** `camelCase`! + +The set of supported options depends on Spark version used. + +```python +from onetl.connection import Hive + +options = Hive.WriteOptions( + if_exists="append", + partitionBy="reg_id", + customSparkOption="value", +) +``` + + + +#### *field* if_exists *: HiveTableExistBehavior* *= HiveTableExistBehavior.APPEND* *(alias 'mode')* + +Behavior of writing data into existing table. + +Possible values: +: * `append` (default) + : Appends data into existing partition/table, or create partition/table if it does not exist. +
+ Same as Spark’s `df.write.insertInto(table, overwrite=False)`. +
+ ### Behavior in details +
+ * Table does not exist + : Table is created using options provided by user (`format`, `compression`, etc). + * Table exists, but not partitioned, [`partition_by`](#onetl.connection.db_connection.hive.options.HiveWriteOptions.partition_by) is set + : Data is appended to a table. Table is still not partitioned (DDL is unchanged). + * Table exists and partitioned, but has different partitioning schema than [`partition_by`](#onetl.connection.db_connection.hive.options.HiveWriteOptions.partition_by) + : Partition is created based on table’s `PARTITIONED BY (...)` options. + Explicit [`partition_by`](#onetl.connection.db_connection.hive.options.HiveWriteOptions.partition_by) value is ignored. + * Table exists and partitioned according [`partition_by`](#onetl.connection.db_connection.hive.options.HiveWriteOptions.partition_by), but partition is present only in dataframe + : Partition is created. + * Table exists and partitioned according [`partition_by`](#onetl.connection.db_connection.hive.options.HiveWriteOptions.partition_by), partition is present in both dataframe and table + : Data is appended to existing partition. +
+ #### WARNING + This mode does not check whether table already contains + rows from dataframe, so duplicated rows can be created. +
+ To implement deduplication, write data to staging table first, + and then perform some deduplication logic using `sql`. + * Table exists and partitioned according [`partition_by`](#onetl.connection.db_connection.hive.options.HiveWriteOptions.partition_by), but partition is present only in table, not dataframe + : Existing partition is left intact. + * `replace_overlapping_partitions` + : Overwrites data in the existing partition, or create partition/table if it does not exist. +
+ Same as Spark’s `df.write.insertInto(table, overwrite=True)` + + `spark.sql.sources.partitionOverwriteMode=dynamic`. +
+ ### Behavior in details +
+ * Table does not exist + : Table is created using options provided by user (`format`, `compression`, etc). + * Table exists, but not partitioned, [`partition_by`](#onetl.connection.db_connection.hive.options.HiveWriteOptions.partition_by) is set + : Data is **overwritten in all the table**. Table is still not partitioned (DDL is unchanged). + * Table exists and partitioned, but has different partitioning schema than [`partition_by`](#onetl.connection.db_connection.hive.options.HiveWriteOptions.partition_by) + : Partition is created based on table’s `PARTITIONED BY (...)` options. + Explicit [`partition_by`](#onetl.connection.db_connection.hive.options.HiveWriteOptions.partition_by) value is ignored. + * Table exists and partitioned according [`partition_by`](#onetl.connection.db_connection.hive.options.HiveWriteOptions.partition_by), but partition is present only in dataframe + : Partition is created. + * Table exists and partitioned according [`partition_by`](#onetl.connection.db_connection.hive.options.HiveWriteOptions.partition_by), partition is present in both dataframe and table + : Existing partition **replaced** with data from dataframe. + * Table exists and partitioned according [`partition_by`](#onetl.connection.db_connection.hive.options.HiveWriteOptions.partition_by), but partition is present only in table, not dataframe + : Existing partition is left intact. + * `replace_entire_table` + : **Recreates table** (via `DROP + CREATE`), **deleting all existing data**. + **All existing partitions are dropped.** +
+ Same as Spark’s `df.write.saveAsTable(table, mode="overwrite")` (NOT `insertInto`)! +
+ #### WARNING + Table is recreated using options provided by user (`format`, `compression`, etc) + **instead of using original table options**. Be careful + * `ignore` + : Ignores the write operation if the table/partition already exists. +
+ ### Behavior in details +
+ * Table does not exist + : Table is created using options provided by user (`format`, `compression`, etc). + * Table exists + : If the table exists, **no further action is taken**. This is true whether or not new partition + values are present and whether the partitioning scheme differs or not + * `error` + : Raises an error if the table/partition already exists. +
+ ### Behavior in details +
+ * Table does not exist + : Table is created using options provided by user (`format`, `compression`, etc). + * Table exists + : If the table exists, **raises an error**. This is true whether or not new partition + values are present and whether the partitioning scheme differs or not + +#### NOTE +Unlike using pure Spark, config option `spark.sql.sources.partitionOverwriteMode` +does not affect behavior. + + + +#### *field* format *: str | [BaseWritableFileFormat](../../../file_df/file_formats/base.md#onetl.base.base_file_format.BaseWritableFileFormat)* *= 'orc'* + +Format of files which should be used for storing table data. + +### Examples + +- string format: `"orc"` (default), `"parquet"`, `"csv"` (NOT recommended). +- format class instance: `ORC(compression="snappy")`, `Parquet()`, `CSV(header=True, delimiter=",")`. + +```default +options = Hive.WriteOptions( + if_exists="append", + partitionBy="reg_id", + format="orc", +) + +# or using an ORC format class instance: + +from onetl.file.format import ORC + +options = Hive.WriteOptions( + if_exists="append", + partitionBy="reg_id", + format=ORC(compression="snappy"), +) +``` + +#### NOTE +It’s better to use column-based formats like `orc` or `parquet`, +not row-based (`csv`, `json`) + +#### WARNING +Used **only** while **creating new table**, or in case of `if_exists=replace_entire_table` + + + +#### *field* partition_by *: List[str] | str | None* *= None* *(alias 'partitionBy')* + +List of columns should be used for data partitioning. `None` means partitioning is disabled. + +Examples: `reg_id` or `["reg_id", "business_dt"]` + +#### WARNING +Used **only** while **creating new table**, or in case of `if_exists=replace_entire_table` + + + +#### *field* bucket_by *: Tuple[int, List[str] | str] | None* *= None* *(alias 'bucketBy')* + +Number of buckets plus bucketing columns. `None` means bucketing is disabled. + +Each bucket is created as a set of files with name containing result of calculation `hash(columns) mod num_buckets`. + +This allows to remove shuffle from queries containing `GROUP BY` or `JOIN` or using `=` / `IN` predicates +on specific columns. + +Examples: `(10, "user_id")`, `(10, ["user_id", "user_phone"])` + +#### NOTE +Bucketing should be used on columns containing a lot of unique values, +like `userId`. + +Columns like `date` should **NOT** be used for bucketing +because of too low number of unique values. + +#### WARNING +It is recommended to use this option **ONLY** if you have a large table +(hundreds of Gb or more), which is used mostly for JOINs with other tables, +and you’re inserting data using `if_exists=overwrite_partitions` or `if_exists=replace_entire_table`. + +Otherwise Spark will create a lot of small files +(one file for each bucket and each executor), drastically **decreasing** HDFS performance. + +#### WARNING +Used **only** while **creating new table**, or in case of `if_exists=replace_entire_table` + + + +#### *field* sort_by *: List[str] | str | None* *= None* *(alias 'sortBy')* + +Each file in a bucket will be sorted by these columns value. `None` means sorting is disabled. + +Examples: `user_id` or `["user_id", "user_phone"]` + +#### NOTE +Sorting columns should contain values which are used in `ORDER BY` clauses. + +#### WARNING +Could be used only with [`bucket_by`](#onetl.connection.db_connection.hive.options.HiveWriteOptions.bucket_by) option + +#### WARNING +Used **only** while **creating new table**, or in case of `if_exists=replace_entire_table` + + + +#### *field* compression *: str | None* *= None* + +Compressing algorithm which should be used for compressing created files in HDFS. +`None` means compression is disabled. + +Examples: `snappy`, `zlib` + +#### WARNING +Used **only** while **creating new table**, or in case of `if_exists=replace_entire_table` + + diff --git a/mddocs/connection/db_connection/index.md b/mddocs/connection/db_connection/index.md new file mode 100644 index 000000000..cd8f76477 --- /dev/null +++ b/mddocs/connection/db_connection/index.md @@ -0,0 +1,16 @@ + + +# DB Connections + +# DB Connections + +* [Clickhouse](clickhouse/index.md) +* [Greenplum](greenplum/index.md) +* [Kafka](kafka/index.md) +* [Hive](hive/index.md) +* [MongoDB](mongodb/index.md) +* [MSSQL](mssql/index.md) +* [MySQL](mysql/index.md) +* [Oracle](oracle/index.md) +* [Postgres](postgres/index.md) +* [Teradata](teradata/index.md) diff --git a/mddocs/connection/db_connection/kafka/auth.md b/mddocs/connection/db_connection/kafka/auth.md new file mode 100644 index 000000000..0bf454521 --- /dev/null +++ b/mddocs/connection/db_connection/kafka/auth.md @@ -0,0 +1,37 @@ + + +# Kafka Auth + +### *class* onetl.connection.db_connection.kafka.kafka_auth.KafkaAuth + +Interface for Kafka connection Auth classes. + +#### Versionadded +Added in version 0.9.0. + + + +#### *abstract* get_options(kafka: [Kafka](connection.md#onetl.connection.db_connection.kafka.connection.Kafka)) → dict + +Get options for Kafka connection + +* **Parameters:** + **kafka** + : Connection instance +* **Returns:** + dict: + : Kafka client options + + + +#### *abstract* cleanup(kafka: [Kafka](connection.md#onetl.connection.db_connection.kafka.connection.Kafka)) → None + +This method is called while closing Kafka connection. + +Implement it to cleanup resources like temporary files. + +* **Parameters:** + **kafka** + : Connection instance + + diff --git a/mddocs/connection/db_connection/kafka/basic_auth.md b/mddocs/connection/db_connection/kafka/basic_auth.md new file mode 100644 index 000000000..586b07500 --- /dev/null +++ b/mddocs/connection/db_connection/kafka/basic_auth.md @@ -0,0 +1,60 @@ + + +# Kafka BasicAuth + +### *pydantic model* onetl.connection.db_connection.kafka.kafka_basic_auth.KafkaBasicAuth + +Connect to Kafka using `sasl.mechanism="PLAIN"`. + +For more details see [Kafka Documentation](https://kafka.apache.org/documentation/#security_sasl_plain). + +#### Versionadded +Added in version 0.9.0. + +### Examples + +Auth in Kafka with user and password: + +```python +from onetl.connection import Kafka + +auth = Kafka.BasicAuth( + user="some_user", + password="abc", +) +``` + + + +#### *field* user *: str* *[Required]* *(alias 'username')* + +#### *field* password *: SecretStr* *[Required]* + +#### get_jaas_conf() → str + + + +#### get_options(kafka: [Kafka](connection.md#onetl.connection.db_connection.kafka.connection.Kafka)) → dict + +Get options for Kafka connection + +* **Parameters:** + **kafka** + : Connection instance +* **Returns:** + dict: + : Kafka client options + + + +#### cleanup(kafka: [Kafka](connection.md#onetl.connection.db_connection.kafka.connection.Kafka)) → None + +This method is called while closing Kafka connection. + +Implement it to cleanup resources like temporary files. + +* **Parameters:** + **kafka** + : Connection instance + + diff --git a/mddocs/connection/db_connection/kafka/connection.md b/mddocs/connection/db_connection/kafka/connection.md new file mode 100644 index 000000000..23a54a42e --- /dev/null +++ b/mddocs/connection/db_connection/kafka/connection.md @@ -0,0 +1,243 @@ + + +# Kafka Connection + +### *class* onetl.connection.db_connection.kafka.connection.Kafka(\*, spark: SparkSession, cluster: Cluster, addresses: List[str], auth: [KafkaAuth](auth.md#onetl.connection.db_connection.kafka.kafka_auth.KafkaAuth) | None = None, protocol: [KafkaProtocol](protocol.md#onetl.connection.db_connection.kafka.kafka_protocol.KafkaProtocol) = KafkaPlaintextProtocol(), extra: KafkaExtra = KafkaExtra()) + +This connector is designed to read and write from Kafka in batch mode. + +Based on [official Kafka Source For Spark](https://spark.apache.org/docs/latest/structured-streaming-kafka-integration.html). + +#### SEE ALSO +Before using this connector please take into account [Prerequisites](prerequisites.md#kafka-prerequisites) + +#### NOTE +This connector is for **batch** ETL processes, not streaming. + +#### Versionadded +Added in version 0.9.0. + +* **Parameters:** + **addresses** + : A list of broker addresses, for example `["192.168.1.10:9092", "192.168.1.11:9092"]`. + + **cluster** + : Cluster name. Used for HWM and lineage. + + **auth** + : Kafka authentication mechanism. `None` means anonymous auth. + + **protocol** + : Kafka security protocol. + + **extra** + : A dictionary of additional properties to be used when connecting to Kafka. +
+ These are Kafka-specific properties that control behavior of the producer or consumer. See: + * [producer options documentation](https://kafka.apache.org/documentation/#producerconfigs) + * [consumer options documentation](https://kafka.apache.org/documentation/#consumerconfigs) + * [Spark Kafka documentation](https://spark.apache.org/docs/latest/structured-streaming-kafka-integration.html#kafka-specific-configurations) +
+ Options are passed without `kafka.` prefix, for example: +
+ For example: + ```python + extra = { + "group.id": "myGroup", + "request.timeout.ms": 120000, + } + ``` +
+ #### WARNING + Options that populated from connection + attributes (like `bootstrap.servers`, `sasl.*`, `ssl.*`) are not allowed to override. + +### Examples + +Create Kafka connection with `PLAINTEXT` protocol and `SCRAM-SHA-256` auth + +```py +from onetl.connection import Kafka +from pyspark.sql import SparkSession + +# Create Spark session with Kafka connector loaded +maven_packages = Kafka.get_packages(spark_version="3.5.5") +exclude_packages = Kafka.get_exclude_packages() +spark = ( + SparkSession.builder.appName("spark-app-name") + .config("spark.jars.packages", ",".join(maven_packages)) + .config("spark.jars.excludes", ",".join(exclude_packages)) + .getOrCreate() +) + +# Create connection +kafka = Kafka( + addresses=["mybroker:9092", "anotherbroker:9092"], + cluster="my-cluster", + auth=Kafka.ScramAuth( + user="me", + password="abc", + digest="SHA-256", + ), + spark=spark, +).check() +``` + +Create Kafka connection with `PLAINTEXT` protocol and Kerberos (`GSSAPI`) auth + +```py +# Create Spark session with Kafka connector loaded +... + +# Create connection +kafka = Kafka( + addresses=["mybroker:9092", "anotherbroker:9092"], + cluster="my-cluster", + auth=Kafka.KerberosAuth( + principal="me@example.com", + keytab="/path/to/keytab", + deploy_keytab=True, + ), + spark=spark, +).check() +``` + +Create Kafka connection with `SASL_SSL` protocol and `SCRAM-SHA-512` auth + +```py +from pathlib import Path + +# Create Spark session with Kafka connector loaded +... + +# Create connection +kafka = Kafka( + addresses=["mybroker:9092", "anotherbroker:9092"], + cluster="my-cluster", + protocol=Kafka.SSLProtocol( + # read client certificate and private key from file + keystore_type="PEM", + keystore_certificate_chain=Path("path/to/user.crt").read_text(), + keystore_key=Path("path/to/user.key").read_text(), + # read server public certificate from file + truststore_type="PEM", + truststore_certificates=Path("/path/to/server.crt").read_text(), + ), + auth=Kafka.ScramAuth( + user="me", + password="abc", + digest="SHA-512", + ), + spark=spark, +).check() +``` + +Create Kafka connection with extra options + +```py +# Create Spark session with Kafka connector loaded +... + +# Create connection +kafka = Kafka( + addresses=["mybroker:9092", "anotherbroker:9092"], + cluster="my-cluster", + protocol=..., + auth=..., + extra={"max.request.size": 1024 * 1024}, # <-- + spark=spark, +).check() +``` + + + +#### check() + +Check source availability. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +If not, an exception will be raised. + +* **Returns:** + Connection itself +* **Raises:** + RuntimeError + : If the connection is not available + +### Examples + +```python +connection.check() +``` + + + +#### close() + +Close all connections created to Kafka. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### NOTE +Connection can be used again after it was closed. + +* **Returns:** + Connection itself + +### Examples + +Close connection automatically: + +```python +with connection: + ... +``` + +Close connection manually: + +```python +connection.close() +``` + + + +#### *classmethod* get_exclude_packages() → list[str] + +Get package names to be excluded by Spark. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### Versionadded +Added in version 0.13.0. + +### Examples + +```python +from onetl.connection import Kafka + +Kafka.get_exclude_packages() +``` + + + +#### *classmethod* get_packages(spark_version: str, scala_version: str | None = None) → list[str] + +Get package names to be downloaded by Spark. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +See [Maven package index](https://mvnrepository.com/artifact/org.apache.spark/spark-sql-kafka-0-10) +for all available packages. + +* **Parameters:** + **spark_version** + : Spark version in format `major.minor.patch`. + + **scala_version** + : Scala version in format `major.minor`. +
+ If `None`, `spark_version` is used to determine Scala version. + +### Examples + +```python +from onetl.connection import Kafka + +Kafka.get_packages(spark_version="3.5.5") +Kafka.get_packages(spark_version="3.5.5", scala_version="2.12") +``` + + diff --git a/mddocs/connection/db_connection/kafka/index.md b/mddocs/connection/db_connection/kafka/index.md new file mode 100644 index 000000000..5e5f45ba1 --- /dev/null +++ b/mddocs/connection/db_connection/kafka/index.md @@ -0,0 +1,31 @@ + + +# Kafka + +# Connection + +* [Prerequisites](prerequisites.md) +* [Kafka Connection](connection.md) +* [Kafka Troubleshooting](troubleshooting.md) + +# Protocols + +* [Kafka PlaintextProtocol](plaintext_protocol.md) +* [Kafka SSLProtocol](ssl_protocol.md) + +# Auth methods + +* [Kafka BasicAuth](basic_auth.md) +* [Kafka KerberosAuth](kerberos_auth.md) +* [Kafka ScramAuth](scram_auth.md) + +# Operations + +* [Reading from Kafka](read.md) +* [Writing to Kafka](write.md) + +# For developers + +* [Kafka Auth](auth.md) +* [Kafka Protocol](protocol.md) +* [Kafka Slots](slots.md) diff --git a/mddocs/connection/db_connection/kafka/kerberos_auth.md b/mddocs/connection/db_connection/kafka/kerberos_auth.md new file mode 100644 index 000000000..1fefd2992 --- /dev/null +++ b/mddocs/connection/db_connection/kafka/kerberos_auth.md @@ -0,0 +1,118 @@ + + +# Kafka KerberosAuth + +### *pydantic model* onetl.connection.db_connection.kafka.kafka_kerberos_auth.KafkaKerberosAuth + +Connect to Kafka using `sasl.mechanism="GSSAPI"`. + +For more details see: + +* [Kafka Documentation](https://kafka.apache.org/documentation/#security_sasl_kerberos_clientconfig) +* [Krb5LoginModule documentation](https://docs.oracle.com/javase/8/docs/jre/api/security/jaas/spec/com/sun/security/auth/module/Krb5LoginModule.html) + +#### Versionadded +Added in version 0.9.0. + +### Examples + +Auth in Kafka with keytab, automatically deploy keytab files to all Spark hosts (driver and executors): + +```python +from onetl.connection import Kafka + +auth = Kafka.KerberosAuth( + principal="user", + keytab="/path/to/keytab", + deploy_keytab=True, +) +``` + +Auth in Kafka with keytab, keytab is **already deployed** on all Spark hosts (driver and executors): + +```python +from onetl.connection import Kafka + +auth = Kafka.KerberosAuth( + principal="user", + keytab="/path/to/keytab", + deploy_keytab=False, +) +``` + +Auth in Kafka with existing Kerberos ticket (only Spark session created with `master=local`): + +```python +from onetl.connection import Kafka + +auth = Kafka.KerberosAuth( + principal="user", + use_keytab=False, + use_ticket_cache=True, +) +``` + +Pass custom options for JAAS config and Kafka SASL: + +```python +from onetl.connection import Kafka + +auth = Kafka.KerberosAuth.parse( + { + "principal": "user", + "keytab": "/path/to/keytab", + # options without sasl.kerberos. prefix are passed to JAAS config + # names are in camel case! + "isInitiator": True, + # options with `sasl.kerberos.` prefix are passed to Kafka client config as-is + "sasl.kerberos.kinit.cmd": "/usr/bin/kinit", + } +) +``` + + + +#### *field* principal *: str* *[Required]* + +#### *field* keytab *: LocalPath | None* *= None* *(alias 'keyTab')* + +#### *field* deploy_keytab *: bool* *= True* + +#### *field* service_name *: str* *= 'kafka'* *(alias 'serviceName')* + +#### *field* renew_ticket *: bool* *= True* *(alias 'renewTicket')* + +#### *field* store_key *: bool* *= True* *(alias 'storeKey')* + +#### *field* use_keytab *: bool* *= True* *(alias 'useKeyTab')* + +#### *field* use_ticket_cache *: bool* *= False* *(alias 'useTicketCache')* + +#### get_jaas_conf(kafka: [Kafka](connection.md#onetl.connection.db_connection.kafka.connection.Kafka)) → str + + + +#### get_options(kafka: [Kafka](connection.md#onetl.connection.db_connection.kafka.connection.Kafka)) → dict + +Get options for Kafka connection + +* **Parameters:** + **kafka** + : Connection instance +* **Returns:** + dict: + : Kafka client options + + + +#### cleanup(kafka: [Kafka](connection.md#onetl.connection.db_connection.kafka.connection.Kafka)) → None + +This method is called while closing Kafka connection. + +Implement it to cleanup resources like temporary files. + +* **Parameters:** + **kafka** + : Connection instance + + diff --git a/mddocs/connection/db_connection/kafka/plaintext_protocol.md b/mddocs/connection/db_connection/kafka/plaintext_protocol.md new file mode 100644 index 000000000..f7ebd316d --- /dev/null +++ b/mddocs/connection/db_connection/kafka/plaintext_protocol.md @@ -0,0 +1,48 @@ + + +# Kafka PlaintextProtocol + +### *pydantic model* onetl.connection.db_connection.kafka.kafka_plaintext_protocol.KafkaPlaintextProtocol + +Connect to Kafka using `PLAINTEXT` or `SASL_PLAINTEXT` security protocols. + +#### WARNING +Not recommended to use on production environments. +Prefer [`SSLProtocol`](ssl_protocol.md#onetl.connection.db_connection.kafka.kafka_ssl_protocol.KafkaSSLProtocol). + +#### Versionadded +Added in version 0.9.0. + +### Examples + +```python +# No options +protocol = Kafka.PlaintextProtocol() +``` + + + +#### get_options(kafka: [Kafka](connection.md#onetl.connection.db_connection.kafka.connection.Kafka)) → dict + +Get options for Kafka connection + +* **Parameters:** + **kafka** + : Connection instance +* **Returns:** + dict: + : Kafka client options + + + +#### cleanup(kafka: [Kafka](connection.md#onetl.connection.db_connection.kafka.connection.Kafka)) → None + +This method is called while closing Kafka connection. + +Implement it to cleanup resources like temporary files. + +* **Parameters:** + **kafka** + : Connection instance + + diff --git a/mddocs/connection/db_connection/kafka/prerequisites.md b/mddocs/connection/db_connection/kafka/prerequisites.md new file mode 100644 index 000000000..fcb90f705 --- /dev/null +++ b/mddocs/connection/db_connection/kafka/prerequisites.md @@ -0,0 +1,65 @@ + + +# Prerequisites + +## Version Compatibility + +* Kafka server versions: + : * Officially declared: 0.10 or higher + * Actually tested: 3.2.3, 3.9.0 (only Kafka 3.x supports message headers) +* Spark versions: 2.4.x - 3.5.x +* Java versions: 8 - 17 + +See [official documentation](https://spark.apache.org/docs/latest/structured-streaming-kafka-integration.html). + +## Installing PySpark + +To use Kafka connector you should have PySpark installed (or injected to `sys.path`) +BEFORE creating the connector instance. + +See [Spark](../../../install/spark.md#install-spark) installation instruction for more details. + +## Connecting to Kafka + +### Connection address + +Kafka is a distributed service, and usually has a list of brokers you can connect to (unlike other connectors, there only one host+port can be set). +Please contact your Kafka administrator to get addresses of these brokers, as there are no defaults. + +Also Kafka has a feature called *advertised listeners* - client connects to one broker, and received list of other brokers in the clusters. +So you don’t have to pass all brokers to `addresses`, it can be some subset. Other broker addresses will be fetched directly from the cluster. + +### Connection protocol + +Kafka can support different connection protocols. List of currently supported protocols: +: * [`PLAINTEXT`](plaintext_protocol.md#onetl.connection.db_connection.kafka.kafka_plaintext_protocol.KafkaPlaintextProtocol) (not secure) + * [`SSL`](ssl_protocol.md#onetl.connection.db_connection.kafka.kafka_ssl_protocol.KafkaSSLProtocol) (secure, recommended) + +Note that specific port can listen for only one of these protocols, so it is important to set +proper port number + protocol combination. + +### Authentication mechanism + +Kafka can support different authentication mechanism (also known as [SASL](https://en.wikipedia.org/wiki/Simple_Authentication_and_Security_Layer)). + +List of currently supported mechanisms: +: * [`PLAIN`](basic_auth.md#onetl.connection.db_connection.kafka.kafka_basic_auth.KafkaBasicAuth). To no confuse this with `PLAINTEXT` connection protocol, onETL uses name `BasicAuth`. + * [`GSSAPI`](kerberos_auth.md#onetl.connection.db_connection.kafka.kafka_kerberos_auth.KafkaKerberosAuth). To simplify naming, onETL uses name `KerberosAuth`. + * [`SCRAM-SHA-256 or SCRAM-SHA-512`](scram_auth.md#onetl.connection.db_connection.kafka.kafka_scram_auth.KafkaScramAuth) (recommended). + +Different mechanisms use different types of credentials (login + password, keytab file, and so on). + +Note that connection protocol and auth mechanism are set in pairs: +: * If you see `SASL_PLAINTEXT` this means `PLAINTEXT` connection protocol + some auth mechanism. + * If you see `SASL_SSL` this means `SSL` connection protocol + some auth mechanism. + * If you see just `PLAINTEXT` or `SSL` (**no** `SASL`), this means that authentication is disabled (anonymous access). + +Please contact your Kafka administrator to get details about enabled auth mechanism in a specific Kafka instance. + +### Required grants + +Ask your Kafka administrator to set following grants for a user, *if Kafka instance uses ACL*: +: * `Describe` + `Read` for reading data from Kafka (Consumer). + * `Describe` + `Write` for writing data from Kafka (Producer). + +More details can be found in [documentation](https://kafka.apache.org/documentation/#operations_in_kafka). diff --git a/mddocs/connection/db_connection/kafka/protocol.md b/mddocs/connection/db_connection/kafka/protocol.md new file mode 100644 index 000000000..720e5f0b4 --- /dev/null +++ b/mddocs/connection/db_connection/kafka/protocol.md @@ -0,0 +1,37 @@ + + +# Kafka Protocol + +### *class* onetl.connection.db_connection.kafka.kafka_protocol.KafkaProtocol + +Interface for Kafka connection Protocol classes. + +#### Versionadded +Added in version 0.9.0. + + + +#### *abstract* get_options(kafka: [Kafka](connection.md#onetl.connection.db_connection.kafka.connection.Kafka)) → dict + +Get options for Kafka connection + +* **Parameters:** + **kafka** + : Connection instance +* **Returns:** + dict: + : Kafka client options + + + +#### *abstract* cleanup(kafka: [Kafka](connection.md#onetl.connection.db_connection.kafka.connection.Kafka)) → None + +This method is called while closing Kafka connection. + +Implement it to cleanup resources like temporary files. + +* **Parameters:** + **kafka** + : Connection instance + + diff --git a/mddocs/connection/db_connection/kafka/read.md b/mddocs/connection/db_connection/kafka/read.md new file mode 100644 index 000000000..52ec5ff33 --- /dev/null +++ b/mddocs/connection/db_connection/kafka/read.md @@ -0,0 +1,172 @@ + + +# Reading from Kafka + +Data can be read from Kafka to Spark using [`DBReader`](../../../db/db_reader.md#onetl.db.db_reader.db_reader.DBReader). +It also supports [Read Strategies](../../../strategy/index.md#strategy) for incremental data reading. + +## Supported DBReader features + +* ❌ `columns` (is not supported by Kafka) +* ❌ `where` (is not supported by Kafka) +* ✅︎ `hwm`, supported strategies: +* * ✅︎ [Snapshot Strategy](../../../strategy/snapshot_strategy.md#snapshot-strategy) +* * ✅︎ [Incremental Strategy](../../../strategy/incremental_strategy.md#incremental-strategy) +* * ❌ [Snapshot Batch Strategy](../../../strategy/snapshot_batch_strategy.md#snapshot-batch-strategy) +* * ❌ [Incremental Batch Strategy](../../../strategy/incremental_batch_strategy.md#incremental-batch-strategy) +* ❌ `hint` (is not supported by Kafka) +* ❌ `df_schema` (see note below) +* ✅︎ `options` (see [`Kafka.ReadOptions`](#onetl.connection.db_connection.kafka.options.KafkaReadOptions)) + +## Dataframe schema + +Unlike other DB connections, Kafka does not have concept of columns. +All the topics messages have the same set of fields, see structure below: + +```text +root +|-- key: binary (nullable = true) +|-- value: binary (nullable = true) +|-- topic: string (nullable = false) +|-- partition: integer (nullable = false) +|-- offset: integer (nullable = false) +|-- timestamp: timestamp (nullable = false) +|-- timestampType: integer (nullable = false) +|-- headers: struct (nullable = true) + |-- key: string (nullable = false) + |-- value: binary (nullable = true) +``` + +`headers` field is present in the dataframe only if `Kafka.ReadOptions(include_headers=True)` is passed (compatibility with Kafka 1.x). + +## Value deserialization + +To read `value` or `key` of other type than bytes (e.g. struct or integer), users have to deserialize values manually. + +This could be done using following methods: +: * [`Avro.parse_column`](../../../file_df/file_formats/avro.md#onetl.file.format.avro.Avro.parse_column) + * [`JSON.parse_column`](../../../file_df/file_formats/json.md#onetl.file.format.json.JSON.parse_column) + * [`CSV.parse_column`](../../../file_df/file_formats/csv.md#onetl.file.format.csv.CSV.parse_column) + * [`XML.parse_column`](../../../file_df/file_formats/xml.md#onetl.file.format.xml.XML.parse_column) + +## Examples + +Snapshot strategy, `value` is Avro binary data: + +```python +from onetl.connection import Kafka +from onetl.db import DBReader, DBWriter +from onetl.file.format import Avro +from pyspark.sql.functions import decode + +# read all topic data from Kafka +kafka = Kafka(...) +reader = DBReader(connection=kafka, source="avro_topic") +read_df = reader.run() + +# parse Avro format to Spark struct +avro = Avro( + schema_dict={ + "type": "record", + "name": "Person", + "fields": [ + {"name": "name", "type": "string"}, + {"name": "age", "type": "int"}, + ], + } +) +deserialized_df = read_df.select( + # cast binary key to string + decode("key", "UTF-8").alias("key"), + avro.parse_column("value"), +) +``` + +Incremental strategy, `value` is JSON string: + +#### NOTE +Currently Kafka connector does support only HWMs based on `offset` field. Other fields, like `timestamp`, are not yet supported. + +```python +from onetl.connection import Kafka +from onetl.db import DBReader, DBWriter +from onetl.file.format import JSON +from pyspark.sql.functions import decode + +kafka = Kafka(...) + +# read only new data from Kafka topic +reader = DBReader( + connection=kafka, + source="topic_name", + hwm=DBReader.AutoDetectHWM(name="kafka_hwm", expression="offset"), +) + +with IncrementalStrategy(): + read_df = reader.run() + +# parse JSON format to Spark struct +json = JSON() +schema = StructType( + [ + StructField("name", StringType(), nullable=True), + StructField("age", IntegerType(), nullable=True), + ], +) +deserialized_df = read_df.select( + # cast binary key to string + decode("key", "UTF-8").alias("key"), + json.parse_column("value", json), +) +``` + +## Options + +### *pydantic model* onetl.connection.db_connection.kafka.options.KafkaReadOptions + +Reading options for Kafka connector. + +#### WARNING +Options: +: * `assign` + * `endingOffsets` + * `endingOffsetsByTimestamp` + * `kafka.*` + * `startingOffsets` + * `startingOffsetsByTimestamp` + * `startingTimestamp` + * `subscribe` + * `subscribePattern` + +are populated from connection attributes, and cannot be overridden by the user in `ReadOptions` to avoid issues. + +#### Versionadded +Added in version 0.9.0. + +### Examples + +#### NOTE +You can pass any value +[supported by connector](https://spark.apache.org/docs/latest/structured-streaming-kafka-integration.html), +even if it is not mentioned in this documentation. **Option names should be in** `camelCase`! + +The set of supported options depends on connector version. + +```python +from onetl.connection import Kafka + +options = Kafka.ReadOptions( + includeHeaders=False, + minPartitions=50, +) +``` + + + +#### *field* include_headers *: bool* *= False* *(alias 'includeHeaders')* + +If `True`, add `headers` column to output DataFrame. + +If `False`, column will not be added. + + diff --git a/mddocs/connection/db_connection/kafka/scram_auth.md b/mddocs/connection/db_connection/kafka/scram_auth.md new file mode 100644 index 000000000..c0c974ad3 --- /dev/null +++ b/mddocs/connection/db_connection/kafka/scram_auth.md @@ -0,0 +1,79 @@ + + +# Kafka ScramAuth + +### *pydantic model* onetl.connection.db_connection.kafka.kafka_scram_auth.KafkaScramAuth + +Connect to Kafka using `sasl.mechanism="SCRAM-SHA-256"` or `sasl.mechanism="SCRAM-SHA-512"`. + +For more details see [Kafka Documentation](https://kafka.apache.org/documentation/#security_sasl_scram_clientconfig). + +#### Versionadded +Added in version 0.9.0. + +### Examples + +Auth in Kafka with `SCRAM-SHA-256` mechanism: + +```python +from onetl.connection import Kafka + +auth = Kafka.ScramAuth( + user="me", + password="abc", + digest="SHA-256", +) +``` + +Auth in Kafka with `SCRAM-SHA-512` mechanism and some custom SASL options passed to Kafka client config: + +```python +from onetl.connection import Kafka + +auth = Kafka.ScramAuth.parse( + { + "user": "me", + "password": "abc", + "digest": "SHA-512", + # options with `sasl.login.` prefix are passed to Kafka client config as-is + "sasl.login.class": "com.example.CustomScramLogin", + } +) +``` + + + +#### *field* user *: str* *[Required]* *(alias 'username')* + +#### *field* password *: SecretStr* *[Required]* + +#### *field* digest *: Literal['SHA-256', 'SHA-512']* *[Required]* + +#### get_jaas_conf() → str + + + +#### get_options(kafka: [Kafka](connection.md#onetl.connection.db_connection.kafka.connection.Kafka)) → dict + +Get options for Kafka connection + +* **Parameters:** + **kafka** + : Connection instance +* **Returns:** + dict: + : Kafka client options + + + +#### cleanup(kafka: [Kafka](connection.md#onetl.connection.db_connection.kafka.connection.Kafka)) → None + +This method is called while closing Kafka connection. + +Implement it to cleanup resources like temporary files. + +* **Parameters:** + **kafka** + : Connection instance + + diff --git a/mddocs/connection/db_connection/kafka/slots.md b/mddocs/connection/db_connection/kafka/slots.md new file mode 100644 index 000000000..e4de1772d --- /dev/null +++ b/mddocs/connection/db_connection/kafka/slots.md @@ -0,0 +1,136 @@ + + +# Kafka Slots + +### *class* onetl.connection.db_connection.kafka.slots.KafkaSlots + +Kafka slots that could be implemented by third-party plugins + +#### Versionadded +Added in version 0.9.0. + + + +#### *static* normalize_cluster_name(cluster: str) → str | None + +Normalize the given Kafka cluster name. + +This can be used to ensure that the Kafka cluster name conforms to specific naming conventions. + +#### Versionadded +Added in version 0.9.0. + +* **Parameters:** + **cluster** + : The original Kafka cluster name. +* **Returns:** + str | None + : The normalized Kafka cluster name. If the hook cannot be applied, return `None`. + +### Examples + +```python +from onetl.connection import Kafka +from onetl.hooks import hook + +@Kafka.Slots.normalize_cluster_name.bind +@hook +def normalize_cluster_name(cluster: str) -> str | None: + return cluster.lower() +``` + + + +#### *static* get_known_clusters() → set[str] | None + +Retrieve the collection of known Kafka clusters. + +This can be used to validate if the provided Kafka cluster name is recognized in the system. + +#### Versionadded +Added in version 0.9.0. + +* **Returns:** + set[str] | None + : A collection of known Kafka cluster names. If the hook cannot be applied, return `None`. + +### Examples + +```python +from onetl.connection import Kafka +from onetl.hooks import hook + +@Kafka.Slots.get_known_clusters.bind +@hook +def get_known_clusters() -> set[str] | None: + return {"kafka-cluster", "local"} +``` + + + +#### *static* normalize_address(address: str, cluster: str) → str | None + +Normalize the given broker address for a specific Kafka cluster. + +This can be used to format the broker address according to specific rules, such as adding default ports. + +#### Versionadded +Added in version 0.9.0. + +* **Parameters:** + **address** + : The original broker address. + + **cluster** + : The Kafka cluster name for which the address should be normalized. +* **Returns:** + str | None + : The normalized broker address. If the hook cannot be applied to the specific address, return `None`. + +### Examples + +```python +from onetl.connection import Kafka +from onetl.hooks import hook + +@Kafka.Slots.normalize_address.bind +@hook +def normalize_address(address: str, cluster: str) -> str | None: + if cluster == "kafka-cluster" and ":" not in address: + return f"{address}:9092" + return None +``` + + + +#### *static* get_cluster_addresses(cluster: str) → list[str] | None + +Retrieve a collection of known broker addresses for the specified Kafka cluster. + +This can be used to obtain the broker addresses dynamically. + +#### Versionadded +Added in version 0.9.0. + +* **Parameters:** + **cluster** + : The Kafka cluster name. +* **Returns:** + list[str] | None + : A collection of broker addresses for the specified Kafka cluster. If the hook cannot be applied, return `None`. + +### Examples + +```python +from onetl.connection import Kafka +from onetl.hooks import hook + +@Kafka.Slots.get_cluster_addresses.bind +@hook +def get_cluster_addresses(cluster: str) -> list[str] | None: + if cluster == "kafka_cluster": + return ["192.168.1.1:9092", "192.168.1.2:9092", "192.168.1.3:9092"] + return None +``` + + diff --git a/mddocs/connection/db_connection/kafka/ssl_protocol.md b/mddocs/connection/db_connection/kafka/ssl_protocol.md new file mode 100644 index 000000000..ca2a59b45 --- /dev/null +++ b/mddocs/connection/db_connection/kafka/ssl_protocol.md @@ -0,0 +1,149 @@ + + +# Kafka SSLProtocol + +### *pydantic model* onetl.connection.db_connection.kafka.kafka_ssl_protocol.KafkaSSLProtocol + +Connect to Kafka using `SSL` or `SASL_SSL` security protocols. + +For more details see: + +* [Kafka Documentation](https://kafka.apache.org/documentation/#producerconfigs_ssl.keystore.location) +* [IBM Documentation](https://www.ibm.com/docs/en/cloud-paks/cp-biz-automation/19.0.x?topic=fcee-kafka-using-ssl-kerberos-authentication) +* [How to use PEM Certificates with Kafka](https://codingharbour.com/apache-kafka/using-pem-certificates-with-apache-kafka/) + +#### Versionadded +Added in version 0.9.0. + +### Examples + +Pass PEM key and certificates as files located on Spark driver host: + +```python +from pathlib import Path + +# Just read existing files located on host, and pass key and certificates as strings +protocol = Kafka.SSLProtocol( + keystore_type="PEM", + keystore_certificate_chain=Path("path/to/user.crt").read_text(), + keystore_key=Path("path/to/user.key").read_text(), + truststore_type="PEM", + truststore_certificates=Path("/path/to/server.crt").read_text(), +) +``` + +Pass PEM key and certificates as raw strings: + +```python +protocol = Kafka.SSLProtocol( + keystore_type="PEM", + keystore_certificate_chain="-----BEGIN CERTIFICATE-----\nMIIDZjC...\n-----END CERTIFICATE-----", + keystore_key="-----BEGIN PRIVATE KEY-----\nMIIEvg..\n-----END PRIVATE KEY-----", + truststore_type="PEM", + truststore_certificates="-----BEGIN CERTIFICATE-----\nMICC...\n-----END CERTIFICATE-----", +) +``` + +Pass custom options: + +```python +protocol = Kafka.SSLProtocol.parse( + { + # Just the same options as above, but using Kafka config naming with dots + "ssl.keystore.type": "PEM", + "ssl.keystore.certificate_chain": "-----BEGIN CERTIFICATE-----\nMIIDZjC...\n-----END CERTIFICATE-----", + "ssl.keystore.key": "-----BEGIN PRIVATE KEY-----\nMIIEvg..\n-----END PRIVATE KEY-----", + "ssl.truststore.type": "PEM", + "ssl.truststore.certificates": "-----BEGIN CERTIFICATE-----\nMICC...\n-----END CERTIFICATE-----", + # Any option starting from "ssl." is passed to Kafka client as-is + "ssl.protocol": "TLSv1.3", + } +) +``` + +### Not recommended + +These options are error-prone and have several drawbacks, so it is not recommended to use them. + +Passing PEM certificates as files: + +* ENCRYPT `user.key` file with password `"some password"` [using PKCS#8 scheme](https://www.mkssoftware.com/docs/man1/openssl_pkcs8.1.asp). +* Save encrypted key to file `/path/to/user/encrypted_key_with_certificate_chain.pem`. +* Then append user certificate to the end of this file. +* Deploy this file (and server certificate too) to **EVERY** host Spark could run (both driver and executors). +* Then pass file locations and password for key decryption to options below. + +```python +protocol = Kafka.SSLProtocol( + keystore_type="PEM", + keystore_location="/path/to/user/encrypted_key_with_certificate_chain.pem", + key_password="some password", + truststore_type="PEM", + truststore_location="/path/to/server.crt", +) +``` + +Passing JKS (Java Key Store) location: + +* [Add user key and certificate to JKS keystore](https://stackoverflow.com/a/4326346). +* [Add server certificate to JKS truststore](https://stackoverflow.com/a/373307). +* This should be done on **EVERY** host Spark could run (both driver and executors). +* Pass keystore and truststore paths to options below, as well as passwords for accessing these stores: + +```python +protocol = Kafka.SSLProtocol( + keystore_type="JKS", + keystore_location="/usr/lib/jvm/default/lib/security/keystore.jks", + keystore_password="changeit", + truststore_type="JKS", + truststore_location="/usr/lib/jvm/default/lib/security/truststore.jks", + truststore_password="changeit", +) +``` + + + +#### *field* keystore_type *: str* *[Required]* *(alias 'ssl.keystore.type')* + +#### *field* keystore_location *: LocalPath | None* *= None* *(alias 'ssl.keystore.location')* + +#### *field* keystore_password *: SecretStr | None* *= None* *(alias 'ssl.keystore.password')* + +#### *field* keystore_certificate_chain *: str | None* *= None* *(alias 'ssl.keystore.certificate.chain')* + +#### *field* keystore_key *: SecretStr | None* *= None* *(alias 'ssl.keystore.key')* + +#### *field* key_password *: SecretStr | None* *= None* *(alias 'ssl.key.password')* + +#### *field* truststore_type *: str* *[Required]* *(alias 'ssl.truststore.type')* + +#### *field* truststore_location *: LocalPath | None* *= None* *(alias 'ssl.truststore.location')* + +#### *field* truststore_password *: SecretStr | None* *= None* *(alias 'ssl.truststore.password')* + +#### *field* truststore_certificates *: str | None* *= None* *(alias 'ssl.truststore.certificates')* + +#### get_options(kafka: [Kafka](connection.md#onetl.connection.db_connection.kafka.connection.Kafka)) → dict + +Get options for Kafka connection + +* **Parameters:** + **kafka** + : Connection instance +* **Returns:** + dict: + : Kafka client options + + + +#### cleanup(kafka: [Kafka](connection.md#onetl.connection.db_connection.kafka.connection.Kafka)) → None + +This method is called while closing Kafka connection. + +Implement it to cleanup resources like temporary files. + +* **Parameters:** + **kafka** + : Connection instance + + diff --git a/mddocs/connection/db_connection/kafka/troubleshooting.md b/mddocs/connection/db_connection/kafka/troubleshooting.md new file mode 100644 index 000000000..cd15b5ba7 --- /dev/null +++ b/mddocs/connection/db_connection/kafka/troubleshooting.md @@ -0,0 +1,10 @@ + + +# Kafka Troubleshooting + +#### NOTE +General guide: [Troubleshooting](../../../troubleshooting/index.md#troubleshooting). + +## Cannot connect using `SSL` protocol + +Please check that certificate files are not Base-64 encoded. diff --git a/mddocs/connection/db_connection/kafka/write.md b/mddocs/connection/db_connection/kafka/write.md new file mode 100644 index 000000000..c85f9c3cc --- /dev/null +++ b/mddocs/connection/db_connection/kafka/write.md @@ -0,0 +1,118 @@ + + +# Writing to Kafka + +For writing data to Kafka, use [`DBWriter`](../../../db/db_writer.md#onetl.db.db_writer.db_writer.DBWriter) with specific options (see below). + +## Dataframe schema + +Unlike other DB connections, Kafka does not have concept of columns. +All the topics messages have the same set of fields. Only some of them can be written: + +```text +root +|-- key: binary (nullable = true) +|-- value: binary (nullable = true) +|-- headers: struct (nullable = true) + |-- key: string (nullable = false) + |-- value: binary (nullable = true) +``` + +`headers` can be passed only with `Kafka.WriteOptions(include_headers=True)` (compatibility with Kafka 1.x). + +Field `topic` should not be present in the dataframe, as it is passed to `DBWriter(target=...)`. + +Other fields, like `partition`, `offset`, `timestamp` are set by Kafka, and cannot be passed explicitly. + +## Value serialization + +To write `value` or `key` of other type than bytes (e.g. struct or integer), users have to serialize values manually. + +This could be done using following methods: +: * [`Avro.serialize_column`](../../../file_df/file_formats/avro.md#onetl.file.format.avro.Avro.serialize_column) + * [`JSON.serialize_column`](../../../file_df/file_formats/json.md#onetl.file.format.json.JSON.serialize_column) + * [`CSV.serialize_column`](../../../file_df/file_formats/csv.md#onetl.file.format.csv.CSV.serialize_column) + +## Examples + +Convert `value` to JSON string, and write to Kafka: + +```python +from onetl.connection import Kafka +from onetl.db import DBWriter +from onetl.file.format import JSON + +df = ... # original data is here + +# serialize struct data as JSON +json = JSON() +write_df = df.select( + df.key, + json.serialize_column(df.value), +) + +# write data to Kafka +kafka = Kafka(...) + +writer = DBWriter( + connection=kafka, + target="topic_name", +) +writer.run(write_df) +``` + +## Options + +### *pydantic model* onetl.connection.db_connection.kafka.options.KafkaWriteOptions + +Writing options for Kafka connector. + +#### WARNING +Options: +: * `kafka.*` + * `topic` + +are populated from connection attributes, and cannot be overridden by the user in `WriteOptions` to avoid issues. + +#### Versionadded +Added in version 0.9.0. + +### Examples + +#### NOTE +You can pass any value +[supported by connector](https://spark.apache.org/docs/latest/structured-streaming-kafka-integration.html), +even if it is not mentioned in this documentation. **Option names should be in** `camelCase`! + +The set of supported options depends on connector version. + +```python +from onetl.connection import Kafka + +options = Kafka.WriteOptions( + if_exists="append", + includeHeaders=True, +) +``` + + + +#### *field* if_exists *: KafkaTopicExistBehaviorKafka* *= KafkaTopicExistBehaviorKafka.APPEND* + +Behavior of writing data into existing topic. + +Same as `df.write.mode(...)`. + +Possible values: +: * `append` (default) - Adds new objects into existing topic. + * `error` - Raises an error if topic already exists. + + + +#### *field* include_headers *: bool* *= False* *(alias 'includeHeaders')* + +If `True`, `headers` column from dataframe can be written to Kafka (requires Kafka 2.0+). + +If `False` and dataframe contains `headers` column, an exception will be raised. + + diff --git a/mddocs/connection/db_connection/mongodb/connection.md b/mddocs/connection/db_connection/mongodb/connection.md new file mode 100644 index 000000000..8a01a11be --- /dev/null +++ b/mddocs/connection/db_connection/mongodb/connection.md @@ -0,0 +1,124 @@ + + +# MongoDB Connection + +### *class* onetl.connection.db_connection.mongodb.connection.MongoDB(\*, spark: SparkSession, database: str, host: Host, user: str, password: SecretStr, port: int = 27017, extra: MongoDBExtra = MongoDBExtra()) + +MongoDB connection. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +Based on package [org.mongodb.spark:mongo-spark-connector:10.4.1](https://mvnrepository.com/artifact/org.mongodb.spark/mongo-spark-connector_2.12/10.4.1) +([MongoDB connector for Spark](https://www.mongodb.com/docs/spark-connector/current/)) + +#### SEE ALSO +Before using this connector please take into account [Prerequisites](prerequisites.md#mongodb-prerequisites) + +#### Versionadded +Added in version 0.7.0. + +* **Parameters:** + **host** + : Host of MongoDB. For example: `test.mongodb.com` or `193.168.1.17`. + + **port** + : Port of MongoDB + + **user** + : User, which have proper access to the database. For example: `some_user`. + + **password** + : Password for database connection. + + **database** + : Database in MongoDB. + + **extra** + : Specifies one or more extra parameters by which clients can connect to the instance. +
+ For example: `{"tls": "false"}` +
+ See [Connection string options documentation](https://www.mongodb.com/docs/manual/reference/connection-string/#std-label-connections-connection-options) + for more details + + **spark** + : Spark session. + +### Examples + +```python +from onetl.connection import MongoDB +from pyspark.sql import SparkSession + +# Create Spark session with MongoDB connector loaded +maven_packages = MongoDB.get_packages(spark_version="3.4") +spark = ( + SparkSession.builder.appName("spark-app-name") + .config("spark.jars.packages", ",".join(maven_packages)) + .getOrCreate() +) + +# Create connection +mongo = MongoDB( + host="master.host.or.ip", + user="user", + password="*****", + database="target_database", + spark=spark, +).check() +``` + + + +#### *classmethod* get_packages(scala_version: str | None = None, spark_version: str | None = None, package_version: str | None = None) → list[str] + +Get package names to be downloaded by Spark. Allows specifying custom MongoDB Spark connector versions. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### Versionadded +Added in version 0.9.0. + +* **Parameters:** + **scala_version** + : Scala version in format `major.minor`. +
+ If `None`, `spark_version` is used to determine Scala version. + + **spark_version** + : Spark version in format `major.minor`. Used only if `scala_version=None`. + + **package_version** + : Specifies the version of the MongoDB Spark connector to use. Defaults to `10.4.1`. +
+ #### Versionadded + Added in version 0.11.0. + +### Examples + +```python +from onetl.connection import MongoDB + +MongoDB.get_packages(scala_version="2.12") + +# specify custom connector version +MongoDB.get_packages(scala_version="2.12", package_version="10.4.1") +``` + + + +#### check() + +Check source availability. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +If not, an exception will be raised. + +* **Returns:** + Connection itself +* **Raises:** + RuntimeError + : If the connection is not available + +### Examples + +```python +connection.check() +``` + + diff --git a/mddocs/connection/db_connection/mongodb/index.md b/mddocs/connection/db_connection/mongodb/index.md new file mode 100644 index 000000000..45ab522a3 --- /dev/null +++ b/mddocs/connection/db_connection/mongodb/index.md @@ -0,0 +1,18 @@ + + +# MongoDB + +# Connection + +* [Prerequisites](prerequisites.md) +* [MongoDB Connection](connection.md) + +# Operations + +* [Reading from MongoDB using `DBReader`](read.md) +* [Reading from MongoDB using `MongoDB.pipeline`](pipeline.md) +* [Writing to MongoDB using `DBWriter`](write.md) + +# Troubleshooting + +* [MongoDB <-> Spark type mapping](types.md) diff --git a/mddocs/connection/db_connection/mongodb/pipeline.md b/mddocs/connection/db_connection/mongodb/pipeline.md new file mode 100644 index 000000000..2bae88c48 --- /dev/null +++ b/mddocs/connection/db_connection/mongodb/pipeline.md @@ -0,0 +1,152 @@ + + +# Reading from MongoDB using `MongoDB.pipeline` + +[`MongoDB.sql`](#onetl.connection.db_connection.mongodb.connection.MongoDB.pipeline) allows passing custom pipeline, +but does not support incremental strategies. + +#### WARNING +Please take into account [MongoDB <-> Spark type mapping](types.md#mongodb-types) + +## Recommendations + +### Pay attention to `pipeline` value + +Instead of filtering data on Spark side using `df.filter(df.column == 'value')` pass proper `mongodb.pipeline(..., pipeline={"$match": {"column": {"$eq": "value"}}})` value. +This both reduces the amount of data send from MongoDB to Spark, and may also improve performance of the query. +Especially if there are indexes for columns used in `pipeline` value. + +## References + +#### MongoDB.pipeline(collection: str, pipeline: dict | list[dict] | None = None, df_schema: StructType | None = None, options: [MongoDBPipelineOptions](#onetl.connection.db_connection.mongodb.options.MongoDBPipelineOptions) | dict | None = None) + +Execute a pipeline for a specific collection, and return DataFrame. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +Almost like [Aggregation pipeline syntax](https://www.mongodb.com/docs/manual/core/aggregation-pipeline/) +in MongoDB: + +```js +db.collection_name.aggregate([{"$match": ...}, {"$group": ...}]) +``` + +but pipeline is executed on Spark executors, in a distributed way. + +#### NOTE +This method does not support [Read Strategies](../../../strategy/index.md#strategy), +use [`DBReader`](../../../db/db_reader.md#onetl.db.db_reader.db_reader.DBReader) instead + +#### Versionadded +Added in version 0.7.0. + +* **Parameters:** + **collection** + : Collection name. + + **pipeline** + : Pipeline containing a database query. + See [Aggregation pipeline syntax](https://www.mongodb.com/docs/manual/core/aggregation-pipeline/). + + **df_schema** + : Schema describing the resulting DataFrame. + + **options** + : Additional pipeline options, see [`MongoDB.PipelineOptions`](#onetl.connection.db_connection.mongodb.options.MongoDBPipelineOptions). + +### Examples + +Get document with a specific `field` value: + +```python +df = connection.pipeline( + collection="collection_name", + pipeline={"$match": {"field": {"$eq": 1}}}, +) +``` + +Calculate aggregation and get result: + +```python +df = connection.pipeline( + collection="collection_name", + pipeline={ + "$group": { + "_id": 1, + "min": {"$min": "$column_int"}, + "max": {"$max": "$column_int"}, + } + }, +) +``` + +Explicitly pass DataFrame schema: + +```python +from pyspark.sql.types import ( + DoubleType, + IntegerType, + StringType, + StructField, + StructType, + TimestampType, +) + +df_schema = StructType( + [ + StructField("_id", StringType()), + StructField("some_string", StringType()), + StructField("some_int", IntegerType()), + StructField("some_datetime", TimestampType()), + StructField("some_float", DoubleType()), + ], +) + +df = connection.pipeline( + collection="collection_name", + df_schema=df_schema, + pipeline={"$match": {"some_int": {"$gt": 999}}}, +) +``` + +Pass additional options to pipeline execution: + +```python +df = connection.pipeline( + collection="collection_name", + pipeline={"$match": {"field": {"$eq": 1}}}, + options=MongoDB.PipelineOptions(hint={"field": 1}), +) +``` + + + +### *pydantic model* onetl.connection.db_connection.mongodb.options.MongoDBPipelineOptions + +Aggregation pipeline options for MongoDB connector. + +The only difference from [`MongoDB.ReadOptions`](read.md#onetl.connection.db_connection.mongodb.options.MongoDBReadOptions) that latter does not allow to pass the `hint` parameter. + +#### WARNING +Options `uri`, `database`, `collection`, `pipeline` are populated from connection attributes, +and cannot be overridden by the user in `PipelineOptions` to avoid issues. + +#### Versionadded +Added in version 0.7.0. + +### Examples + +#### NOTE +You can pass any value +[supported by connector](https://www.mongodb.com/docs/spark-connector/current/batch-mode/batch-read-config/), +even if it is not mentioned in this documentation. **Option names should be in** `camelCase`! + +The set of supported options depends on connector version. + +```python +from onetl.connection import MongoDB + +options = MongoDB.PipelineOptions( + hint={"some_field": 1}, +) +``` + + diff --git a/mddocs/connection/db_connection/mongodb/prerequisites.md b/mddocs/connection/db_connection/mongodb/prerequisites.md new file mode 100644 index 000000000..4572fb4f6 --- /dev/null +++ b/mddocs/connection/db_connection/mongodb/prerequisites.md @@ -0,0 +1,72 @@ + + +# Prerequisites + +## Version Compatibility + +* MongoDB server versions: + : * Officially declared: 4.0 or higher + * Actually tested: 4.0.0, 8.0.4 +* Spark versions: 3.2.x - 3.5.x +* Java versions: 8 - 20 + +See [official documentation](https://www.mongodb.com/docs/spark-connector/). + +## Installing PySpark + +To use MongoDB connector you should have PySpark installed (or injected to `sys.path`) +BEFORE creating the connector instance. + +See [Spark](../../../install/spark.md#install-spark) installation instruction for more details. + +## Connecting to MongoDB + +### Connection host + +It is possible to connect to MongoDB host by using either DNS name of host or it’s IP address. + +It is also possible to connect to MongoDB shared cluster: + +```python +mongo = MongoDB( + host="master.host.or.ip", + user="user", + password="*****", + database="target_database", + spark=spark, + extra={ + # read data from secondary cluster node, switch to primary if not available + "readPreference": "secondaryPreferred", + }, +) +``` + +Supported `readPreference` values are described in [official documentation](https://www.mongodb.com/docs/manual/core/read-preference/). + +### Connection port + +Connection is usually performed to port `27017`. Port may differ for different MongoDB instances. +Please ask your MongoDB administrator to provide required information. + +### Required grants + +Ask your MongoDB cluster administrator to set following grants for a user, +used for creating a connection: + +Read + Write + +```js +// allow writing data to specific database +db.grantRolesToUser("username", [{db: "somedb", role: "readWrite"}]) +``` + +Read only + +```js +// allow reading data from specific database +db.grantRolesToUser("username", [{db: "somedb", role: "read"}]) +``` + +See: +: * [db.grantRolesToUser documentation](https://www.mongodb.com/docs/manual/reference/method/db.grantRolesToUser) + * [MongoDB builtin roles](https://www.mongodb.com/docs/manual/reference/built-in-roles) diff --git a/mddocs/connection/db_connection/mongodb/read.md b/mddocs/connection/db_connection/mongodb/read.md new file mode 100644 index 000000000..2f2ffb73c --- /dev/null +++ b/mddocs/connection/db_connection/mongodb/read.md @@ -0,0 +1,157 @@ + + +# Reading from MongoDB using `DBReader` + +[`DBReader`](../../../db/db_reader.md#onetl.db.db_reader.db_reader.DBReader) supports [Read Strategies](../../../strategy/index.md#strategy) for incremental data reading, +but does not support custom pipelines, e.g. aggregation. + +#### WARNING +Please take into account [MongoDB <-> Spark type mapping](types.md#mongodb-types) + +## Supported DBReader features + +* ❌ `columns` (for now, all document fields are read) +* ✅︎ `where` (passed to `{"$match": ...}` aggregation pipeline) +* ✅︎ `hwm`, supported strategies: +* * ✅︎ [Snapshot Strategy](../../../strategy/snapshot_strategy.md#snapshot-strategy) +* * ✅︎ [Incremental Strategy](../../../strategy/incremental_strategy.md#incremental-strategy) +* * ✅︎ [Snapshot Batch Strategy](../../../strategy/snapshot_batch_strategy.md#snapshot-batch-strategy) +* * ✅︎ [Incremental Batch Strategy](../../../strategy/incremental_batch_strategy.md#incremental-batch-strategy) +* * Note that `expression` field of HWM can only be a field name, not a custom expression +* ✅︎ `hint` (see [official documentation](https://www.mongodb.com/docs/v5.0/reference/operator/meta/hint/)) +* ✅︎ `df_schema` (mandatory) +* ✅︎ `options` (see [`MongoDB.ReadOptions`](#onetl.connection.db_connection.mongodb.options.MongoDBReadOptions)) + +## Examples + +Snapshot strategy: + +```python +from onetl.connection import MongoDB +from onetl.db import DBReader + +from pyspark.sql.types import ( + StructType, + StructField, + IntegerType, + StringType, + TimestampType, +) + +mongodb = MongoDB(...) + +# mandatory +df_schema = StructType( + [ + StructField("_id", StringType()), + StructField("some", StringType()), + StructField( + "field", + StructType( + [ + StructField("nested", IntegerType()), + ], + ), + ), + StructField("updated_dt", TimestampType()), + ] +) + +reader = DBReader( + connection=mongodb, + source="some_collection", + df_schema=df_schema, + where={"field": {"$eq": 123}}, + hint={"field": 1}, + options=MongoDBReadOptions(batchSize=10000), +) +df = reader.run() +``` + +Incremental strategy: + +```python +from onetl.connection import MongoDB +from onetl.db import DBReader +from onetl.strategy import IncrementalStrategy + +from pyspark.sql.types import ( + StructType, + StructField, + IntegerType, + StringType, + TimestampType, +) + +mongodb = MongoDB(...) + +# mandatory +df_schema = StructType( + [ + StructField("_id", StringType()), + StructField("some", StringType()), + StructField( + "field", + StructType( + [ + StructField("nested", IntegerType()), + ], + ), + ), + StructField("updated_dt", TimestampType()), + ] +) + +reader = DBReader( + connection=mongodb, + source="some_collection", + df_schema=df_schema, + where={"field": {"$eq": 123}}, + hint={"field": 1}, + hwm=DBReader.AutoDetectHWM(name="mongodb_hwm", expression="updated_dt"), + options=MongoDBReadOptions(batchSize=10000), +) + +with IncrementalStrategy(): + df = reader.run() +``` + +## Recommendations + +### Pay attention to `where` value + +Instead of filtering data on Spark side using `df.filter(df.column == 'value')` pass proper `DBReader(where={"column": {"$eq": "value"}})` clause. +This both reduces the amount of data send from MongoDB to Spark, and may also improve performance of the query. +Especially if there are indexes for columns used in `where` clause. + +## Read options + +### *pydantic model* onetl.connection.db_connection.mongodb.options.MongoDBReadOptions + +Reading options for MongoDB connector. + +#### WARNING +Options `uri`, `database`, `collection`, `pipeline`, `hint` are populated from connection +attributes, and cannot be overridden by the user in `ReadOptions` to avoid issues. + +#### Versionadded +Added in version 0.7.0. + +### Examples + +#### NOTE +You can pass any value +[supported by connector](https://www.mongodb.com/docs/spark-connector/current/batch-mode/batch-read-config/), +even if it is not mentioned in this documentation. **Option names should be in** `camelCase`! + +The set of supported options depends on connector version. + +```python +from onetl.connection import MongoDB + +options = MongoDB.ReadOptions( + sampleSize=100, +) +``` + + diff --git a/mddocs/connection/db_connection/mongodb/types.md b/mddocs/connection/db_connection/mongodb/types.md new file mode 100644 index 000000000..2d75b5e52 --- /dev/null +++ b/mddocs/connection/db_connection/mongodb/types.md @@ -0,0 +1,214 @@ + + +# MongoDB <-> Spark type mapping + +#### NOTE +The results below are valid for Spark 3.5.5, and may differ on other Spark versions. + +## Type detection & casting + +Spark’s DataFrames always have a `schema` which is a list of fields with corresponding Spark types. All operations on a field are performed using field type. + +MongoDB is, by design, \_\_schemaless_\_. So there are 2 ways how this can be handled: + +* User provides DataFrame schema explicitly: + + ### See example + + ```python + from onetl.connection import MongoDB + from onetl.db import DBReader + + from pyspark.sql.types import ( + StructType, + StructField, + IntegerType, + StringType, + TimestampType, + ) + + mongodb = MongoDB(...) + + df_schema = StructType( + [ + StructField("_id", StringType()), + StructField("some", StringType()), + StructField( + "field", + StructType( + [ + StructField("nested", IntegerType()), + ] + ), + ), + ] + ) + + reader = DBReader( + connection=mongodb, + source="some_collection", + df_schema=df_schema, + ) + df = reader.run() + + # or + + df = mongodb.pipeline( + collection="some_collection", + df_schema=df_schema, + ) + ``` +* Rely on MongoDB connector schema infer: + ```python + df = mongodb.pipeline(collection="some_collection") + ``` + + In this case MongoDB connector read a sample of collection documents, and build DataFrame schema based on document fields and values. + +It is highly recommended to pass `df_schema` explicitly, to avoid type conversion issues. + +### References + +Here you can find source code with type conversions: + +* [MongoDB -> Spark](https://github.com/mongodb/mongo-spark/blob/r10.4.1/src/main/java/com/mongodb/spark/sql/connector/schema/InferSchema.java#L214-L260) +* [Spark -> MongoDB](https://github.com/mongodb/mongo-spark/blob/r10.4.1/src/main/java/com/mongodb/spark/sql/connector/schema/RowToBsonDocumentConverter.java#L157-L260) + +## Supported types + +See [official documentation](https://www.mongodb.com/docs/manual/reference/bson-types/) + +### Numeric types + +| MongoDB type (read) | Spark type | MongoDB type (write) | +|-----------------------|---------------------------|------------------------| +| `Decimal128` | `DecimalType(P=34, S=32)` | `Decimal128` | +| `-` | `FloatType()` | `Double` | +| `Double` | `DoubleType()` | | +| `-` | `ByteType()` | `Int32` | +| `-` | `ShortType()` | | +| `Int32` | `IntegerType()` | | +| `Int64` | `LongType()` | `Int64` | + +### Temporal types + +| MongoDB type (read) | Spark type | MongoDB type (write) | +|-----------------------|---------------------------------|-------------------------------------------------------------------| +| `-` | `DateType()`, days | `Date`, milliseconds | +| `Date`, milliseconds | `TimestampType()`, microseconds | `Date`, milliseconds,
**precision loss** [2](#id2) | +| `Timestamp`, seconds | `TimestampType()`, microseconds | `Date`, milliseconds | +| `-` | `TimestampNTZType()` | unsupported | +| `-` | `DayTimeIntervalType()` | | + +#### WARNING +Note that types in MongoDB and Spark have different value ranges: + +| MongoDB type | Min value | Max value | Spark type | Min value | Max value | +|----------------|-----------------------|-----------------------|-------------------|------------------------------|------------------------------| +| `Date` | -290 million years | 290 million years | `TimestampType()` | `0001-01-01 00:00:00.000000` | `9999-12-31 23:59:59.999999` | +| `Timestamp` | `1970-01-01 00:00:00` | `2106-02-07 09:28:16` | | | | + +So not all values can be read from MongoDB to Spark, and can written from Spark DataFrame to MongoDB. + +References: +: * [MongoDB Date type documentation](https://www.mongodb.com/docs/manual/reference/bson-types/#date) + * [MongoDB Timestamp documentation](https://www.mongodb.com/docs/manual/reference/bson-types/#timestamps) + * [Spark DateType documentation](https://spark.apache.org/docs/latest/api/java/org/apache/spark/sql/types/DateType.html) + * [Spark TimestampType documentation](https://spark.apache.org/docs/latest/api/java/org/apache/spark/sql/types/TimestampType.html) + +* **[2]** MongoDB `Date` type has precision up to milliseconds (`23:59:59.999`). Inserting data with microsecond precision (`23:59:59.999999`) will lead to **throwing away microseconds**. + +### String types + +Note: fields of deprecated MongoDB type `Symbol` are excluded during read. + +| MongoDB type (read) | Spark type | MongoDB type (write) | +|-----------------------|----------------|------------------------| +| `String` | `StringType()` | `String` | +| `Code` | | | +| `RegExp` | | | + +### Binary types + +| MongoDB type (read) | Spark type | MongoDB type (write) | +|-----------------------|-----------------|------------------------| +| `Boolean` | `BooleanType()` | `Boolean` | +| `Binary` | `BinaryType()` | `Binary` | + +### Struct types + +| MongoDB type (read) | Spark type | MongoDB type (write) | +|-----------------------|---------------------|------------------------| +| `Array[T]` | `ArrayType(T)` | `Array[T]` | +| `Object[...]` | `StructType([...])` | `Object[...]` | +| `-` | `MapType(...)` | | + +### Special types + +| MongoDB type (read) | Spark type | MongoDB type (write) | +|-----------------------|-------------------------------------------------------|-------------------------------------| +| `ObjectId` | `StringType()` | `String` | +| `MaxKey` | | | +| `MinKey` | | | +| `Null` | `NullType()` | `Null` | +| `Undefined` | | | +| `DBRef` | `StructType([$ref: StringType(), $id: StringType()])` | `Object[$ref: String, $id: String]` | + +## Explicit type cast + +### `DBReader` + +Currently it is not possible to cast field types using `DBReader`. But this can be done using `MongoDB.pipeline`. + +### `MongoDB.pipeline` + +You can use `$project` aggregation to cast field types: + +```python +from pyspark.sql.types import IntegerType, StructField, StructType + +from onetl.connection import MongoDB +from onetl.db import DBReader + +mongodb = MongoDB(...) + +df = mongodb.pipeline( + collection="my_collection", + pipeline=[ + { + "$project": { + # convert unsupported_field to string + "unsupported_field_str": { + "$convert": { + "input": "$unsupported_field", + "to": "string", + }, + }, + # skip unsupported_field from result + "unsupported_field": 0, + } + } + ], +) + +# cast field content to proper Spark type +df = df.select( + df.id, + df.supported_field, + # explicit cast + df.unsupported_field_str.cast("integer").alias("parsed_integer"), +) +``` + +### `DBWriter` + +Convert dataframe field to string on Spark side, and then write it to MongoDB: + +```python +df = df.select( + df.id, + df.unsupported_field.cast("string").alias("array_field_json"), +) + +writer.run(df) +``` diff --git a/mddocs/connection/db_connection/mongodb/write.md b/mddocs/connection/db_connection/mongodb/write.md new file mode 100644 index 000000000..a0e3778c1 --- /dev/null +++ b/mddocs/connection/db_connection/mongodb/write.md @@ -0,0 +1,118 @@ + + +# Writing to MongoDB using `DBWriter` + +For writing data to MongoDB, use [`DBWriter`](../../../db/db_writer.md#onetl.db.db_writer.db_writer.DBWriter). + +#### WARNING +Please take into account [MongoDB <-> Spark type mapping](types.md#mongodb-types) + +## Examples + +```python +from onetl.connection import MongoDB +from onetl.db import DBWriter + +mongodb = MongoDB(...) + +df = ... # data is here + +writer = DBWriter( + connection=mongodb, + target="schema.table", + options=MongoDB.WriteOptions( + if_exists="append", + ), +) + +writer.run(df) +``` + +## Write options + +Method above accepts [`MongoDB.WriteOptions`](#onetl.connection.db_connection.mongodb.options.MongoDBWriteOptions) + +### *pydantic model* onetl.connection.db_connection.mongodb.options.MongoDBWriteOptions + +Writing options for MongoDB connector. + +#### WARNING +Options `uri`, `database`, `collection` are populated from connection attributes, +and cannot be overridden by the user in `WriteOptions` to avoid issues. + +#### Versionadded +Added in version 0.7.0. + +### Examples + +#### NOTE +You can pass any value +[supported by connector](https://www.mongodb.com/docs/spark-connector/current/batch-mode/batch-write-config/), +even if it is not mentioned in this documentation. **Option names should be in** `camelCase`! + +The set of supported options depends on connector version. + +```python +from onetl.connection import MongoDB + +options = MongoDB.WriteOptions( + if_exists="append", + sampleSize=500, + localThreshold=20, +) +``` + + + +#### *field* if_exists *: MongoDBCollectionExistBehavior* *= MongoDBCollectionExistBehavior.APPEND* *(alias 'mode')* + +Behavior of writing data into existing collection. + +Possible values: +: * `append` (default) + : Adds new objects into existing collection. +
+ ### Behavior in details +
+ * Collection does not exist + : Collection is created using options provided by user + (`shardkey` and others). + * Collection exists + : Data is appended to a collection. +
+ #### WARNING + This mode does not check whether collection already contains + objects from dataframe, so duplicated objects can be created. + * `replace_entire_collection` + : **Collection is deleted and then created**. +
+ ### Behavior in details +
+ * Collection does not exist + : Collection is created using options provided by user + (`shardkey` and others). + * Collection exists + : Collection content is replaced with dataframe content. + * `ignore` + : Ignores the write operation if the collection already exists. +
+ ### Behavior in details +
+ * Collection does not exist + : Collection is created using options provided by user + * Collection exists + : The write operation is ignored, and no data is written to the collection. + * `error` + : Raises an error if the collection already exists. +
+ ### Behavior in details +
+ * Collection does not exist + : Collection is created using options provided by user + * Collection exists + : An error is raised, and no data is written to the collection. + +#### Versionchanged +Changed in version 0.9.0: Renamed `mode` → `if_exists` + + diff --git a/mddocs/connection/db_connection/mssql/connection.md b/mddocs/connection/db_connection/mssql/connection.md new file mode 100644 index 000000000..3e35a34fb --- /dev/null +++ b/mddocs/connection/db_connection/mssql/connection.md @@ -0,0 +1,186 @@ + + +# MSSQL connection + +### *class* onetl.connection.db_connection.mssql.connection.MSSQL(\*, spark: SparkSession, user: str, password: SecretStr, database: str, host: Host, port: int | None = None, extra: MSSQLExtra = MSSQLExtra()) + +MSSQL JDBC connection. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +Based on Maven package [com.microsoft.sqlserver:mssql-jdbc:12.8.1.jre8](https://mvnrepository.com/artifact/com.microsoft.sqlserver/mssql-jdbc/12.8.1.jre8) +([official MSSQL JDBC driver](https://docs.microsoft.com/en-us/sql/connect/jdbc/download-microsoft-jdbc-driver-for-sql-server)). + +#### SEE ALSO +Before using this connector please take into account [Prerequisites](prerequisites.md#mssql-prerequisites) + +* **Parameters:** + **host** + : Host of MSSQL database. For example: `test.mssql.domain.com` or `192.168.1.14` + + **port** + : Port of MSSQL database +
+ #### Versionchanged + Changed in version 0.11.1: Default value was changed from `1433` to `None`, + to allow automatic port discovery with `instanceName`. + + **user** + : User, which have proper access to the database. For example: `some_user` + + **password** + : Password for database connection + + **database** + : Database in RDBMS, NOT schema. +
+ See [this page](https://www.educba.com/postgresql-database-vs-schema/) for more details + + **spark** + : Spark session. + + **extra** + : Specifies one or more extra parameters by which clients can connect to the instance. +
+ For example: `{"connectRetryCount": 3, "connectRetryInterval": 10}` +
+ See [MSSQL JDBC driver properties documentation](https://learn.microsoft.com/en-us/sql/connect/jdbc/setting-the-connection-properties#properties) + for more details + +### Examples + +Create MSSQL connection with plain auth + +```py +from onetl.connection import MSSQL +from pyspark.sql import SparkSession + +# Create Spark session with MSSQL driver loaded +maven_packages = MSSQL.get_packages() +spark = ( + SparkSession.builder.appName("spark-app-name") + .config("spark.jars.packages", ",".join(maven_packages)) + .getOrCreate() +) + +# Create connection +mssql = MSSQL( + host="database.host.or.ip", + port=1433, + user="user", + password="*****", + extra={ + "trustServerCertificate": "true", # add this to avoid SSL certificate issues + }, + spark=spark, +) +``` + +Create MSSQL connection with domain auth + +```py +# Create Spark session with MSSQL driver loaded +... + +# Create connection +mssql = MSSQL( + host="database.host.or.ip", + port=1433, + user="user", + password="*****", + extra={ + "domain": "some.domain.com", # add here your domain + "integratedSecurity": "true", + "authenticationScheme": "NTLM", + "trustServerCertificate": "true", # add this to avoid SSL certificate issues + }, + spark=spark, +) +``` + +Create MSSQL connection with instance name + +```py +# Create Spark session with MSSQL driver loaded +... + +# Create connection +mssql = MSSQL( + host="database.host.or.ip", + # !!! no port !!! + user="user", + password="*****", + extra={ + "instanceName": "myinstance", # add here your instance name + "trustServerCertificate": "true", # add this to avoid SSL certificate issues + }, + spark=spark, +) +``` + +Create MSSQL read-only connection + +```py +# Create Spark session with MSSQL driver loaded +... + +# Create connection +mssql = MSSQL( + host="database.host.or.ip", + port=1433, + user="user", + password="*****", + extra={ + "applicationIntent": "ReadOnly", # driver will open read-only connection, to avoid writing to the database + "trustServerCertificate": "true", # add this to avoid SSL certificate issues + }, + spark=spark, +).check() +``` + + + +#### check() + +Check source availability. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +If not, an exception will be raised. + +* **Returns:** + Connection itself +* **Raises:** + RuntimeError + : If the connection is not available + +### Examples + +```python +connection.check() +``` + + + +#### *classmethod* get_packages(java_version: str | None = None, package_version: str | None = None) → list[str] + +Get package names to be downloaded by Spark. Allows specifying custom JDBC driver versions for MSSQL. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### Versionadded +Added in version 0.9.0. + +* **Parameters:** + **java_version** + : Java major version, defaults to `8`. Must be `8` or `11`. + + **package_version** + : Specifies the version of the MSSQL JDBC driver to use. Defaults to `12.8.1.`. + +### Examples + +```python +from onetl.connection import MSSQL + +MSSQL.get_packages() + +# specify Java and package versions +MSSQL.get_packages(java_version="8", package_version="12.8.1.jre11") +``` + + diff --git a/mddocs/connection/db_connection/mssql/execute.md b/mddocs/connection/db_connection/mssql/execute.md new file mode 100644 index 000000000..7655175c8 --- /dev/null +++ b/mddocs/connection/db_connection/mssql/execute.md @@ -0,0 +1,184 @@ + + +# Executing statements in MSSQL + +#### WARNING +Methods below **read all the rows** returned from DB **to Spark driver memory**, and then convert them to DataFrame. + +Do **NOT** use them to read large amounts of data. Use [DBReader](read.md#mssql-read) or [MSSQL.sql](sql.md#mssql-sql) instead. + +## How to + +There are 2 ways to execute some statement in MSSQL + +### Use `MSSQL.fetch` + +Use this method to perform some `SELECT` query which returns **small number or rows**, like reading +MSSQL config, or reading data from some reference table. Method returns Spark DataFrame. + +Method accepts [`MSSQL.FetchOptions`](#onetl.connection.db_connection.mssql.options.MSSQLFetchOptions). + +Connection opened using this method should be then closed with `connection.close()` or `with connection:`. + +#### WARNING +Please take into account [MSSQL <-> Spark type mapping](types.md#mssql-types). + +#### Syntax support + +This method supports **any** query syntax supported by MSSQL, like: + +* ✅︎ `SELECT ... FROM ...` +* ✅︎ `WITH alias AS (...) SELECT ...` +* ✅︎ `SELECT func(arg1, arg2) FROM DUAL` - call function +* ❌ `SET ...; SELECT ...;` - multiple statements not supported + +#### Examples + +```python +from onetl.connection import MSSQL + +mssql = MSSQL(...) + +df = mssql.fetch( + "SELECT value FROM some.reference_table WHERE key = 'some_constant'", + options=MSSQL.FetchOptions(queryTimeout=10), +) +mssql.close() +value = df.collect()[0][0] # get value from first row and first column +``` + +### Use `MSSQL.execute` + +Use this method to execute DDL and DML operations. Each method call runs operation in a separated transaction, and then commits it. + +Method accepts [`MSSQL.ExecuteOptions`](#onetl.connection.db_connection.mssql.options.MSSQLExecuteOptions). + +Connection opened using this method should be then closed with `connection.close()` or `with connection:`. + +#### Syntax support + +This method supports **any** query syntax supported by MSSQL, like: + +* ✅︎ `CREATE TABLE ...`, `CREATE VIEW ...` +* ✅︎ `ALTER ...` +* ✅︎ `INSERT INTO ... AS SELECT ...` +* ✅︎ `DROP TABLE ...`, `DROP VIEW ...`, `TRUNCATE TABLE`, and so on +* ✅︎ `EXEC procedure(arg1, arg2) ...` or `{call procedure(arg1, arg2)}` - special syntax for calling procedure +* ✅︎ `DECLARE ... BEGIN ... END` - execute PL/SQL statement +* ✅︎ other statements not mentioned here +* ❌ `SET ...; SELECT ...;` - multiple statements not supported + +#### Examples + +```python +from onetl.connection import MSSQL + +mssql = MSSQL(...) + +mssql.execute("DROP TABLE schema.table") +mssql.execute( + """ + CREATE TABLE schema.table ( + id bigint GENERATED ALWAYS AS IDENTITY, + key VARCHAR2(4000), + value NUMBER + ) + """, + options=MSSQL.ExecuteOptions(queryTimeout=10), +) +``` + +## Options + +### *pydantic model* onetl.connection.db_connection.mssql.options.MSSQLFetchOptions + +Options related to fetching data from databases via JDBC. + +#### Versionadded +Added in version 0.11.0: Replace `MSSQL.JDBCOptions` → `MSSQL.FetchOptions` + +### Examples + +#### NOTE +You can pass any value supported by underlying JDBC driver class, +even if it is not mentioned in this documentation. + +```python +from onetl.connection import MSSQL + +options = MSSQL.FetchOptions( + queryTimeout=60_000, + fetchsize=100_000, + customSparkOption="value", +) +``` + + + +#### *field* query_timeout *: int | None* *= None* *(alias 'queryTimeout')* + +The number of seconds the driver will wait for a statement to execute. +Zero means there is no limit. + +This option depends on driver implementation, +some drivers can check the timeout of each query instead of an entire JDBC batch. + + + +#### *field* fetchsize *: int | None* *= None* + +How many rows to fetch per round trip. + +Tuning this option can influence performance of reading. + +#### WARNING +Default value depends on driver. For example, Oracle has +default `fetchsize=10`. + + + +### *pydantic model* onetl.connection.db_connection.mssql.options.MSSQLExecuteOptions + +Options related to executing statements in databases via JDBC. + +#### Versionadded +Added in version 0.11.0: Replace `MSSQL.JDBCOptions` → `MSSQL.ExecuteOptions` + +### Examples + +#### NOTE +You can pass any value supported by underlying JDBC driver class, +even if it is not mentioned in this documentation. + +```python +from onetl.connection import MSSQL + +options = MSSQL.ExecuteOptions( + queryTimeout=60_000, + customSparkOption="value", +) +``` + + + +#### *field* query_timeout *: int | None* *= None* *(alias 'queryTimeout')* + +The number of seconds the driver will wait for a statement to execute. +Zero means there is no limit. + +This option depends on driver implementation, +some drivers can check the timeout of each query instead of an entire JDBC batch. + + + +#### *field* fetchsize *: int | None* *= None* + +How many rows to fetch per round trip. + +Tuning this option can influence performance of reading. + +#### WARNING +Default value depends on driver. For example, Oracle has +default `fetchsize=10`. + + diff --git a/mddocs/connection/db_connection/mssql/index.md b/mddocs/connection/db_connection/mssql/index.md new file mode 100644 index 000000000..a35b4acbb --- /dev/null +++ b/mddocs/connection/db_connection/mssql/index.md @@ -0,0 +1,19 @@ + + +# MSSQL + +# Connection + +* [Prerequisites](prerequisites.md) +* [MSSQL connection](connection.md) + +# Operations + +* [Reading from MSSQL using `DBReader`](read.md) +* [Reading from MSSQL using `MSSQL.sql`](sql.md) +* [Writing to MSSQL using `DBWriter`](write.md) +* [Executing statements in MSSQL](execute.md) + +# Troubleshooting + +* [MSSQL <-> Spark type mapping](types.md) diff --git a/mddocs/connection/db_connection/mssql/prerequisites.md b/mddocs/connection/db_connection/mssql/prerequisites.md new file mode 100644 index 000000000..e103ced3e --- /dev/null +++ b/mddocs/connection/db_connection/mssql/prerequisites.md @@ -0,0 +1,78 @@ + + +# Prerequisites + +## Version Compatibility + +* SQL Server versions: + : * Officially declared: 2016 - 2022 + * Actually tested: 2017, 2022 +* Spark versions: 2.3.x - 3.5.x +* Java versions: 8 - 20 + +See [official documentation](https://learn.microsoft.com/en-us/sql/connect/jdbc/system-requirements-for-the-jdbc-driver) +and [official compatibility matrix](https://learn.microsoft.com/en-us/sql/connect/jdbc/microsoft-jdbc-driver-for-sql-server-support-matrix). + +## Installing PySpark + +To use MSSQL connector you should have PySpark installed (or injected to `sys.path`) +BEFORE creating the connector instance. + +See [Spark](../../../install/spark.md#install-spark) installation instruction for more details. + +## Connecting to MSSQL + +### Connection port + +Connection is usually performed to port 1433. Port may differ for different MSSQL instances. +Please ask your MSSQL administrator to provide required information. + +For named MSSQL instances (`instanceName` option), [port number is optional](https://learn.microsoft.com/en-us/sql/connect/jdbc/building-the-connection-url?view=sql-server-ver16#named-and-multiple-sql-server-instances), and could be omitted. + +### Connection host + +It is possible to connect to MSSQL by using either DNS name of host or it’s IP address. + +If you’re using MSSQL cluster, it is currently possible to connect only to **one specific node**. +Connecting to multiple nodes to perform load balancing, as well as automatic failover to new master/replica are not supported. + +### Required grants + +Ask your MSSQL cluster administrator to set following grants for a user, +used for creating a connection: + +Read + Write (schema is owned by user) + +```sql +-- allow creating tables for user +GRANT CREATE TABLE TO username; + +-- allow read & write access to specific table +GRANT SELECT, INSERT ON username.mytable TO username; + +-- only if if_exists="replace_entire_table" is used: +-- allow dropping/truncating tables in any schema +GRANT ALTER ON username.mytable TO username; +``` + +Read + Write (schema is not owned by user) + +```sql +-- allow creating tables for user +GRANT CREATE TABLE TO username; + +-- allow managing tables in specific schema, and inserting data to tables +GRANT ALTER, SELECT, INSERT ON SCHEMA::someschema TO username; +``` + +Read only + +```sql +-- allow read access to specific table +GRANT SELECT ON someschema.mytable TO username; +``` + +More details can be found in official documentation: +: * [GRANT ON DATABASE](https://learn.microsoft.com/en-us/sql/t-sql/statements/grant-database-permissions-transact-sql) + * [GRANT ON OBJECT](https://learn.microsoft.com/en-us/sql/t-sql/statements/grant-object-permissions-transact-sql) + * [GRANT ON SCHEMA](https://learn.microsoft.com/en-us/sql/t-sql/statements/grant-schema-permissions-transact-sql) diff --git a/mddocs/connection/db_connection/mssql/read.md b/mddocs/connection/db_connection/mssql/read.md new file mode 100644 index 000000000..9dc3dc88d --- /dev/null +++ b/mddocs/connection/db_connection/mssql/read.md @@ -0,0 +1,339 @@ + + +# Reading from MSSQL using `DBReader` + +[`DBReader`](../../../db/db_reader.md#onetl.db.db_reader.db_reader.DBReader) supports [Read Strategies](../../../strategy/index.md#strategy) for incremental data reading, +but does not support custom queries, like `JOIN`. + +#### WARNING +Please take into account [MSSQL <-> Spark type mapping](types.md#mssql-types) + +## Supported DBReader features + +* ✅︎ `columns` +* ✅︎ `where` +* ✅︎ `hwm`, supported strategies: +* * ✅︎ [Snapshot Strategy](../../../strategy/snapshot_strategy.md#snapshot-strategy) +* * ✅︎ [Incremental Strategy](../../../strategy/incremental_strategy.md#incremental-strategy) +* * ✅︎ [Snapshot Batch Strategy](../../../strategy/snapshot_batch_strategy.md#snapshot-batch-strategy) +* * ✅︎ [Incremental Batch Strategy](../../../strategy/incremental_batch_strategy.md#incremental-batch-strategy) +* ❌ `hint` (MSSQL does support hints, but DBReader not, at least for now) +* ❌ `df_schema` +* ✅︎ `options` (see [`MSSQL.ReadOptions`](#onetl.connection.db_connection.mssql.options.MSSQLReadOptions)) + +## Examples + +Snapshot strategy: + +```python +from onetl.connection import MSSQL +from onetl.db import DBReader + +mssql = MSSQL(...) + +reader = DBReader( + connection=mssql, + source="schema.table", + columns=["id", "key", "CAST(value AS text) value", "updated_dt"], + where="key = 'something'", + options=MSSQL.ReadOptions(partitionColumn="id", numPartitions=10), +) +df = reader.run() +``` + +Incremental strategy: + +```python +from onetl.connection import MSSQL +from onetl.db import DBReader +from onetl.strategy import IncrementalStrategy + +mssql = MSSQL(...) + +reader = DBReader( + connection=mssql, + source="schema.table", + columns=["id", "key", "CAST(value AS text) value", "updated_dt"], + where="key = 'something'", + hwm=DBReader.AutoDetectHWM(name="mssql_hwm", expression="updated_dt"), + options=MSSQL.ReadOptions(partitionColumn="id", numPartitions=10), +) + +with IncrementalStrategy(): + df = reader.run() +``` + +## Recommendations + +### Select only required columns + +Instead of passing `"*"` in `DBReader(columns=[...])` prefer passing exact column names. This reduces the amount of data passed from MSSQL to Spark. + +### Pay attention to `where` value + +Instead of filtering data on Spark side using `df.filter(df.column == 'value')` pass proper `DBReader(where="column = 'value'")` clause. +This both reduces the amount of data send from MSSQL to Spark, and may also improve performance of the query. +Especially if there are indexes or partitions for columns used in `where` clause. + +## Options + +### *pydantic model* onetl.connection.db_connection.mssql.options.MSSQLReadOptions + +Spark JDBC reading options. + +#### Versionadded +Added in version 0.5.0: Replace `MSSQL.Options` → `MSSQL.ReadOptions` + +### Examples + +#### NOTE +You can pass any value +[supported by Spark](https://spark.apache.org/docs/latest/sql-data-sources-jdbc.html), +even if it is not mentioned in this documentation. **Option names should be in** `camelCase`! + +The set of supported options depends on Spark version. + +```python +from onetl.connection import MSSQL + +options = MSSQL.ReadOptions( + partitioning_mode="range", + partitionColumn="reg_id", + numPartitions=10, + customSparkOption="value", +) +``` + + + +#### *field* partition_column *: str | None* *= None* *(alias 'partitionColumn')* + +Column used to parallelize reading from a table. + +#### WARNING +It is highly recommended to use primary key, or column with an index +to avoid performance issues. + +#### NOTE +Column type depends on [`partitioning_mode`](#onetl.connection.db_connection.mssql.options.MSSQLReadOptions.partitioning_mode). + +* `partitioning_mode="range"` requires column to be an integer, date or timestamp (can be NULL, but not recommended). +* `partitioning_mode="hash"` accepts any column type (NOT NULL). +* `partitioning_mode="mod"` requires column to be an integer (NOT NULL). + +See documentation for [`partitioning_mode`](#onetl.connection.db_connection.mssql.options.MSSQLReadOptions.partitioning_mode) for more details + + + +#### *field* num_partitions *: PositiveInt* *= 1* *(alias 'numPartitions')* + +Number of jobs created by Spark to read the table content in parallel. +See documentation for [`partitioning_mode`](#onetl.connection.db_connection.mssql.options.MSSQLReadOptions.partitioning_mode) for more details + + + +#### *field* lower_bound *: int | None* *= None* *(alias 'lowerBound')* + +See documentation for [`partitioning_mode`](#onetl.connection.db_connection.mssql.options.MSSQLReadOptions.partitioning_mode) for more details + + + +#### *field* upper_bound *: int | None* *= None* *(alias 'upperBound')* + +See documentation for [`partitioning_mode`](#onetl.connection.db_connection.mssql.options.MSSQLReadOptions.partitioning_mode) for more details + + + +#### *field* session_init_statement *: str | None* *= None* *(alias 'sessionInitStatement')* + +After each database session is opened to the remote DB and before starting to read data, +this option executes a custom SQL statement (or a PL/SQL block). + +Use this to implement session initialization code. + +Example: + +```python +sessionInitStatement = """ + BEGIN + execute immediate + 'alter session set "_serial_direct_read"=true'; + END; +""" +``` + + + +#### *field* query_timeout *: int | None* *= None* *(alias 'queryTimeout')* + +The number of seconds the driver will wait for a statement to execute. +Zero means there is no limit. + +This option depends on driver implementation, +some drivers can check the timeout of each query instead of an entire JDBC batch. + + + +#### *field* fetchsize *: int* *= 100000* + +Fetch N rows from an opened cursor per one read round. + +Tuning this option can influence performance of reading. + +#### WARNING +Default value is different from Spark. + +Spark uses driver’s own value, and it may be different in different drivers, +and even versions of the same driver. For example, Oracle has +default `fetchsize=10`, which is absolutely not usable. + +Thus we’ve overridden default value with `100_000`, which should increase reading performance. + +#### Versionchanged +Changed in version 0.2.0: Set explicit default value to `100_000` + + + +#### *field* partitioning_mode *: JDBCPartitioningMode* *= JDBCPartitioningMode.RANGE* + +Defines how Spark will parallelize reading from table. + +Possible values: + +* `range` (default) + : Allocate each executor a range of values from column passed into [`partition_column`](#onetl.connection.db_connection.mssql.options.MSSQLReadOptions.partition_column). +
+ ### Spark generates for each executor an SQL query +
+ Executor 1: + ```sql + SELECT ... FROM table + WHERE (partition_column >= lowerBound + OR partition_column IS NULL) + AND partition_column < (lower_bound + stride) + ``` +
+ Executor 2: + ```sql + SELECT ... FROM table + WHERE partition_column >= (lower_bound + stride) + AND partition_column < (lower_bound + 2 * stride) + ``` +
+ … +
+ Executor N: + ```sql + SELECT ... FROM table + WHERE partition_column >= (lower_bound + (N-1) * stride) + AND partition_column <= upper_bound + ``` +
+ Where `stride=(upper_bound - lower_bound) / num_partitions`. +
+ #### NOTE + Can be used only with columns of integer, date or timestamp types. +
+ #### NOTE + [`lower_bound`](#onetl.connection.db_connection.mssql.options.MSSQLReadOptions.lower_bound), [`upper_bound`](#onetl.connection.db_connection.mssql.options.MSSQLReadOptions.upper_bound) and [`num_partitions`](#onetl.connection.db_connection.mssql.options.MSSQLReadOptions.num_partitions) are used just to + calculate the partition stride, **NOT** for filtering the rows in table. + So all rows in the table will be returned (unlike *Incremental* [Read Strategies](../../../strategy/index.md#strategy)). +
+ #### NOTE + All queries are executed in parallel. To execute them sequentially, use *Batch* [Read Strategies](../../../strategy/index.md#strategy). +* `hash` + : Allocate each executor a set of values based on hash of the [`partition_column`](#onetl.connection.db_connection.mssql.options.MSSQLReadOptions.partition_column) column. +
+ ### Spark generates for each executor an SQL query +
+ Executor 1: + ```sql + SELECT ... FROM table + WHERE (some_hash(partition_column) mod num_partitions) = 0 -- lower_bound + ``` +
+ Executor 2: + ```sql + SELECT ... FROM table + WHERE (some_hash(partition_column) mod num_partitions) = 1 -- lower_bound + 1 + ``` +
+ … +
+ Executor N: + ```sql + SELECT ... FROM table + WHERE (some_hash(partition_column) mod num_partitions) = num_partitions-1 -- upper_bound + ``` +
+ #### NOTE + The hash function implementation depends on RDBMS. It can be `MD5` or any other fast hash function, + or expression based on this function call. Usually such functions accepts any column type as an input. +* `mod` + : Allocate each executor a set of values based on modulus of the [`partition_column`](#onetl.connection.db_connection.mssql.options.MSSQLReadOptions.partition_column) column. +
+ ### Spark generates for each executor an SQL query +
+ Executor 1: + ```sql + SELECT ... FROM table + WHERE (partition_column mod num_partitions) = 0 -- lower_bound + ``` +
+ Executor 2: + ```sql + SELECT ... FROM table + WHERE (partition_column mod num_partitions) = 1 -- lower_bound + 1 + ``` +
+ Executor N: + ```sql + SELECT ... FROM table + WHERE (partition_column mod num_partitions) = num_partitions-1 -- upper_bound + ``` +
+ #### NOTE + Can be used only with columns of integer type. + +#### Versionadded +Added in version 0.5.0. + +### Examples + +Read data in 10 parallel jobs by range of values in `id_column` column: + +```python +ReadOptions( + partitioning_mode="range", # default mode, can be omitted + partitionColumn="id_column", + numPartitions=10, + # Options below can be discarded because they are + # calculated automatically as MIN and MAX values of `partitionColumn` + lowerBound=0, + upperBound=100_000, +) +``` + +Read data in 10 parallel jobs by hash of values in `some_column` column: + +```python +ReadOptions( + partitioning_mode="hash", + partitionColumn="some_column", + numPartitions=10, + # lowerBound and upperBound are automatically set to `0` and `9` +) +``` + +Read data in 10 parallel jobs by modulus of values in `id_column` column: + +```python +ReadOptions( + partitioning_mode="mod", + partitionColumn="id_column", + numPartitions=10, + # lowerBound and upperBound are automatically set to `0` and `9` +) +``` + + diff --git a/mddocs/connection/db_connection/mssql/sql.md b/mddocs/connection/db_connection/mssql/sql.md new file mode 100644 index 000000000..93d2fb3ef --- /dev/null +++ b/mddocs/connection/db_connection/mssql/sql.md @@ -0,0 +1,191 @@ + + +# Reading from MSSQL using `MSSQL.sql` + +`MSSQL.sql` allows passing custom SQL query, but does not support incremental strategies. + +#### WARNING +Please take into account [MSSQL <-> Spark type mapping](types.md#mssql-types) + +#### WARNING +Statement is executed in **read-write** connection, so if you’re calling some functions/procedures with DDL/DML statements inside, +they can change data in your database. + +## Syntax support + +Only queries with the following syntax are supported: + +* ✅︎ `SELECT ... FROM ...` +* ❌ `WITH alias AS (...) SELECT ...` +* ❌ `SET ...; SELECT ...;` - multiple statements not supported + +## Examples + +```python +from onetl.connection import MSSQL + +mssql = MSSQL(...) +df = mssql.sql( + """ + SELECT + id, + key, + CAST(value AS text) value, + updated_at + FROM + some.mytable + WHERE + key = 'something' + """, + options=MSSQL.SQLOptions( + partitionColumn="id", + numPartitions=10, + lowerBound=0, + upperBound=1000, + ), +) +``` + +## Recommendations + +### Select only required columns + +Instead of passing `SELECT * FROM ...` prefer passing exact column names `SELECT col1, col2, ...`. +This reduces the amount of data passed from MSSQL to Spark. + +### Pay attention to `where` value + +Instead of filtering data on Spark side using `df.filter(df.column == 'value')` pass proper `WHERE column = 'value'` clause. +This both reduces the amount of data send from MSSQL to Spark, and may also improve performance of the query. +Especially if there are indexes or partitions for columns used in `where` clause. + +## Options + +### *pydantic model* onetl.connection.db_connection.mssql.options.MSSQLSQLOptions + +Options specifically for SQL queries + +These options allow you to specify configurations for executing SQL queries +without relying on Spark’s partitioning mechanisms. + +#### Versionadded +Added in version 0.11.0: Split up `MSSQL.ReadOptions` to `MSSQL.SQLOptions` + +### Examples + +#### NOTE +You can pass any JDBC configuration +[supported by Spark](https://spark.apache.org/docs/latest/sql-data-sources-jdbc.html), +tailored to optimize SQL query execution. **Option names should be in** `camelCase`! + +```python +from onetl.connection import MSSQL + +options = MSSQL.SQLOptions( + partitionColumn="reg_id", + numPartitions=10, + lowerBound=0, + upperBound=1000, + customSparkOption="value", +) +``` + + + +#### *field* partition_column *: str | None* *= None* *(alias 'partitionColumn')* + +Column used to partition data across multiple executors for parallel query processing. + +#### WARNING +It is highly recommended to use primary key, or column with an index +to avoid performance issues. + +### Example of using `partitionColumn="id"` with `partitioning_mode="range"` + +```sql +-- If partition_column is 'id', with numPartitions=4, lowerBound=1, and upperBound=100: +-- Executor 1 processes IDs from 1 to 25 +SELECT ... FROM table WHERE id >= 1 AND id < 26 +-- Executor 2 processes IDs from 26 to 50 +SELECT ... FROM table WHERE id >= 26 AND id < 51 +-- Executor 3 processes IDs from 51 to 75 +SELECT ... FROM table WHERE id >= 51 AND id < 76 +-- Executor 4 processes IDs from 76 to 100 +SELECT ... FROM table WHERE id >= 76 AND id <= 100 + +-- General case for Executor N +SELECT ... FROM table +WHERE partition_column >= (lowerBound + (N-1) * stride) +AND partition_column <= upperBound +-- Where ``stride`` is calculated as ``(upperBound - lowerBound) / numPartitions``. +``` + + + +#### *field* num_partitions *: int | None* *= None* *(alias 'numPartitions')* + +Number of jobs created by Spark to read the table content in parallel. + + + +#### *field* lower_bound *: int | None* *= None* *(alias 'lowerBound')* + +Defines the starting boundary for partitioning the query’s data. Mandatory if [`partition_column`](#onetl.connection.db_connection.mssql.options.MSSQLSQLOptions.partition_column) is set + + + +#### *field* upper_bound *: int | None* *= None* *(alias 'upperBound')* + +Sets the ending boundary for data partitioning. Mandatory if [`partition_column`](#onetl.connection.db_connection.mssql.options.MSSQLSQLOptions.partition_column) is set + + + +#### *field* session_init_statement *: str | None* *= None* *(alias 'sessionInitStatement')* + +After each database session is opened to the remote DB and before starting to read data, +this option executes a custom SQL statement (or a PL/SQL block). + +Use this to implement session initialization code. + +Example: + +```python +sessionInitStatement = """ + BEGIN + execute immediate + 'alter session set "_serial_direct_read"=true'; + END; +""" +``` + + + +#### *field* query_timeout *: int | None* *= None* *(alias 'queryTimeout')* + +The number of seconds the driver will wait for a statement to execute. +Zero means there is no limit. + +This option depends on driver implementation, +some drivers can check the timeout of each query instead of an entire JDBC batch. + + + +#### *field* fetchsize *: int* *= 100000* + +Fetch N rows from an opened cursor per one read round. + +Tuning this option can influence performance of reading. + +#### WARNING +Default value is different from Spark. + +Spark uses driver’s own value, and it may be different in different drivers, +and even versions of the same driver. For example, Oracle has +default `fetchsize=10`, which is absolutely not usable. + +Thus we’ve overridden default value with `100_000`, which should increase reading performance. + +#### Versionchanged +Changed in version 0.2.0: Set explicit default value to `100_000` + + diff --git a/mddocs/connection/db_connection/mssql/types.md b/mddocs/connection/db_connection/mssql/types.md new file mode 100644 index 000000000..5981107ad --- /dev/null +++ b/mddocs/connection/db_connection/mssql/types.md @@ -0,0 +1,281 @@ + + +# MSSQL <-> Spark type mapping + +#### NOTE +The results below are valid for Spark 3.5.5, and may differ on other Spark versions. + +## Type detection & casting + +Spark’s DataFrames always have a `schema` which is a list of columns with corresponding Spark types. All operations on a column are performed using column type. + +### Reading from MSSQL + +This is how MSSQL connector performs this: + +* For each column in query result (`SELECT column1, column2, ... FROM table ...`) get column name and MSSQL type. +* Find corresponding `MSSQL type (read)` → `Spark type` combination (see below) for each DataFrame column. If no combination is found, raise exception. +* Create DataFrame from query with specific column names and Spark types. + +### Writing to some existing MSSQL table + +This is how MSSQL connector performs this: + +* Get names of columns in DataFrame. [1](#id3) +* Perform `SELECT * FROM table LIMIT 0` query. +* Take only columns present in DataFrame (by name, case insensitive). For each found column get MSSQL type. +* Find corresponding `Spark type` → `MSSQL type (write)` combination (see below) for each DataFrame column. If no combination is found, raise exception. +* If `MSSQL type (write)` match `MSSQL type (read)`, no additional casts will be performed, DataFrame column will be written to MSSQL as is. +* If `MSSQL type (write)` does not match `MSSQL type (read)`, DataFrame column will be casted to target column type **on MSSQL side**. + For example, you can write column with text data to `int` column, if column contains valid integer values within supported value range and precision [2](#id4). + +* **[1]** This allows to write data to tables with `DEFAULT` and `GENERATED` columns - if DataFrame has no such column, it will be populated by MSSQL. +* **[2]** This is true only if DataFrame column is a `StringType()`, because text value is parsed automatically to target column type. But other types cannot be silently converted, like `int -> text`. This requires explicit casting, see [DBWriter](). + +### Create new table using Spark + +#### WARNING +ABSOLUTELY NOT RECOMMENDED! + +This is how MSSQL connector performs this: + +* Find corresponding `Spark type` → `MSSQL type (create)` combination (see below) for each DataFrame column. If no combination is found, raise exception. +* Generate DDL for creating table in MSSQL, like `CREATE TABLE (col1 ...)`, and run it. +* Write DataFrame to created table as is. + +But some cases this may lead to using wrong column type. For example, Spark creates column of type `timestamp` +which corresponds to MSSQL’s type `timestamp(0)` (precision up to seconds) +instead of more precise `timestamp(6)` (precision up to nanoseconds). +This may lead to incidental precision loss, or sometimes data cannot be written to created table at all. + +So instead of relying on Spark to create tables: + +### See example + +```python +writer = DBWriter( + connection=mssql, + target="myschema.target_tbl", + options=MSSQL.WriteOptions( + if_exists="append", + ), +) +writer.run(df) +``` + +Always prefer creating tables with specific types **BEFORE WRITING DATA**: + +### See example + +```python +mssql.execute( + """ + CREATE TABLE schema.table ( + id bigint, + key text, + value datetime2(6) -- specific type and precision + ) + """, +) + +writer = DBWriter( + connection=mssql, + target="myschema.target_tbl", + options=MSSQL.WriteOptions(if_exists="append"), +) +writer.run(df) +``` + +### References + +Here you can find source code with type conversions: + +* [MSSQL -> JDBC](https://github.com/microsoft/mssql-jdbc/blob/v12.2.0/src/main/java/com/microsoft/sqlserver/jdbc/SQLServerResultSetMetaData.java#L117-L170) +* [JDBC -> Spark](https://github.com/apache/spark/blob/v3.5.5/sql/core/src/main/scala/org/apache/spark/sql/jdbc/MsSqlServerDialect.scala#L135-L152) +* [Spark -> JDBC](https://github.com/apache/spark/blob/v3.5.5/sql/core/src/main/scala/org/apache/spark/sql/jdbc/MsSqlServerDialect.scala#L154-L163) +* [JDBC -> MSSQL](https://github.com/microsoft/mssql-jdbc/blob/v12.2.0/src/main/java/com/microsoft/sqlserver/jdbc/DataTypes.java#L625-L676) + +## Supported types + +See [official documentation](https://learn.microsoft.com/en-us/sql/t-sql/data-types/data-types-transact-sql) + +### Numeric types + +| MSSQL type (read) | Spark type | MSSQL type (write) | MSSQL type (create) | +|-----------------------------|---------------------------------|-----------------------------|-----------------------------| +| `decimal` | `DecimalType(P=18, S=0)` | `decimal(P=18, S=0)` | `decimal(P=18, S=0)` | +| `decimal(P=0..38)` | `DecimalType(P=0..38, S=0)` | `decimal(P=0..38, S=0)` | `decimal(P=0..38, S=0)` | +| `decimal(P=0..38, S=0..38)` | `DecimalType(P=0..38, S=0..38)` | `decimal(P=0..38, S=0..38)` | `decimal(P=0..38, S=0..38)` | +| `real` | `FloatType()` | `real` | `real` | +| `float` | `DoubleType()` | `float` | `float` | +| `smallint` | `ShortType()` | `smallint` | `smallint` | +| `tinyint` | `IntegerType()` | `int` | `int` | +| `int` | | | | +| `bigint` | `LongType()` | `bigint` | `bigint` | + +### Temporal types + +#### NOTE +MSSQL `timestamp` type is alias for `rowversion` (see [Special types]()). It is not a temporal type! + +| MSSQL type (read) | Spark type | MSSQL type (write) | MSSQL type (create) | +|-------------------------------------|----------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------|------------------------------------------------------------------------| +| `date` | `DateType()` | `date` | `date` | +| `smalldatetime`, minutes | `TimestampType()`, microseconds | `datetime2(6)`, microseconds | `datetime`, milliseconds | +| `datetime`, milliseconds | | | | +| `datetime2(0)`, seconds | | | | +| `datetime2(3)`, milliseconds | | | | +| `datetime2(6)`, microseconds | `TimestampType()`, microseconds | `datetime2(6)`, microseconds | `datetime`, milliseconds,
**precision loss** [3](#id14) | +| `datetime2(7)`, 100s of nanoseconds | `TimestampType()`, microseconds,
**precision loss** [4](#id15) | `datetime2(6)`, microseconds,
**precision loss** [4](#id15) | | +| `time(0)`, seconds | `TimestampType()`, microseconds,
with time format quirks [5](#id16) | `datetime2(6)`, microseconds | `datetime`, milliseconds | +| `time(3)`, milliseconds | | | | +| `time(6)`, microseconds | `TimestampType()`, microseconds,
with time format quirks [5](#id16) | `datetime2(6)`, microseconds | `datetime`, milliseconds,
**precision loss** [3](#id14) | +| | | | | +| `time`, 100s of nanoseconds | `TimestampType()`, microseconds,
**precision loss** [4](#id15),
with time format quirks [5](#id16) | `datetime2(6)`, microseconds
**precision loss** [3](#id14) | | +| `time(7)`, 100s of nanoseconds | | | | +| `datetimeoffset` | `StringType()` | `nvarchar` | `nvarchar` | + +#### WARNING +Note that types in MSSQL and Spark have different value ranges: + +| MySQL type | Min value | Max value | Spark type | Min value | Max value | +|-----------------|------------------------------|------------------------------|-------------------|------------------------------|------------------------------| +| `smalldatetime` | `1900-01-01 00:00:00` | `2079-06-06 23:59:00` | `TimestampType()` | `0001-01-01 00:00:00.000000` | `9999-12-31 23:59:59.999999` | +| `datetime` | `1753-01-01 00:00:00.000` | `9999-12-31 23:59:59.997` | | | | +| `datetime2` | `0001-01-01 00:00:00.000000` | `9999-12-31 23:59:59.999999` | | | | +| `time` | `00:00:00.0000000` | `23:59:59.9999999` | | | | + +So not all of values in Spark DataFrame can be written to MSSQL. + +References: +: * [MSSQL date & time types documentation](https://learn.microsoft.com/en-us/sql/t-sql/data-types/date-and-time-types) + * [Spark DateType documentation](https://spark.apache.org/docs/latest/api/java/org/apache/spark/sql/types/DateType.html) + * [Spark TimestampType documentation](https://spark.apache.org/docs/latest/api/java/org/apache/spark/sql/types/TimestampType.html) + +* **[3]** MSSQL dialect for Spark generates DDL with type `datetime` which has precision up to milliseconds (`23:59:59.999`, 10-3 seconds). Inserting data with microsecond and higher precision (`23:59:59.999999` .. `23.59:59.9999999`, 10-6 .. 10-7 seconds) will lead to **throwing away microseconds**. +* **[4]** MSSQL support timestamp up to 100s of nanoseconds precision (`23:59:59.9999999999`, 10-7 seconds), but Spark `TimestampType()` supports datetime up to microseconds precision (`23:59:59.999999`, 10-6 seconds). Last digit will be lost during read or write operations. +* **[5]** `time` type is the same as `datetime2` with date `1970-01-01`. So instead of reading data from MSSQL like `23:59:59.999999` it is actually read `1970-01-01 23:59:59.999999`, and vice versa. + +### String types + +| MSSQL type (read) | Spark type | MSSQL type (write) | MSSQL type (create) | +|---------------------|----------------|----------------------|-----------------------| +| `char` | `StringType()` | `nvarchar` | `nvarchar` | +| `char(N)` | | | | +| `nchar` | | | | +| `nchar(N)` | | | | +| `varchar` | | | | +| `varchar(N)` | | | | +| `nvarchar` | | | | +| `nvarchar(N)` | | | | +| `mediumtext` | | | | +| `text` | | | | +| `ntext` | | | | +| `xml` | | | | + +### Binary types + +| MSSQL type (read) | Spark type | MSSQL type (write) | MSSQL type (create) | +|---------------------|-----------------|----------------------|-----------------------| +| `bit` | `BooleanType()` | `bit` | `bit` | +| `binary` | `BinaryType()` | `varbinary` | `varbinary` | +| `binary(N)` | | | | +| `varbinary` | | | | +| `varbinary(N)` | | | | +| `image` | | | | + +### Special types + +| MSSQL type (read) | Spark type | MSSQL type (write) | MSSQL type (create) | +|---------------------|----------------|----------------------|-----------------------| +| `geography` | `BinaryType()` | `varbinary` | `varbinary` | +| `geometry` | | | | +| `hierarchyid` | | | | +| `rowversion` | | | | +| `sql_variant` | unsupported | | | +| `sysname` | `StringType()` | `nvarchar` | `nvarchar` | +| `uniqueidentifier` | | | | + +## Explicit type cast + +### `DBReader` + +It is possible to explicitly cast column type using `DBReader(columns=...)` syntax. + +For example, you can use `CAST(column AS text)` to convert data to string representation on MSSQL side, and so it will be read as Spark’s `StringType()`: + +```python +from onetl.connection import MSSQL +from onetl.db import DBReader + +mssql = MSSQL(...) + +DBReader( + connection=mssql, + columns=[ + "id", + "supported_column", + "CAST(unsupported_column AS text) unsupported_column_str", + ], +) +df = reader.run() + +# cast column content to proper Spark type +df = df.select( + df.id, + df.supported_column, + # explicit cast + df.unsupported_column_str.cast("integer").alias("parsed_integer"), +) +``` + +### `DBWriter` + +Convert dataframe column to JSON using [to_json](https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.functions.to_json.html), +and write it as `text` column in MSSQL: + +```python +mssql.execute( + """ + CREATE TABLE schema.target_tbl ( + id bigint, + struct_column_json text -- any string type, actually + ) + """, +) + +from pyspark.sql.functions import to_json + +df = df.select( + df.id, + to_json(df.struct_column).alias("struct_column_json"), +) + +writer.run(df) +``` + +Then you can parse this column on MSSQL side - for example, by creating a view: + +```sql +SELECT + id, + JSON_VALUE(struct_column_json, "$.nested.field") AS nested_field +FROM target_tbl +``` + +Or by using [computed column](https://learn.microsoft.com/en-us/sql/relational-databases/tables/specify-computed-columns-in-a-table): + +```sql +CREATE TABLE schema.target_table ( + id bigint, + supported_column datetime2(6), + struct_column_json text, -- any string type, actually + -- computed column + nested_field AS (JSON_VALUE(struct_column_json, "$.nested.field")) + -- or persisted column + -- nested_field AS (JSON_VALUE(struct_column_json, "$.nested.field")) PERSISTED +) +``` + +By default, column value is calculated on every table read. +Column marked as `PERSISTED` is calculated during insert, but this require additional space. diff --git a/mddocs/connection/db_connection/mssql/write.md b/mddocs/connection/db_connection/mssql/write.md new file mode 100644 index 000000000..d29aca762 --- /dev/null +++ b/mddocs/connection/db_connection/mssql/write.md @@ -0,0 +1,183 @@ + + +# Writing to MSSQL using `DBWriter` + +For writing data to MSSQL, use [`DBWriter`](../../../db/db_writer.md#onetl.db.db_writer.db_writer.DBWriter). + +#### WARNING +Please take into account [MSSQL <-> Spark type mapping](types.md#mssql-types) + +#### WARNING +It is always recommended to create table explicitly using [MSSQL.execute](execute.md#mssql-execute) +instead of relying on Spark’s table DDL generation. + +This is because Spark’s DDL generator can create columns with different precision and types than it is expected, +causing precision loss or other issues. + +## Examples + +```python +from onetl.connection import MSSQL +from onetl.db import DBWriter + +mssql = MSSQL(...) + +df = ... # data is here + +writer = DBWriter( + connection=mssql, + target="schema.table", + options=MSSQL.WriteOptions(if_exists="append"), +) + +writer.run(df) +``` + +## Options + +Method above accepts [`MSSQL.WriteOptions`](#onetl.connection.db_connection.mssql.options.MSSQLWriteOptions) + +### *pydantic model* onetl.connection.db_connection.mssql.options.MSSQLWriteOptions + +Spark JDBC writing options. + +#### Versionadded +Added in version 0.5.0: Replace `MSSQL.Options` → `MSSQL.WriteOptions` + +### Examples + +#### NOTE +You can pass any value +[supported by Spark](https://spark.apache.org/docs/latest/sql-data-sources-jdbc.html), +even if it is not mentioned in this documentation. **Option names should be in** `camelCase`! + +The set of supported options depends on Spark version. + +```python +from onetl.connection import MSSQL + +options = MSSQL.WriteOptions( + if_exists="append", + batchsize=20_000, + customSparkOption="value", +) +``` + + + +#### *field* if_exists *: JDBCTableExistBehavior* *= JDBCTableExistBehavior.APPEND* *(alias 'mode')* + +Behavior of writing data into existing table. + +Possible values: +: * `append` (default) + : Adds new rows into existing table. +
+ ### Behavior in details +
+ * Table does not exist + : Table is created using options provided by user + (`createTableOptions`, `createTableColumnTypes`, etc). + * Table exists + : Data is appended to a table. Table has the same DDL as before writing data +
+ #### WARNING + This mode does not check whether table already contains + rows from dataframe, so duplicated rows can be created. +
+ Also Spark does not support passing custom options to + insert statement, like `ON CONFLICT`, so don’t try to + implement deduplication using unique indexes or constraints. +
+ Instead, write to staging table and perform deduplication + using `execute` method. + * `replace_entire_table` + : **Table is dropped and then created, or truncated**. +
+ ### Behavior in details +
+ * Table does not exist + : Table is created using options provided by user + (`createTableOptions`, `createTableColumnTypes`, etc). + * Table exists + : Table content is replaced with dataframe content. +
+ After writing completed, target table could either have the same DDL as + before writing data (`truncate=True`), or can be recreated (`truncate=False` + or source does not support truncation). + * `ignore` + : Ignores the write operation if the table already exists. +
+ ### Behavior in details +
+ * Table does not exist + : Table is created using options provided by user + (`createTableOptions`, `createTableColumnTypes`, etc). + * Table exists + : The write operation is ignored, and no data is written to the table. + * `error` + : Raises an error if the table already exists. +
+ ### Behavior in details +
+ * Table does not exist + : Table is created using options provided by user + (`createTableOptions`, `createTableColumnTypes`, etc). + * Table exists + : An error is raised, and no data is written to the table. + +#### Versionchanged +Changed in version 0.9.0: Renamed `mode` → `if_exists` + + + +#### *field* query_timeout *: int | None* *= None* *(alias 'queryTimeout')* + +The number of seconds the driver will wait for a statement to execute. +Zero means there is no limit. + +This option depends on driver implementation, +some drivers can check the timeout of each query instead of an entire JDBC batch. + + + +#### *field* batchsize *: int* *= 20000* + +How many rows can be inserted per round trip. + +Tuning this option can influence performance of writing. + +#### WARNING +Default value is different from Spark. + +Spark uses quite small value `1000`, which is absolutely not usable +in BigData world. + +Thus we’ve overridden default value with `20_000`, +which should increase writing performance. + +You can increase it even more, up to `50_000`, +but it depends on your database load and number of columns in the row. +Higher values does not increase performance. + +#### Versionchanged +Changed in version 0.4.0: Changed default value from 1000 to 20_000 + + + +#### *field* isolation_level *: str* *= 'READ_UNCOMMITTED'* *(alias 'isolationLevel')* + +The transaction isolation level, which applies to current connection. + +Possible values: +: * `NONE` (as string, not Python’s `None`) + * `READ_COMMITTED` + * `READ_UNCOMMITTED` + * `REPEATABLE_READ` + * `SERIALIZABLE` + +Values correspond to transaction isolation levels defined by JDBC standard. +Please refer the documentation for +[java.sql.Connection](https://docs.oracle.com/javase/8/docs/api/java/sql/Connection.html). + + diff --git a/mddocs/connection/db_connection/mysql/connection.md b/mddocs/connection/db_connection/mysql/connection.md new file mode 100644 index 000000000..93bb348e7 --- /dev/null +++ b/mddocs/connection/db_connection/mysql/connection.md @@ -0,0 +1,120 @@ + + +# MySQL connection + +### *class* onetl.connection.db_connection.mysql.connection.MySQL(\*, spark: SparkSession, user: str, password: SecretStr, host: Host, port: int = 3306, database: str | None = None, extra: MySQLExtra = MySQLExtra(useUnicode='yes', characterEncoding='UTF-8')) + +MySQL JDBC connection. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +Based on Maven package [com.mysql:mysql-connector-j:9.2.0](https://mvnrepository.com/artifact/com.mysql/mysql-connector-j/9.2.0) +([official MySQL JDBC driver](https://dev.mysql.com/doc/connector-j/en/)). + +#### SEE ALSO +Before using this connector please take into account [Prerequisites](prerequisites.md#mysql-prerequisites) + +#### Versionadded +Added in version 0.1.0. + +* **Parameters:** + **host** + : Host of MySQL database. For example: `mysql0012.domain.com` or `192.168.1.11` + + **port** + : Port of MySQL database + + **user** + : User, which have proper access to the database. For example: `some_user` + + **password** + : Password for database connection + + **database** + : Database in RDBMS, NOT schema. +
+ See [this page](https://www.educba.com/postgresql-database-vs-schema/) for more details + + **spark** + : Spark session. + + **extra** + : Specifies one or more extra parameters by which clients can connect to the instance. +
+ For example: `{"useSSL": "false", "allowPublicKeyRetrieval": "true"}` +
+ See [MySQL JDBC driver properties documentation](https://dev.mysql.com/doc/connector-j/en/connector-j-reference-configuration-properties.html) + for more details + +### Examples + +Create and check MySQL connection: + +```python +from onetl.connection import MySQL +from pyspark.sql import SparkSession + +# Create Spark session with MySQL driver loaded +maven_packages = MySQL.get_packages() +spark = ( + SparkSession.builder.appName("spark-app-name") + .config("spark.jars.packages", ",".join(maven_packages)) + .getOrCreate() +) + +# Create connection +mysql = MySQL( + host="database.host.or.ip", + user="user", + password="*****", + extra={"useSSL": "false", "allowPublicKeyRetrieval": "true"}, + spark=spark, +).check() +``` + + + +#### check() + +Check source availability. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +If not, an exception will be raised. + +* **Returns:** + Connection itself +* **Raises:** + RuntimeError + : If the connection is not available + +### Examples + +```python +connection.check() +``` + + + +#### *classmethod* get_packages(package_version: str | None = None) → list[str] + +Get package names to be downloaded by Spark. Allows specifying a custom JDBC driver version for MySQL. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### Versionadded +Added in version 0.9.0. + +* **Parameters:** + **package_version** + : Specifies the version of the MySQL JDBC driver to use. Defaults to `9.2.0`. +
+ #### Versionadded + Added in version 0.11.0. + +### Examples + +```python +from onetl.connection import MySQL + +MySQL.get_packages() + +# specify a custom package version +MySQL.get_packages(package_version="8.2.0") +``` + + diff --git a/mddocs/connection/db_connection/mysql/execute.md b/mddocs/connection/db_connection/mysql/execute.md new file mode 100644 index 000000000..fb345665f --- /dev/null +++ b/mddocs/connection/db_connection/mysql/execute.md @@ -0,0 +1,191 @@ + + +# Executing statements in MySQL + +#### WARNING +Methods below **read all the rows** returned from DB **to Spark driver memory**, and then convert them to DataFrame. + +Do **NOT** use them to read large amounts of data. Use [DBReader](read.md#mysql-read) or [MySQL.sql](sql.md#mysql-sql) instead. + +## How to + +There are 2 ways to execute some statement in MySQL + +### Use `MySQL.fetch` + +Use this method to perform some `SELECT` query which returns **small number or rows**, like reading +MySQL config, or reading data from some reference table. Method returns Spark DataFrame. + +Method accepts [`MySQL.FetchOptions`](#onetl.connection.db_connection.mysql.options.MySQLFetchOptions). + +Connection opened using this method should be then closed with `connection.close()` or `with connection:`. + +#### WARNING +Please take into account [MySQL <-> Spark type mapping](types.md#mysql-types). + +#### Syntax support + +This method supports **any** query syntax supported by MySQL, like: + +* ✅︎ `SELECT ... FROM ...` +* ✅︎ `WITH alias AS (...) SELECT ...` +* ✅︎ `SELECT func(arg1, arg2)` or `{?= call func(arg1, arg2)}` - special syntax for calling function +* ✅︎ `SHOW ...` +* ❌ `SET ...; SELECT ...;` - multiple statements not supported + +#### Examples + +```python +from onetl.connection import MySQL + +mysql = MySQL(...) + +df = mysql.fetch( + "SELECT value FROM some.reference_table WHERE key = 'some_constant'", + options=MySQL.FetchOptions(queryTimeout=10), +) +mysql.close() +value = df.collect()[0][0] # get value from first row and first column +``` + +### Use `MySQL.execute` + +Use this method to execute DDL and DML operations. Each method call runs operation in a separated transaction, and then commits it. + +Method accepts [`MySQL.ExecuteOptions`](#onetl.connection.db_connection.mysql.options.MySQLExecuteOptions). + +Connection opened using this method should be then closed with `connection.close()` or `with connection:`. + +#### Syntax support + +This method supports **any** query syntax supported by MySQL, like: + +* ✅︎ `CREATE TABLE ...`, `CREATE VIEW ...`, and so on +* ✅︎ `ALTER ...` +* ✅︎ `INSERT INTO ... SELECT ...`, `UPDATE ...`, `DELETE ...`, and so on +* ✅︎ `DROP TABLE ...`, `DROP VIEW ...`, and so on +* ✅︎ `CALL procedure(arg1, arg2) ...` or `{call procedure(arg1, arg2)}` - special syntax for calling procedure +* ✅︎ other statements not mentioned here +* ❌ `SET ...; SELECT ...;` - multiple statements not supported + +#### Examples + +```python +from onetl.connection import MySQL + +mysql = MySQL(...) + +mysql.execute("DROP TABLE schema.table") +mysql.execute( + """ + CREATE TABLE schema.table ( + id bigint, + key text, + value float + ) + ENGINE = InnoDB + """, + options=MySQL.ExecuteOptions(queryTimeout=10), +) +``` + +## Options + +### *pydantic model* onetl.connection.db_connection.mysql.options.MySQLFetchOptions + +Options related to fetching data from databases via JDBC. + +#### Versionadded +Added in version 0.11.0: Replace `MySQL.JDBCOptions` → `MySQL.FetchOptions` + +### Examples + +#### NOTE +You can pass any value supported by underlying JDBC driver class, +even if it is not mentioned in this documentation. + +```python +from onetl.connection import MySQL + +options = MySQL.FetchOptions( + queryTimeout=60_000, + fetchsize=100_000, + customSparkOption="value", +) +``` + + +* **Fields:** + - [`fetchsize (int | None)`](#onetl.connection.db_connection.mysql.options.MySQLFetchOptions.fetchsize) + - [`query_timeout (int | None)`](#onetl.connection.db_connection.mysql.options.MySQLFetchOptions.query_timeout) + +#### *field* query_timeout *: int | None* *= None* *(alias 'queryTimeout')* + +The number of seconds the driver will wait for a statement to execute. +Zero means there is no limit. + +This option depends on driver implementation, +some drivers can check the timeout of each query instead of an entire JDBC batch. + + + +#### *field* fetchsize *: int | None* *= None* + +How many rows to fetch per round trip. + +Tuning this option can influence performance of reading. + +#### WARNING +Default value depends on driver. For example, Oracle has +default `fetchsize=10`. + + + +### *pydantic model* onetl.connection.db_connection.mysql.options.MySQLExecuteOptions + +Options related to executing statements in databases via JDBC. + +#### Versionadded +Added in version 0.11.0: Replace `MySQL.JDBCOptions` → `MySQL.ExecuteOptions` + +### Examples + +#### NOTE +You can pass any value supported by underlying JDBC driver class, +even if it is not mentioned in this documentation. + +```python +from onetl.connection import MySQL + +options = MySQL.ExecuteOptions( + queryTimeout=60_000, + customSparkOption="value", +) +``` + + +* **Fields:** + - [`fetchsize (int | None)`](#onetl.connection.db_connection.mysql.options.MySQLExecuteOptions.fetchsize) + - [`query_timeout (int | None)`](#onetl.connection.db_connection.mysql.options.MySQLExecuteOptions.query_timeout) + +#### *field* query_timeout *: int | None* *= None* *(alias 'queryTimeout')* + +The number of seconds the driver will wait for a statement to execute. +Zero means there is no limit. + +This option depends on driver implementation, +some drivers can check the timeout of each query instead of an entire JDBC batch. + + + +#### *field* fetchsize *: int | None* *= None* + +How many rows to fetch per round trip. + +Tuning this option can influence performance of reading. + +#### WARNING +Default value depends on driver. For example, Oracle has +default `fetchsize=10`. + + diff --git a/mddocs/connection/db_connection/mysql/index.md b/mddocs/connection/db_connection/mysql/index.md new file mode 100644 index 000000000..6722ca77d --- /dev/null +++ b/mddocs/connection/db_connection/mysql/index.md @@ -0,0 +1,19 @@ + + +# MySQL + +# Connection + +* [Prerequisites](prerequisites.md) +* [MySQL connection](connection.md) + +# Operations + +* [Reading from MySQL using `DBReader`](read.md) +* [Reading from MySQL using `MySQL.sql`](sql.md) +* [Writing to MySQL using `DBWriter`](write.md) +* [Executing statements in MySQL](execute.md) + +# Troubleshooting + +* [MySQL <-> Spark type mapping](types.md) diff --git a/mddocs/connection/db_connection/mysql/prerequisites.md b/mddocs/connection/db_connection/mysql/prerequisites.md new file mode 100644 index 000000000..d0e551443 --- /dev/null +++ b/mddocs/connection/db_connection/mysql/prerequisites.md @@ -0,0 +1,61 @@ + + +# Prerequisites + +## Version Compatibility + +* MySQL server versions: + : * Officially declared: 8.0 - 9.2 + * Actually tested: 5.7.13, 9.2.0 +* Spark versions: 2.3.x - 3.5.x +* Java versions: 8 - 20 + +See [official documentation](https://dev.mysql.com/doc/connector-j/en/connector-j-versions.html). + +## Installing PySpark + +To use MySQL connector you should have PySpark installed (or injected to `sys.path`) +BEFORE creating the connector instance. + +See [Spark](../../../install/spark.md#install-spark) installation instruction for more details. + +## Connecting to MySQL + +### Connection host + +It is possible to connect to MySQL by using either DNS name of host or it’s IP address. + +If you’re using MySQL cluster, it is currently possible to connect only to **one specific node**. +Connecting to multiple nodes to perform load balancing, as well as automatic failover to new master/replica are not supported. + +### Connection port + +Connection is usually performed to port 3306. Port may differ for different MySQL instances. +Please ask your MySQL administrator to provide required information. + +### Required grants + +Ask your MySQL cluster administrator to set following grants for a user, +used for creating a connection: + +Read + Write + +```sql +-- allow creating tables in the target schema +GRANT CREATE ON myschema.* TO username@'192.168.1.%'; + +-- allow read & write access to specific table +GRANT SELECT, INSERT ON myschema.mytable TO username@'192.168.1.%'; +``` + +Read only + +```sql +-- allow read access to specific table +GRANT SELECT ON myschema.mytable TO username@'192.168.1.%'; +``` + +In example above `'192.168.1.%''` is a network subnet `192.168.1.0 - 192.168.1.255` +where Spark driver and executors are running. To allow connecting user from any IP, use `'%'` (not secure!). + +More details can be found in [official documentation](https://dev.mysql.com/doc/refman/en/grant.html). diff --git a/mddocs/connection/db_connection/mysql/read.md b/mddocs/connection/db_connection/mysql/read.md new file mode 100644 index 000000000..aa66a429d --- /dev/null +++ b/mddocs/connection/db_connection/mysql/read.md @@ -0,0 +1,352 @@ + + +# Reading from MySQL using `DBReader` + +[`DBReader`](../../../db/db_reader.md#onetl.db.db_reader.db_reader.DBReader) supports [Read Strategies](../../../strategy/index.md#strategy) for incremental data reading, +but does not support custom queries, like `JOIN`. + +#### WARNING +Please take into account [MySQL <-> Spark type mapping](types.md#mysql-types) + +## Supported DBReader features + +* ✅︎ `columns` +* ✅︎ `where` +* ✅︎ `hwm`, supported strategies: +* * ✅︎ [Snapshot Strategy](../../../strategy/snapshot_strategy.md#snapshot-strategy) +* * ✅︎ [Incremental Strategy](../../../strategy/incremental_strategy.md#incremental-strategy) +* * ✅︎ [Snapshot Batch Strategy](../../../strategy/snapshot_batch_strategy.md#snapshot-batch-strategy) +* * ✅︎ [Incremental Batch Strategy](../../../strategy/incremental_batch_strategy.md#incremental-batch-strategy) +* ✅︎ `hint` (see [official documentation](https://dev.mysql.com/doc/refman/en/optimizer-hints.html)) +* ❌ `df_schema` +* ✅︎ `options` (see [`MySQL.ReadOptions`](#onetl.connection.db_connection.mysql.options.MySQLReadOptions)) + +## Examples + +Snapshot strategy: + +```python +from onetl.connection import MySQL +from onetl.db import DBReader + +mysql = MySQL(...) + +reader = DBReader( + connection=mysql, + source="schema.table", + columns=["id", "key", "CAST(value AS text) value", "updated_dt"], + where="key = 'something'", + hint="SKIP_SCAN(schema.table key_index)", + options=MySQL.ReadOptions(partitionColumn="id", numPartitions=10), +) +df = reader.run() +``` + +Incremental strategy: + +```python +from onetl.connection import MySQL +from onetl.db import DBReader +from onetl.strategy import IncrementalStrategy + +mysql = MySQL(...) + +reader = DBReader( + connection=mysql, + source="schema.table", + columns=["id", "key", "CAST(value AS text) value", "updated_dt"], + where="key = 'something'", + hint="SKIP_SCAN(schema.table key_index)", + hwm=DBReader.AutoDetectHWM(name="mysql_hwm", expression="updated_dt"), + options=MySQL.ReadOptions(partitionColumn="id", numPartitions=10), +) + +with IncrementalStrategy(): + df = reader.run() +``` + +## Recommendations + +### Select only required columns + +Instead of passing `"*"` in `DBReader(columns=[...])` prefer passing exact column names. This reduces the amount of data passed from Oracle to Spark. + +### Pay attention to `where` value + +Instead of filtering data on Spark side using `df.filter(df.column == 'value')` pass proper `DBReader(where="column = 'value'")` clause. +This both reduces the amount of data send from Oracle to Spark, and may also improve performance of the query. +Especially if there are indexes for columns used in `where` clause. + +## Options + +### *pydantic model* onetl.connection.db_connection.mysql.options.MySQLReadOptions + +Spark JDBC reading options. + +#### Versionadded +Added in version 0.5.0: Replace `MySQL.Options` → `MySQL.ReadOptions` + +### Examples + +#### NOTE +You can pass any value +[supported by Spark](https://spark.apache.org/docs/latest/sql-data-sources-jdbc.html), +even if it is not mentioned in this documentation. **Option names should be in** `camelCase`! + +The set of supported options depends on Spark version. + +```python +from onetl.connection import MySQL + +options = MySQL.ReadOptions( + partitioning_mode="range", + partitionColumn="reg_id", + numPartitions=10, + customSparkOption="value", +) +``` + + +* **Fields:** + - [`fetchsize (int)`](#onetl.connection.db_connection.mysql.options.MySQLReadOptions.fetchsize) + - [`lower_bound (int | None)`](#onetl.connection.db_connection.mysql.options.MySQLReadOptions.lower_bound) + - [`num_partitions (pydantic.types.PositiveInt)`](#onetl.connection.db_connection.mysql.options.MySQLReadOptions.num_partitions) + - [`partition_column (str | None)`](#onetl.connection.db_connection.mysql.options.MySQLReadOptions.partition_column) + - [`partitioning_mode (onetl.connection.db_connection.jdbc_connection.options.JDBCPartitioningMode)`](#onetl.connection.db_connection.mysql.options.MySQLReadOptions.partitioning_mode) + - [`query_timeout (int | None)`](#onetl.connection.db_connection.mysql.options.MySQLReadOptions.query_timeout) + - [`session_init_statement (str | None)`](#onetl.connection.db_connection.mysql.options.MySQLReadOptions.session_init_statement) + - [`upper_bound (int | None)`](#onetl.connection.db_connection.mysql.options.MySQLReadOptions.upper_bound) + +#### *field* partition_column *: str | None* *= None* *(alias 'partitionColumn')* + +Column used to parallelize reading from a table. + +#### WARNING +It is highly recommended to use primary key, or column with an index +to avoid performance issues. + +#### NOTE +Column type depends on [`partitioning_mode`](#onetl.connection.db_connection.mysql.options.MySQLReadOptions.partitioning_mode). + +* `partitioning_mode="range"` requires column to be an integer, date or timestamp (can be NULL, but not recommended). +* `partitioning_mode="hash"` accepts any column type (NOT NULL). +* `partitioning_mode="mod"` requires column to be an integer (NOT NULL). + +See documentation for [`partitioning_mode`](#onetl.connection.db_connection.mysql.options.MySQLReadOptions.partitioning_mode) for more details + + + +#### *field* num_partitions *: PositiveInt* *= 1* *(alias 'numPartitions')* + +Number of jobs created by Spark to read the table content in parallel. +See documentation for [`partitioning_mode`](#onetl.connection.db_connection.mysql.options.MySQLReadOptions.partitioning_mode) for more details + + +* **Constraints:** + - **exclusiveMinimum** = 0 + +#### *field* lower_bound *: int | None* *= None* *(alias 'lowerBound')* + +See documentation for [`partitioning_mode`](#onetl.connection.db_connection.mysql.options.MySQLReadOptions.partitioning_mode) for more details + + + +#### *field* upper_bound *: int | None* *= None* *(alias 'upperBound')* + +See documentation for [`partitioning_mode`](#onetl.connection.db_connection.mysql.options.MySQLReadOptions.partitioning_mode) for more details + + + +#### *field* session_init_statement *: str | None* *= None* *(alias 'sessionInitStatement')* + +After each database session is opened to the remote DB and before starting to read data, +this option executes a custom SQL statement (or a PL/SQL block). + +Use this to implement session initialization code. + +Example: + +```python +sessionInitStatement = """ + BEGIN + execute immediate + 'alter session set "_serial_direct_read"=true'; + END; +""" +``` + + + +#### *field* query_timeout *: int | None* *= None* *(alias 'queryTimeout')* + +The number of seconds the driver will wait for a statement to execute. +Zero means there is no limit. + +This option depends on driver implementation, +some drivers can check the timeout of each query instead of an entire JDBC batch. + + + +#### *field* fetchsize *: int* *= 100000* + +Fetch N rows from an opened cursor per one read round. + +Tuning this option can influence performance of reading. + +#### WARNING +Default value is different from Spark. + +Spark uses driver’s own value, and it may be different in different drivers, +and even versions of the same driver. For example, Oracle has +default `fetchsize=10`, which is absolutely not usable. + +Thus we’ve overridden default value with `100_000`, which should increase reading performance. + +#### Versionchanged +Changed in version 0.2.0: Set explicit default value to `100_000` + + + +#### *field* partitioning_mode *: JDBCPartitioningMode* *= JDBCPartitioningMode.RANGE* + +Defines how Spark will parallelize reading from table. + +Possible values: + +* `range` (default) + : Allocate each executor a range of values from column passed into [`partition_column`](#onetl.connection.db_connection.mysql.options.MySQLReadOptions.partition_column). +
+ ### Spark generates for each executor an SQL query +
+ Executor 1: + ```sql + SELECT ... FROM table + WHERE (partition_column >= lowerBound + OR partition_column IS NULL) + AND partition_column < (lower_bound + stride) + ``` +
+ Executor 2: + ```sql + SELECT ... FROM table + WHERE partition_column >= (lower_bound + stride) + AND partition_column < (lower_bound + 2 * stride) + ``` +
+ … +
+ Executor N: + ```sql + SELECT ... FROM table + WHERE partition_column >= (lower_bound + (N-1) * stride) + AND partition_column <= upper_bound + ``` +
+ Where `stride=(upper_bound - lower_bound) / num_partitions`. +
+ #### NOTE + Can be used only with columns of integer, date or timestamp types. +
+ #### NOTE + [`lower_bound`](#onetl.connection.db_connection.mysql.options.MySQLReadOptions.lower_bound), [`upper_bound`](#onetl.connection.db_connection.mysql.options.MySQLReadOptions.upper_bound) and [`num_partitions`](#onetl.connection.db_connection.mysql.options.MySQLReadOptions.num_partitions) are used just to + calculate the partition stride, **NOT** for filtering the rows in table. + So all rows in the table will be returned (unlike *Incremental* [Read Strategies](../../../strategy/index.md#strategy)). +
+ #### NOTE + All queries are executed in parallel. To execute them sequentially, use *Batch* [Read Strategies](../../../strategy/index.md#strategy). +* `hash` + : Allocate each executor a set of values based on hash of the [`partition_column`](#onetl.connection.db_connection.mysql.options.MySQLReadOptions.partition_column) column. +
+ ### Spark generates for each executor an SQL query +
+ Executor 1: + ```sql + SELECT ... FROM table + WHERE (some_hash(partition_column) mod num_partitions) = 0 -- lower_bound + ``` +
+ Executor 2: + ```sql + SELECT ... FROM table + WHERE (some_hash(partition_column) mod num_partitions) = 1 -- lower_bound + 1 + ``` +
+ … +
+ Executor N: + ```sql + SELECT ... FROM table + WHERE (some_hash(partition_column) mod num_partitions) = num_partitions-1 -- upper_bound + ``` +
+ #### NOTE + The hash function implementation depends on RDBMS. It can be `MD5` or any other fast hash function, + or expression based on this function call. Usually such functions accepts any column type as an input. +* `mod` + : Allocate each executor a set of values based on modulus of the [`partition_column`](#onetl.connection.db_connection.mysql.options.MySQLReadOptions.partition_column) column. +
+ ### Spark generates for each executor an SQL query +
+ Executor 1: + ```sql + SELECT ... FROM table + WHERE (partition_column mod num_partitions) = 0 -- lower_bound + ``` +
+ Executor 2: + ```sql + SELECT ... FROM table + WHERE (partition_column mod num_partitions) = 1 -- lower_bound + 1 + ``` +
+ Executor N: + ```sql + SELECT ... FROM table + WHERE (partition_column mod num_partitions) = num_partitions-1 -- upper_bound + ``` +
+ #### NOTE + Can be used only with columns of integer type. + +#### Versionadded +Added in version 0.5.0. + +### Examples + +Read data in 10 parallel jobs by range of values in `id_column` column: + +```python +ReadOptions( + partitioning_mode="range", # default mode, can be omitted + partitionColumn="id_column", + numPartitions=10, + # Options below can be discarded because they are + # calculated automatically as MIN and MAX values of `partitionColumn` + lowerBound=0, + upperBound=100_000, +) +``` + +Read data in 10 parallel jobs by hash of values in `some_column` column: + +```python +ReadOptions( + partitioning_mode="hash", + partitionColumn="some_column", + numPartitions=10, + # lowerBound and upperBound are automatically set to `0` and `9` +) +``` + +Read data in 10 parallel jobs by modulus of values in `id_column` column: + +```python +ReadOptions( + partitioning_mode="mod", + partitionColumn="id_column", + numPartitions=10, + # lowerBound and upperBound are automatically set to `0` and `9` +) +``` + + diff --git a/mddocs/connection/db_connection/mysql/sql.md b/mddocs/connection/db_connection/mysql/sql.md new file mode 100644 index 000000000..2999cedd1 --- /dev/null +++ b/mddocs/connection/db_connection/mysql/sql.md @@ -0,0 +1,192 @@ + + +# Reading from MySQL using `MySQL.sql` + +`MySQL.sql` allows passing custom SQL query, but does not support incremental strategies. + +#### WARNING +Please take into account [MySQL <-> Spark type mapping](types.md#mysql-types) + +#### WARNING +Statement is executed in **read-write** connection, so if you’re calling some functions/procedures with DDL/DML statements inside, +they can change data in your database. + +## Syntax support + +Only queries with the following syntax are supported: + +* ✅︎ `SELECT ... FROM ...` +* ✅︎ `WITH alias AS (...) SELECT ...` +* ❌ `SHOW ...` +* ❌ `SET ...; SELECT ...;` - multiple statements not supported + +## Examples + +```python +from onetl.connection import MySQL + +mysql = MySQL(...) +df = mysql.sql( + """ + SELECT + id, + key, + CAST(value AS text) value, + updated_at + FROM + some.mytable + WHERE + key = 'something' + """, + options=MySQL.SQLOptions( + partitionColumn="id", + numPartitions=10, + lowerBound=0, + upperBound=1000, + ), +) +``` + +## Recommendations + +### Select only required columns + +Instead of passing `SELECT * FROM ...` prefer passing exact column names `SELECT col1, col2, ...`. +This reduces the amount of data passed from MySQL to Spark. + +### Pay attention to `where` value + +Instead of filtering data on Spark side using `df.filter(df.column == 'value')` pass proper `WHERE column = 'value'` clause. +This both reduces the amount of data send from MySQL to Spark, and may also improve performance of the query. +Especially if there are indexes or partitions for columns used in `where` clause. + +## Options + +### *pydantic model* onetl.connection.db_connection.mysql.options.MySQLSQLOptions + +Options specifically for SQL queries + +These options allow you to specify configurations for executing SQL queries +without relying on Spark’s partitioning mechanisms. + +#### Versionadded +Added in version 0.11.0: Split up `MySQL.ReadOptions` to `MySQL.SQLOptions` + +### Examples + +#### NOTE +You can pass any JDBC configuration +[supported by Spark](https://spark.apache.org/docs/latest/sql-data-sources-jdbc.html), +tailored to optimize SQL query execution. **Option names should be in** `camelCase`! + +```python +from onetl.connection import MySQL + +options = MySQL.SQLOptions( + partitionColumn="reg_id", + numPartitions=10, + lowerBound=0, + upperBound=1000, + customSparkOption="value", +) +``` + + + +#### *field* partition_column *: str | None* *= None* *(alias 'partitionColumn')* + +Column used to partition data across multiple executors for parallel query processing. + +#### WARNING +It is highly recommended to use primary key, or column with an index +to avoid performance issues. + +### Example of using `partitionColumn="id"` with `partitioning_mode="range"` + +```sql +-- If partition_column is 'id', with numPartitions=4, lowerBound=1, and upperBound=100: +-- Executor 1 processes IDs from 1 to 25 +SELECT ... FROM table WHERE id >= 1 AND id < 26 +-- Executor 2 processes IDs from 26 to 50 +SELECT ... FROM table WHERE id >= 26 AND id < 51 +-- Executor 3 processes IDs from 51 to 75 +SELECT ... FROM table WHERE id >= 51 AND id < 76 +-- Executor 4 processes IDs from 76 to 100 +SELECT ... FROM table WHERE id >= 76 AND id <= 100 + +-- General case for Executor N +SELECT ... FROM table +WHERE partition_column >= (lowerBound + (N-1) * stride) +AND partition_column <= upperBound +-- Where ``stride`` is calculated as ``(upperBound - lowerBound) / numPartitions``. +``` + + + +#### *field* num_partitions *: int | None* *= None* *(alias 'numPartitions')* + +Number of jobs created by Spark to read the table content in parallel. + + + +#### *field* lower_bound *: int | None* *= None* *(alias 'lowerBound')* + +Defines the starting boundary for partitioning the query’s data. Mandatory if [`partition_column`](#onetl.connection.db_connection.mysql.options.MySQLSQLOptions.partition_column) is set + + + +#### *field* upper_bound *: int | None* *= None* *(alias 'upperBound')* + +Sets the ending boundary for data partitioning. Mandatory if [`partition_column`](#onetl.connection.db_connection.mysql.options.MySQLSQLOptions.partition_column) is set + + + +#### *field* session_init_statement *: str | None* *= None* *(alias 'sessionInitStatement')* + +After each database session is opened to the remote DB and before starting to read data, +this option executes a custom SQL statement (or a PL/SQL block). + +Use this to implement session initialization code. + +Example: + +```python +sessionInitStatement = """ + BEGIN + execute immediate + 'alter session set "_serial_direct_read"=true'; + END; +""" +``` + + + +#### *field* query_timeout *: int | None* *= None* *(alias 'queryTimeout')* + +The number of seconds the driver will wait for a statement to execute. +Zero means there is no limit. + +This option depends on driver implementation, +some drivers can check the timeout of each query instead of an entire JDBC batch. + + + +#### *field* fetchsize *: int* *= 100000* + +Fetch N rows from an opened cursor per one read round. + +Tuning this option can influence performance of reading. + +#### WARNING +Default value is different from Spark. + +Spark uses driver’s own value, and it may be different in different drivers, +and even versions of the same driver. For example, Oracle has +default `fetchsize=10`, which is absolutely not usable. + +Thus we’ve overridden default value with `100_000`, which should increase reading performance. + +#### Versionchanged +Changed in version 0.2.0: Set explicit default value to `100_000` + + diff --git a/mddocs/connection/db_connection/mysql/types.md b/mddocs/connection/db_connection/mysql/types.md new file mode 100644 index 000000000..cd1e207a9 --- /dev/null +++ b/mddocs/connection/db_connection/mysql/types.md @@ -0,0 +1,292 @@ + + +# MySQL <-> Spark type mapping + +#### NOTE +The results below are valid for Spark 3.5.5, and may differ on other Spark versions. + +## Type detection & casting + +Spark’s DataFrames always have a `schema` which is a list of columns with corresponding Spark types. All operations on a column are performed using column type. + +### Reading from MySQL + +This is how MySQL connector performs this: + +* For each column in query result (`SELECT column1, column2, ... FROM table ...`) get column name and MySQL type. +* Find corresponding `MySQL type (read)` → `Spark type` combination (see below) for each DataFrame column. If no combination is found, raise exception. +* Create DataFrame from query with specific column names and Spark types. + +### Writing to some existing MySQL table + +This is how MySQL connector performs this: + +* Get names of columns in DataFrame. [1](#id2) +* Perform `SELECT * FROM table LIMIT 0` query. +* Take only columns present in DataFrame (by name, case insensitive). For each found column get MySQL type. +* Find corresponding `Spark type` → `MySQL type (write)` combination (see below) for each DataFrame column. If no combination is found, raise exception. +* If `MySQL type (write)` match `MySQL type (read)`, no additional casts will be performed, DataFrame column will be written to MySQL as is. +* If `MySQL type (write)` does not match `MySQL type (read)`, DataFrame column will be casted to target column type **on MySQL side**. For example, you can write column with text data to `int` column, if column contains valid integer values within supported value range and precision. + +* **[1]** This allows to write data to tables with `DEFAULT` and `GENERATED` columns - if DataFrame has no such column, it will be populated by MySQL. + +### Create new table using Spark + +#### WARNING +ABSOLUTELY NOT RECOMMENDED! + +This is how MySQL connector performs this: + +* Find corresponding `Spark type` → `MySQL type (create)` combination (see below) for each DataFrame column. If no combination is found, raise exception. +* Generate DDL for creating table in MySQL, like `CREATE TABLE (col1 ...)`, and run it. +* Write DataFrame to created table as is. + +But some cases this may lead to using wrong column type. For example, Spark creates column of type `timestamp` +which corresponds to MySQL type `timestamp(0)` (precision up to seconds) +instead of more precise `timestamp(6)` (precision up to nanoseconds). +This may lead to incidental precision loss, or sometimes data cannot be written to created table at all. + +So instead of relying on Spark to create tables: + +### See example + +```python +writer = DBWriter( + connection=mysql, + target="myschema.target_tbl", + options=MySQL.WriteOptions( + if_exists="append", + createTableOptions="ENGINE = InnoDB", + ), +) +writer.run(df) +``` + +Always prefer creating tables with specific types **BEFORE WRITING DATA**: + +### See example + +```python +mysql.execute( + """ + CREATE TABLE schema.table ( + id bigint, + key text, + value timestamp(6) -- specific type and precision + ) + ENGINE = InnoDB + """, +) + +writer = DBWriter( + connection=mysql, + target="myschema.target_tbl", + options=MySQL.WriteOptions(if_exists="append"), +) +writer.run(df) +``` + +### References + +Here you can find source code with type conversions: + +* [MySQL -> JDBC](https://github.com/mysql/mysql-connector-j/blob/8.0.33/src/main/core-api/java/com/mysql/cj/MysqlType.java#L44-L623) +* [JDBC -> Spark](https://github.com/apache/spark/blob/v3.5.5/sql/core/src/main/scala/org/apache/spark/sql/jdbc/MySQLDialect.scala#L104-L132) +* [Spark -> JDBC](https://github.com/apache/spark/blob/v3.5.5/sql/core/src/main/scala/org/apache/spark/sql/jdbc/MySQLDialect.scala#L204-L211) +* [JDBC -> MySQL](https://github.com/mysql/mysql-connector-j/blob/8.0.33/src/main/core-api/java/com/mysql/cj/MysqlType.java#L625-L867) + +## Supported types + +See [official documentation](https://dev.mysql.com/doc/refman/en/data-types.html) + +### Numeric types + +| MySQL type (read) | Spark type | MySQL type (write) | MySQL type (create) | +|-----------------------------|----------------------------------|-----------------------------|-----------------------------| +| `decimal` | `DecimalType(P=10, S=0)` | `decimal(P=10, S=0)` | `decimal(P=10, S=0)` | +| `decimal(P=0..38)` | `DecimalType(P=0..38, S=0)` | `decimal(P=0..38, S=0)` | `decimal(P=0..38, S=0)` | +| `decimal(P=0..38, S=0..30)` | `DecimalType(P=0..38, S=0..30)` | `decimal(P=0..38, S=0..30)` | `decimal(P=0..38, S=0..30)` | +| `decimal(P=39..65, S=...)` | unsupported [2](#id4) | | | +| `float` | `DoubleType()` | `double` | `double` | +| `double` | | | | +| `tinyint` | `IntegerType()` | `int` | `int` | +| `smallint` | | | | +| `mediumint` | | | | +| `int` | | | | +| `bigint` | `LongType()` | `bigint` | `bigint` | +* **[2]** MySQL support decimal types with precision `P` up to 65. But Spark’s `DecimalType(P, S)` supports maximum `P=38`. It is impossible to read, write or operate with values of larger precision, this leads to an exception. + +### Temporal types + +| MySQL type (read) | Spark type | MySQL type (write) | MySQL type (create) | +|------------------------------|------------------------------------------------------------------------------------|------------------------------|-----------------------------------------------------------------------| +| `year` | `DateType()` | `date` | `date` | +| `date` | | | | +| `datetime`, seconds | `TimestampType()`, microseconds | `timestamp(6)`, microseconds | `timestamp(0)`, seconds | +| `timestamp`, seconds | | | | +| `datetime(0)`, seconds | | | | +| `timestamp(0)`, seconds | | | | +| `datetime(3)`, milliseconds | `TimestampType()`, microseconds | `timestamp(6)`, microseconds | `timestamp(0)`, seconds,
**precision loss** [4](#id9), | +| `timestamp(3)`, milliseconds | | | | +| `datetime(6)`, microseconds | | | | +| `timestamp(6)`, microseconds | | | | +| `time`, seconds | `TimestampType()`, microseconds,
with time format quirks [5](#id10) | `timestamp(6)`, microseconds | `timestamp(0)`, seconds | +| `time(0)`, seconds | | | | +| `time(3)`, milliseconds | `TimestampType()`, microseconds
with time format quirks [5](#id10) | `timestamp(6)`, microseconds | `timestamp(0)`, seconds,
**precision loss** [4](#id9), | +| `time(6)`, microseconds | | | | + +#### WARNING +Note that types in MySQL and Spark have different value ranges: + +| MySQL type | Min value | Max value | Spark type | Min value | Max value | +|--------------|------------------------------|------------------------------|-------------------|------------------------------|------------------------------| +| `year` | `1901` | `2155` | `DateType()` | `0001-01-01` | `9999-12-31` | +| `date` | `1000-01-01` | `9999-12-31` | | | | +| `datetime` | `1000-01-01 00:00:00.000000` | `9999-12-31 23:59:59.499999` | `TimestampType()` | `0001-01-01 00:00:00.000000` | `9999-12-31 23:59:59.999999` | +| `timestamp` | `1970-01-01 00:00:01.000000` | `9999-12-31 23:59:59.499999` | | | | +| `time` | `-838:59:59.000000` | `838:59:59.000000` | | | | + +So Spark can read all the values from MySQL, but not all of values in Spark DataFrame can be written to MySQL. + +References: +: * [MySQL year documentation](https://dev.mysql.com/doc/refman/en/year.html) + * [MySQL date, datetime & timestamp documentation](https://dev.mysql.com/doc/refman/en/datetime.html) + * [MySQL time documentation](https://dev.mysql.com/doc/refman/en/time.html) + * [Spark DateType documentation](https://spark.apache.org/docs/latest/api/java/org/apache/spark/sql/types/DateType.html) + * [Spark TimestampType documentation](https://spark.apache.org/docs/latest/api/java/org/apache/spark/sql/types/TimestampType.html) + +* **[4]** MySQL dialect generates DDL with MySQL type `timestamp` which is alias for `timestamp(0)` with precision up to seconds (`23:59:59`). Inserting data with microseconds precision (`23:59:59.999999`) will lead to **throwing away microseconds**. +* **[5]** `time` type is the same as `timestamp` with date `1970-01-01`. So instead of reading data from MySQL like `23:59:59` it is actually read `1970-01-01 23:59:59`, and vice versa. + +### String types + +| MySQL type (read) | Spark type | MySQL type (write) | MySQL type (create) | +|-----------------------------|----------------|----------------------|-----------------------| +| `char` | `StringType()` | `longtext` | `longtext` | +| `char(N)` | | | | +| `varchar(N)` | | | | +| `mediumtext` | | | | +| `text` | | | | +| `longtext` | | | | +| `json` | | | | +| `enum("val1", "val2", ...)` | | | | +| `set("val1", "val2", ...)` | | | | + +### Binary types + +| MySQL type (read) | Spark type | MySQL type (write) | MySQL type (create) | +|---------------------|----------------|----------------------|-----------------------| +| `binary` | `BinaryType()` | `blob` | `blob` | +| `binary(N)` | | | | +| `varbinary(N)` | | | | +| `mediumblob` | | | | +| `blob` | | | | +| `longblob` | | | | + +### Geometry types + +| MySQL type (read) | Spark type | MySQL type (write) | MySQL type (create) | +|----------------------|----------------|----------------------|-----------------------| +| `point` | `BinaryType()` | `blob` | `blob` | +| `linestring` | | | | +| `polygon` | | | | +| `geometry` | | | | +| `multipoint` | | | | +| `multilinestring` | | | | +| `multipolygon` | | | | +| `geometrycollection` | | | | + +## Explicit type cast + +### `DBReader` + +It is possible to explicitly cast column type using `DBReader(columns=...)` syntax. + +For example, you can use `CAST(column AS text)` to convert data to string representation on MySQL side, and so it will be read as Spark’s `StringType()`. + +It is also possible to use [JSON_OBJECT](https://dev.mysql.com/doc/refman/en/json.html) MySQL function and parse JSON columns in MySQL with the [`JSON.parse_column`](../../../file_df/file_formats/json.md#onetl.file.format.json.JSON.parse_column) method. + +```python +from pyspark.sql.types import IntegerType, StructType, StructField + +from onetl.connection import MySQL +from onetl.db import DBReader +from onetl.file.format import JSON + +mysql = MySQL(...) + +DBReader( + connection=mysql, + columns=[ + "id", + "supported_column", + "CAST(unsupported_column AS text) unsupported_column_str", + # or + "JSON_OBJECT('key', value_column) json_column", + ], +) +df = reader.run() + +json_scheme = StructType([StructField("key", IntegerType())]) + +df = df.select( + df.id, + df.supported_column, + # explicit cast + df.unsupported_column_str.cast("integer").alias("parsed_integer"), + JSON().parse_column("json_column", json_scheme).alias("struct_column"), +) +``` + +### `DBWriter` + +To write JSON data to a `json` or `text` column in a MySQL table, use the [`JSON.serialize_column`](../../../file_df/file_formats/json.md#onetl.file.format.json.JSON.serialize_column) method. + +```python +from onetl.connection import MySQL +from onetl.db import DBWriter +from onetl.file.format import JSON + +mysql.execute( + """ + CREATE TABLE schema.target_tbl ( + id bigint, + array_column_json json -- any string type, actually + ) + ENGINE = InnoDB + """, +) + +df = df.select( + df.id, + JSON().serialize_column(df.array_column).alias("array_column_json"), +) + +writer.run(df) +``` + +Then you can parse this column on MySQL side - for example, by creating a view: + +```sql +SELECT + id, + array_column_json->"$[0]" AS array_item +FROM target_tbl +``` + +Or by using [GENERATED column](https://dev.mysql.com/doc/refman/en/create-table-generated-columns.html): + +```sql +CREATE TABLE schema.target_table ( + id bigint, + supported_column timestamp, + array_column_json json, -- any string type, actually + -- virtual column + array_item_0 GENERATED ALWAYS AS (array_column_json->"$[0]")) VIRTUAL + -- or stired column + -- array_item_0 GENERATED ALWAYS AS (array_column_json->"$[0]")) STORED +) +``` + +`VIRTUAL` column value is calculated on every table read. +`STORED` column value is calculated during insert, but this require additional space. diff --git a/mddocs/connection/db_connection/mysql/write.md b/mddocs/connection/db_connection/mysql/write.md new file mode 100644 index 000000000..239aacfdb --- /dev/null +++ b/mddocs/connection/db_connection/mysql/write.md @@ -0,0 +1,187 @@ + + +# Writing to MySQL using `DBWriter` + +For writing data to MySQL, use [`DBWriter`](../../../db/db_writer.md#onetl.db.db_writer.db_writer.DBWriter). + +#### WARNING +Please take into account [MySQL <-> Spark type mapping](types.md#mysql-types) + +#### WARNING +It is always recommended to create table explicitly using [MySQL.execute](execute.md#mysql-execute) +instead of relying on Spark’s table DDL generation. + +This is because Spark’s DDL generator can create columns with different precision and types than it is expected, +causing precision loss or other issues. + +## Examples + +```python +from onetl.connection import MySQL +from onetl.db import DBWriter + +mysql = MySQL(...) + +df = ... # data is here + +writer = DBWriter( + connection=mysql, + target="schema.table", + options=MySQL.WriteOptions( + if_exists="append", + # ENGINE is required by MySQL + createTableOptions="ENGINE = MergeTree() ORDER BY id", + ), +) + +writer.run(df) +``` + +## Options + +Method above accepts [`MySQL.WriteOptions`](#onetl.connection.db_connection.mysql.options.MySQLWriteOptions) + +### *pydantic model* onetl.connection.db_connection.mysql.options.MySQLWriteOptions + +Spark JDBC writing options. + +#### Versionadded +Added in version 0.5.0: Replace `MySQL.Options` → `MySQL.WriteOptions` + +### Examples + +#### NOTE +You can pass any value +[supported by Spark](https://spark.apache.org/docs/latest/sql-data-sources-jdbc.html), +even if it is not mentioned in this documentation. **Option names should be in** `camelCase`! + +The set of supported options depends on Spark version. + +```python +from onetl.connection import MySQL + +options = MySQL.WriteOptions( + if_exists="append", + batchsize=20_000, + customSparkOption="value", +) +``` + + + +#### *field* if_exists *: JDBCTableExistBehavior* *= JDBCTableExistBehavior.APPEND* *(alias 'mode')* + +Behavior of writing data into existing table. + +Possible values: +: * `append` (default) + : Adds new rows into existing table. +
+ ### Behavior in details +
+ * Table does not exist + : Table is created using options provided by user + (`createTableOptions`, `createTableColumnTypes`, etc). + * Table exists + : Data is appended to a table. Table has the same DDL as before writing data +
+ #### WARNING + This mode does not check whether table already contains + rows from dataframe, so duplicated rows can be created. +
+ Also Spark does not support passing custom options to + insert statement, like `ON CONFLICT`, so don’t try to + implement deduplication using unique indexes or constraints. +
+ Instead, write to staging table and perform deduplication + using `execute` method. + * `replace_entire_table` + : **Table is dropped and then created, or truncated**. +
+ ### Behavior in details +
+ * Table does not exist + : Table is created using options provided by user + (`createTableOptions`, `createTableColumnTypes`, etc). + * Table exists + : Table content is replaced with dataframe content. +
+ After writing completed, target table could either have the same DDL as + before writing data (`truncate=True`), or can be recreated (`truncate=False` + or source does not support truncation). + * `ignore` + : Ignores the write operation if the table already exists. +
+ ### Behavior in details +
+ * Table does not exist + : Table is created using options provided by user + (`createTableOptions`, `createTableColumnTypes`, etc). + * Table exists + : The write operation is ignored, and no data is written to the table. + * `error` + : Raises an error if the table already exists. +
+ ### Behavior in details +
+ * Table does not exist + : Table is created using options provided by user + (`createTableOptions`, `createTableColumnTypes`, etc). + * Table exists + : An error is raised, and no data is written to the table. + +#### Versionchanged +Changed in version 0.9.0: Renamed `mode` → `if_exists` + + + +#### *field* query_timeout *: int | None* *= None* *(alias 'queryTimeout')* + +The number of seconds the driver will wait for a statement to execute. +Zero means there is no limit. + +This option depends on driver implementation, +some drivers can check the timeout of each query instead of an entire JDBC batch. + + + +#### *field* batchsize *: int* *= 20000* + +How many rows can be inserted per round trip. + +Tuning this option can influence performance of writing. + +#### WARNING +Default value is different from Spark. + +Spark uses quite small value `1000`, which is absolutely not usable +in BigData world. + +Thus we’ve overridden default value with `20_000`, +which should increase writing performance. + +You can increase it even more, up to `50_000`, +but it depends on your database load and number of columns in the row. +Higher values does not increase performance. + +#### Versionchanged +Changed in version 0.4.0: Changed default value from 1000 to 20_000 + + + +#### *field* isolation_level *: str* *= 'READ_UNCOMMITTED'* *(alias 'isolationLevel')* + +The transaction isolation level, which applies to current connection. + +Possible values: +: * `NONE` (as string, not Python’s `None`) + * `READ_COMMITTED` + * `READ_UNCOMMITTED` + * `REPEATABLE_READ` + * `SERIALIZABLE` + +Values correspond to transaction isolation levels defined by JDBC standard. +Please refer the documentation for +[java.sql.Connection](https://docs.oracle.com/javase/8/docs/api/java/sql/Connection.html). + + diff --git a/mddocs/connection/db_connection/oracle/connection.md b/mddocs/connection/db_connection/oracle/connection.md new file mode 100644 index 000000000..6171f3a50 --- /dev/null +++ b/mddocs/connection/db_connection/oracle/connection.md @@ -0,0 +1,143 @@ + + +# Oracle connection + +### *class* onetl.connection.db_connection.oracle.connection.Oracle(\*, spark: SparkSession, user: str, password: SecretStr, host: Host, port: int = 1521, sid: str | None = None, service_name: str | None = None, extra: OracleExtra = OracleExtra()) + +Oracle JDBC connection. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +Based on Maven package [com.oracle.database.jdbc:ojdbc8:23.7.0.25.01](https://mvnrepository.com/artifact/com.oracle.database.jdbc/ojdbc8/23.7.0.25.01) +([official Oracle JDBC driver](https://www.oracle.com/cis/database/technologies/appdev/jdbc-downloads.html)). + +#### SEE ALSO +Before using this connector please take into account [Prerequisites](prerequisites.md#oracle-prerequisites) + +#### Versionadded +Added in version 0.1.0. + +* **Parameters:** + **host** + : Host of Oracle database. For example: `test.oracle.domain.com` or `193.168.1.10` + + **port** + : Port of Oracle database + + **user** + : User, which have proper access to the database. For example: `SOME_USER` + + **password** + : Password for database connection + + **sid** + : Sid of oracle database. For example: `XE` +
+ #### WARNING + You should provide either `sid` or `service_name`, not both of them + + **service_name** + : Specifies one or more names by which clients can connect to the instance. +
+ For example: `PDB1`. +
+ #### WARNING + You should provide either `sid` or `service_name`, not both of them + + **spark** + : Spark session. + + **extra** + : Specifies one or more extra parameters by which clients can connect to the instance. +
+ For example: `{"remarksReporting": "false"}` +
+ See official documentation: + : * [Connection parameters](https://docs.oracle.com/en/database/oracle/oracle-database/23/jajdb/oracle/jdbc/OracleDriver.html) + * [Connection properties](https://docs.oracle.com/cd/A97335_02/apps.102/a83724/basic1.htm#1024018) + +### Examples + +Create and check Oracle connection with `sid`: + +```python +from onetl.connection import Oracle +from pyspark.sql import SparkSession + +# Create Spark session with Oracle driver loaded +maven_packages = Oracle.get_packages() +spark = ( + SparkSession.builder.appName("spark-app-name") + .config("spark.jars.packages", ",".join(maven_packages)) + .getOrCreate() +) + +# Create connection +oracle = Oracle( + host="database.host.or.ip", + user="user", + password="*****", + sid="XE", + extra={"remarksReporting": "false"}, + spark=spark, +).check() +``` + +or with `service_name`: + +```python +... + +oracle = Oracle( + host="database.host.or.ip", + user="user", + password="*****", + service_name="PDB1", # <--- instead of SID + extra={"remarksReporting": "false"}, + spark=spark, +).check() +``` + + + +#### check() + +Check source availability. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +If not, an exception will be raised. + +* **Returns:** + Connection itself +* **Raises:** + RuntimeError + : If the connection is not available + +### Examples + +```python +connection.check() +``` + + + +#### *classmethod* get_packages(java_version: str | None = None, package_version: str | None = None) → list[str] + +Get package names to be downloaded by Spark. Allows specifying custom JDBC driver versions for Oracle. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +* **Parameters:** + **java_version** + : Java major version, defaults to “8”. Must be “8” or “11”. + + **package_version** + : Specifies the version of the Oracle JDBC driver to use. Defaults to “23.7.0.25.01”. + +### Examples + +```python +from onetl.connection import Oracle + +Oracle.get_packages() + +# specify Java and package versions +Oracle.get_packages(java_version="8", package_version="23.7.0.25.01") +``` + + diff --git a/mddocs/connection/db_connection/oracle/execute.md b/mddocs/connection/db_connection/oracle/execute.md new file mode 100644 index 000000000..29293882b --- /dev/null +++ b/mddocs/connection/db_connection/oracle/execute.md @@ -0,0 +1,191 @@ + + +# Executing statements in Oracle + +#### WARNING +Methods below **read all the rows** returned from DB **to Spark driver memory**, and then convert them to DataFrame. + +Do **NOT** use them to read large amounts of data. Use [DBReader](read.md#oracle-read) or [Oracle.sql](sql.md#oracle-sql) instead. + +## How to + +There are 2 ways to execute some statement in Oracle + +### Use `Oracle.fetch` + +Use this method to execute some `SELECT` query which returns **small number or rows**, like reading +Oracle config, or reading data from some reference table. Method returns Spark DataFrame. + +Method accepts [`Oracle.FetchOptions`](#onetl.connection.db_connection.oracle.options.OracleFetchOptions). + +Connection opened using this method should be then closed with `connection.close()` or `with connection:`. + +#### WARNING +Please take into account [Oracle <-> Spark type mapping](types.md#oracle-types). + +#### Syntax support + +This method supports **any** query syntax supported by Oracle, like: + +* ✅︎ `SELECT ... FROM ...` +* ✅︎ `WITH alias AS (...) SELECT ...` +* ✅︎ `SELECT func(arg1, arg2) FROM DUAL` - call function +* ✅︎ `SHOW ...` +* ❌ `SET ...; SELECT ...;` - multiple statements not supported + +#### Examples + +```python +from onetl.connection import Oracle + +oracle = Oracle(...) + +df = oracle.fetch( + "SELECT value FROM some.reference_table WHERE key = 'some_constant'", + options=Oracle.FetchOptions(queryTimeout=10), +) +oracle.close() +value = df.collect()[0][0] # get value from first row and first column +``` + +### Use `Oracle.execute` + +Use this method to execute DDL and DML operations. Each method call runs operation in a separated transaction, and then commits it. + +Method accepts [`Oracle.ExecuteOptions`](#onetl.connection.db_connection.oracle.options.OracleExecuteOptions). + +Connection opened using this method should be then closed with `connection.close()` or `with connection:`. + +#### Syntax support + +This method supports **any** query syntax supported by Oracle, like: + +* ✅︎ `CREATE TABLE ...`, `CREATE VIEW ...` +* ✅︎ `ALTER ...` +* ✅︎ `INSERT INTO ... SELECT ...`, `UPDATE ...`, `DELETE ...`, and so on +* ✅︎ `DROP TABLE ...`, `DROP VIEW ...`, `TRUNCATE TABLE`, and so on +* ✅︎ `CALL procedure(arg1, arg2) ...` or `{call procedure(arg1, arg2)}` - special syntax for calling procedure +* ✅︎ `DECLARE ... BEGIN ... END` - execute PL/SQL statement +* ✅︎ other statements not mentioned here +* ❌ `SET ...; SELECT ...;` - multiple statements not supported + +#### Examples + +```python +from onetl.connection import Oracle + +oracle = Oracle(...) + +oracle.execute("DROP TABLE schema.table") +oracle.execute( + """ + CREATE TABLE schema.table ( + id bigint GENERATED ALWAYS AS IDENTITY, + key VARCHAR2(4000), + value NUMBER + ) + """, + options=Oracle.ExecuteOptions(queryTimeout=10), +) +``` + +## Options + +### *pydantic model* onetl.connection.db_connection.oracle.options.OracleFetchOptions + +Options related to fetching data from databases via JDBC. + +#### Versionadded +Added in version 0.11.0: Replace `Oracle.JDBCOptions` → `Oracle.FetchOptions` + +### Examples + +#### NOTE +You can pass any value supported by underlying JDBC driver class, +even if it is not mentioned in this documentation. + +```python +from onetl.connection import Oracle + +options = Oracle.FetchOptions( + queryTimeout=60_000, + fetchsize=100_000, + customSparkOption="value", +) +``` + + +* **Fields:** + - [`fetchsize (int | None)`](#onetl.connection.db_connection.oracle.options.OracleFetchOptions.fetchsize) + - [`query_timeout (int | None)`](#onetl.connection.db_connection.oracle.options.OracleFetchOptions.query_timeout) + +#### *field* query_timeout *: int | None* *= None* *(alias 'queryTimeout')* + +The number of seconds the driver will wait for a statement to execute. +Zero means there is no limit. + +This option depends on driver implementation, +some drivers can check the timeout of each query instead of an entire JDBC batch. + + + +#### *field* fetchsize *: int | None* *= None* + +How many rows to fetch per round trip. + +Tuning this option can influence performance of reading. + +#### WARNING +Default value depends on driver. For example, Oracle has +default `fetchsize=10`. + + + +### *pydantic model* onetl.connection.db_connection.oracle.options.OracleExecuteOptions + +Options related to executing statements in databases via JDBC. + +#### Versionadded +Added in version 0.11.0: Replace `Oracle.JDBCOptions` → `Oracle.ExecuteOptions` + +### Examples + +#### NOTE +You can pass any value supported by underlying JDBC driver class, +even if it is not mentioned in this documentation. + +```python +from onetl.connection import Oracle + +options = Oracle.ExecuteOptions( + queryTimeout=60_000, + customSparkOption="value", +) +``` + + +* **Fields:** + - [`fetchsize (int | None)`](#onetl.connection.db_connection.oracle.options.OracleExecuteOptions.fetchsize) + - [`query_timeout (int | None)`](#onetl.connection.db_connection.oracle.options.OracleExecuteOptions.query_timeout) + +#### *field* query_timeout *: int | None* *= None* *(alias 'queryTimeout')* + +The number of seconds the driver will wait for a statement to execute. +Zero means there is no limit. + +This option depends on driver implementation, +some drivers can check the timeout of each query instead of an entire JDBC batch. + + + +#### *field* fetchsize *: int | None* *= None* + +How many rows to fetch per round trip. + +Tuning this option can influence performance of reading. + +#### WARNING +Default value depends on driver. For example, Oracle has +default `fetchsize=10`. + + diff --git a/mddocs/connection/db_connection/oracle/index.md b/mddocs/connection/db_connection/oracle/index.md new file mode 100644 index 000000000..39987fb24 --- /dev/null +++ b/mddocs/connection/db_connection/oracle/index.md @@ -0,0 +1,19 @@ + + +# Oracle + +# Connection + +* [Prerequisites](prerequisites.md) +* [Oracle connection](connection.md) + +# Operations + +* [Reading from Oracle using `DBReader`](read.md) +* [Reading from Oracle using `Oracle.sql`](sql.md) +* [Writing to Oracle using `DBWriter`](write.md) +* [Executing statements in Oracle](execute.md) + +# Troubleshooting + +* [Oracle <-> Spark type mapping](types.md) diff --git a/mddocs/connection/db_connection/oracle/prerequisites.md b/mddocs/connection/db_connection/oracle/prerequisites.md new file mode 100644 index 000000000..6dff632ce --- /dev/null +++ b/mddocs/connection/db_connection/oracle/prerequisites.md @@ -0,0 +1,110 @@ + + +# Prerequisites + +## Version Compatibility + +* Oracle Server versions: + : * Officially declared: 19c, 21c, 23ai + * Actually tested: 11.2, 23.5 +* Spark versions: 2.3.x - 3.5.x +* Java versions: 8 - 20 + +See [official documentation](https://www.oracle.com/cis/database/technologies/appdev/jdbc-downloads.html). + +## Installing PySpark + +To use Oracle connector you should have PySpark installed (or injected to `sys.path`) +BEFORE creating the connector instance. + +See [Spark](../../../install/spark.md#install-spark) installation instruction for more details. + +## Connecting to Oracle + +### Connection port + +Connection is usually performed to port 1521. Port may differ for different Oracle instances. +Please ask your Oracle administrator to provide required information. + +### Connection host + +It is possible to connect to Oracle by using either DNS name of host or it’s IP address. + +If you’re using Oracle cluster, it is currently possible to connect only to **one specific node**. +Connecting to multiple nodes to perform load balancing, as well as automatic failover to new master/replica are not supported. + +### Connect as proxy user + +It is possible to connect to database as another user without knowing this user password. + +This can be enabled by granting user a special `CONNECT THROUGH` permission: + +```sql +ALTER USER schema_owner GRANT CONNECT THROUGH proxy_user; +``` + +Then you can connect to Oracle using credentials of `proxy_user` but specify that you need permissions of `schema_owner`: + +```python +oracle = Oracle( + ..., + user="proxy_user[schema_owner]", + password="proxy_user password", +) +``` + +See [official documentation](https://oracle-base.com/articles/misc/proxy-users-and-connect-through). + +### Required grants + +Ask your Oracle cluster administrator to set following grants for a user, +used for creating a connection: + +Read + Write (schema is owned by user) + +```sql +-- allow user to log in +GRANT CREATE SESSION TO username; + +-- allow creating tables in user schema +GRANT CREATE TABLE TO username; + +-- allow read & write access to specific table +GRANT SELECT, INSERT ON username.mytable TO username; +``` + +Read + Write (schema is not owned by user) + +```sql +-- allow user to log in +GRANT CREATE SESSION TO username; + +-- allow creating tables in any schema, +-- as Oracle does not support specifying exact schema name +GRANT CREATE ANY TABLE TO username; + +-- allow read & write access to specific table +GRANT SELECT, INSERT ON someschema.mytable TO username; + +-- only if if_exists="replace_entire_table" is used: +-- allow dropping/truncating tables in any schema, +-- as Oracle does not support specifying exact schema name +GRANT DROP ANY TABLE TO username; +``` + +Read only + +```sql +-- allow user to log in +GRANT CREATE SESSION TO username; + +-- allow read access to specific table +GRANT SELECT ON someschema.mytable TO username; +``` + +More details can be found in official documentation: +: * [GRANT](https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/GRANT.html) + * [SELECT](https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/SELECT.html) + * [CREATE TABLE](https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/SELECT.html) + * [INSERT](https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/INSERT.html) + * [TRUNCATE TABLE](https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/TRUNCATE-TABLE.html) diff --git a/mddocs/connection/db_connection/oracle/read.md b/mddocs/connection/db_connection/oracle/read.md new file mode 100644 index 000000000..fbb28c207 --- /dev/null +++ b/mddocs/connection/db_connection/oracle/read.md @@ -0,0 +1,352 @@ + + +# Reading from Oracle using `DBReader` + +[`DBReader`](../../../db/db_reader.md#onetl.db.db_reader.db_reader.DBReader) supports [Read Strategies](../../../strategy/index.md#strategy) for incremental data reading, +but does not support custom queries, like `JOIN`. + +#### WARNING +Please take into account [Oracle <-> Spark type mapping](types.md#oracle-types) + +## Supported DBReader features + +* ✅︎ `columns` +* ✅︎ `where` +* ✅︎ `hwm`, supported strategies: +* * ✅︎ [Snapshot Strategy](../../../strategy/snapshot_strategy.md#snapshot-strategy) +* * ✅︎ [Incremental Strategy](../../../strategy/incremental_strategy.md#incremental-strategy) +* * ✅︎ [Snapshot Batch Strategy](../../../strategy/snapshot_batch_strategy.md#snapshot-batch-strategy) +* * ✅︎ [Incremental Batch Strategy](../../../strategy/incremental_batch_strategy.md#incremental-batch-strategy) +* ✅︎ `hint` (see [official documentation](https://docs.oracle.com/cd/B10500_01/server.920/a96533/hintsref.htm)) +* ❌ `df_schema` +* ✅︎ `options` (see [`Oracle.ReadOptions`](#onetl.connection.db_connection.oracle.options.OracleReadOptions)) + +## Examples + +Snapshot strategy: + +```python +from onetl.connection import Oracle +from onetl.db import DBReader + +oracle = Oracle(...) + +reader = DBReader( + connection=oracle, + source="schema.table", + columns=["id", "key", "CAST(value AS VARCHAR2(4000)) value", "updated_dt"], + where="key = 'something'", + hint="INDEX(schema.table key_index)", + options=Oracle.ReadOptions(partitionColumn="id", numPartitions=10), +) +df = reader.run() +``` + +Incremental strategy: + +```python +from onetl.connection import Oracle +from onetl.db import DBReader +from onetl.strategy import IncrementalStrategy + +oracle = Oracle(...) + +reader = DBReader( + connection=oracle, + source="schema.table", + columns=["id", "key", "CAST(value AS VARCHAR2(4000)) value", "updated_dt"], + where="key = 'something'", + hint="INDEX(schema.table key_index)", + hwm=DBReader.AutoDetectHWM(name="oracle_hwm", expression="updated_dt"), + options=Oracle.ReadOptions(partitionColumn="id", numPartitions=10), +) + +with IncrementalStrategy(): + df = reader.run() +``` + +## Recommendations + +### Select only required columns + +Instead of passing `"*"` in `DBReader(columns=[...])` prefer passing exact column names. This reduces the amount of data passed from Oracle to Spark. + +### Pay attention to `where` value + +Instead of filtering data on Spark side using `df.filter(df.column == 'value')` pass proper `DBReader(where="column = 'value'")` clause. +This both reduces the amount of data send from Oracle to Spark, and may also improve performance of the query. +Especially if there are indexes or partitions for columns used in `where` clause. + +## Options + +### *pydantic model* onetl.connection.db_connection.oracle.options.OracleReadOptions + +Spark JDBC reading options. + +#### Versionadded +Added in version 0.5.0: Replace `Oracle.Options` → `Oracle.ReadOptions` + +### Examples + +#### NOTE +You can pass any value +[supported by Spark](https://spark.apache.org/docs/latest/sql-data-sources-jdbc.html), +even if it is not mentioned in this documentation. **Option names should be in** `camelCase`! + +The set of supported options depends on Spark version. + +```python +from onetl.connection import Oracle + +options = Oracle.ReadOptions( + partitioning_mode="range", + partitionColumn="reg_id", + numPartitions=10, + customSparkOption="value", +) +``` + + +* **Fields:** + - [`fetchsize (int)`](#onetl.connection.db_connection.oracle.options.OracleReadOptions.fetchsize) + - [`lower_bound (int | None)`](#onetl.connection.db_connection.oracle.options.OracleReadOptions.lower_bound) + - [`num_partitions (pydantic.types.PositiveInt)`](#onetl.connection.db_connection.oracle.options.OracleReadOptions.num_partitions) + - [`partition_column (str | None)`](#onetl.connection.db_connection.oracle.options.OracleReadOptions.partition_column) + - [`partitioning_mode (onetl.connection.db_connection.jdbc_connection.options.JDBCPartitioningMode)`](#onetl.connection.db_connection.oracle.options.OracleReadOptions.partitioning_mode) + - [`query_timeout (int | None)`](#onetl.connection.db_connection.oracle.options.OracleReadOptions.query_timeout) + - [`session_init_statement (str | None)`](#onetl.connection.db_connection.oracle.options.OracleReadOptions.session_init_statement) + - [`upper_bound (int | None)`](#onetl.connection.db_connection.oracle.options.OracleReadOptions.upper_bound) + +#### *field* partition_column *: str | None* *= None* *(alias 'partitionColumn')* + +Column used to parallelize reading from a table. + +#### WARNING +It is highly recommended to use primary key, or column with an index +to avoid performance issues. + +#### NOTE +Column type depends on [`partitioning_mode`](#onetl.connection.db_connection.oracle.options.OracleReadOptions.partitioning_mode). + +* `partitioning_mode="range"` requires column to be an integer, date or timestamp (can be NULL, but not recommended). +* `partitioning_mode="hash"` accepts any column type (NOT NULL). +* `partitioning_mode="mod"` requires column to be an integer (NOT NULL). + +See documentation for [`partitioning_mode`](#onetl.connection.db_connection.oracle.options.OracleReadOptions.partitioning_mode) for more details + + + +#### *field* num_partitions *: PositiveInt* *= 1* *(alias 'numPartitions')* + +Number of jobs created by Spark to read the table content in parallel. +See documentation for [`partitioning_mode`](#onetl.connection.db_connection.oracle.options.OracleReadOptions.partitioning_mode) for more details + + +* **Constraints:** + - **exclusiveMinimum** = 0 + +#### *field* lower_bound *: int | None* *= None* *(alias 'lowerBound')* + +See documentation for [`partitioning_mode`](#onetl.connection.db_connection.oracle.options.OracleReadOptions.partitioning_mode) for more details + + + +#### *field* upper_bound *: int | None* *= None* *(alias 'upperBound')* + +See documentation for [`partitioning_mode`](#onetl.connection.db_connection.oracle.options.OracleReadOptions.partitioning_mode) for more details + + + +#### *field* session_init_statement *: str | None* *= None* *(alias 'sessionInitStatement')* + +After each database session is opened to the remote DB and before starting to read data, +this option executes a custom SQL statement (or a PL/SQL block). + +Use this to implement session initialization code. + +Example: + +```python +sessionInitStatement = """ + BEGIN + execute immediate + 'alter session set "_serial_direct_read"=true'; + END; +""" +``` + + + +#### *field* query_timeout *: int | None* *= None* *(alias 'queryTimeout')* + +The number of seconds the driver will wait for a statement to execute. +Zero means there is no limit. + +This option depends on driver implementation, +some drivers can check the timeout of each query instead of an entire JDBC batch. + + + +#### *field* fetchsize *: int* *= 100000* + +Fetch N rows from an opened cursor per one read round. + +Tuning this option can influence performance of reading. + +#### WARNING +Default value is different from Spark. + +Spark uses driver’s own value, and it may be different in different drivers, +and even versions of the same driver. For example, Oracle has +default `fetchsize=10`, which is absolutely not usable. + +Thus we’ve overridden default value with `100_000`, which should increase reading performance. + +#### Versionchanged +Changed in version 0.2.0: Set explicit default value to `100_000` + + + +#### *field* partitioning_mode *: JDBCPartitioningMode* *= JDBCPartitioningMode.RANGE* + +Defines how Spark will parallelize reading from table. + +Possible values: + +* `range` (default) + : Allocate each executor a range of values from column passed into [`partition_column`](#onetl.connection.db_connection.oracle.options.OracleReadOptions.partition_column). +
+ ### Spark generates for each executor an SQL query +
+ Executor 1: + ```sql + SELECT ... FROM table + WHERE (partition_column >= lowerBound + OR partition_column IS NULL) + AND partition_column < (lower_bound + stride) + ``` +
+ Executor 2: + ```sql + SELECT ... FROM table + WHERE partition_column >= (lower_bound + stride) + AND partition_column < (lower_bound + 2 * stride) + ``` +
+ … +
+ Executor N: + ```sql + SELECT ... FROM table + WHERE partition_column >= (lower_bound + (N-1) * stride) + AND partition_column <= upper_bound + ``` +
+ Where `stride=(upper_bound - lower_bound) / num_partitions`. +
+ #### NOTE + Can be used only with columns of integer, date or timestamp types. +
+ #### NOTE + [`lower_bound`](#onetl.connection.db_connection.oracle.options.OracleReadOptions.lower_bound), [`upper_bound`](#onetl.connection.db_connection.oracle.options.OracleReadOptions.upper_bound) and [`num_partitions`](#onetl.connection.db_connection.oracle.options.OracleReadOptions.num_partitions) are used just to + calculate the partition stride, **NOT** for filtering the rows in table. + So all rows in the table will be returned (unlike *Incremental* [Read Strategies](../../../strategy/index.md#strategy)). +
+ #### NOTE + All queries are executed in parallel. To execute them sequentially, use *Batch* [Read Strategies](../../../strategy/index.md#strategy). +* `hash` + : Allocate each executor a set of values based on hash of the [`partition_column`](#onetl.connection.db_connection.oracle.options.OracleReadOptions.partition_column) column. +
+ ### Spark generates for each executor an SQL query +
+ Executor 1: + ```sql + SELECT ... FROM table + WHERE (some_hash(partition_column) mod num_partitions) = 0 -- lower_bound + ``` +
+ Executor 2: + ```sql + SELECT ... FROM table + WHERE (some_hash(partition_column) mod num_partitions) = 1 -- lower_bound + 1 + ``` +
+ … +
+ Executor N: + ```sql + SELECT ... FROM table + WHERE (some_hash(partition_column) mod num_partitions) = num_partitions-1 -- upper_bound + ``` +
+ #### NOTE + The hash function implementation depends on RDBMS. It can be `MD5` or any other fast hash function, + or expression based on this function call. Usually such functions accepts any column type as an input. +* `mod` + : Allocate each executor a set of values based on modulus of the [`partition_column`](#onetl.connection.db_connection.oracle.options.OracleReadOptions.partition_column) column. +
+ ### Spark generates for each executor an SQL query +
+ Executor 1: + ```sql + SELECT ... FROM table + WHERE (partition_column mod num_partitions) = 0 -- lower_bound + ``` +
+ Executor 2: + ```sql + SELECT ... FROM table + WHERE (partition_column mod num_partitions) = 1 -- lower_bound + 1 + ``` +
+ Executor N: + ```sql + SELECT ... FROM table + WHERE (partition_column mod num_partitions) = num_partitions-1 -- upper_bound + ``` +
+ #### NOTE + Can be used only with columns of integer type. + +#### Versionadded +Added in version 0.5.0. + +### Examples + +Read data in 10 parallel jobs by range of values in `id_column` column: + +```python +ReadOptions( + partitioning_mode="range", # default mode, can be omitted + partitionColumn="id_column", + numPartitions=10, + # Options below can be discarded because they are + # calculated automatically as MIN and MAX values of `partitionColumn` + lowerBound=0, + upperBound=100_000, +) +``` + +Read data in 10 parallel jobs by hash of values in `some_column` column: + +```python +ReadOptions( + partitioning_mode="hash", + partitionColumn="some_column", + numPartitions=10, + # lowerBound and upperBound are automatically set to `0` and `9` +) +``` + +Read data in 10 parallel jobs by modulus of values in `id_column` column: + +```python +ReadOptions( + partitioning_mode="mod", + partitionColumn="id_column", + numPartitions=10, + # lowerBound and upperBound are automatically set to `0` and `9` +) +``` + + diff --git a/mddocs/connection/db_connection/oracle/sql.md b/mddocs/connection/db_connection/oracle/sql.md new file mode 100644 index 000000000..09059e7aa --- /dev/null +++ b/mddocs/connection/db_connection/oracle/sql.md @@ -0,0 +1,192 @@ + + +# Reading from Oracle using `Oracle.sql` + +`Oracle.sql` allows passing custom SQL query, but does not support incremental strategies. + +#### WARNING +Please take into account [Oracle <-> Spark type mapping](types.md#oracle-types) + +#### WARNING +Statement is executed in **read-write** connection, so if you’re calling some functions/procedures with DDL/DML statements inside, +they can change data in your database. + +## Syntax support + +Only queries with the following syntax are supported: + +* ✅︎ `SELECT ... FROM ...` +* ✅︎ `WITH alias AS (...) SELECT ...` +* ❌ `SHOW ...` +* ❌ `SET ...; SELECT ...;` - multiple statements not supported + +## Examples + +```python +from onetl.connection import Oracle + +oracle = Oracle(...) +df = oracle.sql( + """ + SELECT + id, + key, + CAST(value AS VARCHAR2(4000)) value, + updated_at + FROM + some.mytable + WHERE + key = 'something' + """, + options=Oracle.SQLOptions( + partitionColumn="id", + numPartitions=10, + lowerBound=0, + upperBound=1000, + ), +) +``` + +## Recommendations + +### Select only required columns + +Instead of passing `SELECT * FROM ...` prefer passing exact column names `SELECT col1, col2, ...`. +This reduces the amount of data passed from Oracle to Spark. + +### Pay attention to `where` value + +Instead of filtering data on Spark side using `df.filter(df.column == 'value')` pass proper `WHERE column = 'value'` clause. +This both reduces the amount of data send from Oracle to Spark, and may also improve performance of the query. +Especially if there are indexes or partitions for columns used in `where` clause. + +## Options + +### *pydantic model* onetl.connection.db_connection.oracle.options.OracleSQLOptions + +Options specifically for SQL queries + +These options allow you to specify configurations for executing SQL queries +without relying on Spark’s partitioning mechanisms. + +#### Versionadded +Added in version 0.11.0: Split up `Oracle.ReadOptions` to `Oracle.SQLOptions` + +### Examples + +#### NOTE +You can pass any JDBC configuration +[supported by Spark](https://spark.apache.org/docs/latest/sql-data-sources-jdbc.html), +tailored to optimize SQL query execution. **Option names should be in** `camelCase`! + +```python +from onetl.connection import Oracle + +options = Oracle.SQLOptions( + partitionColumn="reg_id", + numPartitions=10, + lowerBound=0, + upperBound=1000, + customSparkOption="value", +) +``` + + + +#### *field* partition_column *: str | None* *= None* *(alias 'partitionColumn')* + +Column used to partition data across multiple executors for parallel query processing. + +#### WARNING +It is highly recommended to use primary key, or column with an index +to avoid performance issues. + +### Example of using `partitionColumn="id"` with `partitioning_mode="range"` + +```sql +-- If partition_column is 'id', with numPartitions=4, lowerBound=1, and upperBound=100: +-- Executor 1 processes IDs from 1 to 25 +SELECT ... FROM table WHERE id >= 1 AND id < 26 +-- Executor 2 processes IDs from 26 to 50 +SELECT ... FROM table WHERE id >= 26 AND id < 51 +-- Executor 3 processes IDs from 51 to 75 +SELECT ... FROM table WHERE id >= 51 AND id < 76 +-- Executor 4 processes IDs from 76 to 100 +SELECT ... FROM table WHERE id >= 76 AND id <= 100 + +-- General case for Executor N +SELECT ... FROM table +WHERE partition_column >= (lowerBound + (N-1) * stride) +AND partition_column <= upperBound +-- Where ``stride`` is calculated as ``(upperBound - lowerBound) / numPartitions``. +``` + + + +#### *field* num_partitions *: int | None* *= None* *(alias 'numPartitions')* + +Number of jobs created by Spark to read the table content in parallel. + + + +#### *field* lower_bound *: int | None* *= None* *(alias 'lowerBound')* + +Defines the starting boundary for partitioning the query’s data. Mandatory if [`partition_column`](#onetl.connection.db_connection.oracle.options.OracleSQLOptions.partition_column) is set + + + +#### *field* upper_bound *: int | None* *= None* *(alias 'upperBound')* + +Sets the ending boundary for data partitioning. Mandatory if [`partition_column`](#onetl.connection.db_connection.oracle.options.OracleSQLOptions.partition_column) is set + + + +#### *field* session_init_statement *: str | None* *= None* *(alias 'sessionInitStatement')* + +After each database session is opened to the remote DB and before starting to read data, +this option executes a custom SQL statement (or a PL/SQL block). + +Use this to implement session initialization code. + +Example: + +```python +sessionInitStatement = """ + BEGIN + execute immediate + 'alter session set "_serial_direct_read"=true'; + END; +""" +``` + + + +#### *field* query_timeout *: int | None* *= None* *(alias 'queryTimeout')* + +The number of seconds the driver will wait for a statement to execute. +Zero means there is no limit. + +This option depends on driver implementation, +some drivers can check the timeout of each query instead of an entire JDBC batch. + + + +#### *field* fetchsize *: int* *= 100000* + +Fetch N rows from an opened cursor per one read round. + +Tuning this option can influence performance of reading. + +#### WARNING +Default value is different from Spark. + +Spark uses driver’s own value, and it may be different in different drivers, +and even versions of the same driver. For example, Oracle has +default `fetchsize=10`, which is absolutely not usable. + +Thus we’ve overridden default value with `100_000`, which should increase reading performance. + +#### Versionchanged +Changed in version 0.2.0: Set explicit default value to `100_000` + + diff --git a/mddocs/connection/db_connection/oracle/types.md b/mddocs/connection/db_connection/oracle/types.md new file mode 100644 index 000000000..130c57855 --- /dev/null +++ b/mddocs/connection/db_connection/oracle/types.md @@ -0,0 +1,303 @@ + + +# Oracle <-> Spark type mapping + +#### NOTE +The results below are valid for Spark 3.5.5, and may differ on other Spark versions. + +## Type detection & casting + +Spark’s DataFrames always have a `schema` which is a list of columns with corresponding Spark types. All operations on a column are performed using column type. + +### Reading from Oracle + +This is how Oracle connector performs this: + +* For each column in query result (`SELECT column1, column2, ... FROM table ...`) get column name and Oracle type. +* Find corresponding `Oracle type (read)` → `Spark type` combination (see below) for each DataFrame column. If no combination is found, raise exception. +* Create DataFrame from query with specific column names and Spark types. + +### Writing to some existing Oracle table + +This is how Oracle connector performs this: + +* Get names of columns in DataFrame. [1](#id3) +* Perform `SELECT * FROM table LIMIT 0` query. +* Take only columns present in DataFrame (by name, case insensitive). For each found column get Clickhouse type. +* **Find corresponding** `Oracle type (read)` → `Spark type` **combination** (see below) for each DataFrame column. If no combination is found, raise exception. [2](#id4) +* Find corresponding `Spark type` → `Oracle type (write)` combination (see below) for each DataFrame column. If no combination is found, raise exception. +* If `Oracle type (write)` match `Oracle type (read)`, no additional casts will be performed, DataFrame column will be written to Oracle as is. +* If `Oracle type (write)` does not match `Oracle type (read)`, DataFrame column will be casted to target column type **on Oracle side**. + For example, you can write column with text data to `int` column, if column contains valid integer values within supported value range and precision. + +* **[1]** This allows to write data to tables with `DEFAULT` and `GENERATED` columns - if DataFrame has no such column, it will be populated by Oracle. +* **[2]** Yes, this is weird. + +### Create new table using Spark + +#### WARNING +ABSOLUTELY NOT RECOMMENDED! + +This is how Oracle connector performs this: + +* Find corresponding `Spark type` → `Oracle type (create)` combination (see below) for each DataFrame column. If no combination is found, raise exception. +* Generate DDL for creating table in Oracle, like `CREATE TABLE (col1 ...)`, and run it. +* Write DataFrame to created table as is. + +But Oracle connector support only limited number of types and almost no custom clauses (like `PARTITION BY`, `INDEX`, etc). +So instead of relying on Spark to create tables: + +### See example + +```python +writer = DBWriter( + connection=oracle, + target="public.table", + options=Oracle.WriteOptions(if_exists="append"), +) +writer.run(df) +``` + +Always prefer creating table with desired DDL **BEFORE WRITING DATA**: + +### See example + +```python +oracle.execute( + """ + CREATE TABLE username.table ( + id NUMBER, + business_dt TIMESTAMP(6), + value VARCHAR2(2000) + ) + """, +) + +writer = DBWriter( + connection=oracle, + target="public.table", + options=Oracle.WriteOptions(if_exists="append"), +) +writer.run(df) +``` + +See Oracle [CREATE TABLE](https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/SELECT.html) documentation. + +## Supported types + +### References + +See [List of Oracle types](https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/Data-Types.html). + +Here you can find source code with type conversions: + +* [JDBC -> Spark](https://github.com/apache/spark/blob/v3.5.5/sql/core/src/main/scala/org/apache/spark/sql/jdbc/OracleDialect.scala#L83-L109) +* [Spark -> JDBC](https://github.com/apache/spark/blob/v3.5.5/sql/core/src/main/scala/org/apache/spark/sql/jdbc/OracleDialect.scala#L111-L123) + +### Numeric types + +| Oracle type (read) | Spark type | Oracle type (write) | Oracle type (create) | +|-----------------------------|----------------------------------|----------------------------|-------------------------| +| `NUMBER` | `DecimalType(P=38, S=10)` | `NUMBER(P=38, S=10)` | `NUMBER(P=38, S=10)` | +| `NUMBER(P=0..38)` | `DecimalType(P=0..38, S=0)` | `NUMBER(P=0..38, S=0)` | `NUMBER(P=38, S=0)` | +| `NUMBER(P=0..38, S=0..38)` | `DecimalType(P=0..38, S=0..38)` | `NUMBER(P=0..38, S=0..38)` | `NUMBER(P=38, S=0..38)` | +| `NUMBER(P=..., S=-127..-1)` | unsupported [3](#id6) | | | +| `FLOAT` | `DecimalType(P=38, S=10)` | `NUMBER(P=38, S=10)` | `NUMBER(P=38, S=10)` | +| `FLOAT(N)` | | | | +| `REAL` | | | | +| `DOUBLE PRECISION` | | | | +| `BINARY_FLOAT` | `FloatType()` | `NUMBER(P=19, S=4)` | `NUMBER(P=19, S=4)` | +| `BINARY_DOUBLE` | `DoubleType()` | | | +| `SMALLINT` | `DecimalType(P=38, S=0)` | `NUMBER(P=38, S=0)` | `NUMBER(P=38, S=0)` | +| `INTEGER` | | | | +| `LONG` | `StringType()` | `CLOB` | `CLOB` | +* **[3]** Oracle support decimal types with negative scale, like `NUMBER(38, -10)`. Spark doesn’t. + +### Temporal types + +| Oracle type (read) | Spark type | Oracle type (write) | Oracle type (create) | +|-------------------------------------|------------------------------------------------------------------------------|------------------------------------------------------|------------------------------------------------------| +| `DATE`, days | `TimestampType()`, microseconds | `TIMESTAMP(6)`, microseconds | `TIMESTAMP(6)`, microseconds | +| `TIMESTAMP`, microseconds | `TimestampType()`, microseconds | `TIMESTAMP(6)`, microseconds | `TIMESTAMP(6)`, microseconds | +| `TIMESTAMP(0)`, seconds | | | | +| `TIMESTAMP(3)`, milliseconds | | | | +| `TIMESTAMP(6)`, microseconds | | | | +| `TIMESTAMP(9)`, nanoseconds | `TimestampType()`, microseconds,
**precision loss** [4](#id8) | `TIMESTAMP(6)`, microseconds,
**precision loss** | `TIMESTAMP(6)`, microseconds,
**precision loss** | +| `TIMESTAMP WITH TIME ZONE` | unsupported | | | +| `TIMESTAMP(N) WITH TIME ZONE` | | | | +| `TIMESTAMP WITH LOCAL TIME ZONE` | | | | +| `TIMESTAMP(N) WITH LOCAL TIME ZONE` | | | | +| `INTERVAL YEAR TO MONTH` | | | | +| `INTERVAL DAY TO SECOND` | | | | + +#### WARNING +Note that types in Oracle and Spark have different value ranges: + +| Oracle type | Min value | Max value | Spark type | Min value | Max value | +|---------------|----------------------------------|---------------------------------|-------------------|------------------------------|------------------------------| +| `date` | `-4712-01-01` | `9999-01-01` | `DateType()` | `0001-01-01` | `9999-12-31` | +| `timestamp` | `-4712-01-01 00:00:00.000000000` | `9999-12-31 23:59:59.999999999` | `TimestampType()` | `0001-01-01 00:00:00.000000` | `9999-12-31 23:59:59.999999` | + +So not all of values can be read from Oracle to Spark. + +References: +: * [Oracle date, timestamp and intervals documentation](https://oracle-base.com/articles/misc/oracle-dates-timestamps-and-intervals#DATE) + * [Spark DateType documentation](https://spark.apache.org/docs/latest/api/java/org/apache/spark/sql/types/DateType.html) + * [Spark TimestampType documentation](https://spark.apache.org/docs/latest/api/java/org/apache/spark/sql/types/TimestampType.html) + +* **[4]** Oracle support timestamp up to nanoseconds precision (`23:59:59.999999999`), but Spark `TimestampType()` supports datetime up to microseconds precision (`23:59:59.999999`). Nanoseconds will be lost during read or write operations. + +### String types + +| Oracle type (read) | Spark type | Oracle type (write) | Oracle type (create) | +|----------------------|----------------|-----------------------|------------------------| +| `CHAR` | `StringType()` | `CLOB` | `CLOB` | +| `CHAR(N CHAR)` | | | | +| `CHAR(N BYTE)` | | | | +| `NCHAR` | | | | +| `NCHAR(N)` | | | | +| `VARCHAR(N)` | | | | +| `LONG VARCHAR` | | | | +| `VARCHAR2(N CHAR)` | | | | +| `VARCHAR2(N BYTE)` | | | | +| `NVARCHAR2(N)` | | | | +| `CLOB` | | | | +| `NCLOB` | | | | + +### Binary types + +| Oracle type (read) | Spark type | Oracle type (write) | Oracle type (create) | +|----------------------|----------------|-----------------------|------------------------| +| `RAW(N)` | `BinaryType()` | `BLOB` | `BLOB` | +| `LONG RAW` | | | | +| `BLOB` | | | | +| `BFILE` | unsupported | | | + +### Struct types + +| Oracle type (read) | Spark type | Oracle type (write) | Oracle type (create) | +|-----------------------------------|----------------|-----------------------|------------------------| +| `XMLType` | `StringType()` | `CLOB` | `CLOB` | +| `URIType` | | | | +| `DBURIType` | | | | +| `XDBURIType` | | | | +| `HTTPURIType` | | | | +| `CREATE TYPE ... AS OBJECT (...)` | | | | +| `JSON` | unsupported | | | +| `CREATE TYPE ... AS VARRAY ...` | | | | +| `CREATE TYPE ... AS TABLE OF ...` | | | | + +### Special types + +| Oracle type (read) | Spark type | Oracle type (write) | Oracle type (create) | +|----------------------|-----------------|-----------------------|------------------------| +| `BOOLEAN` | `BooleanType()` | `BOOLEAN` | `NUMBER(P=1, S=0)` | +| `ROWID` | `StringType()` | `CLOB` | `CLOB` | +| `UROWID` | | | | +| `UROWID(N)` | | | | +| `ANYTYPE` | unsupported | | | +| `ANYDATA` | | | | +| `ANYDATASET` | | | | + +## Explicit type cast + +### `DBReader` + +It is possible to explicitly cast column of unsupported type using `DBReader(columns=...)` syntax. + +For example, you can use `CAST(column AS CLOB)` to convert data to string representation on Oracle side, and so it will be read as Spark’s `StringType()`. + +It is also possible to use [JSON_ARRAY](https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/JSON_ARRAY.html) +or [JSON_OBJECT](https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/JSON_OBJECT.html) Oracle functions +to convert column of any type to string representation. Then this JSON string can then be effectively parsed using the [`JSON.parse_column`](../../../file_df/file_formats/json.md#onetl.file.format.json.JSON.parse_column) method. + +```python +from onetl.file.format import JSON +from pyspark.sql.types import IntegerType, StructType, StructField + +from onetl.connection import Oracle +from onetl.db import DBReader + +oracle = Oracle(...) + +DBReader( + connection=oracle, + columns=[ + "id", + "supported_column", + "CAST(unsupported_column AS VARCHAR2(4000)) unsupported_column_str", + # or + "JSON_ARRAY(array_column) array_column_json", + ], +) +df = reader.run() + +json_scheme = StructType([StructField("key", IntegerType())]) + +df = df.select( + df.id, + df.supported_column, + df.unsupported_column_str.cast("integer").alias("parsed_integer"), + JSON().parse_column("array_column_json", json_scheme).alias("array_column"), +) +``` + +### `DBWriter` + +It is always possible to convert data on Spark side to string, and then write it to text column in Oracle table. + +To serialize and write JSON data to a `text` or `json` column in an Oracle table use the [`JSON.serialize_column`](../../../file_df/file_formats/json.md#onetl.file.format.json.JSON.serialize_column) method. + +```python +from onetl.connection import Oracle +from onetl.db import DBWriter +from onetl.file.format import JSON + +oracle = Oracle(...) + +oracle.execute( + """ + CREATE TABLE schema.target_table ( + id INTEGER, + supported_column TIMESTAMP, + array_column_json VARCHAR2(4000) -- any string type, actually + ) + """, +) + +write_df = df.select( + df.id, + df.supported_column, + JSON().serialize_column(df.unsupported_column).alias("array_column_json"), +) + +writer = DBWriter( + connection=oracle, + target="schema.target_table", +) +writer.run(write_df) +``` + +Then you can parse this column on Oracle side - for example, by creating a view: + +```sql +SELECT + id, + supported_column, + JSON_VALUE(array_column_json, '$[0]' RETURNING NUMBER) AS array_item_0 +FROM + schema.target_table +``` + +Or by using [VIRTUAL column](https://oracle-base.com/articles/11g/virtual-columns-11gr1): + +```sql +CREATE TABLE schema.target_table ( + id INTEGER, + supported_column TIMESTAMP, + array_column_json VARCHAR2(4000), -- any string type, actually + array_item_0 GENERATED ALWAYS AS (JSON_VALUE(array_column_json, '$[0]' RETURNING NUMBER)) VIRTUAL +) +``` + +But data will be parsed on each table read in any case, as Oracle does no support `GENERATED ALWAYS AS (...) STORED` columns. diff --git a/mddocs/connection/db_connection/oracle/write.md b/mddocs/connection/db_connection/oracle/write.md new file mode 100644 index 000000000..f1be20d24 --- /dev/null +++ b/mddocs/connection/db_connection/oracle/write.md @@ -0,0 +1,183 @@ + + +# Writing to Oracle using `DBWriter` + +For writing data to Oracle, use [`DBWriter`](../../../db/db_writer.md#onetl.db.db_writer.db_writer.DBWriter). + +#### WARNING +Please take into account [Oracle <-> Spark type mapping](types.md#oracle-types) + +#### WARNING +It is always recommended to create table explicitly using [Oracle.execute](execute.md#oracle-execute) +instead of relying on Spark’s table DDL generation. + +This is because Spark’s DDL generator can create columns with different precision and types than it is expected, +causing precision loss or other issues. + +## Examples + +```python +from onetl.connection import Oracle +from onetl.db import DBWriter + +oracle = Oracle(...) + +df = ... # data is here + +writer = DBWriter( + connection=oracle, + target="schema.table", + options=Oracle.WriteOptions(if_exists="append"), +) + +writer.run(df) +``` + +## Options + +Method above accepts [`OracleWriteOptions`](#onetl.connection.db_connection.oracle.options.OracleWriteOptions) + +### *pydantic model* onetl.connection.db_connection.oracle.options.OracleWriteOptions + +Spark JDBC writing options. + +#### Versionadded +Added in version 0.5.0: Replace `Oracle.Options` → `Oracle.WriteOptions` + +### Examples + +#### NOTE +You can pass any value +[supported by Spark](https://spark.apache.org/docs/latest/sql-data-sources-jdbc.html), +even if it is not mentioned in this documentation. **Option names should be in** `camelCase`! + +The set of supported options depends on Spark version. + +```python +from onetl.connection import Oracle + +options = Oracle.WriteOptions( + if_exists="append", + batchsize=20_000, + customSparkOption="value", +) +``` + + + +#### *field* if_exists *: JDBCTableExistBehavior* *= JDBCTableExistBehavior.APPEND* *(alias 'mode')* + +Behavior of writing data into existing table. + +Possible values: +: * `append` (default) + : Adds new rows into existing table. +
+ ### Behavior in details +
+ * Table does not exist + : Table is created using options provided by user + (`createTableOptions`, `createTableColumnTypes`, etc). + * Table exists + : Data is appended to a table. Table has the same DDL as before writing data +
+ #### WARNING + This mode does not check whether table already contains + rows from dataframe, so duplicated rows can be created. +
+ Also Spark does not support passing custom options to + insert statement, like `ON CONFLICT`, so don’t try to + implement deduplication using unique indexes or constraints. +
+ Instead, write to staging table and perform deduplication + using `execute` method. + * `replace_entire_table` + : **Table is dropped and then created, or truncated**. +
+ ### Behavior in details +
+ * Table does not exist + : Table is created using options provided by user + (`createTableOptions`, `createTableColumnTypes`, etc). + * Table exists + : Table content is replaced with dataframe content. +
+ After writing completed, target table could either have the same DDL as + before writing data (`truncate=True`), or can be recreated (`truncate=False` + or source does not support truncation). + * `ignore` + : Ignores the write operation if the table already exists. +
+ ### Behavior in details +
+ * Table does not exist + : Table is created using options provided by user + (`createTableOptions`, `createTableColumnTypes`, etc). + * Table exists + : The write operation is ignored, and no data is written to the table. + * `error` + : Raises an error if the table already exists. +
+ ### Behavior in details +
+ * Table does not exist + : Table is created using options provided by user + (`createTableOptions`, `createTableColumnTypes`, etc). + * Table exists + : An error is raised, and no data is written to the table. + +#### Versionchanged +Changed in version 0.9.0: Renamed `mode` → `if_exists` + + + +#### *field* query_timeout *: int | None* *= None* *(alias 'queryTimeout')* + +The number of seconds the driver will wait for a statement to execute. +Zero means there is no limit. + +This option depends on driver implementation, +some drivers can check the timeout of each query instead of an entire JDBC batch. + + + +#### *field* batchsize *: int* *= 20000* + +How many rows can be inserted per round trip. + +Tuning this option can influence performance of writing. + +#### WARNING +Default value is different from Spark. + +Spark uses quite small value `1000`, which is absolutely not usable +in BigData world. + +Thus we’ve overridden default value with `20_000`, +which should increase writing performance. + +You can increase it even more, up to `50_000`, +but it depends on your database load and number of columns in the row. +Higher values does not increase performance. + +#### Versionchanged +Changed in version 0.4.0: Changed default value from 1000 to 20_000 + + + +#### *field* isolation_level *: str* *= 'READ_UNCOMMITTED'* *(alias 'isolationLevel')* + +The transaction isolation level, which applies to current connection. + +Possible values: +: * `NONE` (as string, not Python’s `None`) + * `READ_COMMITTED` + * `READ_UNCOMMITTED` + * `REPEATABLE_READ` + * `SERIALIZABLE` + +Values correspond to transaction isolation levels defined by JDBC standard. +Please refer the documentation for +[java.sql.Connection](https://docs.oracle.com/javase/8/docs/api/java/sql/Connection.html). + + diff --git a/mddocs/connection/db_connection/postgres/connection.md b/mddocs/connection/db_connection/postgres/connection.md new file mode 100644 index 000000000..0ca99169c --- /dev/null +++ b/mddocs/connection/db_connection/postgres/connection.md @@ -0,0 +1,133 @@ + + +# Postgres connection + +### *class* onetl.connection.db_connection.postgres.connection.Postgres(\*, spark: SparkSession, user: str, password: SecretStr, host: Host, database: str, port: int = 5432, extra: PostgresExtra = PostgresExtra(stringtype='unspecified', tcpKeepAlive='true')) + +PostgreSQL JDBC connection. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +Based on Maven package [org.postgresql:postgresql:42.7.5](https://mvnrepository.com/artifact/org.postgresql/postgresql/42.7.5) +([official Postgres JDBC driver](https://jdbc.postgresql.org/)). + +#### SEE ALSO +Before using this connector please take into account [Prerequisites](prerequisites.md#postgres-prerequisites) + +#### Versionadded +Added in version 0.1.0. + +* **Parameters:** + **host** + : Host of Postgres database. For example: `test.postgres.domain.com` or `193.168.1.11` + + **port** + : Port of Postgres database + + **user** + : User, which have proper access to the database. For example: `some_user` + + **password** + : Password for database connection + + **database** + : Database in RDBMS, NOT schema. +
+ See [this page](https://www.educba.com/postgresql-database-vs-schema/) for more details + + **spark** + : Spark session. + + **extra** + : Specifies one or more extra parameters by which clients can connect to the instance. +
+ For example: `{"ssl": "false"}` +
+ See [Postgres JDBC driver properties documentation](https://jdbc.postgresql.org/documentation/use/) + for more details + +### Examples + +Create and check Postgres connection: + +```python +from onetl.connection import Postgres +from pyspark.sql import SparkSession + +# Create Spark session with Postgres driver loaded +maven_packages = Postgres.get_packages() +spark = ( + SparkSession.builder.appName("spark-app-name") + .config("spark.jars.packages", ",".join(maven_packages)) + .getOrCreate() +) + +# Create connection +postgres = Postgres( + host="database.host.or.ip", + user="user", + password="*****", + database="target_database", + spark=spark, +) +``` + +Create read-only connection: + +```python +... + +# Create connection +postgres = Postgres( + host="database.host.or.ip", + user="user", + password="*****", + database="target_database", + extra={"readOnly": True, "readOnlyMode": "always"}, # <-- + spark=spark, +).check() +``` + + + +#### check() + +Check source availability. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +If not, an exception will be raised. + +* **Returns:** + Connection itself +* **Raises:** + RuntimeError + : If the connection is not available + +### Examples + +```python +connection.check() +``` + + + +#### *classmethod* get_packages(package_version: str | None = None) → list[str] + +Get package names to be downloaded by Spark. Allows specifying a custom JDBC driver version. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### Versionadded +Added in version 0.9.0. + +* **Parameters:** + **package_version** + : Specifies the version of the PostgreSQL JDBC driver to use. Defaults to `42.7.5`. + +### Examples + +```python +from onetl.connection import Postgres + +Postgres.get_packages() + +# custom package version +Postgres.get_packages(package_version="42.6.0") +``` + + diff --git a/mddocs/connection/db_connection/postgres/execute.md b/mddocs/connection/db_connection/postgres/execute.md new file mode 100644 index 000000000..e28ebc5da --- /dev/null +++ b/mddocs/connection/db_connection/postgres/execute.md @@ -0,0 +1,189 @@ + + +# Executing statements in Postgres + +#### WARNING +Methods below **read all the rows** returned from DB **to Spark driver memory**, and then convert them to DataFrame. + +Do **NOT** use them to read large amounts of data. Use [DBReader](read.md#postgres-read) or [Postgres.sql](sql.md#postgres-sql) instead. + +## How to + +There are 2 ways to execute some statement in Postgres + +### Use `Postgres.fetch` + +Use this method to execute some `SELECT` query which returns **small number or rows**, like reading +Postgres config, or reading data from some reference table. Method returns Spark DataFrame. + +Method accepts [`Postgres.FetchOptions`](#onetl.connection.db_connection.postgres.options.PostgresFetchOptions). + +Connection opened using this method should be then closed with `connection.close()` or `with connection:`. + +#### WARNING +Please take into account [Postgres <-> Spark type mapping](types.md#postgres-types). + +#### Syntax support + +This method supports **any** query syntax supported by Postgres, like: + +* ✅︎ `SELECT ... FROM ...` +* ✅︎ `WITH alias AS (...) SELECT ...` +* ❌ `SET ...; SELECT ...;` - multiple statements not supported + +#### Examples + +```python +from onetl.connection import Postgres + +postgres = Postgres(...) + +df = postgres.fetch( + "SELECT value FROM some.reference_table WHERE key = 'some_constant'", + options=Postgres.FetchOptions(queryTimeout=10), +) +postgres.close() +value = df.collect()[0][0] # get value from first row and first column +``` + +### Use `Postgres.execute` + +Use this method to execute DDL and DML operations. Each method call runs operation in a separated transaction, and then commits it. + +Method accepts [`Postgres.ExecuteOptions`](#onetl.connection.db_connection.postgres.options.PostgresExecuteOptions). + +Connection opened using this method should be then closed with `connection.close()` or `with connection:`. + +#### Syntax support + +This method supports **any** query syntax supported by Postgres, like: + +* ✅︎ `CREATE TABLE ...`, `CREATE VIEW ...`, and so on +* ✅︎ `ALTER ...` +* ✅︎ `INSERT INTO ... SELECT ...`, `UPDATE ...`, `DELETE ...`, and so on +* ✅︎ `DROP TABLE ...`, `DROP VIEW ...`, `TRUNCATE TABLE`, and so on +* ✅︎ `CALL procedure(arg1, arg2) ...` +* ✅︎ `SELECT func(arg1, arg2)` or `{call func(arg1, arg2)}` - special syntax for calling functions +* ✅︎ other statements not mentioned here +* ❌ `SET ...; SELECT ...;` - multiple statements not supported + +#### Examples + +```python +from onetl.connection import Postgres + +postgres = Postgres(...) + +postgres.execute("DROP TABLE schema.table") +postgres.execute( + """ + CREATE TABLE schema.table ( + id bigint GENERATED ALWAYS AS IDENTITY, + key text, + value real + ) + """, + options=Postgres.ExecuteOptions(queryTimeout=10), +) +``` + +## Options + +### *pydantic model* onetl.connection.db_connection.postgres.options.PostgresFetchOptions + +Options related to fetching data from databases via JDBC. + +#### Versionadded +Added in version 0.11.0: Replace `Postgres.JDBCOptions` → `Postgres.FetchOptions` + +### Examples + +#### NOTE +You can pass any value supported by underlying JDBC driver class, +even if it is not mentioned in this documentation. + +```python +from onetl.connection import Postgres + +options = Postgres.FetchOptions( + queryTimeout=60_000, + fetchsize=100_000, + customSparkOption="value", +) +``` + + +* **Fields:** + - [`fetchsize (int | None)`](#onetl.connection.db_connection.postgres.options.PostgresFetchOptions.fetchsize) + - [`query_timeout (int | None)`](#onetl.connection.db_connection.postgres.options.PostgresFetchOptions.query_timeout) + +#### *field* query_timeout *: int | None* *= None* *(alias 'queryTimeout')* + +The number of seconds the driver will wait for a statement to execute. +Zero means there is no limit. + +This option depends on driver implementation, +some drivers can check the timeout of each query instead of an entire JDBC batch. + + + +#### *field* fetchsize *: int | None* *= None* + +How many rows to fetch per round trip. + +Tuning this option can influence performance of reading. + +#### WARNING +Default value depends on driver. For example, Oracle has +default `fetchsize=10`. + + + +### *pydantic model* onetl.connection.db_connection.postgres.options.PostgresExecuteOptions + +Options related to executing statements in databases via JDBC. + +#### Versionadded +Added in version 0.11.0: Replace `Postgres.JDBCOptions` → `Postgres.ExecuteOptions` + +### Examples + +#### NOTE +You can pass any value supported by underlying JDBC driver class, +even if it is not mentioned in this documentation. + +```python +from onetl.connection import Postgres + +options = Postgres.ExecuteOptions( + queryTimeout=60_000, + customSparkOption="value", +) +``` + + +* **Fields:** + - [`fetchsize (int | None)`](#onetl.connection.db_connection.postgres.options.PostgresExecuteOptions.fetchsize) + - [`query_timeout (int | None)`](#onetl.connection.db_connection.postgres.options.PostgresExecuteOptions.query_timeout) + +#### *field* query_timeout *: int | None* *= None* *(alias 'queryTimeout')* + +The number of seconds the driver will wait for a statement to execute. +Zero means there is no limit. + +This option depends on driver implementation, +some drivers can check the timeout of each query instead of an entire JDBC batch. + + + +#### *field* fetchsize *: int | None* *= None* + +How many rows to fetch per round trip. + +Tuning this option can influence performance of reading. + +#### WARNING +Default value depends on driver. For example, Oracle has +default `fetchsize=10`. + + diff --git a/mddocs/connection/db_connection/postgres/index.md b/mddocs/connection/db_connection/postgres/index.md new file mode 100644 index 000000000..e1a8f76eb --- /dev/null +++ b/mddocs/connection/db_connection/postgres/index.md @@ -0,0 +1,19 @@ + + +# Postgres + +# Connection + +* [Prerequisites](prerequisites.md) +* [Postgres connection](connection.md) + +# Operations + +* [Reading from Postgres using `DBReader`](read.md) +* [Reading from Postgres using `Postgres.sql`](sql.md) +* [Writing to Postgres using `DBWriter`](write.md) +* [Executing statements in Postgres](execute.md) + +# Troubleshooting + +* [Postgres <-> Spark type mapping](types.md) diff --git a/mddocs/connection/db_connection/postgres/prerequisites.md b/mddocs/connection/db_connection/postgres/prerequisites.md new file mode 100644 index 000000000..2bfb04e0f --- /dev/null +++ b/mddocs/connection/db_connection/postgres/prerequisites.md @@ -0,0 +1,71 @@ + + +# Prerequisites + +## Version Compatibility + +* PostgreSQL server versions: + : * Officially declared: 8.2 - 17 + * Actually tested: 9.4.26, 17.3 +* Spark versions: 2.3.x - 3.5.x +* Java versions: 8 - 20 + +See [official documentation](https://jdbc.postgresql.org/). + +## Installing PySpark + +To use Postgres connector you should have PySpark installed (or injected to `sys.path`) +BEFORE creating the connector instance. + +See [Spark](../../../install/spark.md#install-spark) installation instruction for more details. + +## Connecting to Postgres + +### Allowing connection to Postgres instance + +Ask your Postgres administrator to allow your user (and probably IP) to connect to instance, +e.g. by updating `pg_hba.conf` file. + +See [official documentation](https://www.postgresql.org/docs/current/auth-pg-hba-conf.html). + +### Connection port + +Connection is usually performed to port 5432. Port may differ for different Postgres instances. +Please ask your Postgres administrator to provide required information. + +### Connection host + +It is possible to connect to Postgres by using either DNS name of host or it’s IP address. + +If you’re using Postgres cluster, it is currently possible to connect only to **one specific node**. +Connecting to multiple nodes to perform load balancing, as well as automatic failover to new master/replica are not supported. + +### Required grants + +Ask your Postgres cluster administrator to set following grants for a user, +used for creating a connection: + +Read + Write + +```sql +-- allow creating tables in specific schema +GRANT USAGE, CREATE ON SCHEMA myschema TO username; + +-- allow read & write access to specific table +GRANT SELECT, INSERT ON myschema.mytable TO username; + +-- only if if_exists="replace_entire_table" is used: +GRANT TRUNCATE ON myschema.mytable TO username; +``` + +Read only + +```sql +-- allow creating tables in specific schema +GRANT USAGE ON SCHEMA myschema TO username; + +-- allow read access to specific table +GRANT SELECT ON myschema.mytable TO username; +``` + +More details can be found in [official documentation](https://www.postgresql.org/docs/current/sql-grant.html). diff --git a/mddocs/connection/db_connection/postgres/read.md b/mddocs/connection/db_connection/postgres/read.md new file mode 100644 index 000000000..b0047f58a --- /dev/null +++ b/mddocs/connection/db_connection/postgres/read.md @@ -0,0 +1,350 @@ + + +# Reading from Postgres using `DBReader` + +[`DBReader`](../../../db/db_reader.md#onetl.db.db_reader.db_reader.DBReader) supports [Read Strategies](../../../strategy/index.md#strategy) for incremental data reading, +but does not support custom queries, like `JOIN`. + +#### WARNING +Please take into account [Postgres <-> Spark type mapping](types.md#postgres-types) + +## Supported DBReader features + +* ✅︎ `columns` +* ✅︎ `where` +* ✅︎ `hwm`, supported strategies: +* * ✅︎ [Snapshot Strategy](../../../strategy/snapshot_strategy.md#snapshot-strategy) +* * ✅︎ [Incremental Strategy](../../../strategy/incremental_strategy.md#incremental-strategy) +* * ✅︎ [Snapshot Batch Strategy](../../../strategy/snapshot_batch_strategy.md#snapshot-batch-strategy) +* * ✅︎ [Incremental Batch Strategy](../../../strategy/incremental_batch_strategy.md#incremental-batch-strategy) +* ❌ `hint` (is not supported by Postgres) +* ❌ `df_schema` +* ✅︎ `options` (see [`Postgres.ReadOptions`](#onetl.connection.db_connection.postgres.options.PostgresReadOptions)) + +## Examples + +Snapshot strategy: + +```python +from onetl.connection import Postgres +from onetl.db import DBReader + +postgres = Postgres(...) + +reader = DBReader( + connection=postgres, + source="schema.table", + columns=["id", "key", "CAST(value AS text) value", "updated_dt"], + where="key = 'something'", + options=Postgres.ReadOptions(partitionColumn="id", numPartitions=10), +) +df = reader.run() +``` + +Incremental strategy: + +```python +from onetl.connection import Postgres +from onetl.db import DBReader +from onetl.strategy import IncrementalStrategy + +postgres = Postgres(...) + +reader = DBReader( + connection=postgres, + source="schema.table", + columns=["id", "key", "CAST(value AS text) value", "updated_dt"], + where="key = 'something'", + hwm=DBReader.AutoDetectHWM(name="postgres_hwm", expression="updated_dt"), + options=Postgres.ReadOptions(partitionColumn="id", numPartitions=10), +) + +with IncrementalStrategy(): + df = reader.run() +``` + +## Recommendations + +### Select only required columns + +Instead of passing `"*"` in `DBReader(columns=[...])` prefer passing exact column names. This reduces the amount of data passed from Postgres to Spark. + +### Pay attention to `where` value + +Instead of filtering data on Spark side using `df.filter(df.column == 'value')` pass proper `DBReader(where="column = 'value'")` clause. +This both reduces the amount of data send from Postgres to Spark, and may also improve performance of the query. +Especially if there are indexes or partitions for columns used in `where` clause. + +## Options + +### *pydantic model* onetl.connection.db_connection.postgres.options.PostgresReadOptions + +Spark JDBC reading options. + +#### Versionadded +Added in version 0.5.0: Replace `Postgres.Options` → `Postgres.ReadOptions` + +### Examples + +#### NOTE +You can pass any value +[supported by Spark](https://spark.apache.org/docs/latest/sql-data-sources-jdbc.html), +even if it is not mentioned in this documentation. **Option names should be in** `camelCase`! + +The set of supported options depends on Spark version. + +```python +from onetl.connection import Postgres + +options = Postgres.ReadOptions( + partitioning_mode="range", + partitionColumn="reg_id", + numPartitions=10, + customSparkOption="value", +) +``` + + +* **Fields:** + - [`fetchsize (int)`](#onetl.connection.db_connection.postgres.options.PostgresReadOptions.fetchsize) + - [`lower_bound (int | None)`](#onetl.connection.db_connection.postgres.options.PostgresReadOptions.lower_bound) + - [`num_partitions (pydantic.types.PositiveInt)`](#onetl.connection.db_connection.postgres.options.PostgresReadOptions.num_partitions) + - [`partition_column (str | None)`](#onetl.connection.db_connection.postgres.options.PostgresReadOptions.partition_column) + - [`partitioning_mode (onetl.connection.db_connection.jdbc_connection.options.JDBCPartitioningMode)`](#onetl.connection.db_connection.postgres.options.PostgresReadOptions.partitioning_mode) + - [`query_timeout (int | None)`](#onetl.connection.db_connection.postgres.options.PostgresReadOptions.query_timeout) + - [`session_init_statement (str | None)`](#onetl.connection.db_connection.postgres.options.PostgresReadOptions.session_init_statement) + - [`upper_bound (int | None)`](#onetl.connection.db_connection.postgres.options.PostgresReadOptions.upper_bound) + +#### *field* partition_column *: str | None* *= None* *(alias 'partitionColumn')* + +Column used to parallelize reading from a table. + +#### WARNING +It is highly recommended to use primary key, or column with an index +to avoid performance issues. + +#### NOTE +Column type depends on [`partitioning_mode`](#onetl.connection.db_connection.postgres.options.PostgresReadOptions.partitioning_mode). + +* `partitioning_mode="range"` requires column to be an integer, date or timestamp (can be NULL, but not recommended). +* `partitioning_mode="hash"` accepts any column type (NOT NULL). +* `partitioning_mode="mod"` requires column to be an integer (NOT NULL). + +See documentation for [`partitioning_mode`](#onetl.connection.db_connection.postgres.options.PostgresReadOptions.partitioning_mode) for more details + + + +#### *field* num_partitions *: PositiveInt* *= 1* *(alias 'numPartitions')* + +Number of jobs created by Spark to read the table content in parallel. +See documentation for [`partitioning_mode`](#onetl.connection.db_connection.postgres.options.PostgresReadOptions.partitioning_mode) for more details + + +* **Constraints:** + - **exclusiveMinimum** = 0 + +#### *field* lower_bound *: int | None* *= None* *(alias 'lowerBound')* + +See documentation for [`partitioning_mode`](#onetl.connection.db_connection.postgres.options.PostgresReadOptions.partitioning_mode) for more details + + + +#### *field* upper_bound *: int | None* *= None* *(alias 'upperBound')* + +See documentation for [`partitioning_mode`](#onetl.connection.db_connection.postgres.options.PostgresReadOptions.partitioning_mode) for more details + + + +#### *field* session_init_statement *: str | None* *= None* *(alias 'sessionInitStatement')* + +After each database session is opened to the remote DB and before starting to read data, +this option executes a custom SQL statement (or a PL/SQL block). + +Use this to implement session initialization code. + +Example: + +```python +sessionInitStatement = """ + BEGIN + execute immediate + 'alter session set "_serial_direct_read"=true'; + END; +""" +``` + + + +#### *field* query_timeout *: int | None* *= None* *(alias 'queryTimeout')* + +The number of seconds the driver will wait for a statement to execute. +Zero means there is no limit. + +This option depends on driver implementation, +some drivers can check the timeout of each query instead of an entire JDBC batch. + + + +#### *field* fetchsize *: int* *= 100000* + +Fetch N rows from an opened cursor per one read round. + +Tuning this option can influence performance of reading. + +#### WARNING +Default value is different from Spark. + +Spark uses driver’s own value, and it may be different in different drivers, +and even versions of the same driver. For example, Oracle has +default `fetchsize=10`, which is absolutely not usable. + +Thus we’ve overridden default value with `100_000`, which should increase reading performance. + +#### Versionchanged +Changed in version 0.2.0: Set explicit default value to `100_000` + + + +#### *field* partitioning_mode *: JDBCPartitioningMode* *= JDBCPartitioningMode.RANGE* + +Defines how Spark will parallelize reading from table. + +Possible values: + +* `range` (default) + : Allocate each executor a range of values from column passed into [`partition_column`](#onetl.connection.db_connection.postgres.options.PostgresReadOptions.partition_column). +
+ ### Spark generates for each executor an SQL query +
+ Executor 1: + ```sql + SELECT ... FROM table + WHERE (partition_column >= lowerBound + OR partition_column IS NULL) + AND partition_column < (lower_bound + stride) + ``` +
+ Executor 2: + ```sql + SELECT ... FROM table + WHERE partition_column >= (lower_bound + stride) + AND partition_column < (lower_bound + 2 * stride) + ``` +
+ … +
+ Executor N: + ```sql + SELECT ... FROM table + WHERE partition_column >= (lower_bound + (N-1) * stride) + AND partition_column <= upper_bound + ``` +
+ Where `stride=(upper_bound - lower_bound) / num_partitions`. +
+ #### NOTE + Can be used only with columns of integer, date or timestamp types. +
+ #### NOTE + [`lower_bound`](#onetl.connection.db_connection.postgres.options.PostgresReadOptions.lower_bound), [`upper_bound`](#onetl.connection.db_connection.postgres.options.PostgresReadOptions.upper_bound) and [`num_partitions`](#onetl.connection.db_connection.postgres.options.PostgresReadOptions.num_partitions) are used just to + calculate the partition stride, **NOT** for filtering the rows in table. + So all rows in the table will be returned (unlike *Incremental* [Read Strategies](../../../strategy/index.md#strategy)). +
+ #### NOTE + All queries are executed in parallel. To execute them sequentially, use *Batch* [Read Strategies](../../../strategy/index.md#strategy). +* `hash` + : Allocate each executor a set of values based on hash of the [`partition_column`](#onetl.connection.db_connection.postgres.options.PostgresReadOptions.partition_column) column. +
+ ### Spark generates for each executor an SQL query +
+ Executor 1: + ```sql + SELECT ... FROM table + WHERE (some_hash(partition_column) mod num_partitions) = 0 -- lower_bound + ``` +
+ Executor 2: + ```sql + SELECT ... FROM table + WHERE (some_hash(partition_column) mod num_partitions) = 1 -- lower_bound + 1 + ``` +
+ … +
+ Executor N: + ```sql + SELECT ... FROM table + WHERE (some_hash(partition_column) mod num_partitions) = num_partitions-1 -- upper_bound + ``` +
+ #### NOTE + The hash function implementation depends on RDBMS. It can be `MD5` or any other fast hash function, + or expression based on this function call. Usually such functions accepts any column type as an input. +* `mod` + : Allocate each executor a set of values based on modulus of the [`partition_column`](#onetl.connection.db_connection.postgres.options.PostgresReadOptions.partition_column) column. +
+ ### Spark generates for each executor an SQL query +
+ Executor 1: + ```sql + SELECT ... FROM table + WHERE (partition_column mod num_partitions) = 0 -- lower_bound + ``` +
+ Executor 2: + ```sql + SELECT ... FROM table + WHERE (partition_column mod num_partitions) = 1 -- lower_bound + 1 + ``` +
+ Executor N: + ```sql + SELECT ... FROM table + WHERE (partition_column mod num_partitions) = num_partitions-1 -- upper_bound + ``` +
+ #### NOTE + Can be used only with columns of integer type. + +#### Versionadded +Added in version 0.5.0. + +### Examples + +Read data in 10 parallel jobs by range of values in `id_column` column: + +```python +ReadOptions( + partitioning_mode="range", # default mode, can be omitted + partitionColumn="id_column", + numPartitions=10, + # Options below can be discarded because they are + # calculated automatically as MIN and MAX values of `partitionColumn` + lowerBound=0, + upperBound=100_000, +) +``` + +Read data in 10 parallel jobs by hash of values in `some_column` column: + +```python +ReadOptions( + partitioning_mode="hash", + partitionColumn="some_column", + numPartitions=10, + # lowerBound and upperBound are automatically set to `0` and `9` +) +``` + +Read data in 10 parallel jobs by modulus of values in `id_column` column: + +```python +ReadOptions( + partitioning_mode="mod", + partitionColumn="id_column", + numPartitions=10, + # lowerBound and upperBound are automatically set to `0` and `9` +) +``` + + diff --git a/mddocs/connection/db_connection/postgres/sql.md b/mddocs/connection/db_connection/postgres/sql.md new file mode 100644 index 000000000..0a5638d62 --- /dev/null +++ b/mddocs/connection/db_connection/postgres/sql.md @@ -0,0 +1,191 @@ + + +# Reading from Postgres using `Postgres.sql` + +`Postgres.sql` allows passing custom SQL query, but does not support incremental strategies. + +#### WARNING +Please take into account [Postgres <-> Spark type mapping](types.md#postgres-types) + +#### WARNING +Statement is executed in **read-write** connection, so if you’re calling some functions/procedures with DDL/DML statements inside, +they can change data in your database. + +## Syntax support + +Only queries with the following syntax are supported: + +* ✅︎ `SELECT ... FROM ...` +* ✅︎ `WITH alias AS (...) SELECT ...` +* ❌ `SET ...; SELECT ...;` - multiple statements not supported + +## Examples + +```python +from onetl.connection import Postgres + +postgres = Postgres(...) +df = postgres.sql( + """ + SELECT + id, + key, + CAST(value AS text) value, + updated_at + FROM + some.mytable + WHERE + key = 'something' + """, + options=Postgres.SQLOptions( + partitionColumn="id", + numPartitions=10, + lowerBound=0, + upperBound=1000, + ), +) +``` + +## Recommendations + +### Select only required columns + +Instead of passing `SELECT * FROM ...` prefer passing exact column names `SELECT col1, col2, ...`. +This reduces the amount of data passed from Postgres to Spark. + +### Pay attention to `where` value + +Instead of filtering data on Spark side using `df.filter(df.column == 'value')` pass proper `WHERE column = 'value'` clause. +This both reduces the amount of data send from Postgres to Spark, and may also improve performance of the query. +Especially if there are indexes or partitions for columns used in `where` clause. + +## Options + +### *pydantic model* onetl.connection.db_connection.postgres.options.PostgresSQLOptions + +Options specifically for SQL queries + +These options allow you to specify configurations for executing SQL queries +without relying on Spark’s partitioning mechanisms. + +#### Versionadded +Added in version 0.11.0: Split up `Postgres.ReadOptions` to `Postgres.SQLOptions` + +### Examples + +#### NOTE +You can pass any JDBC configuration +[supported by Spark](https://spark.apache.org/docs/latest/sql-data-sources-jdbc.html), +tailored to optimize SQL query execution. **Option names should be in** `camelCase`! + +```python +from onetl.connection import Postgres + +options = Postgres.SQLOptions( + partitionColumn="reg_id", + numPartitions=10, + lowerBound=0, + upperBound=1000, + customSparkOption="value", +) +``` + + + +#### *field* partition_column *: str | None* *= None* *(alias 'partitionColumn')* + +Column used to partition data across multiple executors for parallel query processing. + +#### WARNING +It is highly recommended to use primary key, or column with an index +to avoid performance issues. + +### Example of using `partitionColumn="id"` with `partitioning_mode="range"` + +```sql +-- If partition_column is 'id', with numPartitions=4, lowerBound=1, and upperBound=100: +-- Executor 1 processes IDs from 1 to 25 +SELECT ... FROM table WHERE id >= 1 AND id < 26 +-- Executor 2 processes IDs from 26 to 50 +SELECT ... FROM table WHERE id >= 26 AND id < 51 +-- Executor 3 processes IDs from 51 to 75 +SELECT ... FROM table WHERE id >= 51 AND id < 76 +-- Executor 4 processes IDs from 76 to 100 +SELECT ... FROM table WHERE id >= 76 AND id <= 100 + +-- General case for Executor N +SELECT ... FROM table +WHERE partition_column >= (lowerBound + (N-1) * stride) +AND partition_column <= upperBound +-- Where ``stride`` is calculated as ``(upperBound - lowerBound) / numPartitions``. +``` + + + +#### *field* num_partitions *: int | None* *= None* *(alias 'numPartitions')* + +Number of jobs created by Spark to read the table content in parallel. + + + +#### *field* lower_bound *: int | None* *= None* *(alias 'lowerBound')* + +Defines the starting boundary for partitioning the query’s data. Mandatory if [`partition_column`](#onetl.connection.db_connection.postgres.options.PostgresSQLOptions.partition_column) is set + + + +#### *field* upper_bound *: int | None* *= None* *(alias 'upperBound')* + +Sets the ending boundary for data partitioning. Mandatory if [`partition_column`](#onetl.connection.db_connection.postgres.options.PostgresSQLOptions.partition_column) is set + + + +#### *field* session_init_statement *: str | None* *= None* *(alias 'sessionInitStatement')* + +After each database session is opened to the remote DB and before starting to read data, +this option executes a custom SQL statement (or a PL/SQL block). + +Use this to implement session initialization code. + +Example: + +```python +sessionInitStatement = """ + BEGIN + execute immediate + 'alter session set "_serial_direct_read"=true'; + END; +""" +``` + + + +#### *field* query_timeout *: int | None* *= None* *(alias 'queryTimeout')* + +The number of seconds the driver will wait for a statement to execute. +Zero means there is no limit. + +This option depends on driver implementation, +some drivers can check the timeout of each query instead of an entire JDBC batch. + + + +#### *field* fetchsize *: int* *= 100000* + +Fetch N rows from an opened cursor per one read round. + +Tuning this option can influence performance of reading. + +#### WARNING +Default value is different from Spark. + +Spark uses driver’s own value, and it may be different in different drivers, +and even versions of the same driver. For example, Oracle has +default `fetchsize=10`, which is absolutely not usable. + +Thus we’ve overridden default value with `100_000`, which should increase reading performance. + +#### Versionchanged +Changed in version 0.2.0: Set explicit default value to `100_000` + + diff --git a/mddocs/connection/db_connection/postgres/types.md b/mddocs/connection/db_connection/postgres/types.md new file mode 100644 index 000000000..3baca234d --- /dev/null +++ b/mddocs/connection/db_connection/postgres/types.md @@ -0,0 +1,372 @@ + + +# Postgres <-> Spark type mapping + +#### NOTE +The results below are valid for Spark 3.5.5, and may differ on other Spark versions. + +## Type detection & casting + +Spark’s DataFrames always have a `schema` which is a list of columns with corresponding Spark types. All operations on a column are performed using column type. + +### Reading from Postgres + +This is how Postgres connector performs this: + +* For each column in query result (`SELECT column1, column2, ... FROM table ...`) get column name and Postgres type. +* Find corresponding `Postgres type (read)` → `Spark type` combination (see below) for each DataFrame column [1](#id2). If no combination is found, raise exception. +* Create DataFrame from query with specific column names and Spark types. + +* **[1]** All Postgres types that doesn’t have corresponding Java type are converted to `String`. + +### Writing to some existing Postgres table + +This is how Postgres connector performs this: + +* Get names of columns in DataFrame. [1](#id2) +* Perform `SELECT * FROM table LIMIT 0` query. +* Take only columns present in DataFrame (by name, case insensitive) [2](#id6). For each found column get Postgres type. +* Find corresponding `Spark type` → `Postgres type (write)` combination (see below) for each DataFrame column. If no combination is found, raise exception. +* If `Postgres type (write)` match `Postgres type (read)`, no additional casts will be performed, DataFrame column will be written to Postgres as is. +* If `Postgres type (write)` does not match `Postgres type (read)`, DataFrame column will be casted to target column type **on Postgres side**. + For example, you can write column with text data to `int` column, if column contains valid integer values within supported value range and precision [3](#id7). + +* **[2]** This allows to write data to tables with `DEFAULT` and `GENERATED` columns - if DataFrame has no such column, it will be populated by Postgres. +* **[3]** This is true only if either DataFrame column is a `StringType()`, or target column is `text` type. But other types cannot be silently converted, like `bytea -> bit(N)`. This requires explicit casting, see [Manual conversion to string](). + +### Create new table using Spark + +#### WARNING +ABSOLUTELY NOT RECOMMENDED! + +This is how Postgres connector performs this: + +* Find corresponding `Spark type` → `Postgres type (create)` combination (see below) for each DataFrame column. If no combination is found, raise exception. +* Generate DDL for creating table in Postgres, like `CREATE TABLE (col1 ...)`, and run it. +* Write DataFrame to created table as is. + +But Postgres connector support only limited number of types and almost no custom clauses (like `PARTITION BY`, `INDEX`, etc). +So instead of relying on Spark to create tables: + +### See example + +```python +writer = DBWriter( + connection=postgres, + target="public.table", + options=Postgres.WriteOptions( + if_exists="append", + createTableOptions="PARTITION BY RANGE (id)", + ), +) +writer.run(df) +``` + +Always prefer creating table with desired DDL **BEFORE WRITING DATA**: + +### See example + +```python +postgres.execute( + """ + CREATE TABLE public.table ( + id bigint, + business_dt timestamp(6), + value json + ) + PARTITION BY RANGE (Id) + """, +) + +writer = DBWriter( + connection=postgres, + target="public.table", + options=Postgres.WriteOptions(if_exists="append"), +) +writer.run(df) +``` + +See Postgres [CREATE TABLE](https://www.postgresql.org/docs/current/sql-createtable.html) documentation. + +## Supported types + +### References + +See [List of Postgres types](https://www.postgresql.org/docs/current/datatype.html). + +Here you can find source code with type conversions: + +* [Postgres <-> JDBC](https://github.com/pgjdbc/pgjdbc/blob/REL42.6.0/pgjdbc/src/main/java/org/postgresql/jdbc/TypeInfoCache.java#L78-L112) +* [JDBC -> Spark](https://github.com/apache/spark/blob/v3.5.5/sql/core/src/main/scala/org/apache/spark/sql/jdbc/PostgresDialect.scala#L52-L108) +* [Spark -> JDBC](https://github.com/apache/spark/blob/v3.5.5/sql/core/src/main/scala/org/apache/spark/sql/jdbc/PostgresDialect.scala#L118-L132) + +### Numeric types + +| Postgres type (read) | Spark type | Postgres type (write) | Postgres type (create) | +|-----------------------------|-------------------------------------|-----------------------------|--------------------------| +| `decimal` | `DecimalType(P=38, S=18)` | `decimal(P=38, S=18)` | `decimal` (unbounded) | +| `decimal(P=0..38)` | `DecimalType(P=0..38, S=0)` | `decimal(P=0..38, S=0)` | | +| `decimal(P=0..38, S=0..38)` | `DecimalType(P=0..38, S=0..38)` | `decimal(P=0..38, S=0..38)` | | +| `decimal(P=39.., S=0..)` | unsupported [4](#id11) | | | +| `decimal(P=.., S=..-1)` | unsupported [5](#id12) | | | +| `real` | `FloatType()` | `real` | `real` | +| `double precision` | `DoubleType()` | `double precision` | `double precision` | +| `smallint` | `ShortType()` | `smallint` | `smallint` | +| `-` | `ByteType()` | | | +| `integer` | `IntegerType()` | `integer` | `integer` | +| `bigint` | `LongType()` | `bigint` | `bigint` | +| `money` | `StringType()` [1](#id2) | `text` | `text` | +| `int4range` | | | | +| `int8range` | | | | +| `numrange` | | | | +| `int2vector` | | | | +* **[4]** Postgres support decimal types with unlimited precision. But Spark’s `DecimalType(P, S)` supports maximum `P=38` (128 bit). It is impossible to read, write or operate with values of larger precision, this leads to an exception. +* **[5]** Postgres support decimal types with negative scale, like `decimal(38, -10)`. Spark doesn’t. + +### Temporal types + +| Postgres type (read) | Spark type | Postgres type (write) | Postgres type (create) | +|----------------------------------|----------------------------------------------------------------------|-------------------------|--------------------------| +| `date` | `DateType()` | `date` | `date` | +| `time` | `TimestampType()`,
with time format quirks [6](#id16) | `timestamp(6)` | `timestamp(6)` | +| `time(0..6)` | | | | +| `time with time zone` | | | | +| `time(0..6) with time zone` | | | | +| `timestamp` | `TimestampType()` | `timestamp(6)` | `timestamp(6)` | +| `timestamp(0..6)` | | | | +| `timestamp with time zone` | | | | +| `timestamp(0..6) with time zone` | | | | +| `-` | `TimestampNTZType()` | `timestamp(6)` | `timestamp(6)` | +| `interval` of any precision | `StringType()` [1](#id2) | `text` | `text` | +| `-` | `DayTimeIntervalType()` | unsupported | unsupported | +| `-` | `YearMonthIntervalType()` | unsupported | unsupported | +| `daterange` | `StringType()` [1](#id2) | `text` | `text` | +| `tsrange` | | | | +| `tstzrange` | | | | + +#### WARNING +Note that types in Postgres and Spark have different value ranges: + +| Postgres type | Min value | Max value | Spark type | Min value | Max value | +|-----------------|-------------------------------|--------------------------------|-------------------|------------------------------|------------------------------| +| `date` | `-4713-01-01` | `5874897-01-01` | `DateType()` | `0001-01-01` | `9999-12-31` | +| `timestamp` | `-4713-01-01 00:00:00.000000` | `294276-12-31 23:59:59.999999` | `TimestampType()` | `0001-01-01 00:00:00.000000` | `9999-12-31 23:59:59.999999` | +| `time` | `00:00:00.000000` | `24:00:00.000000` | | | | + +So not all of values can be read from Postgres to Spark. + +References: +: * [Postgres date/time types documentation](https://www.postgresql.org/docs/current/datatype-datetime.html) + * [Spark DateType documentation](https://spark.apache.org/docs/latest/api/java/org/apache/spark/sql/types/DateType.html) + * [Spark TimestampType documentation](https://spark.apache.org/docs/latest/api/java/org/apache/spark/sql/types/TimestampType.html) + +* **[6]** `time` type is the same as `timestamp` with date `1970-01-01`. So instead of reading data from Postgres like `23:59:59` it is actually read `1970-01-01 23:59:59`, and vice versa. + +### String types + +| Postgres type (read) | Spark type | Postgres type (write) | Postgres type (create) | +|---------------------------|-------------------------------------|-------------------------|--------------------------| +| `character` | `StringType()` | `text` | `text` | +| `character(N)` | | | | +| `character varying` | | | | +| `character varying(N)` | | | | +| `text` | | | | +| `json` | | | | +| `jsonb` | | | | +| `xml` | | | | +| `CREATE TYPE ... AS ENUM` | `StringType()` [1](#id2) | | | +| `tsvector` | | | | +| `tsquery` | | | | +| `-` | `CharType()` | `unsupported` | `unsupported` | +| `-` | `VarcharType()` | `unsupported` | `unsupported` | + +### Binary types + +| Postgres type (read) | Spark type | Postgres type (write) | Postgres type (create) | +|------------------------|-------------------------------------|----------------------------------------------------------|--------------------------| +| `boolean` | `BooleanType()` | `boolean` | `boolean` | +| `bit` | `BooleanType()` | `bool`,
**cannot insert data** [3](#id7) | `bool` | +| `bit(N=1)` | | | | +| `bit(N=2..)` | `ByteType()` | `bytea`,
**cannot insert data** [3](#id7) | `bytea` | +| `bit varying` | `StringType()` [1](#id2) | `text` | `text` | +| `bit varying(N)` | | | | +| `bytea` | `BinaryType()` | `bytea` | `bytea` | + +### Struct types + +| Postgres type (read) | Spark type | Postgres type (write) | Postgres type (create) | +|------------------------------|-------------------------------------|-------------------------|--------------------------| +| `T[]` | `ArrayType(T)` | `T[]` | `T[]` | +| `T[][]` | unsupported | | | +| `CREATE TYPE sometype (...)` | `StringType()` [1](#id2) | `text` | `text` | +| `-` | `StructType()` | unsupported | | +| `-` | `MapType()` | | | + +### Network types + +| Postgres type (read) | Spark type | Postgres type (write) | Postgres type (create) | +|------------------------|-------------------------------------|-------------------------|--------------------------| +| `cidr` | `StringType()` [1](#id2) | `text` | `text` | +| `inet` | | | | +| `macaddr` | | | | +| `macaddr8` | | | | + +### Geo types + +| Postgres type (read) | Spark type | Postgres type (write) | Postgres type (create) | +|------------------------|-------------------------------------|-------------------------|--------------------------| +| `circle` | `StringType()` [1](#id2) | `text` | `text` | +| `box` | | | | +| `line` | | | | +| `lseg` | | | | +| `path` | | | | +| `point` | | | | +| `polygon` | | | | +| `polygon` | | | | + +## Explicit type cast + +### `DBReader` + +It is possible to explicitly cast column of unsupported type using `DBReader(columns=...)` syntax. + +For example, you can use `CAST(column AS text)` to convert data to string representation on Postgres side, and so it will be read as Spark’s `StringType()`. + +It is also possible to use [to_json](https://www.postgresql.org/docs/current/functions-json.html) Postgres function to convert column of any type to string representation, and then parse this column on Spark side you can use the [`JSON.parse_column`](../../../file_df/file_formats/json.md#onetl.file.format.json.JSON.parse_column) method: + +```python +from pyspark.sql.types import IntegerType + +from onetl.connection import Postgres +from onetl.db import DBReader +from onetl.file.format import JSON + +postgres = Postgres(...) + +DBReader( + connection=postgres, + columns=[ + "id", + "supported_column", + "CAST(unsupported_column AS text) unsupported_column_str", + # or + "to_json(unsupported_column) array_column_json", + ], +) +df = reader.run() + +json_schema = StructType( + [ + StructField("id", IntegerType(), nullable=True), + StructField("name", StringType(), nullable=True), + ..., + ] +) +df = df.select( + df.id, + df.supported_column, + # explicit cast + df.unsupported_column_str.cast("integer").alias("parsed_integer"), + JSON().parse_column("array_column_json", json_schema).alias("json_string"), +) +``` + +### `DBWriter` + +It is always possible to convert data on the Spark side to a string, and then write it to a text column in a Postgres table. + +#### Using JSON.serialize_column + +You can use the [`JSON.serialize_column`](../../../file_df/file_formats/json.md#onetl.file.format.json.JSON.serialize_column) method for data serialization: + +```python +from onetl.file.format import JSON +from pyspark.sql.functions import col + +from onetl.connection import Postgres +from onetl.db import DBWriter + +postgres = Postgres(...) + +postgres.execute( + """ + CREATE TABLE schema.target_table ( + id int, + supported_column timestamp, + array_column_json jsonb -- any column type, actually + ) + """, +) + +write_df = df.select( + df.id, + df.supported_column, + JSON().serialize_column(df.unsupported_column).alias("array_column_json"), +) + +writer = DBWriter( + connection=postgres, + target="schema.target_table", +) +writer.run(write_df) +``` + +Then you can parse this column on the Postgres side (for example, by creating a view): + +```sql +SELECT + id, + supported_column, + array_column_json->'0' AS array_item_0 +FROM + schema.target_table +``` + +To avoid casting the value on every table read you can use [GENERATED ALWAYS STORED](https://www.postgresql.org/docs/current/ddl-generated-columns.html) column, but this requires 2x space (for original and parsed value). + +#### Manual conversion to string + +Postgres connector also supports conversion text value directly to target column type, if this value has a proper format. + +For example, you can write data like `[123, 345)` to `int8range` type because Postgres allows cast `'[123, 345)'::int8range'`: + +```python +from pyspark.sql.ftypes import StringType +from pyspark.sql.functions import udf + +from onetl.connection import Postgres +from onetl.db import DBReader + +postgres = Postgres(...) + +postgres.execute( + """ + CREATE TABLE schema.target_table ( + id int, + range_column int8range -- any column type, actually + ) + """, +) + + +@udf(returnType=StringType()) +def array_to_range(value: tuple): + """This UDF allows to convert tuple[start, end] to Postgres' range format""" + start, end = value + return f"[{start},{end})" + + +write_df = df.select( + df.id, + array_to_range(df.range_column).alias("range_column"), +) + +writer = DBWriter( + connection=postgres, + target="schema.target_table", +) +writer.run(write_df) +``` + +This can be tricky to implement and may lead to longer write process. +But this does not require extra space on Postgres side, and allows to avoid explicit value cast on every table read. diff --git a/mddocs/connection/db_connection/postgres/write.md b/mddocs/connection/db_connection/postgres/write.md new file mode 100644 index 000000000..3b5822b1f --- /dev/null +++ b/mddocs/connection/db_connection/postgres/write.md @@ -0,0 +1,183 @@ + + +# Writing to Postgres using `DBWriter` + +For writing data to Postgres, use [`DBWriter`](../../../db/db_writer.md#onetl.db.db_writer.db_writer.DBWriter). + +#### WARNING +Please take into account [Postgres <-> Spark type mapping](types.md#postgres-types) + +#### WARNING +It is always recommended to create table explicitly using [Postgres.execute](execute.md#postgres-execute) +instead of relying on Spark’s table DDL generation. + +This is because Spark’s DDL generator can create columns with different precision and types than it is expected, +causing precision loss or other issues. + +## Examples + +```python +from onetl.connection import Postgres +from onetl.db import DBWriter + +postgres = Postgres(...) + +df = ... # data is here + +writer = DBWriter( + connection=postgres, + target="schema.table", + options=Postgres.WriteOptions(if_exists="append"), +) + +writer.run(df) +``` + +## Options + +Method above accepts [`Postgres.WriteOptions`](#onetl.connection.db_connection.postgres.options.PostgresWriteOptions) + +### *pydantic model* onetl.connection.db_connection.postgres.options.PostgresWriteOptions + +Spark JDBC writing options. + +#### Versionadded +Added in version 0.5.0: Replace `Postgres.Options` → `Postgres.WriteOptions` + +### Examples + +#### NOTE +You can pass any value +[supported by Spark](https://spark.apache.org/docs/latest/sql-data-sources-jdbc.html), +even if it is not mentioned in this documentation. **Option names should be in** `camelCase`! + +The set of supported options depends on Spark version. + +```python +from onetl.connection import Postgres + +options = Postgres.WriteOptions( + if_exists="append", + batchsize=20_000, + customSparkOption="value", +) +``` + + + +#### *field* if_exists *: JDBCTableExistBehavior* *= JDBCTableExistBehavior.APPEND* *(alias 'mode')* + +Behavior of writing data into existing table. + +Possible values: +: * `append` (default) + : Adds new rows into existing table. +
+ ### Behavior in details +
+ * Table does not exist + : Table is created using options provided by user + (`createTableOptions`, `createTableColumnTypes`, etc). + * Table exists + : Data is appended to a table. Table has the same DDL as before writing data +
+ #### WARNING + This mode does not check whether table already contains + rows from dataframe, so duplicated rows can be created. +
+ Also Spark does not support passing custom options to + insert statement, like `ON CONFLICT`, so don’t try to + implement deduplication using unique indexes or constraints. +
+ Instead, write to staging table and perform deduplication + using `execute` method. + * `replace_entire_table` + : **Table is dropped and then created, or truncated**. +
+ ### Behavior in details +
+ * Table does not exist + : Table is created using options provided by user + (`createTableOptions`, `createTableColumnTypes`, etc). + * Table exists + : Table content is replaced with dataframe content. +
+ After writing completed, target table could either have the same DDL as + before writing data (`truncate=True`), or can be recreated (`truncate=False` + or source does not support truncation). + * `ignore` + : Ignores the write operation if the table already exists. +
+ ### Behavior in details +
+ * Table does not exist + : Table is created using options provided by user + (`createTableOptions`, `createTableColumnTypes`, etc). + * Table exists + : The write operation is ignored, and no data is written to the table. + * `error` + : Raises an error if the table already exists. +
+ ### Behavior in details +
+ * Table does not exist + : Table is created using options provided by user + (`createTableOptions`, `createTableColumnTypes`, etc). + * Table exists + : An error is raised, and no data is written to the table. + +#### Versionchanged +Changed in version 0.9.0: Renamed `mode` → `if_exists` + + + +#### *field* query_timeout *: int | None* *= None* *(alias 'queryTimeout')* + +The number of seconds the driver will wait for a statement to execute. +Zero means there is no limit. + +This option depends on driver implementation, +some drivers can check the timeout of each query instead of an entire JDBC batch. + + + +#### *field* batchsize *: int* *= 20000* + +How many rows can be inserted per round trip. + +Tuning this option can influence performance of writing. + +#### WARNING +Default value is different from Spark. + +Spark uses quite small value `1000`, which is absolutely not usable +in BigData world. + +Thus we’ve overridden default value with `20_000`, +which should increase writing performance. + +You can increase it even more, up to `50_000`, +but it depends on your database load and number of columns in the row. +Higher values does not increase performance. + +#### Versionchanged +Changed in version 0.4.0: Changed default value from 1000 to 20_000 + + + +#### *field* isolation_level *: str* *= 'READ_UNCOMMITTED'* *(alias 'isolationLevel')* + +The transaction isolation level, which applies to current connection. + +Possible values: +: * `NONE` (as string, not Python’s `None`) + * `READ_COMMITTED` + * `READ_UNCOMMITTED` + * `REPEATABLE_READ` + * `SERIALIZABLE` + +Values correspond to transaction isolation levels defined by JDBC standard. +Please refer the documentation for +[java.sql.Connection](https://docs.oracle.com/javase/8/docs/api/java/sql/Connection.html). + + diff --git a/mddocs/connection/db_connection/teradata/connection.md b/mddocs/connection/db_connection/teradata/connection.md new file mode 100644 index 000000000..de2745c4e --- /dev/null +++ b/mddocs/connection/db_connection/teradata/connection.md @@ -0,0 +1,134 @@ + + +# Teradata connection + +### *class* onetl.connection.db_connection.teradata.connection.Teradata(\*, spark: SparkSession, user: str, password: SecretStr, host: Host, port: int = 1025, database: str | None = None, extra: TeradataExtra = TeradataExtra(CHARSET='UTF8', COLUMN_NAME='ON', FLATTEN='ON', MAYBENULL='ON', STRICT_NAMES='OFF')) + +Teradata JDBC connection. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +Based on package [com.teradata.jdbc:terajdbc:17.20.00.15](https://central.sonatype.com/artifact/com.teradata.jdbc/terajdbc/17.20.00.15) +([official Teradata JDBC driver](https://downloads.teradata.com/download/connectivity/jdbc-driver)). + +#### SEE ALSO +Before using this connector please take into account [Prerequisites](prerequisites.md#teradata-prerequisites) + +#### Versionadded +Added in version 0.1.0. + +* **Parameters:** + **host** + : Host of Teradata database. For example: `test.teradata.domain.com` or `193.168.1.12` + + **port** + : Port of Teradata database + + **user** + : User, which have proper access to the database. For example: `some_user` + + **password** + : Password for database connection + + **database** + : Database in RDBMS, NOT schema. +
+ See [this page](https://www.educba.com/postgresql-database-vs-schema/) for more details + + **spark** + : Spark session. + + **extra** + : Specifies one or more extra parameters which should be appended to a connection string. +
+ For example: `{"TMODE": "TERA", "MAYBENULL": "ON", "CHARSET": "UTF8", "LOGMECH":"LDAP"}` +
+ See [Teradata JDBC driver documentation](https://teradata-docs.s3.amazonaws.com/doc/connectivity/jdbc/reference/current/jdbcug_chapter_2.html#BABJIHBJ) + for more details +
+ #### NOTE + By default, these options are added to extra: + > * `CHARSET = "UTF8"` + > * `COLUMN_NAME = "ON"` - allow reading column title from a table + > * `FLATTEN = "ON"` - improves error messages + > * `MAYBENULL = "ON"` + > * `STRICT_NAMES = "OFF"` - ignore Spark options passed to JDBC URL +
+ It is possible to override default values, for example set `extra={"FLATTEN": "OFF"}` + +### Examples + +Create Teradata connection with LDAP auth: + +```python +from onetl.connection import Teradata +from pyspark.sql import SparkSession + +# Create Spark session with Teradata driver loaded +maven_packages = Teradata.get_packages() +spark = ( + SparkSession.builder.appName("spark-app-name") + .config("spark.jars.packages", ",".join(maven_packages)) + .getOrCreate() +) + +# Create connection +teradata = Teradata( + host="database.host.or.ip", + user="user", + password="*****", + extra={ + "TMODE": "TERA", # "TERA" or "ANSI" + "LOGMECH": "LDAP", + "LOG": "TIMING", # increase log level + }, + spark=spark, +).check() +``` + + + +#### check() + +Check source availability. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +If not, an exception will be raised. + +* **Returns:** + Connection itself +* **Raises:** + RuntimeError + : If the connection is not available + +### Examples + +```python +connection.check() +``` + + + +#### *classmethod* get_packages(package_version: str | None = None) → list[str] + +Get package names to be downloaded by Spark. Allows specifying custom JDBC driver versions for Teradata. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### Versionadded +Added in version 0.9.0. + +* **Parameters:** + **package_version** + : Specifies the version of the Teradata JDBC driver to use. Defaults to `17.20.00.15`. +
+ #### Versionadded + Added in version 0.11.0. + +### Examples + +```python +from onetl.connection import Teradata + +Teradata.get_packages() + +# specify custom driver version +Teradata.get_packages(package_version="20.00.00.18") +``` + + diff --git a/mddocs/connection/db_connection/teradata/execute.md b/mddocs/connection/db_connection/teradata/execute.md new file mode 100644 index 000000000..e836b8a6d --- /dev/null +++ b/mddocs/connection/db_connection/teradata/execute.md @@ -0,0 +1,189 @@ + + +# Executing statements in Teradata + +#### WARNING +Methods below **read all the rows** returned from DB **to Spark driver memory**, and then convert them to DataFrame. + +Do **NOT** use them to read large amounts of data. Use [DBReader](read.md#teradata-read) or [Teradata.sql](sql.md#teradata-sql) instead. + +## How to + +There are 2 ways to execute some statement in Teradata + +### Use `Teradata.fetch` + +Use this method to execute some `SELECT` query which returns **small number or rows**, like reading +Teradata config, or reading data from some reference table. Method returns Spark DataFrame. + +Method accepts [`Teradata.FetchOptions`](#onetl.connection.db_connection.teradata.options.TeradataFetchOptions). + +Connection opened using this method should be then closed with `connection.close()` or `with connection:`. + +#### Syntax support + +This method supports **any** query syntax supported by Teradata, like: + +* ✅︎ `SELECT ... FROM ...` +* ✅︎ `WITH alias AS (...) SELECT ...` +* ✅︎ `SHOW ...` +* ❌ `SET ...; SELECT ...;` - multiple statements not supported + +#### Examples + +```python +from onetl.connection import Teradata + +teradata = Teradata(...) + +df = teradata.fetch( + "SELECT value FROM some.reference_table WHERE key = 'some_constant'", + options=Teradata.FetchOptions(queryTimeout=10), +) +teradata.close() +value = df.collect()[0][0] # get value from first row and first column +``` + +### Use `Teradata.execute` + +Use this method to execute DDL and DML operations. Each method call runs operation in a separated transaction, and then commits it. + +Method accepts [`Teradata.ExecuteOptions`](#onetl.connection.db_connection.teradata.options.TeradataExecuteOptions). + +Connection opened using this method should be then closed with `connection.close()` or `with connection:`. + +#### Syntax support + +This method supports **any** query syntax supported by Teradata, like: + +* ✅︎ `CREATE TABLE ...`, `CREATE VIEW ...`, and so on +* ✅︎ `ALTER ...` +* ✅︎ `INSERT INTO ... SELECT ...`, `UPDATE ...`, `DELETE ...`, and so on +* ✅︎ `DROP TABLE ...`, `DROP VIEW ...`, `TRUNCATE TABLE`, and so on +* ✅︎ `CALL procedure(arg1, arg2) ...` or `{call procedure(arg1, arg2)}` - special syntax for calling procedure +* ✅︎ `EXECUTE macro(arg1, arg2)` +* ✅︎ `EXECUTE FUNCTION ...` +* ✅︎ other statements not mentioned here +* ❌ `SET ...; SELECT ...;` - multiple statements not supported + +#### Examples + +```python +from onetl.connection import Teradata + +teradata = Teradata(...) + +teradata.execute("DROP TABLE database.table") +teradata.execute( + """ + CREATE MULTISET TABLE database.table AS ( + id BIGINT, + key VARCHAR, + value REAL + ) + NO PRIMARY INDEX + """, + options=Teradata.ExecuteOptions(queryTimeout=10), +) +``` + +## Options + +### *pydantic model* onetl.connection.db_connection.teradata.options.TeradataFetchOptions + +Options related to fetching data from databases via JDBC. + +#### Versionadded +Added in version 0.11.0: Replace `Teradata.JDBCOptions` → `Teradata.FetchOptions` + +### Examples + +#### NOTE +You can pass any value supported by underlying JDBC driver class, +even if it is not mentioned in this documentation. + +```python +from onetl.connection import Teradata + +options = Teradata.FetchOptions( + queryTimeout=60_000, + fetchsize=100_000, + customSparkOption="value", +) +``` + + +* **Fields:** + - [`fetchsize (int | None)`](#onetl.connection.db_connection.teradata.options.TeradataFetchOptions.fetchsize) + - [`query_timeout (int | None)`](#onetl.connection.db_connection.teradata.options.TeradataFetchOptions.query_timeout) + +#### *field* query_timeout *: int | None* *= None* *(alias 'queryTimeout')* + +The number of seconds the driver will wait for a statement to execute. +Zero means there is no limit. + +This option depends on driver implementation, +some drivers can check the timeout of each query instead of an entire JDBC batch. + + + +#### *field* fetchsize *: int | None* *= None* + +How many rows to fetch per round trip. + +Tuning this option can influence performance of reading. + +#### WARNING +Default value depends on driver. For example, Oracle has +default `fetchsize=10`. + + + +### *pydantic model* onetl.connection.db_connection.teradata.options.TeradataExecuteOptions + +Options related to executing statements in databases via JDBC. + +#### Versionadded +Added in version 0.11.0: Replace `Teradata.JDBCOptions` → `Teradata.ExecuteOptions` + +### Examples + +#### NOTE +You can pass any value supported by underlying JDBC driver class, +even if it is not mentioned in this documentation. + +```python +from onetl.connection import Teradata + +options = Teradata.ExecuteOptions( + queryTimeout=60_000, + customSparkOption="value", +) +``` + + +* **Fields:** + - [`fetchsize (int | None)`](#onetl.connection.db_connection.teradata.options.TeradataExecuteOptions.fetchsize) + - [`query_timeout (int | None)`](#onetl.connection.db_connection.teradata.options.TeradataExecuteOptions.query_timeout) + +#### *field* query_timeout *: int | None* *= None* *(alias 'queryTimeout')* + +The number of seconds the driver will wait for a statement to execute. +Zero means there is no limit. + +This option depends on driver implementation, +some drivers can check the timeout of each query instead of an entire JDBC batch. + + + +#### *field* fetchsize *: int | None* *= None* + +How many rows to fetch per round trip. + +Tuning this option can influence performance of reading. + +#### WARNING +Default value depends on driver. For example, Oracle has +default `fetchsize=10`. + + diff --git a/mddocs/connection/db_connection/teradata/index.md b/mddocs/connection/db_connection/teradata/index.md new file mode 100644 index 000000000..f35e86981 --- /dev/null +++ b/mddocs/connection/db_connection/teradata/index.md @@ -0,0 +1,15 @@ + + +# Teradata + +# Connection + +* [Prerequisites](prerequisites.md) +* [Teradata connection](connection.md) + +# Operations + +* [Reading from Teradata using `DBReader`](read.md) +* [Reading from Teradata using `Teradata.sql`](sql.md) +* [Writing to Teradata using `DBWriter`](write.md) +* [Executing statements in Teradata](execute.md) diff --git a/mddocs/connection/db_connection/teradata/prerequisites.md b/mddocs/connection/db_connection/teradata/prerequisites.md new file mode 100644 index 000000000..3217390e2 --- /dev/null +++ b/mddocs/connection/db_connection/teradata/prerequisites.md @@ -0,0 +1,57 @@ + + +# Prerequisites + +## Version Compatibility + +* Teradata server versions: + : * Officially declared: 16.10 - 20.0 + * Actually tested: 16.10 +* Spark versions: 2.3.x - 3.5.x +* Java versions: 8 - 20 + +See [official documentation](https://teradata-docs.s3.amazonaws.com/doc/connectivity/jdbc/reference/current/platformMatrix.html). + +## Installing PySpark + +To use Teradata connector you should have PySpark installed (or injected to `sys.path`) +BEFORE creating the connector instance. + +See [Spark](../../../install/spark.md#install-spark) installation instruction for more details. + +## Connecting to Teradata + +### Connection host + +It is possible to connect to Teradata by using either DNS name Parsing Engine (PE) host, or it’s IP address. + +### Connection port + +Connection is usually performed to port `1025`. Port may differ for different Teradata instances. +Please ask your Teradata administrator to provide required information. + +### Required grants + +Ask your Teradata cluster administrator to set following grants for a user, +used for creating a connection: + +Read + Write + +```sql +-- allow creating tables in the target schema +GRANT CREATE TABLE ON database TO username; + +-- allow read & write access to specific table +GRANT SELECT, INSERT ON database.mytable TO username; +``` + +Read only + +```sql +-- allow read access to specific table +GRANT SELECT ON database.mytable TO username; +``` + +See: +: * [Teradata access rights](https://www.dwhpro.com/teradata-access-rights/) + * [GRANT documentation](https://teradata.github.io/presto/docs/0.167-t/sql/grant.html) diff --git a/mddocs/connection/db_connection/teradata/read.md b/mddocs/connection/db_connection/teradata/read.md new file mode 100644 index 000000000..49c91e222 --- /dev/null +++ b/mddocs/connection/db_connection/teradata/read.md @@ -0,0 +1,379 @@ + + +# Reading from Teradata using `DBReader` + +[`DBReader`](../../../db/db_reader.md#onetl.db.db_reader.db_reader.DBReader) supports [Read Strategies](../../../strategy/index.md#strategy) for incremental data reading, +but does not support custom queries, like `JOIN`. + +## Supported DBReader features + +* ✅︎ `columns` +* ✅︎ `where` +* ✅︎ `hwm`, supported strategies: +* * ✅︎ [Snapshot Strategy](../../../strategy/snapshot_strategy.md#snapshot-strategy) +* * ✅︎ [Incremental Strategy](../../../strategy/incremental_strategy.md#incremental-strategy) +* * ✅︎ [Snapshot Batch Strategy](../../../strategy/snapshot_batch_strategy.md#snapshot-batch-strategy) +* * ✅︎ [Incremental Batch Strategy](../../../strategy/incremental_batch_strategy.md#incremental-batch-strategy) +* ❌ `hint` (is not supported by Teradata) +* ❌ `df_schema` +* ✅︎ `options` (see [`Teradata.ReadOptions`](#onetl.connection.db_connection.teradata.options.TeradataReadOptions)) + +## Examples + +Snapshot strategy: + +```python +from onetl.connection import Teradata +from onetl.db import DBReader + +teradata = Teradata(...) + +reader = DBReader( + connection=teradata, + source="database.table", + columns=["id", "key", "CAST(value AS VARCHAR) value", "updated_dt"], + where="key = 'something'", + options=Teradata.ReadOptions( + partitioning_mode="hash", + partitionColumn="id", + numPartitions=10, + ), +) +df = reader.run() +``` + +Incremental strategy: + +```python +from onetl.connection import Teradata +from onetl.db import DBReader +from onetl.strategy import IncrementalStrategy + +teradata = Teradata(...) + +reader = DBReader( + connection=teradata, + source="database.table", + columns=["id", "key", "CAST(value AS VARCHAR) value", "updated_dt"], + where="key = 'something'", + hwm=DBReader.AutoDetectHWM(name="teradata_hwm", expression="updated_dt"), + options=Teradata.ReadOptions( + partitioning_mode="hash", + partitionColumn="id", + numPartitions=10, + ), +) + +with IncrementalStrategy(): + df = reader.run() +``` + +## Recommendations + +### Select only required columns + +Instead of passing `"*"` in `DBReader(columns=[...])` prefer passing exact column names. This reduces the amount of data passed from Teradata to Spark. + +### Pay attention to `where` value + +Instead of filtering data on Spark side using `df.filter(df.column == 'value')` pass proper `DBReader(where="column = 'value'")` clause. +This both reduces the amount of data send from Teradata to Spark, and may also improve performance of the query. +Especially if there are indexes or partitions for columns used in `where` clause. + +### Read data in parallel + +`DBReader` can read data in multiple parallel connections by passing `Teradata.ReadOptions(numPartitions=..., partitionColumn=...)`. + +In the example above, Spark opens 10 parallel connections, and data is evenly distributed between all these connections using expression +`HASHAMP(HASHBUCKET(HASHROW({partition_column}))) MOD {num_partitions}`. +This allows sending each Spark worker only some piece of data, reducing resource consumption. +`partition_column` here can be table column of any type. + +It is also possible to use `partitioning_mode="mod"` or `partitioning_mode="range"`, but in this case +`partition_column` have to be an integer, should not contain `NULL`, and values to be uniformly distributed. +It is also less performant than `partitioning_mode="hash"` due to Teradata `HASHAMP` implementation. + +### Do **NOT** use `TYPE=FASTEXPORT` + +Teradata supports several [different connection types](https://teradata-docs.s3.amazonaws.com/doc/connectivity/jdbc/reference/current/jdbcug_chapter_2.html#BABFGFAF): +: * `TYPE=DEFAULT` - perform plain `SELECT` queries + * `TYPE=FASTEXPORT` - uses special FastExport protocol for select queries + +But `TYPE=FASTEXPORT` uses exclusive lock on the source table, so it is impossible to use multiple Spark workers parallel data read. +This leads to sending all the data to just one Spark worker, which is slow and takes a lot of RAM. + +Prefer using `partitioning_mode="hash"` from example above. + +## Options + +### *pydantic model* onetl.connection.db_connection.teradata.options.TeradataReadOptions + +Spark JDBC reading options. + +#### Versionadded +Added in version 0.5.0: Replace `Teradata.Options` → `Teradata.ReadOptions` + +### Examples + +#### NOTE +You can pass any value +[supported by Spark](https://spark.apache.org/docs/latest/sql-data-sources-jdbc.html), +even if it is not mentioned in this documentation. **Option names should be in** `camelCase`! + +The set of supported options depends on Spark version. + +```python +from onetl.connection import Teradata + +options = Teradata.ReadOptions( + partitioning_mode="range", + partitionColumn="reg_id", + numPartitions=10, + customSparkOption="value", +) +``` + + +* **Fields:** + - [`fetchsize (int)`](#onetl.connection.db_connection.teradata.options.TeradataReadOptions.fetchsize) + - [`lower_bound (int | None)`](#onetl.connection.db_connection.teradata.options.TeradataReadOptions.lower_bound) + - [`num_partitions (pydantic.types.PositiveInt)`](#onetl.connection.db_connection.teradata.options.TeradataReadOptions.num_partitions) + - [`partition_column (str | None)`](#onetl.connection.db_connection.teradata.options.TeradataReadOptions.partition_column) + - [`partitioning_mode (onetl.connection.db_connection.jdbc_connection.options.JDBCPartitioningMode)`](#onetl.connection.db_connection.teradata.options.TeradataReadOptions.partitioning_mode) + - [`query_timeout (int | None)`](#onetl.connection.db_connection.teradata.options.TeradataReadOptions.query_timeout) + - [`session_init_statement (str | None)`](#onetl.connection.db_connection.teradata.options.TeradataReadOptions.session_init_statement) + - [`upper_bound (int | None)`](#onetl.connection.db_connection.teradata.options.TeradataReadOptions.upper_bound) + +#### *field* partition_column *: str | None* *= None* *(alias 'partitionColumn')* + +Column used to parallelize reading from a table. + +#### WARNING +It is highly recommended to use primary key, or column with an index +to avoid performance issues. + +#### NOTE +Column type depends on [`partitioning_mode`](#onetl.connection.db_connection.teradata.options.TeradataReadOptions.partitioning_mode). + +* `partitioning_mode="range"` requires column to be an integer, date or timestamp (can be NULL, but not recommended). +* `partitioning_mode="hash"` accepts any column type (NOT NULL). +* `partitioning_mode="mod"` requires column to be an integer (NOT NULL). + +See documentation for [`partitioning_mode`](#onetl.connection.db_connection.teradata.options.TeradataReadOptions.partitioning_mode) for more details + + + +#### *field* num_partitions *: PositiveInt* *= 1* *(alias 'numPartitions')* + +Number of jobs created by Spark to read the table content in parallel. +See documentation for [`partitioning_mode`](#onetl.connection.db_connection.teradata.options.TeradataReadOptions.partitioning_mode) for more details + + +* **Constraints:** + - **exclusiveMinimum** = 0 + +#### *field* lower_bound *: int | None* *= None* *(alias 'lowerBound')* + +See documentation for [`partitioning_mode`](#onetl.connection.db_connection.teradata.options.TeradataReadOptions.partitioning_mode) for more details + + + +#### *field* upper_bound *: int | None* *= None* *(alias 'upperBound')* + +See documentation for [`partitioning_mode`](#onetl.connection.db_connection.teradata.options.TeradataReadOptions.partitioning_mode) for more details + + + +#### *field* session_init_statement *: str | None* *= None* *(alias 'sessionInitStatement')* + +After each database session is opened to the remote DB and before starting to read data, +this option executes a custom SQL statement (or a PL/SQL block). + +Use this to implement session initialization code. + +Example: + +```python +sessionInitStatement = """ + BEGIN + execute immediate + 'alter session set "_serial_direct_read"=true'; + END; +""" +``` + + + +#### *field* query_timeout *: int | None* *= None* *(alias 'queryTimeout')* + +The number of seconds the driver will wait for a statement to execute. +Zero means there is no limit. + +This option depends on driver implementation, +some drivers can check the timeout of each query instead of an entire JDBC batch. + + + +#### *field* fetchsize *: int* *= 100000* + +Fetch N rows from an opened cursor per one read round. + +Tuning this option can influence performance of reading. + +#### WARNING +Default value is different from Spark. + +Spark uses driver’s own value, and it may be different in different drivers, +and even versions of the same driver. For example, Oracle has +default `fetchsize=10`, which is absolutely not usable. + +Thus we’ve overridden default value with `100_000`, which should increase reading performance. + +#### Versionchanged +Changed in version 0.2.0: Set explicit default value to `100_000` + + + +#### *field* partitioning_mode *: JDBCPartitioningMode* *= JDBCPartitioningMode.RANGE* + +Defines how Spark will parallelize reading from table. + +Possible values: + +* `range` (default) + : Allocate each executor a range of values from column passed into [`partition_column`](#onetl.connection.db_connection.teradata.options.TeradataReadOptions.partition_column). +
+ ### Spark generates for each executor an SQL query +
+ Executor 1: + ```sql + SELECT ... FROM table + WHERE (partition_column >= lowerBound + OR partition_column IS NULL) + AND partition_column < (lower_bound + stride) + ``` +
+ Executor 2: + ```sql + SELECT ... FROM table + WHERE partition_column >= (lower_bound + stride) + AND partition_column < (lower_bound + 2 * stride) + ``` +
+ … +
+ Executor N: + ```sql + SELECT ... FROM table + WHERE partition_column >= (lower_bound + (N-1) * stride) + AND partition_column <= upper_bound + ``` +
+ Where `stride=(upper_bound - lower_bound) / num_partitions`. +
+ #### NOTE + Can be used only with columns of integer, date or timestamp types. +
+ #### NOTE + [`lower_bound`](#onetl.connection.db_connection.teradata.options.TeradataReadOptions.lower_bound), [`upper_bound`](#onetl.connection.db_connection.teradata.options.TeradataReadOptions.upper_bound) and [`num_partitions`](#onetl.connection.db_connection.teradata.options.TeradataReadOptions.num_partitions) are used just to + calculate the partition stride, **NOT** for filtering the rows in table. + So all rows in the table will be returned (unlike *Incremental* [Read Strategies](../../../strategy/index.md#strategy)). +
+ #### NOTE + All queries are executed in parallel. To execute them sequentially, use *Batch* [Read Strategies](../../../strategy/index.md#strategy). +* `hash` + : Allocate each executor a set of values based on hash of the [`partition_column`](#onetl.connection.db_connection.teradata.options.TeradataReadOptions.partition_column) column. +
+ ### Spark generates for each executor an SQL query +
+ Executor 1: + ```sql + SELECT ... FROM table + WHERE (some_hash(partition_column) mod num_partitions) = 0 -- lower_bound + ``` +
+ Executor 2: + ```sql + SELECT ... FROM table + WHERE (some_hash(partition_column) mod num_partitions) = 1 -- lower_bound + 1 + ``` +
+ … +
+ Executor N: + ```sql + SELECT ... FROM table + WHERE (some_hash(partition_column) mod num_partitions) = num_partitions-1 -- upper_bound + ``` +
+ #### NOTE + The hash function implementation depends on RDBMS. It can be `MD5` or any other fast hash function, + or expression based on this function call. Usually such functions accepts any column type as an input. +* `mod` + : Allocate each executor a set of values based on modulus of the [`partition_column`](#onetl.connection.db_connection.teradata.options.TeradataReadOptions.partition_column) column. +
+ ### Spark generates for each executor an SQL query +
+ Executor 1: + ```sql + SELECT ... FROM table + WHERE (partition_column mod num_partitions) = 0 -- lower_bound + ``` +
+ Executor 2: + ```sql + SELECT ... FROM table + WHERE (partition_column mod num_partitions) = 1 -- lower_bound + 1 + ``` +
+ Executor N: + ```sql + SELECT ... FROM table + WHERE (partition_column mod num_partitions) = num_partitions-1 -- upper_bound + ``` +
+ #### NOTE + Can be used only with columns of integer type. + +#### Versionadded +Added in version 0.5.0. + +### Examples + +Read data in 10 parallel jobs by range of values in `id_column` column: + +```python +ReadOptions( + partitioning_mode="range", # default mode, can be omitted + partitionColumn="id_column", + numPartitions=10, + # Options below can be discarded because they are + # calculated automatically as MIN and MAX values of `partitionColumn` + lowerBound=0, + upperBound=100_000, +) +``` + +Read data in 10 parallel jobs by hash of values in `some_column` column: + +```python +ReadOptions( + partitioning_mode="hash", + partitionColumn="some_column", + numPartitions=10, + # lowerBound and upperBound are automatically set to `0` and `9` +) +``` + +Read data in 10 parallel jobs by modulus of values in `id_column` column: + +```python +ReadOptions( + partitioning_mode="mod", + partitionColumn="id_column", + numPartitions=10, + # lowerBound and upperBound are automatically set to `0` and `9` +) +``` + + diff --git a/mddocs/connection/db_connection/teradata/sql.md b/mddocs/connection/db_connection/teradata/sql.md new file mode 100644 index 000000000..651dd6e5f --- /dev/null +++ b/mddocs/connection/db_connection/teradata/sql.md @@ -0,0 +1,190 @@ + + +# Reading from Teradata using `Teradata.sql` + +`Teradata.sql` allows passing custom SQL query, but does not support incremental strategies. + +#### WARNING +Statement is executed in **read-write** connection, so if you’re calling some functions/procedures with DDL/DML statements inside, +they can change data in your database. + +## Syntax support + +Only queries with the following syntax are supported: + +* ✅︎ `SELECT ... FROM ...` +* ✅︎ `WITH alias AS (...) SELECT ...` +* ❌ `SHOW ...` +* ❌ `SET ...; SELECT ...;` - multiple statements not supported + +## Examples + +```python +from onetl.connection import Teradata + +teradata = Teradata(...) +df = teradata.sql( + """ + SELECT + id, + key, + CAST(value AS VARCHAR) AS value, + updated_at, + HASHAMP(HASHBUCKET(HASHROW(id))) MOD 10 AS part_column + FROM + database.mytable + WHERE + key = 'something' + """, + options=Teradata.SQLOptions( + partitionColumn="id", + numPartitions=10, + lowerBound=0, + upperBound=1000, + ), +) +``` + +## Recommendations + +### Select only required columns + +Instead of passing `SELECT * FROM ...` prefer passing exact column names `SELECT col1, col2, ...`. +This reduces the amount of data passed from Teradata to Spark. + +### Pay attention to `where` value + +Instead of filtering data on Spark side using `df.filter(df.column == 'value')` pass proper `WHERE column = 'value'` clause. +This both reduces the amount of data send from Teradata to Spark, and may also improve performance of the query. +Especially if there are indexes or partitions for columns used in `where` clause. + +## Options + +### *pydantic model* onetl.connection.db_connection.teradata.options.TeradataSQLOptions + +Options specifically for SQL queries + +These options allow you to specify configurations for executing SQL queries +without relying on Spark’s partitioning mechanisms. + +#### Versionadded +Added in version 0.11.0: Split up `Teradata.ReadOptions` to `Teradata.SQLOptions` + +### Examples + +#### NOTE +You can pass any JDBC configuration +[supported by Spark](https://spark.apache.org/docs/latest/sql-data-sources-jdbc.html), +tailored to optimize SQL query execution. **Option names should be in** `camelCase`! + +```python +from onetl.connection import Teradata + +options = Teradata.SQLOptions( + partitionColumn="reg_id", + numPartitions=10, + lowerBound=0, + upperBound=1000, + customSparkOption="value", +) +``` + + + +#### *field* partition_column *: str | None* *= None* *(alias 'partitionColumn')* + +Column used to partition data across multiple executors for parallel query processing. + +#### WARNING +It is highly recommended to use primary key, or column with an index +to avoid performance issues. + +### Example of using `partitionColumn="id"` with `partitioning_mode="range"` + +```sql +-- If partition_column is 'id', with numPartitions=4, lowerBound=1, and upperBound=100: +-- Executor 1 processes IDs from 1 to 25 +SELECT ... FROM table WHERE id >= 1 AND id < 26 +-- Executor 2 processes IDs from 26 to 50 +SELECT ... FROM table WHERE id >= 26 AND id < 51 +-- Executor 3 processes IDs from 51 to 75 +SELECT ... FROM table WHERE id >= 51 AND id < 76 +-- Executor 4 processes IDs from 76 to 100 +SELECT ... FROM table WHERE id >= 76 AND id <= 100 + +-- General case for Executor N +SELECT ... FROM table +WHERE partition_column >= (lowerBound + (N-1) * stride) +AND partition_column <= upperBound +-- Where ``stride`` is calculated as ``(upperBound - lowerBound) / numPartitions``. +``` + + + +#### *field* num_partitions *: int | None* *= None* *(alias 'numPartitions')* + +Number of jobs created by Spark to read the table content in parallel. + + + +#### *field* lower_bound *: int | None* *= None* *(alias 'lowerBound')* + +Defines the starting boundary for partitioning the query’s data. Mandatory if [`partition_column`](#onetl.connection.db_connection.teradata.options.TeradataSQLOptions.partition_column) is set + + + +#### *field* upper_bound *: int | None* *= None* *(alias 'upperBound')* + +Sets the ending boundary for data partitioning. Mandatory if [`partition_column`](#onetl.connection.db_connection.teradata.options.TeradataSQLOptions.partition_column) is set + + + +#### *field* session_init_statement *: str | None* *= None* *(alias 'sessionInitStatement')* + +After each database session is opened to the remote DB and before starting to read data, +this option executes a custom SQL statement (or a PL/SQL block). + +Use this to implement session initialization code. + +Example: + +```python +sessionInitStatement = """ + BEGIN + execute immediate + 'alter session set "_serial_direct_read"=true'; + END; +""" +``` + + + +#### *field* query_timeout *: int | None* *= None* *(alias 'queryTimeout')* + +The number of seconds the driver will wait for a statement to execute. +Zero means there is no limit. + +This option depends on driver implementation, +some drivers can check the timeout of each query instead of an entire JDBC batch. + + + +#### *field* fetchsize *: int* *= 100000* + +Fetch N rows from an opened cursor per one read round. + +Tuning this option can influence performance of reading. + +#### WARNING +Default value is different from Spark. + +Spark uses driver’s own value, and it may be different in different drivers, +and even versions of the same driver. For example, Oracle has +default `fetchsize=10`, which is absolutely not usable. + +Thus we’ve overridden default value with `100_000`, which should increase reading performance. + +#### Versionchanged +Changed in version 0.2.0: Set explicit default value to `100_000` + + diff --git a/mddocs/connection/db_connection/teradata/write.md b/mddocs/connection/db_connection/teradata/write.md new file mode 100644 index 000000000..ba160f90c --- /dev/null +++ b/mddocs/connection/db_connection/teradata/write.md @@ -0,0 +1,250 @@ + + +# Writing to Teradata using `DBWriter` + +For writing data to Teradata, use [`DBWriter`](../../../db/db_writer.md#onetl.db.db_writer.db_writer.DBWriter). + +#### WARNING +It is always recommended to create table explicitly using [Teradata.execute](execute.md#teradata-execute) +instead of relying on Spark’s table DDL generation. + +This is because Spark’s DDL generator can create columns with different precision and types than it is expected, +causing precision loss or other issues. + +## Examples + +```python +from onetl.connection import Teradata +from onetl.db import DBWriter + +teradata = Teradata( + ..., + extra={"TYPE": "FASTLOAD", "TMODE": "TERA"}, +) + +df = ... # data is here + +writer = DBWriter( + connection=teradata, + target="database.table", + options=Teradata.WriteOptions( + if_exists="append", + # avoid creating SET table, use MULTISET + createTableOptions="NO PRIMARY INDEX", + ), +) + +writer.run(df.repartition(1)) +``` + +## Recommendations + +### Number of connections + +Teradata is not MVCC based, so write operations take exclusive lock on the entire table. +So **it is impossible to write data to Teradata table in multiple parallel connections**, no exceptions. + +The only way to write to Teradata without making deadlocks is write dataframe with exactly 1 partition. + +It can be implemented using `df.repartition(1)`: + +```python +# do NOT use df.coalesce(1) as it can freeze +writer.run(df.repartition(1)) +``` + +This moves all the data to just one Spark worker, so it may consume a lot of RAM. It is usually require to increase `spark.executor.memory` to handle this. + +Another way is to write all dataframe partitions one-by-one: + +```python +from pyspark.sql.functions import spark_partition_id + +# get list of all partitions in the dataframe +partitions = sorted(df.select(spark_partition_id()).distinct().collect()) + +for partition in partitions: + # get only part of data within this exact partition + part_df = df.where(**partition.asDict()).coalesce(1) + + writer.run(part_df) +``` + +This require even data distribution for all partitions to avoid data skew and spikes of RAM consuming. + +### Choosing connection type + +Teradata supports several [different connection types](https://teradata-docs.s3.amazonaws.com/doc/connectivity/jdbc/reference/current/jdbcug_chapter_2.html#BABFGFAF): +: * `TYPE=DEFAULT` - perform plain `INSERT` queries + * `TYPE=FASTLOAD` - uses special FastLoad protocol for insert queries + +It is always recommended to use `TYPE=FASTLOAD` because: +: * It provides higher performance + * It properly handles inserting `NULL` values (`TYPE=DEFAULT` raises an exception) + +But it can be used only during write, not read. + +### Choosing transaction mode + +Teradata supports [2 different transaction modes](https://teradata-docs.s3.amazonaws.com/doc/connectivity/jdbc/reference/current/jdbcug_chapter_2.html#TMODESEC): +: * `TMODE=ANSI` + * `TMODE=TERA` + +Choosing one of the modes can alter connector behavior. For example: +: * Inserting data which exceeds table column length, like insert `CHAR(25)` to column with type `CHAR(24)`: + * * `TMODE=ANSI` - raises exception + * * `TMODE=TERA` - truncates input string to 24 symbols + * Creating table using Spark: + * * `TMODE=ANSI` - creates `MULTISET` table + * * `TMODE=TERA` - creates `SET` table with `PRIMARY KEY` is a first column in dataframe. + This can lead to slower insert time, because each row will be checked against a unique index. + Fortunately, this can be disabled by passing custom `createTableOptions`. + +## Options + +Method above accepts [`Teradata.WriteOptions`](#onetl.connection.db_connection.teradata.options.TeradataWriteOptions) + +### *pydantic model* onetl.connection.db_connection.teradata.options.TeradataWriteOptions + +Spark JDBC writing options. + +#### Versionadded +Added in version 0.5.0: Replace `Teradata.Options` → `Teradata.WriteOptions` + +### Examples + +#### NOTE +You can pass any value +[supported by Spark](https://spark.apache.org/docs/latest/sql-data-sources-jdbc.html), +even if it is not mentioned in this documentation. **Option names should be in** `camelCase`! + +The set of supported options depends on Spark version. + +```python +from onetl.connection import Teradata + +options = Teradata.WriteOptions( + if_exists="append", + batchsize=20_000, + customSparkOption="value", +) +``` + + + +#### *field* if_exists *: JDBCTableExistBehavior* *= JDBCTableExistBehavior.APPEND* *(alias 'mode')* + +Behavior of writing data into existing table. + +Possible values: +: * `append` (default) + : Adds new rows into existing table. +
+ ### Behavior in details +
+ * Table does not exist + : Table is created using options provided by user + (`createTableOptions`, `createTableColumnTypes`, etc). + * Table exists + : Data is appended to a table. Table has the same DDL as before writing data +
+ #### WARNING + This mode does not check whether table already contains + rows from dataframe, so duplicated rows can be created. +
+ Also Spark does not support passing custom options to + insert statement, like `ON CONFLICT`, so don’t try to + implement deduplication using unique indexes or constraints. +
+ Instead, write to staging table and perform deduplication + using `execute` method. + * `replace_entire_table` + : **Table is dropped and then created, or truncated**. +
+ ### Behavior in details +
+ * Table does not exist + : Table is created using options provided by user + (`createTableOptions`, `createTableColumnTypes`, etc). + * Table exists + : Table content is replaced with dataframe content. +
+ After writing completed, target table could either have the same DDL as + before writing data (`truncate=True`), or can be recreated (`truncate=False` + or source does not support truncation). + * `ignore` + : Ignores the write operation if the table already exists. +
+ ### Behavior in details +
+ * Table does not exist + : Table is created using options provided by user + (`createTableOptions`, `createTableColumnTypes`, etc). + * Table exists + : The write operation is ignored, and no data is written to the table. + * `error` + : Raises an error if the table already exists. +
+ ### Behavior in details +
+ * Table does not exist + : Table is created using options provided by user + (`createTableOptions`, `createTableColumnTypes`, etc). + * Table exists + : An error is raised, and no data is written to the table. + +#### Versionchanged +Changed in version 0.9.0: Renamed `mode` → `if_exists` + + + +#### *field* query_timeout *: int | None* *= None* *(alias 'queryTimeout')* + +The number of seconds the driver will wait for a statement to execute. +Zero means there is no limit. + +This option depends on driver implementation, +some drivers can check the timeout of each query instead of an entire JDBC batch. + + + +#### *field* batchsize *: int* *= 20000* + +How many rows can be inserted per round trip. + +Tuning this option can influence performance of writing. + +#### WARNING +Default value is different from Spark. + +Spark uses quite small value `1000`, which is absolutely not usable +in BigData world. + +Thus we’ve overridden default value with `20_000`, +which should increase writing performance. + +You can increase it even more, up to `50_000`, +but it depends on your database load and number of columns in the row. +Higher values does not increase performance. + +#### Versionchanged +Changed in version 0.4.0: Changed default value from 1000 to 20_000 + + + +#### *field* isolation_level *: str* *= 'READ_UNCOMMITTED'* *(alias 'isolationLevel')* + +The transaction isolation level, which applies to current connection. + +Possible values: +: * `NONE` (as string, not Python’s `None`) + * `READ_COMMITTED` + * `READ_UNCOMMITTED` + * `REPEATABLE_READ` + * `SERIALIZABLE` + +Values correspond to transaction isolation levels defined by JDBC standard. +Please refer the documentation for +[java.sql.Connection](https://docs.oracle.com/javase/8/docs/api/java/sql/Connection.html). + + diff --git a/mddocs/connection/file_connection/ftp.md b/mddocs/connection/file_connection/ftp.md new file mode 100644 index 000000000..6ecce3655 --- /dev/null +++ b/mddocs/connection/file_connection/ftp.md @@ -0,0 +1,627 @@ + + +# FTP connection + +### *class* onetl.connection.file_connection.ftp.FTP(\*, host: Host, port: int = 21, user: str | None = None, password: SecretStr | None = None) + +FTP file connection. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +Based on [FTPUtil library](https://pypi.org/project/ftputil/). + +#### WARNING +Since onETL v0.7.0 to use FTP connector you should install package as follows: + +```bash +pip install onetl[ftp] + +# or +pip install onetl[files] +``` + +See [File connections](../../install/files.md#install-files) installation instruction for more details. + +#### Versionadded +Added in version 0.1.0. + +* **Parameters:** + **host** + : Host of FTP source. For example: `ftp.domain.com` + + **port** + : Port of FTP source + + **user** + : User, which have access to the file source. For example: `someuser`. +
+ `None` means that the user is anonymous. + + **password** + : Password for file source connection. +
+ `None` means that the user is anonymous. + +### Examples + +Create and check FTP connection: + +```python +from onetl.connection import FTP + +ftp = FTP( + host="ftp.domain.com", + user="someuser", + password="*****", +).check() +``` + + + +#### \_\_init_\_(\*\*kwargs) + + + +#### check() + +Check source availability. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +If not, an exception will be raised. + +* **Returns:** + Connection itself +* **Raises:** + RuntimeError + : If the connection is not available + +### Examples + +```python +connection.check() +``` + + + +#### create_dir(path: PathLike | str) → RemoteDirectory + +Creates directory tree on remote filesystem. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **path** + : Directory path +* **Returns:** + Created directory with stats +* **Raises:** + `onetl.exception.NotAFileError` + : Path is not a file + +### Examples + +```pycon +>>> dir_path = connection.create_dir("/path/to/dir") +>>> os.fspath(dir_path) +'/path/to/dir' +``` + + + +#### download_file(remote_file_path: PathLike | str, local_file_path: PathLike | str, replace: bool = True) → LocalPath + +Downloads file from the remote filesystem to a local path. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### WARNING +Supports only one file download per call. Directory download is **NOT** supported, use [File Downloader](../../file/file_downloader/file_downloader.md#file-downloader) instead. + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **remote_file_path** + : Remote file path to read from + + **local_file_path** + : Local file path to create + + **replace** + : If `True`, existing file will be replaced +* **Returns:** + Local file with stats. +* **Raises:** + `onetl.exception.NotAFileError` + : Remote or local path is not a file + + FileNotFoundError + : Remote file does not exist + + FileExistsError + : Local file already exists, and `replace=False` + + `onetl.exception.FileSizeMismatchError` + : Target file size after download is different from source file size. + +### Examples + +```pycon +>>> local_file = connection.download_file( +... remote_file_path="/path/to/source.csv", +... local_file_path="/path/to/target.csv", +... ) +>>> os.fspath(local_file) +'/path/to/target.csv' +>>> local_file.exists() +True +>>> local_file.stat().st_size # in bytes +1024 +>>> connection.get_stat("/path/to/source.csv").st_size # same size +1024 +``` + + + +#### get_stat(path: PathLike | str) → PathStatProtocol + +Returns stats for a specific path. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **path** + : Path to get stats for +* **Returns:** + Stats object +* **Raises:** + Any underlying client exception + +### Examples + +```pycon +>>> stat = connection.get_stat("/path/to/file.csv") +>>> stat.st_size # in bytes +1024 +>>> stat.st_uid # owner id or name +12345 +``` + + + +#### is_dir(path: PathLike | str) → bool + +Check if specified path is a directory. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **path** + : Path to check +* **Returns:** + `True` if path is a directory, `False` otherwise. +* **Raises:** + `onetl.exception.DirectoryNotFoundError` + : Path does not exist + +### Examples + +```pycon +>>> connection.is_dir("/path/to/dir") +True +>>> connection.is_dir("/path/to/dir/file.csv") +False +``` + + + +#### is_file(path: PathLike | str) → bool + +Check if specified path is a file. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **path** + : Path to check +* **Returns:** + `True` if path is a file, `False` otherwise. +* **Raises:** + FileNotFoundError + : Path does not exist + +### Examples + +```pycon +>>> connection.is_file("/path/to/dir/file.csv") +True +>>> connection.is_file("/path/to/dir") +False +``` + + + +#### list_dir(path: PathLike | str, filters: Iterable[[BaseFileFilter](../../file/file_filters/base.md#onetl.base.base_file_filter.BaseFileFilter)] | None = None, limits: Iterable[[BaseFileLimit](../../file/file_limits/base.md#onetl.base.base_file_limit.BaseFileLimit)] | None = None) → list[RemoteDirectory | RemoteFile] + +Return list of child files/directories in a specific directory. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **path** + : Directory path to list contents. + + **filters** + : Return only files/directories matching these filters. See [File Filters](../../file/file_filters/index.md#file-filters) + + **limits** + : Apply limits to the list of files/directories, and stop if one of the limits is reached. + See [File Limits](../../file/file_limits/index.md#file-limits) +* **Returns:** + List of `onetl.base.PathWithStatsProtocol` +* **Raises:** + NotADirectoryError + : Path is not a directory + + `onetl.exception.DirectoryNotFoundError` + : Path does not exist + +### Examples + +```pycon +>>> dir_content = connection.list_dir("/path/to/dir") +>>> os.fspath(dir_content[0]) +'file.csv' +>>> connection.path_exists("/path/to/dir/file.csv") +True +``` + + + +#### path_exists(path: PathLike | str) → bool + +Check if specified path exists on remote filesystem. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html). + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **path** + : Path to check +* **Returns:** + `True` if path exists, `False` otherwise + +### Examples + +```pycon +>>> connection.path_exists("/path/to/file.csv") +True +>>> connection.path_exists("/path/to/dir") +True +>>> connection.path_exists("/path/to/missing") +False +``` + + + +#### remove_dir(path: PathLike | str, recursive: bool = False) → bool + +Remove directory or directory tree. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +If directory does not exist, no exception is raised. + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **path** + : Directory path to remote + + **recursive** + : If `True`, remove directory tree recursively. +* **Returns:** + `True` if directory was removed, `False` if directory does not exist in the first place. +* **Raises:** + NotADirectoryError + : Path is not a directory + +### Examples + +```pycon +>>> connection.remove_dir("/path/to/dir") +True +>>> connection.path_exists("/path/to/dir") +False +>>> connection.path_exists("/path/to/dir/file.csv") +False +>>> connection.remove_dir("/path/to/dir") # already deleted, no error +False +``` + + + +#### remove_file(path: PathLike | str) → bool + +Removes file on remote filesystem. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +If file does not exist, no exception is raised. + +#### WARNING +Supports only one file removal per call. Directory removal is **NOT** supported, use [`remove_dir`](#onetl.connection.file_connection.ftp.FTP.remove_dir) instead. + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **path** + : File path +* **Returns:** + `True` if file was removed, `False` if file does not exist in the first place. +* **Raises:** + `onetl.exception.NotAFileError` + : Path is not a file + +### Examples + +```pycon +>>> connection.remove_file("/path/to/file.csv") +True +>>> connection.path_exists("/path/to/dir/file.csv") +False +>>> connection.remove_file("/path/to/file.csv") # already deleted, no error +False +``` + + + +#### rename_dir(source_dir_path: PathLike | str, target_dir_path: PathLike | str, replace: bool = False) → RemoteDirectory + +Rename or move dir on remote filesystem. + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **source_dir_path** + : Old directory path + + **target_dir_path** + : New directory path + + **replace** + : If `True`, existing directory will be replaced. +* **Returns:** + New directory path with stats. +* **Raises:** + NotADirectoryError + : Path is not a directory + + `onetl.exception.DirectoryNotFoundError` + : Path does not exist + + `onetl.exception.DirectoryExistsError` + : Directory already exists, and `replace=False` + +### Examples + +```pycon +>>> new_dir = connection.rename_dir("/path/to/dir1", "/path/to/dir2") +>>> os.fspath(new_dir) +'/path/to/dir2' +>>> connection.path_exists("/path/to/dir1") +False +>>> connection.path_exists("/path/to/dir2") +True +``` + + + +#### rename_file(source_file_path: PathLike | str, target_file_path: PathLike | str, replace: bool = False) → RemoteFile + +Rename or move file on remote filesystem. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### WARNING +Supports only one file move per call. Directory move/rename is **NOT** supported. + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **source_file_path** + : Old file path + + **target_file_path** + : New file path + + **replace** + : If `True`, existing file will be replaced. +* **Returns:** + New file path with stats. +* **Raises:** + `onetl.exception.NotAFileError` + : Source or target path is not a file + + FileNotFoundError + : File does not exist + + FileExistsError + : File already exists, and `replace=False` + +### Examples + +```pycon +>>> new_file = connection.rename_file("/path/to/file1.csv", "/path/to/file2.csv") +>>> os.fspath(new_file) +'/path/to/file2.csv' +>>> connection.path_exists("/path/to/file2.csv") +True +>>> connection.path_exists("/path/to/file1.csv") +False +``` + + + +#### resolve_dir(path: PathLike | str) → RemoteDirectory + +Returns directory at specific path, with stats. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **path** + : Path to resolve +* **Returns:** + Directory path with stats +* **Raises:** + `onetl.exception.DirectoryNotFoundError` + : Path does not exist + + NotADirectoryError + : Path is not a directory + +### Examples + +```pycon +>>> dir_path = connection.resolve_dir("/path/to/dir") +>>> os.fspath(dir_path) +'/path/to/dir' +>>> dir_path.stat().st_uid # owner id +12345 +``` + + + +#### resolve_file(path: PathLike | str) → RemoteFile + +Returns file at specific path, with stats. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **path** + : Path to resolve +* **Returns:** + File path with stats +* **Raises:** + FileNotFoundError + : Path does not exist + + `onetl.exception.NotAFileError` + : Path is not a file + +### Examples + +```pycon +>>> file_path = connection.resolve_file("/path/to/dir/file.csv") +>>> os.fspath(file_path) +'/path/to/dir/file.csv' +>>> file_path.stat().st_uid # owner id +12345 +``` + + + +#### upload_file(local_file_path: PathLike | str, remote_file_path: PathLike | str, replace: bool = False) → RemoteFile + +Uploads local file to a remote filesystem. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### WARNING +Supports only one file upload per call. Directory upload is **NOT** supported, use [File Uploader](../../file/file_uploader/file_uploader.md#file-uploader) instead. + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **local_file_path** + : Local file path to read from + + **remote_file_path** + : Remote file path to create + + **replace** + : If `True`, existing file will be replaced +* **Returns:** + Remote file with stats. +* **Raises:** + `onetl.exception.NotAFileError` + : Remote or local path is not a file + + FileNotFoundError + : Local file does not exist + + FileExistsError + : Remote file already exists, and `replace=False` + + `onetl.exception.FileSizeMismatchError` + : Target file size after upload is different from source file size. + +### Examples + +```pycon +>>> remote_file = connection.upload( +... local_file_path="/path/to/source.csv", +... remote_file_path="/path/to/target.csv", +... ) +>>> os.fspath(remote_file) +'/path/to/target.csv' +>>> connection.path_exists("/path/to/target.csv") +True +>>> remote_file.stat().st_size # in bytes +1024 +>>> os.stat("/path/to/source.csv").st_size # same as source +1024 +``` + + + +#### walk(root: PathLike | str, topdown: bool = True, filters: Iterable[[BaseFileFilter](../../file/file_filters/base.md#onetl.base.base_file_filter.BaseFileFilter)] | None = None, limits: Iterable[[BaseFileLimit](../../file/file_limits/base.md#onetl.base.base_file_limit.BaseFileLimit)] | None = None) → Iterator[tuple[RemoteDirectory, list[RemoteDirectory], list[RemoteFile]]] + +Walk into directory tree, and iterate over its content in all nesting levels. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +Just like `os.walk`, but with additional filter/limit logic. + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **root** + : Directory path to walk into. + + **topdown** + : If `True`, walk in top-down order, otherwise walk in bottom-up order. + + **filters** + : Return only files/directories matching these filters. See [File Filters](../../file/file_filters/index.md#file-filters). + + **limits** + : Apply limits to the list of files/directories, and immediately stop if any of these limits is reached. + See [File Limits](../../file/file_limits/index.md#file-limits). +* **Returns:** + `Iterator[tuple[root, dirs, files]]`, like `os.walk`. + + But all the paths are not strings, instead path classes with embedded stats are returned. +* **Raises:** + NotADirectoryError + : Path is not a directory + + `onetl.exception.DirectoryNotFoundError` + : Path does not exist + +### Examples + +```pycon +>>> for root, dirs, files in connection.walk("/path/to/dir"): +... break +>>> os.fspath(root) +'/path/to/dir' +>>> dirs +[] +>>> os.fspath(files[0]) +'file.csv' +>>> connection.path_exists("/path/to/dir/file.csv") +True +``` + + diff --git a/mddocs/connection/file_connection/ftps.md b/mddocs/connection/file_connection/ftps.md new file mode 100644 index 000000000..e115be98b --- /dev/null +++ b/mddocs/connection/file_connection/ftps.md @@ -0,0 +1,627 @@ + + +# FTPS connection + +### *class* onetl.connection.file_connection.ftps.FTPS(\*, host: Host, port: int = 21, user: str | None = None, password: SecretStr | None = None) + +FTPS file connection. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +Based on [FTPUtil library](https://pypi.org/project/ftputil/). + +#### WARNING +Since onETL v0.7.0 to use FTPS connector you should install package as follows: + +```bash +pip install onetl[ftps] + +# or +pip install onetl[files] +``` + +See [File connections](../../install/files.md#install-files) installation instruction for more details. + +#### Versionadded +Added in version 0.1.0. + +* **Parameters:** + **host** + : Host of FTPS source. For example: `ftps.domain.com` + + **port** + : Port of FTPS source + + **user** + : User, which have access to the file source. For example: `someuser`. +
+ `None` means that the user is anonymous. + + **password** + : Password for file source connection. +
+ `None` means that the user is anonymous. + +### Examples + +Create and check FTPS connection: + +```python +from onetl.connection import FTPS + +ftps = FTPS( + host="ftps.domain.com", + user="someuser", + password="*****", +).check() +``` + + + +#### \_\_init_\_(\*\*kwargs) + + + +#### check() + +Check source availability. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +If not, an exception will be raised. + +* **Returns:** + Connection itself +* **Raises:** + RuntimeError + : If the connection is not available + +### Examples + +```python +connection.check() +``` + + + +#### create_dir(path: PathLike | str) → RemoteDirectory + +Creates directory tree on remote filesystem. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **path** + : Directory path +* **Returns:** + Created directory with stats +* **Raises:** + `onetl.exception.NotAFileError` + : Path is not a file + +### Examples + +```pycon +>>> dir_path = connection.create_dir("/path/to/dir") +>>> os.fspath(dir_path) +'/path/to/dir' +``` + + + +#### download_file(remote_file_path: PathLike | str, local_file_path: PathLike | str, replace: bool = True) → LocalPath + +Downloads file from the remote filesystem to a local path. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### WARNING +Supports only one file download per call. Directory download is **NOT** supported, use [File Downloader](../../file/file_downloader/file_downloader.md#file-downloader) instead. + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **remote_file_path** + : Remote file path to read from + + **local_file_path** + : Local file path to create + + **replace** + : If `True`, existing file will be replaced +* **Returns:** + Local file with stats. +* **Raises:** + `onetl.exception.NotAFileError` + : Remote or local path is not a file + + FileNotFoundError + : Remote file does not exist + + FileExistsError + : Local file already exists, and `replace=False` + + `onetl.exception.FileSizeMismatchError` + : Target file size after download is different from source file size. + +### Examples + +```pycon +>>> local_file = connection.download_file( +... remote_file_path="/path/to/source.csv", +... local_file_path="/path/to/target.csv", +... ) +>>> os.fspath(local_file) +'/path/to/target.csv' +>>> local_file.exists() +True +>>> local_file.stat().st_size # in bytes +1024 +>>> connection.get_stat("/path/to/source.csv").st_size # same size +1024 +``` + + + +#### get_stat(path: PathLike | str) → PathStatProtocol + +Returns stats for a specific path. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **path** + : Path to get stats for +* **Returns:** + Stats object +* **Raises:** + Any underlying client exception + +### Examples + +```pycon +>>> stat = connection.get_stat("/path/to/file.csv") +>>> stat.st_size # in bytes +1024 +>>> stat.st_uid # owner id or name +12345 +``` + + + +#### is_dir(path: PathLike | str) → bool + +Check if specified path is a directory. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **path** + : Path to check +* **Returns:** + `True` if path is a directory, `False` otherwise. +* **Raises:** + `onetl.exception.DirectoryNotFoundError` + : Path does not exist + +### Examples + +```pycon +>>> connection.is_dir("/path/to/dir") +True +>>> connection.is_dir("/path/to/dir/file.csv") +False +``` + + + +#### is_file(path: PathLike | str) → bool + +Check if specified path is a file. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **path** + : Path to check +* **Returns:** + `True` if path is a file, `False` otherwise. +* **Raises:** + FileNotFoundError + : Path does not exist + +### Examples + +```pycon +>>> connection.is_file("/path/to/dir/file.csv") +True +>>> connection.is_file("/path/to/dir") +False +``` + + + +#### list_dir(path: PathLike | str, filters: Iterable[[BaseFileFilter](../../file/file_filters/base.md#onetl.base.base_file_filter.BaseFileFilter)] | None = None, limits: Iterable[[BaseFileLimit](../../file/file_limits/base.md#onetl.base.base_file_limit.BaseFileLimit)] | None = None) → list[RemoteDirectory | RemoteFile] + +Return list of child files/directories in a specific directory. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **path** + : Directory path to list contents. + + **filters** + : Return only files/directories matching these filters. See [File Filters](../../file/file_filters/index.md#file-filters) + + **limits** + : Apply limits to the list of files/directories, and stop if one of the limits is reached. + See [File Limits](../../file/file_limits/index.md#file-limits) +* **Returns:** + List of `onetl.base.PathWithStatsProtocol` +* **Raises:** + NotADirectoryError + : Path is not a directory + + `onetl.exception.DirectoryNotFoundError` + : Path does not exist + +### Examples + +```pycon +>>> dir_content = connection.list_dir("/path/to/dir") +>>> os.fspath(dir_content[0]) +'file.csv' +>>> connection.path_exists("/path/to/dir/file.csv") +True +``` + + + +#### path_exists(path: PathLike | str) → bool + +Check if specified path exists on remote filesystem. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html). + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **path** + : Path to check +* **Returns:** + `True` if path exists, `False` otherwise + +### Examples + +```pycon +>>> connection.path_exists("/path/to/file.csv") +True +>>> connection.path_exists("/path/to/dir") +True +>>> connection.path_exists("/path/to/missing") +False +``` + + + +#### remove_dir(path: PathLike | str, recursive: bool = False) → bool + +Remove directory or directory tree. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +If directory does not exist, no exception is raised. + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **path** + : Directory path to remote + + **recursive** + : If `True`, remove directory tree recursively. +* **Returns:** + `True` if directory was removed, `False` if directory does not exist in the first place. +* **Raises:** + NotADirectoryError + : Path is not a directory + +### Examples + +```pycon +>>> connection.remove_dir("/path/to/dir") +True +>>> connection.path_exists("/path/to/dir") +False +>>> connection.path_exists("/path/to/dir/file.csv") +False +>>> connection.remove_dir("/path/to/dir") # already deleted, no error +False +``` + + + +#### remove_file(path: PathLike | str) → bool + +Removes file on remote filesystem. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +If file does not exist, no exception is raised. + +#### WARNING +Supports only one file removal per call. Directory removal is **NOT** supported, use [`remove_dir`](#onetl.connection.file_connection.ftps.FTPS.remove_dir) instead. + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **path** + : File path +* **Returns:** + `True` if file was removed, `False` if file does not exist in the first place. +* **Raises:** + `onetl.exception.NotAFileError` + : Path is not a file + +### Examples + +```pycon +>>> connection.remove_file("/path/to/file.csv") +True +>>> connection.path_exists("/path/to/dir/file.csv") +False +>>> connection.remove_file("/path/to/file.csv") # already deleted, no error +False +``` + + + +#### rename_dir(source_dir_path: PathLike | str, target_dir_path: PathLike | str, replace: bool = False) → RemoteDirectory + +Rename or move dir on remote filesystem. + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **source_dir_path** + : Old directory path + + **target_dir_path** + : New directory path + + **replace** + : If `True`, existing directory will be replaced. +* **Returns:** + New directory path with stats. +* **Raises:** + NotADirectoryError + : Path is not a directory + + `onetl.exception.DirectoryNotFoundError` + : Path does not exist + + `onetl.exception.DirectoryExistsError` + : Directory already exists, and `replace=False` + +### Examples + +```pycon +>>> new_dir = connection.rename_dir("/path/to/dir1", "/path/to/dir2") +>>> os.fspath(new_dir) +'/path/to/dir2' +>>> connection.path_exists("/path/to/dir1") +False +>>> connection.path_exists("/path/to/dir2") +True +``` + + + +#### rename_file(source_file_path: PathLike | str, target_file_path: PathLike | str, replace: bool = False) → RemoteFile + +Rename or move file on remote filesystem. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### WARNING +Supports only one file move per call. Directory move/rename is **NOT** supported. + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **source_file_path** + : Old file path + + **target_file_path** + : New file path + + **replace** + : If `True`, existing file will be replaced. +* **Returns:** + New file path with stats. +* **Raises:** + `onetl.exception.NotAFileError` + : Source or target path is not a file + + FileNotFoundError + : File does not exist + + FileExistsError + : File already exists, and `replace=False` + +### Examples + +```pycon +>>> new_file = connection.rename_file("/path/to/file1.csv", "/path/to/file2.csv") +>>> os.fspath(new_file) +'/path/to/file2.csv' +>>> connection.path_exists("/path/to/file2.csv") +True +>>> connection.path_exists("/path/to/file1.csv") +False +``` + + + +#### resolve_dir(path: PathLike | str) → RemoteDirectory + +Returns directory at specific path, with stats. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **path** + : Path to resolve +* **Returns:** + Directory path with stats +* **Raises:** + `onetl.exception.DirectoryNotFoundError` + : Path does not exist + + NotADirectoryError + : Path is not a directory + +### Examples + +```pycon +>>> dir_path = connection.resolve_dir("/path/to/dir") +>>> os.fspath(dir_path) +'/path/to/dir' +>>> dir_path.stat().st_uid # owner id +12345 +``` + + + +#### resolve_file(path: PathLike | str) → RemoteFile + +Returns file at specific path, with stats. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **path** + : Path to resolve +* **Returns:** + File path with stats +* **Raises:** + FileNotFoundError + : Path does not exist + + `onetl.exception.NotAFileError` + : Path is not a file + +### Examples + +```pycon +>>> file_path = connection.resolve_file("/path/to/dir/file.csv") +>>> os.fspath(file_path) +'/path/to/dir/file.csv' +>>> file_path.stat().st_uid # owner id +12345 +``` + + + +#### upload_file(local_file_path: PathLike | str, remote_file_path: PathLike | str, replace: bool = False) → RemoteFile + +Uploads local file to a remote filesystem. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### WARNING +Supports only one file upload per call. Directory upload is **NOT** supported, use [File Uploader](../../file/file_uploader/file_uploader.md#file-uploader) instead. + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **local_file_path** + : Local file path to read from + + **remote_file_path** + : Remote file path to create + + **replace** + : If `True`, existing file will be replaced +* **Returns:** + Remote file with stats. +* **Raises:** + `onetl.exception.NotAFileError` + : Remote or local path is not a file + + FileNotFoundError + : Local file does not exist + + FileExistsError + : Remote file already exists, and `replace=False` + + `onetl.exception.FileSizeMismatchError` + : Target file size after upload is different from source file size. + +### Examples + +```pycon +>>> remote_file = connection.upload( +... local_file_path="/path/to/source.csv", +... remote_file_path="/path/to/target.csv", +... ) +>>> os.fspath(remote_file) +'/path/to/target.csv' +>>> connection.path_exists("/path/to/target.csv") +True +>>> remote_file.stat().st_size # in bytes +1024 +>>> os.stat("/path/to/source.csv").st_size # same as source +1024 +``` + + + +#### walk(root: PathLike | str, topdown: bool = True, filters: Iterable[[BaseFileFilter](../../file/file_filters/base.md#onetl.base.base_file_filter.BaseFileFilter)] | None = None, limits: Iterable[[BaseFileLimit](../../file/file_limits/base.md#onetl.base.base_file_limit.BaseFileLimit)] | None = None) → Iterator[tuple[RemoteDirectory, list[RemoteDirectory], list[RemoteFile]]] + +Walk into directory tree, and iterate over its content in all nesting levels. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +Just like `os.walk`, but with additional filter/limit logic. + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **root** + : Directory path to walk into. + + **topdown** + : If `True`, walk in top-down order, otherwise walk in bottom-up order. + + **filters** + : Return only files/directories matching these filters. See [File Filters](../../file/file_filters/index.md#file-filters). + + **limits** + : Apply limits to the list of files/directories, and immediately stop if any of these limits is reached. + See [File Limits](../../file/file_limits/index.md#file-limits). +* **Returns:** + `Iterator[tuple[root, dirs, files]]`, like `os.walk`. + + But all the paths are not strings, instead path classes with embedded stats are returned. +* **Raises:** + NotADirectoryError + : Path is not a directory + + `onetl.exception.DirectoryNotFoundError` + : Path does not exist + +### Examples + +```pycon +>>> for root, dirs, files in connection.walk("/path/to/dir"): +... break +>>> os.fspath(root) +'/path/to/dir' +>>> dirs +[] +>>> os.fspath(files[0]) +'file.csv' +>>> connection.path_exists("/path/to/dir/file.csv") +True +``` + + diff --git a/mddocs/connection/file_connection/hdfs/connection.md b/mddocs/connection/file_connection/hdfs/connection.md new file mode 100644 index 000000000..d8f53ee60 --- /dev/null +++ b/mddocs/connection/file_connection/hdfs/connection.md @@ -0,0 +1,740 @@ + + +# HDFS connection + +### *class* onetl.connection.file_connection.hdfs.connection.HDFS(\*, cluster: Cluster | None = None, host: Host | None = None, port: int = 50070, user: str | None = None, password: SecretStr | None = None, keytab: FilePath | None = None, timeout: int = 10) + +HDFS file connection. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +Powered by [HDFS Python client](https://pypi.org/project/hdfs/). + +#### WARNING +Since onETL v0.7.0 to use HDFS connector you should install package as follows: + +```bash +pip install onetl[hdfs] + +# or +pip install onetl[files] +``` + +See [File connections](../../../install/files.md#install-files) installation instruction for more details. + +#### NOTE +To access Hadoop cluster with Kerberos installed, you should have `kinit` executable +in some path in `PATH` environment variable. + +See [Kerberos support](../../../install/kerberos.md#install-kerberos) instruction for more details. + +* **Parameters:** + **cluster** + : Hadoop cluster name. For example: `rnd-dwh`. +
+ Used for: + : * HWM and lineage (as instance name for file paths), if set. + * Validation of `host` value, + : if latter is passed and if some hooks are bound to + [`Slots.get_cluster_namenodes`](slots.md#onetl.connection.file_connection.hdfs.slots.HDFSSlots.get_cluster_namenodes) +
+ +
+ #### Versionadded + Added in version 0.7.0. + + **host** + : Hadoop namenode host. For example: `namenode1.domain.com`. +
+ Should be an active namenode (NOT standby). +
+ If value is not set, but there are some hooks bound to + [`Slots.get_cluster_namenodes`](slots.md#onetl.connection.file_connection.hdfs.slots.HDFSSlots.get_cluster_namenodes) + and [`Slots.is_namenode_active`](slots.md#onetl.connection.file_connection.hdfs.slots.HDFSSlots.is_namenode_active), + onETL will iterate over cluster namenodes to detect which one is active. + + + **webhdfs_port** + : Port of Hadoop namenode (WebHDFS protocol). +
+ If omitted, but there are some hooks bound to + [`Slots.get_webhdfs_port`](slots.md#onetl.connection.file_connection.hdfs.slots.HDFSSlots.get_webhdfs_port) slot, + onETL will try to detect port number for a specific `cluster`. + + **user** + : User, which have access to the file source. For example: `someuser`. +
+ If set, Kerberos auth will be used. Otherwise an anonymous connection is created. + + **password** + : User password. +
+ Used for generating Kerberos ticket. +
+ #### WARNING + You can provide only one of the parameters: `password` or `kinit`. + If you provide both, an exception will be raised. + + **keytab** + : LocalPath to keytab file. +
+ Used for generating Kerberos ticket. +
+ #### WARNING + You can provide only one of the parameters: `password` or `kinit`. + If you provide both, an exception will be raised. + + **timeout** + : Connection timeout. + +### Examples + +Create HDFS connection with user+password + +```py +from onetl.connection import HDFS + +hdfs = HDFS( + host="namenode1.domain.com", + user="someuser", + password="*****", +).check() +``` + +Create HDFS connection with user+keytab + +```py +from onetl.connection import HDFS + +hdfs = HDFS( + host="namenode1.domain.com", + user="someuser", + keytab="/path/to/keytab", +).check() +``` + +Create HDFS connection without auth + +```py +from onetl.connection import HDFS + +hdfs = HDFS(host="namenode1.domain.com").check() +``` + +Use cluster name to detect active namenode + +Can be used only if some third-party plugin provides [HDFS Slots](slots.md#hdfs-slots) implementation + +```python +from onetl.connection import HDFS + +hdfs = HDFS( + cluster="rnd-dwh", + user="someuser", + password="*****", +).check() +``` + + + +#### check() + +Check source availability. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +If not, an exception will be raised. + +* **Returns:** + Connection itself +* **Raises:** + RuntimeError + : If the connection is not available + +### Examples + +```python +connection.check() +``` + + + +#### create_dir(path: PathLike | str) → RemoteDirectory + +Creates directory tree on remote filesystem. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **path** + : Directory path +* **Returns:** + Created directory with stats +* **Raises:** + `onetl.exception.NotAFileError` + : Path is not a file + +### Examples + +```pycon +>>> dir_path = connection.create_dir("/path/to/dir") +>>> os.fspath(dir_path) +'/path/to/dir' +``` + + + +#### download_file(remote_file_path: PathLike | str, local_file_path: PathLike | str, replace: bool = True) → LocalPath + +Downloads file from the remote filesystem to a local path. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### WARNING +Supports only one file download per call. Directory download is **NOT** supported, use [File Downloader](../../../file/file_downloader/file_downloader.md#file-downloader) instead. + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **remote_file_path** + : Remote file path to read from + + **local_file_path** + : Local file path to create + + **replace** + : If `True`, existing file will be replaced +* **Returns:** + Local file with stats. +* **Raises:** + `onetl.exception.NotAFileError` + : Remote or local path is not a file + + FileNotFoundError + : Remote file does not exist + + FileExistsError + : Local file already exists, and `replace=False` + + `onetl.exception.FileSizeMismatchError` + : Target file size after download is different from source file size. + +### Examples + +```pycon +>>> local_file = connection.download_file( +... remote_file_path="/path/to/source.csv", +... local_file_path="/path/to/target.csv", +... ) +>>> os.fspath(local_file) +'/path/to/target.csv' +>>> local_file.exists() +True +>>> local_file.stat().st_size # in bytes +1024 +>>> connection.get_stat("/path/to/source.csv").st_size # same size +1024 +``` + + + +#### *classmethod* get_current(\*\*kwargs) + +Create connection for current cluster. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +Automatically sets up current cluster name as `cluster`. + +#### NOTE +Can be used only if there are a some hooks bound to slot +[`Slots.get_current_cluster`](slots.md#onetl.connection.file_connection.hdfs.slots.HDFSSlots.get_current_cluster) + +#### Versionadded +Added in version 0.7.0. + +* **Parameters:** + **user** + + **password** + + **keytab** + + **timeout** + : See [`HDFS`](#onetl.connection.file_connection.hdfs.connection.HDFS) constructor documentation. + +### Examples + +```python +from onetl.connection import HDFS + +# injecting current cluster name via hooks mechanism +hdfs = HDFS.get_current(user="me", password="pass") +``` + + + +#### get_stat(path: PathLike | str) → PathStatProtocol + +Returns stats for a specific path. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **path** + : Path to get stats for +* **Returns:** + Stats object +* **Raises:** + Any underlying client exception + +### Examples + +```pycon +>>> stat = connection.get_stat("/path/to/file.csv") +>>> stat.st_size # in bytes +1024 +>>> stat.st_uid # owner id or name +12345 +``` + + + +#### is_dir(path: PathLike | str) → bool + +Check if specified path is a directory. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **path** + : Path to check +* **Returns:** + `True` if path is a directory, `False` otherwise. +* **Raises:** + `onetl.exception.DirectoryNotFoundError` + : Path does not exist + +### Examples + +```pycon +>>> connection.is_dir("/path/to/dir") +True +>>> connection.is_dir("/path/to/dir/file.csv") +False +``` + + + +#### is_file(path: PathLike | str) → bool + +Check if specified path is a file. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **path** + : Path to check +* **Returns:** + `True` if path is a file, `False` otherwise. +* **Raises:** + FileNotFoundError + : Path does not exist + +### Examples + +```pycon +>>> connection.is_file("/path/to/dir/file.csv") +True +>>> connection.is_file("/path/to/dir") +False +``` + + + +#### list_dir(path: PathLike | str, filters: Iterable[[BaseFileFilter](../../../file/file_filters/base.md#onetl.base.base_file_filter.BaseFileFilter)] | None = None, limits: Iterable[[BaseFileLimit](../../../file/file_limits/base.md#onetl.base.base_file_limit.BaseFileLimit)] | None = None) → list[RemoteDirectory | RemoteFile] + +Return list of child files/directories in a specific directory. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **path** + : Directory path to list contents. + + **filters** + : Return only files/directories matching these filters. See [File Filters](../../../file/file_filters/index.md#file-filters) + + **limits** + : Apply limits to the list of files/directories, and stop if one of the limits is reached. + See [File Limits](../../../file/file_limits/index.md#file-limits) +* **Returns:** + List of `onetl.base.PathWithStatsProtocol` +* **Raises:** + NotADirectoryError + : Path is not a directory + + `onetl.exception.DirectoryNotFoundError` + : Path does not exist + +### Examples + +```pycon +>>> dir_content = connection.list_dir("/path/to/dir") +>>> os.fspath(dir_content[0]) +'file.csv' +>>> connection.path_exists("/path/to/dir/file.csv") +True +``` + + + +#### path_exists(path: PathLike | str) → bool + +Check if specified path exists on remote filesystem. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html). + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **path** + : Path to check +* **Returns:** + `True` if path exists, `False` otherwise + +### Examples + +```pycon +>>> connection.path_exists("/path/to/file.csv") +True +>>> connection.path_exists("/path/to/dir") +True +>>> connection.path_exists("/path/to/missing") +False +``` + + + +#### remove_dir(path: PathLike | str, recursive: bool = False) → bool + +Remove directory or directory tree. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +If directory does not exist, no exception is raised. + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **path** + : Directory path to remote + + **recursive** + : If `True`, remove directory tree recursively. +* **Returns:** + `True` if directory was removed, `False` if directory does not exist in the first place. +* **Raises:** + NotADirectoryError + : Path is not a directory + +### Examples + +```pycon +>>> connection.remove_dir("/path/to/dir") +True +>>> connection.path_exists("/path/to/dir") +False +>>> connection.path_exists("/path/to/dir/file.csv") +False +>>> connection.remove_dir("/path/to/dir") # already deleted, no error +False +``` + + + +#### remove_file(path: PathLike | str) → bool + +Removes file on remote filesystem. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +If file does not exist, no exception is raised. + +#### WARNING +Supports only one file removal per call. Directory removal is **NOT** supported, use [`remove_dir`](#onetl.connection.file_connection.hdfs.connection.HDFS.remove_dir) instead. + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **path** + : File path +* **Returns:** + `True` if file was removed, `False` if file does not exist in the first place. +* **Raises:** + `onetl.exception.NotAFileError` + : Path is not a file + +### Examples + +```pycon +>>> connection.remove_file("/path/to/file.csv") +True +>>> connection.path_exists("/path/to/dir/file.csv") +False +>>> connection.remove_file("/path/to/file.csv") # already deleted, no error +False +``` + + + +#### rename_dir(source_dir_path: PathLike | str, target_dir_path: PathLike | str, replace: bool = False) → RemoteDirectory + +Rename or move dir on remote filesystem. + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **source_dir_path** + : Old directory path + + **target_dir_path** + : New directory path + + **replace** + : If `True`, existing directory will be replaced. +* **Returns:** + New directory path with stats. +* **Raises:** + NotADirectoryError + : Path is not a directory + + `onetl.exception.DirectoryNotFoundError` + : Path does not exist + + `onetl.exception.DirectoryExistsError` + : Directory already exists, and `replace=False` + +### Examples + +```pycon +>>> new_dir = connection.rename_dir("/path/to/dir1", "/path/to/dir2") +>>> os.fspath(new_dir) +'/path/to/dir2' +>>> connection.path_exists("/path/to/dir1") +False +>>> connection.path_exists("/path/to/dir2") +True +``` + + + +#### rename_file(source_file_path: PathLike | str, target_file_path: PathLike | str, replace: bool = False) → RemoteFile + +Rename or move file on remote filesystem. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### WARNING +Supports only one file move per call. Directory move/rename is **NOT** supported. + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **source_file_path** + : Old file path + + **target_file_path** + : New file path + + **replace** + : If `True`, existing file will be replaced. +* **Returns:** + New file path with stats. +* **Raises:** + `onetl.exception.NotAFileError` + : Source or target path is not a file + + FileNotFoundError + : File does not exist + + FileExistsError + : File already exists, and `replace=False` + +### Examples + +```pycon +>>> new_file = connection.rename_file("/path/to/file1.csv", "/path/to/file2.csv") +>>> os.fspath(new_file) +'/path/to/file2.csv' +>>> connection.path_exists("/path/to/file2.csv") +True +>>> connection.path_exists("/path/to/file1.csv") +False +``` + + + +#### resolve_dir(path: PathLike | str) → RemoteDirectory + +Returns directory at specific path, with stats. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **path** + : Path to resolve +* **Returns:** + Directory path with stats +* **Raises:** + `onetl.exception.DirectoryNotFoundError` + : Path does not exist + + NotADirectoryError + : Path is not a directory + +### Examples + +```pycon +>>> dir_path = connection.resolve_dir("/path/to/dir") +>>> os.fspath(dir_path) +'/path/to/dir' +>>> dir_path.stat().st_uid # owner id +12345 +``` + + + +#### resolve_file(path: PathLike | str) → RemoteFile + +Returns file at specific path, with stats. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **path** + : Path to resolve +* **Returns:** + File path with stats +* **Raises:** + FileNotFoundError + : Path does not exist + + `onetl.exception.NotAFileError` + : Path is not a file + +### Examples + +```pycon +>>> file_path = connection.resolve_file("/path/to/dir/file.csv") +>>> os.fspath(file_path) +'/path/to/dir/file.csv' +>>> file_path.stat().st_uid # owner id +12345 +``` + + + +#### upload_file(local_file_path: PathLike | str, remote_file_path: PathLike | str, replace: bool = False) → RemoteFile + +Uploads local file to a remote filesystem. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### WARNING +Supports only one file upload per call. Directory upload is **NOT** supported, use [File Uploader](../../../file/file_uploader/file_uploader.md#file-uploader) instead. + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **local_file_path** + : Local file path to read from + + **remote_file_path** + : Remote file path to create + + **replace** + : If `True`, existing file will be replaced +* **Returns:** + Remote file with stats. +* **Raises:** + `onetl.exception.NotAFileError` + : Remote or local path is not a file + + FileNotFoundError + : Local file does not exist + + FileExistsError + : Remote file already exists, and `replace=False` + + `onetl.exception.FileSizeMismatchError` + : Target file size after upload is different from source file size. + +### Examples + +```pycon +>>> remote_file = connection.upload( +... local_file_path="/path/to/source.csv", +... remote_file_path="/path/to/target.csv", +... ) +>>> os.fspath(remote_file) +'/path/to/target.csv' +>>> connection.path_exists("/path/to/target.csv") +True +>>> remote_file.stat().st_size # in bytes +1024 +>>> os.stat("/path/to/source.csv").st_size # same as source +1024 +``` + + + +#### walk(root: PathLike | str, topdown: bool = True, filters: Iterable[[BaseFileFilter](../../../file/file_filters/base.md#onetl.base.base_file_filter.BaseFileFilter)] | None = None, limits: Iterable[[BaseFileLimit](../../../file/file_limits/base.md#onetl.base.base_file_limit.BaseFileLimit)] | None = None) → Iterator[tuple[RemoteDirectory, list[RemoteDirectory], list[RemoteFile]]] + +Walk into directory tree, and iterate over its content in all nesting levels. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +Just like `os.walk`, but with additional filter/limit logic. + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **root** + : Directory path to walk into. + + **topdown** + : If `True`, walk in top-down order, otherwise walk in bottom-up order. + + **filters** + : Return only files/directories matching these filters. See [File Filters](../../../file/file_filters/index.md#file-filters). + + **limits** + : Apply limits to the list of files/directories, and immediately stop if any of these limits is reached. + See [File Limits](../../../file/file_limits/index.md#file-limits). +* **Returns:** + `Iterator[tuple[root, dirs, files]]`, like `os.walk`. + + But all the paths are not strings, instead path classes with embedded stats are returned. +* **Raises:** + NotADirectoryError + : Path is not a directory + + `onetl.exception.DirectoryNotFoundError` + : Path does not exist + +### Examples + +```pycon +>>> for root, dirs, files in connection.walk("/path/to/dir"): +... break +>>> os.fspath(root) +'/path/to/dir' +>>> dirs +[] +>>> os.fspath(files[0]) +'file.csv' +>>> connection.path_exists("/path/to/dir/file.csv") +True +``` + + diff --git a/mddocs/connection/file_connection/hdfs/index.md b/mddocs/connection/file_connection/hdfs/index.md new file mode 100644 index 000000000..a65c43ea3 --- /dev/null +++ b/mddocs/connection/file_connection/hdfs/index.md @@ -0,0 +1,11 @@ + + +# HDFS + +# Connection + +* [HDFS connection](connection.md) + +# For developers + +* [HDFS Slots](slots.md) diff --git a/mddocs/connection/file_connection/hdfs/slots.md b/mddocs/connection/file_connection/hdfs/slots.md new file mode 100644 index 000000000..6f681805b --- /dev/null +++ b/mddocs/connection/file_connection/hdfs/slots.md @@ -0,0 +1,256 @@ + + +# HDFS Slots + +### *class* onetl.connection.file_connection.hdfs.slots.HDFSSlots + +Slots that could be implemented by third-party plugins. + +#### Versionadded +Added in version 0.7.0. + + + +#### *static* normalize_cluster_name(cluster: str) → str | None + +Normalize cluster name passed into HDFS constructor. + +If hooks didn’t return anything, cluster name is left intact. + +#### Versionadded +Added in version 0.7.0. + +* **Parameters:** + **cluster** + : Cluster name +* **Returns:** + str | None + : Normalized cluster name. +
+ If hook cannot be applied to a specific cluster, it should return `None`. + +### Examples + +```python +from onetl.connection import HDFS +from onetl.hooks import hook + +@HDFS.Slots.normalize_cluster_name.bind +@hook +def normalize_cluster_name(cluster: str) -> str: + return cluster.lower() +``` + + + +#### *static* normalize_namenode_host(host: str, cluster: str | None) → str | None + +Normalize namenode host passed into HDFS constructor. + +If hooks didn’t return anything, host is left intact. + +#### Versionadded +Added in version 0.7.0. + +* **Parameters:** + **host** + : Namenode host (raw) + + **cluster** + : Cluster name (normalized), if set +* **Returns:** + str | None + : Normalized namenode host name. +
+ If hook cannot be applied to a specific host name, it should return `None`. + +### Examples + +```python +from onetl.connection import HDFS +from onetl.hooks import hook + +@HDFS.Slots.normalize_namenode_host.bind +@hook +def normalize_namenode_host(host: str, cluster: str) -> str | None: + if cluster == "rnd-dwh": + if not host.endswith(".domain.com"): + # fix missing domain name + host += ".domain.com" + return host + + return None +``` + + + +#### *static* get_known_clusters() → set[str] | None + +Return collection of known clusters. + +Cluster passed into HDFS constructor should be present in this list. +If hooks didn’t return anything, no validation will be performed. + +#### Versionadded +Added in version 0.7.0. + +* **Returns:** + set[str] | None + : Collection of cluster names (in normalized form). +
+ If hook cannot be applied, it should return `None`. + +### Examples + +```python +from onetl.connection import HDFS +from onetl.hooks import hook + +@HDFS.Slots.get_known_clusters.bind +@hook +def get_known_clusters() -> str[str]: + return {"rnd-dwh", "rnd-prod"} +``` + + + +#### *static* get_cluster_namenodes(cluster: str) → set[str] | None + +Return collection of known namenodes for the cluster. + +Namenode host passed into HDFS constructor should be present in this list. +If hooks didn’t return anything, no validation will be performed. + +#### Versionadded +Added in version 0.7.0. + +* **Parameters:** + **cluster** + : Cluster name (normalized) +* **Returns:** + set[str] | None + : Collection of host names (in normalized form). +
+ If hook cannot be applied, it should return `None`. + +### Examples + +```python +from onetl.connection import HDFS +from onetl.hooks import hook + +@HDFS.Slots.get_cluster_namenodes.bind +@hook +def get_cluster_namenodes(cluster: str) -> str[str] | None: + if cluster == "rnd-dwh": + return {"namenode1.domain.com", "namenode2.domain.com"} + return None +``` + + + +#### *static* get_current_cluster() → str | None + +Get current cluster name. + +Used in [`get_current_cluster`](#onetl.connection.file_connection.hdfs.slots.HDFSSlots.get_current_cluster) to automatically fill up `cluster` attribute of a connection. +If hooks didn’t return anything, calling the method above will raise an exception. + +#### Versionadded +Added in version 0.7.0. + +* **Returns:** + str | None + : Current cluster name (in normalized form). +
+ If hook cannot be applied, it should return `None`. + +### Examples + +```python +from onetl.connection import HDFS +from onetl.hooks import hook + +@HDFS.Slots.get_current_cluster.bind +@hook +def get_current_cluster() -> str: + # some magic here + return "rnd-dwh" +``` + + + +#### *static* get_webhdfs_port(cluster: str) → int | None + +Get WebHDFS port number for a specific cluster. + +Used by constructor to automatically set port number if omitted. + +#### Versionadded +Added in version 0.7.0. + +* **Parameters:** + **cluster** + : Cluster name (normalized) +* **Returns:** + int | None + : WebHDFS port number. +
+ If hook cannot be applied, it should return `None`. + +### Examples + +```python +from onetl.connection import HDFS +from onetl.hooks import hook + +@HDFS.Slots.get_webhdfs_port.bind +@hook +def get_webhdfs_port(cluster: str) -> int | None: + if cluster == "rnd-dwh": + return 50007 # Cloudera + return None +``` + + + +#### *static* is_namenode_active(host: str, cluster: str | None) → bool | None + +Check whether a namenode of a specified cluster is active (=not standby) or not. + +Used for: +: * If HDFS connection is created without `host` + > Connector will iterate over [`get_cluster_namenodes`](#onetl.connection.file_connection.hdfs.slots.HDFSSlots.get_cluster_namenodes) of a cluster to get active namenode, + > and then use it instead of `host` attribute. + * If HDFS connection is created with `host` + > `check` will determine whether this host is active. + +#### Versionadded +Added in version 0.7.0. + +* **Parameters:** + **host** + : Namenode host (normalized) + + **cluster** + : Cluster name (normalized), if set +* **Returns:** + bool | None + : `True` if namenode is active, `False` if not. +
+ If hook cannot be applied, it should return `None`. + +### Examples + +```python +from onetl.connection import HDFS +from onetl.hooks import hook + +@HDFS.Slots.is_namenode_active.bind +@hook +def is_namenode_active(host: str, cluster: str | None) -> bool: + # some magic here + return True +``` + + diff --git a/mddocs/connection/file_connection/index.md b/mddocs/connection/file_connection/index.md new file mode 100644 index 000000000..874845c78 --- /dev/null +++ b/mddocs/connection/file_connection/index.md @@ -0,0 +1,13 @@ + + +# File Connections + +# File Connections + +* [FTP](ftp.md) +* [FTPS](ftps.md) +* [HDFS](hdfs/index.md) +* [Samba](samba.md) +* [SFTP](sftp.md) +* [S3](s3.md) +* [Webdav](webdav.md) diff --git a/mddocs/connection/file_connection/s3.md b/mddocs/connection/file_connection/s3.md new file mode 100644 index 000000000..4fa940aaf --- /dev/null +++ b/mddocs/connection/file_connection/s3.md @@ -0,0 +1,598 @@ + + +# S3 connection + +### *class* onetl.connection.file_connection.s3.S3(\*, host: Host, port: int | None = None, bucket: str, access_key: str, secret_key: SecretStr, protocol: Literal['http', 'https'] = 'https', session_token: SecretStr | None = None, region: str | None = None) + +S3 file connection. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +Based on [minio-py client](https://pypi.org/project/minio/). + +#### WARNING +Since onETL v0.7.0 to use S3 connector you should install package as follows: + +```bash +pip install onetl[s3] + +# or +pip install onetl[files] +``` + +See [File connections](../../install/files.md#install-files) installation instruction for more details. + +#### Versionadded +Added in version 0.5.1. + +* **Parameters:** + **host** + : Host of S3 source. For example: `s3.domain.com` + + **port** + : Port of S3 source + + **bucket** + : Bucket name in the S3 file source + + **access_key** + : Access key (aka user ID) of an account in the S3 service + + **secret_key** + : Secret key (aka password) of an account in the S3 service + + **protocol** + : Connection protocol. Allowed values: `https` or `http` +
+ #### Versionchanged + Changed in version 0.6.0: Renamed `secure: bool` to `protocol: Literal["https", "http"]` + + **session_token** + : Session token of your account in S3 service + + **region** + : Region name of bucket in S3 service + +### Examples + +Create and check S3 connection: + +```python +from onetl.connection import S3 + +s3 = S3( + host="s3.domain.com", + protocol="http", + bucket="my-bucket", + access_key="ACCESS_KEY", + secret_key="SECRET_KEY", +).check() +``` + + + +#### \_\_init_\_(\*\*kwargs) + + + +#### check() + +Check source availability. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +If not, an exception will be raised. + +* **Returns:** + Connection itself +* **Raises:** + RuntimeError + : If the connection is not available + +### Examples + +```python +connection.check() +``` + + + +#### create_dir(path: PathLike | str) → RemoteDirectory + +Creates directory tree on remote filesystem. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **path** + : Directory path +* **Returns:** + Created directory with stats +* **Raises:** + `onetl.exception.NotAFileError` + : Path is not a file + +### Examples + +```pycon +>>> dir_path = connection.create_dir("/path/to/dir") +>>> os.fspath(dir_path) +'/path/to/dir' +``` + + + +#### download_file(remote_file_path: PathLike | str, local_file_path: PathLike | str, replace: bool = True) → LocalPath + +Downloads file from the remote filesystem to a local path. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### WARNING +Supports only one file download per call. Directory download is **NOT** supported, use [File Downloader](../../file/file_downloader/file_downloader.md#file-downloader) instead. + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **remote_file_path** + : Remote file path to read from + + **local_file_path** + : Local file path to create + + **replace** + : If `True`, existing file will be replaced +* **Returns:** + Local file with stats. +* **Raises:** + `onetl.exception.NotAFileError` + : Remote or local path is not a file + + FileNotFoundError + : Remote file does not exist + + FileExistsError + : Local file already exists, and `replace=False` + + `onetl.exception.FileSizeMismatchError` + : Target file size after download is different from source file size. + +### Examples + +```pycon +>>> local_file = connection.download_file( +... remote_file_path="/path/to/source.csv", +... local_file_path="/path/to/target.csv", +... ) +>>> os.fspath(local_file) +'/path/to/target.csv' +>>> local_file.exists() +True +>>> local_file.stat().st_size # in bytes +1024 +>>> connection.get_stat("/path/to/source.csv").st_size # same size +1024 +``` + + + +#### get_stat(path: PathLike | str) → PathStatProtocol + +Returns stats for a specific path. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **path** + : Path to get stats for +* **Returns:** + Stats object +* **Raises:** + Any underlying client exception + +### Examples + +```pycon +>>> stat = connection.get_stat("/path/to/file.csv") +>>> stat.st_size # in bytes +1024 +>>> stat.st_uid # owner id or name +12345 +``` + + + +#### is_dir(path: PathLike | str) → bool + +Check if specified path is a directory. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **path** + : Path to check +* **Returns:** + `True` if path is a directory, `False` otherwise. +* **Raises:** + `onetl.exception.DirectoryNotFoundError` + : Path does not exist + +### Examples + +```pycon +>>> connection.is_dir("/path/to/dir") +True +>>> connection.is_dir("/path/to/dir/file.csv") +False +``` + + + +#### is_file(path: PathLike | str) → bool + +Check if specified path is a file. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **path** + : Path to check +* **Returns:** + `True` if path is a file, `False` otherwise. +* **Raises:** + FileNotFoundError + : Path does not exist + +### Examples + +```pycon +>>> connection.is_file("/path/to/dir/file.csv") +True +>>> connection.is_file("/path/to/dir") +False +``` + + + +#### list_dir(path: PathLike | str, filters: Iterable[[BaseFileFilter](../../file/file_filters/base.md#onetl.base.base_file_filter.BaseFileFilter)] | None = None, limits: Iterable[[BaseFileLimit](../../file/file_limits/base.md#onetl.base.base_file_limit.BaseFileLimit)] | None = None) → list[RemoteDirectory | RemoteFile] + +Return list of child files/directories in a specific directory. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **path** + : Directory path to list contents. + + **filters** + : Return only files/directories matching these filters. See [File Filters](../../file/file_filters/index.md#file-filters) + + **limits** + : Apply limits to the list of files/directories, and stop if one of the limits is reached. + See [File Limits](../../file/file_limits/index.md#file-limits) +* **Returns:** + List of `onetl.base.PathWithStatsProtocol` +* **Raises:** + NotADirectoryError + : Path is not a directory + + `onetl.exception.DirectoryNotFoundError` + : Path does not exist + +### Examples + +```pycon +>>> dir_content = connection.list_dir("/path/to/dir") +>>> os.fspath(dir_content[0]) +'file.csv' +>>> connection.path_exists("/path/to/dir/file.csv") +True +``` + + + +#### path_exists(path: PathLike | str) → bool + +Check if specified path exists on remote filesystem. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html). + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **path** + : Path to check +* **Returns:** + `True` if path exists, `False` otherwise + +### Examples + +```pycon +>>> connection.path_exists("/path/to/file.csv") +True +>>> connection.path_exists("/path/to/dir") +True +>>> connection.path_exists("/path/to/missing") +False +``` + + + +#### remove_dir(path: PathLike | str, recursive: bool = False) → bool + +Remove directory or directory tree. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +If directory does not exist, no exception is raised. + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **path** + : Directory path to remote + + **recursive** + : If `True`, remove directory tree recursively. +* **Returns:** + `True` if directory was removed, `False` if directory does not exist in the first place. +* **Raises:** + NotADirectoryError + : Path is not a directory + +### Examples + +```pycon +>>> connection.remove_dir("/path/to/dir") +True +>>> connection.path_exists("/path/to/dir") +False +>>> connection.path_exists("/path/to/dir/file.csv") +False +>>> connection.remove_dir("/path/to/dir") # already deleted, no error +False +``` + + + +#### remove_file(path: PathLike | str) → bool + +Removes file on remote filesystem. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +If file does not exist, no exception is raised. + +#### WARNING +Supports only one file removal per call. Directory removal is **NOT** supported, use [`remove_dir`](#onetl.connection.file_connection.s3.S3.remove_dir) instead. + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **path** + : File path +* **Returns:** + `True` if file was removed, `False` if file does not exist in the first place. +* **Raises:** + `onetl.exception.NotAFileError` + : Path is not a file + +### Examples + +```pycon +>>> connection.remove_file("/path/to/file.csv") +True +>>> connection.path_exists("/path/to/dir/file.csv") +False +>>> connection.remove_file("/path/to/file.csv") # already deleted, no error +False +``` + + + +#### rename_file(source_file_path: PathLike | str, target_file_path: PathLike | str, replace: bool = False) → RemoteFile + +Rename or move file on remote filesystem. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### WARNING +Supports only one file move per call. Directory move/rename is **NOT** supported. + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **source_file_path** + : Old file path + + **target_file_path** + : New file path + + **replace** + : If `True`, existing file will be replaced. +* **Returns:** + New file path with stats. +* **Raises:** + `onetl.exception.NotAFileError` + : Source or target path is not a file + + FileNotFoundError + : File does not exist + + FileExistsError + : File already exists, and `replace=False` + +### Examples + +```pycon +>>> new_file = connection.rename_file("/path/to/file1.csv", "/path/to/file2.csv") +>>> os.fspath(new_file) +'/path/to/file2.csv' +>>> connection.path_exists("/path/to/file2.csv") +True +>>> connection.path_exists("/path/to/file1.csv") +False +``` + + + +#### resolve_dir(path: PathLike | str) → RemoteDirectory + +Returns directory at specific path, with stats. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **path** + : Path to resolve +* **Returns:** + Directory path with stats +* **Raises:** + `onetl.exception.DirectoryNotFoundError` + : Path does not exist + + NotADirectoryError + : Path is not a directory + +### Examples + +```pycon +>>> dir_path = connection.resolve_dir("/path/to/dir") +>>> os.fspath(dir_path) +'/path/to/dir' +>>> dir_path.stat().st_uid # owner id +12345 +``` + + + +#### resolve_file(path: PathLike | str) → RemoteFile + +Returns file at specific path, with stats. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **path** + : Path to resolve +* **Returns:** + File path with stats +* **Raises:** + FileNotFoundError + : Path does not exist + + `onetl.exception.NotAFileError` + : Path is not a file + +### Examples + +```pycon +>>> file_path = connection.resolve_file("/path/to/dir/file.csv") +>>> os.fspath(file_path) +'/path/to/dir/file.csv' +>>> file_path.stat().st_uid # owner id +12345 +``` + + + +#### upload_file(local_file_path: PathLike | str, remote_file_path: PathLike | str, replace: bool = False) → RemoteFile + +Uploads local file to a remote filesystem. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### WARNING +Supports only one file upload per call. Directory upload is **NOT** supported, use [File Uploader](../../file/file_uploader/file_uploader.md#file-uploader) instead. + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **local_file_path** + : Local file path to read from + + **remote_file_path** + : Remote file path to create + + **replace** + : If `True`, existing file will be replaced +* **Returns:** + Remote file with stats. +* **Raises:** + `onetl.exception.NotAFileError` + : Remote or local path is not a file + + FileNotFoundError + : Local file does not exist + + FileExistsError + : Remote file already exists, and `replace=False` + + `onetl.exception.FileSizeMismatchError` + : Target file size after upload is different from source file size. + +### Examples + +```pycon +>>> remote_file = connection.upload( +... local_file_path="/path/to/source.csv", +... remote_file_path="/path/to/target.csv", +... ) +>>> os.fspath(remote_file) +'/path/to/target.csv' +>>> connection.path_exists("/path/to/target.csv") +True +>>> remote_file.stat().st_size # in bytes +1024 +>>> os.stat("/path/to/source.csv").st_size # same as source +1024 +``` + + + +#### walk(root: PathLike | str, topdown: bool = True, filters: Iterable[[BaseFileFilter](../../file/file_filters/base.md#onetl.base.base_file_filter.BaseFileFilter)] | None = None, limits: Iterable[[BaseFileLimit](../../file/file_limits/base.md#onetl.base.base_file_limit.BaseFileLimit)] | None = None) → Iterator[tuple[RemoteDirectory, list[RemoteDirectory], list[RemoteFile]]] + +Walk into directory tree, and iterate over its content in all nesting levels. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +Just like `os.walk`, but with additional filter/limit logic. + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **root** + : Directory path to walk into. + + **topdown** + : If `True`, walk in top-down order, otherwise walk in bottom-up order. + + **filters** + : Return only files/directories matching these filters. See [File Filters](../../file/file_filters/index.md#file-filters). + + **limits** + : Apply limits to the list of files/directories, and immediately stop if any of these limits is reached. + See [File Limits](../../file/file_limits/index.md#file-limits). +* **Returns:** + `Iterator[tuple[root, dirs, files]]`, like `os.walk`. + + But all the paths are not strings, instead path classes with embedded stats are returned. +* **Raises:** + NotADirectoryError + : Path is not a directory + + `onetl.exception.DirectoryNotFoundError` + : Path does not exist + +### Examples + +```pycon +>>> for root, dirs, files in connection.walk("/path/to/dir"): +... break +>>> os.fspath(root) +'/path/to/dir' +>>> dirs +[] +>>> os.fspath(files[0]) +'file.csv' +>>> connection.path_exists("/path/to/dir/file.csv") +True +``` + + diff --git a/mddocs/connection/file_connection/samba.md b/mddocs/connection/file_connection/samba.md new file mode 100644 index 000000000..81b661ec1 --- /dev/null +++ b/mddocs/connection/file_connection/samba.md @@ -0,0 +1,548 @@ + + +# Samba connection + +### *class* onetl.connection.file_connection.samba.Samba(\*, host: Host, share: str, protocol: Literal['SMB', 'NetBIOS'] = 'SMB', port: int | None = None, domain: str | None = '', auth_type: Literal['NTLMv1', 'NTLMv2'] = 'NTLMv2', user: str | None = None, password: SecretStr | None = None) + +Samba file connection. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +Based on [pysmb library](https://pypi.org/project/pysmb/). + +#### Versionadded +Added in version 0.9.4. + +#### WARNING +To use Samba connector you should install package as follows: + +```bash +pip install onetl[samba] + +# or +pip install onetl[files] +``` + +See [File connections](../../install/files.md#install-files) installation instruction for more details. + +* **Parameters:** + **host** + : Host of Samba source. For example: `mydomain.com`. + + **share** + : The name of the share on the Samba server. + + **protocol** + : The protocol to use for the connection. Either `SMB` or `NetBIOS`. + Affects the default port and the is_direct_tcp flag in SMBConnection. + + **port** + : Port of Samba source. + + **domain** + : Domain name for the Samba connection. Empty strings means use `host` as domain name. + + **auth_type** + : The authentication type to use. Either `NTLMv2` or `NTLMv1`. + Affects the use_ntlm_v2 flag in SMBConnection. + + **user** + : User, which have access to the file source. Can be None for anonymous connection. + + **password** + : Password for file source connection. Can be None for anonymous connection. + +### Examples + +Create and check Samba connection: + +```python +from onetl.connection import Samba + +samba = Samba( + host="mydomain.com", + share="share_name", + protocol="SMB", + port=445, + user="user", + password="password", +).check() +``` + + + +#### \_\_init_\_(\*\*kwargs) + + + +#### check() + +Check source availability. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +If not, an exception will be raised. + +* **Returns:** + Connection itself +* **Raises:** + RuntimeError + : If the connection is not available + +### Examples + +```python +connection.check() +``` + + + +#### create_dir(path: PathLike | str) → RemoteDirectory + +Creates directory tree on remote filesystem. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **path** + : Directory path +* **Returns:** + Created directory with stats +* **Raises:** + `onetl.exception.NotAFileError` + : Path is not a file + +### Examples + +```pycon +>>> dir_path = connection.create_dir("/path/to/dir") +>>> os.fspath(dir_path) +'/path/to/dir' +``` + + + +#### download_file(remote_file_path: PathLike | str, local_file_path: PathLike | str, replace: bool = True) → LocalPath + +Downloads file from the remote filesystem to a local path. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### WARNING +Supports only one file download per call. Directory download is **NOT** supported, use [File Downloader](../../file/file_downloader/file_downloader.md#file-downloader) instead. + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **remote_file_path** + : Remote file path to read from + + **local_file_path** + : Local file path to create + + **replace** + : If `True`, existing file will be replaced +* **Returns:** + Local file with stats. +* **Raises:** + `onetl.exception.NotAFileError` + : Remote or local path is not a file + + FileNotFoundError + : Remote file does not exist + + FileExistsError + : Local file already exists, and `replace=False` + + `onetl.exception.FileSizeMismatchError` + : Target file size after download is different from source file size. + +### Examples + +```pycon +>>> local_file = connection.download_file( +... remote_file_path="/path/to/source.csv", +... local_file_path="/path/to/target.csv", +... ) +>>> os.fspath(local_file) +'/path/to/target.csv' +>>> local_file.exists() +True +>>> local_file.stat().st_size # in bytes +1024 +>>> connection.get_stat("/path/to/source.csv").st_size # same size +1024 +``` + + + +#### get_stat(path: PathLike | str) → PathStatProtocol + +Returns stats for a specific path. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **path** + : Path to get stats for +* **Returns:** + Stats object +* **Raises:** + Any underlying client exception + +### Examples + +```pycon +>>> stat = connection.get_stat("/path/to/file.csv") +>>> stat.st_size # in bytes +1024 +>>> stat.st_uid # owner id or name +12345 +``` + + + +#### is_dir(path: PathLike | str) → bool + +Check if specified path is a directory. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **path** + : Path to check +* **Returns:** + `True` if path is a directory, `False` otherwise. +* **Raises:** + `onetl.exception.DirectoryNotFoundError` + : Path does not exist + +### Examples + +```pycon +>>> connection.is_dir("/path/to/dir") +True +>>> connection.is_dir("/path/to/dir/file.csv") +False +``` + + + +#### is_file(path: PathLike | str) → bool + +Check if specified path is a file. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **path** + : Path to check +* **Returns:** + `True` if path is a file, `False` otherwise. +* **Raises:** + FileNotFoundError + : Path does not exist + +### Examples + +```pycon +>>> connection.is_file("/path/to/dir/file.csv") +True +>>> connection.is_file("/path/to/dir") +False +``` + + + +#### list_dir(path: PathLike | str, filters: Iterable[[BaseFileFilter](../../file/file_filters/base.md#onetl.base.base_file_filter.BaseFileFilter)] | None = None, limits: Iterable[[BaseFileLimit](../../file/file_limits/base.md#onetl.base.base_file_limit.BaseFileLimit)] | None = None) → list[RemoteDirectory | RemoteFile] + +Return list of child files/directories in a specific directory. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **path** + : Directory path to list contents. + + **filters** + : Return only files/directories matching these filters. See [File Filters](../../file/file_filters/index.md#file-filters) + + **limits** + : Apply limits to the list of files/directories, and stop if one of the limits is reached. + See [File Limits](../../file/file_limits/index.md#file-limits) +* **Returns:** + List of `onetl.base.PathWithStatsProtocol` +* **Raises:** + NotADirectoryError + : Path is not a directory + + `onetl.exception.DirectoryNotFoundError` + : Path does not exist + +### Examples + +```pycon +>>> dir_content = connection.list_dir("/path/to/dir") +>>> os.fspath(dir_content[0]) +'file.csv' +>>> connection.path_exists("/path/to/dir/file.csv") +True +``` + + + +#### path_exists(path: PathLike | str) → bool + +Check if specified path exists on remote filesystem. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html). + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **path** + : Path to check +* **Returns:** + `True` if path exists, `False` otherwise + +### Examples + +```pycon +>>> connection.path_exists("/path/to/file.csv") +True +>>> connection.path_exists("/path/to/dir") +True +>>> connection.path_exists("/path/to/missing") +False +``` + + + +#### remove_dir(path: PathLike | str, recursive: bool = False) → bool + +Remove directory or directory tree. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +If directory does not exist, no exception is raised. + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **path** + : Directory path to remote + + **recursive** + : If `True`, remove directory tree recursively. +* **Returns:** + `True` if directory was removed, `False` if directory does not exist in the first place. +* **Raises:** + NotADirectoryError + : Path is not a directory + +### Examples + +```pycon +>>> connection.remove_dir("/path/to/dir") +True +>>> connection.path_exists("/path/to/dir") +False +>>> connection.path_exists("/path/to/dir/file.csv") +False +>>> connection.remove_dir("/path/to/dir") # already deleted, no error +False +``` + + + +#### remove_file(path: PathLike | str) → bool + +Removes file on remote filesystem. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +If file does not exist, no exception is raised. + +#### WARNING +Supports only one file removal per call. Directory removal is **NOT** supported, use [`remove_dir`](#onetl.connection.file_connection.samba.Samba.remove_dir) instead. + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **path** + : File path +* **Returns:** + `True` if file was removed, `False` if file does not exist in the first place. +* **Raises:** + `onetl.exception.NotAFileError` + : Path is not a file + +### Examples + +```pycon +>>> connection.remove_file("/path/to/file.csv") +True +>>> connection.path_exists("/path/to/dir/file.csv") +False +>>> connection.remove_file("/path/to/file.csv") # already deleted, no error +False +``` + + + +#### rename_file(source_file_path: PathLike | str, target_file_path: PathLike | str, replace: bool = False) → RemoteFile + +Rename or move file on remote filesystem. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### WARNING +Supports only one file move per call. Directory move/rename is **NOT** supported. + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **source_file_path** + : Old file path + + **target_file_path** + : New file path + + **replace** + : If `True`, existing file will be replaced. +* **Returns:** + New file path with stats. +* **Raises:** + `onetl.exception.NotAFileError` + : Source or target path is not a file + + FileNotFoundError + : File does not exist + + FileExistsError + : File already exists, and `replace=False` + +### Examples + +```pycon +>>> new_file = connection.rename_file("/path/to/file1.csv", "/path/to/file2.csv") +>>> os.fspath(new_file) +'/path/to/file2.csv' +>>> connection.path_exists("/path/to/file2.csv") +True +>>> connection.path_exists("/path/to/file1.csv") +False +``` + + + +#### resolve_dir(path: PathLike | str) → RemoteDirectory + +Returns directory at specific path, with stats. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **path** + : Path to resolve +* **Returns:** + Directory path with stats +* **Raises:** + `onetl.exception.DirectoryNotFoundError` + : Path does not exist + + NotADirectoryError + : Path is not a directory + +### Examples + +```pycon +>>> dir_path = connection.resolve_dir("/path/to/dir") +>>> os.fspath(dir_path) +'/path/to/dir' +>>> dir_path.stat().st_uid # owner id +12345 +``` + + + +#### resolve_file(path: PathLike | str) → RemoteFile + +Returns file at specific path, with stats. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **path** + : Path to resolve +* **Returns:** + File path with stats +* **Raises:** + FileNotFoundError + : Path does not exist + + `onetl.exception.NotAFileError` + : Path is not a file + +### Examples + +```pycon +>>> file_path = connection.resolve_file("/path/to/dir/file.csv") +>>> os.fspath(file_path) +'/path/to/dir/file.csv' +>>> file_path.stat().st_uid # owner id +12345 +``` + + + +#### upload_file(local_file_path: PathLike | str, remote_file_path: PathLike | str, replace: bool = False) → RemoteFile + +Uploads local file to a remote filesystem. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### WARNING +Supports only one file upload per call. Directory upload is **NOT** supported, use [File Uploader](../../file/file_uploader/file_uploader.md#file-uploader) instead. + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **local_file_path** + : Local file path to read from + + **remote_file_path** + : Remote file path to create + + **replace** + : If `True`, existing file will be replaced +* **Returns:** + Remote file with stats. +* **Raises:** + `onetl.exception.NotAFileError` + : Remote or local path is not a file + + FileNotFoundError + : Local file does not exist + + FileExistsError + : Remote file already exists, and `replace=False` + + `onetl.exception.FileSizeMismatchError` + : Target file size after upload is different from source file size. + +### Examples + +```pycon +>>> remote_file = connection.upload( +... local_file_path="/path/to/source.csv", +... remote_file_path="/path/to/target.csv", +... ) +>>> os.fspath(remote_file) +'/path/to/target.csv' +>>> connection.path_exists("/path/to/target.csv") +True +>>> remote_file.stat().st_size # in bytes +1024 +>>> os.stat("/path/to/source.csv").st_size # same as source +1024 +``` + + diff --git a/mddocs/connection/file_connection/sftp.md b/mddocs/connection/file_connection/sftp.md new file mode 100644 index 000000000..1ee71d53c --- /dev/null +++ b/mddocs/connection/file_connection/sftp.md @@ -0,0 +1,635 @@ + + +# SFTP connection + +### *class* onetl.connection.file_connection.sftp.SFTP(\*, host: Host, port: int = 22, user: str | None = None, password: SecretStr | None = None, key_file: FilePath | None = None, timeout: int = 10, host_key_check: bool = False, compress: bool = True) + +SFTP file connection. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +Based on [Paramiko library](https://pypi.org/project/paramiko/). + +#### WARNING +Since onETL v0.7.0 to use SFTP connector you should install package as follows: + +```bash +pip install onetl[s3] + +# or +pip install onetl[files] +``` + +See [File connections](../../install/files.md#install-files) installation instruction for more details. + +#### Versionadded +Added in version 0.1.0. + +* **Parameters:** + **host** + : Host of SFTP source. For example: `192.168.1.19` + + **port** + : Port of SFTP source + + **user** + : User, which have access to the file source. For example: `someuser` + + **password** + : Password for file source connection + + **key_file** + : the filename of optional private key(s) and/or certs to try for authentication + + **timeout** + : How long to wait for the server to send data before giving up + + **host_key_check** + : set to True to enable searching for discoverable private key files in `~/.ssh/` + + **compress** + : Set to True to turn on compression + +### Examples + +Create and check SFTP connection: + +```python +from onetl.connection import SFTP + +sftp = SFTP( + host="192.168.1.19", + user="someuser", + password="*****", +).check() +``` + + + +#### \_\_init_\_(\*\*kwargs) + + + +#### check() + +Check source availability. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +If not, an exception will be raised. + +* **Returns:** + Connection itself +* **Raises:** + RuntimeError + : If the connection is not available + +### Examples + +```python +connection.check() +``` + + + +#### create_dir(path: PathLike | str) → RemoteDirectory + +Creates directory tree on remote filesystem. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **path** + : Directory path +* **Returns:** + Created directory with stats +* **Raises:** + `onetl.exception.NotAFileError` + : Path is not a file + +### Examples + +```pycon +>>> dir_path = connection.create_dir("/path/to/dir") +>>> os.fspath(dir_path) +'/path/to/dir' +``` + + + +#### download_file(remote_file_path: PathLike | str, local_file_path: PathLike | str, replace: bool = True) → LocalPath + +Downloads file from the remote filesystem to a local path. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### WARNING +Supports only one file download per call. Directory download is **NOT** supported, use [File Downloader](../../file/file_downloader/file_downloader.md#file-downloader) instead. + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **remote_file_path** + : Remote file path to read from + + **local_file_path** + : Local file path to create + + **replace** + : If `True`, existing file will be replaced +* **Returns:** + Local file with stats. +* **Raises:** + `onetl.exception.NotAFileError` + : Remote or local path is not a file + + FileNotFoundError + : Remote file does not exist + + FileExistsError + : Local file already exists, and `replace=False` + + `onetl.exception.FileSizeMismatchError` + : Target file size after download is different from source file size. + +### Examples + +```pycon +>>> local_file = connection.download_file( +... remote_file_path="/path/to/source.csv", +... local_file_path="/path/to/target.csv", +... ) +>>> os.fspath(local_file) +'/path/to/target.csv' +>>> local_file.exists() +True +>>> local_file.stat().st_size # in bytes +1024 +>>> connection.get_stat("/path/to/source.csv").st_size # same size +1024 +``` + + + +#### get_stat(path: PathLike | str) → PathStatProtocol + +Returns stats for a specific path. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **path** + : Path to get stats for +* **Returns:** + Stats object +* **Raises:** + Any underlying client exception + +### Examples + +```pycon +>>> stat = connection.get_stat("/path/to/file.csv") +>>> stat.st_size # in bytes +1024 +>>> stat.st_uid # owner id or name +12345 +``` + + + +#### is_dir(path: PathLike | str) → bool + +Check if specified path is a directory. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **path** + : Path to check +* **Returns:** + `True` if path is a directory, `False` otherwise. +* **Raises:** + `onetl.exception.DirectoryNotFoundError` + : Path does not exist + +### Examples + +```pycon +>>> connection.is_dir("/path/to/dir") +True +>>> connection.is_dir("/path/to/dir/file.csv") +False +``` + + + +#### is_file(path: PathLike | str) → bool + +Check if specified path is a file. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **path** + : Path to check +* **Returns:** + `True` if path is a file, `False` otherwise. +* **Raises:** + FileNotFoundError + : Path does not exist + +### Examples + +```pycon +>>> connection.is_file("/path/to/dir/file.csv") +True +>>> connection.is_file("/path/to/dir") +False +``` + + + +#### list_dir(path: PathLike | str, filters: Iterable[[BaseFileFilter](../../file/file_filters/base.md#onetl.base.base_file_filter.BaseFileFilter)] | None = None, limits: Iterable[[BaseFileLimit](../../file/file_limits/base.md#onetl.base.base_file_limit.BaseFileLimit)] | None = None) → list[RemoteDirectory | RemoteFile] + +Return list of child files/directories in a specific directory. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **path** + : Directory path to list contents. + + **filters** + : Return only files/directories matching these filters. See [File Filters](../../file/file_filters/index.md#file-filters) + + **limits** + : Apply limits to the list of files/directories, and stop if one of the limits is reached. + See [File Limits](../../file/file_limits/index.md#file-limits) +* **Returns:** + List of `onetl.base.PathWithStatsProtocol` +* **Raises:** + NotADirectoryError + : Path is not a directory + + `onetl.exception.DirectoryNotFoundError` + : Path does not exist + +### Examples + +```pycon +>>> dir_content = connection.list_dir("/path/to/dir") +>>> os.fspath(dir_content[0]) +'file.csv' +>>> connection.path_exists("/path/to/dir/file.csv") +True +``` + + + +#### path_exists(path: PathLike | str) → bool + +Check if specified path exists on remote filesystem. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html). + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **path** + : Path to check +* **Returns:** + `True` if path exists, `False` otherwise + +### Examples + +```pycon +>>> connection.path_exists("/path/to/file.csv") +True +>>> connection.path_exists("/path/to/dir") +True +>>> connection.path_exists("/path/to/missing") +False +``` + + + +#### remove_dir(path: PathLike | str, recursive: bool = False) → bool + +Remove directory or directory tree. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +If directory does not exist, no exception is raised. + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **path** + : Directory path to remote + + **recursive** + : If `True`, remove directory tree recursively. +* **Returns:** + `True` if directory was removed, `False` if directory does not exist in the first place. +* **Raises:** + NotADirectoryError + : Path is not a directory + +### Examples + +```pycon +>>> connection.remove_dir("/path/to/dir") +True +>>> connection.path_exists("/path/to/dir") +False +>>> connection.path_exists("/path/to/dir/file.csv") +False +>>> connection.remove_dir("/path/to/dir") # already deleted, no error +False +``` + + + +#### remove_file(path: PathLike | str) → bool + +Removes file on remote filesystem. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +If file does not exist, no exception is raised. + +#### WARNING +Supports only one file removal per call. Directory removal is **NOT** supported, use [`remove_dir`](#onetl.connection.file_connection.sftp.SFTP.remove_dir) instead. + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **path** + : File path +* **Returns:** + `True` if file was removed, `False` if file does not exist in the first place. +* **Raises:** + `onetl.exception.NotAFileError` + : Path is not a file + +### Examples + +```pycon +>>> connection.remove_file("/path/to/file.csv") +True +>>> connection.path_exists("/path/to/dir/file.csv") +False +>>> connection.remove_file("/path/to/file.csv") # already deleted, no error +False +``` + + + +#### rename_dir(source_dir_path: PathLike | str, target_dir_path: PathLike | str, replace: bool = False) → RemoteDirectory + +Rename or move dir on remote filesystem. + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **source_dir_path** + : Old directory path + + **target_dir_path** + : New directory path + + **replace** + : If `True`, existing directory will be replaced. +* **Returns:** + New directory path with stats. +* **Raises:** + NotADirectoryError + : Path is not a directory + + `onetl.exception.DirectoryNotFoundError` + : Path does not exist + + `onetl.exception.DirectoryExistsError` + : Directory already exists, and `replace=False` + +### Examples + +```pycon +>>> new_dir = connection.rename_dir("/path/to/dir1", "/path/to/dir2") +>>> os.fspath(new_dir) +'/path/to/dir2' +>>> connection.path_exists("/path/to/dir1") +False +>>> connection.path_exists("/path/to/dir2") +True +``` + + + +#### rename_file(source_file_path: PathLike | str, target_file_path: PathLike | str, replace: bool = False) → RemoteFile + +Rename or move file on remote filesystem. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### WARNING +Supports only one file move per call. Directory move/rename is **NOT** supported. + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **source_file_path** + : Old file path + + **target_file_path** + : New file path + + **replace** + : If `True`, existing file will be replaced. +* **Returns:** + New file path with stats. +* **Raises:** + `onetl.exception.NotAFileError` + : Source or target path is not a file + + FileNotFoundError + : File does not exist + + FileExistsError + : File already exists, and `replace=False` + +### Examples + +```pycon +>>> new_file = connection.rename_file("/path/to/file1.csv", "/path/to/file2.csv") +>>> os.fspath(new_file) +'/path/to/file2.csv' +>>> connection.path_exists("/path/to/file2.csv") +True +>>> connection.path_exists("/path/to/file1.csv") +False +``` + + + +#### resolve_dir(path: PathLike | str) → RemoteDirectory + +Returns directory at specific path, with stats. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **path** + : Path to resolve +* **Returns:** + Directory path with stats +* **Raises:** + `onetl.exception.DirectoryNotFoundError` + : Path does not exist + + NotADirectoryError + : Path is not a directory + +### Examples + +```pycon +>>> dir_path = connection.resolve_dir("/path/to/dir") +>>> os.fspath(dir_path) +'/path/to/dir' +>>> dir_path.stat().st_uid # owner id +12345 +``` + + + +#### resolve_file(path: PathLike | str) → RemoteFile + +Returns file at specific path, with stats. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **path** + : Path to resolve +* **Returns:** + File path with stats +* **Raises:** + FileNotFoundError + : Path does not exist + + `onetl.exception.NotAFileError` + : Path is not a file + +### Examples + +```pycon +>>> file_path = connection.resolve_file("/path/to/dir/file.csv") +>>> os.fspath(file_path) +'/path/to/dir/file.csv' +>>> file_path.stat().st_uid # owner id +12345 +``` + + + +#### upload_file(local_file_path: PathLike | str, remote_file_path: PathLike | str, replace: bool = False) → RemoteFile + +Uploads local file to a remote filesystem. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### WARNING +Supports only one file upload per call. Directory upload is **NOT** supported, use [File Uploader](../../file/file_uploader/file_uploader.md#file-uploader) instead. + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **local_file_path** + : Local file path to read from + + **remote_file_path** + : Remote file path to create + + **replace** + : If `True`, existing file will be replaced +* **Returns:** + Remote file with stats. +* **Raises:** + `onetl.exception.NotAFileError` + : Remote or local path is not a file + + FileNotFoundError + : Local file does not exist + + FileExistsError + : Remote file already exists, and `replace=False` + + `onetl.exception.FileSizeMismatchError` + : Target file size after upload is different from source file size. + +### Examples + +```pycon +>>> remote_file = connection.upload( +... local_file_path="/path/to/source.csv", +... remote_file_path="/path/to/target.csv", +... ) +>>> os.fspath(remote_file) +'/path/to/target.csv' +>>> connection.path_exists("/path/to/target.csv") +True +>>> remote_file.stat().st_size # in bytes +1024 +>>> os.stat("/path/to/source.csv").st_size # same as source +1024 +``` + + + +#### walk(root: PathLike | str, topdown: bool = True, filters: Iterable[[BaseFileFilter](../../file/file_filters/base.md#onetl.base.base_file_filter.BaseFileFilter)] | None = None, limits: Iterable[[BaseFileLimit](../../file/file_limits/base.md#onetl.base.base_file_limit.BaseFileLimit)] | None = None) → Iterator[tuple[RemoteDirectory, list[RemoteDirectory], list[RemoteFile]]] + +Walk into directory tree, and iterate over its content in all nesting levels. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +Just like `os.walk`, but with additional filter/limit logic. + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **root** + : Directory path to walk into. + + **topdown** + : If `True`, walk in top-down order, otherwise walk in bottom-up order. + + **filters** + : Return only files/directories matching these filters. See [File Filters](../../file/file_filters/index.md#file-filters). + + **limits** + : Apply limits to the list of files/directories, and immediately stop if any of these limits is reached. + See [File Limits](../../file/file_limits/index.md#file-limits). +* **Returns:** + `Iterator[tuple[root, dirs, files]]`, like `os.walk`. + + But all the paths are not strings, instead path classes with embedded stats are returned. +* **Raises:** + NotADirectoryError + : Path is not a directory + + `onetl.exception.DirectoryNotFoundError` + : Path does not exist + +### Examples + +```pycon +>>> for root, dirs, files in connection.walk("/path/to/dir"): +... break +>>> os.fspath(root) +'/path/to/dir' +>>> dirs +[] +>>> os.fspath(files[0]) +'file.csv' +>>> connection.path_exists("/path/to/dir/file.csv") +True +``` + + diff --git a/mddocs/connection/file_connection/webdav.md b/mddocs/connection/file_connection/webdav.md new file mode 100644 index 000000000..ef79f4023 --- /dev/null +++ b/mddocs/connection/file_connection/webdav.md @@ -0,0 +1,592 @@ + + +# WebDAV connection + +### *class* onetl.connection.file_connection.webdav.WebDAV(\*, host: Host, user: str, password: SecretStr, port: int | None = None, ssl_verify: bool | FilePath | DirectoryPath | SSLContext = True, protocol: Literal['http', 'https'] = 'https') + +WebDAV file connection. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +Based on [WebdavClient3 library](https://pypi.org/project/webdavclient3/). + +#### WARNING +Since onETL v0.7.0 to use WebDAV connector you should install package as follows: + +```bash +pip install onetl[webdav] + +# or +pip install onetl[files] +``` + +See [File connections](../../install/files.md#install-files) installation instruction for more details. + +#### Versionadded +Added in version 0.6.0. + +* **Parameters:** + **host** + : Host of WebDAV source. For example: `webdav.domain.com` + + **user** + : User, which have access to the file source. For example: `someuser` + + **password** + : Password for file source connection + + **ssl_verify** + : SSL certificates used to verify the identity of requested hosts. Can be any of + : - `True` (uses default CA bundle), + - a path to an SSL certificate file, + - `False` (disable verification), or + - a `ssl.SSLContext` + + **protocol** + : Connection protocol. Allowed values: `https` or `http` + + **port** + : Connection port + +### Examples + +Create and check WebDAV connection: + +```python +from onetl.connection import WebDAV + +wd = WebDAV( + host="webdav.domain.com", + user="someuser", + password="*****", + protocol="https", +).check() +``` + + + +#### \_\_init_\_(\*\*kwargs) + + + +#### check() + +Check source availability. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +If not, an exception will be raised. + +* **Returns:** + Connection itself +* **Raises:** + RuntimeError + : If the connection is not available + +### Examples + +```python +connection.check() +``` + + + +#### create_dir(path: PathLike | str) → RemoteDirectory + +Creates directory tree on remote filesystem. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **path** + : Directory path +* **Returns:** + Created directory with stats +* **Raises:** + `onetl.exception.NotAFileError` + : Path is not a file + +### Examples + +```pycon +>>> dir_path = connection.create_dir("/path/to/dir") +>>> os.fspath(dir_path) +'/path/to/dir' +``` + + + +#### download_file(remote_file_path: PathLike | str, local_file_path: PathLike | str, replace: bool = True) → LocalPath + +Downloads file from the remote filesystem to a local path. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### WARNING +Supports only one file download per call. Directory download is **NOT** supported, use [File Downloader](../../file/file_downloader/file_downloader.md#file-downloader) instead. + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **remote_file_path** + : Remote file path to read from + + **local_file_path** + : Local file path to create + + **replace** + : If `True`, existing file will be replaced +* **Returns:** + Local file with stats. +* **Raises:** + `onetl.exception.NotAFileError` + : Remote or local path is not a file + + FileNotFoundError + : Remote file does not exist + + FileExistsError + : Local file already exists, and `replace=False` + + `onetl.exception.FileSizeMismatchError` + : Target file size after download is different from source file size. + +### Examples + +```pycon +>>> local_file = connection.download_file( +... remote_file_path="/path/to/source.csv", +... local_file_path="/path/to/target.csv", +... ) +>>> os.fspath(local_file) +'/path/to/target.csv' +>>> local_file.exists() +True +>>> local_file.stat().st_size # in bytes +1024 +>>> connection.get_stat("/path/to/source.csv").st_size # same size +1024 +``` + + + +#### get_stat(path: PathLike | str) → PathStatProtocol + +Returns stats for a specific path. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **path** + : Path to get stats for +* **Returns:** + Stats object +* **Raises:** + Any underlying client exception + +### Examples + +```pycon +>>> stat = connection.get_stat("/path/to/file.csv") +>>> stat.st_size # in bytes +1024 +>>> stat.st_uid # owner id or name +12345 +``` + + + +#### is_dir(path: PathLike | str) → bool + +Check if specified path is a directory. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **path** + : Path to check +* **Returns:** + `True` if path is a directory, `False` otherwise. +* **Raises:** + `onetl.exception.DirectoryNotFoundError` + : Path does not exist + +### Examples + +```pycon +>>> connection.is_dir("/path/to/dir") +True +>>> connection.is_dir("/path/to/dir/file.csv") +False +``` + + + +#### is_file(path: PathLike | str) → bool + +Check if specified path is a file. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **path** + : Path to check +* **Returns:** + `True` if path is a file, `False` otherwise. +* **Raises:** + FileNotFoundError + : Path does not exist + +### Examples + +```pycon +>>> connection.is_file("/path/to/dir/file.csv") +True +>>> connection.is_file("/path/to/dir") +False +``` + + + +#### list_dir(path: PathLike | str, filters: Iterable[[BaseFileFilter](../../file/file_filters/base.md#onetl.base.base_file_filter.BaseFileFilter)] | None = None, limits: Iterable[[BaseFileLimit](../../file/file_limits/base.md#onetl.base.base_file_limit.BaseFileLimit)] | None = None) → list[RemoteDirectory | RemoteFile] + +Return list of child files/directories in a specific directory. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **path** + : Directory path to list contents. + + **filters** + : Return only files/directories matching these filters. See [File Filters](../../file/file_filters/index.md#file-filters) + + **limits** + : Apply limits to the list of files/directories, and stop if one of the limits is reached. + See [File Limits](../../file/file_limits/index.md#file-limits) +* **Returns:** + List of `onetl.base.PathWithStatsProtocol` +* **Raises:** + NotADirectoryError + : Path is not a directory + + `onetl.exception.DirectoryNotFoundError` + : Path does not exist + +### Examples + +```pycon +>>> dir_content = connection.list_dir("/path/to/dir") +>>> os.fspath(dir_content[0]) +'file.csv' +>>> connection.path_exists("/path/to/dir/file.csv") +True +``` + + + +#### path_exists(path: PathLike | str) → bool + +Check if specified path exists on remote filesystem. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html). + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **path** + : Path to check +* **Returns:** + `True` if path exists, `False` otherwise + +### Examples + +```pycon +>>> connection.path_exists("/path/to/file.csv") +True +>>> connection.path_exists("/path/to/dir") +True +>>> connection.path_exists("/path/to/missing") +False +``` + + + +#### remove_dir(path: PathLike | str, recursive: bool = False) → bool + +Remove directory or directory tree. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +If directory does not exist, no exception is raised. + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **path** + : Directory path to remote + + **recursive** + : If `True`, remove directory tree recursively. +* **Returns:** + `True` if directory was removed, `False` if directory does not exist in the first place. +* **Raises:** + NotADirectoryError + : Path is not a directory + +### Examples + +```pycon +>>> connection.remove_dir("/path/to/dir") +True +>>> connection.path_exists("/path/to/dir") +False +>>> connection.path_exists("/path/to/dir/file.csv") +False +>>> connection.remove_dir("/path/to/dir") # already deleted, no error +False +``` + + + +#### remove_file(path: PathLike | str) → bool + +Removes file on remote filesystem. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +If file does not exist, no exception is raised. + +#### WARNING +Supports only one file removal per call. Directory removal is **NOT** supported, use [`remove_dir`](#onetl.connection.file_connection.webdav.WebDAV.remove_dir) instead. + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **path** + : File path +* **Returns:** + `True` if file was removed, `False` if file does not exist in the first place. +* **Raises:** + `onetl.exception.NotAFileError` + : Path is not a file + +### Examples + +```pycon +>>> connection.remove_file("/path/to/file.csv") +True +>>> connection.path_exists("/path/to/dir/file.csv") +False +>>> connection.remove_file("/path/to/file.csv") # already deleted, no error +False +``` + + + +#### rename_file(source_file_path: PathLike | str, target_file_path: PathLike | str, replace: bool = False) → RemoteFile + +Rename or move file on remote filesystem. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### WARNING +Supports only one file move per call. Directory move/rename is **NOT** supported. + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **source_file_path** + : Old file path + + **target_file_path** + : New file path + + **replace** + : If `True`, existing file will be replaced. +* **Returns:** + New file path with stats. +* **Raises:** + `onetl.exception.NotAFileError` + : Source or target path is not a file + + FileNotFoundError + : File does not exist + + FileExistsError + : File already exists, and `replace=False` + +### Examples + +```pycon +>>> new_file = connection.rename_file("/path/to/file1.csv", "/path/to/file2.csv") +>>> os.fspath(new_file) +'/path/to/file2.csv' +>>> connection.path_exists("/path/to/file2.csv") +True +>>> connection.path_exists("/path/to/file1.csv") +False +``` + + + +#### resolve_dir(path: PathLike | str) → RemoteDirectory + +Returns directory at specific path, with stats. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **path** + : Path to resolve +* **Returns:** + Directory path with stats +* **Raises:** + `onetl.exception.DirectoryNotFoundError` + : Path does not exist + + NotADirectoryError + : Path is not a directory + +### Examples + +```pycon +>>> dir_path = connection.resolve_dir("/path/to/dir") +>>> os.fspath(dir_path) +'/path/to/dir' +>>> dir_path.stat().st_uid # owner id +12345 +``` + + + +#### resolve_file(path: PathLike | str) → RemoteFile + +Returns file at specific path, with stats. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **path** + : Path to resolve +* **Returns:** + File path with stats +* **Raises:** + FileNotFoundError + : Path does not exist + + `onetl.exception.NotAFileError` + : Path is not a file + +### Examples + +```pycon +>>> file_path = connection.resolve_file("/path/to/dir/file.csv") +>>> os.fspath(file_path) +'/path/to/dir/file.csv' +>>> file_path.stat().st_uid # owner id +12345 +``` + + + +#### upload_file(local_file_path: PathLike | str, remote_file_path: PathLike | str, replace: bool = False) → RemoteFile + +Uploads local file to a remote filesystem. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### WARNING +Supports only one file upload per call. Directory upload is **NOT** supported, use [File Uploader](../../file/file_uploader/file_uploader.md#file-uploader) instead. + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **local_file_path** + : Local file path to read from + + **remote_file_path** + : Remote file path to create + + **replace** + : If `True`, existing file will be replaced +* **Returns:** + Remote file with stats. +* **Raises:** + `onetl.exception.NotAFileError` + : Remote or local path is not a file + + FileNotFoundError + : Local file does not exist + + FileExistsError + : Remote file already exists, and `replace=False` + + `onetl.exception.FileSizeMismatchError` + : Target file size after upload is different from source file size. + +### Examples + +```pycon +>>> remote_file = connection.upload( +... local_file_path="/path/to/source.csv", +... remote_file_path="/path/to/target.csv", +... ) +>>> os.fspath(remote_file) +'/path/to/target.csv' +>>> connection.path_exists("/path/to/target.csv") +True +>>> remote_file.stat().st_size # in bytes +1024 +>>> os.stat("/path/to/source.csv").st_size # same as source +1024 +``` + + + +#### walk(root: PathLike | str, topdown: bool = True, filters: Iterable[[BaseFileFilter](../../file/file_filters/base.md#onetl.base.base_file_filter.BaseFileFilter)] | None = None, limits: Iterable[[BaseFileLimit](../../file/file_limits/base.md#onetl.base.base_file_limit.BaseFileLimit)] | None = None) → Iterator[tuple[RemoteDirectory, list[RemoteDirectory], list[RemoteFile]]] + +Walk into directory tree, and iterate over its content in all nesting levels. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +Just like `os.walk`, but with additional filter/limit logic. + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **root** + : Directory path to walk into. + + **topdown** + : If `True`, walk in top-down order, otherwise walk in bottom-up order. + + **filters** + : Return only files/directories matching these filters. See [File Filters](../../file/file_filters/index.md#file-filters). + + **limits** + : Apply limits to the list of files/directories, and immediately stop if any of these limits is reached. + See [File Limits](../../file/file_limits/index.md#file-limits). +* **Returns:** + `Iterator[tuple[root, dirs, files]]`, like `os.walk`. + + But all the paths are not strings, instead path classes with embedded stats are returned. +* **Raises:** + NotADirectoryError + : Path is not a directory + + `onetl.exception.DirectoryNotFoundError` + : Path does not exist + +### Examples + +```pycon +>>> for root, dirs, files in connection.walk("/path/to/dir"): +... break +>>> os.fspath(root) +'/path/to/dir' +>>> dirs +[] +>>> os.fspath(files[0]) +'file.csv' +>>> connection.path_exists("/path/to/dir/file.csv") +True +``` + + diff --git a/mddocs/connection/file_df_connection/base.md b/mddocs/connection/file_df_connection/base.md new file mode 100644 index 000000000..8f58267d8 --- /dev/null +++ b/mddocs/connection/file_df_connection/base.md @@ -0,0 +1,63 @@ + + +# Base interface + +### *class* onetl.base.base_file_df_connection.BaseFileDFConnection + +Implements generic methods for reading and writing dataframe as files. + +#### Versionadded +Added in version 0.9.0. + + + +#### *abstract* check() → T + +Check source availability. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +If not, an exception will be raised. + +* **Returns:** + Connection itself +* **Raises:** + RuntimeError + : If the connection is not available + +### Examples + +```python +connection.check() +``` + + + +#### *abstract* check_if_format_supported(format: [BaseReadableFileFormat](../../file_df/file_formats/base.md#onetl.base.base_file_format.BaseReadableFileFormat) | [BaseWritableFileFormat](../../file_df/file_formats/base.md#onetl.base.base_file_format.BaseWritableFileFormat)) → None + +Validate if specific file format is supported. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### Versionadded +Added in version 0.9.0. + +* **Raises:** + RuntimeError + : If file format is not supported. + + + +#### *abstract* read_files_as_df(paths: list[PurePathProtocol], format: [BaseReadableFileFormat](../../file_df/file_formats/base.md#onetl.base.base_file_format.BaseReadableFileFormat), root: PurePathProtocol | None = None, df_schema: StructType | None = None, options: FileDFReadOptions | None = None) → DataFrame + +Read files in some paths list as dataframe. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### Versionadded +Added in version 0.9.0. + + + +#### *abstract* write_df_as_files(df: DataFrame, path: PurePathProtocol, format: [BaseWritableFileFormat](../../file_df/file_formats/base.md#onetl.base.base_file_format.BaseWritableFileFormat), options: FileDFWriteOptions | None = None) → None + +Write dataframe as files in some path. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### Versionadded +Added in version 0.9.0. + + diff --git a/mddocs/connection/file_df_connection/index.md b/mddocs/connection/file_df_connection/index.md new file mode 100644 index 000000000..ef8d51549 --- /dev/null +++ b/mddocs/connection/file_df_connection/index.md @@ -0,0 +1,20 @@ + + +# File DataFrame Connections + +# File DataFrame Connections + +* [Spark LocalFS](spark_local_fs.md) + * [`SparkLocalFS`](spark_local_fs.md#onetl.connection.file_df_connection.spark_local_fs.SparkLocalFS) +* [Spark HDFS](spark_hdfs/index.md) + * [Prerequisites](spark_hdfs/prerequisites.md) + * [Connection](spark_hdfs/connection.md) + * [Slots](spark_hdfs/slots.md) +* [Spark S3](spark_s3/index.md) + * [Prerequisites](spark_s3/prerequisites.md) + * [Connection](spark_s3/connection.md) + * [Troubleshooting](spark_s3/troubleshooting.md) + +# For developers + +* [Base interface](base.md) diff --git a/mddocs/connection/file_df_connection/spark_hdfs/connection.md b/mddocs/connection/file_df_connection/spark_hdfs/connection.md new file mode 100644 index 000000000..4f2364e0e --- /dev/null +++ b/mddocs/connection/file_df_connection/spark_hdfs/connection.md @@ -0,0 +1,169 @@ + + +# Spark HDFS Connection + +### *class* onetl.connection.file_df_connection.spark_hdfs.connection.SparkHDFS(\*, spark: SparkSession, cluster: Cluster, host: Host | None = None, port: int = 8020) + +Spark connection to HDFS. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +Based on [Spark Generic File Data Source](https://spark.apache.org/docs/latest/sql-data-sources-generic-options.html). + +#### SEE ALSO +Before using this connector please take into account [Prerequisites](prerequisites.md#spark-hdfs-prerequisites) + +#### NOTE +Supports only reading files as Spark DataFrame and writing DataFrame to files. + +Does NOT support file operations, like create, delete, rename, etc. For these operations, +use `HDFS` connection. + +#### Versionadded +Added in version 0.9.0. + +* **Parameters:** + **cluster** + : Cluster name. +
+ Used for: + : * HWM and lineage (as instance name for file paths) + * Validation of `host` value, + : if latter is passed and if some hooks are bound to + [`Slots.get_cluster_namenodes`](slots.md#onetl.connection.file_df_connection.spark_hdfs.slots.SparkHDFSSlots.get_cluster_namenodes). + + **host** + : Hadoop namenode host. For example: `namenode1.domain.com`. +
+ Should be an active namenode (NOT standby). +
+ If value is not set, but there are some hooks bound to + [`Slots.get_cluster_namenodes`](slots.md#onetl.connection.file_df_connection.spark_hdfs.slots.SparkHDFSSlots.get_cluster_namenodes) + and + [`Slots.is_namenode_active`](slots.md#onetl.connection.file_df_connection.spark_hdfs.slots.SparkHDFSSlots.is_namenode_active), + onETL will iterate over cluster namenodes to detect which one is active. + + **ipc_port** + : Port of Hadoop namenode (IPC protocol). +
+ If omitted, but there are some hooks bound to + [`Slots.get_ipc_port`](slots.md#onetl.connection.file_df_connection.spark_hdfs.slots.SparkHDFSSlots.get_ipc_port), + onETL will try to detect port number for a specific `cluster`. + + **spark** + : Spark session + +### Examples + +Create SparkHDFS connection with Kerberos auth + +Execute `kinit` consome command before creating Spark Session + +```bash +$ kinit -kt /path/to/keytab user +``` + +```python +from onetl.connection import SparkHDFS +from pyspark.sql import SparkSession + +# Create Spark session. +# Use names "spark.yarn.access.hadoopFileSystems", "spark.yarn.principal" +# and "spark.yarn.keytab" for Spark 2 + +spark = ( + SparkSession.builder.appName("spark-app-name") + .option( + "spark.kerberos.access.hadoopFileSystems", + "hdfs://namenode1.domain.com:8020", + ) + .option("spark.kerberos.principal", "user") + .option("spark.kerberos.keytab", "/path/to/keytab") + .enableHiveSupport() + .getOrCreate() +) + +# Create connection +hdfs = SparkHDFS( + host="namenode1.domain.com", + cluster="rnd-dwh", + spark=spark, +).check() +``` + +Create SparkHDFS connection with anonymous auth + +```py +from onetl.connection import SparkHDFS +from pyspark.sql import SparkSession + +# Create Spark session +spark = SparkSession.builder.master("local").appName("spark-app-name").getOrCreate() + +# Create connection +hdfs = SparkHDFS( + host="namenode1.domain.com", + cluster="rnd-dwh", + spark=spark, +).check() +``` + +Use cluster name to detect active namenode + +Can be used only if some third-party plugin provides [Spark HDFS Slots](slots.md#spark-hdfs-slots) implementation + +```python +# Create Spark session +... + +# Create connection +hdfs = SparkHDFS(cluster="rnd-dwh", spark=spark).check() +``` + + + +#### check() + +Check source availability. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +If not, an exception will be raised. + +* **Returns:** + Connection itself +* **Raises:** + RuntimeError + : If the connection is not available + +### Examples + +```python +connection.check() +``` + + + +#### *classmethod* get_current(spark: SparkSession) + +Create connection for current cluster. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +Automatically sets up current cluster name as `cluster`. + +#### NOTE +Can be used only if there are a some hooks bound to +[`Slots.get_current_cluster`](slots.md#onetl.connection.file_df_connection.spark_hdfs.slots.SparkHDFSSlots.get_current_cluster). + +#### Versionadded +Added in version 0.9.0. + +* **Parameters:** + **spark** + : See [`SparkHDFS`](#onetl.connection.file_df_connection.spark_hdfs.connection.SparkHDFS) constructor documentation. + +### Examples + +```python +from onetl.connection import SparkHDFS + +# injecting current cluster name via hooks mechanism +hdfs = SparkHDFS.get_current(spark=spark) +``` + + diff --git a/mddocs/connection/file_df_connection/spark_hdfs/index.md b/mddocs/connection/file_df_connection/spark_hdfs/index.md new file mode 100644 index 000000000..d5eae4c27 --- /dev/null +++ b/mddocs/connection/file_df_connection/spark_hdfs/index.md @@ -0,0 +1,12 @@ + + +# Spark HDFS + +# Connection + +* [Prerequisites](prerequisites.md) +* [Connection](connection.md) + +# For developers + +* [Slots](slots.md) diff --git a/mddocs/connection/file_df_connection/spark_hdfs/prerequisites.md b/mddocs/connection/file_df_connection/spark_hdfs/prerequisites.md new file mode 100644 index 000000000..2011410da --- /dev/null +++ b/mddocs/connection/file_df_connection/spark_hdfs/prerequisites.md @@ -0,0 +1,46 @@ + + +# Prerequisites + +## Version Compatibility + +* Hadoop versions: 2.x, 3.x (only with Hadoop 3.x libraries) +* Spark versions: 2.3.x - 3.5.x +* Java versions: 8 - 20 + +## Installing PySpark + +To use SparkHDFS connector you should have PySpark installed (or injected to `sys.path`) +BEFORE creating the connector instance. + +See [Spark](../../../install/spark.md#install-spark) installation instruction for more details. + +## Using Kerberos + +Some of Hadoop managed clusters use Kerberos authentication. In this case, you should call [kinit](https://web.mit.edu/kerberos/krb5-1.12/doc/user/user_commands/kinit.html) command +**BEFORE** starting Spark session to generate Kerberos ticket. See [Kerberos support](../../../install/kerberos.md#install-kerberos). + +Sometimes it is also required to pass keytab file to Spark config, allowing Spark executors to generate own Kerberos tickets: + +Spark 3 + +```python +SparkSession.builder + .option("spark.kerberos.access.hadoopFileSystems", "hdfs://namenode1.domain.com:9820,hdfs://namenode2.domain.com:9820") + .option("spark.kerberos.principal", "user") + .option("spark.kerberos.keytab", "/path/to/keytab") + .gerOrCreate() +``` + +Spark 2 + +```python +SparkSession.builder + .option("spark.yarn.access.hadoopFileSystems", "hdfs://namenode1.domain.com:9820,hdfs://namenode2.domain.com:9820") + .option("spark.yarn.principal", "user") + .option("spark.yarn.keytab", "/path/to/keytab") + .gerOrCreate() +``` + +See [Spark security documentation](https://spark.apache.org/docs/latest/security.html#kerberos) +for more details. diff --git a/mddocs/connection/file_df_connection/spark_hdfs/slots.md b/mddocs/connection/file_df_connection/spark_hdfs/slots.md new file mode 100644 index 000000000..a94ddcde0 --- /dev/null +++ b/mddocs/connection/file_df_connection/spark_hdfs/slots.md @@ -0,0 +1,256 @@ + + +# Spark HDFS Slots + +### *class* onetl.connection.file_df_connection.spark_hdfs.slots.SparkHDFSSlots + +Spark HDFS slots that could be implemented by third-party plugins. + +#### Versionadded +Added in version 0.9.0. + + + +#### *static* normalize_cluster_name(cluster: str) → str | None + +Normalize cluster name passed into SparkHDFS constructor. + +If hooks didn’t return anything, cluster name is left intact. + +#### Versionadded +Added in version 0.9.0. + +* **Parameters:** + **cluster** + : Cluster name +* **Returns:** + str | None + : Normalized cluster name. +
+ If hook cannot be applied to a specific cluster, it should return `None`. + +### Examples + +```python +from onetl.connection import SparkHDFS +from onetl.hooks import hook + +@SparkHDFS.Slots.normalize_cluster_name.bind +@hook +def normalize_cluster_name(cluster: str) -> str: + return cluster.lower() +``` + + + +#### *static* normalize_namenode_host(host: str, cluster: str) → str | None + +Normalize namenode host passed into SparkHDFS constructor. + +If hooks didn’t return anything, host is left intact. + +#### Versionadded +Added in version 0.9.0. + +* **Parameters:** + **host** + : Namenode host (raw) + + **cluster** + : Cluster name (normalized) +* **Returns:** + str | None + : Normalized namenode host name. +
+ If hook cannot be applied to a specific host name, it should return `None`. + +### Examples + +```python +from onetl.connection import SparkHDFS +from onetl.hooks import hook + +@SparkHDFS.Slots.normalize_namenode_host.bind +@hook +def normalize_namenode_host(host: str, cluster: str) -> str | None: + if cluster == "rnd-dwh": + if not host.endswith(".domain.com"): + # fix missing domain name + host += ".domain.com" + return host + + return None +``` + + + +#### *static* get_known_clusters() → set[str] | None + +Return collection of known clusters. + +Cluster passed into SparkHDFS constructor should be present in this list. +If hooks didn’t return anything, no validation will be performed. + +#### Versionadded +Added in version 0.9.0. + +* **Returns:** + set[str] | None + : Collection of cluster names (in normalized form). +
+ If hook cannot be applied, it should return `None`. + +### Examples + +```python +from onetl.connection import SparkHDFS +from onetl.hooks import hook + +@SparkHDFS.Slots.get_known_clusters.bind +@hook +def get_known_clusters() -> str[str]: + return {"rnd-dwh", "rnd-prod"} +``` + + + +#### *static* get_cluster_namenodes(cluster: str) → set[str] | None + +Return collection of known namenodes for the cluster. + +Namenode host passed into SparkHDFS constructor should be present in this list. +If hooks didn’t return anything, no validation will be performed. + +#### Versionadded +Added in version 0.9.0. + +* **Parameters:** + **cluster** + : Cluster name (normalized) +* **Returns:** + set[str] | None + : Collection of host names (in normalized form). +
+ If hook cannot be applied, it should return `None`. + +### Examples + +```python +from onetl.connection import SparkHDFS +from onetl.hooks import hook + +@SparkHDFS.Slots.get_cluster_namenodes.bind +@hook +def get_cluster_namenodes(cluster: str) -> str[str] | None: + if cluster == "rnd-dwh": + return {"namenode1.domain.com", "namenode2.domain.com"} + return None +``` + + + +#### *static* get_current_cluster() → str | None + +Get current cluster name. + +Used in [`get_current_cluster`](#onetl.connection.file_df_connection.spark_hdfs.slots.SparkHDFSSlots.get_current_cluster) to automatically fill up `cluster` attribute of a connection. +If hooks didn’t return anything, calling the method above will raise an exception. + +#### Versionadded +Added in version 0.9.0. + +* **Returns:** + str | None + : Current cluster name (in normalized form). +
+ If hook cannot be applied, it should return `None`. + +### Examples + +```python +from onetl.connection import SparkHDFS +from onetl.hooks import hook + +@SparkHDFS.Slots.get_current_cluster.bind +@hook +def get_current_cluster() -> str: + # some magic here + return "rnd-dwh" +``` + + + +#### *static* get_ipc_port(cluster: str) → int | None + +Get IPC port number for a specific cluster. + +Used by constructor to automatically set port number if omitted. + +#### Versionadded +Added in version 0.9.0. + +* **Parameters:** + **cluster** + : Cluster name (normalized) +* **Returns:** + int | None + : IPC port number. +
+ If hook cannot be applied, it should return `None`. + +### Examples + +```python +from onetl.connection import SparkHDFS +from onetl.hooks import hook + +@SparkHDFS.Slots.get_ipc_port.bind +@hook +def get_ipc_port(cluster: str) -> int | None: + if cluster == "rnd-dwh": + return 8020 # Cloudera + return None +``` + + + +#### *static* is_namenode_active(host: str, cluster: str) → bool | None + +Check whether a namenode of a specified cluster is active (=not standby) or not. + +Used for: +: * If SparkHDFS connection is created without `host` + > Connector will iterate over [`get_cluster_namenodes`](#onetl.connection.file_df_connection.spark_hdfs.slots.SparkHDFSSlots.get_cluster_namenodes) of a cluster to get active namenode, + > and then use it instead of `host` attribute. + * If SparkHDFS connection is created with `host` + > `check` will determine whether this host is active. + +#### Versionadded +Added in version 0.9.0. + +* **Parameters:** + **host** + : Namenode host (normalized) + + **cluster** + : Cluster name (normalized) +* **Returns:** + bool | None + : `True` if namenode is active, `False` if not. +
+ If hook cannot be applied, it should return `None`. + +### Examples + +```python +from onetl.connection import SparkHDFS +from onetl.hooks import hook + +@SparkHDFS.Slots.is_namenode_active.bind +@hook +def is_namenode_active(host: str, cluster: str) -> bool: + # some magic here + return True +``` + + diff --git a/mddocs/connection/file_df_connection/spark_local_fs.md b/mddocs/connection/file_df_connection/spark_local_fs.md new file mode 100644 index 000000000..1620f08bc --- /dev/null +++ b/mddocs/connection/file_df_connection/spark_local_fs.md @@ -0,0 +1,65 @@ + + +# Spark LocalFS + +### *class* onetl.connection.file_df_connection.spark_local_fs.SparkLocalFS(\*, spark: SparkSession) + +Spark connection to local filesystem. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +Based on [Spark Generic File Data Source](https://spark.apache.org/docs/latest/sql-data-sources-generic-options.html). + +#### WARNING +To use SparkHDFS connector you should have PySpark installed (or injected to `sys.path`) +BEFORE creating the connector instance. + +See [Spark](../../install/spark.md#install-spark) installation instruction for more details. + +#### WARNING +Currently supports only Spark sessions created with option `spark.master: local`. + +#### NOTE +Supports only reading files as Spark DataFrame and writing DataFrame to files. + +Does NOT support file operations, like create, delete, rename, etc. + +#### Versionadded +Added in version 0.9.0. + +* **Parameters:** + **spark** + : Spark session + +### Examples + +```python +from onetl.connection import SparkLocalFS +from pyspark.sql import SparkSession + +# create Spark session +spark = SparkSession.builder.master("local").appName("spark-app-name").getOrCreate() + +# create connection +local_fs = SparkLocalFS(spark=spark).check() +``` + + + +#### check() + +Check source availability. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +If not, an exception will be raised. + +* **Returns:** + Connection itself +* **Raises:** + RuntimeError + : If the connection is not available + +### Examples + +```python +connection.check() +``` + + diff --git a/mddocs/connection/file_df_connection/spark_s3/connection.md b/mddocs/connection/file_df_connection/spark_s3/connection.md new file mode 100644 index 000000000..5b2c1db31 --- /dev/null +++ b/mddocs/connection/file_df_connection/spark_s3/connection.md @@ -0,0 +1,232 @@ + + +# Spark S3 Connection + +### *class* onetl.connection.file_df_connection.spark_s3.connection.SparkS3(\*, spark: SparkSession, host: Host, port: int | None = None, bucket: str, protocol: Literal['http', 'https'] = 'https', access_key: str | None = None, secret_key: SecretStr | None = None, session_token: SecretStr | None = None, region: str | None = None, extra: SparkS3Extra = SparkS3Extra()) + +Spark connection to S3 filesystem. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +Based on [Hadoop-AWS module](https://hadoop.apache.org/docs/current3/hadoop-aws/tools/hadoop-aws/index.html) +and [Spark integration with Cloud Infrastructures](https://spark.apache.org/docs/latest/cloud-integration.html). + +#### SEE ALSO +Before using this connector please take into account [Prerequisites](prerequisites.md#spark-s3-prerequisites) + +#### NOTE +Supports only reading files as Spark DataFrame and writing DataFrame to files. + +Does NOT support file operations, like create, delete, rename, etc. For these operations, +use `S3` connection. + +#### Versionadded +Added in version 0.9.0. + +* **Parameters:** + **host** + : Host of S3 source. For example: `domain.com` + + **port** + : Port of S3 source + + **bucket** + : Bucket name in the S3 file source + + **protocol** + : Connection protocol. Allowed values: `https` or `http` + + **access_key** + : Access key (aka user ID) of an account in the S3 service + + **secret_key** + : Secret key (aka password) of an account in the S3 service + + **session_token** + : Session token of your account in S3 service + + **region** + : Region name of bucket in S3 service + + **extra** + : A dictionary of additional properties to be used when connecting to S3. +
+ These are Hadoop AWS specific properties, see links below: + * [Hadoop AWS](https://hadoop.apache.org/docs/current/hadoop-aws/tools/hadoop-aws/index.html#General_S3A_Client_configuration) + * [Hadoop AWS committers options](https://hadoop.apache.org/docs/current/hadoop-aws/tools/hadoop-aws/committers.html) +
+ Options are passed without prefixes `spark.hadoop.`, `fs.s3a.` and `fs.s3a.bucket.$BUCKET.`, for example: + ```python + extra = { + "path.style.access": True, + "committer.magic.enabled": True, + "committer.name": "magic", + "connection.timeout": 300000, + } + ``` +
+ #### WARNING + Options that populated from connection + attributes (like `endpoint`, `access.key`) are not allowed to override. +
+ But you may override `aws.credentials.provider` and pass custom credential options. + + **spark** + : Spark session + +### Examples + +Create S3 connection with bucket as subdomain (`my-bucket.domain.com`): + +```py +from onetl.connection import SparkS3 +from pyspark.sql import SparkSession + +# Create Spark session with Hadoop AWS libraries loaded +maven_packages = SparkS3.get_packages(spark_version="3.5.5") +# Some packages are not used, but downloading takes a lot of time. Skipping them. +excluded_packages = SparkS3.get_exclude_packages() +spark = ( + SparkSession.builder.appName("spark-app-name") + .config("spark.jars.packages", ",".join(maven_packages)) + .config("spark.jars.excludes", ",".join(excluded_packages)) + .config("spark.hadoop.fs.s3a.committer.magic.enabled", "true") + .config("spark.hadoop.fs.s3a.committer.name", "magic") + .config( + "spark.hadoop.mapreduce.outputcommitter.factory.scheme.s3a", + "org.apache.hadoop.fs.s3a.commit.S3ACommitterFactory", + ) + .config( + "spark.sql.parquet.output.committer.class", + "org.apache.spark.internal.io.cloud.BindingParquetOutputCommitter", + ) + .config( + "spark.sql.sources.commitProtocolClass", + "org.apache.spark.internal.io.cloud.PathOutputCommitProtocol", + ) + .getOrCreate() +) + +# Create connection +s3 = SparkS3( + host="domain.com", + protocol="http", + bucket="my-bucket", + access_key="ACCESS_KEY", + secret_key="SECRET_KEY", + spark=spark, +).check() +``` + +Create S3 connection with bucket as subpath (`domain.com/my-bucket`) + +```py +# Create Spark session with Hadoop AWS libraries loaded +... + +# Create connection +s3 = SparkS3( + host="domain.com", + protocol="http", + bucket="my-bucket", + access_key="ACCESS_KEY", + secret_key="SECRET_KEY", + extra={ + "path.style.access": True, # <--- + }, + spark=spark, +).check() +``` + + + +#### check() + +Check source availability. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +If not, an exception will be raised. + +* **Returns:** + Connection itself +* **Raises:** + RuntimeError + : If the connection is not available + +### Examples + +```python +connection.check() +``` + + + +#### close() + +Close all connections created to S3. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +Also resets all `fs.s3a.bucket.$BUCKET.*` properties of Hadoop configuration. + +#### NOTE +Connection can be used again after it was closed. + +* **Returns:** + Connection itself + +### Examples + +Close connection automatically: + +```python +with connection: + ... +``` + +Close connection manually: + +```python +connection.close() +``` + + + +#### *classmethod* get_exclude_packages() → list[str] + +Get package names to be excluded by Spark. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### Versionadded +Added in version 0.13.0. + +### Examples + +```python +from onetl.connection import SparkS3 + +SparkS3.get_exclude_packages() +``` + + + +#### *classmethod* get_packages(spark_version: str, scala_version: str | None = None) → list[str] + +Get package names to be downloaded by Spark. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### Versionadded +Added in version 0.9.0. + +* **Parameters:** + **spark_version** + : Spark version in format `major.minor.patch`. + + **scala_version** + : Scala version in format `major.minor`. +
+ If `None`, `spark_version` is used to determine Scala version. + +### Examples + +```python +from onetl.connection import SparkS3 + +SparkS3.get_packages(spark_version="3.5.5") +SparkS3.get_packages(spark_version="3.5.5", scala_version="2.12") +``` + + diff --git a/mddocs/connection/file_df_connection/spark_s3/index.md b/mddocs/connection/file_df_connection/spark_s3/index.md new file mode 100644 index 000000000..be66a0adc --- /dev/null +++ b/mddocs/connection/file_df_connection/spark_s3/index.md @@ -0,0 +1,9 @@ + + +# Spark S3 + +# Connection + +* [Prerequisites](prerequisites.md) +* [Connection](connection.md) +* [Troubleshooting](troubleshooting.md) diff --git a/mddocs/connection/file_df_connection/spark_s3/prerequisites.md b/mddocs/connection/file_df_connection/spark_s3/prerequisites.md new file mode 100644 index 000000000..1bfd0846a --- /dev/null +++ b/mddocs/connection/file_df_connection/spark_s3/prerequisites.md @@ -0,0 +1,61 @@ + + +# Prerequisites + +## Version Compatibility + +* Spark versions: 3.2.x - 3.5.x (only with Hadoop 3.x libraries) +* Java versions: 8 - 20 + +## Installing PySpark + +To use SparkS3 connector you should have PySpark installed (or injected to `sys.path`) +BEFORE creating the connector instance. + +See [Spark](../../../install/spark.md#install-spark) installation instruction for more details. + +## Connecting to S3 + +### Bucket access style + +AWS and some other S3 cloud providers allows bucket access using domain style only, e.g. `https://mybucket.s3provider.com`. + +Other implementations, like Minio, by default allows path style access only, e.g. `https://s3provider.com/mybucket` +(see [MINIO_DOMAIN](https://min.io/docs/minio/linux/reference/minio-server/minio-server.html#envvar.MINIO_DOMAIN)). + +You should set `path.style.access` to `True` or `False`, to choose the preferred style. + +### Authentication + +Different S3 instances can use different authentication methods, like: +: * `access_key + secret_key` (or username + password) + * `access_key + secret_key + session_token` + +Usually these are just passed to SparkS3 constructor: + +```python +SparkS3( + access_key=..., + secret_key=..., + session_token=..., +) +``` + +But some S3 cloud providers, like AWS, may require custom credential providers. You can pass them like: + +```python +SparkS3( + extra={ + # provider class + "aws.credentials.provider": "org.apache.hadoop.fs.s3a.auth.AssumedRoleCredentialProvider", + # other options, if needed + "assumed.role.arn": "arn:aws:iam::90066806600238:role/s3-restricted", + }, +) +``` + +See [Hadoop-AWS](https://hadoop.apache.org/docs/stable/hadoop-aws/tools/hadoop-aws/index.html#Changing_Authentication_Providers) documentation. + +## Troubleshooting + +See [Spark S3 Troubleshooting](troubleshooting.md#spark-s3-troubleshooting). diff --git a/mddocs/connection/file_df_connection/spark_s3/troubleshooting.md b/mddocs/connection/file_df_connection/spark_s3/troubleshooting.md new file mode 100644 index 000000000..32ea15667 --- /dev/null +++ b/mddocs/connection/file_df_connection/spark_s3/troubleshooting.md @@ -0,0 +1,366 @@ + + +# Spark S3 Troubleshooting + +#### NOTE +General guide: [Troubleshooting](../../../troubleshooting/index.md#troubleshooting). + +More details: + +* [Hadoop AWS Troubleshooting Guide](https://hadoop.apache.org/docs/stable/hadoop-aws/tools/hadoop-aws/troubleshooting_s3a.html) +* [Hadoop AWS Performance Guide](https://hadoop.apache.org/docs/stable/hadoop-aws/tools/hadoop-aws/performance.html) +* [Spark integration with Cloud Infrastructures](https://spark.apache.org/docs/latest/cloud-integration.html) + +## `SparkS3.check()` and other methods hang + +### Details + +S3 may not respond for connection attempts for a long time if it’s under heavy load. +To handle this, Hadoop AWS library has retry mechanism. By default it retries 7 times with 500ms interval. + +Hadoop AWS is based on AWS SDK library, which also has retry mechanism. This mechanism is not disabled because it handles different +errors than Hadoop AWS, so they complement each other. Default number of attempts in AWS SDK is 20 with minimal 5s interval, +which is exponentially increasing with each failed attempt. + +It is not a problem if S3 source is not accessible at all, like hostname cannot be resolved, or port is not opened. +These errors are not recoverable, and retry mechanism is not activated. + +But errors like SSL issues, are considered recoverable, and this causing retry of retry over increasing interval. +So user is waiting for [almost 15 minutes](https://issues.apache.org/jira/browse/HADOOP-18839) just to get exception message. + +### How to determine reason + +#### Make logging more verbose + +Change Spark session log level to [DEBUG](../../../troubleshooting/spark.md#troubleshooting-spark) to print result of each attempt. +Resulting logs will look like this + +### See log + +```text +23/08/03 11:25:10 DEBUG S3AFileSystem: Using S3ABlockOutputStream with buffer = disk; block=67108864; queue limit=4 +23/08/03 11:25:10 DEBUG S3Guard: Metastore option source [core-default.xml] +23/08/03 11:25:10 DEBUG S3Guard: Using NullMetadataStore metadata store for s3a filesystem +23/08/03 11:25:10 DEBUG S3AFileSystem: S3Guard is disabled on this bucket: test-bucket +23/08/03 11:25:10 DEBUG DirectoryPolicyImpl: Directory markers will be deleted +23/08/03 11:25:10 DEBUG S3AFileSystem: Directory marker retention policy is DirectoryMarkerRetention{policy='delete'} +23/08/03 11:25:10 DEBUG S3AUtils: Value of fs.s3a.multipart.purge.age is 86400 +23/08/03 11:25:10 DEBUG S3AUtils: Value of fs.s3a.bulk.delete.page.size is 250 +23/08/03 11:25:10 DEBUG FileSystem: Creating FS s3a://test-bucket/fake: duration 0:01.029s +23/08/03 11:25:10 DEBUG IOStatisticsStoreImpl: Incrementing counter op_is_directory by 1 with final value 1 +23/08/03 11:25:10 DEBUG S3AFileSystem: Getting path status for s3a://test-bucket/fake (fake); needEmptyDirectory=false +23/08/03 11:25:10 DEBUG S3AFileSystem: S3GetFileStatus s3a://test-bucket/fake +23/08/03 11:25:10 DEBUG S3AFileSystem: LIST List test-bucket:/fake/ delimiter=/ keys=2 requester pays=false +23/08/03 11:25:10 DEBUG S3AFileSystem: Starting: LIST +23/08/03 11:25:10 DEBUG IOStatisticsStoreImpl: Incrementing counter object_list_request by 1 with final value 1 +23/08/03 11:25:10 DEBUG AWSCredentialProviderList: Using credentials from SimpleAWSCredentialsProvider +23/08/03 11:25:10 DEBUG request: Sending Request: GET https://test-bucket.localhost:9000 / Parameters: ({"list-type":["2"],"delimiter":["/"],"max-keys":["2"],"prefix":["fake/"],"fetch-owner":["false"]}Headers: (amz-sdk-invocation-id: e6d62603-96e4-a80f-10a1-816e0822bc71, Content-Type: application/octet-stream, User-Agent: Hadoop 3.3.4, aws-sdk-java/1.12.262 Linux/6.4.7-1-MANJARO OpenJDK_64-Bit_Server_VM/25.292-b10 java/1.8.0_292 scala/2.12.17 vendor/AdoptOpenJDK cfg/retry-mode/legacy, ) +23/08/03 11:25:10 DEBUG AWS4Signer: AWS4 Canonical Request: '"GET +/ +delimiter=%2F&fetch-owner=false&list-type=2&max-keys=2&prefix=fake%2F +amz-sdk-invocation-id:e6d62603-96e4-a80f-10a1-816e0822bc71 +amz-sdk-request:attempt=1;max=21 +amz-sdk-retry:0/0/500 +content-type:application/octet-stream +host:test-bucket.localhost:9000 +user-agent:Hadoop 3.3.4, aws-sdk-java/1.12.262 Linux/6.4.7-1-MANJARO OpenJDK_64-Bit_Server_VM/25.292-b10 java/1.8.0_292 scala/2.12.17 vendor/AdoptOpenJDK cfg/retry-mode/legacy +x-amz-content-sha256:UNSIGNED-PAYLOAD +x-amz-date:20230803T112510Z + +amz-sdk-invocation-id;amz-sdk-request;amz-sdk-retry;content-type;host;user-agent;x-amz-content-sha256;x-amz-date +UNSIGNED-PAYLOAD" +23/08/03 11:25:10 DEBUG AWS4Signer: AWS4 String to Sign: '"AWS4-HMAC-SHA256 +20230803T112510Z +20230803/us-east-1/s3/aws4_request +31a317bb7f6d97248dd0cf03429d701cbb3e29ce889cfbb98ba7a34c57a3bfba" +23/08/03 11:25:10 DEBUG AWS4Signer: Generating a new signing key as the signing key not available in the cache for the date 1691020800000 +23/08/03 11:25:10 DEBUG RequestAddCookies: CookieSpec selected: default +23/08/03 11:25:10 DEBUG RequestAuthCache: Auth cache not set in the context +23/08/03 11:25:10 DEBUG PoolingHttpClientConnectionManager: Connection request: [route: {s}->https://test-bucket.localhost:9000][total available: 0; route allocated: 0 of 96; total allocated: 0 of 96] +23/08/03 11:25:10 DEBUG PoolingHttpClientConnectionManager: Connection leased: [id: 0][route: {s}->https://test-bucket.localhost:9000][total available: 0; route allocated: 1 of 96; total allocated: 1 of 96] +23/08/03 11:25:10 DEBUG MainClientExec: Opening connection {s}->https://test-bucket.localhost:9000 +23/08/03 11:25:10 DEBUG DefaultHttpClientConnectionOperator: Connecting to test-bucket.localhost/127.0.0.1:9000 +23/08/03 11:25:10 DEBUG SSLConnectionSocketFactory: Connecting socket to test-bucket.localhost/127.0.0.1:9000 with timeout 5000 +23/08/03 11:25:10 DEBUG SSLConnectionSocketFactory: Enabled protocols: [TLSv1.2] +23/08/03 11:25:10 DEBUG SSLConnectionSocketFactory: Enabled cipher suites:[TLS_ECDHE_ECDSA_WITH_AES_256_CBC_SHA384, TLS_ECDHE_RSA_WITH_AES_256_CBC_SHA384, TLS_RSA_WITH_AES_256_CBC_SHA256, TLS_ECDH_ECDSA_WITH_AES_256_CBC_SHA384, TLS_ECDH_RSA_WITH_AES_256_CBC_SHA384, TLS_DHE_RSA_WITH_AES_256_CBC_SHA256, TLS_DHE_DSS_WITH_AES_256_CBC_SHA256, TLS_ECDHE_ECDSA_WITH_AES_256_CBC_SHA, TLS_ECDHE_RSA_WITH_AES_256_CBC_SHA, TLS_RSA_WITH_AES_256_CBC_SHA, TLS_ECDH_ECDSA_WITH_AES_256_CBC_SHA, TLS_ECDH_RSA_WITH_AES_256_CBC_SHA, TLS_DHE_RSA_WITH_AES_256_CBC_SHA, TLS_DHE_DSS_WITH_AES_256_CBC_SHA, TLS_ECDHE_ECDSA_WITH_AES_128_CBC_SHA256, TLS_ECDHE_RSA_WITH_AES_128_CBC_SHA256, TLS_RSA_WITH_AES_128_CBC_SHA256, TLS_ECDH_ECDSA_WITH_AES_128_CBC_SHA256, TLS_ECDH_RSA_WITH_AES_128_CBC_SHA256, TLS_DHE_RSA_WITH_AES_128_CBC_SHA256, TLS_DHE_DSS_WITH_AES_128_CBC_SHA256, TLS_ECDHE_ECDSA_WITH_AES_128_CBC_SHA, TLS_ECDHE_RSA_WITH_AES_128_CBC_SHA, TLS_RSA_WITH_AES_128_CBC_SHA, TLS_ECDH_ECDSA_WITH_AES_128_CBC_SHA, TLS_ECDH_RSA_WITH_AES_128_CBC_SHA, TLS_DHE_RSA_WITH_AES_128_CBC_SHA, TLS_DHE_DSS_WITH_AES_128_CBC_SHA, TLS_EMPTY_RENEGOTIATION_INFO_SCSV] +23/08/03 11:25:10 DEBUG SSLConnectionSocketFactory: Starting handshake +23/08/03 11:25:10 DEBUG ClientConnectionManagerFactory: +java.lang.reflect.InvocationTargetException + at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) + at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62) + at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) + at java.lang.reflect.Method.invoke(Method.java:498) + at com.amazonaws.http.conn.ClientConnectionManagerFactory$Handler.invoke(ClientConnectionManagerFactory.java:76) + at com.amazonaws.http.conn.$Proxy32.connect(Unknown Source) + at com.amazonaws.thirdparty.apache.http.impl.execchain.MainClientExec.establishRoute(MainClientExec.java:393) + at com.amazonaws.thirdparty.apache.http.impl.execchain.MainClientExec.execute(MainClientExec.java:236) + at com.amazonaws.thirdparty.apache.http.impl.execchain.ProtocolExec.execute(ProtocolExec.java:186) + at com.amazonaws.thirdparty.apache.http.impl.client.InternalHttpClient.doExecute(InternalHttpClient.java:185) + at com.amazonaws.thirdparty.apache.http.impl.client.CloseableHttpClient.execute(CloseableHttpClient.java:83) + at com.amazonaws.thirdparty.apache.http.impl.client.CloseableHttpClient.execute(CloseableHttpClient.java:56) + at com.amazonaws.http.apache.client.impl.SdkHttpClient.execute(SdkHttpClient.java:72) + at com.amazonaws.http.AmazonHttpClient$RequestExecutor.executeOneRequest(AmazonHttpClient.java:1346) + at com.amazonaws.http.AmazonHttpClient$RequestExecutor.executeHelper(AmazonHttpClient.java:1157) + at com.amazonaws.http.AmazonHttpClient$RequestExecutor.doExecute(AmazonHttpClient.java:814) + at com.amazonaws.http.AmazonHttpClient$RequestExecutor.executeWithTimer(AmazonHttpClient.java:781) + at com.amazonaws.http.AmazonHttpClient$RequestExecutor.execute(AmazonHttpClient.java:755) + at com.amazonaws.http.AmazonHttpClient$RequestExecutor.access$500(AmazonHttpClient.java:715) + at com.amazonaws.http.AmazonHttpClient$RequestExecutionBuilderImpl.execute(AmazonHttpClient.java:697) + at com.amazonaws.http.AmazonHttpClient.execute(AmazonHttpClient.java:561) + at com.amazonaws.http.AmazonHttpClient.execute(AmazonHttpClient.java:541) + at com.amazonaws.services.s3.AmazonS3Client.invoke(AmazonS3Client.java:5456) + at com.amazonaws.services.s3.AmazonS3Client.invoke(AmazonS3Client.java:5403) + at com.amazonaws.services.s3.AmazonS3Client.invoke(AmazonS3Client.java:5397) + at com.amazonaws.services.s3.AmazonS3Client.listObjectsV2(AmazonS3Client.java:971) + at org.apache.hadoop.fs.s3a.S3AFileSystem.lambda$listObjects$11(S3AFileSystem.java:2595) + at org.apache.hadoop.fs.statistics.impl.IOStatisticsBinding.lambda$trackDurationOfOperation$5(IOStatisticsBinding.java:499) + at org.apache.hadoop.fs.s3a.Invoker.retryUntranslated(Invoker.java:414) + at org.apache.hadoop.fs.s3a.Invoker.retryUntranslated(Invoker.java:377) + at org.apache.hadoop.fs.s3a.S3AFileSystem.listObjects(S3AFileSystem.java:2586) + at org.apache.hadoop.fs.s3a.S3AFileSystem.s3GetFileStatus(S3AFileSystem.java:3832) + at org.apache.hadoop.fs.s3a.S3AFileSystem.innerGetFileStatus(S3AFileSystem.java:3688) + at org.apache.hadoop.fs.s3a.S3AFileSystem.lambda$isDirectory$35(S3AFileSystem.java:4724) + at org.apache.hadoop.fs.statistics.impl.IOStatisticsBinding.lambda$trackDurationOfOperation$5(IOStatisticsBinding.java:499) + at org.apache.hadoop.fs.statistics.impl.IOStatisticsBinding.trackDuration(IOStatisticsBinding.java:444) + at org.apache.hadoop.fs.s3a.S3AFileSystem.trackDurationAndSpan(S3AFileSystem.java:2337) + at org.apache.hadoop.fs.s3a.S3AFileSystem.trackDurationAndSpan(S3AFileSystem.java:2356) + at org.apache.hadoop.fs.s3a.S3AFileSystem.isDirectory(S3AFileSystem.java:4722) + at org.apache.spark.sql.execution.streaming.FileStreamSink$.hasMetadata(FileStreamSink.scala:54) + at org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:366) + at org.apache.spark.sql.DataFrameReader.loadV1Source(DataFrameReader.scala:229) + at org.apache.spark.sql.DataFrameReader.$anonfun$load$2(DataFrameReader.scala:211) + at scala.Option.getOrElse(Option.scala:189) + at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:211) + at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:186) + at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) + at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62) + at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) + at java.lang.reflect.Method.invoke(Method.java:498) + at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244) + at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:374) + at py4j.Gateway.invoke(Gateway.java:282) + at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132) + at py4j.commands.CallCommand.execute(CallCommand.java:79) + at py4j.ClientServerConnection.waitForCommands(ClientServerConnection.java:182) + at py4j.ClientServerConnection.run(ClientServerConnection.java:106) + at java.lang.Thread.run(Thread.java:748) +Caused by: javax.net.ssl.SSLException: Unsupported or unrecognized SSL message + at sun.security.ssl.SSLSocketInputRecord.handleUnknownRecord(SSLSocketInputRecord.java:448) + at sun.security.ssl.SSLSocketInputRecord.decode(SSLSocketInputRecord.java:184) + at sun.security.ssl.SSLTransport.decode(SSLTransport.java:109) + at sun.security.ssl.SSLSocketImpl.decode(SSLSocketImpl.java:1383) + at sun.security.ssl.SSLSocketImpl.readHandshakeRecord(SSLSocketImpl.java:1291) + at sun.security.ssl.SSLSocketImpl.startHandshake(SSLSocketImpl.java:435) + at com.amazonaws.thirdparty.apache.http.conn.ssl.SSLConnectionSocketFactory.createLayeredSocket(SSLConnectionSocketFactory.java:436) + at com.amazonaws.thirdparty.apache.http.conn.ssl.SSLConnectionSocketFactory.connectSocket(SSLConnectionSocketFactory.java:384) + at com.amazonaws.thirdparty.apache.http.impl.conn.DefaultHttpClientConnectionOperator.connect(DefaultHttpClientConnectionOperator.java:142) + at com.amazonaws.thirdparty.apache.http.impl.conn.PoolingHttpClientConnectionManager.connect(PoolingHttpClientConnectionManager.java:376) + ... 58 more +23/08/03 11:25:10 DEBUG DefaultManagedHttpClientConnection: http-outgoing-0: Shutdown connection +23/08/03 11:25:10 DEBUG MainClientExec: Connection discarded +23/08/03 11:25:10 DEBUG PoolingHttpClientConnectionManager: Connection released: [id: 0][route: {s}->https://test-bucket.localhost:9000][total available: 0; route allocated: 0 of 96; total allocated: 0 of 96] +23/08/03 11:25:10 DEBUG AmazonHttpClient: Unable to execute HTTP request: Unsupported or unrecognized SSL message Request will be retried. +23/08/03 11:25:10 DEBUG request: Retrying Request: GET https://test-bucket.localhost:9000 / Parameters: ({"list-type":["2"],"delimiter":["/"],"max-keys":["2"],"prefix":["fake/"],"fetch-owner":["false"]}Headers: (amz-sdk-invocation-id: e6d62603-96e4-a80f-10a1-816e0822bc71, Content-Type: application/octet-stream, User-Agent: Hadoop 3.3.4, aws-sdk-java/1.12.262 Linux/6.4.7-1-MANJARO OpenJDK_64-Bit_Server_VM/25.292-b10 java/1.8.0_292 scala/2.12.17 vendor/AdoptOpenJDK cfg/retry-mode/legacy, ) +23/08/03 11:25:10 DEBUG AmazonHttpClient: Retriable error detected, will retry in 49ms, attempt number: 0 +``` + +#### Change number of retries + +You can also change number of retries performed by both libraries using `extra` parameter: + +```python +spark_s3 = SparkS3( + ..., + extra={ + "attempts.maximum": 1, + "retry.limit": 1, + }, +) +``` + +So accessing S3 will fail almost immediately if there is any error. + +### Most common mistakes + +#### No network access + +```text +Caused by: java.net.ConnectException: Connection refused +``` + +Mostly caused by: + +* Trying to access port number which S3 server does not listen +* You’re trying to access host which is unreachable from your network (e.g. running behind some proxy or VPN) +* There are some firewall restrictions for accessing specific host or port + +#### Using HTTPS protocol for HTTP port + +```text +Caused by: javax.net.ssl.SSLException: Unsupported or unrecognized SSL message +``` + +By default, SparkS3 uses HTTPS protocol for connection. +If you change port number, this does not lead to changing protocol: + +```python +spark_s3 = SparkS3(host="s3provider.com", port=8080, ...) +``` + +You should pass protocol explicitly: + +```python +spark_s3 = SparkS3(host="s3provider.com", port=8080, protocol="http", ...) +``` + +#### SSL certificate is self-signed + +```text +sun.security.provider.certpath.SunCertPathBuilderException: unable to find valid certification path to requested target +``` + +To connect to HTTPS port with self-signed certificate, you should +[add certificate chain to Java TrustedStore](https://stackoverflow.com/questions/373295/digital-certificate-how-to-import-cer-file-in-to-truststore-file-using). + +Another option is to disable SSL check: + +```python +spark_s3 = SparkS3( + ..., + extra={ + "connection.ssl.enabled": False, + }, +) +``` + +But is is **NOT** recommended. + +#### Accessing S3 without domain-style access style support + +```text +Caused by: java.net.UnknownHostException: my-bucket.s3provider.com +``` + +To use path-style access, use option below: + +```python +spark_s3 = SparkS3( + host="s3provider.com", + bucket="my-bucket", + ..., + extra={ + "path.style.access": True, + }, +) +``` + +## Slow or unstable writing to S3 + +Hadoop AWS allows to use different writing strategies for different S3 implementations, depending +on list of supported features by server. + +These strategies are called [committers](https://hadoop.apache.org/docs/stable/hadoop-aws/tools/hadoop-aws/committers.html). +There are [different types of committers](https://hadoop.apache.org/docs/stable/hadoop-aws/tools/hadoop-aws/committers.html#Switching_to_an_S3A_Committer): + +* `file` (default) +* `directory` +* `partitioned` +* `magic` + +### `file` committer + +This committer is quite slow and unstable, so it is not recommended to use: + +```text +WARN AbstractS3ACommitterFactory: Using standard FileOutputCommitter to commit work. This is slow and potentially unsafe. +``` + +This is caused by the fact it creates files in the temp directory on remote filesystem, and after all of them are written successfully, +they are moved to target directory on same remote filesystem. + +This is not an issue for HDFS which does support file move operations and also support renaming directory +as atomic operation with `O(1)` time complexity. + +But S3 does support only file copying, so moving is performed via copy + delete. +Also it does not support atomic directory rename operation. Instead, renaming files with the same prefix has time complexity `O(n)`. + +### `directory` and `partitioned` committers + +These are [staging committers](https://hadoop.apache.org/docs/stable/hadoop-aws/tools/hadoop-aws/committer_architecture.html), +meaning that they create temp directories on local filesystem, and after all files are written successfully, +they will be uploaded to S3. Local filesystems do support file moving and directory renaming, +so these committers does not have issues that `file` committer has. + +But they both require free space on local filesystem, and this may be an issue if user need to write large amount of data. +Also this can be an issue for container environment, like Kubernetes, there resources should be allocated before starting a container. + +### `magic` committer + +This committer uses multipart upload feature of S3 API, allowing to create multiple files +and after all of them were written successfully finish the transaction. Before transaction is finished, +files will not be accessible by other clients. + +Because it does not require neither file moving operations, nor directory atomic rename, +upload process is done in most efficient way S3 support. +This [drastically increases writing performance](https://spot.io/blog/improve-apache-spark-performance-with-the-s3-magic-committer/). + +To use this committer, set [following properties](https://github.com/apache/spark/pull/32518) while creating Spark session. + +S3 your main distributed filesystem (Spark on Kubernetes) + +```py +# https://issues.apache.org/jira/browse/SPARK-23977 +# https://spark.apache.org/docs/latest/cloud-integration.html#committing-work-into-cloud-storage-safely-and-fast +spark = ( + SparkSession.builder.appName("spark-app-name") + .config("spark.hadoop.fs.s3a.committer.magic.enabled", "true") + .config("spark.hadoop.fs.s3a.committer.name", "magic") + .config("spark.hadoop.mapreduce.outputcommitter.factory.scheme.s3a", "org.apache.hadoop.fs.s3a.commit.S3ACommitterFactory") + .config("spark.sql.parquet.output.committer.class", "org.apache.spark.internal.io.cloud.BindingParquetOutputCommitter") + .config("spark.sql.sources.commitProtocolClass", "org.apache.spark.internal.io.cloud.PathOutputCommitProtocol") + .getOrCreate() +) +``` + +HDFS is your main distributed filesystem (Spark on Hadoop) + +```py +# https://community.cloudera.com/t5/Support-Questions/spark-sql-sources-partitionOverwriteMode-dynamic-quot-not/m-p/343483/highlight/true +spark = ( + SparkSession.builder.appName("spark-app-name") + .config("spark.hadoop.fs.s3a.committer.magic.enabled", "true") + .config("spark.hadoop.fs.s3a.committer.name", "magic") + .getOrCreate() +) +``` + +#### WARNING +`magic` committer requires S3 implementation to have strong consistency - file upload API return response only +if it was written on enough number of cluster nodes, and any cluster node error does not lead to missing or corrupting files. + +Some S3 implementations does have strong consistency +(like [AWS S3](https://aws.amazon.com/ru/blogs/aws/amazon-s3-update-strong-read-after-write-consistency/) and +[MinIO](https://blog.min.io/migrating-hdfs-to-object-storage/)), some not. Please contact your S3 provider +to get information about S3 implementation consistency. + +#### WARNING +`magic` committer does not support `if_exists="replace_overlapping_partitions"`. +Either use another `if_exists` value, or use `partitioned` committer. + +### See also + +* [directory.marker.retention=”keep”](https://hadoop.apache.org/docs/stable/hadoop-aws/tools/hadoop-aws/directory_markers.html) + +## Slow reading from S3 + +Please read following documentation: + +* [prefetch.enabled](https://hadoop.apache.org/docs/stable/hadoop-aws/tools/hadoop-aws/prefetching.html) +* [experimental.input.fadvise](https://hadoop.apache.org/docs/stable/hadoop-aws/tools/hadoop-aws/performance.html#Improving_data_input_performance_through_fadvise) +* [Parquet and ORC I/O settings](https://spark.apache.org/docs/latest/cloud-integration.html#parquet-io-settings) + +If you’re reading data from row-based formats, like [CSV](../../../file_df/file_formats/csv.md#csv-file-format), prefer +[experimental.input.fadvise=”sequential” with increased readahead.range](https://issues.apache.org/jira/browse/HADOOP-17789?focusedCommentId=17383559#comment-17383559). + +But for other file formats, especially using compression, prefer +[experimental.input.fadvise=”normal”](https://issues.apache.org/jira/browse/HADOOP-17789?focusedCommentId=17383743#comment-17383743) diff --git a/mddocs/connection/index.md b/mddocs/connection/index.md new file mode 100644 index 000000000..5ad28edef --- /dev/null +++ b/mddocs/connection/index.md @@ -0,0 +1,34 @@ + + + DB Connection + +* [DB Connections](db_connection/index.md) + * [Clickhouse](db_connection/clickhouse/index.md) + * [Greenplum](db_connection/greenplum/index.md) + * [Kafka](db_connection/kafka/index.md) + * [Hive](db_connection/hive/index.md) + * [MongoDB](db_connection/mongodb/index.md) + * [MSSQL](db_connection/mssql/index.md) + * [MySQL](db_connection/mysql/index.md) + * [Oracle](db_connection/oracle/index.md) + * [Postgres](db_connection/postgres/index.md) + * [Teradata](db_connection/teradata/index.md) + + File Connection + +* [File Connections](file_connection/index.md) + * [FTP](file_connection/ftp.md) + * [FTPS](file_connection/ftps.md) + * [HDFS](file_connection/hdfs/index.md) + * [Samba](file_connection/samba.md) + * [SFTP](file_connection/sftp.md) + * [S3](file_connection/s3.md) + * [Webdav](file_connection/webdav.md) + + File DataFrame Connection + +* [File DataFrame Connections](file_df_connection/index.md) + * [Spark LocalFS](file_df_connection/spark_local_fs.md) + * [Spark HDFS](file_df_connection/spark_hdfs/index.md) + * [Spark S3](file_df_connection/spark_s3/index.md) + * [Base interface](file_df_connection/base.md) diff --git a/mddocs/contributing.md b/mddocs/contributing.md new file mode 100644 index 000000000..739798897 --- /dev/null +++ b/mddocs/contributing.md @@ -0,0 +1,391 @@ +# Contributing Guide + +Welcome! There are many ways to contribute, including submitting bug +reports, improving documentation, submitting feature requests, reviewing +new submissions, or contributing code that can be incorporated into the +project. + +## Limitations + +We should keep close to these items during development: + +* Some companies still use old Spark versions, like 2.3.1. So it is required to keep compatibility if possible, e.g. adding branches for different Spark versions. +* Different users uses onETL in different ways - some uses only DB connectors, some only files. Connector-specific dependencies should be optional. +* Instead of creating classes with a lot of different options, prefer splitting them into smaller classes, e.g. options class, context manager, etc, and using composition. + +## Initial setup for local development + +### Install Git + +Please follow [instruction](https://docs.github.com/en/get-started/quickstart/set-up-git). + +### Create a fork + +If you are not a member of a development team building onETL, you should create a fork before making any changes. + +Please follow [instruction](https://docs.github.com/en/get-started/quickstart/fork-a-repo). + +### Clone the repo + +Open terminal and run these commands: + +```bash +git clone git@github.com:myuser/onetl.git -b develop + +cd onetl +``` + +### Setup environment + +Create virtualenv and install dependencies: + +```bash +python -m venv venv +source venv/bin/activate +pip install -U wheel +pip install -U pip setuptools +pip install -U \ + -r requirements/core.txt \ + -r requirements/ftp.txt \ + -r requirements/hdfs.txt \ + -r requirements/kerberos.txt \ + -r requirements/s3.txt \ + -r requirements/sftp.txt \ + -r requirements/webdav.txt \ + -r requirements/dev.txt \ + -r requirements/docs.txt \ + -r requirements/tests/base.txt \ + -r requirements/tests/clickhouse.txt \ + -r requirements/tests/kafka.txt \ + -r requirements/tests/mongodb.txt \ + -r requirements/tests/mssql.txt \ + -r requirements/tests/mysql.txt \ + -r requirements/tests/postgres.txt \ + -r requirements/tests/oracle.txt \ + -r requirements/tests/pydantic-2.txt \ + -r requirements/tests/spark-3.5.5.txt + +# TODO: remove after https://github.com/zqmillet/sphinx-plantuml/pull/4 +pip install sphinx-plantuml --no-deps +``` + +### Enable pre-commit hooks + +Install pre-commit hooks: + +```bash +pre-commit install --install-hooks +``` + +Test pre-commit hooks run: + +```bash +pre-commit run +``` + +## How to + +### Run tests locally + +#### Using docker-compose + +Build image for running tests: + +```bash +docker-compose build +``` + +Start all containers with dependencies: + +```bash +docker-compose --profile all up -d +``` + +You can run limited set of dependencies: + +```bash +docker-compose --profile mongodb up -d +``` + +Run tests: + +```bash +docker-compose run --rm onetl ./run_tests.sh +``` + +You can pass additional arguments, they will be passed to pytest: + +```bash +docker-compose run --rm onetl ./run_tests.sh -m mongodb -lsx -vvvv --log-cli-level=INFO +``` + +You can run interactive bash session and use it: + +```bash +docker-compose run --rm onetl bash + +./run_tests.sh -m mongodb -lsx -vvvv --log-cli-level=INFO +``` + +See logs of test container: + +```bash +docker-compose logs -f onetl +``` + +Stop all containers and remove created volumes: + +```bash +docker-compose --profile all down -v +``` + +#### Without docker-compose + +#### WARNING +To run HDFS tests locally you should add the following line to your `/etc/hosts` (file path depends on OS): + +```default +# HDFS server returns container hostname as connection address, causing error in DNS resolution +127.0.0.1 hdfs +``` + +#### NOTE +To run Oracle tests you need to install [Oracle instantclient](https://www.oracle.com/database/technologies/instant-client.html), +and pass its path to `ONETL_ORA_CLIENT_PATH` and `LD_LIBRARY_PATH` environment variables, +e.g. `ONETL_ORA_CLIENT_PATH=/path/to/client64/lib`. + +It may also require to add the same path into `LD_LIBRARY_PATH` environment variable + +#### NOTE +To run Greenplum tests, you should: + +* Download [VMware Greenplum connector for Spark](https://onetl.readthedocs.io/en/latest/connection/db_connection/greenplum/prerequisites.html) +* Either move it to `~/.ivy2/jars/`, or pass file path to `CLASSPATH` +* Set environment variable `ONETL_GP_PACKAGE_VERSION=local`. + +Start all containers with dependencies: + +```bash +docker-compose --profile all up -d +``` + +You can run limited set of dependencies: + +```bash +docker-compose --profile mongodb up -d +``` + +Load environment variables with connection properties: + +```bash +source .env.local +``` + +Run tests: + +```bash +./run_tests.sh +``` + +You can pass additional arguments, they will be passed to pytest: + +```bash +./run_tests.sh -m mongodb -lsx -vvvv --log-cli-level=INFO +``` + +Stop all containers and remove created volumes: + +```bash +docker-compose --profile all down -v +``` + +### Build documentation + +Build documentation using Sphinx: + +```bash +cd docs +make html +``` + +Then open in browser `docs/_build/index.html`. + +## Review process + +Please create a new GitHub issue for any significant changes and +enhancements that you wish to make. Provide the feature you would like +to see, why you need it, and how it will work. Discuss your ideas +transparently and get community feedback before proceeding. + +Significant Changes that you wish to contribute to the project should be +discussed first in a GitHub issue that clearly outlines the changes and +benefits of the feature. + +Small Changes can directly be crafted and submitted to the GitHub +Repository as a Pull Request. + +### Create pull request + +Commit your changes: + +```bash +git commit -m "Commit message" +git push +``` + +Then open Github interface and [create pull request](https://docs.github.com/en/get-started/quickstart/contributing-to-projects#making-a-pull-request). +Please follow guide from PR body template. + +After pull request is created, it get a corresponding number, e.g. 123 (`pr_number`). + +### Write release notes + +`onETL` uses [towncrier](https://pypi.org/project/towncrier/) +for changelog management. + +To submit a change note about your PR, add a text file into the +[docs/changelog/next_release](./next_release) folder. It should contain an +explanation of what applying this PR will change in the way +end-users interact with the project. One sentence is usually +enough but feel free to add as many details as you feel necessary +for the users to understand what it means. + +**Use the past tense** for the text in your fragment because, +combined with others, it will be a part of the “news digest” +telling the readers **what changed** in a specific version of +the library *since the previous version*. + +You should also use +reStructuredText syntax for highlighting code (inline or block), +linking parts of the docs or external sites. +If you wish to sign your change, feel free to add `-- by +:user:`github-username`` at the end (replace `github-username` +with your own!). + +Finally, name your file following the convention that Towncrier +understands: it should start with the number of an issue or a +PR followed by a dot, then add a patch type, like `feature`, +`doc`, `misc` etc., and add `.rst` as a suffix. If you +need to add more than one fragment, you may add an optional +sequence number (delimited with another period) between the type +and the suffix. + +In general the name will follow `..rst` pattern, +where the categories are: + +- `feature`: Any new feature +- `bugfix`: A bug fix +- `improvement`: An improvement +- `doc`: A change to the documentation +- `dependency`: Dependency-related changes +- `misc`: Changes internal to the repo like CI, test and build changes + +A pull request may have more than one of these components, for example +a code change may introduce a new feature that deprecates an old +feature, in which case two fragments should be added. It is not +necessary to make a separate documentation fragment for documentation +changes accompanying the relevant code changes. + +#### Examples for adding changelog entries to your Pull Requests + +```rst +Added a ``:github:user:`` role to Sphinx config -- by :github:user:`someuser` +``` + +```rst +Fixed behavior of ``WebDAV`` connector -- by :github:user:`someuser` +``` + +```rst +Added support of ``timeout`` in ``S3`` connector +-- by :github:user:`someuser`, :github:user:`anotheruser` and :github:user:`otheruser` +``` + +#### How to skip change notes check? + +Just add `ci:skip-changelog` label to pull request. + +#### Release Process + +Before making a release from the `develop` branch, follow these steps: + +1. Checkout to `develop` branch and update it to the actual state + +```bash +git checkout develop +git pull -p +``` + +1. Backup `NEXT_RELEASE.rst` + +```bash +cp "docs/changelog/NEXT_RELEASE.rst" "docs/changelog/temp_NEXT_RELEASE.rst" +``` + +1. Build the Release notes with Towncrier + +```bash +VERSION=$(cat onetl/VERSION) +towncrier build "--version=${VERSION}" --yes +``` + +1. Change file with changelog to release version number + +```bash +mv docs/changelog/NEXT_RELEASE.rst "docs/changelog/${VERSION}.rst" +``` + +1. Remove content above the version number heading in the `${VERSION}.rst` file + +```bash +awk '!/^.*towncrier release notes start/' "docs/changelog/${VERSION}.rst" > temp && mv temp "docs/changelog/${VERSION}.rst" +``` + +1. Update Changelog Index + +```bash +awk -v version=${VERSION} '/DRAFT/{print;print " " version;next}1' docs/changelog/index.rst > temp && mv temp docs/changelog/index.rst +``` + +1. Restore `NEXT_RELEASE.rst` file from backup + +```bash +mv "docs/changelog/temp_NEXT_RELEASE.rst" "docs/changelog/NEXT_RELEASE.rst" +``` + +1. Commit and push changes to `develop` branch + +```bash +git add . +git commit -m "Prepare for release ${VERSION}" +git push +``` + +1. Merge `develop` branch to `master`, **WITHOUT** squashing + +```bash +git checkout master +git pull +git merge develop +git push +``` + +1. Add git tag to the latest commit in `master` branch + +```bash +git tag "$VERSION" +git push origin "$VERSION" +``` + +1. Update version in `develop` branch **after release**: + +```bash +git checkout develop + +NEXT_VERSION=$(echo "$VERSION" | awk -F. '/[0-9]+\./{$NF++;print}' OFS=.) +echo "$NEXT_VERSION" > onetl/VERSION + +git add . +git commit -m "Bump version" +git push +``` diff --git a/mddocs/db/db_reader.md b/mddocs/db/db_reader.md new file mode 100644 index 000000000..29209279a --- /dev/null +++ b/mddocs/db/db_reader.md @@ -0,0 +1,346 @@ + + +# DB Reader + +| [`DBReader`](#onetl.db.db_reader.db_reader.DBReader) | Allows you to read data from a table with specified database connection and parameters, and return its content as Spark dataframe. | +|------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------| +| [`DBReader.run`](#onetl.db.db_reader.db_reader.DBReader.run)() | Reads data from source table and saves as Spark dataframe. | +| [`DBReader.has_data`](#onetl.db.db_reader.db_reader.DBReader.has_data)() | Returns `True` if there is some data in the source, `False` otherwise. | +| [`DBReader.raise_if_no_data`](#onetl.db.db_reader.db_reader.DBReader.raise_if_no_data)() | Raises exception `NoDataError` if source does not contain any data. | + +### *class* onetl.db.db_reader.db_reader.DBReader(\*, connection: BaseDBConnection, table: str, columns: ConstrainedListValue[str] | None = None, where: Any | None = None, hint: Any | None = None, df_schema: StructType | None = None, hwm_column: str | tuple | None = None, hwm_expression: str | None = None, hwm: AutoDetectHWM | ColumnHWM | KeyValueHWM | None = None, options: GenericOptions | None = None) + +Allows you to read data from a table with specified database connection +and parameters, and return its content as Spark dataframe. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### NOTE +DBReader can return different results depending on [Read Strategies](../strategy/index.md#strategy) + +#### NOTE +This class operates with only one source at a time. It does NOT support executing queries +to multiple source, like `SELECT ... JOIN`. + +#### Versionadded +Added in version 0.1.0. + +#### Versionchanged +Changed in version 0.8.0: Moved `onetl.core.DBReader` → `onetl.db.DBReader` + +* **Parameters:** + **connection** + : Class which contains DB connection properties. See [DB Connections](../connection/db_connection/index.md#db-connections) section + + **source** + : Table/collection/etc name to read data from. +
+ If connection has schema support, you need to specify the full name of the source + including the schema, e.g. `schema.name`. +
+ #### Versionchanged + Changed in version 0.7.0: Renamed `table` → `source` + + **columns** + : The list of columns to be read. +
+ If RDBMS supports any kind of expressions, you can pass them too. + ```python + columns = [ + "mycolumn", + "another_column as alias", + "count(*) over ()", + "some(function) as alias2", + ] + ``` +
+ #### NOTE + Some sources does not have columns. +
+ #### NOTE + It is recommended to pass column names explicitly to avoid selecting too many columns, + and to avoid adding unexpected columns to dataframe if source DDL is changed. +
+ #### Deprecated + Deprecated since version 0.10.0: Syntax `DBReader(columns="col1, col2")` (string instead of list) is not supported, + and will be removed in v1.0.0 + + **where** + : Custom `where` for SQL query or MongoDB pipeline. +
+ `where` syntax depends on the source. For example, SQL sources + accept `where` as a string, but MongoDB sources accept `where` as a dictionary. + ```python + # SQL database connection + where = "column_1 > 2" +
+ # MongoDB connection + where = { + "col_1": {"$gt": 1, "$lt": 100}, + "col_2": {"$gt": 2}, + "col_3": {"$eq": "hello"}, + } + ``` +
+ #### NOTE + Some sources does not support data filtering. + + **hwm** + : HWM class to be used as [HWM](https://etl-entities.readthedocs.io/en/stable/hwm/index.html) value. + ```python + hwm = DBReader.AutoDetectHWM( + name="some_unique_hwm_name", + expression="hwm_column", + ) + ``` +
+ HWM value will be fetched using `hwm_column` SQL query. +
+ If you want to use some SQL expression as HWM value, you can use it as well: + ```python + hwm = DBReader.AutoDetectHWM( + name="some_unique_hwm_name", + expression="cast(hwm_column_orig as date)", + ) + ``` +
+ #### NOTE + Some sources does not support passing expressions and can be used only with column/field + names which present in the source. +
+ #### Versionchanged + Changed in version 0.10.0: Replaces deprecated `hwm_column` and `hwm_expression` attributes + + **hint** + : Hint expression used for querying the data. +
+ `hint` syntax depends on the source. For example, SQL sources + accept `hint` as a string, but MongoDB sources accept `hint` as a dictionary. + ```python + # SQL database connection + hint = "index(myschema.mytable mycolumn)" +
+ # MongoDB connection + hint = { + "mycolumn": 1, + } + ``` +
+ #### NOTE + Some sources does not support hints. + + **df_schema** + : Spark DataFrame schema, used for proper type casting of the rows. + ```python + from pyspark.sql.types import ( + DoubleType, + IntegerType, + StringType, + StructField, + StructType, + TimestampType, + ) +
+ df_schema = StructType( + [ + StructField("_id", IntegerType()), + StructField("text_string", StringType()), + StructField("hwm_int", IntegerType()), + StructField("hwm_datetime", TimestampType()), + StructField("float_value", DoubleType()), + ], + ) +
+ reader = DBReader( + connection=connection, + source="fiddle.dummy", + df_schema=df_schema, + ) + ``` +
+ #### NOTE + Some sources does not support passing dataframe schema. + + **options** + : Spark read options, like partitioning mode. + ```python + Postgres.ReadOptions( + partitioningMode="hash", + partitionColumn="some_column", + numPartitions=20, + fetchsize=1000, + ) + ``` +
+ #### NOTE + Some sources does not support reading options. + +### Examples + +Minimal example + +```py +from onetl.db import DBReader +from onetl.connection import Postgres + +postgres = Postgres(...) + +# create reader +reader = DBReader(connection=postgres, source="fiddle.dummy") + +# read data from table "fiddle.dummy" +df = reader.run() +``` + +With custom reading options + +```py +from onetl.connection import Postgres +from onetl.db import DBReader + +postgres = Postgres(...) +options = Postgres.ReadOptions(sessionInitStatement="select 300", fetchsize="100") + +# create reader and pass some options to the underlying connection object +reader = DBReader(connection=postgres, source="fiddle.dummy", options=options) + +# read data from table "fiddle.dummy" +df = reader.run() +``` + +Full example + +```py +from onetl.db import DBReader +from onetl.connection import Postgres + +postgres = Postgres(...) +options = Postgres.ReadOptions(sessionInitStatement="select 300", fetchsize="100") + +# create reader with specific columns, rows filter +reader = DBReader( + connection=postgres, + source="default.test", + where="d_id > 100", + hint="NOWAIT", + columns=["d_id", "d_name", "d_age"], + options=options, +) + +# read data from table "fiddle.dummy" +df = reader.run() +``` + +Incremental reading + +See [Read Strategies](../strategy/index.md#strategy) for more examples + +```python +from onetl.strategy import IncrementalStrategy + +... + +reader = DBReader( + connection=postgres, + source="fiddle.dummy", + hwm=DBReader.AutoDetectHWM( # mandatory for IncrementalStrategy + name="some_unique_hwm_name", + expression="d_age", + ), +) + +# read data from table "fiddle.dummy" +# but only with new rows (`WHERE d_age > previous_hwm_value`) +with IncrementalStrategy(): + df = reader.run() +``` + + + +#### has_data() → bool + +Returns `True` if there is some data in the source, `False` otherwise. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### NOTE +This method can return different results depending on [Read Strategies](../strategy/index.md#strategy) + +#### WARNING +If [hwm](https://etl-entities.readthedocs.io/en/stable/hwm/index.html) is used, then method should be called inside [Read Strategies](../strategy/index.md#strategy) context. And vise-versa, if HWM is not used, this method should not be called within strategy. + +#### Versionadded +Added in version 0.10.0. + +* **Raises:** + RuntimeError + : Current strategy is not compatible with HWM parameter. + +### Examples + +```python +reader = DBReader(...) + +# handle situation when there is no data in the source +if reader.has_data(): + df = reader.run() +else: + # implement your handling logic here + ... +``` + + + +#### raise_if_no_data() → None + +Raises exception `NoDataError` if source does not contain any data. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### NOTE +This method can return different results depending on [Read Strategies](../strategy/index.md#strategy) + +#### WARNING +If [hwm](https://etl-entities.readthedocs.io/en/stable/hwm/index.html) is used, then method should be called inside [Read Strategies](../strategy/index.md#strategy) context. And vise-versa, if HWM is not used, this method should not be called within strategy. + +#### Versionadded +Added in version 0.10.0. + +* **Raises:** + RuntimeError + : Current strategy is not compatible with HWM parameter. + + `onetl.exception.NoDataError` + : There is no data in source. + +### Examples + +```python +reader = DBReader(...) + +# ensure that there is some data in the source before reading it using Spark +reader.raise_if_no_data() +``` + + + +#### run() → DataFrame + +Reads data from source table and saves as Spark dataframe. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### NOTE +This method can return different results depending on [Read Strategies](../strategy/index.md#strategy) + +#### WARNING +If [hwm](https://etl-entities.readthedocs.io/en/stable/hwm/index.html) is used, then method should be called inside [Read Strategies](../strategy/index.md#strategy) context. And vise-versa, if HWM is not used, this method should not be called within strategy. + +#### Versionadded +Added in version 0.1.0. + +* **Returns:** + **df** + : Spark dataframe + +### Examples + +Read data to Spark dataframe: + +```python +df = reader.run() +``` + + diff --git a/mddocs/db/db_writer.md b/mddocs/db/db_writer.md new file mode 100644 index 000000000..91b8a16fb --- /dev/null +++ b/mddocs/db/db_writer.md @@ -0,0 +1,100 @@ + + +# DB Writer + +| [`DBWriter`](#onetl.db.db_writer.db_writer.DBWriter) | Class specifies schema and table where you can write your dataframe. | +|------------------------------------------------------------------|------------------------------------------------------------------------| +| [`DBWriter.run`](#onetl.db.db_writer.db_writer.DBWriter.run)(df) | Method for writing your df to specified target. | + +### *class* onetl.db.db_writer.db_writer.DBWriter(\*, connection: BaseDBConnection, table: str, options: GenericOptions | None = None) + +Class specifies schema and table where you can write your dataframe. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### Versionadded +Added in version 0.1.0. + +#### Versionchanged +Changed in version 0.8.0: Moved `onetl.core.DBReader` → `onetl.db.DBReader` + +* **Parameters:** + **connection** + : Class which contains DB connection properties. See [DB Connections](../connection/db_connection/index.md#db-connections) section. + + **target** + : Table/collection/etc name to write data to. +
+ If connection has schema support, you need to specify the full name of the source + including the schema, e.g. `schema.name`. +
+ #### Versionchanged + Changed in version 0.7.0: Renamed `table` → `target` + + **options** + : Spark write options. Can be in form of special `WriteOptions` object or a dict. +
+ For example: + `{"if_exists": "replace_entire_table", "compression": "snappy"}` + or + `Hive.WriteOptions(if_exists="replace_entire_table", compression="snappy")` +
+ #### NOTE + Some sources does not support writing options. + +### Examples + +Minimal example + +```py +from onetl.connection import Postgres +from onetl.db import DBWriter + +postgres = Postgres(...) + +writer = DBWriter( + connection=postgres, + target="fiddle.dummy", +) +``` + +With custom write options + +```py +from onetl.connection import Postgres +from onetl.db import DBWriter + +postgres = Postgres(...) + +options = Postgres.WriteOptions(if_exists="replace_entire_table", batchsize=1000) + +writer = DBWriter( + connection=postgres, + target="fiddle.dummy", + options=options, +) +``` + + + +#### run(df: DataFrame) → None + +Method for writing your df to specified target. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### NOTE +Method does support only **batching** DataFrames. + +#### Versionadded +Added in version 0.1.0. + +* **Parameters:** + **df** + : Spark dataframe + +### Examples + +Write dataframe to target: + +```python +writer.run(df) +``` + + diff --git a/mddocs/db/index.md b/mddocs/db/index.md new file mode 100644 index 000000000..861a539ff --- /dev/null +++ b/mddocs/db/index.md @@ -0,0 +1,6 @@ + + + DB classes + +* [DB Reader](db_reader.md) +* [DB Writer](db_writer.md) diff --git a/mddocs/file/file_downloader/file_downloader.md b/mddocs/file/file_downloader/file_downloader.md new file mode 100644 index 000000000..a1c2f361c --- /dev/null +++ b/mddocs/file/file_downloader/file_downloader.md @@ -0,0 +1,352 @@ + + +# File Downloader + +| [`FileDownloader`](#onetl.file.file_downloader.file_downloader.FileDownloader) | Allows you to download files from a remote source with specified file connection and parameters, and return an object with download result summary. | +|--------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------| +| [`FileDownloader.run`](#onetl.file.file_downloader.file_downloader.FileDownloader.run)([files]) | Method for downloading files from source to local directory. | +| [`FileDownloader.view_files`](#onetl.file.file_downloader.file_downloader.FileDownloader.view_files)() | Get file list in the `source_path`, after `filter`, `limit` and `hwm` applied (if any). | + +### *class* onetl.file.file_downloader.file_downloader.FileDownloader(\*, connection: ~onetl.base.base_file_connection.BaseFileConnection, local_path: ~onetl.impl.local_path.LocalPath, source_path: ~onetl.impl.remote_path.RemotePath | None = None, temp_path: ~onetl.impl.local_path.LocalPath | None = None, filter: ~typing.List[~onetl.base.base_file_filter.BaseFileFilter] = None, limit: ~typing.List[~onetl.base.base_file_limit.BaseFileLimit] = None, hwm: ~etl_entities.hwm.file.file_hwm.FileHWM | None = None, hwm_type: ~typing.Type[~etl_entities.old_hwm.file_list_hwm.FileListHWM] | str | None = None, options: ~onetl.file.file_downloader.options.FileDownloaderOptions = FileDownloaderOptions(if_exists=, delete_source=False, workers=1)) + +Allows you to download files from a remote source with specified file connection +and parameters, and return an object with download result summary. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### NOTE +FileDownloader can return different results depending on [Read Strategies](../../strategy/index.md#strategy) + +#### NOTE +This class is used to download files **only** from remote directory to the local one. + +It does NOT support direct file transfer between filesystems, like `FTP -> SFTP`. +You should use FileDownloader + [File Uploader](../file_uploader/file_uploader.md#file-uploader) to implement `FTP -> local dir -> SFTP`. + +#### Versionadded +Added in version 0.1.0. + +#### Versionchanged +Changed in version 0.8.0: Moved `onetl.core.FileDownloader` → `onetl.file.FileDownloader` + +* **Parameters:** + **connection** + : Class which contains File system connection properties. See [File Connections](../../connection/file_connection/index.md#file-connections) section. + + **local_path** + : Local path where you download files + + **source_path** + : Remote path to download files from. +
+ Could be `None`, but only if you pass absolute file paths directly to + [`run`](#onetl.file.file_downloader.file_downloader.FileDownloader.run) method + + **temp_path** + : If set, this path will be used for downloading a file, and then renaming it to the target file path. + If `None` is passed, files are downloaded directly to `target_path`. +
+ #### WARNING + In case of production ETL pipelines, please set a value for `temp_path` (NOT `None`). + This allows to properly handle download interruption, + without creating half-downloaded files in the target, + because unlike file download, `rename` call is atomic. +
+ #### WARNING + In case of connections like SFTP or FTP, which can have multiple underlying filesystems, + please pass to `temp_path` path on the SAME filesystem as `target_path`. + Otherwise instead of `rename`, remote OS will move file between filesystems, + which is NOT atomic operation. +
+ #### Versionadded + Added in version 0.5.0. + + **filters** + : Return only files/directories matching these filters. See [File Filters](../file_filters/index.md#file-filters) +
+ #### Versionchanged + Changed in version 0.3.0: Replaces old `source_path_pattern: str` and `exclude_dirs: str` options. +
+ #### Versionchanged + Changed in version 0.8.0: Renamed `filter` → `filters` + + **limits** + : Apply limits to the list of files/directories, and stop if one of the limits is reached. + See [File Limits](../file_limits/index.md#file-limits) +
+ #### Versionadded + Added in version 0.4.0. +
+ #### Versionchanged + Changed in version 0.8.0: Renamed `limit` → `limits` + + **options** + : File downloading options. See [`FileDownloader.Options`](options.md#onetl.file.file_downloader.options.FileDownloaderOptions) +
+ #### Versionadded + Added in version 0.3.0. + + **hwm** + : HWM class to detect changes in incremental run. See [File HWM](https://etl-entities.readthedocs.io/en/stable/hwm/file/index.html) +
+ #### WARNING + Used only in [`IncrementalStrategy`](../../strategy/incremental_strategy.md#onetl.strategy.incremental_strategy.IncrementalStrategy). +
+ #### Versionadded + Added in version 0.5.0. +
+ #### Versionchanged + Changed in version 0.10.0: Replaces deprecated `hwm_type` attribute + +### Examples + +Minimal example + +```py +from onetl.connection import SFTP +from onetl.file import FileDownloader + +sftp = SFTP(...) + +# create downloader +downloader = FileDownloader( + connection=sftp, + source_path="/path/to/remote/source", + local_path="/path/to/local", +) + +# download files to "/path/to/local" +downloader.run() +``` + +Full example + +```py +from onetl.connection import SFTP +from onetl.file import FileDownloader +from onetl.file.filter import Glob, ExcludeDir +from onetl.file.limit import MaxFilesCount, TotalFileSize + +sftp = SFTP(...) + +# create downloader with a bunch of options +downloader = FileDownloader( + connection=sftp, + source_path="/path/to/remote/source", + local_path="/path/to/local", + temp_path="/tmp", + filters=[ + Glob("*.txt"), + ExcludeDir("/path/to/remote/source/exclude_dir"), + ], + limits=[MaxFilesCount(100), TotalFileSize("10GiB")], + options=FileDownloader.Options(delete_source=True, if_exists="replace_file"), +) + +# download files to "/path/to/local", +# but only *.txt, +# excluding files from "/path/to/remote/source/exclude_dir" directory +# and stop before downloading 101 file +downloader.run() +``` + +Incremental download (by tracking list of file paths) + +```py +from onetl.connection import SFTP +from onetl.file import FileDownloader +from onetl.strategy import IncrementalStrategy +from etl_entities.hwm import FileListHWM + +sftp = SFTP(...) + +# create downloader +downloader = FileDownloader( + connection=sftp, + source_path="/path/to/remote/source", + local_path="/path/to/local", + hwm=FileListHWM( # mandatory for IncrementalStrategy + name="my_unique_hwm_name", + ), +) + +# download files to "/path/to/local", but only added since previous run +with IncrementalStrategy(): + downloader.run() +``` + +Incremental download (by tracking file modification time) + +```py +from onetl.connection import SFTP +from onetl.file import FileDownloader +from onetl.strategy import IncrementalStrategy +from etl_entities.hwm import FileModifiedTimeHWM + +sftp = SFTP(...) + +# create downloader +downloader = FileDownloader( + connection=sftp, + source_path="/path/to/remote/source", + local_path="/path/to/local", + hwm=FileModifiedTimeHWM( # mandatory for IncrementalStrategy + name="my_unique_hwm_name", + ), +) + +# download files to "/path/to/local", but only modified/created since previous run +with IncrementalStrategy(): + downloader.run() +``` + + + +#### run(files: Iterable[str | PathLike] | None = None) → [DownloadResult](result.md#onetl.file.file_downloader.result.DownloadResult) + +Method for downloading files from source to local directory. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### NOTE +This method can return different results depending on [Read Strategies](../../strategy/index.md#strategy) + +#### Versionadded +Added in version 0.1.0. + +* **Parameters:** + **files** + : File list to download. +
+ If empty, download files from `source_path` to `local_path`, + applying `filter`, `limit` and `hwm` to each one (if set). +
+ If not, download to `local_path` **all** input files, **ignoring** + filters, limits and HWM. +
+ #### Versionadded + Added in version 0.3.0. +* **Returns:** + `DownloadResult` + : Download result object +* **Raises:** + `onetl.exception.DirectoryNotFoundError` + : `source_path` does not found + + NotADirectoryError + : `source_path` or `local_path` is not a directory + +### Examples + +Download files from `source_path` to `local_path`: + +```pycon +>>> from onetl.file import FileDownloader +>>> downloader = FileDownloader(source_path="/remote", local_path="/local", ...) +>>> download_result = downloader.run() +>>> download_result +DownloadResult( + successful=FileSet([ + LocalPath("/local/file1.txt"), + LocalPath("/local/file2.txt"), + # directory structure is preserved + LocalPath("/local/nested/path/file3.txt"), + ]), + failed=FileSet([ + FailedRemoteFile("/remote/failed.file"), + ]), + skipped=FileSet([ + RemoteFile("/remote/already.exists"), + ]), + missing=FileSet([ + RemotePath("/remote/missing.file"), + ]), +) +``` + +Download only certain files from `source_path`: + +```pycon +>>> from onetl.file import FileDownloader +>>> downloader = FileDownloader(source_path="/remote", local_path="/local", ...) +>>> # paths could be relative or absolute, but all should be in "/remote" +>>> download_result = downloader.run( +... [ +... "/remote/file1.txt", +... "/remote/nested/path/file3.txt", +... # excluding "/remote/file2.txt" +... ] +... ) +>>> download_result +DownloadResult( + successful=FileSet([ + LocalPath("/local/file1.txt"), + # directory structure is preserved + LocalPath("/local/nested/path/file3.txt"), + ]), + failed=FileSet([]), + skipped=FileSet([]), + missing=FileSet([]), +) +``` + +Download certain files from any folder: + +```pycon +>>> from onetl.file import FileDownloader +>>> downloader = FileDownloader(local_path="/local", ...) # no source_path set +>>> # only absolute paths +>>> download_result = downloader.run( +... [ +... "/remote/file1.txt", +... "/any/nested/path/file2.txt", +... ] +... ) +>>> download_result +DownloadResult( + successful=FileSet([ + LocalPath("/local/file1.txt"), + # directory structure is NOT preserved without source_path + LocalPath("/local/file2.txt"), + ]), + failed=FileSet([]), + skipped=FileSet([]), + missing=FileSet([]), +) +``` + + + +#### view_files() → FileSet[RemoteFile] + +Get file list in the `source_path`, +after `filter`, `limit` and `hwm` applied (if any). [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### NOTE +This method can return different results depending on [Read Strategies](../../strategy/index.md#strategy) + +#### Versionadded +Added in version 0.3.0. + +* **Returns:** + FileSet[RemoteFile] + : Set of files in `source_path`, which will be downloaded by [`run`](#onetl.file.file_downloader.file_downloader.FileDownloader.run) method +* **Raises:** + `onetl.exception.DirectoryNotFoundError` + : `source_path` does not found + + NotADirectoryError + : `source_path` is not a directory + +### Examples + +View files: + +```pycon +>>> from onetl.file import FileDownloader +>>> downloader = FileDownloader(source_path="/remote", ...) +>>> downloader.view_files() +FileSet([ + RemoteFile("/remote/file1.txt"), + RemoteFile("/remote/file3.txt"), + RemoteFile("/remote/nested/file3.txt"), +]) +``` + + diff --git a/mddocs/file/file_downloader/index.md b/mddocs/file/file_downloader/index.md new file mode 100644 index 000000000..3a7b4164b --- /dev/null +++ b/mddocs/file/file_downloader/index.md @@ -0,0 +1,9 @@ + + +# File Downloader + +# File Downloader + +* [File Downloader](file_downloader.md) +* [File Downloader Options](options.md) +* [File Downloader Result](result.md) diff --git a/mddocs/file/file_downloader/options.md b/mddocs/file/file_downloader/options.md new file mode 100644 index 000000000..c40baf9b8 --- /dev/null +++ b/mddocs/file/file_downloader/options.md @@ -0,0 +1,67 @@ + + +# File Downloader Options + +### *pydantic model* onetl.file.file_downloader.options.FileDownloaderOptions + +File downloading options. + +#### Versionadded +Added in version 0.3.0. + +### Examples + +```python +from onetl.file import FileDownloader + +options = FileDownloader.Options( + if_exists="replace_entire_directory", + delete_source=True, + workers=4, +) +``` + + + +#### *field* if_exists *: FileExistBehavior* *= FileExistBehavior.ERROR* *(alias 'mode')* + +How to handle existing files in the local directory. + +Possible values: +: * `error` (default) - mark file as failed + * `ignore` - mark file as skipped + * `replace_file` - replace existing file with a new one + * `replace_entire_directory` - delete local directory content before downloading files + +#### Versionchanged +Changed in version 0.9.0: Renamed `mode` → `if_exists` + + + +#### *field* delete_source *: bool* *= False* + +If `True`, remove source file after successful download. + +If download failed, file will left intact. + +#### Versionadded +Added in version 0.2.0. + +#### Versionchanged +Changed in version 0.3.0: Move `FileUploader.delete_local` to `FileUploaderOptions` + + + +#### *field* workers *: int* *= 1* + +Number of workers to create for parallel file download. + +1 (default) means files will me downloaded sequentially. +2 or more means files will be downloaded in parallel workers. + +Recommended value is `min(32, os.cpu_count() + 4)`, e.g. `5`. + +#### Versionadded +Added in version 0.8.1. + + diff --git a/mddocs/file/file_downloader/result.md b/mddocs/file/file_downloader/result.md new file mode 100644 index 000000000..45b94233a --- /dev/null +++ b/mddocs/file/file_downloader/result.md @@ -0,0 +1,550 @@ + + +# File Downloader Result + +### *class* onetl.file.file_downloader.result.DownloadResult(\*, successful: FileSet[LocalPath] = None, failed: FileSet[FailedRemoteFile] = None, skipped: FileSet[RemoteFile] = None, missing: FileSet[RemotePath] = None) + +Representation of file download result. + +Container for file paths, divided into certain categories: + +* [`successful`](#onetl.file.file_downloader.result.DownloadResult.successful) +* [`failed`](#onetl.file.file_downloader.result.DownloadResult.failed) +* [`skipped`](#onetl.file.file_downloader.result.DownloadResult.skipped) +* [`missing`](#onetl.file.file_downloader.result.DownloadResult.missing) + +#### Versionadded +Added in version 0.3.0. + +### Examples + +```pycon +>>> from onetl.file import FileDownloader +>>> downloader = FileDownloader(local_path="/local", ...) +>>> download_result = downloader.run( +... [ +... "/remote/file1", +... "/remote/file2", +... "/failed/file", +... "/existing/file", +... "/missing/file", +... ] +... ) +>>> download_result +DownloadResult( + successful=FileSet([ + LocalPath("/local/file1"), + LocalPath("/local/file2"), + ]), + failed=FileSet([ + FailedLocalFile("/failed/file") + ]), + skipped=FileSet([ + RemoteFile("/existing/file") + ]), + missing=FileSet([ + RemotePath("/missing/file") + ]), +) +``` + + + +#### *property* details *: str* + +Return detailed information about files in the result object + +### Examples + +```pycon +>>> from onetl.impl import FailedRemoteFile, LocalPath, RemoteFile, RemotePathStat +>>> from onetl.exception import NotAFileError +>>> from onetl.file.file_result import FileResult +>>> file_result1 = FileResult( +... successful={ +... RemoteFile("/local/file", stats=RemotePathStat(st_size=1024)), +... RemoteFile("/local/another.file", stats=RemotePathStat(st_size=1024)), +... }, +... failed={ +... FailedRemoteFile( +... path="/remote/file1", +... stats=RemotePathStat(st_size=0), +... exception=NotAFileError("'/remote/file1' is not a file"), +... ), +... FailedRemoteFile( +... path="/remote/file2", +... stats=RemotePathStat(st_size=0), +... exception=PermissionError("'/remote/file2': [Errno 13] Permission denied"), +... ), +... }, +... skipped={LocalPath("/skipped/file1"), LocalPath("/skipped/file2")}, +... missing={LocalPath("/missing/file1"), LocalPath("/missing/file2")}, +... ) +>>> print(file_result1.details) +Total: 8 files (size='2.0 kB') + +Successful 2 files (size='2.0 kB'): + '/local/another.file' (size='1.0 kB') + '/local/file' (size='1.0 kB') + +Failed 2 files (size='0 Bytes'): + '/remote/file2' (size='0 Bytes') + PermissionError("'/remote/file2': [Errno 13] Permission denied") + '/remote/file1' (size='0 Bytes') + NotAFileError("'/remote/file1' is not a file") + +Skipped 2 files (size='0 Bytes'): + '/skipped/file1' + '/skipped/file2' + +Missing 2 files: + '/missing/file2' + '/missing/file1' +``` + +```pycon +>>> file_result2 = FileResult() +>>> print(file_result2.details) +No successful files + +No failed files + +No skipped files + +No missing files +``` + + + +#### dict(\*, include: 'AbstractSetIntStr' | 'MappingIntStrAny' | None = None, exclude: 'AbstractSetIntStr' | 'MappingIntStrAny' | None = None, by_alias: bool = False, skip_defaults: bool | None = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny + +Generate a dictionary representation of the model, optionally specifying which fields to include or exclude. + + + +#### *field* failed *: FileSet[FailedRemoteFile]* *[Optional]* + +File paths (remote) which were not downloaded because of some failure + + + +#### *property* failed_count *: int* + +Get number of failed files + +### Examples + +```pycon +>>> from onetl.impl import RemoteFile +>>> from onetl.file.file_result import FileResult +>>> file_result = FileResult( +... failed={RemoteFile("/some/file"), RemoteFile("/some/another.file")}, +... ) +>>> file_result.failed_count +2 +``` + + + +#### *property* failed_size *: int* + +Get size (in bytes) of failed files + +### Examples + +```pycon +>>> from onetl.impl import RemoteFile, RemotePathStat +>>> from onetl.file.file_result import FileResult +>>> file_result = FileResult( +... failed={ +... RemoteFile("/some/file", stats=RemotePathStat(st_size=1024)), +... RemoteFile("/some/another.file"), stats=RemotePathStat(st_size=1024)), +... }, +... ) +>>> file_result.failed_size # in bytes +2048 +``` + + + +#### *property* is_empty *: bool* + +Returns `True` if there are no files in `successful`, `failed` and `skipped` attributes + +### Examples + +```pycon +>>> from onetl.impl import LocalPath +>>> from onetl.file.file_result import FileResult +>>> file_result1 = FileResult() +>>> file_result1.is_empty +True +>>> file_result2 = FileResult( +... successful={LocalPath("/local/file"), LocalPath("/local/another.file")}, +... ) +>>> file_result2.is_empty +False +``` + + + +#### json(\*, include: 'AbstractSetIntStr' | 'MappingIntStrAny' | None = None, exclude: 'AbstractSetIntStr' | 'MappingIntStrAny' | None = None, by_alias: bool = False, skip_defaults: bool | None = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Callable[[Any], Any] | None = None, models_as_dict: bool = True, \*\*dumps_kwargs: Any) → str + +Generate a JSON representation of the model, include and exclude arguments as per dict(). + +encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps(). + + + +#### *field* missing *: FileSet[RemotePath]* *[Optional]* + +File paths (remote) which are not present in the remote file system + + + +#### *property* missing_count *: int* + +Get number of missing files + +### Examples + +```pycon +>>> from onetl.impl import LocalPath +>>> from onetl.file.file_result import FileResult +>>> file_result = FileResult( +... missing={LocalPath("/some/file"), LocalPath("/some/another.file")}, +... ) +>>> file_result.missing_count +2 +``` + + + +#### raise_if_contains_zero_size() → None + +Raise exception if `successful` attribute contains a file with zero size + +* **Raises:** + ZeroFileSizeError + : `successful` file set contains a file with zero size + +### Examples + +```pycon +>>> from onetl.exception import ZeroFileSizeError +>>> from onetl.impl import LocalPath +>>> from onetl.file.file_result import FileResult +>>> file_result = FileResult( +... successful={ +... LocalPath("/local/empty1.file"), +... LocalPath("/local/empty2.file"), +... LocalPath("/local/normal.file"), +... }, +... ) +>>> file_result.raise_if_contains_zero_size() +Traceback (most recent call last): + ... +onetl.exception.ZeroFileSizeError: 2 files out of 3 have zero size: + '/local/empty1.file' + '/local/empty2.file' +``` + + + +#### raise_if_empty() → None + +Raise exception if there are no files in `successful`, `failed` and `skipped` attributes + +* **Raises:** + EmptyFilesError + : `successful`, `failed` and `skipped` file sets are empty + +### Examples + +```pycon +>>> from onetl.file.file_result import FileResult +>>> file_result = FileResult() +>>> file_result.raise_if_empty() +Traceback (most recent call last): + ... +onetl.exception.EmptyFilesError: There are no files in the result +``` + + + +#### raise_if_failed() → None + +Raise exception if there are some files in `failed` attribute + +* **Raises:** + FailedFilesError + : `failed` file set is not empty + +### Examples + +```pycon +>>> from onetl.impl import FailedRemoteFile, RemotePathStat +>>> from onetl.exception import NotAFileError, FileMissingError +>>> from onetl.file.file_result import FileResult +>>> files_with_exception = [ +... FailedRemoteFile( +... path="/remote/file1", +... stats=RemotePathStat(st_size=0), +... exception=NotAFileError("'/remote/file1' is not a file"), +... ), +... FailedRemoteFile( +... path="/remote/file2", +... stats=RemotePathStat(st_size=0), +... exception=PermissionError("'/remote/file2': [Errno 13] Permission denied"), +... ), +... ] +>>> file_result = FileResult(failed=files_with_exception) +>>> file_result.raise_if_failed() +Traceback (most recent call last) +... +onetl.exception.FailedFilesError: Failed 2 files (size='0 bytes'): + '/remote/file1' (size='0 bytes') + NotAFileError("'/remote/file1' is not a file") + + '/remote/file2' (size='0 Bytes') + PermissionError("'/remote/file2': [Errno 13] Permission denied") +``` + + + +#### raise_if_missing() → None + +Raise exception if there are some files in `missing` attribute + +* **Raises:** + MissingFilesError + : `missing` file set is not empty + +### Examples + +```pycon +>>> from onetl.impl import LocalPath +>>> from onetl.file.file_result import FileResult +>>> file_result = FileResult( +... missing={ +... LocalPath("/missing/file1"), +... LocalPath("/missing/file2"), +... }, +... ) +>>> file_result.raise_if_missing() +Traceback (most recent call last): + ... +onetl.exception.MissingFilesError: Missing 2 files: + '/missing/file1' + '/missing/file2' +``` + + + +#### raise_if_skipped() → None + +Raise exception if there are some files in `skipped` attribute + +* **Raises:** + SkippedFilesError + : `skipped` file set is not empty + +### Examples + +```pycon +>>> from onetl.impl import LocalPath +>>> from onetl.file.file_result import FileResult +>>> file_result = FileResult( +... skipped={ +... LocalPath("/skipped/file1"), +... LocalPath("/skipped/file2"), +... }, +... ) +>>> file_result.raise_if_skipped() +Traceback (most recent call last): + ... +onetl.exception.SkippedFilesError: Skipped 2 files (15 kB): + '/skipped/file1' (10kB) + '/skipped/file2' (5 kB) +``` + + + +#### *field* skipped *: FileSet[RemoteFile]* *[Optional]* + +File paths (remote) which were skipped because of some reason + + + +#### *property* skipped_count *: int* + +Get number of skipped files + +### Examples + +```pycon +>>> from onetl.impl import LocalPath +>>> from onetl.file.file_result import FileResult +>>> file_result = FileResult( +... skipped={LocalPath("/some/file"), LocalPath("/some/another.file")}, +... ) +>>> file_result.skipped_count +2 +``` + + + +#### *property* skipped_size *: int* + +Get size (in bytes) of skipped files + +### Examples + +```pycon +>>> from onetl.impl import LocalPath +>>> from onetl.file.file_result import FileResult +>>> file_result = FileResult( +... skipped={LocalPath("/some/file"), LocalPath("/some/another.file")}, +... ) +>>> file_result.skipped_size # in bytes +1024 +``` + + + +#### *field* successful *: FileSet[LocalPath]* *[Optional]* + +File paths (local) which were downloaded successfully + + + +#### *property* successful_count *: int* + +Get number of successful files + +### Examples + +```pycon +>>> from onetl.impl import LocalPath +>>> from onetl.file.file_result import FileResult +>>> file_result = FileResult( +... successful={LocalPath("/some/file"), LocalPath("/some/another.file")}, +... ) +>>> file_result.successful_count +2 +``` + + + +#### *property* successful_size *: int* + +Get size (in bytes) of successful files + +### Examples + +```pycon +>>> from onetl.impl import LocalPath +>>> from onetl.file.file_result import FileResult +>>> file_result = FileResult( +... successful={LocalPath("/some/file"), LocalPath("/some/another.file")}, +... ) +>>> file_result.successful_size # in bytes +1024 +``` + + + +#### *property* summary *: str* + +Return short summary about files in the result object + +### Examples + +```pycon +>>> from onetl.impl import FailedRemoteFile, LocalPath, RemoteFile, RemotePathStat +>>> from onetl.exception import NotAFileError +>>> from onetl.file.file_result import FileResult +>>> file_result1 = FileResult( +... successful={ +... RemoteFile("/local/file", stats=RemotePathStat(st_size=1024)), +... RemoteFile("/local/another.file", stats=RemotePathStat(st_size=1024)), +... }, +... failed={ +... FailedRemoteFile( +... path="/remote/file1", +... stats=RemotePathStat(st_size=0), +... exception=NotAFileError("'/remote/file1' is not a file"), +... ), +... FailedRemoteFile( +... path="/remote/file2", +... stats=RemotePathStat(st_size=0), +... exception=PermissionError("'/remote/file2': [Errno 13] Permission denied"), +... ), +... }, +... skipped={LocalPath("/skipped/file1"), LocalPath("/skipped/file2")}, +... missing={LocalPath("/missing/file1"), LocalPath("/missing/file2")}, +... ) +>>> print(file_result1.summary) +Total: 8 files (size='2.0 kB') + +Successful: 2 files (size='2.0 kB') + +Failed: 2 files (size='0 Bytes') + +Skipped: 2 files (size='0 Bytes') + +Missing: 2 files +``` + +```pycon +>>> file_result2 = FileResult() +>>> print(file_result2.summary) +No files +``` + + + +#### *property* total_count *: int* + +Get total number of all files + +### Examples + +```pycon +>>> from onetl.impl import RemoteFile +>>> from onetl.file.file_result import FileResult +>>> file_result = FileResult( +... successful={LocalPath("/local/file"), LocalPath("/local/another.file")}, +... failed={RemoteFile("/remote/file"), RemoteFile("/remote/another.file")}, +... skipped={LocalPath("/skipped/file")}, +... missing={LocalPath("/missing/file")}, +... ) +>>> file_result.total_count +6 +``` + + + +#### *property* total_size *: int* + +Get total size (in bytes) of all files + +### Examples + +```pycon +>>> from onetl.impl import RemoteFile, RemotePathStat, LocalPath +>>> from onetl.file.file_result import FileResult +>>> file_result = FileResult( +... successful={LocalPath("/local/file"), LocalPath("/local/another.file")}, +... failed={ +... RemoteFile("/remote/file", stats=RemotePathStat(st_size=1024)), +... RemoteFile("/remote/another.file", stats=RemotePathStat(st_size=1024)) +... }, +... skipped={LocalPath("/skipped/file")}, +... missing={LocalPath("/missing/file")}, +... ) +>>> file_result.total_size # in bytes +4096 +``` + + diff --git a/mddocs/file/file_filters/base.md b/mddocs/file/file_filters/base.md new file mode 100644 index 000000000..c4f8a9adc --- /dev/null +++ b/mddocs/file/file_filters/base.md @@ -0,0 +1,42 @@ + + +# Base interface + +| [`BaseFileFilter`](#onetl.base.base_file_filter.BaseFileFilter)() | Base file filter interface. | +|-----------------------------------------------------------------------------------|------------------------------------------------------------------| +| [`BaseFileFilter.match`](#onetl.base.base_file_filter.BaseFileFilter.match)(path) | Returns `True` if path is matching the filter, `False` otherwise | + +### *class* onetl.base.base_file_filter.BaseFileFilter + +Base file filter interface. + +Filters used by several onETL components, including [File Downloader](../file_downloader/file_downloader.md#file-downloader) and [File Mover](../file_mover/file_mover.md#file-mover), +to determine if a file should be handled or not. + +All filters are stateless. + +#### Versionadded +Added in version 0.8.0. + + + +#### *abstract* match(path: PathProtocol) → bool + +Returns `True` if path is matching the filter, `False` otherwise + +#### Versionadded +Added in version 0.8.0. + +### Examples + +```pycon +>>> from onetl.impl import LocalPath +>>> filter.match(LocalPath("/path/to/file.csv")) +True +>>> filter.match(LocalPath("/path/to/excluded.csv")) +False +>>> filter.match(LocalPath("/path/to/file.csv")) +True +``` + + diff --git a/mddocs/file/file_filters/exclude_dir.md b/mddocs/file/file_filters/exclude_dir.md new file mode 100644 index 000000000..9ad015e8a --- /dev/null +++ b/mddocs/file/file_filters/exclude_dir.md @@ -0,0 +1,47 @@ + + +# ExcludeDir + +### *class* onetl.file.filter.exclude_dir.ExcludeDir(path: str | os.PathLike) + +Filter files or directories which are included in a specific directory. + +#### Versionadded +Added in version 0.8.0: Replaces deprecated `onetl.core.FileFilter` + +* **Parameters:** + **path** + : Path to directory which should be excluded. + +### Examples + +Create exclude dir filter: + +```python +from onetl.file.filter import ExcludeDir + +exclude_dir = ExcludeDir("/export/news_parse/exclude_dir") +``` + + + +#### match(path: PathProtocol) → bool + +Returns `True` if path is matching the filter, `False` otherwise + +#### Versionadded +Added in version 0.8.0. + +### Examples + +```pycon +>>> from onetl.impl import LocalPath +>>> filter.match(LocalPath("/path/to/file.csv")) +True +>>> filter.match(LocalPath("/path/to/excluded.csv")) +False +>>> filter.match(LocalPath("/path/to/file.csv")) +True +``` + + diff --git a/mddocs/file/file_filters/file_filter.md b/mddocs/file/file_filters/file_filter.md new file mode 100644 index 000000000..31946cb36 --- /dev/null +++ b/mddocs/file/file_filters/file_filter.md @@ -0,0 +1,77 @@ + + +# File Filter (legacy) + +### *class* onetl.core.file_filter.file_filter.FileFilter(\*, glob: str | None = None, regexp: Pattern | None = None, exclude_dirs: List[RemotePath] = None) + +Filter files or directories by their path. + +#### Deprecated +Deprecated since version 0.8.0: Use [`Glob`](glob.md#onetl.file.filter.glob.Glob), [`Regexp`](regexp.md#onetl.file.filter.regexp.Regexp) +or [`ExcludeDir`](exclude_dir.md#onetl.file.filter.exclude_dir.ExcludeDir) instead. + +* **Parameters:** + **glob** + : Pattern (e.g. `*.csv`) for which any **file** (only file) path should match +
+ #### WARNING + Mutually exclusive with `regexp` + + **regexp** + : Regular expression (e.g. `\d+\.csv`) for which any **file** (only file) path should match. +
+ If input is a string, regular expression will be compiles using `re.IGNORECASE` and `re.DOTALL` flags +
+ #### WARNING + Mutually exclusive with `glob` + + **exclude_dirs** + : List of directories which should not be a part of a file or directory path + +### Examples + +Create exclude_dir filter: + +```python +from onetl.core import FileFilter + +file_filter = FileFilter(exclude_dirs=["/export/news_parse/exclude_dir"]) +``` + +Create glob filter: + +```python +from onetl.core import FileFilter + +file_filter = FileFilter(glob="*.csv") +``` + +Create regexp filter: + +```python +from onetl.core import FileFilter + +file_filter = FileFilter(regexp=r"\d+\.csv") + +# or + +import re + +file_filter = FileFilter(regexp=re.compile("\d+\.csv")) +``` + +Not allowed: + +```python +from onetl.core import FileFilter + +FileFilter() # will raise ValueError, at least one argument should be passed +``` + + + +#### match(path: PathProtocol) → bool + +False means it does not match the template by which you want to receive files + + diff --git a/mddocs/file/file_filters/file_mtime_filter.md b/mddocs/file/file_filters/file_mtime_filter.md new file mode 100644 index 000000000..9ec51d939 --- /dev/null +++ b/mddocs/file/file_filters/file_mtime_filter.md @@ -0,0 +1,70 @@ + + +# FileModifiedTime + +### *class* onetl.file.filter.file_mtime.FileModifiedTime(\*, since: datetime | None = None, until: datetime | None = None) + +Filter files matching a specified modification time. + +If file modification time (`.stat().st_mtime`) doesn’t match range, it will be excluded. +Doesn’t affect directories or paths without `.stat()` method. + +#### NOTE +Some filesystems return timestamps truncated to whole seconds (without millisecond part). +obj:~since and :obj\`~until\`\` values should be adjusted accordingly. + +#### Versionadded +Added in version 0.13.0. + +* **Parameters:** + **since** + : Minimal allowed file modification time. `None` means no limit. + + **until** + : Maximum allowed file modification time. `None` means no limit. + +### Examples + +Select files modified between start of the day (`00:00:00`) and hour ago: + +```python +from datetime import datetime, timedelta +from onetl.file.filter import FileModifiedTime + +hour_ago = datetime.now() - timedelta(hours=1) +day_start = datetime.now().replace(hour=0, minute=0, second=0, microsecond=0) +file_mtime = FileModifiedTime(since=day_start, until=hour_ago) +``` + +Select only files modified since hour ago: + +```python +from datetime import datetime, timedelta +from onetl.file.filter import FileModifiedTime + +hour_ago = datetime.now() - timedelta(hours=1) +file_mtime = FileModifiedTime(since=hour_ago) +``` + + + +#### match(path: PathProtocol) → bool + +Returns `True` if path is matching the filter, `False` otherwise + +#### Versionadded +Added in version 0.8.0. + +### Examples + +```pycon +>>> from onetl.impl import LocalPath +>>> filter.match(LocalPath("/path/to/file.csv")) +True +>>> filter.match(LocalPath("/path/to/excluded.csv")) +False +>>> filter.match(LocalPath("/path/to/file.csv")) +True +``` + + diff --git a/mddocs/file/file_filters/file_size_filter.md b/mddocs/file/file_filters/file_size_filter.md new file mode 100644 index 000000000..82255b7c8 --- /dev/null +++ b/mddocs/file/file_filters/file_size_filter.md @@ -0,0 +1,74 @@ + + +# FileSizeRange + +### *class* onetl.file.filter.file_size.FileSizeRange(\*, min: ByteSize | None = None, max: ByteSize | None = None) + +Filter files matching a specified size. + +If file size (`.stat().st_size`) doesn’t match the range, it will be excluded. +Doesn’t affect directories or paths without `.stat()` method. + +#### Versionadded +Added in version 0.13.0. + +#### NOTE +[SI unit prefixes](https://en.wikipedia.org/wiki/Byte#Multiple-byte_units) +means that `1KB` == `1 kilobyte` == `1000 bytes`. +If you need `1024 bytes`, use `1 KiB` == `1 kibibyte`. + +* **Parameters:** + **min** + : Minimal allowed file size. `None` means no limit. + + **max** + : Maximum allowed file size. `None` means no limit. + +### Examples + +Specify min and max file sizes: + +```python +from onetl.file.filter import FileSizeRange + +file_size = FileSizeRange(min="1KiB", max="100MiB") +``` + +Specify only min file size: + +```python +from onetl.file.filter import FileSizeRange + +file_size = FileSizeRange(min="1KiB") +``` + +Specify only max file size: + +```python +from onetl.file.filter import FileSizeRange + +file_size = FileSizeRange(max="100MiB") +``` + + + +#### match(path: PathProtocol) → bool + +Returns `True` if path is matching the filter, `False` otherwise + +#### Versionadded +Added in version 0.8.0. + +### Examples + +```pycon +>>> from onetl.impl import LocalPath +>>> filter.match(LocalPath("/path/to/file.csv")) +True +>>> filter.match(LocalPath("/path/to/excluded.csv")) +False +>>> filter.match(LocalPath("/path/to/file.csv")) +True +``` + + diff --git a/mddocs/file/file_filters/glob.md b/mddocs/file/file_filters/glob.md new file mode 100644 index 000000000..f6b727b77 --- /dev/null +++ b/mddocs/file/file_filters/glob.md @@ -0,0 +1,47 @@ + + +# Glob + +### *class* onetl.file.filter.glob.Glob(pattern: str) + +Filter files or directories with path matching a glob expression. + +#### Versionadded +Added in version 0.8.0: Replaces deprecated `onetl.core.FileFilter` + +* **Parameters:** + **pattern** + : Pattern (e.g. `*.csv`) for which any **file** (only file) path should match + +### Examples + +Create glob filter: + +```python +from onetl.file.filter import Glob + +glob = Glob("*.csv") +``` + + + +#### match(path: PathProtocol) → bool + +Returns `True` if path is matching the filter, `False` otherwise + +#### Versionadded +Added in version 0.8.0. + +### Examples + +```pycon +>>> from onetl.impl import LocalPath +>>> filter.match(LocalPath("/path/to/file.csv")) +True +>>> filter.match(LocalPath("/path/to/excluded.csv")) +False +>>> filter.match(LocalPath("/path/to/file.csv")) +True +``` + + diff --git a/mddocs/file/file_filters/index.md b/mddocs/file/file_filters/index.md new file mode 100644 index 000000000..40155ae9a --- /dev/null +++ b/mddocs/file/file_filters/index.md @@ -0,0 +1,20 @@ + + +# File Filters + +# File filters + +* [Glob](glob.md) +* [Regexp](regexp.md) +* [ExcludeDir](exclude_dir.md) +* [FileSizeRange](file_size_filter.md) +* [FileModifiedTime](file_mtime_filter.md) + +# Legacy + +* [File Filter (legacy)](file_filter.md) + +# For developers + +* [Base interface](base.md) +* [match_all_filters](match_all_filters.md) diff --git a/mddocs/file/file_filters/match_all_filters.md b/mddocs/file/file_filters/match_all_filters.md new file mode 100644 index 000000000..9d09eb421 --- /dev/null +++ b/mddocs/file/file_filters/match_all_filters.md @@ -0,0 +1,37 @@ + + +# match_all_filters + +### onetl.file.filter.match_all_filters.match_all_filters(path: PathProtocol, filters: Iterable[[BaseFileFilter](base.md#onetl.base.base_file_filter.BaseFileFilter)]) → bool + +Check if input path satisfies all the filters. + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **path** + : Path to check. + + **filters** + : Filters to test path against. +* **Returns:** + `True` if path matches all the filters, `False` otherwise. + + If filters are empty, returns `True`. + +### Examples + +```pycon +>>> from onetl.file.filter import Glob, ExcludeDir, match_all_filters +>>> from onetl.impl import RemoteFile, RemotePathStat +>>> filters = [Glob("*.csv"), ExcludeDir("/excluded")] +>>> match_all_filters(RemoteFile("/path/to/file.csv", stats=RemotePathStat()), filters) +True +>>> match_all_filters(RemoteFile("/path/to/file.txt", stats=RemotePathStat()), filters) +False +>>> match_all_filters(RemoteFile("/excluded/path/file.csv", stats=RemotePathStat()), filters) +False +``` + + diff --git a/mddocs/file/file_filters/regexp.md b/mddocs/file/file_filters/regexp.md new file mode 100644 index 000000000..3e252ff74 --- /dev/null +++ b/mddocs/file/file_filters/regexp.md @@ -0,0 +1,59 @@ + + +# Regexp + +### *class* onetl.file.filter.regexp.Regexp(pattern: str) + +Filter files or directories with path matching a regular expression. + +#### Versionadded +Added in version 0.8.0: Replaces deprecated `onetl.core.FileFilter` + +* **Parameters:** + **pattern** + : Regular expression (e.g. `\d+\.csv`) for which any **file** (only file) path should match. +
+ If input is a string, regular expression will be compiles using `re.IGNORECASE` and `re.DOTALL` flags. + +### Examples + +Create regexp filter from string: + +```python +from onetl.file.filter import Regexp + +regexp = Regexp(r"\d+\.csv") +``` + +Create regexp filter from `re.Pattern`: + +```python +import re + +from onetl.file.filter import Regexp + +regexp = Regexp(re.compile(r"\d+\.csv", re.IGNORECASE | re.DOTALL)) +``` + + + +#### match(path: PathProtocol) → bool + +Returns `True` if path is matching the filter, `False` otherwise + +#### Versionadded +Added in version 0.8.0. + +### Examples + +```pycon +>>> from onetl.impl import LocalPath +>>> filter.match(LocalPath("/path/to/file.csv")) +True +>>> filter.match(LocalPath("/path/to/excluded.csv")) +False +>>> filter.match(LocalPath("/path/to/file.csv")) +True +``` + + diff --git a/mddocs/file/file_limits/base.md b/mddocs/file/file_limits/base.md new file mode 100644 index 000000000..4573bb8f4 --- /dev/null +++ b/mddocs/file/file_limits/base.md @@ -0,0 +1,108 @@ + + +# Base interface + +| [`BaseFileLimit`](#onetl.base.base_file_limit.BaseFileLimit)() | Base file limit interface. | +|--------------------------------------------------------------------------------------|-------------------------------------------------| +| [`BaseFileLimit.reset`](#onetl.base.base_file_limit.BaseFileLimit.reset)() | Resets the internal limit state. | +| [`BaseFileLimit.stops_at`](#onetl.base.base_file_limit.BaseFileLimit.stops_at)(path) | Update internal state and return current state. | +| [`BaseFileLimit.is_reached`](#onetl.base.base_file_limit.BaseFileLimit.is_reached) | Check if limit is reached. | + +### *class* onetl.base.base_file_limit.BaseFileLimit + +Base file limit interface. + +Limits used by several onETL components, including [File Downloader](../file_downloader/file_downloader.md#file-downloader) and [File Mover](../file_mover/file_mover.md#file-mover), +to determine if internal loop should be stopped. + +Unlike file filters, limits have internal state which can be updated or reset. + +#### Versionadded +Added in version 0.8.0. + + + +#### *abstract property* is_reached *: bool* + +Check if limit is reached. + +#### Versionadded +Added in version 0.8.0. + +* **Returns:** + `True` if limit is reached, `False` otherwise. + +### Examples + +```pycon +>>> from onetl.impl import LocalPath +>>> limit.is_reached +False +>>> limit.stops_at(LocalPath("/path/to/file.csv")) +False +>>> limit.is_reached +False +>>> # after limit is reached +>>> limit.stops_at(LocalPath("/path/to/file.csv")) +True +>>> limit.is_reached +True +``` + + + +#### *abstract* reset() → Self + +Resets the internal limit state. + +#### Versionadded +Added in version 0.8.0. + +* **Returns:** + Returns a filter of the same type, but with non-reached state. + + It could be the same filter or a new one, this is an implementation detail. + +### Examples + +```pycon +>>> limit.is_reached +True +>>> new_limit = limit.reset() +>>> new_limit.is_reached +False +``` + + + +#### *abstract* stops_at(path: PathProtocol) → bool + +Update internal state and return current state. + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **path** + : Path to check +* **Returns:** + `True` if limit is reached, `False` otherwise. + +### Examples + +```pycon +>>> from onetl.impl import LocalPath +>>> # limit is not reached yet +>>> limit.stops_at(LocalPath("/path/to/file.csv")) +False +>>> # after limit is reached +>>> limit.stops_at(LocalPath("/path/to/another.csv")) +True +>>> # at this point, .stops_at() and .is_reached will always return True, +>>> # even on inputs that returned False before. +>>> # it will be in the same state until .reset() is called +>>> limit.stops_at(LocalPath("/path/to/file.csv")) +True +``` + + diff --git a/mddocs/file/file_limits/file_limit.md b/mddocs/file/file_limits/file_limit.md new file mode 100644 index 000000000..f60fca2e8 --- /dev/null +++ b/mddocs/file/file_limits/file_limit.md @@ -0,0 +1,114 @@ + + +# File Limit (legacy) + +### *class* onetl.core.file_limit.file_limit.FileLimit(\*, count_limit: int = 100) + +Limits the number of downloaded files. + +#### Deprecated +Deprecated since version 0.8.0: Use [`MaxFilesCount`](max_files_count.md#onetl.file.limit.max_files_count.MaxFilesCount) instead. + +* **Parameters:** + **count_limit** + : Number of downloaded files at a time. + +### Examples + +Create a FileLimit object and set the amount in it: + +```python +from onetl.core import FileLimit + +limit = FileLimit(count_limit=1500) +``` + +If you create a [`onetl.file.file_downloader.file_downloader.FileDownloader`](../file_downloader/file_downloader.md#onetl.file.file_downloader.file_downloader.FileDownloader) object without +specifying the limit option, it will download with a limit of 100 files. + + + +#### *property* is_reached *: bool* + +Check if limit is reached. + +#### Versionadded +Added in version 0.8.0. + +* **Returns:** + `True` if limit is reached, `False` otherwise. + +### Examples + +```pycon +>>> from onetl.impl import LocalPath +>>> limit.is_reached +False +>>> limit.stops_at(LocalPath("/path/to/file.csv")) +False +>>> limit.is_reached +False +>>> # after limit is reached +>>> limit.stops_at(LocalPath("/path/to/file.csv")) +True +>>> limit.is_reached +True +``` + + + +#### reset() + +Resets the internal limit state. + +#### Versionadded +Added in version 0.8.0. + +* **Returns:** + Returns a filter of the same type, but with non-reached state. + + It could be the same filter or a new one, this is an implementation detail. + +### Examples + +```pycon +>>> limit.is_reached +True +>>> new_limit = limit.reset() +>>> new_limit.is_reached +False +``` + + + +#### stops_at(path: PathProtocol) → bool + +Update internal state and return current state. + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **path** + : Path to check +* **Returns:** + `True` if limit is reached, `False` otherwise. + +### Examples + +```pycon +>>> from onetl.impl import LocalPath +>>> # limit is not reached yet +>>> limit.stops_at(LocalPath("/path/to/file.csv")) +False +>>> # after limit is reached +>>> limit.stops_at(LocalPath("/path/to/another.csv")) +True +>>> # at this point, .stops_at() and .is_reached will always return True, +>>> # even on inputs that returned False before. +>>> # it will be in the same state until .reset() is called +>>> limit.stops_at(LocalPath("/path/to/file.csv")) +True +``` + + diff --git a/mddocs/file/file_limits/index.md b/mddocs/file/file_limits/index.md new file mode 100644 index 000000000..be23f82da --- /dev/null +++ b/mddocs/file/file_limits/index.md @@ -0,0 +1,19 @@ + + +# File Limits + +# File limits + +* [MaxFilesCount](max_files_count.md) +* [TotalFilesSize](total_files_size.md) + +# Legacy + +* [File Limit (legacy)](file_limit.md) + +# For developers + +* [Base interface](base.md) +* [limits_stop_at](limits_stop_at.md) +* [limits_reached](limits_reached.md) +* [reset_limits](reset_limits.md) diff --git a/mddocs/file/file_limits/limits_reached.md b/mddocs/file/file_limits/limits_reached.md new file mode 100644 index 000000000..bf5aeabbe --- /dev/null +++ b/mddocs/file/file_limits/limits_reached.md @@ -0,0 +1,38 @@ + + +# limits_reached + +### onetl.file.limit.limits_reached.limits_reached(limits: Iterable[[BaseFileLimit](base.md#onetl.base.base_file_limit.BaseFileLimit)]) → bool + +Check if any of limits reached. + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **limits** + : Limits to test. +* **Returns:** + `True` if any of limits is reached, `False` otherwise. + + If no limits are passed, returns `False`. + +### Examples + +```pycon +>>> from onetl.file.limit import MaxFilesCount, limits_reached, limits_stop_at +>>> from onetl.impl import LocalPath +>>> limits = [MaxFilesCount(2)] +>>> limits_reached(limits) +False +>>> limits_stop_at(LocalPath("/path/to/file1.csv"), limits) +False +>>> limits_stop_at(LocalPath("/path/to/file2.csv"), limits) +False +>>> limits_stop_at(LocalPath("/path/to/file3.csv"), limits) +True +>>> limits_reached(limits) +True +``` + + diff --git a/mddocs/file/file_limits/limits_stop_at.md b/mddocs/file/file_limits/limits_stop_at.md new file mode 100644 index 000000000..f41606838 --- /dev/null +++ b/mddocs/file/file_limits/limits_stop_at.md @@ -0,0 +1,37 @@ + + +# limits_stop_at + +### onetl.file.limit.limits_stop_at.limits_stop_at(path: PathProtocol, limits: Iterable[[BaseFileLimit](base.md#onetl.base.base_file_limit.BaseFileLimit)]) → bool + +Check if some of limits stops at given path. + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **path** + : Path to check. + + **limits** + : Limits to test path against. +* **Returns:** + `True` if any of limit is reached while handling the path, `False` otherwise. + + If no limits are passed, returns `False`. + +### Examples + +```pycon +>>> from onetl.file.limit import MaxFilesCount, limits_stop_at +>>> from onetl.impl import LocalPath +>>> limits = [MaxFilesCount(2)] +>>> limits_stop_at(LocalPath("/path/to/file1.csv"), limits) +False +>>> limits_stop_at(LocalPath("/path/to/file2.csv"), limits) +False +>>> limits_stop_at(LocalPath("/path/to/file3.csv"), limits) +True +``` + + diff --git a/mddocs/file/file_limits/max_files_count.md b/mddocs/file/file_limits/max_files_count.md new file mode 100644 index 000000000..1b10149b1 --- /dev/null +++ b/mddocs/file/file_limits/max_files_count.md @@ -0,0 +1,114 @@ + + +# MaxFilesCount + +### *class* onetl.file.limit.max_files_count.MaxFilesCount(limit: int) + +Limits the total number of files handled by [File Downloader](../file_downloader/file_downloader.md#file-downloader) or [File Mover](../file_mover/file_mover.md#file-mover). + +All files until specified limit (including) will be downloaded/moved, but `limit+1` will not. + +This doesn’t apply to directories. + +#### Versionadded +Added in version 0.8.0: Replaces deprecated `onetl.core.FileLimit` + +* **Parameters:** + **limit** + +### Examples + +Create filter which allows to download/move up to 100 files, but stops on 101: + +```python +from onetl.file.limit import MaxFilesCount + +limit = MaxFilesCount(100) +``` + + + +#### *property* is_reached *: bool* + +Check if limit is reached. + +#### Versionadded +Added in version 0.8.0. + +* **Returns:** + `True` if limit is reached, `False` otherwise. + +### Examples + +```pycon +>>> from onetl.impl import LocalPath +>>> limit.is_reached +False +>>> limit.stops_at(LocalPath("/path/to/file.csv")) +False +>>> limit.is_reached +False +>>> # after limit is reached +>>> limit.stops_at(LocalPath("/path/to/file.csv")) +True +>>> limit.is_reached +True +``` + + + +#### reset() + +Resets the internal limit state. + +#### Versionadded +Added in version 0.8.0. + +* **Returns:** + Returns a filter of the same type, but with non-reached state. + + It could be the same filter or a new one, this is an implementation detail. + +### Examples + +```pycon +>>> limit.is_reached +True +>>> new_limit = limit.reset() +>>> new_limit.is_reached +False +``` + + + +#### stops_at(path: PathProtocol) → bool + +Update internal state and return current state. + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **path** + : Path to check +* **Returns:** + `True` if limit is reached, `False` otherwise. + +### Examples + +```pycon +>>> from onetl.impl import LocalPath +>>> # limit is not reached yet +>>> limit.stops_at(LocalPath("/path/to/file.csv")) +False +>>> # after limit is reached +>>> limit.stops_at(LocalPath("/path/to/another.csv")) +True +>>> # at this point, .stops_at() and .is_reached will always return True, +>>> # even on inputs that returned False before. +>>> # it will be in the same state until .reset() is called +>>> limit.stops_at(LocalPath("/path/to/file.csv")) +True +``` + + diff --git a/mddocs/file/file_limits/reset_limits.md b/mddocs/file/file_limits/reset_limits.md new file mode 100644 index 000000000..23a368345 --- /dev/null +++ b/mddocs/file/file_limits/reset_limits.md @@ -0,0 +1,39 @@ + + +# reset_limits + +### onetl.file.limit.reset_limits.reset_limits(limits: Iterable[[BaseFileLimit](base.md#onetl.base.base_file_limit.BaseFileLimit)]) → list[[BaseFileLimit](base.md#onetl.base.base_file_limit.BaseFileLimit)] + +Reset limits state. + +* **Parameters:** + **limits** + : Limits to reset. +* **Returns:** + List with limits, but with reset state. + + List may contain original filters with reset state, or new copies. + + This is an implementation detail of [`reset`](base.md#onetl.base.base_file_limit.BaseFileLimit.reset) method. + +### Examples + +```pycon +>>> from onetl.file.limit import MaxFilesCount, limits_reached, limits_stop_at, reset_limits +>>> from onetl.impl import LocalPath +>>> limits = [MaxFilesCount(1)] +>>> limits_reached(limits) +False +>>> # do something +>>> limits_stop_at(LocalPath("/path/to/file1.csv"), limits) +False +>>> limits_stop_at(LocalPath("/path/to/file2.csv"), limits) +True +>>> limits_reached(limits) +True +>>> new_limits = reset_limits(limits) +>>> limits_reached(new_limits) +False +``` + + diff --git a/mddocs/file/file_limits/total_files_size.md b/mddocs/file/file_limits/total_files_size.md new file mode 100644 index 000000000..48c56bc5b --- /dev/null +++ b/mddocs/file/file_limits/total_files_size.md @@ -0,0 +1,122 @@ + + +# TotalFilesSize + +### *class* onetl.file.limit.total_files_size.TotalFilesSize(limit: int | str) + +Limits the total size of files handled by [File Downloader](../file_downloader/file_downloader.md#file-downloader) or [File Mover](../file_mover/file_mover.md#file-mover). + +Calculates the sum of downloaded/moved files size (`.stat().st_size`), +and checks that this sum is less or equal to specified limit. + +After limit is reached, no more files will be downloaded/moved. + +Doesn’t affect directories, paths without `.stat()` method or files with zero size. + +#### Versionadded +Added in version 0.13.0. + +#### NOTE +[SI unit prefixes](https://en.wikipedia.org/wiki/Byte#Multiple-byte_units) +means that `1KB` == `1 kilobyte` == `1000 bytes`. +If you need `1024 bytes`, use `1 KiB` == `1 kibibyte`. + +* **Parameters:** + **limit** + +### Examples + +Create filter which allows to download/move files with total size up to 1GiB, but not higher: + +```python +from onetl.file.limit import MaxFilesCount + +limit = TotalFilesSize("1GiB") +``` + + + +#### *property* is_reached *: bool* + +Check if limit is reached. + +#### Versionadded +Added in version 0.8.0. + +* **Returns:** + `True` if limit is reached, `False` otherwise. + +### Examples + +```pycon +>>> from onetl.impl import LocalPath +>>> limit.is_reached +False +>>> limit.stops_at(LocalPath("/path/to/file.csv")) +False +>>> limit.is_reached +False +>>> # after limit is reached +>>> limit.stops_at(LocalPath("/path/to/file.csv")) +True +>>> limit.is_reached +True +``` + + + +#### reset() + +Resets the internal limit state. + +#### Versionadded +Added in version 0.8.0. + +* **Returns:** + Returns a filter of the same type, but with non-reached state. + + It could be the same filter or a new one, this is an implementation detail. + +### Examples + +```pycon +>>> limit.is_reached +True +>>> new_limit = limit.reset() +>>> new_limit.is_reached +False +``` + + + +#### stops_at(path: PathProtocol) → bool + +Update internal state and return current state. + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **path** + : Path to check +* **Returns:** + `True` if limit is reached, `False` otherwise. + +### Examples + +```pycon +>>> from onetl.impl import LocalPath +>>> # limit is not reached yet +>>> limit.stops_at(LocalPath("/path/to/file.csv")) +False +>>> # after limit is reached +>>> limit.stops_at(LocalPath("/path/to/another.csv")) +True +>>> # at this point, .stops_at() and .is_reached will always return True, +>>> # even on inputs that returned False before. +>>> # it will be in the same state until .reset() is called +>>> limit.stops_at(LocalPath("/path/to/file.csv")) +True +``` + + diff --git a/mddocs/file/file_mover/file_mover.md b/mddocs/file/file_mover/file_mover.md new file mode 100644 index 000000000..1f7b7ce0b --- /dev/null +++ b/mddocs/file/file_mover/file_mover.md @@ -0,0 +1,243 @@ + + +# File Mover + +| [`FileMover`](#onetl.file.file_mover.file_mover.FileMover) | Allows you to move files between different directories in a filesystem, and return an object with move result summary. | +|------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------| +| [`FileMover.run`](#onetl.file.file_mover.file_mover.FileMover.run)([files]) | Method for moving files from source to target directory. | +| [`FileMover.view_files`](#onetl.file.file_mover.file_mover.FileMover.view_files)() | Get file list in the `source_path`, after `filter` and `limit` applied (if any). | + +### *class* onetl.file.file_mover.file_mover.FileMover(\*, connection: ~onetl.base.base_file_connection.BaseFileConnection, target_path: ~onetl.impl.remote_path.RemotePath, source_path: ~onetl.impl.remote_path.RemotePath | None = None, filters: ~typing.List[~onetl.base.base_file_filter.BaseFileFilter] = None, limits: ~typing.List[~onetl.base.base_file_limit.BaseFileLimit] = None, options: ~onetl.file.file_mover.options.FileMoverOptions = FileMoverOptions(if_exists=, workers=1)) + +Allows you to move files between different directories in a filesystem, +and return an object with move result summary. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### NOTE +This class is used to move files **only** within the same connection, + +It does NOT support direct file transfer between filesystems, like `FTP -> SFTP`. +You should use [File Downloader](../file_downloader/file_downloader.md#file-downloader) + [File Uploader](../file_uploader/file_uploader.md#file-uploader) to implement `FTP -> local dir -> SFTP`. + +#### WARNING +This class does **not** support read strategies. + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **connection** + : Class which contains File system connection properties. See [File Connections](../../connection/file_connection/index.md#file-connections) section. + + **target_path** + : Remote path to move files to + + **source_path** + : Remote path to move files from. +
+ Could be `None`, but only if you pass absolute file paths directly to + [`run`](#onetl.file.file_mover.file_mover.FileMover.run) method + + **filters** + : Return only files/directories matching these filters. See [File Filters](../file_filters/index.md#file-filters) + + **limits** + : Apply limits to the list of files/directories, and stop if one of the limits is reached. + See [File Limits](../file_limits/index.md#file-limits) + + **options** + : File moving options. See [`FileMover.Options`](options.md#onetl.file.file_mover.options.FileMoverOptions) + +### Examples + +Minimal example + +```py +from onetl.connection import SFTP +from onetl.file import FileMover + +sftp = SFTP(...) + +# create mover +mover = FileMover( + connection=sftp, + source_path="/path/to/source/dir", + target_path="/path/to/target/dir", +) + +# move files from "/path/to/source/dir" to "/path/to/target/dir" +mover.run() +``` + +Full example + +```py +from onetl.connection import SFTP +from onetl.file import FileMover +from onetl.file.filter import Glob, ExcludeDir +from onetl.file.limit import MaxFilesCount, TotalFilesSize + +sftp = SFTP(...) + +# create mover with a bunch of options +mover = FileMover( + connection=sftp, + source_path="/path/to/source/dir", + target_path="/path/to/target/dir", + filters=[ + Glob("*.txt"), + ExcludeDir("/path/to/source/dir/exclude"), + ], + limits=[MaxFilesCount(100), TotalFileSize("10GiB")], + options=FileMover.Options(if_exists="replace_file"), +) + +# move files from "/path/to/source/dir" to "/path/to/target/dir", +# but only *.txt files +# excluding files from "/path/to/source/dir/exclude" directory +# and stop before downloading 101 file +mover.run() +``` + + + +#### run(files: Iterable[str | PathLike] | None = None) → [MoveResult](result.md#onetl.file.file_mover.result.MoveResult) + +Method for moving files from source to target directory. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### Versionadded +Added in version 0.8.0. + +* **Parameters:** + **files** + : File list to move. +
+ If empty, move files from `source_path` to `target_path`, + applying `filter` and `limit` to each one (if set). +
+ If not, move to `target_path` **all** input files, **without** + any filtering and limiting. +* **Returns:** + `MoveResult` + : Move result object +* **Raises:** + `onetl.exception.DirectoryNotFoundError` + : `source_path` does not found + + NotADirectoryError + : `source_path` or `target_path` is not a directory + +### Examples + +Move files from `source_path`: + +```pycon +>>> from onetl.file import FileMover +>>> mover = FileMover(source_path="/source", target_path="/target", ...) +>>> move_result = mover.run() +>>> move_result +MoveResult( + successful=FileSet([ + RemoteFile("/target/file1.txt"), + RemoteFile("/target/file2.txt"), + # directory structure is preserved + RemoteFile("/target/nested/path/file3.txt"), + ]), + failed=FileSet([ + FailedRemoteFile("/source/failed.file"), + ]), + skipped=FileSet([ + RemoteFile("/source/already.exists"), + ]), + missing=FileSet([ + RemotePath("/source/missing.file"), + ]), +) +``` + +Move only certain files from `source_path`: + +```pycon +>>> from onetl.file import FileMover +>>> mover = FileMover(source_path="/source", target_path="/target", ...) +>>> # paths could be relative or absolute, but all should be in "/source" +>>> move_result = mover.run( +... [ +... "/source/file1.txt", +... "/source/nested/path/file3.txt", +... # excluding "/source/file2.txt" +... ] +... ) +>>> move_result +MoveResult( + successful=FileSet([ + RemoteFile("/target/file1.txt"), + # directory structure is preserved + RemoteFile("/target/nested/path/file3.txt"), + ]), + failed=FileSet([]), + skipped=FileSet([]), + missing=FileSet([]), +) +``` + +Move certain files from any folder: + +```pycon +>>> from onetl.file import FileMover +>>> mover = FileMover(target_path="/target", ...) # no source_path set +>>> # only absolute paths +>>> move_result = mover.run( +... [ +... "/remote/file1.txt", +... "/any/nested/path/file3.txt", +... ] +... ) +>>> move_result +MoveResult( + successful=FileSet([ + RemoteFile("/target/file1.txt"), + # directory structure is NOT preserved without source_path + RemoteFile("/target/file3.txt"), + ]), + failed=FileSet([]), + skipped=FileSet([]), + missing=FileSet([]), +) +``` + + + +#### view_files() → FileSet[RemoteFile] + +Get file list in the `source_path`, +after `filter` and `limit` applied (if any). [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### Versionadded +Added in version 0.8.0. + +* **Returns:** + FileSet[RemoteFile] + : Set of files in `source_path`, which will be moved by [`run`](#onetl.file.file_mover.file_mover.FileMover.run) method +* **Raises:** + `onetl.exception.DirectoryNotFoundError` + : `source_path` does not found + + NotADirectoryError + : `source_path` is not a directory + +### Examples + +View files: + +```pycon +>>> from onetl.file import FileMover +>>> mover = FileMover(source_path="/remote", ...) +>>> mover.view_files() +FileSet([ + RemoteFile("/remote/file1.txt"), + RemoteFile("/remote/file2.txt"), + RemoteFile("/remote/nested/path/file3.txt"), +]) +``` + + diff --git a/mddocs/file/file_mover/index.md b/mddocs/file/file_mover/index.md new file mode 100644 index 000000000..c8b55c7d0 --- /dev/null +++ b/mddocs/file/file_mover/index.md @@ -0,0 +1,9 @@ + + +# File Mover + +# File Mover + +* [File Mover](file_mover.md) +* [File Mover Options](options.md) +* [File Mover Result](result.md) diff --git a/mddocs/file/file_mover/options.md b/mddocs/file/file_mover/options.md new file mode 100644 index 000000000..66e34176a --- /dev/null +++ b/mddocs/file/file_mover/options.md @@ -0,0 +1,55 @@ + + +# File Mover Options + +### *pydantic model* onetl.file.file_mover.options.FileMoverOptions + +File moving options. + +#### Versionadded +Added in version 0.8.0. + +### Examples + +```python +from onetl.file import FileMover + +options = FileMover.Options( + if_exists="replace_entire_directory", + workers=4, +) +``` + + + +#### *field* if_exists *: FileExistBehavior* *= FileExistBehavior.ERROR* *(alias 'mode')* + +How to handle existing files in the local directory. + +Possible values: +: * `error` (default) - mark file as failed + * `ignore` - mark file as skipped + * `replace_file` - replace existing file with a new one + * `replace_entire_directory` - delete directory content before moving files + +#### Versionadded +Added in version 0.8.0. + +#### Versionchanged +Changed in version 0.9.0: Renamed `mode` → `if_exists` + + + +#### *field* workers *: int* *= 1* + +Number of workers to create for parallel file moving. + +1 (default) means files will me moved sequentially. +2 or more means files will be moved in parallel workers. + +Recommended value is `min(32, os.cpu_count() + 4)`, e.g. `5`. + +#### Versionadded +Added in version 0.8.1. + + diff --git a/mddocs/file/file_mover/result.md b/mddocs/file/file_mover/result.md new file mode 100644 index 000000000..815388b8f --- /dev/null +++ b/mddocs/file/file_mover/result.md @@ -0,0 +1,550 @@ + + +# File Mover Result + +### *class* onetl.file.file_mover.result.MoveResult(\*, successful: FileSet[RemoteFile] = None, failed: FileSet[FailedRemoteFile] = None, skipped: FileSet[RemoteFile] = None, missing: FileSet[RemotePath] = None) + +Representation of file move result. + +Container for file paths, divided into certain categories: + +* [`successful`](#onetl.file.file_mover.result.MoveResult.successful) +* [`failed`](#onetl.file.file_mover.result.MoveResult.failed) +* [`skipped`](#onetl.file.file_mover.result.MoveResult.skipped) +* [`missing`](#onetl.file.file_mover.result.MoveResult.missing) + +#### Versionadded +Added in version 0.8.0. + +### Examples + +```pycon +>>> from onetl.file import FileMover +>>> mover = FileMover(local_path="/local", ...) +>>> move_result = mover.run( +... [ +... "/source/file1", +... "/source/file2", +... "/failed/file", +... "/existing/file", +... "/missing/file", +... ] +... ) +>>> move_result +MoveResult( + successful=FileSet([ + RemoteFile("/target/file1"), + RemoteFile("/target/file2"), + ]), + failed=FileSet([ + FailedLocalFile("/failed/file") + ]), + skipped=FileSet([ + RemoteFile("/existing/file") + ]), + missing=FileSet([ + RemotePath("/missing/file") + ]), +) +``` + + + +#### *property* details *: str* + +Return detailed information about files in the result object + +### Examples + +```pycon +>>> from onetl.impl import FailedRemoteFile, LocalPath, RemoteFile, RemotePathStat +>>> from onetl.exception import NotAFileError +>>> from onetl.file.file_result import FileResult +>>> file_result1 = FileResult( +... successful={ +... RemoteFile("/local/file", stats=RemotePathStat(st_size=1024)), +... RemoteFile("/local/another.file", stats=RemotePathStat(st_size=1024)), +... }, +... failed={ +... FailedRemoteFile( +... path="/remote/file1", +... stats=RemotePathStat(st_size=0), +... exception=NotAFileError("'/remote/file1' is not a file"), +... ), +... FailedRemoteFile( +... path="/remote/file2", +... stats=RemotePathStat(st_size=0), +... exception=PermissionError("'/remote/file2': [Errno 13] Permission denied"), +... ), +... }, +... skipped={LocalPath("/skipped/file1"), LocalPath("/skipped/file2")}, +... missing={LocalPath("/missing/file1"), LocalPath("/missing/file2")}, +... ) +>>> print(file_result1.details) +Total: 8 files (size='2.0 kB') + +Successful 2 files (size='2.0 kB'): + '/local/another.file' (size='1.0 kB') + '/local/file' (size='1.0 kB') + +Failed 2 files (size='0 Bytes'): + '/remote/file2' (size='0 Bytes') + PermissionError("'/remote/file2': [Errno 13] Permission denied") + '/remote/file1' (size='0 Bytes') + NotAFileError("'/remote/file1' is not a file") + +Skipped 2 files (size='0 Bytes'): + '/skipped/file1' + '/skipped/file2' + +Missing 2 files: + '/missing/file2' + '/missing/file1' +``` + +```pycon +>>> file_result2 = FileResult() +>>> print(file_result2.details) +No successful files + +No failed files + +No skipped files + +No missing files +``` + + + +#### dict(\*, include: 'AbstractSetIntStr' | 'MappingIntStrAny' | None = None, exclude: 'AbstractSetIntStr' | 'MappingIntStrAny' | None = None, by_alias: bool = False, skip_defaults: bool | None = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny + +Generate a dictionary representation of the model, optionally specifying which fields to include or exclude. + + + +#### *field* failed *: FileSet[FailedRemoteFile]* *[Optional]* + +File paths (remote) which were not moved because of some failure + + + +#### *property* failed_count *: int* + +Get number of failed files + +### Examples + +```pycon +>>> from onetl.impl import RemoteFile +>>> from onetl.file.file_result import FileResult +>>> file_result = FileResult( +... failed={RemoteFile("/some/file"), RemoteFile("/some/another.file")}, +... ) +>>> file_result.failed_count +2 +``` + + + +#### *property* failed_size *: int* + +Get size (in bytes) of failed files + +### Examples + +```pycon +>>> from onetl.impl import RemoteFile, RemotePathStat +>>> from onetl.file.file_result import FileResult +>>> file_result = FileResult( +... failed={ +... RemoteFile("/some/file", stats=RemotePathStat(st_size=1024)), +... RemoteFile("/some/another.file"), stats=RemotePathStat(st_size=1024)), +... }, +... ) +>>> file_result.failed_size # in bytes +2048 +``` + + + +#### *property* is_empty *: bool* + +Returns `True` if there are no files in `successful`, `failed` and `skipped` attributes + +### Examples + +```pycon +>>> from onetl.impl import LocalPath +>>> from onetl.file.file_result import FileResult +>>> file_result1 = FileResult() +>>> file_result1.is_empty +True +>>> file_result2 = FileResult( +... successful={LocalPath("/local/file"), LocalPath("/local/another.file")}, +... ) +>>> file_result2.is_empty +False +``` + + + +#### json(\*, include: 'AbstractSetIntStr' | 'MappingIntStrAny' | None = None, exclude: 'AbstractSetIntStr' | 'MappingIntStrAny' | None = None, by_alias: bool = False, skip_defaults: bool | None = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Callable[[Any], Any] | None = None, models_as_dict: bool = True, \*\*dumps_kwargs: Any) → str + +Generate a JSON representation of the model, include and exclude arguments as per dict(). + +encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps(). + + + +#### *field* missing *: FileSet[RemotePath]* *[Optional]* + +File paths (remote) which are not present in the remote file system + + + +#### *property* missing_count *: int* + +Get number of missing files + +### Examples + +```pycon +>>> from onetl.impl import LocalPath +>>> from onetl.file.file_result import FileResult +>>> file_result = FileResult( +... missing={LocalPath("/some/file"), LocalPath("/some/another.file")}, +... ) +>>> file_result.missing_count +2 +``` + + + +#### raise_if_contains_zero_size() → None + +Raise exception if `successful` attribute contains a file with zero size + +* **Raises:** + ZeroFileSizeError + : `successful` file set contains a file with zero size + +### Examples + +```pycon +>>> from onetl.exception import ZeroFileSizeError +>>> from onetl.impl import LocalPath +>>> from onetl.file.file_result import FileResult +>>> file_result = FileResult( +... successful={ +... LocalPath("/local/empty1.file"), +... LocalPath("/local/empty2.file"), +... LocalPath("/local/normal.file"), +... }, +... ) +>>> file_result.raise_if_contains_zero_size() +Traceback (most recent call last): + ... +onetl.exception.ZeroFileSizeError: 2 files out of 3 have zero size: + '/local/empty1.file' + '/local/empty2.file' +``` + + + +#### raise_if_empty() → None + +Raise exception if there are no files in `successful`, `failed` and `skipped` attributes + +* **Raises:** + EmptyFilesError + : `successful`, `failed` and `skipped` file sets are empty + +### Examples + +```pycon +>>> from onetl.file.file_result import FileResult +>>> file_result = FileResult() +>>> file_result.raise_if_empty() +Traceback (most recent call last): + ... +onetl.exception.EmptyFilesError: There are no files in the result +``` + + + +#### raise_if_failed() → None + +Raise exception if there are some files in `failed` attribute + +* **Raises:** + FailedFilesError + : `failed` file set is not empty + +### Examples + +```pycon +>>> from onetl.impl import FailedRemoteFile, RemotePathStat +>>> from onetl.exception import NotAFileError, FileMissingError +>>> from onetl.file.file_result import FileResult +>>> files_with_exception = [ +... FailedRemoteFile( +... path="/remote/file1", +... stats=RemotePathStat(st_size=0), +... exception=NotAFileError("'/remote/file1' is not a file"), +... ), +... FailedRemoteFile( +... path="/remote/file2", +... stats=RemotePathStat(st_size=0), +... exception=PermissionError("'/remote/file2': [Errno 13] Permission denied"), +... ), +... ] +>>> file_result = FileResult(failed=files_with_exception) +>>> file_result.raise_if_failed() +Traceback (most recent call last) +... +onetl.exception.FailedFilesError: Failed 2 files (size='0 bytes'): + '/remote/file1' (size='0 bytes') + NotAFileError("'/remote/file1' is not a file") + + '/remote/file2' (size='0 Bytes') + PermissionError("'/remote/file2': [Errno 13] Permission denied") +``` + + + +#### raise_if_missing() → None + +Raise exception if there are some files in `missing` attribute + +* **Raises:** + MissingFilesError + : `missing` file set is not empty + +### Examples + +```pycon +>>> from onetl.impl import LocalPath +>>> from onetl.file.file_result import FileResult +>>> file_result = FileResult( +... missing={ +... LocalPath("/missing/file1"), +... LocalPath("/missing/file2"), +... }, +... ) +>>> file_result.raise_if_missing() +Traceback (most recent call last): + ... +onetl.exception.MissingFilesError: Missing 2 files: + '/missing/file1' + '/missing/file2' +``` + + + +#### raise_if_skipped() → None + +Raise exception if there are some files in `skipped` attribute + +* **Raises:** + SkippedFilesError + : `skipped` file set is not empty + +### Examples + +```pycon +>>> from onetl.impl import LocalPath +>>> from onetl.file.file_result import FileResult +>>> file_result = FileResult( +... skipped={ +... LocalPath("/skipped/file1"), +... LocalPath("/skipped/file2"), +... }, +... ) +>>> file_result.raise_if_skipped() +Traceback (most recent call last): + ... +onetl.exception.SkippedFilesError: Skipped 2 files (15 kB): + '/skipped/file1' (10kB) + '/skipped/file2' (5 kB) +``` + + + +#### *field* skipped *: FileSet[RemoteFile]* *[Optional]* + +File paths (remote) which were skipped because of some reason + + + +#### *property* skipped_count *: int* + +Get number of skipped files + +### Examples + +```pycon +>>> from onetl.impl import LocalPath +>>> from onetl.file.file_result import FileResult +>>> file_result = FileResult( +... skipped={LocalPath("/some/file"), LocalPath("/some/another.file")}, +... ) +>>> file_result.skipped_count +2 +``` + + + +#### *property* skipped_size *: int* + +Get size (in bytes) of skipped files + +### Examples + +```pycon +>>> from onetl.impl import LocalPath +>>> from onetl.file.file_result import FileResult +>>> file_result = FileResult( +... skipped={LocalPath("/some/file"), LocalPath("/some/another.file")}, +... ) +>>> file_result.skipped_size # in bytes +1024 +``` + + + +#### *field* successful *: FileSet[RemoteFile]* *[Optional]* + +File paths (local) which were moved successfully + + + +#### *property* successful_count *: int* + +Get number of successful files + +### Examples + +```pycon +>>> from onetl.impl import LocalPath +>>> from onetl.file.file_result import FileResult +>>> file_result = FileResult( +... successful={LocalPath("/some/file"), LocalPath("/some/another.file")}, +... ) +>>> file_result.successful_count +2 +``` + + + +#### *property* successful_size *: int* + +Get size (in bytes) of successful files + +### Examples + +```pycon +>>> from onetl.impl import LocalPath +>>> from onetl.file.file_result import FileResult +>>> file_result = FileResult( +... successful={LocalPath("/some/file"), LocalPath("/some/another.file")}, +... ) +>>> file_result.successful_size # in bytes +1024 +``` + + + +#### *property* summary *: str* + +Return short summary about files in the result object + +### Examples + +```pycon +>>> from onetl.impl import FailedRemoteFile, LocalPath, RemoteFile, RemotePathStat +>>> from onetl.exception import NotAFileError +>>> from onetl.file.file_result import FileResult +>>> file_result1 = FileResult( +... successful={ +... RemoteFile("/local/file", stats=RemotePathStat(st_size=1024)), +... RemoteFile("/local/another.file", stats=RemotePathStat(st_size=1024)), +... }, +... failed={ +... FailedRemoteFile( +... path="/remote/file1", +... stats=RemotePathStat(st_size=0), +... exception=NotAFileError("'/remote/file1' is not a file"), +... ), +... FailedRemoteFile( +... path="/remote/file2", +... stats=RemotePathStat(st_size=0), +... exception=PermissionError("'/remote/file2': [Errno 13] Permission denied"), +... ), +... }, +... skipped={LocalPath("/skipped/file1"), LocalPath("/skipped/file2")}, +... missing={LocalPath("/missing/file1"), LocalPath("/missing/file2")}, +... ) +>>> print(file_result1.summary) +Total: 8 files (size='2.0 kB') + +Successful: 2 files (size='2.0 kB') + +Failed: 2 files (size='0 Bytes') + +Skipped: 2 files (size='0 Bytes') + +Missing: 2 files +``` + +```pycon +>>> file_result2 = FileResult() +>>> print(file_result2.summary) +No files +``` + + + +#### *property* total_count *: int* + +Get total number of all files + +### Examples + +```pycon +>>> from onetl.impl import RemoteFile +>>> from onetl.file.file_result import FileResult +>>> file_result = FileResult( +... successful={LocalPath("/local/file"), LocalPath("/local/another.file")}, +... failed={RemoteFile("/remote/file"), RemoteFile("/remote/another.file")}, +... skipped={LocalPath("/skipped/file")}, +... missing={LocalPath("/missing/file")}, +... ) +>>> file_result.total_count +6 +``` + + + +#### *property* total_size *: int* + +Get total size (in bytes) of all files + +### Examples + +```pycon +>>> from onetl.impl import RemoteFile, RemotePathStat, LocalPath +>>> from onetl.file.file_result import FileResult +>>> file_result = FileResult( +... successful={LocalPath("/local/file"), LocalPath("/local/another.file")}, +... failed={ +... RemoteFile("/remote/file", stats=RemotePathStat(st_size=1024)), +... RemoteFile("/remote/another.file", stats=RemotePathStat(st_size=1024)) +... }, +... skipped={LocalPath("/skipped/file")}, +... missing={LocalPath("/missing/file")}, +... ) +>>> file_result.total_size # in bytes +4096 +``` + + diff --git a/mddocs/file/file_uploader/file_uploader.md b/mddocs/file/file_uploader/file_uploader.md new file mode 100644 index 000000000..98ebe3165 --- /dev/null +++ b/mddocs/file/file_uploader/file_uploader.md @@ -0,0 +1,241 @@ + + +# File Uploader + +| [`FileUploader`](#onetl.file.file_uploader.file_uploader.FileUploader) | Allows you to upload files to a remote source with specified file connection and parameters, and return an object with upload result summary. | +|------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------| +| [`FileUploader.run`](#onetl.file.file_uploader.file_uploader.FileUploader.run)([files]) | Method for uploading files to remote host. | +| [`FileUploader.view_files`](#onetl.file.file_uploader.file_uploader.FileUploader.view_files)() | Get file list in the `local_path`. | + +### *class* onetl.file.file_uploader.file_uploader.FileUploader(\*, connection: ~onetl.base.base_file_connection.BaseFileConnection, target_path: ~onetl.impl.remote_path.RemotePath, local_path: ~onetl.impl.local_path.LocalPath | None = None, temp_path: ~onetl.impl.remote_path.RemotePath | None = None, options: ~onetl.file.file_uploader.options.FileUploaderOptions = FileUploaderOptions(if_exists=, delete_local=False, workers=1)) + +Allows you to upload files to a remote source with specified file connection +and parameters, and return an object with upload result summary. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### NOTE +This class is used to upload files **only** from local directory to the remote one. + +It does NOT support direct file transfer between filesystems, like `FTP -> SFTP`. +You should use [File Downloader](../file_downloader/file_downloader.md#file-downloader) + FileUploader to implement `FTP -> local dir -> SFTP`. + +#### WARNING +This class does **not** support read strategies. + +#### Versionadded +Added in version 0.1.0. + +#### Versionchanged +Changed in version 0.8.0: Moved `onetl.core.FileDownloader` → `onetl.file.FileDownloader` + +* **Parameters:** + **connection** + : Class which contains File system connection properties. See [File Connections](../../connection/file_connection/index.md#file-connections) section. + + **target_path** + : Remote path where want you upload files to + + **local_path** + : The local directory from which the data is loaded. +
+ Could be `None`, but only if you pass absolute file paths directly to + [`run`](#onetl.file.file_uploader.file_uploader.FileUploader.run) method +
+ #### Versionadded + Added in version 0.3.0. + + **temp_path** + : If set, this path will be used for uploading a file, and then renaming it to the target file path. + If `None` (default since v0.5.0) is passed, files are uploaded directly to `target_path`. +
+ #### WARNING + In case of production ETL pipelines, please set a value for `temp_path` (NOT `None`). + This allows to properly handle upload interruption, + without creating half-uploaded files in the target, + because unlike file upload, `rename` call is atomic. +
+ #### WARNING + In case of connections like SFTP or FTP, which can have multiple underlying filesystems, + please pass `temp_path` path on the SAME filesystem as `target_path`. + Otherwise instead of `rename`, remote OS will move file between filesystems, + which is NOT atomic operation. +
+ #### Versionchanged + Changed in version 0.5.0: Default changed from `/tmp` to `None` + + **options** + : File upload options. See [`FileUploader.Options`](options.md#onetl.file.file_uploader.options.FileUploaderOptions) + +### Examples + +Minimal example + +```py +from onetl.connection import HDFS +from onetl.file import FileUploader + +hdfs = HDFS(...) + +uploader = FileUploader( + connection=hdfs, + target_path="/path/to/remote/source", +) +``` + +Full example + +```py +from onetl.connection import HDFS +from onetl.file import FileUploader + +hdfs = HDFS(...) + +uploader = FileUploader( + connection=hdfs, + target_path="/path/to/remote/source", + temp_path="/user/onetl", + local_path="/some/local/directory", + options=FileUploader.Options(delete_local=True, if_exists="overwrite"), +) +``` + + + +#### run(files: Iterable[str | PathLike] | None = None) → [UploadResult](result.md#onetl.file.file_uploader.result.UploadResult) + +Method for uploading files to remote host. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### Versionadded +Added in version 0.1.0. + +* **Parameters:** + **files** + : File list to upload. +
+ If empty, upload files from `local_path`. +* **Returns:** + `UploadResult` + : Upload result object +* **Raises:** + `onetl.exception.DirectoryNotFoundError` + : `local_path` does not found + + NotADirectoryError + : `local_path` is not a directory + + ValueError + : File in `files` argument does not match `local_path` + +### Examples + +Upload files from `local_path` to `target_path`: + +```pycon +>>> from onetl.file import FileUploader +>>> uploader = FileUploader(local_path="/local", target_path="/remote", ...) +>>> upload_result = uploader.run() +>>> upload_result +UploadResult( + successful=FileSet([ + RemoteFile("/remote/file1"), + RemoteFile("/remote/file2"), + # directory structure is preserved + RemoteFile("/remote/nested/path/file3") + ]), + failed=FileSet([ + FailedLocalFile("/local/failed.file"), + ]), + skipped=FileSet([ + LocalPath("/local/already.exists"), + ]), + missing=FileSet([ + LocalPath("/local/missing.file"), + ]), +) +``` + +Upload only certain files from `local_path`: + +```pycon +>>> from onetl.file import FileUploader +>>> uploader = FileUploader(local_path="/local", target_path="/remote", ...) +>>> # paths could be relative or absolute, but all should be in "/local" +>>> upload_result = uploader.run( +... [ +... "/local/file1", +... "/local/nested/path/file3", +... # excluding "/local/file2", +... ] +... ) +>>> upload_result +UploadResult( + successful=FileSet([ + RemoteFile("/remote/file1"), + # directory structure is preserved + RemoteFile("/remote/nested/path/file3"), + ]), + failed=FileSet([]), + skipped=FileSet([]), + missing=FileSet([]), +) +``` + +Upload only certain files from any folder: + +```pycon +>>> from onetl.file import FileUploader +>>> uploader = FileUploader(target_path="/remote", ...) # no local_path set +>>> # only absolute paths +>>> upload_result = uploader.run( +... [ +... "/local/file1.txt", +... "/any/nested/path/file3.txt", +... ] +... ) +>>> upload_result +UploadResult( + successful=FileSet([ + RemoteFile("/remote/file1.txt"), + # directory structure is NOT preserved without local_path + RemoteFile("/remote/file3.txt"), + ]), + failed=FileSet([]), + skipped=FileSet([]), + missing=FileSet([]), +) +``` + + + +#### view_files() → FileSet[LocalPath] + +Get file list in the `local_path`. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### Versionadded +Added in version 0.3.0. + +* **Returns:** + FileSet[LocalPath] + : Set of files in `local_path` +* **Raises:** + `onetl.exception.DirectoryNotFoundError` + : `local_path` does not found + + NotADirectoryError + : `local_path` is not a directory + +### Examples + +View files: + +```pycon +>>> from onetl.file import FileUploader +>>> uploader = FileUploader(local_path="/local", ...) +>>> uploader.view_files() +FileSet([ + LocalPath("/local/file1.txt"), + LocalPath("/local/file3.txt"), + LocalPath("/local/nested/path/file3.txt"), +]) +``` + + diff --git a/mddocs/file/file_uploader/index.md b/mddocs/file/file_uploader/index.md new file mode 100644 index 000000000..e11581a9c --- /dev/null +++ b/mddocs/file/file_uploader/index.md @@ -0,0 +1,9 @@ + + +# File Uploader + +# File Uploader + +* [File Uploader](file_uploader.md) +* [File Uploader Options](options.md) +* [File Uploader Result](result.md) diff --git a/mddocs/file/file_uploader/options.md b/mddocs/file/file_uploader/options.md new file mode 100644 index 000000000..a32387501 --- /dev/null +++ b/mddocs/file/file_uploader/options.md @@ -0,0 +1,67 @@ + + +# File Uploader Options + +### *pydantic model* onetl.file.file_uploader.options.FileUploaderOptions + +File uploading options. + +#### Versionadded +Added in version 0.3.0. + +### Examples + +```python +from onetl.file import FileUploader + +options = FileUploader.Options( + if_exists="replace_entire_directory", + delete_local=True, + workers=4, +) +``` + + + +#### *field* if_exists *: FileExistBehavior* *= FileExistBehavior.ERROR* *(alias 'mode')* + +How to handle existing files in the target directory. + +Possible values: +: * `error` (default) - mark file as failed + * `ignore` - mark file as skipped + * `replace_file` - replace existing file with a new one + * `replace_entire_directory` - delete local directory content before downloading files + +#### Versionchanged +Changed in version 0.9.0: Renamed `mode` → `if_exists` + + + +#### *field* delete_local *: bool* *= False* + +If `True`, remove local file after successful download. + +If download failed, file will left intact. + +#### Versionadded +Added in version 0.2.0. + +#### Versionchanged +Changed in version 0.3.0: Move `FileUploader.delete_local` to `FileUploaderOptions` + + + +#### *field* workers *: int* *= 1* + +Number of workers to create for parallel file upload. + +1 (default) means files will me uploaded sequentially. +2 or more means files will be uploaded in parallel workers. + +Recommended value is `min(32, os.cpu_count() + 4)`, e.g. `5`. + +#### Versionadded +Added in version 0.8.1. + + diff --git a/mddocs/file/file_uploader/result.md b/mddocs/file/file_uploader/result.md new file mode 100644 index 000000000..59c289bea --- /dev/null +++ b/mddocs/file/file_uploader/result.md @@ -0,0 +1,550 @@ + + +# File Uploader Result + +### *class* onetl.file.file_uploader.result.UploadResult(\*, successful: FileSet[RemoteFile] = None, failed: FileSet[FailedLocalFile] = None, skipped: FileSet[LocalPath] = None, missing: FileSet[LocalPath] = None) + +Representation of file upload result. + +Container for file paths, divided into certain categories: + +* [`successful`](#onetl.file.file_uploader.result.UploadResult.successful) +* [`failed`](#onetl.file.file_uploader.result.UploadResult.failed) +* [`skipped`](#onetl.file.file_uploader.result.UploadResult.skipped) +* [`missing`](#onetl.file.file_uploader.result.UploadResult.missing) + +#### Versionadded +Added in version 0.3.0. + +### Examples + +```pycon +>>> from onetl.file import FileUploader +>>> uploader = FileUploader(target_path="/remote", ...) +>>> upload_result = uploader.run( +... [ +... "/local/file1", +... "/local/file2", +... "/failed/file", +... "/existing/file", +... "/missing/file", +... ] +... ) +>>> upload_result +UploadResult( + successful=FileSet([ + RemoteFile("/remote/file1"), + RemoteFile("/remote/file2"), + ]), + failed=FileSet([ + FailedLocalFile("/failed/file") + ]), + skipped=FileSet([ + LocalPath("/existing/file") + ]), + missing=FileSet([ + LocalPath("/missing/file") + ]), +) +``` + + + +#### *property* details *: str* + +Return detailed information about files in the result object + +### Examples + +```pycon +>>> from onetl.impl import FailedRemoteFile, LocalPath, RemoteFile, RemotePathStat +>>> from onetl.exception import NotAFileError +>>> from onetl.file.file_result import FileResult +>>> file_result1 = FileResult( +... successful={ +... RemoteFile("/local/file", stats=RemotePathStat(st_size=1024)), +... RemoteFile("/local/another.file", stats=RemotePathStat(st_size=1024)), +... }, +... failed={ +... FailedRemoteFile( +... path="/remote/file1", +... stats=RemotePathStat(st_size=0), +... exception=NotAFileError("'/remote/file1' is not a file"), +... ), +... FailedRemoteFile( +... path="/remote/file2", +... stats=RemotePathStat(st_size=0), +... exception=PermissionError("'/remote/file2': [Errno 13] Permission denied"), +... ), +... }, +... skipped={LocalPath("/skipped/file1"), LocalPath("/skipped/file2")}, +... missing={LocalPath("/missing/file1"), LocalPath("/missing/file2")}, +... ) +>>> print(file_result1.details) +Total: 8 files (size='2.0 kB') + +Successful 2 files (size='2.0 kB'): + '/local/another.file' (size='1.0 kB') + '/local/file' (size='1.0 kB') + +Failed 2 files (size='0 Bytes'): + '/remote/file2' (size='0 Bytes') + PermissionError("'/remote/file2': [Errno 13] Permission denied") + '/remote/file1' (size='0 Bytes') + NotAFileError("'/remote/file1' is not a file") + +Skipped 2 files (size='0 Bytes'): + '/skipped/file1' + '/skipped/file2' + +Missing 2 files: + '/missing/file2' + '/missing/file1' +``` + +```pycon +>>> file_result2 = FileResult() +>>> print(file_result2.details) +No successful files + +No failed files + +No skipped files + +No missing files +``` + + + +#### dict(\*, include: 'AbstractSetIntStr' | 'MappingIntStrAny' | None = None, exclude: 'AbstractSetIntStr' | 'MappingIntStrAny' | None = None, by_alias: bool = False, skip_defaults: bool | None = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny + +Generate a dictionary representation of the model, optionally specifying which fields to include or exclude. + + + +#### *field* failed *: FileSet[FailedLocalFile]* *[Optional]* + +File paths (local) which were not uploaded because of some failure + + + +#### *property* failed_count *: int* + +Get number of failed files + +### Examples + +```pycon +>>> from onetl.impl import RemoteFile +>>> from onetl.file.file_result import FileResult +>>> file_result = FileResult( +... failed={RemoteFile("/some/file"), RemoteFile("/some/another.file")}, +... ) +>>> file_result.failed_count +2 +``` + + + +#### *property* failed_size *: int* + +Get size (in bytes) of failed files + +### Examples + +```pycon +>>> from onetl.impl import RemoteFile, RemotePathStat +>>> from onetl.file.file_result import FileResult +>>> file_result = FileResult( +... failed={ +... RemoteFile("/some/file", stats=RemotePathStat(st_size=1024)), +... RemoteFile("/some/another.file"), stats=RemotePathStat(st_size=1024)), +... }, +... ) +>>> file_result.failed_size # in bytes +2048 +``` + + + +#### *property* is_empty *: bool* + +Returns `True` if there are no files in `successful`, `failed` and `skipped` attributes + +### Examples + +```pycon +>>> from onetl.impl import LocalPath +>>> from onetl.file.file_result import FileResult +>>> file_result1 = FileResult() +>>> file_result1.is_empty +True +>>> file_result2 = FileResult( +... successful={LocalPath("/local/file"), LocalPath("/local/another.file")}, +... ) +>>> file_result2.is_empty +False +``` + + + +#### json(\*, include: 'AbstractSetIntStr' | 'MappingIntStrAny' | None = None, exclude: 'AbstractSetIntStr' | 'MappingIntStrAny' | None = None, by_alias: bool = False, skip_defaults: bool | None = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Callable[[Any], Any] | None = None, models_as_dict: bool = True, \*\*dumps_kwargs: Any) → str + +Generate a JSON representation of the model, include and exclude arguments as per dict(). + +encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps(). + + + +#### *field* missing *: FileSet[LocalPath]* *[Optional]* + +File paths (local) which are not present in the local file system + + + +#### *property* missing_count *: int* + +Get number of missing files + +### Examples + +```pycon +>>> from onetl.impl import LocalPath +>>> from onetl.file.file_result import FileResult +>>> file_result = FileResult( +... missing={LocalPath("/some/file"), LocalPath("/some/another.file")}, +... ) +>>> file_result.missing_count +2 +``` + + + +#### raise_if_contains_zero_size() → None + +Raise exception if `successful` attribute contains a file with zero size + +* **Raises:** + ZeroFileSizeError + : `successful` file set contains a file with zero size + +### Examples + +```pycon +>>> from onetl.exception import ZeroFileSizeError +>>> from onetl.impl import LocalPath +>>> from onetl.file.file_result import FileResult +>>> file_result = FileResult( +... successful={ +... LocalPath("/local/empty1.file"), +... LocalPath("/local/empty2.file"), +... LocalPath("/local/normal.file"), +... }, +... ) +>>> file_result.raise_if_contains_zero_size() +Traceback (most recent call last): + ... +onetl.exception.ZeroFileSizeError: 2 files out of 3 have zero size: + '/local/empty1.file' + '/local/empty2.file' +``` + + + +#### raise_if_empty() → None + +Raise exception if there are no files in `successful`, `failed` and `skipped` attributes + +* **Raises:** + EmptyFilesError + : `successful`, `failed` and `skipped` file sets are empty + +### Examples + +```pycon +>>> from onetl.file.file_result import FileResult +>>> file_result = FileResult() +>>> file_result.raise_if_empty() +Traceback (most recent call last): + ... +onetl.exception.EmptyFilesError: There are no files in the result +``` + + + +#### raise_if_failed() → None + +Raise exception if there are some files in `failed` attribute + +* **Raises:** + FailedFilesError + : `failed` file set is not empty + +### Examples + +```pycon +>>> from onetl.impl import FailedRemoteFile, RemotePathStat +>>> from onetl.exception import NotAFileError, FileMissingError +>>> from onetl.file.file_result import FileResult +>>> files_with_exception = [ +... FailedRemoteFile( +... path="/remote/file1", +... stats=RemotePathStat(st_size=0), +... exception=NotAFileError("'/remote/file1' is not a file"), +... ), +... FailedRemoteFile( +... path="/remote/file2", +... stats=RemotePathStat(st_size=0), +... exception=PermissionError("'/remote/file2': [Errno 13] Permission denied"), +... ), +... ] +>>> file_result = FileResult(failed=files_with_exception) +>>> file_result.raise_if_failed() +Traceback (most recent call last) +... +onetl.exception.FailedFilesError: Failed 2 files (size='0 bytes'): + '/remote/file1' (size='0 bytes') + NotAFileError("'/remote/file1' is not a file") + + '/remote/file2' (size='0 Bytes') + PermissionError("'/remote/file2': [Errno 13] Permission denied") +``` + + + +#### raise_if_missing() → None + +Raise exception if there are some files in `missing` attribute + +* **Raises:** + MissingFilesError + : `missing` file set is not empty + +### Examples + +```pycon +>>> from onetl.impl import LocalPath +>>> from onetl.file.file_result import FileResult +>>> file_result = FileResult( +... missing={ +... LocalPath("/missing/file1"), +... LocalPath("/missing/file2"), +... }, +... ) +>>> file_result.raise_if_missing() +Traceback (most recent call last): + ... +onetl.exception.MissingFilesError: Missing 2 files: + '/missing/file1' + '/missing/file2' +``` + + + +#### raise_if_skipped() → None + +Raise exception if there are some files in `skipped` attribute + +* **Raises:** + SkippedFilesError + : `skipped` file set is not empty + +### Examples + +```pycon +>>> from onetl.impl import LocalPath +>>> from onetl.file.file_result import FileResult +>>> file_result = FileResult( +... skipped={ +... LocalPath("/skipped/file1"), +... LocalPath("/skipped/file2"), +... }, +... ) +>>> file_result.raise_if_skipped() +Traceback (most recent call last): + ... +onetl.exception.SkippedFilesError: Skipped 2 files (15 kB): + '/skipped/file1' (10kB) + '/skipped/file2' (5 kB) +``` + + + +#### *field* skipped *: FileSet[LocalPath]* *[Optional]* + +File paths (local) which were skipped because of some reason + + + +#### *property* skipped_count *: int* + +Get number of skipped files + +### Examples + +```pycon +>>> from onetl.impl import LocalPath +>>> from onetl.file.file_result import FileResult +>>> file_result = FileResult( +... skipped={LocalPath("/some/file"), LocalPath("/some/another.file")}, +... ) +>>> file_result.skipped_count +2 +``` + + + +#### *property* skipped_size *: int* + +Get size (in bytes) of skipped files + +### Examples + +```pycon +>>> from onetl.impl import LocalPath +>>> from onetl.file.file_result import FileResult +>>> file_result = FileResult( +... skipped={LocalPath("/some/file"), LocalPath("/some/another.file")}, +... ) +>>> file_result.skipped_size # in bytes +1024 +``` + + + +#### *field* successful *: FileSet[RemoteFile]* *[Optional]* + +File paths (remote) which were uploaded successfully + + + +#### *property* successful_count *: int* + +Get number of successful files + +### Examples + +```pycon +>>> from onetl.impl import LocalPath +>>> from onetl.file.file_result import FileResult +>>> file_result = FileResult( +... successful={LocalPath("/some/file"), LocalPath("/some/another.file")}, +... ) +>>> file_result.successful_count +2 +``` + + + +#### *property* successful_size *: int* + +Get size (in bytes) of successful files + +### Examples + +```pycon +>>> from onetl.impl import LocalPath +>>> from onetl.file.file_result import FileResult +>>> file_result = FileResult( +... successful={LocalPath("/some/file"), LocalPath("/some/another.file")}, +... ) +>>> file_result.successful_size # in bytes +1024 +``` + + + +#### *property* summary *: str* + +Return short summary about files in the result object + +### Examples + +```pycon +>>> from onetl.impl import FailedRemoteFile, LocalPath, RemoteFile, RemotePathStat +>>> from onetl.exception import NotAFileError +>>> from onetl.file.file_result import FileResult +>>> file_result1 = FileResult( +... successful={ +... RemoteFile("/local/file", stats=RemotePathStat(st_size=1024)), +... RemoteFile("/local/another.file", stats=RemotePathStat(st_size=1024)), +... }, +... failed={ +... FailedRemoteFile( +... path="/remote/file1", +... stats=RemotePathStat(st_size=0), +... exception=NotAFileError("'/remote/file1' is not a file"), +... ), +... FailedRemoteFile( +... path="/remote/file2", +... stats=RemotePathStat(st_size=0), +... exception=PermissionError("'/remote/file2': [Errno 13] Permission denied"), +... ), +... }, +... skipped={LocalPath("/skipped/file1"), LocalPath("/skipped/file2")}, +... missing={LocalPath("/missing/file1"), LocalPath("/missing/file2")}, +... ) +>>> print(file_result1.summary) +Total: 8 files (size='2.0 kB') + +Successful: 2 files (size='2.0 kB') + +Failed: 2 files (size='0 Bytes') + +Skipped: 2 files (size='0 Bytes') + +Missing: 2 files +``` + +```pycon +>>> file_result2 = FileResult() +>>> print(file_result2.summary) +No files +``` + + + +#### *property* total_count *: int* + +Get total number of all files + +### Examples + +```pycon +>>> from onetl.impl import RemoteFile +>>> from onetl.file.file_result import FileResult +>>> file_result = FileResult( +... successful={LocalPath("/local/file"), LocalPath("/local/another.file")}, +... failed={RemoteFile("/remote/file"), RemoteFile("/remote/another.file")}, +... skipped={LocalPath("/skipped/file")}, +... missing={LocalPath("/missing/file")}, +... ) +>>> file_result.total_count +6 +``` + + + +#### *property* total_size *: int* + +Get total size (in bytes) of all files + +### Examples + +```pycon +>>> from onetl.impl import RemoteFile, RemotePathStat, LocalPath +>>> from onetl.file.file_result import FileResult +>>> file_result = FileResult( +... successful={LocalPath("/local/file"), LocalPath("/local/another.file")}, +... failed={ +... RemoteFile("/remote/file", stats=RemotePathStat(st_size=1024)), +... RemoteFile("/remote/another.file", stats=RemotePathStat(st_size=1024)) +... }, +... skipped={LocalPath("/skipped/file")}, +... missing={LocalPath("/missing/file")}, +... ) +>>> file_result.total_size # in bytes +4096 +``` + + diff --git a/mddocs/file/index.md b/mddocs/file/index.md new file mode 100644 index 000000000..2da812bab --- /dev/null +++ b/mddocs/file/index.md @@ -0,0 +1,9 @@ + + + File classes + +* [File Downloader](file_downloader/index.md) +* [File Uploader](file_uploader/index.md) +* [File Mover](file_mover/index.md) +* [File Filters](file_filters/index.md) +* [File Limits](file_limits/index.md) diff --git a/mddocs/file_df/file_df_reader/file_df_reader.md b/mddocs/file_df/file_df_reader/file_df_reader.md new file mode 100644 index 000000000..256ab7b59 --- /dev/null +++ b/mddocs/file_df/file_df_reader/file_df_reader.md @@ -0,0 +1,158 @@ + + +# FileDF Reader + +### *class* onetl.file.file_df_reader.file_df_reader.FileDFReader(\*, connection: [BaseFileDFConnection](../../connection/file_df_connection/base.md#onetl.base.base_file_df_connection.BaseFileDFConnection), format: [BaseReadableFileFormat](../file_formats/base.md#onetl.base.base_file_format.BaseReadableFileFormat), source_path: PurePathProtocol | None = None, df_schema: StructType | None = None, options: [FileDFReaderOptions](options.md#onetl.file.file_df_reader.options.FileDFReaderOptions) = FileDFReaderOptions(recursive=None)) + +Allows you to read files from a source path with specified file connection +and parameters, and return a Spark DataFrame. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### WARNING +This class does **not** support read strategies. + +#### Versionadded +Added in version 0.9.0. + +* **Parameters:** + **connection** + : File DataFrame connection. See [File DataFrame Connections](../../connection/file_df_connection/index.md#file-df-connections) section. + + **format** + : File format to read. + + **source_path** + : Directory path to read data from. +
+ Could be `None`, but only if you pass file paths directly to + [`run`](#onetl.file.file_df_reader.file_df_reader.FileDFReader.run) method + + **df_schema** + : Spark DataFrame schema. + + **options** + : Common reading options. + +### Examples + +Read CSV files from local filesystem + +```py +from onetl.connection import SparkLocalFS +from onetl.file import FileDFReader +from onetl.file.format import CSV + +csv = CSV(delimiter=",") +local_fs = SparkLocalFS(spark=spark) + +reader = FileDFReader( + connection=local_fs, + format=csv, + source_path="/path/to/directory", +) +``` + +All supported options + +```py +from onetl.connection import SparkLocalFS +from onetl.file import FileDFReader +from onetl.file.format import CSV + +csv = CSV(delimiter=",") +local_fs = SparkLocalFS(spark=spark) + +reader = FileDFReader( + connection=local_fs, + format=csv, + source_path="/path/to/directory", + options=FileDFReader.Options(recursive=False), +) +``` + + + +#### run(files: Iterable[str | os.PathLike] | None = None) → DataFrame + +Method for reading files as DataFrame. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### Versionadded +Added in version 0.9.0. + +* **Parameters:** + **files** + : File list to read. +
+ If empty, read files from `source_path`. +* **Returns:** + **df** + : Spark DataFrame + +### Examples + +Read CSV files from directory `/path`: + +```python +from onetl.connection import SparkLocalFS +from onetl.file import FileDFReader +from onetl.file.format import CSV + +csv = CSV(delimiter=",") +local_fs = SparkLocalFS(spark=spark) + +reader = FileDFReader( + connection=local_fs, + format=csv, + source_path="/path", +) +df = reader.run() +``` + +Read some CSV files using file paths: + +```python +from onetl.connection import SparkLocalFS +from onetl.file import FileDFReader +from onetl.file.format import CSV + +csv = CSV(delimiter=",") +local_fs = SparkLocalFS(spark=spark) + +reader = FileDFReader( + connection=local_fs, + format=csv, +) + +df = reader.run( + [ + "/path/file1.csv", + "/path/nested/file2.csv", + ] +) +``` + +Read only specific CSV files in directory: + +```python +from onetl.connection import SparkLocalFS +from onetl.file import FileDFReader +from onetl.file.format import CSV + +csv = CSV(delimiter=",") +local_fs = SparkLocalFS(spark=spark) + +reader = FileDFReader( + connection=local_fs, + format=csv, + source_path="/path", +) + +df = reader.run( + [ + # file paths could be relative + "/path/file1.csv", + "/path/nested/file2.csv", + ] +) +``` + + diff --git a/mddocs/file_df/file_df_reader/index.md b/mddocs/file_df/file_df_reader/index.md new file mode 100644 index 000000000..0087e74ac --- /dev/null +++ b/mddocs/file_df/file_df_reader/index.md @@ -0,0 +1,8 @@ + + +# FileDF Reader + +# FileDF Reader + +* [FileDF Reader](file_df_reader.md) +* [Options](options.md) diff --git a/mddocs/file_df/file_df_reader/options.md b/mddocs/file_df/file_df_reader/options.md new file mode 100644 index 000000000..eceb33e5d --- /dev/null +++ b/mddocs/file_df/file_df_reader/options.md @@ -0,0 +1,38 @@ + + +# Options + +### *class* onetl.file.file_df_reader.options.FileDFReaderOptions(\*, recursiveFileLookup: bool | None = None, \*\*kwargs) + +Options for [`FileDFReader`](file_df_reader.md#onetl.file.file_df_reader.file_df_reader.FileDFReader). + +#### Versionadded +Added in version 0.9.0. + +### Examples + +#### NOTE +You can pass any value [supported by Spark](https://spark.apache.org/docs/latest/sql-data-sources-load-save-functions.html), +even if it is not mentioned in this documentation. **Option names should be in** `camelCase`! + +The set of supported options depends on Spark version. + +```python +from onetl.file import FileDFReader + +options = FileDFReader.Options(recursive=True) +``` + + + +#### *field* recursive *: bool | None* *= None* *(alias 'recursiveFileLookup')* + +If `True`, perform recursive file lookup. + +#### WARNING +This disables partition inferring using file paths. + +#### WARNING +Can be used only in Spark 3+. See [SPARK-27990](https://issues.apache.org/jira/browse/SPARK-27990). + + diff --git a/mddocs/file_df/file_df_writer/file_df_writer.md b/mddocs/file_df/file_df_writer/file_df_writer.md new file mode 100644 index 000000000..8f845c598 --- /dev/null +++ b/mddocs/file_df/file_df_writer/file_df_writer.md @@ -0,0 +1,80 @@ + + +# FileDF Writer + +### *class* onetl.file.file_df_writer.file_df_writer.FileDFWriter(\*, connection: ~onetl.base.base_file_df_connection.BaseFileDFConnection, format: ~onetl.base.base_file_format.BaseWritableFileFormat, target_path: ~onetl.base.pure_path_protocol.PurePathProtocol, options: ~onetl.file.file_df_writer.options.FileDFWriterOptions = FileDFWriterOptions(if_exists=, partition_by=None)) + +Allows you to write Spark DataFrame as files in a target path of specified file connection +with parameters. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +* **Parameters:** + **connection** + : File DataFrame connection. See [File DataFrame Connections](../../connection/file_df_connection/index.md#file-df-connections) section. + + **format** + : File format to write. + + **target_path** + : Directory path to write data to. + + **options** + : Common writing options. + +### Examples + +Write CSV files to local filesystem + +```py +from onetl.connection import SparkLocalFS +from onetl.file import FileDFWriter +from onetl.file.format import CSV + +local_fs = SparkLocalFS(spark=spark) + +writer = FileDFWriter( + connection=local_fs, + format=CSV(delimiter=","), + target_path="/path/to/directory", +) +``` + +All supported options + +```py +from onetl.connection import SparkLocalFS +from onetl.file import FileDFWriter +from onetl.file.format import CSV + +csv = CSV(delimiter=",") +local_fs = SparkLocalFS(spark=spark) + +writer = FileDFWriter( + connection=local_fs, + format=csv, + target_path="/path/to/directory", + options=FileDFWriter.Options(if_exists="replace_entire_directory"), +) +``` + + + +#### run(df: DataFrame) → None + +Method for writing DataFrame as files. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### NOTE +Method does support only **batching** DataFrames. + +* **Parameters:** + **df** + : Spark dataframe + +### Examples + +Write df to target: + +```python +writer.run(df) +``` + + diff --git a/mddocs/file_df/file_df_writer/index.md b/mddocs/file_df/file_df_writer/index.md new file mode 100644 index 000000000..9813ed1d4 --- /dev/null +++ b/mddocs/file_df/file_df_writer/index.md @@ -0,0 +1,8 @@ + + +# FileDF Writer + +# FileDF Writer + +* [FileDF Writer](file_df_writer.md) +* [Options](options.md) diff --git a/mddocs/file_df/file_df_writer/options.md b/mddocs/file_df/file_df_writer/options.md new file mode 100644 index 000000000..7a76fecbc --- /dev/null +++ b/mddocs/file_df/file_df_writer/options.md @@ -0,0 +1,140 @@ + + +# Options + +### *class* onetl.file.file_df_writer.options.FileDFWriterOptions(\*, if_exists: FileDFExistBehavior = FileDFExistBehavior.APPEND, partitionBy: List[str] | str | None = None, \*\*kwargs) + +Options for [`FileDFWriter`](file_df_writer.md#onetl.file.file_df_writer.file_df_writer.FileDFWriter). + +#### Versionadded +Added in version 0.9.0. + +### Examples + +#### NOTE +You can pass any value [supported by Spark](https://spark.apache.org/docs/latest/sql-data-sources-load-save-functions.html), +even if it is not mentioned in this documentation. **Option names should be in** `camelCase`! + +The set of supported options depends on Spark version. + +```python +from onetl.file import FileDFWriter + +options = FileDFWriter.Options( + if_exists="replace_overlapping_partitions", + partitionBy="month", +) +``` + + + +#### *field* if_exists *: FileDFExistBehavior* *= FileDFExistBehavior.APPEND* + +Behavior for existing target directory. + +If target directory does not exist, it will be created. +But if it does exist, then behavior is different for each value. + +#### Versionchanged +Changed in version 0.13.0: Default value was changed from `error` to `append` + +Possible values: +: * `error` + : If folder already exists, raise an exception. +
+ Same as Spark’s `df.write.mode("error").save()`. + * `skip_entire_directory` + : If folder already exists, left existing files intact and stop immediately without any errors. +
+ Same as Spark’s `df.write.mode("ignore").save()`. + * `append` (default) + : Appends data into existing directory. +
+ ### Behavior in details +
+ * Directory does not exist + : Directory is created using all the provided options (`format`, `partition_by`, etc). + * Directory exists, does not contain partitions, but [`partition_by`](#onetl.file.file_df_writer.options.FileDFWriterOptions.partition_by) is set + : Data is appended to a directory, but to partitioned directory structure. +
+ #### WARNING + Existing files still present in the root of directory, but Spark will ignore those files while reading, + unless using `recursive=True`. + * Directory exists and contains partitions, but [`partition_by`](#onetl.file.file_df_writer.options.FileDFWriterOptions.partition_by) is not set + : Data is appended to a directory, but to the root of directory instead of nested partition directories. +
+ #### WARNING + Spark will ignore such files while reading, unless using `recursive=True`. + * Directory exists and contains partitions, but with different partitioning schema than [`partition_by`](#onetl.file.file_df_writer.options.FileDFWriterOptions.partition_by) + : Data is appended to a directory with new partitioning schema. +
+ #### WARNING + Spark cannot read directory with multiple partitioning schemas, + unless using `recursive=True` to disable partition scanning. + * Directory exists and partitioned according [`partition_by`](#onetl.file.file_df_writer.options.FileDFWriterOptions.partition_by), but partition is present only in dataframe + : New partition directory is created. + * Directory exists and partitioned according [`partition_by`](#onetl.file.file_df_writer.options.FileDFWriterOptions.partition_by), partition is present in both dataframe and directory + : New files are added to existing partition directory, existing files are sill present. + * Directory exists and partitioned according [`partition_by`](#onetl.file.file_df_writer.options.FileDFWriterOptions.partition_by), but partition is present only in directory, not dataframe + : Existing partition is left intact. + * `replace_overlapping_partitions` + : If partitions from dataframe already exist in directory structure, they will be overwritten. +
+ Same as Spark’s `df.write.mode("overwrite").save()` + + `spark.sql.sources.partitionOverwriteMode=dynamic`. +
+ ### Behavior in details +
+ * Directory does not exist + : Directory is created using all the provided options (`format`, `partition_by`, etc). + * Directory exists, does not contain partitions, but [`partition_by`](#onetl.file.file_df_writer.options.FileDFWriterOptions.partition_by) is set + : Directory **will be deleted**, and will be created with partitions. + * Directory exists and contains partitions, but [`partition_by`](#onetl.file.file_df_writer.options.FileDFWriterOptions.partition_by) is not set + : Directory **will be deleted**, and will be created with partitions. + * Directory exists and contains partitions, but with different partitioning schema than [`partition_by`](#onetl.file.file_df_writer.options.FileDFWriterOptions.partition_by) + : Data is appended to a directory with new partitioning schema. +
+ #### WARNING + Spark cannot read directory with multiple partitioning schemas, + unless using `recursive=True` to disable partition scanning. + * Directory exists and partitioned according [`partition_by`](#onetl.file.file_df_writer.options.FileDFWriterOptions.partition_by), but partition is present only in dataframe + : New partition directory is created. + * Directory exists and partitioned according [`partition_by`](#onetl.file.file_df_writer.options.FileDFWriterOptions.partition_by), partition is present in both dataframe and directory + : Partition directory **will be deleted**, and new one is created with files containing data from dataframe. + * Directory exists and partitioned according [`partition_by`](#onetl.file.file_df_writer.options.FileDFWriterOptions.partition_by), but partition is present only in directory, not dataframe + : Existing partition is left intact. + * `replace_entire_directory` + : Remove existing directory and create new one, **overwriting all existing data**. + **All existing partitions are dropped.** +
+ Same as Spark’s `df.write.mode("overwrite").save()` + + `spark.sql.sources.partitionOverwriteMode=static`. + +#### NOTE +Unlike using pure Spark, config option `spark.sql.sources.partitionOverwriteMode` +does not affect behavior of any `mode` + + + +#### *field* partition_by *: List[str] | str | None* *= None* *(alias 'partitionBy')* + +List of columns should be used for data partitioning. `None` means partitioning is disabled. + +Each partition is a folder which contains only files with the specific column value, +like `some.csv/col1=value1`, `some.csv/col1=value2`, and so on. + +Multiple partitions columns means nested folder structure, like `some.csv/col1=val1/col2=val2`. + +If `WHERE` clause in the query contains expression like `partition = value`, +Spark will scan only files in a specific partition. + +Examples: `reg_id` or `["reg_id", "business_dt"]` + +#### NOTE +Values should be scalars (integers, strings), +and either static (`countryId`) or incrementing (dates, years), with low +number of distinct values. + +Columns like `userId` or `datetime`/`timestamp` should **NOT** be used for partitioning. + + diff --git a/mddocs/file_df/file_formats/avro.md b/mddocs/file_df/file_formats/avro.md new file mode 100644 index 000000000..de41f7796 --- /dev/null +++ b/mddocs/file_df/file_formats/avro.md @@ -0,0 +1,390 @@ + + +# Avro + +### *class* onetl.file.format.avro.Avro(\*, avroSchema: dict | None = None, avroSchemaUrl: str | None = None, recordName: str | None = None, recordNamespace: str | None = None, compression: str | Literal['uncompressed', 'snappy', 'deflate', 'bzip2', 'xz', 'zstandard'] | None = None, mode: Literal['PERMISSIVE', 'FAILFAST'] | None = None, datetimeRebaseMode: Literal['CORRECTED', 'LEGACY', 'EXCEPTION'] | None = None, positionalFieldMatching: bool | None = None, enableStableIdentifiersForUnionType: bool | None = None, \*\*kwargs) + +Avro file format. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +Based on [Spark Avro](https://spark.apache.org/docs/latest/sql-data-sources-avro.html) file format. + +Supports reading/writing files with `.avro` extension. + +### Version compatibility + +* Spark versions: 2.4.x - 3.5.x +* Java versions: 8 - 20 + +See documentation from link above. + +#### Versionadded +Added in version 0.9.0. + +### Examples + +#### NOTE +You can pass any option mentioned in +[official documentation](https://spark.apache.org/docs/latest/sql-data-sources-avro.html). +**Option names should be in** `camelCase`! + +The set of supported options depends on Spark version. + +Reading files + +```py +from pyspark.sql import SparkSession +from onetl.file.format import Avro + +# Create Spark session with Avro package loaded +maven_packages = Avro.get_packages(spark_version="3.5.5") +spark = ( + SparkSession.builder.appName("spark-app-name") + .config("spark.jars.packages", ",".join(maven_packages)) + .getOrCreate() +) + +schema = { + "type": "record", + "name": "Person", + "fields": [ + {"name": "name", "type": "string"}, + {"name": "age", "type": "int"}, + ], +} +avro = Avro(avroSchema=schema) # or avroSchemaUrl=... +``` + +Writing files + +```py +# Create Spark session with Avro package loaded +spark = ... + +from onetl.file.format import Avro + +schema = { + "type": "record", + "name": "Person", + "fields": [ + {"name": "name", "type": "string"}, + {"name": "age", "type": "int"}, + ], +} +avro = Avro( + avroSchema=schema, # or avroSchemaUrl=... + compression="snappy", +) +``` + + + +#### *field* schema_dict *: dict | None* *= None* *(alias 'avroSchema')* + +Avro schema in JSON format representation. + +```python +avro = Avro( + avroSchema={ + "type": "record", + "name": "Person", + "fields": [ + {"name": "name", "type": "string"}, + {"name": "age", "type": "int"}, + ], + }, +) +``` + +If set, all records should match this schema. + +#### WARNING +Mutually exclusive with [`schema_url`](#onetl.file.format.avro.Avro.schema_url). + + + +#### *field* schema_url *: str | None* *= None* *(alias 'avroSchemaUrl')* + +URL to Avro schema in JSON format. Usually points to Schema Registry, like: + +```python +schema_registry = "http://some.schema.registry.domain" +name = "MyAwesomeSchema" +version = "latest" + +schema_url = f"{schema_registry}/subjects/{name}/versions/{version}/schema" +avro = Avro(avroSchemaUrl=schema_url) +``` + +If set, schema is fetched before any records are parsed, so all records should match this schema. + +#### WARNING +Mutually exclusive with [`schema_dict`](#onetl.file.format.avro.Avro.schema_dict). + + + +#### *field* recordName *: str | None* *= None* + +Record name in written Avro schema. +Default is `topLevelRecord`. + +#### NOTE +Used only for writing files and by [`serialize_column`](#onetl.file.format.avro.Avro.serialize_column). + + + +#### *field* recordNamespace *: str | None* *= None* + +Record namespace in written Avro schema. Default is not set. + +#### NOTE +Used only for writing files and by [`serialize_column`](#onetl.file.format.avro.Avro.serialize_column). + + + +#### *field* compression *: str | Literal['uncompressed', 'snappy', 'deflate', 'bzip2', 'xz', 'zstandard'] | None* *= None* + +Compression codec. +By default, Spark config value `spark.sql.avro.compression.codec `` (``snappy`) is used. + +#### NOTE +Used only for writing files. Ignored by [`serialize_column`](#onetl.file.format.avro.Avro.serialize_column). + + + +#### *field* mode *: Literal['PERMISSIVE', 'FAILFAST'] | None* *= None* + +How to handle parsing errors: +: * `PERMISSIVE` - set field value as `null`. + * `FAILFAST` - throw an error immediately. + +Default is `FAILFAST`. + +#### NOTE +Used only by [`parse_column`](#onetl.file.format.avro.Avro.parse_column) method. + + + +#### *field* datetimeRebaseMode *: Literal['CORRECTED', 'LEGACY', 'EXCEPTION'] | None* *= None* + +While converting dates/timestamps from Julian to Proleptic Gregorian calendar, handle value ambiguity: +: * `EXCEPTION` - fail if ancient dates/timestamps are ambiguous between the two calendars. + * `CORRECTED` - load dates/timestamps without as-is. + * `LEGACY` - rebase ancient dates/timestamps from the Julian to Proleptic Gregorian calendar. + +By default, Spark config value `spark.sql.avro.datetimeRebaseModeInRead` (`CORRECTED`) is used. + +#### NOTE +Used only for reading files and by [`parse_column`](#onetl.file.format.avro.Avro.parse_column). + + + +#### *field* positionalFieldMatching *: bool | None* *= None* + +If `True`, match Avro schema field and DataFrame column by position. +If `False`, match by name. + +Default is `False`. + + + +#### *field* enableStableIdentifiersForUnionType *: bool | None* *= None* + +Avro schema may contain union types, which are not supported by Spark. +Different variants of union are split to separated DataFrame columns with respective type. + +If option value is `True`, DataFrame column names are based on Avro variant names, e.g. `member_int`, `member_string`. +If `False`, DataFrame column names are generated using field position, e.g. `member0`, `member1`. + +Default is `False`. + +#### NOTE +Used only for reading files and by [`parse_column`](#onetl.file.format.avro.Avro.parse_column). + + + +#### *classmethod* get_packages(spark_version: str, scala_version: str | None = None) → list[str] + +Get package names to be downloaded by Spark. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +See [Maven package index](https://mvnrepository.com/artifact/org.apache.spark/spark-avro) +for all available packages. + +#### Versionadded +Added in version 0.9.0. + +* **Parameters:** + **spark_version** + : Spark version in format `major.minor.patch`. + + **scala_version** + : Scala version in format `major.minor`. +
+ If `None`, `spark_version` is used to determine Scala version. + +### Examples + +```python +from onetl.file.format import Avro + +Avro.get_packages(spark_version="3.5.5") +Avro.get_packages(spark_version="3.5.5", scala_version="2.12") +``` + + + +#### parse_column(column: str | Column) → Column + +Parses an Avro binary column into a structured Spark SQL column using Spark’s +[from_avro](https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.avro.functions.from_avro.html) function, +based on the schema provided within the class. + +#### NOTE +Can be used only with Spark 3.x+ + +#### WARNING +If `schema_url` is provided, `requests` library is used to fetch the schema from the URL. +It should be installed manually, like this: + +```bash +pip install requests +``` + +#### Versionadded +Added in version 0.11.0. + +* **Parameters:** + **column** + : The name of the column or the column object containing Avro bytes to deserialize. + Schema should match the provided Avro schema. +* **Returns:** + Column with deserialized data. Schema is matching the provided Avro schema. Column name is the same as input column. +* **Raises:** + ValueError + : If the Spark version is less than 3.x or if neither `avroSchema` nor `avroSchemaUrl` are defined. + + ImportError + : If `schema_url` is used and the `requests` library is not installed. + +### Examples + +```pycon +>>> from pyspark.sql.functions import decode +>>> from onetl.file.format import Avro +>>> df.show() ++----+----------------------+----------+---------+------+-----------------------+-------------+ +|key |value |topic |partition|offset|timestamp |timestampType| ++----+----------------------+----------+---------+------+-----------------------+-------------+ +|[31]|[0A 41 6C 69 63 65 28]|topicAvro |0 |0 |2024-04-24 13:02:25.911|0 | +|[32]|[06 42 6F 62 32] |topicAvro |0 |1 |2024-04-24 13:02:25.922|0 | ++----+----------------------+----------+---------+------+-----------------------+-------------+ +>>> df.printSchema() +root +|-- key: binary (nullable = true) +|-- value: binary (nullable = true) +|-- topic: string (nullable = true) +|-- partition: integer (nullable = true) +|-- offset: integer (nullable = true) +|-- timestamp: timestamp (nullable = true) +|-- timestampType: integer (nullable = true) +>>> avro = Avro( +... avroSchema={ # or avroSchemaUrl=... +... "type": "record", +... "name": "Person", +... "fields": [ +... {"name": "name", "type": "string"}, +... {"name": "age", "type": "int"}, +... ], +... } +... ) +>>> parsed_df = df.select(decode("key", "UTF-8").alias("key"), avro.parse_column("value")) +>>> parsed_df.show(truncate=False) ++---+-----------+ +|key|value | ++---+-----------+ +|1 |{Alice, 20}| +|2 |{Bob, 25} | ++---+-----------+ +>>> parsed_df.printSchema() +root +|-- key: string (nullable = true) +|-- value: struct (nullable = true) +| |-- name: string (nullable = true) +| |-- age: integer (nullable = true) +``` + + + +#### serialize_column(column: str | Column) → Column + +Serializes a structured Spark SQL column into an Avro binary column using Spark’s +[to_avro](https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.avro.functions.to_avro.html#pyspark.sql.avro.functions.to_avro) function. + +#### NOTE +Can be used only with Spark 3.x+ + +#### WARNING +If `schema_url` is provided, `requests` library is used to fetch the schema from the URL. It should be installed manually, like this: + +```bash +pip install requests +``` + +#### Versionadded +Added in version 0.11.0. + +* **Parameters:** + **column** + : The name of the column or the column object containing the data to serialize to Avro format. +* **Returns:** + Column with binary Avro data. Column name is the same as input column. +* **Raises:** + ValueError + : If the Spark version is less than 3.x. + + ImportError + : If `schema_url` is used and the `requests` library is not installed. + +### Examples + +```pycon +>>> from pyspark.sql.functions import decode +>>> from onetl.file.format import Avro +>>> df.show() ++---+-----------+ +|key|value | ++---+-----------+ +|1 |{Alice, 20}| +|2 | {Bob, 25}| ++---+-----------+ +>>> df.printSchema() +root +|-- key: string (nullable = true) +|-- value: struct (nullable = true) +| |-- name: string (nullable = true) +| |-- age: integer (nullable = true) +>>> # serializing data into Avro format +>>> avro = Avro( +... avroSchema={ # or avroSchemaUrl=... +... "type": "record", +... "name": "Person", +... "fields": [ +... {"name": "name", "type": "string"}, +... {"name": "age", "type": "int"}, +... ], +... } +... ) +>>> serialized_df = df.select("key", avro.serialize_column("value")) +>>> serialized_df.show(truncate=False) ++---+----------------------+ +|key|value | ++---+----------------------+ +| 1|[0A 41 6C 69 63 65 28]| +| 2|[06 42 6F 62 32] | ++---+----------------------+ +>>> serialized_df.printSchema() +root +|-- key: string (nullable = true) +|-- value: binary (nullable = true) +``` + + diff --git a/mddocs/file_df/file_formats/base.md b/mddocs/file_df/file_formats/base.md new file mode 100644 index 000000000..68ca0e042 --- /dev/null +++ b/mddocs/file_df/file_formats/base.md @@ -0,0 +1,73 @@ + + +# Base interface + +### *class* onetl.base.base_file_format.BaseReadableFileFormat + +Representation of readable file format. + +#### Versionadded +Added in version 0.9.0. + + + +#### *abstract* check_if_supported(spark: SparkSession) → None + +Check if Spark session does support this file format. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### Versionadded +Added in version 0.9.0. + +* **Raises:** + RuntimeError + : If file format is not supported. + + + +#### *abstract* apply_to_reader(reader: DataFrameReader) → DataFrameReader + +Apply provided format to `pyspark.sql.DataFrameReader`. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### Versionadded +Added in version 0.9.0. + +* **Returns:** + `pyspark.sql.DataFrameReader` + : DataFrameReader with options applied. + + + +### *class* onetl.base.base_file_format.BaseWritableFileFormat + +Representation of writable file format. + +#### Versionadded +Added in version 0.9.0. + + + +#### *abstract* check_if_supported(spark: SparkSession) → None + +Check if Spark session does support this file format. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### Versionadded +Added in version 0.9.0. + +* **Raises:** + RuntimeError + : If file format is not supported. + + + +#### *abstract* apply_to_writer(writer: DataFrameWriter) → DataFrameWriter + +Apply provided format to `pyspark.sql.DataFrameWriter`. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### Versionadded +Added in version 0.9.0. + +* **Returns:** + `pyspark.sql.DataFrameWriter` + : DataFrameWriter with options applied. + + diff --git a/mddocs/file_df/file_formats/csv.md b/mddocs/file_df/file_formats/csv.md new file mode 100644 index 000000000..a02edd58e --- /dev/null +++ b/mddocs/file_df/file_formats/csv.md @@ -0,0 +1,539 @@ + + +# CSV + +### *class* onetl.file.format.csv.CSV(\*, sep: str = ',', header: bool | None = None, quote: ConstrainedStrValue = '"', quoteAll: bool | None = None, escape: ConstrainedStrValue = '\\\\', lineSep: ConstrainedStrValue | None = None, encoding: ConstrainedStrValue | None = None, compression: str | Literal['none', 'bzip2', 'gzip', 'lz4', 'snappy', 'deflate'] | None = None, inferSchema: bool | None = None, samplingRatio: ConstrainedFloatValue | None = None, comment: ConstrainedStrValue | None = None, enforceSchema: bool | None = None, escapeQuotes: bool | None = None, unescapedQuoteHandling: None | Literal['STOP_AT_CLOSING_QUOTE', 'BACK_TO_DELIMITER', 'STOP_AT_DELIMITER', 'SKIP_VALUE', 'RAISE_ERROR'] = None, ignoreLeadingWhiteSpace: bool | None = None, ignoreTrailingWhiteSpace: bool | None = None, emptyValue: str | None = None, nullValue: str | None = None, nanValue: str | None = None, positiveInf: ConstrainedStrValue | None = None, negativeInf: ConstrainedStrValue | None = None, preferDate: bool | None = None, dateFormat: ConstrainedStrValue | None = None, timestampFormat: ConstrainedStrValue | None = None, timestampNTZFormat: ConstrainedStrValue | None = None, locale: ConstrainedStrValue | None = None, maxCharsPerColumn: int | None = None, mode: Literal['PERMISSIVE', 'DROPMALFORMED', 'FAILFAST'] | None = None, columnNameOfCorruptRecord: ConstrainedStrValue | None = None, multiLine: bool | None = None, charToEscapeQuoteEscaping: ConstrainedStrValue | None = None, \*\*kwargs) + +CSV file format. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +Based on [Spark CSV](https://spark.apache.org/docs/latest/sql-data-sources-csv.html) file format. + +Supports reading/writing files with `.csv` extension with content like: + +```csv +"some","value" +"another","value" +``` + +#### Versionadded +Added in version 0.9.0. + +### Examples + +#### NOTE +You can pass any option mentioned in +[official documentation](https://spark.apache.org/docs/latest/sql-data-sources-csv.html). +**Option names should be in** `camelCase`! + +The set of supported options depends on Spark version. + +Reading files + +```py +from onetl.file.format import CSV + +csv = CSV(header=True, inferSchema=True, mode="PERMISSIVE") +``` + +Writing files + +```py +from onetl.file.format import CSV + +csv = CSV(header=True, compression="gzip") +``` + + + +#### *field* delimiter *: str* *= ','* *(alias 'sep')* + +Character used to separate fields in CSV row. + + + +#### *field* header *: bool | None* *= None* + +If `True`, the first row of the file is considered a header. +Default `False`. + + + +#### *field* quote *: str* *= '"'* + +Character used to quote field values within CSV field. + +Empty string is considered as `\u0000` (`NUL`) character. + + +* **Constraints:** + - **maxLength** = 1 + +#### *field* quoteAll *: bool | None* *= None* + +If `True`, all fields are quoted: + +```csv +"some","field with \"quote","123","" +``` + +If `False`, only quote fields containing [`quote`](#onetl.file.format.csv.CSV.quote) symbols. + +```csv +any,"field with \"quote",123, +``` + +Default `False`. + +#### NOTE +Used only for writing files. + + + +#### *field* compression *: str | Literal['none', 'bzip2', 'gzip', 'lz4', 'snappy', 'deflate'] | None* *= None* + +Compression codec of the CSV file. +Default `none`. + +#### NOTE +Used only for writing files. Ignored by [`parse_column`](#onetl.file.format.csv.CSV.parse_column) method. + + + +#### *field* inferSchema *: bool | None* *= None* + +If `True`, try to infer the input schema by reading a sample of the file (see [`samplingRatio`](#onetl.file.format.csv.CSV.samplingRatio)). +Default `False` which means that all parsed columns will be `StringType()`. + +#### NOTE +Used only for reading files, and only if user haven’t provider explicit DataFrame schema. +Ignored by [`parse_column`](#onetl.file.format.csv.CSV.parse_column) function. + + + +#### *field* samplingRatio *: float | None* *= None* + +For `inferSchema=True`, read the specified fraction of rows to infer the schema. +Default `1`. + +#### NOTE +Used only for reading files. Ignored by [`parse_column`](#onetl.file.format.csv.CSV.parse_column) function. + + +* **Constraints:** + - **minimum** = 0 + - **maximum** = 1 + +#### *field* comment *: str | None* *= None* + +If set, all lines starting with specified character (e.g. `#`) are considered a comment, and skipped. +Default is not set, meaning that CSV lines should not contain comments. + +#### NOTE +Used only for reading files and [`parse_column`](#onetl.file.format.csv.CSV.parse_column) method. + + +* **Constraints:** + - **maxLength** = 1 + +#### *field* enforceSchema *: bool | None* *= None* + +If `True`, inferred or user-provided schema has higher priority than CSV file headers. +This means that all input files should have the same structure. + +If `False`, CSV headers are used as a primary source of information about column names and their position. + +Default `True`. + +#### NOTE +Used only for reading files. Ignored by [`parse_column`](#onetl.file.format.csv.CSV.parse_column) function. + + + +#### *field* escapeQuotes *: bool | None* *= None* + +If `True`, escape quotes within CSV field. + +```csv +any,field with \"quote,123, +``` + +If `False`, wrap fields containing [`quote`](#onetl.file.format.csv.CSV.quote) symbols with quotes. + +```csv +any,"field with ""quote ",123, +``` + +Default `True`. + +#### NOTE +Used only for writing files. + + + +#### *field* unescapedQuoteHandling *: None | Literal['STOP_AT_CLOSING_QUOTE', 'BACK_TO_DELIMITER', 'STOP_AT_DELIMITER', 'SKIP_VALUE', 'RAISE_ERROR']* *= None* + +Define how to handle unescaped quotes within CSV field. + +* `STOP_AT_CLOSING_QUOTE` - collect all characters until closing quote. +* `BACK_TO_DELIMITER` - collect all characters until delimiter or line end. +* `STOP_AT_DELIMITER` - collect all characters until delimiter or line end. + : If quotes are not closed, this may produce incorrect results (e.g. including delimiter inside field value). +* `SKIP_VALUE` - skip field and consider it as [`nullValue`](#onetl.file.format.csv.CSV.nullValue). +* `RAISE_ERROR` - raise error immediately. + +Default `STOP_AT_DELIMITER`. + +#### NOTE +Used only for reading files and [`parse_column`](#onetl.file.format.csv.CSV.parse_column) method. + + + +#### *field* ignoreLeadingWhiteSpace *: bool | None* *= None* + +If `True`, trim leading whitespaces in field value. + +Defaults: +: * `True` for reading. + * `False` for writing. + + + +#### *field* ignoreTrailingWhiteSpace *: bool | None* *= None* + +If `True`, trim trailing whitespaces in field value. + +Defaults: +: * `True` for reading. + * `False` for writing. + + + +#### *field* emptyValue *: str | None* *= None* + +Value used for empty string fields. + +Defaults: +: * empty string for reading. + * `""` for writing. + + + +#### *field* nullValue *: str | None* *= None* + +If set, this value will be converted to `null`. +Default is empty string. + + + +#### *field* nanValue *: str | None* *= None* + +If set, this string will be considered as Not-A-Number (NaN) value for `FloatType()` and `DoubleType()`. +Default is `NaN`. + +#### NOTE +Used only for reading files and [`parse_column`](#onetl.file.format.csv.CSV.parse_column) method. + + + +#### *field* positiveInf *: str | None* *= None* + +If set, this string will be considered as positive infinity value for `FloatType()` and `DoubleType()`. +Default is `Inf`. + +#### NOTE +Used only for reading files and [`parse_column`](#onetl.file.format.csv.CSV.parse_column) method. + + +* **Constraints:** + - **minLength** = 1 + +#### \_\_init_\_(\*\*kwargs) + + + +#### *field* negativeInf *: str | None* *= None* + +If set, this string will be considered as negative infinity value for `FloatType()` and `DoubleType()`. +Default is `-Inf`. + +#### NOTE +Used only for reading files and [`parse_column`](#onetl.file.format.csv.CSV.parse_column) method. + + +* **Constraints:** + - **minLength** = 1 + +#### *field* preferDate *: bool | None* *= None* + +If `True` and `inferSchema=True` and column does match [`dateFormat`](#onetl.file.format.csv.CSV.dateFormat), consider it as `DateType()`. +For columns matching both [`timestampFormat`](#onetl.file.format.csv.CSV.timestampFormat) and [`dateFormat`](#onetl.file.format.csv.CSV.dateFormat), consider it as `TimestampType()`. + +If `False`, date and timestamp columns will be considered as `StringType()`. + +Default `True`. + +#### NOTE +Used only for reading files. Ignored by [`parse_column`](#onetl.file.format.csv.CSV.parse_column) function. + + + +#### *field* dateFormat *: str | None* *= None* + +String format for `DateType()` representation. +Default is `yyyy-MM-dd`. + + +* **Constraints:** + - **minLength** = 1 + +#### *field* timestampFormat *: str | None* *= None* + +String format for TimestampType()\` representation. +Default is `yyyy-MM-dd'T'HH:mm:ss[.SSS][XXX]`. + + +* **Constraints:** + - **minLength** = 1 + +#### *field* timestampNTZFormat *: str | None* *= None* + +String format for TimestampNTZType()\` representation. +Default is `yyyy-MM-dd'T'HH:mm:ss[.SSS]`. + +#### NOTE +Added in Spark 3.2.0 + + +* **Constraints:** + - **minLength** = 1 + +#### *field* locale *: str | None* *= None* + +Locale name used to parse dates and timestamps. +Default is `en-US` + +#### NOTE +Used only for reading files and [`parse_column`](#onetl.file.format.csv.CSV.parse_column) method. + + +* **Constraints:** + - **minLength** = 1 + +#### *field* maxCharsPerColumn *: int | None* *= None* + +Maximum number of characters to read per column. +Default is `-1`, which means no limit. + +#### NOTE +Used only for reading files and [`parse_column`](#onetl.file.format.csv.CSV.parse_column) method. + + + +#### *field* mode *: Literal['PERMISSIVE', 'DROPMALFORMED', 'FAILFAST'] | None* *= None* + +How to handle parsing errors: +: * `PERMISSIVE` - set field value as `null`, move raw data to [`columnNameOfCorruptRecord`](#onetl.file.format.csv.CSV.columnNameOfCorruptRecord) column. + * `DROPMALFORMED` - skip the malformed row. + * `FAILFAST` - throw an error immediately. + +Default is `PERMISSIVE`. + +#### NOTE +Used only for reading files and [`parse_column`](#onetl.file.format.csv.CSV.parse_column) method. + + + +#### *field* columnNameOfCorruptRecord *: str | None* *= None* + +Name of column to put corrupt records in. +Default is `_corrupt_record`. + +#### WARNING +If DataFrame schema is provided, this column should be added to schema explicitly: + +```python +from onetl.connection import SparkLocalFS +from onetl.file import FileDFReader +from onetl.file.format import CSV + +from pyspark.sql.types import StructType, StructField, TimestampType, StringType + +spark = ... + +schema = StructType( + [ + StructField("my_field", TimestampType()), + StructField("_corrupt_record", StringType()), # <-- important + ] +) + +csv = CSV(mode="PERMISSIVE", columnNameOfCorruptRecord="_corrupt_record") + +reader = FileDFReader( + connection=connection, + format=csv, + schema=schema, # < --- +) +df = reader.run(["/some/file.csv"]) +``` + +#### NOTE +Used only for reading files and [`parse_column`](#onetl.file.format.csv.CSV.parse_column) method. + + +* **Constraints:** + - **minLength** = 1 + +#### *field* multiLine *: bool | None* *= None* + +If `True`, fields may contain line separators. +If `False`, the input is expected to have one record per file. + +Default is `True`. + +#### NOTE +Used only for reading files. +Ignored by [`parse_column`](#onetl.file.format.csv.CSV.parse_column) method, as it expects that each DataFrame row will contain exactly one CSV line. + + + +#### *field* charToEscapeQuoteEscaping *: str | None* *= None* + +If CSV field value contains `escape` character, it should be escaped as well. +For example, if `escape="\"`, when line: + +```csv +"some \" quoted value",other +"some \\ backslashed value",another +``` + +will be parsed as: + +```python +[ + ('some " quoted value', "other"), + ("some \ backslashed value", "another"), +] +``` + +And vice-versa, for writing CSV rows to file. + +Default is same as `escape`. + + +* **Constraints:** + - **maxLength** = 1 + +#### parse_column(column: str | Column, schema: StructType) → Column + +Parses a CSV string column to a structured Spark SQL column using Spark’s +[from_csv](https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.functions.from_csv.html) function, based on the provided schema. + +#### NOTE +Can be used only with Spark 3.x+ + +#### Versionadded +Added in version 0.11.0. + +* **Parameters:** + **column** + : The name of the column or the column object containing CSV strings/bytes to parse. + + **schema** + : The schema to apply when parsing the CSV data. This defines the structure of the output DataFrame column. +* **Returns:** + Column with deserialized data, with the same structure as the provided schema. Column name is the same as input column. + +### Examples + +```pycon +>>> from pyspark.sql.types import StructType, StructField, IntegerType, StringType +>>> from onetl.file.format import CSV +>>> df.show() ++--+--------+ +|id|value | ++--+--------+ +|1 |Alice;20| +|2 |Bob;25 | ++--+--------+ +>>> df.printSchema() +root +|-- id: integer (nullable = true) +|-- value: string (nullable = true) +>>> csv = CSV(delimiter=";") +>>> csv_schema = StructType( +... [ +... StructField("name", StringType(), nullable=True), +... StructField("age", IntegerType(), nullable=True), +... ], +... ) +>>> parsed_df = df.select("id", csv.parse_column("value", csv_schema)) +>>> parsed_df.show() ++--+-----------+ +|id|value | ++--+-----------+ +|1 |{Alice, 20}| +|2 | {Bob, 25}| ++--+-----------+ +>>> parsed_df.printSchema() +root +|-- id: integer (nullable = true) +|-- value: struct (nullable = true) +| |-- name: string (nullable = true) +| |-- age: integer (nullable = true) +``` + + + +#### serialize_column(column: str | Column) → Column + +Serializes a structured Spark SQL column into a CSV string column using Spark’s +[to_csv](https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.functions.to_csv.html) function. + +#### NOTE +Can be used only with Spark 3.x+ + +#### Versionadded +Added in version 0.11.0. + +* **Parameters:** + **column** + : The name of the column or the Column object containing the data to serialize to CSV. +* **Returns:** + Column with string CSV data. Column name is the same as input column. + +### Examples + +```pycon +>>> from pyspark.sql.functions import decode +>>> from onetl.file.format import CSV +>>> df.show() ++--+-----------+ +|id|value | ++--+-----------+ +|1 |{Alice, 20}| +|2 | {Bob, 25}| ++--+-----------+ +>>> df.printSchema() +root +|-- id: integer (nullable = true) +|-- value: struct (nullable = true) +| |-- name: string (nullable = true) +| |-- age: integer (nullable = true) +>>> # serializing data into CSV format +>>> csv = CSV(delimiter=";") +>>> serialized_df = df.select("id", csv.serialize_column("value")) +>>> serialized_df.show(truncate=False) ++--+--------+ +|id|value | ++--+--------+ +|1 |Alice;20| +|2 |Bob;25 | ++--+--------+ +>>> serialized_df.printSchema() +root +|-- id: integer (nullable = true) +|-- value: string (nullable = true) +``` + + diff --git a/mddocs/file_df/file_formats/excel.md b/mddocs/file_df/file_formats/excel.md new file mode 100644 index 000000000..6925c91ca --- /dev/null +++ b/mddocs/file_df/file_formats/excel.md @@ -0,0 +1,232 @@ + + +# Excel + +### *class* onetl.file.format.excel.Excel(\*, header: bool = False, dataAddress: str | None = None, timestampFormat: str | None = None, dateFormat: str | None = None, treatEmptyValuesAsNulls: bool | None = None, setErrorCellsToFallbackValues: bool | None = None, usePlainNumberFormat: bool | None = None, inferSchema: bool | None = None, workbookPassword: SecretStr | None = None, maxRowsInMemory: int | None = None, maxByteArraySize: ByteSize | None = None, tempFileThreshold: ByteSize | None = None, excerptSize: int | None = None, \*\*kwargs) + +Excel file format. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +Based on [Spark Excel](https://github.com/crealytics/spark-excel) file format. + +Supports reading/writing files with `.xlsx` (read/write) and `.xls` (read only) extensions. + +### Version compatibility + +* Spark versions: 3.2.x - 3.5.x + > #### WARNING + > Not all combinations of Spark version and package version are supported. + > See [Maven index](https://mvnrepository.com/artifact/com.crealytics/spark-excel) + > and [official documentation](https://github.com/crealytics/spark-excel). +* Java versions: 8 - 20 + +See documentation from link above. + +#### Versionadded +Added in version 0.9.4. + +### Examples + +#### NOTE +You can pass any option mentioned in +[official documentation](https://github.com/crealytics/spark-excel). +**Option names should be in** `camelCase`! + +The set of supported options depends on `spark-excel` package version. + +Reading files + +```py +from pyspark.sql import SparkSession +from onetl.file.format import Excel + +# Create Spark session with Excel package loaded +maven_packages = Excel.get_packages(spark_version="3.5.1") +spark = ( + SparkSession.builder.appName("spark-app-name") + .config("spark.jars.packages", ",".join(maven_packages)) + .getOrCreate() +) + +excel = Excel(header=True, inferSchema=True) +``` + +Writing files + +```py +# Create Spark session with Excel package loaded +spark = ... + +from onetl.file.format import XML + +excel = Excel(header=True, dataAddress="'Sheet1'!A1") +``` + + + +#### *field* header *: bool* *= False* + +If `True`, the first row in file is conditioned as a header. +Default `False`. + + + +#### *field* dataAddress *: str | None* *= None* + +Cell address used as starting point. +For example: `'A1'` or `'Sheet1'!A1` + + + +#### *field* timestampFormat *: str | None* *= None* + +Format string used for parsing or serializing timestamp values. +Default `yyyy-mm-dd hh:mm:ss[.fffffffff]`. + + + +#### *field* treatEmptyValuesAsNulls *: bool | None* *= None* + +If `True`, empty cells are parsed as `null` values. +If `False`, empty cells are parsed as empty strings. +Default `True`. + +#### NOTE +Used only for reading files. + + + +#### *field* setErrorCellsToFallbackValues *: bool | None* *= None* + +If `True`, cells containing `#N/A` value are replaced with default value for column type, +e.g. 0 for `IntegerType()`. If `False`, `#N/A` values are replaced with `null`. +Default `False`. + +#### NOTE +Used only for reading files. + + + +#### *field* usePlainNumberFormat *: bool | None* *= None* + +If `True`, read or write numeric values with plain format, without using scientific notation or rounding. +Default `False`. + + + +#### *field* inferSchema *: bool | None* *= None* + +If `True`, infer DataFrame schema based on cell content. +If `False` and no explicit DataFrame schema is passed, all columns are `StringType()`. + +#### NOTE +Used only for reading files. + + + +#### *field* workbookPassword *: SecretStr | None* *= None* + +If Excel file is encrypted, provide password to open it. + +#### NOTE +Used only for reading files. Cannot be used to write files. + + +* **Constraints:** + - **type** = string + - **writeOnly** = True + - **format** = password + +#### *field* maxRowsInMemory *: int | None* *= None* + +If set, use streaming reader and fetch only specified number of rows per iteration. +This reduces memory usage for large files. +Default `None`, which means reading the entire file content to memory. + +#### WARNING +Can be used only with `.xlsx` files, but fails on `.xls`. + +#### NOTE +Used only for reading files. + + + +#### *field* maxByteArraySize *: ByteSize | None* *= None* + +If set, overrides memory limit (in bytes) of byte array size used for reading rows from input file. +Default `0`, which means using default limit. + +See [IOUtils.setByteArrayMaxOverride](https://poi.apache.org/apidocs/5.0/org/apache/poi/util/IOUtils.html#setByteArrayMaxOverride-int-) +documentation. + +#### NOTE +Used only for reading files. + + + +#### *field* tempFileThreshold *: ByteSize | None* *= None* + +If value is greater than 0, large zip entries will be written to temporary files after reaching this threshold. +If value is 0, all zip entries will be written to temporary files. +If value is -1, no temp files will be created, which may cause errors if zip entry is larger than 2GiB. + +#### NOTE +Used only for reading files. + + + +#### *field* excerptSize *: int | None* *= None* + +If `inferSchema=True`, set number of rows to infer schema from. +Default `10`. + +#### NOTE +Used only for reading files. + + + +#### *classmethod* get_packages(spark_version: str, scala_version: str | None = None, package_version: str | None = None) → list[str] + +Get package names to be downloaded by Spark. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### WARNING +Not all combinations of Spark version and package version are supported. +See [Maven index](https://mvnrepository.com/artifact/com.crealytics/spark-excel) +and [official documentation](https://github.com/crealytics/spark-excel). + +#### Versionadded +Added in version 0.9.4. + +* **Parameters:** + **spark_version** + : Spark version in format `major.minor.patch`. + + **scala_version** + : Scala version in format `major.minor`. +
+ If `None`, `spark_version` is used to determine Scala version. + + **package_version** + : Package version in format `major.minor.patch`. Default is `0.20.4`. +
+ #### WARNING + Version `0.14` and below are not supported. +
+ #### NOTE + It is not guaranteed that custom package versions are supported. + Tests are performed only for default version. + +### Examples + +```python +from onetl.file.format import Excel + +Excel.get_packages(spark_version="3.5.1") +Excel.get_packages(spark_version="3.5.1", scala_version="2.12") +Excel.get_packages( + spark_version="3.5.1", + scala_version="2.12", + package_version="0.20.4", +) +``` + + diff --git a/mddocs/file_df/file_formats/index.md b/mddocs/file_df/file_formats/index.md new file mode 100644 index 000000000..a6500bdf3 --- /dev/null +++ b/mddocs/file_df/file_formats/index.md @@ -0,0 +1,18 @@ + + +# File Formats + +# File formats + +* [Avro](avro.md) +* [CSV](csv.md) +* [Excel](excel.md) +* [JSON](json.md) +* [JSONLine](jsonline.md) +* [ORC](orc.md) +* [Parquet](parquet.md) +* [XML](xml.md) + +# For developers + +* [Base interface](base.md) diff --git a/mddocs/file_df/file_formats/json.md b/mddocs/file_df/file_formats/json.md new file mode 100644 index 000000000..fd6ee8da7 --- /dev/null +++ b/mddocs/file_df/file_formats/json.md @@ -0,0 +1,414 @@ + + +# JSON + +### *class* onetl.file.format.json.JSON(\*, multiLine: Literal[True] = True, encoding: str | None = None, lineSep: str | None = None, allowComments: bool | None = None, allowUnquotedFieldNames: bool | None = None, allowSingleQuotes: bool | None = None, allowNumericLeadingZeros: bool | None = None, allowNonNumericNumbers: bool | None = None, allowBackslashEscapingAnyCharacter: bool | None = None, allowUnquotedControlChars: bool | None = None, mode: Literal['PERMISSIVE', 'DROPMALFORMED', 'FAILFAST'] | None = None, columnNameOfCorruptRecord: ConstrainedStrValue | None = None, samplingRatio: ConstrainedFloatValue | None = None, primitivesAsString: bool | None = None, prefersDecimal: bool | None = None, dropFieldIfAllNull: bool | None = None, dateFormat: ConstrainedStrValue | None = None, timestampFormat: ConstrainedStrValue | None = None, timestampNTZFormat: ConstrainedStrValue | None = None, timeZone: ConstrainedStrValue | None = None, locale: ConstrainedStrValue | None = None, \*\*kwargs) + +JSON file format. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +Based on [Spark JSON](https://spark.apache.org/docs/latest/sql-data-sources-json.html) file format. + +Supports reading (but **NOT** writing) files with `.json` extension with content like: + +```json +[ + {"key": "value1"}, + {"key": "value2"} +] +``` + +#### Versionadded +Added in version 0.9.0. + +### Examples + +#### NOTE +You can pass any option mentioned in +[official documentation](https://spark.apache.org/docs/latest/sql-data-sources-json.html). +**Option names should be in** `camelCase`! + +The set of supported options depends on Spark version. + +Reading files: + +```python +from onetl.file.format import JSON + +json = JSON(encoding="UTF-8") +``` + +Writing files: + +#### WARNING +Not supported. Use [`JSONLine`](jsonline.md#onetl.file.format.jsonline.JSONLine). + + + +#### *field* encoding *: str | None* *= None* + +Encoding of the JSON file. +Default `UTF-8`. + +#### NOTE +Used only for reading and writing files. + +Ignored by [`parse_column`](#onetl.file.format.json.JSON.parse_column) and [`serialize_column`](#onetl.file.format.json.JSON.serialize_column) methods. + + + +#### *field* lineSep *: str | None* *= None* + +Character used to separate lines in the JSON file. + +Defaults: +: * Try to detect for reading (`\r\n`, `\r`, `\n`) + * `\n` for writing + +#### NOTE +Used only for reading and writing files. + +Ignored by [`parse_column`](#onetl.file.format.json.JSON.parse_column) and [`serialize_column`](#onetl.file.format.json.JSON.serialize_column) methods, +as they handle each DataFrame row separately. + + + +#### *field* allowComments *: bool | None* *= None* + +If `True`, add support for C/C++/Java style comments (`//`, `/* */`). +Default `False`, meaning that JSON files should not contain comments. + +#### NOTE +Used only for reading files and [`parse_column`](#onetl.file.format.json.JSON.parse_column) method. + + + +#### *field* allowUnquotedFieldNames *: bool | None* *= None* + +If `True`, allow JSON object field names without quotes (JavaScript-style). +Default `False`. + +#### NOTE +Used only for reading files and [`parse_column`](#onetl.file.format.json.JSON.parse_column) method. + + + +#### *field* allowSingleQuotes *: bool | None* *= None* + +If `True`, allow JSON object field names to be wrapped with single quotes (`'`). +Default `True`. + +#### NOTE +Used only for reading files and [`parse_column`](#onetl.file.format.json.JSON.parse_column) method. + + + +#### *field* allowNumericLeadingZeros *: bool | None* *= None* + +If `True`, allow leading zeros in numbers (e.g. `00012`). +Default `False`. + +#### NOTE +Used only for reading files and [`parse_column`](#onetl.file.format.json.JSON.parse_column) method. + + + +#### *field* allowNonNumericNumbers *: bool | None* *= None* + +If `True`, allow numbers to contain non-numeric characters, like: +: * scientific notation (e.g. `12e10`). + * positive infinity floating point value (`Infinity`, `+Infinity`, `+INF`). + * negative infinity floating point value (`-Infinity`, `-INF`). + * Not-a-Number floating point value (`NaN`). + +Default `True`. + +#### NOTE +Used only for reading files and [`parse_column`](#onetl.file.format.json.JSON.parse_column) method. + + + +#### *field* allowBackslashEscapingAnyCharacter *: bool | None* *= None* + +If `True`, prefix `\` can escape any character. +Default `False`. + +#### NOTE +Used only for reading files and [`parse_column`](#onetl.file.format.json.JSON.parse_column) method. + + + +#### *field* allowUnquotedControlChars *: bool | None* *= None* + +If `True`, allow unquoted control characters (ASCII values 0-31) in strings without escaping them with `\`. +Default `False`. + +#### NOTE +Used only for reading files and [`parse_column`](#onetl.file.format.json.JSON.parse_column) method. + + + +#### *field* mode *: Literal['PERMISSIVE', 'DROPMALFORMED', 'FAILFAST'] | None* *= None* + +How to handle parsing errors: +: * `PERMISSIVE` - set field value as `null`, move raw data to [`columnNameOfCorruptRecord`](#onetl.file.format.json.JSON.columnNameOfCorruptRecord) column. + * `DROPMALFORMED` - skip the malformed row. + * `FAILFAST` - throw an error immediately. + +Default is `PERMISSIVE`. + +#### NOTE +Used only for reading files and [`parse_column`](#onetl.file.format.json.JSON.parse_column) method. + + + +#### *field* columnNameOfCorruptRecord *: str | None* *= None* + +Name of column to put corrupt records in. +Default is `_corrupt_record`. + +#### WARNING +If DataFrame schema is provided, this column should be added to schema explicitly: + +```python +from onetl.connection import SparkLocalFS +from onetl.file import FileDFReader +from onetl.file.format import JSON + +from pyspark.sql.types import StructType, StructField, TimestampType, StringType + +spark = ... + +schema = StructType( + [ + StructField("my_field", TimestampType()), + StructField("_corrupt_record", StringType()), # <-- important + ] +) + +json = JSON(mode="PERMISSIVE", columnNameOfCorruptRecord="_corrupt_record") + +reader = FileDFReader( + connection=connection, + format=json, + schema=schema, # < --- +) +df = reader.run(["/some/file.json"]) +``` + +#### NOTE +Used only for reading files and [`parse_column`](#onetl.file.format.json.JSON.parse_column) method. + + +* **Constraints:** + - **minLength** = 1 + +#### *field* samplingRatio *: float | None* *= None* + +While inferring schema, read the specified fraction of file rows. +Default `1`. + +#### NOTE +Used only for reading files. Ignored by [`parse_column`](#onetl.file.format.json.JSON.parse_column) function. + + +* **Constraints:** + - **minimum** = 0 + - **maximum** = 1 + +#### *field* primitivesAsString *: bool | None* *= None* + +If `True`, infer all primitive types (string, integer, float, boolean) as strings. +Default `False`. + +#### NOTE +Used only for reading files. Ignored by [`parse_column`](#onetl.file.format.json.JSON.parse_column) method. + + + +#### *field* prefersDecimal *: bool | None* *= None* + +If `True`, infer all floating-point values as `Decimal`. +Default `False`. + +#### NOTE +Used only for reading files. Ignored by [`parse_column`](#onetl.file.format.json.JSON.parse_column) method. + + + +#### \_\_init_\_(\*\*kwargs) + + + +#### *field* dropFieldIfAllNull *: bool | None* *= None* + +If `True` and inferred column is always null or empty array, exclude if from DataFrame schema. +Default `False`. + +#### NOTE +Used only for reading files. Ignored by [`parse_column`](#onetl.file.format.json.JSON.parse_column) method. + + + +#### *field* dateFormat *: str | None* *= None* + +String format for `DateType()` representation. +Default is `yyyy-MM-dd`. + + +* **Constraints:** + - **minLength** = 1 + +#### *field* timestampFormat *: str | None* *= None* + +String format for TimestampType()\` representation. +Default is `yyyy-MM-dd'T'HH:mm:ss[.SSS][XXX]`. + + +* **Constraints:** + - **minLength** = 1 + +#### *field* timestampNTZFormat *: str | None* *= None* + +String format for TimestampNTZType()\` representation. +Default is `yyyy-MM-dd'T'HH:mm:ss[.SSS]`. + +#### NOTE +Added in Spark 3.2.0 + + +* **Constraints:** + - **minLength** = 1 + +#### *field* timezone *: str | None* *= None* *(alias 'timeZone')* + +Allows to override timezone used for parsing or serializing date and timestamp values. +By default, `spark.sql.session.timeZone` is used. + + +* **Constraints:** + - **minLength** = 1 + +#### *field* locale *: str | None* *= None* + +Locale name used to parse dates and timestamps. +Default is `en-US`. + +#### NOTE +Used only for reading files and [`parse_column`](#onetl.file.format.json.JSON.parse_column) method. + + +* **Constraints:** + - **minLength** = 1 + +#### parse_column(column: str | Column, schema: StructType | ArrayType | MapType) → Column + +Parses a JSON string column to a structured Spark SQL column using Spark’s [from_json](https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.functions.from_json.html) function, based on the provided schema. + +#### Versionadded +Added in version 0.11.0. + +* **Parameters:** + **column** + : The name of the column or the column object containing JSON strings/bytes to parse. + + **schema** + : The schema to apply when parsing the JSON data. This defines the structure of the output DataFrame column. +* **Returns:** + Column with deserialized data, with the same structure as the provided schema. Column name is the same as input column. + +### Examples + +```pycon +>>> from pyspark.sql.types import StructType, StructField, IntegerType, StringType +>>> from pyspark.sql.functions import decode +>>> from onetl.file.format import JSON +>>> df.show() ++----+--------------------+----------+---------+------+-----------------------+-------------+ +|key |value |topic |partition|offset|timestamp |timestampType| ++----+--------------------+----------+---------+------+-----------------------+-------------+ +|[31]|[7B 22 6E 61 6D 6...|topicJSON |0 |0 |2024-04-24 16:51:11.739|0 | +|[32]|[7B 22 6E 61 6D 6...|topicJSON |0 |1 |2024-04-24 16:51:11.749|0 | ++----+--------------------+----------+---------+------+-----------------------+-------------+ +>>> df.printSchema() +root +|-- key: binary (nullable = true) +|-- value: binary (nullable = true) +|-- topic: string (nullable = true) +|-- partition: integer (nullable = true) +|-- offset: integer (nullable = true) +|-- timestamp: timestamp (nullable = true) +|-- timestampType: integer (nullable = true) +>>> json = JSON() +>>> json_schema = StructType( +... [ +... StructField("name", StringType(), nullable=True), +... StructField("age", IntegerType(), nullable=True), +... ], +... ) +>>> parsed_df = df.select(decode("key", "UTF-8").alias("key"), json.parse_column("value", json_schema)) +>>> parsed_df.show() ++---+-----------+ +|key|value | ++---+-----------+ +|1 |{Alice, 20}| +|2 | {Bob, 25}| ++---+-----------+ +>>> parsed_df.printSchema() +root +|-- key: string (nullable = true) +|-- value: struct (nullable = true) +| |-- name: string (nullable = true) +| |-- age: integer (nullable = true) +``` + + + +#### serialize_column(column: str | Column) → Column + +Serializes a structured Spark SQL column into a JSON string column using Spark’s +[to_json](https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.functions.to_json.html) function. + +#### Versionadded +Added in version 0.11.0. + +* **Parameters:** + **column** + : The name of the column or the column object containing the data to serialize to JSON format. +* **Returns:** + Column with string JSON data. Column name is the same as input column. + +### Examples + +```pycon +>>> from pyspark.sql.functions import decode +>>> from onetl.file.format import JSON +>>> df.show() ++---+-----------+ +|key|value | ++---+-----------+ +|1 |{Alice, 20}| +|2 | {Bob, 25}| ++---+-----------+ +>>> df.printSchema() +root +|-- key: string (nullable = true) +|-- value: struct (nullable = true) +| |-- name: string (nullable = true) +| |-- age: integer (nullable = true) +>>> # serializing data into JSON format +>>> json = JSON() +>>> serialized_df = df.select("key", json.serialize_column("value")) +>>> serialized_df.show(truncate=False) ++---+-------------------------+ +|key|value | ++---+-------------------------+ +| 1|{"name":"Alice","age":20}| +| 2|{"name":"Bob","age":25} | ++---+-------------------------+ +>>> serialized_df.printSchema() +root +|-- key: string (nullable = true) +|-- value: string (nullable = true) +``` + + diff --git a/mddocs/file_df/file_formats/jsonline.md b/mddocs/file_df/file_formats/jsonline.md new file mode 100644 index 000000000..644b66bcb --- /dev/null +++ b/mddocs/file_df/file_formats/jsonline.md @@ -0,0 +1,314 @@ + + +# JSONLine + +### *class* onetl.file.format.jsonline.JSONLine(\*, multiLine: Literal[False] = False, encoding: str | None = None, lineSep: str | None = None, compression: str | Literal['none', 'bzip2', 'gzip', 'lz4', 'snappy', 'deflate'] | None = None, ignoreNullFields: bool | None = None, allowComments: bool | None = None, allowUnquotedFieldNames: bool | None = None, allowSingleQuotes: bool | None = None, allowNumericLeadingZeros: bool | None = None, allowNonNumericNumbers: bool | None = None, allowBackslashEscapingAnyCharacter: bool | None = None, allowUnquotedControlChars: bool | None = None, mode: Literal['PERMISSIVE', 'DROPMALFORMED', 'FAILFAST'] | None = None, columnNameOfCorruptRecord: ConstrainedStrValue | None = None, samplingRatio: ConstrainedFloatValue | None = None, primitivesAsString: bool | None = None, prefersDecimal: bool | None = None, dropFieldIfAllNull: bool | None = None, dateFormat: ConstrainedStrValue | None = None, timestampFormat: ConstrainedStrValue | None = None, timestampNTZFormat: ConstrainedStrValue | None = None, timeZone: ConstrainedStrValue | None = None, locale: ConstrainedStrValue | None = None, \*\*kwargs) + +JSONLine file format (each line of file contains a JSON object). [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +Based on [Spark JSON](https://spark.apache.org/docs/latest/sql-data-sources-json.html) file format. + +Supports reading/writing files with `.json` extension with content like: + +```json +{"key": "value1"} +{"key": "value2"} +``` + +#### Versionadded +Added in version 0.9.0. + +### Examples + +#### NOTE +You can pass any option mentioned in +[official documentation](https://spark.apache.org/docs/latest/sql-data-sources-json.html). +**Option names should be in** `camelCase`! + +The set of supported options depends on Spark version. + +Reading files + +```py +from onetl.file.format import JSONLine + +jsonline = JSONLine(encoding="UTF-8", mode="PERMISSIVE") +``` + +Writing files + +#### WARNING +Written files have extension `.json`, not `.jsonl` or `.jsonline`. + +```python +from onetl.file.format import JSONLine + +jsonline = JSONLine(encoding="UTF-8", compression="gzip") +``` + + + +#### *field* encoding *: str | None* *= None* + +Encoding of the JSONLine files. +Default `UTF-8`. + + + +#### *field* lineSep *: str | None* *= None* + +Character used to separate lines in the JSONLine files. + +Defaults: +: * Try to detect for reading (`\r\n`, `\r`, `\n`) + * `\n` for writing. + + + +#### *field* compression *: str | Literal['none', 'bzip2', 'gzip', 'lz4', 'snappy', 'deflate'] | None* *= None* + +Compression codec of the JSONLine file. +Default `none`. + +#### NOTE +Used only for writing files. + + + +#### *field* ignoreNullFields *: bool | None* *= None* + +If `True` and field value is `null`, don’t add field into resulting object +Default is value of `spark.sql.jsonGenerator.ignoreNullFields` (`True`). + +#### NOTE +Used only for writing files. + + + +#### *field* allowComments *: bool | None* *= None* + +If `True`, add support for C/C++/Java style comments (`//`, `/* */`). +Default `False`, meaning that JSONLine files should not contain comments. + +#### NOTE +Used only for reading files. + + + +#### *field* allowUnquotedFieldNames *: bool | None* *= None* + +If `True`, allow JSON object field names without quotes (JavaScript-style). +Default `False`. + +#### NOTE +Used only for reading files. + + + +#### *field* allowSingleQuotes *: bool | None* *= None* + +If `True`, allow JSON object field names to be wrapped with single quotes (`'`). +Default `True`. + +#### NOTE +Used only for reading files. + + + +#### *field* allowNumericLeadingZeros *: bool | None* *= None* + +If `True`, allow leading zeros in numbers (e.g. `00012`). +Default `False`. + +#### NOTE +Used only for reading files. + + + +#### *field* allowNonNumericNumbers *: bool | None* *= None* + +If `True`, allow numbers to contain non-numeric characters, like: +: * scientific notation (e.g. `12e10`). + * positive infinity floating point value (`Infinity`, `+Infinity`, `+INF`). + * negative infinity floating point value (`-Infinity`, `-INF`). + * Not-a-Number floating point value (`NaN`). + +Default `True`. + +#### NOTE +Used only for reading files. + + + +#### *field* allowBackslashEscapingAnyCharacter *: bool | None* *= None* + +If `True`, prefix `\` can escape any character. +Default `False`. + +#### NOTE +Used only for reading files. + + + +#### *field* allowUnquotedControlChars *: bool | None* *= None* + +If `True`, allow unquoted control characters (ASCII values 0-31) in strings without escaping them with `\`. +Default `False`. + +#### NOTE +Used only for reading files. + + + +#### *field* mode *: Literal['PERMISSIVE', 'DROPMALFORMED', 'FAILFAST'] | None* *= None* + +How to handle parsing errors: +: * `PERMISSIVE` - set field value as `null`, move raw data to [`columnNameOfCorruptRecord`](#onetl.file.format.jsonline.JSONLine.columnNameOfCorruptRecord) column. + * `DROPMALFORMED` - skip the malformed row. + * `FAILFAST` - throw an error immediately. + +Default is `PERMISSIVE`. + +#### NOTE +Used only for reading files. + + + +#### *field* columnNameOfCorruptRecord *: str | None* *= None* + +Name of column to put corrupt records in. +Default is `_corrupt_record`. + +#### WARNING +If DataFrame schema is provided, this column should be added to schema explicitly: + +```python +from onetl.connection import SparkLocalFS +from onetl.file import FileDFReader +from onetl.file.format import JSONLine + +from pyspark.sql.types import StructType, StructField, TimestampType, StringType + +spark = ... + +schema = StructType( + [ + StructField("my_field", TimestampType()), + StructField("_corrupt_record", StringType()), # <-- important + ] +) + +jsonline = JSONLine(mode="PERMISSIVE", columnNameOfCorruptRecord="_corrupt_record") + +reader = FileDFReader( + connection=connection, + format=jsonline, + schema=schema, # < --- +) +df = reader.run(["/some/file.jsonl"]) +``` + +#### NOTE +Used only for reading files. + + +* **Constraints:** + - **minLength** = 1 + +#### \_\_init_\_(\*\*kwargs) + + + +#### *field* samplingRatio *: float | None* *= None* + +While inferring schema, read the specified fraction of file rows. +Default `1`. + +#### NOTE +Used only for reading files. + + +* **Constraints:** + - **minimum** = 0 + - **maximum** = 1 + +#### *field* primitivesAsString *: bool | None* *= None* + +If `True`, infer all primitive types (string, integer, float, boolean) as strings. +Default `False`. + +#### NOTE +Used only for reading files. + + + +#### *field* prefersDecimal *: bool | None* *= None* + +If `True`, infer all floating-point values as `Decimal`. +Default `False`. + +#### NOTE +Used only for reading files. + + + +#### *field* dropFieldIfAllNull *: bool | None* *= None* + +If `True` and inferred column is always null or empty array, exclude if from DataFrame schema. +Default `False`. + +#### NOTE +Used only for reading files. + + + +#### *field* dateFormat *: str | None* *= None* + +String format for `DateType()` representation. +Default is `yyyy-MM-dd`. + + +* **Constraints:** + - **minLength** = 1 + +#### *field* timestampFormat *: str | None* *= None* + +String format for TimestampType()\` representation. +Default is `yyyy-MM-dd'T'HH:mm:ss[.SSS][XXX]`. + + +* **Constraints:** + - **minLength** = 1 + +#### *field* timestampNTZFormat *: str | None* *= None* + +String format for TimestampNTZType()\` representation. +Default is `yyyy-MM-dd'T'HH:mm:ss[.SSS]`. + +#### NOTE +Added in Spark 3.2.0 + + +* **Constraints:** + - **minLength** = 1 + +#### *field* timezone *: str | None* *= None* *(alias 'timeZone')* + +Allows to override timezone used for parsing or serializing date and timestamp values. +By default, `spark.sql.session.timeZone` is used. + + +* **Constraints:** + - **minLength** = 1 + +#### *field* locale *: str | None* *= None* + +Locale name used to parse dates and timestamps. +Default is `en-US`. + +#### NOTE +Used only for reading files. + + +* **Constraints:** + - **minLength** = 1 diff --git a/mddocs/file_df/file_formats/orc.md b/mddocs/file_df/file_formats/orc.md new file mode 100644 index 000000000..d922a7e47 --- /dev/null +++ b/mddocs/file_df/file_formats/orc.md @@ -0,0 +1,80 @@ + + +# ORC + +### *class* onetl.file.format.orc.ORC(\*, mergeSchema: bool | None = None, compression: str | Literal['uncompressed', 'snappy', 'zlib', 'lzo', 'zstd', 'lz4'] | None = None, \*\*kwargs) + +ORC file format (columnar). [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +Based on [Spark ORC Files](https://spark.apache.org/docs/latest/sql-data-sources-orc.html) file format. + +Supports reading/writing files with `.orc` extension. + +#### Versionadded +Added in version 0.9.0. + +### Examples + +#### NOTE +You can pass any option mentioned in +[official documentation](https://spark.apache.org/docs/latest/sql-data-sources-orc.html). +**Option names should be in** `camelCase`! + +The set of supported options depends on Spark version. + +You may also set options mentioned [orc-java documentation](https://orc.apache.org/docs/core-java-config.html). +They are prefixed with `orc.` with dots in names, so instead of calling constructor `ORC(orc.option=True)` (invalid in Python) +you should call method `ORC.parse({"orc.option": True})`. + +Reading files + +```py +from onetl.file.format import ORC + +orc = ORC(mergeSchema=True) +``` + +Writing files + +```python +from onetl.file.format import ORC + +orc = ORC.parse( + { + "compression": "snappy", + # Enable Bloom filter for columns 'id' and 'name' + "orc.bloom.filter.columns": "id,name", + # Set Bloom filter false positive probability + "orc.bloom.filter.fpp": 0.01, + # Do not use dictionary for 'highly_selective_column' + "orc.column.encoding.direct": "highly_selective_column", + # other options + } +) +``` + + + +#### *field* mergeSchema *: bool | None* *= None* + +Merge schemas of all ORC files being read into a single schema. +By default, Spark config option `spark.sql.orc.mergeSchema` value is used (`False`). + +#### NOTE +Used only for reading files. + + + +#### *field* compression *: str | Literal['uncompressed', 'snappy', 'zlib', 'lzo', 'zstd', 'lz4'] | None* *= None* + +Compression codec of the ORC files. +By default, Spark config option `spark.sql.orc.compression.codec` value is used (`snappy`). + +#### NOTE +Used only for writing files. + + + +#### \_\_init_\_(\*\*kwargs) + + diff --git a/mddocs/file_df/file_formats/parquet.md b/mddocs/file_df/file_formats/parquet.md new file mode 100644 index 000000000..6ff8aa6e0 --- /dev/null +++ b/mddocs/file_df/file_formats/parquet.md @@ -0,0 +1,79 @@ + + +# Parquet + +### *class* onetl.file.format.parquet.Parquet(\*, mergeSchema: bool | None = None, compression: str | Literal['uncompressed', 'snappy', 'gzip', 'lzo', 'brotli', 'lz4', 'lz4raw', 'zstd'] | None = None, \*\*kwargs) + +Parquet file format (columnar). [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +Based on [Spark Parquet Files](https://spark.apache.org/docs/latest/sql-data-sources-parquet.html) file format. + +Supports reading/writing files with `.parquet` extension. + +#### Versionadded +Added in version 0.9.0. + +### Examples + +#### NOTE +You can pass any option mentioned in +[official documentation](https://spark.apache.org/docs/latest/sql-data-sources-parquet.html). +**Option names should be in** `camelCase`! + +The set of supported options depends on Spark version. + +You may also set options mentioned [parquet-hadoop documentation](https://github.com/apache/parquet-java/blob/master/parquet-hadoop/README.md). +They are prefixed with `parquet.` with dots in names, so instead of calling constructor `Parquet(parquet.option=True)` (invalid in Python) +you should call method `Parquet.parse({"parquet.option": True})`. + +Reading files + +```py +from onetl.file.format import Parquet + +parquet = Parquet(mergeSchema=True) +``` + +Writing files + +```py +from onetl.file.format import Parquet + +parquet = Parquet.parse( + { + "compression": "snappy", + # Enable Bloom filter for columns 'id' and 'name' + "parquet.bloom.filter.enabled#id": True, + "parquet.bloom.filter.enabled#name": True, + # Set expected number of distinct values for column 'id' + "parquet.bloom.filter.expected.ndv#id": 10_000_000, + # other options + } +) +``` + + + +#### *field* mergeSchema *: bool | None* *= None* + +Merge schemas of all Parquet files being read into a single schema. +By default, Spark config option `spark.sql.parquet.mergeSchema` value is used (`false`). + +#### NOTE +Used only for reading files. + + + +#### *field* compression *: str | Literal['uncompressed', 'snappy', 'gzip', 'lzo', 'brotli', 'lz4', 'lz4raw', 'zstd'] | None* *= None* + +Compression codec of the Parquet files. +By default, Spark config option `spark.sql.parquet.compression.codec` value is used (`snappy`). + +#### NOTE +Used only for writing files. + + + +#### \_\_init_\_(\*\*kwargs) + + diff --git a/mddocs/file_df/file_formats/xml.md b/mddocs/file_df/file_formats/xml.md new file mode 100644 index 000000000..1da916bc1 --- /dev/null +++ b/mddocs/file_df/file_formats/xml.md @@ -0,0 +1,440 @@ + + +# XML + +### *class* onetl.file.format.xml.XML(\*, rowTag: str, rootTag: str | None = None, compression: str | Literal['bzip2', 'gzip', 'lz4', 'snappy'] | None = None, timestampFormat: str | None = None, dateFormat: str | None = None, timezone: str | None = None, nullValue: str | None = None, ignoreSurroundingSpaces: bool | None = None, mode: Literal['PERMISSIVE', 'DROPMALFORMED', 'FAILFAST'] | None = None, columnNameOfCorruptRecord: str | None = None, inferSchema: bool | None = None, samplingRatio: ConstrainedFloatValue | None = None, charset: str | None = None, valueTag: str | None = None, attributePrefix: str | None = None, excludeAttribute: bool | None = None, wildcardColName: str | None = None, ignoreNamespace: bool | None = None, rowValidationXSDPath: str | None = None, declaration: str | None = None, arrayElementName: str | None = None, \*\*kwargs) + +XML file format. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +Based on [Databricks Spark XML](https://github.com/databricks/spark-xml) file format. + +Supports reading/writing files with `.xml` extension. + +#### Versionadded +Added in version 0.9.5. + +### Version compatibility + +* Spark versions: 3.2.x - 3.5.x +* Java versions: 8 - 20 + +See [official documentation](https://github.com/databricks/spark-xml). + +### Examples + +#### NOTE +You can pass any option mentioned in +[official documentation](https://github.com/databricks/spark-xml). +**Option names should be in** `camelCase`! + +The set of supported options depends on `spark-xml` version. + +Reading files + +```py +from onetl.file.format import XML +from pyspark.sql import SparkSession + +# Create Spark session with XML package loaded +maven_packages = XML.get_packages(spark_version="3.5.5") +spark = ( + SparkSession.builder.appName("spark-app-name") + .config("spark.jars.packages", ",".join(maven_packages)) + .getOrCreate() +) + +xml = XML(rowTag="item", mode="PERMISSIVE") +``` + +Writing files + +#### WARNING +Due to [bug](https://github.com/databricks/spark-xml/issues/664) written files currently does not have `.xml` extension. + +```python +# Create Spark session with XML package loaded +spark = ... + +from onetl.file.format import XML + +xml = XML(rowTag="item", rootTag="data", compression="gzip") +``` + + + +#### *field* row_tag *: str* *[Required]* *(alias 'rowTag')* + +XML tag that encloses each row in XML. Required. + + + +#### *field* rootTag *: str | None* *= None* + +XML tag that encloses content of all DataFrame. Default is `ROWS`. + +#### NOTE +Used only for writing files. + + + +#### *field* compression *: str | Literal['bzip2', 'gzip', 'lz4', 'snappy'] | None* *= None* + +Compression codec. By default no compression is used. + +#### NOTE +Used only for writing files. + + + +#### *field* timestampFormat *: str | None* *= None* + +Format string used for parsing or serializing timestamp values. +By default, ISO 8601 format is used (`yyyy-MM-ddTHH:mm:ss.SSSZ`). + + + +#### *field* dateFormat *: str | None* *= None* + +Format string used for parsing or serializing date values. +By default, ISO 8601 format is used (`yyyy-MM-dd`). + + + +#### *field* nullValue *: str | None* *= None* + +String value used to represent null. Default is `null` string. + + + +#### *field* ignoreSurroundingSpaces *: bool | None* *= None* + +If `True`, trim surrounding spaces while parsing values. Default `false`. + +#### NOTE +Used only for reading files or by [`parse_column`](#onetl.file.format.xml.XML.parse_column) function. + + + +#### *field* mode *: Literal['PERMISSIVE', 'DROPMALFORMED', 'FAILFAST'] | None* *= None* + +How to handle parsing errors: +: * `PERMISSIVE` - set field value as `null`, move raw data to [`columnNameOfCorruptRecord`](#onetl.file.format.xml.XML.columnNameOfCorruptRecord) column. + * `DROPMALFORMED` - skip the malformed row. + * `FAILFAST` - throw an error immediately. + +Default is `PERMISSIVE`. + +#### NOTE +Used only for reading files or by [`parse_column`](#onetl.file.format.xml.XML.parse_column) function. + + + +#### *field* columnNameOfCorruptRecord *: str | None* *= None* + +Name of DataFrame column there corrupted row is stored with `mode=PERMISSIVE`. + +#### WARNING +If DataFrame schema is provided, this column should be added to schema explicitly: + +```python +from onetl.connection import SparkLocalFS +from onetl.file import FileDFReader +from onetl.file.format import XML + +from pyspark.sql.types import StructType, StructField, TImestampType, StringType + +spark = ... +schema = StructType( + [ + StructField("my_field", TimestampType()), + StructField("_corrupt_record", StringType()), # <-- important + ] +) +xml = XML(rowTag="item", columnNameOfCorruptRecord="_corrupt_record") + +reader = FileDFReader( + connection=connection, + format=xml, + schema=schema, # < --- +) +df = reader.run(["/some/file.xml"]) +``` + +#### NOTE +Used only for reading files or by [`parse_column`](#onetl.file.format.xml.XML.parse_column) function. + + + +#### *field* inferSchema *: bool | None* *= None* + +If `True`, try to infer the input schema by reading a sample of the file (see [`samplingRatio`](#onetl.file.format.xml.XML.samplingRatio)). +Default `False` which means that all parsed columns will be `StringType()`. + +#### NOTE +Used only for reading files. Ignored by [`parse_column`](#onetl.file.format.xml.XML.parse_column) function. + + + +#### *field* samplingRatio *: float | None* *= None* + +For `inferSchema=True`, read the specified fraction of rows to infer the schema. +Default `1`. + +#### NOTE +Used only for reading files. Ignored by [`parse_column`](#onetl.file.format.xml.XML.parse_column) function. + + +* **Constraints:** + - **minimum** = 0 + - **maximum** = 1 + +#### *field* charset *: str | None* *= None* + +File encoding. Default is `UTF-8` + +#### NOTE +Used only for reading files or by [`parse_column`](#onetl.file.format.xml.XML.parse_column) function. + + + +#### *field* valueTag *: str | None* *= None* + +Value used to replace missing values while parsing attributes like ``. +Default `_VALUE`. + +#### NOTE +Used only for reading files or by [`parse_column`](#onetl.file.format.xml.XML.parse_column) function. + + + +#### *field* attributePrefix *: str | None* *= None* + +While parsing tags containing attributes like ``, attributes are stored as +DataFrame schema columns with specified prefix, e.g. `_attr`. +Default `_`. + +#### NOTE +Used only for reading files or by [`parse_column`](#onetl.file.format.xml.XML.parse_column) function. + + + +#### *field* excludeAttribute *: bool | None* *= None* + +If `True`, exclude attributes while parsing tags like ``. +Default `false`. + +#### NOTE +Used only for reading files or by [`parse_column`](#onetl.file.format.xml.XML.parse_column) function. + + + +#### *field* wildcardColName *: str | None* *= None* + +Name of column or columns which should be preserved as raw XML string, and not parsed. + +#### WARNING +If DataFrame schema is provided, this column should be added to schema explicitly. +See [`columnNameOfCorruptRecord`](#onetl.file.format.xml.XML.columnNameOfCorruptRecord) example. + +#### NOTE +Used only for reading files or by [`parse_column`](#onetl.file.format.xml.XML.parse_column) function. + + + +#### *field* ignoreNamespace *: bool | None* *= None* + +If `True`, all namespaces like `` will be ignored and treated as just ``. +Default `False`. + +#### NOTE +Used only for reading files or by [`parse_column`](#onetl.file.format.xml.XML.parse_column) function. + + + +#### *field* rowValidationXSDPath *: str | None* *= None* + +Path to XSD file which should be used to validate each row. +If row does not match XSD, it will be treated as error, behavior depends on [`mode`](#onetl.file.format.xml.XML.mode) value. + +Default is no validation. + +#### NOTE +If Spark session is created with `master=yarn` or `master=k8s`, XSD +file should be accessible from all Spark nodes. This can be achieved by calling: + +```python +spark.addFile("/path/to/file.xsd") +``` + +And then by passing `rowValidationXSDPath=file.xsd` (relative path). + +#### NOTE +Used only for reading files or by [`parse_column`](#onetl.file.format.xml.XML.parse_column) function. + + + +#### *field* declaration *: str | None* *= None* + +Content of declaration. +Default is `version="1.0" encoding="UTF-8" standalone="yes"`. + +#### NOTE +Used only for writing files. + + + +#### *field* arrayElementName *: str | None* *= None* + +If DataFrame column is `ArrayType`, its content will be written to XML +inside `...` tag. +Default is `item`. + +#### NOTE +Used only for writing files. + + + +#### *classmethod* get_packages(spark_version: str, scala_version: str | None = None, package_version: str | None = None) → list[str] + +Get package names to be downloaded by Spark. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +#### Versionadded +Added in version 0.9.5. + +* **Parameters:** + **spark_version** + : Spark version in format `major.minor.patch`. + + **scala_version** + : Scala version in format `major.minor`. +
+ If `None`, `spark_version` is used to determine Scala version. + + **package_version** + : Package version in format `major.minor.patch`. Default is `0.18.0`. +
+ See [Maven index](https://mvnrepository.com/artifact/com.databricks/spark-xml) + for list of available versions. +
+ #### WARNING + Version `0.13` and below are not supported. +
+ #### NOTE + It is not guaranteed that custom package versions are supported. + Tests are performed only for default version. + +### Examples + +```python +from onetl.file.format import XML + +XML.get_packages(spark_version="3.5.5") +XML.get_packages(spark_version="3.5.5", scala_version="2.12") +XML.get_packages( + spark_version="3.5.5", + scala_version="2.12", + package_version="0.18.0", +) +``` + + + +#### parse_column(column: str | Column, schema: StructType) → Column + +Parses an XML string column into a structured Spark SQL column using the `from_xml` function +provided by the [Databricks Spark XML library](https://github.com/databricks/spark-xml#pyspark-notes) +based on the provided schema. + +#### NOTE +This method assumes that the `spark-xml` package is installed: [`get_packages`](#onetl.file.format.xml.XML.get_packages). + +#### NOTE +This method parses each DataFrame row individually. Therefore, for a specific column, each row must contain exactly one occurrence of the `rowTag` specified. +If your XML data includes a root tag that encapsulates multiple row tags, you can adjust the schema to use an `ArrayType` to keep all child elements under the single root. + +```xml + + Book OneAuthor A + Book TwoAuthor B + +``` + +And the corresponding schema in Spark using an `ArrayType`: + +```python +from pyspark.sql.types import StructType, StructField, ArrayType, StringType +from onetl.file.format import XML + +# each DataFrame row has exactly one tag +xml = XML(rowTag="books") +# each tag have multiple tags, so using ArrayType for such field +schema = StructType( + [ + StructField( + "book", + ArrayType( + StructType( + [ + StructField("title", StringType(), nullable=True), + StructField("author", StringType(), nullable=True), + ], + ), + ), + nullable=True, + ), + ], +) +``` + +#### Versionadded +Added in version 0.11.0. + +* **Parameters:** + **column** + : The name of the column or the column object containing XML strings/bytes to parse. + + **schema** + : The schema to apply when parsing the XML data. This defines the structure of the output DataFrame column. +* **Returns:** + Column with deserialized data, with the same structure as the provided schema. Column name is the same as input column. + +### Examples + +```pycon +>>> from pyspark.sql.types import StructType, StructField, IntegerType, StringType +>>> from onetl.file.format import XML +>>> df.show() ++--+------------------------------------------------+ +|id|value | ++--+------------------------------------------------+ +|1 |Alice20| +|2 |Bob25 | ++--+------------------------------------------------+ +>>> df.printSchema() +root +|-- id: integer (nullable = true) +|-- value: string (nullable = true) +>>> xml = XML(rowTag="person") +>>> xml_schema = StructType( +... [ +... StructField("name", StringType(), nullable=True), +... StructField("age", IntegerType(), nullable=True), +... ], +... ) +>>> parsed_df = df.select("key", xml.parse_column("value", xml_schema)) +>>> parsed_df.show() ++--+-----------+ +|id|value | ++--+-----------+ +|1 |{Alice, 20}| +|2 | {Bob, 25}| ++--+-----------+ +>>> parsed_df.printSchema() +root +|-- id: integer (nullable = true) +|-- value: struct (nullable = true) +| |-- name: string (nullable = true) +| |-- age: integer (nullable = true) +``` + + diff --git a/mddocs/file_df/index.md b/mddocs/file_df/index.md new file mode 100644 index 000000000..c225567d6 --- /dev/null +++ b/mddocs/file_df/index.md @@ -0,0 +1,7 @@ + + + File DataFrame classes + +* [FileDF Reader](file_df_reader/index.md) +* [FileDF Writer](file_df_writer/index.md) +* [File Formats](file_formats/index.md) diff --git a/mddocs/hooks/design.md b/mddocs/hooks/design.md new file mode 100644 index 000000000..041f206ee --- /dev/null +++ b/mddocs/hooks/design.md @@ -0,0 +1,642 @@ + + +# High level design + +## What are hooks? + +Hook mechanism is a part of onETL which allows to inject some additional behavior into +existing methods of (almost) any class. + +### Features + +Hooks mechanism allows to: + +* Inspect and validate input arguments and output results of method call +* Access, modify or replace method call result (but NOT input arguments) +* Wrap method calls with a context manager and catch raised exceptions + +Hooks can be placed into [Plugins](../plugins.md#plugins), allowing to modify onETL behavior by installing some additional package. + +### Limitations + +* Hooks can be bound to methods of a class only (not functions). +* Only methods decorated with [@slot decorator](slot.md#slot-decorator) implement hooks mechanism. These class and methods are marked as [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html). +* Hooks can be bound to public methods only. + +## Terms + +* [@slot decorator](slot.md#slot-decorator) - method of a class with a special decorator +* `Callback` - function which implements some additional logic which modifies slot behavior +* [@hook decorator](hook.md#hook-decorator) - wrapper around callback which stores hook state, priority and some useful methods +* `Hooks mechanism` - calling `Slot()` will call all enabled hooks which are bound to the slot. Implemented by [@support_hooks decorator](support_hooks.md#support-hooks-decorator). + +## How to implement hooks? + +### TL;DR + +```python +from onetl.hooks import support_hooks, slot, hook + + +@support_hooks # enabling hook mechanism for the class +class MyClass: + def __init__(self, data): + self.data = data + + # this is slot + @slot + def method(self, arg): + pass + + +@MyClass.method.bind # bound hook to the slot +@hook # this is hook +def callback(obj, arg): # this is callback + print(obj.data, arg) + + +obj = MyClass(1) +obj.method(2) # will call callback(obj, 1) + +# prints "1 2" +``` + +#### Define a slot + +* Create a class with a method: + +```python +class MyClass: + def __init__(self, data): + self.data = data + + def method(self, arg): + return self.data, arg +``` + +* Add [@slot decorator](slot.md#slot-decorator) to the method: + +```python +from onetl.hooks import support_hooks, slot, hook + + +class MyClass: + @slot + def method(self, arg): + return self.data, arg +``` + +If method has other decorators like `@classmethod` or `@staticmethod`, `@slot` should be placed on the top: + +```python +from onetl.hooks import support_hooks, slot, hook + + +class MyClass: + @slot + @classmethod + def class_method(cls, arg): + return cls, arg + + @slot + @staticmethod + def static_method(arg): + return arg +``` + +* Add [@support_hooks decorator](support_hooks.md#support-hooks-decorator) to the class: + +```python +from onetl.hooks import support_hooks, slot, hook + + +@support_hooks +class MyClass: + @slot + def method(self, arg): + return self.data, arg +``` + +Slot is created. + +#### Define a callback + +Define some function (a.k.a callback): + +```python +def callback(self, arg): + print(self.data, arg) +``` + +It should have signature *compatible* with `MyClass.method`. *Compatible* does not mean *exactly the same* - +for example, you can rename positional arguments: + +```python +def callback(obj, arg): + print(obj.data, arg) +``` + +Use `*args` and `**kwargs` to omit arguments you don’t care about: + +```python +def callback(obj, *args, **kwargs): + print(obj.data, args, kwargs) +``` + +There is also an argument `method_name` which has a special meaning - +the method name which the callback is bound to is passed into this argument: + +```python +def callback(obj, *args, method_name: str, **kwargs): + print(obj.data, args, method_name, kwargs) +``` + +#### NOTE +`method_name` should always be a keyword argument, **NOT** positional. + +#### WARNING +If callback signature is not compatible with slot signature, an exception will be raised, +but **ONLY** while slot is called. + +#### Define a hook + +Add [@hook decorator](hook.md#hook-decorator) to create a hook from your callback: + +```python +@hook +def callback(obj, arg): + print(obj.data, arg) +``` + +You can pass more options to the `@hook` decorator, like state or priority. +See decorator documentation for more details. + +#### Bind hook to the slot + +Use `Slot.bind` method to bind hook to the slot: + +```python +@MyClass.method.bind +@hook +def callback(obj, arg): + print(obj, arg) +``` + +You can bind more than one hook to the same slot, and bind same hook to multiple slots: + +```python +@MyClass.method1.bind +@MyClass.method2.bind +@hook +def callback1(obj, arg): + "Will be called by both MyClass.method1 and MyClass.method2" + + +@MyClass.method1.bind +@hook +def callback2(obj, arg): + "Will be called by MyClass.method1 too" +``` + +## How hooks are called? + +### General + +Just call the method decorated by `@slot` to trigger the hook: + +```python +obj = MyClass(1) +obj.method(2) # will call callback(obj, 2) + +# prints "1 2" +``` + +There are some special callback types that has a slightly different behavior. + +### Context managers + +`@hook` decorator can be placed on a context manager class: + +```python +@hook +class ContextManager: + def __init__(self, obj, arg): + self.obj = obj + self.arg = arg + + def __enter__(self): + # do something on enter + print(obj.data, arg) + return self + + def __exit__(self, exc_type, exc_value, traceback): + # do something on exit + return False +``` + +Context manager is entered while calling the `Slot()`, and exited then the call is finished. + +If present, method `process_result` has a special meaning - +it can receive `MyClass.method` call result, and also modify/replace it: + +```python +@hook +class ContextManager: + def __init__(self, obj, arg): + self.obj = obj + self.arg = arg + + def __enter__(self): + # do something on enter + print(obj.data, arg) + return self + + def __exit__(self, exc_type, exc_value, traceback): + # do something on exit + return False + + def process_result(self, result): + # do something with method call result + return modified(result) +``` + +See examples below for more information. + +### Generator function + +`@hook` decorator can be placed on a generator function: + +```python +@hook +def callback(obj, arg): + print(obj.data, arg) + # this is called before original method body + + yield # method is called here + + # this is called after original method body +``` + +It is converted to a context manager, in the same manner as +[contextlib.contextmanager](https://docs.python.org/3/library/contextlib.html#contextlib.contextmanager). + +Generator body can be wrapped with `try..except..finally` to catch exceptions: + +```python +@hook +def callback(obj, arg): + print(obj.data, arg) + + try: + # this is called before original method body + + yield # method is called here + except Exception as e: + process_exception(a) + finally: + # this is called after original method body + finalizer() +``` + +There is also a special syntax which allows generator to access and modify/replace method call result: + +```default +@hook +def callback(obj, arg): + original_result = yield # method is called here + + new_result = do_something(original_result) + + yield new_result # modify/replace the result +``` + +### Calling hooks in details + +* The callback will be called with the same arguments as the original method. + * If slot is a regular method: + ```python + callback_result = callback(self, *args, **kwargs) + ``` + + Here `self` is a class instance (`obj`). + * If slot is a class method: + ```python + callback_result = callback(cls, *args, **kwargs) + ``` + + Here `cls` is the class itself (`MyClass`). + * If slot is a static method: + ```python + callback_result = callback(*args, **kwargs) + ``` + + Neither object not class are passed to the callback in this case. +* If `callback_result` is a context manager, enter the context. Context manager can catch all the exceptions raised. + > If there are multiple hooks bound the the slot, every context manager will be entered. +* Then call the original method wrapped by `@slot`: + ```python + original_result = method(*args, **kwargs) + ``` +* Process `original_result`: + * If `callback_result` object has method `process_result`, or is a generator wrapped with `@hook`, call it: + ```python + new_result = callback_result.process_result(original_result) + ``` + * Otherwise set `new_result = callback_result`. + * If there are multiple hooks bound the the method, pass `new_result` through the chain: + ```python + new_result = callback1_result.process_result(original_result) + new_result = callback2_result.process_result(new_result or original_result) + new_result = callback3_result.process_result(new_result or original_result) + ``` +* Finally return: + ```python + return new_result or original_result + ``` + + All `None` values are ignored on every step above. +* Exit all the context managers entered during the slot call. + +### Hooks priority + +Hooks are executed in the following order: + +1. Parent class slot + [`FIRST`](hook.md#onetl.hooks.hook.HookPriority.FIRST) +2. Inherited class slot + [`FIRST`](hook.md#onetl.hooks.hook.HookPriority.FIRST) +3. Parent class slot + [`NORMAL`](hook.md#onetl.hooks.hook.HookPriority.NORMAL) +4. Inherited class slot + [`NORMAL`](hook.md#onetl.hooks.hook.HookPriority.NORMAL) +5. Parent class slot + [`LAST`](hook.md#onetl.hooks.hook.HookPriority.LAST) +6. Inherited class slot + [`LAST`](hook.md#onetl.hooks.hook.HookPriority.LAST) + +Hooks with the same priority and inheritance will be executed in the same order they were registered (`Slot.bind` call). + +#### NOTE +Calls of `super()` inside inherited class methods does not trigger hooks call. +Hooks are triggered only if method is called explicitly. + +This allow to wrap with a hook the entire slot call without influencing its internal logic. + +### Hook types + +Here are several examples of using hooks. These types are not exceptional, they can be mixed - for example, +hook can both modify method result and catch exceptions. + +#### Before hook + +Can be used for inspecting or validating input args of the original function: + +```python +@hook +def before1(obj, arg): + print(obj, arg) + # original method is called after exiting this function + + +@hook +def before2(obj, arg): + if arg == 1: + raise ValueError("arg=1 is not allowed") + return None # return None is the same as no return statement +``` + +Executed before calling the original method wrapped by `@slot`. +If hook raises an exception, method will not be called at all. + +#### After hook + +Can be used for performing some actions after original method was successfully executed: + +```python +@hook +def after1(obj, arg): + yield # original method is called here + print(obj, arg) + + +@hook +def after2(obj, arg): + yield None # yielding None is the same as empty yield + if arg == 1: + raise ValueError("arg=1 is not allowed") +``` + +If original method raises an exception, the block of code after `yield` will not be called. + +#### Context hook + +Can be used for catching and handling some exceptions, or to determine that there was no exception during slot call: + +Generator syntax + +```py +# This is just the same as using @contextlib.contextmanager + +@hook +def context_generator(obj, arg): + try: + yield # original method is called here + print(obj, arg) # <-- this line will not be called if method raised an exception + except SomeException as e: + magic(e) + finally: + finalizer() +``` + +Context manager syntax + +```py +@hook +class ContextManager: + def __init__(self, obj, args): + self.obj = obj + self.args = args + + def __enter__(self): + return self + + # original method is called between __enter__ and __exit__ + + def __exit__(self, exc_type, exc_value, traceback): + result = False + if exc_type is not None and isinstance(exc_value, SomeException): + magic(exc_value) + result = True # suppress exception + else: + print(self.obj, self.arg) + finalizer() + return result +``` + +#### NOTE +Contexts are exited in the reverse order of the hook calls. +So if some hook raised an exception, it will be passed into the previous hook, not the next one. + +It is recommended to specify the proper priority for the hook, e.g. [`FIRST`](hook.md#onetl.hooks.hook.HookPriority.FIRST) + +#### Replacing result hook + +Replaces the output result of the original method. + +Can be used for delegating some implementation details for third-party extensions. +See [Hive](../connection/db_connection/hive/index.md#hive) and [HDFS](../connection/file_connection/hdfs/index.md#hdfs) as an example. + +```python +@hook +def replace1(obj, arg): + result = arg + 10 # any non-None return result + + # original method call result is ignored, output will always be arg + 10 + return result + + +@hook +def replace2(obj, arg): + yield arg + 10 # same as above +``` + +#### NOTE +If there are multiple hooks bound to the same slot, the result of last hook will be used. +It is recommended to specify the proper priority for the hook, e.g. [`LAST`](hook.md#onetl.hooks.hook.HookPriority.LAST) + +#### Accessing result hook + +Can access output result of the original method and inspect or validate it: + +Generator syntax + +```py +@hook +def access_result(obj, arg): + result = yield # original method is called here, and result can be used in the hook + print(result) + yield # does not modify result +``` + +Context manager syntax + +```py +@hook +class ModifiesResult: + def __init__(self, obj, args): + self.obj = obj + self.args = args + + def __enter__(self): + return self + + # original method is called between __enter__ and __exit__ + # result is passed into process_result method of context manager, if present + + def process_result(self, result): + print(result) # result can be used in the hook + return None # does not modify result. same as no return statement in the method + + def __exit__(self, exc_type, exc_value, traceback): + return False +``` + +#### Modifying result hook + +Can access output result of the original method, and return the modified one: + +Generator syntax + +```py +@hook +def modifies_result(obj, arg): + result = yield # original method is called here, and result can be used in the hook + yield result + 10 # modify output result. None values are ignored +``` + +Context manager syntax + +```py +@hook +class ModifiesResult: + def __init__(self, obj, args): + self.obj = obj + self.args = args + + def __enter__(self): + return self + + # original method is called between __enter__ and __exit__ + # result is passed into process_result method of context manager, if present + + def process_result(self, result): + print(result) # result can be used in the hook + return result + 10 # modify output result. None values are ignored + + def __exit__(self, exc_type, exc_value, traceback): + return False +``` + +#### NOTE +If there are multiple hooks bound to the same slot, the result of last hook will be used. +It is recommended to specify the proper priority for the hook, e.g. [`LAST`](hook.md#onetl.hooks.hook.HookPriority.LAST) + +## How to enable/disable hooks? + +You can enable/disable/temporary disable hooks on 4 different levels: + +* Manage global hooks state (level 1): + > * [`onetl.hooks.hooks_state.stop_all_hooks`](global_state.md#onetl.hooks.hooks_state.stop_all_hooks) + > * [`onetl.hooks.hooks_state.resume_all_hooks`](global_state.md#onetl.hooks.hooks_state.resume_all_hooks) + > * [`onetl.hooks.hooks_state.skip_all_hooks`](global_state.md#onetl.hooks.hooks_state.skip_all_hooks) +* Manage all hooks bound to a specific class (level 2): + > * [`onetl.hooks.support_hooks.suspend_hooks`](support_hooks.md#onetl.hooks.support_hooks.suspend_hooks) + > * [`onetl.hooks.support_hooks.resume_hooks`](support_hooks.md#onetl.hooks.support_hooks.resume_hooks) + > * [`onetl.hooks.support_hooks.skip_hooks`](support_hooks.md#onetl.hooks.support_hooks.skip_hooks) +* Manage all hooks bound to a specific slot (level 3): + > * [`onetl.hooks.slot.Slot.suspend_hooks`](slot.md#onetl.hooks.slot.Slot.suspend_hooks) + > * [`onetl.hooks.slot.Slot.resume_hooks`](slot.md#onetl.hooks.slot.Slot.resume_hooks) + > * [`onetl.hooks.slot.Slot.skip_hooks`](slot.md#onetl.hooks.slot.Slot.skip_hooks) +* Manage state of a specific hook (level 4): + > * [`onetl.hooks.hook.Hook.enable`](hook.md#onetl.hooks.hook.Hook.enable) + > * [`onetl.hooks.hook.Hook.disable`](hook.md#onetl.hooks.hook.Hook.disable) + +More details in the documentation above. + +#### NOTE +All of these levels are independent. + +Calling `stop` on the level 1 has higher priority than level 2, and so on. +But calling `resume` on the level 1 does not automatically resume hooks stopped in the level 2, +they should be resumed explicitly. + +## How to see logs of the hook mechanism? + +Hooks registration emits logs with `DEBUG` level: + +```python +from onetl.logs import setup_logging + +setup_logging() +``` + +```text +DEBUG |onETL| Registered hook 'mymodule.callback1' for 'MyClass.method' (enabled=True, priority=HookPriority.NORMAL) +DEBUG |onETL| Registered hook 'mymodule.callback2' for 'MyClass.method' (enabled=True, priority=HookPriority.NORMAL) +DEBUG |onETL| Registered hook 'mymodule.callback3' for 'MyClass.method' (enabled=False, priority=HookPriority.NORMAL) +``` + +But most of logs are emitted with even lower level `NOTICE`, to make output less verbose: + +```python +from onetl.logs import NOTICE, setup_logging + +setup_logging(level=NOTICE) +``` + +```default +NOTICE |Hooks| 2 hooks registered for 'MyClass.method' +NOTICE |Hooks| Calling hook 'mymodule.callback1' (1/2) +NOTICE |Hooks| Hook is finished with returning non-None result +NOTICE |Hooks| Calling hook 'mymodule.callback2' (2/2) +NOTICE |Hooks| This is a context manager, entering ... +NOTICE |Hooks| Calling original method 'MyClass.method' +NOTICE |Hooks| Method call is finished +NOTICE |Hooks| Method call result (*NOT* None) will be replaced with result of hook 'mymodule.callback1' +NOTICE |Hooks| Passing result to 'process_result' method of context manager 'mymodule.callback2' +NOTICE |Hooks| Method call result (*NOT* None) is modified by hook! +``` diff --git a/mddocs/hooks/global_state.md b/mddocs/hooks/global_state.md new file mode 100644 index 000000000..537389dd1 --- /dev/null +++ b/mddocs/hooks/global_state.md @@ -0,0 +1,101 @@ + + +# Hooks global state + +| [`skip_all_hooks`](#onetl.hooks.hooks_state.skip_all_hooks)() | Temporary stop all onETL hooks. | +|-------------------------------------------------------------------|-----------------------------------| +| [`stop_all_hooks`](#onetl.hooks.hooks_state.stop_all_hooks)() | Stop all hooks for all classes. | +| [`resume_all_hooks`](#onetl.hooks.hooks_state.resume_all_hooks)() | Resume all onETL hooks. | + +### onetl.hooks.hooks_state.skip_all_hooks() + +Temporary stop all onETL hooks. Designed to be used as context manager or decorator. + +#### NOTE +If hooks were stopped by [`stop_all_hooks`](#onetl.hooks.hooks_state.stop_all_hooks), they will not be resumed +after exiting the context/decorated function. +You should call [`resume_all_hooks`](#onetl.hooks.hooks_state.resume_all_hooks) explicitly. + +#### Versionadded +Added in version 0.7.0. + +### Examples + +Context manager syntax + +```py +from onetl.hooks import skip_all_hooks + +# hooks are enabled + +with skip_all_hooks(): + # hooks are stopped here + ... + +# hook state is restored +``` + +Decorator syntax + +```py +from onetl.hooks import skip_all_hooks + +# hooks are enabled + +@skip_all_hooks() +def main(): + # hooks are stopped here + ... + +main() +``` + + + +### onetl.hooks.hooks_state.stop_all_hooks() → None + +Stop all hooks for all classes. + +#### Versionadded +Added in version 0.7.0. + +### Examples + +```python +from onetl.hooks import stop_all_hooks + +# hooks are executed + +stop_all_hooks() + +# all hooks are stopped now +``` + + + +### onetl.hooks.hooks_state.resume_all_hooks() → None + +Resume all onETL hooks. + +#### NOTE +This function does not enable hooks which were disabled by [`onetl.hooks.hook.Hook.disable`](hook.md#onetl.hooks.hook.Hook.disable), +or stopped by [`onetl.hooks.support_hooks.suspend_hooks`](support_hooks.md#onetl.hooks.support_hooks.suspend_hooks). + +#### Versionadded +Added in version 0.7.0. + +### Examples + +```python +from onetl.hooks import resume_all_hooks, stop_all_hooks + +stop_all_hooks() + +# hooks are stopped + +resume_all_hooks() + +# all hooks are executed now +``` + + diff --git a/mddocs/hooks/hook.md b/mddocs/hooks/hook.md new file mode 100644 index 000000000..402f79688 --- /dev/null +++ b/mddocs/hooks/hook.md @@ -0,0 +1,223 @@ + + +# `@hook` decorator + +| [`hook`](#onetl.hooks.hook.hook)([inp, enabled, priority]) | Initialize hook from callable/context manager. | +|-------------------------------------------------------------------------------|--------------------------------------------------| +| [`HookPriority`](#onetl.hooks.hook.HookPriority)(value[, names, module, ...]) | Hook priority enum. | +| [`Hook`](#onetl.hooks.hook.Hook)(callback[, enabled, priority]) | Hook representation. | +| [`Hook.enable`](#onetl.hooks.hook.Hook.enable)() | Enable the hook. | +| [`Hook.disable`](#onetl.hooks.hook.Hook.disable)() | Disable the hook. | +| [`Hook.skip`](#onetl.hooks.hook.Hook.skip)() | Temporary disable the hook. | + +### @onetl.hooks.hook.hook(inp: Callable[[...], T] | None = None, enabled: bool = True, priority: [HookPriority](#onetl.hooks.hook.HookPriority) = HookPriority.NORMAL) + +Initialize hook from callable/context manager. + +#### Versionadded +Added in version 0.7.0. + +### Examples + +Decorate a function or generator + +```py +from onetl.hooks import hook, HookPriority + +@hook +def some_func(*args, **kwargs): + ... + +@hook(enabled=True, priority=HookPriority.FIRST) +def another_func(*args, **kwargs): + ... +``` + +Decorate a context manager + +```py +from onetl.hooks import hook, HookPriority + +@hook +class SimpleContextManager: + def __init__(self, *args, **kwargs): + ... + + def __enter__(self): + ... + return self + + def __exit__(self, exc_type, exc_value, traceback): + ... + return False + +@hook(enabled=True, priority=HookPriority.FIRST) +class ContextManagerWithProcessResult: + def __init__(self, *args, **kwargs): + ... + + def __enter__(self): + ... + return self + + def __exit__(self, exc_type, exc_value, traceback): + ... + return False + + def process_result(self, result): + # special method to handle method result call + return modify(result) + + ... +``` + + + +### *class* onetl.hooks.hook.HookPriority(value, names=None, \*, module=None, qualname=None, type=None, start=1, boundary=None) + +Hook priority enum. + +All hooks within the same priority are executed in the same order they were registered. + +#### Versionadded +Added in version 0.7.0. + + + +#### FIRST *= -1* + +Hooks with this priority will run first. + + + +#### NORMAL *= 0* + +Hooks with this priority will run after [`FIRST`](#onetl.hooks.hook.HookPriority.FIRST) but before [`LAST`](#onetl.hooks.hook.HookPriority.LAST). + + + +#### LAST *= 1* + +Hooks with this priority will run last. + + + +### *class* onetl.hooks.hook.Hook(callback: Callable[[...], T], enabled: bool = True, priority: [HookPriority](#onetl.hooks.hook.HookPriority) = HookPriority.NORMAL) + +Hook representation. + +#### Versionadded +Added in version 0.7.0. + +* **Parameters:** + **callback** + : Some callable object which will be wrapped into a Hook, like function or ContextManager class. + + **enabled** + : Will hook be executed or not. Useful for debugging. + + **priority** + : Changes hooks priority, see `HookPriority` documentation. + +### Examples + +```python +from onetl.hooks.hook import Hook, HookPriority + +def some_func(*args, **kwargs): ... + +hook = Hook(callback=some_func, enabled=True, priority=HookPriority.FIRST) +``` + + + +#### enable() + +Enable the hook. + +#### Versionadded +Added in version 0.7.0. + +### Examples + +```pycon +>>> def func1(): ... +>>> hook = Hook(callback=func1, enabled=False) +>>> hook.enabled +False +>>> hook.enable() +>>> hook.enabled +True +``` + + + +#### disable() + +Disable the hook. + +#### Versionadded +Added in version 0.7.0. + +### Examples + +```pycon +>>> def func1(): ... +>>> hook = Hook(callback=func1, enabled=True) +>>> hook.enabled +True +>>> hook.disable() +>>> hook.enabled +False +``` + + + +#### skip() + +Temporary disable the hook. + +#### NOTE +If hook was created with `enabled=False`, or was disabled by [`disable`](#onetl.hooks.hook.Hook.disable), +its state will left intact after exiting the context. + +You should call [`enable`](#onetl.hooks.hook.Hook.enable) explicitly to change its state. + +#### Versionadded +Added in version 0.7.0. + +### Examples + +Context manager syntax + +```pycon +>>> def func1(): ... +>>> hook = Hook(callback=func1, enabled=True) +>>> hook.enabled +True +>>> with hook.skip(): +... print(hook.enabled) +False +>>> # hook state is restored as it was before entering the context manager +>>> hook.enabled +True +``` + +Decorator syntax + +```pycon +>>> def func1(): ... +>>> hook = Hook(callback=func1, enabled=True) +>>> hook.enabled +True +>>> @hook.skip() +... def hook_disabled(): +... print(hook.enabled) +>>> hook_disabled() +False +>>> # hook state is restored as it was before entering the context manager +>>> hook.enabled +True +``` + + diff --git a/mddocs/hooks/index.md b/mddocs/hooks/index.md new file mode 100644 index 000000000..8e1ba7fb9 --- /dev/null +++ b/mddocs/hooks/index.md @@ -0,0 +1,14 @@ + + +# Hooks + +#### Versionadded +Added in version 0.6.0. + +# Hooks + +* [High level design](design.md) +* [@hook decorator](hook.md) +* [@slot decorator](slot.md) +* [@support_hooks decorator](support_hooks.md) +* [Hooks global state](global_state.md) diff --git a/mddocs/hooks/slot.md b/mddocs/hooks/slot.md new file mode 100644 index 000000000..936ee3859 --- /dev/null +++ b/mddocs/hooks/slot.md @@ -0,0 +1,261 @@ + + +# `@slot` decorator + +| [`slot`](#onetl.hooks.slot.slot)(method) | Decorator which enables hooks functionality on a specific class method. | +|----------------------------------------------------------------|------------------------------------------------------------------------------------------------------| +| [`Slot`](#onetl.hooks.slot.Slot) | Protocol which is implemented by a method after applying [`slot`](#onetl.hooks.slot.slot) decorator. | +| [`Slot.bind`](#onetl.hooks.slot.Slot.bind)([inp]) | Bind a hook to the slot. | +| [`Slot.skip_hooks`](#onetl.hooks.slot.Slot.skip_hooks)() | Context manager which temporary stops all the hooks bound to the slot. | +| [`Slot.suspend_hooks`](#onetl.hooks.slot.Slot.suspend_hooks)() | Stop all the hooks bound to the slot. | +| [`Slot.resume_hooks`](#onetl.hooks.slot.Slot.resume_hooks)() | Resume all hooks bound to the slot. | + +### @onetl.hooks.slot.slot + +Decorator which enables hooks functionality on a specific class method. + +Decorated methods get additional nested methods: + +> * [`onetl.hooks.slot.Slot.bind`](#onetl.hooks.slot.Slot.bind) +> * [`onetl.hooks.slot.Slot.suspend_hooks`](#onetl.hooks.slot.Slot.suspend_hooks) +> * [`onetl.hooks.slot.Slot.resume_hooks`](#onetl.hooks.slot.Slot.resume_hooks) +> * [`onetl.hooks.slot.Slot.skip_hooks`](#onetl.hooks.slot.Slot.skip_hooks) + +#### NOTE +Supported method types are: + +> * Regular methods +> * `@classmethod` +> * `@staticmethod` + +It is not allowed to use this decorator over `_private` and `__protected` methods and `@property`. +But is allowed to use on `__dunder__` methods, like `__init__`. + +#### Versionadded +Added in version 0.7.0. + +### Examples + +```python +from onetl.hooks import support_hooks, slot, hook + +@support_hooks +class MyClass: + @slot + def my_method(self, arg): ... + + @slot # decorator should be on top of all other decorators + @classmethod + def class_method(cls): ... + + @slot # decorator should be on top of all other decorators + @staticmethod + def static_method(arg): ... + +@MyClass.my_method.bind +@hook +def callback1(self, arg): ... + +@MyClass.class_method.bind +@hook +def callback2(cls): ... + +@MyClass.static_method.bind +@hook +def callback3(arg): ... + +obj = MyClass() +obj.my_method(1) # will execute callback1(obj, 1) +MyClass.class_method(2) # will execute callback2(MyClass, 2) +MyClass.static_method(3) # will execute callback3(3) +``` + + + +### *protocol* onetl.hooks.slot.Slot + +Protocol which is implemented by a method after applying [`slot`](#onetl.hooks.slot.slot) decorator. + +#### Versionadded +Added in version 0.7.0. + + + +Classes that implement this protocol must have the following methods / attributes: + +#### \_\_call_\_(\*args, \*\*kwargs) + +Call self as a function. + + + +#### *property* \_\_hooks_\_ *: HookCollection* + +Collection of hooks bound to the slot + + + +#### bind(inp=None) + +Bind a hook to the slot. + +See [High level design](design.md#hooks-design) for more details. + +#### Versionadded +Added in version 0.7.0. + +### Examples + +```python +from onetl.hooks import support_hooks, slot, hook, HookPriority + +@support_hooks +class MyClass: + @slot + def method(self, arg): + pass + +@MyClass.method.bind +@hook +def callable(self, arg): + if arg == "some": + do_something() + +@MyClass.method.bind +@hook(priority=HookPriority.FIRST, enabled=True) +def another_callable(self, arg): + if arg == "another": + raise NotAllowed() + +obj = MyClass() +obj.method(1) # will call both callable(obj, 1) and another_callable(obj, 1) +``` + + + +#### resume_hooks() + +Resume all hooks bound to the slot. + +#### NOTE +If hook is disabled by [`onetl.hooks.hook.Hook.disable`](hook.md#onetl.hooks.hook.Hook.disable), it will stay disabled. +You should call [`onetl.hooks.hook.Hook.enable`](hook.md#onetl.hooks.hook.Hook.enable) explicitly. + +### Examples + +```python +from onetl.hooks.hook import hook, support_hooks, slot + +@support_hooks +class MyClass: + @slot + def my_method(self, arg): ... + +@MyClass.my_method.bind +@hook +def callback1(self, arg): ... + +obj = MyClass() +obj.my_method(1) # will call callback1(obj, 1) + +MyClass.my_method.suspend_hooks() +obj.my_method(1) # will NOT call callback1 + +MyClass.my_method.resume_hooks() +obj.my_method(2) # will call callback1(obj, 2) +``` + + + +#### skip_hooks() → ContextManager[None] + +Context manager which temporary stops all the hooks bound to the slot. + +#### NOTE +If hooks were stopped by [`suspend_hooks`](#onetl.hooks.slot.Slot.suspend_hooks), they will not be resumed +after exiting the context/decorated function. +You should call [`resume_hooks`](#onetl.hooks.slot.Slot.resume_hooks) explicitly. + +### Examples + +Context manager syntax + +```py +from onetl.hooks.hook import hook, support_hooks, slot + +@support_hooks +class MyClass: + @slot + def my_method(self, arg): + ... + +@MyClass.my_method.bind +@hook +def callback1(self, arg): + ... + +obj = MyClass() +obj.my_method(1) # will call callback1(obj, 1) + +with MyClass.my_method.skip_hooks(): + obj.my_method(1) # will NOT call callback1 + +obj.my_method(2) # will call callback1(obj, 2) +``` + +Decorator syntax + +```py +from onetl.hooks.hook import hook, support_hooks, slot + +@support_hooks +class MyClass: + @slot + def my_method(self, arg): + ... + +@MyClass.my_method.bind +@hook +def callback1(self, arg): + ... + +@MyClass.my_method.skip_hooks() +def method_without_hooks(obj, arg): + obj.my_method(arg) + +obj = MyClass() +obj.my_method(1) # will call callback1(obj, 1) + +method_without_hooks(obj, 1) # will NOT call callback1 + +obj.my_method(2) # will call callback1(obj, 2) +``` + + + +#### suspend_hooks() + +Stop all the hooks bound to the slot. + +### Examples + +```python +from onetl.hooks.hook import hook, support_hooks, slot + +@support_hooks +class MyClass: + @slot + def my_method(self, arg): ... + +@MyClass.my_method.bind +@hook +def callback1(self, arg): ... + +obj = MyClass() +obj.my_method(1) # will call callback1(obj, 1) + +MyClass.my_method.suspend_hooks() +obj.my_method(1) # will NOT call callback1 +``` + + diff --git a/mddocs/hooks/support_hooks.md b/mddocs/hooks/support_hooks.md new file mode 100644 index 000000000..b876a3e52 --- /dev/null +++ b/mddocs/hooks/support_hooks.md @@ -0,0 +1,161 @@ + + +# `@support_hooks` decorator + +| [`support_hooks`](#onetl.hooks.support_hooks.support_hooks)(cls) | Decorator which adds hooks functionality to a specific class. | +|--------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------| +| [`skip_hooks`](#onetl.hooks.support_hooks.skip_hooks)(cls) | Context manager (and decorator) which temporary disables hooks for all the methods of a specific class. | +| [`suspend_hooks`](#onetl.hooks.support_hooks.suspend_hooks)(cls) | Disables all hooks for all the methods of a specific class. | +| [`resume_hooks`](#onetl.hooks.support_hooks.resume_hooks)(cls) | Enables all hooks for all the methods of a specific class. | + +### @onetl.hooks.support_hooks.support_hooks + +Decorator which adds hooks functionality to a specific class. + +Only methods decorated with `slot` can be used for connecting hooks. + +Adds [`skip_hooks`](#onetl.hooks.support_hooks.skip_hooks), [`suspend_hooks`](#onetl.hooks.support_hooks.suspend_hooks) and [`resume_hooks`](#onetl.hooks.support_hooks.resume_hooks) to the class. + +#### Versionadded +Added in version 0.7.0. + +### Examples + +```python +from onetl.hooks.hook import support_hooks, slot + +@support_hooks +class MyClass: + @slot + def my_method(self, arg): ... + +@MyClass.my_method.hook +def callback(self, arg): ... + +MyClass().my_method() # will execute callback function +``` + + + +### onetl.hooks.support_hooks.skip_hooks(cls: type) + +Context manager (and decorator) which temporary disables hooks for all the methods of a specific class. + +### Examples + +Context manager syntax + +```py +@support_hooks +class MyClass: + @slot + def my_method(self, arg): + ... + +@MyClass.my_method.hook +def callback(self, arg): + ... + +obj = MyClass() +obj.my_method(1) # will execute callback(obj, 1) + +with MyClass.skip_hooks(): + obj.my_method() # will NOT execute callback + +# running outside the context restores previous behavior +obj.my_method(2) # will execute callback(obj, 2) +``` + +Decorator syntax + +```py +@support_hooks +class MyClass: + @slot + def my_method(self, arg): + ... + +@MyClass.my_method.hook +def callback(self, arg): + ... + +def with_hook_enabled(): + obj = MyClass() + obj.my_method(1) + +with_hook_enabled() # will execute callback(obj, 1) + +@MyClass.skip_hooks() +def with_all_hooks_disabled(): + obj = MyClass() + obj.my_method(1) + +with_all_hooks_disabled() # will NOT execute callback function + +# running outside a decorated function restores previous behavior +obj = MyClass() +obj.my_method(2) # will execute callback(obj, 2) +``` + +#### Versionadded +Added in version 0.7.0. + + + +### onetl.hooks.support_hooks.suspend_hooks(cls: type) → None + +Disables all hooks for all the methods of a specific class. + +### Examples + +```python +@support_hooks +class MyClass: + @slot + def my_method(self, arg): ... + +@MyClass.my_method.hook +def callback(self, arg): ... + +obj = MyClass() +obj.my_method(1) # will execute callback(obj, 1) + +MyClass.suspend_hooks() + +obj.my_method(2) # will NOT execute callback +``` + +#### Versionadded +Added in version 0.7.0. + + + +### onetl.hooks.support_hooks.resume_hooks(cls: type) → None + +Enables all hooks for all the methods of a specific class. + +### Examples + +```python +@support_hooks +class MyClass: + @slot + def my_method(self, arg): ... + +@MyClass.my_method.hook +def callback(self, arg): ... + +obj = MyClass() + +MyClass.suspend_hooks() +obj.my_method(1) # will NOT execute callback + +MyClass.resume_hooks() + +obj.my_method(2) # will execute callback(obj, 2) +``` + +#### Versionadded +Added in version 0.7.0. + + diff --git a/mddocs/hwm_store/index.md b/mddocs/hwm_store/index.md new file mode 100644 index 000000000..c59cc0096 --- /dev/null +++ b/mddocs/hwm_store/index.md @@ -0,0 +1,9 @@ + + +# HWM + +Since onETL v0.10.0, the `HWMStore` and `HWM` classes have been moved to a separate library [etl-entities](https://etl-entities.readthedocs.io/en/stable/). + +The only class was left intact is [YAMLHWMStore](yaml_hwm_store.md#yaml-hwm-store), **which is default** in onETL. + +Other known implementation is [HorizonHWMStore](https://horizon-hwm-store.readthedocs.io/). diff --git a/mddocs/hwm_store/yaml_hwm_store.md b/mddocs/hwm_store/yaml_hwm_store.md new file mode 100644 index 000000000..425a7e030 --- /dev/null +++ b/mddocs/hwm_store/yaml_hwm_store.md @@ -0,0 +1,161 @@ + + +# YAMLHWMStore + +### *class* onetl.hwm.store.yaml_hwm_store.YAMLHWMStore(\*, path: LocalPath = LocalPosixPath('/home/sattar/.local/share/onETL/yml_hwm_store'), encoding: str = 'utf-8') + +YAML **local store** for HWM values. Used as default HWM store. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) + +* **Parameters:** + **path** + : Folder name there HWM value files will be stored. +
+ Default: + * `~/.local/share/onETL/yml_hwm_store` on Linux + * `C:\Documents and Settings\\Application Data\oneTools\onETL\yml_hwm_store` on Windows + * `~/Library/Application Support/onETL/yml_hwm_store` on MacOS + + **encoding** + : Encoding of files with HWM value + +### Examples + +Default parameters + +```python +from onetl.connection import Hive, Postgres +from onetl.db import DBReader, DBWriter +from onetl.strategy import IncrementalStrategy +from onetl.hwm.store import YAMLHWMStore + +postgres = Postgres(...) +hive = Hive(...) + +reader = DBReader( + connection=postgres, + source="public.mydata", + columns=["id", "data"], + hwm=DBReader.AutoDetectHWM(name="some_unique_name", expression="id"), +) + +writer = DBWriter(connection=hive, target="db.newtable") + +with YAMLHWMStore(): + with IncrementalStrategy(): + df = reader.run() + writer.run(df) + +# will create file +# "~/.local/share/onETL/id__public.mydata__postgres_postgres.domain.com_5432__myprocess__myhostname.yml" +# with encoding="utf-8" and save a serialized HWM values to this file +``` + +With all options + +```python +with YAMLHWMStore(path="/my/store", encoding="utf-8"): + with IncrementalStrategy(): + df = reader.run() + writer.run(df) + +# will create file +# "/my/store/id__public.mydata__postgres_postgres.domain.com_5432__myprocess__myhostname.yml" +# with encoding="utf-8" and save a serialized HWM values to this file +``` + +File content example: + +```yaml +- column: + name: id + partition: {} + modified_time: '2023-02-11T17:10:49.659019' + process: + dag: '' + host: myhostname + name: myprocess + task: '' + source: + db: public + instance: postgres://postgres.domain.com:5432/target_database + name: mydata + type: int + value: '1500' +- column: + name: id + partition: {} + modified_time: '2023-02-11T16:00:31.962150' + process: + dag: '' + host: myhostname + name: myprocess + task: '' + source: + db: public + instance: postgres://postgres.domain.com:5432/target_database + name: mydata + type: int + value: '1000' +``` + + + +#### \_\_enter_\_() + +HWM store context manager. + +Enter this context to use this HWM store instance as current one (instead default). + +### Examples + +```pycon +>>> from etl_entities.hwm_store import HWMStoreStackManager +>>> with SomeHWMStore(...) as hwm_store: +... print(HWMStoreStackManager.get_current()) +SomeHWMStore(...) +>>> HWMStoreStackManager.get_current() +DefaultHWMStore() +``` + + + +#### get_hwm(name: str) → HWM | None + +Get HWM by name from HWM store. + +* **Parameters:** + **name** + : HWM unique name +* **Returns:** + `HWM` + : HWM object, if it exists in HWM store, or None + +### Examples + +```python +hwm = hwm_store.get_hwm(hwm_unique_name) +``` + + + +#### set_hwm(hwm: HWM) → LocalPath + +Save HWM object to HWM Store. + +* **Parameters:** + **hwm** + : HWM object +* **Returns:** + Any + : HWM location, like URL of file path. Result type is implementation-specific. + +### Examples + +```python +from etl_entities.hwm import ColumnIntHWM + +hwm = ColumnIntHWM(name=..., value=...) +hwm_location = hwm_store.set_hwm(hwm) +``` + + diff --git a/mddocs/index.md b/mddocs/index.md new file mode 100644 index 000000000..ebfe9116e --- /dev/null +++ b/mddocs/index.md @@ -0,0 +1,9 @@ +```{include} README.md +:end-before: +``` + +![onETL logo](_static/logo_wide.svg) + +```{include} README.md +:start-after: +``` \ No newline at end of file diff --git a/mddocs/install/files.md b/mddocs/install/files.md new file mode 100644 index 000000000..60c747462 --- /dev/null +++ b/mddocs/install/files.md @@ -0,0 +1,20 @@ + + +# File connections + +All File (but not *FileDF*) connection classes (`FTP`, `SFTP`, `HDFS` and so on) requires specific Python clients to be installed. + +Each client can be installed explicitly by passing connector name (in lowercase) to `extras`: + +```bash +pip install onetl[ftp] # specific connector +pip install onetl[ftp,ftps,sftp,hdfs,s3,webdav,samba] # multiple connectors +``` + +To install all file connectors at once you can pass `files` to `extras`: + +```bash +pip install onetl[files] +``` + +**Otherwise class import will fail.** diff --git a/mddocs/install/full.md b/mddocs/install/full.md new file mode 100644 index 000000000..889f6c401 --- /dev/null +++ b/mddocs/install/full.md @@ -0,0 +1,15 @@ + + +# Full bundle + +To install all connectors and dependencies, you can pass `all` into `extras`: + +```bash +pip install onetl[all] + +# this is just the same as +pip install onetl[spark,files,kerberos] +``` + +#### WARNING +This method consumes a lot of disk space, and requires for Java & Kerberos libraries to be installed into your OS. diff --git a/mddocs/install/index.md b/mddocs/install/index.md new file mode 100644 index 000000000..1a5fb63cc --- /dev/null +++ b/mddocs/install/index.md @@ -0,0 +1,33 @@ + + +# How to install + +Base `onetl` package contains: + +* `DBReader`, `DBWriter` and related classes +* `FileDownloader`, `FileUploader`, `FileMover` and related classes, like file filters & limits +* `FileDFReader`, `FileDFWriter` and related classes, like file formats +* Read Strategies & HWM classes +* Plugins support + +It can be installed via: + +```bash +pip install onetl +``` + +#### WARNING +This method does NOT include any connections. + +This method is recommended for use in third-party libraries which require for `onetl` to be installed, +but do not use its connection classes. + +## Installation in details + +## How to install + +* [How to install]() +* [Spark](spark.md) +* [File connections](files.md) +* [Kerberos support](kerberos.md) +* [Full bundle](full.md) diff --git a/mddocs/install/kerberos.md b/mddocs/install/kerberos.md new file mode 100644 index 000000000..1826124c2 --- /dev/null +++ b/mddocs/install/kerberos.md @@ -0,0 +1,32 @@ + + +# Kerberos support + +Most of Hadoop instances set up with Kerberos support, +so some connections require additional setup to work properly. + +* `HDFS` + Uses [requests-kerberos](https://pypi.org/project/requests-kerberos/) and + [GSSApi](https://pypi.org/project/gssapi/) for authentication. + It also uses `kinit` executable to generate Kerberos ticket. +* `Hive` and `SparkHDFS` + require Kerberos ticket to exist before creating Spark session. + +So you need to install OS packages with: + +* `krb5` libs +* Headers for `krb5` +* `gcc` or other compiler for C sources + +The exact installation instruction depends on your OS, here are some examples: + +```bash +apt install libkrb5-dev krb5-user gcc # Debian-based +dnf install krb5-devel krb5-libs krb5-workstation gcc # CentOS, OracleLinux +``` + +Also you should pass `kerberos` to `extras` to install required Python packages: + +```bash +pip install onetl[kerberos] +``` diff --git a/mddocs/install/spark.md b/mddocs/install/spark.md new file mode 100644 index 000000000..914f71611 --- /dev/null +++ b/mddocs/install/spark.md @@ -0,0 +1,342 @@ + + +# Spark + +All DB connection classes (`Clickhouse`, `Greenplum`, `Hive` and others) +and all FileDF connection classes (`SparkHDFS`, `SparkLocalFS`, `SparkS3`) +require Spark to be installed. + +## Installing Java + +Firstly, you should install JDK. The exact installation instruction depends on your OS, here are some examples: + +```bash +yum install java-1.8.0-openjdk-devel # CentOS 7 + Spark 2 +dnf install java-11-openjdk-devel # CentOS 8 + Spark 3 +apt-get install openjdk-11-jdk # Debian-based + Spark 3 +``` + + + +### Compatibility matrix + +| Spark | Python | Java | Scala | +|-----------------------------------------------------------|------------|------------|---------| +| [2.3.x](https://spark.apache.org/docs/2.3.1/#downloading) | 3.7 only | 8 only | 2.11 | +| [2.4.x](https://spark.apache.org/docs/2.4.8/#downloading) | 3.7 only | 8 only | 2.11 | +| [3.2.x](https://spark.apache.org/docs/3.2.4/#downloading) | 3.7 - 3.10 | 8u201 - 11 | 2.12 | +| [3.3.x](https://spark.apache.org/docs/3.3.4/#downloading) | 3.7 - 3.12 | 8u201 - 17 | 2.12 | +| [3.4.x](https://spark.apache.org/docs/3.4.4/#downloading) | 3.7 - 3.12 | 8u362 - 20 | 2.12 | +| [3.5.x](https://spark.apache.org/docs/3.5.5/#downloading) | 3.8 - 3.13 | 8u371 - 20 | 2.12 | + +## Installing PySpark + +Then you should install PySpark via passing `spark` to `extras`: + +```bash +pip install onetl[spark] # install latest PySpark +``` + +or install PySpark explicitly: + +```bash +pip install onetl pyspark==3.5.5 # install a specific PySpark version +``` + +or inject PySpark to `sys.path` in some other way BEFORE creating a class instance. +**Otherwise connection object cannot be created.** + + + +## Injecting Java packages + +Some DB and FileDF connection classes require specific packages to be inserted to `CLASSPATH` of Spark session, +like JDBC drivers. + +This is usually done by setting up `spark.jars.packages` option while creating Spark session: + +```python +# here is a list of packages to be downloaded: +maven_packages = ( + Greenplum.get_packages(spark_version="3.2") + + MySQL.get_packages() + + Teradata.get_packages() +) + +spark = ( + SparkSession.builder.config("spark.app.name", "onetl") + .config("spark.jars.packages", ",".join(maven_packages)) + .getOrCreate() +) +``` + +Spark automatically resolves package and all its dependencies, download them and inject to Spark session +(both driver and all executors). + +This requires internet access, because package metadata and `.jar` files are fetched from [Maven Repository](https://mvnrepository.com/). + +But sometimes it is required to: + +* Install package without direct internet access (isolated network) +* Install package which is not available in Maven + +There are several ways to do that. + +### Using `spark.jars` + +The most simple solution, but this requires to store raw `.jar` files somewhere on filesystem or web server. + +* Download `package.jar` files (it’s usually something like `some-package_1.0.0.jar`). Local file name does not matter, but it should be unique. +* (For `spark.submit.deployMode=cluster`) place downloaded files to HDFS or deploy to any HTTP web server serving static files. See [official documentation](https://spark.apache.org/docs/latest/submitting-applications.html#advanced-dependency-management) for more details. +* Create Spark session with passing `.jar` absolute file path to `spark.jars` Spark config option: + +for spark.submit.deployMode=client (default) + +```py +jar_files = ["/path/to/package.jar"] + +# do not pass spark.jars.packages +spark = ( + SparkSession.builder.config("spark.app.name", "onetl") + .config("spark.jars", ",".join(jar_files)) + .getOrCreate() +) +``` + +for spark.submit.deployMode=cluster + +```py +# you can also pass URLs like http://domain.com/path/to/downloadable/package.jar +jar_files = ["hdfs:///path/to/package.jar"] + +# do not pass spark.jars.packages +spark = ( + SparkSession.builder.config("spark.app.name", "onetl") + .config("spark.jars", ",".join(jar_files)) + .getOrCreate() +) +``` + +### Using `spark.jars.repositories` + +#### NOTE +In this case Spark still will try to fetch packages from the internet, so if you don’t have internet access, +Spark session will be created with significant delay because of all attempts to fetch packages. + +Can be used if you have access both to public repos (like Maven) and a private Artifactory/Nexus repo. + +* Setup private Maven repository in [JFrog Artifactory](https://jfrog.com/artifactory/) or [Sonatype Nexus](https://www.sonatype.com/products/sonatype-nexus-repository). +* Download `package.jar` file (it’s usually something like `some-package_1.0.0.jar`). Local file name does not matter. +* Upload `package.jar` file to private repository (with same `groupId` and `artifactoryId` as in source package in Maven). +* Pass repo URL to `spark.jars.repositories` Spark config option. +* Create Spark session with passing Package name to `spark.jars.packages` Spark config option: + +```python +maven_packages = ( + Greenplum.get_packages(spark_version="3.2") + + MySQL.get_packages() + + Teradata.get_packages() +) + +spark = ( + SparkSession.builder.config("spark.app.name", "onetl") + .config("spark.jars.repositories", "http://nexus.mydomain.com/private-repo/") + .config("spark.jars.packages", ",".join(maven_packages)) + .getOrCreate() +) +``` + +### Using `spark.jars.ivySettings` + +Same as above, but can be used even if there is no network access to public repos like Maven. + +* Setup private Maven repository in [JFrog Artifactory](https://jfrog.com/artifactory/) or [Sonatype Nexus](https://www.sonatype.com/products/sonatype-nexus-repository). +* Download `package.jar` file (it’s usually something like `some-package_1.0.0.jar`). Local file name does not matter. +* Upload `package.jar` file to [private repository](https://help.sonatype.com/repomanager3/nexus-repository-administration/repository-management#RepositoryManagement-HostedRepository) (with same `groupId` and `artifactoryId` as in source package in Maven). +* Create `ivysettings.xml` file (see below). +* Add here a resolver with repository URL (and credentials, if required). +* Pass `ivysettings.xml` absolute path to `spark.jars.ivySettings` Spark config option. +* Create Spark session with passing package name to `spark.jars.packages` Spark config option: + +ivysettings-all-packages-uploaded-to-nexus.xml + +```xml + + + + + + + + + + + + + +``` + +ivysettings-private-packages-in-nexus-public-in-maven.xml + +```xml + + + + + + + + + + + + + + + + + +``` + +ivysettings-private-packages-in-nexus-public-fetched-using-proxy-repo.xml + +```xml + + + + + + + + + + + + + + + +``` + +ivysettings-nexus-with-auth-required.xml + +```xml + + + + + + + + + + + + + + + + + + + +``` + +```python +maven_packages = ( + Greenplum.get_packages(spark_version="3.2") + + MySQL.get_packages() + + Teradata.get_packages() +) + +spark = ( + SparkSession.builder.config("spark.app.name", "onetl") + .config("spark.jars.ivySettings", "/path/to/ivysettings.xml") + .config("spark.jars.packages", ",".join(maven_packages)) + .getOrCreate() +) +``` + +### Place `.jar` file to `-/.ivy2/jars/` + +Can be used to pass already downloaded file to Ivy, and skip resolving package from Maven. + +* Download `package.jar` file (it’s usually something like `some-package_1.0.0.jar`). Local file name does not matter, but it should be unique. +* Move it to `-/.ivy2/jars/` folder. +* Create Spark session with passing package name to `spark.jars.packages` Spark config option: + +```python +maven_packages = ( + Greenplum.get_packages(spark_version="3.2") + + MySQL.get_packages() + + Teradata.get_packages() +) + +spark = ( + SparkSession.builder.config("spark.app.name", "onetl") + .config("spark.jars.packages", ",".join(maven_packages)) + .getOrCreate() +) +``` + +### Place `.jar` file to Spark jars folder + +#### NOTE +Package file should be placed on all hosts/containers Spark is running, +both driver and all executors. + +Usually this is used only with either: +: * `spark.master=local` (driver and executors are running on the same host), + * `spark.master=k8s://...` (`.jar` files are added to image or to volume mounted to all pods). + +Can be used to embed `.jar` files to a default Spark classpath. + +* Download `package.jar` file (it’s usually something like `some-package_1.0.0.jar`). Local file name does not matter, but it should be unique. +* Move it to `$SPARK_HOME/jars/` folder, e.g. `^/.local/lib/python3.7/site-packages/pyspark/jars/` or `/opt/spark/3.2.3/jars/`. +* Create Spark session **WITHOUT** passing Package name to `spark.jars.packages` + +```python +# no need to set spark.jars.packages or any other spark.jars.* option +# all jars already present in CLASSPATH, and loaded automatically + +spark = SparkSession.builder.config("spark.app.name", "onetl").getOrCreate() +``` + +### Manually adding `.jar` files to `CLASSPATH` + +#### NOTE +Package file should be placed on all hosts/containers Spark is running, +both driver and all executors. + +Usually this is used only with either: +: * `spark.master=local` (driver and executors are running on the same host), + * `spark.master=k8s://...` (`.jar` files are added to image or to volume mounted to all pods). + +Can be used to embed `.jar` files to a default Java classpath. + +* Download `package.jar` file (it’s usually something like `some-package_1.0.0.jar`). Local file name does not matter. +* Set environment variable `CLASSPATH` to `/path/to/package.jar`. You can set multiple file paths +* Create Spark session **WITHOUT** passing Package name to `spark.jars.packages` + +```python +# no need to set spark.jars.packages or any other spark.jars.* option +# all jars already present in CLASSPATH, and loaded automatically + +import os + +jar_files = ["/path/to/package.jar"] +# different delimiters for Windows and Linux +delimiter = ";" if os.name == "nt" else ":" +spark = ( + SparkSession.builder.config("spark.app.name", "onetl") + .config("spark.driver.extraClassPath", delimiter.join(jar_files)) + .config("spark.executor.extraClassPath", delimiter.join(jar_files)) + .getOrCreate() +) +``` diff --git a/mddocs/logging.md b/mddocs/logging.md new file mode 100644 index 000000000..f9900f789 --- /dev/null +++ b/mddocs/logging.md @@ -0,0 +1,236 @@ + + +# Logging + +Logging is quite important to understand what’s going on under the hood of onETL. + +Default logging level for Python interpreters is `WARNING`, +but most of onETL logs are in `INFO` level, so users usually don’t see much. + +To change logging level, there is a function [`setup_logging`](#onetl.log.setup_logging) +which should be called at the top of the script: + +```python +from onetl.log import setup_logging +from other.lib import some, more, imports + +setup_logging() + +# rest of code +... +``` + +This changes both log level and log formatting to something like this: + +### See logs + +```text +2024-04-12 10:12:10,834 [INFO ] MainThread: |onETL| Using IncrementalStrategy as a strategy +2024-04-12 10:12:10,835 [INFO ] MainThread: =================================== DBReader.run() starts =================================== +2024-04-12 10:12:10,835 [INFO ] MainThread: |DBReader| Getting Spark type for HWM expression: 'updated_at' +2024-04-12 10:12:10,836 [INFO ] MainThread: |MSSQL| Fetching schema of table 'source_schema.table' ... +2024-04-12 10:12:11,636 [INFO ] MainThread: |MSSQL| Schema fetched. +2024-04-12 10:12:11,642 [INFO ] MainThread: |DBReader| Got Spark field: StructField('updated_at', TimestampType(), True) +2024-04-12 10:12:11,642 [INFO ] MainThread: |DBReader| Detected HWM type: 'ColumnDateTimeHWM' +2024-04-12 10:12:11,643 [INFO ] MainThread: |IncrementalStrategy| Fetching HWM from HorizonHWMStore: +2024-04-12 10:12:11,643 [INFO ] MainThread: name = 'updated_at#source_schema.table@mssql:/mssql.host:1433/somedb' +2024-04-12 10:12:12,181 [INFO ] MainThread: |IncrementalStrategy| Fetched HWM: +2024-04-12 10:12:12,182 [INFO ] MainThread: hwm = ColumnDateTimeHWM( +2024-04-12 10:12:12,182 [INFO ] MainThread: name = 'updated_at#source_schema.table@mssql:/mssql.host:1433/somedb', +2024-04-12 10:12:12,182 [INFO ] MainThread: entity = 'source_schema.table', +2024-04-12 10:12:12,182 [INFO ] MainThread: expression = 'updated_at', +2024-04-12 10:12:12,184 [INFO ] MainThread: value = datetime.datetime(2024, 4, 11, 18, 10, 2, 120000), +2024-04-12 10:12:12,184 [INFO ] MainThread: ) +2024-04-12 10:12:12,184 [INFO ] MainThread: |MSSQL| -> |Spark| Reading DataFrame from source using parameters: +2024-04-12 10:12:12,185 [INFO ] MainThread: source = 'source_schema.table' +2024-04-12 10:12:12,185 [INFO ] MainThread: columns = [ +2024-04-12 10:12:12,185 [INFO ] MainThread: 'id', +2024-04-12 10:12:12,186 [INFO ] MainThread: 'new_value', +2024-04-12 10:12:12,186 [INFO ] MainThread: 'old_value', +2024-04-12 10:12:12,186 [INFO ] MainThread: 'updated_at', +2024-04-12 10:12:12,186 [INFO ] MainThread: ] +2024-04-12 10:12:12,187 [INFO ] MainThread: where = "field = 'some'" +2024-04-12 10:12:12,187 [INFO ] MainThread: hwm = AutoDetectHWM( +2024-04-12 10:12:12,187 [INFO ] MainThread: name = 'updated_at#source_schema.table@mssql:/mssql.host:1433/somedb', +2024-04-12 10:12:12,187 [INFO ] MainThread: entity = 'source_schema.table', +2024-04-12 10:12:12,187 [INFO ] MainThread: expression = 'updated_at', +2024-04-12 10:12:12,188 [INFO ] MainThread: ) +2024-04-12 10:12:12,188 [INFO ] MainThread: options = { +2024-04-12 10:12:12,188 [INFO ] MainThread: 'fetchsize': 100000, +2024-04-12 10:12:12,188 [INFO ] MainThread: 'numPartitions': 1, +2024-04-12 10:12:12,189 [INFO ] MainThread: 'partitioningMode': 'range', +2024-04-12 10:12:12,189 [INFO ] MainThread: } +2024-04-12 10:12:12,189 [INFO ] MainThread: |MSSQL| Checking connection availability... +2024-04-12 10:12:12,189 [INFO ] MainThread: |MSSQL| Using connection parameters: +2024-04-12 10:12:12,190 [INFO ] MainThread: user = 'db_user' +2024-04-12 10:12:12,190 [INFO ] MainThread: password = SecretStr('**********') +2024-04-12 10:12:12,190 [INFO ] MainThread: host = 'mssql.host' +2024-04-12 10:12:12,190 [INFO ] MainThread: port = 1433 +2024-04-12 10:12:12,191 [INFO ] MainThread: database = 'somedb' +2024-04-12 10:12:12,191 [INFO ] MainThread: extra = {'applicationIntent': 'ReadOnly', 'trustServerCertificate': 'true'} +2024-04-12 10:12:12,191 [INFO ] MainThread: jdbc_url = 'jdbc:sqlserver:/mssql.host:1433' +2024-04-12 10:12:12,579 [INFO ] MainThread: |MSSQL| Connection is available. +2024-04-12 10:12:12,581 [INFO ] MainThread: |MSSQL| Executing SQL query (on driver): +2024-04-12 10:12:12,581 [INFO ] MainThread: SELECT +2024-04-12 10:12:12,581 [INFO ] MainThread: MIN(updated_at) AS "min", +2024-04-12 10:12:12,582 [INFO ] MainThread: MAX(updated_at) AS "max" +2024-04-12 10:12:12,582 [INFO ] MainThread: FROM +2024-04-12 10:12:12,582 [INFO ] MainThread: source_schema.table +2024-04-12 10:12:12,582 [INFO ] MainThread: WHERE +2024-04-12 10:12:12,582 [INFO ] MainThread: (field = 'some') +2024-04-12 10:12:12,583 [INFO ] MainThread: AND +2024-04-12 10:12:12,583 [INFO ] MainThread: (updated_at >= CAST('2024-04-11T18:10:02.120000' AS datetime2)) +2024-04-12 10:16:22,537 [INFO ] MainThread: |MSSQL| Received values: +2024-04-12 10:16:22,538 [INFO ] MainThread: MIN(updated_at) = datetime.datetime(2024, 4, 11, 21, 10, 7, 397000) +2024-04-12 10:16:22,538 [INFO ] MainThread: MAX(updated_at) = datetime.datetime(2024, 4, 12, 13, 12, 2, 123000) +2024-04-12 10:16:22,540 [INFO ] MainThread: |MSSQL| Executing SQL query (on executor): +2024-04-12 10:16:22,540 [INFO ] MainThread: SELECT +2024-04-12 10:16:22,540 [INFO ] MainThread: id, +2024-04-12 10:16:22,541 [INFO ] MainThread: new_value, +2024-04-12 10:16:22,541 [INFO ] MainThread: old_value, +2024-04-12 10:16:22,541 [INFO ] MainThread: updated_at +2024-04-12 10:16:22,541 [INFO ] MainThread: FROM +2024-04-12 10:16:22,541 [INFO ] MainThread: source_schema.table +2024-04-12 10:16:22,542 [INFO ] MainThread: WHERE +2024-04-12 10:16:22,542 [INFO ] MainThread: (field = 'some') +2024-04-12 10:16:22,542 [INFO ] MainThread: AND +2024-04-12 10:16:22,542 [INFO ] MainThread: (updated_at > CAST('2024-04-11T18:10:02.120000' AS datetime2)) +2024-04-12 10:16:22,542 [INFO ] MainThread: AND +2024-04-12 10:16:22,542 [INFO ] MainThread: (updated_at <= CAST('2024-04-12T13:12:02.123000' AS datetime2)) +2024-04-12 10:16:22,892 [INFO ] MainThread: |Spark| DataFrame successfully created from SQL statement +2024-04-12 10:16:22,892 [INFO ] MainThread: ------------------------------------ DBReader.run() ends ------------------------------------ +2024-04-12 10:40:42,409 [INFO ] MainThread: =================================== DBWriter.run() starts =================================== +2024-04-12 10:40:42,409 [INFO ] MainThread: |Spark| -> |Hive| Writing DataFrame to target using parameters: +2024-04-12 10:40:42,410 [INFO ] MainThread: target = 'target_source_schema.table' +2024-04-12 10:40:42,410 [INFO ] MainThread: options = { +2024-04-12 10:40:42,410 [INFO ] MainThread: 'mode': 'append', +2024-04-12 10:40:42,410 [INFO ] MainThread: 'format': 'orc', +2024-04-12 10:40:42,410 [INFO ] MainThread: 'partitionBy': 'part_dt', +2024-04-12 10:40:42,410 [INFO ] MainThread: } +2024-04-12 10:40:42,411 [INFO ] MainThread: df_schema: +2024-04-12 10:40:42,412 [INFO ] MainThread: root +2024-04-12 10:40:42,412 [INFO ] MainThread: |-- id: integer (nullable = true) +2024-04-12 10:40:42,413 [INFO ] MainThread: |-- new_value: string (nullable = true) +2024-04-12 10:40:42,413 [INFO ] MainThread: |-- old_value: string (nullable = true) +2024-04-12 10:40:42,413 [INFO ] MainThread: |-- updated_at: timestamp (nullable = true) +2024-04-12 10:40:42,413 [INFO ] MainThread: |-- part_dt: date (nullable = true) +2024-04-12 10:40:42,414 [INFO ] MainThread: +2024-04-12 10:40:42,421 [INFO ] MainThread: |Hive| Checking connection availability... +2024-04-12 10:40:42,421 [INFO ] MainThread: |Hive| Using connection parameters: +2024-04-12 10:40:42,421 [INFO ] MainThread: cluster = 'dwh' +2024-04-12 10:40:42,475 [INFO ] MainThread: |Hive| Connection is available. +2024-04-12 10:40:42,476 [INFO ] MainThread: |Hive| Fetching schema of table 'target_source_schema.table' ... +2024-04-12 10:40:43,518 [INFO ] MainThread: |Hive| Schema fetched. +2024-04-12 10:40:43,521 [INFO ] MainThread: |Hive| Table 'target_source_schema.table' already exists +2024-04-12 10:40:43,521 [WARNING ] MainThread: |Hive| User-specified options {'partitionBy': 'part_dt'} are ignored while inserting into existing table. Using only table parameters from Hive metastore +2024-04-12 10:40:43,782 [INFO ] MainThread: |Hive| Inserting data into existing table 'target_source_schema.table' ... +2024-04-12 11:06:07,396 [INFO ] MainThread: |Hive| Data is successfully inserted into table 'target_source_schema.table'. +2024-04-12 11:06:07,397 [INFO ] MainThread: ------------------------------------ DBWriter.run() ends ------------------------------------ +2024-04-12 11:06:07,397 [INFO ] MainThread: |onETL| Exiting IncrementalStrategy +2024-04-12 11:06:07,397 [INFO ] MainThread: |IncrementalStrategy| Saving HWM to 'HorizonHWMStore': +2024-04-12 11:06:07,397 [INFO ] MainThread: hwm = ColumnDateTimeHWM( +2024-04-12 11:06:07,397 [INFO ] MainThread: name = 'updated_at#source_schema.table@mssql:/mssql.host:1433/somedb', +2024-04-12 11:06:07,397 [INFO ] MainThread: entity = 'source_source_schema.table', +2024-04-12 11:06:07,397 [INFO ] MainThread: expression = 'updated_at', +2024-04-12 11:06:07,397 [INFO ] MainThread: value = datetime.datetime(2024, 4, 12, 13, 12, 2, 123000), +2024-04-12 11:06:07,397 [INFO ] MainThread: ) +2024-04-12 11:06:07,495 [INFO ] MainThread: |IncrementalStrategy| HWM has been saved +``` + +Each step performed by onETL is extensively logged, which should help with debugging. + +You can make logs even more verbose by changing level to `DEBUG`: + +```python +from onetl.log import setup_logging + +setup_logging(level="DEBUG", enable_clients=True) + +# rest of code +... +``` + +This also changes log level for all underlying Python libraries, e.g. showing each HTTP request being made, and so on. + +### onetl.log.setup_logging(level: int | str = 20, enable_clients: bool = False) → None + +Set up onETL logging. + +What this function does: +: * Adds stderr logging handler + * Changes root logger format to `2023-05-31 11:22:33.456 [INFO] MainThread: message` + * Changes root logger level to `level` + * Changes onETL logger level to `level` + * Sets up logging level of underlying client modules + +#### NOTE +Should be used only in IDEs (like Jupyter notebooks or PyCharm), +or scripts (ETL pipelines). + +#### Versionchanged +Changed in version 0.5.0: Renamed `setup_notebook_logging` → `setup_logging` + +* **Parameters:** + **level** + : Log level for onETL module + + **enable_clients** + : If `True`, enable logging of underlying client modules. + Otherwise, set client modules log level to `DISABLED`. +
+ #### NOTE + For `level="DEBUG"` it is recommended to use `enable_clients=True` +
+ #### Versionadded + Added in version 0.9.0. + + + +### onetl.log.setup_clients_logging(level: int | str = 9999) → None + +Set logging of underlying client modules used by onETL. + +Affected modules: +: * `ftputil` + * `hdfs` + * `minio` + * `paramiko` + * `py4j` + * `pyspark` + * `webdav3` + +#### NOTE +Can be used in applications, but it is recommended to set up these loggers +according to your framework documentation. + +#### Versionchanged +Changed in version 0.9.0: Renamed `disable_clients_logging` → `setup_clients_logging` + +* **Parameters:** + **level** + : Log level for client modules +
+ #### NOTE + For `py4j`, logging level with maximum verbosity is `INFO` because `DEBUG` logs are + totally unreadable. +
+ #### Versionadded + Added in version 0.9.0. + + + +### onetl.log.set_default_logging_format() → None + +Sets default logging format to preferred by onETL. + +Example log message: `2023-05-31 11:22:33.456 [INFO] MainThread: message` + +#### NOTE +Should be used only in IDEs (like Jupyter notebooks or PyCharm), +or scripts (ETL pipelines). + +#### WARNING +Should **NOT** be used in applications, you should set up logging settings manually, +according to your framework documentation. + + diff --git a/mddocs/plugins.md b/mddocs/plugins.md new file mode 100644 index 000000000..d42ded034 --- /dev/null +++ b/mddocs/plugins.md @@ -0,0 +1,147 @@ + + +# Plugins + +#### Versionadded +Added in version 0.6.0. + +## What are plugins? + +### Terms + +* `Plugin` - some Python package which implements some extra functionality for onETL, like [Hooks](hooks/index.md#hooks) +* `Plugin autoimport` - onETL behavior which allows to automatically import this package if it contains proper metadata (`entry_points`) + +### Features + +Plugins mechanism allows to: + +* Automatically register [Hooks](hooks/index.md#hooks) which can alter onETL behavior +* Automatically register new classes, like HWM type, HWM stores and so on + +### Limitations + +Unlike other projects (like *Airflow 1.x*), plugins does not inject imported classes or functions to `onetl.*` namespace. +Users should import classes from the plugin package **explicitly** to avoid name collisions. + +## How to implement plugin? + +Create a Python package `some-plugin` with a file `some_plugin/setup.py`: + +```python +# some_plugin/setup.py +from setuptools import setup + +setup( + # if you want to import something from onETL, add it to requirements list + install_requires=["onetl"], + entry_points={ + # this key enables plugins autoimport functionality + "onetl.plugins": [ + "some-plugin-name=some_plugin.module", # automatically import all module content + "some-plugin-class=some_plugin.module.internals:MyClass", # import a specific class + "some-plugin-function=some_plugin.module.internals:my_function", # import a specific function + ], + }, +) +``` + +See [setuptools documentation for entry_points](https://setuptools.pypa.io/en/latest/userguide/entry_point.html) + +## How plugins are imported? + +* User should install a package implementing the plugin: + +```bash +pip install some-package +``` + +* Then user should import something from `onetl` module or its submodules: + +```python +import onetl +from onetl.connection import SomeConnection + +# and so on +``` + +* This import automatically executes something like: + +```python +import some_plugin.module +from some_plugin.module.internals import MyClass +from some_plugin.module.internals import my_function +``` + +If specific module/class/function uses some registration capabilities of onETL, +like [@hook decorator](hooks/hook.md#hook-decorator), it will be executed during this import. + +## How to enable/disable plugins? + +#### Versionadded +Added in version 0.7.0. + +### Disable/enable all plugins + +By default plugins are enabled. + +To disabled them, you can set environment variable `ONETL_PLUGINS_ENABLED` to `false` BEFORE +importing onETL. This will disable all plugins autoimport. + +But user is still be able to explicitly import `some_plugin.module`, executing +all decorators and registration capabilities of onETL. + +### Disable a specific plugin (blacklist) + +If some plugin is failing during import, you can disable it by setting up environment variable +`ONETL_PLUGINS_BLACKLIST=some-failing-plugin`. Multiple plugin names could be passed with `,` as delimiter. + +Again, this environment variable should be set BEFORE importing onETL. + +### Disable all plugins except a specific one (whitelist) + +You can also disable all plugins except a specific one by setting up environment variable +`ONETL_PLUGINS_WHITELIST=some-not-failing-plugin`. Multiple plugin names could be passed with `,` as delimiter. + +Again, this environment variable should be set BEFORE importing onETL. + +If both whitelist and blacklist environment variables are set, blacklist has a higher priority. + +## How to see logs of the plugins mechanism? + +Plugins registration emits logs with `INFO` level: + +```python +import logging + +logging.basicConfig(level=logging.INFO) +``` + +```text +INFO |onETL| Found 2 plugins +INFO |onETL| Loading plugin 'my-plugin' +INFO |onETL| Skipping plugin 'failing' because it is in a blacklist +``` + +More detailed logs are emitted with `DEBUG` level, to make output less verbose: + +```python +import logging + +logging.basicConfig(level=logging.DEBUG) +``` + +```text +DEBUG |onETL| Searching for plugins with group 'onetl.plugins' +DEBUG |Plugins| Plugins whitelist: [] +DEBUG |Plugins| Plugins blacklist: ['failing-plugin'] +INFO |Plugins| Found 2 plugins +INFO |onETL| Loading plugin (1/2): +DEBUG name: 'my-plugin' +DEBUG package: 'my-package' +DEBUG version: '0.1.0' +DEBUG importing: 'my_package.my_module:MyClass' +DEBUG |onETL| Successfully loaded plugin 'my-plugin' +DEBUG source: '/usr/lib/python3.11/site-packages/my_package/my_module/my_class.py' +INFO |onETL| Skipping plugin 'failing' because it is in a blacklist +``` diff --git a/mddocs/quickstart.md b/mddocs/quickstart.md new file mode 100644 index 000000000..7a9bd0652 --- /dev/null +++ b/mddocs/quickstart.md @@ -0,0 +1,365 @@ +: + +# Quick start + +## MSSQL → Hive + +Read data from MSSQL, transform & write to Hive. + +```bash +# install onETL and PySpark +pip install onetl[spark] +``` + +```python +# Import pyspark to initialize the SparkSession +from pyspark.sql import SparkSession + +# import function to setup onETL logging +from onetl.log import setup_logging + +# Import required connections +from onetl.connection import MSSQL, Hive + +# Import onETL classes to read & write data +from onetl.db import DBReader, DBWriter + +# change logging level to INFO, and set up default logging format and handler +setup_logging() + +# Initialize new SparkSession with MSSQL driver loaded +maven_packages = MSSQL.get_packages() +spark = ( + SparkSession.builder.appName("spark_app_onetl_demo") + .config("spark.jars.packages", ",".join(maven_packages)) + .enableHiveSupport() # for Hive + .getOrCreate() +) + +# Initialize MSSQL connection and check if database is accessible +mssql = MSSQL( + host="mssqldb.demo.com", + user="onetl", + password="onetl", + database="Telecom", + spark=spark, + # These options are passed to MSSQL JDBC Driver: + extra={"applicationIntent": "ReadOnly"}, +).check() + +# >>> INFO:|MSSQL| Connection is available + +# Initialize DBReader +reader = DBReader( + connection=mssql, + source="dbo.demo_table", + columns=["on", "etl"], + # Set some MSSQL read options: + options=MSSQL.ReadOptions(fetchsize=10000), +) + +# checks that there is data in the table, otherwise raises exception +reader.raise_if_no_data() + +# Read data to DataFrame +df = reader.run() +df.printSchema() +# root +# |-- id: integer (nullable = true) +# |-- phone_number: string (nullable = true) +# |-- region: string (nullable = true) +# |-- birth_date: date (nullable = true) +# |-- registered_at: timestamp (nullable = true) +# |-- account_balance: double (nullable = true) + +# Apply any PySpark transformations +from pyspark.sql.functions import lit + +df_to_write = df.withColumn("engine", lit("onetl")) +df_to_write.printSchema() +# root +# |-- id: integer (nullable = true) +# |-- phone_number: string (nullable = true) +# |-- region: string (nullable = true) +# |-- birth_date: date (nullable = true) +# |-- registered_at: timestamp (nullable = true) +# |-- account_balance: double (nullable = true) +# |-- engine: string (nullable = false) + +# Initialize Hive connection +hive = Hive(cluster="rnd-dwh", spark=spark) + +# Initialize DBWriter +db_writer = DBWriter( + connection=hive, + target="dl_sb.demo_table", + # Set some Hive write options: + options=Hive.WriteOptions(if_exists="replace_entire_table"), +) + +# Write data from DataFrame to Hive +db_writer.run(df_to_write) + +# Success! +``` + +## SFTP → HDFS + +Download files from SFTP & upload them to HDFS. + +```bash +# install onETL with SFTP and HDFS clients, and Kerberos support +pip install onetl[hdfs,sftp,kerberos] +``` + +```python +# import function to setup onETL logging +from onetl.log import setup_logging + +# Import required connections +from onetl.connection import SFTP, HDFS + +# Import onETL classes to download & upload files +from onetl.file import FileDownloader, FileUploader + +# import filter & limit classes +from onetl.file.filter import Glob, ExcludeDir +from onetl.file.limit import MaxFilesCount + +# change logging level to INFO, and set up default logging format and handler +setup_logging() + +# Initialize SFTP connection and check it +sftp = SFTP( + host="sftp.test.com", + user="someuser", + password="somepassword", +).check() + +# >>> INFO:|SFTP| Connection is available + +# Initialize downloader +file_downloader = FileDownloader( + connection=sftp, + source_path="/remote/tests/Report", # path on SFTP + local_path="/local/onetl/Report", # local fs path + filters=[ + # download only files matching the glob + Glob("*.csv"), + # exclude files from this directory + ExcludeDir("/remote/tests/Report/exclude_dir/"), + ], + limits=[ + # download max 1000 files per run + MaxFilesCount(1000), + ], + options=FileDownloader.Options( + # delete files from SFTP after successful download + delete_source=True, + # mark file as failed if it already exist in local_path + if_exists="error", + ), +) + +# Download files to local filesystem +download_result = downloader.run() + +# Method run returns a DownloadResult object, +# which contains collection of downloaded files, divided to 4 categories +download_result + +# DownloadResult( +# successful=[ +# LocalPath('/local/onetl/Report/file_1.json'), +# LocalPath('/local/onetl/Report/file_2.json'), +# ], +# failed=[FailedRemoteFile('/remote/onetl/Report/file_3.json')], +# ignored=[RemoteFile('/remote/onetl/Report/file_4.json')], +# missing=[], +# ) + +# Raise exception if there are failed files, or there were no files in the remote filesystem +download_result.raise_if_failed() or download_result.raise_if_empty() + +# Do any kind of magic with files: rename files, remove header for csv files, ... +renamed_files = my_rename_function(download_result.success) + +# function removed "_" from file names +# [ +# LocalPath('/home/onetl/Report/file1.json'), +# LocalPath('/home/onetl/Report/file2.json'), +# ] + +# Initialize HDFS connection +hdfs = HDFS( + host="my.name.node", + user="someuser", + password="somepassword", # or keytab +) + +# Initialize uploader +file_uploader = FileUploader( + connection=hdfs, + target_path="/user/onetl/Report/", # hdfs path +) + +# Upload files from local fs to HDFS +upload_result = file_uploader.run(renamed_files) + +# Method run returns a UploadResult object, +# which contains collection of uploaded files, divided to 4 categories +upload_result + +# UploadResult( +# successful=[RemoteFile('/user/onetl/Report/file1.json')], +# failed=[FailedLocalFile('/local/onetl/Report/file2.json')], +# ignored=[], +# missing=[], +# ) + +# Raise exception if there are failed files, or there were no files in the local filesystem, or some input file is missing +upload_result.raise_if_failed() or upload_result.raise_if_empty() or upload_result.raise_if_missing() + +# Success! +``` + +## S3 → Postgres + +Read files directly from S3 path, convert them to dataframe, transform it and then write to a database. + +```bash +# install onETL and PySpark +pip install onetl[spark] +``` + +```python +# Import pyspark to initialize the SparkSession +from pyspark.sql import SparkSession + +# import function to setup onETL logging +from onetl.log import setup_logging + +# Import required connections +from onetl.connection import Postgres, SparkS3 + +# Import onETL classes to read files +from onetl.file import FileDFReader +from onetl.file.format import CSV + +# Import onETL classes to write data +from onetl.db import DBWriter + +# change logging level to INFO, and set up default logging format and handler +setup_logging() + +# Initialize new SparkSession with Hadoop AWS libraries and Postgres driver loaded +maven_packages = SparkS3.get_packages(spark_version="3.5.5") + Postgres.get_packages() +exclude_packages = SparkS3.get_exclude_packages() +spark = ( + SparkSession.builder.appName("spark_app_onetl_demo") + .config("spark.jars.packages", ",".join(maven_packages)) + .config("spark.jars.excludes", ",".join(exclude_packages)) + .getOrCreate() +) + +# Initialize S3 connection and check it +spark_s3 = SparkS3( + host="s3.test.com", + protocol="https", + bucket="my-bucket", + access_key="somekey", + secret_key="somesecret", + # Access bucket as s3.test.com/my-bucket + extra={"path.style.access": True}, + spark=spark, +).check() + +# >>> INFO:|SparkS3| Connection is available + +# Describe file format and parsing options +csv = CSV( + delimiter=";", + header=True, + encoding="utf-8", +) + +# Describe DataFrame schema of files +from pyspark.sql.types import ( + DateType, + DoubleType, + IntegerType, + StringType, + StructField, + StructType, + TimestampType, +) + +df_schema = StructType( + [ + StructField("id", IntegerType()), + StructField("phone_number", StringType()), + StructField("region", StringType()), + StructField("birth_date", DateType()), + StructField("registered_at", TimestampType()), + StructField("account_balance", DoubleType()), + ], +) + +# Initialize file df reader +reader = FileDFReader( + connection=spark_s3, + source_path="/remote/tests/Report", # path on S3 there *.csv files are located + format=csv, # file format with specific parsing options + df_schema=df_schema, # columns & types +) + +# Read files directly from S3 as Spark DataFrame +df = reader.run() + +# Check that DataFrame schema is same as expected +df.printSchema() +# root +# |-- id: integer (nullable = true) +# |-- phone_number: string (nullable = true) +# |-- region: string (nullable = true) +# |-- birth_date: date (nullable = true) +# |-- registered_at: timestamp (nullable = true) +# |-- account_balance: double (nullable = true) + +# Apply any PySpark transformations +from pyspark.sql.functions import lit + +df_to_write = df.withColumn("engine", lit("onetl")) +df_to_write.printSchema() +# root +# |-- id: integer (nullable = true) +# |-- phone_number: string (nullable = true) +# |-- region: string (nullable = true) +# |-- birth_date: date (nullable = true) +# |-- registered_at: timestamp (nullable = true) +# |-- account_balance: double (nullable = true) +# |-- engine: string (nullable = false) + +# Initialize Postgres connection +postgres = Postgres( + host="192.169.11.23", + user="onetl", + password="somepassword", + database="mydb", + spark=spark, +) + +# Initialize DBWriter +db_writer = DBWriter( + connection=postgres, + # write to specific table + target="public.my_table", + # with some writing options + options=Postgres.WriteOptions(if_exists="append"), +) + +# Write DataFrame to Postgres table +db_writer.run(df_to_write) + +# Success! +``` diff --git a/mddocs/security.md b/mddocs/security.md new file mode 100644 index 000000000..3048c040b --- /dev/null +++ b/mddocs/security.md @@ -0,0 +1,25 @@ +# Security + +## Supported Python versions + +3.7 or above + +## Product development security recommendations + +1. Update dependencies to last stable version +2. Build SBOM for the project +3. Perform SAST (Static Application Security Testing) where possible + +## Product development security requirements + +1. No binaries in repository +2. No passwords, keys, access tokens in source code +3. No “Critical” and/or “High” vulnerabilities in contributed source code + +## Vulnerability reports + +Please, use email [mailto:onetools@mts.ru](mailto:onetools@mts.ru) for reporting security issues or anything that can cause any consequences for security. + +Please avoid any public disclosure (including registering issues) at least until it is fixed. + +Thank you in advance for understanding. diff --git a/mddocs/strategy/incremental_batch_strategy.md b/mddocs/strategy/incremental_batch_strategy.md new file mode 100644 index 000000000..2d30a6767 --- /dev/null +++ b/mddocs/strategy/incremental_batch_strategy.md @@ -0,0 +1,301 @@ + + +# Incremental Batch Strategy + +### *class* onetl.strategy.incremental_strategy.IncrementalBatchStrategy(\*, hwm: HWM | None = None, step: Any = None, start: Any = None, stop: Any = None, offset: Any = None) + +Incremental batch strategy for [DB Reader](../db/db_reader.md#db-reader). + +#### NOTE +Cannot be used with [File Downloader](../file/file_downloader/file_downloader.md#file-downloader) + +Same as [`IncrementalStrategy`](incremental_strategy.md#onetl.strategy.incremental_strategy.IncrementalStrategy), +but reads data from the source in sequential batches (1..N) like: + +```sql +1: SELECT id, data + FROM public.mydata + WHERE id > 1000 AND id <= 1100; -- previous HWM value is 1000, step is 100 + +2: WHERE id > 1100 AND id <= 1200; -- + step +3: WHERE id > 1200 AND id <= 1200; -- + step +N: WHERE id > 1300 AND id <= 1400; -- until stop +``` + +This allows to use less CPU and RAM than reading all the data in the one batch, +but takes proportionally more time. + +#### WARNING +Unlike [`SnapshotBatchStrategy`](snapshot_batch_strategy.md#onetl.strategy.snapshot_strategy.SnapshotBatchStrategy), +it **saves** current HWM value after **each batch** into [HWM Store](../hwm_store/index.md#hwm). + +So if code inside the context manager raised an exception, like: + +```python +with IncrementalBatchStrategy() as batches: + for _ in batches: + df = reader.run() # something went wrong here + writer.run(df) # or here + # or here... +``` + +DBReader will **NOT** update HWM in HWM Store for the failed batch. + +All of that allows to resume reading process from the *last successful batch*. + +#### WARNING +Not every [DB connection](../connection/db_connection/index.md#db-connections) +supports batch strategy. For example, Kafka connection doesn’t support it. +Make sure the connection you use is compatible with the IncrementalBatchStrategy. + +#### Versionadded +Added in version 0.1.0. + +* **Parameters:** + **step** + : Step size used for generating batch SQL queries like: + ```sql + SELECT id, data + FROM public.mydata + WHERE id > 1000 AND id <= 1100; -- 1000 is previous HWM value, step is 100 + ``` +
+ #### NOTE + Step defines a range of values will be fetched by each batch. This is **not** + a number of rows, it depends on a table content and value distribution across the rows. +
+ #### NOTE + `step` value will be added to the HWM, so it should have a proper type. +
+ For example, for `TIMESTAMP` column `step` type should be `datetime.timedelta`, not `int` + + **stop** + : If passed, the value will be used for generating WHERE clauses with `hwm.expression` filter, + as a stop value for the last batch. +
+ If not set, the value is determined by a separated query: + ```sql + SELECT MAX(id) as stop + FROM public.mydata + WHERE id > 1000; -- 1000 is previous HWM value (if any) + ``` +
+ #### NOTE + `stop` should be the same type as `hwm.expression` value, + e.g. `datetime.datetime` for `TIMESTAMP` column, `datetime.date` for `DATE`, and so on + + **offset** + : If passed, the offset value will be used to read rows which appeared in the source after the previous read. +
+ For example, previous incremental run returned rows: + ```default + 898 + 899 + 900 + 1000 + ``` +
+ Current HWM value is 1000. +
+ But since then few more rows appeared in the source: + ```default + 898 + 899 + 900 + 901 # new + 902 # new + ... + 999 # new + 1000 + ``` +
+ and you need to read them too. +
+ So you can set `offset=100`, so the first batch of a next incremental run will look like: + ```sql + SELECT id, data + FROM public.mydata + WHERE id > 900 AND id <= 1000; -- 900 = 1000 - 100 = HWM - offset + ``` +
+ and return rows from 901 (**not** 900) to **1000** (duplicate). +
+ #### WARNING + This can lead to reading duplicated values from the table. + You probably need additional deduplication step to handle them +
+ #### NOTE + `offset` value will be subtracted from the HWM, so it should have a proper type. +
+ For example, for `TIMESTAMP` column `offset` type should be `datetime.timedelta`, not `int` + +### Examples + +IncrementalBatch run + +```python +from onetl.db import DBReader, DBWriter +from onetl.strategy import IncrementalBatchStrategy + +reader = DBReader( + connection=postgres, + source="public.mydata", + columns=["id", "data"], + hwm=DBReader.AutoDetectHWM(name="some_hwm_name", expression="id"), +) + +writer = DBWriter(connection=hive, target="db.newtable") + +with IncrementalBatchStrategy(step=100) as batches: + for _ in batches: + df = reader.run() + writer.run(df) +``` + +```sql +-- previous HWM value was 1000 +-- each batch (1..N) will perform a query which return some part of input data + +1: SELECT id, data + FROM public.mydata + WHERE id > 1100 AND id <= 1200; --- from HWM to HWM+step (EXCLUDING first row) + +2: WHERE id > 1200 AND id <= 1300; -- + step +N: WHERE id > 1300 AND id <= 1400; -- until max value of HWM column +``` + +IncrementalBatch run with `stop` value + +```python +... + +with IncrementalBatchStrategy(step=100, stop=2000) as batches: + for _ in batches: + df = reader.run() + writer.run(df) +``` + +```sql +-- previous HWM value was 1000 +-- each batch (1..N) will perform a query which return some part of input data + +1: SELECT id, data + FROM public.mydata + WHERE id > 1000 AND id <= 1100; --- from HWM to HWM+step (EXCLUDING first row) + +2: WHERE id > 1100 AND id <= 1200; -- + step +... +N: WHERE id > 1900 AND id <= 2000; -- until stop +``` + +IncrementalBatch run with `offset` value + +```python +... + +with IncrementalBatchStrategy(step=100, offset=100) as batches: + for _ in batches: + df = reader.run() + writer.run(df) +``` + +```sql +-- previous HWM value was 1000 +-- each batch (1..N) will perform a query which return some part of input data + +1: SELECT id, data + FROM public.mydata + WHERE id > 900 AND id <= 1000; --- from HWM-offset to HWM-offset+step (EXCLUDING first row) + +2: WHERE id > 1000 AND id <= 1100; -- + step +3: WHERE id > 1100 AND id <= 1200; -- + step +... +N: WHERE id > 1300 AND id <= 1400; -- until max value of HWM column +``` + +IncrementalBatch run with all possible options + +```python +... + +with IncrementalBatchStrategy( + step=100, + stop=2000, + offset=100, +) as batches: + for _ in batches: + df = reader.run() + writer.run(df) +``` + +```sql +-- previous HWM value was 1000 +-- each batch (1..N) will perform a query which return some part of input data + +1: SELECT id, data + FROM public.mydata + WHERE id > 900 AND id <= 1000; --- from HWM-offset to HWM-offset+step (EXCLUDING first row) + +2: WHERE id > 1000 AND id <= 1100; -- + step +3: WHERE id > 1100 AND id <= 1200; -- + step +... +N: WHERE id > 1900 AND id <= 2000; -- until stop +``` + +IncrementalBatch run over non-integer column + +`hwm.expression`, `offset` and `stop` can be a date or datetime, not only integer: + +```python +from onetl.db import DBReader, DBWriter +from datetime import date, timedelta + +reader = DBReader( + connection=postgres, + source="public.mydata", + columns=["business_dt", "data"], + hwm=DBReader.AutoDetectHWM(name="some_hwm_name", expression="business_dt"), +) + +writer = DBWriter(connection=hive, target="db.newtable") + +with IncrementalBatchStrategy( + step=timedelta(days=5), + stop=date("2021-01-31"), + offset=timedelta(days=1), +) as batches: + for _ in batches: + df = reader.run() + writer.run(df) +``` + +```sql +-- previous HWM value was '2021-01-10' +-- each batch (1..N) will perform a query which return some part of input data + +1: SELECT business_dt, data + FROM public.mydata + WHERE business_dt > CAST('2021-01-09' AS DATE) -- from HWM-offset (EXCLUDING first row) + AND business_dt <= CAST('2021-01-14' AS DATE); -- to HWM-offset+step + +2: WHERE business_dt > CAST('2021-01-14' AS DATE) -- + step + AND business_dt <= CAST('2021-01-19' AS DATE); + +3: WHERE business_dt > CAST('2021-01-19' AS DATE) -- + step + AND business_dt <= CAST('2021-01-24' AS DATE); + +... + +N: WHERE business_dt > CAST('2021-01-29' AS DATE) + AND business_dt <= CAST('2021-01-31' AS DATE); -- until stop +``` + + + +#### \_\_init_\_(\*\*kwargs) + +Create a new model by parsing and validating input data from keyword arguments. + +Raises ValidationError if the input data cannot be parsed to form a valid model. + + diff --git a/mddocs/strategy/incremental_strategy.md b/mddocs/strategy/incremental_strategy.md new file mode 100644 index 000000000..a40dc5b94 --- /dev/null +++ b/mddocs/strategy/incremental_strategy.md @@ -0,0 +1,396 @@ + + +# Incremental Strategy + +### *class* onetl.strategy.incremental_strategy.IncrementalStrategy(\*, hwm: HWM | None = None, offset: Any = None) + +Incremental strategy for [DB Reader](../db/db_reader.md#db-reader)/[File Downloader](../file/file_downloader/file_downloader.md#file-downloader). + +Used for fetching only new rows/files from a source +by filtering items not covered by the previous [HWM](../hwm_store/index.md#hwm) value. + +For [DB Reader](../db/db_reader.md#db-reader): +: First incremental run is just the same as [`SnapshotStrategy`](snapshot_strategy.md#onetl.strategy.snapshot_strategy.SnapshotStrategy): +
+ ```sql + SELECT id, data FROM mydata; + ``` +
+ Then the max value of `id` column (e.g. `1000`) will be saved as `HWM` to [HWM Store](../hwm_store/index.md#hwm). +
+ Next incremental run will read only new data from the source: +
+ ```sql + SELECT id, data FROM mydata WHERE id > 1000; -- hwm value + ``` +
+ Pay attention to resulting dataframe **does not include** row with `id=1000` because it has been read before. +
+ #### WARNING + If code inside the context manager raised an exception, like: +
+ ```python + with IncrementalStrategy(): + df = reader.run() # something went wrong here + writer.run(df) # or here + # or here... + ``` +
+ When DBReader will **NOT** update HWM in HWM Store. + This allows to resume reading process from the *last successful run*. + +For [File Downloader](../file/file_downloader/file_downloader.md#file-downloader): +: Behavior depends on `hwm` type. +
+ FileListHWM +
+ First incremental run is just the same as [`SnapshotStrategy`](snapshot_strategy.md#onetl.strategy.snapshot_strategy.SnapshotStrategy) - + all files are downloaded: +
+ ```bash + $ hdfs dfs -ls /path +
+ /path/my/file1 + /path/my/file2 + ``` +
+ ```python + DownloadResult( + ..., + successful={ + LocalFile("/downloaded/file1"), + LocalFile("/downloaded/file2"), + }, + ) + ``` +
+ Then the list of original file paths is saved as `FileListHWM` object into [HWM Store](../hwm_store/index.md#hwm): +
+ ```python + FileListHWM( + ..., + entity="/path", + value=[ + "/path/my/file1", + "/path/my/file2", + ], + ) + ``` +
+ Next incremental run will download only new files which were added to the source since previous run: +
+ ```bash + $ hdfs dfs -ls /path +
+ /path/my/file1 + /path/my/file2 + /path/my/file3 + ``` +
+ ```python + # only files which are not covered by FileListHWM + DownloadResult( + ..., + successful={ + LocalFile("/downloaded/file3"), + }, + ) + ``` +
+ Value of `FileListHWM` will be updated and saved to [HWM Store](../hwm_store/index.md#hwm): +
+ ```python + FileListHWM( + ..., + directory="/path", + value=[ + "/path/my/file1", + "/path/my/file2", + "/path/my/file3", + ], + ) + ``` +
+ FileModifiedTimeHWM +
+ First incremental run is just the same as [`SnapshotStrategy`](snapshot_strategy.md#onetl.strategy.snapshot_strategy.SnapshotStrategy) - + all files are downloaded: +
+ ```bash + $ hdfs dfs -ls /path +
+ /path/my/file1 + /path/my/file2 + ``` +
+ ```python + DownloadResult( + ..., + successful={ + LocalFile("/downloaded/file1"), + LocalFile("/downloaded/file2"), + }, + ) + ``` +
+ Then the maximum modified time of original files is saved as `FileModifiedTimeHWM` object into [HWM Store](../hwm_store/index.md#hwm): +
+ ```python + FileModifiedTimeHWM( + ..., + directory="/path", + value=datetime.datetime(2025, 1, 1, 11, 22, 33, 456789, tzinfo=timezone.utc), + ) + ``` +
+ Next incremental run will download only files from the source which were modified or created since previous run: +
+ ```bash + $ hdfs dfs -ls /path +
+ /path/my/file1 + /path/my/file2 + /path/my/file3 + ``` +
+ ```python + # only files which are not covered by FileModifiedTimeHWM + DownloadResult( + ..., + successful={ + LocalFile("/downloaded/file3"), + }, + ) + ``` +
+ Value of `FileModifiedTimeHWM` will be updated and and saved to [HWM Store](../hwm_store/index.md#hwm): +
+ ```python + FileModifiedTimeHWM( + ..., + directory="/path", + value=datetime.datetime(2025, 1, 1, 22, 33, 44, 567890, tzinfo=timezone.utc), + ) + ``` +
+ #### WARNING + FileDownload updates HWM in HWM Store at the end of `.run()` call, + **NOT** while exiting strategy context. This is because: +
+ * FileDownloader does not raise exceptions if some file cannot be downloaded. + * FileDownloader creates files on local filesystem, and file content may differ for different `modes`. + * It can remove files from the source if `delete_source` is set to `True`. + +#### Versionadded +Added in version 0.1.0. + +* **Parameters:** + **offset** + : If passed, the offset value will be used to read rows which appeared in the source after the previous read. +
+ For example, previous incremental run returned rows: + ```default + 898 + 899 + 900 + 1000 + ``` +
+ Current HWM value is 1000. +
+ But since then few more rows appeared in the source: + ```default + 898 + 899 + 900 + 901 # new + 902 # new + ... + 999 # new + 1000 + ``` +
+ and you need to read them too. +
+ So you can set `offset=100`, so a next incremental run will generate SQL query like: + ```sql + SELECT id, data FROM public.mydata WHERE id > 900; + -- 900 = 1000 - 100 = hwm - offset + ``` +
+ and return rows since 901 (**not** 900), **including** 1000 which was already captured by HWM. +
+ #### WARNING + This can lead to reading duplicated values from the table. + You probably need additional deduplication step to handle them +
+ #### WARNING + Cannot be used with [File Downloader](../file/file_downloader/file_downloader.md#file-downloader) +
+ #### NOTE + `offset` value will be subtracted from the HWM, so it should have a proper type. +
+ For example, for `TIMESTAMP` column `offset` type should be `datetime.timedelta`, not `int` + +### Examples + +Incremental run with [DB Reader](../db/db_reader.md#db-reader) + +```python +from onetl.db import DBReader, DBWriter +from onetl.strategy import IncrementalStrategy + +reader = DBReader( + connection=postgres, + source="public.mydata", + columns=["id", "data"], + hwm=DBReader.AutoDetectHWM(name="some_hwm_name", expression="id"), +) + +writer = DBWriter(connection=hive, target="db.newtable") + +with IncrementalStrategy(): + df = reader.run() + writer.run(df) +``` + +```sql +-- previous HWM value was 1000 +-- DBReader will generate query like: + +SELECT id, data +FROM public.mydata +WHERE id > 1000; --- from HWM (EXCLUDING first row) +``` + +Incremental run with [DB Reader](../db/db_reader.md#db-reader) and `IncrementalStrategy(offset=...)` + +```python +from onetl.db import DBReader, DBWriter +from onetl.strategy import IncrementalStrategy + +reader = DBReader( + connection=postgres, + source="public.mydata", + columns=["id", "data"], + hwm=DBReader.AutoDetectHWM(name="some_hwm_name", expression="id"), +) + +writer = DBWriter(connection=hive, target="db.newtable") + +with IncrementalStrategy(offset=100): + df = reader.run() + writer.run(df) +``` + +```sql +-- previous HWM value was 1000 +-- DBReader will generate query like: + +SELECT id, data +FROM public.mydata +WHERE id > 900; -- from HWM-offset (EXCLUDING first row) +``` + +`offset` and `hwm.expression` can be a date or datetime, not only integer: + +```python +from onetl.db import DBReader, DBWriter +from datetime import timedelta + +reader = DBReader( + connection=postgres, + source="public.mydata", + columns=["business_dt", "data"], + hwm=DBReader.AutoDetectHWM(name="some_hwm_name", expression="business_dt"), +) + +writer = DBWriter(connection=hive, target="db.newtable") + +with IncrementalStrategy(offset=timedelta(days=1)): + df = reader.run() + writer.run(df) +``` + +```sql +-- previous HWM value was '2021-01-10' +-- DBReader will generate query like: + +SELECT business_dt, data +FROM public.mydata +WHERE business_dt > CAST('2021-01-09' AS DATE); -- from HWM-offset (EXCLUDING first row) +``` + +Incremental run with [DB Reader](../db/db_reader.md#db-reader) and [Kafka](../connection/db_connection/kafka/index.md#kafka) + +```py +from onetl.db import DBReader, DBWriter +from onetl.strategy import IncrementalStrategy + +reader = DBReader( + connection=kafka, + source="topic_name", + hwm=DBReader.AutoDetectHWM(name="some_hwm_name", expression="offset"), +) + +writer = DBWriter(connection=hive, target="db.newtable") + +with IncrementalStrategy(): + df = reader.run() + +# current run will fetch only messages which were added since previous run +``` + +Incremental run with [File Downloader](../file/file_downloader/file_downloader.md#file-downloader) and `hwm=FileListHWM(...)` + +```py +from onetl.file import FileDownloader +from onetl.strategy import SnapshotStrategy +from etl_entities.hwm import FileListHWM + +downloader = FileDownloader( + connection=sftp, + source_path="/remote", + local_path="/local", + hwm=FileListHWM( # mandatory for IncrementalStrategy + name="my_unique_hwm_name", + ), +) + +with IncrementalStrategy(): + df = downloader.run() + +# current run will download only files which were added since previous run +``` + +Incremental run with [File Downloader](../file/file_downloader/file_downloader.md#file-downloader) and `hwm=FileModifiedTimeHWM(...)` + +```py +from onetl.file import FileDownloader +from onetl.strategy import SnapshotStrategy +from etl_entities.hwm import FileModifiedTimeHWM + +downloader = FileDownloader( + connection=sftp, + source_path="/remote", + local_path="/local", + hwm=FileModifiedTimeHWM( # mandatory for IncrementalStrategy + name="my_unique_hwm_name", + ), +) + +with IncrementalStrategy(): + df = downloader.run() + +# current run will download only files which were modified/created since previous run +``` + + + +#### \_\_init_\_(\*\*kwargs) + +Create a new model by parsing and validating input data from keyword arguments. + +Raises ValidationError if the input data cannot be parsed to form a valid model. + + diff --git a/mddocs/strategy/index.md b/mddocs/strategy/index.md new file mode 100644 index 000000000..7791fed74 --- /dev/null +++ b/mddocs/strategy/index.md @@ -0,0 +1,10 @@ + + +# Read Strategies + +onETL have several builtin strategies for reading data: + +1. [Snapshot Strategy](snapshot_strategy.md) +2. [Incremental Strategy](incremental_strategy.md) +3. [Snapshot Batch Strategy](snapshot_batch_strategy.md) +4. [Incremental Batch Strategy](incremental_batch_strategy.md) diff --git a/mddocs/strategy/snapshot_batch_strategy.md b/mddocs/strategy/snapshot_batch_strategy.md new file mode 100644 index 000000000..da95f8133 --- /dev/null +++ b/mddocs/strategy/snapshot_batch_strategy.md @@ -0,0 +1,281 @@ + + +# Snapshot Batch Strategy + +### *class* onetl.strategy.snapshot_strategy.SnapshotBatchStrategy(\*, hwm: HWM | None = None, step: Any = None, start: Any = None, stop: Any = None) + +Snapshot batch strategy for [DB Reader](../db/db_reader.md#db-reader). + +#### NOTE +Cannot be used with [File Downloader](../file/file_downloader/file_downloader.md#file-downloader) + +Same as [`SnapshotStrategy`](snapshot_strategy.md#onetl.strategy.snapshot_strategy.SnapshotStrategy), +but reads data from the source in sequential batches (1..N) like: + +```sql +1: SELECT id, data + FROM public.mydata + WHERE id >= 1000 AND id <= 1100; -- from start to start+step (INCLUDING first row) + +2: WHERE id > 1100 AND id <= 1200; -- + step +3: WHERE id > 1200 AND id <= 1200; -- + step +N: WHERE id > 1300 AND id <= 1400; -- until stop +``` + +This allows to use less CPU and RAM on Spark cluster than reading all the data in parallel, +but takes proportionally more time. + +#### NOTE +This strategy uses HWM column value to filter data for each batch, +but does **NOT** save it into [HWM Store](../hwm_store/index.md#hwm). +So every run starts from the beginning, not from the previous HWM value. + +#### NOTE +If you only need to reduce number of rows read by Spark from opened cursor, +use `onetl.connection.db_connection.postgres.Postgres.ReadOptions.fetchsize` instead + +#### WARNING +Not every [DB connection](../connection/db_connection/index.md#db-connections) +supports batch strategy. For example, Kafka connection doesn’t support it. +Make sure the connection you use is compatible with the SnapshotBatchStrategy. + +#### Versionadded +Added in version 0.1.0. + +* **Parameters:** + **step** + : Step size used for generating batch SQL queries like: + ```sql + SELECT id, data + FROM public.mydata + WHERE id >= 1000 AND id <= 1100; -- 1000 is start value, step is 100 + ``` +
+ #### NOTE + Step defines a range of values will be fetched by each batch. This is **not** + a number of rows, it depends on a table content and value distribution across the rows. +
+ #### NOTE + `step` value will be added to the HWM, so it should have a proper type. +
+ For example, for `TIMESTAMP` column `step` type should be `datetime.timedelta`, not `int` + + **start** + : If passed, the value will be used for generating WHERE clauses with `hwm.expression` filter, + as a start value for the first batch. +
+ If not set, the value is determined by a separated query: + ```sql + SELECT MIN(id) as start + FROM public.mydata + WHERE id <= 1400; -- 1400 here is stop value (if set) + ``` +
+ #### NOTE + `start` should be the same type as `hwm.expression` value, + e.g. `datetime.datetime` for `TIMESTAMP` column, `datetime.date` for `DATE`, and so on + + **stop** + : If passed, the value will be used for generating WHERE clauses with `hwm.expression` filter, + as a stop value for the last batch. +
+ If not set, the value is determined by a separated query: + ```sql + SELECT MAX(id) as stop + FROM public.mydata + WHERE id >= 1000; -- 1000 here is start value (if set) + ``` +
+ #### NOTE + `stop` should be the same type as `hwm.expression` value, + e.g. `datetime.datetime` for `TIMESTAMP` column, `datetime.date` for `DATE`, and so on + +### Examples + +SnapshotBatch run + +```python +from onetl.db import DBReader, DBWriter +from onetl.strategy import SnapshotBatchStrategy + +reader = DBReader( + connection=postgres, + source="public.mydata", + columns=["id", "data"], + hwm=DBReader.AutoDetectHWM(name="some_hwm_name", expression="id"), +) + +writer = DBWriter(connection=hive, target="db.newtable") + +with SnapshotBatchStrategy(step=100) as batches: + for _ in batches: + df = reader.run() + writer.run(df) +``` + +```sql +-- get start and stop values + + SELECT MIN(id) as start, MAX(id) as stop + FROM public.mydata; + +-- for example, start=1000 and stop=2345 + +-- when each batch (1..N) will perform a query which return some part of input data + +1: SELECT id, data + FROM public.mydata + WHERE id >= 1000 AND id <= 1100; -- from start to start+step (INCLUDING first row) + +2: WHERE id > 1100 AND id <= 1200; -- + step +3: WHERE id > 1200 AND id <= 1300; -- + step +N: WHERE id > 2300 AND id <= 2345; -- until stop +``` + +SnapshotBatch run with `stop` value + +```python +... + +with SnapshotBatchStrategy(step=100, stop=1234) as batches: + for _ in batches: + df = reader.run() + writer.run(df) +``` + +```sql +-- stop value is set, so there is no need to fetch it from DB +-- get start value + + SELECT MIN(id) as start + FROM public.mydata + WHERE id <= 1234; -- until stop + +-- for example, start=1000. +-- when each batch (1..N) will perform a query which return some part of input data + +1: SELECT id, data + FROM public.mydata + WHERE id >= 1000 AND id <= 1100; -- from start to start+step (INCLUDING first row) + +2: WHERE id > 1100 AND id <= 1200; -- + step +3: WHERE id > 1200 AND id <= 1300; -- + step +N: WHERE id > 1300 AND id <= 1234; -- until stop +``` + +SnapshotBatch run with `start` value + +```python +... + +with SnapshotBatchStrategy(step=100, start=500) as batches: + for _ in batches: + df = reader.run() + writer.run(df) +``` + +```sql +-- start value is set, so there is no need to fetch it from DB +-- get only stop value + + SELECT MAX(id) as stop + FROM public.mydata + WHERE id >= 500; -- from start + +-- for example, stop=2345. +-- when each batch (1..N) will perform a query which return some part of input data + +1: SELECT id, data + FROM public.mydata + WHERE id >= 500 AND id <= 600; -- from start to start+step (INCLUDING first row) + +2: WHERE id > 600 AND id <= 700; -- + step +3: WHERE id > 700 AND id <= 800; -- + step +... +N: WHERE id > 2300 AND id <= 2345; -- until stop +``` + +SnapshotBatch run with all options + +```python +... + +with SnapshotBatchStrategy( + start=1000, + step=100, + stop=2000, +) as batches: + for _ in batches: + df = reader.run() + writer.run(df) +``` + +```sql +-- start and stop values are set, so no need to fetch boundaries from DB +-- each batch (1..N) will perform a query which return some part of input data + +1: SELECT id, data + FROM public.mydata + WHERE id >= 1000 AND id <= 1100; -- from start to start+step (INCLUDING first row) + +2: WHERE id > 1100 AND id <= 1200; -- + step +3: WHERE id > 1200 AND id <= 1300; -- + step +... +N: WHERE id > 1900 AND id <= 2000; -- until stop +``` + +SnapshotBatch run over non-integer column + +`hwm.expression`, `start` and `stop` can be a date or datetime, not only integer: + +```python +from datetime import date, timedelta + +reader = DBReader( + connection=postgres, + source="public.mydata", + columns=["business_dt", "data"], + hwm=DBReader.AutoDetectHWM(name="some_hwm_name", expression="business_dt"), +) + +with SnapshotBatchStrategy( + start=date("2021-01-01"), + step=timedelta(days=5), + stop=date("2021-01-31"), +) as batches: + for _ in batches: + df = reader.run() + writer.run(df) +``` + +```sql +-- start and stop values are set, so no need to fetch boundaries from DB +-- each batch will perform a query which return some part of input data +-- HWM value will casted to match column type + +1: SELECT business_dt, data + FROM public.mydata + WHERE business_dt >= CAST('2020-01-01' AS DATE) -- from start to start+step (INCLUDING first row) + AND business_dt <= CAST('2021-01-05' AS DATE); + +2: WHERE business_dt > CAST('2021-01-05' AS DATE) -- + step + AND business_dt <= CAST('2021-01-10' AS DATE); + +3: WHERE business_dt > CAST('2021-01-10' AS DATE) -- + step + AND business_dt <= CAST('2021-01-15' AS DATE); + +... + +N: WHERE business_dt > CAST('2021-01-30' AS DATE) + AND business_dt <= CAST('2021-01-31' AS DATE); -- until stop +``` + + + +#### \_\_init_\_(\*\*kwargs) + +Create a new model by parsing and validating input data from keyword arguments. + +Raises ValidationError if the input data cannot be parsed to form a valid model. + + diff --git a/mddocs/strategy/snapshot_strategy.md b/mddocs/strategy/snapshot_strategy.md new file mode 100644 index 000000000..38ef7768c --- /dev/null +++ b/mddocs/strategy/snapshot_strategy.md @@ -0,0 +1,96 @@ + + +# Snapshot Strategy + +### *class* onetl.strategy.snapshot_strategy.SnapshotStrategy + +Snapshot strategy for [DB Reader](../db/db_reader.md#db-reader)/[File Downloader](../file/file_downloader/file_downloader.md#file-downloader). + +Used for fetching all the rows/files from a source. Does not support HWM. + +#### NOTE +This is a default strategy. + +For [DB Reader](../db/db_reader.md#db-reader): +: Every snapshot run is executing the simple query which fetches all the table data: +
+ ```sql + SELECT id, data FROM public.mydata; + ``` + +For [File Downloader](../file/file_downloader/file_downloader.md#file-downloader): +: Every snapshot run is downloading all the files (from the source, or user-defined list): +
+ ```bash + $ hdfs dfs -ls /path +
+ /path/my/file1 + /path/my/file2 + ``` +
+ ```python + DownloadResult( + ..., + successful={ + LocalFile("/downloaded/file1"), + LocalFile("/downloaded/file2"), + }, + ) + ``` + +#### Versionadded +Added in version 0.1.0. + +### Examples + +Snapshot run with [DB Reader](../db/db_reader.md#db-reader) + +```py +from onetl.db import DBReader, DBWriter +from onetl.strategy import SnapshotStrategy + +reader = DBReader( + connection=postgres, + source="public.mydata", + columns=["id", "data"], + hwm=DBReader.AutoDetectHWM(name="some_hwm_name", expression="id"), +) + +writer = DBWriter(connection=hive, target="db.newtable") + +with SnapshotStrategy(): + df = reader.run() + writer.run(df) + +# current run will execute following query: + +# SELECT id, data FROM public.mydata; +``` + +Snapshot run with [File Downloader](../file/file_downloader/file_downloader.md#file-downloader) + +```py +from onetl.file import FileDownloader +from onetl.strategy import SnapshotStrategy + +downloader = FileDownloader( + connection=sftp, + source_path="/remote", + local_path="/local", +) + +with SnapshotStrategy(): + df = downloader.run() + +# current run will download all files from 'source_path' +``` + + + +#### \_\_init_\_(\*\*kwargs) + +Create a new model by parsing and validating input data from keyword arguments. + +Raises ValidationError if the input data cannot be parsed to form a valid model. + + diff --git a/mddocs/troubleshooting/index.md b/mddocs/troubleshooting/index.md new file mode 100644 index 000000000..847f426e9 --- /dev/null +++ b/mddocs/troubleshooting/index.md @@ -0,0 +1,17 @@ + + +# Troubleshooting + +In case of error please follow instructions below: + +* Read the logs or exception messages you’ve faced with. + : * If Python logs are note verbose enough, [increase the log level](../logging.md#logging). + * If Spark logs are note verbose enough, [increase the log level](spark.md#troubleshooting-spark). +* Read documentation related to a class or method you are using. +* [Google](https://google.com) the error message, and carefully read the search result: + : * [StackOverflow](https://stackoverflow.com/) answers. + * [Spark](https://spark.apache.org/docs/latest/) documentation. + * Documentation of database or filesystem you are connecting to. + * Documentation of underlying connector. +* Search for known [issues](https://github.com/MobileTeleSystems/onetl/issues), or create a new one. +* Always use the most resent versions of onETL, PySpark and connector packages, [compatible with your environment](../install/spark.md#install-spark). diff --git a/mddocs/troubleshooting/spark.md b/mddocs/troubleshooting/spark.md new file mode 100644 index 000000000..812b38acc --- /dev/null +++ b/mddocs/troubleshooting/spark.md @@ -0,0 +1,72 @@ + + +# Spark Troubleshooting + +## Restarting Spark session + +Sometimes it is required to stop current Spark session and start a new one, e.g. to add some .jar packages, or change session config. +But PySpark not only starts Spark session, but also starts Java virtual machine (JVM) process in the background, +which. So calling `sparkSession.stop()` [does not shutdown JVM](https://issues.apache.org/jira/browse/SPARK-47740), +and this can cause some issue. + +Also apart from JVM properties, stopping Spark session does not clear Spark context, which is a global object. So new +Spark sessions are created using the same context object, and thus using the same Spark config options. + +To properly stop Spark session, it is **required** to: +\* Stop Spark session by calling `sparkSession.stop()`. +\* **STOP PYTHON INTERPRETER**, e.g. by calling `sys.exit()`. +\* Start new Python interpreter. +\* Start new Spark session with config options you need. + +Skipping some of these steps can lead to issues with creating new Spark session. + +## Driver log level + +Default logging level for Spark session is `WARN`. To show more verbose logs, use: + +```python +spark.sparkContext.setLogLevel("INFO") +``` + +or increase verbosity even more: + +```python +spark.sparkContext.setLogLevel("DEBUG") +``` + +After getting all information you need, you can return back the previous log level: + +```python +spark.sparkContext.setLogLevel("WARN") +``` + +## Executors log level + +`sparkContext.setLogLevel` changes only log level of Spark session on Spark **driver**. +To make Spark executor logs more verbose, perform following steps: + +* Create `log4j.properties` file with content like this: + ```jproperties + log4j.rootCategory=DEBUG, console + + log4j.appender.console=org.apache.log4j.ConsoleAppender + log4j.appender.console.target=System.err + log4j.appender.console.layout=org.apache.log4j.PatternLayout + log4j.appender.console.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss} %p %c{1}: %m%n + ``` +* Stop existing Spark session and create a new one with following options: + ```python + from pyspark.sql import SparkSession + + spark = ( + SparkSesion.builder.config("spark.files", "file:log4j.properties").config( + "spark.executor.extraJavaOptions", "-Dlog4j.configuration=file:log4j.properties" + ) + # you can apply the same logging settings to Spark driver, by uncommenting the line below + # .config("spark.driver.extraJavaOptions", "-Dlog4j.configuration=file:log4j.properties") + .getOrCreate() + ) + ``` + +Each Spark executor will receive a copy of `log4j.properties` file during start, and load it to change own log level. +Same approach can be used for Spark driver as well, to investigate issue when Spark session cannot properly start. From edb8b002a160d40e2bd6005bd4a50284cb39606e Mon Sep 17 00:00:00 2001 From: Sattar Gyulmamedov <64004097+wm-wm-wm@users.noreply.github.com> Date: Tue, 15 Apr 2025 16:55:24 +0300 Subject: [PATCH 02/22] check markup in root before docstrings test --- mddocs/changelog.md | 3 +- mddocs/changelog/DRAFT.md | 1 + mddocs/changelog/NEXT_RELEASE.md | 1 + mddocs/changelog/index.md | 2 +- mddocs/concepts.md | 325 +++++++++++++++-------- mddocs/conf.py | 9 +- mddocs/connection/db_connection/index.md | 2 - mddocs/connection/index.md | 2 +- mddocs/contributing.md | 69 ++--- mddocs/db/index.md | 2 +- mddocs/file/index.md | 2 +- mddocs/file_df/index.md | 2 +- mddocs/index.md | 84 +++++- mddocs/install/index.md | 9 +- mddocs/logging.md | 105 ++++---- mddocs/plugins.md | 10 +- 16 files changed, 400 insertions(+), 228 deletions(-) diff --git a/mddocs/changelog.md b/mddocs/changelog.md index 4ba142635..278395a7f 100644 --- a/mddocs/changelog.md +++ b/mddocs/changelog.md @@ -1,5 +1,4 @@ -# Changelog - + # Changelog * [0.13.4 (2025-03-20)](changelog/0.13.4.md) diff --git a/mddocs/changelog/DRAFT.md b/mddocs/changelog/DRAFT.md index e69de29bb..5b77149b5 100644 --- a/mddocs/changelog/DRAFT.md +++ b/mddocs/changelog/DRAFT.md @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/mddocs/changelog/NEXT_RELEASE.md b/mddocs/changelog/NEXT_RELEASE.md index 8cf66aa33..10ebbf98b 100644 --- a/mddocs/changelog/NEXT_RELEASE.md +++ b/mddocs/changelog/NEXT_RELEASE.md @@ -1 +1,2 @@ + \ No newline at end of file diff --git a/mddocs/changelog/index.md b/mddocs/changelog/index.md index 37d5d85ae..4a2663aee 100644 --- a/mddocs/changelog/index.md +++ b/mddocs/changelog/index.md @@ -1,4 +1,4 @@ -Changelog +# Changelog * [0.13.4 (2025-03-20)](0.13.4.md) * [0.13.3 (2025-03-11)](0.13.3.md) diff --git a/mddocs/concepts.md b/mddocs/concepts.md index cf74e9eaa..352c45886 100644 --- a/mddocs/concepts.md +++ b/mddocs/concepts.md @@ -16,9 +16,9 @@ To create a connection to a specific storage type, you must use a class that mat from onetl.connection import SFTP sftp = SFTP( - host="sftp.test.com", - user="onetl", - password="onetl", + host="sftp.test.com", + user="onetl", + password="onetl", ) ``` @@ -26,6 +26,100 @@ All connection types are inherited from the parent class `BaseConnection`. ### Class diagram + + + + ### DBConnection Classes inherited from `DBConnection` could be used for accessing databases. @@ -36,15 +130,15 @@ A `DBConnection` could be instantiated as follows: from onetl.connection import MSSQL mssql = MSSQL( - host="mssqldb.demo.com", - user="onetl", - password="onetl", - database="Telecom", - spark=spark, + host="mssqldb.demo.com", + user="onetl", + password="onetl", + database="Telecom", + spark=spark, ) ``` -where **spark** is the current SparkSession. +where **spark** is the current SparkSession. `onETL` uses `Spark` and specific Java connectors under the hood to work with databases. For a description of other parameters, see the documentation for the [available DBConnections](connection/db_connection/index.md#db-connections). @@ -59,9 +153,9 @@ A `FileConnection` could be instantiated as follows: from onetl.connection import SFTP sftp = SFTP( - host="sftp.test.com", - user="onetl", - password="onetl", + host="sftp.test.com", + user="onetl", + password="onetl", ) ``` @@ -77,13 +171,13 @@ A `FileDFConnection` could be instantiated as follows: from onetl.connection import SparkHDFS spark_hdfs = SparkHDFS( - host="namenode1.domain.com", - cluster="mycluster", - spark=spark, + host="namenode1.domain.com", + cluster="mycluster", + spark=spark, ) ``` -where **spark** is the current SparkSession. +where **spark** is the current SparkSession. `onETL` uses `Spark` and specific Java connectors under the hood to work with DataFrames. For a description of other parameters, see the documentation for the [available FileDFConnections](connection/file_df_connection/index.md#file-df-connections). @@ -104,12 +198,12 @@ This method returns connection itself, so you can create connection and immediat ```Python mssql = MSSQL( - host="mssqldb.demo.com", - user="onetl", - password="onetl", - database="Telecom", - spark=spark, -).check() # <-- + host="mssqldb.demo.com", + user="onetl", + password="onetl", + database="Telecom", + spark=spark, +).check() # <-- ``` ## Extract/Load data @@ -134,17 +228,17 @@ All of these classes have a method `run()` that starts extracting/loading the da from onetl.db import DBReader, DBWriter reader = DBReader( - connection=mssql, - source="dbo.demo_table", - columns=["column_1", "column_2"], + connection=mssql, + source="dbo.demo_table", + columns=["column_1", "column_2"], ) # Read data as Spark DataFrame df = reader.run() db_writer = DBWriter( - connection=hive, - target="dl_sb.demo_table", + connection=hive, + target="dl_sb.demo_table", ) # Save Spark DataFrame to Hive table @@ -155,28 +249,28 @@ writer.run(df) To extract data you can use classes: -| | Use case | Connection | `run()` gets | `run()` returns | -|-------------------------------------------------------------------------|-------------------------------------------|------------------------------------------------------------------------------------|---------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------| -| [DBReader](db/db_reader.md#db-reader) | Reading data from a database | Any [DBConnection](connection/db_connection/index.md#db-connections) | - | [Spark DataFrame](https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.sql.DataFrame.html#pyspark.sql.DataFrame) | -| [FileDFReader](file_df/file_df_reader/file_df_reader.md#file-df-reader) | Read data from a file or set of files | Any [FileDFConnection](connection/file_df_connection/index.md#file-df-connections) | No input, or List[File path on FileSystem] | [Spark DataFrame](https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.sql.DataFrame.html#pyspark.sql.DataFrame) | -| [FileDownloader](db/db_reader.md#db-reader) | Download files from remote FS to local FS | Any [FileConnection](connection/file_connection/index.md#file-connections) | No input, or List[File path on remote FileSystem] | [DownloadResult](file/file_downloader/result.md#file-downloader-result) | +| | Use case | Connection | `run()` gets | `run()` returns | +|--|--|--|--|--| +| [DBReader](db/db_reader.md#db-reader) | Reading data from a database | Any [DBConnection](connection/db_connection/index.md#db-connections) | - | [Spark DataFrame](https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.sql.DataFrame.html#pyspark.sql.DataFrame) | +| [FileDFReader](file_df/file_df_reader/file_df_reader.md#file-df-reader) | Read data from a file or set of files | Any [FileDFConnection](connection/file_df_connection/index.md#file-df-connections) | No input, or List[File path on FileSystem] | [Spark DataFrame](https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.sql.DataFrame.html#pyspark.sql.DataFrame) | +| [FileDownloader](db/db_reader.md#db-reader) | Download files from remote FS to local FS | Any [FileConnection](connection/file_connection/index.md#file-connections) | No input, or List[File path on remote FileSystem] | [DownloadResult](file/file_downloader/result.md#file-downloader-result) | ### Load data To load data you can use classes: -| | Use case | Connection | `run()` gets | `run()` returns | -|-------------------------------------------------------------------|----------------------------------------------|------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------| -| [DBWriter](db/db_writer.md#db-writer) | Writing data from a DataFrame to a database | Any [DBConnection](connection/db_connection/index.md#db-connections) | [Spark DataFrame](https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.sql.DataFrame.html#pyspark.sql.DataFrame) | None | -| [FileDFWriter](db/db_writer.md#db-writer) | Writing data from a DataFrame to a folder | Any [FileDFConnection](connection/file_df_connection/index.md#file-df-connections) | [Spark DataFrame](https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.sql.DataFrame.html#pyspark.sql.DataFrame) | None | -| [FileUploader](file/file_uploader/file_uploader.md#file-uploader) | Uploading files from a local FS to remote FS | Any [FileConnection](connection/file_connection/index.md#file-connections) | List[File path on local FileSystem] | [UploadResult](file/file_uploader/result.md#file-uploader-result) | +| | Use case | Connection | `run()` gets | `run()` returns | +|--|--|--|--|--| +| [DBWriter](db/db_writer.md#db-writer) | Writing data from a DataFrame to a database | Any [DBConnection](connection/db_connection/index.md#db-connections) | [Spark DataFrame](https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.sql.DataFrame.html#pyspark.sql.DataFrame) | None | +| [FileDFWriter](db/db_writer.md#db-writer) | Writing data from a DataFrame to a folder | Any [FileDFConnection](connection/file_df_connection/index.md#file-df-connections) | [Spark DataFrame](https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.sql.DataFrame.html#pyspark.sql.DataFrame) | None | +| [FileUploader](file/file_uploader/file_uploader.md#file-uploader) | Uploading files from a local FS to remote FS | Any [FileConnection](connection/file_connection/index.md#file-connections) | List[File path on local FileSystem] | [UploadResult](file/file_uploader/result.md#file-uploader-result) | ### Manipulate data To manipulate data you can use classes: -| | Use case | Connection | `run()` gets | `run()` returns | -|-------------------------------------------------------|---------------------------------------------|----------------------------------------------------------------------------|--------------------------------------|-----------------------------------------------------------| +| | Use case | Connection | `run()` gets | `run()` returns | +|--|--|--|--|--| | [FileMover](file/file_mover/file_mover.md#file-mover) | Move files between directories in remote FS | Any [FileConnection](connection/file_connection/index.md#file-connections) | List[File path on remote FileSystem] | [MoveResult](file/file_mover/result.md#file-mover-result) | ### Options @@ -185,88 +279,89 @@ Extract and load classes have a `options` parameter, which has a special meaning > * all other parameters - *WHAT* we extract / *WHERE* we load to > * `options` parameter - *HOW* we extract/load data + ```python db_reader = DBReader( - # WHAT do we read: - connection=mssql, - source="dbo.demo_table", # some table from MSSQL - columns=["column_1", "column_2"], # but only specific set of columns - where="column_2 > 1000", # only rows matching the clause - # HOW do we read: - options=MSSQL.ReadOptions( - numPartitions=10, # read in 10 parallel jobs - partitionColumn="id", # balance data read by assigning each job a part of data using `hash(id) mod N` expression - partitioningMode="hash", - fetchsize=1000, # each job will fetch block of 1000 rows each on every read attempt - ), + # WHAT do we read: + connection=mssql, + source="dbo.demo_table", # some table from MSSQL + columns=["column_1", "column_2"], # but only specific set of columns + where="column_2 > 1000", # only rows matching the clause + # HOW do we read: + options=MSSQL.ReadOptions( + numPartitions=10, # read in 10 parallel jobs + partitionColumn="id", # balance data read by assigning each job a part of data using `hash(id) mod N` expression + partitioningMode="hash", + fetchsize=1000, # each job will fetch block of 1000 rows each on every read attempt + ), ) db_writer = DBWriter( - # WHERE do we write to - to some table in Hive - connection=hive, - target="dl_sb.demo_table", - # HOW do we write - overwrite all the data in the existing table - options=Hive.WriteOptions(if_exists="replace_entire_table"), + # WHERE do we write to - to some table in Hive + connection=hive, + target="dl_sb.demo_table", + # HOW do we write - overwrite all the data in the existing table + options=Hive.WriteOptions(if_exists="replace_entire_table"), ) file_downloader = FileDownloader( - # WHAT do we download - files from some dir in SFTP - connection=sftp, - source_path="/source", - filters=[Glob("*.csv")], # only CSV files - limits=[MaxFilesCount(1000)], # 1000 files max - # WHERE do we download to - a specific dir on local FS - local_path="/some", - # HOW do we download: - options=FileDownloader.Options( - delete_source=True, # after downloading each file remove it from source_path - if_exists="replace_file", # replace existing files in the local_path - ), + # WHAT do we download - files from some dir in SFTP + connection=sftp, + source_path="/source", + filters=[Glob("*.csv")], # only CSV files + limits=[MaxFilesCount(1000)], # 1000 files max + # WHERE do we download to - a specific dir on local FS + local_path="/some", + # HOW do we download: + options=FileDownloader.Options( + delete_source=True, # after downloading each file remove it from source_path + if_exists="replace_file", # replace existing files in the local_path + ), ) file_uploader = FileUploader( - # WHAT do we upload - files from some local dir - local_path="/source", - # WHERE do we upload to- specific remote dir in HDFS - connection=hdfs, - target_path="/some", - # HOW do we upload: - options=FileUploader.Options( - delete_local=True, # after uploading each file remove it from local_path - if_exists="replace_file", # replace existing files in the target_path - ), + # WHAT do we upload - files from some local dir + local_path="/source", + # WHERE do we upload to- specific remote dir in HDFS + connection=hdfs, + target_path="/some", + # HOW do we upload: + options=FileUploader.Options( + delete_local=True, # after uploading each file remove it from local_path + if_exists="replace_file", # replace existing files in the target_path + ), ) file_mover = FileMover( - # WHAT do we move - files in some remote dir in HDFS - source_path="/source", - connection=hdfs, - # WHERE do we move files to - target_path="/some", # a specific remote dir within the same HDFS connection - # HOW do we load - replace existing files in the target_path - options=FileMover.Options(if_exists="replace_file"), + # WHAT do we move - files in some remote dir in HDFS + source_path="/source", + connection=hdfs, + # WHERE do we move files to + target_path="/some", # a specific remote dir within the same HDFS connection + # HOW do we load - replace existing files in the target_path + options=FileMover.Options(if_exists="replace_file"), ) file_df_reader = FileDFReader( - # WHAT do we read - *.csv files from some dir in S3 - connection=s3, - source_path="/source", - file_format=CSV(), - # HOW do we read - load files from /source/*.csv, not from /source/nested/*.csv - options=FileDFReader.Options(recursive=False), + # WHAT do we read - *.csv files from some dir in S3 + connection=s3, + source_path="/source", + file_format=CSV(), + # HOW do we read - load files from /source/*.csv, not from /source/nested/*.csv + options=FileDFReader.Options(recursive=False), ) file_df_writer = FileDFWriter( - # WHERE do we write to - as .csv files in some dir in S3 - connection=s3, - target_path="/target", - file_format=CSV(), - # HOW do we write - replace all existing files in /target, if exists - options=FileDFWriter.Options(if_exists="replace_entire_directory"), + # WHERE do we write to - as .csv files in some dir in S3 + connection=s3, + target_path="/target", + file_format=CSV(), + # HOW do we write - replace all existing files in /target, if exists + options=FileDFWriter.Options(if_exists="replace_entire_directory"), ) ``` -More information about `options` could be found on [DB connection](connection/db_connection/index.md#db-connections). and +More information about `options` could be found on [DB connection](connection/db_connection/index.md#db-connections) and [File Downloader](file/file_downloader/file_downloader.md#file-downloader) / [File Uploader](file/file_uploader/file_uploader.md#file-uploader) / [File Mover](file/file_mover/file_mover.md#file-mover) / [FileDF Reader](file_df/file_df_reader/file_df_reader.md#file-df-reader) / [FileDF Writer](file_df/file_df_writer/file_df_writer.md#file-df-writer) documentation ### Read Strategies @@ -284,21 +379,21 @@ For example, an incremental strategy allows you to get only new data from the ta from onetl.strategy import IncrementalStrategy reader = DBReader( - connection=mssql, - source="dbo.demo_table", - hwm_column="id", # detect new data based on value of "id" column + connection=mssql, + source="dbo.demo_table", + hwm_column="id", # detect new data based on value of "id" column ) # first run with IncrementalStrategy(): - df = reader.run() + df = reader.run() sleep(3600) # second run with IncrementalStrategy(): - # only rows, that appeared in the source since previous run - df = reader.run() + # only rows, that appeared in the source since previous run + df = reader.run() ``` or get only files which were not downloaded before: @@ -307,22 +402,22 @@ or get only files which were not downloaded before: from onetl.strategy import IncrementalStrategy file_downloader = FileDownloader( - connection=sftp, - source_path="/remote", - local_path="/local", - hwm_type="file_list", # save all downloaded files to a list, and exclude files already present in this list + connection=sftp, + source_path="/remote", + local_path="/local", + hwm_type="file_list", # save all downloaded files to a list, and exclude files already present in this list ) # first run with IncrementalStrategy(): - files = file_downloader.run() + files = file_downloader.run() sleep(3600) # second run with IncrementalStrategy(): - # only files, that appeared in the source since previous run - files = file_downloader.run() + # only files, that appeared in the source since previous run + files = file_downloader.run() ``` Most of strategies are based on [HWM](hwm_store/index.md#hwm), Please check each strategy documentation for more details @@ -332,9 +427,9 @@ Most of strategies are based on [HWM](hwm_store/index.md#hwm), Please check each Connections are very simple, they have only a set of some basic operations, like `mkdir`, `remove_file`, `get_table_schema`, and so on. -High-level operations, like -: * [Read Strategies](strategy/index.md#strategy) support - * Handling metadata push/pull - * Handling different options, like `if_exists="replace_file"` in case of file download/upload +High-level operations, like: + * [Read Strategies](strategy/index.md#strategy) support + * Handling metadata push/pull + * Handling different options, like `if_exists="replace_file"` in case of file download/upload is moved to a separate class which calls the connection object methods to perform some complex logic. diff --git a/mddocs/conf.py b/mddocs/conf.py index c69e54521..bb6508a95 100644 --- a/mddocs/conf.py +++ b/mddocs/conf.py @@ -60,9 +60,16 @@ "sphinx_favicon", "sphinxcontrib.autodoc_pydantic", "sphinx_last_updated_by_git", - "sphinx_markdown_builder", + # "sphinx_markdown_builder", "myst_parser", ] + +myst_enable_extensions = [ + 'colon_fence', + 'attrs_block', + # ... other extensions +] + numpydoc_show_class_members = False autodoc_pydantic_model_show_config = False autodoc_pydantic_model_show_config_summary = False diff --git a/mddocs/connection/db_connection/index.md b/mddocs/connection/db_connection/index.md index cd8f76477..6bb1ad53b 100644 --- a/mddocs/connection/db_connection/index.md +++ b/mddocs/connection/db_connection/index.md @@ -2,8 +2,6 @@ # DB Connections -# DB Connections - * [Clickhouse](clickhouse/index.md) * [Greenplum](greenplum/index.md) * [Kafka](kafka/index.md) diff --git a/mddocs/connection/index.md b/mddocs/connection/index.md index 5ad28edef..9775b0ccf 100644 --- a/mddocs/connection/index.md +++ b/mddocs/connection/index.md @@ -1,6 +1,6 @@ - DB Connection +# DB Connection * [DB Connections](db_connection/index.md) * [Clickhouse](db_connection/clickhouse/index.md) diff --git a/mddocs/contributing.md b/mddocs/contributing.md index 739798897..8cb8a1744 100644 --- a/mddocs/contributing.md +++ b/mddocs/contributing.md @@ -122,9 +122,7 @@ docker-compose run --rm onetl ./run_tests.sh -m mongodb -lsx -vvvv --log-cli-lev You can run interactive bash session and use it: ```bash -docker-compose run --rm onetl bash - -./run_tests.sh -m mongodb -lsx -vvvv --log-cli-level=INFO +docker-compose run --rm onetl bash ./run_tests.sh -m mongodb -lsx -vvvv --log-cli-level=INFO ``` See logs of test container: @@ -141,27 +139,30 @@ docker-compose --profile all down -v #### Without docker-compose -#### WARNING +```{admonition} warning To run HDFS tests locally you should add the following line to your `/etc/hosts` (file path depends on OS): +``` ```default # HDFS server returns container hostname as connection address, causing error in DNS resolution 127.0.0.1 hdfs ``` -#### NOTE +```{admonition} note To run Oracle tests you need to install [Oracle instantclient](https://www.oracle.com/database/technologies/instant-client.html), and pass its path to `ONETL_ORA_CLIENT_PATH` and `LD_LIBRARY_PATH` environment variables, e.g. `ONETL_ORA_CLIENT_PATH=/path/to/client64/lib`. It may also require to add the same path into `LD_LIBRARY_PATH` environment variable +``` -#### NOTE +```{admonition} note To run Greenplum tests, you should: * Download [VMware Greenplum connector for Spark](https://onetl.readthedocs.io/en/latest/connection/db_connection/greenplum/prerequisites.html) * Either move it to `~/.ivy2/jars/`, or pass file path to `CLASSPATH` * Set environment variable `ONETL_GP_PACKAGE_VERSION=local`. +``` Start all containers with dependencies: @@ -212,17 +213,11 @@ Then open in browser `docs/_build/index.html`. ## Review process -Please create a new GitHub issue for any significant changes and -enhancements that you wish to make. Provide the feature you would like -to see, why you need it, and how it will work. Discuss your ideas -transparently and get community feedback before proceeding. +Please create a new GitHub issue for any significant changes and enhancements that you wish to make. Provide the feature you would like to see, why you need it, and how it will work. Discuss your ideas transparently and get community feedback before proceeding. -Significant Changes that you wish to contribute to the project should be -discussed first in a GitHub issue that clearly outlines the changes and -benefits of the feature. +Significant Changes that you wish to contribute to the project should be discussed first in a GitHub issue that clearly outlines the changes and benefits of the feature. -Small Changes can directly be crafted and submitted to the GitHub -Repository as a Pull Request. +Small Changes can directly be crafted and submitted to the GitHub Repository as a Pull Request. ### Create pull request @@ -243,35 +238,15 @@ After pull request is created, it get a corresponding number, e.g. 123 (`pr_numb `onETL` uses [towncrier](https://pypi.org/project/towncrier/) for changelog management. -To submit a change note about your PR, add a text file into the -[docs/changelog/next_release](./next_release) folder. It should contain an -explanation of what applying this PR will change in the way -end-users interact with the project. One sentence is usually -enough but feel free to add as many details as you feel necessary -for the users to understand what it means. - -**Use the past tense** for the text in your fragment because, -combined with others, it will be a part of the “news digest” -telling the readers **what changed** in a specific version of -the library *since the previous version*. - -You should also use -reStructuredText syntax for highlighting code (inline or block), -linking parts of the docs or external sites. -If you wish to sign your change, feel free to add `-- by -:user:`github-username`` at the end (replace `github-username` -with your own!). - -Finally, name your file following the convention that Towncrier -understands: it should start with the number of an issue or a -PR followed by a dot, then add a patch type, like `feature`, -`doc`, `misc` etc., and add `.rst` as a suffix. If you -need to add more than one fragment, you may add an optional -sequence number (delimited with another period) between the type -and the suffix. - -In general the name will follow `..rst` pattern, -where the categories are: +To submit a change note about your PR, add a text file into the [docs/changelog/next_release](./next_release) folder. It should contain an explanation of what applying this PR will change in the way end-users interact with the project. One sentence is usually enough but feel free to add as many details as you feel necessary for the users to understand what it means. + +**Use the past tense** for the text in your fragment because, combined with others, it will be a part of the “news digest” telling the readers **what changed** in a specific version of the library *since the previous version*. + +You should also use reStructuredText syntax for highlighting code (inline or block), linking parts of the docs or external sites. If you wish to sign your change, feel free to add `-- by :user:`github-username`` at the end (replace `github-username` with your own!). + +Finally, name your file following the convention that Towncrier understands: it should start with the number of an issue or a PR followed by a dot, then add a patch type, like `feature`, `doc`, `misc` etc., and add `.rst` as a suffix. If you need to add more than one fragment, you may add an optional sequence number (delimited with another period) between the type and the suffix. + +In general the name will follow `..rst` pattern, where the categories are: - `feature`: Any new feature - `bugfix`: A bug fix @@ -280,11 +255,7 @@ where the categories are: - `dependency`: Dependency-related changes - `misc`: Changes internal to the repo like CI, test and build changes -A pull request may have more than one of these components, for example -a code change may introduce a new feature that deprecates an old -feature, in which case two fragments should be added. It is not -necessary to make a separate documentation fragment for documentation -changes accompanying the relevant code changes. +A pull request may have more than one of these components, for example a code change may introduce a new feature that deprecates an old feature, in which case two fragments should be added. It is not necessary to make a separate documentation fragment for documentation changes accompanying the relevant code changes. #### Examples for adding changelog entries to your Pull Requests diff --git a/mddocs/db/index.md b/mddocs/db/index.md index 861a539ff..ef8f4ab30 100644 --- a/mddocs/db/index.md +++ b/mddocs/db/index.md @@ -1,6 +1,6 @@ - DB classes +# DB classes * [DB Reader](db_reader.md) * [DB Writer](db_writer.md) diff --git a/mddocs/file/index.md b/mddocs/file/index.md index 2da812bab..675648e5c 100644 --- a/mddocs/file/index.md +++ b/mddocs/file/index.md @@ -1,6 +1,6 @@ - File classes +# File classes * [File Downloader](file_downloader/index.md) * [File Uploader](file_uploader/index.md) diff --git a/mddocs/file_df/index.md b/mddocs/file_df/index.md index c225567d6..a0868372e 100644 --- a/mddocs/file_df/index.md +++ b/mddocs/file_df/index.md @@ -1,6 +1,6 @@ - File DataFrame classes +# File DataFrame classes * [FileDF Reader](file_df_reader/index.md) * [FileDF Writer](file_df_writer/index.md) diff --git a/mddocs/index.md b/mddocs/index.md index ebfe9116e..7595bd92c 100644 --- a/mddocs/index.md +++ b/mddocs/index.md @@ -6,4 +6,86 @@ ```{include} README.md :start-after: -``` \ No newline at end of file +:end-before: +``` + diff --git a/mddocs/install/index.md b/mddocs/install/index.md index 1a5fb63cc..15f1e36e2 100644 --- a/mddocs/install/index.md +++ b/mddocs/install/index.md @@ -16,11 +16,14 @@ It can be installed via: pip install onetl ``` -#### WARNING +```{admonition} warning + +:class: warning + This method does NOT include any connections. -This method is recommended for use in third-party libraries which require for `onetl` to be installed, -but do not use its connection classes. +This method is recommended for use in third-party libraries which require for `onetl` to be installed, but do not use its connection classes. +``` ## Installation in details diff --git a/mddocs/logging.md b/mddocs/logging.md index f9900f789..c67da0558 100644 --- a/mddocs/logging.md +++ b/mddocs/logging.md @@ -1,14 +1,12 @@ - - # Logging + + Logging is quite important to understand what’s going on under the hood of onETL. -Default logging level for Python interpreters is `WARNING`, -but most of onETL logs are in `INFO` level, so users usually don’t see much. +Default logging level for Python interpreters is `WARNING`, but most of onETL logs are in `INFO` level, so users usually don’t see much. -To change logging level, there is a function [`setup_logging`](#onetl.log.setup_logging) -which should be called at the top of the script: +To change logging level, there is a function [`setup_logging`](#setup_logging) which should be called at the top of the script: ```python from onetl.log import setup_logging @@ -22,7 +20,7 @@ setup_logging() This changes both log level and log formatting to something like this: -### See logs +## See logs ```text 2024-04-12 10:12:10,834 [INFO ] MainThread: |onETL| Using IncrementalStrategy as a strategy @@ -152,85 +150,100 @@ setup_logging(level="DEBUG", enable_clients=True) This also changes log level for all underlying Python libraries, e.g. showing each HTTP request being made, and so on. -### onetl.log.setup_logging(level: int | str = 20, enable_clients: bool = False) → None + +**onetl.log.setup_logging**(level: int | str = 20, enable_clients: bool = False) → None Set up onETL logging. What this function does: -: * Adds stderr logging handler - * Changes root logger format to `2023-05-31 11:22:33.456 [INFO] MainThread: message` - * Changes root logger level to `level` - * Changes onETL logger level to `level` - * Sets up logging level of underlying client modules -#### NOTE +* Adds stderr logging handler +* Changes root logger format to `2023-05-31 11:22:33.456 [INFO] MainThread: message` +* Changes root logger level to `level` +* Changes onETL logger level to `level` +* Sets up logging level of underlying client modules + + + +```{admonition} note Should be used only in IDEs (like Jupyter notebooks or PyCharm), or scripts (ETL pipelines). +``` + + + -#### Versionchanged +````{versionchanged} Changed in version 0.5.0: Renamed `setup_notebook_logging` → `setup_logging` * **Parameters:** **level** - : Log level for onETL module + Log level for onETL module **enable_clients** - : If `True`, enable logging of underlying client modules. + If `True`, enable logging of underlying client modules. Otherwise, set client modules log level to `DISABLED`. -
- #### NOTE - For `level="DEBUG"` it is recommended to use `enable_clients=True` -
- #### Versionadded - Added in version 0.9.0. +```{admonition} note +For `level="DEBUG"` it is recommended to use `enable_clients=True` +``` + +```{versionadded} +Added in version 0.9.0. +``` +```` -### onetl.log.setup_clients_logging(level: int | str = 9999) → None +**onetl.log.setup_clients_logging**(level: int | str = 9999) → None Set logging of underlying client modules used by onETL. Affected modules: -: * `ftputil` - * `hdfs` - * `minio` - * `paramiko` - * `py4j` - * `pyspark` - * `webdav3` - -#### NOTE + +* `ftputil` +* `hdfs` +* `minio` +* `paramiko` +* `py4j` +* `pyspark` +* `webdav3` + +```{admonition} note Can be used in applications, but it is recommended to set up these loggers according to your framework documentation. +``` -#### Versionchanged +````{versionchanged} Changed in version 0.9.0: Renamed `disable_clients_logging` → `setup_clients_logging` * **Parameters:** **level** - : Log level for client modules -
- #### NOTE - For `py4j`, logging level with maximum verbosity is `INFO` because `DEBUG` logs are - totally unreadable. + Log level for client modules
- #### Versionadded - Added in version 0.9.0. - +```{admonition} note +For `py4j`, logging level with maximum verbosity is `INFO` because `DEBUG` logs are +totally unreadable. +``` +
+```{versionadded} +Added in version 0.9.0. +``` +```` -### onetl.log.set_default_logging_format() → None +**onetl.log.set_default_logging_format()** → None Sets default logging format to preferred by onETL. Example log message: `2023-05-31 11:22:33.456 [INFO] MainThread: message` -#### NOTE +```{admonition} note Should be used only in IDEs (like Jupyter notebooks or PyCharm), or scripts (ETL pipelines). +``` -#### WARNING +```{admonition} warning Should **NOT** be used in applications, you should set up logging settings manually, according to your framework documentation. - +``` diff --git a/mddocs/plugins.md b/mddocs/plugins.md index d42ded034..ca50f4a84 100644 --- a/mddocs/plugins.md +++ b/mddocs/plugins.md @@ -1,9 +1,10 @@ - - # Plugins -#### Versionadded + + +```{versionadded} Added in version 0.6.0. +``` ## What are plugins? @@ -78,8 +79,9 @@ like [@hook decorator](hooks/hook.md#hook-decorator), it will be executed during ## How to enable/disable plugins? -#### Versionadded +```{versionadded} Added in version 0.7.0. +``` ### Disable/enable all plugins From c2bff695cb02c21d24adb3cfc36be83f443c142a Mon Sep 17 00:00:00 2001 From: Sattar Gyulmamedov Date: Mon, 21 Apr 2025 23:15:51 +0300 Subject: [PATCH 03/22] experiments with uml --- mddocs/concepts.md | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/mddocs/concepts.md b/mddocs/concepts.md index cf74e9eaa..2e42e2e1c 100644 --- a/mddocs/concepts.md +++ b/mddocs/concepts.md @@ -26,6 +26,14 @@ All connection types are inherited from the parent class `BaseConnection`. ### Class diagram +```{uml} +...uml:: + +@startuml +Bob -> Cat +@enduml +``` + ### DBConnection Classes inherited from `DBConnection` could be used for accessing databases. 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(99%) rename mddocs/{ => markdown}/quickstart.md (100%) rename mddocs/{ => markdown}/security.md (100%) rename mddocs/{ => markdown}/strategy/incremental_batch_strategy.md (100%) rename mddocs/{ => markdown}/strategy/incremental_strategy.md (100%) rename mddocs/{ => markdown}/strategy/index.md (100%) rename mddocs/{ => markdown}/strategy/snapshot_batch_strategy.md (100%) rename mddocs/{ => markdown}/strategy/snapshot_strategy.md (100%) rename mddocs/{ => markdown}/troubleshooting/index.md (100%) rename mddocs/{ => markdown}/troubleshooting/spark.md (100%) diff --git a/mddocs/README.md b/mddocs/README.md deleted file mode 100644 index ffb6fc0c5..000000000 --- a/mddocs/README.md +++ /dev/null @@ -1,657 +0,0 @@ -# onETL - -[![Repo status - Active](https://www.repostatus.org/badges/latest/active.svg)](https://github.com/MobileTeleSystems/onetl) [![PyPI - Latest Release](https://img.shields.io/pypi/v/onetl)](https://pypi.org/project/onetl/) [![PyPI - License](https://img.shields.io/pypi/l/onetl.svg)](https://github.com/MobileTeleSystems/onetl/blob/develop/LICENSE.txt) [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/onetl.svg)](https://pypi.org/project/onetl/) [![PyPI - Downloads](https://img.shields.io/pypi/dm/onetl)](https://pypi.org/project/onetl/) -[![Documentation - ReadTheDocs](https://readthedocs.org/projects/onetl/badge/?version=stable)](https://onetl.readthedocs.io/) [![Github Actions - latest CI build status](https://github.com/MobileTeleSystems/onetl/workflows/Tests/badge.svg)](https://github.com/MobileTeleSystems/onetl/actions) [![Test coverage - percent](https://codecov.io/gh/MobileTeleSystems/onetl/branch/develop/graph/badge.svg?token=RIO8URKNZJ)](https://codecov.io/gh/MobileTeleSystems/onetl) [![pre-commit.ci - status](https://results.pre-commit.ci/badge/github/MobileTeleSystems/onetl/develop.svg)](https://results.pre-commit.ci/latest/github/MobileTeleSystems/onetl/develop) - - - -![onETL logo](_static/logo_wide.svg) - - - -## What is onETL? - -Python ETL/ELT library powered by [Apache Spark](https://spark.apache.org/) & other open-source tools. - -## Goals - -* Provide unified classes to extract data from (**E**) & load data to (**L**) various stores. -* Provides [Spark DataFrame API](https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.DataFrame.html) for performing transformations (**T**) in terms of *ETL*. -* Provide direct assess to database, allowing to execute SQL queries, as well as DDL, DML, and call functions/procedures. This can be used for building up *ELT* pipelines. -* Support different [read strategies](https://onetl.readthedocs.io/en/stable/strategy/index.html) for incremental and batch data fetching. -* Provide [hooks](https://onetl.readthedocs.io/en/stable/hooks/index.html) & [plugins](https://onetl.readthedocs.io/en/stable/plugins.html) mechanism for altering behavior of internal classes. - -## Non-goals - -* onETL is not a Spark replacement. It just provides additional functionality that Spark does not have, and improves UX for end users. -* onETL is not a framework, as it does not have requirements to project structure, naming, the way of running ETL/ELT processes, configuration, etc. All of that should be implemented in some other tool. -* onETL is deliberately developed without any integration with scheduling software like Apache Airflow. All integrations should be implemented as separated tools. -* Only batch operations, no streaming. For streaming prefer [Apache Flink](https://flink.apache.org/). - -## Requirements - -* **Python 3.7 - 3.13** -* PySpark 2.3.x - 3.5.x (depends on used connector) -* Java 8+ (required by Spark, see below) -* Kerberos libs & GCC (required by `Hive`, `HDFS` and `SparkHDFS` connectors) - -## Supported storages - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
TypeStoragePowered by
DatabaseClickhouseApache Spark JDBC Data Source
MSSQL
MySQL
Postgres
Oracle
Teradata
HiveApache Spark Hive integration
KafkaApache Spark Kafka integration
GreenplumVMware Greenplum Spark connector
MongoDBMongoDB Spark connector
FileHDFSHDFS Python client
S3minio-py client
SFTPParamiko library
FTPFTPUtil library
FTPS
WebDAVWebdavClient3 library
Sambapysmb library
Files as DataFrameSparkLocalFSApache Spark File Data Source
SparkHDFS
SparkS3Hadoop AWS library
- - - - -## Documentation - -See [https://onetl.readthedocs.io/](https://onetl.readthedocs.io/) - -## How to install - - - -### Minimal installation - - - -Base `onetl` package contains: - -* `DBReader`, `DBWriter` and related classes -* `FileDownloader`, `FileUploader`, `FileMover` and related classes, like file filters & limits -* `FileDFReader`, `FileDFWriter` and related classes, like file formats -* Read Strategies & HWM classes -* Plugins support - -It can be installed via: - -```bash -pip install onetl -``` - -```{admonition} WARNING -:class: warning - -This method does NOT include any connections. - -This method is recommended for use in third-party libraries which require for `onetl` to be installed, but do not use its connection classes. -``` - -### With DB and FileDF connections - - - -All DB connection classes (`Clickhouse`, `Greenplum`, `Hive` and others) -and all FileDF connection classes (`SparkHDFS`, `SparkLocalFS`, `SparkS3`) -require Spark to be installed. - - - -Firstly, you should install JDK. The exact installation instruction depends on your OS, here are some examples: - -```bash -yum install java-1.8.0-openjdk-devel # CentOS 7 + Spark 2 -dnf install java-11-openjdk-devel # CentOS 8 + Spark 3 -apt-get install openjdk-11-jdk # Debian-based + Spark 3 -``` - - - -#### Compatibility matrix - -| Spark | Python | Java | Scala | -|-----------------------------------------------------------|------------|------------|---------| -| [2.3.x](https://spark.apache.org/docs/2.3.1/#downloading) | 3.7 only | 8 only | 2.11 | -| [2.4.x](https://spark.apache.org/docs/2.4.8/#downloading) | 3.7 only | 8 only | 2.11 | -| [3.2.x](https://spark.apache.org/docs/3.2.4/#downloading) | 3.7 - 3.10 | 8u201 - 11 | 2.12 | -| [3.3.x](https://spark.apache.org/docs/3.3.4/#downloading) | 3.7 - 3.12 | 8u201 - 17 | 2.12 | -| [3.4.x](https://spark.apache.org/docs/3.4.4/#downloading) | 3.7 - 3.12 | 8u362 - 20 | 2.12 | -| [3.5.x](https://spark.apache.org/docs/3.5.5/#downloading) | 3.8 - 3.13 | 8u371 - 20 | 2.12 | - - - -Then you should install PySpark via passing `spark` to `extras`: - -```bash -pip install onetl[spark] # install latest PySpark -``` - -or install PySpark explicitly: - -```bash -pip install onetl pyspark==3.5.5 # install a specific PySpark version -``` - -or inject PySpark to `sys.path` in some other way BEFORE creating a class instance. -**Otherwise connection object cannot be created.** - -### With File connections - - - -All File (but not *FileDF*) connection classes (`FTP`, `SFTP`, `HDFS` and so on) requires specific Python clients to be installed. - -Each client can be installed explicitly by passing connector name (in lowercase) to `extras`: - -```bash -pip install onetl[ftp] # specific connector -pip install onetl[ftp,ftps,sftp,hdfs,s3,webdav,samba] # multiple connectors -``` - -To install all file connectors at once you can pass `files` to `extras`: - -```bash -pip install onetl[files] -``` - -**Otherwise class import will fail.** - -### With Kerberos support - - - -Most of Hadoop instances set up with Kerberos support, -so some connections require additional setup to work properly. - -* `HDFS` - Uses [requests-kerberos](https://pypi.org/project/requests-kerberos/) and - [GSSApi](https://pypi.org/project/gssapi/) for authentication. - It also uses `kinit` executable to generate Kerberos ticket. -* `Hive` and `SparkHDFS` - require Kerberos ticket to exist before creating Spark session. - -So you need to install OS packages with: - -* `krb5` libs -* Headers for `krb5` -* `gcc` or other compiler for C sources - -The exact installation instruction depends on your OS, here are some examples: - -```bash -apt install libkrb5-dev krb5-user gcc # Debian-based -dnf install krb5-devel krb5-libs krb5-workstation gcc # CentOS, OracleLinux -``` - -Also you should pass `kerberos` to `extras` to install required Python packages: - -```bash -pip install onetl[kerberos] -``` - -### Full bundle - - - -To install all connectors and dependencies, you can pass `all` into `extras`: - -```bash -pip install onetl[all] - -# this is just the same as -pip install onetl[spark,files,kerberos] -``` - - -```{admonition} WARNING -:class: warning - -This method consumes a lot of disk space, and requires for Java & Kerberos libraries to be installed into your OS. -``` - - - -## Quick start - -### MSSQL → Hive - -Read data from MSSQL, transform & write to Hive. - -```bash -# install onETL and PySpark -pip install onetl[spark] -``` - -```python -# Import pyspark to initialize the SparkSession -from pyspark.sql import SparkSession - -# import function to setup onETL logging -from onetl.log import setup_logging - -# Import required connections -from onetl.connection import MSSQL, Hive - -# Import onETL classes to read & write data -from onetl.db import DBReader, DBWriter - -# change logging level to INFO, and set up default logging format and handler -setup_logging() - -# Initialize new SparkSession with MSSQL driver loaded -maven_packages = MSSQL.get_packages() -spark = ( - SparkSession.builder.appName("spark_app_onetl_demo") - .config("spark.jars.packages", ",".join(maven_packages)) - .enableHiveSupport() # for Hive - .getOrCreate() -) - -# Initialize MSSQL connection and check if database is accessible -mssql = MSSQL( - host="mssqldb.demo.com", - user="onetl", - password="onetl", - database="Telecom", - spark=spark, - # These options are passed to MSSQL JDBC Driver: - extra={"applicationIntent": "ReadOnly"}, -).check() - -# >>> INFO:|MSSQL| Connection is available - -# Initialize DBReader -reader = DBReader( - connection=mssql, - source="dbo.demo_table", - columns=["on", "etl"], - # Set some MSSQL read options: - options=MSSQL.ReadOptions(fetchsize=10000), -) - -# checks that there is data in the table, otherwise raises exception -reader.raise_if_no_data() - -# Read data to DataFrame -df = reader.run() -df.printSchema() -# root -# |-- id: integer (nullable = true) -# |-- phone_number: string (nullable = true) -# |-- region: string (nullable = true) -# |-- birth_date: date (nullable = true) -# |-- registered_at: timestamp (nullable = true) -# |-- account_balance: double (nullable = true) - -# Apply any PySpark transformations -from pyspark.sql.functions import lit - -df_to_write = df.withColumn("engine", lit("onetl")) -df_to_write.printSchema() -# root -# |-- id: integer (nullable = true) -# |-- phone_number: string (nullable = true) -# |-- region: string (nullable = true) -# |-- birth_date: date (nullable = true) -# |-- registered_at: timestamp (nullable = true) -# |-- account_balance: double (nullable = true) -# |-- engine: string (nullable = false) - -# Initialize Hive connection -hive = Hive(cluster="rnd-dwh", spark=spark) - -# Initialize DBWriter -db_writer = DBWriter( - connection=hive, - target="dl_sb.demo_table", - # Set some Hive write options: - options=Hive.WriteOptions(if_exists="replace_entire_table"), -) - -# Write data from DataFrame to Hive -db_writer.run(df_to_write) - -# Success! -``` - -### SFTP → HDFS - -Download files from SFTP & upload them to HDFS. - -```bash -# install onETL with SFTP and HDFS clients, and Kerberos support -pip install onetl[hdfs,sftp,kerberos] -``` - -```python -# import function to setup onETL logging -from onetl.log import setup_logging - -# Import required connections -from onetl.connection import SFTP, HDFS - -# Import onETL classes to download & upload files -from onetl.file import FileDownloader, FileUploader - -# import filter & limit classes -from onetl.file.filter import Glob, ExcludeDir -from onetl.file.limit import MaxFilesCount - -# change logging level to INFO, and set up default logging format and handler -setup_logging() - -# Initialize SFTP connection and check it -sftp = SFTP( - host="sftp.test.com", - user="someuser", - password="somepassword", -).check() - -# >>> INFO:|SFTP| Connection is available - -# Initialize downloader -file_downloader = FileDownloader( - connection=sftp, - source_path="/remote/tests/Report", # path on SFTP - local_path="/local/onetl/Report", # local fs path - filters=[ - # download only files matching the glob - Glob("*.csv"), - # exclude files from this directory - ExcludeDir("/remote/tests/Report/exclude_dir/"), - ], - limits=[ - # download max 1000 files per run - MaxFilesCount(1000), - ], - options=FileDownloader.Options( - # delete files from SFTP after successful download - delete_source=True, - # mark file as failed if it already exist in local_path - if_exists="error", - ), -) - -# Download files to local filesystem -download_result = downloader.run() - -# Method run returns a DownloadResult object, -# which contains collection of downloaded files, divided to 4 categories -download_result - -# DownloadResult( -# successful=[ -# LocalPath('/local/onetl/Report/file_1.json'), -# LocalPath('/local/onetl/Report/file_2.json'), -# ], -# failed=[FailedRemoteFile('/remote/onetl/Report/file_3.json')], -# ignored=[RemoteFile('/remote/onetl/Report/file_4.json')], -# missing=[], -# ) - -# Raise exception if there are failed files, or there were no files in the remote filesystem -download_result.raise_if_failed() or download_result.raise_if_empty() - -# Do any kind of magic with files: rename files, remove header for csv files, ... -renamed_files = my_rename_function(download_result.success) - -# function removed "_" from file names -# [ -# LocalPath('/home/onetl/Report/file1.json'), -# LocalPath('/home/onetl/Report/file2.json'), -# ] - -# Initialize HDFS connection -hdfs = HDFS( - host="my.name.node", - user="someuser", - password="somepassword", # or keytab -) - -# Initialize uploader -file_uploader = FileUploader( - connection=hdfs, - target_path="/user/onetl/Report/", # hdfs path -) - -# Upload files from local fs to HDFS -upload_result = file_uploader.run(renamed_files) - -# Method run returns a UploadResult object, -# which contains collection of uploaded files, divided to 4 categories -upload_result - -# UploadResult( -# successful=[RemoteFile('/user/onetl/Report/file1.json')], -# failed=[FailedLocalFile('/local/onetl/Report/file2.json')], -# ignored=[], -# missing=[], -# ) - -# Raise exception if there are failed files, or there were no files in the local filesystem, or some input file is missing -upload_result.raise_if_failed() or upload_result.raise_if_empty() or upload_result.raise_if_missing() - -# Success! -``` - -### S3 → Postgres - -Read files directly from S3 path, convert them to dataframe, transform it and then write to a database. - -```bash -# install onETL and PySpark -pip install onetl[spark] -``` - -```python -# Import pyspark to initialize the SparkSession -from pyspark.sql import SparkSession - -# import function to setup onETL logging -from onetl.log import setup_logging - -# Import required connections -from onetl.connection import Postgres, SparkS3 - -# Import onETL classes to read files -from onetl.file import FileDFReader -from onetl.file.format import CSV - -# Import onETL classes to write data -from onetl.db import DBWriter - -# change logging level to INFO, and set up default logging format and handler -setup_logging() - -# Initialize new SparkSession with Hadoop AWS libraries and Postgres driver loaded -maven_packages = SparkS3.get_packages(spark_version="3.5.5") + Postgres.get_packages() -exclude_packages = SparkS3.get_exclude_packages() -spark = ( - SparkSession.builder.appName("spark_app_onetl_demo") - .config("spark.jars.packages", ",".join(maven_packages)) - .config("spark.jars.excludes", ",".join(exclude_packages)) - .getOrCreate() -) - -# Initialize S3 connection and check it -spark_s3 = SparkS3( - host="s3.test.com", - protocol="https", - bucket="my-bucket", - access_key="somekey", - secret_key="somesecret", - # Access bucket as s3.test.com/my-bucket - extra={"path.style.access": True}, - spark=spark, -).check() - -# >>> INFO:|SparkS3| Connection is available - -# Describe file format and parsing options -csv = CSV( - delimiter=";", - header=True, - encoding="utf-8", -) - -# Describe DataFrame schema of files -from pyspark.sql.types import ( - DateType, - DoubleType, - IntegerType, - StringType, - StructField, - StructType, - TimestampType, -) - -df_schema = StructType( - [ - StructField("id", IntegerType()), - StructField("phone_number", StringType()), - StructField("region", StringType()), - StructField("birth_date", DateType()), - StructField("registered_at", TimestampType()), - StructField("account_balance", DoubleType()), - ], -) - -# Initialize file df reader -reader = FileDFReader( - connection=spark_s3, - source_path="/remote/tests/Report", # path on S3 there *.csv files are located - format=csv, # file format with specific parsing options - df_schema=df_schema, # columns & types -) - -# Read files directly from S3 as Spark DataFrame -df = reader.run() - -# Check that DataFrame schema is same as expected -df.printSchema() -# root -# |-- id: integer (nullable = true) -# |-- phone_number: string (nullable = true) -# |-- region: string (nullable = true) -# |-- birth_date: date (nullable = true) -# |-- registered_at: timestamp (nullable = true) -# |-- account_balance: double (nullable = true) - -# Apply any PySpark transformations -from pyspark.sql.functions import lit - -df_to_write = df.withColumn("engine", lit("onetl")) -df_to_write.printSchema() -# root -# |-- id: integer (nullable = true) -# |-- phone_number: string (nullable = true) -# |-- region: string (nullable = true) -# |-- birth_date: date (nullable = true) -# |-- registered_at: timestamp (nullable = true) -# |-- account_balance: double (nullable = true) -# |-- engine: string (nullable = false) - -# Initialize Postgres connection -postgres = Postgres( - host="192.169.11.23", - user="onetl", - password="somepassword", - database="mydb", - spark=spark, -) - -# Initialize DBWriter -db_writer = DBWriter( - connection=postgres, - # write to specific table - target="public.my_table", - # with some writing options - options=Postgres.WriteOptions(if_exists="append"), -) - -# Write DataFrame to Postgres table -db_writer.run(df_to_write) - -# Success! -``` diff --git a/mddocs/_static/icon.svg b/mddocs/_static/icon.svg deleted file mode 100644 index a4d737f81..000000000 --- a/mddocs/_static/icon.svg +++ /dev/null @@ -1,11 +0,0 @@ - - - - - - - - - - - diff --git a/mddocs/_static/logo.svg b/mddocs/_static/logo.svg deleted file mode 100644 index 76527ebf1..000000000 --- a/mddocs/_static/logo.svg +++ /dev/null @@ -1,214 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 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- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/mddocs/changelog/DRAFT.md b/mddocs/changelog/DRAFT.md deleted file mode 100644 index 5b77149b5..000000000 --- a/mddocs/changelog/DRAFT.md +++ /dev/null @@ -1 +0,0 @@ - \ No newline at end of file diff --git a/mddocs/concepts.md b/mddocs/concepts.md deleted file mode 100644 index ef737cdc2..000000000 --- a/mddocs/concepts.md +++ /dev/null @@ -1,349 +0,0 @@ -# Concepts - -Here you can find detailed documentation about each one of the onETL concepts and how to use them. - -## Connection - -### Basics - -onETL is used to pull and push data into other systems, and so it has a first-class `Connection` concept for storing credentials that are used to communicate with external systems. - -A `Connection` is essentially a set of parameters, such as username, password, hostname. - -To create a connection to a specific storage type, you must use a class that matches the storage type. The class name is the same as the storage type name (`Oracle`, `MSSQL`, `SFTP`, etc): - -```python -from onetl.connection import SFTP - -sftp = SFTP( - host="sftp.test.com", - user="onetl", - password="onetl", -) -``` - -All connection types are inherited from the parent class `BaseConnection`. - -### Class diagram - -```{uml} -...uml:: - -@startuml -Bob -> Cat -@enduml -``` - -### DBConnection - -Classes inherited from `DBConnection` could be used for accessing databases. - -A `DBConnection` could be instantiated as follows: - -```python -from onetl.connection import MSSQL - -mssql = MSSQL( - host="mssqldb.demo.com", - user="onetl", - password="onetl", - database="Telecom", - spark=spark, -) -``` - -where **spark** is the current SparkSession. -`onETL` uses `Spark` and specific Java connectors under the hood to work with databases. - -For a description of other parameters, see the documentation for the [available DBConnections](connection/db_connection/index.md#db-connections). - -### FileConnection - -Classes inherited from `FileConnection` could be used to access files stored on the different file systems/file servers - -A `FileConnection` could be instantiated as follows: - -```python -from onetl.connection import SFTP - -sftp = SFTP( - host="sftp.test.com", - user="onetl", - password="onetl", -) -``` - -For a description of other parameters, see the documentation for the [available FileConnections](connection/file_connection/index.md#file-connections). - -### FileDFConnection - -Classes inherited from `FileDFConnection` could be used for accessing files as Spark DataFrames. - -A `FileDFConnection` could be instantiated as follows: - -```python -from onetl.connection import SparkHDFS - -spark_hdfs = SparkHDFS( - host="namenode1.domain.com", - cluster="mycluster", - spark=spark, -) -``` - -where **spark** is the current SparkSession. -`onETL` uses `Spark` and specific Java connectors under the hood to work with DataFrames. - -For a description of other parameters, see the documentation for the [available FileDFConnections](connection/file_df_connection/index.md#file-df-connections). - -### Checking connection availability - -Once you have created a connection, you can check the database/filesystem availability using the method `check()`: - -```python -mssql.check() -sftp.check() -spark_hdfs.check() -``` - -It will raise an exception if database/filesystem cannot be accessed. - -This method returns connection itself, so you can create connection and immediately check its availability: - -```Python -mssql = MSSQL( - host="mssqldb.demo.com", - user="onetl", - password="onetl", - database="Telecom", - spark=spark, -).check() # <-- -``` - -## Extract/Load data - -### Basics - -As we said above, onETL is used to extract data from and load data into remote systems. - -onETL provides several classes for this: - -> * [DBReader](db/db_reader.md#db-reader) -> * [DBWriter](db/db_writer.md#db-writer) -> * [FileDFReader](file_df/file_df_reader/file_df_reader.md#file-df-reader) -> * [FileDFWriter](file_df/file_df_writer/file_df_writer.md#file-df-writer) -> * [FileDownloader](file/file_downloader/file_downloader.md#file-downloader) -> * [FileUploader](file/file_uploader/file_uploader.md#file-uploader) -> * [FileMover](file/file_mover/file_mover.md#file-mover) - -All of these classes have a method `run()` that starts extracting/loading the data: - -```python -from onetl.db import DBReader, DBWriter - -reader = DBReader( - connection=mssql, - source="dbo.demo_table", - columns=["column_1", "column_2"], -) - -# Read data as Spark DataFrame -df = reader.run() - -db_writer = DBWriter( - connection=hive, - target="dl_sb.demo_table", -) - -# Save Spark DataFrame to Hive table -writer.run(df) -``` - -### Extract data - -To extract data you can use classes: - -| | Use case | Connection | `run()` gets | `run()` returns | -|--|--|--|--|--| -| [DBReader](db/db_reader.md#db-reader) | Reading data from a database | Any [DBConnection](connection/db_connection/index.md#db-connections) | - | [Spark DataFrame](https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.sql.DataFrame.html#pyspark.sql.DataFrame) | -| [FileDFReader](file_df/file_df_reader/file_df_reader.md#file-df-reader) | Read data from a file or set of files | Any [FileDFConnection](connection/file_df_connection/index.md#file-df-connections) | No input, or List[File path on FileSystem] | [Spark DataFrame](https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.sql.DataFrame.html#pyspark.sql.DataFrame) | -| [FileDownloader](db/db_reader.md#db-reader) | Download files from remote FS to local FS | Any [FileConnection](connection/file_connection/index.md#file-connections) | No input, or List[File path on remote FileSystem] | [DownloadResult](file/file_downloader/result.md#file-downloader-result) | - -### Load data - -To load data you can use classes: - -| | Use case | Connection | `run()` gets | `run()` returns | -|--|--|--|--|--| -| [DBWriter](db/db_writer.md#db-writer) | Writing data from a DataFrame to a database | Any [DBConnection](connection/db_connection/index.md#db-connections) | [Spark DataFrame](https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.sql.DataFrame.html#pyspark.sql.DataFrame) | None | -| [FileDFWriter](db/db_writer.md#db-writer) | Writing data from a DataFrame to a folder | Any [FileDFConnection](connection/file_df_connection/index.md#file-df-connections) | [Spark DataFrame](https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.sql.DataFrame.html#pyspark.sql.DataFrame) | None | -| [FileUploader](file/file_uploader/file_uploader.md#file-uploader) | Uploading files from a local FS to remote FS | Any [FileConnection](connection/file_connection/index.md#file-connections) | List[File path on local FileSystem] | [UploadResult](file/file_uploader/result.md#file-uploader-result) | - -### Manipulate data - -To manipulate data you can use classes: - -| | Use case | Connection | `run()` gets | `run()` returns | -|--|--|--|--|--| -| [FileMover](file/file_mover/file_mover.md#file-mover) | Move files between directories in remote FS | Any [FileConnection](connection/file_connection/index.md#file-connections) | List[File path on remote FileSystem] | [MoveResult](file/file_mover/result.md#file-mover-result) | - -### Options - -Extract and load classes have a `options` parameter, which has a special meaning: - -> * all other parameters - *WHAT* we extract / *WHERE* we load to -> * `options` parameter - *HOW* we extract/load data - -```python -db_reader = DBReader( - # WHAT do we read: - connection=mssql, - source="dbo.demo_table", # some table from MSSQL - columns=["column_1", "column_2"], # but only specific set of columns - where="column_2 > 1000", # only rows matching the clause - # HOW do we read: - options=MSSQL.ReadOptions( - numPartitions=10, # read in 10 parallel jobs - partitionColumn="id", # balance data read by assigning each job a part of data using `hash(id) mod N` expression - partitioningMode="hash", - fetchsize=1000, # each job will fetch block of 1000 rows each on every read attempt - ), -) - -db_writer = DBWriter( - # WHERE do we write to - to some table in Hive - connection=hive, - target="dl_sb.demo_table", - # HOW do we write - overwrite all the data in the existing table - options=Hive.WriteOptions(if_exists="replace_entire_table"), -) - -file_downloader = FileDownloader( - # WHAT do we download - files from some dir in SFTP - connection=sftp, - source_path="/source", - filters=[Glob("*.csv")], # only CSV files - limits=[MaxFilesCount(1000)], # 1000 files max - # WHERE do we download to - a specific dir on local FS - local_path="/some", - # HOW do we download: - options=FileDownloader.Options( - delete_source=True, # after downloading each file remove it from source_path - if_exists="replace_file", # replace existing files in the local_path - ), -) - -file_uploader = FileUploader( - # WHAT do we upload - files from some local dir - local_path="/source", - # WHERE do we upload to- specific remote dir in HDFS - connection=hdfs, - target_path="/some", - # HOW do we upload: - options=FileUploader.Options( - delete_local=True, # after uploading each file remove it from local_path - if_exists="replace_file", # replace existing files in the target_path - ), -) - -file_mover = FileMover( - # WHAT do we move - files in some remote dir in HDFS - source_path="/source", - connection=hdfs, - # WHERE do we move files to - target_path="/some", # a specific remote dir within the same HDFS connection - # HOW do we load - replace existing files in the target_path - options=FileMover.Options(if_exists="replace_file"), -) - -file_df_reader = FileDFReader( - # WHAT do we read - *.csv files from some dir in S3 - connection=s3, - source_path="/source", - file_format=CSV(), - # HOW do we read - load files from /source/*.csv, not from /source/nested/*.csv - options=FileDFReader.Options(recursive=False), -) - -file_df_writer = FileDFWriter( - # WHERE do we write to - as .csv files in some dir in S3 - connection=s3, - target_path="/target", - file_format=CSV(), - # HOW do we write - replace all existing files in /target, if exists - options=FileDFWriter.Options(if_exists="replace_entire_directory"), -) -``` - -More information about `options` could be found on [DB connection](connection/db_connection/index.md#db-connections) and -[File Downloader](file/file_downloader/file_downloader.md#file-downloader) / [File Uploader](file/file_uploader/file_uploader.md#file-uploader) / [File Mover](file/file_mover/file_mover.md#file-mover) / [FileDF Reader](file_df/file_df_reader/file_df_reader.md#file-df-reader) / [FileDF Writer](file_df/file_df_writer/file_df_writer.md#file-df-writer) documentation - -### Read Strategies - -onETL have several builtin strategies for reading data: - -1. [Snapshot strategy](strategy/snapshot_strategy.html) (default strategy) -2. [Incremental strategy](strategy/incremental_strategy.html) -3. [Snapshot batch strategy](strategy/snapshot_batch_strategy.html) -4. [Incremental batch strategy](strategy/incremental_batch_strategy.html) - -For example, an incremental strategy allows you to get only new data from the table: - -```python -from onetl.strategy import IncrementalStrategy - -reader = DBReader( - connection=mssql, - source="dbo.demo_table", - hwm_column="id", # detect new data based on value of "id" column -) - -# first run -with IncrementalStrategy(): - df = reader.run() - -sleep(3600) - -# second run -with IncrementalStrategy(): - # only rows, that appeared in the source since previous run - df = reader.run() -``` - -or get only files which were not downloaded before: - -```python -from onetl.strategy import IncrementalStrategy - -file_downloader = FileDownloader( - connection=sftp, - source_path="/remote", - local_path="/local", - hwm_type="file_list", # save all downloaded files to a list, and exclude files already present in this list -) - -# first run -with IncrementalStrategy(): - files = file_downloader.run() - -sleep(3600) - -# second run -with IncrementalStrategy(): - # only files, that appeared in the source since previous run - files = file_downloader.run() -``` - -Most of strategies are based on [HWM](hwm_store/index.md#hwm), Please check each strategy documentation for more details - -### Why just not use Connection class for extract/load? - -Connections are very simple, they have only a set of some basic operations, -like `mkdir`, `remove_file`, `get_table_schema`, and so on. - -High-level operations, like: - * [Read Strategies](strategy/index.md#strategy) support - * Handling metadata push/pull - * Handling different options, like `if_exists="replace_file"` in case of file download/upload - -is moved to a separate class which calls the connection object methods to perform some complex logic. diff --git a/mddocs/conf.py b/mddocs/conf.py deleted file mode 100644 index bb6508a95..000000000 --- a/mddocs/conf.py +++ /dev/null @@ -1,157 +0,0 @@ -# Configuration file for the Sphinx documentation builder. -# -# This file only contains a selection of the most common options. For a full -# list see the documentation: -# https://www.sphinx-doc.org/en/master/usage/configuration.html - -# -- Path setup -------------------------------------------------------------- - -# If extensions (or modules to document with autodoc) are in another directory, -# add these directories to sys.path here. If the directory is relative to the -# documentation root, use os.path.abspath to make it absolute, like shown here. - - -import os -import subprocess -import sys -from pathlib import Path - -from packaging.version import Version - -PROJECT_ROOT_DIR = Path(__file__).parent.parent.resolve() - -sys.path.insert(0, os.fspath(PROJECT_ROOT_DIR)) - -# -- Project information ----------------------------------------------------- - -project = "onETL" -copyright = "2021-2024 MTS PJSC" -author = "DataOps.ETL" - -# The version info for the project you're documenting, acts as replacement for -# |version| and |release|, also used in various other places throughout the -# built documents. -# -# The short X.Y version. - -ver = Version(subprocess.check_output("python ../setup.py --version", shell=True, text=True).strip()) -version = ver.base_version -# The full version, including alpha/beta/rc tags. -release = ver.public - -# -- General configuration --------------------------------------------------- - -# Add any Sphinx extension module names here, as strings. They can be -# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom -# ones. -extensions = [ - "numpydoc", - "sphinx_design", - "sphinx_substitution_extensions", - "sphinx_tabs.tabs", - "sphinx_toolbox.more_autodoc.autoprotocol", - "sphinx_toolbox.github", - "sphinx_copybutton", - "sphinx.ext.autodoc", - "sphinx.ext.autosummary", - "sphinxcontrib.towncrier", # provides `towncrier-draft-entries` directive - "sphinxcontrib.plantuml", - "sphinx.ext.extlinks", - "sphinx_favicon", - "sphinxcontrib.autodoc_pydantic", - "sphinx_last_updated_by_git", - # "sphinx_markdown_builder", - "myst_parser", -] - -myst_enable_extensions = [ - 'colon_fence', - 'attrs_block', - # ... other extensions -] - -numpydoc_show_class_members = False -autodoc_pydantic_model_show_config = False -autodoc_pydantic_model_show_config_summary = False -autodoc_pydantic_model_show_config_member = False -autodoc_pydantic_model_show_json = False -autodoc_pydantic_model_show_validator_summary = False -autodoc_pydantic_model_show_validator_members = False -autodoc_pydantic_field_list_validators = False -sphinx_tabs_disable_tab_closing = True - -# prevent >>>, ... and doctest outputs from copying -copybutton_prompt_text = r">>> |\.\.\. |\$ |In \[\d*\]: | {2,5}\.\.\.: | {5,8}: " -copybutton_prompt_is_regexp = True -copybutton_copy_empty_lines = False -copybutton_only_copy_prompt_lines = True - -towncrier_draft_autoversion_mode = "draft" -towncrier_draft_include_empty = False -towncrier_draft_working_directory = PROJECT_ROOT_DIR - -github_username = "MobileTeleSystems" -github_repository = "onetl" - -rst_prolog = f""" -.. |support_hooks| image:: https://img.shields.io/badge/%20-support%20hooks-blue - :target: https://onetl.readthedocs.io/en/{ver}/hooks/index.html -""" - -# Add any paths that contain templates here, relative to this directory. -templates_path = ["_templates"] - -# List of patterns, relative to source directory, that match files and -# directories to ignore when looking for source files. -# This pattern also affects html_static_path and html_extra_path. -exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"] - - -# -- Options for HTML output ------------------------------------------------- - -# The theme to use for HTML and HTML Help pages. See the documentation for -# a list of builtin themes. - -html_theme = "furo" - -# Theme options are theme-specific and customize the look and feel of a theme -# further. For a list of options available for each theme, see the -# documentation. -html_theme_options = {} - -# Add any paths that contain custom static files (such as style sheets) here, -# relative to this directory. They are copied after the builtin static files, -# so a file named "default.css" will overwrite the builtin "default.css". -html_static_path = ["_static"] -html_extra_path = ["robots.txt"] -html_logo = "./_static/logo.svg" -favicons = [ - {"rel": "icon", "href": "icon.svg", "type": "image/svg+xml"}, -] - -# The master toctree document. -master_doc = "index" - -# The language for content autogenerated by Sphinx. Refer to documentation -# for a list of supported languages. -# -# This is also used if you do content translation via gettext catalogs. -# Usually you set "language" from the command line for these cases. -language = "en" - -# The name of the Pygments (syntax highlighting) style to use. -pygments_style = "sphinx" - -# Create an alias for etl-entities lib in onetl documentation -extlinks = { - "etl-entities": ("https://etl-entities.readthedocs.io/en/stable/%s", None), -} - - -# If true, `todo` and `todoList` produce output, else they produce nothing. -todo_include_todos = False - -# -- Options for HTMLHelp output ------------------------------------------ - -# Output file base name for HTML help builder. -htmlhelp_basename = "onetl-doc" diff --git a/mddocs/doctrees/changelog.doctree b/mddocs/doctrees/changelog.doctree new file mode 100644 index 0000000000000000000000000000000000000000..9a5a8b8843ee25c232d790ef7b66528c9e68a0a1 GIT binary patch literal 3515 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diff --git a/mddocs/_static/autodoc_pydantic.css b/mddocs/markdown/_static/autodoc_pydantic.css similarity index 100% rename from mddocs/_static/autodoc_pydantic.css rename to mddocs/markdown/_static/autodoc_pydantic.css diff --git a/mddocs/changelog.md b/mddocs/markdown/changelog.md similarity index 97% rename from mddocs/changelog.md rename to mddocs/markdown/changelog.md index 278395a7f..4ba142635 100644 --- a/mddocs/changelog.md +++ b/mddocs/markdown/changelog.md @@ -1,4 +1,5 @@ - +# Changelog + # Changelog * [0.13.4 (2025-03-20)](changelog/0.13.4.md) diff --git a/mddocs/changelog/0.10.0.md b/mddocs/markdown/changelog/0.10.0.md similarity index 100% rename from mddocs/changelog/0.10.0.md rename to mddocs/markdown/changelog/0.10.0.md diff --git a/mddocs/changelog/0.10.1.md b/mddocs/markdown/changelog/0.10.1.md similarity index 100% rename from mddocs/changelog/0.10.1.md rename to mddocs/markdown/changelog/0.10.1.md diff --git a/mddocs/changelog/0.10.2.md 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diff --git a/mddocs/changelog/0.13.1.md b/mddocs/markdown/changelog/0.13.1.md similarity index 100% rename from mddocs/changelog/0.13.1.md rename to mddocs/markdown/changelog/0.13.1.md diff --git a/mddocs/changelog/0.13.3.md b/mddocs/markdown/changelog/0.13.3.md similarity index 100% rename from mddocs/changelog/0.13.3.md rename to mddocs/markdown/changelog/0.13.3.md diff --git a/mddocs/changelog/0.13.4.md b/mddocs/markdown/changelog/0.13.4.md similarity index 100% rename from mddocs/changelog/0.13.4.md rename to mddocs/markdown/changelog/0.13.4.md diff --git a/mddocs/changelog/0.7.0.md b/mddocs/markdown/changelog/0.7.0.md similarity index 100% rename from mddocs/changelog/0.7.0.md rename to mddocs/markdown/changelog/0.7.0.md diff --git a/mddocs/changelog/0.7.1.md b/mddocs/markdown/changelog/0.7.1.md similarity index 100% rename from mddocs/changelog/0.7.1.md rename to mddocs/markdown/changelog/0.7.1.md diff --git a/mddocs/changelog/0.7.2.md b/mddocs/markdown/changelog/0.7.2.md 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b/mddocs/markdown/changelog/NEXT_RELEASE.md @@ -1,2 +1 @@ - \ No newline at end of file diff --git a/mddocs/changelog/index.md b/mddocs/markdown/changelog/index.md similarity index 98% rename from mddocs/changelog/index.md rename to mddocs/markdown/changelog/index.md index 4a2663aee..37d5d85ae 100644 --- a/mddocs/changelog/index.md +++ b/mddocs/markdown/changelog/index.md @@ -1,4 +1,4 @@ -# Changelog +Changelog * [0.13.4 (2025-03-20)](0.13.4.md) * [0.13.3 (2025-03-11)](0.13.3.md) diff --git a/mddocs/markdown/concepts.md b/mddocs/markdown/concepts.md new file mode 100644 index 000000000..cf74e9eaa --- /dev/null +++ b/mddocs/markdown/concepts.md @@ -0,0 +1,340 @@ +# Concepts + +Here you can find detailed documentation about each one of the onETL concepts and how to use them. + +## Connection + +### Basics + +onETL is used to pull and push data into other systems, and so it has a first-class `Connection` concept for storing credentials that are used to communicate with external systems. + +A `Connection` is essentially a set of parameters, such as username, password, hostname. + +To create a connection to a specific storage type, you must use a class that matches the storage type. The class name is the same as the storage type name (`Oracle`, `MSSQL`, `SFTP`, etc): + +```python +from onetl.connection import SFTP + +sftp = SFTP( + host="sftp.test.com", + user="onetl", + password="onetl", +) +``` + +All connection types are inherited from the parent class `BaseConnection`. + +### Class diagram + +### DBConnection + +Classes inherited from `DBConnection` could be used for accessing databases. + +A `DBConnection` could be instantiated as follows: + +```python +from onetl.connection import MSSQL + +mssql = MSSQL( + host="mssqldb.demo.com", + user="onetl", + password="onetl", + database="Telecom", + spark=spark, +) +``` + +where **spark** is the current SparkSession. +`onETL` uses `Spark` and specific Java connectors under the hood to work with databases. + +For a description of other parameters, see the documentation for the [available DBConnections](connection/db_connection/index.md#db-connections). + +### FileConnection + +Classes inherited from `FileConnection` could be used to access files stored on the different file systems/file servers + +A `FileConnection` could be instantiated as follows: + +```python +from onetl.connection import SFTP + +sftp = SFTP( + host="sftp.test.com", + user="onetl", + password="onetl", +) +``` + +For a description of other parameters, see the documentation for the [available FileConnections](connection/file_connection/index.md#file-connections). + +### FileDFConnection + +Classes inherited from `FileDFConnection` could be used for accessing files as Spark DataFrames. + +A `FileDFConnection` could be instantiated as follows: + +```python +from onetl.connection import SparkHDFS + +spark_hdfs = SparkHDFS( + host="namenode1.domain.com", + cluster="mycluster", + spark=spark, +) +``` + +where **spark** is the current SparkSession. +`onETL` uses `Spark` and specific Java connectors under the hood to work with DataFrames. + +For a description of other parameters, see the documentation for the [available FileDFConnections](connection/file_df_connection/index.md#file-df-connections). + +### Checking connection availability + +Once you have created a connection, you can check the database/filesystem availability using the method `check()`: + +```python +mssql.check() +sftp.check() +spark_hdfs.check() +``` + +It will raise an exception if database/filesystem cannot be accessed. + +This method returns connection itself, so you can create connection and immediately check its availability: + +```Python +mssql = MSSQL( + host="mssqldb.demo.com", + user="onetl", + password="onetl", + database="Telecom", + spark=spark, +).check() # <-- +``` + +## Extract/Load data + +### Basics + +As we said above, onETL is used to extract data from and load data into remote systems. + +onETL provides several classes for this: + +> * [DBReader](db/db_reader.md#db-reader) +> * [DBWriter](db/db_writer.md#db-writer) +> * [FileDFReader](file_df/file_df_reader/file_df_reader.md#file-df-reader) +> * [FileDFWriter](file_df/file_df_writer/file_df_writer.md#file-df-writer) +> * [FileDownloader](file/file_downloader/file_downloader.md#file-downloader) +> * [FileUploader](file/file_uploader/file_uploader.md#file-uploader) +> * [FileMover](file/file_mover/file_mover.md#file-mover) + +All of these classes have a method `run()` that starts extracting/loading the data: + +```python +from onetl.db import DBReader, DBWriter + +reader = DBReader( + connection=mssql, + source="dbo.demo_table", + columns=["column_1", "column_2"], +) + +# Read data as Spark DataFrame +df = reader.run() + +db_writer = DBWriter( + connection=hive, + target="dl_sb.demo_table", +) + +# Save Spark DataFrame to Hive table +writer.run(df) +``` + +### Extract data + +To extract data you can use classes: + +| | Use case | Connection | `run()` gets | `run()` returns | +|-------------------------------------------------------------------------|-------------------------------------------|------------------------------------------------------------------------------------|---------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------| +| [DBReader](db/db_reader.md#db-reader) | Reading data from a database | Any [DBConnection](connection/db_connection/index.md#db-connections) | - | [Spark DataFrame](https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.sql.DataFrame.html#pyspark.sql.DataFrame) | +| [FileDFReader](file_df/file_df_reader/file_df_reader.md#file-df-reader) | Read data from a file or set of files | Any [FileDFConnection](connection/file_df_connection/index.md#file-df-connections) | No input, or List[File path on FileSystem] | [Spark DataFrame](https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.sql.DataFrame.html#pyspark.sql.DataFrame) | +| [FileDownloader](db/db_reader.md#db-reader) | Download files from remote FS to local FS | Any [FileConnection](connection/file_connection/index.md#file-connections) | No input, or List[File path on remote FileSystem] | [DownloadResult](file/file_downloader/result.md#file-downloader-result) | + +### Load data + +To load data you can use classes: + +| | Use case | Connection | `run()` gets | `run()` returns | +|-------------------------------------------------------------------|----------------------------------------------|------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------| +| [DBWriter](db/db_writer.md#db-writer) | Writing data from a DataFrame to a database | Any [DBConnection](connection/db_connection/index.md#db-connections) | [Spark DataFrame](https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.sql.DataFrame.html#pyspark.sql.DataFrame) | None | +| [FileDFWriter](db/db_writer.md#db-writer) | Writing data from a DataFrame to a folder | Any [FileDFConnection](connection/file_df_connection/index.md#file-df-connections) | [Spark DataFrame](https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.sql.DataFrame.html#pyspark.sql.DataFrame) | None | +| [FileUploader](file/file_uploader/file_uploader.md#file-uploader) | Uploading files from a local FS to remote FS | Any [FileConnection](connection/file_connection/index.md#file-connections) | List[File path on local FileSystem] | [UploadResult](file/file_uploader/result.md#file-uploader-result) | + +### Manipulate data + +To manipulate data you can use classes: + +| | Use case | Connection | `run()` gets | `run()` returns | +|-------------------------------------------------------|---------------------------------------------|----------------------------------------------------------------------------|--------------------------------------|-----------------------------------------------------------| +| [FileMover](file/file_mover/file_mover.md#file-mover) | Move files between directories in remote FS | Any [FileConnection](connection/file_connection/index.md#file-connections) | List[File path on remote FileSystem] | [MoveResult](file/file_mover/result.md#file-mover-result) | + +### Options + +Extract and load classes have a `options` parameter, which has a special meaning: + +> * all other parameters - *WHAT* we extract / *WHERE* we load to +> * `options` parameter - *HOW* we extract/load data +```python +db_reader = DBReader( + # WHAT do we read: + connection=mssql, + source="dbo.demo_table", # some table from MSSQL + columns=["column_1", "column_2"], # but only specific set of columns + where="column_2 > 1000", # only rows matching the clause + # HOW do we read: + options=MSSQL.ReadOptions( + numPartitions=10, # read in 10 parallel jobs + partitionColumn="id", # balance data read by assigning each job a part of data using `hash(id) mod N` expression + partitioningMode="hash", + fetchsize=1000, # each job will fetch block of 1000 rows each on every read attempt + ), +) + +db_writer = DBWriter( + # WHERE do we write to - to some table in Hive + connection=hive, + target="dl_sb.demo_table", + # HOW do we write - overwrite all the data in the existing table + options=Hive.WriteOptions(if_exists="replace_entire_table"), +) + +file_downloader = FileDownloader( + # WHAT do we download - files from some dir in SFTP + connection=sftp, + source_path="/source", + filters=[Glob("*.csv")], # only CSV files + limits=[MaxFilesCount(1000)], # 1000 files max + # WHERE do we download to - a specific dir on local FS + local_path="/some", + # HOW do we download: + options=FileDownloader.Options( + delete_source=True, # after downloading each file remove it from source_path + if_exists="replace_file", # replace existing files in the local_path + ), +) + +file_uploader = FileUploader( + # WHAT do we upload - files from some local dir + local_path="/source", + # WHERE do we upload to- specific remote dir in HDFS + connection=hdfs, + target_path="/some", + # HOW do we upload: + options=FileUploader.Options( + delete_local=True, # after uploading each file remove it from local_path + if_exists="replace_file", # replace existing files in the target_path + ), +) + +file_mover = FileMover( + # WHAT do we move - files in some remote dir in HDFS + source_path="/source", + connection=hdfs, + # WHERE do we move files to + target_path="/some", # a specific remote dir within the same HDFS connection + # HOW do we load - replace existing files in the target_path + options=FileMover.Options(if_exists="replace_file"), +) + +file_df_reader = FileDFReader( + # WHAT do we read - *.csv files from some dir in S3 + connection=s3, + source_path="/source", + file_format=CSV(), + # HOW do we read - load files from /source/*.csv, not from /source/nested/*.csv + options=FileDFReader.Options(recursive=False), +) + +file_df_writer = FileDFWriter( + # WHERE do we write to - as .csv files in some dir in S3 + connection=s3, + target_path="/target", + file_format=CSV(), + # HOW do we write - replace all existing files in /target, if exists + options=FileDFWriter.Options(if_exists="replace_entire_directory"), +) +``` + +More information about `options` could be found on [DB connection](connection/db_connection/index.md#db-connections). and +[File Downloader](file/file_downloader/file_downloader.md#file-downloader) / [File Uploader](file/file_uploader/file_uploader.md#file-uploader) / [File Mover](file/file_mover/file_mover.md#file-mover) / [FileDF Reader](file_df/file_df_reader/file_df_reader.md#file-df-reader) / [FileDF Writer](file_df/file_df_writer/file_df_writer.md#file-df-writer) documentation + +### Read Strategies + +onETL have several builtin strategies for reading data: + +1. [Snapshot strategy](strategy/snapshot_strategy.html) (default strategy) +2. [Incremental strategy](strategy/incremental_strategy.html) +3. [Snapshot batch strategy](strategy/snapshot_batch_strategy.html) +4. [Incremental batch strategy](strategy/incremental_batch_strategy.html) + +For example, an incremental strategy allows you to get only new data from the table: + +```python +from onetl.strategy import IncrementalStrategy + +reader = DBReader( + connection=mssql, + source="dbo.demo_table", + hwm_column="id", # detect new data based on value of "id" column +) + +# first run +with IncrementalStrategy(): + df = reader.run() + +sleep(3600) + +# second run +with IncrementalStrategy(): + # only rows, that appeared in the source since previous run + df = reader.run() +``` + +or get only files which were not downloaded before: + +```python +from onetl.strategy import IncrementalStrategy + +file_downloader = FileDownloader( + connection=sftp, + source_path="/remote", + local_path="/local", + hwm_type="file_list", # save all downloaded files to a list, and exclude files already present in this list +) + +# first run +with IncrementalStrategy(): + files = file_downloader.run() + +sleep(3600) + +# second run +with IncrementalStrategy(): + # only files, that appeared in the source since previous run + files = file_downloader.run() +``` + +Most of strategies are based on [HWM](hwm_store/index.md#hwm), Please check each strategy documentation for more details + +### Why just not use Connection class for extract/load? + +Connections are very simple, they have only a set of some basic operations, +like `mkdir`, `remove_file`, `get_table_schema`, and so on. + +High-level operations, like +: * [Read Strategies](strategy/index.md#strategy) support + * Handling metadata push/pull + * Handling different options, like `if_exists="replace_file"` in case of file download/upload + +is moved to a separate class which calls the connection object methods to perform some complex logic. diff --git a/mddocs/connection/db_connection/clickhouse/connection.md b/mddocs/markdown/connection/db_connection/clickhouse/connection.md similarity index 100% rename from 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--git a/mddocs/connection/file_df_connection/index.md b/mddocs/markdown/connection/file_df_connection/index.md similarity index 100% rename from mddocs/connection/file_df_connection/index.md rename to mddocs/markdown/connection/file_df_connection/index.md diff --git a/mddocs/connection/file_df_connection/spark_hdfs/connection.md b/mddocs/markdown/connection/file_df_connection/spark_hdfs/connection.md similarity index 97% rename from mddocs/connection/file_df_connection/spark_hdfs/connection.md rename to mddocs/markdown/connection/file_df_connection/spark_hdfs/connection.md index 4f2364e0e..fe8e621a9 100644 --- a/mddocs/connection/file_df_connection/spark_hdfs/connection.md +++ b/mddocs/markdown/connection/file_df_connection/spark_hdfs/connection.md @@ -15,7 +15,7 @@ Before using this connector please take into account [Prerequisites](prerequisit Supports only reading files as Spark DataFrame and writing DataFrame to files. Does NOT support file operations, like create, delete, rename, etc. For these operations, -use `HDFS` connection. +use [`HDFS`](../../file_connection/hdfs/connection.md#onetl.connection.file_connection.hdfs.connection.HDFS) connection. #### Versionadded Added in version 0.9.0. diff --git a/mddocs/connection/file_df_connection/spark_hdfs/index.md b/mddocs/markdown/connection/file_df_connection/spark_hdfs/index.md similarity index 100% rename from mddocs/connection/file_df_connection/spark_hdfs/index.md rename to mddocs/markdown/connection/file_df_connection/spark_hdfs/index.md diff --git a/mddocs/connection/file_df_connection/spark_hdfs/prerequisites.md b/mddocs/markdown/connection/file_df_connection/spark_hdfs/prerequisites.md similarity index 100% rename from mddocs/connection/file_df_connection/spark_hdfs/prerequisites.md rename to mddocs/markdown/connection/file_df_connection/spark_hdfs/prerequisites.md diff --git a/mddocs/connection/file_df_connection/spark_hdfs/slots.md b/mddocs/markdown/connection/file_df_connection/spark_hdfs/slots.md similarity index 100% rename from mddocs/connection/file_df_connection/spark_hdfs/slots.md rename to mddocs/markdown/connection/file_df_connection/spark_hdfs/slots.md diff --git a/mddocs/connection/file_df_connection/spark_local_fs.md b/mddocs/markdown/connection/file_df_connection/spark_local_fs.md similarity index 100% rename from mddocs/connection/file_df_connection/spark_local_fs.md rename to mddocs/markdown/connection/file_df_connection/spark_local_fs.md diff --git a/mddocs/connection/file_df_connection/spark_s3/connection.md b/mddocs/markdown/connection/file_df_connection/spark_s3/connection.md similarity index 98% rename from mddocs/connection/file_df_connection/spark_s3/connection.md rename to mddocs/markdown/connection/file_df_connection/spark_s3/connection.md index 5b2c1db31..e51ec8421 100644 --- a/mddocs/connection/file_df_connection/spark_s3/connection.md +++ b/mddocs/markdown/connection/file_df_connection/spark_s3/connection.md @@ -16,7 +16,7 @@ Before using this connector please take into account [Prerequisites](prerequisit Supports only reading files as Spark DataFrame and writing DataFrame to files. Does NOT support file operations, like create, delete, rename, etc. For these operations, -use `S3` connection. +use [`S3`](../../file_connection/s3.md#onetl.connection.file_connection.s3.S3) connection. #### Versionadded Added in version 0.9.0. diff --git a/mddocs/connection/file_df_connection/spark_s3/index.md b/mddocs/markdown/connection/file_df_connection/spark_s3/index.md similarity index 100% rename from mddocs/connection/file_df_connection/spark_s3/index.md rename to mddocs/markdown/connection/file_df_connection/spark_s3/index.md diff --git a/mddocs/connection/file_df_connection/spark_s3/prerequisites.md b/mddocs/markdown/connection/file_df_connection/spark_s3/prerequisites.md similarity index 100% rename from mddocs/connection/file_df_connection/spark_s3/prerequisites.md rename to mddocs/markdown/connection/file_df_connection/spark_s3/prerequisites.md diff --git a/mddocs/connection/file_df_connection/spark_s3/troubleshooting.md b/mddocs/markdown/connection/file_df_connection/spark_s3/troubleshooting.md similarity index 100% rename from mddocs/connection/file_df_connection/spark_s3/troubleshooting.md rename to mddocs/markdown/connection/file_df_connection/spark_s3/troubleshooting.md diff --git a/mddocs/connection/index.md b/mddocs/markdown/connection/index.md similarity index 98% rename from mddocs/connection/index.md rename to mddocs/markdown/connection/index.md index 9775b0ccf..5ad28edef 100644 --- a/mddocs/connection/index.md +++ b/mddocs/markdown/connection/index.md @@ -1,6 +1,6 @@ -# DB Connection + DB Connection * [DB Connections](db_connection/index.md) * [Clickhouse](db_connection/clickhouse/index.md) diff --git a/mddocs/contributing.md b/mddocs/markdown/contributing.md similarity index 80% rename from mddocs/contributing.md rename to mddocs/markdown/contributing.md index 8cb8a1744..739798897 100644 --- a/mddocs/contributing.md +++ b/mddocs/markdown/contributing.md @@ -122,7 +122,9 @@ docker-compose run --rm onetl ./run_tests.sh -m mongodb -lsx -vvvv --log-cli-lev You can run interactive bash session and use it: ```bash -docker-compose run --rm onetl bash ./run_tests.sh -m mongodb -lsx -vvvv --log-cli-level=INFO +docker-compose run --rm onetl bash + +./run_tests.sh -m mongodb -lsx -vvvv --log-cli-level=INFO ``` See logs of test container: @@ -139,30 +141,27 @@ docker-compose --profile all down -v #### Without docker-compose -```{admonition} warning +#### WARNING To run HDFS tests locally you should add the following line to your `/etc/hosts` (file path depends on OS): -``` ```default # HDFS server returns container hostname as connection address, causing error in DNS resolution 127.0.0.1 hdfs ``` -```{admonition} note +#### NOTE To run Oracle tests you need to install [Oracle instantclient](https://www.oracle.com/database/technologies/instant-client.html), and pass its path to `ONETL_ORA_CLIENT_PATH` and `LD_LIBRARY_PATH` environment variables, e.g. `ONETL_ORA_CLIENT_PATH=/path/to/client64/lib`. It may also require to add the same path into `LD_LIBRARY_PATH` environment variable -``` -```{admonition} note +#### NOTE To run Greenplum tests, you should: * Download [VMware Greenplum connector for Spark](https://onetl.readthedocs.io/en/latest/connection/db_connection/greenplum/prerequisites.html) * Either move it to `~/.ivy2/jars/`, or pass file path to `CLASSPATH` * Set environment variable `ONETL_GP_PACKAGE_VERSION=local`. -``` Start all containers with dependencies: @@ -213,11 +212,17 @@ Then open in browser `docs/_build/index.html`. ## Review process -Please create a new GitHub issue for any significant changes and enhancements that you wish to make. Provide the feature you would like to see, why you need it, and how it will work. Discuss your ideas transparently and get community feedback before proceeding. +Please create a new GitHub issue for any significant changes and +enhancements that you wish to make. Provide the feature you would like +to see, why you need it, and how it will work. Discuss your ideas +transparently and get community feedback before proceeding. -Significant Changes that you wish to contribute to the project should be discussed first in a GitHub issue that clearly outlines the changes and benefits of the feature. +Significant Changes that you wish to contribute to the project should be +discussed first in a GitHub issue that clearly outlines the changes and +benefits of the feature. -Small Changes can directly be crafted and submitted to the GitHub Repository as a Pull Request. +Small Changes can directly be crafted and submitted to the GitHub +Repository as a Pull Request. ### Create pull request @@ -238,15 +243,35 @@ After pull request is created, it get a corresponding number, e.g. 123 (`pr_numb `onETL` uses [towncrier](https://pypi.org/project/towncrier/) for changelog management. -To submit a change note about your PR, add a text file into the [docs/changelog/next_release](./next_release) folder. It should contain an explanation of what applying this PR will change in the way end-users interact with the project. One sentence is usually enough but feel free to add as many details as you feel necessary for the users to understand what it means. - -**Use the past tense** for the text in your fragment because, combined with others, it will be a part of the “news digest” telling the readers **what changed** in a specific version of the library *since the previous version*. - -You should also use reStructuredText syntax for highlighting code (inline or block), linking parts of the docs or external sites. If you wish to sign your change, feel free to add `-- by :user:`github-username`` at the end (replace `github-username` with your own!). - -Finally, name your file following the convention that Towncrier understands: it should start with the number of an issue or a PR followed by a dot, then add a patch type, like `feature`, `doc`, `misc` etc., and add `.rst` as a suffix. If you need to add more than one fragment, you may add an optional sequence number (delimited with another period) between the type and the suffix. - -In general the name will follow `..rst` pattern, where the categories are: +To submit a change note about your PR, add a text file into the +[docs/changelog/next_release](./next_release) folder. It should contain an +explanation of what applying this PR will change in the way +end-users interact with the project. One sentence is usually +enough but feel free to add as many details as you feel necessary +for the users to understand what it means. + +**Use the past tense** for the text in your fragment because, +combined with others, it will be a part of the “news digest” +telling the readers **what changed** in a specific version of +the library *since the previous version*. + +You should also use +reStructuredText syntax for highlighting code (inline or block), +linking parts of the docs or external sites. +If you wish to sign your change, feel free to add `-- by +:user:`github-username`` at the end (replace `github-username` +with your own!). + +Finally, name your file following the convention that Towncrier +understands: it should start with the number of an issue or a +PR followed by a dot, then add a patch type, like `feature`, +`doc`, `misc` etc., and add `.rst` as a suffix. If you +need to add more than one fragment, you may add an optional +sequence number (delimited with another period) between the type +and the suffix. + +In general the name will follow `..rst` pattern, +where the categories are: - `feature`: Any new feature - `bugfix`: A bug fix @@ -255,7 +280,11 @@ In general the name will follow `..rst` pattern, where the - `dependency`: Dependency-related changes - `misc`: Changes internal to the repo like CI, test and build changes -A pull request may have more than one of these components, for example a code change may introduce a new feature that deprecates an old feature, in which case two fragments should be added. It is not necessary to make a separate documentation fragment for documentation changes accompanying the relevant code changes. +A pull request may have more than one of these components, for example +a code change may introduce a new feature that deprecates an old +feature, in which case two fragments should be added. It is not +necessary to make a separate documentation fragment for documentation +changes accompanying the relevant code changes. #### Examples for adding changelog entries to your Pull Requests diff --git a/mddocs/db/db_reader.md b/mddocs/markdown/db/db_reader.md similarity index 100% rename from mddocs/db/db_reader.md rename to mddocs/markdown/db/db_reader.md diff --git a/mddocs/db/db_writer.md b/mddocs/markdown/db/db_writer.md similarity index 100% rename from mddocs/db/db_writer.md rename to mddocs/markdown/db/db_writer.md diff --git a/mddocs/db/index.md b/mddocs/markdown/db/index.md similarity index 85% rename from mddocs/db/index.md rename to mddocs/markdown/db/index.md index ef8f4ab30..861a539ff 100644 --- a/mddocs/db/index.md +++ b/mddocs/markdown/db/index.md @@ -1,6 +1,6 @@ -# DB classes + DB classes * [DB Reader](db_reader.md) * [DB Writer](db_writer.md) diff --git a/mddocs/file/file_downloader/file_downloader.md b/mddocs/markdown/file/file_downloader/file_downloader.md similarity index 100% rename from mddocs/file/file_downloader/file_downloader.md rename to mddocs/markdown/file/file_downloader/file_downloader.md diff --git a/mddocs/file/file_downloader/index.md b/mddocs/markdown/file/file_downloader/index.md similarity index 100% rename from mddocs/file/file_downloader/index.md rename to mddocs/markdown/file/file_downloader/index.md diff --git a/mddocs/file/file_downloader/options.md b/mddocs/markdown/file/file_downloader/options.md similarity index 100% rename from mddocs/file/file_downloader/options.md rename to mddocs/markdown/file/file_downloader/options.md diff --git a/mddocs/file/file_downloader/result.md b/mddocs/markdown/file/file_downloader/result.md similarity index 100% rename from mddocs/file/file_downloader/result.md rename to mddocs/markdown/file/file_downloader/result.md diff --git a/mddocs/file/file_filters/base.md b/mddocs/markdown/file/file_filters/base.md similarity index 100% rename from mddocs/file/file_filters/base.md rename to 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mddocs/file/file_mover/file_mover.md rename to mddocs/markdown/file/file_mover/file_mover.md diff --git a/mddocs/file/file_mover/index.md b/mddocs/markdown/file/file_mover/index.md similarity index 100% rename from mddocs/file/file_mover/index.md rename to mddocs/markdown/file/file_mover/index.md diff --git a/mddocs/file/file_mover/options.md b/mddocs/markdown/file/file_mover/options.md similarity index 100% rename from mddocs/file/file_mover/options.md rename to mddocs/markdown/file/file_mover/options.md diff --git a/mddocs/file/file_mover/result.md b/mddocs/markdown/file/file_mover/result.md similarity index 100% rename from mddocs/file/file_mover/result.md rename to mddocs/markdown/file/file_mover/result.md diff --git a/mddocs/file/file_uploader/file_uploader.md b/mddocs/markdown/file/file_uploader/file_uploader.md similarity index 100% rename from mddocs/file/file_uploader/file_uploader.md rename to mddocs/markdown/file/file_uploader/file_uploader.md diff --git a/mddocs/file/file_uploader/index.md b/mddocs/markdown/file/file_uploader/index.md similarity index 100% rename from mddocs/file/file_uploader/index.md rename to mddocs/markdown/file/file_uploader/index.md diff --git a/mddocs/file/file_uploader/options.md b/mddocs/markdown/file/file_uploader/options.md similarity index 100% rename from mddocs/file/file_uploader/options.md rename to mddocs/markdown/file/file_uploader/options.md diff --git a/mddocs/file/file_uploader/result.md b/mddocs/markdown/file/file_uploader/result.md similarity index 100% rename from mddocs/file/file_uploader/result.md rename to mddocs/markdown/file/file_uploader/result.md diff --git a/mddocs/file/index.md b/mddocs/markdown/file/index.md similarity index 93% rename from mddocs/file/index.md rename to mddocs/markdown/file/index.md index 675648e5c..2da812bab 100644 --- a/mddocs/file/index.md +++ b/mddocs/markdown/file/index.md @@ -1,6 +1,6 @@ -# File classes + File classes * [File Downloader](file_downloader/index.md) * [File Uploader](file_uploader/index.md) diff --git a/mddocs/file_df/file_df_reader/file_df_reader.md b/mddocs/markdown/file_df/file_df_reader/file_df_reader.md similarity index 100% rename from mddocs/file_df/file_df_reader/file_df_reader.md rename to mddocs/markdown/file_df/file_df_reader/file_df_reader.md diff --git a/mddocs/file_df/file_df_reader/index.md b/mddocs/markdown/file_df/file_df_reader/index.md similarity index 100% rename from mddocs/file_df/file_df_reader/index.md rename to mddocs/markdown/file_df/file_df_reader/index.md diff --git a/mddocs/file_df/file_df_reader/options.md b/mddocs/markdown/file_df/file_df_reader/options.md similarity index 100% rename from mddocs/file_df/file_df_reader/options.md rename to mddocs/markdown/file_df/file_df_reader/options.md diff --git a/mddocs/file_df/file_df_writer/file_df_writer.md b/mddocs/markdown/file_df/file_df_writer/file_df_writer.md similarity index 100% rename from mddocs/file_df/file_df_writer/file_df_writer.md rename to mddocs/markdown/file_df/file_df_writer/file_df_writer.md diff --git a/mddocs/file_df/file_df_writer/index.md b/mddocs/markdown/file_df/file_df_writer/index.md similarity index 100% rename from mddocs/file_df/file_df_writer/index.md rename to mddocs/markdown/file_df/file_df_writer/index.md diff --git a/mddocs/file_df/file_df_writer/options.md b/mddocs/markdown/file_df/file_df_writer/options.md similarity index 100% rename from mddocs/file_df/file_df_writer/options.md rename to mddocs/markdown/file_df/file_df_writer/options.md diff --git a/mddocs/file_df/file_formats/avro.md b/mddocs/markdown/file_df/file_formats/avro.md similarity index 100% rename from mddocs/file_df/file_formats/avro.md rename to mddocs/markdown/file_df/file_formats/avro.md diff --git a/mddocs/file_df/file_formats/base.md b/mddocs/markdown/file_df/file_formats/base.md similarity index 100% rename from mddocs/file_df/file_formats/base.md rename to mddocs/markdown/file_df/file_formats/base.md diff --git a/mddocs/file_df/file_formats/csv.md b/mddocs/markdown/file_df/file_formats/csv.md similarity index 100% rename from mddocs/file_df/file_formats/csv.md rename to mddocs/markdown/file_df/file_formats/csv.md diff --git a/mddocs/file_df/file_formats/excel.md b/mddocs/markdown/file_df/file_formats/excel.md similarity index 100% rename from mddocs/file_df/file_formats/excel.md rename to mddocs/markdown/file_df/file_formats/excel.md diff --git a/mddocs/file_df/file_formats/index.md b/mddocs/markdown/file_df/file_formats/index.md similarity index 100% rename from mddocs/file_df/file_formats/index.md rename to mddocs/markdown/file_df/file_formats/index.md diff --git a/mddocs/file_df/file_formats/json.md b/mddocs/markdown/file_df/file_formats/json.md similarity index 100% rename from mddocs/file_df/file_formats/json.md rename to mddocs/markdown/file_df/file_formats/json.md diff --git a/mddocs/file_df/file_formats/jsonline.md b/mddocs/markdown/file_df/file_formats/jsonline.md similarity index 100% rename from mddocs/file_df/file_formats/jsonline.md rename to mddocs/markdown/file_df/file_formats/jsonline.md diff --git a/mddocs/file_df/file_formats/orc.md b/mddocs/markdown/file_df/file_formats/orc.md similarity index 100% rename from mddocs/file_df/file_formats/orc.md rename to mddocs/markdown/file_df/file_formats/orc.md diff --git a/mddocs/file_df/file_formats/parquet.md b/mddocs/markdown/file_df/file_formats/parquet.md similarity index 100% rename from mddocs/file_df/file_formats/parquet.md rename to mddocs/markdown/file_df/file_formats/parquet.md diff --git a/mddocs/file_df/file_formats/xml.md b/mddocs/markdown/file_df/file_formats/xml.md similarity index 100% rename from mddocs/file_df/file_formats/xml.md rename to mddocs/markdown/file_df/file_formats/xml.md diff --git a/mddocs/file_df/index.md b/mddocs/markdown/file_df/index.md similarity index 86% rename from mddocs/file_df/index.md rename to mddocs/markdown/file_df/index.md index a0868372e..c225567d6 100644 --- a/mddocs/file_df/index.md +++ b/mddocs/markdown/file_df/index.md @@ -1,6 +1,6 @@ -# File DataFrame classes + File DataFrame classes * [FileDF Reader](file_df_reader/index.md) * [FileDF Writer](file_df_writer/index.md) diff --git a/mddocs/hooks/design.md b/mddocs/markdown/hooks/design.md similarity index 100% rename from mddocs/hooks/design.md rename to mddocs/markdown/hooks/design.md diff --git a/mddocs/hooks/global_state.md b/mddocs/markdown/hooks/global_state.md similarity index 100% rename from mddocs/hooks/global_state.md rename to mddocs/markdown/hooks/global_state.md diff --git a/mddocs/hooks/hook.md b/mddocs/markdown/hooks/hook.md similarity index 100% rename from mddocs/hooks/hook.md rename to mddocs/markdown/hooks/hook.md diff --git a/mddocs/hooks/index.md b/mddocs/markdown/hooks/index.md similarity index 100% rename from mddocs/hooks/index.md rename to mddocs/markdown/hooks/index.md diff --git a/mddocs/hooks/slot.md b/mddocs/markdown/hooks/slot.md similarity index 100% rename from mddocs/hooks/slot.md rename to mddocs/markdown/hooks/slot.md diff --git a/mddocs/hooks/support_hooks.md b/mddocs/markdown/hooks/support_hooks.md similarity index 100% rename from mddocs/hooks/support_hooks.md rename to mddocs/markdown/hooks/support_hooks.md diff --git a/mddocs/hwm_store/index.md b/mddocs/markdown/hwm_store/index.md similarity index 100% rename from mddocs/hwm_store/index.md rename to mddocs/markdown/hwm_store/index.md diff --git a/mddocs/hwm_store/yaml_hwm_store.md b/mddocs/markdown/hwm_store/yaml_hwm_store.md similarity index 100% rename from mddocs/hwm_store/yaml_hwm_store.md rename to mddocs/markdown/hwm_store/yaml_hwm_store.md diff --git a/mddocs/markdown/index.md b/mddocs/markdown/index.md new file mode 100644 index 000000000..436e5eb85 --- /dev/null +++ b/mddocs/markdown/index.md @@ -0,0 +1,59 @@ + + +# onETL + +[![Repo status - Active](https://www.repostatus.org/badges/latest/active.svg)](https://github.com/MobileTeleSystems/onetl) [![PyPI - Latest Release](https://img.shields.io/pypi/v/onetl)](https://pypi.org/project/onetl/) [![PyPI - License](https://img.shields.io/pypi/l/onetl.svg)](https://github.com/MobileTeleSystems/onetl/blob/develop/LICENSE.txt) [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/onetl.svg)](https://pypi.org/project/onetl/) [![PyPI - Downloads](https://img.shields.io/pypi/dm/onetl)](https://pypi.org/project/onetl/) +[![Documentation - ReadTheDocs](https://readthedocs.org/projects/onetl/badge/?version=stable)](https://onetl.readthedocs.io/) [![Github Actions - latest CI build status](https://github.com/MobileTeleSystems/onetl/workflows/Tests/badge.svg)](https://github.com/MobileTeleSystems/onetl/actions) [![Test coverage - percent](https://codecov.io/gh/MobileTeleSystems/onetl/branch/develop/graph/badge.svg?token=RIO8URKNZJ)](https://codecov.io/gh/MobileTeleSystems/onetl) [![pre-commit.ci - status](https://results.pre-commit.ci/badge/github/MobileTeleSystems/onetl/develop.svg)](https://results.pre-commit.ci/latest/github/MobileTeleSystems/onetl/develop) + +![onETL logo](_static/logo_wide.svg) + +## What is onETL? + +Python ETL/ELT library powered by [Apache Spark](https://spark.apache.org/) & other open-source tools. + +## Goals + +* Provide unified classes to extract data from (**E**) & load data to (**L**) various stores. +* Provides [Spark DataFrame API](https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.DataFrame.html) for performing transformations (**T**) in terms of *ETL*. +* Provide direct assess to database, allowing to execute SQL queries, as well as DDL, DML, and call functions/procedures. This can be used for building up *ELT* pipelines. +* Support different [read strategies](https://onetl.readthedocs.io/en/stable/strategy/index.html) for incremental and batch data fetching. +* Provide [hooks](https://onetl.readthedocs.io/en/stable/hooks/index.html) & [plugins](https://onetl.readthedocs.io/en/stable/plugins.html) mechanism for altering behavior of internal classes. + +## Non-goals + +* onETL is not a Spark replacement. It just provides additional functionality that Spark does not have, and improves UX for end users. +* onETL is not a framework, as it does not have requirements to project structure, naming, the way of running ETL/ELT processes, configuration, etc. All of that should be implemented in some other tool. +* onETL is deliberately developed without any integration with scheduling software like Apache Airflow. All integrations should be implemented as separated tools. +* Only batch operations, no streaming. For streaming prefer [Apache Flink](https://flink.apache.org/). + +## Requirements + +* **Python 3.7 - 3.13** +* PySpark 2.3.x - 3.5.x (depends on used connector) +* Java 8+ (required by Spark, see below) +* Kerberos libs & GCC (required by `Hive`, `HDFS` and `SparkHDFS` connectors) + +## Supported storages + +| Type | Storage | Powered by | +|--------------------|-----------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------| +| Database | Clickhouse | Apache Spark [JDBC Data Source](https://spark.apache.org/docs/latest/sql-data-sources-jdbc.html) | +| MSSQL | | | +| MySQL | | | +| Postgres | | | +| Oracle | | | +| Teradata | | | +| Hive | Apache Spark [Hive integration](https://spark.apache.org/docs/latest/sql-data-sources-hive-tables.html) | | +| Kafka | Apache Spark [Kafka integration](https://spark.apache.org/docs/latest/structured-streaming-kafka-integration.html) | | +| Greenplum | VMware [Greenplum Spark connector](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/index.html) | | +| MongoDB | [MongoDB Spark connector](https://www.mongodb.com/docs/spark-connector/current) | | +| File | HDFS | [HDFS Python client](https://pypi.org/project/hdfs/) | +| S3 | [minio-py client](https://pypi.org/project/minio/) | | +| SFTP | [Paramiko library](https://pypi.org/project/paramiko/) | | +| FTP | [FTPUtil library](https://pypi.org/project/ftputil/) | | +| FTPS | | | +| WebDAV | [WebdavClient3 library](https://pypi.org/project/webdavclient3/) | | +| Samba | [pysmb library](https://pypi.org/project/pysmb/) | | +| Files as DataFrame | SparkLocalFS | Apache Spark [File Data Source](https://spark.apache.org/docs/latest/sql-data-sources-generic-options.html) | +| SparkHDFS | | | +| SparkS3 | [Hadoop AWS](https://hadoop.apache.org/docs/current3/hadoop-aws/tools/hadoop-aws/index.html) library | | diff --git a/mddocs/install/files.md b/mddocs/markdown/install/files.md similarity index 100% rename from mddocs/install/files.md rename to mddocs/markdown/install/files.md diff --git a/mddocs/install/full.md b/mddocs/markdown/install/full.md similarity index 100% rename from mddocs/install/full.md rename to mddocs/markdown/install/full.md diff --git a/mddocs/install/index.md b/mddocs/markdown/install/index.md similarity index 84% rename from mddocs/install/index.md rename to mddocs/markdown/install/index.md index 15f1e36e2..1a5fb63cc 100644 --- a/mddocs/install/index.md +++ b/mddocs/markdown/install/index.md @@ -16,14 +16,11 @@ It can be installed via: pip install onetl ``` -```{admonition} warning - -:class: warning - +#### WARNING This method does NOT include any connections. -This method is recommended for use in third-party libraries which require for `onetl` to be installed, but do not use its connection classes. -``` +This method is recommended for use in third-party libraries which require for `onetl` to be installed, +but do not use its connection classes. ## Installation in details diff --git a/mddocs/install/kerberos.md b/mddocs/markdown/install/kerberos.md similarity index 100% rename from mddocs/install/kerberos.md rename to mddocs/markdown/install/kerberos.md diff --git a/mddocs/install/spark.md b/mddocs/markdown/install/spark.md similarity index 100% rename from mddocs/install/spark.md rename to mddocs/markdown/install/spark.md diff --git a/mddocs/logging.md b/mddocs/markdown/logging.md similarity index 88% rename from mddocs/logging.md rename to mddocs/markdown/logging.md index c67da0558..f9900f789 100644 --- a/mddocs/logging.md +++ b/mddocs/markdown/logging.md @@ -1,12 +1,14 @@ -# Logging - +# Logging + Logging is quite important to understand what’s going on under the hood of onETL. -Default logging level for Python interpreters is `WARNING`, but most of onETL logs are in `INFO` level, so users usually don’t see much. +Default logging level for Python interpreters is `WARNING`, +but most of onETL logs are in `INFO` level, so users usually don’t see much. -To change logging level, there is a function [`setup_logging`](#setup_logging) which should be called at the top of the script: +To change logging level, there is a function [`setup_logging`](#onetl.log.setup_logging) +which should be called at the top of the script: ```python from onetl.log import setup_logging @@ -20,7 +22,7 @@ setup_logging() This changes both log level and log formatting to something like this: -## See logs +### See logs ```text 2024-04-12 10:12:10,834 [INFO ] MainThread: |onETL| Using IncrementalStrategy as a strategy @@ -150,100 +152,85 @@ setup_logging(level="DEBUG", enable_clients=True) This also changes log level for all underlying Python libraries, e.g. showing each HTTP request being made, and so on. - -**onetl.log.setup_logging**(level: int | str = 20, enable_clients: bool = False) → None +### onetl.log.setup_logging(level: int | str = 20, enable_clients: bool = False) → None Set up onETL logging. What this function does: +: * Adds stderr logging handler + * Changes root logger format to `2023-05-31 11:22:33.456 [INFO] MainThread: message` + * Changes root logger level to `level` + * Changes onETL logger level to `level` + * Sets up logging level of underlying client modules -* Adds stderr logging handler -* Changes root logger format to `2023-05-31 11:22:33.456 [INFO] MainThread: message` -* Changes root logger level to `level` -* Changes onETL logger level to `level` -* Sets up logging level of underlying client modules - - - -```{admonition} note +#### NOTE Should be used only in IDEs (like Jupyter notebooks or PyCharm), or scripts (ETL pipelines). -``` - - - -````{versionchanged} +#### Versionchanged Changed in version 0.5.0: Renamed `setup_notebook_logging` → `setup_logging` * **Parameters:** **level** - Log level for onETL module + : Log level for onETL module **enable_clients** - If `True`, enable logging of underlying client modules. + : If `True`, enable logging of underlying client modules. Otherwise, set client modules log level to `DISABLED`. +
+ #### NOTE + For `level="DEBUG"` it is recommended to use `enable_clients=True` +
+ #### Versionadded + Added in version 0.9.0. -```{admonition} note -For `level="DEBUG"` it is recommended to use `enable_clients=True` -``` - -```{versionadded} -Added in version 0.9.0. -``` -```` -**onetl.log.setup_clients_logging**(level: int | str = 9999) → None +### onetl.log.setup_clients_logging(level: int | str = 9999) → None Set logging of underlying client modules used by onETL. Affected modules: - -* `ftputil` -* `hdfs` -* `minio` -* `paramiko` -* `py4j` -* `pyspark` -* `webdav3` - -```{admonition} note +: * `ftputil` + * `hdfs` + * `minio` + * `paramiko` + * `py4j` + * `pyspark` + * `webdav3` + +#### NOTE Can be used in applications, but it is recommended to set up these loggers according to your framework documentation. -``` -````{versionchanged} +#### Versionchanged Changed in version 0.9.0: Renamed `disable_clients_logging` → `setup_clients_logging` * **Parameters:** **level** - Log level for client modules + : Log level for client modules
-```{admonition} note -For `py4j`, logging level with maximum verbosity is `INFO` because `DEBUG` logs are -totally unreadable. -``` -
-```{versionadded} -Added in version 0.9.0. -``` -```` + #### NOTE + For `py4j`, logging level with maximum verbosity is `INFO` because `DEBUG` logs are + totally unreadable. +
+ #### Versionadded + Added in version 0.9.0. + -**onetl.log.set_default_logging_format()** → None +### onetl.log.set_default_logging_format() → None Sets default logging format to preferred by onETL. Example log message: `2023-05-31 11:22:33.456 [INFO] MainThread: message` -```{admonition} note +#### NOTE Should be used only in IDEs (like Jupyter notebooks or PyCharm), or scripts (ETL pipelines). -``` -```{admonition} warning +#### WARNING Should **NOT** be used in applications, you should set up logging settings manually, according to your framework documentation. -``` + diff --git a/mddocs/plugins.md b/mddocs/markdown/plugins.md similarity index 99% rename from mddocs/plugins.md rename to mddocs/markdown/plugins.md index ca50f4a84..d42ded034 100644 --- a/mddocs/plugins.md +++ b/mddocs/markdown/plugins.md @@ -1,10 +1,9 @@ -# Plugins - -```{versionadded} +# Plugins + +#### Versionadded Added in version 0.6.0. -``` ## What are plugins? @@ -79,9 +78,8 @@ like [@hook decorator](hooks/hook.md#hook-decorator), it will be executed during ## How to enable/disable plugins? -```{versionadded} +#### Versionadded Added in version 0.7.0. -``` ### Disable/enable all plugins diff --git a/mddocs/quickstart.md b/mddocs/markdown/quickstart.md similarity index 100% rename from mddocs/quickstart.md rename to mddocs/markdown/quickstart.md diff --git a/mddocs/security.md b/mddocs/markdown/security.md similarity index 100% rename from mddocs/security.md rename to mddocs/markdown/security.md diff --git a/mddocs/strategy/incremental_batch_strategy.md b/mddocs/markdown/strategy/incremental_batch_strategy.md similarity index 100% rename from mddocs/strategy/incremental_batch_strategy.md rename to mddocs/markdown/strategy/incremental_batch_strategy.md diff --git a/mddocs/strategy/incremental_strategy.md b/mddocs/markdown/strategy/incremental_strategy.md similarity index 100% rename from mddocs/strategy/incremental_strategy.md rename to mddocs/markdown/strategy/incremental_strategy.md diff --git a/mddocs/strategy/index.md b/mddocs/markdown/strategy/index.md similarity index 100% rename from mddocs/strategy/index.md rename to mddocs/markdown/strategy/index.md diff --git a/mddocs/strategy/snapshot_batch_strategy.md b/mddocs/markdown/strategy/snapshot_batch_strategy.md similarity index 100% rename from mddocs/strategy/snapshot_batch_strategy.md rename to mddocs/markdown/strategy/snapshot_batch_strategy.md diff --git a/mddocs/strategy/snapshot_strategy.md b/mddocs/markdown/strategy/snapshot_strategy.md similarity index 100% rename from mddocs/strategy/snapshot_strategy.md rename to mddocs/markdown/strategy/snapshot_strategy.md diff --git a/mddocs/troubleshooting/index.md b/mddocs/markdown/troubleshooting/index.md similarity index 100% rename from mddocs/troubleshooting/index.md rename to mddocs/markdown/troubleshooting/index.md diff --git a/mddocs/troubleshooting/spark.md b/mddocs/markdown/troubleshooting/spark.md similarity index 100% rename from mddocs/troubleshooting/spark.md rename to mddocs/markdown/troubleshooting/spark.md From 08188ef8733169c7dd7e02a705db67b72ae0cc33 Mon Sep 17 00:00:00 2001 From: Sattar Gyulmamedov Date: Sat, 7 Jun 2025 15:52:01 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a/mddocs/markdown/_static/autodoc_pydantic.css +++ /dev/null @@ -1,11 +0,0 @@ -.autodoc_pydantic_validator_arrow { - padding-left: 8px; - } - -.autodoc_pydantic_collapsable_json { - cursor: pointer; - } - -.autodoc_pydantic_collapsable_erd { - cursor: pointer; - } \ No newline at end of file diff --git a/mddocs/markdown/changelog.md b/mddocs/markdown/changelog.md deleted file mode 100644 index 4ba142635..000000000 --- a/mddocs/markdown/changelog.md +++ /dev/null @@ -1,31 +0,0 @@ -# Changelog - -# Changelog - -* [0.13.4 (2025-03-20)](changelog/0.13.4.md) -* [0.13.3 (2025-03-11)](changelog/0.13.3.md) -* [0.13.1 (2025-03-06)](changelog/0.13.1.md) -* [0.13.0 (2025-02-24)](changelog/0.13.0.md) -* [0.12.5 (2024-12-03)](changelog/0.12.5.md) -* [0.12.4 (2024-11-27)](changelog/0.12.4.md) -* [0.12.3 (2024-11-22)](changelog/0.12.3.md) -* [0.12.2 (2024-11-12)](changelog/0.12.2.md) -* [0.12.1 (2024-10-28)](changelog/0.12.1.md) -* [0.12.0 (2024-09-03)](changelog/0.12.0.md) -* [0.11.2 (2024-09-02)](changelog/0.11.2.md) -* [0.11.1 (2024-05-29)](changelog/0.11.1.md) -* [0.11.0 (2024-05-27)](changelog/0.11.0.md) -* [0.10.2 (2024-03-21)](changelog/0.10.2.md) -* [0.10.1 (2024-02-05)](changelog/0.10.1.md) -* [0.10.0 (2023-12-18)](changelog/0.10.0.md) -* [0.9.5 (2023-10-10)](changelog/0.9.5.md) -* [0.9.4 (2023-09-26)](changelog/0.9.4.md) -* [0.9.3 (2023-09-06)](changelog/0.9.3.md) -* [0.9.2 (2023-09-06)](changelog/0.9.2.md) -* [0.9.1 (2023-08-17)](changelog/0.9.1.md) -* [0.9.0 (2023-08-17)](changelog/0.9.0.md) -* [0.8.1 (2023-07-10)](changelog/0.8.1.md) -* [0.8.0 (2023-05-31)](changelog/0.8.0.md) -* [0.7.2 (2023-05-24)](changelog/0.7.2.md) -* [0.7.1 (2023-05-23)](changelog/0.7.1.md) -* [0.7.0 (2023-05-15)](changelog/0.7.0.md) diff --git a/mddocs/markdown/changelog/0.10.0.md b/mddocs/markdown/changelog/0.10.0.md deleted file mode 100644 index ff0fe78bc..000000000 --- a/mddocs/markdown/changelog/0.10.0.md +++ /dev/null @@ -1,359 +0,0 @@ -# 0.10.0 (2023-12-18) - -## Breaking Changes - -- Upgrade `etl-entities` from v1 to v2 ([#172](https://github.com/MobileTeleSystems/onetl/pull/172)). - - This implies that `HWM` classes are now have different internal structure than they used to. - - Before: - ```python - from etl_entities.old_hwm import IntHWM as OldIntHWM - from etl_entities.source import Column, Table - from etl_entities.process import Process - - hwm = OldIntHWM( - process=Process(name="myprocess", task="abc", dag="cde", host="myhost"), - source=Table(name="schema.table", instance="postgres://host:5432/db"), - column=Column(name="col1"), - value=123, - ) - ``` - - After: - ```python - from etl_entities.hwm import ColumnIntHWM - - hwm = ColumnIntHWM( - name="some_unique_name", - description="any value you want", - source="schema.table", - expression="col1", - value=123, - ) - ``` - - **Breaking change:** If you used HWM classes from `etl_entities` module, you should rewrite your code to make it compatible with new version. - - ### More details - - - `HWM` classes used by previous onETL versions were moved from `etl_entities` to `etl_entities.old_hwm` submodule. They are here for compatibility reasons, but are planned to be removed in `etl-entities` v3 release. - - New `HWM` classes have flat structure instead of nested. - - New `HWM` classes have mandatory `name` attribute (it was known as `qualified_name` before). - - Type aliases used while serializing and deserializing `HWM` objects to `dict` representation were changed too: `int` → `column_int`. - - To make migration simpler, you can use new method: - ```python - old_hwm = OldIntHWM(...) - new_hwm = old_hwm.as_new_hwm() - ``` - - Which automatically converts all fields from old structure to new one, including `qualified_name` → `name`. -- **Breaking changes:** - * Methods `BaseHWMStore.get()` and `BaseHWMStore.save()` were renamed to `get_hwm()` and `set_hwm()`. - * They now can be used only with new HWM classes from `etl_entities.hwm`, **old HWM classes are not supported**. - - If you used them in your code, please update it accordingly. -- YAMLHWMStore **CANNOT read files created by older onETL versions** (0.9.x or older). - - ### Update procedure - - ```python - # pip install onetl==0.9.5 - - # Get qualified_name for HWM - - - # Option 1. HWM is built manually - from etl_entities import IntHWM, FileListHWM - from etl_entities.source import Column, Table, RemoteFolder - from etl_entities.process import Process - - # for column HWM - old_column_hwm = IntHWM( - process=Process(name="myprocess", task="abc", dag="cde", host="myhost"), - source=Table(name="schema.table", instance="postgres://host:5432/db"), - column=Column(name="col1"), - ) - qualified_name = old_column_hwm.qualified_name - # "col1#schema.table@postgres://host:5432/db#cde.abc.myprocess@myhost" - - # for file HWM - old_file_hwm = FileListHWM( - process=Process(name="myprocess", task="abc", dag="cde", host="myhost"), - source=RemoteFolder(name="/absolute/path", instance="ftp://ftp.server:21"), - ) - qualified_name = old_file_hwm.qualified_name - # "file_list#/absolute/path@ftp://ftp.server:21#cde.abc.myprocess@myhost" - - - # Option 2. HWM is generated automatically (by DBReader/FileDownloader) - # See onETL logs and search for string like qualified_name = '...' - - qualified_name = "col1#schema.table@postgres://host:5432/db#cde.abc.myprocess@myhost" - - - # Get .yml file path by qualified_name - - import os - from pathlib import PurePosixPath - from onetl.hwm.store import YAMLHWMStore - - # here you should pass the same arguments as used on production, if any - yaml_hwm_store = YAMLHWMStore() - hwm_path = yaml_hwm_store.get_file_path(qualified_name) - print(hwm_path) - - # for column HWM - # LocalPosixPath('/home/maxim/.local/share/onETL/yml_hwm_store/col1__schema.table__postgres_host_5432_db__cde.abc.myprocess__myhost.yml') - - # for file HWM - # LocalPosixPath('/home/maxim/.local/share/onETL/yml_hwm_store/file_list__absolute_path__ftp_ftp.server_21__cde.abc.myprocess__myhost.yml') - - - # Read raw .yml file content - - from yaml import safe_load, dump - - raw_old_hwm_items = safe_load(hwm_path.read_text()) - print(raw_old_hwm_items) - - # for column HWM - # [ - # { - # "column": { "name": "col1", "partition": {} }, - # "modified_time": "2023-12-18T10: 39: 47.377378", - # "process": { "dag": "cde", "host": "myhost", "name": "myprocess", "task": "abc" }, - # "source": { "instance": "postgres: //host:5432/db", "name": "schema.table" }, - # "type": "int", - # "value": "123", - # }, - # ] - - # for file HWM - # [ - # { - # "modified_time": "2023-12-18T11:15:36.478462", - # "process": { "dag": "cde", "host": "myhost", "name": "myprocess", "task": "abc" }, - # "source": { "instance": "ftp://ftp.server:21", "name": "/absolute/path" }, - # "type": "file_list", - # "value": ["file1.txt", "file2.txt"], - # }, - # ] - - - # Convert file content to new structure, compatible with onETL 0.10.x - raw_new_hwm_items = [] - for old_hwm in raw_old_hwm_items: - new_hwm = {"name": qualified_name, "modified_time": old_hwm["modified_time"]} - - if "column" in old_hwm: - new_hwm["expression"] = old_hwm["column"]["name"] - new_hwm["entity"] = old_hwm["source"]["name"] - old_hwm.pop("process", None) - - if old_hwm["type"] == "int": - new_hwm["type"] = "column_int" - new_hwm["value"] = old_hwm["value"] - - elif old_hwm["type"] == "date": - new_hwm["type"] = "column_date" - new_hwm["value"] = old_hwm["value"] - - elif old_hwm["type"] == "datetime": - new_hwm["type"] = "column_datetime" - new_hwm["value"] = old_hwm["value"] - - elif old_hwm["type"] == "file_list": - new_hwm["type"] = "file_list" - new_hwm["value"] = [ - os.fspath(PurePosixPath(old_hwm["source"]["name"]).joinpath(path)) - for path in old_hwm["value"] - ] - - else: - raise ValueError("WAT?") - - raw_new_hwm_items.append(new_hwm) - - - print(raw_new_hwm_items) - # for column HWM - # [ - # { - # "name": "col1#schema.table@postgres://host:5432/db#cde.abc.myprocess@myhost", - # "modified_time": "2023-12-18T10:39:47.377378", - # "expression": "col1", - # "source": "schema.table", - # "type": "column_int", - # "value": 123, - # }, - # ] - - # for file HWM - # [ - # { - # "name": "file_list#/absolute/path@ftp://ftp.server:21#cde.abc.myprocess@myhost", - # "modified_time": "2023-12-18T11:15:36.478462", - # "entity": "/absolute/path", - # "type": "file_list", - # "value": ["/absolute/path/file1.txt", "/absolute/path/file2.txt"], - # }, - # ] - - - # Save file with new content - with open(hwm_path, "w") as file: - dump(raw_new_hwm_items, file) - - - # Stop Python interpreter and update onETL - # pip install onetl==0.10.0 - # Check that new .yml file can be read - - from onetl.hwm.store import YAMLHWMStore - - qualified_name = ... - - # here you should pass the same arguments as used on production, if any - yaml_hwm_store = YAMLHWMStore() - yaml_hwm_store.get_hwm(qualified_name) - - # for column HWM - # ColumnIntHWM( - # name='col1#schema.table@postgres://host:5432/db#cde.abc.myprocess@myhost', - # description='', - # entity='schema.table', - # value=123, - # expression='col1', - # modified_time=datetime.datetime(2023, 12, 18, 10, 39, 47, 377378), - # ) - - # for file HWM - # FileListHWM( - # name='file_list#/absolute/path@ftp://ftp.server:21#cde.abc.myprocess@myhost', - # description='', - # entity=AbsolutePath('/absolute/path'), - # value=frozenset({AbsolutePath('/absolute/path/file1.txt'), AbsolutePath('/absolute/path/file2.txt')}), - # expression=None, - # modified_time=datetime.datetime(2023, 12, 18, 11, 15, 36, 478462) - # ) - - - # That's all! - ``` - - But most of users use other HWM store implementations which do not have such issues. -- Several classes and functions were moved from `onetl` to `etl_entities`: - - | onETL `0.9.x` and older | onETL `0.10.x` and newer | - |-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| - | ```python
from onetl.hwm.store import (
detect_hwm_store,
BaseHWMStore,
HWMStoreClassRegistry,
register_hwm_store_class,
HWMStoreManager,
MemoryHWMStore,
)
``` | ```python
from etl_entities.hwm_store import (
detect_hwm_store,
BaseHWMStore,
HWMStoreClassRegistry,
register_hwm_store_class,
HWMStoreManager,
MemoryHWMStore,
)
``` | - - They still can be imported from old module, but this is deprecated and will be removed in v1.0.0 release. -- Change the way of passing `HWM` to `DBReader` and `FileDownloader` classes: - - | onETL `0.9.x` and older | onETL `0.10.x` and newer | - |----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| - | ```python
reader = DBReader(
connection=...,
source=...,
hwm_column="col1",
)
``` | ```python
reader = DBReader(
connection=...,
source=...,
hwm=DBReader.AutoDetectHWM(
# name is mandatory now!
name="my_unique_hwm_name",
expression="col1",
),
)
``` | - | ```python
reader = DBReader(
connection=...,
source=...,
hwm_column=(
"col1",
"cast(col1 as date)",
),
)
``` | ```python
reader = DBReader(
connection=...,
source=...,
hwm=DBReader.AutoDetectHWM(
# name is mandatory now!
name="my_unique_hwm_name",
expression="cast(col1 as date)",
),
)
``` | - | ```python
downloader = FileDownloader(
connection=...,
source_path=...,
target_path=...,
hwm_type="file_list",
)
``` | ```python
downloader = FileDownloader(
connection=...,
source_path=...,
target_path=...,
hwm=FileListHWM(
# name is mandatory now!
name="another_unique_hwm_name",
),
)
``` | - - New HWM classes have **mandatory** `name` attribute which should be passed explicitly, - instead of generating if automatically under the hood. - - Automatic `name` generation using the old `DBReader.hwm_column` / `FileDownloader.hwm_type` - syntax is still supported, but will be removed in v1.0.0 release. ([#179](https://github.com/MobileTeleSystems/onetl/pull/179)) -- Performance of read Incremental and Batch strategies has been drastically improved. ([#182](https://github.com/MobileTeleSystems/onetl/pull/182)). - - ### Before and after in details - - `DBReader.run()` + incremental/batch strategy behavior in versions 0.9.x and older: - - Get table schema by making query `SELECT * FROM table WHERE 1=0` (if `DBReader.columns` has `*`) - - Expand `*` to real column names from table, add here `hwm_column`, remove duplicates (as some RDBMS does not allow that). - - Create dataframe from query like `SELECT hwm_expression AS hwm_column, ...other table columns... FROM table WHERE hwm_expression > prev_hwm.value`. - - Determine HWM class using dataframe schema: `df.schema[hwm_column].dataType`. - - Determine x HWM column value using Spark: `df.select(max(hwm_column)).collect()`. - - Use `max(hwm_column)` as next HWM value, and save it to HWM Store. - - Return dataframe to user. - - This was far from ideal: - - Dataframe content (all rows or just changed ones) was loaded from the source to Spark only to get min/max values of specific column. - - Step of fetching table schema and then substituting column names in the next query caused some unexpected errors. - > For example, source contains columns with mixed name case, like `"CamelColumn"` or `"spaced column"`. - - > Column names were *not* escaped during query generation, leading to queries that cannot be executed by database. - - > So users have to *explicitly* pass column names `DBReader`, wrapping columns with mixed naming with `"`: - > ```python - > reader = DBReader( - > connection=..., - > source=..., - > columns=[ # passing '*' here leads to wrong SQL query generation - > "normal_column", - > '"CamelColumn"', - > '"spaced column"', - > ..., - > ], - > ) - > ``` - - Using `DBReader` with `IncrementalStrategy` could lead to reading rows already read before. - > Dataframe was created from query with WHERE clause like `hwm.expression > prev_hwm.value`, - > not `hwm.expression > prev_hwm.value AND hwm.expression <= current_hwm.value`. - - > So if new rows appeared in the source **after** HWM value is determined, - > they can be read by accessing dataframe content (because Spark dataframes are lazy), - > leading to inconsistencies between HWM value and dataframe content. - - > This may lead to issues then `DBReader.run()` read some data, updated HWM value, and next call of `DBReader.run()` - > will read rows that were already read in previous run. - - `DBReader.run()` + incremental/batch strategy behavior in versions 0.10.x and newer: - - Detect type of HWM expression: `SELECT hwm.expression FROM table WHERE 1=0`. - - Determine corresponding Spark type `df.schema[0]` and when determine matching HWM class (if `DReader.AutoDetectHWM` is used). - - Get min/max values by querying the source: `SELECT MAX(hwm.expression) FROM table WHERE hwm.expression >= prev_hwm.value`. - - Use `max(hwm.expression)` as next HWM value, and save it to HWM Store. - - Create dataframe from query `SELECT ... table columns ... FROM table WHERE hwm.expression > prev_hwm.value AND hwm.expression <= current_hwm.value`, baking new HWM value into the query. - - Return dataframe to user. - - Improvements: - - Allow source to calculate min/max instead of loading everything to Spark. This should be **faster** on large amounts of data (**up to x2**), because we do not transfer all the data from the source to Spark. This can be even faster if source have indexes for HWM column. - - Columns list is passed to source as-is, without any resolving on `DBReader` side. So you can pass `DBReader(columns=["*"])` to read tables with mixed columns naming. - - Restrict dataframe content to always match HWM values, which leads to never reading the same row twice. - - **Breaking change**: HWM column is not being implicitly added to dataframe. It was a part of `SELECT` clause, but now it is mentioned only in `WHERE` clause. - - So if you had code like this, you have to rewrite it: - - | onETL `0.9.x` and older | onETL `0.10.x` and newer | - |-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| - | ```python
reader = DBReader(
connection=...,
source=...,
columns=[
"col1",
"col2",
],
hwm_column="hwm_col",
)

df = reader.run()
# hwm_column value is in the dataframe
assert df.columns == ["col1", "col2", "hwm_col"]
``` | ```python
reader = DBReader(
connection=...,
source=...,
columns=[
"col1",
"col2",
# add hwm_column explicitly
"hwm_col",
],
hwm_column="hwm_col",
)

df = reader.run()
# if columns list is not updated,
# this fill fail
assert df.columns == ["col1", "col2", "hwm_col"]
``` | - | ```python
reader = DBReader(
connection=...,
source=...,
columns=[
"col1",
"col2",
],
hwm_column=(
"hwm_col",
"cast(hwm_col as int)",
),
)

df = reader.run()
# hwm_expression value is in the dataframe
assert df.columns == ["col1", "col2", "hwm_col"]
``` | ```python
reader = DBReader(
connection=...,
source=...,
columns=[
"col1",
"col2",
# add hwm_expression explicitly
"cast(hwm_col as int) as hwm_col",
],
hwm_column=(
"hwm_col",
"cast(hwm_col as int)",
),
)

df = reader.run()
# if columns list is not updated,
# this fill fail
assert df.columns == ["col1", "col2", "hwm_col"]
``` | - - But most users just use `columns=["*"]` anyway, they won’t see any changes. -- `FileDownloader.run()` now updates HWM in HWM Store not after each file is being successfully downloaded, - but after all files were handled. - - This is because: - * FileDownloader can be used with `DownloadOptions(workers=N)`, which could lead to race condition - one thread can save to HWM store one HWM value when another thread will save different value. - * FileDownloader can download hundreds and thousands of files, and issuing a request to HWM Store for each file could potentially DDoS HWM Store. ([#189](https://github.com/MobileTeleSystems/onetl/pull/189)) - - There is a exception handler which tries to save HWM to HWM store if download process was interrupted. But if it was interrupted by force, like sending `SIGKILL` event, - HWM will not be saved to HWM store, so some already downloaded files may be downloaded again next time. - - But unexpected process kill may produce other negative impact, like some file will be downloaded partially, so this is an expected behavior. - -## Features - -- Add Python 3.12 compatibility. ([#167](https://github.com/MobileTeleSystems/onetl/pull/167)) -- `Excel` file format now can be used with Spark 3.5.0. ([#187](https://github.com/MobileTeleSystems/onetl/pull/187)) -- `SnapshotBatchStagy` and `IncrementalBatchStrategy` does no raise exceptions if source does not contain any data. - Instead they stop at first iteration and return empty dataframe. ([#188](https://github.com/MobileTeleSystems/onetl/pull/188)) -- Cache result of `connection.check()` in high-level classes like `DBReader`, `FileDownloader` and so on. This makes logs less verbose. ([#190](https://github.com/MobileTeleSystems/onetl/pull/190)) - -## Bug Fixes - -- Fix `@slot` and `@hook` decorators returning methods with missing arguments in signature (Pylance, VS Code). ([#183](https://github.com/MobileTeleSystems/onetl/pull/183)) -- Kafka connector documentation said that it does support reading topic data incrementally by passing `group.id` or `groupIdPrefix`. - Actually, this is not true, because Spark does not send information to Kafka which messages were consumed. - So currently users can only read the whole topic, no incremental reads are supported. diff --git a/mddocs/markdown/changelog/0.10.1.md b/mddocs/markdown/changelog/0.10.1.md deleted file mode 100644 index 1bc703cb4..000000000 --- a/mddocs/markdown/changelog/0.10.1.md +++ /dev/null @@ -1,25 +0,0 @@ -# 0.10.1 (2024-02-05) - -## Features - -- Add support of `Incremental Strategies` for `Kafka` connection: - ```python - reader = DBReader( - connection=Kafka(...), - source="topic_name", - hwm=DBReader.AutoDetectHWM(name="some_hwm_name", expression="offset"), - ) - - with IncrementalStrategy(): - df = reader.run() - ``` - - This lets you resume reading data from a Kafka topic starting at the last committed offset from your previous run. ([#202](https://github.com/MobileTeleSystems/onetl/pull/202)) -- Add `has_data`, `raise_if_no_data` methods to `DBReader` class. ([#203](https://github.com/MobileTeleSystems/onetl/pull/203)) -- Updare VMware Greenplum connector from `2.1.4` to `2.3.0`. This implies: - : * Greenplum 7.x support - * [Kubernetes support](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/configure.html#k8scfg) - * New read option [gpdb.matchDistributionPolicy](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/options.html#distpolmotion) - which allows to match each Spark executor with specific Greenplum segment, avoiding redundant data transfer between Greenplum segments - * Allows overriding [Greenplum optimizer parameters](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/options.html#greenplum-gucs) in read/write operations ([#208](https://github.com/MobileTeleSystems/onetl/pull/208)) -- `Greenplum.get_packages()` method now accepts optional arg `package_version` which allows to override version of Greenplum connector package. ([#208](https://github.com/MobileTeleSystems/onetl/pull/208)) diff --git a/mddocs/markdown/changelog/0.10.2.md b/mddocs/markdown/changelog/0.10.2.md deleted file mode 100644 index 3ca2693c2..000000000 --- a/mddocs/markdown/changelog/0.10.2.md +++ /dev/null @@ -1,34 +0,0 @@ -# 0.10.2 (2024-03-21) - -## Features - -- Add support of Pydantic v2. ([#230](https://github.com/MobileTeleSystems/onetl/pull/230)) - -## Improvements - -- Improve database connections documentation: - : * Add “Types” section describing mapping between Clickhouse and Spark types - * Add “Prerequisites” section describing different aspects of connecting to Clickhouse - * Separate documentation of `DBReader` and `.sql()` / `.pipeline(...)` - * Add examples for `.fetch()` and `.execute()` ([#211](https://github.com/MobileTeleSystems/onetl/pull/211), [#228](https://github.com/MobileTeleSystems/onetl/pull/228), [#229](https://github.com/MobileTeleSystems/onetl/pull/229), [#233](https://github.com/MobileTeleSystems/onetl/pull/233), [#234](https://github.com/MobileTeleSystems/onetl/pull/234), [#235](https://github.com/MobileTeleSystems/onetl/pull/235), [#236](https://github.com/MobileTeleSystems/onetl/pull/236), [#240](https://github.com/MobileTeleSystems/onetl/pull/240)) -- Add notes to Greenplum documentation about issues with IP resolution and building `gpfdist` URL ([#228](https://github.com/MobileTeleSystems/onetl/pull/228)) -- Allow calling `MongoDB.pipeline(...)` with passing just collection name, without explicit aggregation pipeline. ([#237](https://github.com/MobileTeleSystems/onetl/pull/237)) -- Update default `Postgres(extra={...})` to include `{"stringtype": "unspecified"}` option. - This allows to write text data to non-text column (or vice versa), relying to Postgres cast capabilities. - - For example, now it is possible to read column of type `money` as Spark’s `StringType()`, and write it back to the same column, - without using intermediate columns or tables. ([#229](https://github.com/MobileTeleSystems/onetl/pull/229)) - -## Bug Fixes - -- Return back handling of `DBReader(columns="string")`. This was a valid syntax up to v0.10 release, but it was removed because - most of users neved used it. It looks that we were wrong, returning this behavior back, but with deprecation warning. ([#238](https://github.com/MobileTeleSystems/onetl/pull/238)) -- Downgrade Greenplum package version from `2.3.0` to `2.2.0`. ([#239](https://github.com/MobileTeleSystems/onetl/pull/239)) - - This is because version 2.3.0 introduced issues with writing data to Greenplum 6.x. - Connector can open transaction with `SELECT * FROM table LIMIT 0` query, but does not close it, which leads to deadlocks. - - For using this connector with Greenplum 7.x, please pass package version explicitly: - ```python - maven_packages = Greenplum.get_packages(package_version="2.3.0", ...) - ``` diff --git a/mddocs/markdown/changelog/0.11.0.md b/mddocs/markdown/changelog/0.11.0.md deleted file mode 100644 index 2069d081f..000000000 --- a/mddocs/markdown/changelog/0.11.0.md +++ /dev/null @@ -1,202 +0,0 @@ -# 0.11.0 (2024-05-27) - -## Breaking Changes - -There can be some changes in connection behavior, related to version upgrades. So we mark these changes as **breaking** although -most of users will not see any differences. - -- Update Clickhouse JDBC driver to latest version ([#249](https://github.com/MobileTeleSystems/onetl/pull/249)): - : * Package was renamed `ru.yandex.clickhouse:clickhouse-jdbc` → `com.clickhouse:clickhouse-jdbc`. - * Package version changed `0.3.2` → `0.6.0-patch5`. - * Driver name changed `ru.yandex.clickhouse.ClickHouseDriver` → `com.clickhouse.jdbc.ClickHouseDriver`. - - This brings up several fixes for Spark <-> Clickhouse type compatibility, and also Clickhouse clusters support. -- Update other JDBC drivers to latest versions: - : * MSSQL `12.2.0` → `12.6.2` ([#254](https://github.com/MobileTeleSystems/onetl/pull/254)). - * MySQL `8.0.33` → `8.4.0` ([#253](https://github.com/MobileTeleSystems/onetl/pull/253), [#285](https://github.com/MobileTeleSystems/onetl/pull/285)). - * Oracle `23.2.0.0` → `23.4.0.24.05` ([#252](https://github.com/MobileTeleSystems/onetl/pull/252), [#284](https://github.com/MobileTeleSystems/onetl/pull/284)). - * Postgres `42.6.0` → `42.7.3` ([#251](https://github.com/MobileTeleSystems/onetl/pull/251)). -- Update MongoDB connector to latest version: `10.1.1` → `10.3.0` ([#255](https://github.com/MobileTeleSystems/onetl/pull/255), [#283](https://github.com/MobileTeleSystems/onetl/pull/283)). - - This brings up Spark 3.5 support. -- Update `XML` package to latest version: `0.17.0` → `0.18.0` ([#259](https://github.com/MobileTeleSystems/onetl/pull/259)). - - This brings few bugfixes with datetime format handling. -- For JDBC connections add new `SQLOptions` class for `DB.sql(query, options=...)` method ([#272](https://github.com/MobileTeleSystems/onetl/pull/272)). - - Firsly, to keep naming more consistent. - - Secondly, some of options are not supported by `DB.sql(...)` method, but supported by `DBReader`. - For example, `SQLOptions` do not support `partitioning_mode` and require explicit definition of `lower_bound` and `upper_bound` when `num_partitions` is greater than 1. - `ReadOptions` does support `partitioning_mode` and allows skipping `lower_bound` and `upper_bound` values. - - This require some code changes. Before: - ```python - from onetl.connection import Postgres - - postgres = Postgres(...) - df = postgres.sql( - """ - SELECT * - FROM some.mytable - WHERE key = 'something' - """, - options=Postgres.ReadOptions( - partitioning_mode="range", - partition_column="id", - num_partitions=10, - ), - ) - ``` - - After: - ```python - from onetl.connection import Postgres - - postgres = Postgres(...) - df = postgres.sql( - """ - SELECT * - FROM some.mytable - WHERE key = 'something' - """, - options=Postgres.SQLOptions( - # partitioning_mode is not supported! - partition_column="id", - num_partitions=10, - lower_bound=0, # <-- set explicitly - upper_bound=1000, # <-- set explicitly - ), - ) - ``` - - For now, `DB.sql(query, options=...)` can accept `ReadOptions` to keep backward compatibility, but emits deprecation warning. - The support will be removed in `v1.0.0`. -- Split up `JDBCOptions` class into `FetchOptions` and `ExecuteOptions` ([#274](https://github.com/MobileTeleSystems/onetl/pull/274)). - - New classes are used by `DB.fetch(query, options=...)` and `DB.execute(query, options=...)` methods respectively. - This is mostly to keep naming more consistent. - - This require some code changes. Before: - ```python - from onetl.connection import Postgres - - postgres = Postgres(...) - df = postgres.fetch( - "SELECT * FROM some.mytable WHERE key = 'something'", - options=Postgres.JDBCOptions( - fetchsize=1000, - query_timeout=30, - ), - ) - - postgres.execute( - "UPDATE some.mytable SET value = 'new' WHERE key = 'something'", - options=Postgres.JDBCOptions(query_timeout=30), - ) - ``` - - After: - ```python - from onetl.connection import Postgres - - # Using FetchOptions for fetching data - postgres = Postgres(...) - df = postgres.fetch( - "SELECT * FROM some.mytable WHERE key = 'something'", - options=Postgres.FetchOptions( # <-- change class name - fetchsize=1000, - query_timeout=30, - ), - ) - - # Using ExecuteOptions for executing statements - postgres.execute( - "UPDATE some.mytable SET value = 'new' WHERE key = 'something'", - options=Postgres.ExecuteOptions(query_timeout=30), # <-- change class name - ) - ``` - - For now, `DB.fetch(query, options=...)` and `DB.execute(query, options=...)` can accept `JDBCOptions`, to keep backward compatibility, - but emit a deprecation warning. The old class will be removed in `v1.0.0`. -- Serialize `ColumnDatetimeHWM` to Clickhouse’s `DateTime64(6)` (precision up to microseconds) instead of `DateTime` (precision up to seconds) ([#267](https://github.com/MobileTeleSystems/onetl/pull/267)). - - In previous onETL versions, `ColumnDatetimeHWM` value was rounded to the second, and thus reading some rows that were read in previous runs, - producing duplicates. - - For Clickhouse versions below 21.1 comparing column of type `DateTime` with a value of type `DateTime64` is not supported, returning an empty dataframe. - To avoid this, replace: - ```python - DBReader( - ..., - hwm=DBReader.AutoDetectHWM( - name="my_hwm", - expression="hwm_column", # <-- - ), - ) - ``` - - with: - ```python - DBReader( - ..., - hwm=DBReader.AutoDetectHWM( - name="my_hwm", - expression="CAST(hwm_column AS DateTime64)", # <-- add explicit CAST - ), - ) - ``` -- Pass JDBC connection extra params as `properties` dict instead of URL with query part ([#268](https://github.com/MobileTeleSystems/onetl/pull/268)). - - This allows passing custom connection parameters like `Clickhouse(extra={"custom_http_options": "option1=value1,option2=value2"})` - without need to apply urlencode to parameter value, like `option1%3Dvalue1%2Coption2%3Dvalue2`. - -## Features - -Improve user experience with Kafka messages and Database tables with serialized columns, like JSON/XML. - -- Allow passing custom package version as argument for `DB.get_packages(...)` method of several DB connectors: - : * `Clickhouse.get_packages(package_version=..., apache_http_client_version=...)` ([#249](https://github.com/MobileTeleSystems/onetl/pull/249)). - * `MongoDB.get_packages(scala_version=..., spark_version=..., package_version=...)` ([#255](https://github.com/MobileTeleSystems/onetl/pull/255)). - * `MySQL.get_packages(package_version=...)` ([#253](https://github.com/MobileTeleSystems/onetl/pull/253)). - * `MSSQL.get_packages(java_version=..., package_version=...)` ([#254](https://github.com/MobileTeleSystems/onetl/pull/254)). - * `Oracle.get_packages(java_version=..., package_version=...)` ([#252](https://github.com/MobileTeleSystems/onetl/pull/252)). - * `Postgres.get_packages(package_version=...)` ([#251](https://github.com/MobileTeleSystems/onetl/pull/251)). - * `Teradata.get_packages(package_version=...)` ([#256](https://github.com/MobileTeleSystems/onetl/pull/256)). - - Now users can downgrade or upgrade connection without waiting for next onETL release. Previously only `Kafka` and `Greenplum` supported this feature. -- Add `FileFormat.parse_column(...)` method to several classes: - : * `Avro.parse_column(col)` ([#265](https://github.com/MobileTeleSystems/onetl/pull/265)). - * `JSON.parse_column(col, schema=...)` ([#257](https://github.com/MobileTeleSystems/onetl/pull/257)). - * `CSV.parse_column(col, schema=...)` ([#258](https://github.com/MobileTeleSystems/onetl/pull/258)). - * `XML.parse_column(col, schema=...)` ([#269](https://github.com/MobileTeleSystems/onetl/pull/269)). - - This allows parsing data in `value` field of Kafka message or string/binary column of some table as a nested Spark structure. -- Add `FileFormat.serialize_column(...)` method to several classes: - : * `Avro.serialize_column(col)` ([#265](https://github.com/MobileTeleSystems/onetl/pull/265)). - * `JSON.serialize_column(col)` ([#257](https://github.com/MobileTeleSystems/onetl/pull/257)). - * `CSV.serialize_column(col)` ([#258](https://github.com/MobileTeleSystems/onetl/pull/258)). - - This allows saving Spark nested structures or arrays to `value` field of Kafka message or string/binary column of some table. - -## Improvements - -Few documentation improvements. - -- Replace all `assert` in documentation with doctest syntax. This should make documentation more readable ([#273](https://github.com/MobileTeleSystems/onetl/pull/273)). -- Add generic `Troubleshooting` guide ([#275](https://github.com/MobileTeleSystems/onetl/pull/275)). -- Improve Kafka documentation: - : * Add “Prerequisites” page describing different aspects of connecting to Kafka. - * Improve “Reading from” and “Writing to” page of Kafka documentation, add more examples and usage notes. - * Add “Troubleshooting” page ([#276](https://github.com/MobileTeleSystems/onetl/pull/276)). -- Improve Hive documentation: - : * Add “Prerequisites” page describing different aspects of connecting to Hive. - * Improve “Reading from” and “Writing to” page of Hive documentation, add more examples and recommendations. - * Improve “Executing statements in Hive” page of Hive documentation. ([#278](https://github.com/MobileTeleSystems/onetl/pull/278)). -- Add “Prerequisites” page describing different aspects of using SparkHDFS and SparkS3 connectors. ([#279](https://github.com/MobileTeleSystems/onetl/pull/279)). -- Add note about connecting to Clickhouse cluster. ([#280](https://github.com/MobileTeleSystems/onetl/pull/280)). -- Add notes about versions when specific class/method/attribute/argument was added, renamed or changed behavior ([#282](https://github.com/MobileTeleSystems/onetl/pull/282)). - -## Bug Fixes - -- Fix missing `pysmb` package after installing `pip install onetl[files]` . diff --git a/mddocs/markdown/changelog/0.11.1.md b/mddocs/markdown/changelog/0.11.1.md deleted file mode 100644 index 823afe3be..000000000 --- a/mddocs/markdown/changelog/0.11.1.md +++ /dev/null @@ -1,9 +0,0 @@ -# 0.11.1 (2024-05-29) - -## Features - -- Change `MSSQL.port` default from `1433` to `None`, allowing use of `instanceName` to detect port number. ([#287](https://github.com/MobileTeleSystems/onetl/pull/287)) - -## Bug Fixes - -- Remove `fetchsize` from `JDBC.WriteOptions`. ([#288](https://github.com/MobileTeleSystems/onetl/pull/288)) diff --git a/mddocs/markdown/changelog/0.11.2.md b/mddocs/markdown/changelog/0.11.2.md deleted file mode 100644 index 9278d22f8..000000000 --- a/mddocs/markdown/changelog/0.11.2.md +++ /dev/null @@ -1,5 +0,0 @@ -# 0.11.2 (2024-09-02) - -## Bug Fixes - -- Fix passing `Greenplum(extra={"options": ...})` during read/write operations. ([#308](https://github.com/MobileTeleSystems/onetl/pull/308)) diff --git a/mddocs/markdown/changelog/0.12.0.md b/mddocs/markdown/changelog/0.12.0.md deleted file mode 100644 index d212c0062..000000000 --- a/mddocs/markdown/changelog/0.12.0.md +++ /dev/null @@ -1,48 +0,0 @@ -# 0.12.0 (2024-09-03) - -## Breaking Changes - -- Change connection URL used for generating HWM names of S3 and Samba sources: - : * `smb://host:port` -> `smb://host:port/share` - * `s3://host:port` -> `s3://host:port/bucket` ([#304](https://github.com/MobileTeleSystems/onetl/pull/304)) -- Update DB connectors/drivers to latest versions: - : * Clickhouse `0.6.0-patch5` → `0.6.5` - * MongoDB `10.3.0` → `10.4.0` - * MSSQL `12.6.2` → `12.8.1` - * MySQL `8.4.0` → `9.0.0` - * Oracle `23.4.0.24.05` → `23.5.0.24.07` - * Postgres `42.7.3` → `42.7.4` -- Update `Excel` package from `0.20.3` to `0.20.4`, to include Spark 3.5.1 support. ([#306](https://github.com/MobileTeleSystems/onetl/pull/306)) - -## Features - -- Add support for specifying file formats (`ORC`, `Parquet`, `CSV`, etc.) in `HiveWriteOptions.format` ([#292](https://github.com/MobileTeleSystems/onetl/pull/292)): - ```python - Hive.WriteOptions(format=ORC(compression="snappy")) - ``` -- Collect Spark execution metrics in following methods, and log then in DEBUG mode: - : * `DBWriter.run()` - * `FileDFWriter.run()` - * `Hive.sql()` - * `Hive.execute()` - - This is implemented using custom `SparkListener` which wraps the entire method call, and - then report collected metrics. But these metrics sometimes may be missing due to Spark architecture, - so they are not reliable source of information. That’s why logs are printed only in DEBUG mode, and - are not returned as method call result. ([#303](https://github.com/MobileTeleSystems/onetl/pull/303)) -- Generate default `jobDescription` based on currently executed method. Examples: - : * `DBWriter.run(schema.table) -> Postgres[host:5432/database]` - * `MongoDB[localhost:27017/admin] -> DBReader.has_data(mycollection)` - * `Hive[cluster].execute()` - - If user already set custom `jobDescription`, it will left intact. ([#304](https://github.com/MobileTeleSystems/onetl/pull/304)) -- Add log.info about JDBC dialect usage ([#305](https://github.com/MobileTeleSystems/onetl/pull/305)): - ```text - |MySQL| Detected dialect: 'org.apache.spark.sql.jdbc.MySQLDialect' - ``` -- Log estimated size of in-memory dataframe created by `JDBC.fetch` and `JDBC.execute` methods. ([#303](https://github.com/MobileTeleSystems/onetl/pull/303)) - -## Bug Fixes - -- Fix passing `Greenplum(extra={"options": ...})` during read/write operations. ([#308](https://github.com/MobileTeleSystems/onetl/pull/308)) -- Do not raise exception if yield-based hook whas something past (and only one) `yield`. diff --git a/mddocs/markdown/changelog/0.12.1.md b/mddocs/markdown/changelog/0.12.1.md deleted file mode 100644 index 3575634d5..000000000 --- a/mddocs/markdown/changelog/0.12.1.md +++ /dev/null @@ -1,17 +0,0 @@ -# 0.12.1 (2024-10-28) - -## Features - -- Log detected JDBC dialect while using `DBWriter`. - -## Bug Fixes - -- Fix `SparkMetricsRecorder` failing when receiving `SparkListenerTaskEnd` without `taskMetrics` (e.g. executor was killed by OOM). ([#313](https://github.com/MobileTeleSystems/onetl/pull/313)) -- Call `kinit` before checking for HDFS active namenode. -- Wrap `kinit` with `threading.Lock` to avoid multithreading issues. -- Immediately show `kinit` errors to user, instead of hiding them. -- Use `AttributeError` instead of `ImportError` in module’s `__getattr__` method, to make code compliant with Python spec. - -## Doc only Changes - -- Add note about [spark-dialect-extension](https://github.com/MobileTeleSystems/spark-dialect-extension) package to Clickhouse connector documentation. ([#310](https://github.com/MobileTeleSystems/onetl/pull/310)) diff --git a/mddocs/markdown/changelog/0.12.2.md b/mddocs/markdown/changelog/0.12.2.md deleted file mode 100644 index 23a8d383d..000000000 --- a/mddocs/markdown/changelog/0.12.2.md +++ /dev/null @@ -1,18 +0,0 @@ -# 0.12.2 (2024-11-12) - -## Improvements - -- Change Spark `jobDescription` for DBReader & FileDFReader from `DBReader.run() -> Connection` to `Connection -> DBReader.run()`. - -## Bug Fixes - -- Fix `log_hwm` result for `KeyValueIntHWM` (used by Kafka). ([#316](https://github.com/MobileTeleSystems/onetl/pull/316)) -- Fix `log_collection` hiding values of `Kafka.addresses` in logs with `INFO` level. ([#316](https://github.com/MobileTeleSystems/onetl/pull/316)) - -## Dependencies - -- Allow using [etl-entities==2.4.0](https://github.com/MobileTeleSystems/etl-entities/releases/tag/2.4.0). - -## Doc only Changes - -- Fix links to MSSQL date & time type documentation. diff --git a/mddocs/markdown/changelog/0.12.3.md b/mddocs/markdown/changelog/0.12.3.md deleted file mode 100644 index 741c74e1f..000000000 --- a/mddocs/markdown/changelog/0.12.3.md +++ /dev/null @@ -1,5 +0,0 @@ -# 0.12.3 (2024-11-22) - -## Bug Fixes - -- Allow passing table names in format `schema."table.with.dots"` to `DBReader(source=...)` and `DBWriter(target=...)`. diff --git a/mddocs/markdown/changelog/0.12.4.md b/mddocs/markdown/changelog/0.12.4.md deleted file mode 100644 index 3ebc57a87..000000000 --- a/mddocs/markdown/changelog/0.12.4.md +++ /dev/null @@ -1,5 +0,0 @@ -# 0.12.4 (2024-11-27) - -## Bug Fixes - -- Fix `DBReader(conn=oracle, options={"partitioning_mode": "hash"})` lead to data skew in last partition due to wrong `ora_hash` usage. ([#319](https://github.com/MobileTeleSystems/onetl/pull/319)) diff --git a/mddocs/markdown/changelog/0.12.5.md b/mddocs/markdown/changelog/0.12.5.md deleted file mode 100644 index c542a50fd..000000000 --- a/mddocs/markdown/changelog/0.12.5.md +++ /dev/null @@ -1,13 +0,0 @@ -# 0.12.5 (2024-12-03) - -## Improvements - -- Use `sipHash64` instead of `md5` in Clickhouse for reading data with `{"partitioning_mode": "hash"}`, as it is 5 times faster. -- Use `hashtext` instead of `md5` in Postgres for reading data with `{"partitioning_mode": "hash"}`, as it is 3-5 times faster. -- Use `BINARY_CHECKSUM` instead of `HASHBYTES` in MSSQL for reading data with `{"partitioning_mode": "hash"}`, as it is 5 times faster. - -## Big fixes - -- In JDBC sources wrap `MOD(partitionColumn, numPartitions)` with `ABS(...)` to make al returned values positive. This prevents data skew. -- Fix reading table data from MSSQL using `{"partitioning_mode": "hash"}` with `partitionColumn` of integer type. -- Fix reading table data from Postgres using `{"partitioning_mode": "hash"}` lead to data skew (all the data was read into one Spark partition). diff --git a/mddocs/markdown/changelog/0.13.0.md b/mddocs/markdown/changelog/0.13.0.md deleted file mode 100644 index 0b7256299..000000000 --- a/mddocs/markdown/changelog/0.13.0.md +++ /dev/null @@ -1,197 +0,0 @@ -# 0.13.0 (2025-02-24) - -🎉 3 years since first release 0.1.0 🎉 - -## Breaking Changes - -- Add Python 3.13. support. ([#298](https://github.com/MobileTeleSystems/onetl/pull/298)) -- Change the logic of `FileConnection.walk` and `FileConnection.list_dir`. ([#327](https://github.com/MobileTeleSystems/onetl/pull/327)) - - Previously `limits.stops_at(path) == True` considered as “return current file and stop”, and could lead to exceeding some limit. - Not it means “stop immediately”. -- Change default value for `FileDFWriter.Options(if_exists=...)` from `error` to `append`, - to make it consistent with other `.Options()` classes within onETL. ([#343](https://github.com/MobileTeleSystems/onetl/pull/343)) - -## Features - -- Add support for `FileModifiedTimeHWM` HWM class (see [etl-entities 2.5.0](https://github.com/MobileTeleSystems/etl-entities/releases/tag/2.5.0)): - ```python - from etl_entitites.hwm import FileModifiedTimeHWM - from onetl.file import FileDownloader - from onetl.strategy import IncrementalStrategy - - downloader = FileDownloader( - ..., - hwm=FileModifiedTimeHWM(name="somename"), - ) - - with IncrementalStrategy(): - downloader.run() - ``` -- Introduce `FileSizeRange(min=..., max=...)` filter class. ([#325](https://github.com/MobileTeleSystems/onetl/pull/325)) - - Now users can set `FileDownloader` / `FileMover` to download/move only files with specific file size range: - ```python - from onetl.file import FileDownloader - from onetl.file.filter import FileSizeRange - - downloader = FileDownloader( - ..., - filters=[FileSizeRange(min="10KiB", max="1GiB")], - ) - ``` -- Introduce `TotalFilesSize(...)` limit class. ([#326](https://github.com/MobileTeleSystems/onetl/pull/326)) - - Now users can set `FileDownloader` / `FileMover` to stop downloading/moving files after reaching a certain amount of data: - ```python - from datetime import datetime, timedelta - from onetl.file import FileDownloader - from onetl.file.limit import TotalFilesSize - - downloader = FileDownloader( - ..., - limits=[TotalFilesSize("1GiB")], - ) - ``` -- Implement `FileModifiedTime(since=..., until=...)` file filter. ([#330](https://github.com/MobileTeleSystems/onetl/pull/330)) - - Now users can set `FileDownloader` / `FileMover` to download/move only files with specific file modification time: - ```python - from datetime import datetime, timedelta - from onetl.file import FileDownloader - from onetl.file.filter import FileModifiedTime - - downloader = FileDownloader( - ..., - filters=[FileModifiedTime(before=datetime.now() - timedelta(hours=1))], - ) - ``` -- Add `SparkS3.get_exclude_packages()` and `Kafka.get_exclude_packages()` methods. ([#341](https://github.com/MobileTeleSystems/onetl/pull/341)) - - Using them allows to skip downloading dependencies not required by this specific connector, or which are already a part of Spark/PySpark: - ```python - from onetl.connection import SparkS3, Kafka - - maven_packages = [ - *SparkS3.get_packages(spark_version="3.5.4"), - *Kafka.get_packages(spark_version="3.5.4"), - ] - exclude_packages = SparkS3.get_exclude_packages() + Kafka.get_exclude_packages() - spark = ( - SparkSession.builder.appName("spark_app_onetl_demo") - .config("spark.jars.packages", ",".join(maven_packages)) - .config("spark.jars.excludes", ",".join(exclude_packages)) - .getOrCreate() - ) - ``` - -## Improvements - -- All DB connections opened by `JDBC.fetch(...)`, `JDBC.execute(...)` or `JDBC.check()` - are immediately closed after the statements is executed. ([#334](https://github.com/MobileTeleSystems/onetl/pull/334)) - - Previously Spark session with `master=local[3]` actually opened up to 5 connections to target DB - one for `JDBC.check()`, - another for Spark driver interaction with DB to create tables, and one for each Spark executor. Now only max 4 connections are opened, - as `JDBC.check()` does not hold opened connection. - - This is important for RDBMS like Postgres or Greenplum where number of connections is strictly limited and limit is usually quite low. -- Set up `ApplicationName` (client info) for Clickhouse, MongoDB, MSSQL, MySQL and Oracle. ([#339](https://github.com/MobileTeleSystems/onetl/pull/339), [#248](https://github.com/MobileTeleSystems/onetl/pull/248)) - - Also update `ApplicationName` format for Greenplum, Postgres, Kafka and SparkS3. - Now all connectors have the same `ApplicationName` format: `${spark.applicationId} ${spark.appName} onETL/${onetl.version} Spark/${spark.version}` - - The only connections not sending `ApplicationName` are Teradata and FileConnection implementations. -- Now `DB.check()` will test connection availability not only on Spark driver, but also from some Spark executor. ([#346](https://github.com/MobileTeleSystems/onetl/pull/346)) - - This allows to fail immediately if Spark driver host has network access to target DB, but Spark executors have not. - - #### NOTE - Now `Greenplum.check()` requires the same user grants as `DBReader(connection=greenplum)`: - ```sql - -- yes, "writable" for reading data from GP, it's not a mistake - ALTER USER username CREATEEXTTABLE(type = 'writable', protocol = 'gpfdist'); - - -- for both reading and writing to GP - -- ALTER USER username CREATEEXTTABLE(type = 'readable', protocol = 'gpfdist') CREATEEXTTABLE(type = 'writable', protocol = 'gpfdist'); - ``` - - Please ask your Greenplum administrators to provide these grants. - -## Bug Fixes - -- Avoid suppressing Hive Metastore errors while using `DBWriter`. ([#329](https://github.com/MobileTeleSystems/onetl/pull/329)) - - Previously this was implemented as: - ```python - try: - spark.sql(f"SELECT * FROM {table}") - table_exists = True - except Exception: - table_exists = False - ``` - - If Hive Metastore was overloaded and responded with an exception, it was considered as non-existing table, resulting - to full table override instead of append or override only partitions subset. -- Fix using onETL to write data to PostgreSQL or Greenplum instances behind *pgbouncer* with `pool_mode=transaction`. ([#336](https://github.com/MobileTeleSystems/onetl/pull/336)) - - Previously `Postgres.check()` opened a read-only transaction, pgbouncer changed the entire connection type from read-write to read-only, - and when `DBWriter.run(df)` executed in read-only connection, producing errors like: - ```default - org.postgresql.util.PSQLException: ERROR: cannot execute INSERT in a read-only transaction - org.postgresql.util.PSQLException: ERROR: cannot execute TRUNCATE TABLE in a read-only transaction - ``` - - Added a workaround by passing `readOnly=True` to JDBC params for read-only connections, so pgbouncer may differ read-only and read-write connections properly. - - After upgrading onETL 0.13.x or higher the same error still may appear of pgbouncer still holds read-only connections and returns them for DBWriter. - To this this, user can manually convert read-only connection to read-write: - ```python - postgres.execute("BEGIN READ WRITE;") # <-- add this line - DBWriter(...).run() - ``` - - After all connections in pgbouncer pool were converted from read-only to read-write, and error fixed, this additional line could be removed. - - See [Postgres JDBC driver documentation](https://jdbc.postgresql.org/documentation/use/). -- Fix `MSSQL.fetch(...)` and `MySQL.fetch(...)` opened a read-write connection instead of read-only. ([#337](https://github.com/MobileTeleSystems/onetl/pull/337)) - - Now this is fixed: - : * `MSSQL.fetch(...)` establishes connection with `ApplicationIntent=ReadOnly`. - * `MySQL.fetch(...)` calls `SET SESSION TRANSACTION READ ONLY` statement. -- Fixed passing multiple filters to `FileDownloader` and `FileMover`. ([#338](https://github.com/MobileTeleSystems/onetl/pull/338)) - If was caused by sorting filters list in internal logging method, but `FileFilter` subclasses are not sortable. -- Fix a false warning about a lof of parallel connections to Grenplum. ([#342](https://github.com/MobileTeleSystems/onetl/pull/342)) - - Creating Spark session with `.master("local[5]")` may open up to 6 connections to Greenplum (=number of Spark executors + 1 for driver), - but onETL instead used number of *CPU cores* on the host as a number of parallel connections. - - This lead to showing a false warning that number of Greenplum connections is too high, - which actually should be the case only if number of executors is higher than 30. -- Fix MongoDB trying to use current database name as `authSource`. ([#347](https://github.com/MobileTeleSystems/onetl/pull/347)) - - Use default connector value which is `admin` database. Previous onETL versions could be fixed by: - ```python - from onetl.connection import MongoDB - - mongodb = MongoDB( - ..., - database="mydb", - extra={ - "authSource": "admin", - }, - ) - ``` - -## Dependencies - -- Minimal `etl-entities` version is now [2.5.0](https://github.com/MobileTeleSystems/etl-entities/releases/tag/2.5.0). ([#331](https://github.com/MobileTeleSystems/onetl/pull/331)) -- Update DB connectors/drivers to latest versions: ([#345](https://github.com/MobileTeleSystems/onetl/pull/345)) - : * Clickhouse `0.6.5` → `0.7.2` - * MongoDB `10.4.0` → `10.4.1` - * MySQL `9.0.0` → `9.2.0` - * Oracle `23.5.0.24.07` → `23.7.0.25.01` - * Postgres `42.7.4` → `42.7.5` - -## Doc only Changes - -- Split large code examples to tabs. ([#344](https://github.com/MobileTeleSystems/onetl/pull/344)) diff --git a/mddocs/markdown/changelog/0.13.1.md b/mddocs/markdown/changelog/0.13.1.md deleted file mode 100644 index e8eb120d2..000000000 --- a/mddocs/markdown/changelog/0.13.1.md +++ /dev/null @@ -1,9 +0,0 @@ -# 0.13.1 (2025-03-06) - -## Bug Fixes - -In 0.13.0, using `DBWriter(connection=hive, target="SOMEDB.SOMETABLE")` lead to executing `df.write.saveAsTable()` -instead of `df.write.insertInto()` if target table `somedb.sometable` already exist. - -This is caused by table name normalization (Hive uses lower-case names), which wasn’t properly handled by method used for checking table existence. -([#350](https://github.com/MobileTeleSystems/onetl/pull/350)) diff --git a/mddocs/markdown/changelog/0.13.3.md b/mddocs/markdown/changelog/0.13.3.md deleted file mode 100644 index 1aa289b49..000000000 --- a/mddocs/markdown/changelog/0.13.3.md +++ /dev/null @@ -1,5 +0,0 @@ -# 0.13.3 (2025-03-11) - -## Dependencies - -Allow using [etl-entities 2.6.0](https://github.com/MobileTeleSystems/etl-entities/releases/tag/2.6.0). diff --git a/mddocs/markdown/changelog/0.13.4.md b/mddocs/markdown/changelog/0.13.4.md deleted file mode 100644 index 10f695e0c..000000000 --- a/mddocs/markdown/changelog/0.13.4.md +++ /dev/null @@ -1,10 +0,0 @@ -# 0.13.4 (2025-03-20) - -## Doc only Changes - -- Prefer `ReadOptions(partitionColumn=..., numPartitions=..., queryTimeout=...)` - instead of `ReadOptions(partition_column=..., num_partitions=..., query_timeout=...)`, - to match Spark documentation. ([#352](https://github.com/MobileTeleSystems/onetl/pull/352)) -- Prefer `WriteOptions(if_exists=...)` instead of `WriteOptions(mode=...)` for IDE suggestions. ([#354](https://github.com/MobileTeleSystems/onetl/pull/354)) -- Document all options of supported file formats. - ([#355](https://github.com/MobileTeleSystems/onetl/pull/355), [#356](https://github.com/MobileTeleSystems/onetl/pull/356), [#357](https://github.com/MobileTeleSystems/onetl/pull/357), [#358](https://github.com/MobileTeleSystems/onetl/pull/358), [#359](https://github.com/MobileTeleSystems/onetl/pull/359), [#360](https://github.com/MobileTeleSystems/onetl/pull/360), [#361](https://github.com/MobileTeleSystems/onetl/pull/361), [#362](https://github.com/MobileTeleSystems/onetl/pull/362)) diff --git a/mddocs/markdown/changelog/0.7.0.md b/mddocs/markdown/changelog/0.7.0.md deleted file mode 100644 index 3f896b82b..000000000 --- a/mddocs/markdown/changelog/0.7.0.md +++ /dev/null @@ -1,211 +0,0 @@ -# 0.7.0 (2023-05-15) - -## 🎉 onETL is now open source 🎉 - -That was long road, but we finally did it! - -## Breaking Changes - -* Changed installation method. - - **TL;DR What should I change to restore previous behavior** - - Simple way: - - | onETL < 0.7.0 | onETL >= 0.7.0 | - |-------------------|-----------------------------------| - | pip install onetl | pip install onetl[files,kerberos] | - - Right way - enumerate connectors should be installed: - ```bash - pip install onetl[hdfs,ftp,kerberos] # except DB connections - ``` - - **Details** - - In onetl<0.7 the package installation looks like: - ```bash - pip install onetl - ``` - - But this includes all dependencies for all connectors, even if user does not use them. - This caused some issues, for example user had to install Kerberos libraries to be able to install onETL, even if user uses only S3 (without Kerberos support). - - Since 0.7.0 installation process was changed: - ```bash - pip install onetl # minimal installation, only onETL core - # there is no extras for DB connections because they are using Java packages which are installed in runtime - - pip install onetl[ftp,ftps,hdfs,sftp,s3,webdav] # install dependencies for specified file connections - pip install onetl[files] # install dependencies for all file connections - - pip install onetl[kerberos] # Kerberos auth support - pip install onetl[spark] # install PySpark to use DB connections - - pip install onetl[spark,kerberos,files] # all file connections + Kerberos + PySpark - pip install onetl[all] # alias for previous case - ``` - - There are corresponding documentation items for each extras. - - Also onETL checks that some requirements are missing, and raises exception with recommendation how to install them: - ```text - Cannot import module "pyspark". - - Since onETL v0.7.0 you should install package as follows: - pip install onetl[spark] - - or inject PySpark to sys.path in some other way BEFORE creating MongoDB instance. - ``` - - ```text - Cannot import module "ftputil". - - Since onETL v0.7.0 you should install package as follows: - pip install onetl[ftp] - - or - pip install onetl[files] - ``` -* Added new `cluster` argument to `Hive` and `HDFS` connections. - - `Hive` qualified name (used in HWM) contains cluster name. But in onETL<0.7.0 cluster name had hard coded value `rnd-dwh` which was not OK for some users. - - `HDFS` connection qualified name contains host (active namenode of Hadoop cluster), but its value can change over time, leading to creating of new HWM. - - Since onETL 0.7.0 both `Hive` and `HDFS` connections have `cluster` attribute which can be set to a specific cluster name. - For `Hive` it is mandatory, for `HDFS` it can be omitted (using host as a fallback). - - But passing cluster name every time could lead to errors. - - Now `Hive` and `HDFS` have nested class named `slots` with methods: - * `normalize_cluster_name` - * `get_known_clusters` - * `get_current_cluster` - * `normalize_namenode_host` (only `HDFS`) - * `get_cluster_namenodes` (only `HDFS`) - * `get_webhdfs_port` (only `HDFS`) - * `is_namenode_active` (only `HDFS`) - - And new method `HDFS.get_current` / `Hive.get_current`. - - Developers can implement hooks validating user input or substituting values for automatic cluster detection. - This should improve user experience while using these connectors. - - See slots documentation. -* Update JDBC connection drivers. - * Greenplum `2.1.3` → `2.1.4`. - * MSSQL `10.2.1.jre8` → `12.2.0.jre8`. Minimal supported version of MSSQL is now 2014 instead 2021. - * MySQL `8.0.30` → `8.0.33`: - \* Package was renamed `mysql:mysql-connector-java` → `com.mysql:mysql-connector-j`. - \* Driver class was renamed `com.mysql.jdbc.Driver` → `com.mysql.cj.jdbc.Driver`. - * Oracle `21.6.0.0.1` → `23.2.0.0`. - * Postgres `42.4.0` → `42.6.0`. - * Teradata `17.20.00.08` → `17.20.00.15`: - \* Package was renamed `com.teradata.jdbc:terajdbc4` → `com.teradata.jdbc:terajdbc`. - \* Teradata driver is now published to Maven. - - See [#31](https://github.com/MobileTeleSystems/onetl/pull/31). - -## Features - -* Added MongoDB connection. - - Using official [MongoDB connector for Spark v10](https://www.mongodb.com/docs/spark-connector/current/). Only Spark 3.2+ is supported. - - There are some differences between MongoDB and other database sources: - * Instead of `mongodb.sql` method there is `mongodb.pipeline`. - * No methods `mongodb.fetch` and `mongodb.execute`. - * `DBReader.hint` and `DBReader.where` have different types than in SQL databases: - - ```python - where = { - "col1": { - "$eq": 10, - }, - } - - hint = { - "col1": 1, - } - ``` - - * Because MongoDB does not have schemas of collections, but Spark cannot create dataframe with dynamic schema, new option `DBReader.df_schema` was introduced. - It is mandatory for MongoDB, but optional for other sources. - * Currently DBReader cannot be used with MongoDB and hwm expression, e.g. `hwm_column=("mycolumn", {"$cast": {"col1": "date"}})` - - Because there are no tables in MongoDB, some options were renamed in core classes: - * `DBReader(table=...)` → `DBReader(source=...)` - * `DBWriter(table=...)` → `DBWriter(target=...)` - - Old names can be used too, they are not deprecated ([#30](https://github.com/MobileTeleSystems/onetl/pull/30)). -* Added option for disabling some plugins during import. - - Previously if some plugin were failing during the import, the only way to import onETL would be to disable all plugins - using environment variable. - - Now there are several variables with different behavior: - * `ONETL_PLUGINS_ENABLED=false` - disable all plugins autoimport. Previously it was named `ONETL_ENABLE_PLUGINS`. - * `ONETL_PLUGINS_BLACKLIST=plugin-name,another-plugin` - set list of plugins which should NOT be imported automatically. - * `ONETL_PLUGINS_WHITELIST=plugin-name,another-plugin` - set list of plugins which should ONLY be imported automatically. - - Also we improved exception message with recommendation how to disable a failing plugin: - ```text - Error while importing plugin 'mtspark' from package 'mtspark' v4.0.0. - - Statement: - import mtspark.onetl - - Check if plugin is compatible with current onETL version 0.7.0. - - You can disable loading this plugin by setting environment variable: - ONETL_PLUGINS_BLACKLIST='mtspark,failing-plugin' - - You can also define a whitelist of packages which can be loaded by onETL: - ONETL_PLUGINS_WHITELIST='not-failing-plugin1,not-failing-plugin2' - - Please take into account that plugin name may differ from package or module name. - See package metadata for more details - ``` - -## Improvements - -* Added compatibility with Python 3.11 and PySpark 3.4.0. - - File connections were OK, but `jdbc.fetch` and `jdbc.execute` were failing. Fixed in [#28](https://github.com/MobileTeleSystems/onetl/pull/28). -* Added check for missing Java packages. - - Previously if DB connection tried to use some Java class which were not loaded into Spark version, it raised an exception - with long Java stacktrace. Most users failed to interpret this trace. - - Now onETL shows the following error message: - ```text - |Spark| Cannot import Java class 'com.mongodb.spark.sql.connector.MongoTableProvider'. - - It looks like you've created Spark session without this option: - SparkSession.builder.config("spark.jars.packages", MongoDB.package_spark_3_2) - - Please call `spark.stop()`, restart the interpreter, - and then create new SparkSession with proper options. - ``` -* Documentation improvements. - * Changed documentation site theme - using [furo](https://github.com/pradyunsg/furo) - instead of default [ReadTheDocs](https://github.com/readthedocs/sphinx_rtd_theme). - - New theme supports wide screens and dark mode. - See [#10](https://github.com/MobileTeleSystems/onetl/pull/10). - * Now each connection class have compatibility table for Spark + Java + Python. - * Added global compatibility table for Spark + Java + Python + Scala. - -## Bug Fixes - -* Fixed several SFTP issues. - * If SSH config file `~/.ssh/config` contains some options not recognized by Paramiko (unknown syntax, unknown option name), - previous versions were raising exception until fixing or removing this file. Since 0.7.0 exception is replaced with warning. - * If user passed `host_key_check=False` but server changed SSH keys, previous versions raised exception until new key is accepted. - Since 0.7.0 exception is replaced with warning if option value is `False`. - - Fixed in [#19](https://github.com/MobileTeleSystems/onetl/pull/19). -* Fixed several S3 issues. - - There was a bug in S3 connection which prevented handling files in the root of a bucket - they were invisible for the connector. Fixed in [#29](https://github.com/MobileTeleSystems/onetl/pull/29). diff --git a/mddocs/markdown/changelog/0.7.1.md b/mddocs/markdown/changelog/0.7.1.md deleted file mode 100644 index fc423f756..000000000 --- a/mddocs/markdown/changelog/0.7.1.md +++ /dev/null @@ -1,31 +0,0 @@ -# 0.7.1 (2023-05-23) - -## Bug Fixes - -* Fixed `setup_logging` function. - - In onETL==0.7.0 calling `onetl.log.setup_logging()` broke the logging: - ```text - Traceback (most recent call last): - File "/opt/anaconda/envs/py39/lib/python3.9/logging/__init__.py", line 434, in format - return self._format(record) - File "/opt/anaconda/envs/py39/lib/python3.9/logging/__init__.py", line 430, in _format - return self._fmt % record.dict - KeyError: 'levelname:8s' - ``` -* Fixed installation examples. - - In onETL==0.7.0 there are examples of installing onETL with extras: - ```bash - pip install onetl[files, kerberos, spark] - ``` - - But pip fails to install such package: - ```text - ERROR: Invalid requirement: 'onet[files,' - ``` - - This is because of spaces in extras clause. Fixed: - ```bash - pip install onetl[files,kerberos,spark] - ``` diff --git a/mddocs/markdown/changelog/0.7.2.md b/mddocs/markdown/changelog/0.7.2.md deleted file mode 100644 index 80bb2cfc5..000000000 --- a/mddocs/markdown/changelog/0.7.2.md +++ /dev/null @@ -1,33 +0,0 @@ -# 0.7.2 (2023-05-24) - -## Dependencies - -* Limited `typing-extensions` version. - - `typing-extensions==4.6.0` release contains some breaking changes causing errors like: - ```text - Traceback (most recent call last): - File "/Users/project/lib/python3.9/typing.py", line 852, in __subclasscheck__ - return issubclass(cls, self.__origin__) - TypeError: issubclass() arg 1 must be a class - ``` - - `typing-extensions==4.6.1` was causing another error: - ```text - Traceback (most recent call last): - File "/home/maxim/Repo/typing_extensions/1.py", line 33, in - isinstance(file, ContainsException) - File "/home/maxim/Repo/typing_extensions/src/typing_extensions.py", line 599, in __instancecheck__ - if super().__instancecheck__(instance): - File "/home/maxim/.pyenv/versions/3.7.8/lib/python3.7/abc.py", line 139, in __instancecheck__ - return _abc_instancecheck(cls, instance) - File "/home/maxim/Repo/typing_extensions/src/typing_extensions.py", line 583, in __subclasscheck__ - return super().__subclasscheck__(other) - File "/home/maxim/.pyenv/versions/3.7.8/lib/python3.7/abc.py", line 143, in __subclasscheck__ - return _abc_subclasscheck(cls, subclass) - File "/home/maxim/Repo/typing_extensions/src/typing_extensions.py", line 661, in _proto_hook - and other._is_protocol - AttributeError: type object 'PathWithFailure' has no attribute '_is_protocol' - ``` - - We updated requirements with `typing-extensions<4.6` until fixing compatibility issues. diff --git a/mddocs/markdown/changelog/0.8.0.md b/mddocs/markdown/changelog/0.8.0.md deleted file mode 100644 index 3fe438549..000000000 --- a/mddocs/markdown/changelog/0.8.0.md +++ /dev/null @@ -1,137 +0,0 @@ -# 0.8.0 (2023-05-31) - -## Breaking Changes - -- Rename methods of `FileConnection` classes: - * `get_directory` → `resolve_dir` - * `get_file` → `resolve_file` - * `listdir` → `list_dir` - * `mkdir` → `create_dir` - * `rmdir` → `remove_dir` - - New naming should be more consistent. - - They were undocumented in previous versions, but someone could use these methods, so this is a breaking change. ([#36](https://github.com/MobileTeleSystems/onetl/pull/36)) -- Deprecate `onetl.core.FileFilter` class, replace it with new classes: - * `onetl.file.filter.Glob` - * `onetl.file.filter.Regexp` - * `onetl.file.filter.ExcludeDir` - - Old class will be removed in v1.0.0. ([#43](https://github.com/MobileTeleSystems/onetl/pull/43)) -- Deprecate `onetl.core.FileLimit` class, replace it with new class `onetl.file.limit.MaxFilesCount`. - - Old class will be removed in v1.0.0. ([#44](https://github.com/MobileTeleSystems/onetl/pull/44)) -- Change behavior of `BaseFileLimit.reset` method. - - This method should now return `self` instead of `None`. - Return value could be the same limit object or a copy, this is an implementation detail. ([#44](https://github.com/MobileTeleSystems/onetl/pull/44)) -- Replaced `FileDownloader.filter` and `.limit` with new options `.filters` and `.limits`: - ```python - FileDownloader( - ..., - filter=FileFilter(glob="*.txt", exclude_dir="/path"), - limit=FileLimit(count_limit=10), - ) - ``` - - ```python - FileDownloader( - ..., - filters=[Glob("*.txt"), ExcludeDir("/path")], - limits=[MaxFilesCount(10)], - ) - ``` - - This allows to developers to implement their own filter and limit classes, and combine them with existing ones. - - Old behavior still supported, but it will be removed in v1.0.0. ([#45](https://github.com/MobileTeleSystems/onetl/pull/45)) -- Removed default value for `FileDownloader.limits`, user should pass limits list explicitly. ([#45](https://github.com/MobileTeleSystems/onetl/pull/45)) -- Move classes from module `onetl.core`: - ```python - from onetl.core import DBReader - from onetl.core import DBWriter - from onetl.core import FileDownloader - from onetl.core import FileUploader - ``` - - with new modules `onetl.db` and `onetl.file`: - ```python - from onetl.db import DBReader - from onetl.db import DBWriter - - from onetl.file import FileDownloader - from onetl.file import FileUploader - ``` - - Imports from old module `onetl.core` still can be used, but marked as deprecated. Module will be removed in v1.0.0. ([#46](https://github.com/MobileTeleSystems/onetl/pull/46)) - -## Features - -- Add `rename_dir` method. - - Method was added to following connections: - * `FTP` - * `FTPS` - * `HDFS` - * `SFTP` - * `WebDAV` - - It allows to rename/move directory to new path with all its content. - - `S3` does not have directories, so there is no such method in that class. ([#40](https://github.com/MobileTeleSystems/onetl/pull/40)) -- Add `onetl.file.FileMover` class. - - It allows to move files between directories of remote file system. - Signature is almost the same as in `FileDownloader`, but without HWM support. ([#42](https://github.com/MobileTeleSystems/onetl/pull/42)) - -## Improvements - -- Document all public methods in `FileConnection` classes: - * `download_file` - * `resolve_dir` - * `resolve_file` - * `get_stat` - * `is_dir` - * `is_file` - * `list_dir` - * `create_dir` - * `path_exists` - * `remove_file` - * `rename_file` - * `remove_dir` - * `upload_file` - * `walk` ([#39](https://github.com/MobileTeleSystems/onetl/pull/39)) -- Update documentation of `check` method of all connections - add usage example and document result type. ([#39](https://github.com/MobileTeleSystems/onetl/pull/39)) -- Add new exception type `FileSizeMismatchError`. - - Methods `connection.download_file` and `connection.upload_file` now raise new exception type instead of `RuntimeError`, - if target file after download/upload has different size than source. ([#39](https://github.com/MobileTeleSystems/onetl/pull/39)) -- Add new exception type `DirectoryExistsError` - it is raised if target directory already exists. ([#40](https://github.com/MobileTeleSystems/onetl/pull/40)) -- Improved `FileDownloader` / `FileUploader` exception logging. - - If `DEBUG` logging is enabled, print exception with stacktrace instead of - printing only exception message. ([#42](https://github.com/MobileTeleSystems/onetl/pull/42)) -- Updated documentation of `FileUploader`. - * Class does not support read strategies, added note to documentation. - * Added examples of using `run` method with explicit files list passing, both absolute and relative paths. - * Fix outdated imports and class names in examples. ([#42](https://github.com/MobileTeleSystems/onetl/pull/42)) -- Updated documentation of `DownloadResult` class - fix outdated imports and class names. ([#42](https://github.com/MobileTeleSystems/onetl/pull/42)) -- Improved file filters documentation section. - - Document interface class `onetl.base.BaseFileFilter` and function `match_all_filters`. ([#43](https://github.com/MobileTeleSystems/onetl/pull/43)) -- Improved file limits documentation section. - - Document interface class `onetl.base.BaseFileLimit` and functions `limits_stop_at` / `limits_reached` / `reset_limits`. ([#44](https://github.com/MobileTeleSystems/onetl/pull/44)) -- Added changelog. - - Changelog is generated from separated news files using [towncrier](https://pypi.org/project/towncrier/). ([#47](https://github.com/MobileTeleSystems/onetl/pull/47)) - -## Misc - -- Improved CI workflow for tests. - * If developer haven’t changed source core of a specific connector or its dependencies, - run tests only against maximum supported versions of Spark, Python, Java and db/file server. - * If developed made some changes in a specific connector, or in core classes, or in dependencies, - run tests for both minimal and maximum versions. - * Once a week run all aganst for minimal and latest versions to detect breaking changes in dependencies - * Minimal tested Spark version is 2.3.1 instead on 2.4.8. ([#32](https://github.com/MobileTeleSystems/onetl/pull/32)) diff --git a/mddocs/markdown/changelog/0.8.1.md b/mddocs/markdown/changelog/0.8.1.md deleted file mode 100644 index d3bd32564..000000000 --- a/mddocs/markdown/changelog/0.8.1.md +++ /dev/null @@ -1,34 +0,0 @@ -# 0.8.1 (2023-07-10) - -## Features - -- Add `@slot` decorator to public methods of: - * `DBConnection` - * `FileConnection` - * `DBReader` - * `DBWriter` - * `FileDownloader` - * `FileUploader` - * `FileMover` ([#49](https://github.com/MobileTeleSystems/onetl/pull/49)) -- Add `workers` field to `FileDownloader` / `FileUploader` / `FileMover`. `Options` classes. - - This allows to speed up all file operations using parallel threads. ([#57](https://github.com/MobileTeleSystems/onetl/pull/57)) - -## Improvements - -- Add documentation for HWM store `.get` and `.save` methods. ([#49](https://github.com/MobileTeleSystems/onetl/pull/49)) -- Improve Readme: - * Move `Quick start` section from documentation - * Add `Non-goals` section - * Fix code blocks indentation ([#50](https://github.com/MobileTeleSystems/onetl/pull/50)) -- Improve Contributing guide: - * Move `Develop` section from Readme - * Move `docs/changelog/README.rst` content - * Add `Limitations` section - * Add instruction of creating a fork and building documentation ([#50](https://github.com/MobileTeleSystems/onetl/pull/50)) -- Remove duplicated checks for source file existence in `FileDownloader` / `FileMover`. ([#57](https://github.com/MobileTeleSystems/onetl/pull/57)) -- Update default logging format to include thread name. ([#57](https://github.com/MobileTeleSystems/onetl/pull/57)) - -## Bug Fixes - -- Fix `S3.list_dir('/')` returns empty list on latest Minio version. ([#58](https://github.com/MobileTeleSystems/onetl/pull/58)) diff --git a/mddocs/markdown/changelog/0.9.0.md b/mddocs/markdown/changelog/0.9.0.md deleted file mode 100644 index b2d10f257..000000000 --- a/mddocs/markdown/changelog/0.9.0.md +++ /dev/null @@ -1,107 +0,0 @@ -# 0.9.0 (2023-08-17) - -## Breaking Changes - -- Rename methods: - * `DBConnection.read_df` → `DBConnection.read_source_as_df` - * `DBConnection.write_df` → `DBConnection.write_df_to_target` ([#66](https://github.com/MobileTeleSystems/onetl/pull/66)) -- Rename classes: - * `HDFS.slots` → `HDFS.Slots` - * `Hive.slots` → `Hive.Slots` - - Old names are left intact, but will be removed in v1.0.0 ([#103](https://github.com/MobileTeleSystems/onetl/pull/103)) -- Rename options to make them self-explanatory: - * `Hive.WriteOptions(mode="append")` → `Hive.WriteOptions(if_exists="append")` - * `Hive.WriteOptions(mode="overwrite_table")` → `Hive.WriteOptions(if_exists="replace_entire_table")` - * `Hive.WriteOptions(mode="overwrite_partitions")` → `Hive.WriteOptions(if_exists="replace_overlapping_partitions")` - * `JDBC.WriteOptions(mode="append")` → `JDBC.WriteOptions(if_exists="append")` - * `JDBC.WriteOptions(mode="overwrite")` → `JDBC.WriteOptions(if_exists="replace_entire_table")` - * `Greenplum.WriteOptions(mode="append")` → `Greenplum.WriteOptions(if_exists="append")` - * `Greenplum.WriteOptions(mode="overwrite")` → `Greenplum.WriteOptions(if_exists="replace_entire_table")` - * `MongoDB.WriteOptions(mode="append")` → `Greenplum.WriteOptions(if_exists="append")` - * `MongoDB.WriteOptions(mode="overwrite")` → `Greenplum.WriteOptions(if_exists="replace_entire_collection")` - * `FileDownloader.Options(mode="error")` → `FileDownloader.Options(if_exists="error")` - * `FileDownloader.Options(mode="ignore")` → `FileDownloader.Options(if_exists="ignore")` - * `FileDownloader.Options(mode="overwrite")` → `FileDownloader.Options(if_exists="replace_file")` - * `FileDownloader.Options(mode="delete_all")` → `FileDownloader.Options(if_exists="replace_entire_directory")` - * `FileUploader.Options(mode="error")` → `FileUploader.Options(if_exists="error")` - * `FileUploader.Options(mode="ignore")` → `FileUploader.Options(if_exists="ignore")` - * `FileUploader.Options(mode="overwrite")` → `FileUploader.Options(if_exists="replace_file")` - * `FileUploader.Options(mode="delete_all")` → `FileUploader.Options(if_exists="replace_entire_directory")` - * `FileMover.Options(mode="error")` → `FileMover.Options(if_exists="error")` - * `FileMover.Options(mode="ignore")` → `FileMover.Options(if_exists="ignore")` - * `FileMover.Options(mode="overwrite")` → `FileMover.Options(if_exists="replace_file")` - * `FileMover.Options(mode="delete_all")` → `FileMover.Options(if_exists="replace_entire_directory")` - - Old names are left intact, but will be removed in v1.0.0 ([#108](https://github.com/MobileTeleSystems/onetl/pull/108)) -- Rename `onetl.log.disable_clients_logging()` to `onetl.log.setup_clients_logging()`. ([#120](https://github.com/MobileTeleSystems/onetl/pull/120)) - -## Features - -- Add new methods returning Maven packages for specific connection class: - * `Clickhouse.get_packages()` - * `MySQL.get_packages()` - * `Postgres.get_packages()` - * `Teradata.get_packages()` - * `MSSQL.get_packages(java_version="8")` - * `Oracle.get_packages(java_version="8")` - * `Greenplum.get_packages(scala_version="2.12")` - * `MongoDB.get_packages(scala_version="2.12")` - * `Kafka.get_packages(spark_version="3.4.1", scala_version="2.12")` - - Deprecate old syntax: - * `Clickhouse.package` - * `MySQL.package` - * `Postgres.package` - * `Teradata.package` - * `MSSQL.package` - * `Oracle.package` - * `Greenplum.package_spark_2_3` - * `Greenplum.package_spark_2_4` - * `Greenplum.package_spark_3_2` - * `MongoDB.package_spark_3_2` - * `MongoDB.package_spark_3_3` - * `MongoDB.package_spark_3_4` ([#87](https://github.com/MobileTeleSystems/onetl/pull/87)) -- Allow to set client modules log level in `onetl.log.setup_clients_logging()`. - - Allow to enable underlying client modules logging in `onetl.log.setup_logging()` by providing additional argument `enable_clients=True`. - This is useful for debug. ([#120](https://github.com/MobileTeleSystems/onetl/pull/120)) -- Added support for reading and writing data to Kafka topics. - - For these operations, new classes were added. - * `Kafka` ([#54](https://github.com/MobileTeleSystems/onetl/pull/54), [#60](https://github.com/MobileTeleSystems/onetl/pull/60), [#72](https://github.com/MobileTeleSystems/onetl/pull/72), [#84](https://github.com/MobileTeleSystems/onetl/pull/84), [#87](https://github.com/MobileTeleSystems/onetl/pull/87), [#89](https://github.com/MobileTeleSystems/onetl/pull/89), [#93](https://github.com/MobileTeleSystems/onetl/pull/93), [#96](https://github.com/MobileTeleSystems/onetl/pull/96), [#102](https://github.com/MobileTeleSystems/onetl/pull/102), [#104](https://github.com/MobileTeleSystems/onetl/pull/104)) - * `Kafka.PlaintextProtocol` ([#79](https://github.com/MobileTeleSystems/onetl/pull/79)) - * `Kafka.SSLProtocol` ([#118](https://github.com/MobileTeleSystems/onetl/pull/118)) - * `Kafka.BasicAuth` ([#63](https://github.com/MobileTeleSystems/onetl/pull/63), [#77](https://github.com/MobileTeleSystems/onetl/pull/77)) - * `Kafka.KerberosAuth` ([#63](https://github.com/MobileTeleSystems/onetl/pull/63), [#77](https://github.com/MobileTeleSystems/onetl/pull/77), [#110](https://github.com/MobileTeleSystems/onetl/pull/110)) - * `Kafka.ScramAuth` ([#115](https://github.com/MobileTeleSystems/onetl/pull/115)) - * `Kafka.Slots` ([#109](https://github.com/MobileTeleSystems/onetl/pull/109)) - * `Kafka.ReadOptions` ([#68](https://github.com/MobileTeleSystems/onetl/pull/68)) - * `Kafka.WriteOptions` ([#68](https://github.com/MobileTeleSystems/onetl/pull/68)) - - Currently, Kafka does not support incremental read strategies, this will be implemented in future releases. -- Added support for reading files as Spark DataFrame and saving DataFrame as Files. - - For these operations, new classes were added. - - FileDFConnections: - * `SparkHDFS` ([#98](https://github.com/MobileTeleSystems/onetl/pull/98)) - * `SparkS3` ([#94](https://github.com/MobileTeleSystems/onetl/pull/94), [#100](https://github.com/MobileTeleSystems/onetl/pull/100), [#124](https://github.com/MobileTeleSystems/onetl/pull/124)) - * `SparkLocalFS` ([#67](https://github.com/MobileTeleSystems/onetl/pull/67)) - - High-level classes: - * `FileDFReader` ([#73](https://github.com/MobileTeleSystems/onetl/pull/73)) - * `FileDFWriter` ([#81](https://github.com/MobileTeleSystems/onetl/pull/81)) - - File formats: - * `Avro` ([#69](https://github.com/MobileTeleSystems/onetl/pull/69)) - * `CSV` ([#92](https://github.com/MobileTeleSystems/onetl/pull/92)) - * `JSON` ([#83](https://github.com/MobileTeleSystems/onetl/pull/83)) - * `JSONLine` ([#83](https://github.com/MobileTeleSystems/onetl/pull/83)) - * `ORC` ([#86](https://github.com/MobileTeleSystems/onetl/pull/86)) - * `Parquet` ([#88](https://github.com/MobileTeleSystems/onetl/pull/88)) - -## Improvements - -- Remove redundant checks for driver availability in Greenplum and MongoDB connections. ([#67](https://github.com/MobileTeleSystems/onetl/pull/67)) -- Check of Java class availability moved from `.check()` method to connection constructor. ([#97](https://github.com/MobileTeleSystems/onetl/pull/97)) diff --git a/mddocs/markdown/changelog/0.9.1.md b/mddocs/markdown/changelog/0.9.1.md deleted file mode 100644 index 1779274b1..000000000 --- a/mddocs/markdown/changelog/0.9.1.md +++ /dev/null @@ -1,7 +0,0 @@ -# 0.9.1 (2023-08-17) - -## Bug Fixes - -- Fixed bug then number of threads created by `FileDownloader` / `FileUploader` / `FileMover` was - not `min(workers, len(files))`, but `max(workers, len(files))`. leading to create too much workers - on large files list. diff --git a/mddocs/markdown/changelog/0.9.2.md b/mddocs/markdown/changelog/0.9.2.md deleted file mode 100644 index 4ca4fcff9..000000000 --- a/mddocs/markdown/changelog/0.9.2.md +++ /dev/null @@ -1,21 +0,0 @@ -# 0.9.2 (2023-09-06) - -## Features - -- Add `if_exists="ignore"` and `error` to `Greenplum.WriteOptions` ([#142](https://github.com/MobileTeleSystems/onetl/pull/142)) - -## Improvements - -- Improve validation messages while writing dataframe to Kafka. ([#131](https://github.com/MobileTeleSystems/onetl/pull/131)) -- Improve documentation: - * Add notes about reading and writing to database connections documentation - * Add notes about executing statements in JDBC and Greenplum connections - -## Bug Fixes - -- Fixed validation of `headers` column is written to Kafka with default `Kafka.WriteOptions()` - default value was `False`, - but instead of raising an exception, column value was just ignored. ([#131](https://github.com/MobileTeleSystems/onetl/pull/131)) -- Fix reading data from Oracle with `partitioningMode="range"` without explicitly set `lowerBound` / `upperBound`. ([#133](https://github.com/MobileTeleSystems/onetl/pull/133)) -- Update Kafka documentation with SSLProtocol usage. ([#136](https://github.com/MobileTeleSystems/onetl/pull/136)) -- Raise exception if someone tries to read data from Kafka topic which does not exist. ([#138](https://github.com/MobileTeleSystems/onetl/pull/138)) -- Allow to pass Kafka topics with name like `some.topic.name` to DBReader. Same for MongoDB collections. ([#139](https://github.com/MobileTeleSystems/onetl/pull/139)) diff --git a/mddocs/markdown/changelog/0.9.3.md b/mddocs/markdown/changelog/0.9.3.md deleted file mode 100644 index 1a8c25d4d..000000000 --- a/mddocs/markdown/changelog/0.9.3.md +++ /dev/null @@ -1,5 +0,0 @@ -# 0.9.3 (2023-09-06) - -## Bug Fixes - -- Fix documentation build diff --git a/mddocs/markdown/changelog/0.9.4.md b/mddocs/markdown/changelog/0.9.4.md deleted file mode 100644 index cf9288760..000000000 --- a/mddocs/markdown/changelog/0.9.4.md +++ /dev/null @@ -1,24 +0,0 @@ -# 0.9.4 (2023-09-26) - -## Features - -- Add `Excel` file format support. ([#148](https://github.com/MobileTeleSystems/onetl/pull/148)) -- Add `Samba` file connection. - It is now possible to download and upload files to Samba shared folders using `FileDownloader`/`FileUploader`. ([#150](https://github.com/MobileTeleSystems/onetl/pull/150)) -- Add `if_exists="ignore"` and `error` to `Hive.WriteOptions` ([#143](https://github.com/MobileTeleSystems/onetl/pull/143)) -- Add `if_exists="ignore"` and `error` to `JDBC.WriteOptions` ([#144](https://github.com/MobileTeleSystems/onetl/pull/144)) -- Add `if_exists="ignore"` and `error` to `MongoDB.WriteOptions` ([#145](https://github.com/MobileTeleSystems/onetl/pull/145)) - -## Improvements - -- Add documentation about different ways of passing packages to Spark session. ([#151](https://github.com/MobileTeleSystems/onetl/pull/151)) -- Drastically improve `Greenplum` documentation: - : * Added information about network ports, grants, `pg_hba.conf` and so on. - * Added interaction schemas for reading, writing and executing statements in Greenplum. - * Added recommendations about reading data from views and `JOIN` results from Greenplum. ([#154](https://github.com/MobileTeleSystems/onetl/pull/154)) -- Make `.fetch` and `.execute` methods of DB connections thread-safe. Each thread works with its own connection. ([#156](https://github.com/MobileTeleSystems/onetl/pull/156)) -- Call `.close()` on `FileConnection` then it is removed by garbage collector. ([#156](https://github.com/MobileTeleSystems/onetl/pull/156)) - -## Bug Fixes - -- Fix issue when stopping Python interpreter calls `JDBCMixin.close()`, but it is finished with exceptions. ([#156](https://github.com/MobileTeleSystems/onetl/pull/156)) diff --git a/mddocs/markdown/changelog/0.9.5.md b/mddocs/markdown/changelog/0.9.5.md deleted file mode 100644 index 1d7358c0b..000000000 --- a/mddocs/markdown/changelog/0.9.5.md +++ /dev/null @@ -1,14 +0,0 @@ -# 0.9.5 (2023-10-10) - -## Features - -- Add `XML` file format support. ([#163](https://github.com/MobileTeleSystems/onetl/pull/163)) -- Tested compatibility with Spark 3.5.0. `MongoDB` and `Excel` are not supported yet, but other packages do. ([#159](https://github.com/MobileTeleSystems/onetl/pull/159)) - -## Improvements - -- Add check to all DB and FileDF connections that Spark session is alive. ([#164](https://github.com/MobileTeleSystems/onetl/pull/164)) - -## Bug Fixes - -- Fix `Hive.check()` behavior when Hive Metastore is not available. ([#164](https://github.com/MobileTeleSystems/onetl/pull/164)) diff --git a/mddocs/markdown/changelog/DRAFT.md b/mddocs/markdown/changelog/DRAFT.md deleted file mode 100644 index e69de29bb..000000000 diff --git a/mddocs/markdown/changelog/NEXT_RELEASE.md b/mddocs/markdown/changelog/NEXT_RELEASE.md deleted file mode 100644 index 8cf66aa33..000000000 --- a/mddocs/markdown/changelog/NEXT_RELEASE.md +++ /dev/null @@ -1 +0,0 @@ - diff --git a/mddocs/markdown/changelog/index.md b/mddocs/markdown/changelog/index.md deleted file mode 100644 index 37d5d85ae..000000000 --- a/mddocs/markdown/changelog/index.md +++ /dev/null @@ -1,29 +0,0 @@ -Changelog - -* [0.13.4 (2025-03-20)](0.13.4.md) -* [0.13.3 (2025-03-11)](0.13.3.md) -* [0.13.1 (2025-03-06)](0.13.1.md) -* [0.13.0 (2025-02-24)](0.13.0.md) -* [0.12.5 (2024-12-03)](0.12.5.md) -* [0.12.4 (2024-11-27)](0.12.4.md) -* [0.12.3 (2024-11-22)](0.12.3.md) -* [0.12.2 (2024-11-12)](0.12.2.md) -* [0.12.1 (2024-10-28)](0.12.1.md) -* [0.12.0 (2024-09-03)](0.12.0.md) -* [0.11.2 (2024-09-02)](0.11.2.md) -* [0.11.1 (2024-05-29)](0.11.1.md) -* [0.11.0 (2024-05-27)](0.11.0.md) -* [0.10.2 (2024-03-21)](0.10.2.md) -* [0.10.1 (2024-02-05)](0.10.1.md) -* [0.10.0 (2023-12-18)](0.10.0.md) -* [0.9.5 (2023-10-10)](0.9.5.md) -* [0.9.4 (2023-09-26)](0.9.4.md) -* [0.9.3 (2023-09-06)](0.9.3.md) -* [0.9.2 (2023-09-06)](0.9.2.md) -* [0.9.1 (2023-08-17)](0.9.1.md) -* [0.9.0 (2023-08-17)](0.9.0.md) -* [0.8.1 (2023-07-10)](0.8.1.md) -* [0.8.0 (2023-05-31)](0.8.0.md) -* [0.7.2 (2023-05-24)](0.7.2.md) -* [0.7.1 (2023-05-23)](0.7.1.md) -* [0.7.0 (2023-05-15)](0.7.0.md) diff --git a/mddocs/markdown/concepts.md b/mddocs/markdown/concepts.md deleted file mode 100644 index cf74e9eaa..000000000 --- a/mddocs/markdown/concepts.md +++ /dev/null @@ -1,340 +0,0 @@ -# Concepts - -Here you can find detailed documentation about each one of the onETL concepts and how to use them. - -## Connection - -### Basics - -onETL is used to pull and push data into other systems, and so it has a first-class `Connection` concept for storing credentials that are used to communicate with external systems. - -A `Connection` is essentially a set of parameters, such as username, password, hostname. - -To create a connection to a specific storage type, you must use a class that matches the storage type. The class name is the same as the storage type name (`Oracle`, `MSSQL`, `SFTP`, etc): - -```python -from onetl.connection import SFTP - -sftp = SFTP( - host="sftp.test.com", - user="onetl", - password="onetl", -) -``` - -All connection types are inherited from the parent class `BaseConnection`. - -### Class diagram - -### DBConnection - -Classes inherited from `DBConnection` could be used for accessing databases. - -A `DBConnection` could be instantiated as follows: - -```python -from onetl.connection import MSSQL - -mssql = MSSQL( - host="mssqldb.demo.com", - user="onetl", - password="onetl", - database="Telecom", - spark=spark, -) -``` - -where **spark** is the current SparkSession. -`onETL` uses `Spark` and specific Java connectors under the hood to work with databases. - -For a description of other parameters, see the documentation for the [available DBConnections](connection/db_connection/index.md#db-connections). - -### FileConnection - -Classes inherited from `FileConnection` could be used to access files stored on the different file systems/file servers - -A `FileConnection` could be instantiated as follows: - -```python -from onetl.connection import SFTP - -sftp = SFTP( - host="sftp.test.com", - user="onetl", - password="onetl", -) -``` - -For a description of other parameters, see the documentation for the [available FileConnections](connection/file_connection/index.md#file-connections). - -### FileDFConnection - -Classes inherited from `FileDFConnection` could be used for accessing files as Spark DataFrames. - -A `FileDFConnection` could be instantiated as follows: - -```python -from onetl.connection import SparkHDFS - -spark_hdfs = SparkHDFS( - host="namenode1.domain.com", - cluster="mycluster", - spark=spark, -) -``` - -where **spark** is the current SparkSession. -`onETL` uses `Spark` and specific Java connectors under the hood to work with DataFrames. - -For a description of other parameters, see the documentation for the [available FileDFConnections](connection/file_df_connection/index.md#file-df-connections). - -### Checking connection availability - -Once you have created a connection, you can check the database/filesystem availability using the method `check()`: - -```python -mssql.check() -sftp.check() -spark_hdfs.check() -``` - -It will raise an exception if database/filesystem cannot be accessed. - -This method returns connection itself, so you can create connection and immediately check its availability: - -```Python -mssql = MSSQL( - host="mssqldb.demo.com", - user="onetl", - password="onetl", - database="Telecom", - spark=spark, -).check() # <-- -``` - -## Extract/Load data - -### Basics - -As we said above, onETL is used to extract data from and load data into remote systems. - -onETL provides several classes for this: - -> * [DBReader](db/db_reader.md#db-reader) -> * [DBWriter](db/db_writer.md#db-writer) -> * [FileDFReader](file_df/file_df_reader/file_df_reader.md#file-df-reader) -> * [FileDFWriter](file_df/file_df_writer/file_df_writer.md#file-df-writer) -> * [FileDownloader](file/file_downloader/file_downloader.md#file-downloader) -> * [FileUploader](file/file_uploader/file_uploader.md#file-uploader) -> * [FileMover](file/file_mover/file_mover.md#file-mover) - -All of these classes have a method `run()` that starts extracting/loading the data: - -```python -from onetl.db import DBReader, DBWriter - -reader = DBReader( - connection=mssql, - source="dbo.demo_table", - columns=["column_1", "column_2"], -) - -# Read data as Spark DataFrame -df = reader.run() - -db_writer = DBWriter( - connection=hive, - target="dl_sb.demo_table", -) - -# Save Spark DataFrame to Hive table -writer.run(df) -``` - -### Extract data - -To extract data you can use classes: - -| | Use case | Connection | `run()` gets | `run()` returns | -|-------------------------------------------------------------------------|-------------------------------------------|------------------------------------------------------------------------------------|---------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------| -| [DBReader](db/db_reader.md#db-reader) | Reading data from a database | Any [DBConnection](connection/db_connection/index.md#db-connections) | - | [Spark DataFrame](https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.sql.DataFrame.html#pyspark.sql.DataFrame) | -| [FileDFReader](file_df/file_df_reader/file_df_reader.md#file-df-reader) | Read data from a file or set of files | Any [FileDFConnection](connection/file_df_connection/index.md#file-df-connections) | No input, or List[File path on FileSystem] | [Spark DataFrame](https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.sql.DataFrame.html#pyspark.sql.DataFrame) | -| [FileDownloader](db/db_reader.md#db-reader) | Download files from remote FS to local FS | Any [FileConnection](connection/file_connection/index.md#file-connections) | No input, or List[File path on remote FileSystem] | [DownloadResult](file/file_downloader/result.md#file-downloader-result) | - -### Load data - -To load data you can use classes: - -| | Use case | Connection | `run()` gets | `run()` returns | -|-------------------------------------------------------------------|----------------------------------------------|------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------| -| [DBWriter](db/db_writer.md#db-writer) | Writing data from a DataFrame to a database | Any [DBConnection](connection/db_connection/index.md#db-connections) | [Spark DataFrame](https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.sql.DataFrame.html#pyspark.sql.DataFrame) | None | -| [FileDFWriter](db/db_writer.md#db-writer) | Writing data from a DataFrame to a folder | Any [FileDFConnection](connection/file_df_connection/index.md#file-df-connections) | [Spark DataFrame](https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.sql.DataFrame.html#pyspark.sql.DataFrame) | None | -| [FileUploader](file/file_uploader/file_uploader.md#file-uploader) | Uploading files from a local FS to remote FS | Any [FileConnection](connection/file_connection/index.md#file-connections) | List[File path on local FileSystem] | [UploadResult](file/file_uploader/result.md#file-uploader-result) | - -### Manipulate data - -To manipulate data you can use classes: - -| | Use case | Connection | `run()` gets | `run()` returns | -|-------------------------------------------------------|---------------------------------------------|----------------------------------------------------------------------------|--------------------------------------|-----------------------------------------------------------| -| [FileMover](file/file_mover/file_mover.md#file-mover) | Move files between directories in remote FS | Any [FileConnection](connection/file_connection/index.md#file-connections) | List[File path on remote FileSystem] | [MoveResult](file/file_mover/result.md#file-mover-result) | - -### Options - -Extract and load classes have a `options` parameter, which has a special meaning: - -> * all other parameters - *WHAT* we extract / *WHERE* we load to -> * `options` parameter - *HOW* we extract/load data -```python -db_reader = DBReader( - # WHAT do we read: - connection=mssql, - source="dbo.demo_table", # some table from MSSQL - columns=["column_1", "column_2"], # but only specific set of columns - where="column_2 > 1000", # only rows matching the clause - # HOW do we read: - options=MSSQL.ReadOptions( - numPartitions=10, # read in 10 parallel jobs - partitionColumn="id", # balance data read by assigning each job a part of data using `hash(id) mod N` expression - partitioningMode="hash", - fetchsize=1000, # each job will fetch block of 1000 rows each on every read attempt - ), -) - -db_writer = DBWriter( - # WHERE do we write to - to some table in Hive - connection=hive, - target="dl_sb.demo_table", - # HOW do we write - overwrite all the data in the existing table - options=Hive.WriteOptions(if_exists="replace_entire_table"), -) - -file_downloader = FileDownloader( - # WHAT do we download - files from some dir in SFTP - connection=sftp, - source_path="/source", - filters=[Glob("*.csv")], # only CSV files - limits=[MaxFilesCount(1000)], # 1000 files max - # WHERE do we download to - a specific dir on local FS - local_path="/some", - # HOW do we download: - options=FileDownloader.Options( - delete_source=True, # after downloading each file remove it from source_path - if_exists="replace_file", # replace existing files in the local_path - ), -) - -file_uploader = FileUploader( - # WHAT do we upload - files from some local dir - local_path="/source", - # WHERE do we upload to- specific remote dir in HDFS - connection=hdfs, - target_path="/some", - # HOW do we upload: - options=FileUploader.Options( - delete_local=True, # after uploading each file remove it from local_path - if_exists="replace_file", # replace existing files in the target_path - ), -) - -file_mover = FileMover( - # WHAT do we move - files in some remote dir in HDFS - source_path="/source", - connection=hdfs, - # WHERE do we move files to - target_path="/some", # a specific remote dir within the same HDFS connection - # HOW do we load - replace existing files in the target_path - options=FileMover.Options(if_exists="replace_file"), -) - -file_df_reader = FileDFReader( - # WHAT do we read - *.csv files from some dir in S3 - connection=s3, - source_path="/source", - file_format=CSV(), - # HOW do we read - load files from /source/*.csv, not from /source/nested/*.csv - options=FileDFReader.Options(recursive=False), -) - -file_df_writer = FileDFWriter( - # WHERE do we write to - as .csv files in some dir in S3 - connection=s3, - target_path="/target", - file_format=CSV(), - # HOW do we write - replace all existing files in /target, if exists - options=FileDFWriter.Options(if_exists="replace_entire_directory"), -) -``` - -More information about `options` could be found on [DB connection](connection/db_connection/index.md#db-connections). and -[File Downloader](file/file_downloader/file_downloader.md#file-downloader) / [File Uploader](file/file_uploader/file_uploader.md#file-uploader) / [File Mover](file/file_mover/file_mover.md#file-mover) / [FileDF Reader](file_df/file_df_reader/file_df_reader.md#file-df-reader) / [FileDF Writer](file_df/file_df_writer/file_df_writer.md#file-df-writer) documentation - -### Read Strategies - -onETL have several builtin strategies for reading data: - -1. [Snapshot strategy](strategy/snapshot_strategy.html) (default strategy) -2. [Incremental strategy](strategy/incremental_strategy.html) -3. [Snapshot batch strategy](strategy/snapshot_batch_strategy.html) -4. [Incremental batch strategy](strategy/incremental_batch_strategy.html) - -For example, an incremental strategy allows you to get only new data from the table: - -```python -from onetl.strategy import IncrementalStrategy - -reader = DBReader( - connection=mssql, - source="dbo.demo_table", - hwm_column="id", # detect new data based on value of "id" column -) - -# first run -with IncrementalStrategy(): - df = reader.run() - -sleep(3600) - -# second run -with IncrementalStrategy(): - # only rows, that appeared in the source since previous run - df = reader.run() -``` - -or get only files which were not downloaded before: - -```python -from onetl.strategy import IncrementalStrategy - -file_downloader = FileDownloader( - connection=sftp, - source_path="/remote", - local_path="/local", - hwm_type="file_list", # save all downloaded files to a list, and exclude files already present in this list -) - -# first run -with IncrementalStrategy(): - files = file_downloader.run() - -sleep(3600) - -# second run -with IncrementalStrategy(): - # only files, that appeared in the source since previous run - files = file_downloader.run() -``` - -Most of strategies are based on [HWM](hwm_store/index.md#hwm), Please check each strategy documentation for more details - -### Why just not use Connection class for extract/load? - -Connections are very simple, they have only a set of some basic operations, -like `mkdir`, `remove_file`, `get_table_schema`, and so on. - -High-level operations, like -: * [Read Strategies](strategy/index.md#strategy) support - * Handling metadata push/pull - * Handling different options, like `if_exists="replace_file"` in case of file download/upload - -is moved to a separate class which calls the connection object methods to perform some complex logic. diff --git a/mddocs/markdown/connection/db_connection/clickhouse/connection.md b/mddocs/markdown/connection/db_connection/clickhouse/connection.md deleted file mode 100644 index 09365e6a8..000000000 --- a/mddocs/markdown/connection/db_connection/clickhouse/connection.md +++ /dev/null @@ -1,128 +0,0 @@ - - -# Clickhouse connection - -### *class* onetl.connection.db_connection.clickhouse.connection.Clickhouse(\*, spark: SparkSession, user: str, password: SecretStr, host: Host, port: int = 8123, database: str | None = None, extra: ClickhouseExtra = ClickhouseExtra()) - -Clickhouse JDBC connection. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -Based on Maven package [com.clickhouse:clickhouse-jdbc:0.7.2](https://mvnrepository.com/artifact/com.clickhouse/clickhouse-jdbc/0.7.2) -([official Clickhouse JDBC driver](https://github.com/ClickHouse/clickhouse-jdbc)). - -#### SEE ALSO -Before using this connector please take into account [Prerequisites](prerequisites.md#clickhouse-prerequisites) - -#### Versionadded -Added in version 0.1.0. - -* **Parameters:** - **host** - : Host of Clickhouse database. For example: `test.clickhouse.domain.com` or `193.168.1.11` - - **port** - : Port of Clickhouse database - - **user** - : User, which have proper access to the database. For example: `some_user` - - **password** - : Password for database connection - - **database** - : Database in RDBMS, NOT schema. -
- See [this page](https://www.educba.com/postgresql-database-vs-schema/) for more details - - **spark** - : Spark session. - - **extra** - : Specifies one or more extra parameters by which clients can connect to the instance. -
- For example: `{"continueBatchOnError": "false"}`. -
- See: - : * [Clickhouse JDBC driver properties documentation](https://clickhouse.com/docs/en/integrations/java#configuration) - * [Clickhouse core settings documentation](https://clickhouse.com/docs/en/operations/settings/settings) - * [Clickhouse query complexity documentation](https://clickhouse.com/docs/en/operations/settings/query-complexity) - * [Clickhouse query level settings](https://clickhouse.com/docs/en/operations/settings/query-level) - -### Examples - -Create and check Clickhouse connection: - -```python -from onetl.connection import Clickhouse -from pyspark.sql import SparkSession - -# Create Spark session with Clickhouse driver loaded -maven_packages = Clickhouse.get_packages() -spark = ( - SparkSession.builder.appName("spark-app-name") - .config("spark.jars.packages", ",".join(maven_packages)) - .getOrCreate() -) - -# Create connection -clickhouse = Clickhouse( - host="database.host.or.ip", - user="user", - password="*****", - extra={"continueBatchOnError": "false"}, - spark=spark, -).check() -``` - - - -#### check() - -Check source availability. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -If not, an exception will be raised. - -* **Returns:** - Connection itself -* **Raises:** - RuntimeError - : If the connection is not available - -### Examples - -```python -connection.check() -``` - - - -#### *classmethod* get_packages(package_version: str | None = None, apache_http_client_version: str | None = None) → list[str] - -Get package names to be downloaded by Spark. Allows specifying custom JDBC and Apache HTTP Client versions. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### Versionadded -Added in version 0.9.0. - -* **Parameters:** - **package_version** - : ClickHouse JDBC version client packages. Defaults to `0.7.2`. -
- #### Versionadded - Added in version 0.11.0. - - **apache_http_client_version** - : Apache HTTP Client version package. Defaults to `5.4.2`. -
- Used only if `package_version` is in range `0.5.0-0.7.0`. -
- #### Versionadded - Added in version 0.11.0. - -### Examples - -```python -from onetl.connection import Clickhouse - -Clickhouse.get_packages(package_version="0.6.0", apache_http_client_version="5.4.2") -``` - - diff --git a/mddocs/markdown/connection/db_connection/clickhouse/execute.md b/mddocs/markdown/connection/db_connection/clickhouse/execute.md deleted file mode 100644 index c65c26a67..000000000 --- a/mddocs/markdown/connection/db_connection/clickhouse/execute.md +++ /dev/null @@ -1,191 +0,0 @@ - - -# Executing statements in Clickhouse - -#### WARNING -Methods below **read all the rows** returned from DB **to Spark driver memory**, and then convert them to DataFrame. - -Do **NOT** use them to read large amounts of data. Use [DBReader](read.md#clickhouse-read) or [Clickhouse.sql](sql.md#clickhouse-sql) instead. - -## How to - -There are 2 ways to execute some statement in Clickhouse - -### Use `Clickhouse.fetch` - -Use this method to perform some `SELECT` query which returns **small number or rows**, like reading -Clickhouse config, or reading data from some reference table. Method returns Spark DataFrame. - -Method accepts [`Clickhouse.FetchOptions`](#onetl.connection.db_connection.clickhouse.options.ClickhouseFetchOptions). - -Connection opened using this method should be then closed with `connection.close()` or `with connection:`. - -#### WARNING -Please take into account [Clickhouse <-> Spark type mapping](types.md#clickhouse-types). - -#### Syntax support - -This method supports **any** query syntax supported by Clickhouse, like: - -* ✅︎ `SELECT ... FROM ...` -* ✅︎ `WITH alias AS (...) SELECT ...` -* ✅︎ `SELECT func(arg1, arg2)` - call function -* ✅︎ `SHOW ...` -* ❌ `SET ...; SELECT ...;` - multiple statements not supported - -#### Examples - -```python -from onetl.connection import Clickhouse - -clickhouse = Clickhouse(...) - -df = clickhouse.fetch( - "SELECT value FROM some.reference_table WHERE key = 'some_constant'", - options=Clickhouse.FetchOptions(queryTimeout=10), -) -clickhouse.close() -value = df.collect()[0][0] # get value from first row and first column -``` - -### Use `Clickhouse.execute` - -Use this method to execute DDL and DML operations. Each method call runs operation in a separated transaction, and then commits it. - -Method accepts [`Clickhouse.ExecuteOptions`](#onetl.connection.db_connection.clickhouse.options.ClickhouseExecuteOptions). - -Connection opened using this method should be then closed with `connection.close()` or `with connection:`. - -#### Syntax support - -This method supports **any** query syntax supported by Clickhouse, like: - -* ✅︎ `CREATE TABLE ...`, `CREATE VIEW ...`, and so on -* ✅︎ `ALTER ...` -* ✅︎ `INSERT INTO ... SELECT ...`, `UPDATE ...`, `DELETE ...`, and so on -* ✅︎ `DROP TABLE ...`, `DROP VIEW ...`, `TRUNCATE TABLE`, and so on -* ✅︎ other statements not mentioned here -* ❌ `SET ...; SELECT ...;` - multiple statements not supported - -#### Examples - -```python -from onetl.connection import Clickhouse - -clickhouse = Clickhouse(...) - -clickhouse.execute("DROP TABLE schema.table") -clickhouse.execute( - """ - CREATE TABLE schema.table ( - id UInt8, - key String, - value Float32 - ) - ENGINE = MergeTree() - ORDER BY id - """, - options=Clickhouse.ExecuteOptions(queryTimeout=10), -) -``` - -## Notes - -These methods **read all the rows** returned from DB **to Spark driver memory**, and then convert them to DataFrame. - -So it should **NOT** be used to read large amounts of data. Use [DBReader](read.md#clickhouse-read) or [Clickhouse.sql](sql.md#clickhouse-sql) instead. - -## Options - -### *pydantic model* onetl.connection.db_connection.clickhouse.options.ClickhouseFetchOptions - -Options related to fetching data from databases via JDBC. - -#### Versionadded -Added in version 0.11.0: Replace `Clickhouse.JDBCOptions` → `Clickhouse.FetchOptions` - -### Examples - -#### NOTE -You can pass any value supported by underlying JDBC driver class, -even if it is not mentioned in this documentation. - -```python -from onetl.connection import Clickhouse - -options = Clickhouse.FetchOptions( - queryTimeout=60_000, - fetchsize=100_000, - customSparkOption="value", -) -``` - - - -#### *field* query_timeout *: int | None* *= None* *(alias 'queryTimeout')* - -The number of seconds the driver will wait for a statement to execute. -Zero means there is no limit. - -This option depends on driver implementation, -some drivers can check the timeout of each query instead of an entire JDBC batch. - - - -#### *field* fetchsize *: int | None* *= None* - -How many rows to fetch per round trip. - -Tuning this option can influence performance of reading. - -#### WARNING -Default value depends on driver. For example, Oracle has -default `fetchsize=10`. - - - -### *pydantic model* onetl.connection.db_connection.clickhouse.options.ClickhouseExecuteOptions - -Options related to executing statements in databases via JDBC. - -#### Versionadded -Added in version 0.11.0: Replace `Clickhouse.JDBCOptions` → `Clickhouse.ExecuteOptions` - -### Examples - -#### NOTE -You can pass any value supported by underlying JDBC driver class, -even if it is not mentioned in this documentation. - -```python -from onetl.connection import Clickhouse - -options = Clickhouse.ExecuteOptions( - queryTimeout=60_000, - customSparkOption="value", -) -``` - - - -#### *field* query_timeout *: int | None* *= None* *(alias 'queryTimeout')* - -The number of seconds the driver will wait for a statement to execute. -Zero means there is no limit. - -This option depends on driver implementation, -some drivers can check the timeout of each query instead of an entire JDBC batch. - - - -#### *field* fetchsize *: int | None* *= None* - -How many rows to fetch per round trip. - -Tuning this option can influence performance of reading. - -#### WARNING -Default value depends on driver. For example, Oracle has -default `fetchsize=10`. - - diff --git a/mddocs/markdown/connection/db_connection/clickhouse/index.md b/mddocs/markdown/connection/db_connection/clickhouse/index.md deleted file mode 100644 index 3b226910d..000000000 --- a/mddocs/markdown/connection/db_connection/clickhouse/index.md +++ /dev/null @@ -1,19 +0,0 @@ - - -# Clickhouse - -# Connection - -* [Prerequisites](prerequisites.md) -* [Clickhouse connection](connection.md) - -# Operations - -* [Reading from Clickhouse using `DBReader`](read.md) -* [Reading from Clickhouse using `Clickhouse.sql`](sql.md) -* [Writing to Clickhouse using `DBWriter`](write.md) -* [Executing statements in Clickhouse](execute.md) - -# Troubleshooting - -* [Clickhouse <-> Spark type mapping](types.md) diff --git a/mddocs/markdown/connection/db_connection/clickhouse/prerequisites.md b/mddocs/markdown/connection/db_connection/clickhouse/prerequisites.md deleted file mode 100644 index 86551b41a..000000000 --- a/mddocs/markdown/connection/db_connection/clickhouse/prerequisites.md +++ /dev/null @@ -1,73 +0,0 @@ - - -# Prerequisites - -## Version Compatibility - -* Clickhouse server versions: - : * Officially declared: 22.8 or higher - * Actually tested: 21.1, 25.1 -* Spark versions: 2.3.x - 3.5.x -* Java versions: 8 - 20 - -See [official documentation](https://clickhouse.com/docs/en/integrations/java#jdbc-driver). - -## Installing PySpark - -To use Clickhouse connector you should have PySpark installed (or injected to `sys.path`) -BEFORE creating the connector instance. - -See [Spark](../../../install/spark.md#install-spark) installation instruction for more details. - -## Connecting to Clickhouse - -### Connection port - -Connector can only use **HTTP** (usually `8123` port) or **HTTPS** (usually `8443` port) protocol. - -TCP and GRPC protocols are NOT supported. - -### Connecting to cluster - -It is possible to connect to Clickhouse cluster, and use it’s load balancing capabilities to read or write data in parallel. -Each Spark executor can connect to random Clickhouse nodes, instead of sending all the data to a node specified in connection params. - -This requires all Clickhouse servers to run on different hosts, and **listen the same HTTP port**. -Set `auto_discovery=True` to enable this feature (disabled by default): - -```python -Clickhouse( - host="node1.of.cluster", - port=8123, - extra={ - "auto_discovery": True, - "load_balancing_policy": "roundRobin", - }, -) -``` - -See [official documentation](https://clickhouse.com/docs/en/integrations/java#configuring-node-discovery-load-balancing-and-failover). - -### Required grants - -Ask your Clickhouse cluster administrator to set following grants for a user, -used for creating a connection: - -Read + Write - -```sql --- allow creating tables in the target schema -GRANT CREATE TABLE ON myschema.* TO username; - --- allow read & write access to specific table -GRANT SELECT, INSERT ON myschema.mytable TO username; -``` - -Read only - -```sql --- allow read access to specific table -GRANT SELECT ON myschema.mytable TO username; -``` - -More details can be found in [official documentation](https://clickhouse.com/docs/en/sql-reference/statements/grant). diff --git a/mddocs/markdown/connection/db_connection/clickhouse/read.md b/mddocs/markdown/connection/db_connection/clickhouse/read.md deleted file mode 100644 index 611930634..000000000 --- a/mddocs/markdown/connection/db_connection/clickhouse/read.md +++ /dev/null @@ -1,339 +0,0 @@ - - -# Reading from Clickhouse using `DBReader` - -[`DBReader`](../../../db/db_reader.md#onetl.db.db_reader.db_reader.DBReader) supports [Read Strategies](../../../strategy/index.md#strategy) for incremental data reading, -but does not support custom queries, like `JOIN`. - -#### WARNING -Please take into account [Clickhouse <-> Spark type mapping](types.md#clickhouse-types) - -## Supported DBReader features - -* ✅︎ `columns` -* ✅︎ `where` -* ✅︎ `hwm`, supported strategies: -* * ✅︎ [Snapshot Strategy](../../../strategy/snapshot_strategy.md#snapshot-strategy) -* * ✅︎ [Incremental Strategy](../../../strategy/incremental_strategy.md#incremental-strategy) -* * ✅︎ [Snapshot Batch Strategy](../../../strategy/snapshot_batch_strategy.md#snapshot-batch-strategy) -* * ✅︎ [Incremental Batch Strategy](../../../strategy/incremental_batch_strategy.md#incremental-batch-strategy) -* ❌ `hint` (is not supported by Clickhouse) -* ❌ `df_schema` -* ✅︎ `options` (see [`Clickhouse.ReadOptions`](#onetl.connection.db_connection.clickhouse.options.ClickhouseReadOptions)) - -## Examples - -Snapshot strategy: - -```python -from onetl.connection import Clickhouse -from onetl.db import DBReader - -clickhouse = Clickhouse(...) - -reader = DBReader( - connection=clickhouse, - source="schema.table", - columns=["id", "key", "CAST(value AS String) value", "updated_dt"], - where="key = 'something'", - options=Clickhouse.ReadOptions(partitionColumn="id", numPartitions=10), -) -df = reader.run() -``` - -Incremental strategy: - -```python -from onetl.connection import Clickhouse -from onetl.db import DBReader -from onetl.strategy import IncrementalStrategy - -clickhouse = Clickhouse(...) - -reader = DBReader( - connection=clickhouse, - source="schema.table", - columns=["id", "key", "CAST(value AS String) value", "updated_dt"], - where="key = 'something'", - hwm=DBReader.AutoDetectHWM(name="clickhouse_hwm", expression="updated_dt"), - options=Clickhouse.ReadOptions(partitionColumn="id", numPartitions=10), -) - -with IncrementalStrategy(): - df = reader.run() -``` - -## Recommendations - -### Select only required columns - -Instead of passing `"*"` in `DBReader(columns=[...])` prefer passing exact column names. This reduces the amount of data passed from Clickhouse to Spark. - -### Pay attention to `where` value - -Instead of filtering data on Spark side using `df.filter(df.column == 'value')` pass proper `DBReader(where="column = 'value'")` clause. -This both reduces the amount of data send from Clickhouse to Spark, and may also improve performance of the query. -Especially if there are indexes or partitions for columns used in `where` clause. - -## Options - -### *pydantic model* onetl.connection.db_connection.clickhouse.options.ClickhouseReadOptions - -Spark JDBC reading options. - -#### Versionadded -Added in version 0.5.0: Replace `Clickhouse.Options` → `Clickhouse.ReadOptions` - -### Examples - -#### NOTE -You can pass any value -[supported by Spark](https://spark.apache.org/docs/latest/sql-data-sources-jdbc.html), -even if it is not mentioned in this documentation. **Option names should be in** `camelCase`! - -The set of supported options depends on Spark version. - -```python -from onetl.connection import Clickhouse - -options = Clickhouse.ReadOptions( - partitioning_mode="range", - partitionColumn="reg_id", - numPartitions=10, - customSparkOption="value", -) -``` - - - -#### *field* partition_column *: str | None* *= None* *(alias 'partitionColumn')* - -Column used to parallelize reading from a table. - -#### WARNING -It is highly recommended to use primary key, or column with an index -to avoid performance issues. - -#### NOTE -Column type depends on [`partitioning_mode`](#onetl.connection.db_connection.clickhouse.options.ClickhouseReadOptions.partitioning_mode). - -* `partitioning_mode="range"` requires column to be an integer, date or timestamp (can be NULL, but not recommended). -* `partitioning_mode="hash"` accepts any column type (NOT NULL). -* `partitioning_mode="mod"` requires column to be an integer (NOT NULL). - -See documentation for [`partitioning_mode`](#onetl.connection.db_connection.clickhouse.options.ClickhouseReadOptions.partitioning_mode) for more details - - - -#### *field* num_partitions *: PositiveInt* *= 1* *(alias 'numPartitions')* - -Number of jobs created by Spark to read the table content in parallel. -See documentation for [`partitioning_mode`](#onetl.connection.db_connection.clickhouse.options.ClickhouseReadOptions.partitioning_mode) for more details - - - -#### *field* lower_bound *: int | None* *= None* *(alias 'lowerBound')* - -See documentation for [`partitioning_mode`](#onetl.connection.db_connection.clickhouse.options.ClickhouseReadOptions.partitioning_mode) for more details - - - -#### *field* upper_bound *: int | None* *= None* *(alias 'upperBound')* - -See documentation for [`partitioning_mode`](#onetl.connection.db_connection.clickhouse.options.ClickhouseReadOptions.partitioning_mode) for more details - - - -#### *field* session_init_statement *: str | None* *= None* *(alias 'sessionInitStatement')* - -After each database session is opened to the remote DB and before starting to read data, -this option executes a custom SQL statement (or a PL/SQL block). - -Use this to implement session initialization code. - -Example: - -```python -sessionInitStatement = """ - BEGIN - execute immediate - 'alter session set "_serial_direct_read"=true'; - END; -""" -``` - - - -#### *field* query_timeout *: int | None* *= None* *(alias 'queryTimeout')* - -The number of seconds the driver will wait for a statement to execute. -Zero means there is no limit. - -This option depends on driver implementation, -some drivers can check the timeout of each query instead of an entire JDBC batch. - - - -#### *field* fetchsize *: int* *= 100000* - -Fetch N rows from an opened cursor per one read round. - -Tuning this option can influence performance of reading. - -#### WARNING -Default value is different from Spark. - -Spark uses driver’s own value, and it may be different in different drivers, -and even versions of the same driver. For example, Oracle has -default `fetchsize=10`, which is absolutely not usable. - -Thus we’ve overridden default value with `100_000`, which should increase reading performance. - -#### Versionchanged -Changed in version 0.2.0: Set explicit default value to `100_000` - - - -#### *field* partitioning_mode *: JDBCPartitioningMode* *= JDBCPartitioningMode.RANGE* - -Defines how Spark will parallelize reading from table. - -Possible values: - -* `range` (default) - : Allocate each executor a range of values from column passed into [`partition_column`](#onetl.connection.db_connection.clickhouse.options.ClickhouseReadOptions.partition_column). -
- ### Spark generates for each executor an SQL query -
- Executor 1: - ```sql - SELECT ... FROM table - WHERE (partition_column >= lowerBound - OR partition_column IS NULL) - AND partition_column < (lower_bound + stride) - ``` -
- Executor 2: - ```sql - SELECT ... FROM table - WHERE partition_column >= (lower_bound + stride) - AND partition_column < (lower_bound + 2 * stride) - ``` -
- … -
- Executor N: - ```sql - SELECT ... FROM table - WHERE partition_column >= (lower_bound + (N-1) * stride) - AND partition_column <= upper_bound - ``` -
- Where `stride=(upper_bound - lower_bound) / num_partitions`. -
- #### NOTE - Can be used only with columns of integer, date or timestamp types. -
- #### NOTE - [`lower_bound`](#onetl.connection.db_connection.clickhouse.options.ClickhouseReadOptions.lower_bound), [`upper_bound`](#onetl.connection.db_connection.clickhouse.options.ClickhouseReadOptions.upper_bound) and [`num_partitions`](#onetl.connection.db_connection.clickhouse.options.ClickhouseReadOptions.num_partitions) are used just to - calculate the partition stride, **NOT** for filtering the rows in table. - So all rows in the table will be returned (unlike *Incremental* [Read Strategies](../../../strategy/index.md#strategy)). -
- #### NOTE - All queries are executed in parallel. To execute them sequentially, use *Batch* [Read Strategies](../../../strategy/index.md#strategy). -* `hash` - : Allocate each executor a set of values based on hash of the [`partition_column`](#onetl.connection.db_connection.clickhouse.options.ClickhouseReadOptions.partition_column) column. -
- ### Spark generates for each executor an SQL query -
- Executor 1: - ```sql - SELECT ... FROM table - WHERE (some_hash(partition_column) mod num_partitions) = 0 -- lower_bound - ``` -
- Executor 2: - ```sql - SELECT ... FROM table - WHERE (some_hash(partition_column) mod num_partitions) = 1 -- lower_bound + 1 - ``` -
- … -
- Executor N: - ```sql - SELECT ... FROM table - WHERE (some_hash(partition_column) mod num_partitions) = num_partitions-1 -- upper_bound - ``` -
- #### NOTE - The hash function implementation depends on RDBMS. It can be `MD5` or any other fast hash function, - or expression based on this function call. Usually such functions accepts any column type as an input. -* `mod` - : Allocate each executor a set of values based on modulus of the [`partition_column`](#onetl.connection.db_connection.clickhouse.options.ClickhouseReadOptions.partition_column) column. -
- ### Spark generates for each executor an SQL query -
- Executor 1: - ```sql - SELECT ... FROM table - WHERE (partition_column mod num_partitions) = 0 -- lower_bound - ``` -
- Executor 2: - ```sql - SELECT ... FROM table - WHERE (partition_column mod num_partitions) = 1 -- lower_bound + 1 - ``` -
- Executor N: - ```sql - SELECT ... FROM table - WHERE (partition_column mod num_partitions) = num_partitions-1 -- upper_bound - ``` -
- #### NOTE - Can be used only with columns of integer type. - -#### Versionadded -Added in version 0.5.0. - -### Examples - -Read data in 10 parallel jobs by range of values in `id_column` column: - -```python -ReadOptions( - partitioning_mode="range", # default mode, can be omitted - partitionColumn="id_column", - numPartitions=10, - # Options below can be discarded because they are - # calculated automatically as MIN and MAX values of `partitionColumn` - lowerBound=0, - upperBound=100_000, -) -``` - -Read data in 10 parallel jobs by hash of values in `some_column` column: - -```python -ReadOptions( - partitioning_mode="hash", - partitionColumn="some_column", - numPartitions=10, - # lowerBound and upperBound are automatically set to `0` and `9` -) -``` - -Read data in 10 parallel jobs by modulus of values in `id_column` column: - -```python -ReadOptions( - partitioning_mode="mod", - partitionColumn="id_column", - numPartitions=10, - # lowerBound and upperBound are automatically set to `0` and `9` -) -``` - - diff --git a/mddocs/markdown/connection/db_connection/clickhouse/sql.md b/mddocs/markdown/connection/db_connection/clickhouse/sql.md deleted file mode 100644 index e2ce4add5..000000000 --- a/mddocs/markdown/connection/db_connection/clickhouse/sql.md +++ /dev/null @@ -1,191 +0,0 @@ - - -# Reading from Clickhouse using `Clickhouse.sql` - -`Clickhouse.sql` allows passing custom SQL query, but does not support incremental strategies. - -#### WARNING -Please take into account [Clickhouse <-> Spark type mapping](types.md#clickhouse-types) - -#### WARNING -Statement is executed in **read-write** connection, so if you’re calling some functions/procedures with DDL/DML statements inside, -they can change data in your database. - -## Syntax support - -Only queries with the following syntax are supported: - -* ✅︎ `SELECT ... FROM ...` -* ✅︎ `WITH alias AS (...) SELECT ...` -* ❌ `SET ...; SELECT ...;` - multiple statements not supported - -## Examples - -```python -from onetl.connection import Clickhouse - -clickhouse = Clickhouse(...) -df = clickhouse.sql( - """ - SELECT - id, - key, - CAST(value AS String) value, - updated_at - FROM - some.mytable - WHERE - key = 'something' - """, - options=Clickhouse.SQLOptions( - partitionColumn="id", - numPartitions=10, - lowerBound=0, - upperBound=1000, - ), -) -``` - -## Recommendations - -### Select only required columns - -Instead of passing `SELECT * FROM ...` prefer passing exact column names `SELECT col1, col2, ...`. -This reduces the amount of data passed from Clickhouse to Spark. - -### Pay attention to `where` value - -Instead of filtering data on Spark side using `df.filter(df.column == 'value')` pass proper `WHERE column = 'value'` clause. -This both reduces the amount of data send from Clickhouse to Spark, and may also improve performance of the query. -Especially if there are indexes or partitions for columns used in `where` clause. - -## Options - -### *pydantic model* onetl.connection.db_connection.clickhouse.options.ClickhouseSQLOptions - -Options specifically for SQL queries - -These options allow you to specify configurations for executing SQL queries -without relying on Spark’s partitioning mechanisms. - -#### Versionadded -Added in version 0.11.0: Split up `Clickhouse.ReadOptions` to `Clickhouse.SQLOptions` - -### Examples - -#### NOTE -You can pass any JDBC configuration -[supported by Spark](https://spark.apache.org/docs/latest/sql-data-sources-jdbc.html), -tailored to optimize SQL query execution. **Option names should be in** `camelCase`! - -```python -from onetl.connection import Clickhouse - -options = Clickhouse.SQLOptions( - partitionColumn="reg_id", - numPartitions=10, - lowerBound=0, - upperBound=1000, - customSparkOption="value", -) -``` - - - -#### *field* partition_column *: str | None* *= None* *(alias 'partitionColumn')* - -Column used to partition data across multiple executors for parallel query processing. - -#### WARNING -It is highly recommended to use primary key, or column with an index -to avoid performance issues. - -### Example of using `partitionColumn="id"` with `partitioning_mode="range"` - -```sql --- If partition_column is 'id', with numPartitions=4, lowerBound=1, and upperBound=100: --- Executor 1 processes IDs from 1 to 25 -SELECT ... FROM table WHERE id >= 1 AND id < 26 --- Executor 2 processes IDs from 26 to 50 -SELECT ... FROM table WHERE id >= 26 AND id < 51 --- Executor 3 processes IDs from 51 to 75 -SELECT ... FROM table WHERE id >= 51 AND id < 76 --- Executor 4 processes IDs from 76 to 100 -SELECT ... FROM table WHERE id >= 76 AND id <= 100 - --- General case for Executor N -SELECT ... FROM table -WHERE partition_column >= (lowerBound + (N-1) * stride) -AND partition_column <= upperBound --- Where ``stride`` is calculated as ``(upperBound - lowerBound) / numPartitions``. -``` - - - -#### *field* num_partitions *: int | None* *= None* *(alias 'numPartitions')* - -Number of jobs created by Spark to read the table content in parallel. - - - -#### *field* lower_bound *: int | None* *= None* *(alias 'lowerBound')* - -Defines the starting boundary for partitioning the query’s data. Mandatory if [`partition_column`](#onetl.connection.db_connection.clickhouse.options.ClickhouseSQLOptions.partition_column) is set - - - -#### *field* upper_bound *: int | None* *= None* *(alias 'upperBound')* - -Sets the ending boundary for data partitioning. Mandatory if [`partition_column`](#onetl.connection.db_connection.clickhouse.options.ClickhouseSQLOptions.partition_column) is set - - - -#### *field* session_init_statement *: str | None* *= None* *(alias 'sessionInitStatement')* - -After each database session is opened to the remote DB and before starting to read data, -this option executes a custom SQL statement (or a PL/SQL block). - -Use this to implement session initialization code. - -Example: - -```python -sessionInitStatement = """ - BEGIN - execute immediate - 'alter session set "_serial_direct_read"=true'; - END; -""" -``` - - - -#### *field* query_timeout *: int | None* *= None* *(alias 'queryTimeout')* - -The number of seconds the driver will wait for a statement to execute. -Zero means there is no limit. - -This option depends on driver implementation, -some drivers can check the timeout of each query instead of an entire JDBC batch. - - - -#### *field* fetchsize *: int* *= 100000* - -Fetch N rows from an opened cursor per one read round. - -Tuning this option can influence performance of reading. - -#### WARNING -Default value is different from Spark. - -Spark uses driver’s own value, and it may be different in different drivers, -and even versions of the same driver. For example, Oracle has -default `fetchsize=10`, which is absolutely not usable. - -Thus we’ve overridden default value with `100_000`, which should increase reading performance. - -#### Versionchanged -Changed in version 0.2.0: Set explicit default value to `100_000` - - diff --git a/mddocs/markdown/connection/db_connection/clickhouse/types.md b/mddocs/markdown/connection/db_connection/clickhouse/types.md deleted file mode 100644 index 447a8ae26..000000000 --- a/mddocs/markdown/connection/db_connection/clickhouse/types.md +++ /dev/null @@ -1,347 +0,0 @@ - - -# Clickhouse <-> Spark type mapping - -#### NOTE -The results below are valid for Spark 3.5.5, and may differ on other Spark versions. - -#### NOTE -It is recommended to use [spark-dialect-extension](https://github.com/MobileTeleSystems/spark-dialect-extension) package, -which implements writing Arrays from Spark to Clickhouse, fixes dropping fractions of seconds in `TimestampType`, -and fixes other type conversion issues. - -## Type detection & casting - -Spark’s DataFrames always have a `schema` which is a list of columns with corresponding Spark types. All operations on a column are performed using column type. - -### Reading from Clickhouse - -This is how Clickhouse connector performs this: - -* For each column in query result (`SELECT column1, column2, ... FROM table ...`) get column name and Clickhouse type. -* Find corresponding `Clickhouse type (read)` → `Spark type` combination (see below) for each DataFrame column. If no combination is found, raise exception. -* Create DataFrame from query with specific column names and Spark types. - -### Writing to some existing Clickhouse table - -This is how Clickhouse connector performs this: - -* Get names of columns in DataFrame. [1](#id3) -* Perform `SELECT * FROM table LIMIT 0` query. -* Take only columns present in DataFrame (by name, case insensitive). For each found column get Clickhouse type. -* **Find corresponding** `Clickhouse type (read)` → `Spark type` **combination** (see below) for each DataFrame column. If no combination is found, raise exception. [2](#id4) -* Find corresponding `Spark type` → `Clickhousetype (write)` combination (see below) for each DataFrame column. If no combination is found, raise exception. -* If `Clickhousetype (write)` match `Clickhouse type (read)`, no additional casts will be performed, DataFrame column will be written to Clickhouse as is. -* If `Clickhousetype (write)` does not match `Clickhouse type (read)`, DataFrame column will be casted to target column type **on Clickhouse side**. For example, you can write column with text data to `Int32` column, if column contains valid integer values within supported value range and precision. - -* **[1]** This allows to write data to tables with `DEFAULT` columns - if DataFrame has no such column, it will be populated by Clickhouse. -* **[2]** Yes, this is weird. - -### Create new table using Spark - -#### WARNING -ABSOLUTELY NOT RECOMMENDED! - -This is how Clickhouse connector performs this: - -* Find corresponding `Spark type` → `Clickhouse type (create)` combination (see below) for each DataFrame column. If no combination is found, raise exception. -* Generate DDL for creating table in Clickhouse, like `CREATE TABLE (col1 ...)`, and run it. -* Write DataFrame to created table as is. - -But Spark does not have specific dialect for Clickhouse, so Generic JDBC dialect is used. -Generic dialect is using SQL ANSI type names while creating tables in target database, not database-specific types. - -If some cases this may lead to using wrong column type. For example, Spark creates column of type `TIMESTAMP` -which corresponds to Clickhouse type `DateTime32` (precision up to seconds) -instead of more precise `DateTime64` (precision up to nanoseconds). -This may lead to incidental precision loss, or sometimes data cannot be written to created table at all. - -So instead of relying on Spark to create tables: - -### See example - -```python -writer = DBWriter( - connection=clickhouse, - target="default.target_tbl", - options=Clickhouse.WriteOptions( - if_exists="append", - # ENGINE is required by Clickhouse - createTableOptions="ENGINE = MergeTree() ORDER BY id", - ), -) -writer.run(df) -``` - -Always prefer creating tables with specific types **BEFORE WRITING DATA**: - -### See example - -```python -clickhouse.execute( - """ - CREATE TABLE default.target_tbl ( - id UInt8, - value DateTime64(6) -- specific type and precision - ) - ENGINE = MergeTree() - ORDER BY id - """, -) - -writer = DBWriter( - connection=clickhouse, - target="default.target_tbl", - options=Clickhouse.WriteOptions(if_exists="append"), -) -writer.run(df) -``` - -### References - -Here you can find source code with type conversions: - -* [Clickhouse -> JDBC](https://github.com/ClickHouse/clickhouse-java/blob/0.3.2/clickhouse-jdbc/src/main/java/com/clickhouse/jdbc/JdbcTypeMapping.java#L39-L176) -* [JDBC -> Spark](https://github.com/apache/spark/blob/v3.5.5/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JdbcUtils.scala#L307) -* [Spark -> JDBC](https://github.com/apache/spark/blob/v3.5.5/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JdbcUtils.scala#L141-L164) -* [JDBC -> Clickhouse](https://github.com/ClickHouse/clickhouse-java/blob/0.3.2/clickhouse-jdbc/src/main/java/com/clickhouse/jdbc/JdbcTypeMapping.java#L185-L311) - -## Supported types - -See [official documentation](https://clickhouse.com/docs/en/sql-reference/data-types) - -### Generic types - -* `LowCardinality(T)` is same as `T` -* `Nullable(T)` is same as `T`, but Spark column is inferred as `nullable=True` - -### Numeric types - -| Clickhouse type (read) | Spark type | Clickhouse type (write) | Clickhouse type (create) | -|------------------------------|----------------------------------|-----------------------------|-----------------------------| -| `Bool` | `BooleanType()` | `Bool` | `UInt64` | -| `Decimal` | `DecimalType(P=10, S=0)` | `Decimal(P=10, S=0)` | `Decimal(P=10, S=0)` | -| `Decimal(P=0..38)` | `DecimalType(P=0..38, S=0)` | `Decimal(P=0..38, S=0)` | `Decimal(P=0..38, S=0)` | -| `Decimal(P=0..38, S=0..38)` | `DecimalType(P=0..38, S=0..38)` | `Decimal(P=0..38, S=0..38)` | `Decimal(P=0..38, S=0..38)` | -| `Decimal(P=39..76, S=0..76)` | unsupported [3](#id9) | | | -| `Decimal32(P=0..9)` | `DecimalType(P=9, S=0..9)` | `Decimal(P=9, S=0..9)` | `Decimal(P=9, S=0..9)` | -| `Decimal64(S=0..18)` | `DecimalType(P=18, S=0..18)` | `Decimal(P=18, S=0..18)` | `Decimal(P=18, S=0..18)` | -| `Decimal128(S=0..38)` | `DecimalType(P=38, S=0..38)` | `Decimal(P=38, S=0..38)` | `Decimal(P=38, S=0..38)` | -| `Decimal256(S=0..76)` | unsupported [3](#id9) | | | -| `Float32` | `FloatType()` | `Float32` | `Float32` | -| `Float64` | `DoubleType()` | `Float64` | `Float64` | -| `Int8` | `IntegerType()` | `Int32` | `Int32` | -| `Int16` | | | | -| `Int32` | | | | -| `Int64` | `LongType()` | `Int64` | `Int64` | -| `Int128` | unsupported [3](#id9) | | | -| `Int256` | | | | -| `-` | `ByteType()` | `Int8` | `Int8` | -| `-` | `ShortType()` | `Int32` | `Int32` | -| `UInt8` | `IntegerType()` | `Int32` | `Int32` | -| `UInt16` | `LongType()` | `Int64` | `Int64` | -| `UInt32` | `DecimalType(20,0)` | `Decimal(20,0)` | `Decimal(20,0)` | -| `UInt64` | | | | -| `UInt128` | unsupported [3](#id9) | | | -| `UInt256` | | | | -* **[3]** Clickhouse support numeric types up to 256 bit - `Int256`, `UInt256`, `Decimal256(S)`, `Decimal(P=39..76, S=0..76)`. But Spark’s `DecimalType(P, S)` supports maximum `P=38` (128 bit). It is impossible to read, write or operate with values of larger precision, this leads to an exception. - -### Temporal types - -Notes: -: * Datetime with timezone has the same precision as without timezone - * `DateTime` is alias for `DateTime32` - * `TIMESTAMP` is alias for `DateTime32`, but `TIMESTAMP(N)` is alias for `DateTime64(N)` - -| Clickhouse type (read) | Spark type | Clickhouse type (write) | Clickhouse type (create) | -|---------------------------------|-------------------------------------------------------------------------------|-------------------------------|---------------------------------------------------------------------| -| `Date` | `DateType()` | `Date` | `Date` | -| `Date32` | `DateType()` | `Date` | `Date`,
**cannot insert data** [4](#id15) | -| `DateTime32`, seconds | `TimestampType()`, microseconds | `DateTime64(6)`, microseconds | `DateTime32`, seconds | -| `DateTime64(3)`, milliseconds | `TimestampType()`, microseconds | `DateTime64(6)`, microseconds | `DateTime32`, seconds,
**precision loss** [5](#id16) | -| `DateTime64(6)`, microseconds | `TimestampType()`, microseconds | | `DateTime32`, seconds,
**precision loss** [7](#id18) | -| `DateTime64(7..9)`, nanoseconds | `TimestampType()`, microseconds,
**precision loss** [6](#id17) | | | -| `-` | `TimestampNTZType()`, microseconds | | | -| `DateTime32(TZ)` | unsupported [7](#id18) | | | -| `DateTime64(P, TZ)` | | | | -| `IntervalNanosecond` | `LongType()` | `Int64` | `Int64` | -| `IntervalMicrosecond` | | | | -| `IntervalMillisecond` | | | | -| `IntervalSecond` | | | | -| `IntervalMinute` | | | | -| `IntervalHour` | | | | -| `IntervalDay` | | | | -| `IntervalMonth` | | | | -| `IntervalQuarter` | | | | -| `IntervalWeek` | | | | -| `IntervalYear` | | | | - -#### WARNING -Note that types in Clickhouse and Spark have different value ranges: - -| Clickhouse type | Min value | Max value | Spark type | Min value | Max value | -|----------------------|---------------------------------|---------------------------------|-------------------|------------------------------|------------------------------| -| `Date` | `1970-01-01` | `2149-06-06` | `DateType()` | `0001-01-01` | `9999-12-31` | -| `DateTime32` | `1970-01-01 00:00:00` | `2106-02-07 06:28:15` | `TimestampType()` | `0001-01-01 00:00:00.000000` | `9999-12-31 23:59:59.999999` | -| `DateTime64(P=0..8)` | `1900-01-01 00:00:00.00000000` | `2299-12-31 23:59:59.99999999` | | | | -| `DateTime64(P=9)` | `1900-01-01 00:00:00.000000000` | `2262-04-11 23:47:16.999999999` | | | | - -So not all of values in Spark DataFrame can be written to Clickhouse. - -References: -: * [Clickhouse Date documentation](https://clickhouse.com/docs/en/sql-reference/data-types/date) - * [Clickhouse Datetime32 documentation](https://clickhouse.com/docs/en/sql-reference/data-types/datetime) - * [Clickhouse Datetime64 documentation](https://clickhouse.com/docs/en/sql-reference/data-types/datetime64) - * [Spark DateType documentation](https://spark.apache.org/docs/latest/api/java/org/apache/spark/sql/types/DateType.html) - * [Spark TimestampType documentation](https://spark.apache.org/docs/latest/api/java/org/apache/spark/sql/types/TimestampType.html) - -* **[4]** `Date32` has different bytes representation than `Date`, and inserting value of type `Date32` to `Date` column leads to errors on Clickhouse side, e.g. `Date(106617) should be between 0 and 65535 inclusive of both values`. Although Spark does properly read the `Date32` column as `DateType()`, and there should be no difference at all. Probably this is some bug in Clickhouse driver. -* **[5]** Generic JDBC dialect generates DDL with Clickhouse type `TIMESTAMP` which is alias for `DateTime32` with precision up to seconds (`23:59:59`). Inserting data with milliseconds precision (`23:59:59.999`) will lead to **throwing away milliseconds**. Solution: create table manually, with proper column type. -* **[6]** Clickhouse support datetime up to nanoseconds precision (`23:59:59.999999999`), but Spark `TimestampType()` supports datetime up to microseconds precision (`23:59:59.999999`). Nanoseconds will be lost during read or write operations. Solution: create table manually, with proper column type. -* **[7]** Clickhouse will raise an exception that data in format `2001-01-01 23:59:59.999999` has data `.999999` which does not match format `YYYY-MM-DD hh:mm:ss` of `DateTime32` column type (see [5](#id16)). So Spark can create Clickhouse table, but cannot write data to column of this type. Solution: create table manually, with proper column type. - -### String types - -| Clickhouse type (read) | Spark type | Clickhousetype (write) | Clickhouse type (create) | -|--------------------------|----------------|--------------------------|----------------------------| -| `FixedString(N)` | `StringType()` | `String` | `String` | -| `String` | | | | -| `Enum8` | | | | -| `Enum16` | | | | -| `IPv4` | | | | -| `IPv6` | | | | -| `UUID` | | | | -| `-` | `BinaryType()` | | | - -## Unsupported types - -Columns of these Clickhouse types cannot be read by Spark: -: * `AggregateFunction(func, T)` - * `Array(T)` - * `JSON` - * `Map(K, V)` - * `MultiPolygon` - * `Nested(field1 T1, ...)` - * `Nothing` - * `Point` - * `Polygon` - * `Ring` - * `SimpleAggregateFunction(func, T)` - * `Tuple(T1, T2, ...)` - -Dataframe with these Spark types cannot be written to Clickhouse: -: * `ArrayType(T)` - * `BinaryType()` - * `CharType(N)` - * `DayTimeIntervalType(P, S)` - * `MapType(K, V)` - * `NullType()` - * `StructType([...])` - * `TimestampNTZType()` - * `VarcharType(N)` - -This is because Spark does not have dedicated Clickhouse dialect, and uses Generic JDBC dialect instead. -This dialect does not have type conversion between some types, like Clickhouse `Array` -> Spark `ArrayType()`, and vice versa. - -The is a way to avoid this - just cast everything to `String`. - -## Explicit type cast - -### `DBReader` - -Use `CAST` or `toJSONString` to get column data as string in JSON format, - -For parsing JSON columns in ClickHouse, [`JSON.parse_column`](../../../file_df/file_formats/json.md#onetl.file.format.json.JSON.parse_column) method. - -```python -from pyspark.sql.types import ArrayType, IntegerType - -from onetl.file.format import JSON -from onetl.connection import ClickHouse -from onetl.db import DBReader - -reader = DBReader( - connection=clickhouse, - target="default.source_tbl", - columns=[ - "id", - "toJSONString(array_column) array_column", - ], -) -df = reader.run() - -# Spark requires all columns to have some specific type, describe it -column_type = ArrayType(IntegerType()) - -json = JSON() -df = df.select( - df.id, - json.parse_column("array_column", column_type), -) -``` - -### `DBWriter` - -For writing JSON data to ClickHouse, use the [`JSON.serialize_column`](../../../file_df/file_formats/json.md#onetl.file.format.json.JSON.serialize_column) method to convert a DataFrame column to JSON format efficiently and write it as a `String` column in Clickhouse. - -```python -from onetl.file.format import JSON -from onetl.connection import ClickHouse -from onetl.db import DBWriter - -clickhouse = ClickHouse(...) - -clickhouse.execute( - """ - CREATE TABLE default.target_tbl ( - id Int32, - array_column_json String, - ) - ENGINE = MergeTree() - ORDER BY id - """, -) - -json = JSON() -df = df.select( - df.id, - json.serialize_column(df.array_column).alias("array_column_json"), -) - -writer.run(df) -``` - -Then you can parse this column on Clickhouse side - for example, by creating a view: - -```sql -SELECT - id, - JSONExtract(json_column, 'Array(String)') AS array_column -FROM target_tbl -``` - -You can also use [ALIAS](https://clickhouse.com/docs/en/sql-reference/statements/create/table#alias) -or [MATERIALIZED](https://clickhouse.com/docs/en/sql-reference/statements/create/table#materialized) columns -to avoid writing such expression in every `SELECT` clause all the time: - -```sql -CREATE TABLE default.target_tbl ( - id Int32, - array_column_json String, - -- computed column - array_column Array(String) ALIAS JSONExtract(json_column, 'Array(String)') - -- or materialized column - -- array_column Array(String) MATERIALIZED JSONExtract(json_column, 'Array(String)') -) -ENGINE = MergeTree() -ORDER BY id -``` - -Downsides: - -* Using `SELECT JSONExtract(...)` or `ALIAS` column can be expensive, because value is calculated on every row access. This can be especially harmful if such column is used in `WHERE` clause. -* `ALIAS` and `MATERIALIZED` columns are not included in `SELECT *` clause, they should be added explicitly: `SELECT *, calculated_column FROM table`. - -#### WARNING -[EPHEMERAL](https://clickhouse.com/docs/en/sql-reference/statements/create/table#ephemeral) columns are not supported by Spark -because they cannot be selected to determine target column type. diff --git a/mddocs/markdown/connection/db_connection/clickhouse/write.md b/mddocs/markdown/connection/db_connection/clickhouse/write.md deleted file mode 100644 index dd8ca31cc..000000000 --- a/mddocs/markdown/connection/db_connection/clickhouse/write.md +++ /dev/null @@ -1,187 +0,0 @@ - - -# Writing to Clickhouse using `DBWriter` - -For writing data to Clickhouse, use [`DBWriter`](../../../db/db_writer.md#onetl.db.db_writer.db_writer.DBWriter). - -#### WARNING -Please take into account [Clickhouse <-> Spark type mapping](types.md#clickhouse-types) - -#### WARNING -It is always recommended to create table explicitly using [Clickhouse.execute](execute.md#clickhouse-execute) -instead of relying on Spark’s table DDL generation. - -This is because Spark’s DDL generator can create columns with different precision and types than it is expected, -causing precision loss or other issues. - -## Examples - -```python -from onetl.connection import Clickhouse -from onetl.db import DBWriter - -clickhouse = Clickhouse(...) - -df = ... # data is here - -writer = DBWriter( - connection=clickhouse, - target="schema.table", - options=Clickhouse.WriteOptions( - if_exists="append", - # ENGINE is required by Clickhouse - createTableOptions="ENGINE = MergeTree() ORDER BY id", - ), -) - -writer.run(df) -``` - -## Options - -Method above accepts [`Clickhouse.WriteOptions`](#onetl.connection.db_connection.clickhouse.options.ClickhouseWriteOptions) - -### *pydantic model* onetl.connection.db_connection.clickhouse.options.ClickhouseWriteOptions - -Spark JDBC writing options. - -#### Versionadded -Added in version 0.5.0: Replace `Clickhouse.Options` → `Clickhouse.WriteOptions` - -### Examples - -#### NOTE -You can pass any value -[supported by Spark](https://spark.apache.org/docs/latest/sql-data-sources-jdbc.html), -even if it is not mentioned in this documentation. **Option names should be in** `camelCase`! - -The set of supported options depends on Spark version. - -```python -from onetl.connection import Clickhouse - -options = Clickhouse.WriteOptions( - if_exists="append", - batchsize=20_000, - customSparkOption="value", -) -``` - - - -#### *field* if_exists *: JDBCTableExistBehavior* *= JDBCTableExistBehavior.APPEND* *(alias 'mode')* - -Behavior of writing data into existing table. - -Possible values: -: * `append` (default) - : Adds new rows into existing table. -
- ### Behavior in details -
- * Table does not exist - : Table is created using options provided by user - (`createTableOptions`, `createTableColumnTypes`, etc). - * Table exists - : Data is appended to a table. Table has the same DDL as before writing data -
- #### WARNING - This mode does not check whether table already contains - rows from dataframe, so duplicated rows can be created. -
- Also Spark does not support passing custom options to - insert statement, like `ON CONFLICT`, so don’t try to - implement deduplication using unique indexes or constraints. -
- Instead, write to staging table and perform deduplication - using `execute` method. - * `replace_entire_table` - : **Table is dropped and then created, or truncated**. -
- ### Behavior in details -
- * Table does not exist - : Table is created using options provided by user - (`createTableOptions`, `createTableColumnTypes`, etc). - * Table exists - : Table content is replaced with dataframe content. -
- After writing completed, target table could either have the same DDL as - before writing data (`truncate=True`), or can be recreated (`truncate=False` - or source does not support truncation). - * `ignore` - : Ignores the write operation if the table already exists. -
- ### Behavior in details -
- * Table does not exist - : Table is created using options provided by user - (`createTableOptions`, `createTableColumnTypes`, etc). - * Table exists - : The write operation is ignored, and no data is written to the table. - * `error` - : Raises an error if the table already exists. -
- ### Behavior in details -
- * Table does not exist - : Table is created using options provided by user - (`createTableOptions`, `createTableColumnTypes`, etc). - * Table exists - : An error is raised, and no data is written to the table. - -#### Versionchanged -Changed in version 0.9.0: Renamed `mode` → `if_exists` - - - -#### *field* query_timeout *: int | None* *= None* *(alias 'queryTimeout')* - -The number of seconds the driver will wait for a statement to execute. -Zero means there is no limit. - -This option depends on driver implementation, -some drivers can check the timeout of each query instead of an entire JDBC batch. - - - -#### *field* batchsize *: int* *= 20000* - -How many rows can be inserted per round trip. - -Tuning this option can influence performance of writing. - -#### WARNING -Default value is different from Spark. - -Spark uses quite small value `1000`, which is absolutely not usable -in BigData world. - -Thus we’ve overridden default value with `20_000`, -which should increase writing performance. - -You can increase it even more, up to `50_000`, -but it depends on your database load and number of columns in the row. -Higher values does not increase performance. - -#### Versionchanged -Changed in version 0.4.0: Changed default value from 1000 to 20_000 - - - -#### *field* isolation_level *: str* *= 'READ_UNCOMMITTED'* *(alias 'isolationLevel')* - -The transaction isolation level, which applies to current connection. - -Possible values: -: * `NONE` (as string, not Python’s `None`) - * `READ_COMMITTED` - * `READ_UNCOMMITTED` - * `REPEATABLE_READ` - * `SERIALIZABLE` - -Values correspond to transaction isolation levels defined by JDBC standard. -Please refer the documentation for -[java.sql.Connection](https://docs.oracle.com/javase/8/docs/api/java/sql/Connection.html). - - diff --git a/mddocs/markdown/connection/db_connection/greenplum/connection.md b/mddocs/markdown/connection/db_connection/greenplum/connection.md deleted file mode 100644 index b180a5fc7..000000000 --- a/mddocs/markdown/connection/db_connection/greenplum/connection.md +++ /dev/null @@ -1,144 +0,0 @@ - - -# Greenplum connection - -### *class* onetl.connection.db_connection.greenplum.connection.Greenplum(\*, spark: SparkSession, host: Host, user: str, password: SecretStr, database: str, port: int = 5432, extra: GreenplumExtra = GreenplumExtra(tcpKeepAlive='true')) - -Greenplum connection. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -Based on package `io.pivotal:greenplum-spark:2.2.0` -([VMware Greenplum connector for Spark](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/index.html)). - -#### SEE ALSO -Before using this connector please take into account [Prerequisites](prerequisites.md#greenplum-prerequisites) - -#### Versionadded -Added in version 0.5.0. - -* **Parameters:** - **host** - : Host of Greenplum master. For example: `test.greenplum.domain.com` or `193.168.1.17` - - **port** - : Port of Greenplum master - - **user** - : User, which have proper access to the database. For example: `some_user` - - **password** - : Password for database connection - - **database** - : Database in RDBMS, NOT schema. -
- See [this page](https://www.educba.com/postgresql-database-vs-schema/) for more details - - **spark** - : Spark session. - - **extra** - : Specifies one or more extra parameters by which clients can connect to the instance. -
- For example: `{"tcpKeepAlive": "true", "server.port": "50000-65535"}` -
- Supported options are: - : * All [Postgres JDBC driver properties](https://jdbc.postgresql.org/documentation/use/) - * Properties from [Greenplum connector for Spark documentation](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/options.html) page, but only starting with `server.` or `pool.` - -### Examples - -Create and check Greenplum connection: - -```python -from onetl.connection import Greenplum -from pyspark.sql import SparkSession - -# Create Spark session with Greenplum connector loaded -maven_packages = Greenplum.get_packages(spark_version="3.2") -spark = ( - SparkSession.builder.appName("spark-app-name") - .config("spark.jars.packages", ",".join(maven_packages)) - .config("spark.executor.allowSparkContext", "true") - # IMPORTANT!!! - # Set number of executors according to "Prerequisites" -> "Number of executors" - .config("spark.dynamicAllocation.maxExecutors", 10) - .config("spark.executor.cores", 1) - .getOrCreate() -) - -# IMPORTANT!!! -# Set port range of executors according to "Prerequisites" -> "Network ports" -extra = { - "server.port": "41000-42000", -} - -# Create connection -greenplum = Greenplum( - host="master.host.or.ip", - user="user", - password="*****", - database="target_database", - extra=extra, - spark=spark, -).check() -``` - - - -#### check() - -Check source availability. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -If not, an exception will be raised. - -* **Returns:** - Connection itself -* **Raises:** - RuntimeError - : If the connection is not available - -### Examples - -```python -connection.check() -``` - - - -#### *classmethod* get_packages(\*, scala_version: str | None = None, spark_version: str | None = None, package_version: str | None = None) → list[str] - -Get package names to be downloaded by Spark. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### WARNING -You should pass either `scala_version` or `spark_version`. - -#### Versionadded -Added in version 0.9.0. - -* **Parameters:** - **scala_version** - : Scala version in format `major.minor`. -
- If `None`, `spark_version` is used to determine Scala version. - - **spark_version** - : Spark version in format `major.minor`. -
- Used only if `scala_version=None`. - - **package_version** - : Package version in format `major.minor.patch` -
- #### Versionadded - Added in version 0.10.1. - -### Examples - -```python -from onetl.connection import Greenplum - -Greenplum.get_packages(scala_version="2.12") -Greenplum.get_packages(spark_version="3.2", package_version="2.3.0") -``` - - diff --git a/mddocs/markdown/connection/db_connection/greenplum/execute.md b/mddocs/markdown/connection/db_connection/greenplum/execute.md deleted file mode 100644 index c923512a3..000000000 --- a/mddocs/markdown/connection/db_connection/greenplum/execute.md +++ /dev/null @@ -1,195 +0,0 @@ - - -# Executing statements in Greenplum - -#### WARNING -Methods below **read all the rows** returned from DB **to Spark driver memory**, and then convert them to DataFrame. - -Do **NOT** use them to read large amounts of data. Use [DBReader](read.md#greenplum-read) instead. - -## How to - -There are 2 ways to execute some statement in Greenplum - -### Use `Greenplum.fetch` - -Use this method to perform some `SELECT` query which returns **small number or rows**, like reading -Greenplum config, or reading data from some reference table. Method returns Spark DataFrame. - -Method accepts [`Greenplum.FetchOptions`](#onetl.connection.db_connection.greenplum.options.GreenplumFetchOptions). - -Connection opened using this method should be then closed with `connection.close()` or `with connection:`. - -#### WARNING -`Greenplum.fetch` is implemented using Postgres JDBC connection, -so types are handled a bit differently than in `DBReader`. See [Postgres <-> Spark type mapping](../postgres/types.md#postgres-types). - -#### Syntax support - -This method supports **any** query syntax supported by Greenplum, like: - -* ✅︎ `SELECT ... FROM ...` -* ✅︎ `WITH alias AS (...) SELECT ...` -* ✅︎ `SELECT func(arg1, arg2)` or `{call func(arg1, arg2)}` - special syntax for calling functions -* ❌ `SET ...; SELECT ...;` - multiple statements not supported - -#### Examples - -```python -from onetl.connection import Greenplum - -greenplum = Greenplum(...) - -df = greenplum.fetch( - "SELECT value FROM some.reference_table WHERE key = 'some_constant'", - options=Greenplum.FetchOptions(queryTimeout=10), -) -greenplum.close() -value = df.collect()[0][0] # get value from first row and first column -``` - -### Use `Greenplum.execute` - -Use this method to execute DDL and DML operations. Each method call runs operation in a separated transaction, and then commits it. - -Method accepts [`Greenplum.ExecuteOptions`](#onetl.connection.db_connection.greenplum.options.GreenplumExecuteOptions). - -Connection opened using this method should be then closed with `connection.close()` or `with connection:`. - -#### Syntax support - -This method supports **any** query syntax supported by Greenplum, like: - -* ✅︎ `CREATE TABLE ...`, `CREATE VIEW ...`, and so on -* ✅︎ `ALTER ...` -* ✅︎ `INSERT INTO ... SELECT ...`, `UPDATE ...`, `DELETE ...`, and so on -* ✅︎ `DROP TABLE ...`, `DROP VIEW ...`, `TRUNCATE TABLE`, and so on -* ✅︎ `CALL procedure(arg1, arg2) ...` -* ✅︎ `SELECT func(arg1, arg2)` or `{call func(arg1, arg2)}` - special syntax for calling functions -* ✅︎ other statements not mentioned here -* ❌ `SET ...; SELECT ...;` - multiple statements not supported - -#### Examples - -```python -from onetl.connection import Greenplum - -greenplum = Greenplum(...) - -greenplum.execute("DROP TABLE schema.table") -greenplum.execute( - """ - CREATE TABLE schema.table ( - id int, - key text, - value real - ) - DISTRIBUTED BY id - """, - options=Greenplum.ExecuteOptions(queryTimeout=10), -) -``` - -## Interaction schema - -Unlike reading & writing, executing statements in Greenplum is done **only** through Greenplum master node, -without any interaction between Greenplum segments and Spark executors. More than that, Spark executors are not used in this case. - -The only port used while interacting with Greenplum in this case is `5432` (Greenplum master port). - -### Spark <-> Greenplum interaction during Greenplum.execute()/Greenplum.fetch() - -## Options - -### *pydantic model* onetl.connection.db_connection.greenplum.options.GreenplumFetchOptions - -Options related to fetching data from databases via JDBC. - -#### Versionadded -Added in version 0.11.0: Replace `Greenplum.JDBCOptions` → `Greenplum.FetchOptions` - -### Examples - -#### NOTE -You can pass any value supported by underlying JDBC driver class, -even if it is not mentioned in this documentation. - -```python -from onetl.connection import Greenplum - -options = Greenplum.FetchOptions( - queryTimeout=60_000, - fetchsize=100_000, - customSparkOption="value", -) -``` - - - -#### *field* query_timeout *: int | None* *= None* *(alias 'queryTimeout')* - -The number of seconds the driver will wait for a statement to execute. -Zero means there is no limit. - -This option depends on driver implementation, -some drivers can check the timeout of each query instead of an entire JDBC batch. - - - -#### *field* fetchsize *: int | None* *= None* - -How many rows to fetch per round trip. - -Tuning this option can influence performance of reading. - -#### WARNING -Default value depends on driver. For example, Oracle has -default `fetchsize=10`. - - - -### *pydantic model* onetl.connection.db_connection.greenplum.options.GreenplumExecuteOptions - -Options related to executing statements in databases via JDBC. - -#### Versionadded -Added in version 0.11.0: Replace `Greenplum.JDBCOptions` → `Greenplum.ExecuteOptions` - -### Examples - -#### NOTE -You can pass any value supported by underlying JDBC driver class, -even if it is not mentioned in this documentation. - -```python -from onetl.connection import Greenplum - -options = Greenplum.ExecuteOptions( - queryTimeout=60_000, - customSparkOption="value", -) -``` - - - -#### *field* query_timeout *: int | None* *= None* *(alias 'queryTimeout')* - -The number of seconds the driver will wait for a statement to execute. -Zero means there is no limit. - -This option depends on driver implementation, -some drivers can check the timeout of each query instead of an entire JDBC batch. - - - -#### *field* fetchsize *: int | None* *= None* - -How many rows to fetch per round trip. - -Tuning this option can influence performance of reading. - -#### WARNING -Default value depends on driver. For example, Oracle has -default `fetchsize=10`. - - diff --git a/mddocs/markdown/connection/db_connection/greenplum/index.md b/mddocs/markdown/connection/db_connection/greenplum/index.md deleted file mode 100644 index 9fcc68f66..000000000 --- a/mddocs/markdown/connection/db_connection/greenplum/index.md +++ /dev/null @@ -1,18 +0,0 @@ - - -# Greenplum - -# Connection - -* [Prerequisites](prerequisites.md) -* [Greenplum connection](connection.md) - -# Operations - -* [Reading from Greenplum using `DBReader`](read.md) -* [Writing to Greenplum using `DBWriter`](write.md) -* [Executing statements in Greenplum](execute.md) - -# Troubleshooting - -* [Greenplum <-> Spark type mapping](types.md) diff --git a/mddocs/markdown/connection/db_connection/greenplum/prerequisites.md b/mddocs/markdown/connection/db_connection/greenplum/prerequisites.md deleted file mode 100644 index 15c06300b..000000000 --- a/mddocs/markdown/connection/db_connection/greenplum/prerequisites.md +++ /dev/null @@ -1,358 +0,0 @@ - - -# Prerequisites - -## Version Compatibility - -* Greenplum server versions: - : * Officially declared: 5.x, 6.x, and 7.x (which requires `Greenplum.get_packages(package_version="2.3.0")` or higher) - * Actually tested: 6.23, 7.0 -* Spark versions: 2.3.x - 3.2.x (Spark 3.3+ is not supported yet) -* Java versions: 8 - 11 - -See [official documentation](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.2/greenplum-connector-spark/release_notes.html). - -## Installing PySpark - -To use Greenplum connector you should have PySpark installed (or injected to `sys.path`) -BEFORE creating the connector instance. - -See [Spark](../../../install/spark.md#install-spark) installation instruction for more details. - -## Downloading VMware package - -To use Greenplum connector you should download connector `.jar` file from -[VMware website](https://network.tanzu.vmware.com/products/vmware-greenplum#/releases/1413479/file_groups/16966) -and then pass it to Spark session. - -#### WARNING -Please pay attention to [Spark & Scala version compatibility](../../../install/spark.md#spark-compatibility-matrix). - -#### WARNING -There are issues with using package of version 2.3.0/2.3.1 with Greenplum 6.x - connector can -open transaction with `SELECT * FROM table LIMIT 0` query, but does not close it, which leads to deadlocks -during write. - -There are several ways to do that. See [Injecting Java packages](../../../install/spark.md#java-packages) for details. - -#### NOTE -If you’re uploading package to private package repo, use `groupId=io.pivotal` and `artifactoryId=greenplum-spark_2.12` -(`2.12` is Scala version) to give uploaded package a proper name. - -## Connecting to Greenplum - -### Interaction schema - -Spark executors open ports to listen incoming requests. -Greenplum segments are initiating connections to Spark executors using [EXTERNAL TABLE](https://docs.vmware.com/en/VMware-Greenplum/7/greenplum-database/ref_guide-sql_commands-CREATE_EXTERNAL_TABLE.html) -functionality, and send/read data using [gpfdist protocol](https://docs.vmware.com/en/VMware-Greenplum/7/greenplum-database/admin_guide-external-g-using-the-greenplum-parallel-file-server--gpfdist-.html#about-gpfdist-setup-and-performance-1). - -Data is **not** send through Greenplum master. -Greenplum master only receives commands to start reading/writing process, and manages all the metadata (external table location, schema and so on). - -More details can be found in [official documentation](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/overview.html). - -### Set number of connections - -#### WARNING -This is very important!!! - -If you don’t limit number of connections, you can exceed the [max_connections](https://docs.vmware.com/en/VMware-Greenplum/7/greenplum-database/admin_guide-client_auth.html#limiting-concurrent-connections#limiting-concurrent-connections-2) -limit set on the Greenplum side. It’s usually not so high, e.g. 500-1000 connections max, -depending on your Greenplum instance settings and using connection balancers like `pgbouncer`. - -Consuming all available connections means **nobody** (even admin users) can connect to Greenplum. - -Each job on the Spark executor makes its own connection to Greenplum master node, -so you need to limit number of connections to avoid opening too many of them. - -* Reading about `5-10Gb` of data requires about `3-5` parallel connections. -* Reading about `20-30Gb` of data requires about `5-10` parallel connections. -* Reading about `50Gb` of data requires ~ `10-20` parallel connections. -* Reading about `100+Gb` of data requires `20-30` parallel connections. -* Opening more than `30-50` connections is not recommended. - -Number of connections can be limited by 2 ways: - -* By limiting number of Spark executors and number of cores per-executor. Max number of parallel jobs is `executors * cores`. - -Spark with master=local - -```py -spark = ( - SparkSession.builder - # Spark will run with 5 threads in local mode, allowing up to 5 parallel tasks - .config("spark.master", "local[5]") - .config("spark.executor.cores", 1) -).getOrCreate() -``` - -Spark with master=yarn or master=k8s, dynamic allocation - -```py -spark = ( - SparkSession.builder - .config("spark.master", "yarn") - # Spark will start MAX 10 executors with 1 core each (dynamically), so max number of parallel jobs is 10 - .config("spark.dynamicAllocation.maxExecutors", 10) - .config("spark.executor.cores", 1) -).getOrCreate() -``` - -Spark with master=yarn or master=k8s, static allocation - -```py -spark = ( - SparkSession.builder - .config("spark.master", "yarn") - # Spark will start EXACTLY 10 executors with 1 core each, so max number of parallel jobs is 10 - .config("spark.executor.instances", 10) - .config("spark.executor.cores", 1) -).getOrCreate() -``` - -* By limiting connection pool size user by Spark (**only** for Spark with `master=local`): - -```python -spark = SparkSession.builder.config("spark.master", "local[*]").getOrCreate() - -# No matter how many executors are started and how many cores they have, -# number of connections cannot exceed pool size: -Greenplum( - ..., - extra={ - "pool.maxSize": 10, - }, -) -``` - -See [connection pooling](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/using_the_connector.html#jdbcconnpool) -documentation. - -* By setting [`num_partitions`](read.md#onetl.connection.db_connection.greenplum.options.GreenplumReadOptions.num_partitions) - and [`partition_column`](read.md#onetl.connection.db_connection.greenplum.options.GreenplumReadOptions.partition_column) (not recommended). - -### Allowing connection to Greenplum master - -Ask your Greenplum cluster administrator to allow your user to connect to Greenplum master node, -e.g. by updating `pg_hba.conf` file. - -More details can be found in [official documentation](https://docs.vmware.com/en/VMware-Greenplum/7/greenplum-database/admin_guide-client_auth.html#limiting-concurrent-connections#allowing-connections-to-greenplum-database-0). - -### Set connection port - -#### Spark with `master=k8s` - -Please follow [the official documentation](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/configure.html#k8scfg) - -#### Spark with `master=yarn` or `master=local` - -To read data from Greenplum using Spark, following ports should be opened in firewall between Spark and Greenplum: - -* Spark driver and all Spark executors -> port `5432` on Greenplum master node. - - This port number should be set while connecting to Greenplum: - ```python - greenplum = Greenplum(host="master.host", port=5432, ...) - ``` -* Greenplum segments -> some port range (e.g. `41000-42000`) **listened by Spark executors**. - - This range should be set in `extra` option: - ```python - greenplum = Greenplum( - ..., - extra={ - "server.port": "41000-42000", - }, - ) - ``` - - Number of ports in this range is `number of parallel running Spark sessions` \* `number of parallel connections per session`. - - Number of connections per session (see below) is usually less than `30` (see above). - - Number of session depends on your environment: - : * For `master=local` only few ones-tens sessions can be started on the same host, depends on available RAM and CPU. - * For `master=yarn` hundreds or thousands of sessions can be started simultaneously, - but they are executing on different cluster nodes, so one port can be opened on different nodes at the same time. - -More details can be found in official documentation: -: * [port requirements](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/sys_reqs.html#network-port-requirements) - * [format of server.port value](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/options.html#server.port) - * [port troubleshooting](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/troubleshooting.html#port-errors) - -### Set connection host - -#### Spark with `master=k8s` - -Please follow [the official documentation](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/configure.html#k8scfg) - -#### Spark with `master=local` - -By default, Greenplum connector tries to resolve IP of current host, and then pass it as `gpfdist` URL to Greenplum segment. -This may fail in some cases. - -For example, IP can be resolved using `/etc/hosts` content like this: - -```text -127.0.0.1 localhost real-host-name -``` - -```bash -$ hostname -f -localhost - -$ hostname -i -127.0.0.1 -``` - -Reading/writing data to Greenplum will fail with following exception: - -```text -org.postgresql.util.PSQLException: ERROR: connection with gpfdist failed for -"gpfdist://127.0.0.1:49152/local-1709739764667/exec/driver", -effective url: "http://127.0.0.1:49152/local-1709739764667/exec/driver": -error code = 111 (Connection refused); (seg3 slice1 12.34.56.78:10003 pid=123456) -``` - -There are 2 ways to fix that: - -* Explicitly pass your host IP address to connector, like this - ```python - import os - - # pass here real host IP (accessible from GP segments) - os.environ["HOST_IP"] = "192.168.1.1" - - greenplum = Greenplum( - ..., - extra={ - # connector will read IP from this environment variable - "server.hostEnv": "env.HOST_IP", - }, - spark=spark, - ) - ``` - - More details can be found in [official documentation](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/options.html#server.hostenv). -* Update `/etc/hosts` file to include real host IP: - ```text - 127.0.0.1 localhost - # this IP should be accessible from GP segments - 192.168.1.1 driver-host-name - ``` - - So Greenplum connector will properly resolve host IP. - -#### Spark with `master=yarn` - -The same issue with resolving IP address can occur on Hadoop cluster node, but it’s tricky to fix, because each node has a different IP. - -There are 3 ways to fix that: - -* Pass node hostname to `gpfdist` URL. So IP will be resolved on segment side: - ```python - greenplum = Greenplum( - ..., - extra={ - "server.useHostname": "true", - }, - ) - ``` - - But this may fail if Hadoop cluster node hostname cannot be resolved from Greenplum segment side. - - More details can be found in [official documentation](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/options.html#server.usehostname). -* Set specific network interface to get IP address from: - ```python - greenplum = Greenplum( - ..., - extra={ - "server.nic": "eth0", - }, - ) - ``` - - You can get list of network interfaces using this command. - - #### NOTE - This command should be executed on Hadoop cluster node, **not** Spark driver host! - - ```bash - $ ip address - 1: lo: mtu 65536 qdisc noqueue state UNKNOWN group default qlen 1000 - inet 127.0.0.1/8 scope host lo - valid_lft forever preferred_lft forever - 2: eth0: mtu 1500 qdisc fq_codel state UP group default qlen 1000 - inet 192.168.1.1/24 brd 192.168.1.255 scope global dynamic noprefixroute eth0 - valid_lft 83457sec preferred_lft 83457sec - ``` - - Note that in this case **each** Hadoop cluster node node should have network interface with name `eth0`. - - More details can be found in [official documentation](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/options.html#server.nic). -* Update `/etc/hosts` on each Hadoop cluster node to include real node IP: - ```text - 127.0.0.1 localhost - # this IP should be accessible from GP segments - 192.168.1.1 cluster-node-name - ``` - - So Greenplum connector will properly resolve node IP. - -### Set required grants - -Ask your Greenplum cluster administrator to set following grants for a user, -used for creating a connection: - -Read + Write - -```sql --- get access to get tables metadata & cluster information -GRANT SELECT ON information_schema.tables TO username; -GRANT SELECT ON pg_attribute TO username; -GRANT SELECT ON pg_class TO username; -GRANT SELECT ON pg_namespace TO username; -GRANT SELECT ON pg_settings TO username; -GRANT SELECT ON pg_stats TO username; -GRANT SELECT ON gp_distributed_xacts TO username; -GRANT SELECT ON gp_segment_configuration TO username; --- Greenplum 5.x only -GRANT SELECT ON gp_distribution_policy TO username; - --- allow creating external tables in the same schema as source/target table -GRANT USAGE ON SCHEMA myschema TO username; -GRANT CREATE ON SCHEMA myschema TO username; -ALTER USER username CREATEEXTTABLE(type = 'readable', protocol = 'gpfdist') CREATEEXTTABLE(type = 'writable', protocol = 'gpfdist'); - --- allow read access to specific table (to get column types) --- allow write access to specific table -GRANT SELECT, INSERT ON myschema.mytable TO username; -``` - -Read only - -```sql --- get access to get tables metadata & cluster information -GRANT SELECT ON information_schema.tables TO username; -GRANT SELECT ON pg_attribute TO username; -GRANT SELECT ON pg_class TO username; -GRANT SELECT ON pg_namespace TO username; -GRANT SELECT ON pg_settings TO username; -GRANT SELECT ON pg_stats TO username; -GRANT SELECT ON gp_distributed_xacts TO username; -GRANT SELECT ON gp_segment_configuration TO username; --- Greenplum 5.x only -GRANT SELECT ON gp_distribution_policy TO username; - --- allow creating external tables in the same schema as source table -GRANT USAGE ON SCHEMA schema_to_read TO username; -GRANT CREATE ON SCHEMA schema_to_read TO username; --- yes, ``writable`` for reading from GP, because data is written from Greenplum to Spark executor. -ALTER USER username CREATEEXTTABLE(type = 'writable', protocol = 'gpfdist'); - --- allow read access to specific table -GRANT SELECT ON schema_to_read.table_to_read TO username; -``` - -More details can be found in [official documentation](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/install_cfg.html#role-privileges). diff --git a/mddocs/markdown/connection/db_connection/greenplum/read.md b/mddocs/markdown/connection/db_connection/greenplum/read.md deleted file mode 100644 index 476f6f0db..000000000 --- a/mddocs/markdown/connection/db_connection/greenplum/read.md +++ /dev/null @@ -1,405 +0,0 @@ - - -# Reading from Greenplum using `DBReader` - -Data can be read from Greenplum to Spark using [`DBReader`](../../../db/db_reader.md#onetl.db.db_reader.db_reader.DBReader). -It also supports [Read Strategies](../../../strategy/index.md#strategy) for incremental data reading. - -#### WARNING -Please take into account [Greenplum <-> Spark type mapping](types.md#greenplum-types). - -#### NOTE -Unlike JDBC connectors, *Greenplum connector for Spark* does not support -executing **custom** SQL queries using `.sql` method. Connector can be used to only read data from a table or view. - -## Supported DBReader features - -* ✅︎ `columns` (see note below) -* ✅︎ `where` (see note below) -* ✅︎ `hwm` (see note below), supported strategies: -* * ✅︎ [Snapshot Strategy](../../../strategy/snapshot_strategy.md#snapshot-strategy) -* * ✅︎ [Incremental Strategy](../../../strategy/incremental_strategy.md#incremental-strategy) -* * ✅︎ [Snapshot Batch Strategy](../../../strategy/snapshot_batch_strategy.md#snapshot-batch-strategy) -* * ✅︎ [Incremental Batch Strategy](../../../strategy/incremental_batch_strategy.md#incremental-batch-strategy) -* ❌ `hint` (is not supported by Greenplum) -* ❌ `df_schema` -* ✅︎ `options` (see [`Greenplum.ReadOptions`](#onetl.connection.db_connection.greenplum.options.GreenplumReadOptions)) - -#### WARNING -In case of Greenplum connector, `DBReader` does not generate raw `SELECT` query. Instead it relies on Spark SQL syntax -which in some cases (using column projection and predicate pushdown) can be converted to Greenplum SQL. - -So `columns`, `where` and `hwm.expression` should be specified in [Spark SQL](https://spark.apache.org/docs/latest/sql-ref-syntax.html) syntax, -not Greenplum SQL. - -This is OK: - -```python -DBReader( - columns=[ - "some_column", - # this cast is executed on Spark side - "CAST(another_column AS STRING)", - ], - # this predicate is parsed by Spark, and can be pushed down to Greenplum - where="some_column LIKE 'val1%'", -) -``` - -This is will fail: - -```python -DBReader( - columns=[ - "some_column", - # Spark does not have `text` type - "CAST(another_column AS text)", - ], - # Spark does not support ~ syntax for regexp matching - where="some_column ~ 'val1.*'", -) -``` - -## Examples - -Snapshot strategy: - -```python -from onetl.connection import Greenplum -from onetl.db import DBReader - -greenplum = Greenplum(...) - -reader = DBReader( - connection=greenplum, - source="schema.table", - columns=["id", "key", "CAST(value AS string) value", "updated_dt"], - where="key = 'something'", -) -df = reader.run() -``` - -Incremental strategy: - -```python -from onetl.connection import Greenplum -from onetl.db import DBReader -from onetl.strategy import IncrementalStrategy - -greenplum = Greenplum(...) - -reader = DBReader( - connection=greenplum, - source="schema.table", - columns=["id", "key", "CAST(value AS string) value", "updated_dt"], - where="key = 'something'", - hwm=DBReader.AutoDetectHWM(name="greenplum_hwm", expression="updated_dt"), -) - -with IncrementalStrategy(): - df = reader.run() -``` - -## Interaction schema - -High-level schema is described in [Prerequisites](prerequisites.md#greenplum-prerequisites). You can find detailed interaction schema below. - -### Spark <-> Greenplum interaction during DBReader.run() - -## Recommendations - -### Select only required columns - -Instead of passing `"*"` in `DBReader(columns=[...])` prefer passing exact column names. This reduces the amount of data passed from Greenplum to Spark. - -### Pay attention to `where` value - -Instead of filtering data on Spark side using `df.filter(df.column == 'value')` pass proper `DBReader(where="column = 'value'")` clause. -This both reduces the amount of data send from Greenplum to Spark, and may also improve performance of the query. -Especially if there are indexes or partitions for columns used in `where` clause. - -### Read data in parallel - -`DBReader` in case of Greenplum connector requires view or table to have a column which is used by Spark -for parallel reads. - -Choosing proper column allows each Spark executor to read only part of data stored in the specified segment, -avoiding moving large amounts of data between segments, which improves reading performance. - -#### Using `gp_segment_id` - -By default, `DBReader` will use [gp_segment_id](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/troubleshooting.html#reading-from-a-view) -column for parallel data reading. Each DataFrame partition will contain data of a specific Greenplum segment. - -This allows each Spark executor read only data from specific Greenplum segment, avoiding moving large amounts of data between segments. - -If view is used, it is recommended to include `gp_segment_id` column to this view: - -### Reading from view with gp_segment_id column - -```python -from onetl.connection import Greenplum -from onetl.db import DBReader - -greenplum = Greenplum(...) - -greenplum.execute( - """ - CREATE VIEW schema.view_with_gp_segment_id AS - SELECT - id, - some_column, - another_column, - gp_segment_id -- IMPORTANT - FROM schema.some_table - """, -) - -reader = DBReader( - connection=greenplum, - source="schema.view_with_gp_segment_id", -) -df = reader.run() -``` - -#### Using custom `partition_column` - -Sometimes table or view is lack of `gp_segment_id` column, but there is some column -with value range correlated with Greenplum segment distribution. - -In this case, custom column can be used instead: - -### Reading from view with custom partition_column - -```python -from onetl.connection import Greenplum -from onetl.db import DBReader - -greenplum = Greenplum(...) - -greenplum.execute( - """ - CREATE VIEW schema.view_with_partition_column AS - SELECT - id, - some_column, - part_column -- correlated to greenplum segment ID - FROM schema.some_table - """, -) - -reader = DBReader( - connection=greenplum, - source="schema.view_with_partition_column", - options=Greenplum.ReadOptions( - # parallelize data using specified column - partitionColumn="part_column", - # create 10 Spark tasks, each will read only part of table data - partitions=10, - ), -) -df = reader.run() -``` - -#### Reading `DISTRIBUTED REPLICATED` tables - -Replicated tables do not have `gp_segment_id` column at all, so you need to set `partition_column` to some column name -of type integer/bigint/smallint. - -### Parallel `JOIN` execution - -In case of using views which require some data motion between Greenplum segments, like `JOIN` queries, another approach should be used. - -Each Spark executor N will run the same query, so each of N query will start its own JOIN process, leading to really heavy load on Greenplum segments. -**This should be avoided**. - -Instead is recommended to run `JOIN` query on Greenplum side, save the result to an intermediate table, -and then read this table using `DBReader`: - -### Reading from view using intermediate table - -```python -from onetl.connection import Greenplum -from onetl.db import DBReader - -greenplum = Greenplum(...) - -greenplum.execute( - """ - CREATE UNLOGGED TABLE schema.intermediate_table AS - SELECT - id, - tbl1.col1, - tbl1.data, - tbl2.another_data - FROM - schema.table1 as tbl1 - JOIN - schema.table2 as tbl2 - ON - tbl1.col1 = tbl2.col2 - WHERE ... - """, -) - -reader = DBReader( - connection=greenplum, - source="schema.intermediate_table", -) -df = reader.run() - -# write dataframe somethere - -greenplum.execute( - """ - DROP TABLE schema.intermediate_table - """, -) -``` - -#### WARNING -**NEVER** do that: - -```python -df1 = DBReader(connection=greenplum, target="public.table1", ...).run() -df2 = DBReader(connection=greenplum, target="public.table2", ...).run() - -joined_df = df1.join(df2, on="col") -``` - -This will lead to sending all the data from both `table1` and `table2` to Spark executor memory, and then `JOIN` -will be performed on Spark side, not inside Greenplum. This is **VERY** inefficient. - -#### `TEMPORARY` tables notice - -Someone could think that writing data from view or result of `JOIN` to `TEMPORARY` table, -and then passing it to `DBReader`, is an efficient way to read data from Greenplum. This is because temp tables are not generating WAL files, -and are automatically deleted after finishing the transaction. - -That will **NOT** work. Each Spark executor establishes its own connection to Greenplum. -And each connection starts its own transaction which means that every executor will read empty temporary table. - -You should use [UNLOGGED](https://docs.vmware.com/en/VMware-Greenplum/7/greenplum-database/ref_guide-sql_commands-CREATE_TABLE.html) tables -to write data to intermediate table without generating WAL logs. - -## Options - -### *pydantic model* onetl.connection.db_connection.greenplum.options.GreenplumReadOptions - -VMware’s Greenplum Spark connector reading options. - -#### WARNING -Some options, like `url`, `dbtable`, `server.*`, `pool.*`, -etc are populated from connection attributes, and cannot be overridden by the user in `ReadOptions` to avoid issues. - -### Examples - -#### NOTE -You can pass any value -[supported by connector](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/read_from_gpdb.html), -even if it is not mentioned in this documentation. **Option names should be in** `camelCase`! - -The set of supported options depends on connector version. - -```python -from onetl.connection import Greenplum - -options = Greenplum.ReadOptions( - partitionColumn="reg_id", - partitions=10, -) -``` - - - -#### *field* partition_column *: str | None* *= None* *(alias 'partitionColumn')* - -Column used to parallelize reading from a table. - -#### WARNING -You should not change this option, unless you know what you’re doing. - -It’s preferable to use default values to read data parallel by number of segments in Greenplum cluster. - -Possible values: -: * `None` (default): - : Spark generates N jobs (where N == number of segments in Greenplum cluster), - each job is reading only data from a specific segment - (filtering data by `gp_segment_id` column). -
- This is very effective way to fetch the data from a cluster. - * table column - : Allocate each executor a range of values from a specific column. -
- Spark generates for each executor an SQL query: -
- Executor 1: - ```sql - SELECT ... FROM table - WHERE (partition_column >= lowerBound - OR partition_column IS NULL) - AND partition_column < (lower_bound + stride) - ``` -
- Executor 2: - ```sql - SELECT ... FROM table - WHERE partition_column >= (lower_bound + stride) - AND partition_column < (lower_bound + 2 * stride) - ``` -
- … -
- Executor N: - ```sql - SELECT ... FROM table - WHERE partition_column >= (lower_bound + (N-1) * stride) - AND partition_column <= upper_bound - ``` -
- Where `stride=(upper_bound - lower_bound) / num_partitions`, - `lower_bound=MIN(partition_column)`, `upper_bound=MAX(partition_column)`. -
- #### NOTE - Column type must be numeric. Other types are not supported. -
- #### NOTE - [`num_partitions`](#onetl.connection.db_connection.greenplum.options.GreenplumReadOptions.num_partitions) is used just to - calculate the partition stride, **NOT** for filtering the rows in table. - So all rows in the table will be returned (unlike *Incremental* [Read Strategies](../../../strategy/index.md#strategy)). -
- #### NOTE - All queries are executed in parallel. To execute them sequentially, use *Batch* [Read Strategies](../../../strategy/index.md#strategy). - -#### WARNING -Both options [`partition_column`](#onetl.connection.db_connection.greenplum.options.GreenplumReadOptions.partition_column) and [`num_partitions`](#onetl.connection.db_connection.greenplum.options.GreenplumReadOptions.num_partitions) should have a value, -or both should be `None` - -### Examples - -Read data in 10 parallel jobs by range of values in `id_column` column: - -```python -Greenplum.ReadOptions( - partitionColumn="id_column", - partitions=10, -) -``` - - - -#### *field* num_partitions *: int | None* *= None* *(alias 'partitions')* - -Number of jobs created by Spark to read the table content in parallel. - -See documentation for [`partition_column`](#onetl.connection.db_connection.greenplum.options.GreenplumReadOptions.partition_column) for more details - -#### WARNING -By default connector uses number of segments in the Greenplum cluster. -You should not change this option, unless you know what you’re doing - -#### WARNING -Both options [`partition_column`](#onetl.connection.db_connection.greenplum.options.GreenplumReadOptions.partition_column) and [`num_partitions`](#onetl.connection.db_connection.greenplum.options.GreenplumReadOptions.num_partitions) should have a value, -or both should be `None` - - diff --git a/mddocs/markdown/connection/db_connection/greenplum/types.md b/mddocs/markdown/connection/db_connection/greenplum/types.md deleted file mode 100644 index 6a6c0effb..000000000 --- a/mddocs/markdown/connection/db_connection/greenplum/types.md +++ /dev/null @@ -1,318 +0,0 @@ - - -# Greenplum <-> Spark type mapping - -#### NOTE -The results below are valid for Spark 3.2.4, and may differ on other Spark versions. - -## Type detection & casting - -Spark’s DataFrames always have a `schema` which is a list of columns with corresponding Spark types. All operations on a column are performed using column type. - -### Reading from Greenplum - -This is how Greenplum connector performs this: - -* Execute query `SELECT * FROM table LIMIT 0` [1](#id2). -* For each column in query result get column name and Greenplum type. -* Find corresponding `Greenplum type (read)` → `Spark type` combination (see below) for each DataFrame column. If no combination is found, raise exception. -* Use Spark column projection and predicate pushdown features to build a final query. -* Create DataFrame from generated query with inferred schema. - -* **[1]** Yes, **all columns of a table**, not just selected ones. This means that if source table **contains** columns with unsupported type, the entire table cannot be read. - -### Writing to some existing Greenplum table - -This is how Greenplum connector performs this: - -* Get names of columns in DataFrame. -* Perform `SELECT * FROM table LIMIT 0` query. -* For each column in query result get column name and Greenplum type. -* Match table columns with DataFrame columns (by name, case insensitive). - If some column is present only in target table, but not in DataFrame (like `DEFAULT` or `SERIAL` column), and vice versa, raise an exception. - See [Explicit type cast](). -* Find corresponding `Spark type` → `Greenplumtype (write)` combination (see below) for each DataFrame column. If no combination is found, raise exception. -* If `Greenplumtype (write)` match `Greenplum type (read)`, no additional casts will be performed, DataFrame column will be written to Greenplum as is. -* If `Greenplumtype (write)` does not match `Greenplum type (read)`, DataFrame column will be casted to target column type **on Greenplum side**. For example, you can write column with text data to `json` column which Greenplum connector currently does not support. - -### Create new table using Spark - -#### WARNING -ABSOLUTELY NOT RECOMMENDED! - -This is how Greenplum connector performs this: - -* Find corresponding `Spark type` → `Greenplum type (create)` combination (see below) for each DataFrame column. If no combination is found, raise exception. -* Generate DDL for creating table in Greenplum, like `CREATE TABLE (col1 ...)`, and run it. -* Write DataFrame to created table as is. - -More details [can be found here](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/write_to_gpdb.html). - -But Greenplum connector support only limited number of types and almost no custom clauses (like `PARTITION BY`). -So instead of relying on Spark to create tables: - -### See example - -```python -writer = DBWriter( - connection=greenplum, - target="public.table", - options=Greenplum.WriteOptions( - if_exists="append", - # by default distribution is random - distributedBy="id", - # partitionBy is not supported - ), -) -writer.run(df) -``` - -Always prefer creating table with desired DDL **BEFORE WRITING DATA**: - -### See example - -```python -greenplum.execute( - """ - CREATE TABLE public.table ( - id int32, - business_dt timestamp(6), - value json - ) - PARTITION BY RANGE (business_dt) - DISTRIBUTED BY id - """, -) - -writer = DBWriter( - connection=greenplum, - target="public.table", - options=Greenplum.WriteOptions(if_exists="append"), -) -writer.run(df) -``` - -See Greenplum [CREATE TABLE](https://docs.vmware.com/en/VMware-Greenplum/7/greenplum-database/ref_guide-sql_commands-CREATE_TABLE.html) documentation. - -## Supported types - -See: -: * [official connector documentation](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/reference-datatype_mapping.html) - * [list of Greenplum types](https://docs.vmware.com/en/VMware-Greenplum/7/greenplum-database/ref_guide-data_types.html) - -### Numeric types - -| Greenplum type (read) | Spark type | Greenplumtype (write) | Greenplum type (create) | -|-----------------------------|----------------------------------|-----------------------------|---------------------------| -| `decimal` | `DecimalType(P=38, S=18)` | `decimal(P=38, S=18)` | `decimal` (unbounded) | -| `decimal(P=0..38)` | `DecimalType(P=0..38, S=0)` | `decimal(P=0..38, S=0)` | | -| `decimal(P=0..38, S=0..38)` | `DecimalType(P=0..38, S=0..38)` | `decimal(P=0..38, S=0..38)` | | -| `decimal(P=39.., S=0..)` | unsupported [2](#id4) | | | -| `real` | `FloatType()` | `real` | `real` | -| `double precision` | `DoubleType()` | `double precision` | `double precision` | -| `-` | `ByteType()` | unsupported | unsupported | -| `smallint` | `ShortType()` | `smallint` | `smallint` | -| `integer` | `IntegerType()` | `integer` | `integer` | -| `bigint` | `LongType()` | `bigint` | `bigint` | -| `money` | unsupported | | | -| `int4range` | | | | -| `int8range` | | | | -| `numrange` | | | | -| `int2vector` | | | | -* **[2]** Greenplum support decimal types with unlimited precision. But Spark’s `DecimalType(P, S)` supports maximum `P=38` (128 bit). It is impossible to read, write or operate with values of larger precision, this leads to an exception. - -### Temporal types - -| Greenplum type (read) | Spark type | Greenplumtype (write) | Greenplum type (create) | -|----------------------------------|----------------------------------------------------------------|-------------------------|---------------------------| -| `date` | `DateType()` | `date` | `date` | -| `time` | `TimestampType()`,
time format quirks [3](#id6) | `timestamp` | `timestamp` | -| `time(0..6)` | | | | -| `time with time zone` | | | | -| `time(0..6) with time zone` | | | | -| `timestamp` | `TimestampType()` | `timestamp` | `timestamp` | -| `timestamp(0..6)` | | | | -| `timestamp with time zone` | | | | -| `timestamp(0..6) with time zone` | | | | -| `interval` or any precision | unsupported | | | -| `daterange` | | | | -| `tsrange` | | | | -| `tstzrange` | | | | - -#### WARNING -Note that types in Greenplum and Spark have different value ranges: - -| Greenplum type | Min value | Max value | Spark type | Min value | Max value | -|------------------|-------------------------------|--------------------------------|-------------------|------------------------------|------------------------------| -| `date` | `-4713-01-01` | `5874897-01-01` | `DateType()` | `0001-01-01` | `9999-12-31` | -| `timestamp` | `-4713-01-01 00:00:00.000000` | `294276-12-31 23:59:59.999999` | `TimestampType()` | `0001-01-01 00:00:00.000000` | `9999-12-31 23:59:59.999999` | -| `time` | `00:00:00.000000` | `24:00:00.000000` | | | | - -So not all of values can be read from Greenplum to Spark. - -References: -: * [Greenplum types documentation](https://docs.vmware.com/en/VMware-Greenplum/7/greenplum-database/ref_guide-data_types.html) - * [Spark DateType documentation](https://spark.apache.org/docs/latest/api/java/org/apache/spark/sql/types/DateType.html) - * [Spark TimestampType documentation](https://spark.apache.org/docs/latest/api/java/org/apache/spark/sql/types/TimestampType.html) - -* **[3]** `time` type is the same as `timestamp` with date `1970-01-01`. So instead of reading data from Postgres like `23:59:59` it is actually read `1970-01-01 23:59:59`, and vice versa. - -### String types - -| Greenplum type (read) | Spark type | Greenplumtype (write) | Greenplum type (create) | -|---------------------------|----------------|-------------------------|---------------------------| -| `character` | `StringType()` | `text` | `text` | -| `character(N)` | | | | -| `character varying` | | | | -| `character varying(N)` | | | | -| `text` | | | | -| `xml` | | | | -| `CREATE TYPE ... AS ENUM` | | | | -| `json` | unsupported | | | -| `jsonb` | | | | - -### Binary types - -| Greenplum type (read) | Spark type | Greenplumtype (write) | Greenplum type (create) | -|-------------------------|----------------------------------|-------------------------|---------------------------| -| `boolean` | `BooleanType()` | `boolean` | `boolean` | -| `bit` | unsupported | | | -| `bit(N)` | | | | -| `bit varying` | | | | -| `bit varying(N)` | | | | -| `bytea` | unsupported [4](#id8) | | | -| `-` | `BinaryType()` | `bytea` | `bytea` | -* **[4]** Yes, that’s weird. - -### Struct types - -| Greenplum type (read) | Spark type | Greenplumtype (write) | Greenplum type (create) | -|------------------------------|----------------|-------------------------|---------------------------| -| `T[]` | unsupported | | | -| `-` | `ArrayType()` | unsupported | | -| `CREATE TYPE sometype (...)` | `StringType()` | `text` | `text` | -| `-` | `StructType()` | unsupported | | -| `-` | `MapType()` | | | - -## Unsupported types - -Columns of these types cannot be read/written by Spark: -: * `cidr` - * `inet` - * `macaddr` - * `macaddr8` - * `circle` - * `box` - * `line` - * `lseg` - * `path` - * `point` - * `polygon` - * `tsvector` - * `tsquery` - * `uuid` - -The is a way to avoid this - just cast unsupported types to `text`. But the way this can be done is not a straightforward. - -## Explicit type cast - -### `DBReader` - -Direct casting of Greenplum types is not supported by DBReader due to the connector’s implementation specifics. - -```python -reader = DBReader( - connection=greenplum, - # will fail - columns=["CAST(unsupported_column AS text)"], -) -``` - -But there is a workaround - create a view with casting unsupported column to text (or any other supported type). -For example, you can use [to_json](https://www.postgresql.org/docs/current/functions-json.html) Postgres function to convert column of any type to string representation and then parse this column on Spark side using [`JSON.parse_column`](../../../file_df/file_formats/json.md#onetl.file.format.json.JSON.parse_column) method. - -```python -from pyspark.sql.types import ArrayType, IntegerType - -from onetl.connection import Greenplum -from onetl.db import DBReader -from onetl.file.format import JSON - -greenplum = Greenplum(...) - -greenplum.execute( - """ - CREATE VIEW schema.view_with_json_column AS - SELECT - id, - supported_column, - to_json(array_column) array_column_as_json, - gp_segment_id -- ! important ! - FROM - schema.table_with_unsupported_columns - """, -) - -# create dataframe using this view -reader = DBReader( - connection=greenplum, - source="schema.view_with_json_column", -) -df = reader.run() - -# Define the schema for the JSON data -json_scheme = ArrayType(IntegerType()) - -df = df.select( - df.id, - df.supported_column, - JSON().parse_column(df.array_column_as_json, json_scheme).alias("array_column"), -) -``` - -### `DBWriter` - -To write data to a `text` or `json` column in a Greenplum table, use [`JSON.serialize_column`](../../../file_df/file_formats/json.md#onetl.file.format.json.JSON.serialize_column) method. - -```python -from onetl.connection import Greenplum -from onetl.db import DBWriter -from onetl.file.format import JSON - -greenplum = Greenplum(...) - -greenplum.execute( - """ - CREATE TABLE schema.target_table ( - id int, - supported_column timestamp, - array_column_as_json jsonb, -- or text - ) - DISTRIBUTED BY id - """, -) - -write_df = df.select( - df.id, - df.supported_column, - JSON().serialize_column(df.array_column).alias("array_column_json"), -) - -writer = DBWriter( - connection=greenplum, - target="schema.target_table", -) -writer.run(write_df) -``` - -Then you can parse this column on Greenplum side: - -```sql -SELECT - id, - supported_column, - -- access first item of an array - array_column_as_json->0 -FROM - schema.target_table -``` diff --git a/mddocs/markdown/connection/db_connection/greenplum/write.md b/mddocs/markdown/connection/db_connection/greenplum/write.md deleted file mode 100644 index 0815e0760..000000000 --- a/mddocs/markdown/connection/db_connection/greenplum/write.md +++ /dev/null @@ -1,141 +0,0 @@ - - -# Writing to Greenplum using `DBWriter` - -For writing data to Greenplum, use [`DBWriter`](../../../db/db_writer.md#onetl.db.db_writer.db_writer.DBWriter) -with [`GreenplumWriteOptions`](#onetl.connection.db_connection.greenplum.options.GreenplumWriteOptions). - -#### WARNING -Please take into account [Greenplum <-> Spark type mapping](types.md#greenplum-types). - -#### WARNING -It is always recommended to create table explicitly using [Greenplum.execute](execute.md#greenplum-execute) -instead of relying on Spark’s table DDL generation. - -This is because Spark’s DDL generator can create columns with different types than it is expected. - -## Examples - -```python -from onetl.connection import Greenplum -from onetl.db import DBWriter - -greenplum = Greenplum(...) - -df = ... # data is here - -writer = DBWriter( - connection=greenplum, - target="schema.table", - options=Greenplum.WriteOptions( - if_exists="append", - # by default distribution is random - distributedBy="id", - # partitionBy is not supported - ), -) - -writer.run(df) -``` - -## Interaction schema - -High-level schema is described in [Prerequisites](prerequisites.md#greenplum-prerequisites). You can find detailed interaction schema below. - -### Spark <-> Greenplum interaction during DBWriter.run() - -## Options - -### *pydantic model* onetl.connection.db_connection.greenplum.options.GreenplumWriteOptions - -VMware’s Greenplum Spark connector writing options. - -#### WARNING -Some options, like `url`, `dbtable`, `server.*`, `pool.*`, etc -are populated from connection attributes, and cannot be overridden by the user in `WriteOptions` to avoid issues. - -### Examples - -#### NOTE -You can pass any value -[supported by connector](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/write_to_gpdb.html), -even if it is not mentioned in this documentation. **Option names should be in** `camelCase`! - -The set of supported options depends on connector version. - -```python -from onetl.connection import Greenplum - -options = Greenplum.WriteOptions( - if_exists="append", - truncate="false", - distributedBy="mycolumn", -) -``` - - - -#### *field* if_exists *: GreenplumTableExistBehavior* *= GreenplumTableExistBehavior.APPEND* *(alias 'mode')* - -Behavior of writing data into existing table. - -Possible values: -: * `append` (default) - : Adds new rows into existing table. -
- ### Behavior in details -
- * Table does not exist - : Table is created using options provided by user - (`distributedBy` and others). - * Table exists - : Data is appended to a table. Table has the same DDL as before writing data. -
- #### WARNING - This mode does not check whether table already contains - rows from dataframe, so duplicated rows can be created. -
- Also Spark does not support passing custom options to - insert statement, like `ON CONFLICT`, so don’t try to - implement deduplication using unique indexes or constraints. -
- Instead, write to staging table and perform deduplication - using `execute` method. - * `replace_entire_table` - : **Table is dropped and then created**. -
- ### Behavior in details -
- * Table does not exist - : Table is created using options provided by user - (`distributedBy` and others). - * Table exists - : Table content is replaced with dataframe content. -
- After writing completed, target table could either have the same DDL as - before writing data (`truncate=True`), or can be recreated (`truncate=False`). - * `ignore` - : Ignores the write operation if the table already exists. -
- ### Behavior in details -
- * Table does not exist - : Table is created using options provided by user - (`distributedBy` and others). - * Table exists - : The write operation is ignored, and no data is written to the table. - * `error` - : Raises an error if the table already exists. -
- ### Behavior in details -
- * Table does not exist - : Table is created using options provided by user - (`distributedBy` and others). - * Table exists - : An error is raised, and no data is written to the table. - -#### Versionchanged -Changed in version 0.9.0: Renamed `mode` → `if_exists` - - diff --git a/mddocs/markdown/connection/db_connection/hive/connection.md b/mddocs/markdown/connection/db_connection/hive/connection.md deleted file mode 100644 index 7a8470de1..000000000 --- a/mddocs/markdown/connection/db_connection/hive/connection.md +++ /dev/null @@ -1,118 +0,0 @@ - - -# Hive Connection - -### *class* onetl.connection.db_connection.hive.connection.Hive(\*, spark: SparkSession, cluster: Cluster) - -Spark connection with Hive MetaStore support. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### SEE ALSO -Before using this connector please take into account [Prerequisites](prerequisites.md#hive-prerequisites) - -#### Versionadded -Added in version 0.1.0. - -* **Parameters:** - **cluster** - : Cluster name. Used for HWM and lineage. -
- #### Versionadded - Added in version 0.7.0. - - **spark** - : Spark session with Hive metastore support enabled - -### Examples - -Create Hive connection with Kerberos auth - -Execute `kinit` consome command before creating Spark Session - -```bash -$ kinit -kt /path/to/keytab user -``` - -```python -from onetl.connection import Hive -from pyspark.sql import SparkSession - -# Create Spark session -# Use names "spark.yarn.access.hadoopFileSystems", "spark.yarn.principal" -# and "spark.yarn.keytab" for Spark 2 - -spark = ( - SparkSession.builder.appName("spark-app-name") - .option("spark.kerberos.access.hadoopFileSystems", "hdfs://cluster.name.node:8020") - .option("spark.kerberos.principal", "user") - .option("spark.kerberos.keytab", "/path/to/keytab") - .enableHiveSupport() - .getOrCreate() -) - -# Create connection -hive = Hive(cluster="rnd-dwh", spark=spark).check() -``` - -Create Hive connection with anonymous auth - -```py -from onetl.connection import Hive -from pyspark.sql import SparkSession - -# Create Spark session -spark = SparkSession.builder.appName("spark-app-name").enableHiveSupport().getOrCreate() - -# Create connection -hive = Hive(cluster="rnd-dwh", spark=spark).check() -``` - - - -#### *classmethod* get_current(spark: SparkSession) - -Create connection for current cluster. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### NOTE -Can be used only if there are some hooks bound to -[`Slots.get_current_cluster`](slots.md#onetl.connection.db_connection.hive.slots.HiveSlots.get_current_cluster) slot. - -#### Versionadded -Added in version 0.7.0. - -* **Parameters:** - **spark** - : Spark session - -### Examples - -```python -from onetl.connection import Hive -from pyspark.sql import SparkSession - -spark = SparkSession.builder.appName("spark-app-name").enableHiveSupport().getOrCreate() - -# injecting current cluster name via hooks mechanism -hive = Hive.get_current(spark=spark) -``` - - - -#### check() - -Check source availability. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -If not, an exception will be raised. - -* **Returns:** - Connection itself -* **Raises:** - RuntimeError - : If the connection is not available - -### Examples - -```python -connection.check() -``` - - diff --git a/mddocs/markdown/connection/db_connection/hive/execute.md b/mddocs/markdown/connection/db_connection/hive/execute.md deleted file mode 100644 index c1d77874c..000000000 --- a/mddocs/markdown/connection/db_connection/hive/execute.md +++ /dev/null @@ -1,57 +0,0 @@ - - -# Executing statements in Hive - -Use `Hive.execute(...)` to execute DDL and DML operations. - -## Syntax support - -This method supports **any** query syntax supported by Hive, like: - -* ✅︎ `CREATE TABLE ...`, `CREATE VIEW ...`, and so on -* ✅︎ `LOAD DATA ...`, and so on -* ✅︎ `ALTER ...` -* ✅︎ `INSERT INTO ... SELECT ...`, and so on -* ✅︎ `DROP TABLE ...`, `DROP VIEW ...`, and so on -* ✅︎ `MSCK REPAIR TABLE ...`, and so on -* ✅︎ other statements not mentioned here -* ❌ `SET ...; SELECT ...;` - multiple statements not supported - -#### WARNING -Actually, query should be written using [SparkSQL](https://spark.apache.org/docs/latest/sql-ref-syntax.html#ddl-statements) syntax, not HiveQL. - -## Examples - -```python -from onetl.connection import Hive - -hive = Hive(...) - -hive.execute("DROP TABLE schema.table") -hive.execute( - """ - CREATE TABLE schema.table ( - id NUMBER, - key VARCHAR, - value DOUBLE - ) - PARTITION BY (business_date DATE) - STORED AS orc - """ -) -``` - -### Details - -#### Hive.execute(statement: str) → None - -Execute DDL or DML statement. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### Versionadded -Added in version 0.2.0. - -* **Parameters:** - **statement** - : Statement to be executed. - - diff --git a/mddocs/markdown/connection/db_connection/hive/index.md b/mddocs/markdown/connection/db_connection/hive/index.md deleted file mode 100644 index bc551b745..000000000 --- a/mddocs/markdown/connection/db_connection/hive/index.md +++ /dev/null @@ -1,19 +0,0 @@ - - -# Hive - -# Connection - -* [Prerequisites](prerequisites.md) -* [Hive Connection](connection.md) - -# Operations - -* [Reading from Hive using `DBReader`](read.md) -* [Reading from Hive using `Hive.sql`](sql.md) -* [Writing to Hive using `DBWriter`](write.md) -* [Executing statements in Hive](execute.md) - -# For developers - -* [Hive Slots](slots.md) diff --git a/mddocs/markdown/connection/db_connection/hive/prerequisites.md b/mddocs/markdown/connection/db_connection/hive/prerequisites.md deleted file mode 100644 index aa0fb49c7..000000000 --- a/mddocs/markdown/connection/db_connection/hive/prerequisites.md +++ /dev/null @@ -1,128 +0,0 @@ - - -# Prerequisites - -#### NOTE -onETL’s Hive connection is actually SparkSession with access to [Hive Thrift Metastore](https://docs.cloudera.com/cdw-runtime/1.5.0/hive-hms-overview/topics/hive-hms-introduction.html) -and HDFS/S3. -All data motion is made using Spark. Hive Metastore is used only to store tables and partitions metadata. - -This connector does **NOT** require Hive server. It also does **NOT** use Hive JDBC connector. - -## Version Compatibility - -* Hive Metastore version: - : * Officially declared: 0.12 - 3.1.3 (may require to add proper .jar file explicitly) - * Actually tested: 1.2.100, 2.3.10, 3.1.3 -* Spark versions: 2.3.x - 3.5.x -* Java versions: 8 - 20 - -See [official documentation](https://spark.apache.org/docs/latest/sql-data-sources-hive-tables.html). - -## Installing PySpark - -To use Hive connector you should have PySpark installed (or injected to `sys.path`) -BEFORE creating the connector instance. - -See [Spark](../../../install/spark.md#install-spark) installation instruction for more details. - -## Connecting to Hive Metastore - -#### NOTE -If you’re using managed Hadoop cluster, skip this step, as all Spark configs are should already present on the host. - -Create `$SPARK_CONF_DIR/hive-site.xml` with Hive Metastore URL: - -```xml - - - - - hive.metastore.uris - thrift://metastore.host.name:9083 - - -``` - -Create `$SPARK_CONF_DIR/core-site.xml` with warehouse location ,e.g. HDFS IPC port of Hadoop namenode, or S3 bucket address & credentials: - -HDFS - -```xml - - - - - fs.defaultFS - hdfs://myhadoopcluster:9820 - - -``` - -S3 - -```xml - - - - !-- See https://hadoop.apache.org/docs/current/hadoop-aws/tools/hadoop-aws/index.html#General_S3A_Client_configuration - - fs.defaultFS - s3a://mys3bucket/ - - - fs.s3a.bucket.mybucket.endpoint - http://s3.somain - - - fs.s3a.bucket.mybucket.connection.ssl.enabled - false - - - fs.s3a.bucket.mybucket.path.style.access - true - - - fs.s3a.bucket.mybucket.aws.credentials.provider - org.apache.hadoop.fs.s3a.SimpleAWSCredentialsProvider - - - fs.s3a.bucket.mybucket.access.key - some-user - - - fs.s3a.bucket.mybucket.secret.key - mysecrettoken - - -``` - -## Using Kerberos - -Some of Hadoop managed clusters use Kerberos authentication. In this case, you should call [kinit](https://web.mit.edu/kerberos/krb5-1.12/doc/user/user_commands/kinit.html) command -**BEFORE** starting Spark session to generate Kerberos ticket. See [Kerberos support](../../../install/kerberos.md#install-kerberos). - -Sometimes it is also required to pass keytab file to Spark config, allowing Spark executors to generate own Kerberos tickets: - -Spark 3 - -```python -SparkSession.builder - .option("spark.kerberos.access.hadoopFileSystems", "hdfs://namenode1.domain.com:9820,hdfs://namenode2.domain.com:9820") - .option("spark.kerberos.principal", "user") - .option("spark.kerberos.keytab", "/path/to/keytab") - .gerOrCreate() -``` - -Spark 2 - -```python -SparkSession.builder - .option("spark.yarn.access.hadoopFileSystems", "hdfs://namenode1.domain.com:9820,hdfs://namenode2.domain.com:9820") - .option("spark.yarn.principal", "user") - .option("spark.yarn.keytab", "/path/to/keytab") - .gerOrCreate() -``` - -See [Spark security documentation](https://spark.apache.org/docs/latest/security.html#kerberos) -for more details. diff --git a/mddocs/markdown/connection/db_connection/hive/read.md b/mddocs/markdown/connection/db_connection/hive/read.md deleted file mode 100644 index cbe3469d9..000000000 --- a/mddocs/markdown/connection/db_connection/hive/read.md +++ /dev/null @@ -1,92 +0,0 @@ - - -# Reading from Hive using `DBReader` - -[`DBReader`](../../../db/db_reader.md#onetl.db.db_reader.db_reader.DBReader) supports [Read Strategies](../../../strategy/index.md#strategy) for incremental data reading, -but does not support custom queries, like `JOIN`. - -## Supported DBReader features - -* ✅︎ `columns` -* ✅︎ `where` -* ✅︎ `hwm`, supported strategies: -* * ✅︎ [Snapshot Strategy](../../../strategy/snapshot_strategy.md#snapshot-strategy) -* * ✅︎ [Incremental Strategy](../../../strategy/incremental_strategy.md#incremental-strategy) -* * ✅︎ [Snapshot Batch Strategy](../../../strategy/snapshot_batch_strategy.md#snapshot-batch-strategy) -* * ✅︎ [Incremental Batch Strategy](../../../strategy/incremental_batch_strategy.md#incremental-batch-strategy) -* ❌ `hint` (is not supported by Hive) -* ❌ `df_schema` -* ❌ `options` (only Spark config params are used) - -#### WARNING -Actually, `columns`, `where` and `hwm.expression` should be written using [SparkSQL](https://spark.apache.org/docs/latest/sql-ref-syntax.html#data-retrieval-statements) syntax, -not HiveQL. - -## Examples - -Snapshot strategy: - -```python -from onetl.connection import Hive -from onetl.db import DBReader - -hive = Hive(...) - -reader = DBReader( - connection=hive, - source="schema.table", - columns=["id", "key", "CAST(value AS text) value", "updated_dt"], - where="key = 'something'", -) -df = reader.run() -``` - -Incremental strategy: - -```python -from onetl.connection import Hive -from onetl.db import DBReader -from onetl.strategy import IncrementalStrategy - -hive = Hive(...) - -reader = DBReader( - connection=hive, - source="schema.table", - columns=["id", "key", "CAST(value AS text) value", "updated_dt"], - where="key = 'something'", - hwm=DBReader.AutoDetectHWM(name="hive_hwm", expression="updated_dt"), -) - -with IncrementalStrategy(): - df = reader.run() -``` - -## Recommendations - -### Use column-based write formats - -Prefer these write formats: -: * [ORC](https://spark.apache.org/docs/latest/sql-data-sources-orc.html) - * [Parquet](https://spark.apache.org/docs/latest/sql-data-sources-parquet.html) - * [Iceberg](https://iceberg.apache.org/spark-quickstart/) - * [Hudi](https://hudi.apache.org/docs/quick-start-guide/) - * [Delta](https://docs.delta.io/latest/quick-start.html#set-up-apache-spark-with-delta-lake) - -For colum-based write formats, each file contains separated sections there column data is stored. The file footer contains -location of each column section/group. Spark can use this information to load only sections required by specific query, e.g. only selected columns, -to drastically speed up the query. - -Another advantage is high compression ratio, e.g. 10x-100x in comparison to JSON or CSV. - -### Select only required columns - -Instead of passing `"*"` in `DBReader(columns=[...])` prefer passing exact column names. -This drastically reduces the amount of data read by Spark, **if column-based file formats are used**. - -### Use partition columns in `where` clause - -Queries should include `WHERE` clause with filters on Hive partitioning columns. -This allows Spark to read only small set of files (*partition pruning*) instead of scanning the entire table, so this drastically increases performance. - -Supported operators are: `=`, `>`, `<` and `BETWEEN`, and only against some **static** value. diff --git a/mddocs/markdown/connection/db_connection/hive/slots.md b/mddocs/markdown/connection/db_connection/hive/slots.md deleted file mode 100644 index 75bad02ff..000000000 --- a/mddocs/markdown/connection/db_connection/hive/slots.md +++ /dev/null @@ -1,105 +0,0 @@ - - -# Hive Slots - -### *class* onetl.connection.db_connection.hive.slots.HiveSlots - -[Slots](../../../hooks/slot.md#slot-decorator) that could be implemented by third-party plugins. - -#### Versionadded -Added in version 0.7.0. - - - -#### *static* normalize_cluster_name(cluster: str) → str | None - -Normalize cluster name passed into Hive constructor. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -If hooks didn’t return anything, cluster name is left intact. - -#### Versionadded -Added in version 0.7.0. - -* **Parameters:** - **cluster** - : Cluster name (raw) -* **Returns:** - str | None - : Normalized cluster name. -
- If hook cannot be applied to a specific cluster, it should return `None`. - -### Examples - -```python -from onetl.connection import Hive -from onetl.hooks import hook - -@Hive.Slots.normalize_cluster_name.bind -@hook -def normalize_cluster_name(cluster: str) -> str: - return cluster.lower() -``` - - - -#### *static* get_known_clusters() → set[str] | None - -Return collection of known clusters. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -Cluster passed into Hive constructor should be present in this list. -If hooks didn’t return anything, no validation will be performed. - -#### Versionadded -Added in version 0.7.0. - -* **Returns:** - set[str] | None - : Collection of cluster names (normalized). -
- If hook cannot be applied, it should return `None`. - -### Examples - -```python -from onetl.connection import Hive -from onetl.hooks import hook - -@Hive.Slots.get_known_clusters.bind -@hook -def get_known_clusters() -> str[str]: - return {"rnd-dwh", "rnd-prod"} -``` - - - -#### *static* get_current_cluster() → str | None - -Get current cluster name. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -Used in `check` method to verify that connection is created only from the same cluster. -If hooks didn’t return anything, no validation will be performed. - -#### Versionadded -Added in version 0.7.0. - -* **Returns:** - str | None - : Current cluster name (normalized). -
- If hook cannot be applied, it should return `None`. - -### Examples - -```python -from onetl.connection import Hive -from onetl.hooks import hook - -@Hive.Slots.get_current_cluster.bind -@hook -def get_current_cluster() -> str: - # some magic here - return "rnd-dwh" -``` - - diff --git a/mddocs/markdown/connection/db_connection/hive/sql.md b/mddocs/markdown/connection/db_connection/hive/sql.md deleted file mode 100644 index 9e6d15f1c..000000000 --- a/mddocs/markdown/connection/db_connection/hive/sql.md +++ /dev/null @@ -1,86 +0,0 @@ - - -# Reading from Hive using `Hive.sql` - -`Hive.sql` allows passing custom SQL query, but does not support incremental strategies. - -## Syntax support - -Only queries with the following syntax are supported: - -* ✅︎ `SELECT ... FROM ...` -* ✅︎ `WITH alias AS (...) SELECT ...` -* ❌ `SET ...; SELECT ...;` - multiple statements not supported - -#### WARNING -Actually, query should be written using [SparkSQL](https://spark.apache.org/docs/latest/sql-ref-syntax.html#data-retrieval-statements) syntax, not HiveQL. - -## Examples - -```python -from onetl.connection import Hive - -hive = Hive(...) -df = hive.sql( - """ - SELECT - id, - key, - CAST(value AS text) value, - updated_at - FROM - some.mytable - WHERE - key = 'something' - """ -) -``` - -## Recommendations - -### Use column-based write formats - -Prefer these write formats: -: * [ORC](https://spark.apache.org/docs/latest/sql-data-sources-orc.html) - * [Parquet](https://spark.apache.org/docs/latest/sql-data-sources-parquet.html) - * [Iceberg](https://iceberg.apache.org/spark-quickstart/) - * [Hudi](https://hudi.apache.org/docs/quick-start-guide/) - * [Delta](https://docs.delta.io/latest/quick-start.html#set-up-apache-spark-with-delta-lake) - -For colum-based write formats, each file contains separated sections there column data is stored. The file footer contains -location of each column section/group. Spark can use this information to load only sections required by specific query, e.g. only selected columns, -to drastically speed up the query. - -Another advantage is high compression ratio, e.g. 10x-100x in comparison to JSON or CSV. - -### Select only required columns - -Instead of passing `SELECT * FROM ...` prefer passing exact column names `SELECT col1, col2, ...`. -This drastically reduces the amount of data read by Spark, **if column-based file formats are used**. - -### Use partition columns in `where` clause - -Queries should include `WHERE` clause with filters on Hive partitioning columns. -This allows Spark to read only small set of files (*partition pruning*) instead of scanning the entire table, so this drastically increases performance. - -Supported operators are: `=`, `>`, `<` and `BETWEEN`, and only against some **static** value. - -## Details - -#### Hive.sql(query: str) → DataFrame - -Lazily execute SELECT statement and return DataFrame. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -Same as `spark.sql(query)`. - -#### Versionadded -Added in version 0.2.0. - -* **Parameters:** - **query** - : SQL query to be executed. -* **Returns:** - **df** - : Spark dataframe - - diff --git a/mddocs/markdown/connection/db_connection/hive/write.md b/mddocs/markdown/connection/db_connection/hive/write.md deleted file mode 100644 index 1c61846df..000000000 --- a/mddocs/markdown/connection/db_connection/hive/write.md +++ /dev/null @@ -1,394 +0,0 @@ - - -# Writing to Hive using `DBWriter` - -For writing data to Hive, use [`DBWriter`](../../../db/db_writer.md#onetl.db.db_writer.db_writer.DBWriter). - -## Examples - -```python -from onetl.connection import Hive -from onetl.db import DBWriter - -hive = Hive(...) - -df = ... # data is here - -# Create dataframe with specific number of Spark partitions. -# Use the Hive partitioning columns to group the data. Create max 20 files per Hive partition. -# Also sort the data by column which most data is correlated with (e.g. user_id), reducing files size. - -num_files_per_partition = 20 -partition_columns = ["country", "business_date"] -sort_columns = ["user_id"] -write_df = df.repartition( - num_files_per_partition, - *partition_columns, - *sort_columns, -).sortWithinPartitions(*partition_columns, *sort_columns) - -writer = DBWriter( - connection=hive, - target="schema.table", - options=Hive.WriteOptions( - if_exists="append", - # Hive partitioning columns. - partitionBy=partition_columns, - ), -) - -writer.run(write_df) -``` - -## Recommendations - -### Use column-based write formats - -Prefer these write formats: -: * [ORC](https://spark.apache.org/docs/latest/sql-data-sources-orc.html) (**default**) - * [Parquet](https://spark.apache.org/docs/latest/sql-data-sources-parquet.html) - * [Iceberg](https://iceberg.apache.org/spark-quickstart/) - * [Hudi](https://hudi.apache.org/docs/quick-start-guide/) - * [Delta](https://docs.delta.io/latest/quick-start.html#set-up-apache-spark-with-delta-lake) - -#### WARNING -When using `DBWriter`, the default spark data format configured in `spark.sql.sources.default` is ignored, as `Hive.WriteOptions(format=...)` default value is explicitly set to `orc`. - -For column-based write formats, each file contains separated sections where column data is stored. The file footer contains -location of each column section/group. Spark can use this information to load only sections required by specific query, e.g. only selected columns, -to drastically speed up the query. - -Another advantage is high compression ratio, e.g. 10x-100x in comparison to JSON or CSV. - -### Use partitioning - -#### How does it work - -Hive support splitting data to partitions, which are different directories in filesystem with names like `some_col=value1/another_col=value2`. - -For example, dataframe with content like this: - -| country: string | business_date: date | user_id: int | bytes: long | -|-------------------|-----------------------|----------------|---------------| -| RU | 2024-01-01 | 1234 | 25325253525 | -| RU | 2024-01-01 | 2345 | 23234535243 | -| RU | 2024-01-02 | 1234 | 62346634564 | -| US | 2024-01-01 | 5678 | 4252345354 | -| US | 2024-01-02 | 5678 | 5474575745 | -| US | 2024-01-03 | 5678 | 3464574567 | - -With `partitionBy=["country", "business_dt"]` data will be stored as files in the following subfolders: -: * `/country=RU/business_date=2024-01-01/` - * `/country=RU/business_date=2024-01-02/` - * `/country=US/business_date=2024-01-01/` - * `/country=US/business_date=2024-01-02/` - * `/country=US/business_date=2024-01-03/` - -A separated subdirectory is created for each distinct combination of column values in the dataframe. - -Please do not confuse Spark dataframe partitions (a.k.a batches of data handled by Spark executors, usually in parallel) -and Hive partitioning (store data in different subdirectories). -Number of Spark dataframe partitions is correlated the number of files created in **each** Hive partition. -For example, Spark dataframe with 10 partitions and 5 distinct values of Hive partition columns will be saved as 5 subfolders with 10 files each = 50 files in total. -Without Hive partitioning, all the files are placed into one flat directory. - -#### But why? - -Queries which has `WHERE` clause with filters on Hive partitioning columns, like `WHERE country = 'RU' AND business_date='2024-01-01'`, will -read only files from this exact partitions, like `/country=RU/business_date=2024-01-01/`, and skip files from other partitions. - -This drastically increases performance and reduces the amount of memory used by Spark. -Consider using Hive partitioning in all tables. - -#### Which columns should I use? - -Usually Hive partitioning columns are based on event date or location, like `country: string`, `business_date: date`, `run_date: date` and so on. - -**Partition columns should contain data with low cardinality.** -Dates, small integers, strings with low number of possible values are OK. -But timestamp, float, decimals, longs (like user id), strings with lots oj unique values (like user name or email) should **NOT** be used as Hive partitioning columns. -Unlike some other databases, range and hash-based partitions are not supported. - -Partition column should be a part of a dataframe. If you want to partition values by date component of `business_dt: timestamp` column, -add a new column to dataframe like this: `df.withColumn("business_date", date(df.business_dt))`. - -### Use compression - -Using compression algorithms like `snappy`, `lz4` or `zstd` can reduce the size of files (up to 10x). - -### Prefer creating large files - -Storing millions of small files is not that HDFS and S3 are designed for. Minimal file size should be at least 10Mb, but usually it is like 128Mb+ or 256Mb+ (HDFS block size). -**NEVER** create files with few Kbytes in size. - -Number of files can be different in different cases. -On one hand, Spark Adaptive Query Execution (AQE) can merge small Spark dataframe partitions into one larger. -On the other hand, dataframes with skewed data can produce a larger number of files than expected. - -To create small amount of large files, you can reduce number of Spark dataframe partitions. -Use [df.repartition(N, columns…)](https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.DataFrame.repartition.html) function, -like this: `df.repartition(20, "col1", "col2")`. -This creates new Spark dataframe with partitions using `hash(df.col1 + df.col2) mod 20` expression, avoiding data skew. - -Note: larger dataframe partitions requires more resources (CPU, RAM) on Spark executor. The exact number of partitions -should be determined empirically, as it depends on the amount of data and available resources. - -### Sort data before writing - -Dataframe with sorted content: - -| country: string | business_date: date | user_id: int | business_dt: timestamp | bytes: long | -|-------------------|-----------------------|----------------|--------------------------|---------------| -| RU | 2024-01-01 | 1234 | 2024-01-01T11:22:33.456 | 25325253525 | -| RU | 2024-01-01 | 1234 | 2024-01-01T12:23:44.567 | 25325253525 | -| RU | 2024-01-02 | 1234 | 2024-01-01T13:25:56.789 | 34335645635 | -| US | 2024-01-01 | 2345 | 2024-01-01T10:00:00.000 | 12341 | -| US | 2024-01-02 | 2345 | 2024-01-01T15:11:22.345 | 13435 | -| US | 2024-01-03 | 2345 | 2024-01-01T20:22:33.567 | 14564 | - -Has a much better compression rate than unsorted one, e.g. 2x or even higher: - -| country: string | business_date: date | user_id: int | business_dt: timestamp | bytes: long | -|-------------------|-----------------------|----------------|--------------------------|---------------| -| RU | 2024-01-01 | 1234 | 2024-01-01T11:22:33.456 | 25325253525 | -| RU | 2024-01-01 | 6345 | 2024-12-01T23:03:44.567 | 25365 | -| RU | 2024-01-02 | 5234 | 2024-07-01T06:10:56.789 | 45643456747 | -| US | 2024-01-01 | 4582 | 2024-04-01T17:59:00.000 | 362546475 | -| US | 2024-01-02 | 2345 | 2024-09-01T04:24:22.345 | 3235 | -| US | 2024-01-03 | 3575 | 2024-03-01T21:37:33.567 | 346345764 | - -Choosing columns to sort data by is really depends on the data. If data is correlated with some specific -column, like in example above the amount of traffic is correlated with both `user_id` and `timestamp`, -use `df.sortWithinPartitions("user_id", "timestamp")` before writing the data. - -If `df.repartition(N, repartition_columns...)` is used in combination with `df.sortWithinPartitions(sort_columns...)`, -then `sort_columns` should start with `repartition_columns` or be equal to it. - -## Options - -### *pydantic model* onetl.connection.db_connection.hive.options.HiveWriteOptions - -Hive source writing options. - -You can pass here key-value items which then will be converted to calls -of `pyspark.sql.readwriter.DataFrameWriter` methods. - -For example, `Hive.WriteOptions(if_exists="append", partitionBy="reg_id")` will -be converted to `df.write.mode("append").partitionBy("reg_id")` call, and so on. - -### Examples - -#### NOTE -You can pass any method name and its value -[supported by Spark](https://spark.apache.org/docs/latest/sql-data-sources-load-save-functions.html), -even if it is not mentioned in this documentation. **Option names should be in** `camelCase`! - -The set of supported options depends on Spark version used. - -```python -from onetl.connection import Hive - -options = Hive.WriteOptions( - if_exists="append", - partitionBy="reg_id", - customSparkOption="value", -) -``` - - - -#### *field* if_exists *: HiveTableExistBehavior* *= HiveTableExistBehavior.APPEND* *(alias 'mode')* - -Behavior of writing data into existing table. - -Possible values: -: * `append` (default) - : Appends data into existing partition/table, or create partition/table if it does not exist. -
- Same as Spark’s `df.write.insertInto(table, overwrite=False)`. -
- ### Behavior in details -
- * Table does not exist - : Table is created using options provided by user (`format`, `compression`, etc). - * Table exists, but not partitioned, [`partition_by`](#onetl.connection.db_connection.hive.options.HiveWriteOptions.partition_by) is set - : Data is appended to a table. Table is still not partitioned (DDL is unchanged). - * Table exists and partitioned, but has different partitioning schema than [`partition_by`](#onetl.connection.db_connection.hive.options.HiveWriteOptions.partition_by) - : Partition is created based on table’s `PARTITIONED BY (...)` options. - Explicit [`partition_by`](#onetl.connection.db_connection.hive.options.HiveWriteOptions.partition_by) value is ignored. - * Table exists and partitioned according [`partition_by`](#onetl.connection.db_connection.hive.options.HiveWriteOptions.partition_by), but partition is present only in dataframe - : Partition is created. - * Table exists and partitioned according [`partition_by`](#onetl.connection.db_connection.hive.options.HiveWriteOptions.partition_by), partition is present in both dataframe and table - : Data is appended to existing partition. -
- #### WARNING - This mode does not check whether table already contains - rows from dataframe, so duplicated rows can be created. -
- To implement deduplication, write data to staging table first, - and then perform some deduplication logic using `sql`. - * Table exists and partitioned according [`partition_by`](#onetl.connection.db_connection.hive.options.HiveWriteOptions.partition_by), but partition is present only in table, not dataframe - : Existing partition is left intact. - * `replace_overlapping_partitions` - : Overwrites data in the existing partition, or create partition/table if it does not exist. -
- Same as Spark’s `df.write.insertInto(table, overwrite=True)` + - `spark.sql.sources.partitionOverwriteMode=dynamic`. -
- ### Behavior in details -
- * Table does not exist - : Table is created using options provided by user (`format`, `compression`, etc). - * Table exists, but not partitioned, [`partition_by`](#onetl.connection.db_connection.hive.options.HiveWriteOptions.partition_by) is set - : Data is **overwritten in all the table**. Table is still not partitioned (DDL is unchanged). - * Table exists and partitioned, but has different partitioning schema than [`partition_by`](#onetl.connection.db_connection.hive.options.HiveWriteOptions.partition_by) - : Partition is created based on table’s `PARTITIONED BY (...)` options. - Explicit [`partition_by`](#onetl.connection.db_connection.hive.options.HiveWriteOptions.partition_by) value is ignored. - * Table exists and partitioned according [`partition_by`](#onetl.connection.db_connection.hive.options.HiveWriteOptions.partition_by), but partition is present only in dataframe - : Partition is created. - * Table exists and partitioned according [`partition_by`](#onetl.connection.db_connection.hive.options.HiveWriteOptions.partition_by), partition is present in both dataframe and table - : Existing partition **replaced** with data from dataframe. - * Table exists and partitioned according [`partition_by`](#onetl.connection.db_connection.hive.options.HiveWriteOptions.partition_by), but partition is present only in table, not dataframe - : Existing partition is left intact. - * `replace_entire_table` - : **Recreates table** (via `DROP + CREATE`), **deleting all existing data**. - **All existing partitions are dropped.** -
- Same as Spark’s `df.write.saveAsTable(table, mode="overwrite")` (NOT `insertInto`)! -
- #### WARNING - Table is recreated using options provided by user (`format`, `compression`, etc) - **instead of using original table options**. Be careful - * `ignore` - : Ignores the write operation if the table/partition already exists. -
- ### Behavior in details -
- * Table does not exist - : Table is created using options provided by user (`format`, `compression`, etc). - * Table exists - : If the table exists, **no further action is taken**. This is true whether or not new partition - values are present and whether the partitioning scheme differs or not - * `error` - : Raises an error if the table/partition already exists. -
- ### Behavior in details -
- * Table does not exist - : Table is created using options provided by user (`format`, `compression`, etc). - * Table exists - : If the table exists, **raises an error**. This is true whether or not new partition - values are present and whether the partitioning scheme differs or not - -#### NOTE -Unlike using pure Spark, config option `spark.sql.sources.partitionOverwriteMode` -does not affect behavior. - - - -#### *field* format *: str | [BaseWritableFileFormat](../../../file_df/file_formats/base.md#onetl.base.base_file_format.BaseWritableFileFormat)* *= 'orc'* - -Format of files which should be used for storing table data. - -### Examples - -- string format: `"orc"` (default), `"parquet"`, `"csv"` (NOT recommended). -- format class instance: `ORC(compression="snappy")`, `Parquet()`, `CSV(header=True, delimiter=",")`. - -```default -options = Hive.WriteOptions( - if_exists="append", - partitionBy="reg_id", - format="orc", -) - -# or using an ORC format class instance: - -from onetl.file.format import ORC - -options = Hive.WriteOptions( - if_exists="append", - partitionBy="reg_id", - format=ORC(compression="snappy"), -) -``` - -#### NOTE -It’s better to use column-based formats like `orc` or `parquet`, -not row-based (`csv`, `json`) - -#### WARNING -Used **only** while **creating new table**, or in case of `if_exists=replace_entire_table` - - - -#### *field* partition_by *: List[str] | str | None* *= None* *(alias 'partitionBy')* - -List of columns should be used for data partitioning. `None` means partitioning is disabled. - -Examples: `reg_id` or `["reg_id", "business_dt"]` - -#### WARNING -Used **only** while **creating new table**, or in case of `if_exists=replace_entire_table` - - - -#### *field* bucket_by *: Tuple[int, List[str] | str] | None* *= None* *(alias 'bucketBy')* - -Number of buckets plus bucketing columns. `None` means bucketing is disabled. - -Each bucket is created as a set of files with name containing result of calculation `hash(columns) mod num_buckets`. - -This allows to remove shuffle from queries containing `GROUP BY` or `JOIN` or using `=` / `IN` predicates -on specific columns. - -Examples: `(10, "user_id")`, `(10, ["user_id", "user_phone"])` - -#### NOTE -Bucketing should be used on columns containing a lot of unique values, -like `userId`. - -Columns like `date` should **NOT** be used for bucketing -because of too low number of unique values. - -#### WARNING -It is recommended to use this option **ONLY** if you have a large table -(hundreds of Gb or more), which is used mostly for JOINs with other tables, -and you’re inserting data using `if_exists=overwrite_partitions` or `if_exists=replace_entire_table`. - -Otherwise Spark will create a lot of small files -(one file for each bucket and each executor), drastically **decreasing** HDFS performance. - -#### WARNING -Used **only** while **creating new table**, or in case of `if_exists=replace_entire_table` - - - -#### *field* sort_by *: List[str] | str | None* *= None* *(alias 'sortBy')* - -Each file in a bucket will be sorted by these columns value. `None` means sorting is disabled. - -Examples: `user_id` or `["user_id", "user_phone"]` - -#### NOTE -Sorting columns should contain values which are used in `ORDER BY` clauses. - -#### WARNING -Could be used only with [`bucket_by`](#onetl.connection.db_connection.hive.options.HiveWriteOptions.bucket_by) option - -#### WARNING -Used **only** while **creating new table**, or in case of `if_exists=replace_entire_table` - - - -#### *field* compression *: str | None* *= None* - -Compressing algorithm which should be used for compressing created files in HDFS. -`None` means compression is disabled. - -Examples: `snappy`, `zlib` - -#### WARNING -Used **only** while **creating new table**, or in case of `if_exists=replace_entire_table` - - diff --git a/mddocs/markdown/connection/db_connection/index.md b/mddocs/markdown/connection/db_connection/index.md deleted file mode 100644 index cd8f76477..000000000 --- a/mddocs/markdown/connection/db_connection/index.md +++ /dev/null @@ -1,16 +0,0 @@ - - -# DB Connections - -# DB Connections - -* [Clickhouse](clickhouse/index.md) -* [Greenplum](greenplum/index.md) -* [Kafka](kafka/index.md) -* [Hive](hive/index.md) -* [MongoDB](mongodb/index.md) -* [MSSQL](mssql/index.md) -* [MySQL](mysql/index.md) -* [Oracle](oracle/index.md) -* [Postgres](postgres/index.md) -* [Teradata](teradata/index.md) diff --git a/mddocs/markdown/connection/db_connection/kafka/auth.md b/mddocs/markdown/connection/db_connection/kafka/auth.md deleted file mode 100644 index 0bf454521..000000000 --- a/mddocs/markdown/connection/db_connection/kafka/auth.md +++ /dev/null @@ -1,37 +0,0 @@ - - -# Kafka Auth - -### *class* onetl.connection.db_connection.kafka.kafka_auth.KafkaAuth - -Interface for Kafka connection Auth classes. - -#### Versionadded -Added in version 0.9.0. - - - -#### *abstract* get_options(kafka: [Kafka](connection.md#onetl.connection.db_connection.kafka.connection.Kafka)) → dict - -Get options for Kafka connection - -* **Parameters:** - **kafka** - : Connection instance -* **Returns:** - dict: - : Kafka client options - - - -#### *abstract* cleanup(kafka: [Kafka](connection.md#onetl.connection.db_connection.kafka.connection.Kafka)) → None - -This method is called while closing Kafka connection. - -Implement it to cleanup resources like temporary files. - -* **Parameters:** - **kafka** - : Connection instance - - diff --git a/mddocs/markdown/connection/db_connection/kafka/basic_auth.md b/mddocs/markdown/connection/db_connection/kafka/basic_auth.md deleted file mode 100644 index 586b07500..000000000 --- a/mddocs/markdown/connection/db_connection/kafka/basic_auth.md +++ /dev/null @@ -1,60 +0,0 @@ - - -# Kafka BasicAuth - -### *pydantic model* onetl.connection.db_connection.kafka.kafka_basic_auth.KafkaBasicAuth - -Connect to Kafka using `sasl.mechanism="PLAIN"`. - -For more details see [Kafka Documentation](https://kafka.apache.org/documentation/#security_sasl_plain). - -#### Versionadded -Added in version 0.9.0. - -### Examples - -Auth in Kafka with user and password: - -```python -from onetl.connection import Kafka - -auth = Kafka.BasicAuth( - user="some_user", - password="abc", -) -``` - - - -#### *field* user *: str* *[Required]* *(alias 'username')* - -#### *field* password *: SecretStr* *[Required]* - -#### get_jaas_conf() → str - - - -#### get_options(kafka: [Kafka](connection.md#onetl.connection.db_connection.kafka.connection.Kafka)) → dict - -Get options for Kafka connection - -* **Parameters:** - **kafka** - : Connection instance -* **Returns:** - dict: - : Kafka client options - - - -#### cleanup(kafka: [Kafka](connection.md#onetl.connection.db_connection.kafka.connection.Kafka)) → None - -This method is called while closing Kafka connection. - -Implement it to cleanup resources like temporary files. - -* **Parameters:** - **kafka** - : Connection instance - - diff --git a/mddocs/markdown/connection/db_connection/kafka/connection.md b/mddocs/markdown/connection/db_connection/kafka/connection.md deleted file mode 100644 index 23a54a42e..000000000 --- a/mddocs/markdown/connection/db_connection/kafka/connection.md +++ /dev/null @@ -1,243 +0,0 @@ - - -# Kafka Connection - -### *class* onetl.connection.db_connection.kafka.connection.Kafka(\*, spark: SparkSession, cluster: Cluster, addresses: List[str], auth: [KafkaAuth](auth.md#onetl.connection.db_connection.kafka.kafka_auth.KafkaAuth) | None = None, protocol: [KafkaProtocol](protocol.md#onetl.connection.db_connection.kafka.kafka_protocol.KafkaProtocol) = KafkaPlaintextProtocol(), extra: KafkaExtra = KafkaExtra()) - -This connector is designed to read and write from Kafka in batch mode. - -Based on [official Kafka Source For Spark](https://spark.apache.org/docs/latest/structured-streaming-kafka-integration.html). - -#### SEE ALSO -Before using this connector please take into account [Prerequisites](prerequisites.md#kafka-prerequisites) - -#### NOTE -This connector is for **batch** ETL processes, not streaming. - -#### Versionadded -Added in version 0.9.0. - -* **Parameters:** - **addresses** - : A list of broker addresses, for example `["192.168.1.10:9092", "192.168.1.11:9092"]`. - - **cluster** - : Cluster name. Used for HWM and lineage. - - **auth** - : Kafka authentication mechanism. `None` means anonymous auth. - - **protocol** - : Kafka security protocol. - - **extra** - : A dictionary of additional properties to be used when connecting to Kafka. -
- These are Kafka-specific properties that control behavior of the producer or consumer. See: - * [producer options documentation](https://kafka.apache.org/documentation/#producerconfigs) - * [consumer options documentation](https://kafka.apache.org/documentation/#consumerconfigs) - * [Spark Kafka documentation](https://spark.apache.org/docs/latest/structured-streaming-kafka-integration.html#kafka-specific-configurations) -
- Options are passed without `kafka.` prefix, for example: -
- For example: - ```python - extra = { - "group.id": "myGroup", - "request.timeout.ms": 120000, - } - ``` -
- #### WARNING - Options that populated from connection - attributes (like `bootstrap.servers`, `sasl.*`, `ssl.*`) are not allowed to override. - -### Examples - -Create Kafka connection with `PLAINTEXT` protocol and `SCRAM-SHA-256` auth - -```py -from onetl.connection import Kafka -from pyspark.sql import SparkSession - -# Create Spark session with Kafka connector loaded -maven_packages = Kafka.get_packages(spark_version="3.5.5") -exclude_packages = Kafka.get_exclude_packages() -spark = ( - SparkSession.builder.appName("spark-app-name") - .config("spark.jars.packages", ",".join(maven_packages)) - .config("spark.jars.excludes", ",".join(exclude_packages)) - .getOrCreate() -) - -# Create connection -kafka = Kafka( - addresses=["mybroker:9092", "anotherbroker:9092"], - cluster="my-cluster", - auth=Kafka.ScramAuth( - user="me", - password="abc", - digest="SHA-256", - ), - spark=spark, -).check() -``` - -Create Kafka connection with `PLAINTEXT` protocol and Kerberos (`GSSAPI`) auth - -```py -# Create Spark session with Kafka connector loaded -... - -# Create connection -kafka = Kafka( - addresses=["mybroker:9092", "anotherbroker:9092"], - cluster="my-cluster", - auth=Kafka.KerberosAuth( - principal="me@example.com", - keytab="/path/to/keytab", - deploy_keytab=True, - ), - spark=spark, -).check() -``` - -Create Kafka connection with `SASL_SSL` protocol and `SCRAM-SHA-512` auth - -```py -from pathlib import Path - -# Create Spark session with Kafka connector loaded -... - -# Create connection -kafka = Kafka( - addresses=["mybroker:9092", "anotherbroker:9092"], - cluster="my-cluster", - protocol=Kafka.SSLProtocol( - # read client certificate and private key from file - keystore_type="PEM", - keystore_certificate_chain=Path("path/to/user.crt").read_text(), - keystore_key=Path("path/to/user.key").read_text(), - # read server public certificate from file - truststore_type="PEM", - truststore_certificates=Path("/path/to/server.crt").read_text(), - ), - auth=Kafka.ScramAuth( - user="me", - password="abc", - digest="SHA-512", - ), - spark=spark, -).check() -``` - -Create Kafka connection with extra options - -```py -# Create Spark session with Kafka connector loaded -... - -# Create connection -kafka = Kafka( - addresses=["mybroker:9092", "anotherbroker:9092"], - cluster="my-cluster", - protocol=..., - auth=..., - extra={"max.request.size": 1024 * 1024}, # <-- - spark=spark, -).check() -``` - - - -#### check() - -Check source availability. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -If not, an exception will be raised. - -* **Returns:** - Connection itself -* **Raises:** - RuntimeError - : If the connection is not available - -### Examples - -```python -connection.check() -``` - - - -#### close() - -Close all connections created to Kafka. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### NOTE -Connection can be used again after it was closed. - -* **Returns:** - Connection itself - -### Examples - -Close connection automatically: - -```python -with connection: - ... -``` - -Close connection manually: - -```python -connection.close() -``` - - - -#### *classmethod* get_exclude_packages() → list[str] - -Get package names to be excluded by Spark. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### Versionadded -Added in version 0.13.0. - -### Examples - -```python -from onetl.connection import Kafka - -Kafka.get_exclude_packages() -``` - - - -#### *classmethod* get_packages(spark_version: str, scala_version: str | None = None) → list[str] - -Get package names to be downloaded by Spark. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -See [Maven package index](https://mvnrepository.com/artifact/org.apache.spark/spark-sql-kafka-0-10) -for all available packages. - -* **Parameters:** - **spark_version** - : Spark version in format `major.minor.patch`. - - **scala_version** - : Scala version in format `major.minor`. -
- If `None`, `spark_version` is used to determine Scala version. - -### Examples - -```python -from onetl.connection import Kafka - -Kafka.get_packages(spark_version="3.5.5") -Kafka.get_packages(spark_version="3.5.5", scala_version="2.12") -``` - - diff --git a/mddocs/markdown/connection/db_connection/kafka/index.md b/mddocs/markdown/connection/db_connection/kafka/index.md deleted file mode 100644 index 5e5f45ba1..000000000 --- a/mddocs/markdown/connection/db_connection/kafka/index.md +++ /dev/null @@ -1,31 +0,0 @@ - - -# Kafka - -# Connection - -* [Prerequisites](prerequisites.md) -* [Kafka Connection](connection.md) -* [Kafka Troubleshooting](troubleshooting.md) - -# Protocols - -* [Kafka PlaintextProtocol](plaintext_protocol.md) -* [Kafka SSLProtocol](ssl_protocol.md) - -# Auth methods - -* [Kafka BasicAuth](basic_auth.md) -* [Kafka KerberosAuth](kerberos_auth.md) -* [Kafka ScramAuth](scram_auth.md) - -# Operations - -* [Reading from Kafka](read.md) -* [Writing to Kafka](write.md) - -# For developers - -* [Kafka Auth](auth.md) -* [Kafka Protocol](protocol.md) -* [Kafka Slots](slots.md) diff --git a/mddocs/markdown/connection/db_connection/kafka/kerberos_auth.md b/mddocs/markdown/connection/db_connection/kafka/kerberos_auth.md deleted file mode 100644 index 1fefd2992..000000000 --- a/mddocs/markdown/connection/db_connection/kafka/kerberos_auth.md +++ /dev/null @@ -1,118 +0,0 @@ - - -# Kafka KerberosAuth - -### *pydantic model* onetl.connection.db_connection.kafka.kafka_kerberos_auth.KafkaKerberosAuth - -Connect to Kafka using `sasl.mechanism="GSSAPI"`. - -For more details see: - -* [Kafka Documentation](https://kafka.apache.org/documentation/#security_sasl_kerberos_clientconfig) -* [Krb5LoginModule documentation](https://docs.oracle.com/javase/8/docs/jre/api/security/jaas/spec/com/sun/security/auth/module/Krb5LoginModule.html) - -#### Versionadded -Added in version 0.9.0. - -### Examples - -Auth in Kafka with keytab, automatically deploy keytab files to all Spark hosts (driver and executors): - -```python -from onetl.connection import Kafka - -auth = Kafka.KerberosAuth( - principal="user", - keytab="/path/to/keytab", - deploy_keytab=True, -) -``` - -Auth in Kafka with keytab, keytab is **already deployed** on all Spark hosts (driver and executors): - -```python -from onetl.connection import Kafka - -auth = Kafka.KerberosAuth( - principal="user", - keytab="/path/to/keytab", - deploy_keytab=False, -) -``` - -Auth in Kafka with existing Kerberos ticket (only Spark session created with `master=local`): - -```python -from onetl.connection import Kafka - -auth = Kafka.KerberosAuth( - principal="user", - use_keytab=False, - use_ticket_cache=True, -) -``` - -Pass custom options for JAAS config and Kafka SASL: - -```python -from onetl.connection import Kafka - -auth = Kafka.KerberosAuth.parse( - { - "principal": "user", - "keytab": "/path/to/keytab", - # options without sasl.kerberos. prefix are passed to JAAS config - # names are in camel case! - "isInitiator": True, - # options with `sasl.kerberos.` prefix are passed to Kafka client config as-is - "sasl.kerberos.kinit.cmd": "/usr/bin/kinit", - } -) -``` - - - -#### *field* principal *: str* *[Required]* - -#### *field* keytab *: LocalPath | None* *= None* *(alias 'keyTab')* - -#### *field* deploy_keytab *: bool* *= True* - -#### *field* service_name *: str* *= 'kafka'* *(alias 'serviceName')* - -#### *field* renew_ticket *: bool* *= True* *(alias 'renewTicket')* - -#### *field* store_key *: bool* *= True* *(alias 'storeKey')* - -#### *field* use_keytab *: bool* *= True* *(alias 'useKeyTab')* - -#### *field* use_ticket_cache *: bool* *= False* *(alias 'useTicketCache')* - -#### get_jaas_conf(kafka: [Kafka](connection.md#onetl.connection.db_connection.kafka.connection.Kafka)) → str - - - -#### get_options(kafka: [Kafka](connection.md#onetl.connection.db_connection.kafka.connection.Kafka)) → dict - -Get options for Kafka connection - -* **Parameters:** - **kafka** - : Connection instance -* **Returns:** - dict: - : Kafka client options - - - -#### cleanup(kafka: [Kafka](connection.md#onetl.connection.db_connection.kafka.connection.Kafka)) → None - -This method is called while closing Kafka connection. - -Implement it to cleanup resources like temporary files. - -* **Parameters:** - **kafka** - : Connection instance - - diff --git a/mddocs/markdown/connection/db_connection/kafka/plaintext_protocol.md b/mddocs/markdown/connection/db_connection/kafka/plaintext_protocol.md deleted file mode 100644 index f7ebd316d..000000000 --- a/mddocs/markdown/connection/db_connection/kafka/plaintext_protocol.md +++ /dev/null @@ -1,48 +0,0 @@ - - -# Kafka PlaintextProtocol - -### *pydantic model* onetl.connection.db_connection.kafka.kafka_plaintext_protocol.KafkaPlaintextProtocol - -Connect to Kafka using `PLAINTEXT` or `SASL_PLAINTEXT` security protocols. - -#### WARNING -Not recommended to use on production environments. -Prefer [`SSLProtocol`](ssl_protocol.md#onetl.connection.db_connection.kafka.kafka_ssl_protocol.KafkaSSLProtocol). - -#### Versionadded -Added in version 0.9.0. - -### Examples - -```python -# No options -protocol = Kafka.PlaintextProtocol() -``` - - - -#### get_options(kafka: [Kafka](connection.md#onetl.connection.db_connection.kafka.connection.Kafka)) → dict - -Get options for Kafka connection - -* **Parameters:** - **kafka** - : Connection instance -* **Returns:** - dict: - : Kafka client options - - - -#### cleanup(kafka: [Kafka](connection.md#onetl.connection.db_connection.kafka.connection.Kafka)) → None - -This method is called while closing Kafka connection. - -Implement it to cleanup resources like temporary files. - -* **Parameters:** - **kafka** - : Connection instance - - diff --git a/mddocs/markdown/connection/db_connection/kafka/prerequisites.md b/mddocs/markdown/connection/db_connection/kafka/prerequisites.md deleted file mode 100644 index fcb90f705..000000000 --- a/mddocs/markdown/connection/db_connection/kafka/prerequisites.md +++ /dev/null @@ -1,65 +0,0 @@ - - -# Prerequisites - -## Version Compatibility - -* Kafka server versions: - : * Officially declared: 0.10 or higher - * Actually tested: 3.2.3, 3.9.0 (only Kafka 3.x supports message headers) -* Spark versions: 2.4.x - 3.5.x -* Java versions: 8 - 17 - -See [official documentation](https://spark.apache.org/docs/latest/structured-streaming-kafka-integration.html). - -## Installing PySpark - -To use Kafka connector you should have PySpark installed (or injected to `sys.path`) -BEFORE creating the connector instance. - -See [Spark](../../../install/spark.md#install-spark) installation instruction for more details. - -## Connecting to Kafka - -### Connection address - -Kafka is a distributed service, and usually has a list of brokers you can connect to (unlike other connectors, there only one host+port can be set). -Please contact your Kafka administrator to get addresses of these brokers, as there are no defaults. - -Also Kafka has a feature called *advertised listeners* - client connects to one broker, and received list of other brokers in the clusters. -So you don’t have to pass all brokers to `addresses`, it can be some subset. Other broker addresses will be fetched directly from the cluster. - -### Connection protocol - -Kafka can support different connection protocols. List of currently supported protocols: -: * [`PLAINTEXT`](plaintext_protocol.md#onetl.connection.db_connection.kafka.kafka_plaintext_protocol.KafkaPlaintextProtocol) (not secure) - * [`SSL`](ssl_protocol.md#onetl.connection.db_connection.kafka.kafka_ssl_protocol.KafkaSSLProtocol) (secure, recommended) - -Note that specific port can listen for only one of these protocols, so it is important to set -proper port number + protocol combination. - -### Authentication mechanism - -Kafka can support different authentication mechanism (also known as [SASL](https://en.wikipedia.org/wiki/Simple_Authentication_and_Security_Layer)). - -List of currently supported mechanisms: -: * [`PLAIN`](basic_auth.md#onetl.connection.db_connection.kafka.kafka_basic_auth.KafkaBasicAuth). To no confuse this with `PLAINTEXT` connection protocol, onETL uses name `BasicAuth`. - * [`GSSAPI`](kerberos_auth.md#onetl.connection.db_connection.kafka.kafka_kerberos_auth.KafkaKerberosAuth). To simplify naming, onETL uses name `KerberosAuth`. - * [`SCRAM-SHA-256 or SCRAM-SHA-512`](scram_auth.md#onetl.connection.db_connection.kafka.kafka_scram_auth.KafkaScramAuth) (recommended). - -Different mechanisms use different types of credentials (login + password, keytab file, and so on). - -Note that connection protocol and auth mechanism are set in pairs: -: * If you see `SASL_PLAINTEXT` this means `PLAINTEXT` connection protocol + some auth mechanism. - * If you see `SASL_SSL` this means `SSL` connection protocol + some auth mechanism. - * If you see just `PLAINTEXT` or `SSL` (**no** `SASL`), this means that authentication is disabled (anonymous access). - -Please contact your Kafka administrator to get details about enabled auth mechanism in a specific Kafka instance. - -### Required grants - -Ask your Kafka administrator to set following grants for a user, *if Kafka instance uses ACL*: -: * `Describe` + `Read` for reading data from Kafka (Consumer). - * `Describe` + `Write` for writing data from Kafka (Producer). - -More details can be found in [documentation](https://kafka.apache.org/documentation/#operations_in_kafka). diff --git a/mddocs/markdown/connection/db_connection/kafka/protocol.md b/mddocs/markdown/connection/db_connection/kafka/protocol.md deleted file mode 100644 index 720e5f0b4..000000000 --- a/mddocs/markdown/connection/db_connection/kafka/protocol.md +++ /dev/null @@ -1,37 +0,0 @@ - - -# Kafka Protocol - -### *class* onetl.connection.db_connection.kafka.kafka_protocol.KafkaProtocol - -Interface for Kafka connection Protocol classes. - -#### Versionadded -Added in version 0.9.0. - - - -#### *abstract* get_options(kafka: [Kafka](connection.md#onetl.connection.db_connection.kafka.connection.Kafka)) → dict - -Get options for Kafka connection - -* **Parameters:** - **kafka** - : Connection instance -* **Returns:** - dict: - : Kafka client options - - - -#### *abstract* cleanup(kafka: [Kafka](connection.md#onetl.connection.db_connection.kafka.connection.Kafka)) → None - -This method is called while closing Kafka connection. - -Implement it to cleanup resources like temporary files. - -* **Parameters:** - **kafka** - : Connection instance - - diff --git a/mddocs/markdown/connection/db_connection/kafka/read.md b/mddocs/markdown/connection/db_connection/kafka/read.md deleted file mode 100644 index 52ec5ff33..000000000 --- a/mddocs/markdown/connection/db_connection/kafka/read.md +++ /dev/null @@ -1,172 +0,0 @@ - - -# Reading from Kafka - -Data can be read from Kafka to Spark using [`DBReader`](../../../db/db_reader.md#onetl.db.db_reader.db_reader.DBReader). -It also supports [Read Strategies](../../../strategy/index.md#strategy) for incremental data reading. - -## Supported DBReader features - -* ❌ `columns` (is not supported by Kafka) -* ❌ `where` (is not supported by Kafka) -* ✅︎ `hwm`, supported strategies: -* * ✅︎ [Snapshot Strategy](../../../strategy/snapshot_strategy.md#snapshot-strategy) -* * ✅︎ [Incremental Strategy](../../../strategy/incremental_strategy.md#incremental-strategy) -* * ❌ [Snapshot Batch Strategy](../../../strategy/snapshot_batch_strategy.md#snapshot-batch-strategy) -* * ❌ [Incremental Batch Strategy](../../../strategy/incremental_batch_strategy.md#incremental-batch-strategy) -* ❌ `hint` (is not supported by Kafka) -* ❌ `df_schema` (see note below) -* ✅︎ `options` (see [`Kafka.ReadOptions`](#onetl.connection.db_connection.kafka.options.KafkaReadOptions)) - -## Dataframe schema - -Unlike other DB connections, Kafka does not have concept of columns. -All the topics messages have the same set of fields, see structure below: - -```text -root -|-- key: binary (nullable = true) -|-- value: binary (nullable = true) -|-- topic: string (nullable = false) -|-- partition: integer (nullable = false) -|-- offset: integer (nullable = false) -|-- timestamp: timestamp (nullable = false) -|-- timestampType: integer (nullable = false) -|-- headers: struct (nullable = true) - |-- key: string (nullable = false) - |-- value: binary (nullable = true) -``` - -`headers` field is present in the dataframe only if `Kafka.ReadOptions(include_headers=True)` is passed (compatibility with Kafka 1.x). - -## Value deserialization - -To read `value` or `key` of other type than bytes (e.g. struct or integer), users have to deserialize values manually. - -This could be done using following methods: -: * [`Avro.parse_column`](../../../file_df/file_formats/avro.md#onetl.file.format.avro.Avro.parse_column) - * [`JSON.parse_column`](../../../file_df/file_formats/json.md#onetl.file.format.json.JSON.parse_column) - * [`CSV.parse_column`](../../../file_df/file_formats/csv.md#onetl.file.format.csv.CSV.parse_column) - * [`XML.parse_column`](../../../file_df/file_formats/xml.md#onetl.file.format.xml.XML.parse_column) - -## Examples - -Snapshot strategy, `value` is Avro binary data: - -```python -from onetl.connection import Kafka -from onetl.db import DBReader, DBWriter -from onetl.file.format import Avro -from pyspark.sql.functions import decode - -# read all topic data from Kafka -kafka = Kafka(...) -reader = DBReader(connection=kafka, source="avro_topic") -read_df = reader.run() - -# parse Avro format to Spark struct -avro = Avro( - schema_dict={ - "type": "record", - "name": "Person", - "fields": [ - {"name": "name", "type": "string"}, - {"name": "age", "type": "int"}, - ], - } -) -deserialized_df = read_df.select( - # cast binary key to string - decode("key", "UTF-8").alias("key"), - avro.parse_column("value"), -) -``` - -Incremental strategy, `value` is JSON string: - -#### NOTE -Currently Kafka connector does support only HWMs based on `offset` field. Other fields, like `timestamp`, are not yet supported. - -```python -from onetl.connection import Kafka -from onetl.db import DBReader, DBWriter -from onetl.file.format import JSON -from pyspark.sql.functions import decode - -kafka = Kafka(...) - -# read only new data from Kafka topic -reader = DBReader( - connection=kafka, - source="topic_name", - hwm=DBReader.AutoDetectHWM(name="kafka_hwm", expression="offset"), -) - -with IncrementalStrategy(): - read_df = reader.run() - -# parse JSON format to Spark struct -json = JSON() -schema = StructType( - [ - StructField("name", StringType(), nullable=True), - StructField("age", IntegerType(), nullable=True), - ], -) -deserialized_df = read_df.select( - # cast binary key to string - decode("key", "UTF-8").alias("key"), - json.parse_column("value", json), -) -``` - -## Options - -### *pydantic model* onetl.connection.db_connection.kafka.options.KafkaReadOptions - -Reading options for Kafka connector. - -#### WARNING -Options: -: * `assign` - * `endingOffsets` - * `endingOffsetsByTimestamp` - * `kafka.*` - * `startingOffsets` - * `startingOffsetsByTimestamp` - * `startingTimestamp` - * `subscribe` - * `subscribePattern` - -are populated from connection attributes, and cannot be overridden by the user in `ReadOptions` to avoid issues. - -#### Versionadded -Added in version 0.9.0. - -### Examples - -#### NOTE -You can pass any value -[supported by connector](https://spark.apache.org/docs/latest/structured-streaming-kafka-integration.html), -even if it is not mentioned in this documentation. **Option names should be in** `camelCase`! - -The set of supported options depends on connector version. - -```python -from onetl.connection import Kafka - -options = Kafka.ReadOptions( - includeHeaders=False, - minPartitions=50, -) -``` - - - -#### *field* include_headers *: bool* *= False* *(alias 'includeHeaders')* - -If `True`, add `headers` column to output DataFrame. - -If `False`, column will not be added. - - diff --git a/mddocs/markdown/connection/db_connection/kafka/scram_auth.md b/mddocs/markdown/connection/db_connection/kafka/scram_auth.md deleted file mode 100644 index c0c974ad3..000000000 --- a/mddocs/markdown/connection/db_connection/kafka/scram_auth.md +++ /dev/null @@ -1,79 +0,0 @@ - - -# Kafka ScramAuth - -### *pydantic model* onetl.connection.db_connection.kafka.kafka_scram_auth.KafkaScramAuth - -Connect to Kafka using `sasl.mechanism="SCRAM-SHA-256"` or `sasl.mechanism="SCRAM-SHA-512"`. - -For more details see [Kafka Documentation](https://kafka.apache.org/documentation/#security_sasl_scram_clientconfig). - -#### Versionadded -Added in version 0.9.0. - -### Examples - -Auth in Kafka with `SCRAM-SHA-256` mechanism: - -```python -from onetl.connection import Kafka - -auth = Kafka.ScramAuth( - user="me", - password="abc", - digest="SHA-256", -) -``` - -Auth in Kafka with `SCRAM-SHA-512` mechanism and some custom SASL options passed to Kafka client config: - -```python -from onetl.connection import Kafka - -auth = Kafka.ScramAuth.parse( - { - "user": "me", - "password": "abc", - "digest": "SHA-512", - # options with `sasl.login.` prefix are passed to Kafka client config as-is - "sasl.login.class": "com.example.CustomScramLogin", - } -) -``` - - - -#### *field* user *: str* *[Required]* *(alias 'username')* - -#### *field* password *: SecretStr* *[Required]* - -#### *field* digest *: Literal['SHA-256', 'SHA-512']* *[Required]* - -#### get_jaas_conf() → str - - - -#### get_options(kafka: [Kafka](connection.md#onetl.connection.db_connection.kafka.connection.Kafka)) → dict - -Get options for Kafka connection - -* **Parameters:** - **kafka** - : Connection instance -* **Returns:** - dict: - : Kafka client options - - - -#### cleanup(kafka: [Kafka](connection.md#onetl.connection.db_connection.kafka.connection.Kafka)) → None - -This method is called while closing Kafka connection. - -Implement it to cleanup resources like temporary files. - -* **Parameters:** - **kafka** - : Connection instance - - diff --git a/mddocs/markdown/connection/db_connection/kafka/slots.md b/mddocs/markdown/connection/db_connection/kafka/slots.md deleted file mode 100644 index e4de1772d..000000000 --- a/mddocs/markdown/connection/db_connection/kafka/slots.md +++ /dev/null @@ -1,136 +0,0 @@ - - -# Kafka Slots - -### *class* onetl.connection.db_connection.kafka.slots.KafkaSlots - -Kafka slots that could be implemented by third-party plugins - -#### Versionadded -Added in version 0.9.0. - - - -#### *static* normalize_cluster_name(cluster: str) → str | None - -Normalize the given Kafka cluster name. - -This can be used to ensure that the Kafka cluster name conforms to specific naming conventions. - -#### Versionadded -Added in version 0.9.0. - -* **Parameters:** - **cluster** - : The original Kafka cluster name. -* **Returns:** - str | None - : The normalized Kafka cluster name. If the hook cannot be applied, return `None`. - -### Examples - -```python -from onetl.connection import Kafka -from onetl.hooks import hook - -@Kafka.Slots.normalize_cluster_name.bind -@hook -def normalize_cluster_name(cluster: str) -> str | None: - return cluster.lower() -``` - - - -#### *static* get_known_clusters() → set[str] | None - -Retrieve the collection of known Kafka clusters. - -This can be used to validate if the provided Kafka cluster name is recognized in the system. - -#### Versionadded -Added in version 0.9.0. - -* **Returns:** - set[str] | None - : A collection of known Kafka cluster names. If the hook cannot be applied, return `None`. - -### Examples - -```python -from onetl.connection import Kafka -from onetl.hooks import hook - -@Kafka.Slots.get_known_clusters.bind -@hook -def get_known_clusters() -> set[str] | None: - return {"kafka-cluster", "local"} -``` - - - -#### *static* normalize_address(address: str, cluster: str) → str | None - -Normalize the given broker address for a specific Kafka cluster. - -This can be used to format the broker address according to specific rules, such as adding default ports. - -#### Versionadded -Added in version 0.9.0. - -* **Parameters:** - **address** - : The original broker address. - - **cluster** - : The Kafka cluster name for which the address should be normalized. -* **Returns:** - str | None - : The normalized broker address. If the hook cannot be applied to the specific address, return `None`. - -### Examples - -```python -from onetl.connection import Kafka -from onetl.hooks import hook - -@Kafka.Slots.normalize_address.bind -@hook -def normalize_address(address: str, cluster: str) -> str | None: - if cluster == "kafka-cluster" and ":" not in address: - return f"{address}:9092" - return None -``` - - - -#### *static* get_cluster_addresses(cluster: str) → list[str] | None - -Retrieve a collection of known broker addresses for the specified Kafka cluster. - -This can be used to obtain the broker addresses dynamically. - -#### Versionadded -Added in version 0.9.0. - -* **Parameters:** - **cluster** - : The Kafka cluster name. -* **Returns:** - list[str] | None - : A collection of broker addresses for the specified Kafka cluster. If the hook cannot be applied, return `None`. - -### Examples - -```python -from onetl.connection import Kafka -from onetl.hooks import hook - -@Kafka.Slots.get_cluster_addresses.bind -@hook -def get_cluster_addresses(cluster: str) -> list[str] | None: - if cluster == "kafka_cluster": - return ["192.168.1.1:9092", "192.168.1.2:9092", "192.168.1.3:9092"] - return None -``` - - diff --git a/mddocs/markdown/connection/db_connection/kafka/ssl_protocol.md b/mddocs/markdown/connection/db_connection/kafka/ssl_protocol.md deleted file mode 100644 index ca2a59b45..000000000 --- a/mddocs/markdown/connection/db_connection/kafka/ssl_protocol.md +++ /dev/null @@ -1,149 +0,0 @@ - - -# Kafka SSLProtocol - -### *pydantic model* onetl.connection.db_connection.kafka.kafka_ssl_protocol.KafkaSSLProtocol - -Connect to Kafka using `SSL` or `SASL_SSL` security protocols. - -For more details see: - -* [Kafka Documentation](https://kafka.apache.org/documentation/#producerconfigs_ssl.keystore.location) -* [IBM Documentation](https://www.ibm.com/docs/en/cloud-paks/cp-biz-automation/19.0.x?topic=fcee-kafka-using-ssl-kerberos-authentication) -* [How to use PEM Certificates with Kafka](https://codingharbour.com/apache-kafka/using-pem-certificates-with-apache-kafka/) - -#### Versionadded -Added in version 0.9.0. - -### Examples - -Pass PEM key and certificates as files located on Spark driver host: - -```python -from pathlib import Path - -# Just read existing files located on host, and pass key and certificates as strings -protocol = Kafka.SSLProtocol( - keystore_type="PEM", - keystore_certificate_chain=Path("path/to/user.crt").read_text(), - keystore_key=Path("path/to/user.key").read_text(), - truststore_type="PEM", - truststore_certificates=Path("/path/to/server.crt").read_text(), -) -``` - -Pass PEM key and certificates as raw strings: - -```python -protocol = Kafka.SSLProtocol( - keystore_type="PEM", - keystore_certificate_chain="-----BEGIN CERTIFICATE-----\nMIIDZjC...\n-----END CERTIFICATE-----", - keystore_key="-----BEGIN PRIVATE KEY-----\nMIIEvg..\n-----END PRIVATE KEY-----", - truststore_type="PEM", - truststore_certificates="-----BEGIN CERTIFICATE-----\nMICC...\n-----END CERTIFICATE-----", -) -``` - -Pass custom options: - -```python -protocol = Kafka.SSLProtocol.parse( - { - # Just the same options as above, but using Kafka config naming with dots - "ssl.keystore.type": "PEM", - "ssl.keystore.certificate_chain": "-----BEGIN CERTIFICATE-----\nMIIDZjC...\n-----END CERTIFICATE-----", - "ssl.keystore.key": "-----BEGIN PRIVATE KEY-----\nMIIEvg..\n-----END PRIVATE KEY-----", - "ssl.truststore.type": "PEM", - "ssl.truststore.certificates": "-----BEGIN CERTIFICATE-----\nMICC...\n-----END CERTIFICATE-----", - # Any option starting from "ssl." is passed to Kafka client as-is - "ssl.protocol": "TLSv1.3", - } -) -``` - -### Not recommended - -These options are error-prone and have several drawbacks, so it is not recommended to use them. - -Passing PEM certificates as files: - -* ENCRYPT `user.key` file with password `"some password"` [using PKCS#8 scheme](https://www.mkssoftware.com/docs/man1/openssl_pkcs8.1.asp). -* Save encrypted key to file `/path/to/user/encrypted_key_with_certificate_chain.pem`. -* Then append user certificate to the end of this file. -* Deploy this file (and server certificate too) to **EVERY** host Spark could run (both driver and executors). -* Then pass file locations and password for key decryption to options below. - -```python -protocol = Kafka.SSLProtocol( - keystore_type="PEM", - keystore_location="/path/to/user/encrypted_key_with_certificate_chain.pem", - key_password="some password", - truststore_type="PEM", - truststore_location="/path/to/server.crt", -) -``` - -Passing JKS (Java Key Store) location: - -* [Add user key and certificate to JKS keystore](https://stackoverflow.com/a/4326346). -* [Add server certificate to JKS truststore](https://stackoverflow.com/a/373307). -* This should be done on **EVERY** host Spark could run (both driver and executors). -* Pass keystore and truststore paths to options below, as well as passwords for accessing these stores: - -```python -protocol = Kafka.SSLProtocol( - keystore_type="JKS", - keystore_location="/usr/lib/jvm/default/lib/security/keystore.jks", - keystore_password="changeit", - truststore_type="JKS", - truststore_location="/usr/lib/jvm/default/lib/security/truststore.jks", - truststore_password="changeit", -) -``` - - - -#### *field* keystore_type *: str* *[Required]* *(alias 'ssl.keystore.type')* - -#### *field* keystore_location *: LocalPath | None* *= None* *(alias 'ssl.keystore.location')* - -#### *field* keystore_password *: SecretStr | None* *= None* *(alias 'ssl.keystore.password')* - -#### *field* keystore_certificate_chain *: str | None* *= None* *(alias 'ssl.keystore.certificate.chain')* - -#### *field* keystore_key *: SecretStr | None* *= None* *(alias 'ssl.keystore.key')* - -#### *field* key_password *: SecretStr | None* *= None* *(alias 'ssl.key.password')* - -#### *field* truststore_type *: str* *[Required]* *(alias 'ssl.truststore.type')* - -#### *field* truststore_location *: LocalPath | None* *= None* *(alias 'ssl.truststore.location')* - -#### *field* truststore_password *: SecretStr | None* *= None* *(alias 'ssl.truststore.password')* - -#### *field* truststore_certificates *: str | None* *= None* *(alias 'ssl.truststore.certificates')* - -#### get_options(kafka: [Kafka](connection.md#onetl.connection.db_connection.kafka.connection.Kafka)) → dict - -Get options for Kafka connection - -* **Parameters:** - **kafka** - : Connection instance -* **Returns:** - dict: - : Kafka client options - - - -#### cleanup(kafka: [Kafka](connection.md#onetl.connection.db_connection.kafka.connection.Kafka)) → None - -This method is called while closing Kafka connection. - -Implement it to cleanup resources like temporary files. - -* **Parameters:** - **kafka** - : Connection instance - - diff --git a/mddocs/markdown/connection/db_connection/kafka/troubleshooting.md b/mddocs/markdown/connection/db_connection/kafka/troubleshooting.md deleted file mode 100644 index cd15b5ba7..000000000 --- a/mddocs/markdown/connection/db_connection/kafka/troubleshooting.md +++ /dev/null @@ -1,10 +0,0 @@ - - -# Kafka Troubleshooting - -#### NOTE -General guide: [Troubleshooting](../../../troubleshooting/index.md#troubleshooting). - -## Cannot connect using `SSL` protocol - -Please check that certificate files are not Base-64 encoded. diff --git a/mddocs/markdown/connection/db_connection/kafka/write.md b/mddocs/markdown/connection/db_connection/kafka/write.md deleted file mode 100644 index c85f9c3cc..000000000 --- a/mddocs/markdown/connection/db_connection/kafka/write.md +++ /dev/null @@ -1,118 +0,0 @@ - - -# Writing to Kafka - -For writing data to Kafka, use [`DBWriter`](../../../db/db_writer.md#onetl.db.db_writer.db_writer.DBWriter) with specific options (see below). - -## Dataframe schema - -Unlike other DB connections, Kafka does not have concept of columns. -All the topics messages have the same set of fields. Only some of them can be written: - -```text -root -|-- key: binary (nullable = true) -|-- value: binary (nullable = true) -|-- headers: struct (nullable = true) - |-- key: string (nullable = false) - |-- value: binary (nullable = true) -``` - -`headers` can be passed only with `Kafka.WriteOptions(include_headers=True)` (compatibility with Kafka 1.x). - -Field `topic` should not be present in the dataframe, as it is passed to `DBWriter(target=...)`. - -Other fields, like `partition`, `offset`, `timestamp` are set by Kafka, and cannot be passed explicitly. - -## Value serialization - -To write `value` or `key` of other type than bytes (e.g. struct or integer), users have to serialize values manually. - -This could be done using following methods: -: * [`Avro.serialize_column`](../../../file_df/file_formats/avro.md#onetl.file.format.avro.Avro.serialize_column) - * [`JSON.serialize_column`](../../../file_df/file_formats/json.md#onetl.file.format.json.JSON.serialize_column) - * [`CSV.serialize_column`](../../../file_df/file_formats/csv.md#onetl.file.format.csv.CSV.serialize_column) - -## Examples - -Convert `value` to JSON string, and write to Kafka: - -```python -from onetl.connection import Kafka -from onetl.db import DBWriter -from onetl.file.format import JSON - -df = ... # original data is here - -# serialize struct data as JSON -json = JSON() -write_df = df.select( - df.key, - json.serialize_column(df.value), -) - -# write data to Kafka -kafka = Kafka(...) - -writer = DBWriter( - connection=kafka, - target="topic_name", -) -writer.run(write_df) -``` - -## Options - -### *pydantic model* onetl.connection.db_connection.kafka.options.KafkaWriteOptions - -Writing options for Kafka connector. - -#### WARNING -Options: -: * `kafka.*` - * `topic` - -are populated from connection attributes, and cannot be overridden by the user in `WriteOptions` to avoid issues. - -#### Versionadded -Added in version 0.9.0. - -### Examples - -#### NOTE -You can pass any value -[supported by connector](https://spark.apache.org/docs/latest/structured-streaming-kafka-integration.html), -even if it is not mentioned in this documentation. **Option names should be in** `camelCase`! - -The set of supported options depends on connector version. - -```python -from onetl.connection import Kafka - -options = Kafka.WriteOptions( - if_exists="append", - includeHeaders=True, -) -``` - - - -#### *field* if_exists *: KafkaTopicExistBehaviorKafka* *= KafkaTopicExistBehaviorKafka.APPEND* - -Behavior of writing data into existing topic. - -Same as `df.write.mode(...)`. - -Possible values: -: * `append` (default) - Adds new objects into existing topic. - * `error` - Raises an error if topic already exists. - - - -#### *field* include_headers *: bool* *= False* *(alias 'includeHeaders')* - -If `True`, `headers` column from dataframe can be written to Kafka (requires Kafka 2.0+). - -If `False` and dataframe contains `headers` column, an exception will be raised. - - diff --git a/mddocs/markdown/connection/db_connection/mongodb/connection.md b/mddocs/markdown/connection/db_connection/mongodb/connection.md deleted file mode 100644 index 8a01a11be..000000000 --- a/mddocs/markdown/connection/db_connection/mongodb/connection.md +++ /dev/null @@ -1,124 +0,0 @@ - - -# MongoDB Connection - -### *class* onetl.connection.db_connection.mongodb.connection.MongoDB(\*, spark: SparkSession, database: str, host: Host, user: str, password: SecretStr, port: int = 27017, extra: MongoDBExtra = MongoDBExtra()) - -MongoDB connection. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -Based on package [org.mongodb.spark:mongo-spark-connector:10.4.1](https://mvnrepository.com/artifact/org.mongodb.spark/mongo-spark-connector_2.12/10.4.1) -([MongoDB connector for Spark](https://www.mongodb.com/docs/spark-connector/current/)) - -#### SEE ALSO -Before using this connector please take into account [Prerequisites](prerequisites.md#mongodb-prerequisites) - -#### Versionadded -Added in version 0.7.0. - -* **Parameters:** - **host** - : Host of MongoDB. For example: `test.mongodb.com` or `193.168.1.17`. - - **port** - : Port of MongoDB - - **user** - : User, which have proper access to the database. For example: `some_user`. - - **password** - : Password for database connection. - - **database** - : Database in MongoDB. - - **extra** - : Specifies one or more extra parameters by which clients can connect to the instance. -
- For example: `{"tls": "false"}` -
- See [Connection string options documentation](https://www.mongodb.com/docs/manual/reference/connection-string/#std-label-connections-connection-options) - for more details - - **spark** - : Spark session. - -### Examples - -```python -from onetl.connection import MongoDB -from pyspark.sql import SparkSession - -# Create Spark session with MongoDB connector loaded -maven_packages = MongoDB.get_packages(spark_version="3.4") -spark = ( - SparkSession.builder.appName("spark-app-name") - .config("spark.jars.packages", ",".join(maven_packages)) - .getOrCreate() -) - -# Create connection -mongo = MongoDB( - host="master.host.or.ip", - user="user", - password="*****", - database="target_database", - spark=spark, -).check() -``` - - - -#### *classmethod* get_packages(scala_version: str | None = None, spark_version: str | None = None, package_version: str | None = None) → list[str] - -Get package names to be downloaded by Spark. Allows specifying custom MongoDB Spark connector versions. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### Versionadded -Added in version 0.9.0. - -* **Parameters:** - **scala_version** - : Scala version in format `major.minor`. -
- If `None`, `spark_version` is used to determine Scala version. - - **spark_version** - : Spark version in format `major.minor`. Used only if `scala_version=None`. - - **package_version** - : Specifies the version of the MongoDB Spark connector to use. Defaults to `10.4.1`. -
- #### Versionadded - Added in version 0.11.0. - -### Examples - -```python -from onetl.connection import MongoDB - -MongoDB.get_packages(scala_version="2.12") - -# specify custom connector version -MongoDB.get_packages(scala_version="2.12", package_version="10.4.1") -``` - - - -#### check() - -Check source availability. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -If not, an exception will be raised. - -* **Returns:** - Connection itself -* **Raises:** - RuntimeError - : If the connection is not available - -### Examples - -```python -connection.check() -``` - - diff --git a/mddocs/markdown/connection/db_connection/mongodb/index.md b/mddocs/markdown/connection/db_connection/mongodb/index.md deleted file mode 100644 index 45ab522a3..000000000 --- a/mddocs/markdown/connection/db_connection/mongodb/index.md +++ /dev/null @@ -1,18 +0,0 @@ - - -# MongoDB - -# Connection - -* [Prerequisites](prerequisites.md) -* [MongoDB Connection](connection.md) - -# Operations - -* [Reading from MongoDB using `DBReader`](read.md) -* [Reading from MongoDB using `MongoDB.pipeline`](pipeline.md) -* [Writing to MongoDB using `DBWriter`](write.md) - -# Troubleshooting - -* [MongoDB <-> Spark type mapping](types.md) diff --git a/mddocs/markdown/connection/db_connection/mongodb/pipeline.md b/mddocs/markdown/connection/db_connection/mongodb/pipeline.md deleted file mode 100644 index 2bae88c48..000000000 --- a/mddocs/markdown/connection/db_connection/mongodb/pipeline.md +++ /dev/null @@ -1,152 +0,0 @@ - - -# Reading from MongoDB using `MongoDB.pipeline` - -[`MongoDB.sql`](#onetl.connection.db_connection.mongodb.connection.MongoDB.pipeline) allows passing custom pipeline, -but does not support incremental strategies. - -#### WARNING -Please take into account [MongoDB <-> Spark type mapping](types.md#mongodb-types) - -## Recommendations - -### Pay attention to `pipeline` value - -Instead of filtering data on Spark side using `df.filter(df.column == 'value')` pass proper `mongodb.pipeline(..., pipeline={"$match": {"column": {"$eq": "value"}}})` value. -This both reduces the amount of data send from MongoDB to Spark, and may also improve performance of the query. -Especially if there are indexes for columns used in `pipeline` value. - -## References - -#### MongoDB.pipeline(collection: str, pipeline: dict | list[dict] | None = None, df_schema: StructType | None = None, options: [MongoDBPipelineOptions](#onetl.connection.db_connection.mongodb.options.MongoDBPipelineOptions) | dict | None = None) - -Execute a pipeline for a specific collection, and return DataFrame. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -Almost like [Aggregation pipeline syntax](https://www.mongodb.com/docs/manual/core/aggregation-pipeline/) -in MongoDB: - -```js -db.collection_name.aggregate([{"$match": ...}, {"$group": ...}]) -``` - -but pipeline is executed on Spark executors, in a distributed way. - -#### NOTE -This method does not support [Read Strategies](../../../strategy/index.md#strategy), -use [`DBReader`](../../../db/db_reader.md#onetl.db.db_reader.db_reader.DBReader) instead - -#### Versionadded -Added in version 0.7.0. - -* **Parameters:** - **collection** - : Collection name. - - **pipeline** - : Pipeline containing a database query. - See [Aggregation pipeline syntax](https://www.mongodb.com/docs/manual/core/aggregation-pipeline/). - - **df_schema** - : Schema describing the resulting DataFrame. - - **options** - : Additional pipeline options, see [`MongoDB.PipelineOptions`](#onetl.connection.db_connection.mongodb.options.MongoDBPipelineOptions). - -### Examples - -Get document with a specific `field` value: - -```python -df = connection.pipeline( - collection="collection_name", - pipeline={"$match": {"field": {"$eq": 1}}}, -) -``` - -Calculate aggregation and get result: - -```python -df = connection.pipeline( - collection="collection_name", - pipeline={ - "$group": { - "_id": 1, - "min": {"$min": "$column_int"}, - "max": {"$max": "$column_int"}, - } - }, -) -``` - -Explicitly pass DataFrame schema: - -```python -from pyspark.sql.types import ( - DoubleType, - IntegerType, - StringType, - StructField, - StructType, - TimestampType, -) - -df_schema = StructType( - [ - StructField("_id", StringType()), - StructField("some_string", StringType()), - StructField("some_int", IntegerType()), - StructField("some_datetime", TimestampType()), - StructField("some_float", DoubleType()), - ], -) - -df = connection.pipeline( - collection="collection_name", - df_schema=df_schema, - pipeline={"$match": {"some_int": {"$gt": 999}}}, -) -``` - -Pass additional options to pipeline execution: - -```python -df = connection.pipeline( - collection="collection_name", - pipeline={"$match": {"field": {"$eq": 1}}}, - options=MongoDB.PipelineOptions(hint={"field": 1}), -) -``` - - - -### *pydantic model* onetl.connection.db_connection.mongodb.options.MongoDBPipelineOptions - -Aggregation pipeline options for MongoDB connector. - -The only difference from [`MongoDB.ReadOptions`](read.md#onetl.connection.db_connection.mongodb.options.MongoDBReadOptions) that latter does not allow to pass the `hint` parameter. - -#### WARNING -Options `uri`, `database`, `collection`, `pipeline` are populated from connection attributes, -and cannot be overridden by the user in `PipelineOptions` to avoid issues. - -#### Versionadded -Added in version 0.7.0. - -### Examples - -#### NOTE -You can pass any value -[supported by connector](https://www.mongodb.com/docs/spark-connector/current/batch-mode/batch-read-config/), -even if it is not mentioned in this documentation. **Option names should be in** `camelCase`! - -The set of supported options depends on connector version. - -```python -from onetl.connection import MongoDB - -options = MongoDB.PipelineOptions( - hint={"some_field": 1}, -) -``` - - diff --git a/mddocs/markdown/connection/db_connection/mongodb/prerequisites.md b/mddocs/markdown/connection/db_connection/mongodb/prerequisites.md deleted file mode 100644 index 4572fb4f6..000000000 --- a/mddocs/markdown/connection/db_connection/mongodb/prerequisites.md +++ /dev/null @@ -1,72 +0,0 @@ - - -# Prerequisites - -## Version Compatibility - -* MongoDB server versions: - : * Officially declared: 4.0 or higher - * Actually tested: 4.0.0, 8.0.4 -* Spark versions: 3.2.x - 3.5.x -* Java versions: 8 - 20 - -See [official documentation](https://www.mongodb.com/docs/spark-connector/). - -## Installing PySpark - -To use MongoDB connector you should have PySpark installed (or injected to `sys.path`) -BEFORE creating the connector instance. - -See [Spark](../../../install/spark.md#install-spark) installation instruction for more details. - -## Connecting to MongoDB - -### Connection host - -It is possible to connect to MongoDB host by using either DNS name of host or it’s IP address. - -It is also possible to connect to MongoDB shared cluster: - -```python -mongo = MongoDB( - host="master.host.or.ip", - user="user", - password="*****", - database="target_database", - spark=spark, - extra={ - # read data from secondary cluster node, switch to primary if not available - "readPreference": "secondaryPreferred", - }, -) -``` - -Supported `readPreference` values are described in [official documentation](https://www.mongodb.com/docs/manual/core/read-preference/). - -### Connection port - -Connection is usually performed to port `27017`. Port may differ for different MongoDB instances. -Please ask your MongoDB administrator to provide required information. - -### Required grants - -Ask your MongoDB cluster administrator to set following grants for a user, -used for creating a connection: - -Read + Write - -```js -// allow writing data to specific database -db.grantRolesToUser("username", [{db: "somedb", role: "readWrite"}]) -``` - -Read only - -```js -// allow reading data from specific database -db.grantRolesToUser("username", [{db: "somedb", role: "read"}]) -``` - -See: -: * [db.grantRolesToUser documentation](https://www.mongodb.com/docs/manual/reference/method/db.grantRolesToUser) - * [MongoDB builtin roles](https://www.mongodb.com/docs/manual/reference/built-in-roles) diff --git a/mddocs/markdown/connection/db_connection/mongodb/read.md b/mddocs/markdown/connection/db_connection/mongodb/read.md deleted file mode 100644 index 2f2ffb73c..000000000 --- a/mddocs/markdown/connection/db_connection/mongodb/read.md +++ /dev/null @@ -1,157 +0,0 @@ - - -# Reading from MongoDB using `DBReader` - -[`DBReader`](../../../db/db_reader.md#onetl.db.db_reader.db_reader.DBReader) supports [Read Strategies](../../../strategy/index.md#strategy) for incremental data reading, -but does not support custom pipelines, e.g. aggregation. - -#### WARNING -Please take into account [MongoDB <-> Spark type mapping](types.md#mongodb-types) - -## Supported DBReader features - -* ❌ `columns` (for now, all document fields are read) -* ✅︎ `where` (passed to `{"$match": ...}` aggregation pipeline) -* ✅︎ `hwm`, supported strategies: -* * ✅︎ [Snapshot Strategy](../../../strategy/snapshot_strategy.md#snapshot-strategy) -* * ✅︎ [Incremental Strategy](../../../strategy/incremental_strategy.md#incremental-strategy) -* * ✅︎ [Snapshot Batch Strategy](../../../strategy/snapshot_batch_strategy.md#snapshot-batch-strategy) -* * ✅︎ [Incremental Batch Strategy](../../../strategy/incremental_batch_strategy.md#incremental-batch-strategy) -* * Note that `expression` field of HWM can only be a field name, not a custom expression -* ✅︎ `hint` (see [official documentation](https://www.mongodb.com/docs/v5.0/reference/operator/meta/hint/)) -* ✅︎ `df_schema` (mandatory) -* ✅︎ `options` (see [`MongoDB.ReadOptions`](#onetl.connection.db_connection.mongodb.options.MongoDBReadOptions)) - -## Examples - -Snapshot strategy: - -```python -from onetl.connection import MongoDB -from onetl.db import DBReader - -from pyspark.sql.types import ( - StructType, - StructField, - IntegerType, - StringType, - TimestampType, -) - -mongodb = MongoDB(...) - -# mandatory -df_schema = StructType( - [ - StructField("_id", StringType()), - StructField("some", StringType()), - StructField( - "field", - StructType( - [ - StructField("nested", IntegerType()), - ], - ), - ), - StructField("updated_dt", TimestampType()), - ] -) - -reader = DBReader( - connection=mongodb, - source="some_collection", - df_schema=df_schema, - where={"field": {"$eq": 123}}, - hint={"field": 1}, - options=MongoDBReadOptions(batchSize=10000), -) -df = reader.run() -``` - -Incremental strategy: - -```python -from onetl.connection import MongoDB -from onetl.db import DBReader -from onetl.strategy import IncrementalStrategy - -from pyspark.sql.types import ( - StructType, - StructField, - IntegerType, - StringType, - TimestampType, -) - -mongodb = MongoDB(...) - -# mandatory -df_schema = StructType( - [ - StructField("_id", StringType()), - StructField("some", StringType()), - StructField( - "field", - StructType( - [ - StructField("nested", IntegerType()), - ], - ), - ), - StructField("updated_dt", TimestampType()), - ] -) - -reader = DBReader( - connection=mongodb, - source="some_collection", - df_schema=df_schema, - where={"field": {"$eq": 123}}, - hint={"field": 1}, - hwm=DBReader.AutoDetectHWM(name="mongodb_hwm", expression="updated_dt"), - options=MongoDBReadOptions(batchSize=10000), -) - -with IncrementalStrategy(): - df = reader.run() -``` - -## Recommendations - -### Pay attention to `where` value - -Instead of filtering data on Spark side using `df.filter(df.column == 'value')` pass proper `DBReader(where={"column": {"$eq": "value"}})` clause. -This both reduces the amount of data send from MongoDB to Spark, and may also improve performance of the query. -Especially if there are indexes for columns used in `where` clause. - -## Read options - -### *pydantic model* onetl.connection.db_connection.mongodb.options.MongoDBReadOptions - -Reading options for MongoDB connector. - -#### WARNING -Options `uri`, `database`, `collection`, `pipeline`, `hint` are populated from connection -attributes, and cannot be overridden by the user in `ReadOptions` to avoid issues. - -#### Versionadded -Added in version 0.7.0. - -### Examples - -#### NOTE -You can pass any value -[supported by connector](https://www.mongodb.com/docs/spark-connector/current/batch-mode/batch-read-config/), -even if it is not mentioned in this documentation. **Option names should be in** `camelCase`! - -The set of supported options depends on connector version. - -```python -from onetl.connection import MongoDB - -options = MongoDB.ReadOptions( - sampleSize=100, -) -``` - - diff --git a/mddocs/markdown/connection/db_connection/mongodb/types.md b/mddocs/markdown/connection/db_connection/mongodb/types.md deleted file mode 100644 index 2d75b5e52..000000000 --- a/mddocs/markdown/connection/db_connection/mongodb/types.md +++ /dev/null @@ -1,214 +0,0 @@ - - -# MongoDB <-> Spark type mapping - -#### NOTE -The results below are valid for Spark 3.5.5, and may differ on other Spark versions. - -## Type detection & casting - -Spark’s DataFrames always have a `schema` which is a list of fields with corresponding Spark types. All operations on a field are performed using field type. - -MongoDB is, by design, \_\_schemaless_\_. So there are 2 ways how this can be handled: - -* User provides DataFrame schema explicitly: - - ### See example - - ```python - from onetl.connection import MongoDB - from onetl.db import DBReader - - from pyspark.sql.types import ( - StructType, - StructField, - IntegerType, - StringType, - TimestampType, - ) - - mongodb = MongoDB(...) - - df_schema = StructType( - [ - StructField("_id", StringType()), - StructField("some", StringType()), - StructField( - "field", - StructType( - [ - StructField("nested", IntegerType()), - ] - ), - ), - ] - ) - - reader = DBReader( - connection=mongodb, - source="some_collection", - df_schema=df_schema, - ) - df = reader.run() - - # or - - df = mongodb.pipeline( - collection="some_collection", - df_schema=df_schema, - ) - ``` -* Rely on MongoDB connector schema infer: - ```python - df = mongodb.pipeline(collection="some_collection") - ``` - - In this case MongoDB connector read a sample of collection documents, and build DataFrame schema based on document fields and values. - -It is highly recommended to pass `df_schema` explicitly, to avoid type conversion issues. - -### References - -Here you can find source code with type conversions: - -* [MongoDB -> Spark](https://github.com/mongodb/mongo-spark/blob/r10.4.1/src/main/java/com/mongodb/spark/sql/connector/schema/InferSchema.java#L214-L260) -* [Spark -> MongoDB](https://github.com/mongodb/mongo-spark/blob/r10.4.1/src/main/java/com/mongodb/spark/sql/connector/schema/RowToBsonDocumentConverter.java#L157-L260) - -## Supported types - -See [official documentation](https://www.mongodb.com/docs/manual/reference/bson-types/) - -### Numeric types - -| MongoDB type (read) | Spark type | MongoDB type (write) | -|-----------------------|---------------------------|------------------------| -| `Decimal128` | `DecimalType(P=34, S=32)` | `Decimal128` | -| `-` | `FloatType()` | `Double` | -| `Double` | `DoubleType()` | | -| `-` | `ByteType()` | `Int32` | -| `-` | `ShortType()` | | -| `Int32` | `IntegerType()` | | -| `Int64` | `LongType()` | `Int64` | - -### Temporal types - -| MongoDB type (read) | Spark type | MongoDB type (write) | -|-----------------------|---------------------------------|-------------------------------------------------------------------| -| `-` | `DateType()`, days | `Date`, milliseconds | -| `Date`, milliseconds | `TimestampType()`, microseconds | `Date`, milliseconds,
**precision loss** [2](#id2) | -| `Timestamp`, seconds | `TimestampType()`, microseconds | `Date`, milliseconds | -| `-` | `TimestampNTZType()` | unsupported | -| `-` | `DayTimeIntervalType()` | | - -#### WARNING -Note that types in MongoDB and Spark have different value ranges: - -| MongoDB type | Min value | Max value | Spark type | Min value | Max value | -|----------------|-----------------------|-----------------------|-------------------|------------------------------|------------------------------| -| `Date` | -290 million years | 290 million years | `TimestampType()` | `0001-01-01 00:00:00.000000` | `9999-12-31 23:59:59.999999` | -| `Timestamp` | `1970-01-01 00:00:00` | `2106-02-07 09:28:16` | | | | - -So not all values can be read from MongoDB to Spark, and can written from Spark DataFrame to MongoDB. - -References: -: * [MongoDB Date type documentation](https://www.mongodb.com/docs/manual/reference/bson-types/#date) - * [MongoDB Timestamp documentation](https://www.mongodb.com/docs/manual/reference/bson-types/#timestamps) - * [Spark DateType documentation](https://spark.apache.org/docs/latest/api/java/org/apache/spark/sql/types/DateType.html) - * [Spark TimestampType documentation](https://spark.apache.org/docs/latest/api/java/org/apache/spark/sql/types/TimestampType.html) - -* **[2]** MongoDB `Date` type has precision up to milliseconds (`23:59:59.999`). Inserting data with microsecond precision (`23:59:59.999999`) will lead to **throwing away microseconds**. - -### String types - -Note: fields of deprecated MongoDB type `Symbol` are excluded during read. - -| MongoDB type (read) | Spark type | MongoDB type (write) | -|-----------------------|----------------|------------------------| -| `String` | `StringType()` | `String` | -| `Code` | | | -| `RegExp` | | | - -### Binary types - -| MongoDB type (read) | Spark type | MongoDB type (write) | -|-----------------------|-----------------|------------------------| -| `Boolean` | `BooleanType()` | `Boolean` | -| `Binary` | `BinaryType()` | `Binary` | - -### Struct types - -| MongoDB type (read) | Spark type | MongoDB type (write) | -|-----------------------|---------------------|------------------------| -| `Array[T]` | `ArrayType(T)` | `Array[T]` | -| `Object[...]` | `StructType([...])` | `Object[...]` | -| `-` | `MapType(...)` | | - -### Special types - -| MongoDB type (read) | Spark type | MongoDB type (write) | -|-----------------------|-------------------------------------------------------|-------------------------------------| -| `ObjectId` | `StringType()` | `String` | -| `MaxKey` | | | -| `MinKey` | | | -| `Null` | `NullType()` | `Null` | -| `Undefined` | | | -| `DBRef` | `StructType([$ref: StringType(), $id: StringType()])` | `Object[$ref: String, $id: String]` | - -## Explicit type cast - -### `DBReader` - -Currently it is not possible to cast field types using `DBReader`. But this can be done using `MongoDB.pipeline`. - -### `MongoDB.pipeline` - -You can use `$project` aggregation to cast field types: - -```python -from pyspark.sql.types import IntegerType, StructField, StructType - -from onetl.connection import MongoDB -from onetl.db import DBReader - -mongodb = MongoDB(...) - -df = mongodb.pipeline( - collection="my_collection", - pipeline=[ - { - "$project": { - # convert unsupported_field to string - "unsupported_field_str": { - "$convert": { - "input": "$unsupported_field", - "to": "string", - }, - }, - # skip unsupported_field from result - "unsupported_field": 0, - } - } - ], -) - -# cast field content to proper Spark type -df = df.select( - df.id, - df.supported_field, - # explicit cast - df.unsupported_field_str.cast("integer").alias("parsed_integer"), -) -``` - -### `DBWriter` - -Convert dataframe field to string on Spark side, and then write it to MongoDB: - -```python -df = df.select( - df.id, - df.unsupported_field.cast("string").alias("array_field_json"), -) - -writer.run(df) -``` diff --git a/mddocs/markdown/connection/db_connection/mongodb/write.md b/mddocs/markdown/connection/db_connection/mongodb/write.md deleted file mode 100644 index a0e3778c1..000000000 --- a/mddocs/markdown/connection/db_connection/mongodb/write.md +++ /dev/null @@ -1,118 +0,0 @@ - - -# Writing to MongoDB using `DBWriter` - -For writing data to MongoDB, use [`DBWriter`](../../../db/db_writer.md#onetl.db.db_writer.db_writer.DBWriter). - -#### WARNING -Please take into account [MongoDB <-> Spark type mapping](types.md#mongodb-types) - -## Examples - -```python -from onetl.connection import MongoDB -from onetl.db import DBWriter - -mongodb = MongoDB(...) - -df = ... # data is here - -writer = DBWriter( - connection=mongodb, - target="schema.table", - options=MongoDB.WriteOptions( - if_exists="append", - ), -) - -writer.run(df) -``` - -## Write options - -Method above accepts [`MongoDB.WriteOptions`](#onetl.connection.db_connection.mongodb.options.MongoDBWriteOptions) - -### *pydantic model* onetl.connection.db_connection.mongodb.options.MongoDBWriteOptions - -Writing options for MongoDB connector. - -#### WARNING -Options `uri`, `database`, `collection` are populated from connection attributes, -and cannot be overridden by the user in `WriteOptions` to avoid issues. - -#### Versionadded -Added in version 0.7.0. - -### Examples - -#### NOTE -You can pass any value -[supported by connector](https://www.mongodb.com/docs/spark-connector/current/batch-mode/batch-write-config/), -even if it is not mentioned in this documentation. **Option names should be in** `camelCase`! - -The set of supported options depends on connector version. - -```python -from onetl.connection import MongoDB - -options = MongoDB.WriteOptions( - if_exists="append", - sampleSize=500, - localThreshold=20, -) -``` - - - -#### *field* if_exists *: MongoDBCollectionExistBehavior* *= MongoDBCollectionExistBehavior.APPEND* *(alias 'mode')* - -Behavior of writing data into existing collection. - -Possible values: -: * `append` (default) - : Adds new objects into existing collection. -
- ### Behavior in details -
- * Collection does not exist - : Collection is created using options provided by user - (`shardkey` and others). - * Collection exists - : Data is appended to a collection. -
- #### WARNING - This mode does not check whether collection already contains - objects from dataframe, so duplicated objects can be created. - * `replace_entire_collection` - : **Collection is deleted and then created**. -
- ### Behavior in details -
- * Collection does not exist - : Collection is created using options provided by user - (`shardkey` and others). - * Collection exists - : Collection content is replaced with dataframe content. - * `ignore` - : Ignores the write operation if the collection already exists. -
- ### Behavior in details -
- * Collection does not exist - : Collection is created using options provided by user - * Collection exists - : The write operation is ignored, and no data is written to the collection. - * `error` - : Raises an error if the collection already exists. -
- ### Behavior in details -
- * Collection does not exist - : Collection is created using options provided by user - * Collection exists - : An error is raised, and no data is written to the collection. - -#### Versionchanged -Changed in version 0.9.0: Renamed `mode` → `if_exists` - - diff --git a/mddocs/markdown/connection/db_connection/mssql/connection.md b/mddocs/markdown/connection/db_connection/mssql/connection.md deleted file mode 100644 index 3e35a34fb..000000000 --- a/mddocs/markdown/connection/db_connection/mssql/connection.md +++ /dev/null @@ -1,186 +0,0 @@ - - -# MSSQL connection - -### *class* onetl.connection.db_connection.mssql.connection.MSSQL(\*, spark: SparkSession, user: str, password: SecretStr, database: str, host: Host, port: int | None = None, extra: MSSQLExtra = MSSQLExtra()) - -MSSQL JDBC connection. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -Based on Maven package [com.microsoft.sqlserver:mssql-jdbc:12.8.1.jre8](https://mvnrepository.com/artifact/com.microsoft.sqlserver/mssql-jdbc/12.8.1.jre8) -([official MSSQL JDBC driver](https://docs.microsoft.com/en-us/sql/connect/jdbc/download-microsoft-jdbc-driver-for-sql-server)). - -#### SEE ALSO -Before using this connector please take into account [Prerequisites](prerequisites.md#mssql-prerequisites) - -* **Parameters:** - **host** - : Host of MSSQL database. For example: `test.mssql.domain.com` or `192.168.1.14` - - **port** - : Port of MSSQL database -
- #### Versionchanged - Changed in version 0.11.1: Default value was changed from `1433` to `None`, - to allow automatic port discovery with `instanceName`. - - **user** - : User, which have proper access to the database. For example: `some_user` - - **password** - : Password for database connection - - **database** - : Database in RDBMS, NOT schema. -
- See [this page](https://www.educba.com/postgresql-database-vs-schema/) for more details - - **spark** - : Spark session. - - **extra** - : Specifies one or more extra parameters by which clients can connect to the instance. -
- For example: `{"connectRetryCount": 3, "connectRetryInterval": 10}` -
- See [MSSQL JDBC driver properties documentation](https://learn.microsoft.com/en-us/sql/connect/jdbc/setting-the-connection-properties#properties) - for more details - -### Examples - -Create MSSQL connection with plain auth - -```py -from onetl.connection import MSSQL -from pyspark.sql import SparkSession - -# Create Spark session with MSSQL driver loaded -maven_packages = MSSQL.get_packages() -spark = ( - SparkSession.builder.appName("spark-app-name") - .config("spark.jars.packages", ",".join(maven_packages)) - .getOrCreate() -) - -# Create connection -mssql = MSSQL( - host="database.host.or.ip", - port=1433, - user="user", - password="*****", - extra={ - "trustServerCertificate": "true", # add this to avoid SSL certificate issues - }, - spark=spark, -) -``` - -Create MSSQL connection with domain auth - -```py -# Create Spark session with MSSQL driver loaded -... - -# Create connection -mssql = MSSQL( - host="database.host.or.ip", - port=1433, - user="user", - password="*****", - extra={ - "domain": "some.domain.com", # add here your domain - "integratedSecurity": "true", - "authenticationScheme": "NTLM", - "trustServerCertificate": "true", # add this to avoid SSL certificate issues - }, - spark=spark, -) -``` - -Create MSSQL connection with instance name - -```py -# Create Spark session with MSSQL driver loaded -... - -# Create connection -mssql = MSSQL( - host="database.host.or.ip", - # !!! no port !!! - user="user", - password="*****", - extra={ - "instanceName": "myinstance", # add here your instance name - "trustServerCertificate": "true", # add this to avoid SSL certificate issues - }, - spark=spark, -) -``` - -Create MSSQL read-only connection - -```py -# Create Spark session with MSSQL driver loaded -... - -# Create connection -mssql = MSSQL( - host="database.host.or.ip", - port=1433, - user="user", - password="*****", - extra={ - "applicationIntent": "ReadOnly", # driver will open read-only connection, to avoid writing to the database - "trustServerCertificate": "true", # add this to avoid SSL certificate issues - }, - spark=spark, -).check() -``` - - - -#### check() - -Check source availability. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -If not, an exception will be raised. - -* **Returns:** - Connection itself -* **Raises:** - RuntimeError - : If the connection is not available - -### Examples - -```python -connection.check() -``` - - - -#### *classmethod* get_packages(java_version: str | None = None, package_version: str | None = None) → list[str] - -Get package names to be downloaded by Spark. Allows specifying custom JDBC driver versions for MSSQL. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### Versionadded -Added in version 0.9.0. - -* **Parameters:** - **java_version** - : Java major version, defaults to `8`. Must be `8` or `11`. - - **package_version** - : Specifies the version of the MSSQL JDBC driver to use. Defaults to `12.8.1.`. - -### Examples - -```python -from onetl.connection import MSSQL - -MSSQL.get_packages() - -# specify Java and package versions -MSSQL.get_packages(java_version="8", package_version="12.8.1.jre11") -``` - - diff --git a/mddocs/markdown/connection/db_connection/mssql/execute.md b/mddocs/markdown/connection/db_connection/mssql/execute.md deleted file mode 100644 index 7655175c8..000000000 --- a/mddocs/markdown/connection/db_connection/mssql/execute.md +++ /dev/null @@ -1,184 +0,0 @@ - - -# Executing statements in MSSQL - -#### WARNING -Methods below **read all the rows** returned from DB **to Spark driver memory**, and then convert them to DataFrame. - -Do **NOT** use them to read large amounts of data. Use [DBReader](read.md#mssql-read) or [MSSQL.sql](sql.md#mssql-sql) instead. - -## How to - -There are 2 ways to execute some statement in MSSQL - -### Use `MSSQL.fetch` - -Use this method to perform some `SELECT` query which returns **small number or rows**, like reading -MSSQL config, or reading data from some reference table. Method returns Spark DataFrame. - -Method accepts [`MSSQL.FetchOptions`](#onetl.connection.db_connection.mssql.options.MSSQLFetchOptions). - -Connection opened using this method should be then closed with `connection.close()` or `with connection:`. - -#### WARNING -Please take into account [MSSQL <-> Spark type mapping](types.md#mssql-types). - -#### Syntax support - -This method supports **any** query syntax supported by MSSQL, like: - -* ✅︎ `SELECT ... FROM ...` -* ✅︎ `WITH alias AS (...) SELECT ...` -* ✅︎ `SELECT func(arg1, arg2) FROM DUAL` - call function -* ❌ `SET ...; SELECT ...;` - multiple statements not supported - -#### Examples - -```python -from onetl.connection import MSSQL - -mssql = MSSQL(...) - -df = mssql.fetch( - "SELECT value FROM some.reference_table WHERE key = 'some_constant'", - options=MSSQL.FetchOptions(queryTimeout=10), -) -mssql.close() -value = df.collect()[0][0] # get value from first row and first column -``` - -### Use `MSSQL.execute` - -Use this method to execute DDL and DML operations. Each method call runs operation in a separated transaction, and then commits it. - -Method accepts [`MSSQL.ExecuteOptions`](#onetl.connection.db_connection.mssql.options.MSSQLExecuteOptions). - -Connection opened using this method should be then closed with `connection.close()` or `with connection:`. - -#### Syntax support - -This method supports **any** query syntax supported by MSSQL, like: - -* ✅︎ `CREATE TABLE ...`, `CREATE VIEW ...` -* ✅︎ `ALTER ...` -* ✅︎ `INSERT INTO ... AS SELECT ...` -* ✅︎ `DROP TABLE ...`, `DROP VIEW ...`, `TRUNCATE TABLE`, and so on -* ✅︎ `EXEC procedure(arg1, arg2) ...` or `{call procedure(arg1, arg2)}` - special syntax for calling procedure -* ✅︎ `DECLARE ... BEGIN ... END` - execute PL/SQL statement -* ✅︎ other statements not mentioned here -* ❌ `SET ...; SELECT ...;` - multiple statements not supported - -#### Examples - -```python -from onetl.connection import MSSQL - -mssql = MSSQL(...) - -mssql.execute("DROP TABLE schema.table") -mssql.execute( - """ - CREATE TABLE schema.table ( - id bigint GENERATED ALWAYS AS IDENTITY, - key VARCHAR2(4000), - value NUMBER - ) - """, - options=MSSQL.ExecuteOptions(queryTimeout=10), -) -``` - -## Options - -### *pydantic model* onetl.connection.db_connection.mssql.options.MSSQLFetchOptions - -Options related to fetching data from databases via JDBC. - -#### Versionadded -Added in version 0.11.0: Replace `MSSQL.JDBCOptions` → `MSSQL.FetchOptions` - -### Examples - -#### NOTE -You can pass any value supported by underlying JDBC driver class, -even if it is not mentioned in this documentation. - -```python -from onetl.connection import MSSQL - -options = MSSQL.FetchOptions( - queryTimeout=60_000, - fetchsize=100_000, - customSparkOption="value", -) -``` - - - -#### *field* query_timeout *: int | None* *= None* *(alias 'queryTimeout')* - -The number of seconds the driver will wait for a statement to execute. -Zero means there is no limit. - -This option depends on driver implementation, -some drivers can check the timeout of each query instead of an entire JDBC batch. - - - -#### *field* fetchsize *: int | None* *= None* - -How many rows to fetch per round trip. - -Tuning this option can influence performance of reading. - -#### WARNING -Default value depends on driver. For example, Oracle has -default `fetchsize=10`. - - - -### *pydantic model* onetl.connection.db_connection.mssql.options.MSSQLExecuteOptions - -Options related to executing statements in databases via JDBC. - -#### Versionadded -Added in version 0.11.0: Replace `MSSQL.JDBCOptions` → `MSSQL.ExecuteOptions` - -### Examples - -#### NOTE -You can pass any value supported by underlying JDBC driver class, -even if it is not mentioned in this documentation. - -```python -from onetl.connection import MSSQL - -options = MSSQL.ExecuteOptions( - queryTimeout=60_000, - customSparkOption="value", -) -``` - - - -#### *field* query_timeout *: int | None* *= None* *(alias 'queryTimeout')* - -The number of seconds the driver will wait for a statement to execute. -Zero means there is no limit. - -This option depends on driver implementation, -some drivers can check the timeout of each query instead of an entire JDBC batch. - - - -#### *field* fetchsize *: int | None* *= None* - -How many rows to fetch per round trip. - -Tuning this option can influence performance of reading. - -#### WARNING -Default value depends on driver. For example, Oracle has -default `fetchsize=10`. - - diff --git a/mddocs/markdown/connection/db_connection/mssql/index.md b/mddocs/markdown/connection/db_connection/mssql/index.md deleted file mode 100644 index a35b4acbb..000000000 --- a/mddocs/markdown/connection/db_connection/mssql/index.md +++ /dev/null @@ -1,19 +0,0 @@ - - -# MSSQL - -# Connection - -* [Prerequisites](prerequisites.md) -* [MSSQL connection](connection.md) - -# Operations - -* [Reading from MSSQL using `DBReader`](read.md) -* [Reading from MSSQL using `MSSQL.sql`](sql.md) -* [Writing to MSSQL using `DBWriter`](write.md) -* [Executing statements in MSSQL](execute.md) - -# Troubleshooting - -* [MSSQL <-> Spark type mapping](types.md) diff --git a/mddocs/markdown/connection/db_connection/mssql/prerequisites.md b/mddocs/markdown/connection/db_connection/mssql/prerequisites.md deleted file mode 100644 index e103ced3e..000000000 --- a/mddocs/markdown/connection/db_connection/mssql/prerequisites.md +++ /dev/null @@ -1,78 +0,0 @@ - - -# Prerequisites - -## Version Compatibility - -* SQL Server versions: - : * Officially declared: 2016 - 2022 - * Actually tested: 2017, 2022 -* Spark versions: 2.3.x - 3.5.x -* Java versions: 8 - 20 - -See [official documentation](https://learn.microsoft.com/en-us/sql/connect/jdbc/system-requirements-for-the-jdbc-driver) -and [official compatibility matrix](https://learn.microsoft.com/en-us/sql/connect/jdbc/microsoft-jdbc-driver-for-sql-server-support-matrix). - -## Installing PySpark - -To use MSSQL connector you should have PySpark installed (or injected to `sys.path`) -BEFORE creating the connector instance. - -See [Spark](../../../install/spark.md#install-spark) installation instruction for more details. - -## Connecting to MSSQL - -### Connection port - -Connection is usually performed to port 1433. Port may differ for different MSSQL instances. -Please ask your MSSQL administrator to provide required information. - -For named MSSQL instances (`instanceName` option), [port number is optional](https://learn.microsoft.com/en-us/sql/connect/jdbc/building-the-connection-url?view=sql-server-ver16#named-and-multiple-sql-server-instances), and could be omitted. - -### Connection host - -It is possible to connect to MSSQL by using either DNS name of host or it’s IP address. - -If you’re using MSSQL cluster, it is currently possible to connect only to **one specific node**. -Connecting to multiple nodes to perform load balancing, as well as automatic failover to new master/replica are not supported. - -### Required grants - -Ask your MSSQL cluster administrator to set following grants for a user, -used for creating a connection: - -Read + Write (schema is owned by user) - -```sql --- allow creating tables for user -GRANT CREATE TABLE TO username; - --- allow read & write access to specific table -GRANT SELECT, INSERT ON username.mytable TO username; - --- only if if_exists="replace_entire_table" is used: --- allow dropping/truncating tables in any schema -GRANT ALTER ON username.mytable TO username; -``` - -Read + Write (schema is not owned by user) - -```sql --- allow creating tables for user -GRANT CREATE TABLE TO username; - --- allow managing tables in specific schema, and inserting data to tables -GRANT ALTER, SELECT, INSERT ON SCHEMA::someschema TO username; -``` - -Read only - -```sql --- allow read access to specific table -GRANT SELECT ON someschema.mytable TO username; -``` - -More details can be found in official documentation: -: * [GRANT ON DATABASE](https://learn.microsoft.com/en-us/sql/t-sql/statements/grant-database-permissions-transact-sql) - * [GRANT ON OBJECT](https://learn.microsoft.com/en-us/sql/t-sql/statements/grant-object-permissions-transact-sql) - * [GRANT ON SCHEMA](https://learn.microsoft.com/en-us/sql/t-sql/statements/grant-schema-permissions-transact-sql) diff --git a/mddocs/markdown/connection/db_connection/mssql/read.md b/mddocs/markdown/connection/db_connection/mssql/read.md deleted file mode 100644 index 9dc3dc88d..000000000 --- a/mddocs/markdown/connection/db_connection/mssql/read.md +++ /dev/null @@ -1,339 +0,0 @@ - - -# Reading from MSSQL using `DBReader` - -[`DBReader`](../../../db/db_reader.md#onetl.db.db_reader.db_reader.DBReader) supports [Read Strategies](../../../strategy/index.md#strategy) for incremental data reading, -but does not support custom queries, like `JOIN`. - -#### WARNING -Please take into account [MSSQL <-> Spark type mapping](types.md#mssql-types) - -## Supported DBReader features - -* ✅︎ `columns` -* ✅︎ `where` -* ✅︎ `hwm`, supported strategies: -* * ✅︎ [Snapshot Strategy](../../../strategy/snapshot_strategy.md#snapshot-strategy) -* * ✅︎ [Incremental Strategy](../../../strategy/incremental_strategy.md#incremental-strategy) -* * ✅︎ [Snapshot Batch Strategy](../../../strategy/snapshot_batch_strategy.md#snapshot-batch-strategy) -* * ✅︎ [Incremental Batch Strategy](../../../strategy/incremental_batch_strategy.md#incremental-batch-strategy) -* ❌ `hint` (MSSQL does support hints, but DBReader not, at least for now) -* ❌ `df_schema` -* ✅︎ `options` (see [`MSSQL.ReadOptions`](#onetl.connection.db_connection.mssql.options.MSSQLReadOptions)) - -## Examples - -Snapshot strategy: - -```python -from onetl.connection import MSSQL -from onetl.db import DBReader - -mssql = MSSQL(...) - -reader = DBReader( - connection=mssql, - source="schema.table", - columns=["id", "key", "CAST(value AS text) value", "updated_dt"], - where="key = 'something'", - options=MSSQL.ReadOptions(partitionColumn="id", numPartitions=10), -) -df = reader.run() -``` - -Incremental strategy: - -```python -from onetl.connection import MSSQL -from onetl.db import DBReader -from onetl.strategy import IncrementalStrategy - -mssql = MSSQL(...) - -reader = DBReader( - connection=mssql, - source="schema.table", - columns=["id", "key", "CAST(value AS text) value", "updated_dt"], - where="key = 'something'", - hwm=DBReader.AutoDetectHWM(name="mssql_hwm", expression="updated_dt"), - options=MSSQL.ReadOptions(partitionColumn="id", numPartitions=10), -) - -with IncrementalStrategy(): - df = reader.run() -``` - -## Recommendations - -### Select only required columns - -Instead of passing `"*"` in `DBReader(columns=[...])` prefer passing exact column names. This reduces the amount of data passed from MSSQL to Spark. - -### Pay attention to `where` value - -Instead of filtering data on Spark side using `df.filter(df.column == 'value')` pass proper `DBReader(where="column = 'value'")` clause. -This both reduces the amount of data send from MSSQL to Spark, and may also improve performance of the query. -Especially if there are indexes or partitions for columns used in `where` clause. - -## Options - -### *pydantic model* onetl.connection.db_connection.mssql.options.MSSQLReadOptions - -Spark JDBC reading options. - -#### Versionadded -Added in version 0.5.0: Replace `MSSQL.Options` → `MSSQL.ReadOptions` - -### Examples - -#### NOTE -You can pass any value -[supported by Spark](https://spark.apache.org/docs/latest/sql-data-sources-jdbc.html), -even if it is not mentioned in this documentation. **Option names should be in** `camelCase`! - -The set of supported options depends on Spark version. - -```python -from onetl.connection import MSSQL - -options = MSSQL.ReadOptions( - partitioning_mode="range", - partitionColumn="reg_id", - numPartitions=10, - customSparkOption="value", -) -``` - - - -#### *field* partition_column *: str | None* *= None* *(alias 'partitionColumn')* - -Column used to parallelize reading from a table. - -#### WARNING -It is highly recommended to use primary key, or column with an index -to avoid performance issues. - -#### NOTE -Column type depends on [`partitioning_mode`](#onetl.connection.db_connection.mssql.options.MSSQLReadOptions.partitioning_mode). - -* `partitioning_mode="range"` requires column to be an integer, date or timestamp (can be NULL, but not recommended). -* `partitioning_mode="hash"` accepts any column type (NOT NULL). -* `partitioning_mode="mod"` requires column to be an integer (NOT NULL). - -See documentation for [`partitioning_mode`](#onetl.connection.db_connection.mssql.options.MSSQLReadOptions.partitioning_mode) for more details - - - -#### *field* num_partitions *: PositiveInt* *= 1* *(alias 'numPartitions')* - -Number of jobs created by Spark to read the table content in parallel. -See documentation for [`partitioning_mode`](#onetl.connection.db_connection.mssql.options.MSSQLReadOptions.partitioning_mode) for more details - - - -#### *field* lower_bound *: int | None* *= None* *(alias 'lowerBound')* - -See documentation for [`partitioning_mode`](#onetl.connection.db_connection.mssql.options.MSSQLReadOptions.partitioning_mode) for more details - - - -#### *field* upper_bound *: int | None* *= None* *(alias 'upperBound')* - -See documentation for [`partitioning_mode`](#onetl.connection.db_connection.mssql.options.MSSQLReadOptions.partitioning_mode) for more details - - - -#### *field* session_init_statement *: str | None* *= None* *(alias 'sessionInitStatement')* - -After each database session is opened to the remote DB and before starting to read data, -this option executes a custom SQL statement (or a PL/SQL block). - -Use this to implement session initialization code. - -Example: - -```python -sessionInitStatement = """ - BEGIN - execute immediate - 'alter session set "_serial_direct_read"=true'; - END; -""" -``` - - - -#### *field* query_timeout *: int | None* *= None* *(alias 'queryTimeout')* - -The number of seconds the driver will wait for a statement to execute. -Zero means there is no limit. - -This option depends on driver implementation, -some drivers can check the timeout of each query instead of an entire JDBC batch. - - - -#### *field* fetchsize *: int* *= 100000* - -Fetch N rows from an opened cursor per one read round. - -Tuning this option can influence performance of reading. - -#### WARNING -Default value is different from Spark. - -Spark uses driver’s own value, and it may be different in different drivers, -and even versions of the same driver. For example, Oracle has -default `fetchsize=10`, which is absolutely not usable. - -Thus we’ve overridden default value with `100_000`, which should increase reading performance. - -#### Versionchanged -Changed in version 0.2.0: Set explicit default value to `100_000` - - - -#### *field* partitioning_mode *: JDBCPartitioningMode* *= JDBCPartitioningMode.RANGE* - -Defines how Spark will parallelize reading from table. - -Possible values: - -* `range` (default) - : Allocate each executor a range of values from column passed into [`partition_column`](#onetl.connection.db_connection.mssql.options.MSSQLReadOptions.partition_column). -
- ### Spark generates for each executor an SQL query -
- Executor 1: - ```sql - SELECT ... FROM table - WHERE (partition_column >= lowerBound - OR partition_column IS NULL) - AND partition_column < (lower_bound + stride) - ``` -
- Executor 2: - ```sql - SELECT ... FROM table - WHERE partition_column >= (lower_bound + stride) - AND partition_column < (lower_bound + 2 * stride) - ``` -
- … -
- Executor N: - ```sql - SELECT ... FROM table - WHERE partition_column >= (lower_bound + (N-1) * stride) - AND partition_column <= upper_bound - ``` -
- Where `stride=(upper_bound - lower_bound) / num_partitions`. -
- #### NOTE - Can be used only with columns of integer, date or timestamp types. -
- #### NOTE - [`lower_bound`](#onetl.connection.db_connection.mssql.options.MSSQLReadOptions.lower_bound), [`upper_bound`](#onetl.connection.db_connection.mssql.options.MSSQLReadOptions.upper_bound) and [`num_partitions`](#onetl.connection.db_connection.mssql.options.MSSQLReadOptions.num_partitions) are used just to - calculate the partition stride, **NOT** for filtering the rows in table. - So all rows in the table will be returned (unlike *Incremental* [Read Strategies](../../../strategy/index.md#strategy)). -
- #### NOTE - All queries are executed in parallel. To execute them sequentially, use *Batch* [Read Strategies](../../../strategy/index.md#strategy). -* `hash` - : Allocate each executor a set of values based on hash of the [`partition_column`](#onetl.connection.db_connection.mssql.options.MSSQLReadOptions.partition_column) column. -
- ### Spark generates for each executor an SQL query -
- Executor 1: - ```sql - SELECT ... FROM table - WHERE (some_hash(partition_column) mod num_partitions) = 0 -- lower_bound - ``` -
- Executor 2: - ```sql - SELECT ... FROM table - WHERE (some_hash(partition_column) mod num_partitions) = 1 -- lower_bound + 1 - ``` -
- … -
- Executor N: - ```sql - SELECT ... FROM table - WHERE (some_hash(partition_column) mod num_partitions) = num_partitions-1 -- upper_bound - ``` -
- #### NOTE - The hash function implementation depends on RDBMS. It can be `MD5` or any other fast hash function, - or expression based on this function call. Usually such functions accepts any column type as an input. -* `mod` - : Allocate each executor a set of values based on modulus of the [`partition_column`](#onetl.connection.db_connection.mssql.options.MSSQLReadOptions.partition_column) column. -
- ### Spark generates for each executor an SQL query -
- Executor 1: - ```sql - SELECT ... FROM table - WHERE (partition_column mod num_partitions) = 0 -- lower_bound - ``` -
- Executor 2: - ```sql - SELECT ... FROM table - WHERE (partition_column mod num_partitions) = 1 -- lower_bound + 1 - ``` -
- Executor N: - ```sql - SELECT ... FROM table - WHERE (partition_column mod num_partitions) = num_partitions-1 -- upper_bound - ``` -
- #### NOTE - Can be used only with columns of integer type. - -#### Versionadded -Added in version 0.5.0. - -### Examples - -Read data in 10 parallel jobs by range of values in `id_column` column: - -```python -ReadOptions( - partitioning_mode="range", # default mode, can be omitted - partitionColumn="id_column", - numPartitions=10, - # Options below can be discarded because they are - # calculated automatically as MIN and MAX values of `partitionColumn` - lowerBound=0, - upperBound=100_000, -) -``` - -Read data in 10 parallel jobs by hash of values in `some_column` column: - -```python -ReadOptions( - partitioning_mode="hash", - partitionColumn="some_column", - numPartitions=10, - # lowerBound and upperBound are automatically set to `0` and `9` -) -``` - -Read data in 10 parallel jobs by modulus of values in `id_column` column: - -```python -ReadOptions( - partitioning_mode="mod", - partitionColumn="id_column", - numPartitions=10, - # lowerBound and upperBound are automatically set to `0` and `9` -) -``` - - diff --git a/mddocs/markdown/connection/db_connection/mssql/sql.md b/mddocs/markdown/connection/db_connection/mssql/sql.md deleted file mode 100644 index 93d2fb3ef..000000000 --- a/mddocs/markdown/connection/db_connection/mssql/sql.md +++ /dev/null @@ -1,191 +0,0 @@ - - -# Reading from MSSQL using `MSSQL.sql` - -`MSSQL.sql` allows passing custom SQL query, but does not support incremental strategies. - -#### WARNING -Please take into account [MSSQL <-> Spark type mapping](types.md#mssql-types) - -#### WARNING -Statement is executed in **read-write** connection, so if you’re calling some functions/procedures with DDL/DML statements inside, -they can change data in your database. - -## Syntax support - -Only queries with the following syntax are supported: - -* ✅︎ `SELECT ... FROM ...` -* ❌ `WITH alias AS (...) SELECT ...` -* ❌ `SET ...; SELECT ...;` - multiple statements not supported - -## Examples - -```python -from onetl.connection import MSSQL - -mssql = MSSQL(...) -df = mssql.sql( - """ - SELECT - id, - key, - CAST(value AS text) value, - updated_at - FROM - some.mytable - WHERE - key = 'something' - """, - options=MSSQL.SQLOptions( - partitionColumn="id", - numPartitions=10, - lowerBound=0, - upperBound=1000, - ), -) -``` - -## Recommendations - -### Select only required columns - -Instead of passing `SELECT * FROM ...` prefer passing exact column names `SELECT col1, col2, ...`. -This reduces the amount of data passed from MSSQL to Spark. - -### Pay attention to `where` value - -Instead of filtering data on Spark side using `df.filter(df.column == 'value')` pass proper `WHERE column = 'value'` clause. -This both reduces the amount of data send from MSSQL to Spark, and may also improve performance of the query. -Especially if there are indexes or partitions for columns used in `where` clause. - -## Options - -### *pydantic model* onetl.connection.db_connection.mssql.options.MSSQLSQLOptions - -Options specifically for SQL queries - -These options allow you to specify configurations for executing SQL queries -without relying on Spark’s partitioning mechanisms. - -#### Versionadded -Added in version 0.11.0: Split up `MSSQL.ReadOptions` to `MSSQL.SQLOptions` - -### Examples - -#### NOTE -You can pass any JDBC configuration -[supported by Spark](https://spark.apache.org/docs/latest/sql-data-sources-jdbc.html), -tailored to optimize SQL query execution. **Option names should be in** `camelCase`! - -```python -from onetl.connection import MSSQL - -options = MSSQL.SQLOptions( - partitionColumn="reg_id", - numPartitions=10, - lowerBound=0, - upperBound=1000, - customSparkOption="value", -) -``` - - - -#### *field* partition_column *: str | None* *= None* *(alias 'partitionColumn')* - -Column used to partition data across multiple executors for parallel query processing. - -#### WARNING -It is highly recommended to use primary key, or column with an index -to avoid performance issues. - -### Example of using `partitionColumn="id"` with `partitioning_mode="range"` - -```sql --- If partition_column is 'id', with numPartitions=4, lowerBound=1, and upperBound=100: --- Executor 1 processes IDs from 1 to 25 -SELECT ... FROM table WHERE id >= 1 AND id < 26 --- Executor 2 processes IDs from 26 to 50 -SELECT ... FROM table WHERE id >= 26 AND id < 51 --- Executor 3 processes IDs from 51 to 75 -SELECT ... FROM table WHERE id >= 51 AND id < 76 --- Executor 4 processes IDs from 76 to 100 -SELECT ... FROM table WHERE id >= 76 AND id <= 100 - --- General case for Executor N -SELECT ... FROM table -WHERE partition_column >= (lowerBound + (N-1) * stride) -AND partition_column <= upperBound --- Where ``stride`` is calculated as ``(upperBound - lowerBound) / numPartitions``. -``` - - - -#### *field* num_partitions *: int | None* *= None* *(alias 'numPartitions')* - -Number of jobs created by Spark to read the table content in parallel. - - - -#### *field* lower_bound *: int | None* *= None* *(alias 'lowerBound')* - -Defines the starting boundary for partitioning the query’s data. Mandatory if [`partition_column`](#onetl.connection.db_connection.mssql.options.MSSQLSQLOptions.partition_column) is set - - - -#### *field* upper_bound *: int | None* *= None* *(alias 'upperBound')* - -Sets the ending boundary for data partitioning. Mandatory if [`partition_column`](#onetl.connection.db_connection.mssql.options.MSSQLSQLOptions.partition_column) is set - - - -#### *field* session_init_statement *: str | None* *= None* *(alias 'sessionInitStatement')* - -After each database session is opened to the remote DB and before starting to read data, -this option executes a custom SQL statement (or a PL/SQL block). - -Use this to implement session initialization code. - -Example: - -```python -sessionInitStatement = """ - BEGIN - execute immediate - 'alter session set "_serial_direct_read"=true'; - END; -""" -``` - - - -#### *field* query_timeout *: int | None* *= None* *(alias 'queryTimeout')* - -The number of seconds the driver will wait for a statement to execute. -Zero means there is no limit. - -This option depends on driver implementation, -some drivers can check the timeout of each query instead of an entire JDBC batch. - - - -#### *field* fetchsize *: int* *= 100000* - -Fetch N rows from an opened cursor per one read round. - -Tuning this option can influence performance of reading. - -#### WARNING -Default value is different from Spark. - -Spark uses driver’s own value, and it may be different in different drivers, -and even versions of the same driver. For example, Oracle has -default `fetchsize=10`, which is absolutely not usable. - -Thus we’ve overridden default value with `100_000`, which should increase reading performance. - -#### Versionchanged -Changed in version 0.2.0: Set explicit default value to `100_000` - - diff --git a/mddocs/markdown/connection/db_connection/mssql/types.md b/mddocs/markdown/connection/db_connection/mssql/types.md deleted file mode 100644 index 5981107ad..000000000 --- a/mddocs/markdown/connection/db_connection/mssql/types.md +++ /dev/null @@ -1,281 +0,0 @@ - - -# MSSQL <-> Spark type mapping - -#### NOTE -The results below are valid for Spark 3.5.5, and may differ on other Spark versions. - -## Type detection & casting - -Spark’s DataFrames always have a `schema` which is a list of columns with corresponding Spark types. All operations on a column are performed using column type. - -### Reading from MSSQL - -This is how MSSQL connector performs this: - -* For each column in query result (`SELECT column1, column2, ... FROM table ...`) get column name and MSSQL type. -* Find corresponding `MSSQL type (read)` → `Spark type` combination (see below) for each DataFrame column. If no combination is found, raise exception. -* Create DataFrame from query with specific column names and Spark types. - -### Writing to some existing MSSQL table - -This is how MSSQL connector performs this: - -* Get names of columns in DataFrame. [1](#id3) -* Perform `SELECT * FROM table LIMIT 0` query. -* Take only columns present in DataFrame (by name, case insensitive). For each found column get MSSQL type. -* Find corresponding `Spark type` → `MSSQL type (write)` combination (see below) for each DataFrame column. If no combination is found, raise exception. -* If `MSSQL type (write)` match `MSSQL type (read)`, no additional casts will be performed, DataFrame column will be written to MSSQL as is. -* If `MSSQL type (write)` does not match `MSSQL type (read)`, DataFrame column will be casted to target column type **on MSSQL side**. - For example, you can write column with text data to `int` column, if column contains valid integer values within supported value range and precision [2](#id4). - -* **[1]** This allows to write data to tables with `DEFAULT` and `GENERATED` columns - if DataFrame has no such column, it will be populated by MSSQL. -* **[2]** This is true only if DataFrame column is a `StringType()`, because text value is parsed automatically to target column type. But other types cannot be silently converted, like `int -> text`. This requires explicit casting, see [DBWriter](). - -### Create new table using Spark - -#### WARNING -ABSOLUTELY NOT RECOMMENDED! - -This is how MSSQL connector performs this: - -* Find corresponding `Spark type` → `MSSQL type (create)` combination (see below) for each DataFrame column. If no combination is found, raise exception. -* Generate DDL for creating table in MSSQL, like `CREATE TABLE (col1 ...)`, and run it. -* Write DataFrame to created table as is. - -But some cases this may lead to using wrong column type. For example, Spark creates column of type `timestamp` -which corresponds to MSSQL’s type `timestamp(0)` (precision up to seconds) -instead of more precise `timestamp(6)` (precision up to nanoseconds). -This may lead to incidental precision loss, or sometimes data cannot be written to created table at all. - -So instead of relying on Spark to create tables: - -### See example - -```python -writer = DBWriter( - connection=mssql, - target="myschema.target_tbl", - options=MSSQL.WriteOptions( - if_exists="append", - ), -) -writer.run(df) -``` - -Always prefer creating tables with specific types **BEFORE WRITING DATA**: - -### See example - -```python -mssql.execute( - """ - CREATE TABLE schema.table ( - id bigint, - key text, - value datetime2(6) -- specific type and precision - ) - """, -) - -writer = DBWriter( - connection=mssql, - target="myschema.target_tbl", - options=MSSQL.WriteOptions(if_exists="append"), -) -writer.run(df) -``` - -### References - -Here you can find source code with type conversions: - -* [MSSQL -> JDBC](https://github.com/microsoft/mssql-jdbc/blob/v12.2.0/src/main/java/com/microsoft/sqlserver/jdbc/SQLServerResultSetMetaData.java#L117-L170) -* [JDBC -> Spark](https://github.com/apache/spark/blob/v3.5.5/sql/core/src/main/scala/org/apache/spark/sql/jdbc/MsSqlServerDialect.scala#L135-L152) -* [Spark -> JDBC](https://github.com/apache/spark/blob/v3.5.5/sql/core/src/main/scala/org/apache/spark/sql/jdbc/MsSqlServerDialect.scala#L154-L163) -* [JDBC -> MSSQL](https://github.com/microsoft/mssql-jdbc/blob/v12.2.0/src/main/java/com/microsoft/sqlserver/jdbc/DataTypes.java#L625-L676) - -## Supported types - -See [official documentation](https://learn.microsoft.com/en-us/sql/t-sql/data-types/data-types-transact-sql) - -### Numeric types - -| MSSQL type (read) | Spark type | MSSQL type (write) | MSSQL type (create) | -|-----------------------------|---------------------------------|-----------------------------|-----------------------------| -| `decimal` | `DecimalType(P=18, S=0)` | `decimal(P=18, S=0)` | `decimal(P=18, S=0)` | -| `decimal(P=0..38)` | `DecimalType(P=0..38, S=0)` | `decimal(P=0..38, S=0)` | `decimal(P=0..38, S=0)` | -| `decimal(P=0..38, S=0..38)` | `DecimalType(P=0..38, S=0..38)` | `decimal(P=0..38, S=0..38)` | `decimal(P=0..38, S=0..38)` | -| `real` | `FloatType()` | `real` | `real` | -| `float` | `DoubleType()` | `float` | `float` | -| `smallint` | `ShortType()` | `smallint` | `smallint` | -| `tinyint` | `IntegerType()` | `int` | `int` | -| `int` | | | | -| `bigint` | `LongType()` | `bigint` | `bigint` | - -### Temporal types - -#### NOTE -MSSQL `timestamp` type is alias for `rowversion` (see [Special types]()). It is not a temporal type! - -| MSSQL type (read) | Spark type | MSSQL type (write) | MSSQL type (create) | -|-------------------------------------|----------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------|------------------------------------------------------------------------| -| `date` | `DateType()` | `date` | `date` | -| `smalldatetime`, minutes | `TimestampType()`, microseconds | `datetime2(6)`, microseconds | `datetime`, milliseconds | -| `datetime`, milliseconds | | | | -| `datetime2(0)`, seconds | | | | -| `datetime2(3)`, milliseconds | | | | -| `datetime2(6)`, microseconds | `TimestampType()`, microseconds | `datetime2(6)`, microseconds | `datetime`, milliseconds,
**precision loss** [3](#id14) | -| `datetime2(7)`, 100s of nanoseconds | `TimestampType()`, microseconds,
**precision loss** [4](#id15) | `datetime2(6)`, microseconds,
**precision loss** [4](#id15) | | -| `time(0)`, seconds | `TimestampType()`, microseconds,
with time format quirks [5](#id16) | `datetime2(6)`, microseconds | `datetime`, milliseconds | -| `time(3)`, milliseconds | | | | -| `time(6)`, microseconds | `TimestampType()`, microseconds,
with time format quirks [5](#id16) | `datetime2(6)`, microseconds | `datetime`, milliseconds,
**precision loss** [3](#id14) | -| | | | | -| `time`, 100s of nanoseconds | `TimestampType()`, microseconds,
**precision loss** [4](#id15),
with time format quirks [5](#id16) | `datetime2(6)`, microseconds
**precision loss** [3](#id14) | | -| `time(7)`, 100s of nanoseconds | | | | -| `datetimeoffset` | `StringType()` | `nvarchar` | `nvarchar` | - -#### WARNING -Note that types in MSSQL and Spark have different value ranges: - -| MySQL type | Min value | Max value | Spark type | Min value | Max value | -|-----------------|------------------------------|------------------------------|-------------------|------------------------------|------------------------------| -| `smalldatetime` | `1900-01-01 00:00:00` | `2079-06-06 23:59:00` | `TimestampType()` | `0001-01-01 00:00:00.000000` | `9999-12-31 23:59:59.999999` | -| `datetime` | `1753-01-01 00:00:00.000` | `9999-12-31 23:59:59.997` | | | | -| `datetime2` | `0001-01-01 00:00:00.000000` | `9999-12-31 23:59:59.999999` | | | | -| `time` | `00:00:00.0000000` | `23:59:59.9999999` | | | | - -So not all of values in Spark DataFrame can be written to MSSQL. - -References: -: * [MSSQL date & time types documentation](https://learn.microsoft.com/en-us/sql/t-sql/data-types/date-and-time-types) - * [Spark DateType documentation](https://spark.apache.org/docs/latest/api/java/org/apache/spark/sql/types/DateType.html) - * [Spark TimestampType documentation](https://spark.apache.org/docs/latest/api/java/org/apache/spark/sql/types/TimestampType.html) - -* **[3]** MSSQL dialect for Spark generates DDL with type `datetime` which has precision up to milliseconds (`23:59:59.999`, 10-3 seconds). Inserting data with microsecond and higher precision (`23:59:59.999999` .. `23.59:59.9999999`, 10-6 .. 10-7 seconds) will lead to **throwing away microseconds**. -* **[4]** MSSQL support timestamp up to 100s of nanoseconds precision (`23:59:59.9999999999`, 10-7 seconds), but Spark `TimestampType()` supports datetime up to microseconds precision (`23:59:59.999999`, 10-6 seconds). Last digit will be lost during read or write operations. -* **[5]** `time` type is the same as `datetime2` with date `1970-01-01`. So instead of reading data from MSSQL like `23:59:59.999999` it is actually read `1970-01-01 23:59:59.999999`, and vice versa. - -### String types - -| MSSQL type (read) | Spark type | MSSQL type (write) | MSSQL type (create) | -|---------------------|----------------|----------------------|-----------------------| -| `char` | `StringType()` | `nvarchar` | `nvarchar` | -| `char(N)` | | | | -| `nchar` | | | | -| `nchar(N)` | | | | -| `varchar` | | | | -| `varchar(N)` | | | | -| `nvarchar` | | | | -| `nvarchar(N)` | | | | -| `mediumtext` | | | | -| `text` | | | | -| `ntext` | | | | -| `xml` | | | | - -### Binary types - -| MSSQL type (read) | Spark type | MSSQL type (write) | MSSQL type (create) | -|---------------------|-----------------|----------------------|-----------------------| -| `bit` | `BooleanType()` | `bit` | `bit` | -| `binary` | `BinaryType()` | `varbinary` | `varbinary` | -| `binary(N)` | | | | -| `varbinary` | | | | -| `varbinary(N)` | | | | -| `image` | | | | - -### Special types - -| MSSQL type (read) | Spark type | MSSQL type (write) | MSSQL type (create) | -|---------------------|----------------|----------------------|-----------------------| -| `geography` | `BinaryType()` | `varbinary` | `varbinary` | -| `geometry` | | | | -| `hierarchyid` | | | | -| `rowversion` | | | | -| `sql_variant` | unsupported | | | -| `sysname` | `StringType()` | `nvarchar` | `nvarchar` | -| `uniqueidentifier` | | | | - -## Explicit type cast - -### `DBReader` - -It is possible to explicitly cast column type using `DBReader(columns=...)` syntax. - -For example, you can use `CAST(column AS text)` to convert data to string representation on MSSQL side, and so it will be read as Spark’s `StringType()`: - -```python -from onetl.connection import MSSQL -from onetl.db import DBReader - -mssql = MSSQL(...) - -DBReader( - connection=mssql, - columns=[ - "id", - "supported_column", - "CAST(unsupported_column AS text) unsupported_column_str", - ], -) -df = reader.run() - -# cast column content to proper Spark type -df = df.select( - df.id, - df.supported_column, - # explicit cast - df.unsupported_column_str.cast("integer").alias("parsed_integer"), -) -``` - -### `DBWriter` - -Convert dataframe column to JSON using [to_json](https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.functions.to_json.html), -and write it as `text` column in MSSQL: - -```python -mssql.execute( - """ - CREATE TABLE schema.target_tbl ( - id bigint, - struct_column_json text -- any string type, actually - ) - """, -) - -from pyspark.sql.functions import to_json - -df = df.select( - df.id, - to_json(df.struct_column).alias("struct_column_json"), -) - -writer.run(df) -``` - -Then you can parse this column on MSSQL side - for example, by creating a view: - -```sql -SELECT - id, - JSON_VALUE(struct_column_json, "$.nested.field") AS nested_field -FROM target_tbl -``` - -Or by using [computed column](https://learn.microsoft.com/en-us/sql/relational-databases/tables/specify-computed-columns-in-a-table): - -```sql -CREATE TABLE schema.target_table ( - id bigint, - supported_column datetime2(6), - struct_column_json text, -- any string type, actually - -- computed column - nested_field AS (JSON_VALUE(struct_column_json, "$.nested.field")) - -- or persisted column - -- nested_field AS (JSON_VALUE(struct_column_json, "$.nested.field")) PERSISTED -) -``` - -By default, column value is calculated on every table read. -Column marked as `PERSISTED` is calculated during insert, but this require additional space. diff --git a/mddocs/markdown/connection/db_connection/mssql/write.md b/mddocs/markdown/connection/db_connection/mssql/write.md deleted file mode 100644 index d29aca762..000000000 --- a/mddocs/markdown/connection/db_connection/mssql/write.md +++ /dev/null @@ -1,183 +0,0 @@ - - -# Writing to MSSQL using `DBWriter` - -For writing data to MSSQL, use [`DBWriter`](../../../db/db_writer.md#onetl.db.db_writer.db_writer.DBWriter). - -#### WARNING -Please take into account [MSSQL <-> Spark type mapping](types.md#mssql-types) - -#### WARNING -It is always recommended to create table explicitly using [MSSQL.execute](execute.md#mssql-execute) -instead of relying on Spark’s table DDL generation. - -This is because Spark’s DDL generator can create columns with different precision and types than it is expected, -causing precision loss or other issues. - -## Examples - -```python -from onetl.connection import MSSQL -from onetl.db import DBWriter - -mssql = MSSQL(...) - -df = ... # data is here - -writer = DBWriter( - connection=mssql, - target="schema.table", - options=MSSQL.WriteOptions(if_exists="append"), -) - -writer.run(df) -``` - -## Options - -Method above accepts [`MSSQL.WriteOptions`](#onetl.connection.db_connection.mssql.options.MSSQLWriteOptions) - -### *pydantic model* onetl.connection.db_connection.mssql.options.MSSQLWriteOptions - -Spark JDBC writing options. - -#### Versionadded -Added in version 0.5.0: Replace `MSSQL.Options` → `MSSQL.WriteOptions` - -### Examples - -#### NOTE -You can pass any value -[supported by Spark](https://spark.apache.org/docs/latest/sql-data-sources-jdbc.html), -even if it is not mentioned in this documentation. **Option names should be in** `camelCase`! - -The set of supported options depends on Spark version. - -```python -from onetl.connection import MSSQL - -options = MSSQL.WriteOptions( - if_exists="append", - batchsize=20_000, - customSparkOption="value", -) -``` - - - -#### *field* if_exists *: JDBCTableExistBehavior* *= JDBCTableExistBehavior.APPEND* *(alias 'mode')* - -Behavior of writing data into existing table. - -Possible values: -: * `append` (default) - : Adds new rows into existing table. -
- ### Behavior in details -
- * Table does not exist - : Table is created using options provided by user - (`createTableOptions`, `createTableColumnTypes`, etc). - * Table exists - : Data is appended to a table. Table has the same DDL as before writing data -
- #### WARNING - This mode does not check whether table already contains - rows from dataframe, so duplicated rows can be created. -
- Also Spark does not support passing custom options to - insert statement, like `ON CONFLICT`, so don’t try to - implement deduplication using unique indexes or constraints. -
- Instead, write to staging table and perform deduplication - using `execute` method. - * `replace_entire_table` - : **Table is dropped and then created, or truncated**. -
- ### Behavior in details -
- * Table does not exist - : Table is created using options provided by user - (`createTableOptions`, `createTableColumnTypes`, etc). - * Table exists - : Table content is replaced with dataframe content. -
- After writing completed, target table could either have the same DDL as - before writing data (`truncate=True`), or can be recreated (`truncate=False` - or source does not support truncation). - * `ignore` - : Ignores the write operation if the table already exists. -
- ### Behavior in details -
- * Table does not exist - : Table is created using options provided by user - (`createTableOptions`, `createTableColumnTypes`, etc). - * Table exists - : The write operation is ignored, and no data is written to the table. - * `error` - : Raises an error if the table already exists. -
- ### Behavior in details -
- * Table does not exist - : Table is created using options provided by user - (`createTableOptions`, `createTableColumnTypes`, etc). - * Table exists - : An error is raised, and no data is written to the table. - -#### Versionchanged -Changed in version 0.9.0: Renamed `mode` → `if_exists` - - - -#### *field* query_timeout *: int | None* *= None* *(alias 'queryTimeout')* - -The number of seconds the driver will wait for a statement to execute. -Zero means there is no limit. - -This option depends on driver implementation, -some drivers can check the timeout of each query instead of an entire JDBC batch. - - - -#### *field* batchsize *: int* *= 20000* - -How many rows can be inserted per round trip. - -Tuning this option can influence performance of writing. - -#### WARNING -Default value is different from Spark. - -Spark uses quite small value `1000`, which is absolutely not usable -in BigData world. - -Thus we’ve overridden default value with `20_000`, -which should increase writing performance. - -You can increase it even more, up to `50_000`, -but it depends on your database load and number of columns in the row. -Higher values does not increase performance. - -#### Versionchanged -Changed in version 0.4.0: Changed default value from 1000 to 20_000 - - - -#### *field* isolation_level *: str* *= 'READ_UNCOMMITTED'* *(alias 'isolationLevel')* - -The transaction isolation level, which applies to current connection. - -Possible values: -: * `NONE` (as string, not Python’s `None`) - * `READ_COMMITTED` - * `READ_UNCOMMITTED` - * `REPEATABLE_READ` - * `SERIALIZABLE` - -Values correspond to transaction isolation levels defined by JDBC standard. -Please refer the documentation for -[java.sql.Connection](https://docs.oracle.com/javase/8/docs/api/java/sql/Connection.html). - - diff --git a/mddocs/markdown/connection/db_connection/mysql/connection.md b/mddocs/markdown/connection/db_connection/mysql/connection.md deleted file mode 100644 index 93bb348e7..000000000 --- a/mddocs/markdown/connection/db_connection/mysql/connection.md +++ /dev/null @@ -1,120 +0,0 @@ - - -# MySQL connection - -### *class* onetl.connection.db_connection.mysql.connection.MySQL(\*, spark: SparkSession, user: str, password: SecretStr, host: Host, port: int = 3306, database: str | None = None, extra: MySQLExtra = MySQLExtra(useUnicode='yes', characterEncoding='UTF-8')) - -MySQL JDBC connection. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -Based on Maven package [com.mysql:mysql-connector-j:9.2.0](https://mvnrepository.com/artifact/com.mysql/mysql-connector-j/9.2.0) -([official MySQL JDBC driver](https://dev.mysql.com/doc/connector-j/en/)). - -#### SEE ALSO -Before using this connector please take into account [Prerequisites](prerequisites.md#mysql-prerequisites) - -#### Versionadded -Added in version 0.1.0. - -* **Parameters:** - **host** - : Host of MySQL database. For example: `mysql0012.domain.com` or `192.168.1.11` - - **port** - : Port of MySQL database - - **user** - : User, which have proper access to the database. For example: `some_user` - - **password** - : Password for database connection - - **database** - : Database in RDBMS, NOT schema. -
- See [this page](https://www.educba.com/postgresql-database-vs-schema/) for more details - - **spark** - : Spark session. - - **extra** - : Specifies one or more extra parameters by which clients can connect to the instance. -
- For example: `{"useSSL": "false", "allowPublicKeyRetrieval": "true"}` -
- See [MySQL JDBC driver properties documentation](https://dev.mysql.com/doc/connector-j/en/connector-j-reference-configuration-properties.html) - for more details - -### Examples - -Create and check MySQL connection: - -```python -from onetl.connection import MySQL -from pyspark.sql import SparkSession - -# Create Spark session with MySQL driver loaded -maven_packages = MySQL.get_packages() -spark = ( - SparkSession.builder.appName("spark-app-name") - .config("spark.jars.packages", ",".join(maven_packages)) - .getOrCreate() -) - -# Create connection -mysql = MySQL( - host="database.host.or.ip", - user="user", - password="*****", - extra={"useSSL": "false", "allowPublicKeyRetrieval": "true"}, - spark=spark, -).check() -``` - - - -#### check() - -Check source availability. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -If not, an exception will be raised. - -* **Returns:** - Connection itself -* **Raises:** - RuntimeError - : If the connection is not available - -### Examples - -```python -connection.check() -``` - - - -#### *classmethod* get_packages(package_version: str | None = None) → list[str] - -Get package names to be downloaded by Spark. Allows specifying a custom JDBC driver version for MySQL. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### Versionadded -Added in version 0.9.0. - -* **Parameters:** - **package_version** - : Specifies the version of the MySQL JDBC driver to use. Defaults to `9.2.0`. -
- #### Versionadded - Added in version 0.11.0. - -### Examples - -```python -from onetl.connection import MySQL - -MySQL.get_packages() - -# specify a custom package version -MySQL.get_packages(package_version="8.2.0") -``` - - diff --git a/mddocs/markdown/connection/db_connection/mysql/execute.md b/mddocs/markdown/connection/db_connection/mysql/execute.md deleted file mode 100644 index fb345665f..000000000 --- a/mddocs/markdown/connection/db_connection/mysql/execute.md +++ /dev/null @@ -1,191 +0,0 @@ - - -# Executing statements in MySQL - -#### WARNING -Methods below **read all the rows** returned from DB **to Spark driver memory**, and then convert them to DataFrame. - -Do **NOT** use them to read large amounts of data. Use [DBReader](read.md#mysql-read) or [MySQL.sql](sql.md#mysql-sql) instead. - -## How to - -There are 2 ways to execute some statement in MySQL - -### Use `MySQL.fetch` - -Use this method to perform some `SELECT` query which returns **small number or rows**, like reading -MySQL config, or reading data from some reference table. Method returns Spark DataFrame. - -Method accepts [`MySQL.FetchOptions`](#onetl.connection.db_connection.mysql.options.MySQLFetchOptions). - -Connection opened using this method should be then closed with `connection.close()` or `with connection:`. - -#### WARNING -Please take into account [MySQL <-> Spark type mapping](types.md#mysql-types). - -#### Syntax support - -This method supports **any** query syntax supported by MySQL, like: - -* ✅︎ `SELECT ... FROM ...` -* ✅︎ `WITH alias AS (...) SELECT ...` -* ✅︎ `SELECT func(arg1, arg2)` or `{?= call func(arg1, arg2)}` - special syntax for calling function -* ✅︎ `SHOW ...` -* ❌ `SET ...; SELECT ...;` - multiple statements not supported - -#### Examples - -```python -from onetl.connection import MySQL - -mysql = MySQL(...) - -df = mysql.fetch( - "SELECT value FROM some.reference_table WHERE key = 'some_constant'", - options=MySQL.FetchOptions(queryTimeout=10), -) -mysql.close() -value = df.collect()[0][0] # get value from first row and first column -``` - -### Use `MySQL.execute` - -Use this method to execute DDL and DML operations. Each method call runs operation in a separated transaction, and then commits it. - -Method accepts [`MySQL.ExecuteOptions`](#onetl.connection.db_connection.mysql.options.MySQLExecuteOptions). - -Connection opened using this method should be then closed with `connection.close()` or `with connection:`. - -#### Syntax support - -This method supports **any** query syntax supported by MySQL, like: - -* ✅︎ `CREATE TABLE ...`, `CREATE VIEW ...`, and so on -* ✅︎ `ALTER ...` -* ✅︎ `INSERT INTO ... SELECT ...`, `UPDATE ...`, `DELETE ...`, and so on -* ✅︎ `DROP TABLE ...`, `DROP VIEW ...`, and so on -* ✅︎ `CALL procedure(arg1, arg2) ...` or `{call procedure(arg1, arg2)}` - special syntax for calling procedure -* ✅︎ other statements not mentioned here -* ❌ `SET ...; SELECT ...;` - multiple statements not supported - -#### Examples - -```python -from onetl.connection import MySQL - -mysql = MySQL(...) - -mysql.execute("DROP TABLE schema.table") -mysql.execute( - """ - CREATE TABLE schema.table ( - id bigint, - key text, - value float - ) - ENGINE = InnoDB - """, - options=MySQL.ExecuteOptions(queryTimeout=10), -) -``` - -## Options - -### *pydantic model* onetl.connection.db_connection.mysql.options.MySQLFetchOptions - -Options related to fetching data from databases via JDBC. - -#### Versionadded -Added in version 0.11.0: Replace `MySQL.JDBCOptions` → `MySQL.FetchOptions` - -### Examples - -#### NOTE -You can pass any value supported by underlying JDBC driver class, -even if it is not mentioned in this documentation. - -```python -from onetl.connection import MySQL - -options = MySQL.FetchOptions( - queryTimeout=60_000, - fetchsize=100_000, - customSparkOption="value", -) -``` - - -* **Fields:** - - [`fetchsize (int | None)`](#onetl.connection.db_connection.mysql.options.MySQLFetchOptions.fetchsize) - - [`query_timeout (int | None)`](#onetl.connection.db_connection.mysql.options.MySQLFetchOptions.query_timeout) - -#### *field* query_timeout *: int | None* *= None* *(alias 'queryTimeout')* - -The number of seconds the driver will wait for a statement to execute. -Zero means there is no limit. - -This option depends on driver implementation, -some drivers can check the timeout of each query instead of an entire JDBC batch. - - - -#### *field* fetchsize *: int | None* *= None* - -How many rows to fetch per round trip. - -Tuning this option can influence performance of reading. - -#### WARNING -Default value depends on driver. For example, Oracle has -default `fetchsize=10`. - - - -### *pydantic model* onetl.connection.db_connection.mysql.options.MySQLExecuteOptions - -Options related to executing statements in databases via JDBC. - -#### Versionadded -Added in version 0.11.0: Replace `MySQL.JDBCOptions` → `MySQL.ExecuteOptions` - -### Examples - -#### NOTE -You can pass any value supported by underlying JDBC driver class, -even if it is not mentioned in this documentation. - -```python -from onetl.connection import MySQL - -options = MySQL.ExecuteOptions( - queryTimeout=60_000, - customSparkOption="value", -) -``` - - -* **Fields:** - - [`fetchsize (int | None)`](#onetl.connection.db_connection.mysql.options.MySQLExecuteOptions.fetchsize) - - [`query_timeout (int | None)`](#onetl.connection.db_connection.mysql.options.MySQLExecuteOptions.query_timeout) - -#### *field* query_timeout *: int | None* *= None* *(alias 'queryTimeout')* - -The number of seconds the driver will wait for a statement to execute. -Zero means there is no limit. - -This option depends on driver implementation, -some drivers can check the timeout of each query instead of an entire JDBC batch. - - - -#### *field* fetchsize *: int | None* *= None* - -How many rows to fetch per round trip. - -Tuning this option can influence performance of reading. - -#### WARNING -Default value depends on driver. For example, Oracle has -default `fetchsize=10`. - - diff --git a/mddocs/markdown/connection/db_connection/mysql/index.md b/mddocs/markdown/connection/db_connection/mysql/index.md deleted file mode 100644 index 6722ca77d..000000000 --- a/mddocs/markdown/connection/db_connection/mysql/index.md +++ /dev/null @@ -1,19 +0,0 @@ - - -# MySQL - -# Connection - -* [Prerequisites](prerequisites.md) -* [MySQL connection](connection.md) - -# Operations - -* [Reading from MySQL using `DBReader`](read.md) -* [Reading from MySQL using `MySQL.sql`](sql.md) -* [Writing to MySQL using `DBWriter`](write.md) -* [Executing statements in MySQL](execute.md) - -# Troubleshooting - -* [MySQL <-> Spark type mapping](types.md) diff --git a/mddocs/markdown/connection/db_connection/mysql/prerequisites.md b/mddocs/markdown/connection/db_connection/mysql/prerequisites.md deleted file mode 100644 index d0e551443..000000000 --- a/mddocs/markdown/connection/db_connection/mysql/prerequisites.md +++ /dev/null @@ -1,61 +0,0 @@ - - -# Prerequisites - -## Version Compatibility - -* MySQL server versions: - : * Officially declared: 8.0 - 9.2 - * Actually tested: 5.7.13, 9.2.0 -* Spark versions: 2.3.x - 3.5.x -* Java versions: 8 - 20 - -See [official documentation](https://dev.mysql.com/doc/connector-j/en/connector-j-versions.html). - -## Installing PySpark - -To use MySQL connector you should have PySpark installed (or injected to `sys.path`) -BEFORE creating the connector instance. - -See [Spark](../../../install/spark.md#install-spark) installation instruction for more details. - -## Connecting to MySQL - -### Connection host - -It is possible to connect to MySQL by using either DNS name of host or it’s IP address. - -If you’re using MySQL cluster, it is currently possible to connect only to **one specific node**. -Connecting to multiple nodes to perform load balancing, as well as automatic failover to new master/replica are not supported. - -### Connection port - -Connection is usually performed to port 3306. Port may differ for different MySQL instances. -Please ask your MySQL administrator to provide required information. - -### Required grants - -Ask your MySQL cluster administrator to set following grants for a user, -used for creating a connection: - -Read + Write - -```sql --- allow creating tables in the target schema -GRANT CREATE ON myschema.* TO username@'192.168.1.%'; - --- allow read & write access to specific table -GRANT SELECT, INSERT ON myschema.mytable TO username@'192.168.1.%'; -``` - -Read only - -```sql --- allow read access to specific table -GRANT SELECT ON myschema.mytable TO username@'192.168.1.%'; -``` - -In example above `'192.168.1.%''` is a network subnet `192.168.1.0 - 192.168.1.255` -where Spark driver and executors are running. To allow connecting user from any IP, use `'%'` (not secure!). - -More details can be found in [official documentation](https://dev.mysql.com/doc/refman/en/grant.html). diff --git a/mddocs/markdown/connection/db_connection/mysql/read.md b/mddocs/markdown/connection/db_connection/mysql/read.md deleted file mode 100644 index aa66a429d..000000000 --- a/mddocs/markdown/connection/db_connection/mysql/read.md +++ /dev/null @@ -1,352 +0,0 @@ - - -# Reading from MySQL using `DBReader` - -[`DBReader`](../../../db/db_reader.md#onetl.db.db_reader.db_reader.DBReader) supports [Read Strategies](../../../strategy/index.md#strategy) for incremental data reading, -but does not support custom queries, like `JOIN`. - -#### WARNING -Please take into account [MySQL <-> Spark type mapping](types.md#mysql-types) - -## Supported DBReader features - -* ✅︎ `columns` -* ✅︎ `where` -* ✅︎ `hwm`, supported strategies: -* * ✅︎ [Snapshot Strategy](../../../strategy/snapshot_strategy.md#snapshot-strategy) -* * ✅︎ [Incremental Strategy](../../../strategy/incremental_strategy.md#incremental-strategy) -* * ✅︎ [Snapshot Batch Strategy](../../../strategy/snapshot_batch_strategy.md#snapshot-batch-strategy) -* * ✅︎ [Incremental Batch Strategy](../../../strategy/incremental_batch_strategy.md#incremental-batch-strategy) -* ✅︎ `hint` (see [official documentation](https://dev.mysql.com/doc/refman/en/optimizer-hints.html)) -* ❌ `df_schema` -* ✅︎ `options` (see [`MySQL.ReadOptions`](#onetl.connection.db_connection.mysql.options.MySQLReadOptions)) - -## Examples - -Snapshot strategy: - -```python -from onetl.connection import MySQL -from onetl.db import DBReader - -mysql = MySQL(...) - -reader = DBReader( - connection=mysql, - source="schema.table", - columns=["id", "key", "CAST(value AS text) value", "updated_dt"], - where="key = 'something'", - hint="SKIP_SCAN(schema.table key_index)", - options=MySQL.ReadOptions(partitionColumn="id", numPartitions=10), -) -df = reader.run() -``` - -Incremental strategy: - -```python -from onetl.connection import MySQL -from onetl.db import DBReader -from onetl.strategy import IncrementalStrategy - -mysql = MySQL(...) - -reader = DBReader( - connection=mysql, - source="schema.table", - columns=["id", "key", "CAST(value AS text) value", "updated_dt"], - where="key = 'something'", - hint="SKIP_SCAN(schema.table key_index)", - hwm=DBReader.AutoDetectHWM(name="mysql_hwm", expression="updated_dt"), - options=MySQL.ReadOptions(partitionColumn="id", numPartitions=10), -) - -with IncrementalStrategy(): - df = reader.run() -``` - -## Recommendations - -### Select only required columns - -Instead of passing `"*"` in `DBReader(columns=[...])` prefer passing exact column names. This reduces the amount of data passed from Oracle to Spark. - -### Pay attention to `where` value - -Instead of filtering data on Spark side using `df.filter(df.column == 'value')` pass proper `DBReader(where="column = 'value'")` clause. -This both reduces the amount of data send from Oracle to Spark, and may also improve performance of the query. -Especially if there are indexes for columns used in `where` clause. - -## Options - -### *pydantic model* onetl.connection.db_connection.mysql.options.MySQLReadOptions - -Spark JDBC reading options. - -#### Versionadded -Added in version 0.5.0: Replace `MySQL.Options` → `MySQL.ReadOptions` - -### Examples - -#### NOTE -You can pass any value -[supported by Spark](https://spark.apache.org/docs/latest/sql-data-sources-jdbc.html), -even if it is not mentioned in this documentation. **Option names should be in** `camelCase`! - -The set of supported options depends on Spark version. - -```python -from onetl.connection import MySQL - -options = MySQL.ReadOptions( - partitioning_mode="range", - partitionColumn="reg_id", - numPartitions=10, - customSparkOption="value", -) -``` - - -* **Fields:** - - [`fetchsize (int)`](#onetl.connection.db_connection.mysql.options.MySQLReadOptions.fetchsize) - - [`lower_bound (int | None)`](#onetl.connection.db_connection.mysql.options.MySQLReadOptions.lower_bound) - - [`num_partitions (pydantic.types.PositiveInt)`](#onetl.connection.db_connection.mysql.options.MySQLReadOptions.num_partitions) - - [`partition_column (str | None)`](#onetl.connection.db_connection.mysql.options.MySQLReadOptions.partition_column) - - [`partitioning_mode (onetl.connection.db_connection.jdbc_connection.options.JDBCPartitioningMode)`](#onetl.connection.db_connection.mysql.options.MySQLReadOptions.partitioning_mode) - - [`query_timeout (int | None)`](#onetl.connection.db_connection.mysql.options.MySQLReadOptions.query_timeout) - - [`session_init_statement (str | None)`](#onetl.connection.db_connection.mysql.options.MySQLReadOptions.session_init_statement) - - [`upper_bound (int | None)`](#onetl.connection.db_connection.mysql.options.MySQLReadOptions.upper_bound) - -#### *field* partition_column *: str | None* *= None* *(alias 'partitionColumn')* - -Column used to parallelize reading from a table. - -#### WARNING -It is highly recommended to use primary key, or column with an index -to avoid performance issues. - -#### NOTE -Column type depends on [`partitioning_mode`](#onetl.connection.db_connection.mysql.options.MySQLReadOptions.partitioning_mode). - -* `partitioning_mode="range"` requires column to be an integer, date or timestamp (can be NULL, but not recommended). -* `partitioning_mode="hash"` accepts any column type (NOT NULL). -* `partitioning_mode="mod"` requires column to be an integer (NOT NULL). - -See documentation for [`partitioning_mode`](#onetl.connection.db_connection.mysql.options.MySQLReadOptions.partitioning_mode) for more details - - - -#### *field* num_partitions *: PositiveInt* *= 1* *(alias 'numPartitions')* - -Number of jobs created by Spark to read the table content in parallel. -See documentation for [`partitioning_mode`](#onetl.connection.db_connection.mysql.options.MySQLReadOptions.partitioning_mode) for more details - - -* **Constraints:** - - **exclusiveMinimum** = 0 - -#### *field* lower_bound *: int | None* *= None* *(alias 'lowerBound')* - -See documentation for [`partitioning_mode`](#onetl.connection.db_connection.mysql.options.MySQLReadOptions.partitioning_mode) for more details - - - -#### *field* upper_bound *: int | None* *= None* *(alias 'upperBound')* - -See documentation for [`partitioning_mode`](#onetl.connection.db_connection.mysql.options.MySQLReadOptions.partitioning_mode) for more details - - - -#### *field* session_init_statement *: str | None* *= None* *(alias 'sessionInitStatement')* - -After each database session is opened to the remote DB and before starting to read data, -this option executes a custom SQL statement (or a PL/SQL block). - -Use this to implement session initialization code. - -Example: - -```python -sessionInitStatement = """ - BEGIN - execute immediate - 'alter session set "_serial_direct_read"=true'; - END; -""" -``` - - - -#### *field* query_timeout *: int | None* *= None* *(alias 'queryTimeout')* - -The number of seconds the driver will wait for a statement to execute. -Zero means there is no limit. - -This option depends on driver implementation, -some drivers can check the timeout of each query instead of an entire JDBC batch. - - - -#### *field* fetchsize *: int* *= 100000* - -Fetch N rows from an opened cursor per one read round. - -Tuning this option can influence performance of reading. - -#### WARNING -Default value is different from Spark. - -Spark uses driver’s own value, and it may be different in different drivers, -and even versions of the same driver. For example, Oracle has -default `fetchsize=10`, which is absolutely not usable. - -Thus we’ve overridden default value with `100_000`, which should increase reading performance. - -#### Versionchanged -Changed in version 0.2.0: Set explicit default value to `100_000` - - - -#### *field* partitioning_mode *: JDBCPartitioningMode* *= JDBCPartitioningMode.RANGE* - -Defines how Spark will parallelize reading from table. - -Possible values: - -* `range` (default) - : Allocate each executor a range of values from column passed into [`partition_column`](#onetl.connection.db_connection.mysql.options.MySQLReadOptions.partition_column). -
- ### Spark generates for each executor an SQL query -
- Executor 1: - ```sql - SELECT ... FROM table - WHERE (partition_column >= lowerBound - OR partition_column IS NULL) - AND partition_column < (lower_bound + stride) - ``` -
- Executor 2: - ```sql - SELECT ... FROM table - WHERE partition_column >= (lower_bound + stride) - AND partition_column < (lower_bound + 2 * stride) - ``` -
- … -
- Executor N: - ```sql - SELECT ... FROM table - WHERE partition_column >= (lower_bound + (N-1) * stride) - AND partition_column <= upper_bound - ``` -
- Where `stride=(upper_bound - lower_bound) / num_partitions`. -
- #### NOTE - Can be used only with columns of integer, date or timestamp types. -
- #### NOTE - [`lower_bound`](#onetl.connection.db_connection.mysql.options.MySQLReadOptions.lower_bound), [`upper_bound`](#onetl.connection.db_connection.mysql.options.MySQLReadOptions.upper_bound) and [`num_partitions`](#onetl.connection.db_connection.mysql.options.MySQLReadOptions.num_partitions) are used just to - calculate the partition stride, **NOT** for filtering the rows in table. - So all rows in the table will be returned (unlike *Incremental* [Read Strategies](../../../strategy/index.md#strategy)). -
- #### NOTE - All queries are executed in parallel. To execute them sequentially, use *Batch* [Read Strategies](../../../strategy/index.md#strategy). -* `hash` - : Allocate each executor a set of values based on hash of the [`partition_column`](#onetl.connection.db_connection.mysql.options.MySQLReadOptions.partition_column) column. -
- ### Spark generates for each executor an SQL query -
- Executor 1: - ```sql - SELECT ... FROM table - WHERE (some_hash(partition_column) mod num_partitions) = 0 -- lower_bound - ``` -
- Executor 2: - ```sql - SELECT ... FROM table - WHERE (some_hash(partition_column) mod num_partitions) = 1 -- lower_bound + 1 - ``` -
- … -
- Executor N: - ```sql - SELECT ... FROM table - WHERE (some_hash(partition_column) mod num_partitions) = num_partitions-1 -- upper_bound - ``` -
- #### NOTE - The hash function implementation depends on RDBMS. It can be `MD5` or any other fast hash function, - or expression based on this function call. Usually such functions accepts any column type as an input. -* `mod` - : Allocate each executor a set of values based on modulus of the [`partition_column`](#onetl.connection.db_connection.mysql.options.MySQLReadOptions.partition_column) column. -
- ### Spark generates for each executor an SQL query -
- Executor 1: - ```sql - SELECT ... FROM table - WHERE (partition_column mod num_partitions) = 0 -- lower_bound - ``` -
- Executor 2: - ```sql - SELECT ... FROM table - WHERE (partition_column mod num_partitions) = 1 -- lower_bound + 1 - ``` -
- Executor N: - ```sql - SELECT ... FROM table - WHERE (partition_column mod num_partitions) = num_partitions-1 -- upper_bound - ``` -
- #### NOTE - Can be used only with columns of integer type. - -#### Versionadded -Added in version 0.5.0. - -### Examples - -Read data in 10 parallel jobs by range of values in `id_column` column: - -```python -ReadOptions( - partitioning_mode="range", # default mode, can be omitted - partitionColumn="id_column", - numPartitions=10, - # Options below can be discarded because they are - # calculated automatically as MIN and MAX values of `partitionColumn` - lowerBound=0, - upperBound=100_000, -) -``` - -Read data in 10 parallel jobs by hash of values in `some_column` column: - -```python -ReadOptions( - partitioning_mode="hash", - partitionColumn="some_column", - numPartitions=10, - # lowerBound and upperBound are automatically set to `0` and `9` -) -``` - -Read data in 10 parallel jobs by modulus of values in `id_column` column: - -```python -ReadOptions( - partitioning_mode="mod", - partitionColumn="id_column", - numPartitions=10, - # lowerBound and upperBound are automatically set to `0` and `9` -) -``` - - diff --git a/mddocs/markdown/connection/db_connection/mysql/sql.md b/mddocs/markdown/connection/db_connection/mysql/sql.md deleted file mode 100644 index 2999cedd1..000000000 --- a/mddocs/markdown/connection/db_connection/mysql/sql.md +++ /dev/null @@ -1,192 +0,0 @@ - - -# Reading from MySQL using `MySQL.sql` - -`MySQL.sql` allows passing custom SQL query, but does not support incremental strategies. - -#### WARNING -Please take into account [MySQL <-> Spark type mapping](types.md#mysql-types) - -#### WARNING -Statement is executed in **read-write** connection, so if you’re calling some functions/procedures with DDL/DML statements inside, -they can change data in your database. - -## Syntax support - -Only queries with the following syntax are supported: - -* ✅︎ `SELECT ... FROM ...` -* ✅︎ `WITH alias AS (...) SELECT ...` -* ❌ `SHOW ...` -* ❌ `SET ...; SELECT ...;` - multiple statements not supported - -## Examples - -```python -from onetl.connection import MySQL - -mysql = MySQL(...) -df = mysql.sql( - """ - SELECT - id, - key, - CAST(value AS text) value, - updated_at - FROM - some.mytable - WHERE - key = 'something' - """, - options=MySQL.SQLOptions( - partitionColumn="id", - numPartitions=10, - lowerBound=0, - upperBound=1000, - ), -) -``` - -## Recommendations - -### Select only required columns - -Instead of passing `SELECT * FROM ...` prefer passing exact column names `SELECT col1, col2, ...`. -This reduces the amount of data passed from MySQL to Spark. - -### Pay attention to `where` value - -Instead of filtering data on Spark side using `df.filter(df.column == 'value')` pass proper `WHERE column = 'value'` clause. -This both reduces the amount of data send from MySQL to Spark, and may also improve performance of the query. -Especially if there are indexes or partitions for columns used in `where` clause. - -## Options - -### *pydantic model* onetl.connection.db_connection.mysql.options.MySQLSQLOptions - -Options specifically for SQL queries - -These options allow you to specify configurations for executing SQL queries -without relying on Spark’s partitioning mechanisms. - -#### Versionadded -Added in version 0.11.0: Split up `MySQL.ReadOptions` to `MySQL.SQLOptions` - -### Examples - -#### NOTE -You can pass any JDBC configuration -[supported by Spark](https://spark.apache.org/docs/latest/sql-data-sources-jdbc.html), -tailored to optimize SQL query execution. **Option names should be in** `camelCase`! - -```python -from onetl.connection import MySQL - -options = MySQL.SQLOptions( - partitionColumn="reg_id", - numPartitions=10, - lowerBound=0, - upperBound=1000, - customSparkOption="value", -) -``` - - - -#### *field* partition_column *: str | None* *= None* *(alias 'partitionColumn')* - -Column used to partition data across multiple executors for parallel query processing. - -#### WARNING -It is highly recommended to use primary key, or column with an index -to avoid performance issues. - -### Example of using `partitionColumn="id"` with `partitioning_mode="range"` - -```sql --- If partition_column is 'id', with numPartitions=4, lowerBound=1, and upperBound=100: --- Executor 1 processes IDs from 1 to 25 -SELECT ... FROM table WHERE id >= 1 AND id < 26 --- Executor 2 processes IDs from 26 to 50 -SELECT ... FROM table WHERE id >= 26 AND id < 51 --- Executor 3 processes IDs from 51 to 75 -SELECT ... FROM table WHERE id >= 51 AND id < 76 --- Executor 4 processes IDs from 76 to 100 -SELECT ... FROM table WHERE id >= 76 AND id <= 100 - --- General case for Executor N -SELECT ... FROM table -WHERE partition_column >= (lowerBound + (N-1) * stride) -AND partition_column <= upperBound --- Where ``stride`` is calculated as ``(upperBound - lowerBound) / numPartitions``. -``` - - - -#### *field* num_partitions *: int | None* *= None* *(alias 'numPartitions')* - -Number of jobs created by Spark to read the table content in parallel. - - - -#### *field* lower_bound *: int | None* *= None* *(alias 'lowerBound')* - -Defines the starting boundary for partitioning the query’s data. Mandatory if [`partition_column`](#onetl.connection.db_connection.mysql.options.MySQLSQLOptions.partition_column) is set - - - -#### *field* upper_bound *: int | None* *= None* *(alias 'upperBound')* - -Sets the ending boundary for data partitioning. Mandatory if [`partition_column`](#onetl.connection.db_connection.mysql.options.MySQLSQLOptions.partition_column) is set - - - -#### *field* session_init_statement *: str | None* *= None* *(alias 'sessionInitStatement')* - -After each database session is opened to the remote DB and before starting to read data, -this option executes a custom SQL statement (or a PL/SQL block). - -Use this to implement session initialization code. - -Example: - -```python -sessionInitStatement = """ - BEGIN - execute immediate - 'alter session set "_serial_direct_read"=true'; - END; -""" -``` - - - -#### *field* query_timeout *: int | None* *= None* *(alias 'queryTimeout')* - -The number of seconds the driver will wait for a statement to execute. -Zero means there is no limit. - -This option depends on driver implementation, -some drivers can check the timeout of each query instead of an entire JDBC batch. - - - -#### *field* fetchsize *: int* *= 100000* - -Fetch N rows from an opened cursor per one read round. - -Tuning this option can influence performance of reading. - -#### WARNING -Default value is different from Spark. - -Spark uses driver’s own value, and it may be different in different drivers, -and even versions of the same driver. For example, Oracle has -default `fetchsize=10`, which is absolutely not usable. - -Thus we’ve overridden default value with `100_000`, which should increase reading performance. - -#### Versionchanged -Changed in version 0.2.0: Set explicit default value to `100_000` - - diff --git a/mddocs/markdown/connection/db_connection/mysql/types.md b/mddocs/markdown/connection/db_connection/mysql/types.md deleted file mode 100644 index cd1e207a9..000000000 --- a/mddocs/markdown/connection/db_connection/mysql/types.md +++ /dev/null @@ -1,292 +0,0 @@ - - -# MySQL <-> Spark type mapping - -#### NOTE -The results below are valid for Spark 3.5.5, and may differ on other Spark versions. - -## Type detection & casting - -Spark’s DataFrames always have a `schema` which is a list of columns with corresponding Spark types. All operations on a column are performed using column type. - -### Reading from MySQL - -This is how MySQL connector performs this: - -* For each column in query result (`SELECT column1, column2, ... FROM table ...`) get column name and MySQL type. -* Find corresponding `MySQL type (read)` → `Spark type` combination (see below) for each DataFrame column. If no combination is found, raise exception. -* Create DataFrame from query with specific column names and Spark types. - -### Writing to some existing MySQL table - -This is how MySQL connector performs this: - -* Get names of columns in DataFrame. [1](#id2) -* Perform `SELECT * FROM table LIMIT 0` query. -* Take only columns present in DataFrame (by name, case insensitive). For each found column get MySQL type. -* Find corresponding `Spark type` → `MySQL type (write)` combination (see below) for each DataFrame column. If no combination is found, raise exception. -* If `MySQL type (write)` match `MySQL type (read)`, no additional casts will be performed, DataFrame column will be written to MySQL as is. -* If `MySQL type (write)` does not match `MySQL type (read)`, DataFrame column will be casted to target column type **on MySQL side**. For example, you can write column with text data to `int` column, if column contains valid integer values within supported value range and precision. - -* **[1]** This allows to write data to tables with `DEFAULT` and `GENERATED` columns - if DataFrame has no such column, it will be populated by MySQL. - -### Create new table using Spark - -#### WARNING -ABSOLUTELY NOT RECOMMENDED! - -This is how MySQL connector performs this: - -* Find corresponding `Spark type` → `MySQL type (create)` combination (see below) for each DataFrame column. If no combination is found, raise exception. -* Generate DDL for creating table in MySQL, like `CREATE TABLE (col1 ...)`, and run it. -* Write DataFrame to created table as is. - -But some cases this may lead to using wrong column type. For example, Spark creates column of type `timestamp` -which corresponds to MySQL type `timestamp(0)` (precision up to seconds) -instead of more precise `timestamp(6)` (precision up to nanoseconds). -This may lead to incidental precision loss, or sometimes data cannot be written to created table at all. - -So instead of relying on Spark to create tables: - -### See example - -```python -writer = DBWriter( - connection=mysql, - target="myschema.target_tbl", - options=MySQL.WriteOptions( - if_exists="append", - createTableOptions="ENGINE = InnoDB", - ), -) -writer.run(df) -``` - -Always prefer creating tables with specific types **BEFORE WRITING DATA**: - -### See example - -```python -mysql.execute( - """ - CREATE TABLE schema.table ( - id bigint, - key text, - value timestamp(6) -- specific type and precision - ) - ENGINE = InnoDB - """, -) - -writer = DBWriter( - connection=mysql, - target="myschema.target_tbl", - options=MySQL.WriteOptions(if_exists="append"), -) -writer.run(df) -``` - -### References - -Here you can find source code with type conversions: - -* [MySQL -> JDBC](https://github.com/mysql/mysql-connector-j/blob/8.0.33/src/main/core-api/java/com/mysql/cj/MysqlType.java#L44-L623) -* [JDBC -> Spark](https://github.com/apache/spark/blob/v3.5.5/sql/core/src/main/scala/org/apache/spark/sql/jdbc/MySQLDialect.scala#L104-L132) -* [Spark -> JDBC](https://github.com/apache/spark/blob/v3.5.5/sql/core/src/main/scala/org/apache/spark/sql/jdbc/MySQLDialect.scala#L204-L211) -* [JDBC -> MySQL](https://github.com/mysql/mysql-connector-j/blob/8.0.33/src/main/core-api/java/com/mysql/cj/MysqlType.java#L625-L867) - -## Supported types - -See [official documentation](https://dev.mysql.com/doc/refman/en/data-types.html) - -### Numeric types - -| MySQL type (read) | Spark type | MySQL type (write) | MySQL type (create) | -|-----------------------------|----------------------------------|-----------------------------|-----------------------------| -| `decimal` | `DecimalType(P=10, S=0)` | `decimal(P=10, S=0)` | `decimal(P=10, S=0)` | -| `decimal(P=0..38)` | `DecimalType(P=0..38, S=0)` | `decimal(P=0..38, S=0)` | `decimal(P=0..38, S=0)` | -| `decimal(P=0..38, S=0..30)` | `DecimalType(P=0..38, S=0..30)` | `decimal(P=0..38, S=0..30)` | `decimal(P=0..38, S=0..30)` | -| `decimal(P=39..65, S=...)` | unsupported [2](#id4) | | | -| `float` | `DoubleType()` | `double` | `double` | -| `double` | | | | -| `tinyint` | `IntegerType()` | `int` | `int` | -| `smallint` | | | | -| `mediumint` | | | | -| `int` | | | | -| `bigint` | `LongType()` | `bigint` | `bigint` | -* **[2]** MySQL support decimal types with precision `P` up to 65. But Spark’s `DecimalType(P, S)` supports maximum `P=38`. It is impossible to read, write or operate with values of larger precision, this leads to an exception. - -### Temporal types - -| MySQL type (read) | Spark type | MySQL type (write) | MySQL type (create) | -|------------------------------|------------------------------------------------------------------------------------|------------------------------|-----------------------------------------------------------------------| -| `year` | `DateType()` | `date` | `date` | -| `date` | | | | -| `datetime`, seconds | `TimestampType()`, microseconds | `timestamp(6)`, microseconds | `timestamp(0)`, seconds | -| `timestamp`, seconds | | | | -| `datetime(0)`, seconds | | | | -| `timestamp(0)`, seconds | | | | -| `datetime(3)`, milliseconds | `TimestampType()`, microseconds | `timestamp(6)`, microseconds | `timestamp(0)`, seconds,
**precision loss** [4](#id9), | -| `timestamp(3)`, milliseconds | | | | -| `datetime(6)`, microseconds | | | | -| `timestamp(6)`, microseconds | | | | -| `time`, seconds | `TimestampType()`, microseconds,
with time format quirks [5](#id10) | `timestamp(6)`, microseconds | `timestamp(0)`, seconds | -| `time(0)`, seconds | | | | -| `time(3)`, milliseconds | `TimestampType()`, microseconds
with time format quirks [5](#id10) | `timestamp(6)`, microseconds | `timestamp(0)`, seconds,
**precision loss** [4](#id9), | -| `time(6)`, microseconds | | | | - -#### WARNING -Note that types in MySQL and Spark have different value ranges: - -| MySQL type | Min value | Max value | Spark type | Min value | Max value | -|--------------|------------------------------|------------------------------|-------------------|------------------------------|------------------------------| -| `year` | `1901` | `2155` | `DateType()` | `0001-01-01` | `9999-12-31` | -| `date` | `1000-01-01` | `9999-12-31` | | | | -| `datetime` | `1000-01-01 00:00:00.000000` | `9999-12-31 23:59:59.499999` | `TimestampType()` | `0001-01-01 00:00:00.000000` | `9999-12-31 23:59:59.999999` | -| `timestamp` | `1970-01-01 00:00:01.000000` | `9999-12-31 23:59:59.499999` | | | | -| `time` | `-838:59:59.000000` | `838:59:59.000000` | | | | - -So Spark can read all the values from MySQL, but not all of values in Spark DataFrame can be written to MySQL. - -References: -: * [MySQL year documentation](https://dev.mysql.com/doc/refman/en/year.html) - * [MySQL date, datetime & timestamp documentation](https://dev.mysql.com/doc/refman/en/datetime.html) - * [MySQL time documentation](https://dev.mysql.com/doc/refman/en/time.html) - * [Spark DateType documentation](https://spark.apache.org/docs/latest/api/java/org/apache/spark/sql/types/DateType.html) - * [Spark TimestampType documentation](https://spark.apache.org/docs/latest/api/java/org/apache/spark/sql/types/TimestampType.html) - -* **[4]** MySQL dialect generates DDL with MySQL type `timestamp` which is alias for `timestamp(0)` with precision up to seconds (`23:59:59`). Inserting data with microseconds precision (`23:59:59.999999`) will lead to **throwing away microseconds**. -* **[5]** `time` type is the same as `timestamp` with date `1970-01-01`. So instead of reading data from MySQL like `23:59:59` it is actually read `1970-01-01 23:59:59`, and vice versa. - -### String types - -| MySQL type (read) | Spark type | MySQL type (write) | MySQL type (create) | -|-----------------------------|----------------|----------------------|-----------------------| -| `char` | `StringType()` | `longtext` | `longtext` | -| `char(N)` | | | | -| `varchar(N)` | | | | -| `mediumtext` | | | | -| `text` | | | | -| `longtext` | | | | -| `json` | | | | -| `enum("val1", "val2", ...)` | | | | -| `set("val1", "val2", ...)` | | | | - -### Binary types - -| MySQL type (read) | Spark type | MySQL type (write) | MySQL type (create) | -|---------------------|----------------|----------------------|-----------------------| -| `binary` | `BinaryType()` | `blob` | `blob` | -| `binary(N)` | | | | -| `varbinary(N)` | | | | -| `mediumblob` | | | | -| `blob` | | | | -| `longblob` | | | | - -### Geometry types - -| MySQL type (read) | Spark type | MySQL type (write) | MySQL type (create) | -|----------------------|----------------|----------------------|-----------------------| -| `point` | `BinaryType()` | `blob` | `blob` | -| `linestring` | | | | -| `polygon` | | | | -| `geometry` | | | | -| `multipoint` | | | | -| `multilinestring` | | | | -| `multipolygon` | | | | -| `geometrycollection` | | | | - -## Explicit type cast - -### `DBReader` - -It is possible to explicitly cast column type using `DBReader(columns=...)` syntax. - -For example, you can use `CAST(column AS text)` to convert data to string representation on MySQL side, and so it will be read as Spark’s `StringType()`. - -It is also possible to use [JSON_OBJECT](https://dev.mysql.com/doc/refman/en/json.html) MySQL function and parse JSON columns in MySQL with the [`JSON.parse_column`](../../../file_df/file_formats/json.md#onetl.file.format.json.JSON.parse_column) method. - -```python -from pyspark.sql.types import IntegerType, StructType, StructField - -from onetl.connection import MySQL -from onetl.db import DBReader -from onetl.file.format import JSON - -mysql = MySQL(...) - -DBReader( - connection=mysql, - columns=[ - "id", - "supported_column", - "CAST(unsupported_column AS text) unsupported_column_str", - # or - "JSON_OBJECT('key', value_column) json_column", - ], -) -df = reader.run() - -json_scheme = StructType([StructField("key", IntegerType())]) - -df = df.select( - df.id, - df.supported_column, - # explicit cast - df.unsupported_column_str.cast("integer").alias("parsed_integer"), - JSON().parse_column("json_column", json_scheme).alias("struct_column"), -) -``` - -### `DBWriter` - -To write JSON data to a `json` or `text` column in a MySQL table, use the [`JSON.serialize_column`](../../../file_df/file_formats/json.md#onetl.file.format.json.JSON.serialize_column) method. - -```python -from onetl.connection import MySQL -from onetl.db import DBWriter -from onetl.file.format import JSON - -mysql.execute( - """ - CREATE TABLE schema.target_tbl ( - id bigint, - array_column_json json -- any string type, actually - ) - ENGINE = InnoDB - """, -) - -df = df.select( - df.id, - JSON().serialize_column(df.array_column).alias("array_column_json"), -) - -writer.run(df) -``` - -Then you can parse this column on MySQL side - for example, by creating a view: - -```sql -SELECT - id, - array_column_json->"$[0]" AS array_item -FROM target_tbl -``` - -Or by using [GENERATED column](https://dev.mysql.com/doc/refman/en/create-table-generated-columns.html): - -```sql -CREATE TABLE schema.target_table ( - id bigint, - supported_column timestamp, - array_column_json json, -- any string type, actually - -- virtual column - array_item_0 GENERATED ALWAYS AS (array_column_json->"$[0]")) VIRTUAL - -- or stired column - -- array_item_0 GENERATED ALWAYS AS (array_column_json->"$[0]")) STORED -) -``` - -`VIRTUAL` column value is calculated on every table read. -`STORED` column value is calculated during insert, but this require additional space. diff --git a/mddocs/markdown/connection/db_connection/mysql/write.md b/mddocs/markdown/connection/db_connection/mysql/write.md deleted file mode 100644 index 239aacfdb..000000000 --- a/mddocs/markdown/connection/db_connection/mysql/write.md +++ /dev/null @@ -1,187 +0,0 @@ - - -# Writing to MySQL using `DBWriter` - -For writing data to MySQL, use [`DBWriter`](../../../db/db_writer.md#onetl.db.db_writer.db_writer.DBWriter). - -#### WARNING -Please take into account [MySQL <-> Spark type mapping](types.md#mysql-types) - -#### WARNING -It is always recommended to create table explicitly using [MySQL.execute](execute.md#mysql-execute) -instead of relying on Spark’s table DDL generation. - -This is because Spark’s DDL generator can create columns with different precision and types than it is expected, -causing precision loss or other issues. - -## Examples - -```python -from onetl.connection import MySQL -from onetl.db import DBWriter - -mysql = MySQL(...) - -df = ... # data is here - -writer = DBWriter( - connection=mysql, - target="schema.table", - options=MySQL.WriteOptions( - if_exists="append", - # ENGINE is required by MySQL - createTableOptions="ENGINE = MergeTree() ORDER BY id", - ), -) - -writer.run(df) -``` - -## Options - -Method above accepts [`MySQL.WriteOptions`](#onetl.connection.db_connection.mysql.options.MySQLWriteOptions) - -### *pydantic model* onetl.connection.db_connection.mysql.options.MySQLWriteOptions - -Spark JDBC writing options. - -#### Versionadded -Added in version 0.5.0: Replace `MySQL.Options` → `MySQL.WriteOptions` - -### Examples - -#### NOTE -You can pass any value -[supported by Spark](https://spark.apache.org/docs/latest/sql-data-sources-jdbc.html), -even if it is not mentioned in this documentation. **Option names should be in** `camelCase`! - -The set of supported options depends on Spark version. - -```python -from onetl.connection import MySQL - -options = MySQL.WriteOptions( - if_exists="append", - batchsize=20_000, - customSparkOption="value", -) -``` - - - -#### *field* if_exists *: JDBCTableExistBehavior* *= JDBCTableExistBehavior.APPEND* *(alias 'mode')* - -Behavior of writing data into existing table. - -Possible values: -: * `append` (default) - : Adds new rows into existing table. -
- ### Behavior in details -
- * Table does not exist - : Table is created using options provided by user - (`createTableOptions`, `createTableColumnTypes`, etc). - * Table exists - : Data is appended to a table. Table has the same DDL as before writing data -
- #### WARNING - This mode does not check whether table already contains - rows from dataframe, so duplicated rows can be created. -
- Also Spark does not support passing custom options to - insert statement, like `ON CONFLICT`, so don’t try to - implement deduplication using unique indexes or constraints. -
- Instead, write to staging table and perform deduplication - using `execute` method. - * `replace_entire_table` - : **Table is dropped and then created, or truncated**. -
- ### Behavior in details -
- * Table does not exist - : Table is created using options provided by user - (`createTableOptions`, `createTableColumnTypes`, etc). - * Table exists - : Table content is replaced with dataframe content. -
- After writing completed, target table could either have the same DDL as - before writing data (`truncate=True`), or can be recreated (`truncate=False` - or source does not support truncation). - * `ignore` - : Ignores the write operation if the table already exists. -
- ### Behavior in details -
- * Table does not exist - : Table is created using options provided by user - (`createTableOptions`, `createTableColumnTypes`, etc). - * Table exists - : The write operation is ignored, and no data is written to the table. - * `error` - : Raises an error if the table already exists. -
- ### Behavior in details -
- * Table does not exist - : Table is created using options provided by user - (`createTableOptions`, `createTableColumnTypes`, etc). - * Table exists - : An error is raised, and no data is written to the table. - -#### Versionchanged -Changed in version 0.9.0: Renamed `mode` → `if_exists` - - - -#### *field* query_timeout *: int | None* *= None* *(alias 'queryTimeout')* - -The number of seconds the driver will wait for a statement to execute. -Zero means there is no limit. - -This option depends on driver implementation, -some drivers can check the timeout of each query instead of an entire JDBC batch. - - - -#### *field* batchsize *: int* *= 20000* - -How many rows can be inserted per round trip. - -Tuning this option can influence performance of writing. - -#### WARNING -Default value is different from Spark. - -Spark uses quite small value `1000`, which is absolutely not usable -in BigData world. - -Thus we’ve overridden default value with `20_000`, -which should increase writing performance. - -You can increase it even more, up to `50_000`, -but it depends on your database load and number of columns in the row. -Higher values does not increase performance. - -#### Versionchanged -Changed in version 0.4.0: Changed default value from 1000 to 20_000 - - - -#### *field* isolation_level *: str* *= 'READ_UNCOMMITTED'* *(alias 'isolationLevel')* - -The transaction isolation level, which applies to current connection. - -Possible values: -: * `NONE` (as string, not Python’s `None`) - * `READ_COMMITTED` - * `READ_UNCOMMITTED` - * `REPEATABLE_READ` - * `SERIALIZABLE` - -Values correspond to transaction isolation levels defined by JDBC standard. -Please refer the documentation for -[java.sql.Connection](https://docs.oracle.com/javase/8/docs/api/java/sql/Connection.html). - - diff --git a/mddocs/markdown/connection/db_connection/oracle/connection.md b/mddocs/markdown/connection/db_connection/oracle/connection.md deleted file mode 100644 index 6171f3a50..000000000 --- a/mddocs/markdown/connection/db_connection/oracle/connection.md +++ /dev/null @@ -1,143 +0,0 @@ - - -# Oracle connection - -### *class* onetl.connection.db_connection.oracle.connection.Oracle(\*, spark: SparkSession, user: str, password: SecretStr, host: Host, port: int = 1521, sid: str | None = None, service_name: str | None = None, extra: OracleExtra = OracleExtra()) - -Oracle JDBC connection. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -Based on Maven package [com.oracle.database.jdbc:ojdbc8:23.7.0.25.01](https://mvnrepository.com/artifact/com.oracle.database.jdbc/ojdbc8/23.7.0.25.01) -([official Oracle JDBC driver](https://www.oracle.com/cis/database/technologies/appdev/jdbc-downloads.html)). - -#### SEE ALSO -Before using this connector please take into account [Prerequisites](prerequisites.md#oracle-prerequisites) - -#### Versionadded -Added in version 0.1.0. - -* **Parameters:** - **host** - : Host of Oracle database. For example: `test.oracle.domain.com` or `193.168.1.10` - - **port** - : Port of Oracle database - - **user** - : User, which have proper access to the database. For example: `SOME_USER` - - **password** - : Password for database connection - - **sid** - : Sid of oracle database. For example: `XE` -
- #### WARNING - You should provide either `sid` or `service_name`, not both of them - - **service_name** - : Specifies one or more names by which clients can connect to the instance. -
- For example: `PDB1`. -
- #### WARNING - You should provide either `sid` or `service_name`, not both of them - - **spark** - : Spark session. - - **extra** - : Specifies one or more extra parameters by which clients can connect to the instance. -
- For example: `{"remarksReporting": "false"}` -
- See official documentation: - : * [Connection parameters](https://docs.oracle.com/en/database/oracle/oracle-database/23/jajdb/oracle/jdbc/OracleDriver.html) - * [Connection properties](https://docs.oracle.com/cd/A97335_02/apps.102/a83724/basic1.htm#1024018) - -### Examples - -Create and check Oracle connection with `sid`: - -```python -from onetl.connection import Oracle -from pyspark.sql import SparkSession - -# Create Spark session with Oracle driver loaded -maven_packages = Oracle.get_packages() -spark = ( - SparkSession.builder.appName("spark-app-name") - .config("spark.jars.packages", ",".join(maven_packages)) - .getOrCreate() -) - -# Create connection -oracle = Oracle( - host="database.host.or.ip", - user="user", - password="*****", - sid="XE", - extra={"remarksReporting": "false"}, - spark=spark, -).check() -``` - -or with `service_name`: - -```python -... - -oracle = Oracle( - host="database.host.or.ip", - user="user", - password="*****", - service_name="PDB1", # <--- instead of SID - extra={"remarksReporting": "false"}, - spark=spark, -).check() -``` - - - -#### check() - -Check source availability. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -If not, an exception will be raised. - -* **Returns:** - Connection itself -* **Raises:** - RuntimeError - : If the connection is not available - -### Examples - -```python -connection.check() -``` - - - -#### *classmethod* get_packages(java_version: str | None = None, package_version: str | None = None) → list[str] - -Get package names to be downloaded by Spark. Allows specifying custom JDBC driver versions for Oracle. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -* **Parameters:** - **java_version** - : Java major version, defaults to “8”. Must be “8” or “11”. - - **package_version** - : Specifies the version of the Oracle JDBC driver to use. Defaults to “23.7.0.25.01”. - -### Examples - -```python -from onetl.connection import Oracle - -Oracle.get_packages() - -# specify Java and package versions -Oracle.get_packages(java_version="8", package_version="23.7.0.25.01") -``` - - diff --git a/mddocs/markdown/connection/db_connection/oracle/execute.md b/mddocs/markdown/connection/db_connection/oracle/execute.md deleted file mode 100644 index 29293882b..000000000 --- a/mddocs/markdown/connection/db_connection/oracle/execute.md +++ /dev/null @@ -1,191 +0,0 @@ - - -# Executing statements in Oracle - -#### WARNING -Methods below **read all the rows** returned from DB **to Spark driver memory**, and then convert them to DataFrame. - -Do **NOT** use them to read large amounts of data. Use [DBReader](read.md#oracle-read) or [Oracle.sql](sql.md#oracle-sql) instead. - -## How to - -There are 2 ways to execute some statement in Oracle - -### Use `Oracle.fetch` - -Use this method to execute some `SELECT` query which returns **small number or rows**, like reading -Oracle config, or reading data from some reference table. Method returns Spark DataFrame. - -Method accepts [`Oracle.FetchOptions`](#onetl.connection.db_connection.oracle.options.OracleFetchOptions). - -Connection opened using this method should be then closed with `connection.close()` or `with connection:`. - -#### WARNING -Please take into account [Oracle <-> Spark type mapping](types.md#oracle-types). - -#### Syntax support - -This method supports **any** query syntax supported by Oracle, like: - -* ✅︎ `SELECT ... FROM ...` -* ✅︎ `WITH alias AS (...) SELECT ...` -* ✅︎ `SELECT func(arg1, arg2) FROM DUAL` - call function -* ✅︎ `SHOW ...` -* ❌ `SET ...; SELECT ...;` - multiple statements not supported - -#### Examples - -```python -from onetl.connection import Oracle - -oracle = Oracle(...) - -df = oracle.fetch( - "SELECT value FROM some.reference_table WHERE key = 'some_constant'", - options=Oracle.FetchOptions(queryTimeout=10), -) -oracle.close() -value = df.collect()[0][0] # get value from first row and first column -``` - -### Use `Oracle.execute` - -Use this method to execute DDL and DML operations. Each method call runs operation in a separated transaction, and then commits it. - -Method accepts [`Oracle.ExecuteOptions`](#onetl.connection.db_connection.oracle.options.OracleExecuteOptions). - -Connection opened using this method should be then closed with `connection.close()` or `with connection:`. - -#### Syntax support - -This method supports **any** query syntax supported by Oracle, like: - -* ✅︎ `CREATE TABLE ...`, `CREATE VIEW ...` -* ✅︎ `ALTER ...` -* ✅︎ `INSERT INTO ... SELECT ...`, `UPDATE ...`, `DELETE ...`, and so on -* ✅︎ `DROP TABLE ...`, `DROP VIEW ...`, `TRUNCATE TABLE`, and so on -* ✅︎ `CALL procedure(arg1, arg2) ...` or `{call procedure(arg1, arg2)}` - special syntax for calling procedure -* ✅︎ `DECLARE ... BEGIN ... END` - execute PL/SQL statement -* ✅︎ other statements not mentioned here -* ❌ `SET ...; SELECT ...;` - multiple statements not supported - -#### Examples - -```python -from onetl.connection import Oracle - -oracle = Oracle(...) - -oracle.execute("DROP TABLE schema.table") -oracle.execute( - """ - CREATE TABLE schema.table ( - id bigint GENERATED ALWAYS AS IDENTITY, - key VARCHAR2(4000), - value NUMBER - ) - """, - options=Oracle.ExecuteOptions(queryTimeout=10), -) -``` - -## Options - -### *pydantic model* onetl.connection.db_connection.oracle.options.OracleFetchOptions - -Options related to fetching data from databases via JDBC. - -#### Versionadded -Added in version 0.11.0: Replace `Oracle.JDBCOptions` → `Oracle.FetchOptions` - -### Examples - -#### NOTE -You can pass any value supported by underlying JDBC driver class, -even if it is not mentioned in this documentation. - -```python -from onetl.connection import Oracle - -options = Oracle.FetchOptions( - queryTimeout=60_000, - fetchsize=100_000, - customSparkOption="value", -) -``` - - -* **Fields:** - - [`fetchsize (int | None)`](#onetl.connection.db_connection.oracle.options.OracleFetchOptions.fetchsize) - - [`query_timeout (int | None)`](#onetl.connection.db_connection.oracle.options.OracleFetchOptions.query_timeout) - -#### *field* query_timeout *: int | None* *= None* *(alias 'queryTimeout')* - -The number of seconds the driver will wait for a statement to execute. -Zero means there is no limit. - -This option depends on driver implementation, -some drivers can check the timeout of each query instead of an entire JDBC batch. - - - -#### *field* fetchsize *: int | None* *= None* - -How many rows to fetch per round trip. - -Tuning this option can influence performance of reading. - -#### WARNING -Default value depends on driver. For example, Oracle has -default `fetchsize=10`. - - - -### *pydantic model* onetl.connection.db_connection.oracle.options.OracleExecuteOptions - -Options related to executing statements in databases via JDBC. - -#### Versionadded -Added in version 0.11.0: Replace `Oracle.JDBCOptions` → `Oracle.ExecuteOptions` - -### Examples - -#### NOTE -You can pass any value supported by underlying JDBC driver class, -even if it is not mentioned in this documentation. - -```python -from onetl.connection import Oracle - -options = Oracle.ExecuteOptions( - queryTimeout=60_000, - customSparkOption="value", -) -``` - - -* **Fields:** - - [`fetchsize (int | None)`](#onetl.connection.db_connection.oracle.options.OracleExecuteOptions.fetchsize) - - [`query_timeout (int | None)`](#onetl.connection.db_connection.oracle.options.OracleExecuteOptions.query_timeout) - -#### *field* query_timeout *: int | None* *= None* *(alias 'queryTimeout')* - -The number of seconds the driver will wait for a statement to execute. -Zero means there is no limit. - -This option depends on driver implementation, -some drivers can check the timeout of each query instead of an entire JDBC batch. - - - -#### *field* fetchsize *: int | None* *= None* - -How many rows to fetch per round trip. - -Tuning this option can influence performance of reading. - -#### WARNING -Default value depends on driver. For example, Oracle has -default `fetchsize=10`. - - diff --git a/mddocs/markdown/connection/db_connection/oracle/index.md b/mddocs/markdown/connection/db_connection/oracle/index.md deleted file mode 100644 index 39987fb24..000000000 --- a/mddocs/markdown/connection/db_connection/oracle/index.md +++ /dev/null @@ -1,19 +0,0 @@ - - -# Oracle - -# Connection - -* [Prerequisites](prerequisites.md) -* [Oracle connection](connection.md) - -# Operations - -* [Reading from Oracle using `DBReader`](read.md) -* [Reading from Oracle using `Oracle.sql`](sql.md) -* [Writing to Oracle using `DBWriter`](write.md) -* [Executing statements in Oracle](execute.md) - -# Troubleshooting - -* [Oracle <-> Spark type mapping](types.md) diff --git a/mddocs/markdown/connection/db_connection/oracle/prerequisites.md b/mddocs/markdown/connection/db_connection/oracle/prerequisites.md deleted file mode 100644 index 6dff632ce..000000000 --- a/mddocs/markdown/connection/db_connection/oracle/prerequisites.md +++ /dev/null @@ -1,110 +0,0 @@ - - -# Prerequisites - -## Version Compatibility - -* Oracle Server versions: - : * Officially declared: 19c, 21c, 23ai - * Actually tested: 11.2, 23.5 -* Spark versions: 2.3.x - 3.5.x -* Java versions: 8 - 20 - -See [official documentation](https://www.oracle.com/cis/database/technologies/appdev/jdbc-downloads.html). - -## Installing PySpark - -To use Oracle connector you should have PySpark installed (or injected to `sys.path`) -BEFORE creating the connector instance. - -See [Spark](../../../install/spark.md#install-spark) installation instruction for more details. - -## Connecting to Oracle - -### Connection port - -Connection is usually performed to port 1521. Port may differ for different Oracle instances. -Please ask your Oracle administrator to provide required information. - -### Connection host - -It is possible to connect to Oracle by using either DNS name of host or it’s IP address. - -If you’re using Oracle cluster, it is currently possible to connect only to **one specific node**. -Connecting to multiple nodes to perform load balancing, as well as automatic failover to new master/replica are not supported. - -### Connect as proxy user - -It is possible to connect to database as another user without knowing this user password. - -This can be enabled by granting user a special `CONNECT THROUGH` permission: - -```sql -ALTER USER schema_owner GRANT CONNECT THROUGH proxy_user; -``` - -Then you can connect to Oracle using credentials of `proxy_user` but specify that you need permissions of `schema_owner`: - -```python -oracle = Oracle( - ..., - user="proxy_user[schema_owner]", - password="proxy_user password", -) -``` - -See [official documentation](https://oracle-base.com/articles/misc/proxy-users-and-connect-through). - -### Required grants - -Ask your Oracle cluster administrator to set following grants for a user, -used for creating a connection: - -Read + Write (schema is owned by user) - -```sql --- allow user to log in -GRANT CREATE SESSION TO username; - --- allow creating tables in user schema -GRANT CREATE TABLE TO username; - --- allow read & write access to specific table -GRANT SELECT, INSERT ON username.mytable TO username; -``` - -Read + Write (schema is not owned by user) - -```sql --- allow user to log in -GRANT CREATE SESSION TO username; - --- allow creating tables in any schema, --- as Oracle does not support specifying exact schema name -GRANT CREATE ANY TABLE TO username; - --- allow read & write access to specific table -GRANT SELECT, INSERT ON someschema.mytable TO username; - --- only if if_exists="replace_entire_table" is used: --- allow dropping/truncating tables in any schema, --- as Oracle does not support specifying exact schema name -GRANT DROP ANY TABLE TO username; -``` - -Read only - -```sql --- allow user to log in -GRANT CREATE SESSION TO username; - --- allow read access to specific table -GRANT SELECT ON someschema.mytable TO username; -``` - -More details can be found in official documentation: -: * [GRANT](https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/GRANT.html) - * [SELECT](https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/SELECT.html) - * [CREATE TABLE](https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/SELECT.html) - * [INSERT](https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/INSERT.html) - * [TRUNCATE TABLE](https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/TRUNCATE-TABLE.html) diff --git a/mddocs/markdown/connection/db_connection/oracle/read.md b/mddocs/markdown/connection/db_connection/oracle/read.md deleted file mode 100644 index fbb28c207..000000000 --- a/mddocs/markdown/connection/db_connection/oracle/read.md +++ /dev/null @@ -1,352 +0,0 @@ - - -# Reading from Oracle using `DBReader` - -[`DBReader`](../../../db/db_reader.md#onetl.db.db_reader.db_reader.DBReader) supports [Read Strategies](../../../strategy/index.md#strategy) for incremental data reading, -but does not support custom queries, like `JOIN`. - -#### WARNING -Please take into account [Oracle <-> Spark type mapping](types.md#oracle-types) - -## Supported DBReader features - -* ✅︎ `columns` -* ✅︎ `where` -* ✅︎ `hwm`, supported strategies: -* * ✅︎ [Snapshot Strategy](../../../strategy/snapshot_strategy.md#snapshot-strategy) -* * ✅︎ [Incremental Strategy](../../../strategy/incremental_strategy.md#incremental-strategy) -* * ✅︎ [Snapshot Batch Strategy](../../../strategy/snapshot_batch_strategy.md#snapshot-batch-strategy) -* * ✅︎ [Incremental Batch Strategy](../../../strategy/incremental_batch_strategy.md#incremental-batch-strategy) -* ✅︎ `hint` (see [official documentation](https://docs.oracle.com/cd/B10500_01/server.920/a96533/hintsref.htm)) -* ❌ `df_schema` -* ✅︎ `options` (see [`Oracle.ReadOptions`](#onetl.connection.db_connection.oracle.options.OracleReadOptions)) - -## Examples - -Snapshot strategy: - -```python -from onetl.connection import Oracle -from onetl.db import DBReader - -oracle = Oracle(...) - -reader = DBReader( - connection=oracle, - source="schema.table", - columns=["id", "key", "CAST(value AS VARCHAR2(4000)) value", "updated_dt"], - where="key = 'something'", - hint="INDEX(schema.table key_index)", - options=Oracle.ReadOptions(partitionColumn="id", numPartitions=10), -) -df = reader.run() -``` - -Incremental strategy: - -```python -from onetl.connection import Oracle -from onetl.db import DBReader -from onetl.strategy import IncrementalStrategy - -oracle = Oracle(...) - -reader = DBReader( - connection=oracle, - source="schema.table", - columns=["id", "key", "CAST(value AS VARCHAR2(4000)) value", "updated_dt"], - where="key = 'something'", - hint="INDEX(schema.table key_index)", - hwm=DBReader.AutoDetectHWM(name="oracle_hwm", expression="updated_dt"), - options=Oracle.ReadOptions(partitionColumn="id", numPartitions=10), -) - -with IncrementalStrategy(): - df = reader.run() -``` - -## Recommendations - -### Select only required columns - -Instead of passing `"*"` in `DBReader(columns=[...])` prefer passing exact column names. This reduces the amount of data passed from Oracle to Spark. - -### Pay attention to `where` value - -Instead of filtering data on Spark side using `df.filter(df.column == 'value')` pass proper `DBReader(where="column = 'value'")` clause. -This both reduces the amount of data send from Oracle to Spark, and may also improve performance of the query. -Especially if there are indexes or partitions for columns used in `where` clause. - -## Options - -### *pydantic model* onetl.connection.db_connection.oracle.options.OracleReadOptions - -Spark JDBC reading options. - -#### Versionadded -Added in version 0.5.0: Replace `Oracle.Options` → `Oracle.ReadOptions` - -### Examples - -#### NOTE -You can pass any value -[supported by Spark](https://spark.apache.org/docs/latest/sql-data-sources-jdbc.html), -even if it is not mentioned in this documentation. **Option names should be in** `camelCase`! - -The set of supported options depends on Spark version. - -```python -from onetl.connection import Oracle - -options = Oracle.ReadOptions( - partitioning_mode="range", - partitionColumn="reg_id", - numPartitions=10, - customSparkOption="value", -) -``` - - -* **Fields:** - - [`fetchsize (int)`](#onetl.connection.db_connection.oracle.options.OracleReadOptions.fetchsize) - - [`lower_bound (int | None)`](#onetl.connection.db_connection.oracle.options.OracleReadOptions.lower_bound) - - [`num_partitions (pydantic.types.PositiveInt)`](#onetl.connection.db_connection.oracle.options.OracleReadOptions.num_partitions) - - [`partition_column (str | None)`](#onetl.connection.db_connection.oracle.options.OracleReadOptions.partition_column) - - [`partitioning_mode (onetl.connection.db_connection.jdbc_connection.options.JDBCPartitioningMode)`](#onetl.connection.db_connection.oracle.options.OracleReadOptions.partitioning_mode) - - [`query_timeout (int | None)`](#onetl.connection.db_connection.oracle.options.OracleReadOptions.query_timeout) - - [`session_init_statement (str | None)`](#onetl.connection.db_connection.oracle.options.OracleReadOptions.session_init_statement) - - [`upper_bound (int | None)`](#onetl.connection.db_connection.oracle.options.OracleReadOptions.upper_bound) - -#### *field* partition_column *: str | None* *= None* *(alias 'partitionColumn')* - -Column used to parallelize reading from a table. - -#### WARNING -It is highly recommended to use primary key, or column with an index -to avoid performance issues. - -#### NOTE -Column type depends on [`partitioning_mode`](#onetl.connection.db_connection.oracle.options.OracleReadOptions.partitioning_mode). - -* `partitioning_mode="range"` requires column to be an integer, date or timestamp (can be NULL, but not recommended). -* `partitioning_mode="hash"` accepts any column type (NOT NULL). -* `partitioning_mode="mod"` requires column to be an integer (NOT NULL). - -See documentation for [`partitioning_mode`](#onetl.connection.db_connection.oracle.options.OracleReadOptions.partitioning_mode) for more details - - - -#### *field* num_partitions *: PositiveInt* *= 1* *(alias 'numPartitions')* - -Number of jobs created by Spark to read the table content in parallel. -See documentation for [`partitioning_mode`](#onetl.connection.db_connection.oracle.options.OracleReadOptions.partitioning_mode) for more details - - -* **Constraints:** - - **exclusiveMinimum** = 0 - -#### *field* lower_bound *: int | None* *= None* *(alias 'lowerBound')* - -See documentation for [`partitioning_mode`](#onetl.connection.db_connection.oracle.options.OracleReadOptions.partitioning_mode) for more details - - - -#### *field* upper_bound *: int | None* *= None* *(alias 'upperBound')* - -See documentation for [`partitioning_mode`](#onetl.connection.db_connection.oracle.options.OracleReadOptions.partitioning_mode) for more details - - - -#### *field* session_init_statement *: str | None* *= None* *(alias 'sessionInitStatement')* - -After each database session is opened to the remote DB and before starting to read data, -this option executes a custom SQL statement (or a PL/SQL block). - -Use this to implement session initialization code. - -Example: - -```python -sessionInitStatement = """ - BEGIN - execute immediate - 'alter session set "_serial_direct_read"=true'; - END; -""" -``` - - - -#### *field* query_timeout *: int | None* *= None* *(alias 'queryTimeout')* - -The number of seconds the driver will wait for a statement to execute. -Zero means there is no limit. - -This option depends on driver implementation, -some drivers can check the timeout of each query instead of an entire JDBC batch. - - - -#### *field* fetchsize *: int* *= 100000* - -Fetch N rows from an opened cursor per one read round. - -Tuning this option can influence performance of reading. - -#### WARNING -Default value is different from Spark. - -Spark uses driver’s own value, and it may be different in different drivers, -and even versions of the same driver. For example, Oracle has -default `fetchsize=10`, which is absolutely not usable. - -Thus we’ve overridden default value with `100_000`, which should increase reading performance. - -#### Versionchanged -Changed in version 0.2.0: Set explicit default value to `100_000` - - - -#### *field* partitioning_mode *: JDBCPartitioningMode* *= JDBCPartitioningMode.RANGE* - -Defines how Spark will parallelize reading from table. - -Possible values: - -* `range` (default) - : Allocate each executor a range of values from column passed into [`partition_column`](#onetl.connection.db_connection.oracle.options.OracleReadOptions.partition_column). -
- ### Spark generates for each executor an SQL query -
- Executor 1: - ```sql - SELECT ... FROM table - WHERE (partition_column >= lowerBound - OR partition_column IS NULL) - AND partition_column < (lower_bound + stride) - ``` -
- Executor 2: - ```sql - SELECT ... FROM table - WHERE partition_column >= (lower_bound + stride) - AND partition_column < (lower_bound + 2 * stride) - ``` -
- … -
- Executor N: - ```sql - SELECT ... FROM table - WHERE partition_column >= (lower_bound + (N-1) * stride) - AND partition_column <= upper_bound - ``` -
- Where `stride=(upper_bound - lower_bound) / num_partitions`. -
- #### NOTE - Can be used only with columns of integer, date or timestamp types. -
- #### NOTE - [`lower_bound`](#onetl.connection.db_connection.oracle.options.OracleReadOptions.lower_bound), [`upper_bound`](#onetl.connection.db_connection.oracle.options.OracleReadOptions.upper_bound) and [`num_partitions`](#onetl.connection.db_connection.oracle.options.OracleReadOptions.num_partitions) are used just to - calculate the partition stride, **NOT** for filtering the rows in table. - So all rows in the table will be returned (unlike *Incremental* [Read Strategies](../../../strategy/index.md#strategy)). -
- #### NOTE - All queries are executed in parallel. To execute them sequentially, use *Batch* [Read Strategies](../../../strategy/index.md#strategy). -* `hash` - : Allocate each executor a set of values based on hash of the [`partition_column`](#onetl.connection.db_connection.oracle.options.OracleReadOptions.partition_column) column. -
- ### Spark generates for each executor an SQL query -
- Executor 1: - ```sql - SELECT ... FROM table - WHERE (some_hash(partition_column) mod num_partitions) = 0 -- lower_bound - ``` -
- Executor 2: - ```sql - SELECT ... FROM table - WHERE (some_hash(partition_column) mod num_partitions) = 1 -- lower_bound + 1 - ``` -
- … -
- Executor N: - ```sql - SELECT ... FROM table - WHERE (some_hash(partition_column) mod num_partitions) = num_partitions-1 -- upper_bound - ``` -
- #### NOTE - The hash function implementation depends on RDBMS. It can be `MD5` or any other fast hash function, - or expression based on this function call. Usually such functions accepts any column type as an input. -* `mod` - : Allocate each executor a set of values based on modulus of the [`partition_column`](#onetl.connection.db_connection.oracle.options.OracleReadOptions.partition_column) column. -
- ### Spark generates for each executor an SQL query -
- Executor 1: - ```sql - SELECT ... FROM table - WHERE (partition_column mod num_partitions) = 0 -- lower_bound - ``` -
- Executor 2: - ```sql - SELECT ... FROM table - WHERE (partition_column mod num_partitions) = 1 -- lower_bound + 1 - ``` -
- Executor N: - ```sql - SELECT ... FROM table - WHERE (partition_column mod num_partitions) = num_partitions-1 -- upper_bound - ``` -
- #### NOTE - Can be used only with columns of integer type. - -#### Versionadded -Added in version 0.5.0. - -### Examples - -Read data in 10 parallel jobs by range of values in `id_column` column: - -```python -ReadOptions( - partitioning_mode="range", # default mode, can be omitted - partitionColumn="id_column", - numPartitions=10, - # Options below can be discarded because they are - # calculated automatically as MIN and MAX values of `partitionColumn` - lowerBound=0, - upperBound=100_000, -) -``` - -Read data in 10 parallel jobs by hash of values in `some_column` column: - -```python -ReadOptions( - partitioning_mode="hash", - partitionColumn="some_column", - numPartitions=10, - # lowerBound and upperBound are automatically set to `0` and `9` -) -``` - -Read data in 10 parallel jobs by modulus of values in `id_column` column: - -```python -ReadOptions( - partitioning_mode="mod", - partitionColumn="id_column", - numPartitions=10, - # lowerBound and upperBound are automatically set to `0` and `9` -) -``` - - diff --git a/mddocs/markdown/connection/db_connection/oracle/sql.md b/mddocs/markdown/connection/db_connection/oracle/sql.md deleted file mode 100644 index 09059e7aa..000000000 --- a/mddocs/markdown/connection/db_connection/oracle/sql.md +++ /dev/null @@ -1,192 +0,0 @@ - - -# Reading from Oracle using `Oracle.sql` - -`Oracle.sql` allows passing custom SQL query, but does not support incremental strategies. - -#### WARNING -Please take into account [Oracle <-> Spark type mapping](types.md#oracle-types) - -#### WARNING -Statement is executed in **read-write** connection, so if you’re calling some functions/procedures with DDL/DML statements inside, -they can change data in your database. - -## Syntax support - -Only queries with the following syntax are supported: - -* ✅︎ `SELECT ... FROM ...` -* ✅︎ `WITH alias AS (...) SELECT ...` -* ❌ `SHOW ...` -* ❌ `SET ...; SELECT ...;` - multiple statements not supported - -## Examples - -```python -from onetl.connection import Oracle - -oracle = Oracle(...) -df = oracle.sql( - """ - SELECT - id, - key, - CAST(value AS VARCHAR2(4000)) value, - updated_at - FROM - some.mytable - WHERE - key = 'something' - """, - options=Oracle.SQLOptions( - partitionColumn="id", - numPartitions=10, - lowerBound=0, - upperBound=1000, - ), -) -``` - -## Recommendations - -### Select only required columns - -Instead of passing `SELECT * FROM ...` prefer passing exact column names `SELECT col1, col2, ...`. -This reduces the amount of data passed from Oracle to Spark. - -### Pay attention to `where` value - -Instead of filtering data on Spark side using `df.filter(df.column == 'value')` pass proper `WHERE column = 'value'` clause. -This both reduces the amount of data send from Oracle to Spark, and may also improve performance of the query. -Especially if there are indexes or partitions for columns used in `where` clause. - -## Options - -### *pydantic model* onetl.connection.db_connection.oracle.options.OracleSQLOptions - -Options specifically for SQL queries - -These options allow you to specify configurations for executing SQL queries -without relying on Spark’s partitioning mechanisms. - -#### Versionadded -Added in version 0.11.0: Split up `Oracle.ReadOptions` to `Oracle.SQLOptions` - -### Examples - -#### NOTE -You can pass any JDBC configuration -[supported by Spark](https://spark.apache.org/docs/latest/sql-data-sources-jdbc.html), -tailored to optimize SQL query execution. **Option names should be in** `camelCase`! - -```python -from onetl.connection import Oracle - -options = Oracle.SQLOptions( - partitionColumn="reg_id", - numPartitions=10, - lowerBound=0, - upperBound=1000, - customSparkOption="value", -) -``` - - - -#### *field* partition_column *: str | None* *= None* *(alias 'partitionColumn')* - -Column used to partition data across multiple executors for parallel query processing. - -#### WARNING -It is highly recommended to use primary key, or column with an index -to avoid performance issues. - -### Example of using `partitionColumn="id"` with `partitioning_mode="range"` - -```sql --- If partition_column is 'id', with numPartitions=4, lowerBound=1, and upperBound=100: --- Executor 1 processes IDs from 1 to 25 -SELECT ... FROM table WHERE id >= 1 AND id < 26 --- Executor 2 processes IDs from 26 to 50 -SELECT ... FROM table WHERE id >= 26 AND id < 51 --- Executor 3 processes IDs from 51 to 75 -SELECT ... FROM table WHERE id >= 51 AND id < 76 --- Executor 4 processes IDs from 76 to 100 -SELECT ... FROM table WHERE id >= 76 AND id <= 100 - --- General case for Executor N -SELECT ... FROM table -WHERE partition_column >= (lowerBound + (N-1) * stride) -AND partition_column <= upperBound --- Where ``stride`` is calculated as ``(upperBound - lowerBound) / numPartitions``. -``` - - - -#### *field* num_partitions *: int | None* *= None* *(alias 'numPartitions')* - -Number of jobs created by Spark to read the table content in parallel. - - - -#### *field* lower_bound *: int | None* *= None* *(alias 'lowerBound')* - -Defines the starting boundary for partitioning the query’s data. Mandatory if [`partition_column`](#onetl.connection.db_connection.oracle.options.OracleSQLOptions.partition_column) is set - - - -#### *field* upper_bound *: int | None* *= None* *(alias 'upperBound')* - -Sets the ending boundary for data partitioning. Mandatory if [`partition_column`](#onetl.connection.db_connection.oracle.options.OracleSQLOptions.partition_column) is set - - - -#### *field* session_init_statement *: str | None* *= None* *(alias 'sessionInitStatement')* - -After each database session is opened to the remote DB and before starting to read data, -this option executes a custom SQL statement (or a PL/SQL block). - -Use this to implement session initialization code. - -Example: - -```python -sessionInitStatement = """ - BEGIN - execute immediate - 'alter session set "_serial_direct_read"=true'; - END; -""" -``` - - - -#### *field* query_timeout *: int | None* *= None* *(alias 'queryTimeout')* - -The number of seconds the driver will wait for a statement to execute. -Zero means there is no limit. - -This option depends on driver implementation, -some drivers can check the timeout of each query instead of an entire JDBC batch. - - - -#### *field* fetchsize *: int* *= 100000* - -Fetch N rows from an opened cursor per one read round. - -Tuning this option can influence performance of reading. - -#### WARNING -Default value is different from Spark. - -Spark uses driver’s own value, and it may be different in different drivers, -and even versions of the same driver. For example, Oracle has -default `fetchsize=10`, which is absolutely not usable. - -Thus we’ve overridden default value with `100_000`, which should increase reading performance. - -#### Versionchanged -Changed in version 0.2.0: Set explicit default value to `100_000` - - diff --git a/mddocs/markdown/connection/db_connection/oracle/types.md b/mddocs/markdown/connection/db_connection/oracle/types.md deleted file mode 100644 index 130c57855..000000000 --- a/mddocs/markdown/connection/db_connection/oracle/types.md +++ /dev/null @@ -1,303 +0,0 @@ - - -# Oracle <-> Spark type mapping - -#### NOTE -The results below are valid for Spark 3.5.5, and may differ on other Spark versions. - -## Type detection & casting - -Spark’s DataFrames always have a `schema` which is a list of columns with corresponding Spark types. All operations on a column are performed using column type. - -### Reading from Oracle - -This is how Oracle connector performs this: - -* For each column in query result (`SELECT column1, column2, ... FROM table ...`) get column name and Oracle type. -* Find corresponding `Oracle type (read)` → `Spark type` combination (see below) for each DataFrame column. If no combination is found, raise exception. -* Create DataFrame from query with specific column names and Spark types. - -### Writing to some existing Oracle table - -This is how Oracle connector performs this: - -* Get names of columns in DataFrame. [1](#id3) -* Perform `SELECT * FROM table LIMIT 0` query. -* Take only columns present in DataFrame (by name, case insensitive). For each found column get Clickhouse type. -* **Find corresponding** `Oracle type (read)` → `Spark type` **combination** (see below) for each DataFrame column. If no combination is found, raise exception. [2](#id4) -* Find corresponding `Spark type` → `Oracle type (write)` combination (see below) for each DataFrame column. If no combination is found, raise exception. -* If `Oracle type (write)` match `Oracle type (read)`, no additional casts will be performed, DataFrame column will be written to Oracle as is. -* If `Oracle type (write)` does not match `Oracle type (read)`, DataFrame column will be casted to target column type **on Oracle side**. - For example, you can write column with text data to `int` column, if column contains valid integer values within supported value range and precision. - -* **[1]** This allows to write data to tables with `DEFAULT` and `GENERATED` columns - if DataFrame has no such column, it will be populated by Oracle. -* **[2]** Yes, this is weird. - -### Create new table using Spark - -#### WARNING -ABSOLUTELY NOT RECOMMENDED! - -This is how Oracle connector performs this: - -* Find corresponding `Spark type` → `Oracle type (create)` combination (see below) for each DataFrame column. If no combination is found, raise exception. -* Generate DDL for creating table in Oracle, like `CREATE TABLE (col1 ...)`, and run it. -* Write DataFrame to created table as is. - -But Oracle connector support only limited number of types and almost no custom clauses (like `PARTITION BY`, `INDEX`, etc). -So instead of relying on Spark to create tables: - -### See example - -```python -writer = DBWriter( - connection=oracle, - target="public.table", - options=Oracle.WriteOptions(if_exists="append"), -) -writer.run(df) -``` - -Always prefer creating table with desired DDL **BEFORE WRITING DATA**: - -### See example - -```python -oracle.execute( - """ - CREATE TABLE username.table ( - id NUMBER, - business_dt TIMESTAMP(6), - value VARCHAR2(2000) - ) - """, -) - -writer = DBWriter( - connection=oracle, - target="public.table", - options=Oracle.WriteOptions(if_exists="append"), -) -writer.run(df) -``` - -See Oracle [CREATE TABLE](https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/SELECT.html) documentation. - -## Supported types - -### References - -See [List of Oracle types](https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/Data-Types.html). - -Here you can find source code with type conversions: - -* [JDBC -> Spark](https://github.com/apache/spark/blob/v3.5.5/sql/core/src/main/scala/org/apache/spark/sql/jdbc/OracleDialect.scala#L83-L109) -* [Spark -> JDBC](https://github.com/apache/spark/blob/v3.5.5/sql/core/src/main/scala/org/apache/spark/sql/jdbc/OracleDialect.scala#L111-L123) - -### Numeric types - -| Oracle type (read) | Spark type | Oracle type (write) | Oracle type (create) | -|-----------------------------|----------------------------------|----------------------------|-------------------------| -| `NUMBER` | `DecimalType(P=38, S=10)` | `NUMBER(P=38, S=10)` | `NUMBER(P=38, S=10)` | -| `NUMBER(P=0..38)` | `DecimalType(P=0..38, S=0)` | `NUMBER(P=0..38, S=0)` | `NUMBER(P=38, S=0)` | -| `NUMBER(P=0..38, S=0..38)` | `DecimalType(P=0..38, S=0..38)` | `NUMBER(P=0..38, S=0..38)` | `NUMBER(P=38, S=0..38)` | -| `NUMBER(P=..., S=-127..-1)` | unsupported [3](#id6) | | | -| `FLOAT` | `DecimalType(P=38, S=10)` | `NUMBER(P=38, S=10)` | `NUMBER(P=38, S=10)` | -| `FLOAT(N)` | | | | -| `REAL` | | | | -| `DOUBLE PRECISION` | | | | -| `BINARY_FLOAT` | `FloatType()` | `NUMBER(P=19, S=4)` | `NUMBER(P=19, S=4)` | -| `BINARY_DOUBLE` | `DoubleType()` | | | -| `SMALLINT` | `DecimalType(P=38, S=0)` | `NUMBER(P=38, S=0)` | `NUMBER(P=38, S=0)` | -| `INTEGER` | | | | -| `LONG` | `StringType()` | `CLOB` | `CLOB` | -* **[3]** Oracle support decimal types with negative scale, like `NUMBER(38, -10)`. Spark doesn’t. - -### Temporal types - -| Oracle type (read) | Spark type | Oracle type (write) | Oracle type (create) | -|-------------------------------------|------------------------------------------------------------------------------|------------------------------------------------------|------------------------------------------------------| -| `DATE`, days | `TimestampType()`, microseconds | `TIMESTAMP(6)`, microseconds | `TIMESTAMP(6)`, microseconds | -| `TIMESTAMP`, microseconds | `TimestampType()`, microseconds | `TIMESTAMP(6)`, microseconds | `TIMESTAMP(6)`, microseconds | -| `TIMESTAMP(0)`, seconds | | | | -| `TIMESTAMP(3)`, milliseconds | | | | -| `TIMESTAMP(6)`, microseconds | | | | -| `TIMESTAMP(9)`, nanoseconds | `TimestampType()`, microseconds,
**precision loss** [4](#id8) | `TIMESTAMP(6)`, microseconds,
**precision loss** | `TIMESTAMP(6)`, microseconds,
**precision loss** | -| `TIMESTAMP WITH TIME ZONE` | unsupported | | | -| `TIMESTAMP(N) WITH TIME ZONE` | | | | -| `TIMESTAMP WITH LOCAL TIME ZONE` | | | | -| `TIMESTAMP(N) WITH LOCAL TIME ZONE` | | | | -| `INTERVAL YEAR TO MONTH` | | | | -| `INTERVAL DAY TO SECOND` | | | | - -#### WARNING -Note that types in Oracle and Spark have different value ranges: - -| Oracle type | Min value | Max value | Spark type | Min value | Max value | -|---------------|----------------------------------|---------------------------------|-------------------|------------------------------|------------------------------| -| `date` | `-4712-01-01` | `9999-01-01` | `DateType()` | `0001-01-01` | `9999-12-31` | -| `timestamp` | `-4712-01-01 00:00:00.000000000` | `9999-12-31 23:59:59.999999999` | `TimestampType()` | `0001-01-01 00:00:00.000000` | `9999-12-31 23:59:59.999999` | - -So not all of values can be read from Oracle to Spark. - -References: -: * [Oracle date, timestamp and intervals documentation](https://oracle-base.com/articles/misc/oracle-dates-timestamps-and-intervals#DATE) - * [Spark DateType documentation](https://spark.apache.org/docs/latest/api/java/org/apache/spark/sql/types/DateType.html) - * [Spark TimestampType documentation](https://spark.apache.org/docs/latest/api/java/org/apache/spark/sql/types/TimestampType.html) - -* **[4]** Oracle support timestamp up to nanoseconds precision (`23:59:59.999999999`), but Spark `TimestampType()` supports datetime up to microseconds precision (`23:59:59.999999`). Nanoseconds will be lost during read or write operations. - -### String types - -| Oracle type (read) | Spark type | Oracle type (write) | Oracle type (create) | -|----------------------|----------------|-----------------------|------------------------| -| `CHAR` | `StringType()` | `CLOB` | `CLOB` | -| `CHAR(N CHAR)` | | | | -| `CHAR(N BYTE)` | | | | -| `NCHAR` | | | | -| `NCHAR(N)` | | | | -| `VARCHAR(N)` | | | | -| `LONG VARCHAR` | | | | -| `VARCHAR2(N CHAR)` | | | | -| `VARCHAR2(N BYTE)` | | | | -| `NVARCHAR2(N)` | | | | -| `CLOB` | | | | -| `NCLOB` | | | | - -### Binary types - -| Oracle type (read) | Spark type | Oracle type (write) | Oracle type (create) | -|----------------------|----------------|-----------------------|------------------------| -| `RAW(N)` | `BinaryType()` | `BLOB` | `BLOB` | -| `LONG RAW` | | | | -| `BLOB` | | | | -| `BFILE` | unsupported | | | - -### Struct types - -| Oracle type (read) | Spark type | Oracle type (write) | Oracle type (create) | -|-----------------------------------|----------------|-----------------------|------------------------| -| `XMLType` | `StringType()` | `CLOB` | `CLOB` | -| `URIType` | | | | -| `DBURIType` | | | | -| `XDBURIType` | | | | -| `HTTPURIType` | | | | -| `CREATE TYPE ... AS OBJECT (...)` | | | | -| `JSON` | unsupported | | | -| `CREATE TYPE ... AS VARRAY ...` | | | | -| `CREATE TYPE ... AS TABLE OF ...` | | | | - -### Special types - -| Oracle type (read) | Spark type | Oracle type (write) | Oracle type (create) | -|----------------------|-----------------|-----------------------|------------------------| -| `BOOLEAN` | `BooleanType()` | `BOOLEAN` | `NUMBER(P=1, S=0)` | -| `ROWID` | `StringType()` | `CLOB` | `CLOB` | -| `UROWID` | | | | -| `UROWID(N)` | | | | -| `ANYTYPE` | unsupported | | | -| `ANYDATA` | | | | -| `ANYDATASET` | | | | - -## Explicit type cast - -### `DBReader` - -It is possible to explicitly cast column of unsupported type using `DBReader(columns=...)` syntax. - -For example, you can use `CAST(column AS CLOB)` to convert data to string representation on Oracle side, and so it will be read as Spark’s `StringType()`. - -It is also possible to use [JSON_ARRAY](https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/JSON_ARRAY.html) -or [JSON_OBJECT](https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/JSON_OBJECT.html) Oracle functions -to convert column of any type to string representation. Then this JSON string can then be effectively parsed using the [`JSON.parse_column`](../../../file_df/file_formats/json.md#onetl.file.format.json.JSON.parse_column) method. - -```python -from onetl.file.format import JSON -from pyspark.sql.types import IntegerType, StructType, StructField - -from onetl.connection import Oracle -from onetl.db import DBReader - -oracle = Oracle(...) - -DBReader( - connection=oracle, - columns=[ - "id", - "supported_column", - "CAST(unsupported_column AS VARCHAR2(4000)) unsupported_column_str", - # or - "JSON_ARRAY(array_column) array_column_json", - ], -) -df = reader.run() - -json_scheme = StructType([StructField("key", IntegerType())]) - -df = df.select( - df.id, - df.supported_column, - df.unsupported_column_str.cast("integer").alias("parsed_integer"), - JSON().parse_column("array_column_json", json_scheme).alias("array_column"), -) -``` - -### `DBWriter` - -It is always possible to convert data on Spark side to string, and then write it to text column in Oracle table. - -To serialize and write JSON data to a `text` or `json` column in an Oracle table use the [`JSON.serialize_column`](../../../file_df/file_formats/json.md#onetl.file.format.json.JSON.serialize_column) method. - -```python -from onetl.connection import Oracle -from onetl.db import DBWriter -from onetl.file.format import JSON - -oracle = Oracle(...) - -oracle.execute( - """ - CREATE TABLE schema.target_table ( - id INTEGER, - supported_column TIMESTAMP, - array_column_json VARCHAR2(4000) -- any string type, actually - ) - """, -) - -write_df = df.select( - df.id, - df.supported_column, - JSON().serialize_column(df.unsupported_column).alias("array_column_json"), -) - -writer = DBWriter( - connection=oracle, - target="schema.target_table", -) -writer.run(write_df) -``` - -Then you can parse this column on Oracle side - for example, by creating a view: - -```sql -SELECT - id, - supported_column, - JSON_VALUE(array_column_json, '$[0]' RETURNING NUMBER) AS array_item_0 -FROM - schema.target_table -``` - -Or by using [VIRTUAL column](https://oracle-base.com/articles/11g/virtual-columns-11gr1): - -```sql -CREATE TABLE schema.target_table ( - id INTEGER, - supported_column TIMESTAMP, - array_column_json VARCHAR2(4000), -- any string type, actually - array_item_0 GENERATED ALWAYS AS (JSON_VALUE(array_column_json, '$[0]' RETURNING NUMBER)) VIRTUAL -) -``` - -But data will be parsed on each table read in any case, as Oracle does no support `GENERATED ALWAYS AS (...) STORED` columns. diff --git a/mddocs/markdown/connection/db_connection/oracle/write.md b/mddocs/markdown/connection/db_connection/oracle/write.md deleted file mode 100644 index f1be20d24..000000000 --- a/mddocs/markdown/connection/db_connection/oracle/write.md +++ /dev/null @@ -1,183 +0,0 @@ - - -# Writing to Oracle using `DBWriter` - -For writing data to Oracle, use [`DBWriter`](../../../db/db_writer.md#onetl.db.db_writer.db_writer.DBWriter). - -#### WARNING -Please take into account [Oracle <-> Spark type mapping](types.md#oracle-types) - -#### WARNING -It is always recommended to create table explicitly using [Oracle.execute](execute.md#oracle-execute) -instead of relying on Spark’s table DDL generation. - -This is because Spark’s DDL generator can create columns with different precision and types than it is expected, -causing precision loss or other issues. - -## Examples - -```python -from onetl.connection import Oracle -from onetl.db import DBWriter - -oracle = Oracle(...) - -df = ... # data is here - -writer = DBWriter( - connection=oracle, - target="schema.table", - options=Oracle.WriteOptions(if_exists="append"), -) - -writer.run(df) -``` - -## Options - -Method above accepts [`OracleWriteOptions`](#onetl.connection.db_connection.oracle.options.OracleWriteOptions) - -### *pydantic model* onetl.connection.db_connection.oracle.options.OracleWriteOptions - -Spark JDBC writing options. - -#### Versionadded -Added in version 0.5.0: Replace `Oracle.Options` → `Oracle.WriteOptions` - -### Examples - -#### NOTE -You can pass any value -[supported by Spark](https://spark.apache.org/docs/latest/sql-data-sources-jdbc.html), -even if it is not mentioned in this documentation. **Option names should be in** `camelCase`! - -The set of supported options depends on Spark version. - -```python -from onetl.connection import Oracle - -options = Oracle.WriteOptions( - if_exists="append", - batchsize=20_000, - customSparkOption="value", -) -``` - - - -#### *field* if_exists *: JDBCTableExistBehavior* *= JDBCTableExistBehavior.APPEND* *(alias 'mode')* - -Behavior of writing data into existing table. - -Possible values: -: * `append` (default) - : Adds new rows into existing table. -
- ### Behavior in details -
- * Table does not exist - : Table is created using options provided by user - (`createTableOptions`, `createTableColumnTypes`, etc). - * Table exists - : Data is appended to a table. Table has the same DDL as before writing data -
- #### WARNING - This mode does not check whether table already contains - rows from dataframe, so duplicated rows can be created. -
- Also Spark does not support passing custom options to - insert statement, like `ON CONFLICT`, so don’t try to - implement deduplication using unique indexes or constraints. -
- Instead, write to staging table and perform deduplication - using `execute` method. - * `replace_entire_table` - : **Table is dropped and then created, or truncated**. -
- ### Behavior in details -
- * Table does not exist - : Table is created using options provided by user - (`createTableOptions`, `createTableColumnTypes`, etc). - * Table exists - : Table content is replaced with dataframe content. -
- After writing completed, target table could either have the same DDL as - before writing data (`truncate=True`), or can be recreated (`truncate=False` - or source does not support truncation). - * `ignore` - : Ignores the write operation if the table already exists. -
- ### Behavior in details -
- * Table does not exist - : Table is created using options provided by user - (`createTableOptions`, `createTableColumnTypes`, etc). - * Table exists - : The write operation is ignored, and no data is written to the table. - * `error` - : Raises an error if the table already exists. -
- ### Behavior in details -
- * Table does not exist - : Table is created using options provided by user - (`createTableOptions`, `createTableColumnTypes`, etc). - * Table exists - : An error is raised, and no data is written to the table. - -#### Versionchanged -Changed in version 0.9.0: Renamed `mode` → `if_exists` - - - -#### *field* query_timeout *: int | None* *= None* *(alias 'queryTimeout')* - -The number of seconds the driver will wait for a statement to execute. -Zero means there is no limit. - -This option depends on driver implementation, -some drivers can check the timeout of each query instead of an entire JDBC batch. - - - -#### *field* batchsize *: int* *= 20000* - -How many rows can be inserted per round trip. - -Tuning this option can influence performance of writing. - -#### WARNING -Default value is different from Spark. - -Spark uses quite small value `1000`, which is absolutely not usable -in BigData world. - -Thus we’ve overridden default value with `20_000`, -which should increase writing performance. - -You can increase it even more, up to `50_000`, -but it depends on your database load and number of columns in the row. -Higher values does not increase performance. - -#### Versionchanged -Changed in version 0.4.0: Changed default value from 1000 to 20_000 - - - -#### *field* isolation_level *: str* *= 'READ_UNCOMMITTED'* *(alias 'isolationLevel')* - -The transaction isolation level, which applies to current connection. - -Possible values: -: * `NONE` (as string, not Python’s `None`) - * `READ_COMMITTED` - * `READ_UNCOMMITTED` - * `REPEATABLE_READ` - * `SERIALIZABLE` - -Values correspond to transaction isolation levels defined by JDBC standard. -Please refer the documentation for -[java.sql.Connection](https://docs.oracle.com/javase/8/docs/api/java/sql/Connection.html). - - diff --git a/mddocs/markdown/connection/db_connection/postgres/connection.md b/mddocs/markdown/connection/db_connection/postgres/connection.md deleted file mode 100644 index 0ca99169c..000000000 --- a/mddocs/markdown/connection/db_connection/postgres/connection.md +++ /dev/null @@ -1,133 +0,0 @@ - - -# Postgres connection - -### *class* onetl.connection.db_connection.postgres.connection.Postgres(\*, spark: SparkSession, user: str, password: SecretStr, host: Host, database: str, port: int = 5432, extra: PostgresExtra = PostgresExtra(stringtype='unspecified', tcpKeepAlive='true')) - -PostgreSQL JDBC connection. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -Based on Maven package [org.postgresql:postgresql:42.7.5](https://mvnrepository.com/artifact/org.postgresql/postgresql/42.7.5) -([official Postgres JDBC driver](https://jdbc.postgresql.org/)). - -#### SEE ALSO -Before using this connector please take into account [Prerequisites](prerequisites.md#postgres-prerequisites) - -#### Versionadded -Added in version 0.1.0. - -* **Parameters:** - **host** - : Host of Postgres database. For example: `test.postgres.domain.com` or `193.168.1.11` - - **port** - : Port of Postgres database - - **user** - : User, which have proper access to the database. For example: `some_user` - - **password** - : Password for database connection - - **database** - : Database in RDBMS, NOT schema. -
- See [this page](https://www.educba.com/postgresql-database-vs-schema/) for more details - - **spark** - : Spark session. - - **extra** - : Specifies one or more extra parameters by which clients can connect to the instance. -
- For example: `{"ssl": "false"}` -
- See [Postgres JDBC driver properties documentation](https://jdbc.postgresql.org/documentation/use/) - for more details - -### Examples - -Create and check Postgres connection: - -```python -from onetl.connection import Postgres -from pyspark.sql import SparkSession - -# Create Spark session with Postgres driver loaded -maven_packages = Postgres.get_packages() -spark = ( - SparkSession.builder.appName("spark-app-name") - .config("spark.jars.packages", ",".join(maven_packages)) - .getOrCreate() -) - -# Create connection -postgres = Postgres( - host="database.host.or.ip", - user="user", - password="*****", - database="target_database", - spark=spark, -) -``` - -Create read-only connection: - -```python -... - -# Create connection -postgres = Postgres( - host="database.host.or.ip", - user="user", - password="*****", - database="target_database", - extra={"readOnly": True, "readOnlyMode": "always"}, # <-- - spark=spark, -).check() -``` - - - -#### check() - -Check source availability. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -If not, an exception will be raised. - -* **Returns:** - Connection itself -* **Raises:** - RuntimeError - : If the connection is not available - -### Examples - -```python -connection.check() -``` - - - -#### *classmethod* get_packages(package_version: str | None = None) → list[str] - -Get package names to be downloaded by Spark. Allows specifying a custom JDBC driver version. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### Versionadded -Added in version 0.9.0. - -* **Parameters:** - **package_version** - : Specifies the version of the PostgreSQL JDBC driver to use. Defaults to `42.7.5`. - -### Examples - -```python -from onetl.connection import Postgres - -Postgres.get_packages() - -# custom package version -Postgres.get_packages(package_version="42.6.0") -``` - - diff --git a/mddocs/markdown/connection/db_connection/postgres/execute.md b/mddocs/markdown/connection/db_connection/postgres/execute.md deleted file mode 100644 index e28ebc5da..000000000 --- a/mddocs/markdown/connection/db_connection/postgres/execute.md +++ /dev/null @@ -1,189 +0,0 @@ - - -# Executing statements in Postgres - -#### WARNING -Methods below **read all the rows** returned from DB **to Spark driver memory**, and then convert them to DataFrame. - -Do **NOT** use them to read large amounts of data. Use [DBReader](read.md#postgres-read) or [Postgres.sql](sql.md#postgres-sql) instead. - -## How to - -There are 2 ways to execute some statement in Postgres - -### Use `Postgres.fetch` - -Use this method to execute some `SELECT` query which returns **small number or rows**, like reading -Postgres config, or reading data from some reference table. Method returns Spark DataFrame. - -Method accepts [`Postgres.FetchOptions`](#onetl.connection.db_connection.postgres.options.PostgresFetchOptions). - -Connection opened using this method should be then closed with `connection.close()` or `with connection:`. - -#### WARNING -Please take into account [Postgres <-> Spark type mapping](types.md#postgres-types). - -#### Syntax support - -This method supports **any** query syntax supported by Postgres, like: - -* ✅︎ `SELECT ... FROM ...` -* ✅︎ `WITH alias AS (...) SELECT ...` -* ❌ `SET ...; SELECT ...;` - multiple statements not supported - -#### Examples - -```python -from onetl.connection import Postgres - -postgres = Postgres(...) - -df = postgres.fetch( - "SELECT value FROM some.reference_table WHERE key = 'some_constant'", - options=Postgres.FetchOptions(queryTimeout=10), -) -postgres.close() -value = df.collect()[0][0] # get value from first row and first column -``` - -### Use `Postgres.execute` - -Use this method to execute DDL and DML operations. Each method call runs operation in a separated transaction, and then commits it. - -Method accepts [`Postgres.ExecuteOptions`](#onetl.connection.db_connection.postgres.options.PostgresExecuteOptions). - -Connection opened using this method should be then closed with `connection.close()` or `with connection:`. - -#### Syntax support - -This method supports **any** query syntax supported by Postgres, like: - -* ✅︎ `CREATE TABLE ...`, `CREATE VIEW ...`, and so on -* ✅︎ `ALTER ...` -* ✅︎ `INSERT INTO ... SELECT ...`, `UPDATE ...`, `DELETE ...`, and so on -* ✅︎ `DROP TABLE ...`, `DROP VIEW ...`, `TRUNCATE TABLE`, and so on -* ✅︎ `CALL procedure(arg1, arg2) ...` -* ✅︎ `SELECT func(arg1, arg2)` or `{call func(arg1, arg2)}` - special syntax for calling functions -* ✅︎ other statements not mentioned here -* ❌ `SET ...; SELECT ...;` - multiple statements not supported - -#### Examples - -```python -from onetl.connection import Postgres - -postgres = Postgres(...) - -postgres.execute("DROP TABLE schema.table") -postgres.execute( - """ - CREATE TABLE schema.table ( - id bigint GENERATED ALWAYS AS IDENTITY, - key text, - value real - ) - """, - options=Postgres.ExecuteOptions(queryTimeout=10), -) -``` - -## Options - -### *pydantic model* onetl.connection.db_connection.postgres.options.PostgresFetchOptions - -Options related to fetching data from databases via JDBC. - -#### Versionadded -Added in version 0.11.0: Replace `Postgres.JDBCOptions` → `Postgres.FetchOptions` - -### Examples - -#### NOTE -You can pass any value supported by underlying JDBC driver class, -even if it is not mentioned in this documentation. - -```python -from onetl.connection import Postgres - -options = Postgres.FetchOptions( - queryTimeout=60_000, - fetchsize=100_000, - customSparkOption="value", -) -``` - - -* **Fields:** - - [`fetchsize (int | None)`](#onetl.connection.db_connection.postgres.options.PostgresFetchOptions.fetchsize) - - [`query_timeout (int | None)`](#onetl.connection.db_connection.postgres.options.PostgresFetchOptions.query_timeout) - -#### *field* query_timeout *: int | None* *= None* *(alias 'queryTimeout')* - -The number of seconds the driver will wait for a statement to execute. -Zero means there is no limit. - -This option depends on driver implementation, -some drivers can check the timeout of each query instead of an entire JDBC batch. - - - -#### *field* fetchsize *: int | None* *= None* - -How many rows to fetch per round trip. - -Tuning this option can influence performance of reading. - -#### WARNING -Default value depends on driver. For example, Oracle has -default `fetchsize=10`. - - - -### *pydantic model* onetl.connection.db_connection.postgres.options.PostgresExecuteOptions - -Options related to executing statements in databases via JDBC. - -#### Versionadded -Added in version 0.11.0: Replace `Postgres.JDBCOptions` → `Postgres.ExecuteOptions` - -### Examples - -#### NOTE -You can pass any value supported by underlying JDBC driver class, -even if it is not mentioned in this documentation. - -```python -from onetl.connection import Postgres - -options = Postgres.ExecuteOptions( - queryTimeout=60_000, - customSparkOption="value", -) -``` - - -* **Fields:** - - [`fetchsize (int | None)`](#onetl.connection.db_connection.postgres.options.PostgresExecuteOptions.fetchsize) - - [`query_timeout (int | None)`](#onetl.connection.db_connection.postgres.options.PostgresExecuteOptions.query_timeout) - -#### *field* query_timeout *: int | None* *= None* *(alias 'queryTimeout')* - -The number of seconds the driver will wait for a statement to execute. -Zero means there is no limit. - -This option depends on driver implementation, -some drivers can check the timeout of each query instead of an entire JDBC batch. - - - -#### *field* fetchsize *: int | None* *= None* - -How many rows to fetch per round trip. - -Tuning this option can influence performance of reading. - -#### WARNING -Default value depends on driver. For example, Oracle has -default `fetchsize=10`. - - diff --git a/mddocs/markdown/connection/db_connection/postgres/index.md b/mddocs/markdown/connection/db_connection/postgres/index.md deleted file mode 100644 index e1a8f76eb..000000000 --- a/mddocs/markdown/connection/db_connection/postgres/index.md +++ /dev/null @@ -1,19 +0,0 @@ - - -# Postgres - -# Connection - -* [Prerequisites](prerequisites.md) -* [Postgres connection](connection.md) - -# Operations - -* [Reading from Postgres using `DBReader`](read.md) -* [Reading from Postgres using `Postgres.sql`](sql.md) -* [Writing to Postgres using `DBWriter`](write.md) -* [Executing statements in Postgres](execute.md) - -# Troubleshooting - -* [Postgres <-> Spark type mapping](types.md) diff --git a/mddocs/markdown/connection/db_connection/postgres/prerequisites.md b/mddocs/markdown/connection/db_connection/postgres/prerequisites.md deleted file mode 100644 index 2bfb04e0f..000000000 --- a/mddocs/markdown/connection/db_connection/postgres/prerequisites.md +++ /dev/null @@ -1,71 +0,0 @@ - - -# Prerequisites - -## Version Compatibility - -* PostgreSQL server versions: - : * Officially declared: 8.2 - 17 - * Actually tested: 9.4.26, 17.3 -* Spark versions: 2.3.x - 3.5.x -* Java versions: 8 - 20 - -See [official documentation](https://jdbc.postgresql.org/). - -## Installing PySpark - -To use Postgres connector you should have PySpark installed (or injected to `sys.path`) -BEFORE creating the connector instance. - -See [Spark](../../../install/spark.md#install-spark) installation instruction for more details. - -## Connecting to Postgres - -### Allowing connection to Postgres instance - -Ask your Postgres administrator to allow your user (and probably IP) to connect to instance, -e.g. by updating `pg_hba.conf` file. - -See [official documentation](https://www.postgresql.org/docs/current/auth-pg-hba-conf.html). - -### Connection port - -Connection is usually performed to port 5432. Port may differ for different Postgres instances. -Please ask your Postgres administrator to provide required information. - -### Connection host - -It is possible to connect to Postgres by using either DNS name of host or it’s IP address. - -If you’re using Postgres cluster, it is currently possible to connect only to **one specific node**. -Connecting to multiple nodes to perform load balancing, as well as automatic failover to new master/replica are not supported. - -### Required grants - -Ask your Postgres cluster administrator to set following grants for a user, -used for creating a connection: - -Read + Write - -```sql --- allow creating tables in specific schema -GRANT USAGE, CREATE ON SCHEMA myschema TO username; - --- allow read & write access to specific table -GRANT SELECT, INSERT ON myschema.mytable TO username; - --- only if if_exists="replace_entire_table" is used: -GRANT TRUNCATE ON myschema.mytable TO username; -``` - -Read only - -```sql --- allow creating tables in specific schema -GRANT USAGE ON SCHEMA myschema TO username; - --- allow read access to specific table -GRANT SELECT ON myschema.mytable TO username; -``` - -More details can be found in [official documentation](https://www.postgresql.org/docs/current/sql-grant.html). diff --git a/mddocs/markdown/connection/db_connection/postgres/read.md b/mddocs/markdown/connection/db_connection/postgres/read.md deleted file mode 100644 index b0047f58a..000000000 --- a/mddocs/markdown/connection/db_connection/postgres/read.md +++ /dev/null @@ -1,350 +0,0 @@ - - -# Reading from Postgres using `DBReader` - -[`DBReader`](../../../db/db_reader.md#onetl.db.db_reader.db_reader.DBReader) supports [Read Strategies](../../../strategy/index.md#strategy) for incremental data reading, -but does not support custom queries, like `JOIN`. - -#### WARNING -Please take into account [Postgres <-> Spark type mapping](types.md#postgres-types) - -## Supported DBReader features - -* ✅︎ `columns` -* ✅︎ `where` -* ✅︎ `hwm`, supported strategies: -* * ✅︎ [Snapshot Strategy](../../../strategy/snapshot_strategy.md#snapshot-strategy) -* * ✅︎ [Incremental Strategy](../../../strategy/incremental_strategy.md#incremental-strategy) -* * ✅︎ [Snapshot Batch Strategy](../../../strategy/snapshot_batch_strategy.md#snapshot-batch-strategy) -* * ✅︎ [Incremental Batch Strategy](../../../strategy/incremental_batch_strategy.md#incremental-batch-strategy) -* ❌ `hint` (is not supported by Postgres) -* ❌ `df_schema` -* ✅︎ `options` (see [`Postgres.ReadOptions`](#onetl.connection.db_connection.postgres.options.PostgresReadOptions)) - -## Examples - -Snapshot strategy: - -```python -from onetl.connection import Postgres -from onetl.db import DBReader - -postgres = Postgres(...) - -reader = DBReader( - connection=postgres, - source="schema.table", - columns=["id", "key", "CAST(value AS text) value", "updated_dt"], - where="key = 'something'", - options=Postgres.ReadOptions(partitionColumn="id", numPartitions=10), -) -df = reader.run() -``` - -Incremental strategy: - -```python -from onetl.connection import Postgres -from onetl.db import DBReader -from onetl.strategy import IncrementalStrategy - -postgres = Postgres(...) - -reader = DBReader( - connection=postgres, - source="schema.table", - columns=["id", "key", "CAST(value AS text) value", "updated_dt"], - where="key = 'something'", - hwm=DBReader.AutoDetectHWM(name="postgres_hwm", expression="updated_dt"), - options=Postgres.ReadOptions(partitionColumn="id", numPartitions=10), -) - -with IncrementalStrategy(): - df = reader.run() -``` - -## Recommendations - -### Select only required columns - -Instead of passing `"*"` in `DBReader(columns=[...])` prefer passing exact column names. This reduces the amount of data passed from Postgres to Spark. - -### Pay attention to `where` value - -Instead of filtering data on Spark side using `df.filter(df.column == 'value')` pass proper `DBReader(where="column = 'value'")` clause. -This both reduces the amount of data send from Postgres to Spark, and may also improve performance of the query. -Especially if there are indexes or partitions for columns used in `where` clause. - -## Options - -### *pydantic model* onetl.connection.db_connection.postgres.options.PostgresReadOptions - -Spark JDBC reading options. - -#### Versionadded -Added in version 0.5.0: Replace `Postgres.Options` → `Postgres.ReadOptions` - -### Examples - -#### NOTE -You can pass any value -[supported by Spark](https://spark.apache.org/docs/latest/sql-data-sources-jdbc.html), -even if it is not mentioned in this documentation. **Option names should be in** `camelCase`! - -The set of supported options depends on Spark version. - -```python -from onetl.connection import Postgres - -options = Postgres.ReadOptions( - partitioning_mode="range", - partitionColumn="reg_id", - numPartitions=10, - customSparkOption="value", -) -``` - - -* **Fields:** - - [`fetchsize (int)`](#onetl.connection.db_connection.postgres.options.PostgresReadOptions.fetchsize) - - [`lower_bound (int | None)`](#onetl.connection.db_connection.postgres.options.PostgresReadOptions.lower_bound) - - [`num_partitions (pydantic.types.PositiveInt)`](#onetl.connection.db_connection.postgres.options.PostgresReadOptions.num_partitions) - - [`partition_column (str | None)`](#onetl.connection.db_connection.postgres.options.PostgresReadOptions.partition_column) - - [`partitioning_mode (onetl.connection.db_connection.jdbc_connection.options.JDBCPartitioningMode)`](#onetl.connection.db_connection.postgres.options.PostgresReadOptions.partitioning_mode) - - [`query_timeout (int | None)`](#onetl.connection.db_connection.postgres.options.PostgresReadOptions.query_timeout) - - [`session_init_statement (str | None)`](#onetl.connection.db_connection.postgres.options.PostgresReadOptions.session_init_statement) - - [`upper_bound (int | None)`](#onetl.connection.db_connection.postgres.options.PostgresReadOptions.upper_bound) - -#### *field* partition_column *: str | None* *= None* *(alias 'partitionColumn')* - -Column used to parallelize reading from a table. - -#### WARNING -It is highly recommended to use primary key, or column with an index -to avoid performance issues. - -#### NOTE -Column type depends on [`partitioning_mode`](#onetl.connection.db_connection.postgres.options.PostgresReadOptions.partitioning_mode). - -* `partitioning_mode="range"` requires column to be an integer, date or timestamp (can be NULL, but not recommended). -* `partitioning_mode="hash"` accepts any column type (NOT NULL). -* `partitioning_mode="mod"` requires column to be an integer (NOT NULL). - -See documentation for [`partitioning_mode`](#onetl.connection.db_connection.postgres.options.PostgresReadOptions.partitioning_mode) for more details - - - -#### *field* num_partitions *: PositiveInt* *= 1* *(alias 'numPartitions')* - -Number of jobs created by Spark to read the table content in parallel. -See documentation for [`partitioning_mode`](#onetl.connection.db_connection.postgres.options.PostgresReadOptions.partitioning_mode) for more details - - -* **Constraints:** - - **exclusiveMinimum** = 0 - -#### *field* lower_bound *: int | None* *= None* *(alias 'lowerBound')* - -See documentation for [`partitioning_mode`](#onetl.connection.db_connection.postgres.options.PostgresReadOptions.partitioning_mode) for more details - - - -#### *field* upper_bound *: int | None* *= None* *(alias 'upperBound')* - -See documentation for [`partitioning_mode`](#onetl.connection.db_connection.postgres.options.PostgresReadOptions.partitioning_mode) for more details - - - -#### *field* session_init_statement *: str | None* *= None* *(alias 'sessionInitStatement')* - -After each database session is opened to the remote DB and before starting to read data, -this option executes a custom SQL statement (or a PL/SQL block). - -Use this to implement session initialization code. - -Example: - -```python -sessionInitStatement = """ - BEGIN - execute immediate - 'alter session set "_serial_direct_read"=true'; - END; -""" -``` - - - -#### *field* query_timeout *: int | None* *= None* *(alias 'queryTimeout')* - -The number of seconds the driver will wait for a statement to execute. -Zero means there is no limit. - -This option depends on driver implementation, -some drivers can check the timeout of each query instead of an entire JDBC batch. - - - -#### *field* fetchsize *: int* *= 100000* - -Fetch N rows from an opened cursor per one read round. - -Tuning this option can influence performance of reading. - -#### WARNING -Default value is different from Spark. - -Spark uses driver’s own value, and it may be different in different drivers, -and even versions of the same driver. For example, Oracle has -default `fetchsize=10`, which is absolutely not usable. - -Thus we’ve overridden default value with `100_000`, which should increase reading performance. - -#### Versionchanged -Changed in version 0.2.0: Set explicit default value to `100_000` - - - -#### *field* partitioning_mode *: JDBCPartitioningMode* *= JDBCPartitioningMode.RANGE* - -Defines how Spark will parallelize reading from table. - -Possible values: - -* `range` (default) - : Allocate each executor a range of values from column passed into [`partition_column`](#onetl.connection.db_connection.postgres.options.PostgresReadOptions.partition_column). -
- ### Spark generates for each executor an SQL query -
- Executor 1: - ```sql - SELECT ... FROM table - WHERE (partition_column >= lowerBound - OR partition_column IS NULL) - AND partition_column < (lower_bound + stride) - ``` -
- Executor 2: - ```sql - SELECT ... FROM table - WHERE partition_column >= (lower_bound + stride) - AND partition_column < (lower_bound + 2 * stride) - ``` -
- … -
- Executor N: - ```sql - SELECT ... FROM table - WHERE partition_column >= (lower_bound + (N-1) * stride) - AND partition_column <= upper_bound - ``` -
- Where `stride=(upper_bound - lower_bound) / num_partitions`. -
- #### NOTE - Can be used only with columns of integer, date or timestamp types. -
- #### NOTE - [`lower_bound`](#onetl.connection.db_connection.postgres.options.PostgresReadOptions.lower_bound), [`upper_bound`](#onetl.connection.db_connection.postgres.options.PostgresReadOptions.upper_bound) and [`num_partitions`](#onetl.connection.db_connection.postgres.options.PostgresReadOptions.num_partitions) are used just to - calculate the partition stride, **NOT** for filtering the rows in table. - So all rows in the table will be returned (unlike *Incremental* [Read Strategies](../../../strategy/index.md#strategy)). -
- #### NOTE - All queries are executed in parallel. To execute them sequentially, use *Batch* [Read Strategies](../../../strategy/index.md#strategy). -* `hash` - : Allocate each executor a set of values based on hash of the [`partition_column`](#onetl.connection.db_connection.postgres.options.PostgresReadOptions.partition_column) column. -
- ### Spark generates for each executor an SQL query -
- Executor 1: - ```sql - SELECT ... FROM table - WHERE (some_hash(partition_column) mod num_partitions) = 0 -- lower_bound - ``` -
- Executor 2: - ```sql - SELECT ... FROM table - WHERE (some_hash(partition_column) mod num_partitions) = 1 -- lower_bound + 1 - ``` -
- … -
- Executor N: - ```sql - SELECT ... FROM table - WHERE (some_hash(partition_column) mod num_partitions) = num_partitions-1 -- upper_bound - ``` -
- #### NOTE - The hash function implementation depends on RDBMS. It can be `MD5` or any other fast hash function, - or expression based on this function call. Usually such functions accepts any column type as an input. -* `mod` - : Allocate each executor a set of values based on modulus of the [`partition_column`](#onetl.connection.db_connection.postgres.options.PostgresReadOptions.partition_column) column. -
- ### Spark generates for each executor an SQL query -
- Executor 1: - ```sql - SELECT ... FROM table - WHERE (partition_column mod num_partitions) = 0 -- lower_bound - ``` -
- Executor 2: - ```sql - SELECT ... FROM table - WHERE (partition_column mod num_partitions) = 1 -- lower_bound + 1 - ``` -
- Executor N: - ```sql - SELECT ... FROM table - WHERE (partition_column mod num_partitions) = num_partitions-1 -- upper_bound - ``` -
- #### NOTE - Can be used only with columns of integer type. - -#### Versionadded -Added in version 0.5.0. - -### Examples - -Read data in 10 parallel jobs by range of values in `id_column` column: - -```python -ReadOptions( - partitioning_mode="range", # default mode, can be omitted - partitionColumn="id_column", - numPartitions=10, - # Options below can be discarded because they are - # calculated automatically as MIN and MAX values of `partitionColumn` - lowerBound=0, - upperBound=100_000, -) -``` - -Read data in 10 parallel jobs by hash of values in `some_column` column: - -```python -ReadOptions( - partitioning_mode="hash", - partitionColumn="some_column", - numPartitions=10, - # lowerBound and upperBound are automatically set to `0` and `9` -) -``` - -Read data in 10 parallel jobs by modulus of values in `id_column` column: - -```python -ReadOptions( - partitioning_mode="mod", - partitionColumn="id_column", - numPartitions=10, - # lowerBound and upperBound are automatically set to `0` and `9` -) -``` - - diff --git a/mddocs/markdown/connection/db_connection/postgres/sql.md b/mddocs/markdown/connection/db_connection/postgres/sql.md deleted file mode 100644 index 0a5638d62..000000000 --- a/mddocs/markdown/connection/db_connection/postgres/sql.md +++ /dev/null @@ -1,191 +0,0 @@ - - -# Reading from Postgres using `Postgres.sql` - -`Postgres.sql` allows passing custom SQL query, but does not support incremental strategies. - -#### WARNING -Please take into account [Postgres <-> Spark type mapping](types.md#postgres-types) - -#### WARNING -Statement is executed in **read-write** connection, so if you’re calling some functions/procedures with DDL/DML statements inside, -they can change data in your database. - -## Syntax support - -Only queries with the following syntax are supported: - -* ✅︎ `SELECT ... FROM ...` -* ✅︎ `WITH alias AS (...) SELECT ...` -* ❌ `SET ...; SELECT ...;` - multiple statements not supported - -## Examples - -```python -from onetl.connection import Postgres - -postgres = Postgres(...) -df = postgres.sql( - """ - SELECT - id, - key, - CAST(value AS text) value, - updated_at - FROM - some.mytable - WHERE - key = 'something' - """, - options=Postgres.SQLOptions( - partitionColumn="id", - numPartitions=10, - lowerBound=0, - upperBound=1000, - ), -) -``` - -## Recommendations - -### Select only required columns - -Instead of passing `SELECT * FROM ...` prefer passing exact column names `SELECT col1, col2, ...`. -This reduces the amount of data passed from Postgres to Spark. - -### Pay attention to `where` value - -Instead of filtering data on Spark side using `df.filter(df.column == 'value')` pass proper `WHERE column = 'value'` clause. -This both reduces the amount of data send from Postgres to Spark, and may also improve performance of the query. -Especially if there are indexes or partitions for columns used in `where` clause. - -## Options - -### *pydantic model* onetl.connection.db_connection.postgres.options.PostgresSQLOptions - -Options specifically for SQL queries - -These options allow you to specify configurations for executing SQL queries -without relying on Spark’s partitioning mechanisms. - -#### Versionadded -Added in version 0.11.0: Split up `Postgres.ReadOptions` to `Postgres.SQLOptions` - -### Examples - -#### NOTE -You can pass any JDBC configuration -[supported by Spark](https://spark.apache.org/docs/latest/sql-data-sources-jdbc.html), -tailored to optimize SQL query execution. **Option names should be in** `camelCase`! - -```python -from onetl.connection import Postgres - -options = Postgres.SQLOptions( - partitionColumn="reg_id", - numPartitions=10, - lowerBound=0, - upperBound=1000, - customSparkOption="value", -) -``` - - - -#### *field* partition_column *: str | None* *= None* *(alias 'partitionColumn')* - -Column used to partition data across multiple executors for parallel query processing. - -#### WARNING -It is highly recommended to use primary key, or column with an index -to avoid performance issues. - -### Example of using `partitionColumn="id"` with `partitioning_mode="range"` - -```sql --- If partition_column is 'id', with numPartitions=4, lowerBound=1, and upperBound=100: --- Executor 1 processes IDs from 1 to 25 -SELECT ... FROM table WHERE id >= 1 AND id < 26 --- Executor 2 processes IDs from 26 to 50 -SELECT ... FROM table WHERE id >= 26 AND id < 51 --- Executor 3 processes IDs from 51 to 75 -SELECT ... FROM table WHERE id >= 51 AND id < 76 --- Executor 4 processes IDs from 76 to 100 -SELECT ... FROM table WHERE id >= 76 AND id <= 100 - --- General case for Executor N -SELECT ... FROM table -WHERE partition_column >= (lowerBound + (N-1) * stride) -AND partition_column <= upperBound --- Where ``stride`` is calculated as ``(upperBound - lowerBound) / numPartitions``. -``` - - - -#### *field* num_partitions *: int | None* *= None* *(alias 'numPartitions')* - -Number of jobs created by Spark to read the table content in parallel. - - - -#### *field* lower_bound *: int | None* *= None* *(alias 'lowerBound')* - -Defines the starting boundary for partitioning the query’s data. Mandatory if [`partition_column`](#onetl.connection.db_connection.postgres.options.PostgresSQLOptions.partition_column) is set - - - -#### *field* upper_bound *: int | None* *= None* *(alias 'upperBound')* - -Sets the ending boundary for data partitioning. Mandatory if [`partition_column`](#onetl.connection.db_connection.postgres.options.PostgresSQLOptions.partition_column) is set - - - -#### *field* session_init_statement *: str | None* *= None* *(alias 'sessionInitStatement')* - -After each database session is opened to the remote DB and before starting to read data, -this option executes a custom SQL statement (or a PL/SQL block). - -Use this to implement session initialization code. - -Example: - -```python -sessionInitStatement = """ - BEGIN - execute immediate - 'alter session set "_serial_direct_read"=true'; - END; -""" -``` - - - -#### *field* query_timeout *: int | None* *= None* *(alias 'queryTimeout')* - -The number of seconds the driver will wait for a statement to execute. -Zero means there is no limit. - -This option depends on driver implementation, -some drivers can check the timeout of each query instead of an entire JDBC batch. - - - -#### *field* fetchsize *: int* *= 100000* - -Fetch N rows from an opened cursor per one read round. - -Tuning this option can influence performance of reading. - -#### WARNING -Default value is different from Spark. - -Spark uses driver’s own value, and it may be different in different drivers, -and even versions of the same driver. For example, Oracle has -default `fetchsize=10`, which is absolutely not usable. - -Thus we’ve overridden default value with `100_000`, which should increase reading performance. - -#### Versionchanged -Changed in version 0.2.0: Set explicit default value to `100_000` - - diff --git a/mddocs/markdown/connection/db_connection/postgres/types.md b/mddocs/markdown/connection/db_connection/postgres/types.md deleted file mode 100644 index 3baca234d..000000000 --- a/mddocs/markdown/connection/db_connection/postgres/types.md +++ /dev/null @@ -1,372 +0,0 @@ - - -# Postgres <-> Spark type mapping - -#### NOTE -The results below are valid for Spark 3.5.5, and may differ on other Spark versions. - -## Type detection & casting - -Spark’s DataFrames always have a `schema` which is a list of columns with corresponding Spark types. All operations on a column are performed using column type. - -### Reading from Postgres - -This is how Postgres connector performs this: - -* For each column in query result (`SELECT column1, column2, ... FROM table ...`) get column name and Postgres type. -* Find corresponding `Postgres type (read)` → `Spark type` combination (see below) for each DataFrame column [1](#id2). If no combination is found, raise exception. -* Create DataFrame from query with specific column names and Spark types. - -* **[1]** All Postgres types that doesn’t have corresponding Java type are converted to `String`. - -### Writing to some existing Postgres table - -This is how Postgres connector performs this: - -* Get names of columns in DataFrame. [1](#id2) -* Perform `SELECT * FROM table LIMIT 0` query. -* Take only columns present in DataFrame (by name, case insensitive) [2](#id6). For each found column get Postgres type. -* Find corresponding `Spark type` → `Postgres type (write)` combination (see below) for each DataFrame column. If no combination is found, raise exception. -* If `Postgres type (write)` match `Postgres type (read)`, no additional casts will be performed, DataFrame column will be written to Postgres as is. -* If `Postgres type (write)` does not match `Postgres type (read)`, DataFrame column will be casted to target column type **on Postgres side**. - For example, you can write column with text data to `int` column, if column contains valid integer values within supported value range and precision [3](#id7). - -* **[2]** This allows to write data to tables with `DEFAULT` and `GENERATED` columns - if DataFrame has no such column, it will be populated by Postgres. -* **[3]** This is true only if either DataFrame column is a `StringType()`, or target column is `text` type. But other types cannot be silently converted, like `bytea -> bit(N)`. This requires explicit casting, see [Manual conversion to string](). - -### Create new table using Spark - -#### WARNING -ABSOLUTELY NOT RECOMMENDED! - -This is how Postgres connector performs this: - -* Find corresponding `Spark type` → `Postgres type (create)` combination (see below) for each DataFrame column. If no combination is found, raise exception. -* Generate DDL for creating table in Postgres, like `CREATE TABLE (col1 ...)`, and run it. -* Write DataFrame to created table as is. - -But Postgres connector support only limited number of types and almost no custom clauses (like `PARTITION BY`, `INDEX`, etc). -So instead of relying on Spark to create tables: - -### See example - -```python -writer = DBWriter( - connection=postgres, - target="public.table", - options=Postgres.WriteOptions( - if_exists="append", - createTableOptions="PARTITION BY RANGE (id)", - ), -) -writer.run(df) -``` - -Always prefer creating table with desired DDL **BEFORE WRITING DATA**: - -### See example - -```python -postgres.execute( - """ - CREATE TABLE public.table ( - id bigint, - business_dt timestamp(6), - value json - ) - PARTITION BY RANGE (Id) - """, -) - -writer = DBWriter( - connection=postgres, - target="public.table", - options=Postgres.WriteOptions(if_exists="append"), -) -writer.run(df) -``` - -See Postgres [CREATE TABLE](https://www.postgresql.org/docs/current/sql-createtable.html) documentation. - -## Supported types - -### References - -See [List of Postgres types](https://www.postgresql.org/docs/current/datatype.html). - -Here you can find source code with type conversions: - -* [Postgres <-> JDBC](https://github.com/pgjdbc/pgjdbc/blob/REL42.6.0/pgjdbc/src/main/java/org/postgresql/jdbc/TypeInfoCache.java#L78-L112) -* [JDBC -> Spark](https://github.com/apache/spark/blob/v3.5.5/sql/core/src/main/scala/org/apache/spark/sql/jdbc/PostgresDialect.scala#L52-L108) -* [Spark -> JDBC](https://github.com/apache/spark/blob/v3.5.5/sql/core/src/main/scala/org/apache/spark/sql/jdbc/PostgresDialect.scala#L118-L132) - -### Numeric types - -| Postgres type (read) | Spark type | Postgres type (write) | Postgres type (create) | -|-----------------------------|-------------------------------------|-----------------------------|--------------------------| -| `decimal` | `DecimalType(P=38, S=18)` | `decimal(P=38, S=18)` | `decimal` (unbounded) | -| `decimal(P=0..38)` | `DecimalType(P=0..38, S=0)` | `decimal(P=0..38, S=0)` | | -| `decimal(P=0..38, S=0..38)` | `DecimalType(P=0..38, S=0..38)` | `decimal(P=0..38, S=0..38)` | | -| `decimal(P=39.., S=0..)` | unsupported [4](#id11) | | | -| `decimal(P=.., S=..-1)` | unsupported [5](#id12) | | | -| `real` | `FloatType()` | `real` | `real` | -| `double precision` | `DoubleType()` | `double precision` | `double precision` | -| `smallint` | `ShortType()` | `smallint` | `smallint` | -| `-` | `ByteType()` | | | -| `integer` | `IntegerType()` | `integer` | `integer` | -| `bigint` | `LongType()` | `bigint` | `bigint` | -| `money` | `StringType()` [1](#id2) | `text` | `text` | -| `int4range` | | | | -| `int8range` | | | | -| `numrange` | | | | -| `int2vector` | | | | -* **[4]** Postgres support decimal types with unlimited precision. But Spark’s `DecimalType(P, S)` supports maximum `P=38` (128 bit). It is impossible to read, write or operate with values of larger precision, this leads to an exception. -* **[5]** Postgres support decimal types with negative scale, like `decimal(38, -10)`. Spark doesn’t. - -### Temporal types - -| Postgres type (read) | Spark type | Postgres type (write) | Postgres type (create) | -|----------------------------------|----------------------------------------------------------------------|-------------------------|--------------------------| -| `date` | `DateType()` | `date` | `date` | -| `time` | `TimestampType()`,
with time format quirks [6](#id16) | `timestamp(6)` | `timestamp(6)` | -| `time(0..6)` | | | | -| `time with time zone` | | | | -| `time(0..6) with time zone` | | | | -| `timestamp` | `TimestampType()` | `timestamp(6)` | `timestamp(6)` | -| `timestamp(0..6)` | | | | -| `timestamp with time zone` | | | | -| `timestamp(0..6) with time zone` | | | | -| `-` | `TimestampNTZType()` | `timestamp(6)` | `timestamp(6)` | -| `interval` of any precision | `StringType()` [1](#id2) | `text` | `text` | -| `-` | `DayTimeIntervalType()` | unsupported | unsupported | -| `-` | `YearMonthIntervalType()` | unsupported | unsupported | -| `daterange` | `StringType()` [1](#id2) | `text` | `text` | -| `tsrange` | | | | -| `tstzrange` | | | | - -#### WARNING -Note that types in Postgres and Spark have different value ranges: - -| Postgres type | Min value | Max value | Spark type | Min value | Max value | -|-----------------|-------------------------------|--------------------------------|-------------------|------------------------------|------------------------------| -| `date` | `-4713-01-01` | `5874897-01-01` | `DateType()` | `0001-01-01` | `9999-12-31` | -| `timestamp` | `-4713-01-01 00:00:00.000000` | `294276-12-31 23:59:59.999999` | `TimestampType()` | `0001-01-01 00:00:00.000000` | `9999-12-31 23:59:59.999999` | -| `time` | `00:00:00.000000` | `24:00:00.000000` | | | | - -So not all of values can be read from Postgres to Spark. - -References: -: * [Postgres date/time types documentation](https://www.postgresql.org/docs/current/datatype-datetime.html) - * [Spark DateType documentation](https://spark.apache.org/docs/latest/api/java/org/apache/spark/sql/types/DateType.html) - * [Spark TimestampType documentation](https://spark.apache.org/docs/latest/api/java/org/apache/spark/sql/types/TimestampType.html) - -* **[6]** `time` type is the same as `timestamp` with date `1970-01-01`. So instead of reading data from Postgres like `23:59:59` it is actually read `1970-01-01 23:59:59`, and vice versa. - -### String types - -| Postgres type (read) | Spark type | Postgres type (write) | Postgres type (create) | -|---------------------------|-------------------------------------|-------------------------|--------------------------| -| `character` | `StringType()` | `text` | `text` | -| `character(N)` | | | | -| `character varying` | | | | -| `character varying(N)` | | | | -| `text` | | | | -| `json` | | | | -| `jsonb` | | | | -| `xml` | | | | -| `CREATE TYPE ... AS ENUM` | `StringType()` [1](#id2) | | | -| `tsvector` | | | | -| `tsquery` | | | | -| `-` | `CharType()` | `unsupported` | `unsupported` | -| `-` | `VarcharType()` | `unsupported` | `unsupported` | - -### Binary types - -| Postgres type (read) | Spark type | Postgres type (write) | Postgres type (create) | -|------------------------|-------------------------------------|----------------------------------------------------------|--------------------------| -| `boolean` | `BooleanType()` | `boolean` | `boolean` | -| `bit` | `BooleanType()` | `bool`,
**cannot insert data** [3](#id7) | `bool` | -| `bit(N=1)` | | | | -| `bit(N=2..)` | `ByteType()` | `bytea`,
**cannot insert data** [3](#id7) | `bytea` | -| `bit varying` | `StringType()` [1](#id2) | `text` | `text` | -| `bit varying(N)` | | | | -| `bytea` | `BinaryType()` | `bytea` | `bytea` | - -### Struct types - -| Postgres type (read) | Spark type | Postgres type (write) | Postgres type (create) | -|------------------------------|-------------------------------------|-------------------------|--------------------------| -| `T[]` | `ArrayType(T)` | `T[]` | `T[]` | -| `T[][]` | unsupported | | | -| `CREATE TYPE sometype (...)` | `StringType()` [1](#id2) | `text` | `text` | -| `-` | `StructType()` | unsupported | | -| `-` | `MapType()` | | | - -### Network types - -| Postgres type (read) | Spark type | Postgres type (write) | Postgres type (create) | -|------------------------|-------------------------------------|-------------------------|--------------------------| -| `cidr` | `StringType()` [1](#id2) | `text` | `text` | -| `inet` | | | | -| `macaddr` | | | | -| `macaddr8` | | | | - -### Geo types - -| Postgres type (read) | Spark type | Postgres type (write) | Postgres type (create) | -|------------------------|-------------------------------------|-------------------------|--------------------------| -| `circle` | `StringType()` [1](#id2) | `text` | `text` | -| `box` | | | | -| `line` | | | | -| `lseg` | | | | -| `path` | | | | -| `point` | | | | -| `polygon` | | | | -| `polygon` | | | | - -## Explicit type cast - -### `DBReader` - -It is possible to explicitly cast column of unsupported type using `DBReader(columns=...)` syntax. - -For example, you can use `CAST(column AS text)` to convert data to string representation on Postgres side, and so it will be read as Spark’s `StringType()`. - -It is also possible to use [to_json](https://www.postgresql.org/docs/current/functions-json.html) Postgres function to convert column of any type to string representation, and then parse this column on Spark side you can use the [`JSON.parse_column`](../../../file_df/file_formats/json.md#onetl.file.format.json.JSON.parse_column) method: - -```python -from pyspark.sql.types import IntegerType - -from onetl.connection import Postgres -from onetl.db import DBReader -from onetl.file.format import JSON - -postgres = Postgres(...) - -DBReader( - connection=postgres, - columns=[ - "id", - "supported_column", - "CAST(unsupported_column AS text) unsupported_column_str", - # or - "to_json(unsupported_column) array_column_json", - ], -) -df = reader.run() - -json_schema = StructType( - [ - StructField("id", IntegerType(), nullable=True), - StructField("name", StringType(), nullable=True), - ..., - ] -) -df = df.select( - df.id, - df.supported_column, - # explicit cast - df.unsupported_column_str.cast("integer").alias("parsed_integer"), - JSON().parse_column("array_column_json", json_schema).alias("json_string"), -) -``` - -### `DBWriter` - -It is always possible to convert data on the Spark side to a string, and then write it to a text column in a Postgres table. - -#### Using JSON.serialize_column - -You can use the [`JSON.serialize_column`](../../../file_df/file_formats/json.md#onetl.file.format.json.JSON.serialize_column) method for data serialization: - -```python -from onetl.file.format import JSON -from pyspark.sql.functions import col - -from onetl.connection import Postgres -from onetl.db import DBWriter - -postgres = Postgres(...) - -postgres.execute( - """ - CREATE TABLE schema.target_table ( - id int, - supported_column timestamp, - array_column_json jsonb -- any column type, actually - ) - """, -) - -write_df = df.select( - df.id, - df.supported_column, - JSON().serialize_column(df.unsupported_column).alias("array_column_json"), -) - -writer = DBWriter( - connection=postgres, - target="schema.target_table", -) -writer.run(write_df) -``` - -Then you can parse this column on the Postgres side (for example, by creating a view): - -```sql -SELECT - id, - supported_column, - array_column_json->'0' AS array_item_0 -FROM - schema.target_table -``` - -To avoid casting the value on every table read you can use [GENERATED ALWAYS STORED](https://www.postgresql.org/docs/current/ddl-generated-columns.html) column, but this requires 2x space (for original and parsed value). - -#### Manual conversion to string - -Postgres connector also supports conversion text value directly to target column type, if this value has a proper format. - -For example, you can write data like `[123, 345)` to `int8range` type because Postgres allows cast `'[123, 345)'::int8range'`: - -```python -from pyspark.sql.ftypes import StringType -from pyspark.sql.functions import udf - -from onetl.connection import Postgres -from onetl.db import DBReader - -postgres = Postgres(...) - -postgres.execute( - """ - CREATE TABLE schema.target_table ( - id int, - range_column int8range -- any column type, actually - ) - """, -) - - -@udf(returnType=StringType()) -def array_to_range(value: tuple): - """This UDF allows to convert tuple[start, end] to Postgres' range format""" - start, end = value - return f"[{start},{end})" - - -write_df = df.select( - df.id, - array_to_range(df.range_column).alias("range_column"), -) - -writer = DBWriter( - connection=postgres, - target="schema.target_table", -) -writer.run(write_df) -``` - -This can be tricky to implement and may lead to longer write process. -But this does not require extra space on Postgres side, and allows to avoid explicit value cast on every table read. diff --git a/mddocs/markdown/connection/db_connection/postgres/write.md b/mddocs/markdown/connection/db_connection/postgres/write.md deleted file mode 100644 index 3b5822b1f..000000000 --- a/mddocs/markdown/connection/db_connection/postgres/write.md +++ /dev/null @@ -1,183 +0,0 @@ - - -# Writing to Postgres using `DBWriter` - -For writing data to Postgres, use [`DBWriter`](../../../db/db_writer.md#onetl.db.db_writer.db_writer.DBWriter). - -#### WARNING -Please take into account [Postgres <-> Spark type mapping](types.md#postgres-types) - -#### WARNING -It is always recommended to create table explicitly using [Postgres.execute](execute.md#postgres-execute) -instead of relying on Spark’s table DDL generation. - -This is because Spark’s DDL generator can create columns with different precision and types than it is expected, -causing precision loss or other issues. - -## Examples - -```python -from onetl.connection import Postgres -from onetl.db import DBWriter - -postgres = Postgres(...) - -df = ... # data is here - -writer = DBWriter( - connection=postgres, - target="schema.table", - options=Postgres.WriteOptions(if_exists="append"), -) - -writer.run(df) -``` - -## Options - -Method above accepts [`Postgres.WriteOptions`](#onetl.connection.db_connection.postgres.options.PostgresWriteOptions) - -### *pydantic model* onetl.connection.db_connection.postgres.options.PostgresWriteOptions - -Spark JDBC writing options. - -#### Versionadded -Added in version 0.5.0: Replace `Postgres.Options` → `Postgres.WriteOptions` - -### Examples - -#### NOTE -You can pass any value -[supported by Spark](https://spark.apache.org/docs/latest/sql-data-sources-jdbc.html), -even if it is not mentioned in this documentation. **Option names should be in** `camelCase`! - -The set of supported options depends on Spark version. - -```python -from onetl.connection import Postgres - -options = Postgres.WriteOptions( - if_exists="append", - batchsize=20_000, - customSparkOption="value", -) -``` - - - -#### *field* if_exists *: JDBCTableExistBehavior* *= JDBCTableExistBehavior.APPEND* *(alias 'mode')* - -Behavior of writing data into existing table. - -Possible values: -: * `append` (default) - : Adds new rows into existing table. -
- ### Behavior in details -
- * Table does not exist - : Table is created using options provided by user - (`createTableOptions`, `createTableColumnTypes`, etc). - * Table exists - : Data is appended to a table. Table has the same DDL as before writing data -
- #### WARNING - This mode does not check whether table already contains - rows from dataframe, so duplicated rows can be created. -
- Also Spark does not support passing custom options to - insert statement, like `ON CONFLICT`, so don’t try to - implement deduplication using unique indexes or constraints. -
- Instead, write to staging table and perform deduplication - using `execute` method. - * `replace_entire_table` - : **Table is dropped and then created, or truncated**. -
- ### Behavior in details -
- * Table does not exist - : Table is created using options provided by user - (`createTableOptions`, `createTableColumnTypes`, etc). - * Table exists - : Table content is replaced with dataframe content. -
- After writing completed, target table could either have the same DDL as - before writing data (`truncate=True`), or can be recreated (`truncate=False` - or source does not support truncation). - * `ignore` - : Ignores the write operation if the table already exists. -
- ### Behavior in details -
- * Table does not exist - : Table is created using options provided by user - (`createTableOptions`, `createTableColumnTypes`, etc). - * Table exists - : The write operation is ignored, and no data is written to the table. - * `error` - : Raises an error if the table already exists. -
- ### Behavior in details -
- * Table does not exist - : Table is created using options provided by user - (`createTableOptions`, `createTableColumnTypes`, etc). - * Table exists - : An error is raised, and no data is written to the table. - -#### Versionchanged -Changed in version 0.9.0: Renamed `mode` → `if_exists` - - - -#### *field* query_timeout *: int | None* *= None* *(alias 'queryTimeout')* - -The number of seconds the driver will wait for a statement to execute. -Zero means there is no limit. - -This option depends on driver implementation, -some drivers can check the timeout of each query instead of an entire JDBC batch. - - - -#### *field* batchsize *: int* *= 20000* - -How many rows can be inserted per round trip. - -Tuning this option can influence performance of writing. - -#### WARNING -Default value is different from Spark. - -Spark uses quite small value `1000`, which is absolutely not usable -in BigData world. - -Thus we’ve overridden default value with `20_000`, -which should increase writing performance. - -You can increase it even more, up to `50_000`, -but it depends on your database load and number of columns in the row. -Higher values does not increase performance. - -#### Versionchanged -Changed in version 0.4.0: Changed default value from 1000 to 20_000 - - - -#### *field* isolation_level *: str* *= 'READ_UNCOMMITTED'* *(alias 'isolationLevel')* - -The transaction isolation level, which applies to current connection. - -Possible values: -: * `NONE` (as string, not Python’s `None`) - * `READ_COMMITTED` - * `READ_UNCOMMITTED` - * `REPEATABLE_READ` - * `SERIALIZABLE` - -Values correspond to transaction isolation levels defined by JDBC standard. -Please refer the documentation for -[java.sql.Connection](https://docs.oracle.com/javase/8/docs/api/java/sql/Connection.html). - - diff --git a/mddocs/markdown/connection/db_connection/teradata/connection.md b/mddocs/markdown/connection/db_connection/teradata/connection.md deleted file mode 100644 index de2745c4e..000000000 --- a/mddocs/markdown/connection/db_connection/teradata/connection.md +++ /dev/null @@ -1,134 +0,0 @@ - - -# Teradata connection - -### *class* onetl.connection.db_connection.teradata.connection.Teradata(\*, spark: SparkSession, user: str, password: SecretStr, host: Host, port: int = 1025, database: str | None = None, extra: TeradataExtra = TeradataExtra(CHARSET='UTF8', COLUMN_NAME='ON', FLATTEN='ON', MAYBENULL='ON', STRICT_NAMES='OFF')) - -Teradata JDBC connection. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -Based on package [com.teradata.jdbc:terajdbc:17.20.00.15](https://central.sonatype.com/artifact/com.teradata.jdbc/terajdbc/17.20.00.15) -([official Teradata JDBC driver](https://downloads.teradata.com/download/connectivity/jdbc-driver)). - -#### SEE ALSO -Before using this connector please take into account [Prerequisites](prerequisites.md#teradata-prerequisites) - -#### Versionadded -Added in version 0.1.0. - -* **Parameters:** - **host** - : Host of Teradata database. For example: `test.teradata.domain.com` or `193.168.1.12` - - **port** - : Port of Teradata database - - **user** - : User, which have proper access to the database. For example: `some_user` - - **password** - : Password for database connection - - **database** - : Database in RDBMS, NOT schema. -
- See [this page](https://www.educba.com/postgresql-database-vs-schema/) for more details - - **spark** - : Spark session. - - **extra** - : Specifies one or more extra parameters which should be appended to a connection string. -
- For example: `{"TMODE": "TERA", "MAYBENULL": "ON", "CHARSET": "UTF8", "LOGMECH":"LDAP"}` -
- See [Teradata JDBC driver documentation](https://teradata-docs.s3.amazonaws.com/doc/connectivity/jdbc/reference/current/jdbcug_chapter_2.html#BABJIHBJ) - for more details -
- #### NOTE - By default, these options are added to extra: - > * `CHARSET = "UTF8"` - > * `COLUMN_NAME = "ON"` - allow reading column title from a table - > * `FLATTEN = "ON"` - improves error messages - > * `MAYBENULL = "ON"` - > * `STRICT_NAMES = "OFF"` - ignore Spark options passed to JDBC URL -
- It is possible to override default values, for example set `extra={"FLATTEN": "OFF"}` - -### Examples - -Create Teradata connection with LDAP auth: - -```python -from onetl.connection import Teradata -from pyspark.sql import SparkSession - -# Create Spark session with Teradata driver loaded -maven_packages = Teradata.get_packages() -spark = ( - SparkSession.builder.appName("spark-app-name") - .config("spark.jars.packages", ",".join(maven_packages)) - .getOrCreate() -) - -# Create connection -teradata = Teradata( - host="database.host.or.ip", - user="user", - password="*****", - extra={ - "TMODE": "TERA", # "TERA" or "ANSI" - "LOGMECH": "LDAP", - "LOG": "TIMING", # increase log level - }, - spark=spark, -).check() -``` - - - -#### check() - -Check source availability. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -If not, an exception will be raised. - -* **Returns:** - Connection itself -* **Raises:** - RuntimeError - : If the connection is not available - -### Examples - -```python -connection.check() -``` - - - -#### *classmethod* get_packages(package_version: str | None = None) → list[str] - -Get package names to be downloaded by Spark. Allows specifying custom JDBC driver versions for Teradata. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### Versionadded -Added in version 0.9.0. - -* **Parameters:** - **package_version** - : Specifies the version of the Teradata JDBC driver to use. Defaults to `17.20.00.15`. -
- #### Versionadded - Added in version 0.11.0. - -### Examples - -```python -from onetl.connection import Teradata - -Teradata.get_packages() - -# specify custom driver version -Teradata.get_packages(package_version="20.00.00.18") -``` - - diff --git a/mddocs/markdown/connection/db_connection/teradata/execute.md b/mddocs/markdown/connection/db_connection/teradata/execute.md deleted file mode 100644 index e836b8a6d..000000000 --- a/mddocs/markdown/connection/db_connection/teradata/execute.md +++ /dev/null @@ -1,189 +0,0 @@ - - -# Executing statements in Teradata - -#### WARNING -Methods below **read all the rows** returned from DB **to Spark driver memory**, and then convert them to DataFrame. - -Do **NOT** use them to read large amounts of data. Use [DBReader](read.md#teradata-read) or [Teradata.sql](sql.md#teradata-sql) instead. - -## How to - -There are 2 ways to execute some statement in Teradata - -### Use `Teradata.fetch` - -Use this method to execute some `SELECT` query which returns **small number or rows**, like reading -Teradata config, or reading data from some reference table. Method returns Spark DataFrame. - -Method accepts [`Teradata.FetchOptions`](#onetl.connection.db_connection.teradata.options.TeradataFetchOptions). - -Connection opened using this method should be then closed with `connection.close()` or `with connection:`. - -#### Syntax support - -This method supports **any** query syntax supported by Teradata, like: - -* ✅︎ `SELECT ... FROM ...` -* ✅︎ `WITH alias AS (...) SELECT ...` -* ✅︎ `SHOW ...` -* ❌ `SET ...; SELECT ...;` - multiple statements not supported - -#### Examples - -```python -from onetl.connection import Teradata - -teradata = Teradata(...) - -df = teradata.fetch( - "SELECT value FROM some.reference_table WHERE key = 'some_constant'", - options=Teradata.FetchOptions(queryTimeout=10), -) -teradata.close() -value = df.collect()[0][0] # get value from first row and first column -``` - -### Use `Teradata.execute` - -Use this method to execute DDL and DML operations. Each method call runs operation in a separated transaction, and then commits it. - -Method accepts [`Teradata.ExecuteOptions`](#onetl.connection.db_connection.teradata.options.TeradataExecuteOptions). - -Connection opened using this method should be then closed with `connection.close()` or `with connection:`. - -#### Syntax support - -This method supports **any** query syntax supported by Teradata, like: - -* ✅︎ `CREATE TABLE ...`, `CREATE VIEW ...`, and so on -* ✅︎ `ALTER ...` -* ✅︎ `INSERT INTO ... SELECT ...`, `UPDATE ...`, `DELETE ...`, and so on -* ✅︎ `DROP TABLE ...`, `DROP VIEW ...`, `TRUNCATE TABLE`, and so on -* ✅︎ `CALL procedure(arg1, arg2) ...` or `{call procedure(arg1, arg2)}` - special syntax for calling procedure -* ✅︎ `EXECUTE macro(arg1, arg2)` -* ✅︎ `EXECUTE FUNCTION ...` -* ✅︎ other statements not mentioned here -* ❌ `SET ...; SELECT ...;` - multiple statements not supported - -#### Examples - -```python -from onetl.connection import Teradata - -teradata = Teradata(...) - -teradata.execute("DROP TABLE database.table") -teradata.execute( - """ - CREATE MULTISET TABLE database.table AS ( - id BIGINT, - key VARCHAR, - value REAL - ) - NO PRIMARY INDEX - """, - options=Teradata.ExecuteOptions(queryTimeout=10), -) -``` - -## Options - -### *pydantic model* onetl.connection.db_connection.teradata.options.TeradataFetchOptions - -Options related to fetching data from databases via JDBC. - -#### Versionadded -Added in version 0.11.0: Replace `Teradata.JDBCOptions` → `Teradata.FetchOptions` - -### Examples - -#### NOTE -You can pass any value supported by underlying JDBC driver class, -even if it is not mentioned in this documentation. - -```python -from onetl.connection import Teradata - -options = Teradata.FetchOptions( - queryTimeout=60_000, - fetchsize=100_000, - customSparkOption="value", -) -``` - - -* **Fields:** - - [`fetchsize (int | None)`](#onetl.connection.db_connection.teradata.options.TeradataFetchOptions.fetchsize) - - [`query_timeout (int | None)`](#onetl.connection.db_connection.teradata.options.TeradataFetchOptions.query_timeout) - -#### *field* query_timeout *: int | None* *= None* *(alias 'queryTimeout')* - -The number of seconds the driver will wait for a statement to execute. -Zero means there is no limit. - -This option depends on driver implementation, -some drivers can check the timeout of each query instead of an entire JDBC batch. - - - -#### *field* fetchsize *: int | None* *= None* - -How many rows to fetch per round trip. - -Tuning this option can influence performance of reading. - -#### WARNING -Default value depends on driver. For example, Oracle has -default `fetchsize=10`. - - - -### *pydantic model* onetl.connection.db_connection.teradata.options.TeradataExecuteOptions - -Options related to executing statements in databases via JDBC. - -#### Versionadded -Added in version 0.11.0: Replace `Teradata.JDBCOptions` → `Teradata.ExecuteOptions` - -### Examples - -#### NOTE -You can pass any value supported by underlying JDBC driver class, -even if it is not mentioned in this documentation. - -```python -from onetl.connection import Teradata - -options = Teradata.ExecuteOptions( - queryTimeout=60_000, - customSparkOption="value", -) -``` - - -* **Fields:** - - [`fetchsize (int | None)`](#onetl.connection.db_connection.teradata.options.TeradataExecuteOptions.fetchsize) - - [`query_timeout (int | None)`](#onetl.connection.db_connection.teradata.options.TeradataExecuteOptions.query_timeout) - -#### *field* query_timeout *: int | None* *= None* *(alias 'queryTimeout')* - -The number of seconds the driver will wait for a statement to execute. -Zero means there is no limit. - -This option depends on driver implementation, -some drivers can check the timeout of each query instead of an entire JDBC batch. - - - -#### *field* fetchsize *: int | None* *= None* - -How many rows to fetch per round trip. - -Tuning this option can influence performance of reading. - -#### WARNING -Default value depends on driver. For example, Oracle has -default `fetchsize=10`. - - diff --git a/mddocs/markdown/connection/db_connection/teradata/index.md b/mddocs/markdown/connection/db_connection/teradata/index.md deleted file mode 100644 index f35e86981..000000000 --- a/mddocs/markdown/connection/db_connection/teradata/index.md +++ /dev/null @@ -1,15 +0,0 @@ - - -# Teradata - -# Connection - -* [Prerequisites](prerequisites.md) -* [Teradata connection](connection.md) - -# Operations - -* [Reading from Teradata using `DBReader`](read.md) -* [Reading from Teradata using `Teradata.sql`](sql.md) -* [Writing to Teradata using `DBWriter`](write.md) -* [Executing statements in Teradata](execute.md) diff --git a/mddocs/markdown/connection/db_connection/teradata/prerequisites.md b/mddocs/markdown/connection/db_connection/teradata/prerequisites.md deleted file mode 100644 index 3217390e2..000000000 --- a/mddocs/markdown/connection/db_connection/teradata/prerequisites.md +++ /dev/null @@ -1,57 +0,0 @@ - - -# Prerequisites - -## Version Compatibility - -* Teradata server versions: - : * Officially declared: 16.10 - 20.0 - * Actually tested: 16.10 -* Spark versions: 2.3.x - 3.5.x -* Java versions: 8 - 20 - -See [official documentation](https://teradata-docs.s3.amazonaws.com/doc/connectivity/jdbc/reference/current/platformMatrix.html). - -## Installing PySpark - -To use Teradata connector you should have PySpark installed (or injected to `sys.path`) -BEFORE creating the connector instance. - -See [Spark](../../../install/spark.md#install-spark) installation instruction for more details. - -## Connecting to Teradata - -### Connection host - -It is possible to connect to Teradata by using either DNS name Parsing Engine (PE) host, or it’s IP address. - -### Connection port - -Connection is usually performed to port `1025`. Port may differ for different Teradata instances. -Please ask your Teradata administrator to provide required information. - -### Required grants - -Ask your Teradata cluster administrator to set following grants for a user, -used for creating a connection: - -Read + Write - -```sql --- allow creating tables in the target schema -GRANT CREATE TABLE ON database TO username; - --- allow read & write access to specific table -GRANT SELECT, INSERT ON database.mytable TO username; -``` - -Read only - -```sql --- allow read access to specific table -GRANT SELECT ON database.mytable TO username; -``` - -See: -: * [Teradata access rights](https://www.dwhpro.com/teradata-access-rights/) - * [GRANT documentation](https://teradata.github.io/presto/docs/0.167-t/sql/grant.html) diff --git a/mddocs/markdown/connection/db_connection/teradata/read.md b/mddocs/markdown/connection/db_connection/teradata/read.md deleted file mode 100644 index 49c91e222..000000000 --- a/mddocs/markdown/connection/db_connection/teradata/read.md +++ /dev/null @@ -1,379 +0,0 @@ - - -# Reading from Teradata using `DBReader` - -[`DBReader`](../../../db/db_reader.md#onetl.db.db_reader.db_reader.DBReader) supports [Read Strategies](../../../strategy/index.md#strategy) for incremental data reading, -but does not support custom queries, like `JOIN`. - -## Supported DBReader features - -* ✅︎ `columns` -* ✅︎ `where` -* ✅︎ `hwm`, supported strategies: -* * ✅︎ [Snapshot Strategy](../../../strategy/snapshot_strategy.md#snapshot-strategy) -* * ✅︎ [Incremental Strategy](../../../strategy/incremental_strategy.md#incremental-strategy) -* * ✅︎ [Snapshot Batch Strategy](../../../strategy/snapshot_batch_strategy.md#snapshot-batch-strategy) -* * ✅︎ [Incremental Batch Strategy](../../../strategy/incremental_batch_strategy.md#incremental-batch-strategy) -* ❌ `hint` (is not supported by Teradata) -* ❌ `df_schema` -* ✅︎ `options` (see [`Teradata.ReadOptions`](#onetl.connection.db_connection.teradata.options.TeradataReadOptions)) - -## Examples - -Snapshot strategy: - -```python -from onetl.connection import Teradata -from onetl.db import DBReader - -teradata = Teradata(...) - -reader = DBReader( - connection=teradata, - source="database.table", - columns=["id", "key", "CAST(value AS VARCHAR) value", "updated_dt"], - where="key = 'something'", - options=Teradata.ReadOptions( - partitioning_mode="hash", - partitionColumn="id", - numPartitions=10, - ), -) -df = reader.run() -``` - -Incremental strategy: - -```python -from onetl.connection import Teradata -from onetl.db import DBReader -from onetl.strategy import IncrementalStrategy - -teradata = Teradata(...) - -reader = DBReader( - connection=teradata, - source="database.table", - columns=["id", "key", "CAST(value AS VARCHAR) value", "updated_dt"], - where="key = 'something'", - hwm=DBReader.AutoDetectHWM(name="teradata_hwm", expression="updated_dt"), - options=Teradata.ReadOptions( - partitioning_mode="hash", - partitionColumn="id", - numPartitions=10, - ), -) - -with IncrementalStrategy(): - df = reader.run() -``` - -## Recommendations - -### Select only required columns - -Instead of passing `"*"` in `DBReader(columns=[...])` prefer passing exact column names. This reduces the amount of data passed from Teradata to Spark. - -### Pay attention to `where` value - -Instead of filtering data on Spark side using `df.filter(df.column == 'value')` pass proper `DBReader(where="column = 'value'")` clause. -This both reduces the amount of data send from Teradata to Spark, and may also improve performance of the query. -Especially if there are indexes or partitions for columns used in `where` clause. - -### Read data in parallel - -`DBReader` can read data in multiple parallel connections by passing `Teradata.ReadOptions(numPartitions=..., partitionColumn=...)`. - -In the example above, Spark opens 10 parallel connections, and data is evenly distributed between all these connections using expression -`HASHAMP(HASHBUCKET(HASHROW({partition_column}))) MOD {num_partitions}`. -This allows sending each Spark worker only some piece of data, reducing resource consumption. -`partition_column` here can be table column of any type. - -It is also possible to use `partitioning_mode="mod"` or `partitioning_mode="range"`, but in this case -`partition_column` have to be an integer, should not contain `NULL`, and values to be uniformly distributed. -It is also less performant than `partitioning_mode="hash"` due to Teradata `HASHAMP` implementation. - -### Do **NOT** use `TYPE=FASTEXPORT` - -Teradata supports several [different connection types](https://teradata-docs.s3.amazonaws.com/doc/connectivity/jdbc/reference/current/jdbcug_chapter_2.html#BABFGFAF): -: * `TYPE=DEFAULT` - perform plain `SELECT` queries - * `TYPE=FASTEXPORT` - uses special FastExport protocol for select queries - -But `TYPE=FASTEXPORT` uses exclusive lock on the source table, so it is impossible to use multiple Spark workers parallel data read. -This leads to sending all the data to just one Spark worker, which is slow and takes a lot of RAM. - -Prefer using `partitioning_mode="hash"` from example above. - -## Options - -### *pydantic model* onetl.connection.db_connection.teradata.options.TeradataReadOptions - -Spark JDBC reading options. - -#### Versionadded -Added in version 0.5.0: Replace `Teradata.Options` → `Teradata.ReadOptions` - -### Examples - -#### NOTE -You can pass any value -[supported by Spark](https://spark.apache.org/docs/latest/sql-data-sources-jdbc.html), -even if it is not mentioned in this documentation. **Option names should be in** `camelCase`! - -The set of supported options depends on Spark version. - -```python -from onetl.connection import Teradata - -options = Teradata.ReadOptions( - partitioning_mode="range", - partitionColumn="reg_id", - numPartitions=10, - customSparkOption="value", -) -``` - - -* **Fields:** - - [`fetchsize (int)`](#onetl.connection.db_connection.teradata.options.TeradataReadOptions.fetchsize) - - [`lower_bound (int | None)`](#onetl.connection.db_connection.teradata.options.TeradataReadOptions.lower_bound) - - [`num_partitions (pydantic.types.PositiveInt)`](#onetl.connection.db_connection.teradata.options.TeradataReadOptions.num_partitions) - - [`partition_column (str | None)`](#onetl.connection.db_connection.teradata.options.TeradataReadOptions.partition_column) - - [`partitioning_mode (onetl.connection.db_connection.jdbc_connection.options.JDBCPartitioningMode)`](#onetl.connection.db_connection.teradata.options.TeradataReadOptions.partitioning_mode) - - [`query_timeout (int | None)`](#onetl.connection.db_connection.teradata.options.TeradataReadOptions.query_timeout) - - [`session_init_statement (str | None)`](#onetl.connection.db_connection.teradata.options.TeradataReadOptions.session_init_statement) - - [`upper_bound (int | None)`](#onetl.connection.db_connection.teradata.options.TeradataReadOptions.upper_bound) - -#### *field* partition_column *: str | None* *= None* *(alias 'partitionColumn')* - -Column used to parallelize reading from a table. - -#### WARNING -It is highly recommended to use primary key, or column with an index -to avoid performance issues. - -#### NOTE -Column type depends on [`partitioning_mode`](#onetl.connection.db_connection.teradata.options.TeradataReadOptions.partitioning_mode). - -* `partitioning_mode="range"` requires column to be an integer, date or timestamp (can be NULL, but not recommended). -* `partitioning_mode="hash"` accepts any column type (NOT NULL). -* `partitioning_mode="mod"` requires column to be an integer (NOT NULL). - -See documentation for [`partitioning_mode`](#onetl.connection.db_connection.teradata.options.TeradataReadOptions.partitioning_mode) for more details - - - -#### *field* num_partitions *: PositiveInt* *= 1* *(alias 'numPartitions')* - -Number of jobs created by Spark to read the table content in parallel. -See documentation for [`partitioning_mode`](#onetl.connection.db_connection.teradata.options.TeradataReadOptions.partitioning_mode) for more details - - -* **Constraints:** - - **exclusiveMinimum** = 0 - -#### *field* lower_bound *: int | None* *= None* *(alias 'lowerBound')* - -See documentation for [`partitioning_mode`](#onetl.connection.db_connection.teradata.options.TeradataReadOptions.partitioning_mode) for more details - - - -#### *field* upper_bound *: int | None* *= None* *(alias 'upperBound')* - -See documentation for [`partitioning_mode`](#onetl.connection.db_connection.teradata.options.TeradataReadOptions.partitioning_mode) for more details - - - -#### *field* session_init_statement *: str | None* *= None* *(alias 'sessionInitStatement')* - -After each database session is opened to the remote DB and before starting to read data, -this option executes a custom SQL statement (or a PL/SQL block). - -Use this to implement session initialization code. - -Example: - -```python -sessionInitStatement = """ - BEGIN - execute immediate - 'alter session set "_serial_direct_read"=true'; - END; -""" -``` - - - -#### *field* query_timeout *: int | None* *= None* *(alias 'queryTimeout')* - -The number of seconds the driver will wait for a statement to execute. -Zero means there is no limit. - -This option depends on driver implementation, -some drivers can check the timeout of each query instead of an entire JDBC batch. - - - -#### *field* fetchsize *: int* *= 100000* - -Fetch N rows from an opened cursor per one read round. - -Tuning this option can influence performance of reading. - -#### WARNING -Default value is different from Spark. - -Spark uses driver’s own value, and it may be different in different drivers, -and even versions of the same driver. For example, Oracle has -default `fetchsize=10`, which is absolutely not usable. - -Thus we’ve overridden default value with `100_000`, which should increase reading performance. - -#### Versionchanged -Changed in version 0.2.0: Set explicit default value to `100_000` - - - -#### *field* partitioning_mode *: JDBCPartitioningMode* *= JDBCPartitioningMode.RANGE* - -Defines how Spark will parallelize reading from table. - -Possible values: - -* `range` (default) - : Allocate each executor a range of values from column passed into [`partition_column`](#onetl.connection.db_connection.teradata.options.TeradataReadOptions.partition_column). -
- ### Spark generates for each executor an SQL query -
- Executor 1: - ```sql - SELECT ... FROM table - WHERE (partition_column >= lowerBound - OR partition_column IS NULL) - AND partition_column < (lower_bound + stride) - ``` -
- Executor 2: - ```sql - SELECT ... FROM table - WHERE partition_column >= (lower_bound + stride) - AND partition_column < (lower_bound + 2 * stride) - ``` -
- … -
- Executor N: - ```sql - SELECT ... FROM table - WHERE partition_column >= (lower_bound + (N-1) * stride) - AND partition_column <= upper_bound - ``` -
- Where `stride=(upper_bound - lower_bound) / num_partitions`. -
- #### NOTE - Can be used only with columns of integer, date or timestamp types. -
- #### NOTE - [`lower_bound`](#onetl.connection.db_connection.teradata.options.TeradataReadOptions.lower_bound), [`upper_bound`](#onetl.connection.db_connection.teradata.options.TeradataReadOptions.upper_bound) and [`num_partitions`](#onetl.connection.db_connection.teradata.options.TeradataReadOptions.num_partitions) are used just to - calculate the partition stride, **NOT** for filtering the rows in table. - So all rows in the table will be returned (unlike *Incremental* [Read Strategies](../../../strategy/index.md#strategy)). -
- #### NOTE - All queries are executed in parallel. To execute them sequentially, use *Batch* [Read Strategies](../../../strategy/index.md#strategy). -* `hash` - : Allocate each executor a set of values based on hash of the [`partition_column`](#onetl.connection.db_connection.teradata.options.TeradataReadOptions.partition_column) column. -
- ### Spark generates for each executor an SQL query -
- Executor 1: - ```sql - SELECT ... FROM table - WHERE (some_hash(partition_column) mod num_partitions) = 0 -- lower_bound - ``` -
- Executor 2: - ```sql - SELECT ... FROM table - WHERE (some_hash(partition_column) mod num_partitions) = 1 -- lower_bound + 1 - ``` -
- … -
- Executor N: - ```sql - SELECT ... FROM table - WHERE (some_hash(partition_column) mod num_partitions) = num_partitions-1 -- upper_bound - ``` -
- #### NOTE - The hash function implementation depends on RDBMS. It can be `MD5` or any other fast hash function, - or expression based on this function call. Usually such functions accepts any column type as an input. -* `mod` - : Allocate each executor a set of values based on modulus of the [`partition_column`](#onetl.connection.db_connection.teradata.options.TeradataReadOptions.partition_column) column. -
- ### Spark generates for each executor an SQL query -
- Executor 1: - ```sql - SELECT ... FROM table - WHERE (partition_column mod num_partitions) = 0 -- lower_bound - ``` -
- Executor 2: - ```sql - SELECT ... FROM table - WHERE (partition_column mod num_partitions) = 1 -- lower_bound + 1 - ``` -
- Executor N: - ```sql - SELECT ... FROM table - WHERE (partition_column mod num_partitions) = num_partitions-1 -- upper_bound - ``` -
- #### NOTE - Can be used only with columns of integer type. - -#### Versionadded -Added in version 0.5.0. - -### Examples - -Read data in 10 parallel jobs by range of values in `id_column` column: - -```python -ReadOptions( - partitioning_mode="range", # default mode, can be omitted - partitionColumn="id_column", - numPartitions=10, - # Options below can be discarded because they are - # calculated automatically as MIN and MAX values of `partitionColumn` - lowerBound=0, - upperBound=100_000, -) -``` - -Read data in 10 parallel jobs by hash of values in `some_column` column: - -```python -ReadOptions( - partitioning_mode="hash", - partitionColumn="some_column", - numPartitions=10, - # lowerBound and upperBound are automatically set to `0` and `9` -) -``` - -Read data in 10 parallel jobs by modulus of values in `id_column` column: - -```python -ReadOptions( - partitioning_mode="mod", - partitionColumn="id_column", - numPartitions=10, - # lowerBound and upperBound are automatically set to `0` and `9` -) -``` - - diff --git a/mddocs/markdown/connection/db_connection/teradata/sql.md b/mddocs/markdown/connection/db_connection/teradata/sql.md deleted file mode 100644 index 651dd6e5f..000000000 --- a/mddocs/markdown/connection/db_connection/teradata/sql.md +++ /dev/null @@ -1,190 +0,0 @@ - - -# Reading from Teradata using `Teradata.sql` - -`Teradata.sql` allows passing custom SQL query, but does not support incremental strategies. - -#### WARNING -Statement is executed in **read-write** connection, so if you’re calling some functions/procedures with DDL/DML statements inside, -they can change data in your database. - -## Syntax support - -Only queries with the following syntax are supported: - -* ✅︎ `SELECT ... FROM ...` -* ✅︎ `WITH alias AS (...) SELECT ...` -* ❌ `SHOW ...` -* ❌ `SET ...; SELECT ...;` - multiple statements not supported - -## Examples - -```python -from onetl.connection import Teradata - -teradata = Teradata(...) -df = teradata.sql( - """ - SELECT - id, - key, - CAST(value AS VARCHAR) AS value, - updated_at, - HASHAMP(HASHBUCKET(HASHROW(id))) MOD 10 AS part_column - FROM - database.mytable - WHERE - key = 'something' - """, - options=Teradata.SQLOptions( - partitionColumn="id", - numPartitions=10, - lowerBound=0, - upperBound=1000, - ), -) -``` - -## Recommendations - -### Select only required columns - -Instead of passing `SELECT * FROM ...` prefer passing exact column names `SELECT col1, col2, ...`. -This reduces the amount of data passed from Teradata to Spark. - -### Pay attention to `where` value - -Instead of filtering data on Spark side using `df.filter(df.column == 'value')` pass proper `WHERE column = 'value'` clause. -This both reduces the amount of data send from Teradata to Spark, and may also improve performance of the query. -Especially if there are indexes or partitions for columns used in `where` clause. - -## Options - -### *pydantic model* onetl.connection.db_connection.teradata.options.TeradataSQLOptions - -Options specifically for SQL queries - -These options allow you to specify configurations for executing SQL queries -without relying on Spark’s partitioning mechanisms. - -#### Versionadded -Added in version 0.11.0: Split up `Teradata.ReadOptions` to `Teradata.SQLOptions` - -### Examples - -#### NOTE -You can pass any JDBC configuration -[supported by Spark](https://spark.apache.org/docs/latest/sql-data-sources-jdbc.html), -tailored to optimize SQL query execution. **Option names should be in** `camelCase`! - -```python -from onetl.connection import Teradata - -options = Teradata.SQLOptions( - partitionColumn="reg_id", - numPartitions=10, - lowerBound=0, - upperBound=1000, - customSparkOption="value", -) -``` - - - -#### *field* partition_column *: str | None* *= None* *(alias 'partitionColumn')* - -Column used to partition data across multiple executors for parallel query processing. - -#### WARNING -It is highly recommended to use primary key, or column with an index -to avoid performance issues. - -### Example of using `partitionColumn="id"` with `partitioning_mode="range"` - -```sql --- If partition_column is 'id', with numPartitions=4, lowerBound=1, and upperBound=100: --- Executor 1 processes IDs from 1 to 25 -SELECT ... FROM table WHERE id >= 1 AND id < 26 --- Executor 2 processes IDs from 26 to 50 -SELECT ... FROM table WHERE id >= 26 AND id < 51 --- Executor 3 processes IDs from 51 to 75 -SELECT ... FROM table WHERE id >= 51 AND id < 76 --- Executor 4 processes IDs from 76 to 100 -SELECT ... FROM table WHERE id >= 76 AND id <= 100 - --- General case for Executor N -SELECT ... FROM table -WHERE partition_column >= (lowerBound + (N-1) * stride) -AND partition_column <= upperBound --- Where ``stride`` is calculated as ``(upperBound - lowerBound) / numPartitions``. -``` - - - -#### *field* num_partitions *: int | None* *= None* *(alias 'numPartitions')* - -Number of jobs created by Spark to read the table content in parallel. - - - -#### *field* lower_bound *: int | None* *= None* *(alias 'lowerBound')* - -Defines the starting boundary for partitioning the query’s data. Mandatory if [`partition_column`](#onetl.connection.db_connection.teradata.options.TeradataSQLOptions.partition_column) is set - - - -#### *field* upper_bound *: int | None* *= None* *(alias 'upperBound')* - -Sets the ending boundary for data partitioning. Mandatory if [`partition_column`](#onetl.connection.db_connection.teradata.options.TeradataSQLOptions.partition_column) is set - - - -#### *field* session_init_statement *: str | None* *= None* *(alias 'sessionInitStatement')* - -After each database session is opened to the remote DB and before starting to read data, -this option executes a custom SQL statement (or a PL/SQL block). - -Use this to implement session initialization code. - -Example: - -```python -sessionInitStatement = """ - BEGIN - execute immediate - 'alter session set "_serial_direct_read"=true'; - END; -""" -``` - - - -#### *field* query_timeout *: int | None* *= None* *(alias 'queryTimeout')* - -The number of seconds the driver will wait for a statement to execute. -Zero means there is no limit. - -This option depends on driver implementation, -some drivers can check the timeout of each query instead of an entire JDBC batch. - - - -#### *field* fetchsize *: int* *= 100000* - -Fetch N rows from an opened cursor per one read round. - -Tuning this option can influence performance of reading. - -#### WARNING -Default value is different from Spark. - -Spark uses driver’s own value, and it may be different in different drivers, -and even versions of the same driver. For example, Oracle has -default `fetchsize=10`, which is absolutely not usable. - -Thus we’ve overridden default value with `100_000`, which should increase reading performance. - -#### Versionchanged -Changed in version 0.2.0: Set explicit default value to `100_000` - - diff --git a/mddocs/markdown/connection/db_connection/teradata/write.md b/mddocs/markdown/connection/db_connection/teradata/write.md deleted file mode 100644 index ba160f90c..000000000 --- a/mddocs/markdown/connection/db_connection/teradata/write.md +++ /dev/null @@ -1,250 +0,0 @@ - - -# Writing to Teradata using `DBWriter` - -For writing data to Teradata, use [`DBWriter`](../../../db/db_writer.md#onetl.db.db_writer.db_writer.DBWriter). - -#### WARNING -It is always recommended to create table explicitly using [Teradata.execute](execute.md#teradata-execute) -instead of relying on Spark’s table DDL generation. - -This is because Spark’s DDL generator can create columns with different precision and types than it is expected, -causing precision loss or other issues. - -## Examples - -```python -from onetl.connection import Teradata -from onetl.db import DBWriter - -teradata = Teradata( - ..., - extra={"TYPE": "FASTLOAD", "TMODE": "TERA"}, -) - -df = ... # data is here - -writer = DBWriter( - connection=teradata, - target="database.table", - options=Teradata.WriteOptions( - if_exists="append", - # avoid creating SET table, use MULTISET - createTableOptions="NO PRIMARY INDEX", - ), -) - -writer.run(df.repartition(1)) -``` - -## Recommendations - -### Number of connections - -Teradata is not MVCC based, so write operations take exclusive lock on the entire table. -So **it is impossible to write data to Teradata table in multiple parallel connections**, no exceptions. - -The only way to write to Teradata without making deadlocks is write dataframe with exactly 1 partition. - -It can be implemented using `df.repartition(1)`: - -```python -# do NOT use df.coalesce(1) as it can freeze -writer.run(df.repartition(1)) -``` - -This moves all the data to just one Spark worker, so it may consume a lot of RAM. It is usually require to increase `spark.executor.memory` to handle this. - -Another way is to write all dataframe partitions one-by-one: - -```python -from pyspark.sql.functions import spark_partition_id - -# get list of all partitions in the dataframe -partitions = sorted(df.select(spark_partition_id()).distinct().collect()) - -for partition in partitions: - # get only part of data within this exact partition - part_df = df.where(**partition.asDict()).coalesce(1) - - writer.run(part_df) -``` - -This require even data distribution for all partitions to avoid data skew and spikes of RAM consuming. - -### Choosing connection type - -Teradata supports several [different connection types](https://teradata-docs.s3.amazonaws.com/doc/connectivity/jdbc/reference/current/jdbcug_chapter_2.html#BABFGFAF): -: * `TYPE=DEFAULT` - perform plain `INSERT` queries - * `TYPE=FASTLOAD` - uses special FastLoad protocol for insert queries - -It is always recommended to use `TYPE=FASTLOAD` because: -: * It provides higher performance - * It properly handles inserting `NULL` values (`TYPE=DEFAULT` raises an exception) - -But it can be used only during write, not read. - -### Choosing transaction mode - -Teradata supports [2 different transaction modes](https://teradata-docs.s3.amazonaws.com/doc/connectivity/jdbc/reference/current/jdbcug_chapter_2.html#TMODESEC): -: * `TMODE=ANSI` - * `TMODE=TERA` - -Choosing one of the modes can alter connector behavior. For example: -: * Inserting data which exceeds table column length, like insert `CHAR(25)` to column with type `CHAR(24)`: - * * `TMODE=ANSI` - raises exception - * * `TMODE=TERA` - truncates input string to 24 symbols - * Creating table using Spark: - * * `TMODE=ANSI` - creates `MULTISET` table - * * `TMODE=TERA` - creates `SET` table with `PRIMARY KEY` is a first column in dataframe. - This can lead to slower insert time, because each row will be checked against a unique index. - Fortunately, this can be disabled by passing custom `createTableOptions`. - -## Options - -Method above accepts [`Teradata.WriteOptions`](#onetl.connection.db_connection.teradata.options.TeradataWriteOptions) - -### *pydantic model* onetl.connection.db_connection.teradata.options.TeradataWriteOptions - -Spark JDBC writing options. - -#### Versionadded -Added in version 0.5.0: Replace `Teradata.Options` → `Teradata.WriteOptions` - -### Examples - -#### NOTE -You can pass any value -[supported by Spark](https://spark.apache.org/docs/latest/sql-data-sources-jdbc.html), -even if it is not mentioned in this documentation. **Option names should be in** `camelCase`! - -The set of supported options depends on Spark version. - -```python -from onetl.connection import Teradata - -options = Teradata.WriteOptions( - if_exists="append", - batchsize=20_000, - customSparkOption="value", -) -``` - - - -#### *field* if_exists *: JDBCTableExistBehavior* *= JDBCTableExistBehavior.APPEND* *(alias 'mode')* - -Behavior of writing data into existing table. - -Possible values: -: * `append` (default) - : Adds new rows into existing table. -
- ### Behavior in details -
- * Table does not exist - : Table is created using options provided by user - (`createTableOptions`, `createTableColumnTypes`, etc). - * Table exists - : Data is appended to a table. Table has the same DDL as before writing data -
- #### WARNING - This mode does not check whether table already contains - rows from dataframe, so duplicated rows can be created. -
- Also Spark does not support passing custom options to - insert statement, like `ON CONFLICT`, so don’t try to - implement deduplication using unique indexes or constraints. -
- Instead, write to staging table and perform deduplication - using `execute` method. - * `replace_entire_table` - : **Table is dropped and then created, or truncated**. -
- ### Behavior in details -
- * Table does not exist - : Table is created using options provided by user - (`createTableOptions`, `createTableColumnTypes`, etc). - * Table exists - : Table content is replaced with dataframe content. -
- After writing completed, target table could either have the same DDL as - before writing data (`truncate=True`), or can be recreated (`truncate=False` - or source does not support truncation). - * `ignore` - : Ignores the write operation if the table already exists. -
- ### Behavior in details -
- * Table does not exist - : Table is created using options provided by user - (`createTableOptions`, `createTableColumnTypes`, etc). - * Table exists - : The write operation is ignored, and no data is written to the table. - * `error` - : Raises an error if the table already exists. -
- ### Behavior in details -
- * Table does not exist - : Table is created using options provided by user - (`createTableOptions`, `createTableColumnTypes`, etc). - * Table exists - : An error is raised, and no data is written to the table. - -#### Versionchanged -Changed in version 0.9.0: Renamed `mode` → `if_exists` - - - -#### *field* query_timeout *: int | None* *= None* *(alias 'queryTimeout')* - -The number of seconds the driver will wait for a statement to execute. -Zero means there is no limit. - -This option depends on driver implementation, -some drivers can check the timeout of each query instead of an entire JDBC batch. - - - -#### *field* batchsize *: int* *= 20000* - -How many rows can be inserted per round trip. - -Tuning this option can influence performance of writing. - -#### WARNING -Default value is different from Spark. - -Spark uses quite small value `1000`, which is absolutely not usable -in BigData world. - -Thus we’ve overridden default value with `20_000`, -which should increase writing performance. - -You can increase it even more, up to `50_000`, -but it depends on your database load and number of columns in the row. -Higher values does not increase performance. - -#### Versionchanged -Changed in version 0.4.0: Changed default value from 1000 to 20_000 - - - -#### *field* isolation_level *: str* *= 'READ_UNCOMMITTED'* *(alias 'isolationLevel')* - -The transaction isolation level, which applies to current connection. - -Possible values: -: * `NONE` (as string, not Python’s `None`) - * `READ_COMMITTED` - * `READ_UNCOMMITTED` - * `REPEATABLE_READ` - * `SERIALIZABLE` - -Values correspond to transaction isolation levels defined by JDBC standard. -Please refer the documentation for -[java.sql.Connection](https://docs.oracle.com/javase/8/docs/api/java/sql/Connection.html). - - diff --git a/mddocs/markdown/connection/file_connection/ftp.md b/mddocs/markdown/connection/file_connection/ftp.md deleted file mode 100644 index 6ecce3655..000000000 --- a/mddocs/markdown/connection/file_connection/ftp.md +++ /dev/null @@ -1,627 +0,0 @@ - - -# FTP connection - -### *class* onetl.connection.file_connection.ftp.FTP(\*, host: Host, port: int = 21, user: str | None = None, password: SecretStr | None = None) - -FTP file connection. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -Based on [FTPUtil library](https://pypi.org/project/ftputil/). - -#### WARNING -Since onETL v0.7.0 to use FTP connector you should install package as follows: - -```bash -pip install onetl[ftp] - -# or -pip install onetl[files] -``` - -See [File connections](../../install/files.md#install-files) installation instruction for more details. - -#### Versionadded -Added in version 0.1.0. - -* **Parameters:** - **host** - : Host of FTP source. For example: `ftp.domain.com` - - **port** - : Port of FTP source - - **user** - : User, which have access to the file source. For example: `someuser`. -
- `None` means that the user is anonymous. - - **password** - : Password for file source connection. -
- `None` means that the user is anonymous. - -### Examples - -Create and check FTP connection: - -```python -from onetl.connection import FTP - -ftp = FTP( - host="ftp.domain.com", - user="someuser", - password="*****", -).check() -``` - - - -#### \_\_init_\_(\*\*kwargs) - - - -#### check() - -Check source availability. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -If not, an exception will be raised. - -* **Returns:** - Connection itself -* **Raises:** - RuntimeError - : If the connection is not available - -### Examples - -```python -connection.check() -``` - - - -#### create_dir(path: PathLike | str) → RemoteDirectory - -Creates directory tree on remote filesystem. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **path** - : Directory path -* **Returns:** - Created directory with stats -* **Raises:** - `onetl.exception.NotAFileError` - : Path is not a file - -### Examples - -```pycon ->>> dir_path = connection.create_dir("/path/to/dir") ->>> os.fspath(dir_path) -'/path/to/dir' -``` - - - -#### download_file(remote_file_path: PathLike | str, local_file_path: PathLike | str, replace: bool = True) → LocalPath - -Downloads file from the remote filesystem to a local path. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### WARNING -Supports only one file download per call. Directory download is **NOT** supported, use [File Downloader](../../file/file_downloader/file_downloader.md#file-downloader) instead. - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **remote_file_path** - : Remote file path to read from - - **local_file_path** - : Local file path to create - - **replace** - : If `True`, existing file will be replaced -* **Returns:** - Local file with stats. -* **Raises:** - `onetl.exception.NotAFileError` - : Remote or local path is not a file - - FileNotFoundError - : Remote file does not exist - - FileExistsError - : Local file already exists, and `replace=False` - - `onetl.exception.FileSizeMismatchError` - : Target file size after download is different from source file size. - -### Examples - -```pycon ->>> local_file = connection.download_file( -... remote_file_path="/path/to/source.csv", -... local_file_path="/path/to/target.csv", -... ) ->>> os.fspath(local_file) -'/path/to/target.csv' ->>> local_file.exists() -True ->>> local_file.stat().st_size # in bytes -1024 ->>> connection.get_stat("/path/to/source.csv").st_size # same size -1024 -``` - - - -#### get_stat(path: PathLike | str) → PathStatProtocol - -Returns stats for a specific path. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **path** - : Path to get stats for -* **Returns:** - Stats object -* **Raises:** - Any underlying client exception - -### Examples - -```pycon ->>> stat = connection.get_stat("/path/to/file.csv") ->>> stat.st_size # in bytes -1024 ->>> stat.st_uid # owner id or name -12345 -``` - - - -#### is_dir(path: PathLike | str) → bool - -Check if specified path is a directory. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **path** - : Path to check -* **Returns:** - `True` if path is a directory, `False` otherwise. -* **Raises:** - `onetl.exception.DirectoryNotFoundError` - : Path does not exist - -### Examples - -```pycon ->>> connection.is_dir("/path/to/dir") -True ->>> connection.is_dir("/path/to/dir/file.csv") -False -``` - - - -#### is_file(path: PathLike | str) → bool - -Check if specified path is a file. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **path** - : Path to check -* **Returns:** - `True` if path is a file, `False` otherwise. -* **Raises:** - FileNotFoundError - : Path does not exist - -### Examples - -```pycon ->>> connection.is_file("/path/to/dir/file.csv") -True ->>> connection.is_file("/path/to/dir") -False -``` - - - -#### list_dir(path: PathLike | str, filters: Iterable[[BaseFileFilter](../../file/file_filters/base.md#onetl.base.base_file_filter.BaseFileFilter)] | None = None, limits: Iterable[[BaseFileLimit](../../file/file_limits/base.md#onetl.base.base_file_limit.BaseFileLimit)] | None = None) → list[RemoteDirectory | RemoteFile] - -Return list of child files/directories in a specific directory. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **path** - : Directory path to list contents. - - **filters** - : Return only files/directories matching these filters. See [File Filters](../../file/file_filters/index.md#file-filters) - - **limits** - : Apply limits to the list of files/directories, and stop if one of the limits is reached. - See [File Limits](../../file/file_limits/index.md#file-limits) -* **Returns:** - List of `onetl.base.PathWithStatsProtocol` -* **Raises:** - NotADirectoryError - : Path is not a directory - - `onetl.exception.DirectoryNotFoundError` - : Path does not exist - -### Examples - -```pycon ->>> dir_content = connection.list_dir("/path/to/dir") ->>> os.fspath(dir_content[0]) -'file.csv' ->>> connection.path_exists("/path/to/dir/file.csv") -True -``` - - - -#### path_exists(path: PathLike | str) → bool - -Check if specified path exists on remote filesystem. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html). - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **path** - : Path to check -* **Returns:** - `True` if path exists, `False` otherwise - -### Examples - -```pycon ->>> connection.path_exists("/path/to/file.csv") -True ->>> connection.path_exists("/path/to/dir") -True ->>> connection.path_exists("/path/to/missing") -False -``` - - - -#### remove_dir(path: PathLike | str, recursive: bool = False) → bool - -Remove directory or directory tree. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -If directory does not exist, no exception is raised. - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **path** - : Directory path to remote - - **recursive** - : If `True`, remove directory tree recursively. -* **Returns:** - `True` if directory was removed, `False` if directory does not exist in the first place. -* **Raises:** - NotADirectoryError - : Path is not a directory - -### Examples - -```pycon ->>> connection.remove_dir("/path/to/dir") -True ->>> connection.path_exists("/path/to/dir") -False ->>> connection.path_exists("/path/to/dir/file.csv") -False ->>> connection.remove_dir("/path/to/dir") # already deleted, no error -False -``` - - - -#### remove_file(path: PathLike | str) → bool - -Removes file on remote filesystem. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -If file does not exist, no exception is raised. - -#### WARNING -Supports only one file removal per call. Directory removal is **NOT** supported, use [`remove_dir`](#onetl.connection.file_connection.ftp.FTP.remove_dir) instead. - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **path** - : File path -* **Returns:** - `True` if file was removed, `False` if file does not exist in the first place. -* **Raises:** - `onetl.exception.NotAFileError` - : Path is not a file - -### Examples - -```pycon ->>> connection.remove_file("/path/to/file.csv") -True ->>> connection.path_exists("/path/to/dir/file.csv") -False ->>> connection.remove_file("/path/to/file.csv") # already deleted, no error -False -``` - - - -#### rename_dir(source_dir_path: PathLike | str, target_dir_path: PathLike | str, replace: bool = False) → RemoteDirectory - -Rename or move dir on remote filesystem. - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **source_dir_path** - : Old directory path - - **target_dir_path** - : New directory path - - **replace** - : If `True`, existing directory will be replaced. -* **Returns:** - New directory path with stats. -* **Raises:** - NotADirectoryError - : Path is not a directory - - `onetl.exception.DirectoryNotFoundError` - : Path does not exist - - `onetl.exception.DirectoryExistsError` - : Directory already exists, and `replace=False` - -### Examples - -```pycon ->>> new_dir = connection.rename_dir("/path/to/dir1", "/path/to/dir2") ->>> os.fspath(new_dir) -'/path/to/dir2' ->>> connection.path_exists("/path/to/dir1") -False ->>> connection.path_exists("/path/to/dir2") -True -``` - - - -#### rename_file(source_file_path: PathLike | str, target_file_path: PathLike | str, replace: bool = False) → RemoteFile - -Rename or move file on remote filesystem. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### WARNING -Supports only one file move per call. Directory move/rename is **NOT** supported. - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **source_file_path** - : Old file path - - **target_file_path** - : New file path - - **replace** - : If `True`, existing file will be replaced. -* **Returns:** - New file path with stats. -* **Raises:** - `onetl.exception.NotAFileError` - : Source or target path is not a file - - FileNotFoundError - : File does not exist - - FileExistsError - : File already exists, and `replace=False` - -### Examples - -```pycon ->>> new_file = connection.rename_file("/path/to/file1.csv", "/path/to/file2.csv") ->>> os.fspath(new_file) -'/path/to/file2.csv' ->>> connection.path_exists("/path/to/file2.csv") -True ->>> connection.path_exists("/path/to/file1.csv") -False -``` - - - -#### resolve_dir(path: PathLike | str) → RemoteDirectory - -Returns directory at specific path, with stats. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **path** - : Path to resolve -* **Returns:** - Directory path with stats -* **Raises:** - `onetl.exception.DirectoryNotFoundError` - : Path does not exist - - NotADirectoryError - : Path is not a directory - -### Examples - -```pycon ->>> dir_path = connection.resolve_dir("/path/to/dir") ->>> os.fspath(dir_path) -'/path/to/dir' ->>> dir_path.stat().st_uid # owner id -12345 -``` - - - -#### resolve_file(path: PathLike | str) → RemoteFile - -Returns file at specific path, with stats. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **path** - : Path to resolve -* **Returns:** - File path with stats -* **Raises:** - FileNotFoundError - : Path does not exist - - `onetl.exception.NotAFileError` - : Path is not a file - -### Examples - -```pycon ->>> file_path = connection.resolve_file("/path/to/dir/file.csv") ->>> os.fspath(file_path) -'/path/to/dir/file.csv' ->>> file_path.stat().st_uid # owner id -12345 -``` - - - -#### upload_file(local_file_path: PathLike | str, remote_file_path: PathLike | str, replace: bool = False) → RemoteFile - -Uploads local file to a remote filesystem. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### WARNING -Supports only one file upload per call. Directory upload is **NOT** supported, use [File Uploader](../../file/file_uploader/file_uploader.md#file-uploader) instead. - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **local_file_path** - : Local file path to read from - - **remote_file_path** - : Remote file path to create - - **replace** - : If `True`, existing file will be replaced -* **Returns:** - Remote file with stats. -* **Raises:** - `onetl.exception.NotAFileError` - : Remote or local path is not a file - - FileNotFoundError - : Local file does not exist - - FileExistsError - : Remote file already exists, and `replace=False` - - `onetl.exception.FileSizeMismatchError` - : Target file size after upload is different from source file size. - -### Examples - -```pycon ->>> remote_file = connection.upload( -... local_file_path="/path/to/source.csv", -... remote_file_path="/path/to/target.csv", -... ) ->>> os.fspath(remote_file) -'/path/to/target.csv' ->>> connection.path_exists("/path/to/target.csv") -True ->>> remote_file.stat().st_size # in bytes -1024 ->>> os.stat("/path/to/source.csv").st_size # same as source -1024 -``` - - - -#### walk(root: PathLike | str, topdown: bool = True, filters: Iterable[[BaseFileFilter](../../file/file_filters/base.md#onetl.base.base_file_filter.BaseFileFilter)] | None = None, limits: Iterable[[BaseFileLimit](../../file/file_limits/base.md#onetl.base.base_file_limit.BaseFileLimit)] | None = None) → Iterator[tuple[RemoteDirectory, list[RemoteDirectory], list[RemoteFile]]] - -Walk into directory tree, and iterate over its content in all nesting levels. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -Just like `os.walk`, but with additional filter/limit logic. - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **root** - : Directory path to walk into. - - **topdown** - : If `True`, walk in top-down order, otherwise walk in bottom-up order. - - **filters** - : Return only files/directories matching these filters. See [File Filters](../../file/file_filters/index.md#file-filters). - - **limits** - : Apply limits to the list of files/directories, and immediately stop if any of these limits is reached. - See [File Limits](../../file/file_limits/index.md#file-limits). -* **Returns:** - `Iterator[tuple[root, dirs, files]]`, like `os.walk`. - - But all the paths are not strings, instead path classes with embedded stats are returned. -* **Raises:** - NotADirectoryError - : Path is not a directory - - `onetl.exception.DirectoryNotFoundError` - : Path does not exist - -### Examples - -```pycon ->>> for root, dirs, files in connection.walk("/path/to/dir"): -... break ->>> os.fspath(root) -'/path/to/dir' ->>> dirs -[] ->>> os.fspath(files[0]) -'file.csv' ->>> connection.path_exists("/path/to/dir/file.csv") -True -``` - - diff --git a/mddocs/markdown/connection/file_connection/ftps.md b/mddocs/markdown/connection/file_connection/ftps.md deleted file mode 100644 index e115be98b..000000000 --- a/mddocs/markdown/connection/file_connection/ftps.md +++ /dev/null @@ -1,627 +0,0 @@ - - -# FTPS connection - -### *class* onetl.connection.file_connection.ftps.FTPS(\*, host: Host, port: int = 21, user: str | None = None, password: SecretStr | None = None) - -FTPS file connection. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -Based on [FTPUtil library](https://pypi.org/project/ftputil/). - -#### WARNING -Since onETL v0.7.0 to use FTPS connector you should install package as follows: - -```bash -pip install onetl[ftps] - -# or -pip install onetl[files] -``` - -See [File connections](../../install/files.md#install-files) installation instruction for more details. - -#### Versionadded -Added in version 0.1.0. - -* **Parameters:** - **host** - : Host of FTPS source. For example: `ftps.domain.com` - - **port** - : Port of FTPS source - - **user** - : User, which have access to the file source. For example: `someuser`. -
- `None` means that the user is anonymous. - - **password** - : Password for file source connection. -
- `None` means that the user is anonymous. - -### Examples - -Create and check FTPS connection: - -```python -from onetl.connection import FTPS - -ftps = FTPS( - host="ftps.domain.com", - user="someuser", - password="*****", -).check() -``` - - - -#### \_\_init_\_(\*\*kwargs) - - - -#### check() - -Check source availability. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -If not, an exception will be raised. - -* **Returns:** - Connection itself -* **Raises:** - RuntimeError - : If the connection is not available - -### Examples - -```python -connection.check() -``` - - - -#### create_dir(path: PathLike | str) → RemoteDirectory - -Creates directory tree on remote filesystem. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **path** - : Directory path -* **Returns:** - Created directory with stats -* **Raises:** - `onetl.exception.NotAFileError` - : Path is not a file - -### Examples - -```pycon ->>> dir_path = connection.create_dir("/path/to/dir") ->>> os.fspath(dir_path) -'/path/to/dir' -``` - - - -#### download_file(remote_file_path: PathLike | str, local_file_path: PathLike | str, replace: bool = True) → LocalPath - -Downloads file from the remote filesystem to a local path. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### WARNING -Supports only one file download per call. Directory download is **NOT** supported, use [File Downloader](../../file/file_downloader/file_downloader.md#file-downloader) instead. - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **remote_file_path** - : Remote file path to read from - - **local_file_path** - : Local file path to create - - **replace** - : If `True`, existing file will be replaced -* **Returns:** - Local file with stats. -* **Raises:** - `onetl.exception.NotAFileError` - : Remote or local path is not a file - - FileNotFoundError - : Remote file does not exist - - FileExistsError - : Local file already exists, and `replace=False` - - `onetl.exception.FileSizeMismatchError` - : Target file size after download is different from source file size. - -### Examples - -```pycon ->>> local_file = connection.download_file( -... remote_file_path="/path/to/source.csv", -... local_file_path="/path/to/target.csv", -... ) ->>> os.fspath(local_file) -'/path/to/target.csv' ->>> local_file.exists() -True ->>> local_file.stat().st_size # in bytes -1024 ->>> connection.get_stat("/path/to/source.csv").st_size # same size -1024 -``` - - - -#### get_stat(path: PathLike | str) → PathStatProtocol - -Returns stats for a specific path. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **path** - : Path to get stats for -* **Returns:** - Stats object -* **Raises:** - Any underlying client exception - -### Examples - -```pycon ->>> stat = connection.get_stat("/path/to/file.csv") ->>> stat.st_size # in bytes -1024 ->>> stat.st_uid # owner id or name -12345 -``` - - - -#### is_dir(path: PathLike | str) → bool - -Check if specified path is a directory. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **path** - : Path to check -* **Returns:** - `True` if path is a directory, `False` otherwise. -* **Raises:** - `onetl.exception.DirectoryNotFoundError` - : Path does not exist - -### Examples - -```pycon ->>> connection.is_dir("/path/to/dir") -True ->>> connection.is_dir("/path/to/dir/file.csv") -False -``` - - - -#### is_file(path: PathLike | str) → bool - -Check if specified path is a file. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **path** - : Path to check -* **Returns:** - `True` if path is a file, `False` otherwise. -* **Raises:** - FileNotFoundError - : Path does not exist - -### Examples - -```pycon ->>> connection.is_file("/path/to/dir/file.csv") -True ->>> connection.is_file("/path/to/dir") -False -``` - - - -#### list_dir(path: PathLike | str, filters: Iterable[[BaseFileFilter](../../file/file_filters/base.md#onetl.base.base_file_filter.BaseFileFilter)] | None = None, limits: Iterable[[BaseFileLimit](../../file/file_limits/base.md#onetl.base.base_file_limit.BaseFileLimit)] | None = None) → list[RemoteDirectory | RemoteFile] - -Return list of child files/directories in a specific directory. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **path** - : Directory path to list contents. - - **filters** - : Return only files/directories matching these filters. See [File Filters](../../file/file_filters/index.md#file-filters) - - **limits** - : Apply limits to the list of files/directories, and stop if one of the limits is reached. - See [File Limits](../../file/file_limits/index.md#file-limits) -* **Returns:** - List of `onetl.base.PathWithStatsProtocol` -* **Raises:** - NotADirectoryError - : Path is not a directory - - `onetl.exception.DirectoryNotFoundError` - : Path does not exist - -### Examples - -```pycon ->>> dir_content = connection.list_dir("/path/to/dir") ->>> os.fspath(dir_content[0]) -'file.csv' ->>> connection.path_exists("/path/to/dir/file.csv") -True -``` - - - -#### path_exists(path: PathLike | str) → bool - -Check if specified path exists on remote filesystem. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html). - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **path** - : Path to check -* **Returns:** - `True` if path exists, `False` otherwise - -### Examples - -```pycon ->>> connection.path_exists("/path/to/file.csv") -True ->>> connection.path_exists("/path/to/dir") -True ->>> connection.path_exists("/path/to/missing") -False -``` - - - -#### remove_dir(path: PathLike | str, recursive: bool = False) → bool - -Remove directory or directory tree. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -If directory does not exist, no exception is raised. - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **path** - : Directory path to remote - - **recursive** - : If `True`, remove directory tree recursively. -* **Returns:** - `True` if directory was removed, `False` if directory does not exist in the first place. -* **Raises:** - NotADirectoryError - : Path is not a directory - -### Examples - -```pycon ->>> connection.remove_dir("/path/to/dir") -True ->>> connection.path_exists("/path/to/dir") -False ->>> connection.path_exists("/path/to/dir/file.csv") -False ->>> connection.remove_dir("/path/to/dir") # already deleted, no error -False -``` - - - -#### remove_file(path: PathLike | str) → bool - -Removes file on remote filesystem. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -If file does not exist, no exception is raised. - -#### WARNING -Supports only one file removal per call. Directory removal is **NOT** supported, use [`remove_dir`](#onetl.connection.file_connection.ftps.FTPS.remove_dir) instead. - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **path** - : File path -* **Returns:** - `True` if file was removed, `False` if file does not exist in the first place. -* **Raises:** - `onetl.exception.NotAFileError` - : Path is not a file - -### Examples - -```pycon ->>> connection.remove_file("/path/to/file.csv") -True ->>> connection.path_exists("/path/to/dir/file.csv") -False ->>> connection.remove_file("/path/to/file.csv") # already deleted, no error -False -``` - - - -#### rename_dir(source_dir_path: PathLike | str, target_dir_path: PathLike | str, replace: bool = False) → RemoteDirectory - -Rename or move dir on remote filesystem. - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **source_dir_path** - : Old directory path - - **target_dir_path** - : New directory path - - **replace** - : If `True`, existing directory will be replaced. -* **Returns:** - New directory path with stats. -* **Raises:** - NotADirectoryError - : Path is not a directory - - `onetl.exception.DirectoryNotFoundError` - : Path does not exist - - `onetl.exception.DirectoryExistsError` - : Directory already exists, and `replace=False` - -### Examples - -```pycon ->>> new_dir = connection.rename_dir("/path/to/dir1", "/path/to/dir2") ->>> os.fspath(new_dir) -'/path/to/dir2' ->>> connection.path_exists("/path/to/dir1") -False ->>> connection.path_exists("/path/to/dir2") -True -``` - - - -#### rename_file(source_file_path: PathLike | str, target_file_path: PathLike | str, replace: bool = False) → RemoteFile - -Rename or move file on remote filesystem. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### WARNING -Supports only one file move per call. Directory move/rename is **NOT** supported. - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **source_file_path** - : Old file path - - **target_file_path** - : New file path - - **replace** - : If `True`, existing file will be replaced. -* **Returns:** - New file path with stats. -* **Raises:** - `onetl.exception.NotAFileError` - : Source or target path is not a file - - FileNotFoundError - : File does not exist - - FileExistsError - : File already exists, and `replace=False` - -### Examples - -```pycon ->>> new_file = connection.rename_file("/path/to/file1.csv", "/path/to/file2.csv") ->>> os.fspath(new_file) -'/path/to/file2.csv' ->>> connection.path_exists("/path/to/file2.csv") -True ->>> connection.path_exists("/path/to/file1.csv") -False -``` - - - -#### resolve_dir(path: PathLike | str) → RemoteDirectory - -Returns directory at specific path, with stats. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **path** - : Path to resolve -* **Returns:** - Directory path with stats -* **Raises:** - `onetl.exception.DirectoryNotFoundError` - : Path does not exist - - NotADirectoryError - : Path is not a directory - -### Examples - -```pycon ->>> dir_path = connection.resolve_dir("/path/to/dir") ->>> os.fspath(dir_path) -'/path/to/dir' ->>> dir_path.stat().st_uid # owner id -12345 -``` - - - -#### resolve_file(path: PathLike | str) → RemoteFile - -Returns file at specific path, with stats. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **path** - : Path to resolve -* **Returns:** - File path with stats -* **Raises:** - FileNotFoundError - : Path does not exist - - `onetl.exception.NotAFileError` - : Path is not a file - -### Examples - -```pycon ->>> file_path = connection.resolve_file("/path/to/dir/file.csv") ->>> os.fspath(file_path) -'/path/to/dir/file.csv' ->>> file_path.stat().st_uid # owner id -12345 -``` - - - -#### upload_file(local_file_path: PathLike | str, remote_file_path: PathLike | str, replace: bool = False) → RemoteFile - -Uploads local file to a remote filesystem. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### WARNING -Supports only one file upload per call. Directory upload is **NOT** supported, use [File Uploader](../../file/file_uploader/file_uploader.md#file-uploader) instead. - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **local_file_path** - : Local file path to read from - - **remote_file_path** - : Remote file path to create - - **replace** - : If `True`, existing file will be replaced -* **Returns:** - Remote file with stats. -* **Raises:** - `onetl.exception.NotAFileError` - : Remote or local path is not a file - - FileNotFoundError - : Local file does not exist - - FileExistsError - : Remote file already exists, and `replace=False` - - `onetl.exception.FileSizeMismatchError` - : Target file size after upload is different from source file size. - -### Examples - -```pycon ->>> remote_file = connection.upload( -... local_file_path="/path/to/source.csv", -... remote_file_path="/path/to/target.csv", -... ) ->>> os.fspath(remote_file) -'/path/to/target.csv' ->>> connection.path_exists("/path/to/target.csv") -True ->>> remote_file.stat().st_size # in bytes -1024 ->>> os.stat("/path/to/source.csv").st_size # same as source -1024 -``` - - - -#### walk(root: PathLike | str, topdown: bool = True, filters: Iterable[[BaseFileFilter](../../file/file_filters/base.md#onetl.base.base_file_filter.BaseFileFilter)] | None = None, limits: Iterable[[BaseFileLimit](../../file/file_limits/base.md#onetl.base.base_file_limit.BaseFileLimit)] | None = None) → Iterator[tuple[RemoteDirectory, list[RemoteDirectory], list[RemoteFile]]] - -Walk into directory tree, and iterate over its content in all nesting levels. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -Just like `os.walk`, but with additional filter/limit logic. - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **root** - : Directory path to walk into. - - **topdown** - : If `True`, walk in top-down order, otherwise walk in bottom-up order. - - **filters** - : Return only files/directories matching these filters. See [File Filters](../../file/file_filters/index.md#file-filters). - - **limits** - : Apply limits to the list of files/directories, and immediately stop if any of these limits is reached. - See [File Limits](../../file/file_limits/index.md#file-limits). -* **Returns:** - `Iterator[tuple[root, dirs, files]]`, like `os.walk`. - - But all the paths are not strings, instead path classes with embedded stats are returned. -* **Raises:** - NotADirectoryError - : Path is not a directory - - `onetl.exception.DirectoryNotFoundError` - : Path does not exist - -### Examples - -```pycon ->>> for root, dirs, files in connection.walk("/path/to/dir"): -... break ->>> os.fspath(root) -'/path/to/dir' ->>> dirs -[] ->>> os.fspath(files[0]) -'file.csv' ->>> connection.path_exists("/path/to/dir/file.csv") -True -``` - - diff --git a/mddocs/markdown/connection/file_connection/hdfs/connection.md b/mddocs/markdown/connection/file_connection/hdfs/connection.md deleted file mode 100644 index d8f53ee60..000000000 --- a/mddocs/markdown/connection/file_connection/hdfs/connection.md +++ /dev/null @@ -1,740 +0,0 @@ - - -# HDFS connection - -### *class* onetl.connection.file_connection.hdfs.connection.HDFS(\*, cluster: Cluster | None = None, host: Host | None = None, port: int = 50070, user: str | None = None, password: SecretStr | None = None, keytab: FilePath | None = None, timeout: int = 10) - -HDFS file connection. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -Powered by [HDFS Python client](https://pypi.org/project/hdfs/). - -#### WARNING -Since onETL v0.7.0 to use HDFS connector you should install package as follows: - -```bash -pip install onetl[hdfs] - -# or -pip install onetl[files] -``` - -See [File connections](../../../install/files.md#install-files) installation instruction for more details. - -#### NOTE -To access Hadoop cluster with Kerberos installed, you should have `kinit` executable -in some path in `PATH` environment variable. - -See [Kerberos support](../../../install/kerberos.md#install-kerberos) instruction for more details. - -* **Parameters:** - **cluster** - : Hadoop cluster name. For example: `rnd-dwh`. -
- Used for: - : * HWM and lineage (as instance name for file paths), if set. - * Validation of `host` value, - : if latter is passed and if some hooks are bound to - [`Slots.get_cluster_namenodes`](slots.md#onetl.connection.file_connection.hdfs.slots.HDFSSlots.get_cluster_namenodes) -
- -
- #### Versionadded - Added in version 0.7.0. - - **host** - : Hadoop namenode host. For example: `namenode1.domain.com`. -
- Should be an active namenode (NOT standby). -
- If value is not set, but there are some hooks bound to - [`Slots.get_cluster_namenodes`](slots.md#onetl.connection.file_connection.hdfs.slots.HDFSSlots.get_cluster_namenodes) - and [`Slots.is_namenode_active`](slots.md#onetl.connection.file_connection.hdfs.slots.HDFSSlots.is_namenode_active), - onETL will iterate over cluster namenodes to detect which one is active. - - - **webhdfs_port** - : Port of Hadoop namenode (WebHDFS protocol). -
- If omitted, but there are some hooks bound to - [`Slots.get_webhdfs_port`](slots.md#onetl.connection.file_connection.hdfs.slots.HDFSSlots.get_webhdfs_port) slot, - onETL will try to detect port number for a specific `cluster`. - - **user** - : User, which have access to the file source. For example: `someuser`. -
- If set, Kerberos auth will be used. Otherwise an anonymous connection is created. - - **password** - : User password. -
- Used for generating Kerberos ticket. -
- #### WARNING - You can provide only one of the parameters: `password` or `kinit`. - If you provide both, an exception will be raised. - - **keytab** - : LocalPath to keytab file. -
- Used for generating Kerberos ticket. -
- #### WARNING - You can provide only one of the parameters: `password` or `kinit`. - If you provide both, an exception will be raised. - - **timeout** - : Connection timeout. - -### Examples - -Create HDFS connection with user+password - -```py -from onetl.connection import HDFS - -hdfs = HDFS( - host="namenode1.domain.com", - user="someuser", - password="*****", -).check() -``` - -Create HDFS connection with user+keytab - -```py -from onetl.connection import HDFS - -hdfs = HDFS( - host="namenode1.domain.com", - user="someuser", - keytab="/path/to/keytab", -).check() -``` - -Create HDFS connection without auth - -```py -from onetl.connection import HDFS - -hdfs = HDFS(host="namenode1.domain.com").check() -``` - -Use cluster name to detect active namenode - -Can be used only if some third-party plugin provides [HDFS Slots](slots.md#hdfs-slots) implementation - -```python -from onetl.connection import HDFS - -hdfs = HDFS( - cluster="rnd-dwh", - user="someuser", - password="*****", -).check() -``` - - - -#### check() - -Check source availability. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -If not, an exception will be raised. - -* **Returns:** - Connection itself -* **Raises:** - RuntimeError - : If the connection is not available - -### Examples - -```python -connection.check() -``` - - - -#### create_dir(path: PathLike | str) → RemoteDirectory - -Creates directory tree on remote filesystem. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **path** - : Directory path -* **Returns:** - Created directory with stats -* **Raises:** - `onetl.exception.NotAFileError` - : Path is not a file - -### Examples - -```pycon ->>> dir_path = connection.create_dir("/path/to/dir") ->>> os.fspath(dir_path) -'/path/to/dir' -``` - - - -#### download_file(remote_file_path: PathLike | str, local_file_path: PathLike | str, replace: bool = True) → LocalPath - -Downloads file from the remote filesystem to a local path. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### WARNING -Supports only one file download per call. Directory download is **NOT** supported, use [File Downloader](../../../file/file_downloader/file_downloader.md#file-downloader) instead. - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **remote_file_path** - : Remote file path to read from - - **local_file_path** - : Local file path to create - - **replace** - : If `True`, existing file will be replaced -* **Returns:** - Local file with stats. -* **Raises:** - `onetl.exception.NotAFileError` - : Remote or local path is not a file - - FileNotFoundError - : Remote file does not exist - - FileExistsError - : Local file already exists, and `replace=False` - - `onetl.exception.FileSizeMismatchError` - : Target file size after download is different from source file size. - -### Examples - -```pycon ->>> local_file = connection.download_file( -... remote_file_path="/path/to/source.csv", -... local_file_path="/path/to/target.csv", -... ) ->>> os.fspath(local_file) -'/path/to/target.csv' ->>> local_file.exists() -True ->>> local_file.stat().st_size # in bytes -1024 ->>> connection.get_stat("/path/to/source.csv").st_size # same size -1024 -``` - - - -#### *classmethod* get_current(\*\*kwargs) - -Create connection for current cluster. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -Automatically sets up current cluster name as `cluster`. - -#### NOTE -Can be used only if there are a some hooks bound to slot -[`Slots.get_current_cluster`](slots.md#onetl.connection.file_connection.hdfs.slots.HDFSSlots.get_current_cluster) - -#### Versionadded -Added in version 0.7.0. - -* **Parameters:** - **user** - - **password** - - **keytab** - - **timeout** - : See [`HDFS`](#onetl.connection.file_connection.hdfs.connection.HDFS) constructor documentation. - -### Examples - -```python -from onetl.connection import HDFS - -# injecting current cluster name via hooks mechanism -hdfs = HDFS.get_current(user="me", password="pass") -``` - - - -#### get_stat(path: PathLike | str) → PathStatProtocol - -Returns stats for a specific path. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **path** - : Path to get stats for -* **Returns:** - Stats object -* **Raises:** - Any underlying client exception - -### Examples - -```pycon ->>> stat = connection.get_stat("/path/to/file.csv") ->>> stat.st_size # in bytes -1024 ->>> stat.st_uid # owner id or name -12345 -``` - - - -#### is_dir(path: PathLike | str) → bool - -Check if specified path is a directory. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **path** - : Path to check -* **Returns:** - `True` if path is a directory, `False` otherwise. -* **Raises:** - `onetl.exception.DirectoryNotFoundError` - : Path does not exist - -### Examples - -```pycon ->>> connection.is_dir("/path/to/dir") -True ->>> connection.is_dir("/path/to/dir/file.csv") -False -``` - - - -#### is_file(path: PathLike | str) → bool - -Check if specified path is a file. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **path** - : Path to check -* **Returns:** - `True` if path is a file, `False` otherwise. -* **Raises:** - FileNotFoundError - : Path does not exist - -### Examples - -```pycon ->>> connection.is_file("/path/to/dir/file.csv") -True ->>> connection.is_file("/path/to/dir") -False -``` - - - -#### list_dir(path: PathLike | str, filters: Iterable[[BaseFileFilter](../../../file/file_filters/base.md#onetl.base.base_file_filter.BaseFileFilter)] | None = None, limits: Iterable[[BaseFileLimit](../../../file/file_limits/base.md#onetl.base.base_file_limit.BaseFileLimit)] | None = None) → list[RemoteDirectory | RemoteFile] - -Return list of child files/directories in a specific directory. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **path** - : Directory path to list contents. - - **filters** - : Return only files/directories matching these filters. See [File Filters](../../../file/file_filters/index.md#file-filters) - - **limits** - : Apply limits to the list of files/directories, and stop if one of the limits is reached. - See [File Limits](../../../file/file_limits/index.md#file-limits) -* **Returns:** - List of `onetl.base.PathWithStatsProtocol` -* **Raises:** - NotADirectoryError - : Path is not a directory - - `onetl.exception.DirectoryNotFoundError` - : Path does not exist - -### Examples - -```pycon ->>> dir_content = connection.list_dir("/path/to/dir") ->>> os.fspath(dir_content[0]) -'file.csv' ->>> connection.path_exists("/path/to/dir/file.csv") -True -``` - - - -#### path_exists(path: PathLike | str) → bool - -Check if specified path exists on remote filesystem. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html). - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **path** - : Path to check -* **Returns:** - `True` if path exists, `False` otherwise - -### Examples - -```pycon ->>> connection.path_exists("/path/to/file.csv") -True ->>> connection.path_exists("/path/to/dir") -True ->>> connection.path_exists("/path/to/missing") -False -``` - - - -#### remove_dir(path: PathLike | str, recursive: bool = False) → bool - -Remove directory or directory tree. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -If directory does not exist, no exception is raised. - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **path** - : Directory path to remote - - **recursive** - : If `True`, remove directory tree recursively. -* **Returns:** - `True` if directory was removed, `False` if directory does not exist in the first place. -* **Raises:** - NotADirectoryError - : Path is not a directory - -### Examples - -```pycon ->>> connection.remove_dir("/path/to/dir") -True ->>> connection.path_exists("/path/to/dir") -False ->>> connection.path_exists("/path/to/dir/file.csv") -False ->>> connection.remove_dir("/path/to/dir") # already deleted, no error -False -``` - - - -#### remove_file(path: PathLike | str) → bool - -Removes file on remote filesystem. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -If file does not exist, no exception is raised. - -#### WARNING -Supports only one file removal per call. Directory removal is **NOT** supported, use [`remove_dir`](#onetl.connection.file_connection.hdfs.connection.HDFS.remove_dir) instead. - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **path** - : File path -* **Returns:** - `True` if file was removed, `False` if file does not exist in the first place. -* **Raises:** - `onetl.exception.NotAFileError` - : Path is not a file - -### Examples - -```pycon ->>> connection.remove_file("/path/to/file.csv") -True ->>> connection.path_exists("/path/to/dir/file.csv") -False ->>> connection.remove_file("/path/to/file.csv") # already deleted, no error -False -``` - - - -#### rename_dir(source_dir_path: PathLike | str, target_dir_path: PathLike | str, replace: bool = False) → RemoteDirectory - -Rename or move dir on remote filesystem. - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **source_dir_path** - : Old directory path - - **target_dir_path** - : New directory path - - **replace** - : If `True`, existing directory will be replaced. -* **Returns:** - New directory path with stats. -* **Raises:** - NotADirectoryError - : Path is not a directory - - `onetl.exception.DirectoryNotFoundError` - : Path does not exist - - `onetl.exception.DirectoryExistsError` - : Directory already exists, and `replace=False` - -### Examples - -```pycon ->>> new_dir = connection.rename_dir("/path/to/dir1", "/path/to/dir2") ->>> os.fspath(new_dir) -'/path/to/dir2' ->>> connection.path_exists("/path/to/dir1") -False ->>> connection.path_exists("/path/to/dir2") -True -``` - - - -#### rename_file(source_file_path: PathLike | str, target_file_path: PathLike | str, replace: bool = False) → RemoteFile - -Rename or move file on remote filesystem. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### WARNING -Supports only one file move per call. Directory move/rename is **NOT** supported. - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **source_file_path** - : Old file path - - **target_file_path** - : New file path - - **replace** - : If `True`, existing file will be replaced. -* **Returns:** - New file path with stats. -* **Raises:** - `onetl.exception.NotAFileError` - : Source or target path is not a file - - FileNotFoundError - : File does not exist - - FileExistsError - : File already exists, and `replace=False` - -### Examples - -```pycon ->>> new_file = connection.rename_file("/path/to/file1.csv", "/path/to/file2.csv") ->>> os.fspath(new_file) -'/path/to/file2.csv' ->>> connection.path_exists("/path/to/file2.csv") -True ->>> connection.path_exists("/path/to/file1.csv") -False -``` - - - -#### resolve_dir(path: PathLike | str) → RemoteDirectory - -Returns directory at specific path, with stats. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **path** - : Path to resolve -* **Returns:** - Directory path with stats -* **Raises:** - `onetl.exception.DirectoryNotFoundError` - : Path does not exist - - NotADirectoryError - : Path is not a directory - -### Examples - -```pycon ->>> dir_path = connection.resolve_dir("/path/to/dir") ->>> os.fspath(dir_path) -'/path/to/dir' ->>> dir_path.stat().st_uid # owner id -12345 -``` - - - -#### resolve_file(path: PathLike | str) → RemoteFile - -Returns file at specific path, with stats. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **path** - : Path to resolve -* **Returns:** - File path with stats -* **Raises:** - FileNotFoundError - : Path does not exist - - `onetl.exception.NotAFileError` - : Path is not a file - -### Examples - -```pycon ->>> file_path = connection.resolve_file("/path/to/dir/file.csv") ->>> os.fspath(file_path) -'/path/to/dir/file.csv' ->>> file_path.stat().st_uid # owner id -12345 -``` - - - -#### upload_file(local_file_path: PathLike | str, remote_file_path: PathLike | str, replace: bool = False) → RemoteFile - -Uploads local file to a remote filesystem. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### WARNING -Supports only one file upload per call. Directory upload is **NOT** supported, use [File Uploader](../../../file/file_uploader/file_uploader.md#file-uploader) instead. - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **local_file_path** - : Local file path to read from - - **remote_file_path** - : Remote file path to create - - **replace** - : If `True`, existing file will be replaced -* **Returns:** - Remote file with stats. -* **Raises:** - `onetl.exception.NotAFileError` - : Remote or local path is not a file - - FileNotFoundError - : Local file does not exist - - FileExistsError - : Remote file already exists, and `replace=False` - - `onetl.exception.FileSizeMismatchError` - : Target file size after upload is different from source file size. - -### Examples - -```pycon ->>> remote_file = connection.upload( -... local_file_path="/path/to/source.csv", -... remote_file_path="/path/to/target.csv", -... ) ->>> os.fspath(remote_file) -'/path/to/target.csv' ->>> connection.path_exists("/path/to/target.csv") -True ->>> remote_file.stat().st_size # in bytes -1024 ->>> os.stat("/path/to/source.csv").st_size # same as source -1024 -``` - - - -#### walk(root: PathLike | str, topdown: bool = True, filters: Iterable[[BaseFileFilter](../../../file/file_filters/base.md#onetl.base.base_file_filter.BaseFileFilter)] | None = None, limits: Iterable[[BaseFileLimit](../../../file/file_limits/base.md#onetl.base.base_file_limit.BaseFileLimit)] | None = None) → Iterator[tuple[RemoteDirectory, list[RemoteDirectory], list[RemoteFile]]] - -Walk into directory tree, and iterate over its content in all nesting levels. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -Just like `os.walk`, but with additional filter/limit logic. - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **root** - : Directory path to walk into. - - **topdown** - : If `True`, walk in top-down order, otherwise walk in bottom-up order. - - **filters** - : Return only files/directories matching these filters. See [File Filters](../../../file/file_filters/index.md#file-filters). - - **limits** - : Apply limits to the list of files/directories, and immediately stop if any of these limits is reached. - See [File Limits](../../../file/file_limits/index.md#file-limits). -* **Returns:** - `Iterator[tuple[root, dirs, files]]`, like `os.walk`. - - But all the paths are not strings, instead path classes with embedded stats are returned. -* **Raises:** - NotADirectoryError - : Path is not a directory - - `onetl.exception.DirectoryNotFoundError` - : Path does not exist - -### Examples - -```pycon ->>> for root, dirs, files in connection.walk("/path/to/dir"): -... break ->>> os.fspath(root) -'/path/to/dir' ->>> dirs -[] ->>> os.fspath(files[0]) -'file.csv' ->>> connection.path_exists("/path/to/dir/file.csv") -True -``` - - diff --git a/mddocs/markdown/connection/file_connection/hdfs/index.md b/mddocs/markdown/connection/file_connection/hdfs/index.md deleted file mode 100644 index a65c43ea3..000000000 --- a/mddocs/markdown/connection/file_connection/hdfs/index.md +++ /dev/null @@ -1,11 +0,0 @@ - - -# HDFS - -# Connection - -* [HDFS connection](connection.md) - -# For developers - -* [HDFS Slots](slots.md) diff --git a/mddocs/markdown/connection/file_connection/hdfs/slots.md b/mddocs/markdown/connection/file_connection/hdfs/slots.md deleted file mode 100644 index 6f681805b..000000000 --- a/mddocs/markdown/connection/file_connection/hdfs/slots.md +++ /dev/null @@ -1,256 +0,0 @@ - - -# HDFS Slots - -### *class* onetl.connection.file_connection.hdfs.slots.HDFSSlots - -Slots that could be implemented by third-party plugins. - -#### Versionadded -Added in version 0.7.0. - - - -#### *static* normalize_cluster_name(cluster: str) → str | None - -Normalize cluster name passed into HDFS constructor. - -If hooks didn’t return anything, cluster name is left intact. - -#### Versionadded -Added in version 0.7.0. - -* **Parameters:** - **cluster** - : Cluster name -* **Returns:** - str | None - : Normalized cluster name. -
- If hook cannot be applied to a specific cluster, it should return `None`. - -### Examples - -```python -from onetl.connection import HDFS -from onetl.hooks import hook - -@HDFS.Slots.normalize_cluster_name.bind -@hook -def normalize_cluster_name(cluster: str) -> str: - return cluster.lower() -``` - - - -#### *static* normalize_namenode_host(host: str, cluster: str | None) → str | None - -Normalize namenode host passed into HDFS constructor. - -If hooks didn’t return anything, host is left intact. - -#### Versionadded -Added in version 0.7.0. - -* **Parameters:** - **host** - : Namenode host (raw) - - **cluster** - : Cluster name (normalized), if set -* **Returns:** - str | None - : Normalized namenode host name. -
- If hook cannot be applied to a specific host name, it should return `None`. - -### Examples - -```python -from onetl.connection import HDFS -from onetl.hooks import hook - -@HDFS.Slots.normalize_namenode_host.bind -@hook -def normalize_namenode_host(host: str, cluster: str) -> str | None: - if cluster == "rnd-dwh": - if not host.endswith(".domain.com"): - # fix missing domain name - host += ".domain.com" - return host - - return None -``` - - - -#### *static* get_known_clusters() → set[str] | None - -Return collection of known clusters. - -Cluster passed into HDFS constructor should be present in this list. -If hooks didn’t return anything, no validation will be performed. - -#### Versionadded -Added in version 0.7.0. - -* **Returns:** - set[str] | None - : Collection of cluster names (in normalized form). -
- If hook cannot be applied, it should return `None`. - -### Examples - -```python -from onetl.connection import HDFS -from onetl.hooks import hook - -@HDFS.Slots.get_known_clusters.bind -@hook -def get_known_clusters() -> str[str]: - return {"rnd-dwh", "rnd-prod"} -``` - - - -#### *static* get_cluster_namenodes(cluster: str) → set[str] | None - -Return collection of known namenodes for the cluster. - -Namenode host passed into HDFS constructor should be present in this list. -If hooks didn’t return anything, no validation will be performed. - -#### Versionadded -Added in version 0.7.0. - -* **Parameters:** - **cluster** - : Cluster name (normalized) -* **Returns:** - set[str] | None - : Collection of host names (in normalized form). -
- If hook cannot be applied, it should return `None`. - -### Examples - -```python -from onetl.connection import HDFS -from onetl.hooks import hook - -@HDFS.Slots.get_cluster_namenodes.bind -@hook -def get_cluster_namenodes(cluster: str) -> str[str] | None: - if cluster == "rnd-dwh": - return {"namenode1.domain.com", "namenode2.domain.com"} - return None -``` - - - -#### *static* get_current_cluster() → str | None - -Get current cluster name. - -Used in [`get_current_cluster`](#onetl.connection.file_connection.hdfs.slots.HDFSSlots.get_current_cluster) to automatically fill up `cluster` attribute of a connection. -If hooks didn’t return anything, calling the method above will raise an exception. - -#### Versionadded -Added in version 0.7.0. - -* **Returns:** - str | None - : Current cluster name (in normalized form). -
- If hook cannot be applied, it should return `None`. - -### Examples - -```python -from onetl.connection import HDFS -from onetl.hooks import hook - -@HDFS.Slots.get_current_cluster.bind -@hook -def get_current_cluster() -> str: - # some magic here - return "rnd-dwh" -``` - - - -#### *static* get_webhdfs_port(cluster: str) → int | None - -Get WebHDFS port number for a specific cluster. - -Used by constructor to automatically set port number if omitted. - -#### Versionadded -Added in version 0.7.0. - -* **Parameters:** - **cluster** - : Cluster name (normalized) -* **Returns:** - int | None - : WebHDFS port number. -
- If hook cannot be applied, it should return `None`. - -### Examples - -```python -from onetl.connection import HDFS -from onetl.hooks import hook - -@HDFS.Slots.get_webhdfs_port.bind -@hook -def get_webhdfs_port(cluster: str) -> int | None: - if cluster == "rnd-dwh": - return 50007 # Cloudera - return None -``` - - - -#### *static* is_namenode_active(host: str, cluster: str | None) → bool | None - -Check whether a namenode of a specified cluster is active (=not standby) or not. - -Used for: -: * If HDFS connection is created without `host` - > Connector will iterate over [`get_cluster_namenodes`](#onetl.connection.file_connection.hdfs.slots.HDFSSlots.get_cluster_namenodes) of a cluster to get active namenode, - > and then use it instead of `host` attribute. - * If HDFS connection is created with `host` - > `check` will determine whether this host is active. - -#### Versionadded -Added in version 0.7.0. - -* **Parameters:** - **host** - : Namenode host (normalized) - - **cluster** - : Cluster name (normalized), if set -* **Returns:** - bool | None - : `True` if namenode is active, `False` if not. -
- If hook cannot be applied, it should return `None`. - -### Examples - -```python -from onetl.connection import HDFS -from onetl.hooks import hook - -@HDFS.Slots.is_namenode_active.bind -@hook -def is_namenode_active(host: str, cluster: str | None) -> bool: - # some magic here - return True -``` - - diff --git a/mddocs/markdown/connection/file_connection/index.md b/mddocs/markdown/connection/file_connection/index.md deleted file mode 100644 index 874845c78..000000000 --- a/mddocs/markdown/connection/file_connection/index.md +++ /dev/null @@ -1,13 +0,0 @@ - - -# File Connections - -# File Connections - -* [FTP](ftp.md) -* [FTPS](ftps.md) -* [HDFS](hdfs/index.md) -* [Samba](samba.md) -* [SFTP](sftp.md) -* [S3](s3.md) -* [Webdav](webdav.md) diff --git a/mddocs/markdown/connection/file_connection/s3.md b/mddocs/markdown/connection/file_connection/s3.md deleted file mode 100644 index 4fa940aaf..000000000 --- a/mddocs/markdown/connection/file_connection/s3.md +++ /dev/null @@ -1,598 +0,0 @@ - - -# S3 connection - -### *class* onetl.connection.file_connection.s3.S3(\*, host: Host, port: int | None = None, bucket: str, access_key: str, secret_key: SecretStr, protocol: Literal['http', 'https'] = 'https', session_token: SecretStr | None = None, region: str | None = None) - -S3 file connection. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -Based on [minio-py client](https://pypi.org/project/minio/). - -#### WARNING -Since onETL v0.7.0 to use S3 connector you should install package as follows: - -```bash -pip install onetl[s3] - -# or -pip install onetl[files] -``` - -See [File connections](../../install/files.md#install-files) installation instruction for more details. - -#### Versionadded -Added in version 0.5.1. - -* **Parameters:** - **host** - : Host of S3 source. For example: `s3.domain.com` - - **port** - : Port of S3 source - - **bucket** - : Bucket name in the S3 file source - - **access_key** - : Access key (aka user ID) of an account in the S3 service - - **secret_key** - : Secret key (aka password) of an account in the S3 service - - **protocol** - : Connection protocol. Allowed values: `https` or `http` -
- #### Versionchanged - Changed in version 0.6.0: Renamed `secure: bool` to `protocol: Literal["https", "http"]` - - **session_token** - : Session token of your account in S3 service - - **region** - : Region name of bucket in S3 service - -### Examples - -Create and check S3 connection: - -```python -from onetl.connection import S3 - -s3 = S3( - host="s3.domain.com", - protocol="http", - bucket="my-bucket", - access_key="ACCESS_KEY", - secret_key="SECRET_KEY", -).check() -``` - - - -#### \_\_init_\_(\*\*kwargs) - - - -#### check() - -Check source availability. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -If not, an exception will be raised. - -* **Returns:** - Connection itself -* **Raises:** - RuntimeError - : If the connection is not available - -### Examples - -```python -connection.check() -``` - - - -#### create_dir(path: PathLike | str) → RemoteDirectory - -Creates directory tree on remote filesystem. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **path** - : Directory path -* **Returns:** - Created directory with stats -* **Raises:** - `onetl.exception.NotAFileError` - : Path is not a file - -### Examples - -```pycon ->>> dir_path = connection.create_dir("/path/to/dir") ->>> os.fspath(dir_path) -'/path/to/dir' -``` - - - -#### download_file(remote_file_path: PathLike | str, local_file_path: PathLike | str, replace: bool = True) → LocalPath - -Downloads file from the remote filesystem to a local path. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### WARNING -Supports only one file download per call. Directory download is **NOT** supported, use [File Downloader](../../file/file_downloader/file_downloader.md#file-downloader) instead. - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **remote_file_path** - : Remote file path to read from - - **local_file_path** - : Local file path to create - - **replace** - : If `True`, existing file will be replaced -* **Returns:** - Local file with stats. -* **Raises:** - `onetl.exception.NotAFileError` - : Remote or local path is not a file - - FileNotFoundError - : Remote file does not exist - - FileExistsError - : Local file already exists, and `replace=False` - - `onetl.exception.FileSizeMismatchError` - : Target file size after download is different from source file size. - -### Examples - -```pycon ->>> local_file = connection.download_file( -... remote_file_path="/path/to/source.csv", -... local_file_path="/path/to/target.csv", -... ) ->>> os.fspath(local_file) -'/path/to/target.csv' ->>> local_file.exists() -True ->>> local_file.stat().st_size # in bytes -1024 ->>> connection.get_stat("/path/to/source.csv").st_size # same size -1024 -``` - - - -#### get_stat(path: PathLike | str) → PathStatProtocol - -Returns stats for a specific path. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **path** - : Path to get stats for -* **Returns:** - Stats object -* **Raises:** - Any underlying client exception - -### Examples - -```pycon ->>> stat = connection.get_stat("/path/to/file.csv") ->>> stat.st_size # in bytes -1024 ->>> stat.st_uid # owner id or name -12345 -``` - - - -#### is_dir(path: PathLike | str) → bool - -Check if specified path is a directory. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **path** - : Path to check -* **Returns:** - `True` if path is a directory, `False` otherwise. -* **Raises:** - `onetl.exception.DirectoryNotFoundError` - : Path does not exist - -### Examples - -```pycon ->>> connection.is_dir("/path/to/dir") -True ->>> connection.is_dir("/path/to/dir/file.csv") -False -``` - - - -#### is_file(path: PathLike | str) → bool - -Check if specified path is a file. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **path** - : Path to check -* **Returns:** - `True` if path is a file, `False` otherwise. -* **Raises:** - FileNotFoundError - : Path does not exist - -### Examples - -```pycon ->>> connection.is_file("/path/to/dir/file.csv") -True ->>> connection.is_file("/path/to/dir") -False -``` - - - -#### list_dir(path: PathLike | str, filters: Iterable[[BaseFileFilter](../../file/file_filters/base.md#onetl.base.base_file_filter.BaseFileFilter)] | None = None, limits: Iterable[[BaseFileLimit](../../file/file_limits/base.md#onetl.base.base_file_limit.BaseFileLimit)] | None = None) → list[RemoteDirectory | RemoteFile] - -Return list of child files/directories in a specific directory. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **path** - : Directory path to list contents. - - **filters** - : Return only files/directories matching these filters. See [File Filters](../../file/file_filters/index.md#file-filters) - - **limits** - : Apply limits to the list of files/directories, and stop if one of the limits is reached. - See [File Limits](../../file/file_limits/index.md#file-limits) -* **Returns:** - List of `onetl.base.PathWithStatsProtocol` -* **Raises:** - NotADirectoryError - : Path is not a directory - - `onetl.exception.DirectoryNotFoundError` - : Path does not exist - -### Examples - -```pycon ->>> dir_content = connection.list_dir("/path/to/dir") ->>> os.fspath(dir_content[0]) -'file.csv' ->>> connection.path_exists("/path/to/dir/file.csv") -True -``` - - - -#### path_exists(path: PathLike | str) → bool - -Check if specified path exists on remote filesystem. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html). - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **path** - : Path to check -* **Returns:** - `True` if path exists, `False` otherwise - -### Examples - -```pycon ->>> connection.path_exists("/path/to/file.csv") -True ->>> connection.path_exists("/path/to/dir") -True ->>> connection.path_exists("/path/to/missing") -False -``` - - - -#### remove_dir(path: PathLike | str, recursive: bool = False) → bool - -Remove directory or directory tree. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -If directory does not exist, no exception is raised. - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **path** - : Directory path to remote - - **recursive** - : If `True`, remove directory tree recursively. -* **Returns:** - `True` if directory was removed, `False` if directory does not exist in the first place. -* **Raises:** - NotADirectoryError - : Path is not a directory - -### Examples - -```pycon ->>> connection.remove_dir("/path/to/dir") -True ->>> connection.path_exists("/path/to/dir") -False ->>> connection.path_exists("/path/to/dir/file.csv") -False ->>> connection.remove_dir("/path/to/dir") # already deleted, no error -False -``` - - - -#### remove_file(path: PathLike | str) → bool - -Removes file on remote filesystem. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -If file does not exist, no exception is raised. - -#### WARNING -Supports only one file removal per call. Directory removal is **NOT** supported, use [`remove_dir`](#onetl.connection.file_connection.s3.S3.remove_dir) instead. - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **path** - : File path -* **Returns:** - `True` if file was removed, `False` if file does not exist in the first place. -* **Raises:** - `onetl.exception.NotAFileError` - : Path is not a file - -### Examples - -```pycon ->>> connection.remove_file("/path/to/file.csv") -True ->>> connection.path_exists("/path/to/dir/file.csv") -False ->>> connection.remove_file("/path/to/file.csv") # already deleted, no error -False -``` - - - -#### rename_file(source_file_path: PathLike | str, target_file_path: PathLike | str, replace: bool = False) → RemoteFile - -Rename or move file on remote filesystem. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### WARNING -Supports only one file move per call. Directory move/rename is **NOT** supported. - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **source_file_path** - : Old file path - - **target_file_path** - : New file path - - **replace** - : If `True`, existing file will be replaced. -* **Returns:** - New file path with stats. -* **Raises:** - `onetl.exception.NotAFileError` - : Source or target path is not a file - - FileNotFoundError - : File does not exist - - FileExistsError - : File already exists, and `replace=False` - -### Examples - -```pycon ->>> new_file = connection.rename_file("/path/to/file1.csv", "/path/to/file2.csv") ->>> os.fspath(new_file) -'/path/to/file2.csv' ->>> connection.path_exists("/path/to/file2.csv") -True ->>> connection.path_exists("/path/to/file1.csv") -False -``` - - - -#### resolve_dir(path: PathLike | str) → RemoteDirectory - -Returns directory at specific path, with stats. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **path** - : Path to resolve -* **Returns:** - Directory path with stats -* **Raises:** - `onetl.exception.DirectoryNotFoundError` - : Path does not exist - - NotADirectoryError - : Path is not a directory - -### Examples - -```pycon ->>> dir_path = connection.resolve_dir("/path/to/dir") ->>> os.fspath(dir_path) -'/path/to/dir' ->>> dir_path.stat().st_uid # owner id -12345 -``` - - - -#### resolve_file(path: PathLike | str) → RemoteFile - -Returns file at specific path, with stats. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **path** - : Path to resolve -* **Returns:** - File path with stats -* **Raises:** - FileNotFoundError - : Path does not exist - - `onetl.exception.NotAFileError` - : Path is not a file - -### Examples - -```pycon ->>> file_path = connection.resolve_file("/path/to/dir/file.csv") ->>> os.fspath(file_path) -'/path/to/dir/file.csv' ->>> file_path.stat().st_uid # owner id -12345 -``` - - - -#### upload_file(local_file_path: PathLike | str, remote_file_path: PathLike | str, replace: bool = False) → RemoteFile - -Uploads local file to a remote filesystem. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### WARNING -Supports only one file upload per call. Directory upload is **NOT** supported, use [File Uploader](../../file/file_uploader/file_uploader.md#file-uploader) instead. - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **local_file_path** - : Local file path to read from - - **remote_file_path** - : Remote file path to create - - **replace** - : If `True`, existing file will be replaced -* **Returns:** - Remote file with stats. -* **Raises:** - `onetl.exception.NotAFileError` - : Remote or local path is not a file - - FileNotFoundError - : Local file does not exist - - FileExistsError - : Remote file already exists, and `replace=False` - - `onetl.exception.FileSizeMismatchError` - : Target file size after upload is different from source file size. - -### Examples - -```pycon ->>> remote_file = connection.upload( -... local_file_path="/path/to/source.csv", -... remote_file_path="/path/to/target.csv", -... ) ->>> os.fspath(remote_file) -'/path/to/target.csv' ->>> connection.path_exists("/path/to/target.csv") -True ->>> remote_file.stat().st_size # in bytes -1024 ->>> os.stat("/path/to/source.csv").st_size # same as source -1024 -``` - - - -#### walk(root: PathLike | str, topdown: bool = True, filters: Iterable[[BaseFileFilter](../../file/file_filters/base.md#onetl.base.base_file_filter.BaseFileFilter)] | None = None, limits: Iterable[[BaseFileLimit](../../file/file_limits/base.md#onetl.base.base_file_limit.BaseFileLimit)] | None = None) → Iterator[tuple[RemoteDirectory, list[RemoteDirectory], list[RemoteFile]]] - -Walk into directory tree, and iterate over its content in all nesting levels. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -Just like `os.walk`, but with additional filter/limit logic. - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **root** - : Directory path to walk into. - - **topdown** - : If `True`, walk in top-down order, otherwise walk in bottom-up order. - - **filters** - : Return only files/directories matching these filters. See [File Filters](../../file/file_filters/index.md#file-filters). - - **limits** - : Apply limits to the list of files/directories, and immediately stop if any of these limits is reached. - See [File Limits](../../file/file_limits/index.md#file-limits). -* **Returns:** - `Iterator[tuple[root, dirs, files]]`, like `os.walk`. - - But all the paths are not strings, instead path classes with embedded stats are returned. -* **Raises:** - NotADirectoryError - : Path is not a directory - - `onetl.exception.DirectoryNotFoundError` - : Path does not exist - -### Examples - -```pycon ->>> for root, dirs, files in connection.walk("/path/to/dir"): -... break ->>> os.fspath(root) -'/path/to/dir' ->>> dirs -[] ->>> os.fspath(files[0]) -'file.csv' ->>> connection.path_exists("/path/to/dir/file.csv") -True -``` - - diff --git a/mddocs/markdown/connection/file_connection/samba.md b/mddocs/markdown/connection/file_connection/samba.md deleted file mode 100644 index 81b661ec1..000000000 --- a/mddocs/markdown/connection/file_connection/samba.md +++ /dev/null @@ -1,548 +0,0 @@ - - -# Samba connection - -### *class* onetl.connection.file_connection.samba.Samba(\*, host: Host, share: str, protocol: Literal['SMB', 'NetBIOS'] = 'SMB', port: int | None = None, domain: str | None = '', auth_type: Literal['NTLMv1', 'NTLMv2'] = 'NTLMv2', user: str | None = None, password: SecretStr | None = None) - -Samba file connection. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -Based on [pysmb library](https://pypi.org/project/pysmb/). - -#### Versionadded -Added in version 0.9.4. - -#### WARNING -To use Samba connector you should install package as follows: - -```bash -pip install onetl[samba] - -# or -pip install onetl[files] -``` - -See [File connections](../../install/files.md#install-files) installation instruction for more details. - -* **Parameters:** - **host** - : Host of Samba source. For example: `mydomain.com`. - - **share** - : The name of the share on the Samba server. - - **protocol** - : The protocol to use for the connection. Either `SMB` or `NetBIOS`. - Affects the default port and the is_direct_tcp flag in SMBConnection. - - **port** - : Port of Samba source. - - **domain** - : Domain name for the Samba connection. Empty strings means use `host` as domain name. - - **auth_type** - : The authentication type to use. Either `NTLMv2` or `NTLMv1`. - Affects the use_ntlm_v2 flag in SMBConnection. - - **user** - : User, which have access to the file source. Can be None for anonymous connection. - - **password** - : Password for file source connection. Can be None for anonymous connection. - -### Examples - -Create and check Samba connection: - -```python -from onetl.connection import Samba - -samba = Samba( - host="mydomain.com", - share="share_name", - protocol="SMB", - port=445, - user="user", - password="password", -).check() -``` - - - -#### \_\_init_\_(\*\*kwargs) - - - -#### check() - -Check source availability. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -If not, an exception will be raised. - -* **Returns:** - Connection itself -* **Raises:** - RuntimeError - : If the connection is not available - -### Examples - -```python -connection.check() -``` - - - -#### create_dir(path: PathLike | str) → RemoteDirectory - -Creates directory tree on remote filesystem. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **path** - : Directory path -* **Returns:** - Created directory with stats -* **Raises:** - `onetl.exception.NotAFileError` - : Path is not a file - -### Examples - -```pycon ->>> dir_path = connection.create_dir("/path/to/dir") ->>> os.fspath(dir_path) -'/path/to/dir' -``` - - - -#### download_file(remote_file_path: PathLike | str, local_file_path: PathLike | str, replace: bool = True) → LocalPath - -Downloads file from the remote filesystem to a local path. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### WARNING -Supports only one file download per call. Directory download is **NOT** supported, use [File Downloader](../../file/file_downloader/file_downloader.md#file-downloader) instead. - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **remote_file_path** - : Remote file path to read from - - **local_file_path** - : Local file path to create - - **replace** - : If `True`, existing file will be replaced -* **Returns:** - Local file with stats. -* **Raises:** - `onetl.exception.NotAFileError` - : Remote or local path is not a file - - FileNotFoundError - : Remote file does not exist - - FileExistsError - : Local file already exists, and `replace=False` - - `onetl.exception.FileSizeMismatchError` - : Target file size after download is different from source file size. - -### Examples - -```pycon ->>> local_file = connection.download_file( -... remote_file_path="/path/to/source.csv", -... local_file_path="/path/to/target.csv", -... ) ->>> os.fspath(local_file) -'/path/to/target.csv' ->>> local_file.exists() -True ->>> local_file.stat().st_size # in bytes -1024 ->>> connection.get_stat("/path/to/source.csv").st_size # same size -1024 -``` - - - -#### get_stat(path: PathLike | str) → PathStatProtocol - -Returns stats for a specific path. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **path** - : Path to get stats for -* **Returns:** - Stats object -* **Raises:** - Any underlying client exception - -### Examples - -```pycon ->>> stat = connection.get_stat("/path/to/file.csv") ->>> stat.st_size # in bytes -1024 ->>> stat.st_uid # owner id or name -12345 -``` - - - -#### is_dir(path: PathLike | str) → bool - -Check if specified path is a directory. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **path** - : Path to check -* **Returns:** - `True` if path is a directory, `False` otherwise. -* **Raises:** - `onetl.exception.DirectoryNotFoundError` - : Path does not exist - -### Examples - -```pycon ->>> connection.is_dir("/path/to/dir") -True ->>> connection.is_dir("/path/to/dir/file.csv") -False -``` - - - -#### is_file(path: PathLike | str) → bool - -Check if specified path is a file. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **path** - : Path to check -* **Returns:** - `True` if path is a file, `False` otherwise. -* **Raises:** - FileNotFoundError - : Path does not exist - -### Examples - -```pycon ->>> connection.is_file("/path/to/dir/file.csv") -True ->>> connection.is_file("/path/to/dir") -False -``` - - - -#### list_dir(path: PathLike | str, filters: Iterable[[BaseFileFilter](../../file/file_filters/base.md#onetl.base.base_file_filter.BaseFileFilter)] | None = None, limits: Iterable[[BaseFileLimit](../../file/file_limits/base.md#onetl.base.base_file_limit.BaseFileLimit)] | None = None) → list[RemoteDirectory | RemoteFile] - -Return list of child files/directories in a specific directory. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **path** - : Directory path to list contents. - - **filters** - : Return only files/directories matching these filters. See [File Filters](../../file/file_filters/index.md#file-filters) - - **limits** - : Apply limits to the list of files/directories, and stop if one of the limits is reached. - See [File Limits](../../file/file_limits/index.md#file-limits) -* **Returns:** - List of `onetl.base.PathWithStatsProtocol` -* **Raises:** - NotADirectoryError - : Path is not a directory - - `onetl.exception.DirectoryNotFoundError` - : Path does not exist - -### Examples - -```pycon ->>> dir_content = connection.list_dir("/path/to/dir") ->>> os.fspath(dir_content[0]) -'file.csv' ->>> connection.path_exists("/path/to/dir/file.csv") -True -``` - - - -#### path_exists(path: PathLike | str) → bool - -Check if specified path exists on remote filesystem. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html). - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **path** - : Path to check -* **Returns:** - `True` if path exists, `False` otherwise - -### Examples - -```pycon ->>> connection.path_exists("/path/to/file.csv") -True ->>> connection.path_exists("/path/to/dir") -True ->>> connection.path_exists("/path/to/missing") -False -``` - - - -#### remove_dir(path: PathLike | str, recursive: bool = False) → bool - -Remove directory or directory tree. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -If directory does not exist, no exception is raised. - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **path** - : Directory path to remote - - **recursive** - : If `True`, remove directory tree recursively. -* **Returns:** - `True` if directory was removed, `False` if directory does not exist in the first place. -* **Raises:** - NotADirectoryError - : Path is not a directory - -### Examples - -```pycon ->>> connection.remove_dir("/path/to/dir") -True ->>> connection.path_exists("/path/to/dir") -False ->>> connection.path_exists("/path/to/dir/file.csv") -False ->>> connection.remove_dir("/path/to/dir") # already deleted, no error -False -``` - - - -#### remove_file(path: PathLike | str) → bool - -Removes file on remote filesystem. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -If file does not exist, no exception is raised. - -#### WARNING -Supports only one file removal per call. Directory removal is **NOT** supported, use [`remove_dir`](#onetl.connection.file_connection.samba.Samba.remove_dir) instead. - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **path** - : File path -* **Returns:** - `True` if file was removed, `False` if file does not exist in the first place. -* **Raises:** - `onetl.exception.NotAFileError` - : Path is not a file - -### Examples - -```pycon ->>> connection.remove_file("/path/to/file.csv") -True ->>> connection.path_exists("/path/to/dir/file.csv") -False ->>> connection.remove_file("/path/to/file.csv") # already deleted, no error -False -``` - - - -#### rename_file(source_file_path: PathLike | str, target_file_path: PathLike | str, replace: bool = False) → RemoteFile - -Rename or move file on remote filesystem. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### WARNING -Supports only one file move per call. Directory move/rename is **NOT** supported. - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **source_file_path** - : Old file path - - **target_file_path** - : New file path - - **replace** - : If `True`, existing file will be replaced. -* **Returns:** - New file path with stats. -* **Raises:** - `onetl.exception.NotAFileError` - : Source or target path is not a file - - FileNotFoundError - : File does not exist - - FileExistsError - : File already exists, and `replace=False` - -### Examples - -```pycon ->>> new_file = connection.rename_file("/path/to/file1.csv", "/path/to/file2.csv") ->>> os.fspath(new_file) -'/path/to/file2.csv' ->>> connection.path_exists("/path/to/file2.csv") -True ->>> connection.path_exists("/path/to/file1.csv") -False -``` - - - -#### resolve_dir(path: PathLike | str) → RemoteDirectory - -Returns directory at specific path, with stats. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **path** - : Path to resolve -* **Returns:** - Directory path with stats -* **Raises:** - `onetl.exception.DirectoryNotFoundError` - : Path does not exist - - NotADirectoryError - : Path is not a directory - -### Examples - -```pycon ->>> dir_path = connection.resolve_dir("/path/to/dir") ->>> os.fspath(dir_path) -'/path/to/dir' ->>> dir_path.stat().st_uid # owner id -12345 -``` - - - -#### resolve_file(path: PathLike | str) → RemoteFile - -Returns file at specific path, with stats. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **path** - : Path to resolve -* **Returns:** - File path with stats -* **Raises:** - FileNotFoundError - : Path does not exist - - `onetl.exception.NotAFileError` - : Path is not a file - -### Examples - -```pycon ->>> file_path = connection.resolve_file("/path/to/dir/file.csv") ->>> os.fspath(file_path) -'/path/to/dir/file.csv' ->>> file_path.stat().st_uid # owner id -12345 -``` - - - -#### upload_file(local_file_path: PathLike | str, remote_file_path: PathLike | str, replace: bool = False) → RemoteFile - -Uploads local file to a remote filesystem. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### WARNING -Supports only one file upload per call. Directory upload is **NOT** supported, use [File Uploader](../../file/file_uploader/file_uploader.md#file-uploader) instead. - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **local_file_path** - : Local file path to read from - - **remote_file_path** - : Remote file path to create - - **replace** - : If `True`, existing file will be replaced -* **Returns:** - Remote file with stats. -* **Raises:** - `onetl.exception.NotAFileError` - : Remote or local path is not a file - - FileNotFoundError - : Local file does not exist - - FileExistsError - : Remote file already exists, and `replace=False` - - `onetl.exception.FileSizeMismatchError` - : Target file size after upload is different from source file size. - -### Examples - -```pycon ->>> remote_file = connection.upload( -... local_file_path="/path/to/source.csv", -... remote_file_path="/path/to/target.csv", -... ) ->>> os.fspath(remote_file) -'/path/to/target.csv' ->>> connection.path_exists("/path/to/target.csv") -True ->>> remote_file.stat().st_size # in bytes -1024 ->>> os.stat("/path/to/source.csv").st_size # same as source -1024 -``` - - diff --git a/mddocs/markdown/connection/file_connection/sftp.md b/mddocs/markdown/connection/file_connection/sftp.md deleted file mode 100644 index 1ee71d53c..000000000 --- a/mddocs/markdown/connection/file_connection/sftp.md +++ /dev/null @@ -1,635 +0,0 @@ - - -# SFTP connection - -### *class* onetl.connection.file_connection.sftp.SFTP(\*, host: Host, port: int = 22, user: str | None = None, password: SecretStr | None = None, key_file: FilePath | None = None, timeout: int = 10, host_key_check: bool = False, compress: bool = True) - -SFTP file connection. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -Based on [Paramiko library](https://pypi.org/project/paramiko/). - -#### WARNING -Since onETL v0.7.0 to use SFTP connector you should install package as follows: - -```bash -pip install onetl[s3] - -# or -pip install onetl[files] -``` - -See [File connections](../../install/files.md#install-files) installation instruction for more details. - -#### Versionadded -Added in version 0.1.0. - -* **Parameters:** - **host** - : Host of SFTP source. For example: `192.168.1.19` - - **port** - : Port of SFTP source - - **user** - : User, which have access to the file source. For example: `someuser` - - **password** - : Password for file source connection - - **key_file** - : the filename of optional private key(s) and/or certs to try for authentication - - **timeout** - : How long to wait for the server to send data before giving up - - **host_key_check** - : set to True to enable searching for discoverable private key files in `~/.ssh/` - - **compress** - : Set to True to turn on compression - -### Examples - -Create and check SFTP connection: - -```python -from onetl.connection import SFTP - -sftp = SFTP( - host="192.168.1.19", - user="someuser", - password="*****", -).check() -``` - - - -#### \_\_init_\_(\*\*kwargs) - - - -#### check() - -Check source availability. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -If not, an exception will be raised. - -* **Returns:** - Connection itself -* **Raises:** - RuntimeError - : If the connection is not available - -### Examples - -```python -connection.check() -``` - - - -#### create_dir(path: PathLike | str) → RemoteDirectory - -Creates directory tree on remote filesystem. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **path** - : Directory path -* **Returns:** - Created directory with stats -* **Raises:** - `onetl.exception.NotAFileError` - : Path is not a file - -### Examples - -```pycon ->>> dir_path = connection.create_dir("/path/to/dir") ->>> os.fspath(dir_path) -'/path/to/dir' -``` - - - -#### download_file(remote_file_path: PathLike | str, local_file_path: PathLike | str, replace: bool = True) → LocalPath - -Downloads file from the remote filesystem to a local path. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### WARNING -Supports only one file download per call. Directory download is **NOT** supported, use [File Downloader](../../file/file_downloader/file_downloader.md#file-downloader) instead. - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **remote_file_path** - : Remote file path to read from - - **local_file_path** - : Local file path to create - - **replace** - : If `True`, existing file will be replaced -* **Returns:** - Local file with stats. -* **Raises:** - `onetl.exception.NotAFileError` - : Remote or local path is not a file - - FileNotFoundError - : Remote file does not exist - - FileExistsError - : Local file already exists, and `replace=False` - - `onetl.exception.FileSizeMismatchError` - : Target file size after download is different from source file size. - -### Examples - -```pycon ->>> local_file = connection.download_file( -... remote_file_path="/path/to/source.csv", -... local_file_path="/path/to/target.csv", -... ) ->>> os.fspath(local_file) -'/path/to/target.csv' ->>> local_file.exists() -True ->>> local_file.stat().st_size # in bytes -1024 ->>> connection.get_stat("/path/to/source.csv").st_size # same size -1024 -``` - - - -#### get_stat(path: PathLike | str) → PathStatProtocol - -Returns stats for a specific path. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **path** - : Path to get stats for -* **Returns:** - Stats object -* **Raises:** - Any underlying client exception - -### Examples - -```pycon ->>> stat = connection.get_stat("/path/to/file.csv") ->>> stat.st_size # in bytes -1024 ->>> stat.st_uid # owner id or name -12345 -``` - - - -#### is_dir(path: PathLike | str) → bool - -Check if specified path is a directory. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **path** - : Path to check -* **Returns:** - `True` if path is a directory, `False` otherwise. -* **Raises:** - `onetl.exception.DirectoryNotFoundError` - : Path does not exist - -### Examples - -```pycon ->>> connection.is_dir("/path/to/dir") -True ->>> connection.is_dir("/path/to/dir/file.csv") -False -``` - - - -#### is_file(path: PathLike | str) → bool - -Check if specified path is a file. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **path** - : Path to check -* **Returns:** - `True` if path is a file, `False` otherwise. -* **Raises:** - FileNotFoundError - : Path does not exist - -### Examples - -```pycon ->>> connection.is_file("/path/to/dir/file.csv") -True ->>> connection.is_file("/path/to/dir") -False -``` - - - -#### list_dir(path: PathLike | str, filters: Iterable[[BaseFileFilter](../../file/file_filters/base.md#onetl.base.base_file_filter.BaseFileFilter)] | None = None, limits: Iterable[[BaseFileLimit](../../file/file_limits/base.md#onetl.base.base_file_limit.BaseFileLimit)] | None = None) → list[RemoteDirectory | RemoteFile] - -Return list of child files/directories in a specific directory. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **path** - : Directory path to list contents. - - **filters** - : Return only files/directories matching these filters. See [File Filters](../../file/file_filters/index.md#file-filters) - - **limits** - : Apply limits to the list of files/directories, and stop if one of the limits is reached. - See [File Limits](../../file/file_limits/index.md#file-limits) -* **Returns:** - List of `onetl.base.PathWithStatsProtocol` -* **Raises:** - NotADirectoryError - : Path is not a directory - - `onetl.exception.DirectoryNotFoundError` - : Path does not exist - -### Examples - -```pycon ->>> dir_content = connection.list_dir("/path/to/dir") ->>> os.fspath(dir_content[0]) -'file.csv' ->>> connection.path_exists("/path/to/dir/file.csv") -True -``` - - - -#### path_exists(path: PathLike | str) → bool - -Check if specified path exists on remote filesystem. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html). - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **path** - : Path to check -* **Returns:** - `True` if path exists, `False` otherwise - -### Examples - -```pycon ->>> connection.path_exists("/path/to/file.csv") -True ->>> connection.path_exists("/path/to/dir") -True ->>> connection.path_exists("/path/to/missing") -False -``` - - - -#### remove_dir(path: PathLike | str, recursive: bool = False) → bool - -Remove directory or directory tree. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -If directory does not exist, no exception is raised. - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **path** - : Directory path to remote - - **recursive** - : If `True`, remove directory tree recursively. -* **Returns:** - `True` if directory was removed, `False` if directory does not exist in the first place. -* **Raises:** - NotADirectoryError - : Path is not a directory - -### Examples - -```pycon ->>> connection.remove_dir("/path/to/dir") -True ->>> connection.path_exists("/path/to/dir") -False ->>> connection.path_exists("/path/to/dir/file.csv") -False ->>> connection.remove_dir("/path/to/dir") # already deleted, no error -False -``` - - - -#### remove_file(path: PathLike | str) → bool - -Removes file on remote filesystem. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -If file does not exist, no exception is raised. - -#### WARNING -Supports only one file removal per call. Directory removal is **NOT** supported, use [`remove_dir`](#onetl.connection.file_connection.sftp.SFTP.remove_dir) instead. - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **path** - : File path -* **Returns:** - `True` if file was removed, `False` if file does not exist in the first place. -* **Raises:** - `onetl.exception.NotAFileError` - : Path is not a file - -### Examples - -```pycon ->>> connection.remove_file("/path/to/file.csv") -True ->>> connection.path_exists("/path/to/dir/file.csv") -False ->>> connection.remove_file("/path/to/file.csv") # already deleted, no error -False -``` - - - -#### rename_dir(source_dir_path: PathLike | str, target_dir_path: PathLike | str, replace: bool = False) → RemoteDirectory - -Rename or move dir on remote filesystem. - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **source_dir_path** - : Old directory path - - **target_dir_path** - : New directory path - - **replace** - : If `True`, existing directory will be replaced. -* **Returns:** - New directory path with stats. -* **Raises:** - NotADirectoryError - : Path is not a directory - - `onetl.exception.DirectoryNotFoundError` - : Path does not exist - - `onetl.exception.DirectoryExistsError` - : Directory already exists, and `replace=False` - -### Examples - -```pycon ->>> new_dir = connection.rename_dir("/path/to/dir1", "/path/to/dir2") ->>> os.fspath(new_dir) -'/path/to/dir2' ->>> connection.path_exists("/path/to/dir1") -False ->>> connection.path_exists("/path/to/dir2") -True -``` - - - -#### rename_file(source_file_path: PathLike | str, target_file_path: PathLike | str, replace: bool = False) → RemoteFile - -Rename or move file on remote filesystem. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### WARNING -Supports only one file move per call. Directory move/rename is **NOT** supported. - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **source_file_path** - : Old file path - - **target_file_path** - : New file path - - **replace** - : If `True`, existing file will be replaced. -* **Returns:** - New file path with stats. -* **Raises:** - `onetl.exception.NotAFileError` - : Source or target path is not a file - - FileNotFoundError - : File does not exist - - FileExistsError - : File already exists, and `replace=False` - -### Examples - -```pycon ->>> new_file = connection.rename_file("/path/to/file1.csv", "/path/to/file2.csv") ->>> os.fspath(new_file) -'/path/to/file2.csv' ->>> connection.path_exists("/path/to/file2.csv") -True ->>> connection.path_exists("/path/to/file1.csv") -False -``` - - - -#### resolve_dir(path: PathLike | str) → RemoteDirectory - -Returns directory at specific path, with stats. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **path** - : Path to resolve -* **Returns:** - Directory path with stats -* **Raises:** - `onetl.exception.DirectoryNotFoundError` - : Path does not exist - - NotADirectoryError - : Path is not a directory - -### Examples - -```pycon ->>> dir_path = connection.resolve_dir("/path/to/dir") ->>> os.fspath(dir_path) -'/path/to/dir' ->>> dir_path.stat().st_uid # owner id -12345 -``` - - - -#### resolve_file(path: PathLike | str) → RemoteFile - -Returns file at specific path, with stats. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **path** - : Path to resolve -* **Returns:** - File path with stats -* **Raises:** - FileNotFoundError - : Path does not exist - - `onetl.exception.NotAFileError` - : Path is not a file - -### Examples - -```pycon ->>> file_path = connection.resolve_file("/path/to/dir/file.csv") ->>> os.fspath(file_path) -'/path/to/dir/file.csv' ->>> file_path.stat().st_uid # owner id -12345 -``` - - - -#### upload_file(local_file_path: PathLike | str, remote_file_path: PathLike | str, replace: bool = False) → RemoteFile - -Uploads local file to a remote filesystem. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### WARNING -Supports only one file upload per call. Directory upload is **NOT** supported, use [File Uploader](../../file/file_uploader/file_uploader.md#file-uploader) instead. - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **local_file_path** - : Local file path to read from - - **remote_file_path** - : Remote file path to create - - **replace** - : If `True`, existing file will be replaced -* **Returns:** - Remote file with stats. -* **Raises:** - `onetl.exception.NotAFileError` - : Remote or local path is not a file - - FileNotFoundError - : Local file does not exist - - FileExistsError - : Remote file already exists, and `replace=False` - - `onetl.exception.FileSizeMismatchError` - : Target file size after upload is different from source file size. - -### Examples - -```pycon ->>> remote_file = connection.upload( -... local_file_path="/path/to/source.csv", -... remote_file_path="/path/to/target.csv", -... ) ->>> os.fspath(remote_file) -'/path/to/target.csv' ->>> connection.path_exists("/path/to/target.csv") -True ->>> remote_file.stat().st_size # in bytes -1024 ->>> os.stat("/path/to/source.csv").st_size # same as source -1024 -``` - - - -#### walk(root: PathLike | str, topdown: bool = True, filters: Iterable[[BaseFileFilter](../../file/file_filters/base.md#onetl.base.base_file_filter.BaseFileFilter)] | None = None, limits: Iterable[[BaseFileLimit](../../file/file_limits/base.md#onetl.base.base_file_limit.BaseFileLimit)] | None = None) → Iterator[tuple[RemoteDirectory, list[RemoteDirectory], list[RemoteFile]]] - -Walk into directory tree, and iterate over its content in all nesting levels. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -Just like `os.walk`, but with additional filter/limit logic. - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **root** - : Directory path to walk into. - - **topdown** - : If `True`, walk in top-down order, otherwise walk in bottom-up order. - - **filters** - : Return only files/directories matching these filters. See [File Filters](../../file/file_filters/index.md#file-filters). - - **limits** - : Apply limits to the list of files/directories, and immediately stop if any of these limits is reached. - See [File Limits](../../file/file_limits/index.md#file-limits). -* **Returns:** - `Iterator[tuple[root, dirs, files]]`, like `os.walk`. - - But all the paths are not strings, instead path classes with embedded stats are returned. -* **Raises:** - NotADirectoryError - : Path is not a directory - - `onetl.exception.DirectoryNotFoundError` - : Path does not exist - -### Examples - -```pycon ->>> for root, dirs, files in connection.walk("/path/to/dir"): -... break ->>> os.fspath(root) -'/path/to/dir' ->>> dirs -[] ->>> os.fspath(files[0]) -'file.csv' ->>> connection.path_exists("/path/to/dir/file.csv") -True -``` - - diff --git a/mddocs/markdown/connection/file_connection/webdav.md b/mddocs/markdown/connection/file_connection/webdav.md deleted file mode 100644 index ef79f4023..000000000 --- a/mddocs/markdown/connection/file_connection/webdav.md +++ /dev/null @@ -1,592 +0,0 @@ - - -# WebDAV connection - -### *class* onetl.connection.file_connection.webdav.WebDAV(\*, host: Host, user: str, password: SecretStr, port: int | None = None, ssl_verify: bool | FilePath | DirectoryPath | SSLContext = True, protocol: Literal['http', 'https'] = 'https') - -WebDAV file connection. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -Based on [WebdavClient3 library](https://pypi.org/project/webdavclient3/). - -#### WARNING -Since onETL v0.7.0 to use WebDAV connector you should install package as follows: - -```bash -pip install onetl[webdav] - -# or -pip install onetl[files] -``` - -See [File connections](../../install/files.md#install-files) installation instruction for more details. - -#### Versionadded -Added in version 0.6.0. - -* **Parameters:** - **host** - : Host of WebDAV source. For example: `webdav.domain.com` - - **user** - : User, which have access to the file source. For example: `someuser` - - **password** - : Password for file source connection - - **ssl_verify** - : SSL certificates used to verify the identity of requested hosts. Can be any of - : - `True` (uses default CA bundle), - - a path to an SSL certificate file, - - `False` (disable verification), or - - a `ssl.SSLContext` - - **protocol** - : Connection protocol. Allowed values: `https` or `http` - - **port** - : Connection port - -### Examples - -Create and check WebDAV connection: - -```python -from onetl.connection import WebDAV - -wd = WebDAV( - host="webdav.domain.com", - user="someuser", - password="*****", - protocol="https", -).check() -``` - - - -#### \_\_init_\_(\*\*kwargs) - - - -#### check() - -Check source availability. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -If not, an exception will be raised. - -* **Returns:** - Connection itself -* **Raises:** - RuntimeError - : If the connection is not available - -### Examples - -```python -connection.check() -``` - - - -#### create_dir(path: PathLike | str) → RemoteDirectory - -Creates directory tree on remote filesystem. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **path** - : Directory path -* **Returns:** - Created directory with stats -* **Raises:** - `onetl.exception.NotAFileError` - : Path is not a file - -### Examples - -```pycon ->>> dir_path = connection.create_dir("/path/to/dir") ->>> os.fspath(dir_path) -'/path/to/dir' -``` - - - -#### download_file(remote_file_path: PathLike | str, local_file_path: PathLike | str, replace: bool = True) → LocalPath - -Downloads file from the remote filesystem to a local path. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### WARNING -Supports only one file download per call. Directory download is **NOT** supported, use [File Downloader](../../file/file_downloader/file_downloader.md#file-downloader) instead. - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **remote_file_path** - : Remote file path to read from - - **local_file_path** - : Local file path to create - - **replace** - : If `True`, existing file will be replaced -* **Returns:** - Local file with stats. -* **Raises:** - `onetl.exception.NotAFileError` - : Remote or local path is not a file - - FileNotFoundError - : Remote file does not exist - - FileExistsError - : Local file already exists, and `replace=False` - - `onetl.exception.FileSizeMismatchError` - : Target file size after download is different from source file size. - -### Examples - -```pycon ->>> local_file = connection.download_file( -... remote_file_path="/path/to/source.csv", -... local_file_path="/path/to/target.csv", -... ) ->>> os.fspath(local_file) -'/path/to/target.csv' ->>> local_file.exists() -True ->>> local_file.stat().st_size # in bytes -1024 ->>> connection.get_stat("/path/to/source.csv").st_size # same size -1024 -``` - - - -#### get_stat(path: PathLike | str) → PathStatProtocol - -Returns stats for a specific path. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **path** - : Path to get stats for -* **Returns:** - Stats object -* **Raises:** - Any underlying client exception - -### Examples - -```pycon ->>> stat = connection.get_stat("/path/to/file.csv") ->>> stat.st_size # in bytes -1024 ->>> stat.st_uid # owner id or name -12345 -``` - - - -#### is_dir(path: PathLike | str) → bool - -Check if specified path is a directory. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **path** - : Path to check -* **Returns:** - `True` if path is a directory, `False` otherwise. -* **Raises:** - `onetl.exception.DirectoryNotFoundError` - : Path does not exist - -### Examples - -```pycon ->>> connection.is_dir("/path/to/dir") -True ->>> connection.is_dir("/path/to/dir/file.csv") -False -``` - - - -#### is_file(path: PathLike | str) → bool - -Check if specified path is a file. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **path** - : Path to check -* **Returns:** - `True` if path is a file, `False` otherwise. -* **Raises:** - FileNotFoundError - : Path does not exist - -### Examples - -```pycon ->>> connection.is_file("/path/to/dir/file.csv") -True ->>> connection.is_file("/path/to/dir") -False -``` - - - -#### list_dir(path: PathLike | str, filters: Iterable[[BaseFileFilter](../../file/file_filters/base.md#onetl.base.base_file_filter.BaseFileFilter)] | None = None, limits: Iterable[[BaseFileLimit](../../file/file_limits/base.md#onetl.base.base_file_limit.BaseFileLimit)] | None = None) → list[RemoteDirectory | RemoteFile] - -Return list of child files/directories in a specific directory. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **path** - : Directory path to list contents. - - **filters** - : Return only files/directories matching these filters. See [File Filters](../../file/file_filters/index.md#file-filters) - - **limits** - : Apply limits to the list of files/directories, and stop if one of the limits is reached. - See [File Limits](../../file/file_limits/index.md#file-limits) -* **Returns:** - List of `onetl.base.PathWithStatsProtocol` -* **Raises:** - NotADirectoryError - : Path is not a directory - - `onetl.exception.DirectoryNotFoundError` - : Path does not exist - -### Examples - -```pycon ->>> dir_content = connection.list_dir("/path/to/dir") ->>> os.fspath(dir_content[0]) -'file.csv' ->>> connection.path_exists("/path/to/dir/file.csv") -True -``` - - - -#### path_exists(path: PathLike | str) → bool - -Check if specified path exists on remote filesystem. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html). - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **path** - : Path to check -* **Returns:** - `True` if path exists, `False` otherwise - -### Examples - -```pycon ->>> connection.path_exists("/path/to/file.csv") -True ->>> connection.path_exists("/path/to/dir") -True ->>> connection.path_exists("/path/to/missing") -False -``` - - - -#### remove_dir(path: PathLike | str, recursive: bool = False) → bool - -Remove directory or directory tree. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -If directory does not exist, no exception is raised. - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **path** - : Directory path to remote - - **recursive** - : If `True`, remove directory tree recursively. -* **Returns:** - `True` if directory was removed, `False` if directory does not exist in the first place. -* **Raises:** - NotADirectoryError - : Path is not a directory - -### Examples - -```pycon ->>> connection.remove_dir("/path/to/dir") -True ->>> connection.path_exists("/path/to/dir") -False ->>> connection.path_exists("/path/to/dir/file.csv") -False ->>> connection.remove_dir("/path/to/dir") # already deleted, no error -False -``` - - - -#### remove_file(path: PathLike | str) → bool - -Removes file on remote filesystem. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -If file does not exist, no exception is raised. - -#### WARNING -Supports only one file removal per call. Directory removal is **NOT** supported, use [`remove_dir`](#onetl.connection.file_connection.webdav.WebDAV.remove_dir) instead. - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **path** - : File path -* **Returns:** - `True` if file was removed, `False` if file does not exist in the first place. -* **Raises:** - `onetl.exception.NotAFileError` - : Path is not a file - -### Examples - -```pycon ->>> connection.remove_file("/path/to/file.csv") -True ->>> connection.path_exists("/path/to/dir/file.csv") -False ->>> connection.remove_file("/path/to/file.csv") # already deleted, no error -False -``` - - - -#### rename_file(source_file_path: PathLike | str, target_file_path: PathLike | str, replace: bool = False) → RemoteFile - -Rename or move file on remote filesystem. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### WARNING -Supports only one file move per call. Directory move/rename is **NOT** supported. - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **source_file_path** - : Old file path - - **target_file_path** - : New file path - - **replace** - : If `True`, existing file will be replaced. -* **Returns:** - New file path with stats. -* **Raises:** - `onetl.exception.NotAFileError` - : Source or target path is not a file - - FileNotFoundError - : File does not exist - - FileExistsError - : File already exists, and `replace=False` - -### Examples - -```pycon ->>> new_file = connection.rename_file("/path/to/file1.csv", "/path/to/file2.csv") ->>> os.fspath(new_file) -'/path/to/file2.csv' ->>> connection.path_exists("/path/to/file2.csv") -True ->>> connection.path_exists("/path/to/file1.csv") -False -``` - - - -#### resolve_dir(path: PathLike | str) → RemoteDirectory - -Returns directory at specific path, with stats. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **path** - : Path to resolve -* **Returns:** - Directory path with stats -* **Raises:** - `onetl.exception.DirectoryNotFoundError` - : Path does not exist - - NotADirectoryError - : Path is not a directory - -### Examples - -```pycon ->>> dir_path = connection.resolve_dir("/path/to/dir") ->>> os.fspath(dir_path) -'/path/to/dir' ->>> dir_path.stat().st_uid # owner id -12345 -``` - - - -#### resolve_file(path: PathLike | str) → RemoteFile - -Returns file at specific path, with stats. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **path** - : Path to resolve -* **Returns:** - File path with stats -* **Raises:** - FileNotFoundError - : Path does not exist - - `onetl.exception.NotAFileError` - : Path is not a file - -### Examples - -```pycon ->>> file_path = connection.resolve_file("/path/to/dir/file.csv") ->>> os.fspath(file_path) -'/path/to/dir/file.csv' ->>> file_path.stat().st_uid # owner id -12345 -``` - - - -#### upload_file(local_file_path: PathLike | str, remote_file_path: PathLike | str, replace: bool = False) → RemoteFile - -Uploads local file to a remote filesystem. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### WARNING -Supports only one file upload per call. Directory upload is **NOT** supported, use [File Uploader](../../file/file_uploader/file_uploader.md#file-uploader) instead. - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **local_file_path** - : Local file path to read from - - **remote_file_path** - : Remote file path to create - - **replace** - : If `True`, existing file will be replaced -* **Returns:** - Remote file with stats. -* **Raises:** - `onetl.exception.NotAFileError` - : Remote or local path is not a file - - FileNotFoundError - : Local file does not exist - - FileExistsError - : Remote file already exists, and `replace=False` - - `onetl.exception.FileSizeMismatchError` - : Target file size after upload is different from source file size. - -### Examples - -```pycon ->>> remote_file = connection.upload( -... local_file_path="/path/to/source.csv", -... remote_file_path="/path/to/target.csv", -... ) ->>> os.fspath(remote_file) -'/path/to/target.csv' ->>> connection.path_exists("/path/to/target.csv") -True ->>> remote_file.stat().st_size # in bytes -1024 ->>> os.stat("/path/to/source.csv").st_size # same as source -1024 -``` - - - -#### walk(root: PathLike | str, topdown: bool = True, filters: Iterable[[BaseFileFilter](../../file/file_filters/base.md#onetl.base.base_file_filter.BaseFileFilter)] | None = None, limits: Iterable[[BaseFileLimit](../../file/file_limits/base.md#onetl.base.base_file_limit.BaseFileLimit)] | None = None) → Iterator[tuple[RemoteDirectory, list[RemoteDirectory], list[RemoteFile]]] - -Walk into directory tree, and iterate over its content in all nesting levels. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -Just like `os.walk`, but with additional filter/limit logic. - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **root** - : Directory path to walk into. - - **topdown** - : If `True`, walk in top-down order, otherwise walk in bottom-up order. - - **filters** - : Return only files/directories matching these filters. See [File Filters](../../file/file_filters/index.md#file-filters). - - **limits** - : Apply limits to the list of files/directories, and immediately stop if any of these limits is reached. - See [File Limits](../../file/file_limits/index.md#file-limits). -* **Returns:** - `Iterator[tuple[root, dirs, files]]`, like `os.walk`. - - But all the paths are not strings, instead path classes with embedded stats are returned. -* **Raises:** - NotADirectoryError - : Path is not a directory - - `onetl.exception.DirectoryNotFoundError` - : Path does not exist - -### Examples - -```pycon ->>> for root, dirs, files in connection.walk("/path/to/dir"): -... break ->>> os.fspath(root) -'/path/to/dir' ->>> dirs -[] ->>> os.fspath(files[0]) -'file.csv' ->>> connection.path_exists("/path/to/dir/file.csv") -True -``` - - diff --git a/mddocs/markdown/connection/file_df_connection/base.md b/mddocs/markdown/connection/file_df_connection/base.md deleted file mode 100644 index 8f58267d8..000000000 --- a/mddocs/markdown/connection/file_df_connection/base.md +++ /dev/null @@ -1,63 +0,0 @@ - - -# Base interface - -### *class* onetl.base.base_file_df_connection.BaseFileDFConnection - -Implements generic methods for reading and writing dataframe as files. - -#### Versionadded -Added in version 0.9.0. - - - -#### *abstract* check() → T - -Check source availability. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -If not, an exception will be raised. - -* **Returns:** - Connection itself -* **Raises:** - RuntimeError - : If the connection is not available - -### Examples - -```python -connection.check() -``` - - - -#### *abstract* check_if_format_supported(format: [BaseReadableFileFormat](../../file_df/file_formats/base.md#onetl.base.base_file_format.BaseReadableFileFormat) | [BaseWritableFileFormat](../../file_df/file_formats/base.md#onetl.base.base_file_format.BaseWritableFileFormat)) → None - -Validate if specific file format is supported. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### Versionadded -Added in version 0.9.0. - -* **Raises:** - RuntimeError - : If file format is not supported. - - - -#### *abstract* read_files_as_df(paths: list[PurePathProtocol], format: [BaseReadableFileFormat](../../file_df/file_formats/base.md#onetl.base.base_file_format.BaseReadableFileFormat), root: PurePathProtocol | None = None, df_schema: StructType | None = None, options: FileDFReadOptions | None = None) → DataFrame - -Read files in some paths list as dataframe. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### Versionadded -Added in version 0.9.0. - - - -#### *abstract* write_df_as_files(df: DataFrame, path: PurePathProtocol, format: [BaseWritableFileFormat](../../file_df/file_formats/base.md#onetl.base.base_file_format.BaseWritableFileFormat), options: FileDFWriteOptions | None = None) → None - -Write dataframe as files in some path. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### Versionadded -Added in version 0.9.0. - - diff --git a/mddocs/markdown/connection/file_df_connection/index.md b/mddocs/markdown/connection/file_df_connection/index.md deleted file mode 100644 index ef8d51549..000000000 --- a/mddocs/markdown/connection/file_df_connection/index.md +++ /dev/null @@ -1,20 +0,0 @@ - - -# File DataFrame Connections - -# File DataFrame Connections - -* [Spark LocalFS](spark_local_fs.md) - * [`SparkLocalFS`](spark_local_fs.md#onetl.connection.file_df_connection.spark_local_fs.SparkLocalFS) -* [Spark HDFS](spark_hdfs/index.md) - * [Prerequisites](spark_hdfs/prerequisites.md) - * [Connection](spark_hdfs/connection.md) - * [Slots](spark_hdfs/slots.md) -* [Spark S3](spark_s3/index.md) - * [Prerequisites](spark_s3/prerequisites.md) - * [Connection](spark_s3/connection.md) - * [Troubleshooting](spark_s3/troubleshooting.md) - -# For developers - -* [Base interface](base.md) diff --git a/mddocs/markdown/connection/file_df_connection/spark_hdfs/connection.md b/mddocs/markdown/connection/file_df_connection/spark_hdfs/connection.md deleted file mode 100644 index fe8e621a9..000000000 --- a/mddocs/markdown/connection/file_df_connection/spark_hdfs/connection.md +++ /dev/null @@ -1,169 +0,0 @@ - - -# Spark HDFS Connection - -### *class* onetl.connection.file_df_connection.spark_hdfs.connection.SparkHDFS(\*, spark: SparkSession, cluster: Cluster, host: Host | None = None, port: int = 8020) - -Spark connection to HDFS. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -Based on [Spark Generic File Data Source](https://spark.apache.org/docs/latest/sql-data-sources-generic-options.html). - -#### SEE ALSO -Before using this connector please take into account [Prerequisites](prerequisites.md#spark-hdfs-prerequisites) - -#### NOTE -Supports only reading files as Spark DataFrame and writing DataFrame to files. - -Does NOT support file operations, like create, delete, rename, etc. For these operations, -use [`HDFS`](../../file_connection/hdfs/connection.md#onetl.connection.file_connection.hdfs.connection.HDFS) connection. - -#### Versionadded -Added in version 0.9.0. - -* **Parameters:** - **cluster** - : Cluster name. -
- Used for: - : * HWM and lineage (as instance name for file paths) - * Validation of `host` value, - : if latter is passed and if some hooks are bound to - [`Slots.get_cluster_namenodes`](slots.md#onetl.connection.file_df_connection.spark_hdfs.slots.SparkHDFSSlots.get_cluster_namenodes). - - **host** - : Hadoop namenode host. For example: `namenode1.domain.com`. -
- Should be an active namenode (NOT standby). -
- If value is not set, but there are some hooks bound to - [`Slots.get_cluster_namenodes`](slots.md#onetl.connection.file_df_connection.spark_hdfs.slots.SparkHDFSSlots.get_cluster_namenodes) - and - [`Slots.is_namenode_active`](slots.md#onetl.connection.file_df_connection.spark_hdfs.slots.SparkHDFSSlots.is_namenode_active), - onETL will iterate over cluster namenodes to detect which one is active. - - **ipc_port** - : Port of Hadoop namenode (IPC protocol). -
- If omitted, but there are some hooks bound to - [`Slots.get_ipc_port`](slots.md#onetl.connection.file_df_connection.spark_hdfs.slots.SparkHDFSSlots.get_ipc_port), - onETL will try to detect port number for a specific `cluster`. - - **spark** - : Spark session - -### Examples - -Create SparkHDFS connection with Kerberos auth - -Execute `kinit` consome command before creating Spark Session - -```bash -$ kinit -kt /path/to/keytab user -``` - -```python -from onetl.connection import SparkHDFS -from pyspark.sql import SparkSession - -# Create Spark session. -# Use names "spark.yarn.access.hadoopFileSystems", "spark.yarn.principal" -# and "spark.yarn.keytab" for Spark 2 - -spark = ( - SparkSession.builder.appName("spark-app-name") - .option( - "spark.kerberos.access.hadoopFileSystems", - "hdfs://namenode1.domain.com:8020", - ) - .option("spark.kerberos.principal", "user") - .option("spark.kerberos.keytab", "/path/to/keytab") - .enableHiveSupport() - .getOrCreate() -) - -# Create connection -hdfs = SparkHDFS( - host="namenode1.domain.com", - cluster="rnd-dwh", - spark=spark, -).check() -``` - -Create SparkHDFS connection with anonymous auth - -```py -from onetl.connection import SparkHDFS -from pyspark.sql import SparkSession - -# Create Spark session -spark = SparkSession.builder.master("local").appName("spark-app-name").getOrCreate() - -# Create connection -hdfs = SparkHDFS( - host="namenode1.domain.com", - cluster="rnd-dwh", - spark=spark, -).check() -``` - -Use cluster name to detect active namenode - -Can be used only if some third-party plugin provides [Spark HDFS Slots](slots.md#spark-hdfs-slots) implementation - -```python -# Create Spark session -... - -# Create connection -hdfs = SparkHDFS(cluster="rnd-dwh", spark=spark).check() -``` - - - -#### check() - -Check source availability. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -If not, an exception will be raised. - -* **Returns:** - Connection itself -* **Raises:** - RuntimeError - : If the connection is not available - -### Examples - -```python -connection.check() -``` - - - -#### *classmethod* get_current(spark: SparkSession) - -Create connection for current cluster. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -Automatically sets up current cluster name as `cluster`. - -#### NOTE -Can be used only if there are a some hooks bound to -[`Slots.get_current_cluster`](slots.md#onetl.connection.file_df_connection.spark_hdfs.slots.SparkHDFSSlots.get_current_cluster). - -#### Versionadded -Added in version 0.9.0. - -* **Parameters:** - **spark** - : See [`SparkHDFS`](#onetl.connection.file_df_connection.spark_hdfs.connection.SparkHDFS) constructor documentation. - -### Examples - -```python -from onetl.connection import SparkHDFS - -# injecting current cluster name via hooks mechanism -hdfs = SparkHDFS.get_current(spark=spark) -``` - - diff --git a/mddocs/markdown/connection/file_df_connection/spark_hdfs/index.md b/mddocs/markdown/connection/file_df_connection/spark_hdfs/index.md deleted file mode 100644 index d5eae4c27..000000000 --- a/mddocs/markdown/connection/file_df_connection/spark_hdfs/index.md +++ /dev/null @@ -1,12 +0,0 @@ - - -# Spark HDFS - -# Connection - -* [Prerequisites](prerequisites.md) -* [Connection](connection.md) - -# For developers - -* [Slots](slots.md) diff --git a/mddocs/markdown/connection/file_df_connection/spark_hdfs/prerequisites.md b/mddocs/markdown/connection/file_df_connection/spark_hdfs/prerequisites.md deleted file mode 100644 index 2011410da..000000000 --- a/mddocs/markdown/connection/file_df_connection/spark_hdfs/prerequisites.md +++ /dev/null @@ -1,46 +0,0 @@ - - -# Prerequisites - -## Version Compatibility - -* Hadoop versions: 2.x, 3.x (only with Hadoop 3.x libraries) -* Spark versions: 2.3.x - 3.5.x -* Java versions: 8 - 20 - -## Installing PySpark - -To use SparkHDFS connector you should have PySpark installed (or injected to `sys.path`) -BEFORE creating the connector instance. - -See [Spark](../../../install/spark.md#install-spark) installation instruction for more details. - -## Using Kerberos - -Some of Hadoop managed clusters use Kerberos authentication. In this case, you should call [kinit](https://web.mit.edu/kerberos/krb5-1.12/doc/user/user_commands/kinit.html) command -**BEFORE** starting Spark session to generate Kerberos ticket. See [Kerberos support](../../../install/kerberos.md#install-kerberos). - -Sometimes it is also required to pass keytab file to Spark config, allowing Spark executors to generate own Kerberos tickets: - -Spark 3 - -```python -SparkSession.builder - .option("spark.kerberos.access.hadoopFileSystems", "hdfs://namenode1.domain.com:9820,hdfs://namenode2.domain.com:9820") - .option("spark.kerberos.principal", "user") - .option("spark.kerberos.keytab", "/path/to/keytab") - .gerOrCreate() -``` - -Spark 2 - -```python -SparkSession.builder - .option("spark.yarn.access.hadoopFileSystems", "hdfs://namenode1.domain.com:9820,hdfs://namenode2.domain.com:9820") - .option("spark.yarn.principal", "user") - .option("spark.yarn.keytab", "/path/to/keytab") - .gerOrCreate() -``` - -See [Spark security documentation](https://spark.apache.org/docs/latest/security.html#kerberos) -for more details. diff --git a/mddocs/markdown/connection/file_df_connection/spark_hdfs/slots.md b/mddocs/markdown/connection/file_df_connection/spark_hdfs/slots.md deleted file mode 100644 index a94ddcde0..000000000 --- a/mddocs/markdown/connection/file_df_connection/spark_hdfs/slots.md +++ /dev/null @@ -1,256 +0,0 @@ - - -# Spark HDFS Slots - -### *class* onetl.connection.file_df_connection.spark_hdfs.slots.SparkHDFSSlots - -Spark HDFS slots that could be implemented by third-party plugins. - -#### Versionadded -Added in version 0.9.0. - - - -#### *static* normalize_cluster_name(cluster: str) → str | None - -Normalize cluster name passed into SparkHDFS constructor. - -If hooks didn’t return anything, cluster name is left intact. - -#### Versionadded -Added in version 0.9.0. - -* **Parameters:** - **cluster** - : Cluster name -* **Returns:** - str | None - : Normalized cluster name. -
- If hook cannot be applied to a specific cluster, it should return `None`. - -### Examples - -```python -from onetl.connection import SparkHDFS -from onetl.hooks import hook - -@SparkHDFS.Slots.normalize_cluster_name.bind -@hook -def normalize_cluster_name(cluster: str) -> str: - return cluster.lower() -``` - - - -#### *static* normalize_namenode_host(host: str, cluster: str) → str | None - -Normalize namenode host passed into SparkHDFS constructor. - -If hooks didn’t return anything, host is left intact. - -#### Versionadded -Added in version 0.9.0. - -* **Parameters:** - **host** - : Namenode host (raw) - - **cluster** - : Cluster name (normalized) -* **Returns:** - str | None - : Normalized namenode host name. -
- If hook cannot be applied to a specific host name, it should return `None`. - -### Examples - -```python -from onetl.connection import SparkHDFS -from onetl.hooks import hook - -@SparkHDFS.Slots.normalize_namenode_host.bind -@hook -def normalize_namenode_host(host: str, cluster: str) -> str | None: - if cluster == "rnd-dwh": - if not host.endswith(".domain.com"): - # fix missing domain name - host += ".domain.com" - return host - - return None -``` - - - -#### *static* get_known_clusters() → set[str] | None - -Return collection of known clusters. - -Cluster passed into SparkHDFS constructor should be present in this list. -If hooks didn’t return anything, no validation will be performed. - -#### Versionadded -Added in version 0.9.0. - -* **Returns:** - set[str] | None - : Collection of cluster names (in normalized form). -
- If hook cannot be applied, it should return `None`. - -### Examples - -```python -from onetl.connection import SparkHDFS -from onetl.hooks import hook - -@SparkHDFS.Slots.get_known_clusters.bind -@hook -def get_known_clusters() -> str[str]: - return {"rnd-dwh", "rnd-prod"} -``` - - - -#### *static* get_cluster_namenodes(cluster: str) → set[str] | None - -Return collection of known namenodes for the cluster. - -Namenode host passed into SparkHDFS constructor should be present in this list. -If hooks didn’t return anything, no validation will be performed. - -#### Versionadded -Added in version 0.9.0. - -* **Parameters:** - **cluster** - : Cluster name (normalized) -* **Returns:** - set[str] | None - : Collection of host names (in normalized form). -
- If hook cannot be applied, it should return `None`. - -### Examples - -```python -from onetl.connection import SparkHDFS -from onetl.hooks import hook - -@SparkHDFS.Slots.get_cluster_namenodes.bind -@hook -def get_cluster_namenodes(cluster: str) -> str[str] | None: - if cluster == "rnd-dwh": - return {"namenode1.domain.com", "namenode2.domain.com"} - return None -``` - - - -#### *static* get_current_cluster() → str | None - -Get current cluster name. - -Used in [`get_current_cluster`](#onetl.connection.file_df_connection.spark_hdfs.slots.SparkHDFSSlots.get_current_cluster) to automatically fill up `cluster` attribute of a connection. -If hooks didn’t return anything, calling the method above will raise an exception. - -#### Versionadded -Added in version 0.9.0. - -* **Returns:** - str | None - : Current cluster name (in normalized form). -
- If hook cannot be applied, it should return `None`. - -### Examples - -```python -from onetl.connection import SparkHDFS -from onetl.hooks import hook - -@SparkHDFS.Slots.get_current_cluster.bind -@hook -def get_current_cluster() -> str: - # some magic here - return "rnd-dwh" -``` - - - -#### *static* get_ipc_port(cluster: str) → int | None - -Get IPC port number for a specific cluster. - -Used by constructor to automatically set port number if omitted. - -#### Versionadded -Added in version 0.9.0. - -* **Parameters:** - **cluster** - : Cluster name (normalized) -* **Returns:** - int | None - : IPC port number. -
- If hook cannot be applied, it should return `None`. - -### Examples - -```python -from onetl.connection import SparkHDFS -from onetl.hooks import hook - -@SparkHDFS.Slots.get_ipc_port.bind -@hook -def get_ipc_port(cluster: str) -> int | None: - if cluster == "rnd-dwh": - return 8020 # Cloudera - return None -``` - - - -#### *static* is_namenode_active(host: str, cluster: str) → bool | None - -Check whether a namenode of a specified cluster is active (=not standby) or not. - -Used for: -: * If SparkHDFS connection is created without `host` - > Connector will iterate over [`get_cluster_namenodes`](#onetl.connection.file_df_connection.spark_hdfs.slots.SparkHDFSSlots.get_cluster_namenodes) of a cluster to get active namenode, - > and then use it instead of `host` attribute. - * If SparkHDFS connection is created with `host` - > `check` will determine whether this host is active. - -#### Versionadded -Added in version 0.9.0. - -* **Parameters:** - **host** - : Namenode host (normalized) - - **cluster** - : Cluster name (normalized) -* **Returns:** - bool | None - : `True` if namenode is active, `False` if not. -
- If hook cannot be applied, it should return `None`. - -### Examples - -```python -from onetl.connection import SparkHDFS -from onetl.hooks import hook - -@SparkHDFS.Slots.is_namenode_active.bind -@hook -def is_namenode_active(host: str, cluster: str) -> bool: - # some magic here - return True -``` - - diff --git a/mddocs/markdown/connection/file_df_connection/spark_local_fs.md b/mddocs/markdown/connection/file_df_connection/spark_local_fs.md deleted file mode 100644 index 1620f08bc..000000000 --- a/mddocs/markdown/connection/file_df_connection/spark_local_fs.md +++ /dev/null @@ -1,65 +0,0 @@ - - -# Spark LocalFS - -### *class* onetl.connection.file_df_connection.spark_local_fs.SparkLocalFS(\*, spark: SparkSession) - -Spark connection to local filesystem. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -Based on [Spark Generic File Data Source](https://spark.apache.org/docs/latest/sql-data-sources-generic-options.html). - -#### WARNING -To use SparkHDFS connector you should have PySpark installed (or injected to `sys.path`) -BEFORE creating the connector instance. - -See [Spark](../../install/spark.md#install-spark) installation instruction for more details. - -#### WARNING -Currently supports only Spark sessions created with option `spark.master: local`. - -#### NOTE -Supports only reading files as Spark DataFrame and writing DataFrame to files. - -Does NOT support file operations, like create, delete, rename, etc. - -#### Versionadded -Added in version 0.9.0. - -* **Parameters:** - **spark** - : Spark session - -### Examples - -```python -from onetl.connection import SparkLocalFS -from pyspark.sql import SparkSession - -# create Spark session -spark = SparkSession.builder.master("local").appName("spark-app-name").getOrCreate() - -# create connection -local_fs = SparkLocalFS(spark=spark).check() -``` - - - -#### check() - -Check source availability. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -If not, an exception will be raised. - -* **Returns:** - Connection itself -* **Raises:** - RuntimeError - : If the connection is not available - -### Examples - -```python -connection.check() -``` - - diff --git a/mddocs/markdown/connection/file_df_connection/spark_s3/connection.md b/mddocs/markdown/connection/file_df_connection/spark_s3/connection.md deleted file mode 100644 index e51ec8421..000000000 --- a/mddocs/markdown/connection/file_df_connection/spark_s3/connection.md +++ /dev/null @@ -1,232 +0,0 @@ - - -# Spark S3 Connection - -### *class* onetl.connection.file_df_connection.spark_s3.connection.SparkS3(\*, spark: SparkSession, host: Host, port: int | None = None, bucket: str, protocol: Literal['http', 'https'] = 'https', access_key: str | None = None, secret_key: SecretStr | None = None, session_token: SecretStr | None = None, region: str | None = None, extra: SparkS3Extra = SparkS3Extra()) - -Spark connection to S3 filesystem. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -Based on [Hadoop-AWS module](https://hadoop.apache.org/docs/current3/hadoop-aws/tools/hadoop-aws/index.html) -and [Spark integration with Cloud Infrastructures](https://spark.apache.org/docs/latest/cloud-integration.html). - -#### SEE ALSO -Before using this connector please take into account [Prerequisites](prerequisites.md#spark-s3-prerequisites) - -#### NOTE -Supports only reading files as Spark DataFrame and writing DataFrame to files. - -Does NOT support file operations, like create, delete, rename, etc. For these operations, -use [`S3`](../../file_connection/s3.md#onetl.connection.file_connection.s3.S3) connection. - -#### Versionadded -Added in version 0.9.0. - -* **Parameters:** - **host** - : Host of S3 source. For example: `domain.com` - - **port** - : Port of S3 source - - **bucket** - : Bucket name in the S3 file source - - **protocol** - : Connection protocol. Allowed values: `https` or `http` - - **access_key** - : Access key (aka user ID) of an account in the S3 service - - **secret_key** - : Secret key (aka password) of an account in the S3 service - - **session_token** - : Session token of your account in S3 service - - **region** - : Region name of bucket in S3 service - - **extra** - : A dictionary of additional properties to be used when connecting to S3. -
- These are Hadoop AWS specific properties, see links below: - * [Hadoop AWS](https://hadoop.apache.org/docs/current/hadoop-aws/tools/hadoop-aws/index.html#General_S3A_Client_configuration) - * [Hadoop AWS committers options](https://hadoop.apache.org/docs/current/hadoop-aws/tools/hadoop-aws/committers.html) -
- Options are passed without prefixes `spark.hadoop.`, `fs.s3a.` and `fs.s3a.bucket.$BUCKET.`, for example: - ```python - extra = { - "path.style.access": True, - "committer.magic.enabled": True, - "committer.name": "magic", - "connection.timeout": 300000, - } - ``` -
- #### WARNING - Options that populated from connection - attributes (like `endpoint`, `access.key`) are not allowed to override. -
- But you may override `aws.credentials.provider` and pass custom credential options. - - **spark** - : Spark session - -### Examples - -Create S3 connection with bucket as subdomain (`my-bucket.domain.com`): - -```py -from onetl.connection import SparkS3 -from pyspark.sql import SparkSession - -# Create Spark session with Hadoop AWS libraries loaded -maven_packages = SparkS3.get_packages(spark_version="3.5.5") -# Some packages are not used, but downloading takes a lot of time. Skipping them. -excluded_packages = SparkS3.get_exclude_packages() -spark = ( - SparkSession.builder.appName("spark-app-name") - .config("spark.jars.packages", ",".join(maven_packages)) - .config("spark.jars.excludes", ",".join(excluded_packages)) - .config("spark.hadoop.fs.s3a.committer.magic.enabled", "true") - .config("spark.hadoop.fs.s3a.committer.name", "magic") - .config( - "spark.hadoop.mapreduce.outputcommitter.factory.scheme.s3a", - "org.apache.hadoop.fs.s3a.commit.S3ACommitterFactory", - ) - .config( - "spark.sql.parquet.output.committer.class", - "org.apache.spark.internal.io.cloud.BindingParquetOutputCommitter", - ) - .config( - "spark.sql.sources.commitProtocolClass", - "org.apache.spark.internal.io.cloud.PathOutputCommitProtocol", - ) - .getOrCreate() -) - -# Create connection -s3 = SparkS3( - host="domain.com", - protocol="http", - bucket="my-bucket", - access_key="ACCESS_KEY", - secret_key="SECRET_KEY", - spark=spark, -).check() -``` - -Create S3 connection with bucket as subpath (`domain.com/my-bucket`) - -```py -# Create Spark session with Hadoop AWS libraries loaded -... - -# Create connection -s3 = SparkS3( - host="domain.com", - protocol="http", - bucket="my-bucket", - access_key="ACCESS_KEY", - secret_key="SECRET_KEY", - extra={ - "path.style.access": True, # <--- - }, - spark=spark, -).check() -``` - - - -#### check() - -Check source availability. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -If not, an exception will be raised. - -* **Returns:** - Connection itself -* **Raises:** - RuntimeError - : If the connection is not available - -### Examples - -```python -connection.check() -``` - - - -#### close() - -Close all connections created to S3. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -Also resets all `fs.s3a.bucket.$BUCKET.*` properties of Hadoop configuration. - -#### NOTE -Connection can be used again after it was closed. - -* **Returns:** - Connection itself - -### Examples - -Close connection automatically: - -```python -with connection: - ... -``` - -Close connection manually: - -```python -connection.close() -``` - - - -#### *classmethod* get_exclude_packages() → list[str] - -Get package names to be excluded by Spark. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### Versionadded -Added in version 0.13.0. - -### Examples - -```python -from onetl.connection import SparkS3 - -SparkS3.get_exclude_packages() -``` - - - -#### *classmethod* get_packages(spark_version: str, scala_version: str | None = None) → list[str] - -Get package names to be downloaded by Spark. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### Versionadded -Added in version 0.9.0. - -* **Parameters:** - **spark_version** - : Spark version in format `major.minor.patch`. - - **scala_version** - : Scala version in format `major.minor`. -
- If `None`, `spark_version` is used to determine Scala version. - -### Examples - -```python -from onetl.connection import SparkS3 - -SparkS3.get_packages(spark_version="3.5.5") -SparkS3.get_packages(spark_version="3.5.5", scala_version="2.12") -``` - - diff --git a/mddocs/markdown/connection/file_df_connection/spark_s3/index.md b/mddocs/markdown/connection/file_df_connection/spark_s3/index.md deleted file mode 100644 index be66a0adc..000000000 --- a/mddocs/markdown/connection/file_df_connection/spark_s3/index.md +++ /dev/null @@ -1,9 +0,0 @@ - - -# Spark S3 - -# Connection - -* [Prerequisites](prerequisites.md) -* [Connection](connection.md) -* [Troubleshooting](troubleshooting.md) diff --git a/mddocs/markdown/connection/file_df_connection/spark_s3/prerequisites.md b/mddocs/markdown/connection/file_df_connection/spark_s3/prerequisites.md deleted file mode 100644 index 1bfd0846a..000000000 --- a/mddocs/markdown/connection/file_df_connection/spark_s3/prerequisites.md +++ /dev/null @@ -1,61 +0,0 @@ - - -# Prerequisites - -## Version Compatibility - -* Spark versions: 3.2.x - 3.5.x (only with Hadoop 3.x libraries) -* Java versions: 8 - 20 - -## Installing PySpark - -To use SparkS3 connector you should have PySpark installed (or injected to `sys.path`) -BEFORE creating the connector instance. - -See [Spark](../../../install/spark.md#install-spark) installation instruction for more details. - -## Connecting to S3 - -### Bucket access style - -AWS and some other S3 cloud providers allows bucket access using domain style only, e.g. `https://mybucket.s3provider.com`. - -Other implementations, like Minio, by default allows path style access only, e.g. `https://s3provider.com/mybucket` -(see [MINIO_DOMAIN](https://min.io/docs/minio/linux/reference/minio-server/minio-server.html#envvar.MINIO_DOMAIN)). - -You should set `path.style.access` to `True` or `False`, to choose the preferred style. - -### Authentication - -Different S3 instances can use different authentication methods, like: -: * `access_key + secret_key` (or username + password) - * `access_key + secret_key + session_token` - -Usually these are just passed to SparkS3 constructor: - -```python -SparkS3( - access_key=..., - secret_key=..., - session_token=..., -) -``` - -But some S3 cloud providers, like AWS, may require custom credential providers. You can pass them like: - -```python -SparkS3( - extra={ - # provider class - "aws.credentials.provider": "org.apache.hadoop.fs.s3a.auth.AssumedRoleCredentialProvider", - # other options, if needed - "assumed.role.arn": "arn:aws:iam::90066806600238:role/s3-restricted", - }, -) -``` - -See [Hadoop-AWS](https://hadoop.apache.org/docs/stable/hadoop-aws/tools/hadoop-aws/index.html#Changing_Authentication_Providers) documentation. - -## Troubleshooting - -See [Spark S3 Troubleshooting](troubleshooting.md#spark-s3-troubleshooting). diff --git a/mddocs/markdown/connection/file_df_connection/spark_s3/troubleshooting.md b/mddocs/markdown/connection/file_df_connection/spark_s3/troubleshooting.md deleted file mode 100644 index 32ea15667..000000000 --- a/mddocs/markdown/connection/file_df_connection/spark_s3/troubleshooting.md +++ /dev/null @@ -1,366 +0,0 @@ - - -# Spark S3 Troubleshooting - -#### NOTE -General guide: [Troubleshooting](../../../troubleshooting/index.md#troubleshooting). - -More details: - -* [Hadoop AWS Troubleshooting Guide](https://hadoop.apache.org/docs/stable/hadoop-aws/tools/hadoop-aws/troubleshooting_s3a.html) -* [Hadoop AWS Performance Guide](https://hadoop.apache.org/docs/stable/hadoop-aws/tools/hadoop-aws/performance.html) -* [Spark integration with Cloud Infrastructures](https://spark.apache.org/docs/latest/cloud-integration.html) - -## `SparkS3.check()` and other methods hang - -### Details - -S3 may not respond for connection attempts for a long time if it’s under heavy load. -To handle this, Hadoop AWS library has retry mechanism. By default it retries 7 times with 500ms interval. - -Hadoop AWS is based on AWS SDK library, which also has retry mechanism. This mechanism is not disabled because it handles different -errors than Hadoop AWS, so they complement each other. Default number of attempts in AWS SDK is 20 with minimal 5s interval, -which is exponentially increasing with each failed attempt. - -It is not a problem if S3 source is not accessible at all, like hostname cannot be resolved, or port is not opened. -These errors are not recoverable, and retry mechanism is not activated. - -But errors like SSL issues, are considered recoverable, and this causing retry of retry over increasing interval. -So user is waiting for [almost 15 minutes](https://issues.apache.org/jira/browse/HADOOP-18839) just to get exception message. - -### How to determine reason - -#### Make logging more verbose - -Change Spark session log level to [DEBUG](../../../troubleshooting/spark.md#troubleshooting-spark) to print result of each attempt. -Resulting logs will look like this - -### See log - -```text -23/08/03 11:25:10 DEBUG S3AFileSystem: Using S3ABlockOutputStream with buffer = disk; block=67108864; queue limit=4 -23/08/03 11:25:10 DEBUG S3Guard: Metastore option source [core-default.xml] -23/08/03 11:25:10 DEBUG S3Guard: Using NullMetadataStore metadata store for s3a filesystem -23/08/03 11:25:10 DEBUG S3AFileSystem: S3Guard is disabled on this bucket: test-bucket -23/08/03 11:25:10 DEBUG DirectoryPolicyImpl: Directory markers will be deleted -23/08/03 11:25:10 DEBUG S3AFileSystem: Directory marker retention policy is DirectoryMarkerRetention{policy='delete'} -23/08/03 11:25:10 DEBUG S3AUtils: Value of fs.s3a.multipart.purge.age is 86400 -23/08/03 11:25:10 DEBUG S3AUtils: Value of fs.s3a.bulk.delete.page.size is 250 -23/08/03 11:25:10 DEBUG FileSystem: Creating FS s3a://test-bucket/fake: duration 0:01.029s -23/08/03 11:25:10 DEBUG IOStatisticsStoreImpl: Incrementing counter op_is_directory by 1 with final value 1 -23/08/03 11:25:10 DEBUG S3AFileSystem: Getting path status for s3a://test-bucket/fake (fake); needEmptyDirectory=false -23/08/03 11:25:10 DEBUG S3AFileSystem: S3GetFileStatus s3a://test-bucket/fake -23/08/03 11:25:10 DEBUG S3AFileSystem: LIST List test-bucket:/fake/ delimiter=/ keys=2 requester pays=false -23/08/03 11:25:10 DEBUG S3AFileSystem: Starting: LIST -23/08/03 11:25:10 DEBUG IOStatisticsStoreImpl: Incrementing counter object_list_request by 1 with final value 1 -23/08/03 11:25:10 DEBUG AWSCredentialProviderList: Using credentials from SimpleAWSCredentialsProvider -23/08/03 11:25:10 DEBUG request: Sending Request: GET https://test-bucket.localhost:9000 / Parameters: ({"list-type":["2"],"delimiter":["/"],"max-keys":["2"],"prefix":["fake/"],"fetch-owner":["false"]}Headers: (amz-sdk-invocation-id: e6d62603-96e4-a80f-10a1-816e0822bc71, Content-Type: application/octet-stream, User-Agent: Hadoop 3.3.4, aws-sdk-java/1.12.262 Linux/6.4.7-1-MANJARO OpenJDK_64-Bit_Server_VM/25.292-b10 java/1.8.0_292 scala/2.12.17 vendor/AdoptOpenJDK cfg/retry-mode/legacy, ) -23/08/03 11:25:10 DEBUG AWS4Signer: AWS4 Canonical Request: '"GET -/ -delimiter=%2F&fetch-owner=false&list-type=2&max-keys=2&prefix=fake%2F -amz-sdk-invocation-id:e6d62603-96e4-a80f-10a1-816e0822bc71 -amz-sdk-request:attempt=1;max=21 -amz-sdk-retry:0/0/500 -content-type:application/octet-stream -host:test-bucket.localhost:9000 -user-agent:Hadoop 3.3.4, aws-sdk-java/1.12.262 Linux/6.4.7-1-MANJARO OpenJDK_64-Bit_Server_VM/25.292-b10 java/1.8.0_292 scala/2.12.17 vendor/AdoptOpenJDK cfg/retry-mode/legacy -x-amz-content-sha256:UNSIGNED-PAYLOAD -x-amz-date:20230803T112510Z - -amz-sdk-invocation-id;amz-sdk-request;amz-sdk-retry;content-type;host;user-agent;x-amz-content-sha256;x-amz-date -UNSIGNED-PAYLOAD" -23/08/03 11:25:10 DEBUG AWS4Signer: AWS4 String to Sign: '"AWS4-HMAC-SHA256 -20230803T112510Z -20230803/us-east-1/s3/aws4_request -31a317bb7f6d97248dd0cf03429d701cbb3e29ce889cfbb98ba7a34c57a3bfba" -23/08/03 11:25:10 DEBUG AWS4Signer: Generating a new signing key as the signing key not available in the cache for the date 1691020800000 -23/08/03 11:25:10 DEBUG RequestAddCookies: CookieSpec selected: default -23/08/03 11:25:10 DEBUG RequestAuthCache: Auth cache not set in the context -23/08/03 11:25:10 DEBUG PoolingHttpClientConnectionManager: Connection request: [route: {s}->https://test-bucket.localhost:9000][total available: 0; route allocated: 0 of 96; total allocated: 0 of 96] -23/08/03 11:25:10 DEBUG PoolingHttpClientConnectionManager: Connection leased: [id: 0][route: {s}->https://test-bucket.localhost:9000][total available: 0; route allocated: 1 of 96; total allocated: 1 of 96] -23/08/03 11:25:10 DEBUG MainClientExec: Opening connection {s}->https://test-bucket.localhost:9000 -23/08/03 11:25:10 DEBUG DefaultHttpClientConnectionOperator: Connecting to test-bucket.localhost/127.0.0.1:9000 -23/08/03 11:25:10 DEBUG SSLConnectionSocketFactory: Connecting socket to test-bucket.localhost/127.0.0.1:9000 with timeout 5000 -23/08/03 11:25:10 DEBUG SSLConnectionSocketFactory: Enabled protocols: [TLSv1.2] -23/08/03 11:25:10 DEBUG SSLConnectionSocketFactory: Enabled cipher suites:[TLS_ECDHE_ECDSA_WITH_AES_256_CBC_SHA384, TLS_ECDHE_RSA_WITH_AES_256_CBC_SHA384, TLS_RSA_WITH_AES_256_CBC_SHA256, TLS_ECDH_ECDSA_WITH_AES_256_CBC_SHA384, TLS_ECDH_RSA_WITH_AES_256_CBC_SHA384, TLS_DHE_RSA_WITH_AES_256_CBC_SHA256, TLS_DHE_DSS_WITH_AES_256_CBC_SHA256, TLS_ECDHE_ECDSA_WITH_AES_256_CBC_SHA, TLS_ECDHE_RSA_WITH_AES_256_CBC_SHA, TLS_RSA_WITH_AES_256_CBC_SHA, TLS_ECDH_ECDSA_WITH_AES_256_CBC_SHA, TLS_ECDH_RSA_WITH_AES_256_CBC_SHA, TLS_DHE_RSA_WITH_AES_256_CBC_SHA, TLS_DHE_DSS_WITH_AES_256_CBC_SHA, TLS_ECDHE_ECDSA_WITH_AES_128_CBC_SHA256, TLS_ECDHE_RSA_WITH_AES_128_CBC_SHA256, TLS_RSA_WITH_AES_128_CBC_SHA256, TLS_ECDH_ECDSA_WITH_AES_128_CBC_SHA256, TLS_ECDH_RSA_WITH_AES_128_CBC_SHA256, TLS_DHE_RSA_WITH_AES_128_CBC_SHA256, TLS_DHE_DSS_WITH_AES_128_CBC_SHA256, TLS_ECDHE_ECDSA_WITH_AES_128_CBC_SHA, TLS_ECDHE_RSA_WITH_AES_128_CBC_SHA, TLS_RSA_WITH_AES_128_CBC_SHA, TLS_ECDH_ECDSA_WITH_AES_128_CBC_SHA, TLS_ECDH_RSA_WITH_AES_128_CBC_SHA, TLS_DHE_RSA_WITH_AES_128_CBC_SHA, TLS_DHE_DSS_WITH_AES_128_CBC_SHA, TLS_EMPTY_RENEGOTIATION_INFO_SCSV] -23/08/03 11:25:10 DEBUG SSLConnectionSocketFactory: Starting handshake -23/08/03 11:25:10 DEBUG ClientConnectionManagerFactory: -java.lang.reflect.InvocationTargetException - at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) - at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62) - at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) - at java.lang.reflect.Method.invoke(Method.java:498) - at com.amazonaws.http.conn.ClientConnectionManagerFactory$Handler.invoke(ClientConnectionManagerFactory.java:76) - at com.amazonaws.http.conn.$Proxy32.connect(Unknown Source) - at com.amazonaws.thirdparty.apache.http.impl.execchain.MainClientExec.establishRoute(MainClientExec.java:393) - at com.amazonaws.thirdparty.apache.http.impl.execchain.MainClientExec.execute(MainClientExec.java:236) - at com.amazonaws.thirdparty.apache.http.impl.execchain.ProtocolExec.execute(ProtocolExec.java:186) - at com.amazonaws.thirdparty.apache.http.impl.client.InternalHttpClient.doExecute(InternalHttpClient.java:185) - at com.amazonaws.thirdparty.apache.http.impl.client.CloseableHttpClient.execute(CloseableHttpClient.java:83) - at com.amazonaws.thirdparty.apache.http.impl.client.CloseableHttpClient.execute(CloseableHttpClient.java:56) - at com.amazonaws.http.apache.client.impl.SdkHttpClient.execute(SdkHttpClient.java:72) - at com.amazonaws.http.AmazonHttpClient$RequestExecutor.executeOneRequest(AmazonHttpClient.java:1346) - at com.amazonaws.http.AmazonHttpClient$RequestExecutor.executeHelper(AmazonHttpClient.java:1157) - at com.amazonaws.http.AmazonHttpClient$RequestExecutor.doExecute(AmazonHttpClient.java:814) - at com.amazonaws.http.AmazonHttpClient$RequestExecutor.executeWithTimer(AmazonHttpClient.java:781) - at com.amazonaws.http.AmazonHttpClient$RequestExecutor.execute(AmazonHttpClient.java:755) - at com.amazonaws.http.AmazonHttpClient$RequestExecutor.access$500(AmazonHttpClient.java:715) - at com.amazonaws.http.AmazonHttpClient$RequestExecutionBuilderImpl.execute(AmazonHttpClient.java:697) - at com.amazonaws.http.AmazonHttpClient.execute(AmazonHttpClient.java:561) - at com.amazonaws.http.AmazonHttpClient.execute(AmazonHttpClient.java:541) - at com.amazonaws.services.s3.AmazonS3Client.invoke(AmazonS3Client.java:5456) - at com.amazonaws.services.s3.AmazonS3Client.invoke(AmazonS3Client.java:5403) - at com.amazonaws.services.s3.AmazonS3Client.invoke(AmazonS3Client.java:5397) - at com.amazonaws.services.s3.AmazonS3Client.listObjectsV2(AmazonS3Client.java:971) - at org.apache.hadoop.fs.s3a.S3AFileSystem.lambda$listObjects$11(S3AFileSystem.java:2595) - at org.apache.hadoop.fs.statistics.impl.IOStatisticsBinding.lambda$trackDurationOfOperation$5(IOStatisticsBinding.java:499) - at org.apache.hadoop.fs.s3a.Invoker.retryUntranslated(Invoker.java:414) - at org.apache.hadoop.fs.s3a.Invoker.retryUntranslated(Invoker.java:377) - at org.apache.hadoop.fs.s3a.S3AFileSystem.listObjects(S3AFileSystem.java:2586) - at org.apache.hadoop.fs.s3a.S3AFileSystem.s3GetFileStatus(S3AFileSystem.java:3832) - at org.apache.hadoop.fs.s3a.S3AFileSystem.innerGetFileStatus(S3AFileSystem.java:3688) - at org.apache.hadoop.fs.s3a.S3AFileSystem.lambda$isDirectory$35(S3AFileSystem.java:4724) - at org.apache.hadoop.fs.statistics.impl.IOStatisticsBinding.lambda$trackDurationOfOperation$5(IOStatisticsBinding.java:499) - at org.apache.hadoop.fs.statistics.impl.IOStatisticsBinding.trackDuration(IOStatisticsBinding.java:444) - at org.apache.hadoop.fs.s3a.S3AFileSystem.trackDurationAndSpan(S3AFileSystem.java:2337) - at org.apache.hadoop.fs.s3a.S3AFileSystem.trackDurationAndSpan(S3AFileSystem.java:2356) - at org.apache.hadoop.fs.s3a.S3AFileSystem.isDirectory(S3AFileSystem.java:4722) - at org.apache.spark.sql.execution.streaming.FileStreamSink$.hasMetadata(FileStreamSink.scala:54) - at org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:366) - at org.apache.spark.sql.DataFrameReader.loadV1Source(DataFrameReader.scala:229) - at org.apache.spark.sql.DataFrameReader.$anonfun$load$2(DataFrameReader.scala:211) - at scala.Option.getOrElse(Option.scala:189) - at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:211) - at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:186) - at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) - at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62) - at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) - at java.lang.reflect.Method.invoke(Method.java:498) - at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244) - at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:374) - at py4j.Gateway.invoke(Gateway.java:282) - at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132) - at py4j.commands.CallCommand.execute(CallCommand.java:79) - at py4j.ClientServerConnection.waitForCommands(ClientServerConnection.java:182) - at py4j.ClientServerConnection.run(ClientServerConnection.java:106) - at java.lang.Thread.run(Thread.java:748) -Caused by: javax.net.ssl.SSLException: Unsupported or unrecognized SSL message - at sun.security.ssl.SSLSocketInputRecord.handleUnknownRecord(SSLSocketInputRecord.java:448) - at sun.security.ssl.SSLSocketInputRecord.decode(SSLSocketInputRecord.java:184) - at sun.security.ssl.SSLTransport.decode(SSLTransport.java:109) - at sun.security.ssl.SSLSocketImpl.decode(SSLSocketImpl.java:1383) - at sun.security.ssl.SSLSocketImpl.readHandshakeRecord(SSLSocketImpl.java:1291) - at sun.security.ssl.SSLSocketImpl.startHandshake(SSLSocketImpl.java:435) - at com.amazonaws.thirdparty.apache.http.conn.ssl.SSLConnectionSocketFactory.createLayeredSocket(SSLConnectionSocketFactory.java:436) - at com.amazonaws.thirdparty.apache.http.conn.ssl.SSLConnectionSocketFactory.connectSocket(SSLConnectionSocketFactory.java:384) - at com.amazonaws.thirdparty.apache.http.impl.conn.DefaultHttpClientConnectionOperator.connect(DefaultHttpClientConnectionOperator.java:142) - at com.amazonaws.thirdparty.apache.http.impl.conn.PoolingHttpClientConnectionManager.connect(PoolingHttpClientConnectionManager.java:376) - ... 58 more -23/08/03 11:25:10 DEBUG DefaultManagedHttpClientConnection: http-outgoing-0: Shutdown connection -23/08/03 11:25:10 DEBUG MainClientExec: Connection discarded -23/08/03 11:25:10 DEBUG PoolingHttpClientConnectionManager: Connection released: [id: 0][route: {s}->https://test-bucket.localhost:9000][total available: 0; route allocated: 0 of 96; total allocated: 0 of 96] -23/08/03 11:25:10 DEBUG AmazonHttpClient: Unable to execute HTTP request: Unsupported or unrecognized SSL message Request will be retried. -23/08/03 11:25:10 DEBUG request: Retrying Request: GET https://test-bucket.localhost:9000 / Parameters: ({"list-type":["2"],"delimiter":["/"],"max-keys":["2"],"prefix":["fake/"],"fetch-owner":["false"]}Headers: (amz-sdk-invocation-id: e6d62603-96e4-a80f-10a1-816e0822bc71, Content-Type: application/octet-stream, User-Agent: Hadoop 3.3.4, aws-sdk-java/1.12.262 Linux/6.4.7-1-MANJARO OpenJDK_64-Bit_Server_VM/25.292-b10 java/1.8.0_292 scala/2.12.17 vendor/AdoptOpenJDK cfg/retry-mode/legacy, ) -23/08/03 11:25:10 DEBUG AmazonHttpClient: Retriable error detected, will retry in 49ms, attempt number: 0 -``` - -#### Change number of retries - -You can also change number of retries performed by both libraries using `extra` parameter: - -```python -spark_s3 = SparkS3( - ..., - extra={ - "attempts.maximum": 1, - "retry.limit": 1, - }, -) -``` - -So accessing S3 will fail almost immediately if there is any error. - -### Most common mistakes - -#### No network access - -```text -Caused by: java.net.ConnectException: Connection refused -``` - -Mostly caused by: - -* Trying to access port number which S3 server does not listen -* You’re trying to access host which is unreachable from your network (e.g. running behind some proxy or VPN) -* There are some firewall restrictions for accessing specific host or port - -#### Using HTTPS protocol for HTTP port - -```text -Caused by: javax.net.ssl.SSLException: Unsupported or unrecognized SSL message -``` - -By default, SparkS3 uses HTTPS protocol for connection. -If you change port number, this does not lead to changing protocol: - -```python -spark_s3 = SparkS3(host="s3provider.com", port=8080, ...) -``` - -You should pass protocol explicitly: - -```python -spark_s3 = SparkS3(host="s3provider.com", port=8080, protocol="http", ...) -``` - -#### SSL certificate is self-signed - -```text -sun.security.provider.certpath.SunCertPathBuilderException: unable to find valid certification path to requested target -``` - -To connect to HTTPS port with self-signed certificate, you should -[add certificate chain to Java TrustedStore](https://stackoverflow.com/questions/373295/digital-certificate-how-to-import-cer-file-in-to-truststore-file-using). - -Another option is to disable SSL check: - -```python -spark_s3 = SparkS3( - ..., - extra={ - "connection.ssl.enabled": False, - }, -) -``` - -But is is **NOT** recommended. - -#### Accessing S3 without domain-style access style support - -```text -Caused by: java.net.UnknownHostException: my-bucket.s3provider.com -``` - -To use path-style access, use option below: - -```python -spark_s3 = SparkS3( - host="s3provider.com", - bucket="my-bucket", - ..., - extra={ - "path.style.access": True, - }, -) -``` - -## Slow or unstable writing to S3 - -Hadoop AWS allows to use different writing strategies for different S3 implementations, depending -on list of supported features by server. - -These strategies are called [committers](https://hadoop.apache.org/docs/stable/hadoop-aws/tools/hadoop-aws/committers.html). -There are [different types of committers](https://hadoop.apache.org/docs/stable/hadoop-aws/tools/hadoop-aws/committers.html#Switching_to_an_S3A_Committer): - -* `file` (default) -* `directory` -* `partitioned` -* `magic` - -### `file` committer - -This committer is quite slow and unstable, so it is not recommended to use: - -```text -WARN AbstractS3ACommitterFactory: Using standard FileOutputCommitter to commit work. This is slow and potentially unsafe. -``` - -This is caused by the fact it creates files in the temp directory on remote filesystem, and after all of them are written successfully, -they are moved to target directory on same remote filesystem. - -This is not an issue for HDFS which does support file move operations and also support renaming directory -as atomic operation with `O(1)` time complexity. - -But S3 does support only file copying, so moving is performed via copy + delete. -Also it does not support atomic directory rename operation. Instead, renaming files with the same prefix has time complexity `O(n)`. - -### `directory` and `partitioned` committers - -These are [staging committers](https://hadoop.apache.org/docs/stable/hadoop-aws/tools/hadoop-aws/committer_architecture.html), -meaning that they create temp directories on local filesystem, and after all files are written successfully, -they will be uploaded to S3. Local filesystems do support file moving and directory renaming, -so these committers does not have issues that `file` committer has. - -But they both require free space on local filesystem, and this may be an issue if user need to write large amount of data. -Also this can be an issue for container environment, like Kubernetes, there resources should be allocated before starting a container. - -### `magic` committer - -This committer uses multipart upload feature of S3 API, allowing to create multiple files -and after all of them were written successfully finish the transaction. Before transaction is finished, -files will not be accessible by other clients. - -Because it does not require neither file moving operations, nor directory atomic rename, -upload process is done in most efficient way S3 support. -This [drastically increases writing performance](https://spot.io/blog/improve-apache-spark-performance-with-the-s3-magic-committer/). - -To use this committer, set [following properties](https://github.com/apache/spark/pull/32518) while creating Spark session. - -S3 your main distributed filesystem (Spark on Kubernetes) - -```py -# https://issues.apache.org/jira/browse/SPARK-23977 -# https://spark.apache.org/docs/latest/cloud-integration.html#committing-work-into-cloud-storage-safely-and-fast -spark = ( - SparkSession.builder.appName("spark-app-name") - .config("spark.hadoop.fs.s3a.committer.magic.enabled", "true") - .config("spark.hadoop.fs.s3a.committer.name", "magic") - .config("spark.hadoop.mapreduce.outputcommitter.factory.scheme.s3a", "org.apache.hadoop.fs.s3a.commit.S3ACommitterFactory") - .config("spark.sql.parquet.output.committer.class", "org.apache.spark.internal.io.cloud.BindingParquetOutputCommitter") - .config("spark.sql.sources.commitProtocolClass", "org.apache.spark.internal.io.cloud.PathOutputCommitProtocol") - .getOrCreate() -) -``` - -HDFS is your main distributed filesystem (Spark on Hadoop) - -```py -# https://community.cloudera.com/t5/Support-Questions/spark-sql-sources-partitionOverwriteMode-dynamic-quot-not/m-p/343483/highlight/true -spark = ( - SparkSession.builder.appName("spark-app-name") - .config("spark.hadoop.fs.s3a.committer.magic.enabled", "true") - .config("spark.hadoop.fs.s3a.committer.name", "magic") - .getOrCreate() -) -``` - -#### WARNING -`magic` committer requires S3 implementation to have strong consistency - file upload API return response only -if it was written on enough number of cluster nodes, and any cluster node error does not lead to missing or corrupting files. - -Some S3 implementations does have strong consistency -(like [AWS S3](https://aws.amazon.com/ru/blogs/aws/amazon-s3-update-strong-read-after-write-consistency/) and -[MinIO](https://blog.min.io/migrating-hdfs-to-object-storage/)), some not. Please contact your S3 provider -to get information about S3 implementation consistency. - -#### WARNING -`magic` committer does not support `if_exists="replace_overlapping_partitions"`. -Either use another `if_exists` value, or use `partitioned` committer. - -### See also - -* [directory.marker.retention=”keep”](https://hadoop.apache.org/docs/stable/hadoop-aws/tools/hadoop-aws/directory_markers.html) - -## Slow reading from S3 - -Please read following documentation: - -* [prefetch.enabled](https://hadoop.apache.org/docs/stable/hadoop-aws/tools/hadoop-aws/prefetching.html) -* [experimental.input.fadvise](https://hadoop.apache.org/docs/stable/hadoop-aws/tools/hadoop-aws/performance.html#Improving_data_input_performance_through_fadvise) -* [Parquet and ORC I/O settings](https://spark.apache.org/docs/latest/cloud-integration.html#parquet-io-settings) - -If you’re reading data from row-based formats, like [CSV](../../../file_df/file_formats/csv.md#csv-file-format), prefer -[experimental.input.fadvise=”sequential” with increased readahead.range](https://issues.apache.org/jira/browse/HADOOP-17789?focusedCommentId=17383559#comment-17383559). - -But for other file formats, especially using compression, prefer -[experimental.input.fadvise=”normal”](https://issues.apache.org/jira/browse/HADOOP-17789?focusedCommentId=17383743#comment-17383743) diff --git a/mddocs/markdown/connection/index.md b/mddocs/markdown/connection/index.md deleted file mode 100644 index 5ad28edef..000000000 --- a/mddocs/markdown/connection/index.md +++ /dev/null @@ -1,34 +0,0 @@ - - - DB Connection - -* [DB Connections](db_connection/index.md) - * [Clickhouse](db_connection/clickhouse/index.md) - * [Greenplum](db_connection/greenplum/index.md) - * [Kafka](db_connection/kafka/index.md) - * [Hive](db_connection/hive/index.md) - * [MongoDB](db_connection/mongodb/index.md) - * [MSSQL](db_connection/mssql/index.md) - * [MySQL](db_connection/mysql/index.md) - * [Oracle](db_connection/oracle/index.md) - * [Postgres](db_connection/postgres/index.md) - * [Teradata](db_connection/teradata/index.md) - - File Connection - -* [File Connections](file_connection/index.md) - * [FTP](file_connection/ftp.md) - * [FTPS](file_connection/ftps.md) - * [HDFS](file_connection/hdfs/index.md) - * [Samba](file_connection/samba.md) - * [SFTP](file_connection/sftp.md) - * [S3](file_connection/s3.md) - * [Webdav](file_connection/webdav.md) - - File DataFrame Connection - -* [File DataFrame Connections](file_df_connection/index.md) - * [Spark LocalFS](file_df_connection/spark_local_fs.md) - * [Spark HDFS](file_df_connection/spark_hdfs/index.md) - * [Spark S3](file_df_connection/spark_s3/index.md) - * [Base interface](file_df_connection/base.md) diff --git a/mddocs/markdown/contributing.md b/mddocs/markdown/contributing.md deleted file mode 100644 index 739798897..000000000 --- a/mddocs/markdown/contributing.md +++ /dev/null @@ -1,391 +0,0 @@ -# Contributing Guide - -Welcome! There are many ways to contribute, including submitting bug -reports, improving documentation, submitting feature requests, reviewing -new submissions, or contributing code that can be incorporated into the -project. - -## Limitations - -We should keep close to these items during development: - -* Some companies still use old Spark versions, like 2.3.1. So it is required to keep compatibility if possible, e.g. adding branches for different Spark versions. -* Different users uses onETL in different ways - some uses only DB connectors, some only files. Connector-specific dependencies should be optional. -* Instead of creating classes with a lot of different options, prefer splitting them into smaller classes, e.g. options class, context manager, etc, and using composition. - -## Initial setup for local development - -### Install Git - -Please follow [instruction](https://docs.github.com/en/get-started/quickstart/set-up-git). - -### Create a fork - -If you are not a member of a development team building onETL, you should create a fork before making any changes. - -Please follow [instruction](https://docs.github.com/en/get-started/quickstart/fork-a-repo). - -### Clone the repo - -Open terminal and run these commands: - -```bash -git clone git@github.com:myuser/onetl.git -b develop - -cd onetl -``` - -### Setup environment - -Create virtualenv and install dependencies: - -```bash -python -m venv venv -source venv/bin/activate -pip install -U wheel -pip install -U pip setuptools -pip install -U \ - -r requirements/core.txt \ - -r requirements/ftp.txt \ - -r requirements/hdfs.txt \ - -r requirements/kerberos.txt \ - -r requirements/s3.txt \ - -r requirements/sftp.txt \ - -r requirements/webdav.txt \ - -r requirements/dev.txt \ - -r requirements/docs.txt \ - -r requirements/tests/base.txt \ - -r requirements/tests/clickhouse.txt \ - -r requirements/tests/kafka.txt \ - -r requirements/tests/mongodb.txt \ - -r requirements/tests/mssql.txt \ - -r requirements/tests/mysql.txt \ - -r requirements/tests/postgres.txt \ - -r requirements/tests/oracle.txt \ - -r requirements/tests/pydantic-2.txt \ - -r requirements/tests/spark-3.5.5.txt - -# TODO: remove after https://github.com/zqmillet/sphinx-plantuml/pull/4 -pip install sphinx-plantuml --no-deps -``` - -### Enable pre-commit hooks - -Install pre-commit hooks: - -```bash -pre-commit install --install-hooks -``` - -Test pre-commit hooks run: - -```bash -pre-commit run -``` - -## How to - -### Run tests locally - -#### Using docker-compose - -Build image for running tests: - -```bash -docker-compose build -``` - -Start all containers with dependencies: - -```bash -docker-compose --profile all up -d -``` - -You can run limited set of dependencies: - -```bash -docker-compose --profile mongodb up -d -``` - -Run tests: - -```bash -docker-compose run --rm onetl ./run_tests.sh -``` - -You can pass additional arguments, they will be passed to pytest: - -```bash -docker-compose run --rm onetl ./run_tests.sh -m mongodb -lsx -vvvv --log-cli-level=INFO -``` - -You can run interactive bash session and use it: - -```bash -docker-compose run --rm onetl bash - -./run_tests.sh -m mongodb -lsx -vvvv --log-cli-level=INFO -``` - -See logs of test container: - -```bash -docker-compose logs -f onetl -``` - -Stop all containers and remove created volumes: - -```bash -docker-compose --profile all down -v -``` - -#### Without docker-compose - -#### WARNING -To run HDFS tests locally you should add the following line to your `/etc/hosts` (file path depends on OS): - -```default -# HDFS server returns container hostname as connection address, causing error in DNS resolution -127.0.0.1 hdfs -``` - -#### NOTE -To run Oracle tests you need to install [Oracle instantclient](https://www.oracle.com/database/technologies/instant-client.html), -and pass its path to `ONETL_ORA_CLIENT_PATH` and `LD_LIBRARY_PATH` environment variables, -e.g. `ONETL_ORA_CLIENT_PATH=/path/to/client64/lib`. - -It may also require to add the same path into `LD_LIBRARY_PATH` environment variable - -#### NOTE -To run Greenplum tests, you should: - -* Download [VMware Greenplum connector for Spark](https://onetl.readthedocs.io/en/latest/connection/db_connection/greenplum/prerequisites.html) -* Either move it to `~/.ivy2/jars/`, or pass file path to `CLASSPATH` -* Set environment variable `ONETL_GP_PACKAGE_VERSION=local`. - -Start all containers with dependencies: - -```bash -docker-compose --profile all up -d -``` - -You can run limited set of dependencies: - -```bash -docker-compose --profile mongodb up -d -``` - -Load environment variables with connection properties: - -```bash -source .env.local -``` - -Run tests: - -```bash -./run_tests.sh -``` - -You can pass additional arguments, they will be passed to pytest: - -```bash -./run_tests.sh -m mongodb -lsx -vvvv --log-cli-level=INFO -``` - -Stop all containers and remove created volumes: - -```bash -docker-compose --profile all down -v -``` - -### Build documentation - -Build documentation using Sphinx: - -```bash -cd docs -make html -``` - -Then open in browser `docs/_build/index.html`. - -## Review process - -Please create a new GitHub issue for any significant changes and -enhancements that you wish to make. Provide the feature you would like -to see, why you need it, and how it will work. Discuss your ideas -transparently and get community feedback before proceeding. - -Significant Changes that you wish to contribute to the project should be -discussed first in a GitHub issue that clearly outlines the changes and -benefits of the feature. - -Small Changes can directly be crafted and submitted to the GitHub -Repository as a Pull Request. - -### Create pull request - -Commit your changes: - -```bash -git commit -m "Commit message" -git push -``` - -Then open Github interface and [create pull request](https://docs.github.com/en/get-started/quickstart/contributing-to-projects#making-a-pull-request). -Please follow guide from PR body template. - -After pull request is created, it get a corresponding number, e.g. 123 (`pr_number`). - -### Write release notes - -`onETL` uses [towncrier](https://pypi.org/project/towncrier/) -for changelog management. - -To submit a change note about your PR, add a text file into the -[docs/changelog/next_release](./next_release) folder. It should contain an -explanation of what applying this PR will change in the way -end-users interact with the project. One sentence is usually -enough but feel free to add as many details as you feel necessary -for the users to understand what it means. - -**Use the past tense** for the text in your fragment because, -combined with others, it will be a part of the “news digest” -telling the readers **what changed** in a specific version of -the library *since the previous version*. - -You should also use -reStructuredText syntax for highlighting code (inline or block), -linking parts of the docs or external sites. -If you wish to sign your change, feel free to add `-- by -:user:`github-username`` at the end (replace `github-username` -with your own!). - -Finally, name your file following the convention that Towncrier -understands: it should start with the number of an issue or a -PR followed by a dot, then add a patch type, like `feature`, -`doc`, `misc` etc., and add `.rst` as a suffix. If you -need to add more than one fragment, you may add an optional -sequence number (delimited with another period) between the type -and the suffix. - -In general the name will follow `..rst` pattern, -where the categories are: - -- `feature`: Any new feature -- `bugfix`: A bug fix -- `improvement`: An improvement -- `doc`: A change to the documentation -- `dependency`: Dependency-related changes -- `misc`: Changes internal to the repo like CI, test and build changes - -A pull request may have more than one of these components, for example -a code change may introduce a new feature that deprecates an old -feature, in which case two fragments should be added. It is not -necessary to make a separate documentation fragment for documentation -changes accompanying the relevant code changes. - -#### Examples for adding changelog entries to your Pull Requests - -```rst -Added a ``:github:user:`` role to Sphinx config -- by :github:user:`someuser` -``` - -```rst -Fixed behavior of ``WebDAV`` connector -- by :github:user:`someuser` -``` - -```rst -Added support of ``timeout`` in ``S3`` connector --- by :github:user:`someuser`, :github:user:`anotheruser` and :github:user:`otheruser` -``` - -#### How to skip change notes check? - -Just add `ci:skip-changelog` label to pull request. - -#### Release Process - -Before making a release from the `develop` branch, follow these steps: - -1. Checkout to `develop` branch and update it to the actual state - -```bash -git checkout develop -git pull -p -``` - -1. Backup `NEXT_RELEASE.rst` - -```bash -cp "docs/changelog/NEXT_RELEASE.rst" "docs/changelog/temp_NEXT_RELEASE.rst" -``` - -1. Build the Release notes with Towncrier - -```bash -VERSION=$(cat onetl/VERSION) -towncrier build "--version=${VERSION}" --yes -``` - -1. Change file with changelog to release version number - -```bash -mv docs/changelog/NEXT_RELEASE.rst "docs/changelog/${VERSION}.rst" -``` - -1. Remove content above the version number heading in the `${VERSION}.rst` file - -```bash -awk '!/^.*towncrier release notes start/' "docs/changelog/${VERSION}.rst" > temp && mv temp "docs/changelog/${VERSION}.rst" -``` - -1. Update Changelog Index - -```bash -awk -v version=${VERSION} '/DRAFT/{print;print " " version;next}1' docs/changelog/index.rst > temp && mv temp docs/changelog/index.rst -``` - -1. Restore `NEXT_RELEASE.rst` file from backup - -```bash -mv "docs/changelog/temp_NEXT_RELEASE.rst" "docs/changelog/NEXT_RELEASE.rst" -``` - -1. Commit and push changes to `develop` branch - -```bash -git add . -git commit -m "Prepare for release ${VERSION}" -git push -``` - -1. Merge `develop` branch to `master`, **WITHOUT** squashing - -```bash -git checkout master -git pull -git merge develop -git push -``` - -1. Add git tag to the latest commit in `master` branch - -```bash -git tag "$VERSION" -git push origin "$VERSION" -``` - -1. Update version in `develop` branch **after release**: - -```bash -git checkout develop - -NEXT_VERSION=$(echo "$VERSION" | awk -F. '/[0-9]+\./{$NF++;print}' OFS=.) -echo "$NEXT_VERSION" > onetl/VERSION - -git add . -git commit -m "Bump version" -git push -``` diff --git a/mddocs/markdown/db/db_reader.md b/mddocs/markdown/db/db_reader.md deleted file mode 100644 index 29209279a..000000000 --- a/mddocs/markdown/db/db_reader.md +++ /dev/null @@ -1,346 +0,0 @@ - - -# DB Reader - -| [`DBReader`](#onetl.db.db_reader.db_reader.DBReader) | Allows you to read data from a table with specified database connection and parameters, and return its content as Spark dataframe. | -|------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------| -| [`DBReader.run`](#onetl.db.db_reader.db_reader.DBReader.run)() | Reads data from source table and saves as Spark dataframe. | -| [`DBReader.has_data`](#onetl.db.db_reader.db_reader.DBReader.has_data)() | Returns `True` if there is some data in the source, `False` otherwise. | -| [`DBReader.raise_if_no_data`](#onetl.db.db_reader.db_reader.DBReader.raise_if_no_data)() | Raises exception `NoDataError` if source does not contain any data. | - -### *class* onetl.db.db_reader.db_reader.DBReader(\*, connection: BaseDBConnection, table: str, columns: ConstrainedListValue[str] | None = None, where: Any | None = None, hint: Any | None = None, df_schema: StructType | None = None, hwm_column: str | tuple | None = None, hwm_expression: str | None = None, hwm: AutoDetectHWM | ColumnHWM | KeyValueHWM | None = None, options: GenericOptions | None = None) - -Allows you to read data from a table with specified database connection -and parameters, and return its content as Spark dataframe. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### NOTE -DBReader can return different results depending on [Read Strategies](../strategy/index.md#strategy) - -#### NOTE -This class operates with only one source at a time. It does NOT support executing queries -to multiple source, like `SELECT ... JOIN`. - -#### Versionadded -Added in version 0.1.0. - -#### Versionchanged -Changed in version 0.8.0: Moved `onetl.core.DBReader` → `onetl.db.DBReader` - -* **Parameters:** - **connection** - : Class which contains DB connection properties. See [DB Connections](../connection/db_connection/index.md#db-connections) section - - **source** - : Table/collection/etc name to read data from. -
- If connection has schema support, you need to specify the full name of the source - including the schema, e.g. `schema.name`. -
- #### Versionchanged - Changed in version 0.7.0: Renamed `table` → `source` - - **columns** - : The list of columns to be read. -
- If RDBMS supports any kind of expressions, you can pass them too. - ```python - columns = [ - "mycolumn", - "another_column as alias", - "count(*) over ()", - "some(function) as alias2", - ] - ``` -
- #### NOTE - Some sources does not have columns. -
- #### NOTE - It is recommended to pass column names explicitly to avoid selecting too many columns, - and to avoid adding unexpected columns to dataframe if source DDL is changed. -
- #### Deprecated - Deprecated since version 0.10.0: Syntax `DBReader(columns="col1, col2")` (string instead of list) is not supported, - and will be removed in v1.0.0 - - **where** - : Custom `where` for SQL query or MongoDB pipeline. -
- `where` syntax depends on the source. For example, SQL sources - accept `where` as a string, but MongoDB sources accept `where` as a dictionary. - ```python - # SQL database connection - where = "column_1 > 2" -
- # MongoDB connection - where = { - "col_1": {"$gt": 1, "$lt": 100}, - "col_2": {"$gt": 2}, - "col_3": {"$eq": "hello"}, - } - ``` -
- #### NOTE - Some sources does not support data filtering. - - **hwm** - : HWM class to be used as [HWM](https://etl-entities.readthedocs.io/en/stable/hwm/index.html) value. - ```python - hwm = DBReader.AutoDetectHWM( - name="some_unique_hwm_name", - expression="hwm_column", - ) - ``` -
- HWM value will be fetched using `hwm_column` SQL query. -
- If you want to use some SQL expression as HWM value, you can use it as well: - ```python - hwm = DBReader.AutoDetectHWM( - name="some_unique_hwm_name", - expression="cast(hwm_column_orig as date)", - ) - ``` -
- #### NOTE - Some sources does not support passing expressions and can be used only with column/field - names which present in the source. -
- #### Versionchanged - Changed in version 0.10.0: Replaces deprecated `hwm_column` and `hwm_expression` attributes - - **hint** - : Hint expression used for querying the data. -
- `hint` syntax depends on the source. For example, SQL sources - accept `hint` as a string, but MongoDB sources accept `hint` as a dictionary. - ```python - # SQL database connection - hint = "index(myschema.mytable mycolumn)" -
- # MongoDB connection - hint = { - "mycolumn": 1, - } - ``` -
- #### NOTE - Some sources does not support hints. - - **df_schema** - : Spark DataFrame schema, used for proper type casting of the rows. - ```python - from pyspark.sql.types import ( - DoubleType, - IntegerType, - StringType, - StructField, - StructType, - TimestampType, - ) -
- df_schema = StructType( - [ - StructField("_id", IntegerType()), - StructField("text_string", StringType()), - StructField("hwm_int", IntegerType()), - StructField("hwm_datetime", TimestampType()), - StructField("float_value", DoubleType()), - ], - ) -
- reader = DBReader( - connection=connection, - source="fiddle.dummy", - df_schema=df_schema, - ) - ``` -
- #### NOTE - Some sources does not support passing dataframe schema. - - **options** - : Spark read options, like partitioning mode. - ```python - Postgres.ReadOptions( - partitioningMode="hash", - partitionColumn="some_column", - numPartitions=20, - fetchsize=1000, - ) - ``` -
- #### NOTE - Some sources does not support reading options. - -### Examples - -Minimal example - -```py -from onetl.db import DBReader -from onetl.connection import Postgres - -postgres = Postgres(...) - -# create reader -reader = DBReader(connection=postgres, source="fiddle.dummy") - -# read data from table "fiddle.dummy" -df = reader.run() -``` - -With custom reading options - -```py -from onetl.connection import Postgres -from onetl.db import DBReader - -postgres = Postgres(...) -options = Postgres.ReadOptions(sessionInitStatement="select 300", fetchsize="100") - -# create reader and pass some options to the underlying connection object -reader = DBReader(connection=postgres, source="fiddle.dummy", options=options) - -# read data from table "fiddle.dummy" -df = reader.run() -``` - -Full example - -```py -from onetl.db import DBReader -from onetl.connection import Postgres - -postgres = Postgres(...) -options = Postgres.ReadOptions(sessionInitStatement="select 300", fetchsize="100") - -# create reader with specific columns, rows filter -reader = DBReader( - connection=postgres, - source="default.test", - where="d_id > 100", - hint="NOWAIT", - columns=["d_id", "d_name", "d_age"], - options=options, -) - -# read data from table "fiddle.dummy" -df = reader.run() -``` - -Incremental reading - -See [Read Strategies](../strategy/index.md#strategy) for more examples - -```python -from onetl.strategy import IncrementalStrategy - -... - -reader = DBReader( - connection=postgres, - source="fiddle.dummy", - hwm=DBReader.AutoDetectHWM( # mandatory for IncrementalStrategy - name="some_unique_hwm_name", - expression="d_age", - ), -) - -# read data from table "fiddle.dummy" -# but only with new rows (`WHERE d_age > previous_hwm_value`) -with IncrementalStrategy(): - df = reader.run() -``` - - - -#### has_data() → bool - -Returns `True` if there is some data in the source, `False` otherwise. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### NOTE -This method can return different results depending on [Read Strategies](../strategy/index.md#strategy) - -#### WARNING -If [hwm](https://etl-entities.readthedocs.io/en/stable/hwm/index.html) is used, then method should be called inside [Read Strategies](../strategy/index.md#strategy) context. And vise-versa, if HWM is not used, this method should not be called within strategy. - -#### Versionadded -Added in version 0.10.0. - -* **Raises:** - RuntimeError - : Current strategy is not compatible with HWM parameter. - -### Examples - -```python -reader = DBReader(...) - -# handle situation when there is no data in the source -if reader.has_data(): - df = reader.run() -else: - # implement your handling logic here - ... -``` - - - -#### raise_if_no_data() → None - -Raises exception `NoDataError` if source does not contain any data. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### NOTE -This method can return different results depending on [Read Strategies](../strategy/index.md#strategy) - -#### WARNING -If [hwm](https://etl-entities.readthedocs.io/en/stable/hwm/index.html) is used, then method should be called inside [Read Strategies](../strategy/index.md#strategy) context. And vise-versa, if HWM is not used, this method should not be called within strategy. - -#### Versionadded -Added in version 0.10.0. - -* **Raises:** - RuntimeError - : Current strategy is not compatible with HWM parameter. - - `onetl.exception.NoDataError` - : There is no data in source. - -### Examples - -```python -reader = DBReader(...) - -# ensure that there is some data in the source before reading it using Spark -reader.raise_if_no_data() -``` - - - -#### run() → DataFrame - -Reads data from source table and saves as Spark dataframe. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### NOTE -This method can return different results depending on [Read Strategies](../strategy/index.md#strategy) - -#### WARNING -If [hwm](https://etl-entities.readthedocs.io/en/stable/hwm/index.html) is used, then method should be called inside [Read Strategies](../strategy/index.md#strategy) context. And vise-versa, if HWM is not used, this method should not be called within strategy. - -#### Versionadded -Added in version 0.1.0. - -* **Returns:** - **df** - : Spark dataframe - -### Examples - -Read data to Spark dataframe: - -```python -df = reader.run() -``` - - diff --git a/mddocs/markdown/db/db_writer.md b/mddocs/markdown/db/db_writer.md deleted file mode 100644 index 91b8a16fb..000000000 --- a/mddocs/markdown/db/db_writer.md +++ /dev/null @@ -1,100 +0,0 @@ - - -# DB Writer - -| [`DBWriter`](#onetl.db.db_writer.db_writer.DBWriter) | Class specifies schema and table where you can write your dataframe. | -|------------------------------------------------------------------|------------------------------------------------------------------------| -| [`DBWriter.run`](#onetl.db.db_writer.db_writer.DBWriter.run)(df) | Method for writing your df to specified target. | - -### *class* onetl.db.db_writer.db_writer.DBWriter(\*, connection: BaseDBConnection, table: str, options: GenericOptions | None = None) - -Class specifies schema and table where you can write your dataframe. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### Versionadded -Added in version 0.1.0. - -#### Versionchanged -Changed in version 0.8.0: Moved `onetl.core.DBReader` → `onetl.db.DBReader` - -* **Parameters:** - **connection** - : Class which contains DB connection properties. See [DB Connections](../connection/db_connection/index.md#db-connections) section. - - **target** - : Table/collection/etc name to write data to. -
- If connection has schema support, you need to specify the full name of the source - including the schema, e.g. `schema.name`. -
- #### Versionchanged - Changed in version 0.7.0: Renamed `table` → `target` - - **options** - : Spark write options. Can be in form of special `WriteOptions` object or a dict. -
- For example: - `{"if_exists": "replace_entire_table", "compression": "snappy"}` - or - `Hive.WriteOptions(if_exists="replace_entire_table", compression="snappy")` -
- #### NOTE - Some sources does not support writing options. - -### Examples - -Minimal example - -```py -from onetl.connection import Postgres -from onetl.db import DBWriter - -postgres = Postgres(...) - -writer = DBWriter( - connection=postgres, - target="fiddle.dummy", -) -``` - -With custom write options - -```py -from onetl.connection import Postgres -from onetl.db import DBWriter - -postgres = Postgres(...) - -options = Postgres.WriteOptions(if_exists="replace_entire_table", batchsize=1000) - -writer = DBWriter( - connection=postgres, - target="fiddle.dummy", - options=options, -) -``` - - - -#### run(df: DataFrame) → None - -Method for writing your df to specified target. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### NOTE -Method does support only **batching** DataFrames. - -#### Versionadded -Added in version 0.1.0. - -* **Parameters:** - **df** - : Spark dataframe - -### Examples - -Write dataframe to target: - -```python -writer.run(df) -``` - - diff --git a/mddocs/markdown/db/index.md b/mddocs/markdown/db/index.md deleted file mode 100644 index 861a539ff..000000000 --- a/mddocs/markdown/db/index.md +++ /dev/null @@ -1,6 +0,0 @@ - - - DB classes - -* [DB Reader](db_reader.md) -* [DB Writer](db_writer.md) diff --git a/mddocs/markdown/file/file_downloader/file_downloader.md b/mddocs/markdown/file/file_downloader/file_downloader.md deleted file mode 100644 index a1c2f361c..000000000 --- a/mddocs/markdown/file/file_downloader/file_downloader.md +++ /dev/null @@ -1,352 +0,0 @@ - - -# File Downloader - -| [`FileDownloader`](#onetl.file.file_downloader.file_downloader.FileDownloader) | Allows you to download files from a remote source with specified file connection and parameters, and return an object with download result summary. | -|--------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------| -| [`FileDownloader.run`](#onetl.file.file_downloader.file_downloader.FileDownloader.run)([files]) | Method for downloading files from source to local directory. | -| [`FileDownloader.view_files`](#onetl.file.file_downloader.file_downloader.FileDownloader.view_files)() | Get file list in the `source_path`, after `filter`, `limit` and `hwm` applied (if any). | - -### *class* onetl.file.file_downloader.file_downloader.FileDownloader(\*, connection: ~onetl.base.base_file_connection.BaseFileConnection, local_path: ~onetl.impl.local_path.LocalPath, source_path: ~onetl.impl.remote_path.RemotePath | None = None, temp_path: ~onetl.impl.local_path.LocalPath | None = None, filter: ~typing.List[~onetl.base.base_file_filter.BaseFileFilter] = None, limit: ~typing.List[~onetl.base.base_file_limit.BaseFileLimit] = None, hwm: ~etl_entities.hwm.file.file_hwm.FileHWM | None = None, hwm_type: ~typing.Type[~etl_entities.old_hwm.file_list_hwm.FileListHWM] | str | None = None, options: ~onetl.file.file_downloader.options.FileDownloaderOptions = FileDownloaderOptions(if_exists=, delete_source=False, workers=1)) - -Allows you to download files from a remote source with specified file connection -and parameters, and return an object with download result summary. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### NOTE -FileDownloader can return different results depending on [Read Strategies](../../strategy/index.md#strategy) - -#### NOTE -This class is used to download files **only** from remote directory to the local one. - -It does NOT support direct file transfer between filesystems, like `FTP -> SFTP`. -You should use FileDownloader + [File Uploader](../file_uploader/file_uploader.md#file-uploader) to implement `FTP -> local dir -> SFTP`. - -#### Versionadded -Added in version 0.1.0. - -#### Versionchanged -Changed in version 0.8.0: Moved `onetl.core.FileDownloader` → `onetl.file.FileDownloader` - -* **Parameters:** - **connection** - : Class which contains File system connection properties. See [File Connections](../../connection/file_connection/index.md#file-connections) section. - - **local_path** - : Local path where you download files - - **source_path** - : Remote path to download files from. -
- Could be `None`, but only if you pass absolute file paths directly to - [`run`](#onetl.file.file_downloader.file_downloader.FileDownloader.run) method - - **temp_path** - : If set, this path will be used for downloading a file, and then renaming it to the target file path. - If `None` is passed, files are downloaded directly to `target_path`. -
- #### WARNING - In case of production ETL pipelines, please set a value for `temp_path` (NOT `None`). - This allows to properly handle download interruption, - without creating half-downloaded files in the target, - because unlike file download, `rename` call is atomic. -
- #### WARNING - In case of connections like SFTP or FTP, which can have multiple underlying filesystems, - please pass to `temp_path` path on the SAME filesystem as `target_path`. - Otherwise instead of `rename`, remote OS will move file between filesystems, - which is NOT atomic operation. -
- #### Versionadded - Added in version 0.5.0. - - **filters** - : Return only files/directories matching these filters. See [File Filters](../file_filters/index.md#file-filters) -
- #### Versionchanged - Changed in version 0.3.0: Replaces old `source_path_pattern: str` and `exclude_dirs: str` options. -
- #### Versionchanged - Changed in version 0.8.0: Renamed `filter` → `filters` - - **limits** - : Apply limits to the list of files/directories, and stop if one of the limits is reached. - See [File Limits](../file_limits/index.md#file-limits) -
- #### Versionadded - Added in version 0.4.0. -
- #### Versionchanged - Changed in version 0.8.0: Renamed `limit` → `limits` - - **options** - : File downloading options. See [`FileDownloader.Options`](options.md#onetl.file.file_downloader.options.FileDownloaderOptions) -
- #### Versionadded - Added in version 0.3.0. - - **hwm** - : HWM class to detect changes in incremental run. See [File HWM](https://etl-entities.readthedocs.io/en/stable/hwm/file/index.html) -
- #### WARNING - Used only in [`IncrementalStrategy`](../../strategy/incremental_strategy.md#onetl.strategy.incremental_strategy.IncrementalStrategy). -
- #### Versionadded - Added in version 0.5.0. -
- #### Versionchanged - Changed in version 0.10.0: Replaces deprecated `hwm_type` attribute - -### Examples - -Minimal example - -```py -from onetl.connection import SFTP -from onetl.file import FileDownloader - -sftp = SFTP(...) - -# create downloader -downloader = FileDownloader( - connection=sftp, - source_path="/path/to/remote/source", - local_path="/path/to/local", -) - -# download files to "/path/to/local" -downloader.run() -``` - -Full example - -```py -from onetl.connection import SFTP -from onetl.file import FileDownloader -from onetl.file.filter import Glob, ExcludeDir -from onetl.file.limit import MaxFilesCount, TotalFileSize - -sftp = SFTP(...) - -# create downloader with a bunch of options -downloader = FileDownloader( - connection=sftp, - source_path="/path/to/remote/source", - local_path="/path/to/local", - temp_path="/tmp", - filters=[ - Glob("*.txt"), - ExcludeDir("/path/to/remote/source/exclude_dir"), - ], - limits=[MaxFilesCount(100), TotalFileSize("10GiB")], - options=FileDownloader.Options(delete_source=True, if_exists="replace_file"), -) - -# download files to "/path/to/local", -# but only *.txt, -# excluding files from "/path/to/remote/source/exclude_dir" directory -# and stop before downloading 101 file -downloader.run() -``` - -Incremental download (by tracking list of file paths) - -```py -from onetl.connection import SFTP -from onetl.file import FileDownloader -from onetl.strategy import IncrementalStrategy -from etl_entities.hwm import FileListHWM - -sftp = SFTP(...) - -# create downloader -downloader = FileDownloader( - connection=sftp, - source_path="/path/to/remote/source", - local_path="/path/to/local", - hwm=FileListHWM( # mandatory for IncrementalStrategy - name="my_unique_hwm_name", - ), -) - -# download files to "/path/to/local", but only added since previous run -with IncrementalStrategy(): - downloader.run() -``` - -Incremental download (by tracking file modification time) - -```py -from onetl.connection import SFTP -from onetl.file import FileDownloader -from onetl.strategy import IncrementalStrategy -from etl_entities.hwm import FileModifiedTimeHWM - -sftp = SFTP(...) - -# create downloader -downloader = FileDownloader( - connection=sftp, - source_path="/path/to/remote/source", - local_path="/path/to/local", - hwm=FileModifiedTimeHWM( # mandatory for IncrementalStrategy - name="my_unique_hwm_name", - ), -) - -# download files to "/path/to/local", but only modified/created since previous run -with IncrementalStrategy(): - downloader.run() -``` - - - -#### run(files: Iterable[str | PathLike] | None = None) → [DownloadResult](result.md#onetl.file.file_downloader.result.DownloadResult) - -Method for downloading files from source to local directory. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### NOTE -This method can return different results depending on [Read Strategies](../../strategy/index.md#strategy) - -#### Versionadded -Added in version 0.1.0. - -* **Parameters:** - **files** - : File list to download. -
- If empty, download files from `source_path` to `local_path`, - applying `filter`, `limit` and `hwm` to each one (if set). -
- If not, download to `local_path` **all** input files, **ignoring** - filters, limits and HWM. -
- #### Versionadded - Added in version 0.3.0. -* **Returns:** - `DownloadResult` - : Download result object -* **Raises:** - `onetl.exception.DirectoryNotFoundError` - : `source_path` does not found - - NotADirectoryError - : `source_path` or `local_path` is not a directory - -### Examples - -Download files from `source_path` to `local_path`: - -```pycon ->>> from onetl.file import FileDownloader ->>> downloader = FileDownloader(source_path="/remote", local_path="/local", ...) ->>> download_result = downloader.run() ->>> download_result -DownloadResult( - successful=FileSet([ - LocalPath("/local/file1.txt"), - LocalPath("/local/file2.txt"), - # directory structure is preserved - LocalPath("/local/nested/path/file3.txt"), - ]), - failed=FileSet([ - FailedRemoteFile("/remote/failed.file"), - ]), - skipped=FileSet([ - RemoteFile("/remote/already.exists"), - ]), - missing=FileSet([ - RemotePath("/remote/missing.file"), - ]), -) -``` - -Download only certain files from `source_path`: - -```pycon ->>> from onetl.file import FileDownloader ->>> downloader = FileDownloader(source_path="/remote", local_path="/local", ...) ->>> # paths could be relative or absolute, but all should be in "/remote" ->>> download_result = downloader.run( -... [ -... "/remote/file1.txt", -... "/remote/nested/path/file3.txt", -... # excluding "/remote/file2.txt" -... ] -... ) ->>> download_result -DownloadResult( - successful=FileSet([ - LocalPath("/local/file1.txt"), - # directory structure is preserved - LocalPath("/local/nested/path/file3.txt"), - ]), - failed=FileSet([]), - skipped=FileSet([]), - missing=FileSet([]), -) -``` - -Download certain files from any folder: - -```pycon ->>> from onetl.file import FileDownloader ->>> downloader = FileDownloader(local_path="/local", ...) # no source_path set ->>> # only absolute paths ->>> download_result = downloader.run( -... [ -... "/remote/file1.txt", -... "/any/nested/path/file2.txt", -... ] -... ) ->>> download_result -DownloadResult( - successful=FileSet([ - LocalPath("/local/file1.txt"), - # directory structure is NOT preserved without source_path - LocalPath("/local/file2.txt"), - ]), - failed=FileSet([]), - skipped=FileSet([]), - missing=FileSet([]), -) -``` - - - -#### view_files() → FileSet[RemoteFile] - -Get file list in the `source_path`, -after `filter`, `limit` and `hwm` applied (if any). [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### NOTE -This method can return different results depending on [Read Strategies](../../strategy/index.md#strategy) - -#### Versionadded -Added in version 0.3.0. - -* **Returns:** - FileSet[RemoteFile] - : Set of files in `source_path`, which will be downloaded by [`run`](#onetl.file.file_downloader.file_downloader.FileDownloader.run) method -* **Raises:** - `onetl.exception.DirectoryNotFoundError` - : `source_path` does not found - - NotADirectoryError - : `source_path` is not a directory - -### Examples - -View files: - -```pycon ->>> from onetl.file import FileDownloader ->>> downloader = FileDownloader(source_path="/remote", ...) ->>> downloader.view_files() -FileSet([ - RemoteFile("/remote/file1.txt"), - RemoteFile("/remote/file3.txt"), - RemoteFile("/remote/nested/file3.txt"), -]) -``` - - diff --git a/mddocs/markdown/file/file_downloader/index.md b/mddocs/markdown/file/file_downloader/index.md deleted file mode 100644 index 3a7b4164b..000000000 --- a/mddocs/markdown/file/file_downloader/index.md +++ /dev/null @@ -1,9 +0,0 @@ - - -# File Downloader - -# File Downloader - -* [File Downloader](file_downloader.md) -* [File Downloader Options](options.md) -* [File Downloader Result](result.md) diff --git a/mddocs/markdown/file/file_downloader/options.md b/mddocs/markdown/file/file_downloader/options.md deleted file mode 100644 index c40baf9b8..000000000 --- a/mddocs/markdown/file/file_downloader/options.md +++ /dev/null @@ -1,67 +0,0 @@ - - -# File Downloader Options - -### *pydantic model* onetl.file.file_downloader.options.FileDownloaderOptions - -File downloading options. - -#### Versionadded -Added in version 0.3.0. - -### Examples - -```python -from onetl.file import FileDownloader - -options = FileDownloader.Options( - if_exists="replace_entire_directory", - delete_source=True, - workers=4, -) -``` - - - -#### *field* if_exists *: FileExistBehavior* *= FileExistBehavior.ERROR* *(alias 'mode')* - -How to handle existing files in the local directory. - -Possible values: -: * `error` (default) - mark file as failed - * `ignore` - mark file as skipped - * `replace_file` - replace existing file with a new one - * `replace_entire_directory` - delete local directory content before downloading files - -#### Versionchanged -Changed in version 0.9.0: Renamed `mode` → `if_exists` - - - -#### *field* delete_source *: bool* *= False* - -If `True`, remove source file after successful download. - -If download failed, file will left intact. - -#### Versionadded -Added in version 0.2.0. - -#### Versionchanged -Changed in version 0.3.0: Move `FileUploader.delete_local` to `FileUploaderOptions` - - - -#### *field* workers *: int* *= 1* - -Number of workers to create for parallel file download. - -1 (default) means files will me downloaded sequentially. -2 or more means files will be downloaded in parallel workers. - -Recommended value is `min(32, os.cpu_count() + 4)`, e.g. `5`. - -#### Versionadded -Added in version 0.8.1. - - diff --git a/mddocs/markdown/file/file_downloader/result.md b/mddocs/markdown/file/file_downloader/result.md deleted file mode 100644 index 45b94233a..000000000 --- a/mddocs/markdown/file/file_downloader/result.md +++ /dev/null @@ -1,550 +0,0 @@ - - -# File Downloader Result - -### *class* onetl.file.file_downloader.result.DownloadResult(\*, successful: FileSet[LocalPath] = None, failed: FileSet[FailedRemoteFile] = None, skipped: FileSet[RemoteFile] = None, missing: FileSet[RemotePath] = None) - -Representation of file download result. - -Container for file paths, divided into certain categories: - -* [`successful`](#onetl.file.file_downloader.result.DownloadResult.successful) -* [`failed`](#onetl.file.file_downloader.result.DownloadResult.failed) -* [`skipped`](#onetl.file.file_downloader.result.DownloadResult.skipped) -* [`missing`](#onetl.file.file_downloader.result.DownloadResult.missing) - -#### Versionadded -Added in version 0.3.0. - -### Examples - -```pycon ->>> from onetl.file import FileDownloader ->>> downloader = FileDownloader(local_path="/local", ...) ->>> download_result = downloader.run( -... [ -... "/remote/file1", -... "/remote/file2", -... "/failed/file", -... "/existing/file", -... "/missing/file", -... ] -... ) ->>> download_result -DownloadResult( - successful=FileSet([ - LocalPath("/local/file1"), - LocalPath("/local/file2"), - ]), - failed=FileSet([ - FailedLocalFile("/failed/file") - ]), - skipped=FileSet([ - RemoteFile("/existing/file") - ]), - missing=FileSet([ - RemotePath("/missing/file") - ]), -) -``` - - - -#### *property* details *: str* - -Return detailed information about files in the result object - -### Examples - -```pycon ->>> from onetl.impl import FailedRemoteFile, LocalPath, RemoteFile, RemotePathStat ->>> from onetl.exception import NotAFileError ->>> from onetl.file.file_result import FileResult ->>> file_result1 = FileResult( -... successful={ -... RemoteFile("/local/file", stats=RemotePathStat(st_size=1024)), -... RemoteFile("/local/another.file", stats=RemotePathStat(st_size=1024)), -... }, -... failed={ -... FailedRemoteFile( -... path="/remote/file1", -... stats=RemotePathStat(st_size=0), -... exception=NotAFileError("'/remote/file1' is not a file"), -... ), -... FailedRemoteFile( -... path="/remote/file2", -... stats=RemotePathStat(st_size=0), -... exception=PermissionError("'/remote/file2': [Errno 13] Permission denied"), -... ), -... }, -... skipped={LocalPath("/skipped/file1"), LocalPath("/skipped/file2")}, -... missing={LocalPath("/missing/file1"), LocalPath("/missing/file2")}, -... ) ->>> print(file_result1.details) -Total: 8 files (size='2.0 kB') - -Successful 2 files (size='2.0 kB'): - '/local/another.file' (size='1.0 kB') - '/local/file' (size='1.0 kB') - -Failed 2 files (size='0 Bytes'): - '/remote/file2' (size='0 Bytes') - PermissionError("'/remote/file2': [Errno 13] Permission denied") - '/remote/file1' (size='0 Bytes') - NotAFileError("'/remote/file1' is not a file") - -Skipped 2 files (size='0 Bytes'): - '/skipped/file1' - '/skipped/file2' - -Missing 2 files: - '/missing/file2' - '/missing/file1' -``` - -```pycon ->>> file_result2 = FileResult() ->>> print(file_result2.details) -No successful files - -No failed files - -No skipped files - -No missing files -``` - - - -#### dict(\*, include: 'AbstractSetIntStr' | 'MappingIntStrAny' | None = None, exclude: 'AbstractSetIntStr' | 'MappingIntStrAny' | None = None, by_alias: bool = False, skip_defaults: bool | None = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny - -Generate a dictionary representation of the model, optionally specifying which fields to include or exclude. - - - -#### *field* failed *: FileSet[FailedRemoteFile]* *[Optional]* - -File paths (remote) which were not downloaded because of some failure - - - -#### *property* failed_count *: int* - -Get number of failed files - -### Examples - -```pycon ->>> from onetl.impl import RemoteFile ->>> from onetl.file.file_result import FileResult ->>> file_result = FileResult( -... failed={RemoteFile("/some/file"), RemoteFile("/some/another.file")}, -... ) ->>> file_result.failed_count -2 -``` - - - -#### *property* failed_size *: int* - -Get size (in bytes) of failed files - -### Examples - -```pycon ->>> from onetl.impl import RemoteFile, RemotePathStat ->>> from onetl.file.file_result import FileResult ->>> file_result = FileResult( -... failed={ -... RemoteFile("/some/file", stats=RemotePathStat(st_size=1024)), -... RemoteFile("/some/another.file"), stats=RemotePathStat(st_size=1024)), -... }, -... ) ->>> file_result.failed_size # in bytes -2048 -``` - - - -#### *property* is_empty *: bool* - -Returns `True` if there are no files in `successful`, `failed` and `skipped` attributes - -### Examples - -```pycon ->>> from onetl.impl import LocalPath ->>> from onetl.file.file_result import FileResult ->>> file_result1 = FileResult() ->>> file_result1.is_empty -True ->>> file_result2 = FileResult( -... successful={LocalPath("/local/file"), LocalPath("/local/another.file")}, -... ) ->>> file_result2.is_empty -False -``` - - - -#### json(\*, include: 'AbstractSetIntStr' | 'MappingIntStrAny' | None = None, exclude: 'AbstractSetIntStr' | 'MappingIntStrAny' | None = None, by_alias: bool = False, skip_defaults: bool | None = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Callable[[Any], Any] | None = None, models_as_dict: bool = True, \*\*dumps_kwargs: Any) → str - -Generate a JSON representation of the model, include and exclude arguments as per dict(). - -encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps(). - - - -#### *field* missing *: FileSet[RemotePath]* *[Optional]* - -File paths (remote) which are not present in the remote file system - - - -#### *property* missing_count *: int* - -Get number of missing files - -### Examples - -```pycon ->>> from onetl.impl import LocalPath ->>> from onetl.file.file_result import FileResult ->>> file_result = FileResult( -... missing={LocalPath("/some/file"), LocalPath("/some/another.file")}, -... ) ->>> file_result.missing_count -2 -``` - - - -#### raise_if_contains_zero_size() → None - -Raise exception if `successful` attribute contains a file with zero size - -* **Raises:** - ZeroFileSizeError - : `successful` file set contains a file with zero size - -### Examples - -```pycon ->>> from onetl.exception import ZeroFileSizeError ->>> from onetl.impl import LocalPath ->>> from onetl.file.file_result import FileResult ->>> file_result = FileResult( -... successful={ -... LocalPath("/local/empty1.file"), -... LocalPath("/local/empty2.file"), -... LocalPath("/local/normal.file"), -... }, -... ) ->>> file_result.raise_if_contains_zero_size() -Traceback (most recent call last): - ... -onetl.exception.ZeroFileSizeError: 2 files out of 3 have zero size: - '/local/empty1.file' - '/local/empty2.file' -``` - - - -#### raise_if_empty() → None - -Raise exception if there are no files in `successful`, `failed` and `skipped` attributes - -* **Raises:** - EmptyFilesError - : `successful`, `failed` and `skipped` file sets are empty - -### Examples - -```pycon ->>> from onetl.file.file_result import FileResult ->>> file_result = FileResult() ->>> file_result.raise_if_empty() -Traceback (most recent call last): - ... -onetl.exception.EmptyFilesError: There are no files in the result -``` - - - -#### raise_if_failed() → None - -Raise exception if there are some files in `failed` attribute - -* **Raises:** - FailedFilesError - : `failed` file set is not empty - -### Examples - -```pycon ->>> from onetl.impl import FailedRemoteFile, RemotePathStat ->>> from onetl.exception import NotAFileError, FileMissingError ->>> from onetl.file.file_result import FileResult ->>> files_with_exception = [ -... FailedRemoteFile( -... path="/remote/file1", -... stats=RemotePathStat(st_size=0), -... exception=NotAFileError("'/remote/file1' is not a file"), -... ), -... FailedRemoteFile( -... path="/remote/file2", -... stats=RemotePathStat(st_size=0), -... exception=PermissionError("'/remote/file2': [Errno 13] Permission denied"), -... ), -... ] ->>> file_result = FileResult(failed=files_with_exception) ->>> file_result.raise_if_failed() -Traceback (most recent call last) -... -onetl.exception.FailedFilesError: Failed 2 files (size='0 bytes'): - '/remote/file1' (size='0 bytes') - NotAFileError("'/remote/file1' is not a file") - - '/remote/file2' (size='0 Bytes') - PermissionError("'/remote/file2': [Errno 13] Permission denied") -``` - - - -#### raise_if_missing() → None - -Raise exception if there are some files in `missing` attribute - -* **Raises:** - MissingFilesError - : `missing` file set is not empty - -### Examples - -```pycon ->>> from onetl.impl import LocalPath ->>> from onetl.file.file_result import FileResult ->>> file_result = FileResult( -... missing={ -... LocalPath("/missing/file1"), -... LocalPath("/missing/file2"), -... }, -... ) ->>> file_result.raise_if_missing() -Traceback (most recent call last): - ... -onetl.exception.MissingFilesError: Missing 2 files: - '/missing/file1' - '/missing/file2' -``` - - - -#### raise_if_skipped() → None - -Raise exception if there are some files in `skipped` attribute - -* **Raises:** - SkippedFilesError - : `skipped` file set is not empty - -### Examples - -```pycon ->>> from onetl.impl import LocalPath ->>> from onetl.file.file_result import FileResult ->>> file_result = FileResult( -... skipped={ -... LocalPath("/skipped/file1"), -... LocalPath("/skipped/file2"), -... }, -... ) ->>> file_result.raise_if_skipped() -Traceback (most recent call last): - ... -onetl.exception.SkippedFilesError: Skipped 2 files (15 kB): - '/skipped/file1' (10kB) - '/skipped/file2' (5 kB) -``` - - - -#### *field* skipped *: FileSet[RemoteFile]* *[Optional]* - -File paths (remote) which were skipped because of some reason - - - -#### *property* skipped_count *: int* - -Get number of skipped files - -### Examples - -```pycon ->>> from onetl.impl import LocalPath ->>> from onetl.file.file_result import FileResult ->>> file_result = FileResult( -... skipped={LocalPath("/some/file"), LocalPath("/some/another.file")}, -... ) ->>> file_result.skipped_count -2 -``` - - - -#### *property* skipped_size *: int* - -Get size (in bytes) of skipped files - -### Examples - -```pycon ->>> from onetl.impl import LocalPath ->>> from onetl.file.file_result import FileResult ->>> file_result = FileResult( -... skipped={LocalPath("/some/file"), LocalPath("/some/another.file")}, -... ) ->>> file_result.skipped_size # in bytes -1024 -``` - - - -#### *field* successful *: FileSet[LocalPath]* *[Optional]* - -File paths (local) which were downloaded successfully - - - -#### *property* successful_count *: int* - -Get number of successful files - -### Examples - -```pycon ->>> from onetl.impl import LocalPath ->>> from onetl.file.file_result import FileResult ->>> file_result = FileResult( -... successful={LocalPath("/some/file"), LocalPath("/some/another.file")}, -... ) ->>> file_result.successful_count -2 -``` - - - -#### *property* successful_size *: int* - -Get size (in bytes) of successful files - -### Examples - -```pycon ->>> from onetl.impl import LocalPath ->>> from onetl.file.file_result import FileResult ->>> file_result = FileResult( -... successful={LocalPath("/some/file"), LocalPath("/some/another.file")}, -... ) ->>> file_result.successful_size # in bytes -1024 -``` - - - -#### *property* summary *: str* - -Return short summary about files in the result object - -### Examples - -```pycon ->>> from onetl.impl import FailedRemoteFile, LocalPath, RemoteFile, RemotePathStat ->>> from onetl.exception import NotAFileError ->>> from onetl.file.file_result import FileResult ->>> file_result1 = FileResult( -... successful={ -... RemoteFile("/local/file", stats=RemotePathStat(st_size=1024)), -... RemoteFile("/local/another.file", stats=RemotePathStat(st_size=1024)), -... }, -... failed={ -... FailedRemoteFile( -... path="/remote/file1", -... stats=RemotePathStat(st_size=0), -... exception=NotAFileError("'/remote/file1' is not a file"), -... ), -... FailedRemoteFile( -... path="/remote/file2", -... stats=RemotePathStat(st_size=0), -... exception=PermissionError("'/remote/file2': [Errno 13] Permission denied"), -... ), -... }, -... skipped={LocalPath("/skipped/file1"), LocalPath("/skipped/file2")}, -... missing={LocalPath("/missing/file1"), LocalPath("/missing/file2")}, -... ) ->>> print(file_result1.summary) -Total: 8 files (size='2.0 kB') - -Successful: 2 files (size='2.0 kB') - -Failed: 2 files (size='0 Bytes') - -Skipped: 2 files (size='0 Bytes') - -Missing: 2 files -``` - -```pycon ->>> file_result2 = FileResult() ->>> print(file_result2.summary) -No files -``` - - - -#### *property* total_count *: int* - -Get total number of all files - -### Examples - -```pycon ->>> from onetl.impl import RemoteFile ->>> from onetl.file.file_result import FileResult ->>> file_result = FileResult( -... successful={LocalPath("/local/file"), LocalPath("/local/another.file")}, -... failed={RemoteFile("/remote/file"), RemoteFile("/remote/another.file")}, -... skipped={LocalPath("/skipped/file")}, -... missing={LocalPath("/missing/file")}, -... ) ->>> file_result.total_count -6 -``` - - - -#### *property* total_size *: int* - -Get total size (in bytes) of all files - -### Examples - -```pycon ->>> from onetl.impl import RemoteFile, RemotePathStat, LocalPath ->>> from onetl.file.file_result import FileResult ->>> file_result = FileResult( -... successful={LocalPath("/local/file"), LocalPath("/local/another.file")}, -... failed={ -... RemoteFile("/remote/file", stats=RemotePathStat(st_size=1024)), -... RemoteFile("/remote/another.file", stats=RemotePathStat(st_size=1024)) -... }, -... skipped={LocalPath("/skipped/file")}, -... missing={LocalPath("/missing/file")}, -... ) ->>> file_result.total_size # in bytes -4096 -``` - - diff --git a/mddocs/markdown/file/file_filters/base.md b/mddocs/markdown/file/file_filters/base.md deleted file mode 100644 index c4f8a9adc..000000000 --- a/mddocs/markdown/file/file_filters/base.md +++ /dev/null @@ -1,42 +0,0 @@ - - -# Base interface - -| [`BaseFileFilter`](#onetl.base.base_file_filter.BaseFileFilter)() | Base file filter interface. | -|-----------------------------------------------------------------------------------|------------------------------------------------------------------| -| [`BaseFileFilter.match`](#onetl.base.base_file_filter.BaseFileFilter.match)(path) | Returns `True` if path is matching the filter, `False` otherwise | - -### *class* onetl.base.base_file_filter.BaseFileFilter - -Base file filter interface. - -Filters used by several onETL components, including [File Downloader](../file_downloader/file_downloader.md#file-downloader) and [File Mover](../file_mover/file_mover.md#file-mover), -to determine if a file should be handled or not. - -All filters are stateless. - -#### Versionadded -Added in version 0.8.0. - - - -#### *abstract* match(path: PathProtocol) → bool - -Returns `True` if path is matching the filter, `False` otherwise - -#### Versionadded -Added in version 0.8.0. - -### Examples - -```pycon ->>> from onetl.impl import LocalPath ->>> filter.match(LocalPath("/path/to/file.csv")) -True ->>> filter.match(LocalPath("/path/to/excluded.csv")) -False ->>> filter.match(LocalPath("/path/to/file.csv")) -True -``` - - diff --git a/mddocs/markdown/file/file_filters/exclude_dir.md b/mddocs/markdown/file/file_filters/exclude_dir.md deleted file mode 100644 index 9ad015e8a..000000000 --- a/mddocs/markdown/file/file_filters/exclude_dir.md +++ /dev/null @@ -1,47 +0,0 @@ - - -# ExcludeDir - -### *class* onetl.file.filter.exclude_dir.ExcludeDir(path: str | os.PathLike) - -Filter files or directories which are included in a specific directory. - -#### Versionadded -Added in version 0.8.0: Replaces deprecated `onetl.core.FileFilter` - -* **Parameters:** - **path** - : Path to directory which should be excluded. - -### Examples - -Create exclude dir filter: - -```python -from onetl.file.filter import ExcludeDir - -exclude_dir = ExcludeDir("/export/news_parse/exclude_dir") -``` - - - -#### match(path: PathProtocol) → bool - -Returns `True` if path is matching the filter, `False` otherwise - -#### Versionadded -Added in version 0.8.0. - -### Examples - -```pycon ->>> from onetl.impl import LocalPath ->>> filter.match(LocalPath("/path/to/file.csv")) -True ->>> filter.match(LocalPath("/path/to/excluded.csv")) -False ->>> filter.match(LocalPath("/path/to/file.csv")) -True -``` - - diff --git a/mddocs/markdown/file/file_filters/file_filter.md b/mddocs/markdown/file/file_filters/file_filter.md deleted file mode 100644 index 31946cb36..000000000 --- a/mddocs/markdown/file/file_filters/file_filter.md +++ /dev/null @@ -1,77 +0,0 @@ - - -# File Filter (legacy) - -### *class* onetl.core.file_filter.file_filter.FileFilter(\*, glob: str | None = None, regexp: Pattern | None = None, exclude_dirs: List[RemotePath] = None) - -Filter files or directories by their path. - -#### Deprecated -Deprecated since version 0.8.0: Use [`Glob`](glob.md#onetl.file.filter.glob.Glob), [`Regexp`](regexp.md#onetl.file.filter.regexp.Regexp) -or [`ExcludeDir`](exclude_dir.md#onetl.file.filter.exclude_dir.ExcludeDir) instead. - -* **Parameters:** - **glob** - : Pattern (e.g. `*.csv`) for which any **file** (only file) path should match -
- #### WARNING - Mutually exclusive with `regexp` - - **regexp** - : Regular expression (e.g. `\d+\.csv`) for which any **file** (only file) path should match. -
- If input is a string, regular expression will be compiles using `re.IGNORECASE` and `re.DOTALL` flags -
- #### WARNING - Mutually exclusive with `glob` - - **exclude_dirs** - : List of directories which should not be a part of a file or directory path - -### Examples - -Create exclude_dir filter: - -```python -from onetl.core import FileFilter - -file_filter = FileFilter(exclude_dirs=["/export/news_parse/exclude_dir"]) -``` - -Create glob filter: - -```python -from onetl.core import FileFilter - -file_filter = FileFilter(glob="*.csv") -``` - -Create regexp filter: - -```python -from onetl.core import FileFilter - -file_filter = FileFilter(regexp=r"\d+\.csv") - -# or - -import re - -file_filter = FileFilter(regexp=re.compile("\d+\.csv")) -``` - -Not allowed: - -```python -from onetl.core import FileFilter - -FileFilter() # will raise ValueError, at least one argument should be passed -``` - - - -#### match(path: PathProtocol) → bool - -False means it does not match the template by which you want to receive files - - diff --git a/mddocs/markdown/file/file_filters/file_mtime_filter.md b/mddocs/markdown/file/file_filters/file_mtime_filter.md deleted file mode 100644 index 9ec51d939..000000000 --- a/mddocs/markdown/file/file_filters/file_mtime_filter.md +++ /dev/null @@ -1,70 +0,0 @@ - - -# FileModifiedTime - -### *class* onetl.file.filter.file_mtime.FileModifiedTime(\*, since: datetime | None = None, until: datetime | None = None) - -Filter files matching a specified modification time. - -If file modification time (`.stat().st_mtime`) doesn’t match range, it will be excluded. -Doesn’t affect directories or paths without `.stat()` method. - -#### NOTE -Some filesystems return timestamps truncated to whole seconds (without millisecond part). -obj:~since and :obj\`~until\`\` values should be adjusted accordingly. - -#### Versionadded -Added in version 0.13.0. - -* **Parameters:** - **since** - : Minimal allowed file modification time. `None` means no limit. - - **until** - : Maximum allowed file modification time. `None` means no limit. - -### Examples - -Select files modified between start of the day (`00:00:00`) and hour ago: - -```python -from datetime import datetime, timedelta -from onetl.file.filter import FileModifiedTime - -hour_ago = datetime.now() - timedelta(hours=1) -day_start = datetime.now().replace(hour=0, minute=0, second=0, microsecond=0) -file_mtime = FileModifiedTime(since=day_start, until=hour_ago) -``` - -Select only files modified since hour ago: - -```python -from datetime import datetime, timedelta -from onetl.file.filter import FileModifiedTime - -hour_ago = datetime.now() - timedelta(hours=1) -file_mtime = FileModifiedTime(since=hour_ago) -``` - - - -#### match(path: PathProtocol) → bool - -Returns `True` if path is matching the filter, `False` otherwise - -#### Versionadded -Added in version 0.8.0. - -### Examples - -```pycon ->>> from onetl.impl import LocalPath ->>> filter.match(LocalPath("/path/to/file.csv")) -True ->>> filter.match(LocalPath("/path/to/excluded.csv")) -False ->>> filter.match(LocalPath("/path/to/file.csv")) -True -``` - - diff --git a/mddocs/markdown/file/file_filters/file_size_filter.md b/mddocs/markdown/file/file_filters/file_size_filter.md deleted file mode 100644 index 82255b7c8..000000000 --- a/mddocs/markdown/file/file_filters/file_size_filter.md +++ /dev/null @@ -1,74 +0,0 @@ - - -# FileSizeRange - -### *class* onetl.file.filter.file_size.FileSizeRange(\*, min: ByteSize | None = None, max: ByteSize | None = None) - -Filter files matching a specified size. - -If file size (`.stat().st_size`) doesn’t match the range, it will be excluded. -Doesn’t affect directories or paths without `.stat()` method. - -#### Versionadded -Added in version 0.13.0. - -#### NOTE -[SI unit prefixes](https://en.wikipedia.org/wiki/Byte#Multiple-byte_units) -means that `1KB` == `1 kilobyte` == `1000 bytes`. -If you need `1024 bytes`, use `1 KiB` == `1 kibibyte`. - -* **Parameters:** - **min** - : Minimal allowed file size. `None` means no limit. - - **max** - : Maximum allowed file size. `None` means no limit. - -### Examples - -Specify min and max file sizes: - -```python -from onetl.file.filter import FileSizeRange - -file_size = FileSizeRange(min="1KiB", max="100MiB") -``` - -Specify only min file size: - -```python -from onetl.file.filter import FileSizeRange - -file_size = FileSizeRange(min="1KiB") -``` - -Specify only max file size: - -```python -from onetl.file.filter import FileSizeRange - -file_size = FileSizeRange(max="100MiB") -``` - - - -#### match(path: PathProtocol) → bool - -Returns `True` if path is matching the filter, `False` otherwise - -#### Versionadded -Added in version 0.8.0. - -### Examples - -```pycon ->>> from onetl.impl import LocalPath ->>> filter.match(LocalPath("/path/to/file.csv")) -True ->>> filter.match(LocalPath("/path/to/excluded.csv")) -False ->>> filter.match(LocalPath("/path/to/file.csv")) -True -``` - - diff --git a/mddocs/markdown/file/file_filters/glob.md b/mddocs/markdown/file/file_filters/glob.md deleted file mode 100644 index f6b727b77..000000000 --- a/mddocs/markdown/file/file_filters/glob.md +++ /dev/null @@ -1,47 +0,0 @@ - - -# Glob - -### *class* onetl.file.filter.glob.Glob(pattern: str) - -Filter files or directories with path matching a glob expression. - -#### Versionadded -Added in version 0.8.0: Replaces deprecated `onetl.core.FileFilter` - -* **Parameters:** - **pattern** - : Pattern (e.g. `*.csv`) for which any **file** (only file) path should match - -### Examples - -Create glob filter: - -```python -from onetl.file.filter import Glob - -glob = Glob("*.csv") -``` - - - -#### match(path: PathProtocol) → bool - -Returns `True` if path is matching the filter, `False` otherwise - -#### Versionadded -Added in version 0.8.0. - -### Examples - -```pycon ->>> from onetl.impl import LocalPath ->>> filter.match(LocalPath("/path/to/file.csv")) -True ->>> filter.match(LocalPath("/path/to/excluded.csv")) -False ->>> filter.match(LocalPath("/path/to/file.csv")) -True -``` - - diff --git a/mddocs/markdown/file/file_filters/index.md b/mddocs/markdown/file/file_filters/index.md deleted file mode 100644 index 40155ae9a..000000000 --- a/mddocs/markdown/file/file_filters/index.md +++ /dev/null @@ -1,20 +0,0 @@ - - -# File Filters - -# File filters - -* [Glob](glob.md) -* [Regexp](regexp.md) -* [ExcludeDir](exclude_dir.md) -* [FileSizeRange](file_size_filter.md) -* [FileModifiedTime](file_mtime_filter.md) - -# Legacy - -* [File Filter (legacy)](file_filter.md) - -# For developers - -* [Base interface](base.md) -* [match_all_filters](match_all_filters.md) diff --git a/mddocs/markdown/file/file_filters/match_all_filters.md b/mddocs/markdown/file/file_filters/match_all_filters.md deleted file mode 100644 index 9d09eb421..000000000 --- a/mddocs/markdown/file/file_filters/match_all_filters.md +++ /dev/null @@ -1,37 +0,0 @@ - - -# match_all_filters - -### onetl.file.filter.match_all_filters.match_all_filters(path: PathProtocol, filters: Iterable[[BaseFileFilter](base.md#onetl.base.base_file_filter.BaseFileFilter)]) → bool - -Check if input path satisfies all the filters. - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **path** - : Path to check. - - **filters** - : Filters to test path against. -* **Returns:** - `True` if path matches all the filters, `False` otherwise. - - If filters are empty, returns `True`. - -### Examples - -```pycon ->>> from onetl.file.filter import Glob, ExcludeDir, match_all_filters ->>> from onetl.impl import RemoteFile, RemotePathStat ->>> filters = [Glob("*.csv"), ExcludeDir("/excluded")] ->>> match_all_filters(RemoteFile("/path/to/file.csv", stats=RemotePathStat()), filters) -True ->>> match_all_filters(RemoteFile("/path/to/file.txt", stats=RemotePathStat()), filters) -False ->>> match_all_filters(RemoteFile("/excluded/path/file.csv", stats=RemotePathStat()), filters) -False -``` - - diff --git a/mddocs/markdown/file/file_filters/regexp.md b/mddocs/markdown/file/file_filters/regexp.md deleted file mode 100644 index 3e252ff74..000000000 --- a/mddocs/markdown/file/file_filters/regexp.md +++ /dev/null @@ -1,59 +0,0 @@ - - -# Regexp - -### *class* onetl.file.filter.regexp.Regexp(pattern: str) - -Filter files or directories with path matching a regular expression. - -#### Versionadded -Added in version 0.8.0: Replaces deprecated `onetl.core.FileFilter` - -* **Parameters:** - **pattern** - : Regular expression (e.g. `\d+\.csv`) for which any **file** (only file) path should match. -
- If input is a string, regular expression will be compiles using `re.IGNORECASE` and `re.DOTALL` flags. - -### Examples - -Create regexp filter from string: - -```python -from onetl.file.filter import Regexp - -regexp = Regexp(r"\d+\.csv") -``` - -Create regexp filter from `re.Pattern`: - -```python -import re - -from onetl.file.filter import Regexp - -regexp = Regexp(re.compile(r"\d+\.csv", re.IGNORECASE | re.DOTALL)) -``` - - - -#### match(path: PathProtocol) → bool - -Returns `True` if path is matching the filter, `False` otherwise - -#### Versionadded -Added in version 0.8.0. - -### Examples - -```pycon ->>> from onetl.impl import LocalPath ->>> filter.match(LocalPath("/path/to/file.csv")) -True ->>> filter.match(LocalPath("/path/to/excluded.csv")) -False ->>> filter.match(LocalPath("/path/to/file.csv")) -True -``` - - diff --git a/mddocs/markdown/file/file_limits/base.md b/mddocs/markdown/file/file_limits/base.md deleted file mode 100644 index 4573bb8f4..000000000 --- a/mddocs/markdown/file/file_limits/base.md +++ /dev/null @@ -1,108 +0,0 @@ - - -# Base interface - -| [`BaseFileLimit`](#onetl.base.base_file_limit.BaseFileLimit)() | Base file limit interface. | -|--------------------------------------------------------------------------------------|-------------------------------------------------| -| [`BaseFileLimit.reset`](#onetl.base.base_file_limit.BaseFileLimit.reset)() | Resets the internal limit state. | -| [`BaseFileLimit.stops_at`](#onetl.base.base_file_limit.BaseFileLimit.stops_at)(path) | Update internal state and return current state. | -| [`BaseFileLimit.is_reached`](#onetl.base.base_file_limit.BaseFileLimit.is_reached) | Check if limit is reached. | - -### *class* onetl.base.base_file_limit.BaseFileLimit - -Base file limit interface. - -Limits used by several onETL components, including [File Downloader](../file_downloader/file_downloader.md#file-downloader) and [File Mover](../file_mover/file_mover.md#file-mover), -to determine if internal loop should be stopped. - -Unlike file filters, limits have internal state which can be updated or reset. - -#### Versionadded -Added in version 0.8.0. - - - -#### *abstract property* is_reached *: bool* - -Check if limit is reached. - -#### Versionadded -Added in version 0.8.0. - -* **Returns:** - `True` if limit is reached, `False` otherwise. - -### Examples - -```pycon ->>> from onetl.impl import LocalPath ->>> limit.is_reached -False ->>> limit.stops_at(LocalPath("/path/to/file.csv")) -False ->>> limit.is_reached -False ->>> # after limit is reached ->>> limit.stops_at(LocalPath("/path/to/file.csv")) -True ->>> limit.is_reached -True -``` - - - -#### *abstract* reset() → Self - -Resets the internal limit state. - -#### Versionadded -Added in version 0.8.0. - -* **Returns:** - Returns a filter of the same type, but with non-reached state. - - It could be the same filter or a new one, this is an implementation detail. - -### Examples - -```pycon ->>> limit.is_reached -True ->>> new_limit = limit.reset() ->>> new_limit.is_reached -False -``` - - - -#### *abstract* stops_at(path: PathProtocol) → bool - -Update internal state and return current state. - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **path** - : Path to check -* **Returns:** - `True` if limit is reached, `False` otherwise. - -### Examples - -```pycon ->>> from onetl.impl import LocalPath ->>> # limit is not reached yet ->>> limit.stops_at(LocalPath("/path/to/file.csv")) -False ->>> # after limit is reached ->>> limit.stops_at(LocalPath("/path/to/another.csv")) -True ->>> # at this point, .stops_at() and .is_reached will always return True, ->>> # even on inputs that returned False before. ->>> # it will be in the same state until .reset() is called ->>> limit.stops_at(LocalPath("/path/to/file.csv")) -True -``` - - diff --git a/mddocs/markdown/file/file_limits/file_limit.md b/mddocs/markdown/file/file_limits/file_limit.md deleted file mode 100644 index f60fca2e8..000000000 --- a/mddocs/markdown/file/file_limits/file_limit.md +++ /dev/null @@ -1,114 +0,0 @@ - - -# File Limit (legacy) - -### *class* onetl.core.file_limit.file_limit.FileLimit(\*, count_limit: int = 100) - -Limits the number of downloaded files. - -#### Deprecated -Deprecated since version 0.8.0: Use [`MaxFilesCount`](max_files_count.md#onetl.file.limit.max_files_count.MaxFilesCount) instead. - -* **Parameters:** - **count_limit** - : Number of downloaded files at a time. - -### Examples - -Create a FileLimit object and set the amount in it: - -```python -from onetl.core import FileLimit - -limit = FileLimit(count_limit=1500) -``` - -If you create a [`onetl.file.file_downloader.file_downloader.FileDownloader`](../file_downloader/file_downloader.md#onetl.file.file_downloader.file_downloader.FileDownloader) object without -specifying the limit option, it will download with a limit of 100 files. - - - -#### *property* is_reached *: bool* - -Check if limit is reached. - -#### Versionadded -Added in version 0.8.0. - -* **Returns:** - `True` if limit is reached, `False` otherwise. - -### Examples - -```pycon ->>> from onetl.impl import LocalPath ->>> limit.is_reached -False ->>> limit.stops_at(LocalPath("/path/to/file.csv")) -False ->>> limit.is_reached -False ->>> # after limit is reached ->>> limit.stops_at(LocalPath("/path/to/file.csv")) -True ->>> limit.is_reached -True -``` - - - -#### reset() - -Resets the internal limit state. - -#### Versionadded -Added in version 0.8.0. - -* **Returns:** - Returns a filter of the same type, but with non-reached state. - - It could be the same filter or a new one, this is an implementation detail. - -### Examples - -```pycon ->>> limit.is_reached -True ->>> new_limit = limit.reset() ->>> new_limit.is_reached -False -``` - - - -#### stops_at(path: PathProtocol) → bool - -Update internal state and return current state. - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **path** - : Path to check -* **Returns:** - `True` if limit is reached, `False` otherwise. - -### Examples - -```pycon ->>> from onetl.impl import LocalPath ->>> # limit is not reached yet ->>> limit.stops_at(LocalPath("/path/to/file.csv")) -False ->>> # after limit is reached ->>> limit.stops_at(LocalPath("/path/to/another.csv")) -True ->>> # at this point, .stops_at() and .is_reached will always return True, ->>> # even on inputs that returned False before. ->>> # it will be in the same state until .reset() is called ->>> limit.stops_at(LocalPath("/path/to/file.csv")) -True -``` - - diff --git a/mddocs/markdown/file/file_limits/index.md b/mddocs/markdown/file/file_limits/index.md deleted file mode 100644 index be23f82da..000000000 --- a/mddocs/markdown/file/file_limits/index.md +++ /dev/null @@ -1,19 +0,0 @@ - - -# File Limits - -# File limits - -* [MaxFilesCount](max_files_count.md) -* [TotalFilesSize](total_files_size.md) - -# Legacy - -* [File Limit (legacy)](file_limit.md) - -# For developers - -* [Base interface](base.md) -* [limits_stop_at](limits_stop_at.md) -* [limits_reached](limits_reached.md) -* [reset_limits](reset_limits.md) diff --git a/mddocs/markdown/file/file_limits/limits_reached.md b/mddocs/markdown/file/file_limits/limits_reached.md deleted file mode 100644 index bf5aeabbe..000000000 --- a/mddocs/markdown/file/file_limits/limits_reached.md +++ /dev/null @@ -1,38 +0,0 @@ - - -# limits_reached - -### onetl.file.limit.limits_reached.limits_reached(limits: Iterable[[BaseFileLimit](base.md#onetl.base.base_file_limit.BaseFileLimit)]) → bool - -Check if any of limits reached. - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **limits** - : Limits to test. -* **Returns:** - `True` if any of limits is reached, `False` otherwise. - - If no limits are passed, returns `False`. - -### Examples - -```pycon ->>> from onetl.file.limit import MaxFilesCount, limits_reached, limits_stop_at ->>> from onetl.impl import LocalPath ->>> limits = [MaxFilesCount(2)] ->>> limits_reached(limits) -False ->>> limits_stop_at(LocalPath("/path/to/file1.csv"), limits) -False ->>> limits_stop_at(LocalPath("/path/to/file2.csv"), limits) -False ->>> limits_stop_at(LocalPath("/path/to/file3.csv"), limits) -True ->>> limits_reached(limits) -True -``` - - diff --git a/mddocs/markdown/file/file_limits/limits_stop_at.md b/mddocs/markdown/file/file_limits/limits_stop_at.md deleted file mode 100644 index f41606838..000000000 --- a/mddocs/markdown/file/file_limits/limits_stop_at.md +++ /dev/null @@ -1,37 +0,0 @@ - - -# limits_stop_at - -### onetl.file.limit.limits_stop_at.limits_stop_at(path: PathProtocol, limits: Iterable[[BaseFileLimit](base.md#onetl.base.base_file_limit.BaseFileLimit)]) → bool - -Check if some of limits stops at given path. - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **path** - : Path to check. - - **limits** - : Limits to test path against. -* **Returns:** - `True` if any of limit is reached while handling the path, `False` otherwise. - - If no limits are passed, returns `False`. - -### Examples - -```pycon ->>> from onetl.file.limit import MaxFilesCount, limits_stop_at ->>> from onetl.impl import LocalPath ->>> limits = [MaxFilesCount(2)] ->>> limits_stop_at(LocalPath("/path/to/file1.csv"), limits) -False ->>> limits_stop_at(LocalPath("/path/to/file2.csv"), limits) -False ->>> limits_stop_at(LocalPath("/path/to/file3.csv"), limits) -True -``` - - diff --git a/mddocs/markdown/file/file_limits/max_files_count.md b/mddocs/markdown/file/file_limits/max_files_count.md deleted file mode 100644 index 1b10149b1..000000000 --- a/mddocs/markdown/file/file_limits/max_files_count.md +++ /dev/null @@ -1,114 +0,0 @@ - - -# MaxFilesCount - -### *class* onetl.file.limit.max_files_count.MaxFilesCount(limit: int) - -Limits the total number of files handled by [File Downloader](../file_downloader/file_downloader.md#file-downloader) or [File Mover](../file_mover/file_mover.md#file-mover). - -All files until specified limit (including) will be downloaded/moved, but `limit+1` will not. - -This doesn’t apply to directories. - -#### Versionadded -Added in version 0.8.0: Replaces deprecated `onetl.core.FileLimit` - -* **Parameters:** - **limit** - -### Examples - -Create filter which allows to download/move up to 100 files, but stops on 101: - -```python -from onetl.file.limit import MaxFilesCount - -limit = MaxFilesCount(100) -``` - - - -#### *property* is_reached *: bool* - -Check if limit is reached. - -#### Versionadded -Added in version 0.8.0. - -* **Returns:** - `True` if limit is reached, `False` otherwise. - -### Examples - -```pycon ->>> from onetl.impl import LocalPath ->>> limit.is_reached -False ->>> limit.stops_at(LocalPath("/path/to/file.csv")) -False ->>> limit.is_reached -False ->>> # after limit is reached ->>> limit.stops_at(LocalPath("/path/to/file.csv")) -True ->>> limit.is_reached -True -``` - - - -#### reset() - -Resets the internal limit state. - -#### Versionadded -Added in version 0.8.0. - -* **Returns:** - Returns a filter of the same type, but with non-reached state. - - It could be the same filter or a new one, this is an implementation detail. - -### Examples - -```pycon ->>> limit.is_reached -True ->>> new_limit = limit.reset() ->>> new_limit.is_reached -False -``` - - - -#### stops_at(path: PathProtocol) → bool - -Update internal state and return current state. - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **path** - : Path to check -* **Returns:** - `True` if limit is reached, `False` otherwise. - -### Examples - -```pycon ->>> from onetl.impl import LocalPath ->>> # limit is not reached yet ->>> limit.stops_at(LocalPath("/path/to/file.csv")) -False ->>> # after limit is reached ->>> limit.stops_at(LocalPath("/path/to/another.csv")) -True ->>> # at this point, .stops_at() and .is_reached will always return True, ->>> # even on inputs that returned False before. ->>> # it will be in the same state until .reset() is called ->>> limit.stops_at(LocalPath("/path/to/file.csv")) -True -``` - - diff --git a/mddocs/markdown/file/file_limits/reset_limits.md b/mddocs/markdown/file/file_limits/reset_limits.md deleted file mode 100644 index 23a368345..000000000 --- a/mddocs/markdown/file/file_limits/reset_limits.md +++ /dev/null @@ -1,39 +0,0 @@ - - -# reset_limits - -### onetl.file.limit.reset_limits.reset_limits(limits: Iterable[[BaseFileLimit](base.md#onetl.base.base_file_limit.BaseFileLimit)]) → list[[BaseFileLimit](base.md#onetl.base.base_file_limit.BaseFileLimit)] - -Reset limits state. - -* **Parameters:** - **limits** - : Limits to reset. -* **Returns:** - List with limits, but with reset state. - - List may contain original filters with reset state, or new copies. - - This is an implementation detail of [`reset`](base.md#onetl.base.base_file_limit.BaseFileLimit.reset) method. - -### Examples - -```pycon ->>> from onetl.file.limit import MaxFilesCount, limits_reached, limits_stop_at, reset_limits ->>> from onetl.impl import LocalPath ->>> limits = [MaxFilesCount(1)] ->>> limits_reached(limits) -False ->>> # do something ->>> limits_stop_at(LocalPath("/path/to/file1.csv"), limits) -False ->>> limits_stop_at(LocalPath("/path/to/file2.csv"), limits) -True ->>> limits_reached(limits) -True ->>> new_limits = reset_limits(limits) ->>> limits_reached(new_limits) -False -``` - - diff --git a/mddocs/markdown/file/file_limits/total_files_size.md b/mddocs/markdown/file/file_limits/total_files_size.md deleted file mode 100644 index 48c56bc5b..000000000 --- a/mddocs/markdown/file/file_limits/total_files_size.md +++ /dev/null @@ -1,122 +0,0 @@ - - -# TotalFilesSize - -### *class* onetl.file.limit.total_files_size.TotalFilesSize(limit: int | str) - -Limits the total size of files handled by [File Downloader](../file_downloader/file_downloader.md#file-downloader) or [File Mover](../file_mover/file_mover.md#file-mover). - -Calculates the sum of downloaded/moved files size (`.stat().st_size`), -and checks that this sum is less or equal to specified limit. - -After limit is reached, no more files will be downloaded/moved. - -Doesn’t affect directories, paths without `.stat()` method or files with zero size. - -#### Versionadded -Added in version 0.13.0. - -#### NOTE -[SI unit prefixes](https://en.wikipedia.org/wiki/Byte#Multiple-byte_units) -means that `1KB` == `1 kilobyte` == `1000 bytes`. -If you need `1024 bytes`, use `1 KiB` == `1 kibibyte`. - -* **Parameters:** - **limit** - -### Examples - -Create filter which allows to download/move files with total size up to 1GiB, but not higher: - -```python -from onetl.file.limit import MaxFilesCount - -limit = TotalFilesSize("1GiB") -``` - - - -#### *property* is_reached *: bool* - -Check if limit is reached. - -#### Versionadded -Added in version 0.8.0. - -* **Returns:** - `True` if limit is reached, `False` otherwise. - -### Examples - -```pycon ->>> from onetl.impl import LocalPath ->>> limit.is_reached -False ->>> limit.stops_at(LocalPath("/path/to/file.csv")) -False ->>> limit.is_reached -False ->>> # after limit is reached ->>> limit.stops_at(LocalPath("/path/to/file.csv")) -True ->>> limit.is_reached -True -``` - - - -#### reset() - -Resets the internal limit state. - -#### Versionadded -Added in version 0.8.0. - -* **Returns:** - Returns a filter of the same type, but with non-reached state. - - It could be the same filter or a new one, this is an implementation detail. - -### Examples - -```pycon ->>> limit.is_reached -True ->>> new_limit = limit.reset() ->>> new_limit.is_reached -False -``` - - - -#### stops_at(path: PathProtocol) → bool - -Update internal state and return current state. - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **path** - : Path to check -* **Returns:** - `True` if limit is reached, `False` otherwise. - -### Examples - -```pycon ->>> from onetl.impl import LocalPath ->>> # limit is not reached yet ->>> limit.stops_at(LocalPath("/path/to/file.csv")) -False ->>> # after limit is reached ->>> limit.stops_at(LocalPath("/path/to/another.csv")) -True ->>> # at this point, .stops_at() and .is_reached will always return True, ->>> # even on inputs that returned False before. ->>> # it will be in the same state until .reset() is called ->>> limit.stops_at(LocalPath("/path/to/file.csv")) -True -``` - - diff --git a/mddocs/markdown/file/file_mover/file_mover.md b/mddocs/markdown/file/file_mover/file_mover.md deleted file mode 100644 index 1f7b7ce0b..000000000 --- a/mddocs/markdown/file/file_mover/file_mover.md +++ /dev/null @@ -1,243 +0,0 @@ - - -# File Mover - -| [`FileMover`](#onetl.file.file_mover.file_mover.FileMover) | Allows you to move files between different directories in a filesystem, and return an object with move result summary. | -|------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------| -| [`FileMover.run`](#onetl.file.file_mover.file_mover.FileMover.run)([files]) | Method for moving files from source to target directory. | -| [`FileMover.view_files`](#onetl.file.file_mover.file_mover.FileMover.view_files)() | Get file list in the `source_path`, after `filter` and `limit` applied (if any). | - -### *class* onetl.file.file_mover.file_mover.FileMover(\*, connection: ~onetl.base.base_file_connection.BaseFileConnection, target_path: ~onetl.impl.remote_path.RemotePath, source_path: ~onetl.impl.remote_path.RemotePath | None = None, filters: ~typing.List[~onetl.base.base_file_filter.BaseFileFilter] = None, limits: ~typing.List[~onetl.base.base_file_limit.BaseFileLimit] = None, options: ~onetl.file.file_mover.options.FileMoverOptions = FileMoverOptions(if_exists=, workers=1)) - -Allows you to move files between different directories in a filesystem, -and return an object with move result summary. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### NOTE -This class is used to move files **only** within the same connection, - -It does NOT support direct file transfer between filesystems, like `FTP -> SFTP`. -You should use [File Downloader](../file_downloader/file_downloader.md#file-downloader) + [File Uploader](../file_uploader/file_uploader.md#file-uploader) to implement `FTP -> local dir -> SFTP`. - -#### WARNING -This class does **not** support read strategies. - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **connection** - : Class which contains File system connection properties. See [File Connections](../../connection/file_connection/index.md#file-connections) section. - - **target_path** - : Remote path to move files to - - **source_path** - : Remote path to move files from. -
- Could be `None`, but only if you pass absolute file paths directly to - [`run`](#onetl.file.file_mover.file_mover.FileMover.run) method - - **filters** - : Return only files/directories matching these filters. See [File Filters](../file_filters/index.md#file-filters) - - **limits** - : Apply limits to the list of files/directories, and stop if one of the limits is reached. - See [File Limits](../file_limits/index.md#file-limits) - - **options** - : File moving options. See [`FileMover.Options`](options.md#onetl.file.file_mover.options.FileMoverOptions) - -### Examples - -Minimal example - -```py -from onetl.connection import SFTP -from onetl.file import FileMover - -sftp = SFTP(...) - -# create mover -mover = FileMover( - connection=sftp, - source_path="/path/to/source/dir", - target_path="/path/to/target/dir", -) - -# move files from "/path/to/source/dir" to "/path/to/target/dir" -mover.run() -``` - -Full example - -```py -from onetl.connection import SFTP -from onetl.file import FileMover -from onetl.file.filter import Glob, ExcludeDir -from onetl.file.limit import MaxFilesCount, TotalFilesSize - -sftp = SFTP(...) - -# create mover with a bunch of options -mover = FileMover( - connection=sftp, - source_path="/path/to/source/dir", - target_path="/path/to/target/dir", - filters=[ - Glob("*.txt"), - ExcludeDir("/path/to/source/dir/exclude"), - ], - limits=[MaxFilesCount(100), TotalFileSize("10GiB")], - options=FileMover.Options(if_exists="replace_file"), -) - -# move files from "/path/to/source/dir" to "/path/to/target/dir", -# but only *.txt files -# excluding files from "/path/to/source/dir/exclude" directory -# and stop before downloading 101 file -mover.run() -``` - - - -#### run(files: Iterable[str | PathLike] | None = None) → [MoveResult](result.md#onetl.file.file_mover.result.MoveResult) - -Method for moving files from source to target directory. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### Versionadded -Added in version 0.8.0. - -* **Parameters:** - **files** - : File list to move. -
- If empty, move files from `source_path` to `target_path`, - applying `filter` and `limit` to each one (if set). -
- If not, move to `target_path` **all** input files, **without** - any filtering and limiting. -* **Returns:** - `MoveResult` - : Move result object -* **Raises:** - `onetl.exception.DirectoryNotFoundError` - : `source_path` does not found - - NotADirectoryError - : `source_path` or `target_path` is not a directory - -### Examples - -Move files from `source_path`: - -```pycon ->>> from onetl.file import FileMover ->>> mover = FileMover(source_path="/source", target_path="/target", ...) ->>> move_result = mover.run() ->>> move_result -MoveResult( - successful=FileSet([ - RemoteFile("/target/file1.txt"), - RemoteFile("/target/file2.txt"), - # directory structure is preserved - RemoteFile("/target/nested/path/file3.txt"), - ]), - failed=FileSet([ - FailedRemoteFile("/source/failed.file"), - ]), - skipped=FileSet([ - RemoteFile("/source/already.exists"), - ]), - missing=FileSet([ - RemotePath("/source/missing.file"), - ]), -) -``` - -Move only certain files from `source_path`: - -```pycon ->>> from onetl.file import FileMover ->>> mover = FileMover(source_path="/source", target_path="/target", ...) ->>> # paths could be relative or absolute, but all should be in "/source" ->>> move_result = mover.run( -... [ -... "/source/file1.txt", -... "/source/nested/path/file3.txt", -... # excluding "/source/file2.txt" -... ] -... ) ->>> move_result -MoveResult( - successful=FileSet([ - RemoteFile("/target/file1.txt"), - # directory structure is preserved - RemoteFile("/target/nested/path/file3.txt"), - ]), - failed=FileSet([]), - skipped=FileSet([]), - missing=FileSet([]), -) -``` - -Move certain files from any folder: - -```pycon ->>> from onetl.file import FileMover ->>> mover = FileMover(target_path="/target", ...) # no source_path set ->>> # only absolute paths ->>> move_result = mover.run( -... [ -... "/remote/file1.txt", -... "/any/nested/path/file3.txt", -... ] -... ) ->>> move_result -MoveResult( - successful=FileSet([ - RemoteFile("/target/file1.txt"), - # directory structure is NOT preserved without source_path - RemoteFile("/target/file3.txt"), - ]), - failed=FileSet([]), - skipped=FileSet([]), - missing=FileSet([]), -) -``` - - - -#### view_files() → FileSet[RemoteFile] - -Get file list in the `source_path`, -after `filter` and `limit` applied (if any). [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### Versionadded -Added in version 0.8.0. - -* **Returns:** - FileSet[RemoteFile] - : Set of files in `source_path`, which will be moved by [`run`](#onetl.file.file_mover.file_mover.FileMover.run) method -* **Raises:** - `onetl.exception.DirectoryNotFoundError` - : `source_path` does not found - - NotADirectoryError - : `source_path` is not a directory - -### Examples - -View files: - -```pycon ->>> from onetl.file import FileMover ->>> mover = FileMover(source_path="/remote", ...) ->>> mover.view_files() -FileSet([ - RemoteFile("/remote/file1.txt"), - RemoteFile("/remote/file2.txt"), - RemoteFile("/remote/nested/path/file3.txt"), -]) -``` - - diff --git a/mddocs/markdown/file/file_mover/index.md b/mddocs/markdown/file/file_mover/index.md deleted file mode 100644 index c8b55c7d0..000000000 --- a/mddocs/markdown/file/file_mover/index.md +++ /dev/null @@ -1,9 +0,0 @@ - - -# File Mover - -# File Mover - -* [File Mover](file_mover.md) -* [File Mover Options](options.md) -* [File Mover Result](result.md) diff --git a/mddocs/markdown/file/file_mover/options.md b/mddocs/markdown/file/file_mover/options.md deleted file mode 100644 index 66e34176a..000000000 --- a/mddocs/markdown/file/file_mover/options.md +++ /dev/null @@ -1,55 +0,0 @@ - - -# File Mover Options - -### *pydantic model* onetl.file.file_mover.options.FileMoverOptions - -File moving options. - -#### Versionadded -Added in version 0.8.0. - -### Examples - -```python -from onetl.file import FileMover - -options = FileMover.Options( - if_exists="replace_entire_directory", - workers=4, -) -``` - - - -#### *field* if_exists *: FileExistBehavior* *= FileExistBehavior.ERROR* *(alias 'mode')* - -How to handle existing files in the local directory. - -Possible values: -: * `error` (default) - mark file as failed - * `ignore` - mark file as skipped - * `replace_file` - replace existing file with a new one - * `replace_entire_directory` - delete directory content before moving files - -#### Versionadded -Added in version 0.8.0. - -#### Versionchanged -Changed in version 0.9.0: Renamed `mode` → `if_exists` - - - -#### *field* workers *: int* *= 1* - -Number of workers to create for parallel file moving. - -1 (default) means files will me moved sequentially. -2 or more means files will be moved in parallel workers. - -Recommended value is `min(32, os.cpu_count() + 4)`, e.g. `5`. - -#### Versionadded -Added in version 0.8.1. - - diff --git a/mddocs/markdown/file/file_mover/result.md b/mddocs/markdown/file/file_mover/result.md deleted file mode 100644 index 815388b8f..000000000 --- a/mddocs/markdown/file/file_mover/result.md +++ /dev/null @@ -1,550 +0,0 @@ - - -# File Mover Result - -### *class* onetl.file.file_mover.result.MoveResult(\*, successful: FileSet[RemoteFile] = None, failed: FileSet[FailedRemoteFile] = None, skipped: FileSet[RemoteFile] = None, missing: FileSet[RemotePath] = None) - -Representation of file move result. - -Container for file paths, divided into certain categories: - -* [`successful`](#onetl.file.file_mover.result.MoveResult.successful) -* [`failed`](#onetl.file.file_mover.result.MoveResult.failed) -* [`skipped`](#onetl.file.file_mover.result.MoveResult.skipped) -* [`missing`](#onetl.file.file_mover.result.MoveResult.missing) - -#### Versionadded -Added in version 0.8.0. - -### Examples - -```pycon ->>> from onetl.file import FileMover ->>> mover = FileMover(local_path="/local", ...) ->>> move_result = mover.run( -... [ -... "/source/file1", -... "/source/file2", -... "/failed/file", -... "/existing/file", -... "/missing/file", -... ] -... ) ->>> move_result -MoveResult( - successful=FileSet([ - RemoteFile("/target/file1"), - RemoteFile("/target/file2"), - ]), - failed=FileSet([ - FailedLocalFile("/failed/file") - ]), - skipped=FileSet([ - RemoteFile("/existing/file") - ]), - missing=FileSet([ - RemotePath("/missing/file") - ]), -) -``` - - - -#### *property* details *: str* - -Return detailed information about files in the result object - -### Examples - -```pycon ->>> from onetl.impl import FailedRemoteFile, LocalPath, RemoteFile, RemotePathStat ->>> from onetl.exception import NotAFileError ->>> from onetl.file.file_result import FileResult ->>> file_result1 = FileResult( -... successful={ -... RemoteFile("/local/file", stats=RemotePathStat(st_size=1024)), -... RemoteFile("/local/another.file", stats=RemotePathStat(st_size=1024)), -... }, -... failed={ -... FailedRemoteFile( -... path="/remote/file1", -... stats=RemotePathStat(st_size=0), -... exception=NotAFileError("'/remote/file1' is not a file"), -... ), -... FailedRemoteFile( -... path="/remote/file2", -... stats=RemotePathStat(st_size=0), -... exception=PermissionError("'/remote/file2': [Errno 13] Permission denied"), -... ), -... }, -... skipped={LocalPath("/skipped/file1"), LocalPath("/skipped/file2")}, -... missing={LocalPath("/missing/file1"), LocalPath("/missing/file2")}, -... ) ->>> print(file_result1.details) -Total: 8 files (size='2.0 kB') - -Successful 2 files (size='2.0 kB'): - '/local/another.file' (size='1.0 kB') - '/local/file' (size='1.0 kB') - -Failed 2 files (size='0 Bytes'): - '/remote/file2' (size='0 Bytes') - PermissionError("'/remote/file2': [Errno 13] Permission denied") - '/remote/file1' (size='0 Bytes') - NotAFileError("'/remote/file1' is not a file") - -Skipped 2 files (size='0 Bytes'): - '/skipped/file1' - '/skipped/file2' - -Missing 2 files: - '/missing/file2' - '/missing/file1' -``` - -```pycon ->>> file_result2 = FileResult() ->>> print(file_result2.details) -No successful files - -No failed files - -No skipped files - -No missing files -``` - - - -#### dict(\*, include: 'AbstractSetIntStr' | 'MappingIntStrAny' | None = None, exclude: 'AbstractSetIntStr' | 'MappingIntStrAny' | None = None, by_alias: bool = False, skip_defaults: bool | None = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny - -Generate a dictionary representation of the model, optionally specifying which fields to include or exclude. - - - -#### *field* failed *: FileSet[FailedRemoteFile]* *[Optional]* - -File paths (remote) which were not moved because of some failure - - - -#### *property* failed_count *: int* - -Get number of failed files - -### Examples - -```pycon ->>> from onetl.impl import RemoteFile ->>> from onetl.file.file_result import FileResult ->>> file_result = FileResult( -... failed={RemoteFile("/some/file"), RemoteFile("/some/another.file")}, -... ) ->>> file_result.failed_count -2 -``` - - - -#### *property* failed_size *: int* - -Get size (in bytes) of failed files - -### Examples - -```pycon ->>> from onetl.impl import RemoteFile, RemotePathStat ->>> from onetl.file.file_result import FileResult ->>> file_result = FileResult( -... failed={ -... RemoteFile("/some/file", stats=RemotePathStat(st_size=1024)), -... RemoteFile("/some/another.file"), stats=RemotePathStat(st_size=1024)), -... }, -... ) ->>> file_result.failed_size # in bytes -2048 -``` - - - -#### *property* is_empty *: bool* - -Returns `True` if there are no files in `successful`, `failed` and `skipped` attributes - -### Examples - -```pycon ->>> from onetl.impl import LocalPath ->>> from onetl.file.file_result import FileResult ->>> file_result1 = FileResult() ->>> file_result1.is_empty -True ->>> file_result2 = FileResult( -... successful={LocalPath("/local/file"), LocalPath("/local/another.file")}, -... ) ->>> file_result2.is_empty -False -``` - - - -#### json(\*, include: 'AbstractSetIntStr' | 'MappingIntStrAny' | None = None, exclude: 'AbstractSetIntStr' | 'MappingIntStrAny' | None = None, by_alias: bool = False, skip_defaults: bool | None = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Callable[[Any], Any] | None = None, models_as_dict: bool = True, \*\*dumps_kwargs: Any) → str - -Generate a JSON representation of the model, include and exclude arguments as per dict(). - -encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps(). - - - -#### *field* missing *: FileSet[RemotePath]* *[Optional]* - -File paths (remote) which are not present in the remote file system - - - -#### *property* missing_count *: int* - -Get number of missing files - -### Examples - -```pycon ->>> from onetl.impl import LocalPath ->>> from onetl.file.file_result import FileResult ->>> file_result = FileResult( -... missing={LocalPath("/some/file"), LocalPath("/some/another.file")}, -... ) ->>> file_result.missing_count -2 -``` - - - -#### raise_if_contains_zero_size() → None - -Raise exception if `successful` attribute contains a file with zero size - -* **Raises:** - ZeroFileSizeError - : `successful` file set contains a file with zero size - -### Examples - -```pycon ->>> from onetl.exception import ZeroFileSizeError ->>> from onetl.impl import LocalPath ->>> from onetl.file.file_result import FileResult ->>> file_result = FileResult( -... successful={ -... LocalPath("/local/empty1.file"), -... LocalPath("/local/empty2.file"), -... LocalPath("/local/normal.file"), -... }, -... ) ->>> file_result.raise_if_contains_zero_size() -Traceback (most recent call last): - ... -onetl.exception.ZeroFileSizeError: 2 files out of 3 have zero size: - '/local/empty1.file' - '/local/empty2.file' -``` - - - -#### raise_if_empty() → None - -Raise exception if there are no files in `successful`, `failed` and `skipped` attributes - -* **Raises:** - EmptyFilesError - : `successful`, `failed` and `skipped` file sets are empty - -### Examples - -```pycon ->>> from onetl.file.file_result import FileResult ->>> file_result = FileResult() ->>> file_result.raise_if_empty() -Traceback (most recent call last): - ... -onetl.exception.EmptyFilesError: There are no files in the result -``` - - - -#### raise_if_failed() → None - -Raise exception if there are some files in `failed` attribute - -* **Raises:** - FailedFilesError - : `failed` file set is not empty - -### Examples - -```pycon ->>> from onetl.impl import FailedRemoteFile, RemotePathStat ->>> from onetl.exception import NotAFileError, FileMissingError ->>> from onetl.file.file_result import FileResult ->>> files_with_exception = [ -... FailedRemoteFile( -... path="/remote/file1", -... stats=RemotePathStat(st_size=0), -... exception=NotAFileError("'/remote/file1' is not a file"), -... ), -... FailedRemoteFile( -... path="/remote/file2", -... stats=RemotePathStat(st_size=0), -... exception=PermissionError("'/remote/file2': [Errno 13] Permission denied"), -... ), -... ] ->>> file_result = FileResult(failed=files_with_exception) ->>> file_result.raise_if_failed() -Traceback (most recent call last) -... -onetl.exception.FailedFilesError: Failed 2 files (size='0 bytes'): - '/remote/file1' (size='0 bytes') - NotAFileError("'/remote/file1' is not a file") - - '/remote/file2' (size='0 Bytes') - PermissionError("'/remote/file2': [Errno 13] Permission denied") -``` - - - -#### raise_if_missing() → None - -Raise exception if there are some files in `missing` attribute - -* **Raises:** - MissingFilesError - : `missing` file set is not empty - -### Examples - -```pycon ->>> from onetl.impl import LocalPath ->>> from onetl.file.file_result import FileResult ->>> file_result = FileResult( -... missing={ -... LocalPath("/missing/file1"), -... LocalPath("/missing/file2"), -... }, -... ) ->>> file_result.raise_if_missing() -Traceback (most recent call last): - ... -onetl.exception.MissingFilesError: Missing 2 files: - '/missing/file1' - '/missing/file2' -``` - - - -#### raise_if_skipped() → None - -Raise exception if there are some files in `skipped` attribute - -* **Raises:** - SkippedFilesError - : `skipped` file set is not empty - -### Examples - -```pycon ->>> from onetl.impl import LocalPath ->>> from onetl.file.file_result import FileResult ->>> file_result = FileResult( -... skipped={ -... LocalPath("/skipped/file1"), -... LocalPath("/skipped/file2"), -... }, -... ) ->>> file_result.raise_if_skipped() -Traceback (most recent call last): - ... -onetl.exception.SkippedFilesError: Skipped 2 files (15 kB): - '/skipped/file1' (10kB) - '/skipped/file2' (5 kB) -``` - - - -#### *field* skipped *: FileSet[RemoteFile]* *[Optional]* - -File paths (remote) which were skipped because of some reason - - - -#### *property* skipped_count *: int* - -Get number of skipped files - -### Examples - -```pycon ->>> from onetl.impl import LocalPath ->>> from onetl.file.file_result import FileResult ->>> file_result = FileResult( -... skipped={LocalPath("/some/file"), LocalPath("/some/another.file")}, -... ) ->>> file_result.skipped_count -2 -``` - - - -#### *property* skipped_size *: int* - -Get size (in bytes) of skipped files - -### Examples - -```pycon ->>> from onetl.impl import LocalPath ->>> from onetl.file.file_result import FileResult ->>> file_result = FileResult( -... skipped={LocalPath("/some/file"), LocalPath("/some/another.file")}, -... ) ->>> file_result.skipped_size # in bytes -1024 -``` - - - -#### *field* successful *: FileSet[RemoteFile]* *[Optional]* - -File paths (local) which were moved successfully - - - -#### *property* successful_count *: int* - -Get number of successful files - -### Examples - -```pycon ->>> from onetl.impl import LocalPath ->>> from onetl.file.file_result import FileResult ->>> file_result = FileResult( -... successful={LocalPath("/some/file"), LocalPath("/some/another.file")}, -... ) ->>> file_result.successful_count -2 -``` - - - -#### *property* successful_size *: int* - -Get size (in bytes) of successful files - -### Examples - -```pycon ->>> from onetl.impl import LocalPath ->>> from onetl.file.file_result import FileResult ->>> file_result = FileResult( -... successful={LocalPath("/some/file"), LocalPath("/some/another.file")}, -... ) ->>> file_result.successful_size # in bytes -1024 -``` - - - -#### *property* summary *: str* - -Return short summary about files in the result object - -### Examples - -```pycon ->>> from onetl.impl import FailedRemoteFile, LocalPath, RemoteFile, RemotePathStat ->>> from onetl.exception import NotAFileError ->>> from onetl.file.file_result import FileResult ->>> file_result1 = FileResult( -... successful={ -... RemoteFile("/local/file", stats=RemotePathStat(st_size=1024)), -... RemoteFile("/local/another.file", stats=RemotePathStat(st_size=1024)), -... }, -... failed={ -... FailedRemoteFile( -... path="/remote/file1", -... stats=RemotePathStat(st_size=0), -... exception=NotAFileError("'/remote/file1' is not a file"), -... ), -... FailedRemoteFile( -... path="/remote/file2", -... stats=RemotePathStat(st_size=0), -... exception=PermissionError("'/remote/file2': [Errno 13] Permission denied"), -... ), -... }, -... skipped={LocalPath("/skipped/file1"), LocalPath("/skipped/file2")}, -... missing={LocalPath("/missing/file1"), LocalPath("/missing/file2")}, -... ) ->>> print(file_result1.summary) -Total: 8 files (size='2.0 kB') - -Successful: 2 files (size='2.0 kB') - -Failed: 2 files (size='0 Bytes') - -Skipped: 2 files (size='0 Bytes') - -Missing: 2 files -``` - -```pycon ->>> file_result2 = FileResult() ->>> print(file_result2.summary) -No files -``` - - - -#### *property* total_count *: int* - -Get total number of all files - -### Examples - -```pycon ->>> from onetl.impl import RemoteFile ->>> from onetl.file.file_result import FileResult ->>> file_result = FileResult( -... successful={LocalPath("/local/file"), LocalPath("/local/another.file")}, -... failed={RemoteFile("/remote/file"), RemoteFile("/remote/another.file")}, -... skipped={LocalPath("/skipped/file")}, -... missing={LocalPath("/missing/file")}, -... ) ->>> file_result.total_count -6 -``` - - - -#### *property* total_size *: int* - -Get total size (in bytes) of all files - -### Examples - -```pycon ->>> from onetl.impl import RemoteFile, RemotePathStat, LocalPath ->>> from onetl.file.file_result import FileResult ->>> file_result = FileResult( -... successful={LocalPath("/local/file"), LocalPath("/local/another.file")}, -... failed={ -... RemoteFile("/remote/file", stats=RemotePathStat(st_size=1024)), -... RemoteFile("/remote/another.file", stats=RemotePathStat(st_size=1024)) -... }, -... skipped={LocalPath("/skipped/file")}, -... missing={LocalPath("/missing/file")}, -... ) ->>> file_result.total_size # in bytes -4096 -``` - - diff --git a/mddocs/markdown/file/file_uploader/file_uploader.md b/mddocs/markdown/file/file_uploader/file_uploader.md deleted file mode 100644 index 98ebe3165..000000000 --- a/mddocs/markdown/file/file_uploader/file_uploader.md +++ /dev/null @@ -1,241 +0,0 @@ - - -# File Uploader - -| [`FileUploader`](#onetl.file.file_uploader.file_uploader.FileUploader) | Allows you to upload files to a remote source with specified file connection and parameters, and return an object with upload result summary. | -|------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------| -| [`FileUploader.run`](#onetl.file.file_uploader.file_uploader.FileUploader.run)([files]) | Method for uploading files to remote host. | -| [`FileUploader.view_files`](#onetl.file.file_uploader.file_uploader.FileUploader.view_files)() | Get file list in the `local_path`. | - -### *class* onetl.file.file_uploader.file_uploader.FileUploader(\*, connection: ~onetl.base.base_file_connection.BaseFileConnection, target_path: ~onetl.impl.remote_path.RemotePath, local_path: ~onetl.impl.local_path.LocalPath | None = None, temp_path: ~onetl.impl.remote_path.RemotePath | None = None, options: ~onetl.file.file_uploader.options.FileUploaderOptions = FileUploaderOptions(if_exists=, delete_local=False, workers=1)) - -Allows you to upload files to a remote source with specified file connection -and parameters, and return an object with upload result summary. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### NOTE -This class is used to upload files **only** from local directory to the remote one. - -It does NOT support direct file transfer between filesystems, like `FTP -> SFTP`. -You should use [File Downloader](../file_downloader/file_downloader.md#file-downloader) + FileUploader to implement `FTP -> local dir -> SFTP`. - -#### WARNING -This class does **not** support read strategies. - -#### Versionadded -Added in version 0.1.0. - -#### Versionchanged -Changed in version 0.8.0: Moved `onetl.core.FileDownloader` → `onetl.file.FileDownloader` - -* **Parameters:** - **connection** - : Class which contains File system connection properties. See [File Connections](../../connection/file_connection/index.md#file-connections) section. - - **target_path** - : Remote path where want you upload files to - - **local_path** - : The local directory from which the data is loaded. -
- Could be `None`, but only if you pass absolute file paths directly to - [`run`](#onetl.file.file_uploader.file_uploader.FileUploader.run) method -
- #### Versionadded - Added in version 0.3.0. - - **temp_path** - : If set, this path will be used for uploading a file, and then renaming it to the target file path. - If `None` (default since v0.5.0) is passed, files are uploaded directly to `target_path`. -
- #### WARNING - In case of production ETL pipelines, please set a value for `temp_path` (NOT `None`). - This allows to properly handle upload interruption, - without creating half-uploaded files in the target, - because unlike file upload, `rename` call is atomic. -
- #### WARNING - In case of connections like SFTP or FTP, which can have multiple underlying filesystems, - please pass `temp_path` path on the SAME filesystem as `target_path`. - Otherwise instead of `rename`, remote OS will move file between filesystems, - which is NOT atomic operation. -
- #### Versionchanged - Changed in version 0.5.0: Default changed from `/tmp` to `None` - - **options** - : File upload options. See [`FileUploader.Options`](options.md#onetl.file.file_uploader.options.FileUploaderOptions) - -### Examples - -Minimal example - -```py -from onetl.connection import HDFS -from onetl.file import FileUploader - -hdfs = HDFS(...) - -uploader = FileUploader( - connection=hdfs, - target_path="/path/to/remote/source", -) -``` - -Full example - -```py -from onetl.connection import HDFS -from onetl.file import FileUploader - -hdfs = HDFS(...) - -uploader = FileUploader( - connection=hdfs, - target_path="/path/to/remote/source", - temp_path="/user/onetl", - local_path="/some/local/directory", - options=FileUploader.Options(delete_local=True, if_exists="overwrite"), -) -``` - - - -#### run(files: Iterable[str | PathLike] | None = None) → [UploadResult](result.md#onetl.file.file_uploader.result.UploadResult) - -Method for uploading files to remote host. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### Versionadded -Added in version 0.1.0. - -* **Parameters:** - **files** - : File list to upload. -
- If empty, upload files from `local_path`. -* **Returns:** - `UploadResult` - : Upload result object -* **Raises:** - `onetl.exception.DirectoryNotFoundError` - : `local_path` does not found - - NotADirectoryError - : `local_path` is not a directory - - ValueError - : File in `files` argument does not match `local_path` - -### Examples - -Upload files from `local_path` to `target_path`: - -```pycon ->>> from onetl.file import FileUploader ->>> uploader = FileUploader(local_path="/local", target_path="/remote", ...) ->>> upload_result = uploader.run() ->>> upload_result -UploadResult( - successful=FileSet([ - RemoteFile("/remote/file1"), - RemoteFile("/remote/file2"), - # directory structure is preserved - RemoteFile("/remote/nested/path/file3") - ]), - failed=FileSet([ - FailedLocalFile("/local/failed.file"), - ]), - skipped=FileSet([ - LocalPath("/local/already.exists"), - ]), - missing=FileSet([ - LocalPath("/local/missing.file"), - ]), -) -``` - -Upload only certain files from `local_path`: - -```pycon ->>> from onetl.file import FileUploader ->>> uploader = FileUploader(local_path="/local", target_path="/remote", ...) ->>> # paths could be relative or absolute, but all should be in "/local" ->>> upload_result = uploader.run( -... [ -... "/local/file1", -... "/local/nested/path/file3", -... # excluding "/local/file2", -... ] -... ) ->>> upload_result -UploadResult( - successful=FileSet([ - RemoteFile("/remote/file1"), - # directory structure is preserved - RemoteFile("/remote/nested/path/file3"), - ]), - failed=FileSet([]), - skipped=FileSet([]), - missing=FileSet([]), -) -``` - -Upload only certain files from any folder: - -```pycon ->>> from onetl.file import FileUploader ->>> uploader = FileUploader(target_path="/remote", ...) # no local_path set ->>> # only absolute paths ->>> upload_result = uploader.run( -... [ -... "/local/file1.txt", -... "/any/nested/path/file3.txt", -... ] -... ) ->>> upload_result -UploadResult( - successful=FileSet([ - RemoteFile("/remote/file1.txt"), - # directory structure is NOT preserved without local_path - RemoteFile("/remote/file3.txt"), - ]), - failed=FileSet([]), - skipped=FileSet([]), - missing=FileSet([]), -) -``` - - - -#### view_files() → FileSet[LocalPath] - -Get file list in the `local_path`. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### Versionadded -Added in version 0.3.0. - -* **Returns:** - FileSet[LocalPath] - : Set of files in `local_path` -* **Raises:** - `onetl.exception.DirectoryNotFoundError` - : `local_path` does not found - - NotADirectoryError - : `local_path` is not a directory - -### Examples - -View files: - -```pycon ->>> from onetl.file import FileUploader ->>> uploader = FileUploader(local_path="/local", ...) ->>> uploader.view_files() -FileSet([ - LocalPath("/local/file1.txt"), - LocalPath("/local/file3.txt"), - LocalPath("/local/nested/path/file3.txt"), -]) -``` - - diff --git a/mddocs/markdown/file/file_uploader/index.md b/mddocs/markdown/file/file_uploader/index.md deleted file mode 100644 index e11581a9c..000000000 --- a/mddocs/markdown/file/file_uploader/index.md +++ /dev/null @@ -1,9 +0,0 @@ - - -# File Uploader - -# File Uploader - -* [File Uploader](file_uploader.md) -* [File Uploader Options](options.md) -* [File Uploader Result](result.md) diff --git a/mddocs/markdown/file/file_uploader/options.md b/mddocs/markdown/file/file_uploader/options.md deleted file mode 100644 index a32387501..000000000 --- a/mddocs/markdown/file/file_uploader/options.md +++ /dev/null @@ -1,67 +0,0 @@ - - -# File Uploader Options - -### *pydantic model* onetl.file.file_uploader.options.FileUploaderOptions - -File uploading options. - -#### Versionadded -Added in version 0.3.0. - -### Examples - -```python -from onetl.file import FileUploader - -options = FileUploader.Options( - if_exists="replace_entire_directory", - delete_local=True, - workers=4, -) -``` - - - -#### *field* if_exists *: FileExistBehavior* *= FileExistBehavior.ERROR* *(alias 'mode')* - -How to handle existing files in the target directory. - -Possible values: -: * `error` (default) - mark file as failed - * `ignore` - mark file as skipped - * `replace_file` - replace existing file with a new one - * `replace_entire_directory` - delete local directory content before downloading files - -#### Versionchanged -Changed in version 0.9.0: Renamed `mode` → `if_exists` - - - -#### *field* delete_local *: bool* *= False* - -If `True`, remove local file after successful download. - -If download failed, file will left intact. - -#### Versionadded -Added in version 0.2.0. - -#### Versionchanged -Changed in version 0.3.0: Move `FileUploader.delete_local` to `FileUploaderOptions` - - - -#### *field* workers *: int* *= 1* - -Number of workers to create for parallel file upload. - -1 (default) means files will me uploaded sequentially. -2 or more means files will be uploaded in parallel workers. - -Recommended value is `min(32, os.cpu_count() + 4)`, e.g. `5`. - -#### Versionadded -Added in version 0.8.1. - - diff --git a/mddocs/markdown/file/file_uploader/result.md b/mddocs/markdown/file/file_uploader/result.md deleted file mode 100644 index 59c289bea..000000000 --- a/mddocs/markdown/file/file_uploader/result.md +++ /dev/null @@ -1,550 +0,0 @@ - - -# File Uploader Result - -### *class* onetl.file.file_uploader.result.UploadResult(\*, successful: FileSet[RemoteFile] = None, failed: FileSet[FailedLocalFile] = None, skipped: FileSet[LocalPath] = None, missing: FileSet[LocalPath] = None) - -Representation of file upload result. - -Container for file paths, divided into certain categories: - -* [`successful`](#onetl.file.file_uploader.result.UploadResult.successful) -* [`failed`](#onetl.file.file_uploader.result.UploadResult.failed) -* [`skipped`](#onetl.file.file_uploader.result.UploadResult.skipped) -* [`missing`](#onetl.file.file_uploader.result.UploadResult.missing) - -#### Versionadded -Added in version 0.3.0. - -### Examples - -```pycon ->>> from onetl.file import FileUploader ->>> uploader = FileUploader(target_path="/remote", ...) ->>> upload_result = uploader.run( -... [ -... "/local/file1", -... "/local/file2", -... "/failed/file", -... "/existing/file", -... "/missing/file", -... ] -... ) ->>> upload_result -UploadResult( - successful=FileSet([ - RemoteFile("/remote/file1"), - RemoteFile("/remote/file2"), - ]), - failed=FileSet([ - FailedLocalFile("/failed/file") - ]), - skipped=FileSet([ - LocalPath("/existing/file") - ]), - missing=FileSet([ - LocalPath("/missing/file") - ]), -) -``` - - - -#### *property* details *: str* - -Return detailed information about files in the result object - -### Examples - -```pycon ->>> from onetl.impl import FailedRemoteFile, LocalPath, RemoteFile, RemotePathStat ->>> from onetl.exception import NotAFileError ->>> from onetl.file.file_result import FileResult ->>> file_result1 = FileResult( -... successful={ -... RemoteFile("/local/file", stats=RemotePathStat(st_size=1024)), -... RemoteFile("/local/another.file", stats=RemotePathStat(st_size=1024)), -... }, -... failed={ -... FailedRemoteFile( -... path="/remote/file1", -... stats=RemotePathStat(st_size=0), -... exception=NotAFileError("'/remote/file1' is not a file"), -... ), -... FailedRemoteFile( -... path="/remote/file2", -... stats=RemotePathStat(st_size=0), -... exception=PermissionError("'/remote/file2': [Errno 13] Permission denied"), -... ), -... }, -... skipped={LocalPath("/skipped/file1"), LocalPath("/skipped/file2")}, -... missing={LocalPath("/missing/file1"), LocalPath("/missing/file2")}, -... ) ->>> print(file_result1.details) -Total: 8 files (size='2.0 kB') - -Successful 2 files (size='2.0 kB'): - '/local/another.file' (size='1.0 kB') - '/local/file' (size='1.0 kB') - -Failed 2 files (size='0 Bytes'): - '/remote/file2' (size='0 Bytes') - PermissionError("'/remote/file2': [Errno 13] Permission denied") - '/remote/file1' (size='0 Bytes') - NotAFileError("'/remote/file1' is not a file") - -Skipped 2 files (size='0 Bytes'): - '/skipped/file1' - '/skipped/file2' - -Missing 2 files: - '/missing/file2' - '/missing/file1' -``` - -```pycon ->>> file_result2 = FileResult() ->>> print(file_result2.details) -No successful files - -No failed files - -No skipped files - -No missing files -``` - - - -#### dict(\*, include: 'AbstractSetIntStr' | 'MappingIntStrAny' | None = None, exclude: 'AbstractSetIntStr' | 'MappingIntStrAny' | None = None, by_alias: bool = False, skip_defaults: bool | None = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny - -Generate a dictionary representation of the model, optionally specifying which fields to include or exclude. - - - -#### *field* failed *: FileSet[FailedLocalFile]* *[Optional]* - -File paths (local) which were not uploaded because of some failure - - - -#### *property* failed_count *: int* - -Get number of failed files - -### Examples - -```pycon ->>> from onetl.impl import RemoteFile ->>> from onetl.file.file_result import FileResult ->>> file_result = FileResult( -... failed={RemoteFile("/some/file"), RemoteFile("/some/another.file")}, -... ) ->>> file_result.failed_count -2 -``` - - - -#### *property* failed_size *: int* - -Get size (in bytes) of failed files - -### Examples - -```pycon ->>> from onetl.impl import RemoteFile, RemotePathStat ->>> from onetl.file.file_result import FileResult ->>> file_result = FileResult( -... failed={ -... RemoteFile("/some/file", stats=RemotePathStat(st_size=1024)), -... RemoteFile("/some/another.file"), stats=RemotePathStat(st_size=1024)), -... }, -... ) ->>> file_result.failed_size # in bytes -2048 -``` - - - -#### *property* is_empty *: bool* - -Returns `True` if there are no files in `successful`, `failed` and `skipped` attributes - -### Examples - -```pycon ->>> from onetl.impl import LocalPath ->>> from onetl.file.file_result import FileResult ->>> file_result1 = FileResult() ->>> file_result1.is_empty -True ->>> file_result2 = FileResult( -... successful={LocalPath("/local/file"), LocalPath("/local/another.file")}, -... ) ->>> file_result2.is_empty -False -``` - - - -#### json(\*, include: 'AbstractSetIntStr' | 'MappingIntStrAny' | None = None, exclude: 'AbstractSetIntStr' | 'MappingIntStrAny' | None = None, by_alias: bool = False, skip_defaults: bool | None = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Callable[[Any], Any] | None = None, models_as_dict: bool = True, \*\*dumps_kwargs: Any) → str - -Generate a JSON representation of the model, include and exclude arguments as per dict(). - -encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps(). - - - -#### *field* missing *: FileSet[LocalPath]* *[Optional]* - -File paths (local) which are not present in the local file system - - - -#### *property* missing_count *: int* - -Get number of missing files - -### Examples - -```pycon ->>> from onetl.impl import LocalPath ->>> from onetl.file.file_result import FileResult ->>> file_result = FileResult( -... missing={LocalPath("/some/file"), LocalPath("/some/another.file")}, -... ) ->>> file_result.missing_count -2 -``` - - - -#### raise_if_contains_zero_size() → None - -Raise exception if `successful` attribute contains a file with zero size - -* **Raises:** - ZeroFileSizeError - : `successful` file set contains a file with zero size - -### Examples - -```pycon ->>> from onetl.exception import ZeroFileSizeError ->>> from onetl.impl import LocalPath ->>> from onetl.file.file_result import FileResult ->>> file_result = FileResult( -... successful={ -... LocalPath("/local/empty1.file"), -... LocalPath("/local/empty2.file"), -... LocalPath("/local/normal.file"), -... }, -... ) ->>> file_result.raise_if_contains_zero_size() -Traceback (most recent call last): - ... -onetl.exception.ZeroFileSizeError: 2 files out of 3 have zero size: - '/local/empty1.file' - '/local/empty2.file' -``` - - - -#### raise_if_empty() → None - -Raise exception if there are no files in `successful`, `failed` and `skipped` attributes - -* **Raises:** - EmptyFilesError - : `successful`, `failed` and `skipped` file sets are empty - -### Examples - -```pycon ->>> from onetl.file.file_result import FileResult ->>> file_result = FileResult() ->>> file_result.raise_if_empty() -Traceback (most recent call last): - ... -onetl.exception.EmptyFilesError: There are no files in the result -``` - - - -#### raise_if_failed() → None - -Raise exception if there are some files in `failed` attribute - -* **Raises:** - FailedFilesError - : `failed` file set is not empty - -### Examples - -```pycon ->>> from onetl.impl import FailedRemoteFile, RemotePathStat ->>> from onetl.exception import NotAFileError, FileMissingError ->>> from onetl.file.file_result import FileResult ->>> files_with_exception = [ -... FailedRemoteFile( -... path="/remote/file1", -... stats=RemotePathStat(st_size=0), -... exception=NotAFileError("'/remote/file1' is not a file"), -... ), -... FailedRemoteFile( -... path="/remote/file2", -... stats=RemotePathStat(st_size=0), -... exception=PermissionError("'/remote/file2': [Errno 13] Permission denied"), -... ), -... ] ->>> file_result = FileResult(failed=files_with_exception) ->>> file_result.raise_if_failed() -Traceback (most recent call last) -... -onetl.exception.FailedFilesError: Failed 2 files (size='0 bytes'): - '/remote/file1' (size='0 bytes') - NotAFileError("'/remote/file1' is not a file") - - '/remote/file2' (size='0 Bytes') - PermissionError("'/remote/file2': [Errno 13] Permission denied") -``` - - - -#### raise_if_missing() → None - -Raise exception if there are some files in `missing` attribute - -* **Raises:** - MissingFilesError - : `missing` file set is not empty - -### Examples - -```pycon ->>> from onetl.impl import LocalPath ->>> from onetl.file.file_result import FileResult ->>> file_result = FileResult( -... missing={ -... LocalPath("/missing/file1"), -... LocalPath("/missing/file2"), -... }, -... ) ->>> file_result.raise_if_missing() -Traceback (most recent call last): - ... -onetl.exception.MissingFilesError: Missing 2 files: - '/missing/file1' - '/missing/file2' -``` - - - -#### raise_if_skipped() → None - -Raise exception if there are some files in `skipped` attribute - -* **Raises:** - SkippedFilesError - : `skipped` file set is not empty - -### Examples - -```pycon ->>> from onetl.impl import LocalPath ->>> from onetl.file.file_result import FileResult ->>> file_result = FileResult( -... skipped={ -... LocalPath("/skipped/file1"), -... LocalPath("/skipped/file2"), -... }, -... ) ->>> file_result.raise_if_skipped() -Traceback (most recent call last): - ... -onetl.exception.SkippedFilesError: Skipped 2 files (15 kB): - '/skipped/file1' (10kB) - '/skipped/file2' (5 kB) -``` - - - -#### *field* skipped *: FileSet[LocalPath]* *[Optional]* - -File paths (local) which were skipped because of some reason - - - -#### *property* skipped_count *: int* - -Get number of skipped files - -### Examples - -```pycon ->>> from onetl.impl import LocalPath ->>> from onetl.file.file_result import FileResult ->>> file_result = FileResult( -... skipped={LocalPath("/some/file"), LocalPath("/some/another.file")}, -... ) ->>> file_result.skipped_count -2 -``` - - - -#### *property* skipped_size *: int* - -Get size (in bytes) of skipped files - -### Examples - -```pycon ->>> from onetl.impl import LocalPath ->>> from onetl.file.file_result import FileResult ->>> file_result = FileResult( -... skipped={LocalPath("/some/file"), LocalPath("/some/another.file")}, -... ) ->>> file_result.skipped_size # in bytes -1024 -``` - - - -#### *field* successful *: FileSet[RemoteFile]* *[Optional]* - -File paths (remote) which were uploaded successfully - - - -#### *property* successful_count *: int* - -Get number of successful files - -### Examples - -```pycon ->>> from onetl.impl import LocalPath ->>> from onetl.file.file_result import FileResult ->>> file_result = FileResult( -... successful={LocalPath("/some/file"), LocalPath("/some/another.file")}, -... ) ->>> file_result.successful_count -2 -``` - - - -#### *property* successful_size *: int* - -Get size (in bytes) of successful files - -### Examples - -```pycon ->>> from onetl.impl import LocalPath ->>> from onetl.file.file_result import FileResult ->>> file_result = FileResult( -... successful={LocalPath("/some/file"), LocalPath("/some/another.file")}, -... ) ->>> file_result.successful_size # in bytes -1024 -``` - - - -#### *property* summary *: str* - -Return short summary about files in the result object - -### Examples - -```pycon ->>> from onetl.impl import FailedRemoteFile, LocalPath, RemoteFile, RemotePathStat ->>> from onetl.exception import NotAFileError ->>> from onetl.file.file_result import FileResult ->>> file_result1 = FileResult( -... successful={ -... RemoteFile("/local/file", stats=RemotePathStat(st_size=1024)), -... RemoteFile("/local/another.file", stats=RemotePathStat(st_size=1024)), -... }, -... failed={ -... FailedRemoteFile( -... path="/remote/file1", -... stats=RemotePathStat(st_size=0), -... exception=NotAFileError("'/remote/file1' is not a file"), -... ), -... FailedRemoteFile( -... path="/remote/file2", -... stats=RemotePathStat(st_size=0), -... exception=PermissionError("'/remote/file2': [Errno 13] Permission denied"), -... ), -... }, -... skipped={LocalPath("/skipped/file1"), LocalPath("/skipped/file2")}, -... missing={LocalPath("/missing/file1"), LocalPath("/missing/file2")}, -... ) ->>> print(file_result1.summary) -Total: 8 files (size='2.0 kB') - -Successful: 2 files (size='2.0 kB') - -Failed: 2 files (size='0 Bytes') - -Skipped: 2 files (size='0 Bytes') - -Missing: 2 files -``` - -```pycon ->>> file_result2 = FileResult() ->>> print(file_result2.summary) -No files -``` - - - -#### *property* total_count *: int* - -Get total number of all files - -### Examples - -```pycon ->>> from onetl.impl import RemoteFile ->>> from onetl.file.file_result import FileResult ->>> file_result = FileResult( -... successful={LocalPath("/local/file"), LocalPath("/local/another.file")}, -... failed={RemoteFile("/remote/file"), RemoteFile("/remote/another.file")}, -... skipped={LocalPath("/skipped/file")}, -... missing={LocalPath("/missing/file")}, -... ) ->>> file_result.total_count -6 -``` - - - -#### *property* total_size *: int* - -Get total size (in bytes) of all files - -### Examples - -```pycon ->>> from onetl.impl import RemoteFile, RemotePathStat, LocalPath ->>> from onetl.file.file_result import FileResult ->>> file_result = FileResult( -... successful={LocalPath("/local/file"), LocalPath("/local/another.file")}, -... failed={ -... RemoteFile("/remote/file", stats=RemotePathStat(st_size=1024)), -... RemoteFile("/remote/another.file", stats=RemotePathStat(st_size=1024)) -... }, -... skipped={LocalPath("/skipped/file")}, -... missing={LocalPath("/missing/file")}, -... ) ->>> file_result.total_size # in bytes -4096 -``` - - diff --git a/mddocs/markdown/file/index.md b/mddocs/markdown/file/index.md deleted file mode 100644 index 2da812bab..000000000 --- a/mddocs/markdown/file/index.md +++ /dev/null @@ -1,9 +0,0 @@ - - - File classes - -* [File Downloader](file_downloader/index.md) -* [File Uploader](file_uploader/index.md) -* [File Mover](file_mover/index.md) -* [File Filters](file_filters/index.md) -* [File Limits](file_limits/index.md) diff --git a/mddocs/markdown/file_df/file_df_reader/file_df_reader.md b/mddocs/markdown/file_df/file_df_reader/file_df_reader.md deleted file mode 100644 index 256ab7b59..000000000 --- a/mddocs/markdown/file_df/file_df_reader/file_df_reader.md +++ /dev/null @@ -1,158 +0,0 @@ - - -# FileDF Reader - -### *class* onetl.file.file_df_reader.file_df_reader.FileDFReader(\*, connection: [BaseFileDFConnection](../../connection/file_df_connection/base.md#onetl.base.base_file_df_connection.BaseFileDFConnection), format: [BaseReadableFileFormat](../file_formats/base.md#onetl.base.base_file_format.BaseReadableFileFormat), source_path: PurePathProtocol | None = None, df_schema: StructType | None = None, options: [FileDFReaderOptions](options.md#onetl.file.file_df_reader.options.FileDFReaderOptions) = FileDFReaderOptions(recursive=None)) - -Allows you to read files from a source path with specified file connection -and parameters, and return a Spark DataFrame. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### WARNING -This class does **not** support read strategies. - -#### Versionadded -Added in version 0.9.0. - -* **Parameters:** - **connection** - : File DataFrame connection. See [File DataFrame Connections](../../connection/file_df_connection/index.md#file-df-connections) section. - - **format** - : File format to read. - - **source_path** - : Directory path to read data from. -
- Could be `None`, but only if you pass file paths directly to - [`run`](#onetl.file.file_df_reader.file_df_reader.FileDFReader.run) method - - **df_schema** - : Spark DataFrame schema. - - **options** - : Common reading options. - -### Examples - -Read CSV files from local filesystem - -```py -from onetl.connection import SparkLocalFS -from onetl.file import FileDFReader -from onetl.file.format import CSV - -csv = CSV(delimiter=",") -local_fs = SparkLocalFS(spark=spark) - -reader = FileDFReader( - connection=local_fs, - format=csv, - source_path="/path/to/directory", -) -``` - -All supported options - -```py -from onetl.connection import SparkLocalFS -from onetl.file import FileDFReader -from onetl.file.format import CSV - -csv = CSV(delimiter=",") -local_fs = SparkLocalFS(spark=spark) - -reader = FileDFReader( - connection=local_fs, - format=csv, - source_path="/path/to/directory", - options=FileDFReader.Options(recursive=False), -) -``` - - - -#### run(files: Iterable[str | os.PathLike] | None = None) → DataFrame - -Method for reading files as DataFrame. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### Versionadded -Added in version 0.9.0. - -* **Parameters:** - **files** - : File list to read. -
- If empty, read files from `source_path`. -* **Returns:** - **df** - : Spark DataFrame - -### Examples - -Read CSV files from directory `/path`: - -```python -from onetl.connection import SparkLocalFS -from onetl.file import FileDFReader -from onetl.file.format import CSV - -csv = CSV(delimiter=",") -local_fs = SparkLocalFS(spark=spark) - -reader = FileDFReader( - connection=local_fs, - format=csv, - source_path="/path", -) -df = reader.run() -``` - -Read some CSV files using file paths: - -```python -from onetl.connection import SparkLocalFS -from onetl.file import FileDFReader -from onetl.file.format import CSV - -csv = CSV(delimiter=",") -local_fs = SparkLocalFS(spark=spark) - -reader = FileDFReader( - connection=local_fs, - format=csv, -) - -df = reader.run( - [ - "/path/file1.csv", - "/path/nested/file2.csv", - ] -) -``` - -Read only specific CSV files in directory: - -```python -from onetl.connection import SparkLocalFS -from onetl.file import FileDFReader -from onetl.file.format import CSV - -csv = CSV(delimiter=",") -local_fs = SparkLocalFS(spark=spark) - -reader = FileDFReader( - connection=local_fs, - format=csv, - source_path="/path", -) - -df = reader.run( - [ - # file paths could be relative - "/path/file1.csv", - "/path/nested/file2.csv", - ] -) -``` - - diff --git a/mddocs/markdown/file_df/file_df_reader/index.md b/mddocs/markdown/file_df/file_df_reader/index.md deleted file mode 100644 index 0087e74ac..000000000 --- a/mddocs/markdown/file_df/file_df_reader/index.md +++ /dev/null @@ -1,8 +0,0 @@ - - -# FileDF Reader - -# FileDF Reader - -* [FileDF Reader](file_df_reader.md) -* [Options](options.md) diff --git a/mddocs/markdown/file_df/file_df_reader/options.md b/mddocs/markdown/file_df/file_df_reader/options.md deleted file mode 100644 index eceb33e5d..000000000 --- a/mddocs/markdown/file_df/file_df_reader/options.md +++ /dev/null @@ -1,38 +0,0 @@ - - -# Options - -### *class* onetl.file.file_df_reader.options.FileDFReaderOptions(\*, recursiveFileLookup: bool | None = None, \*\*kwargs) - -Options for [`FileDFReader`](file_df_reader.md#onetl.file.file_df_reader.file_df_reader.FileDFReader). - -#### Versionadded -Added in version 0.9.0. - -### Examples - -#### NOTE -You can pass any value [supported by Spark](https://spark.apache.org/docs/latest/sql-data-sources-load-save-functions.html), -even if it is not mentioned in this documentation. **Option names should be in** `camelCase`! - -The set of supported options depends on Spark version. - -```python -from onetl.file import FileDFReader - -options = FileDFReader.Options(recursive=True) -``` - - - -#### *field* recursive *: bool | None* *= None* *(alias 'recursiveFileLookup')* - -If `True`, perform recursive file lookup. - -#### WARNING -This disables partition inferring using file paths. - -#### WARNING -Can be used only in Spark 3+. See [SPARK-27990](https://issues.apache.org/jira/browse/SPARK-27990). - - diff --git a/mddocs/markdown/file_df/file_df_writer/file_df_writer.md b/mddocs/markdown/file_df/file_df_writer/file_df_writer.md deleted file mode 100644 index 8f845c598..000000000 --- a/mddocs/markdown/file_df/file_df_writer/file_df_writer.md +++ /dev/null @@ -1,80 +0,0 @@ - - -# FileDF Writer - -### *class* onetl.file.file_df_writer.file_df_writer.FileDFWriter(\*, connection: ~onetl.base.base_file_df_connection.BaseFileDFConnection, format: ~onetl.base.base_file_format.BaseWritableFileFormat, target_path: ~onetl.base.pure_path_protocol.PurePathProtocol, options: ~onetl.file.file_df_writer.options.FileDFWriterOptions = FileDFWriterOptions(if_exists=, partition_by=None)) - -Allows you to write Spark DataFrame as files in a target path of specified file connection -with parameters. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -* **Parameters:** - **connection** - : File DataFrame connection. See [File DataFrame Connections](../../connection/file_df_connection/index.md#file-df-connections) section. - - **format** - : File format to write. - - **target_path** - : Directory path to write data to. - - **options** - : Common writing options. - -### Examples - -Write CSV files to local filesystem - -```py -from onetl.connection import SparkLocalFS -from onetl.file import FileDFWriter -from onetl.file.format import CSV - -local_fs = SparkLocalFS(spark=spark) - -writer = FileDFWriter( - connection=local_fs, - format=CSV(delimiter=","), - target_path="/path/to/directory", -) -``` - -All supported options - -```py -from onetl.connection import SparkLocalFS -from onetl.file import FileDFWriter -from onetl.file.format import CSV - -csv = CSV(delimiter=",") -local_fs = SparkLocalFS(spark=spark) - -writer = FileDFWriter( - connection=local_fs, - format=csv, - target_path="/path/to/directory", - options=FileDFWriter.Options(if_exists="replace_entire_directory"), -) -``` - - - -#### run(df: DataFrame) → None - -Method for writing DataFrame as files. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### NOTE -Method does support only **batching** DataFrames. - -* **Parameters:** - **df** - : Spark dataframe - -### Examples - -Write df to target: - -```python -writer.run(df) -``` - - diff --git a/mddocs/markdown/file_df/file_df_writer/index.md b/mddocs/markdown/file_df/file_df_writer/index.md deleted file mode 100644 index 9813ed1d4..000000000 --- a/mddocs/markdown/file_df/file_df_writer/index.md +++ /dev/null @@ -1,8 +0,0 @@ - - -# FileDF Writer - -# FileDF Writer - -* [FileDF Writer](file_df_writer.md) -* [Options](options.md) diff --git a/mddocs/markdown/file_df/file_df_writer/options.md b/mddocs/markdown/file_df/file_df_writer/options.md deleted file mode 100644 index 7a76fecbc..000000000 --- a/mddocs/markdown/file_df/file_df_writer/options.md +++ /dev/null @@ -1,140 +0,0 @@ - - -# Options - -### *class* onetl.file.file_df_writer.options.FileDFWriterOptions(\*, if_exists: FileDFExistBehavior = FileDFExistBehavior.APPEND, partitionBy: List[str] | str | None = None, \*\*kwargs) - -Options for [`FileDFWriter`](file_df_writer.md#onetl.file.file_df_writer.file_df_writer.FileDFWriter). - -#### Versionadded -Added in version 0.9.0. - -### Examples - -#### NOTE -You can pass any value [supported by Spark](https://spark.apache.org/docs/latest/sql-data-sources-load-save-functions.html), -even if it is not mentioned in this documentation. **Option names should be in** `camelCase`! - -The set of supported options depends on Spark version. - -```python -from onetl.file import FileDFWriter - -options = FileDFWriter.Options( - if_exists="replace_overlapping_partitions", - partitionBy="month", -) -``` - - - -#### *field* if_exists *: FileDFExistBehavior* *= FileDFExistBehavior.APPEND* - -Behavior for existing target directory. - -If target directory does not exist, it will be created. -But if it does exist, then behavior is different for each value. - -#### Versionchanged -Changed in version 0.13.0: Default value was changed from `error` to `append` - -Possible values: -: * `error` - : If folder already exists, raise an exception. -
- Same as Spark’s `df.write.mode("error").save()`. - * `skip_entire_directory` - : If folder already exists, left existing files intact and stop immediately without any errors. -
- Same as Spark’s `df.write.mode("ignore").save()`. - * `append` (default) - : Appends data into existing directory. -
- ### Behavior in details -
- * Directory does not exist - : Directory is created using all the provided options (`format`, `partition_by`, etc). - * Directory exists, does not contain partitions, but [`partition_by`](#onetl.file.file_df_writer.options.FileDFWriterOptions.partition_by) is set - : Data is appended to a directory, but to partitioned directory structure. -
- #### WARNING - Existing files still present in the root of directory, but Spark will ignore those files while reading, - unless using `recursive=True`. - * Directory exists and contains partitions, but [`partition_by`](#onetl.file.file_df_writer.options.FileDFWriterOptions.partition_by) is not set - : Data is appended to a directory, but to the root of directory instead of nested partition directories. -
- #### WARNING - Spark will ignore such files while reading, unless using `recursive=True`. - * Directory exists and contains partitions, but with different partitioning schema than [`partition_by`](#onetl.file.file_df_writer.options.FileDFWriterOptions.partition_by) - : Data is appended to a directory with new partitioning schema. -
- #### WARNING - Spark cannot read directory with multiple partitioning schemas, - unless using `recursive=True` to disable partition scanning. - * Directory exists and partitioned according [`partition_by`](#onetl.file.file_df_writer.options.FileDFWriterOptions.partition_by), but partition is present only in dataframe - : New partition directory is created. - * Directory exists and partitioned according [`partition_by`](#onetl.file.file_df_writer.options.FileDFWriterOptions.partition_by), partition is present in both dataframe and directory - : New files are added to existing partition directory, existing files are sill present. - * Directory exists and partitioned according [`partition_by`](#onetl.file.file_df_writer.options.FileDFWriterOptions.partition_by), but partition is present only in directory, not dataframe - : Existing partition is left intact. - * `replace_overlapping_partitions` - : If partitions from dataframe already exist in directory structure, they will be overwritten. -
- Same as Spark’s `df.write.mode("overwrite").save()` + - `spark.sql.sources.partitionOverwriteMode=dynamic`. -
- ### Behavior in details -
- * Directory does not exist - : Directory is created using all the provided options (`format`, `partition_by`, etc). - * Directory exists, does not contain partitions, but [`partition_by`](#onetl.file.file_df_writer.options.FileDFWriterOptions.partition_by) is set - : Directory **will be deleted**, and will be created with partitions. - * Directory exists and contains partitions, but [`partition_by`](#onetl.file.file_df_writer.options.FileDFWriterOptions.partition_by) is not set - : Directory **will be deleted**, and will be created with partitions. - * Directory exists and contains partitions, but with different partitioning schema than [`partition_by`](#onetl.file.file_df_writer.options.FileDFWriterOptions.partition_by) - : Data is appended to a directory with new partitioning schema. -
- #### WARNING - Spark cannot read directory with multiple partitioning schemas, - unless using `recursive=True` to disable partition scanning. - * Directory exists and partitioned according [`partition_by`](#onetl.file.file_df_writer.options.FileDFWriterOptions.partition_by), but partition is present only in dataframe - : New partition directory is created. - * Directory exists and partitioned according [`partition_by`](#onetl.file.file_df_writer.options.FileDFWriterOptions.partition_by), partition is present in both dataframe and directory - : Partition directory **will be deleted**, and new one is created with files containing data from dataframe. - * Directory exists and partitioned according [`partition_by`](#onetl.file.file_df_writer.options.FileDFWriterOptions.partition_by), but partition is present only in directory, not dataframe - : Existing partition is left intact. - * `replace_entire_directory` - : Remove existing directory and create new one, **overwriting all existing data**. - **All existing partitions are dropped.** -
- Same as Spark’s `df.write.mode("overwrite").save()` + - `spark.sql.sources.partitionOverwriteMode=static`. - -#### NOTE -Unlike using pure Spark, config option `spark.sql.sources.partitionOverwriteMode` -does not affect behavior of any `mode` - - - -#### *field* partition_by *: List[str] | str | None* *= None* *(alias 'partitionBy')* - -List of columns should be used for data partitioning. `None` means partitioning is disabled. - -Each partition is a folder which contains only files with the specific column value, -like `some.csv/col1=value1`, `some.csv/col1=value2`, and so on. - -Multiple partitions columns means nested folder structure, like `some.csv/col1=val1/col2=val2`. - -If `WHERE` clause in the query contains expression like `partition = value`, -Spark will scan only files in a specific partition. - -Examples: `reg_id` or `["reg_id", "business_dt"]` - -#### NOTE -Values should be scalars (integers, strings), -and either static (`countryId`) or incrementing (dates, years), with low -number of distinct values. - -Columns like `userId` or `datetime`/`timestamp` should **NOT** be used for partitioning. - - diff --git a/mddocs/markdown/file_df/file_formats/avro.md b/mddocs/markdown/file_df/file_formats/avro.md deleted file mode 100644 index de41f7796..000000000 --- a/mddocs/markdown/file_df/file_formats/avro.md +++ /dev/null @@ -1,390 +0,0 @@ - - -# Avro - -### *class* onetl.file.format.avro.Avro(\*, avroSchema: dict | None = None, avroSchemaUrl: str | None = None, recordName: str | None = None, recordNamespace: str | None = None, compression: str | Literal['uncompressed', 'snappy', 'deflate', 'bzip2', 'xz', 'zstandard'] | None = None, mode: Literal['PERMISSIVE', 'FAILFAST'] | None = None, datetimeRebaseMode: Literal['CORRECTED', 'LEGACY', 'EXCEPTION'] | None = None, positionalFieldMatching: bool | None = None, enableStableIdentifiersForUnionType: bool | None = None, \*\*kwargs) - -Avro file format. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -Based on [Spark Avro](https://spark.apache.org/docs/latest/sql-data-sources-avro.html) file format. - -Supports reading/writing files with `.avro` extension. - -### Version compatibility - -* Spark versions: 2.4.x - 3.5.x -* Java versions: 8 - 20 - -See documentation from link above. - -#### Versionadded -Added in version 0.9.0. - -### Examples - -#### NOTE -You can pass any option mentioned in -[official documentation](https://spark.apache.org/docs/latest/sql-data-sources-avro.html). -**Option names should be in** `camelCase`! - -The set of supported options depends on Spark version. - -Reading files - -```py -from pyspark.sql import SparkSession -from onetl.file.format import Avro - -# Create Spark session with Avro package loaded -maven_packages = Avro.get_packages(spark_version="3.5.5") -spark = ( - SparkSession.builder.appName("spark-app-name") - .config("spark.jars.packages", ",".join(maven_packages)) - .getOrCreate() -) - -schema = { - "type": "record", - "name": "Person", - "fields": [ - {"name": "name", "type": "string"}, - {"name": "age", "type": "int"}, - ], -} -avro = Avro(avroSchema=schema) # or avroSchemaUrl=... -``` - -Writing files - -```py -# Create Spark session with Avro package loaded -spark = ... - -from onetl.file.format import Avro - -schema = { - "type": "record", - "name": "Person", - "fields": [ - {"name": "name", "type": "string"}, - {"name": "age", "type": "int"}, - ], -} -avro = Avro( - avroSchema=schema, # or avroSchemaUrl=... - compression="snappy", -) -``` - - - -#### *field* schema_dict *: dict | None* *= None* *(alias 'avroSchema')* - -Avro schema in JSON format representation. - -```python -avro = Avro( - avroSchema={ - "type": "record", - "name": "Person", - "fields": [ - {"name": "name", "type": "string"}, - {"name": "age", "type": "int"}, - ], - }, -) -``` - -If set, all records should match this schema. - -#### WARNING -Mutually exclusive with [`schema_url`](#onetl.file.format.avro.Avro.schema_url). - - - -#### *field* schema_url *: str | None* *= None* *(alias 'avroSchemaUrl')* - -URL to Avro schema in JSON format. Usually points to Schema Registry, like: - -```python -schema_registry = "http://some.schema.registry.domain" -name = "MyAwesomeSchema" -version = "latest" - -schema_url = f"{schema_registry}/subjects/{name}/versions/{version}/schema" -avro = Avro(avroSchemaUrl=schema_url) -``` - -If set, schema is fetched before any records are parsed, so all records should match this schema. - -#### WARNING -Mutually exclusive with [`schema_dict`](#onetl.file.format.avro.Avro.schema_dict). - - - -#### *field* recordName *: str | None* *= None* - -Record name in written Avro schema. -Default is `topLevelRecord`. - -#### NOTE -Used only for writing files and by [`serialize_column`](#onetl.file.format.avro.Avro.serialize_column). - - - -#### *field* recordNamespace *: str | None* *= None* - -Record namespace in written Avro schema. Default is not set. - -#### NOTE -Used only for writing files and by [`serialize_column`](#onetl.file.format.avro.Avro.serialize_column). - - - -#### *field* compression *: str | Literal['uncompressed', 'snappy', 'deflate', 'bzip2', 'xz', 'zstandard'] | None* *= None* - -Compression codec. -By default, Spark config value `spark.sql.avro.compression.codec `` (``snappy`) is used. - -#### NOTE -Used only for writing files. Ignored by [`serialize_column`](#onetl.file.format.avro.Avro.serialize_column). - - - -#### *field* mode *: Literal['PERMISSIVE', 'FAILFAST'] | None* *= None* - -How to handle parsing errors: -: * `PERMISSIVE` - set field value as `null`. - * `FAILFAST` - throw an error immediately. - -Default is `FAILFAST`. - -#### NOTE -Used only by [`parse_column`](#onetl.file.format.avro.Avro.parse_column) method. - - - -#### *field* datetimeRebaseMode *: Literal['CORRECTED', 'LEGACY', 'EXCEPTION'] | None* *= None* - -While converting dates/timestamps from Julian to Proleptic Gregorian calendar, handle value ambiguity: -: * `EXCEPTION` - fail if ancient dates/timestamps are ambiguous between the two calendars. - * `CORRECTED` - load dates/timestamps without as-is. - * `LEGACY` - rebase ancient dates/timestamps from the Julian to Proleptic Gregorian calendar. - -By default, Spark config value `spark.sql.avro.datetimeRebaseModeInRead` (`CORRECTED`) is used. - -#### NOTE -Used only for reading files and by [`parse_column`](#onetl.file.format.avro.Avro.parse_column). - - - -#### *field* positionalFieldMatching *: bool | None* *= None* - -If `True`, match Avro schema field and DataFrame column by position. -If `False`, match by name. - -Default is `False`. - - - -#### *field* enableStableIdentifiersForUnionType *: bool | None* *= None* - -Avro schema may contain union types, which are not supported by Spark. -Different variants of union are split to separated DataFrame columns with respective type. - -If option value is `True`, DataFrame column names are based on Avro variant names, e.g. `member_int`, `member_string`. -If `False`, DataFrame column names are generated using field position, e.g. `member0`, `member1`. - -Default is `False`. - -#### NOTE -Used only for reading files and by [`parse_column`](#onetl.file.format.avro.Avro.parse_column). - - - -#### *classmethod* get_packages(spark_version: str, scala_version: str | None = None) → list[str] - -Get package names to be downloaded by Spark. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -See [Maven package index](https://mvnrepository.com/artifact/org.apache.spark/spark-avro) -for all available packages. - -#### Versionadded -Added in version 0.9.0. - -* **Parameters:** - **spark_version** - : Spark version in format `major.minor.patch`. - - **scala_version** - : Scala version in format `major.minor`. -
- If `None`, `spark_version` is used to determine Scala version. - -### Examples - -```python -from onetl.file.format import Avro - -Avro.get_packages(spark_version="3.5.5") -Avro.get_packages(spark_version="3.5.5", scala_version="2.12") -``` - - - -#### parse_column(column: str | Column) → Column - -Parses an Avro binary column into a structured Spark SQL column using Spark’s -[from_avro](https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.avro.functions.from_avro.html) function, -based on the schema provided within the class. - -#### NOTE -Can be used only with Spark 3.x+ - -#### WARNING -If `schema_url` is provided, `requests` library is used to fetch the schema from the URL. -It should be installed manually, like this: - -```bash -pip install requests -``` - -#### Versionadded -Added in version 0.11.0. - -* **Parameters:** - **column** - : The name of the column or the column object containing Avro bytes to deserialize. - Schema should match the provided Avro schema. -* **Returns:** - Column with deserialized data. Schema is matching the provided Avro schema. Column name is the same as input column. -* **Raises:** - ValueError - : If the Spark version is less than 3.x or if neither `avroSchema` nor `avroSchemaUrl` are defined. - - ImportError - : If `schema_url` is used and the `requests` library is not installed. - -### Examples - -```pycon ->>> from pyspark.sql.functions import decode ->>> from onetl.file.format import Avro ->>> df.show() -+----+----------------------+----------+---------+------+-----------------------+-------------+ -|key |value |topic |partition|offset|timestamp |timestampType| -+----+----------------------+----------+---------+------+-----------------------+-------------+ -|[31]|[0A 41 6C 69 63 65 28]|topicAvro |0 |0 |2024-04-24 13:02:25.911|0 | -|[32]|[06 42 6F 62 32] |topicAvro |0 |1 |2024-04-24 13:02:25.922|0 | -+----+----------------------+----------+---------+------+-----------------------+-------------+ ->>> df.printSchema() -root -|-- key: binary (nullable = true) -|-- value: binary (nullable = true) -|-- topic: string (nullable = true) -|-- partition: integer (nullable = true) -|-- offset: integer (nullable = true) -|-- timestamp: timestamp (nullable = true) -|-- timestampType: integer (nullable = true) ->>> avro = Avro( -... avroSchema={ # or avroSchemaUrl=... -... "type": "record", -... "name": "Person", -... "fields": [ -... {"name": "name", "type": "string"}, -... {"name": "age", "type": "int"}, -... ], -... } -... ) ->>> parsed_df = df.select(decode("key", "UTF-8").alias("key"), avro.parse_column("value")) ->>> parsed_df.show(truncate=False) -+---+-----------+ -|key|value | -+---+-----------+ -|1 |{Alice, 20}| -|2 |{Bob, 25} | -+---+-----------+ ->>> parsed_df.printSchema() -root -|-- key: string (nullable = true) -|-- value: struct (nullable = true) -| |-- name: string (nullable = true) -| |-- age: integer (nullable = true) -``` - - - -#### serialize_column(column: str | Column) → Column - -Serializes a structured Spark SQL column into an Avro binary column using Spark’s -[to_avro](https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.avro.functions.to_avro.html#pyspark.sql.avro.functions.to_avro) function. - -#### NOTE -Can be used only with Spark 3.x+ - -#### WARNING -If `schema_url` is provided, `requests` library is used to fetch the schema from the URL. It should be installed manually, like this: - -```bash -pip install requests -``` - -#### Versionadded -Added in version 0.11.0. - -* **Parameters:** - **column** - : The name of the column or the column object containing the data to serialize to Avro format. -* **Returns:** - Column with binary Avro data. Column name is the same as input column. -* **Raises:** - ValueError - : If the Spark version is less than 3.x. - - ImportError - : If `schema_url` is used and the `requests` library is not installed. - -### Examples - -```pycon ->>> from pyspark.sql.functions import decode ->>> from onetl.file.format import Avro ->>> df.show() -+---+-----------+ -|key|value | -+---+-----------+ -|1 |{Alice, 20}| -|2 | {Bob, 25}| -+---+-----------+ ->>> df.printSchema() -root -|-- key: string (nullable = true) -|-- value: struct (nullable = true) -| |-- name: string (nullable = true) -| |-- age: integer (nullable = true) ->>> # serializing data into Avro format ->>> avro = Avro( -... avroSchema={ # or avroSchemaUrl=... -... "type": "record", -... "name": "Person", -... "fields": [ -... {"name": "name", "type": "string"}, -... {"name": "age", "type": "int"}, -... ], -... } -... ) ->>> serialized_df = df.select("key", avro.serialize_column("value")) ->>> serialized_df.show(truncate=False) -+---+----------------------+ -|key|value | -+---+----------------------+ -| 1|[0A 41 6C 69 63 65 28]| -| 2|[06 42 6F 62 32] | -+---+----------------------+ ->>> serialized_df.printSchema() -root -|-- key: string (nullable = true) -|-- value: binary (nullable = true) -``` - - diff --git a/mddocs/markdown/file_df/file_formats/base.md b/mddocs/markdown/file_df/file_formats/base.md deleted file mode 100644 index 68ca0e042..000000000 --- a/mddocs/markdown/file_df/file_formats/base.md +++ /dev/null @@ -1,73 +0,0 @@ - - -# Base interface - -### *class* onetl.base.base_file_format.BaseReadableFileFormat - -Representation of readable file format. - -#### Versionadded -Added in version 0.9.0. - - - -#### *abstract* check_if_supported(spark: SparkSession) → None - -Check if Spark session does support this file format. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### Versionadded -Added in version 0.9.0. - -* **Raises:** - RuntimeError - : If file format is not supported. - - - -#### *abstract* apply_to_reader(reader: DataFrameReader) → DataFrameReader - -Apply provided format to `pyspark.sql.DataFrameReader`. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### Versionadded -Added in version 0.9.0. - -* **Returns:** - `pyspark.sql.DataFrameReader` - : DataFrameReader with options applied. - - - -### *class* onetl.base.base_file_format.BaseWritableFileFormat - -Representation of writable file format. - -#### Versionadded -Added in version 0.9.0. - - - -#### *abstract* check_if_supported(spark: SparkSession) → None - -Check if Spark session does support this file format. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### Versionadded -Added in version 0.9.0. - -* **Raises:** - RuntimeError - : If file format is not supported. - - - -#### *abstract* apply_to_writer(writer: DataFrameWriter) → DataFrameWriter - -Apply provided format to `pyspark.sql.DataFrameWriter`. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### Versionadded -Added in version 0.9.0. - -* **Returns:** - `pyspark.sql.DataFrameWriter` - : DataFrameWriter with options applied. - - diff --git a/mddocs/markdown/file_df/file_formats/csv.md b/mddocs/markdown/file_df/file_formats/csv.md deleted file mode 100644 index a02edd58e..000000000 --- a/mddocs/markdown/file_df/file_formats/csv.md +++ /dev/null @@ -1,539 +0,0 @@ - - -# CSV - -### *class* onetl.file.format.csv.CSV(\*, sep: str = ',', header: bool | None = None, quote: ConstrainedStrValue = '"', quoteAll: bool | None = None, escape: ConstrainedStrValue = '\\\\', lineSep: ConstrainedStrValue | None = None, encoding: ConstrainedStrValue | None = None, compression: str | Literal['none', 'bzip2', 'gzip', 'lz4', 'snappy', 'deflate'] | None = None, inferSchema: bool | None = None, samplingRatio: ConstrainedFloatValue | None = None, comment: ConstrainedStrValue | None = None, enforceSchema: bool | None = None, escapeQuotes: bool | None = None, unescapedQuoteHandling: None | Literal['STOP_AT_CLOSING_QUOTE', 'BACK_TO_DELIMITER', 'STOP_AT_DELIMITER', 'SKIP_VALUE', 'RAISE_ERROR'] = None, ignoreLeadingWhiteSpace: bool | None = None, ignoreTrailingWhiteSpace: bool | None = None, emptyValue: str | None = None, nullValue: str | None = None, nanValue: str | None = None, positiveInf: ConstrainedStrValue | None = None, negativeInf: ConstrainedStrValue | None = None, preferDate: bool | None = None, dateFormat: ConstrainedStrValue | None = None, timestampFormat: ConstrainedStrValue | None = None, timestampNTZFormat: ConstrainedStrValue | None = None, locale: ConstrainedStrValue | None = None, maxCharsPerColumn: int | None = None, mode: Literal['PERMISSIVE', 'DROPMALFORMED', 'FAILFAST'] | None = None, columnNameOfCorruptRecord: ConstrainedStrValue | None = None, multiLine: bool | None = None, charToEscapeQuoteEscaping: ConstrainedStrValue | None = None, \*\*kwargs) - -CSV file format. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -Based on [Spark CSV](https://spark.apache.org/docs/latest/sql-data-sources-csv.html) file format. - -Supports reading/writing files with `.csv` extension with content like: - -```csv -"some","value" -"another","value" -``` - -#### Versionadded -Added in version 0.9.0. - -### Examples - -#### NOTE -You can pass any option mentioned in -[official documentation](https://spark.apache.org/docs/latest/sql-data-sources-csv.html). -**Option names should be in** `camelCase`! - -The set of supported options depends on Spark version. - -Reading files - -```py -from onetl.file.format import CSV - -csv = CSV(header=True, inferSchema=True, mode="PERMISSIVE") -``` - -Writing files - -```py -from onetl.file.format import CSV - -csv = CSV(header=True, compression="gzip") -``` - - - -#### *field* delimiter *: str* *= ','* *(alias 'sep')* - -Character used to separate fields in CSV row. - - - -#### *field* header *: bool | None* *= None* - -If `True`, the first row of the file is considered a header. -Default `False`. - - - -#### *field* quote *: str* *= '"'* - -Character used to quote field values within CSV field. - -Empty string is considered as `\u0000` (`NUL`) character. - - -* **Constraints:** - - **maxLength** = 1 - -#### *field* quoteAll *: bool | None* *= None* - -If `True`, all fields are quoted: - -```csv -"some","field with \"quote","123","" -``` - -If `False`, only quote fields containing [`quote`](#onetl.file.format.csv.CSV.quote) symbols. - -```csv -any,"field with \"quote",123, -``` - -Default `False`. - -#### NOTE -Used only for writing files. - - - -#### *field* compression *: str | Literal['none', 'bzip2', 'gzip', 'lz4', 'snappy', 'deflate'] | None* *= None* - -Compression codec of the CSV file. -Default `none`. - -#### NOTE -Used only for writing files. Ignored by [`parse_column`](#onetl.file.format.csv.CSV.parse_column) method. - - - -#### *field* inferSchema *: bool | None* *= None* - -If `True`, try to infer the input schema by reading a sample of the file (see [`samplingRatio`](#onetl.file.format.csv.CSV.samplingRatio)). -Default `False` which means that all parsed columns will be `StringType()`. - -#### NOTE -Used only for reading files, and only if user haven’t provider explicit DataFrame schema. -Ignored by [`parse_column`](#onetl.file.format.csv.CSV.parse_column) function. - - - -#### *field* samplingRatio *: float | None* *= None* - -For `inferSchema=True`, read the specified fraction of rows to infer the schema. -Default `1`. - -#### NOTE -Used only for reading files. Ignored by [`parse_column`](#onetl.file.format.csv.CSV.parse_column) function. - - -* **Constraints:** - - **minimum** = 0 - - **maximum** = 1 - -#### *field* comment *: str | None* *= None* - -If set, all lines starting with specified character (e.g. `#`) are considered a comment, and skipped. -Default is not set, meaning that CSV lines should not contain comments. - -#### NOTE -Used only for reading files and [`parse_column`](#onetl.file.format.csv.CSV.parse_column) method. - - -* **Constraints:** - - **maxLength** = 1 - -#### *field* enforceSchema *: bool | None* *= None* - -If `True`, inferred or user-provided schema has higher priority than CSV file headers. -This means that all input files should have the same structure. - -If `False`, CSV headers are used as a primary source of information about column names and their position. - -Default `True`. - -#### NOTE -Used only for reading files. Ignored by [`parse_column`](#onetl.file.format.csv.CSV.parse_column) function. - - - -#### *field* escapeQuotes *: bool | None* *= None* - -If `True`, escape quotes within CSV field. - -```csv -any,field with \"quote,123, -``` - -If `False`, wrap fields containing [`quote`](#onetl.file.format.csv.CSV.quote) symbols with quotes. - -```csv -any,"field with ""quote ",123, -``` - -Default `True`. - -#### NOTE -Used only for writing files. - - - -#### *field* unescapedQuoteHandling *: None | Literal['STOP_AT_CLOSING_QUOTE', 'BACK_TO_DELIMITER', 'STOP_AT_DELIMITER', 'SKIP_VALUE', 'RAISE_ERROR']* *= None* - -Define how to handle unescaped quotes within CSV field. - -* `STOP_AT_CLOSING_QUOTE` - collect all characters until closing quote. -* `BACK_TO_DELIMITER` - collect all characters until delimiter or line end. -* `STOP_AT_DELIMITER` - collect all characters until delimiter or line end. - : If quotes are not closed, this may produce incorrect results (e.g. including delimiter inside field value). -* `SKIP_VALUE` - skip field and consider it as [`nullValue`](#onetl.file.format.csv.CSV.nullValue). -* `RAISE_ERROR` - raise error immediately. - -Default `STOP_AT_DELIMITER`. - -#### NOTE -Used only for reading files and [`parse_column`](#onetl.file.format.csv.CSV.parse_column) method. - - - -#### *field* ignoreLeadingWhiteSpace *: bool | None* *= None* - -If `True`, trim leading whitespaces in field value. - -Defaults: -: * `True` for reading. - * `False` for writing. - - - -#### *field* ignoreTrailingWhiteSpace *: bool | None* *= None* - -If `True`, trim trailing whitespaces in field value. - -Defaults: -: * `True` for reading. - * `False` for writing. - - - -#### *field* emptyValue *: str | None* *= None* - -Value used for empty string fields. - -Defaults: -: * empty string for reading. - * `""` for writing. - - - -#### *field* nullValue *: str | None* *= None* - -If set, this value will be converted to `null`. -Default is empty string. - - - -#### *field* nanValue *: str | None* *= None* - -If set, this string will be considered as Not-A-Number (NaN) value for `FloatType()` and `DoubleType()`. -Default is `NaN`. - -#### NOTE -Used only for reading files and [`parse_column`](#onetl.file.format.csv.CSV.parse_column) method. - - - -#### *field* positiveInf *: str | None* *= None* - -If set, this string will be considered as positive infinity value for `FloatType()` and `DoubleType()`. -Default is `Inf`. - -#### NOTE -Used only for reading files and [`parse_column`](#onetl.file.format.csv.CSV.parse_column) method. - - -* **Constraints:** - - **minLength** = 1 - -#### \_\_init_\_(\*\*kwargs) - - - -#### *field* negativeInf *: str | None* *= None* - -If set, this string will be considered as negative infinity value for `FloatType()` and `DoubleType()`. -Default is `-Inf`. - -#### NOTE -Used only for reading files and [`parse_column`](#onetl.file.format.csv.CSV.parse_column) method. - - -* **Constraints:** - - **minLength** = 1 - -#### *field* preferDate *: bool | None* *= None* - -If `True` and `inferSchema=True` and column does match [`dateFormat`](#onetl.file.format.csv.CSV.dateFormat), consider it as `DateType()`. -For columns matching both [`timestampFormat`](#onetl.file.format.csv.CSV.timestampFormat) and [`dateFormat`](#onetl.file.format.csv.CSV.dateFormat), consider it as `TimestampType()`. - -If `False`, date and timestamp columns will be considered as `StringType()`. - -Default `True`. - -#### NOTE -Used only for reading files. Ignored by [`parse_column`](#onetl.file.format.csv.CSV.parse_column) function. - - - -#### *field* dateFormat *: str | None* *= None* - -String format for `DateType()` representation. -Default is `yyyy-MM-dd`. - - -* **Constraints:** - - **minLength** = 1 - -#### *field* timestampFormat *: str | None* *= None* - -String format for TimestampType()\` representation. -Default is `yyyy-MM-dd'T'HH:mm:ss[.SSS][XXX]`. - - -* **Constraints:** - - **minLength** = 1 - -#### *field* timestampNTZFormat *: str | None* *= None* - -String format for TimestampNTZType()\` representation. -Default is `yyyy-MM-dd'T'HH:mm:ss[.SSS]`. - -#### NOTE -Added in Spark 3.2.0 - - -* **Constraints:** - - **minLength** = 1 - -#### *field* locale *: str | None* *= None* - -Locale name used to parse dates and timestamps. -Default is `en-US` - -#### NOTE -Used only for reading files and [`parse_column`](#onetl.file.format.csv.CSV.parse_column) method. - - -* **Constraints:** - - **minLength** = 1 - -#### *field* maxCharsPerColumn *: int | None* *= None* - -Maximum number of characters to read per column. -Default is `-1`, which means no limit. - -#### NOTE -Used only for reading files and [`parse_column`](#onetl.file.format.csv.CSV.parse_column) method. - - - -#### *field* mode *: Literal['PERMISSIVE', 'DROPMALFORMED', 'FAILFAST'] | None* *= None* - -How to handle parsing errors: -: * `PERMISSIVE` - set field value as `null`, move raw data to [`columnNameOfCorruptRecord`](#onetl.file.format.csv.CSV.columnNameOfCorruptRecord) column. - * `DROPMALFORMED` - skip the malformed row. - * `FAILFAST` - throw an error immediately. - -Default is `PERMISSIVE`. - -#### NOTE -Used only for reading files and [`parse_column`](#onetl.file.format.csv.CSV.parse_column) method. - - - -#### *field* columnNameOfCorruptRecord *: str | None* *= None* - -Name of column to put corrupt records in. -Default is `_corrupt_record`. - -#### WARNING -If DataFrame schema is provided, this column should be added to schema explicitly: - -```python -from onetl.connection import SparkLocalFS -from onetl.file import FileDFReader -from onetl.file.format import CSV - -from pyspark.sql.types import StructType, StructField, TimestampType, StringType - -spark = ... - -schema = StructType( - [ - StructField("my_field", TimestampType()), - StructField("_corrupt_record", StringType()), # <-- important - ] -) - -csv = CSV(mode="PERMISSIVE", columnNameOfCorruptRecord="_corrupt_record") - -reader = FileDFReader( - connection=connection, - format=csv, - schema=schema, # < --- -) -df = reader.run(["/some/file.csv"]) -``` - -#### NOTE -Used only for reading files and [`parse_column`](#onetl.file.format.csv.CSV.parse_column) method. - - -* **Constraints:** - - **minLength** = 1 - -#### *field* multiLine *: bool | None* *= None* - -If `True`, fields may contain line separators. -If `False`, the input is expected to have one record per file. - -Default is `True`. - -#### NOTE -Used only for reading files. -Ignored by [`parse_column`](#onetl.file.format.csv.CSV.parse_column) method, as it expects that each DataFrame row will contain exactly one CSV line. - - - -#### *field* charToEscapeQuoteEscaping *: str | None* *= None* - -If CSV field value contains `escape` character, it should be escaped as well. -For example, if `escape="\"`, when line: - -```csv -"some \" quoted value",other -"some \\ backslashed value",another -``` - -will be parsed as: - -```python -[ - ('some " quoted value', "other"), - ("some \ backslashed value", "another"), -] -``` - -And vice-versa, for writing CSV rows to file. - -Default is same as `escape`. - - -* **Constraints:** - - **maxLength** = 1 - -#### parse_column(column: str | Column, schema: StructType) → Column - -Parses a CSV string column to a structured Spark SQL column using Spark’s -[from_csv](https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.functions.from_csv.html) function, based on the provided schema. - -#### NOTE -Can be used only with Spark 3.x+ - -#### Versionadded -Added in version 0.11.0. - -* **Parameters:** - **column** - : The name of the column or the column object containing CSV strings/bytes to parse. - - **schema** - : The schema to apply when parsing the CSV data. This defines the structure of the output DataFrame column. -* **Returns:** - Column with deserialized data, with the same structure as the provided schema. Column name is the same as input column. - -### Examples - -```pycon ->>> from pyspark.sql.types import StructType, StructField, IntegerType, StringType ->>> from onetl.file.format import CSV ->>> df.show() -+--+--------+ -|id|value | -+--+--------+ -|1 |Alice;20| -|2 |Bob;25 | -+--+--------+ ->>> df.printSchema() -root -|-- id: integer (nullable = true) -|-- value: string (nullable = true) ->>> csv = CSV(delimiter=";") ->>> csv_schema = StructType( -... [ -... StructField("name", StringType(), nullable=True), -... StructField("age", IntegerType(), nullable=True), -... ], -... ) ->>> parsed_df = df.select("id", csv.parse_column("value", csv_schema)) ->>> parsed_df.show() -+--+-----------+ -|id|value | -+--+-----------+ -|1 |{Alice, 20}| -|2 | {Bob, 25}| -+--+-----------+ ->>> parsed_df.printSchema() -root -|-- id: integer (nullable = true) -|-- value: struct (nullable = true) -| |-- name: string (nullable = true) -| |-- age: integer (nullable = true) -``` - - - -#### serialize_column(column: str | Column) → Column - -Serializes a structured Spark SQL column into a CSV string column using Spark’s -[to_csv](https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.functions.to_csv.html) function. - -#### NOTE -Can be used only with Spark 3.x+ - -#### Versionadded -Added in version 0.11.0. - -* **Parameters:** - **column** - : The name of the column or the Column object containing the data to serialize to CSV. -* **Returns:** - Column with string CSV data. Column name is the same as input column. - -### Examples - -```pycon ->>> from pyspark.sql.functions import decode ->>> from onetl.file.format import CSV ->>> df.show() -+--+-----------+ -|id|value | -+--+-----------+ -|1 |{Alice, 20}| -|2 | {Bob, 25}| -+--+-----------+ ->>> df.printSchema() -root -|-- id: integer (nullable = true) -|-- value: struct (nullable = true) -| |-- name: string (nullable = true) -| |-- age: integer (nullable = true) ->>> # serializing data into CSV format ->>> csv = CSV(delimiter=";") ->>> serialized_df = df.select("id", csv.serialize_column("value")) ->>> serialized_df.show(truncate=False) -+--+--------+ -|id|value | -+--+--------+ -|1 |Alice;20| -|2 |Bob;25 | -+--+--------+ ->>> serialized_df.printSchema() -root -|-- id: integer (nullable = true) -|-- value: string (nullable = true) -``` - - diff --git a/mddocs/markdown/file_df/file_formats/excel.md b/mddocs/markdown/file_df/file_formats/excel.md deleted file mode 100644 index 6925c91ca..000000000 --- a/mddocs/markdown/file_df/file_formats/excel.md +++ /dev/null @@ -1,232 +0,0 @@ - - -# Excel - -### *class* onetl.file.format.excel.Excel(\*, header: bool = False, dataAddress: str | None = None, timestampFormat: str | None = None, dateFormat: str | None = None, treatEmptyValuesAsNulls: bool | None = None, setErrorCellsToFallbackValues: bool | None = None, usePlainNumberFormat: bool | None = None, inferSchema: bool | None = None, workbookPassword: SecretStr | None = None, maxRowsInMemory: int | None = None, maxByteArraySize: ByteSize | None = None, tempFileThreshold: ByteSize | None = None, excerptSize: int | None = None, \*\*kwargs) - -Excel file format. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -Based on [Spark Excel](https://github.com/crealytics/spark-excel) file format. - -Supports reading/writing files with `.xlsx` (read/write) and `.xls` (read only) extensions. - -### Version compatibility - -* Spark versions: 3.2.x - 3.5.x - > #### WARNING - > Not all combinations of Spark version and package version are supported. - > See [Maven index](https://mvnrepository.com/artifact/com.crealytics/spark-excel) - > and [official documentation](https://github.com/crealytics/spark-excel). -* Java versions: 8 - 20 - -See documentation from link above. - -#### Versionadded -Added in version 0.9.4. - -### Examples - -#### NOTE -You can pass any option mentioned in -[official documentation](https://github.com/crealytics/spark-excel). -**Option names should be in** `camelCase`! - -The set of supported options depends on `spark-excel` package version. - -Reading files - -```py -from pyspark.sql import SparkSession -from onetl.file.format import Excel - -# Create Spark session with Excel package loaded -maven_packages = Excel.get_packages(spark_version="3.5.1") -spark = ( - SparkSession.builder.appName("spark-app-name") - .config("spark.jars.packages", ",".join(maven_packages)) - .getOrCreate() -) - -excel = Excel(header=True, inferSchema=True) -``` - -Writing files - -```py -# Create Spark session with Excel package loaded -spark = ... - -from onetl.file.format import XML - -excel = Excel(header=True, dataAddress="'Sheet1'!A1") -``` - - - -#### *field* header *: bool* *= False* - -If `True`, the first row in file is conditioned as a header. -Default `False`. - - - -#### *field* dataAddress *: str | None* *= None* - -Cell address used as starting point. -For example: `'A1'` or `'Sheet1'!A1` - - - -#### *field* timestampFormat *: str | None* *= None* - -Format string used for parsing or serializing timestamp values. -Default `yyyy-mm-dd hh:mm:ss[.fffffffff]`. - - - -#### *field* treatEmptyValuesAsNulls *: bool | None* *= None* - -If `True`, empty cells are parsed as `null` values. -If `False`, empty cells are parsed as empty strings. -Default `True`. - -#### NOTE -Used only for reading files. - - - -#### *field* setErrorCellsToFallbackValues *: bool | None* *= None* - -If `True`, cells containing `#N/A` value are replaced with default value for column type, -e.g. 0 for `IntegerType()`. If `False`, `#N/A` values are replaced with `null`. -Default `False`. - -#### NOTE -Used only for reading files. - - - -#### *field* usePlainNumberFormat *: bool | None* *= None* - -If `True`, read or write numeric values with plain format, without using scientific notation or rounding. -Default `False`. - - - -#### *field* inferSchema *: bool | None* *= None* - -If `True`, infer DataFrame schema based on cell content. -If `False` and no explicit DataFrame schema is passed, all columns are `StringType()`. - -#### NOTE -Used only for reading files. - - - -#### *field* workbookPassword *: SecretStr | None* *= None* - -If Excel file is encrypted, provide password to open it. - -#### NOTE -Used only for reading files. Cannot be used to write files. - - -* **Constraints:** - - **type** = string - - **writeOnly** = True - - **format** = password - -#### *field* maxRowsInMemory *: int | None* *= None* - -If set, use streaming reader and fetch only specified number of rows per iteration. -This reduces memory usage for large files. -Default `None`, which means reading the entire file content to memory. - -#### WARNING -Can be used only with `.xlsx` files, but fails on `.xls`. - -#### NOTE -Used only for reading files. - - - -#### *field* maxByteArraySize *: ByteSize | None* *= None* - -If set, overrides memory limit (in bytes) of byte array size used for reading rows from input file. -Default `0`, which means using default limit. - -See [IOUtils.setByteArrayMaxOverride](https://poi.apache.org/apidocs/5.0/org/apache/poi/util/IOUtils.html#setByteArrayMaxOverride-int-) -documentation. - -#### NOTE -Used only for reading files. - - - -#### *field* tempFileThreshold *: ByteSize | None* *= None* - -If value is greater than 0, large zip entries will be written to temporary files after reaching this threshold. -If value is 0, all zip entries will be written to temporary files. -If value is -1, no temp files will be created, which may cause errors if zip entry is larger than 2GiB. - -#### NOTE -Used only for reading files. - - - -#### *field* excerptSize *: int | None* *= None* - -If `inferSchema=True`, set number of rows to infer schema from. -Default `10`. - -#### NOTE -Used only for reading files. - - - -#### *classmethod* get_packages(spark_version: str, scala_version: str | None = None, package_version: str | None = None) → list[str] - -Get package names to be downloaded by Spark. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### WARNING -Not all combinations of Spark version and package version are supported. -See [Maven index](https://mvnrepository.com/artifact/com.crealytics/spark-excel) -and [official documentation](https://github.com/crealytics/spark-excel). - -#### Versionadded -Added in version 0.9.4. - -* **Parameters:** - **spark_version** - : Spark version in format `major.minor.patch`. - - **scala_version** - : Scala version in format `major.minor`. -
- If `None`, `spark_version` is used to determine Scala version. - - **package_version** - : Package version in format `major.minor.patch`. Default is `0.20.4`. -
- #### WARNING - Version `0.14` and below are not supported. -
- #### NOTE - It is not guaranteed that custom package versions are supported. - Tests are performed only for default version. - -### Examples - -```python -from onetl.file.format import Excel - -Excel.get_packages(spark_version="3.5.1") -Excel.get_packages(spark_version="3.5.1", scala_version="2.12") -Excel.get_packages( - spark_version="3.5.1", - scala_version="2.12", - package_version="0.20.4", -) -``` - - diff --git a/mddocs/markdown/file_df/file_formats/index.md b/mddocs/markdown/file_df/file_formats/index.md deleted file mode 100644 index a6500bdf3..000000000 --- a/mddocs/markdown/file_df/file_formats/index.md +++ /dev/null @@ -1,18 +0,0 @@ - - -# File Formats - -# File formats - -* [Avro](avro.md) -* [CSV](csv.md) -* [Excel](excel.md) -* [JSON](json.md) -* [JSONLine](jsonline.md) -* [ORC](orc.md) -* [Parquet](parquet.md) -* [XML](xml.md) - -# For developers - -* [Base interface](base.md) diff --git a/mddocs/markdown/file_df/file_formats/json.md b/mddocs/markdown/file_df/file_formats/json.md deleted file mode 100644 index fd6ee8da7..000000000 --- a/mddocs/markdown/file_df/file_formats/json.md +++ /dev/null @@ -1,414 +0,0 @@ - - -# JSON - -### *class* onetl.file.format.json.JSON(\*, multiLine: Literal[True] = True, encoding: str | None = None, lineSep: str | None = None, allowComments: bool | None = None, allowUnquotedFieldNames: bool | None = None, allowSingleQuotes: bool | None = None, allowNumericLeadingZeros: bool | None = None, allowNonNumericNumbers: bool | None = None, allowBackslashEscapingAnyCharacter: bool | None = None, allowUnquotedControlChars: bool | None = None, mode: Literal['PERMISSIVE', 'DROPMALFORMED', 'FAILFAST'] | None = None, columnNameOfCorruptRecord: ConstrainedStrValue | None = None, samplingRatio: ConstrainedFloatValue | None = None, primitivesAsString: bool | None = None, prefersDecimal: bool | None = None, dropFieldIfAllNull: bool | None = None, dateFormat: ConstrainedStrValue | None = None, timestampFormat: ConstrainedStrValue | None = None, timestampNTZFormat: ConstrainedStrValue | None = None, timeZone: ConstrainedStrValue | None = None, locale: ConstrainedStrValue | None = None, \*\*kwargs) - -JSON file format. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -Based on [Spark JSON](https://spark.apache.org/docs/latest/sql-data-sources-json.html) file format. - -Supports reading (but **NOT** writing) files with `.json` extension with content like: - -```json -[ - {"key": "value1"}, - {"key": "value2"} -] -``` - -#### Versionadded -Added in version 0.9.0. - -### Examples - -#### NOTE -You can pass any option mentioned in -[official documentation](https://spark.apache.org/docs/latest/sql-data-sources-json.html). -**Option names should be in** `camelCase`! - -The set of supported options depends on Spark version. - -Reading files: - -```python -from onetl.file.format import JSON - -json = JSON(encoding="UTF-8") -``` - -Writing files: - -#### WARNING -Not supported. Use [`JSONLine`](jsonline.md#onetl.file.format.jsonline.JSONLine). - - - -#### *field* encoding *: str | None* *= None* - -Encoding of the JSON file. -Default `UTF-8`. - -#### NOTE -Used only for reading and writing files. - -Ignored by [`parse_column`](#onetl.file.format.json.JSON.parse_column) and [`serialize_column`](#onetl.file.format.json.JSON.serialize_column) methods. - - - -#### *field* lineSep *: str | None* *= None* - -Character used to separate lines in the JSON file. - -Defaults: -: * Try to detect for reading (`\r\n`, `\r`, `\n`) - * `\n` for writing - -#### NOTE -Used only for reading and writing files. - -Ignored by [`parse_column`](#onetl.file.format.json.JSON.parse_column) and [`serialize_column`](#onetl.file.format.json.JSON.serialize_column) methods, -as they handle each DataFrame row separately. - - - -#### *field* allowComments *: bool | None* *= None* - -If `True`, add support for C/C++/Java style comments (`//`, `/* */`). -Default `False`, meaning that JSON files should not contain comments. - -#### NOTE -Used only for reading files and [`parse_column`](#onetl.file.format.json.JSON.parse_column) method. - - - -#### *field* allowUnquotedFieldNames *: bool | None* *= None* - -If `True`, allow JSON object field names without quotes (JavaScript-style). -Default `False`. - -#### NOTE -Used only for reading files and [`parse_column`](#onetl.file.format.json.JSON.parse_column) method. - - - -#### *field* allowSingleQuotes *: bool | None* *= None* - -If `True`, allow JSON object field names to be wrapped with single quotes (`'`). -Default `True`. - -#### NOTE -Used only for reading files and [`parse_column`](#onetl.file.format.json.JSON.parse_column) method. - - - -#### *field* allowNumericLeadingZeros *: bool | None* *= None* - -If `True`, allow leading zeros in numbers (e.g. `00012`). -Default `False`. - -#### NOTE -Used only for reading files and [`parse_column`](#onetl.file.format.json.JSON.parse_column) method. - - - -#### *field* allowNonNumericNumbers *: bool | None* *= None* - -If `True`, allow numbers to contain non-numeric characters, like: -: * scientific notation (e.g. `12e10`). - * positive infinity floating point value (`Infinity`, `+Infinity`, `+INF`). - * negative infinity floating point value (`-Infinity`, `-INF`). - * Not-a-Number floating point value (`NaN`). - -Default `True`. - -#### NOTE -Used only for reading files and [`parse_column`](#onetl.file.format.json.JSON.parse_column) method. - - - -#### *field* allowBackslashEscapingAnyCharacter *: bool | None* *= None* - -If `True`, prefix `\` can escape any character. -Default `False`. - -#### NOTE -Used only for reading files and [`parse_column`](#onetl.file.format.json.JSON.parse_column) method. - - - -#### *field* allowUnquotedControlChars *: bool | None* *= None* - -If `True`, allow unquoted control characters (ASCII values 0-31) in strings without escaping them with `\`. -Default `False`. - -#### NOTE -Used only for reading files and [`parse_column`](#onetl.file.format.json.JSON.parse_column) method. - - - -#### *field* mode *: Literal['PERMISSIVE', 'DROPMALFORMED', 'FAILFAST'] | None* *= None* - -How to handle parsing errors: -: * `PERMISSIVE` - set field value as `null`, move raw data to [`columnNameOfCorruptRecord`](#onetl.file.format.json.JSON.columnNameOfCorruptRecord) column. - * `DROPMALFORMED` - skip the malformed row. - * `FAILFAST` - throw an error immediately. - -Default is `PERMISSIVE`. - -#### NOTE -Used only for reading files and [`parse_column`](#onetl.file.format.json.JSON.parse_column) method. - - - -#### *field* columnNameOfCorruptRecord *: str | None* *= None* - -Name of column to put corrupt records in. -Default is `_corrupt_record`. - -#### WARNING -If DataFrame schema is provided, this column should be added to schema explicitly: - -```python -from onetl.connection import SparkLocalFS -from onetl.file import FileDFReader -from onetl.file.format import JSON - -from pyspark.sql.types import StructType, StructField, TimestampType, StringType - -spark = ... - -schema = StructType( - [ - StructField("my_field", TimestampType()), - StructField("_corrupt_record", StringType()), # <-- important - ] -) - -json = JSON(mode="PERMISSIVE", columnNameOfCorruptRecord="_corrupt_record") - -reader = FileDFReader( - connection=connection, - format=json, - schema=schema, # < --- -) -df = reader.run(["/some/file.json"]) -``` - -#### NOTE -Used only for reading files and [`parse_column`](#onetl.file.format.json.JSON.parse_column) method. - - -* **Constraints:** - - **minLength** = 1 - -#### *field* samplingRatio *: float | None* *= None* - -While inferring schema, read the specified fraction of file rows. -Default `1`. - -#### NOTE -Used only for reading files. Ignored by [`parse_column`](#onetl.file.format.json.JSON.parse_column) function. - - -* **Constraints:** - - **minimum** = 0 - - **maximum** = 1 - -#### *field* primitivesAsString *: bool | None* *= None* - -If `True`, infer all primitive types (string, integer, float, boolean) as strings. -Default `False`. - -#### NOTE -Used only for reading files. Ignored by [`parse_column`](#onetl.file.format.json.JSON.parse_column) method. - - - -#### *field* prefersDecimal *: bool | None* *= None* - -If `True`, infer all floating-point values as `Decimal`. -Default `False`. - -#### NOTE -Used only for reading files. Ignored by [`parse_column`](#onetl.file.format.json.JSON.parse_column) method. - - - -#### \_\_init_\_(\*\*kwargs) - - - -#### *field* dropFieldIfAllNull *: bool | None* *= None* - -If `True` and inferred column is always null or empty array, exclude if from DataFrame schema. -Default `False`. - -#### NOTE -Used only for reading files. Ignored by [`parse_column`](#onetl.file.format.json.JSON.parse_column) method. - - - -#### *field* dateFormat *: str | None* *= None* - -String format for `DateType()` representation. -Default is `yyyy-MM-dd`. - - -* **Constraints:** - - **minLength** = 1 - -#### *field* timestampFormat *: str | None* *= None* - -String format for TimestampType()\` representation. -Default is `yyyy-MM-dd'T'HH:mm:ss[.SSS][XXX]`. - - -* **Constraints:** - - **minLength** = 1 - -#### *field* timestampNTZFormat *: str | None* *= None* - -String format for TimestampNTZType()\` representation. -Default is `yyyy-MM-dd'T'HH:mm:ss[.SSS]`. - -#### NOTE -Added in Spark 3.2.0 - - -* **Constraints:** - - **minLength** = 1 - -#### *field* timezone *: str | None* *= None* *(alias 'timeZone')* - -Allows to override timezone used for parsing or serializing date and timestamp values. -By default, `spark.sql.session.timeZone` is used. - - -* **Constraints:** - - **minLength** = 1 - -#### *field* locale *: str | None* *= None* - -Locale name used to parse dates and timestamps. -Default is `en-US`. - -#### NOTE -Used only for reading files and [`parse_column`](#onetl.file.format.json.JSON.parse_column) method. - - -* **Constraints:** - - **minLength** = 1 - -#### parse_column(column: str | Column, schema: StructType | ArrayType | MapType) → Column - -Parses a JSON string column to a structured Spark SQL column using Spark’s [from_json](https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.functions.from_json.html) function, based on the provided schema. - -#### Versionadded -Added in version 0.11.0. - -* **Parameters:** - **column** - : The name of the column or the column object containing JSON strings/bytes to parse. - - **schema** - : The schema to apply when parsing the JSON data. This defines the structure of the output DataFrame column. -* **Returns:** - Column with deserialized data, with the same structure as the provided schema. Column name is the same as input column. - -### Examples - -```pycon ->>> from pyspark.sql.types import StructType, StructField, IntegerType, StringType ->>> from pyspark.sql.functions import decode ->>> from onetl.file.format import JSON ->>> df.show() -+----+--------------------+----------+---------+------+-----------------------+-------------+ -|key |value |topic |partition|offset|timestamp |timestampType| -+----+--------------------+----------+---------+------+-----------------------+-------------+ -|[31]|[7B 22 6E 61 6D 6...|topicJSON |0 |0 |2024-04-24 16:51:11.739|0 | -|[32]|[7B 22 6E 61 6D 6...|topicJSON |0 |1 |2024-04-24 16:51:11.749|0 | -+----+--------------------+----------+---------+------+-----------------------+-------------+ ->>> df.printSchema() -root -|-- key: binary (nullable = true) -|-- value: binary (nullable = true) -|-- topic: string (nullable = true) -|-- partition: integer (nullable = true) -|-- offset: integer (nullable = true) -|-- timestamp: timestamp (nullable = true) -|-- timestampType: integer (nullable = true) ->>> json = JSON() ->>> json_schema = StructType( -... [ -... StructField("name", StringType(), nullable=True), -... StructField("age", IntegerType(), nullable=True), -... ], -... ) ->>> parsed_df = df.select(decode("key", "UTF-8").alias("key"), json.parse_column("value", json_schema)) ->>> parsed_df.show() -+---+-----------+ -|key|value | -+---+-----------+ -|1 |{Alice, 20}| -|2 | {Bob, 25}| -+---+-----------+ ->>> parsed_df.printSchema() -root -|-- key: string (nullable = true) -|-- value: struct (nullable = true) -| |-- name: string (nullable = true) -| |-- age: integer (nullable = true) -``` - - - -#### serialize_column(column: str | Column) → Column - -Serializes a structured Spark SQL column into a JSON string column using Spark’s -[to_json](https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.functions.to_json.html) function. - -#### Versionadded -Added in version 0.11.0. - -* **Parameters:** - **column** - : The name of the column or the column object containing the data to serialize to JSON format. -* **Returns:** - Column with string JSON data. Column name is the same as input column. - -### Examples - -```pycon ->>> from pyspark.sql.functions import decode ->>> from onetl.file.format import JSON ->>> df.show() -+---+-----------+ -|key|value | -+---+-----------+ -|1 |{Alice, 20}| -|2 | {Bob, 25}| -+---+-----------+ ->>> df.printSchema() -root -|-- key: string (nullable = true) -|-- value: struct (nullable = true) -| |-- name: string (nullable = true) -| |-- age: integer (nullable = true) ->>> # serializing data into JSON format ->>> json = JSON() ->>> serialized_df = df.select("key", json.serialize_column("value")) ->>> serialized_df.show(truncate=False) -+---+-------------------------+ -|key|value | -+---+-------------------------+ -| 1|{"name":"Alice","age":20}| -| 2|{"name":"Bob","age":25} | -+---+-------------------------+ ->>> serialized_df.printSchema() -root -|-- key: string (nullable = true) -|-- value: string (nullable = true) -``` - - diff --git a/mddocs/markdown/file_df/file_formats/jsonline.md b/mddocs/markdown/file_df/file_formats/jsonline.md deleted file mode 100644 index 644b66bcb..000000000 --- a/mddocs/markdown/file_df/file_formats/jsonline.md +++ /dev/null @@ -1,314 +0,0 @@ - - -# JSONLine - -### *class* onetl.file.format.jsonline.JSONLine(\*, multiLine: Literal[False] = False, encoding: str | None = None, lineSep: str | None = None, compression: str | Literal['none', 'bzip2', 'gzip', 'lz4', 'snappy', 'deflate'] | None = None, ignoreNullFields: bool | None = None, allowComments: bool | None = None, allowUnquotedFieldNames: bool | None = None, allowSingleQuotes: bool | None = None, allowNumericLeadingZeros: bool | None = None, allowNonNumericNumbers: bool | None = None, allowBackslashEscapingAnyCharacter: bool | None = None, allowUnquotedControlChars: bool | None = None, mode: Literal['PERMISSIVE', 'DROPMALFORMED', 'FAILFAST'] | None = None, columnNameOfCorruptRecord: ConstrainedStrValue | None = None, samplingRatio: ConstrainedFloatValue | None = None, primitivesAsString: bool | None = None, prefersDecimal: bool | None = None, dropFieldIfAllNull: bool | None = None, dateFormat: ConstrainedStrValue | None = None, timestampFormat: ConstrainedStrValue | None = None, timestampNTZFormat: ConstrainedStrValue | None = None, timeZone: ConstrainedStrValue | None = None, locale: ConstrainedStrValue | None = None, \*\*kwargs) - -JSONLine file format (each line of file contains a JSON object). [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -Based on [Spark JSON](https://spark.apache.org/docs/latest/sql-data-sources-json.html) file format. - -Supports reading/writing files with `.json` extension with content like: - -```json -{"key": "value1"} -{"key": "value2"} -``` - -#### Versionadded -Added in version 0.9.0. - -### Examples - -#### NOTE -You can pass any option mentioned in -[official documentation](https://spark.apache.org/docs/latest/sql-data-sources-json.html). -**Option names should be in** `camelCase`! - -The set of supported options depends on Spark version. - -Reading files - -```py -from onetl.file.format import JSONLine - -jsonline = JSONLine(encoding="UTF-8", mode="PERMISSIVE") -``` - -Writing files - -#### WARNING -Written files have extension `.json`, not `.jsonl` or `.jsonline`. - -```python -from onetl.file.format import JSONLine - -jsonline = JSONLine(encoding="UTF-8", compression="gzip") -``` - - - -#### *field* encoding *: str | None* *= None* - -Encoding of the JSONLine files. -Default `UTF-8`. - - - -#### *field* lineSep *: str | None* *= None* - -Character used to separate lines in the JSONLine files. - -Defaults: -: * Try to detect for reading (`\r\n`, `\r`, `\n`) - * `\n` for writing. - - - -#### *field* compression *: str | Literal['none', 'bzip2', 'gzip', 'lz4', 'snappy', 'deflate'] | None* *= None* - -Compression codec of the JSONLine file. -Default `none`. - -#### NOTE -Used only for writing files. - - - -#### *field* ignoreNullFields *: bool | None* *= None* - -If `True` and field value is `null`, don’t add field into resulting object -Default is value of `spark.sql.jsonGenerator.ignoreNullFields` (`True`). - -#### NOTE -Used only for writing files. - - - -#### *field* allowComments *: bool | None* *= None* - -If `True`, add support for C/C++/Java style comments (`//`, `/* */`). -Default `False`, meaning that JSONLine files should not contain comments. - -#### NOTE -Used only for reading files. - - - -#### *field* allowUnquotedFieldNames *: bool | None* *= None* - -If `True`, allow JSON object field names without quotes (JavaScript-style). -Default `False`. - -#### NOTE -Used only for reading files. - - - -#### *field* allowSingleQuotes *: bool | None* *= None* - -If `True`, allow JSON object field names to be wrapped with single quotes (`'`). -Default `True`. - -#### NOTE -Used only for reading files. - - - -#### *field* allowNumericLeadingZeros *: bool | None* *= None* - -If `True`, allow leading zeros in numbers (e.g. `00012`). -Default `False`. - -#### NOTE -Used only for reading files. - - - -#### *field* allowNonNumericNumbers *: bool | None* *= None* - -If `True`, allow numbers to contain non-numeric characters, like: -: * scientific notation (e.g. `12e10`). - * positive infinity floating point value (`Infinity`, `+Infinity`, `+INF`). - * negative infinity floating point value (`-Infinity`, `-INF`). - * Not-a-Number floating point value (`NaN`). - -Default `True`. - -#### NOTE -Used only for reading files. - - - -#### *field* allowBackslashEscapingAnyCharacter *: bool | None* *= None* - -If `True`, prefix `\` can escape any character. -Default `False`. - -#### NOTE -Used only for reading files. - - - -#### *field* allowUnquotedControlChars *: bool | None* *= None* - -If `True`, allow unquoted control characters (ASCII values 0-31) in strings without escaping them with `\`. -Default `False`. - -#### NOTE -Used only for reading files. - - - -#### *field* mode *: Literal['PERMISSIVE', 'DROPMALFORMED', 'FAILFAST'] | None* *= None* - -How to handle parsing errors: -: * `PERMISSIVE` - set field value as `null`, move raw data to [`columnNameOfCorruptRecord`](#onetl.file.format.jsonline.JSONLine.columnNameOfCorruptRecord) column. - * `DROPMALFORMED` - skip the malformed row. - * `FAILFAST` - throw an error immediately. - -Default is `PERMISSIVE`. - -#### NOTE -Used only for reading files. - - - -#### *field* columnNameOfCorruptRecord *: str | None* *= None* - -Name of column to put corrupt records in. -Default is `_corrupt_record`. - -#### WARNING -If DataFrame schema is provided, this column should be added to schema explicitly: - -```python -from onetl.connection import SparkLocalFS -from onetl.file import FileDFReader -from onetl.file.format import JSONLine - -from pyspark.sql.types import StructType, StructField, TimestampType, StringType - -spark = ... - -schema = StructType( - [ - StructField("my_field", TimestampType()), - StructField("_corrupt_record", StringType()), # <-- important - ] -) - -jsonline = JSONLine(mode="PERMISSIVE", columnNameOfCorruptRecord="_corrupt_record") - -reader = FileDFReader( - connection=connection, - format=jsonline, - schema=schema, # < --- -) -df = reader.run(["/some/file.jsonl"]) -``` - -#### NOTE -Used only for reading files. - - -* **Constraints:** - - **minLength** = 1 - -#### \_\_init_\_(\*\*kwargs) - - - -#### *field* samplingRatio *: float | None* *= None* - -While inferring schema, read the specified fraction of file rows. -Default `1`. - -#### NOTE -Used only for reading files. - - -* **Constraints:** - - **minimum** = 0 - - **maximum** = 1 - -#### *field* primitivesAsString *: bool | None* *= None* - -If `True`, infer all primitive types (string, integer, float, boolean) as strings. -Default `False`. - -#### NOTE -Used only for reading files. - - - -#### *field* prefersDecimal *: bool | None* *= None* - -If `True`, infer all floating-point values as `Decimal`. -Default `False`. - -#### NOTE -Used only for reading files. - - - -#### *field* dropFieldIfAllNull *: bool | None* *= None* - -If `True` and inferred column is always null or empty array, exclude if from DataFrame schema. -Default `False`. - -#### NOTE -Used only for reading files. - - - -#### *field* dateFormat *: str | None* *= None* - -String format for `DateType()` representation. -Default is `yyyy-MM-dd`. - - -* **Constraints:** - - **minLength** = 1 - -#### *field* timestampFormat *: str | None* *= None* - -String format for TimestampType()\` representation. -Default is `yyyy-MM-dd'T'HH:mm:ss[.SSS][XXX]`. - - -* **Constraints:** - - **minLength** = 1 - -#### *field* timestampNTZFormat *: str | None* *= None* - -String format for TimestampNTZType()\` representation. -Default is `yyyy-MM-dd'T'HH:mm:ss[.SSS]`. - -#### NOTE -Added in Spark 3.2.0 - - -* **Constraints:** - - **minLength** = 1 - -#### *field* timezone *: str | None* *= None* *(alias 'timeZone')* - -Allows to override timezone used for parsing or serializing date and timestamp values. -By default, `spark.sql.session.timeZone` is used. - - -* **Constraints:** - - **minLength** = 1 - -#### *field* locale *: str | None* *= None* - -Locale name used to parse dates and timestamps. -Default is `en-US`. - -#### NOTE -Used only for reading files. - - -* **Constraints:** - - **minLength** = 1 diff --git a/mddocs/markdown/file_df/file_formats/orc.md b/mddocs/markdown/file_df/file_formats/orc.md deleted file mode 100644 index d922a7e47..000000000 --- a/mddocs/markdown/file_df/file_formats/orc.md +++ /dev/null @@ -1,80 +0,0 @@ - - -# ORC - -### *class* onetl.file.format.orc.ORC(\*, mergeSchema: bool | None = None, compression: str | Literal['uncompressed', 'snappy', 'zlib', 'lzo', 'zstd', 'lz4'] | None = None, \*\*kwargs) - -ORC file format (columnar). [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -Based on [Spark ORC Files](https://spark.apache.org/docs/latest/sql-data-sources-orc.html) file format. - -Supports reading/writing files with `.orc` extension. - -#### Versionadded -Added in version 0.9.0. - -### Examples - -#### NOTE -You can pass any option mentioned in -[official documentation](https://spark.apache.org/docs/latest/sql-data-sources-orc.html). -**Option names should be in** `camelCase`! - -The set of supported options depends on Spark version. - -You may also set options mentioned [orc-java documentation](https://orc.apache.org/docs/core-java-config.html). -They are prefixed with `orc.` with dots in names, so instead of calling constructor `ORC(orc.option=True)` (invalid in Python) -you should call method `ORC.parse({"orc.option": True})`. - -Reading files - -```py -from onetl.file.format import ORC - -orc = ORC(mergeSchema=True) -``` - -Writing files - -```python -from onetl.file.format import ORC - -orc = ORC.parse( - { - "compression": "snappy", - # Enable Bloom filter for columns 'id' and 'name' - "orc.bloom.filter.columns": "id,name", - # Set Bloom filter false positive probability - "orc.bloom.filter.fpp": 0.01, - # Do not use dictionary for 'highly_selective_column' - "orc.column.encoding.direct": "highly_selective_column", - # other options - } -) -``` - - - -#### *field* mergeSchema *: bool | None* *= None* - -Merge schemas of all ORC files being read into a single schema. -By default, Spark config option `spark.sql.orc.mergeSchema` value is used (`False`). - -#### NOTE -Used only for reading files. - - - -#### *field* compression *: str | Literal['uncompressed', 'snappy', 'zlib', 'lzo', 'zstd', 'lz4'] | None* *= None* - -Compression codec of the ORC files. -By default, Spark config option `spark.sql.orc.compression.codec` value is used (`snappy`). - -#### NOTE -Used only for writing files. - - - -#### \_\_init_\_(\*\*kwargs) - - diff --git a/mddocs/markdown/file_df/file_formats/parquet.md b/mddocs/markdown/file_df/file_formats/parquet.md deleted file mode 100644 index 6ff8aa6e0..000000000 --- a/mddocs/markdown/file_df/file_formats/parquet.md +++ /dev/null @@ -1,79 +0,0 @@ - - -# Parquet - -### *class* onetl.file.format.parquet.Parquet(\*, mergeSchema: bool | None = None, compression: str | Literal['uncompressed', 'snappy', 'gzip', 'lzo', 'brotli', 'lz4', 'lz4raw', 'zstd'] | None = None, \*\*kwargs) - -Parquet file format (columnar). [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -Based on [Spark Parquet Files](https://spark.apache.org/docs/latest/sql-data-sources-parquet.html) file format. - -Supports reading/writing files with `.parquet` extension. - -#### Versionadded -Added in version 0.9.0. - -### Examples - -#### NOTE -You can pass any option mentioned in -[official documentation](https://spark.apache.org/docs/latest/sql-data-sources-parquet.html). -**Option names should be in** `camelCase`! - -The set of supported options depends on Spark version. - -You may also set options mentioned [parquet-hadoop documentation](https://github.com/apache/parquet-java/blob/master/parquet-hadoop/README.md). -They are prefixed with `parquet.` with dots in names, so instead of calling constructor `Parquet(parquet.option=True)` (invalid in Python) -you should call method `Parquet.parse({"parquet.option": True})`. - -Reading files - -```py -from onetl.file.format import Parquet - -parquet = Parquet(mergeSchema=True) -``` - -Writing files - -```py -from onetl.file.format import Parquet - -parquet = Parquet.parse( - { - "compression": "snappy", - # Enable Bloom filter for columns 'id' and 'name' - "parquet.bloom.filter.enabled#id": True, - "parquet.bloom.filter.enabled#name": True, - # Set expected number of distinct values for column 'id' - "parquet.bloom.filter.expected.ndv#id": 10_000_000, - # other options - } -) -``` - - - -#### *field* mergeSchema *: bool | None* *= None* - -Merge schemas of all Parquet files being read into a single schema. -By default, Spark config option `spark.sql.parquet.mergeSchema` value is used (`false`). - -#### NOTE -Used only for reading files. - - - -#### *field* compression *: str | Literal['uncompressed', 'snappy', 'gzip', 'lzo', 'brotli', 'lz4', 'lz4raw', 'zstd'] | None* *= None* - -Compression codec of the Parquet files. -By default, Spark config option `spark.sql.parquet.compression.codec` value is used (`snappy`). - -#### NOTE -Used only for writing files. - - - -#### \_\_init_\_(\*\*kwargs) - - diff --git a/mddocs/markdown/file_df/file_formats/xml.md b/mddocs/markdown/file_df/file_formats/xml.md deleted file mode 100644 index 1da916bc1..000000000 --- a/mddocs/markdown/file_df/file_formats/xml.md +++ /dev/null @@ -1,440 +0,0 @@ - - -# XML - -### *class* onetl.file.format.xml.XML(\*, rowTag: str, rootTag: str | None = None, compression: str | Literal['bzip2', 'gzip', 'lz4', 'snappy'] | None = None, timestampFormat: str | None = None, dateFormat: str | None = None, timezone: str | None = None, nullValue: str | None = None, ignoreSurroundingSpaces: bool | None = None, mode: Literal['PERMISSIVE', 'DROPMALFORMED', 'FAILFAST'] | None = None, columnNameOfCorruptRecord: str | None = None, inferSchema: bool | None = None, samplingRatio: ConstrainedFloatValue | None = None, charset: str | None = None, valueTag: str | None = None, attributePrefix: str | None = None, excludeAttribute: bool | None = None, wildcardColName: str | None = None, ignoreNamespace: bool | None = None, rowValidationXSDPath: str | None = None, declaration: str | None = None, arrayElementName: str | None = None, \*\*kwargs) - -XML file format. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -Based on [Databricks Spark XML](https://github.com/databricks/spark-xml) file format. - -Supports reading/writing files with `.xml` extension. - -#### Versionadded -Added in version 0.9.5. - -### Version compatibility - -* Spark versions: 3.2.x - 3.5.x -* Java versions: 8 - 20 - -See [official documentation](https://github.com/databricks/spark-xml). - -### Examples - -#### NOTE -You can pass any option mentioned in -[official documentation](https://github.com/databricks/spark-xml). -**Option names should be in** `camelCase`! - -The set of supported options depends on `spark-xml` version. - -Reading files - -```py -from onetl.file.format import XML -from pyspark.sql import SparkSession - -# Create Spark session with XML package loaded -maven_packages = XML.get_packages(spark_version="3.5.5") -spark = ( - SparkSession.builder.appName("spark-app-name") - .config("spark.jars.packages", ",".join(maven_packages)) - .getOrCreate() -) - -xml = XML(rowTag="item", mode="PERMISSIVE") -``` - -Writing files - -#### WARNING -Due to [bug](https://github.com/databricks/spark-xml/issues/664) written files currently does not have `.xml` extension. - -```python -# Create Spark session with XML package loaded -spark = ... - -from onetl.file.format import XML - -xml = XML(rowTag="item", rootTag="data", compression="gzip") -``` - - - -#### *field* row_tag *: str* *[Required]* *(alias 'rowTag')* - -XML tag that encloses each row in XML. Required. - - - -#### *field* rootTag *: str | None* *= None* - -XML tag that encloses content of all DataFrame. Default is `ROWS`. - -#### NOTE -Used only for writing files. - - - -#### *field* compression *: str | Literal['bzip2', 'gzip', 'lz4', 'snappy'] | None* *= None* - -Compression codec. By default no compression is used. - -#### NOTE -Used only for writing files. - - - -#### *field* timestampFormat *: str | None* *= None* - -Format string used for parsing or serializing timestamp values. -By default, ISO 8601 format is used (`yyyy-MM-ddTHH:mm:ss.SSSZ`). - - - -#### *field* dateFormat *: str | None* *= None* - -Format string used for parsing or serializing date values. -By default, ISO 8601 format is used (`yyyy-MM-dd`). - - - -#### *field* nullValue *: str | None* *= None* - -String value used to represent null. Default is `null` string. - - - -#### *field* ignoreSurroundingSpaces *: bool | None* *= None* - -If `True`, trim surrounding spaces while parsing values. Default `false`. - -#### NOTE -Used only for reading files or by [`parse_column`](#onetl.file.format.xml.XML.parse_column) function. - - - -#### *field* mode *: Literal['PERMISSIVE', 'DROPMALFORMED', 'FAILFAST'] | None* *= None* - -How to handle parsing errors: -: * `PERMISSIVE` - set field value as `null`, move raw data to [`columnNameOfCorruptRecord`](#onetl.file.format.xml.XML.columnNameOfCorruptRecord) column. - * `DROPMALFORMED` - skip the malformed row. - * `FAILFAST` - throw an error immediately. - -Default is `PERMISSIVE`. - -#### NOTE -Used only for reading files or by [`parse_column`](#onetl.file.format.xml.XML.parse_column) function. - - - -#### *field* columnNameOfCorruptRecord *: str | None* *= None* - -Name of DataFrame column there corrupted row is stored with `mode=PERMISSIVE`. - -#### WARNING -If DataFrame schema is provided, this column should be added to schema explicitly: - -```python -from onetl.connection import SparkLocalFS -from onetl.file import FileDFReader -from onetl.file.format import XML - -from pyspark.sql.types import StructType, StructField, TImestampType, StringType - -spark = ... -schema = StructType( - [ - StructField("my_field", TimestampType()), - StructField("_corrupt_record", StringType()), # <-- important - ] -) -xml = XML(rowTag="item", columnNameOfCorruptRecord="_corrupt_record") - -reader = FileDFReader( - connection=connection, - format=xml, - schema=schema, # < --- -) -df = reader.run(["/some/file.xml"]) -``` - -#### NOTE -Used only for reading files or by [`parse_column`](#onetl.file.format.xml.XML.parse_column) function. - - - -#### *field* inferSchema *: bool | None* *= None* - -If `True`, try to infer the input schema by reading a sample of the file (see [`samplingRatio`](#onetl.file.format.xml.XML.samplingRatio)). -Default `False` which means that all parsed columns will be `StringType()`. - -#### NOTE -Used only for reading files. Ignored by [`parse_column`](#onetl.file.format.xml.XML.parse_column) function. - - - -#### *field* samplingRatio *: float | None* *= None* - -For `inferSchema=True`, read the specified fraction of rows to infer the schema. -Default `1`. - -#### NOTE -Used only for reading files. Ignored by [`parse_column`](#onetl.file.format.xml.XML.parse_column) function. - - -* **Constraints:** - - **minimum** = 0 - - **maximum** = 1 - -#### *field* charset *: str | None* *= None* - -File encoding. Default is `UTF-8` - -#### NOTE -Used only for reading files or by [`parse_column`](#onetl.file.format.xml.XML.parse_column) function. - - - -#### *field* valueTag *: str | None* *= None* - -Value used to replace missing values while parsing attributes like ``. -Default `_VALUE`. - -#### NOTE -Used only for reading files or by [`parse_column`](#onetl.file.format.xml.XML.parse_column) function. - - - -#### *field* attributePrefix *: str | None* *= None* - -While parsing tags containing attributes like ``, attributes are stored as -DataFrame schema columns with specified prefix, e.g. `_attr`. -Default `_`. - -#### NOTE -Used only for reading files or by [`parse_column`](#onetl.file.format.xml.XML.parse_column) function. - - - -#### *field* excludeAttribute *: bool | None* *= None* - -If `True`, exclude attributes while parsing tags like ``. -Default `false`. - -#### NOTE -Used only for reading files or by [`parse_column`](#onetl.file.format.xml.XML.parse_column) function. - - - -#### *field* wildcardColName *: str | None* *= None* - -Name of column or columns which should be preserved as raw XML string, and not parsed. - -#### WARNING -If DataFrame schema is provided, this column should be added to schema explicitly. -See [`columnNameOfCorruptRecord`](#onetl.file.format.xml.XML.columnNameOfCorruptRecord) example. - -#### NOTE -Used only for reading files or by [`parse_column`](#onetl.file.format.xml.XML.parse_column) function. - - - -#### *field* ignoreNamespace *: bool | None* *= None* - -If `True`, all namespaces like `` will be ignored and treated as just ``. -Default `False`. - -#### NOTE -Used only for reading files or by [`parse_column`](#onetl.file.format.xml.XML.parse_column) function. - - - -#### *field* rowValidationXSDPath *: str | None* *= None* - -Path to XSD file which should be used to validate each row. -If row does not match XSD, it will be treated as error, behavior depends on [`mode`](#onetl.file.format.xml.XML.mode) value. - -Default is no validation. - -#### NOTE -If Spark session is created with `master=yarn` or `master=k8s`, XSD -file should be accessible from all Spark nodes. This can be achieved by calling: - -```python -spark.addFile("/path/to/file.xsd") -``` - -And then by passing `rowValidationXSDPath=file.xsd` (relative path). - -#### NOTE -Used only for reading files or by [`parse_column`](#onetl.file.format.xml.XML.parse_column) function. - - - -#### *field* declaration *: str | None* *= None* - -Content of declaration. -Default is `version="1.0" encoding="UTF-8" standalone="yes"`. - -#### NOTE -Used only for writing files. - - - -#### *field* arrayElementName *: str | None* *= None* - -If DataFrame column is `ArrayType`, its content will be written to XML -inside `...` tag. -Default is `item`. - -#### NOTE -Used only for writing files. - - - -#### *classmethod* get_packages(spark_version: str, scala_version: str | None = None, package_version: str | None = None) → list[str] - -Get package names to be downloaded by Spark. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -#### Versionadded -Added in version 0.9.5. - -* **Parameters:** - **spark_version** - : Spark version in format `major.minor.patch`. - - **scala_version** - : Scala version in format `major.minor`. -
- If `None`, `spark_version` is used to determine Scala version. - - **package_version** - : Package version in format `major.minor.patch`. Default is `0.18.0`. -
- See [Maven index](https://mvnrepository.com/artifact/com.databricks/spark-xml) - for list of available versions. -
- #### WARNING - Version `0.13` and below are not supported. -
- #### NOTE - It is not guaranteed that custom package versions are supported. - Tests are performed only for default version. - -### Examples - -```python -from onetl.file.format import XML - -XML.get_packages(spark_version="3.5.5") -XML.get_packages(spark_version="3.5.5", scala_version="2.12") -XML.get_packages( - spark_version="3.5.5", - scala_version="2.12", - package_version="0.18.0", -) -``` - - - -#### parse_column(column: str | Column, schema: StructType) → Column - -Parses an XML string column into a structured Spark SQL column using the `from_xml` function -provided by the [Databricks Spark XML library](https://github.com/databricks/spark-xml#pyspark-notes) -based on the provided schema. - -#### NOTE -This method assumes that the `spark-xml` package is installed: [`get_packages`](#onetl.file.format.xml.XML.get_packages). - -#### NOTE -This method parses each DataFrame row individually. Therefore, for a specific column, each row must contain exactly one occurrence of the `rowTag` specified. -If your XML data includes a root tag that encapsulates multiple row tags, you can adjust the schema to use an `ArrayType` to keep all child elements under the single root. - -```xml - - Book OneAuthor A - Book TwoAuthor B - -``` - -And the corresponding schema in Spark using an `ArrayType`: - -```python -from pyspark.sql.types import StructType, StructField, ArrayType, StringType -from onetl.file.format import XML - -# each DataFrame row has exactly one tag -xml = XML(rowTag="books") -# each tag have multiple tags, so using ArrayType for such field -schema = StructType( - [ - StructField( - "book", - ArrayType( - StructType( - [ - StructField("title", StringType(), nullable=True), - StructField("author", StringType(), nullable=True), - ], - ), - ), - nullable=True, - ), - ], -) -``` - -#### Versionadded -Added in version 0.11.0. - -* **Parameters:** - **column** - : The name of the column or the column object containing XML strings/bytes to parse. - - **schema** - : The schema to apply when parsing the XML data. This defines the structure of the output DataFrame column. -* **Returns:** - Column with deserialized data, with the same structure as the provided schema. Column name is the same as input column. - -### Examples - -```pycon ->>> from pyspark.sql.types import StructType, StructField, IntegerType, StringType ->>> from onetl.file.format import XML ->>> df.show() -+--+------------------------------------------------+ -|id|value | -+--+------------------------------------------------+ -|1 |Alice20| -|2 |Bob25 | -+--+------------------------------------------------+ ->>> df.printSchema() -root -|-- id: integer (nullable = true) -|-- value: string (nullable = true) ->>> xml = XML(rowTag="person") ->>> xml_schema = StructType( -... [ -... StructField("name", StringType(), nullable=True), -... StructField("age", IntegerType(), nullable=True), -... ], -... ) ->>> parsed_df = df.select("key", xml.parse_column("value", xml_schema)) ->>> parsed_df.show() -+--+-----------+ -|id|value | -+--+-----------+ -|1 |{Alice, 20}| -|2 | {Bob, 25}| -+--+-----------+ ->>> parsed_df.printSchema() -root -|-- id: integer (nullable = true) -|-- value: struct (nullable = true) -| |-- name: string (nullable = true) -| |-- age: integer (nullable = true) -``` - - diff --git a/mddocs/markdown/file_df/index.md b/mddocs/markdown/file_df/index.md deleted file mode 100644 index c225567d6..000000000 --- a/mddocs/markdown/file_df/index.md +++ /dev/null @@ -1,7 +0,0 @@ - - - File DataFrame classes - -* [FileDF Reader](file_df_reader/index.md) -* [FileDF Writer](file_df_writer/index.md) -* [File Formats](file_formats/index.md) diff --git a/mddocs/markdown/hooks/design.md b/mddocs/markdown/hooks/design.md deleted file mode 100644 index 041f206ee..000000000 --- a/mddocs/markdown/hooks/design.md +++ /dev/null @@ -1,642 +0,0 @@ - - -# High level design - -## What are hooks? - -Hook mechanism is a part of onETL which allows to inject some additional behavior into -existing methods of (almost) any class. - -### Features - -Hooks mechanism allows to: - -* Inspect and validate input arguments and output results of method call -* Access, modify or replace method call result (but NOT input arguments) -* Wrap method calls with a context manager and catch raised exceptions - -Hooks can be placed into [Plugins](../plugins.md#plugins), allowing to modify onETL behavior by installing some additional package. - -### Limitations - -* Hooks can be bound to methods of a class only (not functions). -* Only methods decorated with [@slot decorator](slot.md#slot-decorator) implement hooks mechanism. These class and methods are marked as [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html). -* Hooks can be bound to public methods only. - -## Terms - -* [@slot decorator](slot.md#slot-decorator) - method of a class with a special decorator -* `Callback` - function which implements some additional logic which modifies slot behavior -* [@hook decorator](hook.md#hook-decorator) - wrapper around callback which stores hook state, priority and some useful methods -* `Hooks mechanism` - calling `Slot()` will call all enabled hooks which are bound to the slot. Implemented by [@support_hooks decorator](support_hooks.md#support-hooks-decorator). - -## How to implement hooks? - -### TL;DR - -```python -from onetl.hooks import support_hooks, slot, hook - - -@support_hooks # enabling hook mechanism for the class -class MyClass: - def __init__(self, data): - self.data = data - - # this is slot - @slot - def method(self, arg): - pass - - -@MyClass.method.bind # bound hook to the slot -@hook # this is hook -def callback(obj, arg): # this is callback - print(obj.data, arg) - - -obj = MyClass(1) -obj.method(2) # will call callback(obj, 1) - -# prints "1 2" -``` - -#### Define a slot - -* Create a class with a method: - -```python -class MyClass: - def __init__(self, data): - self.data = data - - def method(self, arg): - return self.data, arg -``` - -* Add [@slot decorator](slot.md#slot-decorator) to the method: - -```python -from onetl.hooks import support_hooks, slot, hook - - -class MyClass: - @slot - def method(self, arg): - return self.data, arg -``` - -If method has other decorators like `@classmethod` or `@staticmethod`, `@slot` should be placed on the top: - -```python -from onetl.hooks import support_hooks, slot, hook - - -class MyClass: - @slot - @classmethod - def class_method(cls, arg): - return cls, arg - - @slot - @staticmethod - def static_method(arg): - return arg -``` - -* Add [@support_hooks decorator](support_hooks.md#support-hooks-decorator) to the class: - -```python -from onetl.hooks import support_hooks, slot, hook - - -@support_hooks -class MyClass: - @slot - def method(self, arg): - return self.data, arg -``` - -Slot is created. - -#### Define a callback - -Define some function (a.k.a callback): - -```python -def callback(self, arg): - print(self.data, arg) -``` - -It should have signature *compatible* with `MyClass.method`. *Compatible* does not mean *exactly the same* - -for example, you can rename positional arguments: - -```python -def callback(obj, arg): - print(obj.data, arg) -``` - -Use `*args` and `**kwargs` to omit arguments you don’t care about: - -```python -def callback(obj, *args, **kwargs): - print(obj.data, args, kwargs) -``` - -There is also an argument `method_name` which has a special meaning - -the method name which the callback is bound to is passed into this argument: - -```python -def callback(obj, *args, method_name: str, **kwargs): - print(obj.data, args, method_name, kwargs) -``` - -#### NOTE -`method_name` should always be a keyword argument, **NOT** positional. - -#### WARNING -If callback signature is not compatible with slot signature, an exception will be raised, -but **ONLY** while slot is called. - -#### Define a hook - -Add [@hook decorator](hook.md#hook-decorator) to create a hook from your callback: - -```python -@hook -def callback(obj, arg): - print(obj.data, arg) -``` - -You can pass more options to the `@hook` decorator, like state or priority. -See decorator documentation for more details. - -#### Bind hook to the slot - -Use `Slot.bind` method to bind hook to the slot: - -```python -@MyClass.method.bind -@hook -def callback(obj, arg): - print(obj, arg) -``` - -You can bind more than one hook to the same slot, and bind same hook to multiple slots: - -```python -@MyClass.method1.bind -@MyClass.method2.bind -@hook -def callback1(obj, arg): - "Will be called by both MyClass.method1 and MyClass.method2" - - -@MyClass.method1.bind -@hook -def callback2(obj, arg): - "Will be called by MyClass.method1 too" -``` - -## How hooks are called? - -### General - -Just call the method decorated by `@slot` to trigger the hook: - -```python -obj = MyClass(1) -obj.method(2) # will call callback(obj, 2) - -# prints "1 2" -``` - -There are some special callback types that has a slightly different behavior. - -### Context managers - -`@hook` decorator can be placed on a context manager class: - -```python -@hook -class ContextManager: - def __init__(self, obj, arg): - self.obj = obj - self.arg = arg - - def __enter__(self): - # do something on enter - print(obj.data, arg) - return self - - def __exit__(self, exc_type, exc_value, traceback): - # do something on exit - return False -``` - -Context manager is entered while calling the `Slot()`, and exited then the call is finished. - -If present, method `process_result` has a special meaning - -it can receive `MyClass.method` call result, and also modify/replace it: - -```python -@hook -class ContextManager: - def __init__(self, obj, arg): - self.obj = obj - self.arg = arg - - def __enter__(self): - # do something on enter - print(obj.data, arg) - return self - - def __exit__(self, exc_type, exc_value, traceback): - # do something on exit - return False - - def process_result(self, result): - # do something with method call result - return modified(result) -``` - -See examples below for more information. - -### Generator function - -`@hook` decorator can be placed on a generator function: - -```python -@hook -def callback(obj, arg): - print(obj.data, arg) - # this is called before original method body - - yield # method is called here - - # this is called after original method body -``` - -It is converted to a context manager, in the same manner as -[contextlib.contextmanager](https://docs.python.org/3/library/contextlib.html#contextlib.contextmanager). - -Generator body can be wrapped with `try..except..finally` to catch exceptions: - -```python -@hook -def callback(obj, arg): - print(obj.data, arg) - - try: - # this is called before original method body - - yield # method is called here - except Exception as e: - process_exception(a) - finally: - # this is called after original method body - finalizer() -``` - -There is also a special syntax which allows generator to access and modify/replace method call result: - -```default -@hook -def callback(obj, arg): - original_result = yield # method is called here - - new_result = do_something(original_result) - - yield new_result # modify/replace the result -``` - -### Calling hooks in details - -* The callback will be called with the same arguments as the original method. - * If slot is a regular method: - ```python - callback_result = callback(self, *args, **kwargs) - ``` - - Here `self` is a class instance (`obj`). - * If slot is a class method: - ```python - callback_result = callback(cls, *args, **kwargs) - ``` - - Here `cls` is the class itself (`MyClass`). - * If slot is a static method: - ```python - callback_result = callback(*args, **kwargs) - ``` - - Neither object not class are passed to the callback in this case. -* If `callback_result` is a context manager, enter the context. Context manager can catch all the exceptions raised. - > If there are multiple hooks bound the the slot, every context manager will be entered. -* Then call the original method wrapped by `@slot`: - ```python - original_result = method(*args, **kwargs) - ``` -* Process `original_result`: - * If `callback_result` object has method `process_result`, or is a generator wrapped with `@hook`, call it: - ```python - new_result = callback_result.process_result(original_result) - ``` - * Otherwise set `new_result = callback_result`. - * If there are multiple hooks bound the the method, pass `new_result` through the chain: - ```python - new_result = callback1_result.process_result(original_result) - new_result = callback2_result.process_result(new_result or original_result) - new_result = callback3_result.process_result(new_result or original_result) - ``` -* Finally return: - ```python - return new_result or original_result - ``` - - All `None` values are ignored on every step above. -* Exit all the context managers entered during the slot call. - -### Hooks priority - -Hooks are executed in the following order: - -1. Parent class slot + [`FIRST`](hook.md#onetl.hooks.hook.HookPriority.FIRST) -2. Inherited class slot + [`FIRST`](hook.md#onetl.hooks.hook.HookPriority.FIRST) -3. Parent class slot + [`NORMAL`](hook.md#onetl.hooks.hook.HookPriority.NORMAL) -4. Inherited class slot + [`NORMAL`](hook.md#onetl.hooks.hook.HookPriority.NORMAL) -5. Parent class slot + [`LAST`](hook.md#onetl.hooks.hook.HookPriority.LAST) -6. Inherited class slot + [`LAST`](hook.md#onetl.hooks.hook.HookPriority.LAST) - -Hooks with the same priority and inheritance will be executed in the same order they were registered (`Slot.bind` call). - -#### NOTE -Calls of `super()` inside inherited class methods does not trigger hooks call. -Hooks are triggered only if method is called explicitly. - -This allow to wrap with a hook the entire slot call without influencing its internal logic. - -### Hook types - -Here are several examples of using hooks. These types are not exceptional, they can be mixed - for example, -hook can both modify method result and catch exceptions. - -#### Before hook - -Can be used for inspecting or validating input args of the original function: - -```python -@hook -def before1(obj, arg): - print(obj, arg) - # original method is called after exiting this function - - -@hook -def before2(obj, arg): - if arg == 1: - raise ValueError("arg=1 is not allowed") - return None # return None is the same as no return statement -``` - -Executed before calling the original method wrapped by `@slot`. -If hook raises an exception, method will not be called at all. - -#### After hook - -Can be used for performing some actions after original method was successfully executed: - -```python -@hook -def after1(obj, arg): - yield # original method is called here - print(obj, arg) - - -@hook -def after2(obj, arg): - yield None # yielding None is the same as empty yield - if arg == 1: - raise ValueError("arg=1 is not allowed") -``` - -If original method raises an exception, the block of code after `yield` will not be called. - -#### Context hook - -Can be used for catching and handling some exceptions, or to determine that there was no exception during slot call: - -Generator syntax - -```py -# This is just the same as using @contextlib.contextmanager - -@hook -def context_generator(obj, arg): - try: - yield # original method is called here - print(obj, arg) # <-- this line will not be called if method raised an exception - except SomeException as e: - magic(e) - finally: - finalizer() -``` - -Context manager syntax - -```py -@hook -class ContextManager: - def __init__(self, obj, args): - self.obj = obj - self.args = args - - def __enter__(self): - return self - - # original method is called between __enter__ and __exit__ - - def __exit__(self, exc_type, exc_value, traceback): - result = False - if exc_type is not None and isinstance(exc_value, SomeException): - magic(exc_value) - result = True # suppress exception - else: - print(self.obj, self.arg) - finalizer() - return result -``` - -#### NOTE -Contexts are exited in the reverse order of the hook calls. -So if some hook raised an exception, it will be passed into the previous hook, not the next one. - -It is recommended to specify the proper priority for the hook, e.g. [`FIRST`](hook.md#onetl.hooks.hook.HookPriority.FIRST) - -#### Replacing result hook - -Replaces the output result of the original method. - -Can be used for delegating some implementation details for third-party extensions. -See [Hive](../connection/db_connection/hive/index.md#hive) and [HDFS](../connection/file_connection/hdfs/index.md#hdfs) as an example. - -```python -@hook -def replace1(obj, arg): - result = arg + 10 # any non-None return result - - # original method call result is ignored, output will always be arg + 10 - return result - - -@hook -def replace2(obj, arg): - yield arg + 10 # same as above -``` - -#### NOTE -If there are multiple hooks bound to the same slot, the result of last hook will be used. -It is recommended to specify the proper priority for the hook, e.g. [`LAST`](hook.md#onetl.hooks.hook.HookPriority.LAST) - -#### Accessing result hook - -Can access output result of the original method and inspect or validate it: - -Generator syntax - -```py -@hook -def access_result(obj, arg): - result = yield # original method is called here, and result can be used in the hook - print(result) - yield # does not modify result -``` - -Context manager syntax - -```py -@hook -class ModifiesResult: - def __init__(self, obj, args): - self.obj = obj - self.args = args - - def __enter__(self): - return self - - # original method is called between __enter__ and __exit__ - # result is passed into process_result method of context manager, if present - - def process_result(self, result): - print(result) # result can be used in the hook - return None # does not modify result. same as no return statement in the method - - def __exit__(self, exc_type, exc_value, traceback): - return False -``` - -#### Modifying result hook - -Can access output result of the original method, and return the modified one: - -Generator syntax - -```py -@hook -def modifies_result(obj, arg): - result = yield # original method is called here, and result can be used in the hook - yield result + 10 # modify output result. None values are ignored -``` - -Context manager syntax - -```py -@hook -class ModifiesResult: - def __init__(self, obj, args): - self.obj = obj - self.args = args - - def __enter__(self): - return self - - # original method is called between __enter__ and __exit__ - # result is passed into process_result method of context manager, if present - - def process_result(self, result): - print(result) # result can be used in the hook - return result + 10 # modify output result. None values are ignored - - def __exit__(self, exc_type, exc_value, traceback): - return False -``` - -#### NOTE -If there are multiple hooks bound to the same slot, the result of last hook will be used. -It is recommended to specify the proper priority for the hook, e.g. [`LAST`](hook.md#onetl.hooks.hook.HookPriority.LAST) - -## How to enable/disable hooks? - -You can enable/disable/temporary disable hooks on 4 different levels: - -* Manage global hooks state (level 1): - > * [`onetl.hooks.hooks_state.stop_all_hooks`](global_state.md#onetl.hooks.hooks_state.stop_all_hooks) - > * [`onetl.hooks.hooks_state.resume_all_hooks`](global_state.md#onetl.hooks.hooks_state.resume_all_hooks) - > * [`onetl.hooks.hooks_state.skip_all_hooks`](global_state.md#onetl.hooks.hooks_state.skip_all_hooks) -* Manage all hooks bound to a specific class (level 2): - > * [`onetl.hooks.support_hooks.suspend_hooks`](support_hooks.md#onetl.hooks.support_hooks.suspend_hooks) - > * [`onetl.hooks.support_hooks.resume_hooks`](support_hooks.md#onetl.hooks.support_hooks.resume_hooks) - > * [`onetl.hooks.support_hooks.skip_hooks`](support_hooks.md#onetl.hooks.support_hooks.skip_hooks) -* Manage all hooks bound to a specific slot (level 3): - > * [`onetl.hooks.slot.Slot.suspend_hooks`](slot.md#onetl.hooks.slot.Slot.suspend_hooks) - > * [`onetl.hooks.slot.Slot.resume_hooks`](slot.md#onetl.hooks.slot.Slot.resume_hooks) - > * [`onetl.hooks.slot.Slot.skip_hooks`](slot.md#onetl.hooks.slot.Slot.skip_hooks) -* Manage state of a specific hook (level 4): - > * [`onetl.hooks.hook.Hook.enable`](hook.md#onetl.hooks.hook.Hook.enable) - > * [`onetl.hooks.hook.Hook.disable`](hook.md#onetl.hooks.hook.Hook.disable) - -More details in the documentation above. - -#### NOTE -All of these levels are independent. - -Calling `stop` on the level 1 has higher priority than level 2, and so on. -But calling `resume` on the level 1 does not automatically resume hooks stopped in the level 2, -they should be resumed explicitly. - -## How to see logs of the hook mechanism? - -Hooks registration emits logs with `DEBUG` level: - -```python -from onetl.logs import setup_logging - -setup_logging() -``` - -```text -DEBUG |onETL| Registered hook 'mymodule.callback1' for 'MyClass.method' (enabled=True, priority=HookPriority.NORMAL) -DEBUG |onETL| Registered hook 'mymodule.callback2' for 'MyClass.method' (enabled=True, priority=HookPriority.NORMAL) -DEBUG |onETL| Registered hook 'mymodule.callback3' for 'MyClass.method' (enabled=False, priority=HookPriority.NORMAL) -``` - -But most of logs are emitted with even lower level `NOTICE`, to make output less verbose: - -```python -from onetl.logs import NOTICE, setup_logging - -setup_logging(level=NOTICE) -``` - -```default -NOTICE |Hooks| 2 hooks registered for 'MyClass.method' -NOTICE |Hooks| Calling hook 'mymodule.callback1' (1/2) -NOTICE |Hooks| Hook is finished with returning non-None result -NOTICE |Hooks| Calling hook 'mymodule.callback2' (2/2) -NOTICE |Hooks| This is a context manager, entering ... -NOTICE |Hooks| Calling original method 'MyClass.method' -NOTICE |Hooks| Method call is finished -NOTICE |Hooks| Method call result (*NOT* None) will be replaced with result of hook 'mymodule.callback1' -NOTICE |Hooks| Passing result to 'process_result' method of context manager 'mymodule.callback2' -NOTICE |Hooks| Method call result (*NOT* None) is modified by hook! -``` diff --git a/mddocs/markdown/hooks/global_state.md b/mddocs/markdown/hooks/global_state.md deleted file mode 100644 index 537389dd1..000000000 --- a/mddocs/markdown/hooks/global_state.md +++ /dev/null @@ -1,101 +0,0 @@ - - -# Hooks global state - -| [`skip_all_hooks`](#onetl.hooks.hooks_state.skip_all_hooks)() | Temporary stop all onETL hooks. | -|-------------------------------------------------------------------|-----------------------------------| -| [`stop_all_hooks`](#onetl.hooks.hooks_state.stop_all_hooks)() | Stop all hooks for all classes. | -| [`resume_all_hooks`](#onetl.hooks.hooks_state.resume_all_hooks)() | Resume all onETL hooks. | - -### onetl.hooks.hooks_state.skip_all_hooks() - -Temporary stop all onETL hooks. Designed to be used as context manager or decorator. - -#### NOTE -If hooks were stopped by [`stop_all_hooks`](#onetl.hooks.hooks_state.stop_all_hooks), they will not be resumed -after exiting the context/decorated function. -You should call [`resume_all_hooks`](#onetl.hooks.hooks_state.resume_all_hooks) explicitly. - -#### Versionadded -Added in version 0.7.0. - -### Examples - -Context manager syntax - -```py -from onetl.hooks import skip_all_hooks - -# hooks are enabled - -with skip_all_hooks(): - # hooks are stopped here - ... - -# hook state is restored -``` - -Decorator syntax - -```py -from onetl.hooks import skip_all_hooks - -# hooks are enabled - -@skip_all_hooks() -def main(): - # hooks are stopped here - ... - -main() -``` - - - -### onetl.hooks.hooks_state.stop_all_hooks() → None - -Stop all hooks for all classes. - -#### Versionadded -Added in version 0.7.0. - -### Examples - -```python -from onetl.hooks import stop_all_hooks - -# hooks are executed - -stop_all_hooks() - -# all hooks are stopped now -``` - - - -### onetl.hooks.hooks_state.resume_all_hooks() → None - -Resume all onETL hooks. - -#### NOTE -This function does not enable hooks which were disabled by [`onetl.hooks.hook.Hook.disable`](hook.md#onetl.hooks.hook.Hook.disable), -or stopped by [`onetl.hooks.support_hooks.suspend_hooks`](support_hooks.md#onetl.hooks.support_hooks.suspend_hooks). - -#### Versionadded -Added in version 0.7.0. - -### Examples - -```python -from onetl.hooks import resume_all_hooks, stop_all_hooks - -stop_all_hooks() - -# hooks are stopped - -resume_all_hooks() - -# all hooks are executed now -``` - - diff --git a/mddocs/markdown/hooks/hook.md b/mddocs/markdown/hooks/hook.md deleted file mode 100644 index 402f79688..000000000 --- a/mddocs/markdown/hooks/hook.md +++ /dev/null @@ -1,223 +0,0 @@ - - -# `@hook` decorator - -| [`hook`](#onetl.hooks.hook.hook)([inp, enabled, priority]) | Initialize hook from callable/context manager. | -|-------------------------------------------------------------------------------|--------------------------------------------------| -| [`HookPriority`](#onetl.hooks.hook.HookPriority)(value[, names, module, ...]) | Hook priority enum. | -| [`Hook`](#onetl.hooks.hook.Hook)(callback[, enabled, priority]) | Hook representation. | -| [`Hook.enable`](#onetl.hooks.hook.Hook.enable)() | Enable the hook. | -| [`Hook.disable`](#onetl.hooks.hook.Hook.disable)() | Disable the hook. | -| [`Hook.skip`](#onetl.hooks.hook.Hook.skip)() | Temporary disable the hook. | - -### @onetl.hooks.hook.hook(inp: Callable[[...], T] | None = None, enabled: bool = True, priority: [HookPriority](#onetl.hooks.hook.HookPriority) = HookPriority.NORMAL) - -Initialize hook from callable/context manager. - -#### Versionadded -Added in version 0.7.0. - -### Examples - -Decorate a function or generator - -```py -from onetl.hooks import hook, HookPriority - -@hook -def some_func(*args, **kwargs): - ... - -@hook(enabled=True, priority=HookPriority.FIRST) -def another_func(*args, **kwargs): - ... -``` - -Decorate a context manager - -```py -from onetl.hooks import hook, HookPriority - -@hook -class SimpleContextManager: - def __init__(self, *args, **kwargs): - ... - - def __enter__(self): - ... - return self - - def __exit__(self, exc_type, exc_value, traceback): - ... - return False - -@hook(enabled=True, priority=HookPriority.FIRST) -class ContextManagerWithProcessResult: - def __init__(self, *args, **kwargs): - ... - - def __enter__(self): - ... - return self - - def __exit__(self, exc_type, exc_value, traceback): - ... - return False - - def process_result(self, result): - # special method to handle method result call - return modify(result) - - ... -``` - - - -### *class* onetl.hooks.hook.HookPriority(value, names=None, \*, module=None, qualname=None, type=None, start=1, boundary=None) - -Hook priority enum. - -All hooks within the same priority are executed in the same order they were registered. - -#### Versionadded -Added in version 0.7.0. - - - -#### FIRST *= -1* - -Hooks with this priority will run first. - - - -#### NORMAL *= 0* - -Hooks with this priority will run after [`FIRST`](#onetl.hooks.hook.HookPriority.FIRST) but before [`LAST`](#onetl.hooks.hook.HookPriority.LAST). - - - -#### LAST *= 1* - -Hooks with this priority will run last. - - - -### *class* onetl.hooks.hook.Hook(callback: Callable[[...], T], enabled: bool = True, priority: [HookPriority](#onetl.hooks.hook.HookPriority) = HookPriority.NORMAL) - -Hook representation. - -#### Versionadded -Added in version 0.7.0. - -* **Parameters:** - **callback** - : Some callable object which will be wrapped into a Hook, like function or ContextManager class. - - **enabled** - : Will hook be executed or not. Useful for debugging. - - **priority** - : Changes hooks priority, see `HookPriority` documentation. - -### Examples - -```python -from onetl.hooks.hook import Hook, HookPriority - -def some_func(*args, **kwargs): ... - -hook = Hook(callback=some_func, enabled=True, priority=HookPriority.FIRST) -``` - - - -#### enable() - -Enable the hook. - -#### Versionadded -Added in version 0.7.0. - -### Examples - -```pycon ->>> def func1(): ... ->>> hook = Hook(callback=func1, enabled=False) ->>> hook.enabled -False ->>> hook.enable() ->>> hook.enabled -True -``` - - - -#### disable() - -Disable the hook. - -#### Versionadded -Added in version 0.7.0. - -### Examples - -```pycon ->>> def func1(): ... ->>> hook = Hook(callback=func1, enabled=True) ->>> hook.enabled -True ->>> hook.disable() ->>> hook.enabled -False -``` - - - -#### skip() - -Temporary disable the hook. - -#### NOTE -If hook was created with `enabled=False`, or was disabled by [`disable`](#onetl.hooks.hook.Hook.disable), -its state will left intact after exiting the context. - -You should call [`enable`](#onetl.hooks.hook.Hook.enable) explicitly to change its state. - -#### Versionadded -Added in version 0.7.0. - -### Examples - -Context manager syntax - -```pycon ->>> def func1(): ... ->>> hook = Hook(callback=func1, enabled=True) ->>> hook.enabled -True ->>> with hook.skip(): -... print(hook.enabled) -False ->>> # hook state is restored as it was before entering the context manager ->>> hook.enabled -True -``` - -Decorator syntax - -```pycon ->>> def func1(): ... ->>> hook = Hook(callback=func1, enabled=True) ->>> hook.enabled -True ->>> @hook.skip() -... def hook_disabled(): -... print(hook.enabled) ->>> hook_disabled() -False ->>> # hook state is restored as it was before entering the context manager ->>> hook.enabled -True -``` - - diff --git a/mddocs/markdown/hooks/index.md b/mddocs/markdown/hooks/index.md deleted file mode 100644 index 8e1ba7fb9..000000000 --- a/mddocs/markdown/hooks/index.md +++ /dev/null @@ -1,14 +0,0 @@ - - -# Hooks - -#### Versionadded -Added in version 0.6.0. - -# Hooks - -* [High level design](design.md) -* [@hook decorator](hook.md) -* [@slot decorator](slot.md) -* [@support_hooks decorator](support_hooks.md) -* [Hooks global state](global_state.md) diff --git a/mddocs/markdown/hooks/slot.md b/mddocs/markdown/hooks/slot.md deleted file mode 100644 index 936ee3859..000000000 --- a/mddocs/markdown/hooks/slot.md +++ /dev/null @@ -1,261 +0,0 @@ - - -# `@slot` decorator - -| [`slot`](#onetl.hooks.slot.slot)(method) | Decorator which enables hooks functionality on a specific class method. | -|----------------------------------------------------------------|------------------------------------------------------------------------------------------------------| -| [`Slot`](#onetl.hooks.slot.Slot) | Protocol which is implemented by a method after applying [`slot`](#onetl.hooks.slot.slot) decorator. | -| [`Slot.bind`](#onetl.hooks.slot.Slot.bind)([inp]) | Bind a hook to the slot. | -| [`Slot.skip_hooks`](#onetl.hooks.slot.Slot.skip_hooks)() | Context manager which temporary stops all the hooks bound to the slot. | -| [`Slot.suspend_hooks`](#onetl.hooks.slot.Slot.suspend_hooks)() | Stop all the hooks bound to the slot. | -| [`Slot.resume_hooks`](#onetl.hooks.slot.Slot.resume_hooks)() | Resume all hooks bound to the slot. | - -### @onetl.hooks.slot.slot - -Decorator which enables hooks functionality on a specific class method. - -Decorated methods get additional nested methods: - -> * [`onetl.hooks.slot.Slot.bind`](#onetl.hooks.slot.Slot.bind) -> * [`onetl.hooks.slot.Slot.suspend_hooks`](#onetl.hooks.slot.Slot.suspend_hooks) -> * [`onetl.hooks.slot.Slot.resume_hooks`](#onetl.hooks.slot.Slot.resume_hooks) -> * [`onetl.hooks.slot.Slot.skip_hooks`](#onetl.hooks.slot.Slot.skip_hooks) - -#### NOTE -Supported method types are: - -> * Regular methods -> * `@classmethod` -> * `@staticmethod` - -It is not allowed to use this decorator over `_private` and `__protected` methods and `@property`. -But is allowed to use on `__dunder__` methods, like `__init__`. - -#### Versionadded -Added in version 0.7.0. - -### Examples - -```python -from onetl.hooks import support_hooks, slot, hook - -@support_hooks -class MyClass: - @slot - def my_method(self, arg): ... - - @slot # decorator should be on top of all other decorators - @classmethod - def class_method(cls): ... - - @slot # decorator should be on top of all other decorators - @staticmethod - def static_method(arg): ... - -@MyClass.my_method.bind -@hook -def callback1(self, arg): ... - -@MyClass.class_method.bind -@hook -def callback2(cls): ... - -@MyClass.static_method.bind -@hook -def callback3(arg): ... - -obj = MyClass() -obj.my_method(1) # will execute callback1(obj, 1) -MyClass.class_method(2) # will execute callback2(MyClass, 2) -MyClass.static_method(3) # will execute callback3(3) -``` - - - -### *protocol* onetl.hooks.slot.Slot - -Protocol which is implemented by a method after applying [`slot`](#onetl.hooks.slot.slot) decorator. - -#### Versionadded -Added in version 0.7.0. - - - -Classes that implement this protocol must have the following methods / attributes: - -#### \_\_call_\_(\*args, \*\*kwargs) - -Call self as a function. - - - -#### *property* \_\_hooks_\_ *: HookCollection* - -Collection of hooks bound to the slot - - - -#### bind(inp=None) - -Bind a hook to the slot. - -See [High level design](design.md#hooks-design) for more details. - -#### Versionadded -Added in version 0.7.0. - -### Examples - -```python -from onetl.hooks import support_hooks, slot, hook, HookPriority - -@support_hooks -class MyClass: - @slot - def method(self, arg): - pass - -@MyClass.method.bind -@hook -def callable(self, arg): - if arg == "some": - do_something() - -@MyClass.method.bind -@hook(priority=HookPriority.FIRST, enabled=True) -def another_callable(self, arg): - if arg == "another": - raise NotAllowed() - -obj = MyClass() -obj.method(1) # will call both callable(obj, 1) and another_callable(obj, 1) -``` - - - -#### resume_hooks() - -Resume all hooks bound to the slot. - -#### NOTE -If hook is disabled by [`onetl.hooks.hook.Hook.disable`](hook.md#onetl.hooks.hook.Hook.disable), it will stay disabled. -You should call [`onetl.hooks.hook.Hook.enable`](hook.md#onetl.hooks.hook.Hook.enable) explicitly. - -### Examples - -```python -from onetl.hooks.hook import hook, support_hooks, slot - -@support_hooks -class MyClass: - @slot - def my_method(self, arg): ... - -@MyClass.my_method.bind -@hook -def callback1(self, arg): ... - -obj = MyClass() -obj.my_method(1) # will call callback1(obj, 1) - -MyClass.my_method.suspend_hooks() -obj.my_method(1) # will NOT call callback1 - -MyClass.my_method.resume_hooks() -obj.my_method(2) # will call callback1(obj, 2) -``` - - - -#### skip_hooks() → ContextManager[None] - -Context manager which temporary stops all the hooks bound to the slot. - -#### NOTE -If hooks were stopped by [`suspend_hooks`](#onetl.hooks.slot.Slot.suspend_hooks), they will not be resumed -after exiting the context/decorated function. -You should call [`resume_hooks`](#onetl.hooks.slot.Slot.resume_hooks) explicitly. - -### Examples - -Context manager syntax - -```py -from onetl.hooks.hook import hook, support_hooks, slot - -@support_hooks -class MyClass: - @slot - def my_method(self, arg): - ... - -@MyClass.my_method.bind -@hook -def callback1(self, arg): - ... - -obj = MyClass() -obj.my_method(1) # will call callback1(obj, 1) - -with MyClass.my_method.skip_hooks(): - obj.my_method(1) # will NOT call callback1 - -obj.my_method(2) # will call callback1(obj, 2) -``` - -Decorator syntax - -```py -from onetl.hooks.hook import hook, support_hooks, slot - -@support_hooks -class MyClass: - @slot - def my_method(self, arg): - ... - -@MyClass.my_method.bind -@hook -def callback1(self, arg): - ... - -@MyClass.my_method.skip_hooks() -def method_without_hooks(obj, arg): - obj.my_method(arg) - -obj = MyClass() -obj.my_method(1) # will call callback1(obj, 1) - -method_without_hooks(obj, 1) # will NOT call callback1 - -obj.my_method(2) # will call callback1(obj, 2) -``` - - - -#### suspend_hooks() - -Stop all the hooks bound to the slot. - -### Examples - -```python -from onetl.hooks.hook import hook, support_hooks, slot - -@support_hooks -class MyClass: - @slot - def my_method(self, arg): ... - -@MyClass.my_method.bind -@hook -def callback1(self, arg): ... - -obj = MyClass() -obj.my_method(1) # will call callback1(obj, 1) - -MyClass.my_method.suspend_hooks() -obj.my_method(1) # will NOT call callback1 -``` - - diff --git a/mddocs/markdown/hooks/support_hooks.md b/mddocs/markdown/hooks/support_hooks.md deleted file mode 100644 index b876a3e52..000000000 --- a/mddocs/markdown/hooks/support_hooks.md +++ /dev/null @@ -1,161 +0,0 @@ - - -# `@support_hooks` decorator - -| [`support_hooks`](#onetl.hooks.support_hooks.support_hooks)(cls) | Decorator which adds hooks functionality to a specific class. | -|--------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------| -| [`skip_hooks`](#onetl.hooks.support_hooks.skip_hooks)(cls) | Context manager (and decorator) which temporary disables hooks for all the methods of a specific class. | -| [`suspend_hooks`](#onetl.hooks.support_hooks.suspend_hooks)(cls) | Disables all hooks for all the methods of a specific class. | -| [`resume_hooks`](#onetl.hooks.support_hooks.resume_hooks)(cls) | Enables all hooks for all the methods of a specific class. | - -### @onetl.hooks.support_hooks.support_hooks - -Decorator which adds hooks functionality to a specific class. - -Only methods decorated with `slot` can be used for connecting hooks. - -Adds [`skip_hooks`](#onetl.hooks.support_hooks.skip_hooks), [`suspend_hooks`](#onetl.hooks.support_hooks.suspend_hooks) and [`resume_hooks`](#onetl.hooks.support_hooks.resume_hooks) to the class. - -#### Versionadded -Added in version 0.7.0. - -### Examples - -```python -from onetl.hooks.hook import support_hooks, slot - -@support_hooks -class MyClass: - @slot - def my_method(self, arg): ... - -@MyClass.my_method.hook -def callback(self, arg): ... - -MyClass().my_method() # will execute callback function -``` - - - -### onetl.hooks.support_hooks.skip_hooks(cls: type) - -Context manager (and decorator) which temporary disables hooks for all the methods of a specific class. - -### Examples - -Context manager syntax - -```py -@support_hooks -class MyClass: - @slot - def my_method(self, arg): - ... - -@MyClass.my_method.hook -def callback(self, arg): - ... - -obj = MyClass() -obj.my_method(1) # will execute callback(obj, 1) - -with MyClass.skip_hooks(): - obj.my_method() # will NOT execute callback - -# running outside the context restores previous behavior -obj.my_method(2) # will execute callback(obj, 2) -``` - -Decorator syntax - -```py -@support_hooks -class MyClass: - @slot - def my_method(self, arg): - ... - -@MyClass.my_method.hook -def callback(self, arg): - ... - -def with_hook_enabled(): - obj = MyClass() - obj.my_method(1) - -with_hook_enabled() # will execute callback(obj, 1) - -@MyClass.skip_hooks() -def with_all_hooks_disabled(): - obj = MyClass() - obj.my_method(1) - -with_all_hooks_disabled() # will NOT execute callback function - -# running outside a decorated function restores previous behavior -obj = MyClass() -obj.my_method(2) # will execute callback(obj, 2) -``` - -#### Versionadded -Added in version 0.7.0. - - - -### onetl.hooks.support_hooks.suspend_hooks(cls: type) → None - -Disables all hooks for all the methods of a specific class. - -### Examples - -```python -@support_hooks -class MyClass: - @slot - def my_method(self, arg): ... - -@MyClass.my_method.hook -def callback(self, arg): ... - -obj = MyClass() -obj.my_method(1) # will execute callback(obj, 1) - -MyClass.suspend_hooks() - -obj.my_method(2) # will NOT execute callback -``` - -#### Versionadded -Added in version 0.7.0. - - - -### onetl.hooks.support_hooks.resume_hooks(cls: type) → None - -Enables all hooks for all the methods of a specific class. - -### Examples - -```python -@support_hooks -class MyClass: - @slot - def my_method(self, arg): ... - -@MyClass.my_method.hook -def callback(self, arg): ... - -obj = MyClass() - -MyClass.suspend_hooks() -obj.my_method(1) # will NOT execute callback - -MyClass.resume_hooks() - -obj.my_method(2) # will execute callback(obj, 2) -``` - -#### Versionadded -Added in version 0.7.0. - - diff --git a/mddocs/markdown/hwm_store/index.md b/mddocs/markdown/hwm_store/index.md deleted file mode 100644 index c59cc0096..000000000 --- a/mddocs/markdown/hwm_store/index.md +++ /dev/null @@ -1,9 +0,0 @@ - - -# HWM - -Since onETL v0.10.0, the `HWMStore` and `HWM` classes have been moved to a separate library [etl-entities](https://etl-entities.readthedocs.io/en/stable/). - -The only class was left intact is [YAMLHWMStore](yaml_hwm_store.md#yaml-hwm-store), **which is default** in onETL. - -Other known implementation is [HorizonHWMStore](https://horizon-hwm-store.readthedocs.io/). diff --git a/mddocs/markdown/hwm_store/yaml_hwm_store.md b/mddocs/markdown/hwm_store/yaml_hwm_store.md deleted file mode 100644 index 425a7e030..000000000 --- a/mddocs/markdown/hwm_store/yaml_hwm_store.md +++ /dev/null @@ -1,161 +0,0 @@ - - -# YAMLHWMStore - -### *class* onetl.hwm.store.yaml_hwm_store.YAMLHWMStore(\*, path: LocalPath = LocalPosixPath('/home/sattar/.local/share/onETL/yml_hwm_store'), encoding: str = 'utf-8') - -YAML **local store** for HWM values. Used as default HWM store. [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html) - -* **Parameters:** - **path** - : Folder name there HWM value files will be stored. -
- Default: - * `~/.local/share/onETL/yml_hwm_store` on Linux - * `C:\Documents and Settings\\Application Data\oneTools\onETL\yml_hwm_store` on Windows - * `~/Library/Application Support/onETL/yml_hwm_store` on MacOS - - **encoding** - : Encoding of files with HWM value - -### Examples - -Default parameters - -```python -from onetl.connection import Hive, Postgres -from onetl.db import DBReader, DBWriter -from onetl.strategy import IncrementalStrategy -from onetl.hwm.store import YAMLHWMStore - -postgres = Postgres(...) -hive = Hive(...) - -reader = DBReader( - connection=postgres, - source="public.mydata", - columns=["id", "data"], - hwm=DBReader.AutoDetectHWM(name="some_unique_name", expression="id"), -) - -writer = DBWriter(connection=hive, target="db.newtable") - -with YAMLHWMStore(): - with IncrementalStrategy(): - df = reader.run() - writer.run(df) - -# will create file -# "~/.local/share/onETL/id__public.mydata__postgres_postgres.domain.com_5432__myprocess__myhostname.yml" -# with encoding="utf-8" and save a serialized HWM values to this file -``` - -With all options - -```python -with YAMLHWMStore(path="/my/store", encoding="utf-8"): - with IncrementalStrategy(): - df = reader.run() - writer.run(df) - -# will create file -# "/my/store/id__public.mydata__postgres_postgres.domain.com_5432__myprocess__myhostname.yml" -# with encoding="utf-8" and save a serialized HWM values to this file -``` - -File content example: - -```yaml -- column: - name: id - partition: {} - modified_time: '2023-02-11T17:10:49.659019' - process: - dag: '' - host: myhostname - name: myprocess - task: '' - source: - db: public - instance: postgres://postgres.domain.com:5432/target_database - name: mydata - type: int - value: '1500' -- column: - name: id - partition: {} - modified_time: '2023-02-11T16:00:31.962150' - process: - dag: '' - host: myhostname - name: myprocess - task: '' - source: - db: public - instance: postgres://postgres.domain.com:5432/target_database - name: mydata - type: int - value: '1000' -``` - - - -#### \_\_enter_\_() - -HWM store context manager. - -Enter this context to use this HWM store instance as current one (instead default). - -### Examples - -```pycon ->>> from etl_entities.hwm_store import HWMStoreStackManager ->>> with SomeHWMStore(...) as hwm_store: -... print(HWMStoreStackManager.get_current()) -SomeHWMStore(...) ->>> HWMStoreStackManager.get_current() -DefaultHWMStore() -``` - - - -#### get_hwm(name: str) → HWM | None - -Get HWM by name from HWM store. - -* **Parameters:** - **name** - : HWM unique name -* **Returns:** - `HWM` - : HWM object, if it exists in HWM store, or None - -### Examples - -```python -hwm = hwm_store.get_hwm(hwm_unique_name) -``` - - - -#### set_hwm(hwm: HWM) → LocalPath - -Save HWM object to HWM Store. - -* **Parameters:** - **hwm** - : HWM object -* **Returns:** - Any - : HWM location, like URL of file path. Result type is implementation-specific. - -### Examples - -```python -from etl_entities.hwm import ColumnIntHWM - -hwm = ColumnIntHWM(name=..., value=...) -hwm_location = hwm_store.set_hwm(hwm) -``` - - diff --git a/mddocs/markdown/index.md b/mddocs/markdown/index.md deleted file mode 100644 index 436e5eb85..000000000 --- a/mddocs/markdown/index.md +++ /dev/null @@ -1,59 +0,0 @@ - - -# onETL - -[![Repo status - Active](https://www.repostatus.org/badges/latest/active.svg)](https://github.com/MobileTeleSystems/onetl) [![PyPI - Latest Release](https://img.shields.io/pypi/v/onetl)](https://pypi.org/project/onetl/) [![PyPI - License](https://img.shields.io/pypi/l/onetl.svg)](https://github.com/MobileTeleSystems/onetl/blob/develop/LICENSE.txt) [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/onetl.svg)](https://pypi.org/project/onetl/) [![PyPI - Downloads](https://img.shields.io/pypi/dm/onetl)](https://pypi.org/project/onetl/) -[![Documentation - ReadTheDocs](https://readthedocs.org/projects/onetl/badge/?version=stable)](https://onetl.readthedocs.io/) [![Github Actions - latest CI build status](https://github.com/MobileTeleSystems/onetl/workflows/Tests/badge.svg)](https://github.com/MobileTeleSystems/onetl/actions) [![Test coverage - percent](https://codecov.io/gh/MobileTeleSystems/onetl/branch/develop/graph/badge.svg?token=RIO8URKNZJ)](https://codecov.io/gh/MobileTeleSystems/onetl) [![pre-commit.ci - status](https://results.pre-commit.ci/badge/github/MobileTeleSystems/onetl/develop.svg)](https://results.pre-commit.ci/latest/github/MobileTeleSystems/onetl/develop) - -![onETL logo](_static/logo_wide.svg) - -## What is onETL? - -Python ETL/ELT library powered by [Apache Spark](https://spark.apache.org/) & other open-source tools. - -## Goals - -* Provide unified classes to extract data from (**E**) & load data to (**L**) various stores. -* Provides [Spark DataFrame API](https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.DataFrame.html) for performing transformations (**T**) in terms of *ETL*. -* Provide direct assess to database, allowing to execute SQL queries, as well as DDL, DML, and call functions/procedures. This can be used for building up *ELT* pipelines. -* Support different [read strategies](https://onetl.readthedocs.io/en/stable/strategy/index.html) for incremental and batch data fetching. -* Provide [hooks](https://onetl.readthedocs.io/en/stable/hooks/index.html) & [plugins](https://onetl.readthedocs.io/en/stable/plugins.html) mechanism for altering behavior of internal classes. - -## Non-goals - -* onETL is not a Spark replacement. It just provides additional functionality that Spark does not have, and improves UX for end users. -* onETL is not a framework, as it does not have requirements to project structure, naming, the way of running ETL/ELT processes, configuration, etc. All of that should be implemented in some other tool. -* onETL is deliberately developed without any integration with scheduling software like Apache Airflow. All integrations should be implemented as separated tools. -* Only batch operations, no streaming. For streaming prefer [Apache Flink](https://flink.apache.org/). - -## Requirements - -* **Python 3.7 - 3.13** -* PySpark 2.3.x - 3.5.x (depends on used connector) -* Java 8+ (required by Spark, see below) -* Kerberos libs & GCC (required by `Hive`, `HDFS` and `SparkHDFS` connectors) - -## Supported storages - -| Type | Storage | Powered by | -|--------------------|-----------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------| -| Database | Clickhouse | Apache Spark [JDBC Data Source](https://spark.apache.org/docs/latest/sql-data-sources-jdbc.html) | -| MSSQL | | | -| MySQL | | | -| Postgres | | | -| Oracle | | | -| Teradata | | | -| Hive | Apache Spark [Hive integration](https://spark.apache.org/docs/latest/sql-data-sources-hive-tables.html) | | -| Kafka | Apache Spark [Kafka integration](https://spark.apache.org/docs/latest/structured-streaming-kafka-integration.html) | | -| Greenplum | VMware [Greenplum Spark connector](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/index.html) | | -| MongoDB | [MongoDB Spark connector](https://www.mongodb.com/docs/spark-connector/current) | | -| File | HDFS | [HDFS Python client](https://pypi.org/project/hdfs/) | -| S3 | [minio-py client](https://pypi.org/project/minio/) | | -| SFTP | [Paramiko library](https://pypi.org/project/paramiko/) | | -| FTP | [FTPUtil library](https://pypi.org/project/ftputil/) | | -| FTPS | | | -| WebDAV | [WebdavClient3 library](https://pypi.org/project/webdavclient3/) | | -| Samba | [pysmb library](https://pypi.org/project/pysmb/) | | -| Files as DataFrame | SparkLocalFS | Apache Spark [File Data Source](https://spark.apache.org/docs/latest/sql-data-sources-generic-options.html) | -| SparkHDFS | | | -| SparkS3 | [Hadoop AWS](https://hadoop.apache.org/docs/current3/hadoop-aws/tools/hadoop-aws/index.html) library | | diff --git a/mddocs/markdown/install/files.md b/mddocs/markdown/install/files.md deleted file mode 100644 index 60c747462..000000000 --- a/mddocs/markdown/install/files.md +++ /dev/null @@ -1,20 +0,0 @@ - - -# File connections - -All File (but not *FileDF*) connection classes (`FTP`, `SFTP`, `HDFS` and so on) requires specific Python clients to be installed. - -Each client can be installed explicitly by passing connector name (in lowercase) to `extras`: - -```bash -pip install onetl[ftp] # specific connector -pip install onetl[ftp,ftps,sftp,hdfs,s3,webdav,samba] # multiple connectors -``` - -To install all file connectors at once you can pass `files` to `extras`: - -```bash -pip install onetl[files] -``` - -**Otherwise class import will fail.** diff --git a/mddocs/markdown/install/full.md b/mddocs/markdown/install/full.md deleted file mode 100644 index 889f6c401..000000000 --- a/mddocs/markdown/install/full.md +++ /dev/null @@ -1,15 +0,0 @@ - - -# Full bundle - -To install all connectors and dependencies, you can pass `all` into `extras`: - -```bash -pip install onetl[all] - -# this is just the same as -pip install onetl[spark,files,kerberos] -``` - -#### WARNING -This method consumes a lot of disk space, and requires for Java & Kerberos libraries to be installed into your OS. diff --git a/mddocs/markdown/install/index.md b/mddocs/markdown/install/index.md deleted file mode 100644 index 1a5fb63cc..000000000 --- a/mddocs/markdown/install/index.md +++ /dev/null @@ -1,33 +0,0 @@ - - -# How to install - -Base `onetl` package contains: - -* `DBReader`, `DBWriter` and related classes -* `FileDownloader`, `FileUploader`, `FileMover` and related classes, like file filters & limits -* `FileDFReader`, `FileDFWriter` and related classes, like file formats -* Read Strategies & HWM classes -* Plugins support - -It can be installed via: - -```bash -pip install onetl -``` - -#### WARNING -This method does NOT include any connections. - -This method is recommended for use in third-party libraries which require for `onetl` to be installed, -but do not use its connection classes. - -## Installation in details - -## How to install - -* [How to install]() -* [Spark](spark.md) -* [File connections](files.md) -* [Kerberos support](kerberos.md) -* [Full bundle](full.md) diff --git a/mddocs/markdown/install/kerberos.md b/mddocs/markdown/install/kerberos.md deleted file mode 100644 index 1826124c2..000000000 --- a/mddocs/markdown/install/kerberos.md +++ /dev/null @@ -1,32 +0,0 @@ - - -# Kerberos support - -Most of Hadoop instances set up with Kerberos support, -so some connections require additional setup to work properly. - -* `HDFS` - Uses [requests-kerberos](https://pypi.org/project/requests-kerberos/) and - [GSSApi](https://pypi.org/project/gssapi/) for authentication. - It also uses `kinit` executable to generate Kerberos ticket. -* `Hive` and `SparkHDFS` - require Kerberos ticket to exist before creating Spark session. - -So you need to install OS packages with: - -* `krb5` libs -* Headers for `krb5` -* `gcc` or other compiler for C sources - -The exact installation instruction depends on your OS, here are some examples: - -```bash -apt install libkrb5-dev krb5-user gcc # Debian-based -dnf install krb5-devel krb5-libs krb5-workstation gcc # CentOS, OracleLinux -``` - -Also you should pass `kerberos` to `extras` to install required Python packages: - -```bash -pip install onetl[kerberos] -``` diff --git a/mddocs/markdown/install/spark.md b/mddocs/markdown/install/spark.md deleted file mode 100644 index 914f71611..000000000 --- a/mddocs/markdown/install/spark.md +++ /dev/null @@ -1,342 +0,0 @@ - - -# Spark - -All DB connection classes (`Clickhouse`, `Greenplum`, `Hive` and others) -and all FileDF connection classes (`SparkHDFS`, `SparkLocalFS`, `SparkS3`) -require Spark to be installed. - -## Installing Java - -Firstly, you should install JDK. The exact installation instruction depends on your OS, here are some examples: - -```bash -yum install java-1.8.0-openjdk-devel # CentOS 7 + Spark 2 -dnf install java-11-openjdk-devel # CentOS 8 + Spark 3 -apt-get install openjdk-11-jdk # Debian-based + Spark 3 -``` - - - -### Compatibility matrix - -| Spark | Python | Java | Scala | -|-----------------------------------------------------------|------------|------------|---------| -| [2.3.x](https://spark.apache.org/docs/2.3.1/#downloading) | 3.7 only | 8 only | 2.11 | -| [2.4.x](https://spark.apache.org/docs/2.4.8/#downloading) | 3.7 only | 8 only | 2.11 | -| [3.2.x](https://spark.apache.org/docs/3.2.4/#downloading) | 3.7 - 3.10 | 8u201 - 11 | 2.12 | -| [3.3.x](https://spark.apache.org/docs/3.3.4/#downloading) | 3.7 - 3.12 | 8u201 - 17 | 2.12 | -| [3.4.x](https://spark.apache.org/docs/3.4.4/#downloading) | 3.7 - 3.12 | 8u362 - 20 | 2.12 | -| [3.5.x](https://spark.apache.org/docs/3.5.5/#downloading) | 3.8 - 3.13 | 8u371 - 20 | 2.12 | - -## Installing PySpark - -Then you should install PySpark via passing `spark` to `extras`: - -```bash -pip install onetl[spark] # install latest PySpark -``` - -or install PySpark explicitly: - -```bash -pip install onetl pyspark==3.5.5 # install a specific PySpark version -``` - -or inject PySpark to `sys.path` in some other way BEFORE creating a class instance. -**Otherwise connection object cannot be created.** - - - -## Injecting Java packages - -Some DB and FileDF connection classes require specific packages to be inserted to `CLASSPATH` of Spark session, -like JDBC drivers. - -This is usually done by setting up `spark.jars.packages` option while creating Spark session: - -```python -# here is a list of packages to be downloaded: -maven_packages = ( - Greenplum.get_packages(spark_version="3.2") - + MySQL.get_packages() - + Teradata.get_packages() -) - -spark = ( - SparkSession.builder.config("spark.app.name", "onetl") - .config("spark.jars.packages", ",".join(maven_packages)) - .getOrCreate() -) -``` - -Spark automatically resolves package and all its dependencies, download them and inject to Spark session -(both driver and all executors). - -This requires internet access, because package metadata and `.jar` files are fetched from [Maven Repository](https://mvnrepository.com/). - -But sometimes it is required to: - -* Install package without direct internet access (isolated network) -* Install package which is not available in Maven - -There are several ways to do that. - -### Using `spark.jars` - -The most simple solution, but this requires to store raw `.jar` files somewhere on filesystem or web server. - -* Download `package.jar` files (it’s usually something like `some-package_1.0.0.jar`). Local file name does not matter, but it should be unique. -* (For `spark.submit.deployMode=cluster`) place downloaded files to HDFS or deploy to any HTTP web server serving static files. See [official documentation](https://spark.apache.org/docs/latest/submitting-applications.html#advanced-dependency-management) for more details. -* Create Spark session with passing `.jar` absolute file path to `spark.jars` Spark config option: - -for spark.submit.deployMode=client (default) - -```py -jar_files = ["/path/to/package.jar"] - -# do not pass spark.jars.packages -spark = ( - SparkSession.builder.config("spark.app.name", "onetl") - .config("spark.jars", ",".join(jar_files)) - .getOrCreate() -) -``` - -for spark.submit.deployMode=cluster - -```py -# you can also pass URLs like http://domain.com/path/to/downloadable/package.jar -jar_files = ["hdfs:///path/to/package.jar"] - -# do not pass spark.jars.packages -spark = ( - SparkSession.builder.config("spark.app.name", "onetl") - .config("spark.jars", ",".join(jar_files)) - .getOrCreate() -) -``` - -### Using `spark.jars.repositories` - -#### NOTE -In this case Spark still will try to fetch packages from the internet, so if you don’t have internet access, -Spark session will be created with significant delay because of all attempts to fetch packages. - -Can be used if you have access both to public repos (like Maven) and a private Artifactory/Nexus repo. - -* Setup private Maven repository in [JFrog Artifactory](https://jfrog.com/artifactory/) or [Sonatype Nexus](https://www.sonatype.com/products/sonatype-nexus-repository). -* Download `package.jar` file (it’s usually something like `some-package_1.0.0.jar`). Local file name does not matter. -* Upload `package.jar` file to private repository (with same `groupId` and `artifactoryId` as in source package in Maven). -* Pass repo URL to `spark.jars.repositories` Spark config option. -* Create Spark session with passing Package name to `spark.jars.packages` Spark config option: - -```python -maven_packages = ( - Greenplum.get_packages(spark_version="3.2") - + MySQL.get_packages() - + Teradata.get_packages() -) - -spark = ( - SparkSession.builder.config("spark.app.name", "onetl") - .config("spark.jars.repositories", "http://nexus.mydomain.com/private-repo/") - .config("spark.jars.packages", ",".join(maven_packages)) - .getOrCreate() -) -``` - -### Using `spark.jars.ivySettings` - -Same as above, but can be used even if there is no network access to public repos like Maven. - -* Setup private Maven repository in [JFrog Artifactory](https://jfrog.com/artifactory/) or [Sonatype Nexus](https://www.sonatype.com/products/sonatype-nexus-repository). -* Download `package.jar` file (it’s usually something like `some-package_1.0.0.jar`). Local file name does not matter. -* Upload `package.jar` file to [private repository](https://help.sonatype.com/repomanager3/nexus-repository-administration/repository-management#RepositoryManagement-HostedRepository) (with same `groupId` and `artifactoryId` as in source package in Maven). -* Create `ivysettings.xml` file (see below). -* Add here a resolver with repository URL (and credentials, if required). -* Pass `ivysettings.xml` absolute path to `spark.jars.ivySettings` Spark config option. -* Create Spark session with passing package name to `spark.jars.packages` Spark config option: - -ivysettings-all-packages-uploaded-to-nexus.xml - -```xml - - - - - - - - - - - - - -``` - -ivysettings-private-packages-in-nexus-public-in-maven.xml - -```xml - - - - - - - - - - - - - - - - - -``` - -ivysettings-private-packages-in-nexus-public-fetched-using-proxy-repo.xml - -```xml - - - - - - - - - - - - - - - -``` - -ivysettings-nexus-with-auth-required.xml - -```xml - - - - - - - - - - - - - - - - - - - -``` - -```python -maven_packages = ( - Greenplum.get_packages(spark_version="3.2") - + MySQL.get_packages() - + Teradata.get_packages() -) - -spark = ( - SparkSession.builder.config("spark.app.name", "onetl") - .config("spark.jars.ivySettings", "/path/to/ivysettings.xml") - .config("spark.jars.packages", ",".join(maven_packages)) - .getOrCreate() -) -``` - -### Place `.jar` file to `-/.ivy2/jars/` - -Can be used to pass already downloaded file to Ivy, and skip resolving package from Maven. - -* Download `package.jar` file (it’s usually something like `some-package_1.0.0.jar`). Local file name does not matter, but it should be unique. -* Move it to `-/.ivy2/jars/` folder. -* Create Spark session with passing package name to `spark.jars.packages` Spark config option: - -```python -maven_packages = ( - Greenplum.get_packages(spark_version="3.2") - + MySQL.get_packages() - + Teradata.get_packages() -) - -spark = ( - SparkSession.builder.config("spark.app.name", "onetl") - .config("spark.jars.packages", ",".join(maven_packages)) - .getOrCreate() -) -``` - -### Place `.jar` file to Spark jars folder - -#### NOTE -Package file should be placed on all hosts/containers Spark is running, -both driver and all executors. - -Usually this is used only with either: -: * `spark.master=local` (driver and executors are running on the same host), - * `spark.master=k8s://...` (`.jar` files are added to image or to volume mounted to all pods). - -Can be used to embed `.jar` files to a default Spark classpath. - -* Download `package.jar` file (it’s usually something like `some-package_1.0.0.jar`). Local file name does not matter, but it should be unique. -* Move it to `$SPARK_HOME/jars/` folder, e.g. `^/.local/lib/python3.7/site-packages/pyspark/jars/` or `/opt/spark/3.2.3/jars/`. -* Create Spark session **WITHOUT** passing Package name to `spark.jars.packages` - -```python -# no need to set spark.jars.packages or any other spark.jars.* option -# all jars already present in CLASSPATH, and loaded automatically - -spark = SparkSession.builder.config("spark.app.name", "onetl").getOrCreate() -``` - -### Manually adding `.jar` files to `CLASSPATH` - -#### NOTE -Package file should be placed on all hosts/containers Spark is running, -both driver and all executors. - -Usually this is used only with either: -: * `spark.master=local` (driver and executors are running on the same host), - * `spark.master=k8s://...` (`.jar` files are added to image or to volume mounted to all pods). - -Can be used to embed `.jar` files to a default Java classpath. - -* Download `package.jar` file (it’s usually something like `some-package_1.0.0.jar`). Local file name does not matter. -* Set environment variable `CLASSPATH` to `/path/to/package.jar`. You can set multiple file paths -* Create Spark session **WITHOUT** passing Package name to `spark.jars.packages` - -```python -# no need to set spark.jars.packages or any other spark.jars.* option -# all jars already present in CLASSPATH, and loaded automatically - -import os - -jar_files = ["/path/to/package.jar"] -# different delimiters for Windows and Linux -delimiter = ";" if os.name == "nt" else ":" -spark = ( - SparkSession.builder.config("spark.app.name", "onetl") - .config("spark.driver.extraClassPath", delimiter.join(jar_files)) - .config("spark.executor.extraClassPath", delimiter.join(jar_files)) - .getOrCreate() -) -``` diff --git a/mddocs/markdown/logging.md b/mddocs/markdown/logging.md deleted file mode 100644 index f9900f789..000000000 --- a/mddocs/markdown/logging.md +++ /dev/null @@ -1,236 +0,0 @@ - - -# Logging - -Logging is quite important to understand what’s going on under the hood of onETL. - -Default logging level for Python interpreters is `WARNING`, -but most of onETL logs are in `INFO` level, so users usually don’t see much. - -To change logging level, there is a function [`setup_logging`](#onetl.log.setup_logging) -which should be called at the top of the script: - -```python -from onetl.log import setup_logging -from other.lib import some, more, imports - -setup_logging() - -# rest of code -... -``` - -This changes both log level and log formatting to something like this: - -### See logs - -```text -2024-04-12 10:12:10,834 [INFO ] MainThread: |onETL| Using IncrementalStrategy as a strategy -2024-04-12 10:12:10,835 [INFO ] MainThread: =================================== DBReader.run() starts =================================== -2024-04-12 10:12:10,835 [INFO ] MainThread: |DBReader| Getting Spark type for HWM expression: 'updated_at' -2024-04-12 10:12:10,836 [INFO ] MainThread: |MSSQL| Fetching schema of table 'source_schema.table' ... -2024-04-12 10:12:11,636 [INFO ] MainThread: |MSSQL| Schema fetched. -2024-04-12 10:12:11,642 [INFO ] MainThread: |DBReader| Got Spark field: StructField('updated_at', TimestampType(), True) -2024-04-12 10:12:11,642 [INFO ] MainThread: |DBReader| Detected HWM type: 'ColumnDateTimeHWM' -2024-04-12 10:12:11,643 [INFO ] MainThread: |IncrementalStrategy| Fetching HWM from HorizonHWMStore: -2024-04-12 10:12:11,643 [INFO ] MainThread: name = 'updated_at#source_schema.table@mssql:/mssql.host:1433/somedb' -2024-04-12 10:12:12,181 [INFO ] MainThread: |IncrementalStrategy| Fetched HWM: -2024-04-12 10:12:12,182 [INFO ] MainThread: hwm = ColumnDateTimeHWM( -2024-04-12 10:12:12,182 [INFO ] MainThread: name = 'updated_at#source_schema.table@mssql:/mssql.host:1433/somedb', -2024-04-12 10:12:12,182 [INFO ] MainThread: entity = 'source_schema.table', -2024-04-12 10:12:12,182 [INFO ] MainThread: expression = 'updated_at', -2024-04-12 10:12:12,184 [INFO ] MainThread: value = datetime.datetime(2024, 4, 11, 18, 10, 2, 120000), -2024-04-12 10:12:12,184 [INFO ] MainThread: ) -2024-04-12 10:12:12,184 [INFO ] MainThread: |MSSQL| -> |Spark| Reading DataFrame from source using parameters: -2024-04-12 10:12:12,185 [INFO ] MainThread: source = 'source_schema.table' -2024-04-12 10:12:12,185 [INFO ] MainThread: columns = [ -2024-04-12 10:12:12,185 [INFO ] MainThread: 'id', -2024-04-12 10:12:12,186 [INFO ] MainThread: 'new_value', -2024-04-12 10:12:12,186 [INFO ] MainThread: 'old_value', -2024-04-12 10:12:12,186 [INFO ] MainThread: 'updated_at', -2024-04-12 10:12:12,186 [INFO ] MainThread: ] -2024-04-12 10:12:12,187 [INFO ] MainThread: where = "field = 'some'" -2024-04-12 10:12:12,187 [INFO ] MainThread: hwm = AutoDetectHWM( -2024-04-12 10:12:12,187 [INFO ] MainThread: name = 'updated_at#source_schema.table@mssql:/mssql.host:1433/somedb', -2024-04-12 10:12:12,187 [INFO ] MainThread: entity = 'source_schema.table', -2024-04-12 10:12:12,187 [INFO ] MainThread: expression = 'updated_at', -2024-04-12 10:12:12,188 [INFO ] MainThread: ) -2024-04-12 10:12:12,188 [INFO ] MainThread: options = { -2024-04-12 10:12:12,188 [INFO ] MainThread: 'fetchsize': 100000, -2024-04-12 10:12:12,188 [INFO ] MainThread: 'numPartitions': 1, -2024-04-12 10:12:12,189 [INFO ] MainThread: 'partitioningMode': 'range', -2024-04-12 10:12:12,189 [INFO ] MainThread: } -2024-04-12 10:12:12,189 [INFO ] MainThread: |MSSQL| Checking connection availability... -2024-04-12 10:12:12,189 [INFO ] MainThread: |MSSQL| Using connection parameters: -2024-04-12 10:12:12,190 [INFO ] MainThread: user = 'db_user' -2024-04-12 10:12:12,190 [INFO ] MainThread: password = SecretStr('**********') -2024-04-12 10:12:12,190 [INFO ] MainThread: host = 'mssql.host' -2024-04-12 10:12:12,190 [INFO ] MainThread: port = 1433 -2024-04-12 10:12:12,191 [INFO ] MainThread: database = 'somedb' -2024-04-12 10:12:12,191 [INFO ] MainThread: extra = {'applicationIntent': 'ReadOnly', 'trustServerCertificate': 'true'} -2024-04-12 10:12:12,191 [INFO ] MainThread: jdbc_url = 'jdbc:sqlserver:/mssql.host:1433' -2024-04-12 10:12:12,579 [INFO ] MainThread: |MSSQL| Connection is available. -2024-04-12 10:12:12,581 [INFO ] MainThread: |MSSQL| Executing SQL query (on driver): -2024-04-12 10:12:12,581 [INFO ] MainThread: SELECT -2024-04-12 10:12:12,581 [INFO ] MainThread: MIN(updated_at) AS "min", -2024-04-12 10:12:12,582 [INFO ] MainThread: MAX(updated_at) AS "max" -2024-04-12 10:12:12,582 [INFO ] MainThread: FROM -2024-04-12 10:12:12,582 [INFO ] MainThread: source_schema.table -2024-04-12 10:12:12,582 [INFO ] MainThread: WHERE -2024-04-12 10:12:12,582 [INFO ] MainThread: (field = 'some') -2024-04-12 10:12:12,583 [INFO ] MainThread: AND -2024-04-12 10:12:12,583 [INFO ] MainThread: (updated_at >= CAST('2024-04-11T18:10:02.120000' AS datetime2)) -2024-04-12 10:16:22,537 [INFO ] MainThread: |MSSQL| Received values: -2024-04-12 10:16:22,538 [INFO ] MainThread: MIN(updated_at) = datetime.datetime(2024, 4, 11, 21, 10, 7, 397000) -2024-04-12 10:16:22,538 [INFO ] MainThread: MAX(updated_at) = datetime.datetime(2024, 4, 12, 13, 12, 2, 123000) -2024-04-12 10:16:22,540 [INFO ] MainThread: |MSSQL| Executing SQL query (on executor): -2024-04-12 10:16:22,540 [INFO ] MainThread: SELECT -2024-04-12 10:16:22,540 [INFO ] MainThread: id, -2024-04-12 10:16:22,541 [INFO ] MainThread: new_value, -2024-04-12 10:16:22,541 [INFO ] MainThread: old_value, -2024-04-12 10:16:22,541 [INFO ] MainThread: updated_at -2024-04-12 10:16:22,541 [INFO ] MainThread: FROM -2024-04-12 10:16:22,541 [INFO ] MainThread: source_schema.table -2024-04-12 10:16:22,542 [INFO ] MainThread: WHERE -2024-04-12 10:16:22,542 [INFO ] MainThread: (field = 'some') -2024-04-12 10:16:22,542 [INFO ] MainThread: AND -2024-04-12 10:16:22,542 [INFO ] MainThread: (updated_at > CAST('2024-04-11T18:10:02.120000' AS datetime2)) -2024-04-12 10:16:22,542 [INFO ] MainThread: AND -2024-04-12 10:16:22,542 [INFO ] MainThread: (updated_at <= CAST('2024-04-12T13:12:02.123000' AS datetime2)) -2024-04-12 10:16:22,892 [INFO ] MainThread: |Spark| DataFrame successfully created from SQL statement -2024-04-12 10:16:22,892 [INFO ] MainThread: ------------------------------------ DBReader.run() ends ------------------------------------ -2024-04-12 10:40:42,409 [INFO ] MainThread: =================================== DBWriter.run() starts =================================== -2024-04-12 10:40:42,409 [INFO ] MainThread: |Spark| -> |Hive| Writing DataFrame to target using parameters: -2024-04-12 10:40:42,410 [INFO ] MainThread: target = 'target_source_schema.table' -2024-04-12 10:40:42,410 [INFO ] MainThread: options = { -2024-04-12 10:40:42,410 [INFO ] MainThread: 'mode': 'append', -2024-04-12 10:40:42,410 [INFO ] MainThread: 'format': 'orc', -2024-04-12 10:40:42,410 [INFO ] MainThread: 'partitionBy': 'part_dt', -2024-04-12 10:40:42,410 [INFO ] MainThread: } -2024-04-12 10:40:42,411 [INFO ] MainThread: df_schema: -2024-04-12 10:40:42,412 [INFO ] MainThread: root -2024-04-12 10:40:42,412 [INFO ] MainThread: |-- id: integer (nullable = true) -2024-04-12 10:40:42,413 [INFO ] MainThread: |-- new_value: string (nullable = true) -2024-04-12 10:40:42,413 [INFO ] MainThread: |-- old_value: string (nullable = true) -2024-04-12 10:40:42,413 [INFO ] MainThread: |-- updated_at: timestamp (nullable = true) -2024-04-12 10:40:42,413 [INFO ] MainThread: |-- part_dt: date (nullable = true) -2024-04-12 10:40:42,414 [INFO ] MainThread: -2024-04-12 10:40:42,421 [INFO ] MainThread: |Hive| Checking connection availability... -2024-04-12 10:40:42,421 [INFO ] MainThread: |Hive| Using connection parameters: -2024-04-12 10:40:42,421 [INFO ] MainThread: cluster = 'dwh' -2024-04-12 10:40:42,475 [INFO ] MainThread: |Hive| Connection is available. -2024-04-12 10:40:42,476 [INFO ] MainThread: |Hive| Fetching schema of table 'target_source_schema.table' ... -2024-04-12 10:40:43,518 [INFO ] MainThread: |Hive| Schema fetched. -2024-04-12 10:40:43,521 [INFO ] MainThread: |Hive| Table 'target_source_schema.table' already exists -2024-04-12 10:40:43,521 [WARNING ] MainThread: |Hive| User-specified options {'partitionBy': 'part_dt'} are ignored while inserting into existing table. Using only table parameters from Hive metastore -2024-04-12 10:40:43,782 [INFO ] MainThread: |Hive| Inserting data into existing table 'target_source_schema.table' ... -2024-04-12 11:06:07,396 [INFO ] MainThread: |Hive| Data is successfully inserted into table 'target_source_schema.table'. -2024-04-12 11:06:07,397 [INFO ] MainThread: ------------------------------------ DBWriter.run() ends ------------------------------------ -2024-04-12 11:06:07,397 [INFO ] MainThread: |onETL| Exiting IncrementalStrategy -2024-04-12 11:06:07,397 [INFO ] MainThread: |IncrementalStrategy| Saving HWM to 'HorizonHWMStore': -2024-04-12 11:06:07,397 [INFO ] MainThread: hwm = ColumnDateTimeHWM( -2024-04-12 11:06:07,397 [INFO ] MainThread: name = 'updated_at#source_schema.table@mssql:/mssql.host:1433/somedb', -2024-04-12 11:06:07,397 [INFO ] MainThread: entity = 'source_source_schema.table', -2024-04-12 11:06:07,397 [INFO ] MainThread: expression = 'updated_at', -2024-04-12 11:06:07,397 [INFO ] MainThread: value = datetime.datetime(2024, 4, 12, 13, 12, 2, 123000), -2024-04-12 11:06:07,397 [INFO ] MainThread: ) -2024-04-12 11:06:07,495 [INFO ] MainThread: |IncrementalStrategy| HWM has been saved -``` - -Each step performed by onETL is extensively logged, which should help with debugging. - -You can make logs even more verbose by changing level to `DEBUG`: - -```python -from onetl.log import setup_logging - -setup_logging(level="DEBUG", enable_clients=True) - -# rest of code -... -``` - -This also changes log level for all underlying Python libraries, e.g. showing each HTTP request being made, and so on. - -### onetl.log.setup_logging(level: int | str = 20, enable_clients: bool = False) → None - -Set up onETL logging. - -What this function does: -: * Adds stderr logging handler - * Changes root logger format to `2023-05-31 11:22:33.456 [INFO] MainThread: message` - * Changes root logger level to `level` - * Changes onETL logger level to `level` - * Sets up logging level of underlying client modules - -#### NOTE -Should be used only in IDEs (like Jupyter notebooks or PyCharm), -or scripts (ETL pipelines). - -#### Versionchanged -Changed in version 0.5.0: Renamed `setup_notebook_logging` → `setup_logging` - -* **Parameters:** - **level** - : Log level for onETL module - - **enable_clients** - : If `True`, enable logging of underlying client modules. - Otherwise, set client modules log level to `DISABLED`. -
- #### NOTE - For `level="DEBUG"` it is recommended to use `enable_clients=True` -
- #### Versionadded - Added in version 0.9.0. - - - -### onetl.log.setup_clients_logging(level: int | str = 9999) → None - -Set logging of underlying client modules used by onETL. - -Affected modules: -: * `ftputil` - * `hdfs` - * `minio` - * `paramiko` - * `py4j` - * `pyspark` - * `webdav3` - -#### NOTE -Can be used in applications, but it is recommended to set up these loggers -according to your framework documentation. - -#### Versionchanged -Changed in version 0.9.0: Renamed `disable_clients_logging` → `setup_clients_logging` - -* **Parameters:** - **level** - : Log level for client modules -
- #### NOTE - For `py4j`, logging level with maximum verbosity is `INFO` because `DEBUG` logs are - totally unreadable. -
- #### Versionadded - Added in version 0.9.0. - - - -### onetl.log.set_default_logging_format() → None - -Sets default logging format to preferred by onETL. - -Example log message: `2023-05-31 11:22:33.456 [INFO] MainThread: message` - -#### NOTE -Should be used only in IDEs (like Jupyter notebooks or PyCharm), -or scripts (ETL pipelines). - -#### WARNING -Should **NOT** be used in applications, you should set up logging settings manually, -according to your framework documentation. - - diff --git a/mddocs/markdown/plugins.md b/mddocs/markdown/plugins.md deleted file mode 100644 index d42ded034..000000000 --- a/mddocs/markdown/plugins.md +++ /dev/null @@ -1,147 +0,0 @@ - - -# Plugins - -#### Versionadded -Added in version 0.6.0. - -## What are plugins? - -### Terms - -* `Plugin` - some Python package which implements some extra functionality for onETL, like [Hooks](hooks/index.md#hooks) -* `Plugin autoimport` - onETL behavior which allows to automatically import this package if it contains proper metadata (`entry_points`) - -### Features - -Plugins mechanism allows to: - -* Automatically register [Hooks](hooks/index.md#hooks) which can alter onETL behavior -* Automatically register new classes, like HWM type, HWM stores and so on - -### Limitations - -Unlike other projects (like *Airflow 1.x*), plugins does not inject imported classes or functions to `onetl.*` namespace. -Users should import classes from the plugin package **explicitly** to avoid name collisions. - -## How to implement plugin? - -Create a Python package `some-plugin` with a file `some_plugin/setup.py`: - -```python -# some_plugin/setup.py -from setuptools import setup - -setup( - # if you want to import something from onETL, add it to requirements list - install_requires=["onetl"], - entry_points={ - # this key enables plugins autoimport functionality - "onetl.plugins": [ - "some-plugin-name=some_plugin.module", # automatically import all module content - "some-plugin-class=some_plugin.module.internals:MyClass", # import a specific class - "some-plugin-function=some_plugin.module.internals:my_function", # import a specific function - ], - }, -) -``` - -See [setuptools documentation for entry_points](https://setuptools.pypa.io/en/latest/userguide/entry_point.html) - -## How plugins are imported? - -* User should install a package implementing the plugin: - -```bash -pip install some-package -``` - -* Then user should import something from `onetl` module or its submodules: - -```python -import onetl -from onetl.connection import SomeConnection - -# and so on -``` - -* This import automatically executes something like: - -```python -import some_plugin.module -from some_plugin.module.internals import MyClass -from some_plugin.module.internals import my_function -``` - -If specific module/class/function uses some registration capabilities of onETL, -like [@hook decorator](hooks/hook.md#hook-decorator), it will be executed during this import. - -## How to enable/disable plugins? - -#### Versionadded -Added in version 0.7.0. - -### Disable/enable all plugins - -By default plugins are enabled. - -To disabled them, you can set environment variable `ONETL_PLUGINS_ENABLED` to `false` BEFORE -importing onETL. This will disable all plugins autoimport. - -But user is still be able to explicitly import `some_plugin.module`, executing -all decorators and registration capabilities of onETL. - -### Disable a specific plugin (blacklist) - -If some plugin is failing during import, you can disable it by setting up environment variable -`ONETL_PLUGINS_BLACKLIST=some-failing-plugin`. Multiple plugin names could be passed with `,` as delimiter. - -Again, this environment variable should be set BEFORE importing onETL. - -### Disable all plugins except a specific one (whitelist) - -You can also disable all plugins except a specific one by setting up environment variable -`ONETL_PLUGINS_WHITELIST=some-not-failing-plugin`. Multiple plugin names could be passed with `,` as delimiter. - -Again, this environment variable should be set BEFORE importing onETL. - -If both whitelist and blacklist environment variables are set, blacklist has a higher priority. - -## How to see logs of the plugins mechanism? - -Plugins registration emits logs with `INFO` level: - -```python -import logging - -logging.basicConfig(level=logging.INFO) -``` - -```text -INFO |onETL| Found 2 plugins -INFO |onETL| Loading plugin 'my-plugin' -INFO |onETL| Skipping plugin 'failing' because it is in a blacklist -``` - -More detailed logs are emitted with `DEBUG` level, to make output less verbose: - -```python -import logging - -logging.basicConfig(level=logging.DEBUG) -``` - -```text -DEBUG |onETL| Searching for plugins with group 'onetl.plugins' -DEBUG |Plugins| Plugins whitelist: [] -DEBUG |Plugins| Plugins blacklist: ['failing-plugin'] -INFO |Plugins| Found 2 plugins -INFO |onETL| Loading plugin (1/2): -DEBUG name: 'my-plugin' -DEBUG package: 'my-package' -DEBUG version: '0.1.0' -DEBUG importing: 'my_package.my_module:MyClass' -DEBUG |onETL| Successfully loaded plugin 'my-plugin' -DEBUG source: '/usr/lib/python3.11/site-packages/my_package/my_module/my_class.py' -INFO |onETL| Skipping plugin 'failing' because it is in a blacklist -``` diff --git a/mddocs/markdown/quickstart.md b/mddocs/markdown/quickstart.md deleted file mode 100644 index 7a9bd0652..000000000 --- a/mddocs/markdown/quickstart.md +++ /dev/null @@ -1,365 +0,0 @@ -: - -# Quick start - -## MSSQL → Hive - -Read data from MSSQL, transform & write to Hive. - -```bash -# install onETL and PySpark -pip install onetl[spark] -``` - -```python -# Import pyspark to initialize the SparkSession -from pyspark.sql import SparkSession - -# import function to setup onETL logging -from onetl.log import setup_logging - -# Import required connections -from onetl.connection import MSSQL, Hive - -# Import onETL classes to read & write data -from onetl.db import DBReader, DBWriter - -# change logging level to INFO, and set up default logging format and handler -setup_logging() - -# Initialize new SparkSession with MSSQL driver loaded -maven_packages = MSSQL.get_packages() -spark = ( - SparkSession.builder.appName("spark_app_onetl_demo") - .config("spark.jars.packages", ",".join(maven_packages)) - .enableHiveSupport() # for Hive - .getOrCreate() -) - -# Initialize MSSQL connection and check if database is accessible -mssql = MSSQL( - host="mssqldb.demo.com", - user="onetl", - password="onetl", - database="Telecom", - spark=spark, - # These options are passed to MSSQL JDBC Driver: - extra={"applicationIntent": "ReadOnly"}, -).check() - -# >>> INFO:|MSSQL| Connection is available - -# Initialize DBReader -reader = DBReader( - connection=mssql, - source="dbo.demo_table", - columns=["on", "etl"], - # Set some MSSQL read options: - options=MSSQL.ReadOptions(fetchsize=10000), -) - -# checks that there is data in the table, otherwise raises exception -reader.raise_if_no_data() - -# Read data to DataFrame -df = reader.run() -df.printSchema() -# root -# |-- id: integer (nullable = true) -# |-- phone_number: string (nullable = true) -# |-- region: string (nullable = true) -# |-- birth_date: date (nullable = true) -# |-- registered_at: timestamp (nullable = true) -# |-- account_balance: double (nullable = true) - -# Apply any PySpark transformations -from pyspark.sql.functions import lit - -df_to_write = df.withColumn("engine", lit("onetl")) -df_to_write.printSchema() -# root -# |-- id: integer (nullable = true) -# |-- phone_number: string (nullable = true) -# |-- region: string (nullable = true) -# |-- birth_date: date (nullable = true) -# |-- registered_at: timestamp (nullable = true) -# |-- account_balance: double (nullable = true) -# |-- engine: string (nullable = false) - -# Initialize Hive connection -hive = Hive(cluster="rnd-dwh", spark=spark) - -# Initialize DBWriter -db_writer = DBWriter( - connection=hive, - target="dl_sb.demo_table", - # Set some Hive write options: - options=Hive.WriteOptions(if_exists="replace_entire_table"), -) - -# Write data from DataFrame to Hive -db_writer.run(df_to_write) - -# Success! -``` - -## SFTP → HDFS - -Download files from SFTP & upload them to HDFS. - -```bash -# install onETL with SFTP and HDFS clients, and Kerberos support -pip install onetl[hdfs,sftp,kerberos] -``` - -```python -# import function to setup onETL logging -from onetl.log import setup_logging - -# Import required connections -from onetl.connection import SFTP, HDFS - -# Import onETL classes to download & upload files -from onetl.file import FileDownloader, FileUploader - -# import filter & limit classes -from onetl.file.filter import Glob, ExcludeDir -from onetl.file.limit import MaxFilesCount - -# change logging level to INFO, and set up default logging format and handler -setup_logging() - -# Initialize SFTP connection and check it -sftp = SFTP( - host="sftp.test.com", - user="someuser", - password="somepassword", -).check() - -# >>> INFO:|SFTP| Connection is available - -# Initialize downloader -file_downloader = FileDownloader( - connection=sftp, - source_path="/remote/tests/Report", # path on SFTP - local_path="/local/onetl/Report", # local fs path - filters=[ - # download only files matching the glob - Glob("*.csv"), - # exclude files from this directory - ExcludeDir("/remote/tests/Report/exclude_dir/"), - ], - limits=[ - # download max 1000 files per run - MaxFilesCount(1000), - ], - options=FileDownloader.Options( - # delete files from SFTP after successful download - delete_source=True, - # mark file as failed if it already exist in local_path - if_exists="error", - ), -) - -# Download files to local filesystem -download_result = downloader.run() - -# Method run returns a DownloadResult object, -# which contains collection of downloaded files, divided to 4 categories -download_result - -# DownloadResult( -# successful=[ -# LocalPath('/local/onetl/Report/file_1.json'), -# LocalPath('/local/onetl/Report/file_2.json'), -# ], -# failed=[FailedRemoteFile('/remote/onetl/Report/file_3.json')], -# ignored=[RemoteFile('/remote/onetl/Report/file_4.json')], -# missing=[], -# ) - -# Raise exception if there are failed files, or there were no files in the remote filesystem -download_result.raise_if_failed() or download_result.raise_if_empty() - -# Do any kind of magic with files: rename files, remove header for csv files, ... -renamed_files = my_rename_function(download_result.success) - -# function removed "_" from file names -# [ -# LocalPath('/home/onetl/Report/file1.json'), -# LocalPath('/home/onetl/Report/file2.json'), -# ] - -# Initialize HDFS connection -hdfs = HDFS( - host="my.name.node", - user="someuser", - password="somepassword", # or keytab -) - -# Initialize uploader -file_uploader = FileUploader( - connection=hdfs, - target_path="/user/onetl/Report/", # hdfs path -) - -# Upload files from local fs to HDFS -upload_result = file_uploader.run(renamed_files) - -# Method run returns a UploadResult object, -# which contains collection of uploaded files, divided to 4 categories -upload_result - -# UploadResult( -# successful=[RemoteFile('/user/onetl/Report/file1.json')], -# failed=[FailedLocalFile('/local/onetl/Report/file2.json')], -# ignored=[], -# missing=[], -# ) - -# Raise exception if there are failed files, or there were no files in the local filesystem, or some input file is missing -upload_result.raise_if_failed() or upload_result.raise_if_empty() or upload_result.raise_if_missing() - -# Success! -``` - -## S3 → Postgres - -Read files directly from S3 path, convert them to dataframe, transform it and then write to a database. - -```bash -# install onETL and PySpark -pip install onetl[spark] -``` - -```python -# Import pyspark to initialize the SparkSession -from pyspark.sql import SparkSession - -# import function to setup onETL logging -from onetl.log import setup_logging - -# Import required connections -from onetl.connection import Postgres, SparkS3 - -# Import onETL classes to read files -from onetl.file import FileDFReader -from onetl.file.format import CSV - -# Import onETL classes to write data -from onetl.db import DBWriter - -# change logging level to INFO, and set up default logging format and handler -setup_logging() - -# Initialize new SparkSession with Hadoop AWS libraries and Postgres driver loaded -maven_packages = SparkS3.get_packages(spark_version="3.5.5") + Postgres.get_packages() -exclude_packages = SparkS3.get_exclude_packages() -spark = ( - SparkSession.builder.appName("spark_app_onetl_demo") - .config("spark.jars.packages", ",".join(maven_packages)) - .config("spark.jars.excludes", ",".join(exclude_packages)) - .getOrCreate() -) - -# Initialize S3 connection and check it -spark_s3 = SparkS3( - host="s3.test.com", - protocol="https", - bucket="my-bucket", - access_key="somekey", - secret_key="somesecret", - # Access bucket as s3.test.com/my-bucket - extra={"path.style.access": True}, - spark=spark, -).check() - -# >>> INFO:|SparkS3| Connection is available - -# Describe file format and parsing options -csv = CSV( - delimiter=";", - header=True, - encoding="utf-8", -) - -# Describe DataFrame schema of files -from pyspark.sql.types import ( - DateType, - DoubleType, - IntegerType, - StringType, - StructField, - StructType, - TimestampType, -) - -df_schema = StructType( - [ - StructField("id", IntegerType()), - StructField("phone_number", StringType()), - StructField("region", StringType()), - StructField("birth_date", DateType()), - StructField("registered_at", TimestampType()), - StructField("account_balance", DoubleType()), - ], -) - -# Initialize file df reader -reader = FileDFReader( - connection=spark_s3, - source_path="/remote/tests/Report", # path on S3 there *.csv files are located - format=csv, # file format with specific parsing options - df_schema=df_schema, # columns & types -) - -# Read files directly from S3 as Spark DataFrame -df = reader.run() - -# Check that DataFrame schema is same as expected -df.printSchema() -# root -# |-- id: integer (nullable = true) -# |-- phone_number: string (nullable = true) -# |-- region: string (nullable = true) -# |-- birth_date: date (nullable = true) -# |-- registered_at: timestamp (nullable = true) -# |-- account_balance: double (nullable = true) - -# Apply any PySpark transformations -from pyspark.sql.functions import lit - -df_to_write = df.withColumn("engine", lit("onetl")) -df_to_write.printSchema() -# root -# |-- id: integer (nullable = true) -# |-- phone_number: string (nullable = true) -# |-- region: string (nullable = true) -# |-- birth_date: date (nullable = true) -# |-- registered_at: timestamp (nullable = true) -# |-- account_balance: double (nullable = true) -# |-- engine: string (nullable = false) - -# Initialize Postgres connection -postgres = Postgres( - host="192.169.11.23", - user="onetl", - password="somepassword", - database="mydb", - spark=spark, -) - -# Initialize DBWriter -db_writer = DBWriter( - connection=postgres, - # write to specific table - target="public.my_table", - # with some writing options - options=Postgres.WriteOptions(if_exists="append"), -) - -# Write DataFrame to Postgres table -db_writer.run(df_to_write) - -# Success! -``` diff --git a/mddocs/markdown/security.md b/mddocs/markdown/security.md deleted file mode 100644 index 3048c040b..000000000 --- a/mddocs/markdown/security.md +++ /dev/null @@ -1,25 +0,0 @@ -# Security - -## Supported Python versions - -3.7 or above - -## Product development security recommendations - -1. Update dependencies to last stable version -2. Build SBOM for the project -3. Perform SAST (Static Application Security Testing) where possible - -## Product development security requirements - -1. No binaries in repository -2. No passwords, keys, access tokens in source code -3. No “Critical” and/or “High” vulnerabilities in contributed source code - -## Vulnerability reports - -Please, use email [mailto:onetools@mts.ru](mailto:onetools@mts.ru) for reporting security issues or anything that can cause any consequences for security. - -Please avoid any public disclosure (including registering issues) at least until it is fixed. - -Thank you in advance for understanding. diff --git a/mddocs/markdown/strategy/incremental_batch_strategy.md b/mddocs/markdown/strategy/incremental_batch_strategy.md deleted file mode 100644 index 2d30a6767..000000000 --- a/mddocs/markdown/strategy/incremental_batch_strategy.md +++ /dev/null @@ -1,301 +0,0 @@ - - -# Incremental Batch Strategy - -### *class* onetl.strategy.incremental_strategy.IncrementalBatchStrategy(\*, hwm: HWM | None = None, step: Any = None, start: Any = None, stop: Any = None, offset: Any = None) - -Incremental batch strategy for [DB Reader](../db/db_reader.md#db-reader). - -#### NOTE -Cannot be used with [File Downloader](../file/file_downloader/file_downloader.md#file-downloader) - -Same as [`IncrementalStrategy`](incremental_strategy.md#onetl.strategy.incremental_strategy.IncrementalStrategy), -but reads data from the source in sequential batches (1..N) like: - -```sql -1: SELECT id, data - FROM public.mydata - WHERE id > 1000 AND id <= 1100; -- previous HWM value is 1000, step is 100 - -2: WHERE id > 1100 AND id <= 1200; -- + step -3: WHERE id > 1200 AND id <= 1200; -- + step -N: WHERE id > 1300 AND id <= 1400; -- until stop -``` - -This allows to use less CPU and RAM than reading all the data in the one batch, -but takes proportionally more time. - -#### WARNING -Unlike [`SnapshotBatchStrategy`](snapshot_batch_strategy.md#onetl.strategy.snapshot_strategy.SnapshotBatchStrategy), -it **saves** current HWM value after **each batch** into [HWM Store](../hwm_store/index.md#hwm). - -So if code inside the context manager raised an exception, like: - -```python -with IncrementalBatchStrategy() as batches: - for _ in batches: - df = reader.run() # something went wrong here - writer.run(df) # or here - # or here... -``` - -DBReader will **NOT** update HWM in HWM Store for the failed batch. - -All of that allows to resume reading process from the *last successful batch*. - -#### WARNING -Not every [DB connection](../connection/db_connection/index.md#db-connections) -supports batch strategy. For example, Kafka connection doesn’t support it. -Make sure the connection you use is compatible with the IncrementalBatchStrategy. - -#### Versionadded -Added in version 0.1.0. - -* **Parameters:** - **step** - : Step size used for generating batch SQL queries like: - ```sql - SELECT id, data - FROM public.mydata - WHERE id > 1000 AND id <= 1100; -- 1000 is previous HWM value, step is 100 - ``` -
- #### NOTE - Step defines a range of values will be fetched by each batch. This is **not** - a number of rows, it depends on a table content and value distribution across the rows. -
- #### NOTE - `step` value will be added to the HWM, so it should have a proper type. -
- For example, for `TIMESTAMP` column `step` type should be `datetime.timedelta`, not `int` - - **stop** - : If passed, the value will be used for generating WHERE clauses with `hwm.expression` filter, - as a stop value for the last batch. -
- If not set, the value is determined by a separated query: - ```sql - SELECT MAX(id) as stop - FROM public.mydata - WHERE id > 1000; -- 1000 is previous HWM value (if any) - ``` -
- #### NOTE - `stop` should be the same type as `hwm.expression` value, - e.g. `datetime.datetime` for `TIMESTAMP` column, `datetime.date` for `DATE`, and so on - - **offset** - : If passed, the offset value will be used to read rows which appeared in the source after the previous read. -
- For example, previous incremental run returned rows: - ```default - 898 - 899 - 900 - 1000 - ``` -
- Current HWM value is 1000. -
- But since then few more rows appeared in the source: - ```default - 898 - 899 - 900 - 901 # new - 902 # new - ... - 999 # new - 1000 - ``` -
- and you need to read them too. -
- So you can set `offset=100`, so the first batch of a next incremental run will look like: - ```sql - SELECT id, data - FROM public.mydata - WHERE id > 900 AND id <= 1000; -- 900 = 1000 - 100 = HWM - offset - ``` -
- and return rows from 901 (**not** 900) to **1000** (duplicate). -
- #### WARNING - This can lead to reading duplicated values from the table. - You probably need additional deduplication step to handle them -
- #### NOTE - `offset` value will be subtracted from the HWM, so it should have a proper type. -
- For example, for `TIMESTAMP` column `offset` type should be `datetime.timedelta`, not `int` - -### Examples - -IncrementalBatch run - -```python -from onetl.db import DBReader, DBWriter -from onetl.strategy import IncrementalBatchStrategy - -reader = DBReader( - connection=postgres, - source="public.mydata", - columns=["id", "data"], - hwm=DBReader.AutoDetectHWM(name="some_hwm_name", expression="id"), -) - -writer = DBWriter(connection=hive, target="db.newtable") - -with IncrementalBatchStrategy(step=100) as batches: - for _ in batches: - df = reader.run() - writer.run(df) -``` - -```sql --- previous HWM value was 1000 --- each batch (1..N) will perform a query which return some part of input data - -1: SELECT id, data - FROM public.mydata - WHERE id > 1100 AND id <= 1200; --- from HWM to HWM+step (EXCLUDING first row) - -2: WHERE id > 1200 AND id <= 1300; -- + step -N: WHERE id > 1300 AND id <= 1400; -- until max value of HWM column -``` - -IncrementalBatch run with `stop` value - -```python -... - -with IncrementalBatchStrategy(step=100, stop=2000) as batches: - for _ in batches: - df = reader.run() - writer.run(df) -``` - -```sql --- previous HWM value was 1000 --- each batch (1..N) will perform a query which return some part of input data - -1: SELECT id, data - FROM public.mydata - WHERE id > 1000 AND id <= 1100; --- from HWM to HWM+step (EXCLUDING first row) - -2: WHERE id > 1100 AND id <= 1200; -- + step -... -N: WHERE id > 1900 AND id <= 2000; -- until stop -``` - -IncrementalBatch run with `offset` value - -```python -... - -with IncrementalBatchStrategy(step=100, offset=100) as batches: - for _ in batches: - df = reader.run() - writer.run(df) -``` - -```sql --- previous HWM value was 1000 --- each batch (1..N) will perform a query which return some part of input data - -1: SELECT id, data - FROM public.mydata - WHERE id > 900 AND id <= 1000; --- from HWM-offset to HWM-offset+step (EXCLUDING first row) - -2: WHERE id > 1000 AND id <= 1100; -- + step -3: WHERE id > 1100 AND id <= 1200; -- + step -... -N: WHERE id > 1300 AND id <= 1400; -- until max value of HWM column -``` - -IncrementalBatch run with all possible options - -```python -... - -with IncrementalBatchStrategy( - step=100, - stop=2000, - offset=100, -) as batches: - for _ in batches: - df = reader.run() - writer.run(df) -``` - -```sql --- previous HWM value was 1000 --- each batch (1..N) will perform a query which return some part of input data - -1: SELECT id, data - FROM public.mydata - WHERE id > 900 AND id <= 1000; --- from HWM-offset to HWM-offset+step (EXCLUDING first row) - -2: WHERE id > 1000 AND id <= 1100; -- + step -3: WHERE id > 1100 AND id <= 1200; -- + step -... -N: WHERE id > 1900 AND id <= 2000; -- until stop -``` - -IncrementalBatch run over non-integer column - -`hwm.expression`, `offset` and `stop` can be a date or datetime, not only integer: - -```python -from onetl.db import DBReader, DBWriter -from datetime import date, timedelta - -reader = DBReader( - connection=postgres, - source="public.mydata", - columns=["business_dt", "data"], - hwm=DBReader.AutoDetectHWM(name="some_hwm_name", expression="business_dt"), -) - -writer = DBWriter(connection=hive, target="db.newtable") - -with IncrementalBatchStrategy( - step=timedelta(days=5), - stop=date("2021-01-31"), - offset=timedelta(days=1), -) as batches: - for _ in batches: - df = reader.run() - writer.run(df) -``` - -```sql --- previous HWM value was '2021-01-10' --- each batch (1..N) will perform a query which return some part of input data - -1: SELECT business_dt, data - FROM public.mydata - WHERE business_dt > CAST('2021-01-09' AS DATE) -- from HWM-offset (EXCLUDING first row) - AND business_dt <= CAST('2021-01-14' AS DATE); -- to HWM-offset+step - -2: WHERE business_dt > CAST('2021-01-14' AS DATE) -- + step - AND business_dt <= CAST('2021-01-19' AS DATE); - -3: WHERE business_dt > CAST('2021-01-19' AS DATE) -- + step - AND business_dt <= CAST('2021-01-24' AS DATE); - -... - -N: WHERE business_dt > CAST('2021-01-29' AS DATE) - AND business_dt <= CAST('2021-01-31' AS DATE); -- until stop -``` - - - -#### \_\_init_\_(\*\*kwargs) - -Create a new model by parsing and validating input data from keyword arguments. - -Raises ValidationError if the input data cannot be parsed to form a valid model. - - diff --git a/mddocs/markdown/strategy/incremental_strategy.md b/mddocs/markdown/strategy/incremental_strategy.md deleted file mode 100644 index a40dc5b94..000000000 --- a/mddocs/markdown/strategy/incremental_strategy.md +++ /dev/null @@ -1,396 +0,0 @@ - - -# Incremental Strategy - -### *class* onetl.strategy.incremental_strategy.IncrementalStrategy(\*, hwm: HWM | None = None, offset: Any = None) - -Incremental strategy for [DB Reader](../db/db_reader.md#db-reader)/[File Downloader](../file/file_downloader/file_downloader.md#file-downloader). - -Used for fetching only new rows/files from a source -by filtering items not covered by the previous [HWM](../hwm_store/index.md#hwm) value. - -For [DB Reader](../db/db_reader.md#db-reader): -: First incremental run is just the same as [`SnapshotStrategy`](snapshot_strategy.md#onetl.strategy.snapshot_strategy.SnapshotStrategy): -
- ```sql - SELECT id, data FROM mydata; - ``` -
- Then the max value of `id` column (e.g. `1000`) will be saved as `HWM` to [HWM Store](../hwm_store/index.md#hwm). -
- Next incremental run will read only new data from the source: -
- ```sql - SELECT id, data FROM mydata WHERE id > 1000; -- hwm value - ``` -
- Pay attention to resulting dataframe **does not include** row with `id=1000` because it has been read before. -
- #### WARNING - If code inside the context manager raised an exception, like: -
- ```python - with IncrementalStrategy(): - df = reader.run() # something went wrong here - writer.run(df) # or here - # or here... - ``` -
- When DBReader will **NOT** update HWM in HWM Store. - This allows to resume reading process from the *last successful run*. - -For [File Downloader](../file/file_downloader/file_downloader.md#file-downloader): -: Behavior depends on `hwm` type. -
- FileListHWM -
- First incremental run is just the same as [`SnapshotStrategy`](snapshot_strategy.md#onetl.strategy.snapshot_strategy.SnapshotStrategy) - - all files are downloaded: -
- ```bash - $ hdfs dfs -ls /path -
- /path/my/file1 - /path/my/file2 - ``` -
- ```python - DownloadResult( - ..., - successful={ - LocalFile("/downloaded/file1"), - LocalFile("/downloaded/file2"), - }, - ) - ``` -
- Then the list of original file paths is saved as `FileListHWM` object into [HWM Store](../hwm_store/index.md#hwm): -
- ```python - FileListHWM( - ..., - entity="/path", - value=[ - "/path/my/file1", - "/path/my/file2", - ], - ) - ``` -
- Next incremental run will download only new files which were added to the source since previous run: -
- ```bash - $ hdfs dfs -ls /path -
- /path/my/file1 - /path/my/file2 - /path/my/file3 - ``` -
- ```python - # only files which are not covered by FileListHWM - DownloadResult( - ..., - successful={ - LocalFile("/downloaded/file3"), - }, - ) - ``` -
- Value of `FileListHWM` will be updated and saved to [HWM Store](../hwm_store/index.md#hwm): -
- ```python - FileListHWM( - ..., - directory="/path", - value=[ - "/path/my/file1", - "/path/my/file2", - "/path/my/file3", - ], - ) - ``` -
- FileModifiedTimeHWM -
- First incremental run is just the same as [`SnapshotStrategy`](snapshot_strategy.md#onetl.strategy.snapshot_strategy.SnapshotStrategy) - - all files are downloaded: -
- ```bash - $ hdfs dfs -ls /path -
- /path/my/file1 - /path/my/file2 - ``` -
- ```python - DownloadResult( - ..., - successful={ - LocalFile("/downloaded/file1"), - LocalFile("/downloaded/file2"), - }, - ) - ``` -
- Then the maximum modified time of original files is saved as `FileModifiedTimeHWM` object into [HWM Store](../hwm_store/index.md#hwm): -
- ```python - FileModifiedTimeHWM( - ..., - directory="/path", - value=datetime.datetime(2025, 1, 1, 11, 22, 33, 456789, tzinfo=timezone.utc), - ) - ``` -
- Next incremental run will download only files from the source which were modified or created since previous run: -
- ```bash - $ hdfs dfs -ls /path -
- /path/my/file1 - /path/my/file2 - /path/my/file3 - ``` -
- ```python - # only files which are not covered by FileModifiedTimeHWM - DownloadResult( - ..., - successful={ - LocalFile("/downloaded/file3"), - }, - ) - ``` -
- Value of `FileModifiedTimeHWM` will be updated and and saved to [HWM Store](../hwm_store/index.md#hwm): -
- ```python - FileModifiedTimeHWM( - ..., - directory="/path", - value=datetime.datetime(2025, 1, 1, 22, 33, 44, 567890, tzinfo=timezone.utc), - ) - ``` -
- #### WARNING - FileDownload updates HWM in HWM Store at the end of `.run()` call, - **NOT** while exiting strategy context. This is because: -
- * FileDownloader does not raise exceptions if some file cannot be downloaded. - * FileDownloader creates files on local filesystem, and file content may differ for different `modes`. - * It can remove files from the source if `delete_source` is set to `True`. - -#### Versionadded -Added in version 0.1.0. - -* **Parameters:** - **offset** - : If passed, the offset value will be used to read rows which appeared in the source after the previous read. -
- For example, previous incremental run returned rows: - ```default - 898 - 899 - 900 - 1000 - ``` -
- Current HWM value is 1000. -
- But since then few more rows appeared in the source: - ```default - 898 - 899 - 900 - 901 # new - 902 # new - ... - 999 # new - 1000 - ``` -
- and you need to read them too. -
- So you can set `offset=100`, so a next incremental run will generate SQL query like: - ```sql - SELECT id, data FROM public.mydata WHERE id > 900; - -- 900 = 1000 - 100 = hwm - offset - ``` -
- and return rows since 901 (**not** 900), **including** 1000 which was already captured by HWM. -
- #### WARNING - This can lead to reading duplicated values from the table. - You probably need additional deduplication step to handle them -
- #### WARNING - Cannot be used with [File Downloader](../file/file_downloader/file_downloader.md#file-downloader) -
- #### NOTE - `offset` value will be subtracted from the HWM, so it should have a proper type. -
- For example, for `TIMESTAMP` column `offset` type should be `datetime.timedelta`, not `int` - -### Examples - -Incremental run with [DB Reader](../db/db_reader.md#db-reader) - -```python -from onetl.db import DBReader, DBWriter -from onetl.strategy import IncrementalStrategy - -reader = DBReader( - connection=postgres, - source="public.mydata", - columns=["id", "data"], - hwm=DBReader.AutoDetectHWM(name="some_hwm_name", expression="id"), -) - -writer = DBWriter(connection=hive, target="db.newtable") - -with IncrementalStrategy(): - df = reader.run() - writer.run(df) -``` - -```sql --- previous HWM value was 1000 --- DBReader will generate query like: - -SELECT id, data -FROM public.mydata -WHERE id > 1000; --- from HWM (EXCLUDING first row) -``` - -Incremental run with [DB Reader](../db/db_reader.md#db-reader) and `IncrementalStrategy(offset=...)` - -```python -from onetl.db import DBReader, DBWriter -from onetl.strategy import IncrementalStrategy - -reader = DBReader( - connection=postgres, - source="public.mydata", - columns=["id", "data"], - hwm=DBReader.AutoDetectHWM(name="some_hwm_name", expression="id"), -) - -writer = DBWriter(connection=hive, target="db.newtable") - -with IncrementalStrategy(offset=100): - df = reader.run() - writer.run(df) -``` - -```sql --- previous HWM value was 1000 --- DBReader will generate query like: - -SELECT id, data -FROM public.mydata -WHERE id > 900; -- from HWM-offset (EXCLUDING first row) -``` - -`offset` and `hwm.expression` can be a date or datetime, not only integer: - -```python -from onetl.db import DBReader, DBWriter -from datetime import timedelta - -reader = DBReader( - connection=postgres, - source="public.mydata", - columns=["business_dt", "data"], - hwm=DBReader.AutoDetectHWM(name="some_hwm_name", expression="business_dt"), -) - -writer = DBWriter(connection=hive, target="db.newtable") - -with IncrementalStrategy(offset=timedelta(days=1)): - df = reader.run() - writer.run(df) -``` - -```sql --- previous HWM value was '2021-01-10' --- DBReader will generate query like: - -SELECT business_dt, data -FROM public.mydata -WHERE business_dt > CAST('2021-01-09' AS DATE); -- from HWM-offset (EXCLUDING first row) -``` - -Incremental run with [DB Reader](../db/db_reader.md#db-reader) and [Kafka](../connection/db_connection/kafka/index.md#kafka) - -```py -from onetl.db import DBReader, DBWriter -from onetl.strategy import IncrementalStrategy - -reader = DBReader( - connection=kafka, - source="topic_name", - hwm=DBReader.AutoDetectHWM(name="some_hwm_name", expression="offset"), -) - -writer = DBWriter(connection=hive, target="db.newtable") - -with IncrementalStrategy(): - df = reader.run() - -# current run will fetch only messages which were added since previous run -``` - -Incremental run with [File Downloader](../file/file_downloader/file_downloader.md#file-downloader) and `hwm=FileListHWM(...)` - -```py -from onetl.file import FileDownloader -from onetl.strategy import SnapshotStrategy -from etl_entities.hwm import FileListHWM - -downloader = FileDownloader( - connection=sftp, - source_path="/remote", - local_path="/local", - hwm=FileListHWM( # mandatory for IncrementalStrategy - name="my_unique_hwm_name", - ), -) - -with IncrementalStrategy(): - df = downloader.run() - -# current run will download only files which were added since previous run -``` - -Incremental run with [File Downloader](../file/file_downloader/file_downloader.md#file-downloader) and `hwm=FileModifiedTimeHWM(...)` - -```py -from onetl.file import FileDownloader -from onetl.strategy import SnapshotStrategy -from etl_entities.hwm import FileModifiedTimeHWM - -downloader = FileDownloader( - connection=sftp, - source_path="/remote", - local_path="/local", - hwm=FileModifiedTimeHWM( # mandatory for IncrementalStrategy - name="my_unique_hwm_name", - ), -) - -with IncrementalStrategy(): - df = downloader.run() - -# current run will download only files which were modified/created since previous run -``` - - - -#### \_\_init_\_(\*\*kwargs) - -Create a new model by parsing and validating input data from keyword arguments. - -Raises ValidationError if the input data cannot be parsed to form a valid model. - - diff --git a/mddocs/markdown/strategy/index.md b/mddocs/markdown/strategy/index.md deleted file mode 100644 index 7791fed74..000000000 --- a/mddocs/markdown/strategy/index.md +++ /dev/null @@ -1,10 +0,0 @@ - - -# Read Strategies - -onETL have several builtin strategies for reading data: - -1. [Snapshot Strategy](snapshot_strategy.md) -2. [Incremental Strategy](incremental_strategy.md) -3. [Snapshot Batch Strategy](snapshot_batch_strategy.md) -4. [Incremental Batch Strategy](incremental_batch_strategy.md) diff --git a/mddocs/markdown/strategy/snapshot_batch_strategy.md b/mddocs/markdown/strategy/snapshot_batch_strategy.md deleted file mode 100644 index da95f8133..000000000 --- a/mddocs/markdown/strategy/snapshot_batch_strategy.md +++ /dev/null @@ -1,281 +0,0 @@ - - -# Snapshot Batch Strategy - -### *class* onetl.strategy.snapshot_strategy.SnapshotBatchStrategy(\*, hwm: HWM | None = None, step: Any = None, start: Any = None, stop: Any = None) - -Snapshot batch strategy for [DB Reader](../db/db_reader.md#db-reader). - -#### NOTE -Cannot be used with [File Downloader](../file/file_downloader/file_downloader.md#file-downloader) - -Same as [`SnapshotStrategy`](snapshot_strategy.md#onetl.strategy.snapshot_strategy.SnapshotStrategy), -but reads data from the source in sequential batches (1..N) like: - -```sql -1: SELECT id, data - FROM public.mydata - WHERE id >= 1000 AND id <= 1100; -- from start to start+step (INCLUDING first row) - -2: WHERE id > 1100 AND id <= 1200; -- + step -3: WHERE id > 1200 AND id <= 1200; -- + step -N: WHERE id > 1300 AND id <= 1400; -- until stop -``` - -This allows to use less CPU and RAM on Spark cluster than reading all the data in parallel, -but takes proportionally more time. - -#### NOTE -This strategy uses HWM column value to filter data for each batch, -but does **NOT** save it into [HWM Store](../hwm_store/index.md#hwm). -So every run starts from the beginning, not from the previous HWM value. - -#### NOTE -If you only need to reduce number of rows read by Spark from opened cursor, -use `onetl.connection.db_connection.postgres.Postgres.ReadOptions.fetchsize` instead - -#### WARNING -Not every [DB connection](../connection/db_connection/index.md#db-connections) -supports batch strategy. For example, Kafka connection doesn’t support it. -Make sure the connection you use is compatible with the SnapshotBatchStrategy. - -#### Versionadded -Added in version 0.1.0. - -* **Parameters:** - **step** - : Step size used for generating batch SQL queries like: - ```sql - SELECT id, data - FROM public.mydata - WHERE id >= 1000 AND id <= 1100; -- 1000 is start value, step is 100 - ``` -
- #### NOTE - Step defines a range of values will be fetched by each batch. This is **not** - a number of rows, it depends on a table content and value distribution across the rows. -
- #### NOTE - `step` value will be added to the HWM, so it should have a proper type. -
- For example, for `TIMESTAMP` column `step` type should be `datetime.timedelta`, not `int` - - **start** - : If passed, the value will be used for generating WHERE clauses with `hwm.expression` filter, - as a start value for the first batch. -
- If not set, the value is determined by a separated query: - ```sql - SELECT MIN(id) as start - FROM public.mydata - WHERE id <= 1400; -- 1400 here is stop value (if set) - ``` -
- #### NOTE - `start` should be the same type as `hwm.expression` value, - e.g. `datetime.datetime` for `TIMESTAMP` column, `datetime.date` for `DATE`, and so on - - **stop** - : If passed, the value will be used for generating WHERE clauses with `hwm.expression` filter, - as a stop value for the last batch. -
- If not set, the value is determined by a separated query: - ```sql - SELECT MAX(id) as stop - FROM public.mydata - WHERE id >= 1000; -- 1000 here is start value (if set) - ``` -
- #### NOTE - `stop` should be the same type as `hwm.expression` value, - e.g. `datetime.datetime` for `TIMESTAMP` column, `datetime.date` for `DATE`, and so on - -### Examples - -SnapshotBatch run - -```python -from onetl.db import DBReader, DBWriter -from onetl.strategy import SnapshotBatchStrategy - -reader = DBReader( - connection=postgres, - source="public.mydata", - columns=["id", "data"], - hwm=DBReader.AutoDetectHWM(name="some_hwm_name", expression="id"), -) - -writer = DBWriter(connection=hive, target="db.newtable") - -with SnapshotBatchStrategy(step=100) as batches: - for _ in batches: - df = reader.run() - writer.run(df) -``` - -```sql --- get start and stop values - - SELECT MIN(id) as start, MAX(id) as stop - FROM public.mydata; - --- for example, start=1000 and stop=2345 - --- when each batch (1..N) will perform a query which return some part of input data - -1: SELECT id, data - FROM public.mydata - WHERE id >= 1000 AND id <= 1100; -- from start to start+step (INCLUDING first row) - -2: WHERE id > 1100 AND id <= 1200; -- + step -3: WHERE id > 1200 AND id <= 1300; -- + step -N: WHERE id > 2300 AND id <= 2345; -- until stop -``` - -SnapshotBatch run with `stop` value - -```python -... - -with SnapshotBatchStrategy(step=100, stop=1234) as batches: - for _ in batches: - df = reader.run() - writer.run(df) -``` - -```sql --- stop value is set, so there is no need to fetch it from DB --- get start value - - SELECT MIN(id) as start - FROM public.mydata - WHERE id <= 1234; -- until stop - --- for example, start=1000. --- when each batch (1..N) will perform a query which return some part of input data - -1: SELECT id, data - FROM public.mydata - WHERE id >= 1000 AND id <= 1100; -- from start to start+step (INCLUDING first row) - -2: WHERE id > 1100 AND id <= 1200; -- + step -3: WHERE id > 1200 AND id <= 1300; -- + step -N: WHERE id > 1300 AND id <= 1234; -- until stop -``` - -SnapshotBatch run with `start` value - -```python -... - -with SnapshotBatchStrategy(step=100, start=500) as batches: - for _ in batches: - df = reader.run() - writer.run(df) -``` - -```sql --- start value is set, so there is no need to fetch it from DB --- get only stop value - - SELECT MAX(id) as stop - FROM public.mydata - WHERE id >= 500; -- from start - --- for example, stop=2345. --- when each batch (1..N) will perform a query which return some part of input data - -1: SELECT id, data - FROM public.mydata - WHERE id >= 500 AND id <= 600; -- from start to start+step (INCLUDING first row) - -2: WHERE id > 600 AND id <= 700; -- + step -3: WHERE id > 700 AND id <= 800; -- + step -... -N: WHERE id > 2300 AND id <= 2345; -- until stop -``` - -SnapshotBatch run with all options - -```python -... - -with SnapshotBatchStrategy( - start=1000, - step=100, - stop=2000, -) as batches: - for _ in batches: - df = reader.run() - writer.run(df) -``` - -```sql --- start and stop values are set, so no need to fetch boundaries from DB --- each batch (1..N) will perform a query which return some part of input data - -1: SELECT id, data - FROM public.mydata - WHERE id >= 1000 AND id <= 1100; -- from start to start+step (INCLUDING first row) - -2: WHERE id > 1100 AND id <= 1200; -- + step -3: WHERE id > 1200 AND id <= 1300; -- + step -... -N: WHERE id > 1900 AND id <= 2000; -- until stop -``` - -SnapshotBatch run over non-integer column - -`hwm.expression`, `start` and `stop` can be a date or datetime, not only integer: - -```python -from datetime import date, timedelta - -reader = DBReader( - connection=postgres, - source="public.mydata", - columns=["business_dt", "data"], - hwm=DBReader.AutoDetectHWM(name="some_hwm_name", expression="business_dt"), -) - -with SnapshotBatchStrategy( - start=date("2021-01-01"), - step=timedelta(days=5), - stop=date("2021-01-31"), -) as batches: - for _ in batches: - df = reader.run() - writer.run(df) -``` - -```sql --- start and stop values are set, so no need to fetch boundaries from DB --- each batch will perform a query which return some part of input data --- HWM value will casted to match column type - -1: SELECT business_dt, data - FROM public.mydata - WHERE business_dt >= CAST('2020-01-01' AS DATE) -- from start to start+step (INCLUDING first row) - AND business_dt <= CAST('2021-01-05' AS DATE); - -2: WHERE business_dt > CAST('2021-01-05' AS DATE) -- + step - AND business_dt <= CAST('2021-01-10' AS DATE); - -3: WHERE business_dt > CAST('2021-01-10' AS DATE) -- + step - AND business_dt <= CAST('2021-01-15' AS DATE); - -... - -N: WHERE business_dt > CAST('2021-01-30' AS DATE) - AND business_dt <= CAST('2021-01-31' AS DATE); -- until stop -``` - - - -#### \_\_init_\_(\*\*kwargs) - -Create a new model by parsing and validating input data from keyword arguments. - -Raises ValidationError if the input data cannot be parsed to form a valid model. - - diff --git a/mddocs/markdown/strategy/snapshot_strategy.md b/mddocs/markdown/strategy/snapshot_strategy.md deleted file mode 100644 index 38ef7768c..000000000 --- a/mddocs/markdown/strategy/snapshot_strategy.md +++ /dev/null @@ -1,96 +0,0 @@ - - -# Snapshot Strategy - -### *class* onetl.strategy.snapshot_strategy.SnapshotStrategy - -Snapshot strategy for [DB Reader](../db/db_reader.md#db-reader)/[File Downloader](../file/file_downloader/file_downloader.md#file-downloader). - -Used for fetching all the rows/files from a source. Does not support HWM. - -#### NOTE -This is a default strategy. - -For [DB Reader](../db/db_reader.md#db-reader): -: Every snapshot run is executing the simple query which fetches all the table data: -
- ```sql - SELECT id, data FROM public.mydata; - ``` - -For [File Downloader](../file/file_downloader/file_downloader.md#file-downloader): -: Every snapshot run is downloading all the files (from the source, or user-defined list): -
- ```bash - $ hdfs dfs -ls /path -
- /path/my/file1 - /path/my/file2 - ``` -
- ```python - DownloadResult( - ..., - successful={ - LocalFile("/downloaded/file1"), - LocalFile("/downloaded/file2"), - }, - ) - ``` - -#### Versionadded -Added in version 0.1.0. - -### Examples - -Snapshot run with [DB Reader](../db/db_reader.md#db-reader) - -```py -from onetl.db import DBReader, DBWriter -from onetl.strategy import SnapshotStrategy - -reader = DBReader( - connection=postgres, - source="public.mydata", - columns=["id", "data"], - hwm=DBReader.AutoDetectHWM(name="some_hwm_name", expression="id"), -) - -writer = DBWriter(connection=hive, target="db.newtable") - -with SnapshotStrategy(): - df = reader.run() - writer.run(df) - -# current run will execute following query: - -# SELECT id, data FROM public.mydata; -``` - -Snapshot run with [File Downloader](../file/file_downloader/file_downloader.md#file-downloader) - -```py -from onetl.file import FileDownloader -from onetl.strategy import SnapshotStrategy - -downloader = FileDownloader( - connection=sftp, - source_path="/remote", - local_path="/local", -) - -with SnapshotStrategy(): - df = downloader.run() - -# current run will download all files from 'source_path' -``` - - - -#### \_\_init_\_(\*\*kwargs) - -Create a new model by parsing and validating input data from keyword arguments. - -Raises ValidationError if the input data cannot be parsed to form a valid model. - - diff --git a/mddocs/markdown/troubleshooting/index.md b/mddocs/markdown/troubleshooting/index.md deleted file mode 100644 index 847f426e9..000000000 --- a/mddocs/markdown/troubleshooting/index.md +++ /dev/null @@ -1,17 +0,0 @@ - - -# Troubleshooting - -In case of error please follow instructions below: - -* Read the logs or exception messages you’ve faced with. - : * If Python logs are note verbose enough, [increase the log level](../logging.md#logging). - * If Spark logs are note verbose enough, [increase the log level](spark.md#troubleshooting-spark). -* Read documentation related to a class or method you are using. -* [Google](https://google.com) the error message, and carefully read the search result: - : * [StackOverflow](https://stackoverflow.com/) answers. - * [Spark](https://spark.apache.org/docs/latest/) documentation. - * Documentation of database or filesystem you are connecting to. - * Documentation of underlying connector. -* Search for known [issues](https://github.com/MobileTeleSystems/onetl/issues), or create a new one. -* Always use the most resent versions of onETL, PySpark and connector packages, [compatible with your environment](../install/spark.md#install-spark). diff --git a/mddocs/markdown/troubleshooting/spark.md b/mddocs/markdown/troubleshooting/spark.md deleted file mode 100644 index 812b38acc..000000000 --- a/mddocs/markdown/troubleshooting/spark.md +++ /dev/null @@ -1,72 +0,0 @@ - - -# Spark Troubleshooting - -## Restarting Spark session - -Sometimes it is required to stop current Spark session and start a new one, e.g. to add some .jar packages, or change session config. -But PySpark not only starts Spark session, but also starts Java virtual machine (JVM) process in the background, -which. So calling `sparkSession.stop()` [does not shutdown JVM](https://issues.apache.org/jira/browse/SPARK-47740), -and this can cause some issue. - -Also apart from JVM properties, stopping Spark session does not clear Spark context, which is a global object. So new -Spark sessions are created using the same context object, and thus using the same Spark config options. - -To properly stop Spark session, it is **required** to: -\* Stop Spark session by calling `sparkSession.stop()`. -\* **STOP PYTHON INTERPRETER**, e.g. by calling `sys.exit()`. -\* Start new Python interpreter. -\* Start new Spark session with config options you need. - -Skipping some of these steps can lead to issues with creating new Spark session. - -## Driver log level - -Default logging level for Spark session is `WARN`. To show more verbose logs, use: - -```python -spark.sparkContext.setLogLevel("INFO") -``` - -or increase verbosity even more: - -```python -spark.sparkContext.setLogLevel("DEBUG") -``` - -After getting all information you need, you can return back the previous log level: - -```python -spark.sparkContext.setLogLevel("WARN") -``` - -## Executors log level - -`sparkContext.setLogLevel` changes only log level of Spark session on Spark **driver**. -To make Spark executor logs more verbose, perform following steps: - -* Create `log4j.properties` file with content like this: - ```jproperties - log4j.rootCategory=DEBUG, console - - log4j.appender.console=org.apache.log4j.ConsoleAppender - log4j.appender.console.target=System.err - log4j.appender.console.layout=org.apache.log4j.PatternLayout - log4j.appender.console.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss} %p %c{1}: %m%n - ``` -* Stop existing Spark session and create a new one with following options: - ```python - from pyspark.sql import SparkSession - - spark = ( - SparkSesion.builder.config("spark.files", "file:log4j.properties").config( - "spark.executor.extraJavaOptions", "-Dlog4j.configuration=file:log4j.properties" - ) - # you can apply the same logging settings to Spark driver, by uncommenting the line below - # .config("spark.driver.extraJavaOptions", "-Dlog4j.configuration=file:log4j.properties") - .getOrCreate() - ) - ``` - -Each Spark executor will receive a copy of `log4j.properties` file during start, and load it to change own log level. -Same approach can be used for Spark driver as well, to investigate issue when Spark session cannot properly start. 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+ let data = create_key(label); + if (data) { + let [group, id, key] = data; + + // add click event listener + // @ts-ignore + label.onclick = onSDLabelClick; + + // store map of key to elements + if (!sd_id_to_elements[key]) { + sd_id_to_elements[key] = []; + } + sd_id_to_elements[key].push(label); + + if (groups.indexOf(group) === -1) { + groups.push(group); + // Check if a specific tab has been selected via URL parameter + const tabParam = new URLSearchParams(window.location.search).get( + group + ); + if (tabParam) { + console.log( + "sphinx-design: Selecting tab id for group '" + + group + + "' from URL parameter: " + + tabParam + ); + window.sessionStorage.setItem(storageKeyPrefix + group, tabParam); + } + } + + // Check is a specific tab has been selected previously + let previousId = window.sessionStorage.getItem( + storageKeyPrefix + group + ); + if (previousId === id) { + // console.log( + // "sphinx-design: Selecting tab from session storage: " + id + // ); + // @ts-ignore + 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b/mddocs/docs/_build/markdown/_static/autodoc_pydantic.css @@ -0,0 +1,11 @@ +.autodoc_pydantic_validator_arrow { + padding-left: 8px; + } + +.autodoc_pydantic_collapsable_json { + cursor: pointer; + } + +.autodoc_pydantic_collapsable_erd { + cursor: pointer; + } \ No newline at end of file diff --git a/mddocs/docs/_build/markdown/changelog.md b/mddocs/docs/_build/markdown/changelog.md new file mode 100644 index 000000000..4ba142635 --- /dev/null +++ b/mddocs/docs/_build/markdown/changelog.md @@ -0,0 +1,31 @@ +# Changelog + +# Changelog + +* [0.13.4 (2025-03-20)](changelog/0.13.4.md) +* [0.13.3 (2025-03-11)](changelog/0.13.3.md) +* [0.13.1 (2025-03-06)](changelog/0.13.1.md) +* [0.13.0 (2025-02-24)](changelog/0.13.0.md) +* [0.12.5 (2024-12-03)](changelog/0.12.5.md) +* [0.12.4 (2024-11-27)](changelog/0.12.4.md) +* [0.12.3 (2024-11-22)](changelog/0.12.3.md) +* [0.12.2 (2024-11-12)](changelog/0.12.2.md) +* [0.12.1 (2024-10-28)](changelog/0.12.1.md) +* [0.12.0 (2024-09-03)](changelog/0.12.0.md) +* [0.11.2 (2024-09-02)](changelog/0.11.2.md) +* [0.11.1 (2024-05-29)](changelog/0.11.1.md) +* [0.11.0 (2024-05-27)](changelog/0.11.0.md) +* [0.10.2 (2024-03-21)](changelog/0.10.2.md) +* [0.10.1 (2024-02-05)](changelog/0.10.1.md) +* [0.10.0 (2023-12-18)](changelog/0.10.0.md) +* [0.9.5 (2023-10-10)](changelog/0.9.5.md) +* [0.9.4 (2023-09-26)](changelog/0.9.4.md) +* [0.9.3 (2023-09-06)](changelog/0.9.3.md) +* [0.9.2 (2023-09-06)](changelog/0.9.2.md) +* [0.9.1 (2023-08-17)](changelog/0.9.1.md) +* [0.9.0 (2023-08-17)](changelog/0.9.0.md) +* [0.8.1 (2023-07-10)](changelog/0.8.1.md) +* [0.8.0 (2023-05-31)](changelog/0.8.0.md) +* [0.7.2 (2023-05-24)](changelog/0.7.2.md) +* [0.7.1 (2023-05-23)](changelog/0.7.1.md) +* [0.7.0 (2023-05-15)](changelog/0.7.0.md) diff --git a/mddocs/docs/_build/markdown/changelog/0.10.0.md b/mddocs/docs/_build/markdown/changelog/0.10.0.md new file mode 100644 index 000000000..ff0fe78bc --- /dev/null +++ b/mddocs/docs/_build/markdown/changelog/0.10.0.md @@ -0,0 +1,359 @@ +# 0.10.0 (2023-12-18) + +## Breaking Changes + +- Upgrade `etl-entities` from v1 to v2 ([#172](https://github.com/MobileTeleSystems/onetl/pull/172)). + + This implies that `HWM` classes are now have different internal structure than they used to. + + Before: + ```python + from etl_entities.old_hwm import IntHWM as OldIntHWM + from etl_entities.source import Column, Table + from etl_entities.process import Process + + hwm = OldIntHWM( + process=Process(name="myprocess", task="abc", dag="cde", host="myhost"), + source=Table(name="schema.table", instance="postgres://host:5432/db"), + column=Column(name="col1"), + value=123, + ) + ``` + + After: + ```python + from etl_entities.hwm import ColumnIntHWM + + hwm = ColumnIntHWM( + name="some_unique_name", + description="any value you want", + source="schema.table", + expression="col1", + value=123, + ) + ``` + + **Breaking change:** If you used HWM classes from `etl_entities` module, you should rewrite your code to make it compatible with new version. + + ### More details + + - `HWM` classes used by previous onETL versions were moved from `etl_entities` to `etl_entities.old_hwm` submodule. They are here for compatibility reasons, but are planned to be removed in `etl-entities` v3 release. + - New `HWM` classes have flat structure instead of nested. + - New `HWM` classes have mandatory `name` attribute (it was known as `qualified_name` before). + - Type aliases used while serializing and deserializing `HWM` objects to `dict` representation were changed too: `int` → `column_int`. + + To make migration simpler, you can use new method: + ```python + old_hwm = OldIntHWM(...) + new_hwm = old_hwm.as_new_hwm() + ``` + + Which automatically converts all fields from old structure to new one, including `qualified_name` → `name`. +- **Breaking changes:** + * Methods `BaseHWMStore.get()` and `BaseHWMStore.save()` were renamed to `get_hwm()` and `set_hwm()`. + * They now can be used only with new HWM classes from `etl_entities.hwm`, **old HWM classes are not supported**. + + If you used them in your code, please update it accordingly. +- YAMLHWMStore **CANNOT read files created by older onETL versions** (0.9.x or older). + + ### Update procedure + + ```python + # pip install onetl==0.9.5 + + # Get qualified_name for HWM + + + # Option 1. HWM is built manually + from etl_entities import IntHWM, FileListHWM + from etl_entities.source import Column, Table, RemoteFolder + from etl_entities.process import Process + + # for column HWM + old_column_hwm = IntHWM( + process=Process(name="myprocess", task="abc", dag="cde", host="myhost"), + source=Table(name="schema.table", instance="postgres://host:5432/db"), + column=Column(name="col1"), + ) + qualified_name = old_column_hwm.qualified_name + # "col1#schema.table@postgres://host:5432/db#cde.abc.myprocess@myhost" + + # for file HWM + old_file_hwm = FileListHWM( + process=Process(name="myprocess", task="abc", dag="cde", host="myhost"), + source=RemoteFolder(name="/absolute/path", instance="ftp://ftp.server:21"), + ) + qualified_name = old_file_hwm.qualified_name + # "file_list#/absolute/path@ftp://ftp.server:21#cde.abc.myprocess@myhost" + + + # Option 2. HWM is generated automatically (by DBReader/FileDownloader) + # See onETL logs and search for string like qualified_name = '...' + + qualified_name = "col1#schema.table@postgres://host:5432/db#cde.abc.myprocess@myhost" + + + # Get .yml file path by qualified_name + + import os + from pathlib import PurePosixPath + from onetl.hwm.store import YAMLHWMStore + + # here you should pass the same arguments as used on production, if any + yaml_hwm_store = YAMLHWMStore() + hwm_path = yaml_hwm_store.get_file_path(qualified_name) + print(hwm_path) + + # for column HWM + # LocalPosixPath('/home/maxim/.local/share/onETL/yml_hwm_store/col1__schema.table__postgres_host_5432_db__cde.abc.myprocess__myhost.yml') + + # for file HWM + # LocalPosixPath('/home/maxim/.local/share/onETL/yml_hwm_store/file_list__absolute_path__ftp_ftp.server_21__cde.abc.myprocess__myhost.yml') + + + # Read raw .yml file content + + from yaml import safe_load, dump + + raw_old_hwm_items = safe_load(hwm_path.read_text()) + print(raw_old_hwm_items) + + # for column HWM + # [ + # { + # "column": { "name": "col1", "partition": {} }, + # "modified_time": "2023-12-18T10: 39: 47.377378", + # "process": { "dag": "cde", "host": "myhost", "name": "myprocess", "task": "abc" }, + # "source": { "instance": "postgres: //host:5432/db", "name": "schema.table" }, + # "type": "int", + # "value": "123", + # }, + # ] + + # for file HWM + # [ + # { + # "modified_time": "2023-12-18T11:15:36.478462", + # "process": { "dag": "cde", "host": "myhost", "name": "myprocess", "task": "abc" }, + # "source": { "instance": "ftp://ftp.server:21", "name": "/absolute/path" }, + # "type": "file_list", + # "value": ["file1.txt", "file2.txt"], + # }, + # ] + + + # Convert file content to new structure, compatible with onETL 0.10.x + raw_new_hwm_items = [] + for old_hwm in raw_old_hwm_items: + new_hwm = {"name": qualified_name, "modified_time": old_hwm["modified_time"]} + + if "column" in old_hwm: + new_hwm["expression"] = old_hwm["column"]["name"] + new_hwm["entity"] = old_hwm["source"]["name"] + old_hwm.pop("process", None) + + if old_hwm["type"] == "int": + new_hwm["type"] = "column_int" + new_hwm["value"] = old_hwm["value"] + + elif old_hwm["type"] == "date": + new_hwm["type"] = "column_date" + new_hwm["value"] = old_hwm["value"] + + elif old_hwm["type"] == "datetime": + new_hwm["type"] = "column_datetime" + new_hwm["value"] = old_hwm["value"] + + elif old_hwm["type"] == "file_list": + new_hwm["type"] = "file_list" + new_hwm["value"] = [ + os.fspath(PurePosixPath(old_hwm["source"]["name"]).joinpath(path)) + for path in old_hwm["value"] + ] + + else: + raise ValueError("WAT?") + + raw_new_hwm_items.append(new_hwm) + + + print(raw_new_hwm_items) + # for column HWM + # [ + # { + # "name": "col1#schema.table@postgres://host:5432/db#cde.abc.myprocess@myhost", + # "modified_time": "2023-12-18T10:39:47.377378", + # "expression": "col1", + # "source": "schema.table", + # "type": "column_int", + # "value": 123, + # }, + # ] + + # for file HWM + # [ + # { + # "name": "file_list#/absolute/path@ftp://ftp.server:21#cde.abc.myprocess@myhost", + # "modified_time": "2023-12-18T11:15:36.478462", + # "entity": "/absolute/path", + # "type": "file_list", + # "value": ["/absolute/path/file1.txt", "/absolute/path/file2.txt"], + # }, + # ] + + + # Save file with new content + with open(hwm_path, "w") as file: + dump(raw_new_hwm_items, file) + + + # Stop Python interpreter and update onETL + # pip install onetl==0.10.0 + # Check that new .yml file can be read + + from onetl.hwm.store import YAMLHWMStore + + qualified_name = ... + + # here you should pass the same arguments as used on production, if any + yaml_hwm_store = YAMLHWMStore() + yaml_hwm_store.get_hwm(qualified_name) + + # for column HWM + # ColumnIntHWM( + # name='col1#schema.table@postgres://host:5432/db#cde.abc.myprocess@myhost', + # description='', + # entity='schema.table', + # value=123, + # expression='col1', + # modified_time=datetime.datetime(2023, 12, 18, 10, 39, 47, 377378), + # ) + + # for file HWM + # FileListHWM( + # name='file_list#/absolute/path@ftp://ftp.server:21#cde.abc.myprocess@myhost', + # description='', + # entity=AbsolutePath('/absolute/path'), + # value=frozenset({AbsolutePath('/absolute/path/file1.txt'), AbsolutePath('/absolute/path/file2.txt')}), + # expression=None, + # modified_time=datetime.datetime(2023, 12, 18, 11, 15, 36, 478462) + # ) + + + # That's all! + ``` + + But most of users use other HWM store implementations which do not have such issues. +- Several classes and functions were moved from `onetl` to `etl_entities`: + + | onETL `0.9.x` and older | onETL `0.10.x` and newer | + |-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| + | ```python
from onetl.hwm.store import (
detect_hwm_store,
BaseHWMStore,
HWMStoreClassRegistry,
register_hwm_store_class,
HWMStoreManager,
MemoryHWMStore,
)
``` | ```python
from etl_entities.hwm_store import (
detect_hwm_store,
BaseHWMStore,
HWMStoreClassRegistry,
register_hwm_store_class,
HWMStoreManager,
MemoryHWMStore,
)
``` | + + They still can be imported from old module, but this is deprecated and will be removed in v1.0.0 release. +- Change the way of passing `HWM` to `DBReader` and `FileDownloader` classes: + + | onETL `0.9.x` and older | onETL `0.10.x` and newer | + |----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| + | ```python
reader = DBReader(
connection=...,
source=...,
hwm_column="col1",
)
``` | ```python
reader = DBReader(
connection=...,
source=...,
hwm=DBReader.AutoDetectHWM(
# name is mandatory now!
name="my_unique_hwm_name",
expression="col1",
),
)
``` | + | ```python
reader = DBReader(
connection=...,
source=...,
hwm_column=(
"col1",
"cast(col1 as date)",
),
)
``` | ```python
reader = DBReader(
connection=...,
source=...,
hwm=DBReader.AutoDetectHWM(
# name is mandatory now!
name="my_unique_hwm_name",
expression="cast(col1 as date)",
),
)
``` | + | ```python
downloader = FileDownloader(
connection=...,
source_path=...,
target_path=...,
hwm_type="file_list",
)
``` | ```python
downloader = FileDownloader(
connection=...,
source_path=...,
target_path=...,
hwm=FileListHWM(
# name is mandatory now!
name="another_unique_hwm_name",
),
)
``` | + + New HWM classes have **mandatory** `name` attribute which should be passed explicitly, + instead of generating if automatically under the hood. + + Automatic `name` generation using the old `DBReader.hwm_column` / `FileDownloader.hwm_type` + syntax is still supported, but will be removed in v1.0.0 release. ([#179](https://github.com/MobileTeleSystems/onetl/pull/179)) +- Performance of read Incremental and Batch strategies has been drastically improved. ([#182](https://github.com/MobileTeleSystems/onetl/pull/182)). + + ### Before and after in details + + `DBReader.run()` + incremental/batch strategy behavior in versions 0.9.x and older: + - Get table schema by making query `SELECT * FROM table WHERE 1=0` (if `DBReader.columns` has `*`) + - Expand `*` to real column names from table, add here `hwm_column`, remove duplicates (as some RDBMS does not allow that). + - Create dataframe from query like `SELECT hwm_expression AS hwm_column, ...other table columns... FROM table WHERE hwm_expression > prev_hwm.value`. + - Determine HWM class using dataframe schema: `df.schema[hwm_column].dataType`. + - Determine x HWM column value using Spark: `df.select(max(hwm_column)).collect()`. + - Use `max(hwm_column)` as next HWM value, and save it to HWM Store. + - Return dataframe to user. + + This was far from ideal: + - Dataframe content (all rows or just changed ones) was loaded from the source to Spark only to get min/max values of specific column. + - Step of fetching table schema and then substituting column names in the next query caused some unexpected errors. + > For example, source contains columns with mixed name case, like `"CamelColumn"` or `"spaced column"`. + + > Column names were *not* escaped during query generation, leading to queries that cannot be executed by database. + + > So users have to *explicitly* pass column names `DBReader`, wrapping columns with mixed naming with `"`: + > ```python + > reader = DBReader( + > connection=..., + > source=..., + > columns=[ # passing '*' here leads to wrong SQL query generation + > "normal_column", + > '"CamelColumn"', + > '"spaced column"', + > ..., + > ], + > ) + > ``` + - Using `DBReader` with `IncrementalStrategy` could lead to reading rows already read before. + > Dataframe was created from query with WHERE clause like `hwm.expression > prev_hwm.value`, + > not `hwm.expression > prev_hwm.value AND hwm.expression <= current_hwm.value`. + + > So if new rows appeared in the source **after** HWM value is determined, + > they can be read by accessing dataframe content (because Spark dataframes are lazy), + > leading to inconsistencies between HWM value and dataframe content. + + > This may lead to issues then `DBReader.run()` read some data, updated HWM value, and next call of `DBReader.run()` + > will read rows that were already read in previous run. + + `DBReader.run()` + incremental/batch strategy behavior in versions 0.10.x and newer: + - Detect type of HWM expression: `SELECT hwm.expression FROM table WHERE 1=0`. + - Determine corresponding Spark type `df.schema[0]` and when determine matching HWM class (if `DReader.AutoDetectHWM` is used). + - Get min/max values by querying the source: `SELECT MAX(hwm.expression) FROM table WHERE hwm.expression >= prev_hwm.value`. + - Use `max(hwm.expression)` as next HWM value, and save it to HWM Store. + - Create dataframe from query `SELECT ... table columns ... FROM table WHERE hwm.expression > prev_hwm.value AND hwm.expression <= current_hwm.value`, baking new HWM value into the query. + - Return dataframe to user. + + Improvements: + - Allow source to calculate min/max instead of loading everything to Spark. This should be **faster** on large amounts of data (**up to x2**), because we do not transfer all the data from the source to Spark. This can be even faster if source have indexes for HWM column. + - Columns list is passed to source as-is, without any resolving on `DBReader` side. So you can pass `DBReader(columns=["*"])` to read tables with mixed columns naming. + - Restrict dataframe content to always match HWM values, which leads to never reading the same row twice. + + **Breaking change**: HWM column is not being implicitly added to dataframe. It was a part of `SELECT` clause, but now it is mentioned only in `WHERE` clause. + + So if you had code like this, you have to rewrite it: + + | onETL `0.9.x` and older | onETL `0.10.x` and newer | + |-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| + | ```python
reader = DBReader(
connection=...,
source=...,
columns=[
"col1",
"col2",
],
hwm_column="hwm_col",
)

df = reader.run()
# hwm_column value is in the dataframe
assert df.columns == ["col1", "col2", "hwm_col"]
``` | ```python
reader = DBReader(
connection=...,
source=...,
columns=[
"col1",
"col2",
# add hwm_column explicitly
"hwm_col",
],
hwm_column="hwm_col",
)

df = reader.run()
# if columns list is not updated,
# this fill fail
assert df.columns == ["col1", "col2", "hwm_col"]
``` | + | ```python
reader = DBReader(
connection=...,
source=...,
columns=[
"col1",
"col2",
],
hwm_column=(
"hwm_col",
"cast(hwm_col as int)",
),
)

df = reader.run()
# hwm_expression value is in the dataframe
assert df.columns == ["col1", "col2", "hwm_col"]
``` | ```python
reader = DBReader(
connection=...,
source=...,
columns=[
"col1",
"col2",
# add hwm_expression explicitly
"cast(hwm_col as int) as hwm_col",
],
hwm_column=(
"hwm_col",
"cast(hwm_col as int)",
),
)

df = reader.run()
# if columns list is not updated,
# this fill fail
assert df.columns == ["col1", "col2", "hwm_col"]
``` | + + But most users just use `columns=["*"]` anyway, they won’t see any changes. +- `FileDownloader.run()` now updates HWM in HWM Store not after each file is being successfully downloaded, + but after all files were handled. + + This is because: + * FileDownloader can be used with `DownloadOptions(workers=N)`, which could lead to race condition - one thread can save to HWM store one HWM value when another thread will save different value. + * FileDownloader can download hundreds and thousands of files, and issuing a request to HWM Store for each file could potentially DDoS HWM Store. ([#189](https://github.com/MobileTeleSystems/onetl/pull/189)) + + There is a exception handler which tries to save HWM to HWM store if download process was interrupted. But if it was interrupted by force, like sending `SIGKILL` event, + HWM will not be saved to HWM store, so some already downloaded files may be downloaded again next time. + + But unexpected process kill may produce other negative impact, like some file will be downloaded partially, so this is an expected behavior. + +## Features + +- Add Python 3.12 compatibility. ([#167](https://github.com/MobileTeleSystems/onetl/pull/167)) +- `Excel` file format now can be used with Spark 3.5.0. ([#187](https://github.com/MobileTeleSystems/onetl/pull/187)) +- `SnapshotBatchStagy` and `IncrementalBatchStrategy` does no raise exceptions if source does not contain any data. + Instead they stop at first iteration and return empty dataframe. ([#188](https://github.com/MobileTeleSystems/onetl/pull/188)) +- Cache result of `connection.check()` in high-level classes like `DBReader`, `FileDownloader` and so on. This makes logs less verbose. ([#190](https://github.com/MobileTeleSystems/onetl/pull/190)) + +## Bug Fixes + +- Fix `@slot` and `@hook` decorators returning methods with missing arguments in signature (Pylance, VS Code). ([#183](https://github.com/MobileTeleSystems/onetl/pull/183)) +- Kafka connector documentation said that it does support reading topic data incrementally by passing `group.id` or `groupIdPrefix`. + Actually, this is not true, because Spark does not send information to Kafka which messages were consumed. + So currently users can only read the whole topic, no incremental reads are supported. diff --git a/mddocs/docs/_build/markdown/changelog/0.10.1.md b/mddocs/docs/_build/markdown/changelog/0.10.1.md new file mode 100644 index 000000000..1bc703cb4 --- /dev/null +++ b/mddocs/docs/_build/markdown/changelog/0.10.1.md @@ -0,0 +1,25 @@ +# 0.10.1 (2024-02-05) + +## Features + +- Add support of `Incremental Strategies` for `Kafka` connection: + ```python + reader = DBReader( + connection=Kafka(...), + source="topic_name", + hwm=DBReader.AutoDetectHWM(name="some_hwm_name", expression="offset"), + ) + + with IncrementalStrategy(): + df = reader.run() + ``` + + This lets you resume reading data from a Kafka topic starting at the last committed offset from your previous run. ([#202](https://github.com/MobileTeleSystems/onetl/pull/202)) +- Add `has_data`, `raise_if_no_data` methods to `DBReader` class. ([#203](https://github.com/MobileTeleSystems/onetl/pull/203)) +- Updare VMware Greenplum connector from `2.1.4` to `2.3.0`. This implies: + : * Greenplum 7.x support + * [Kubernetes support](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/configure.html#k8scfg) + * New read option [gpdb.matchDistributionPolicy](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/options.html#distpolmotion) + which allows to match each Spark executor with specific Greenplum segment, avoiding redundant data transfer between Greenplum segments + * Allows overriding [Greenplum optimizer parameters](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/options.html#greenplum-gucs) in read/write operations ([#208](https://github.com/MobileTeleSystems/onetl/pull/208)) +- `Greenplum.get_packages()` method now accepts optional arg `package_version` which allows to override version of Greenplum connector package. ([#208](https://github.com/MobileTeleSystems/onetl/pull/208)) diff --git a/mddocs/docs/_build/markdown/changelog/0.10.2.md b/mddocs/docs/_build/markdown/changelog/0.10.2.md new file mode 100644 index 000000000..3ca2693c2 --- /dev/null +++ b/mddocs/docs/_build/markdown/changelog/0.10.2.md @@ -0,0 +1,34 @@ +# 0.10.2 (2024-03-21) + +## Features + +- Add support of Pydantic v2. ([#230](https://github.com/MobileTeleSystems/onetl/pull/230)) + +## Improvements + +- Improve database connections documentation: + : * Add “Types” section describing mapping between Clickhouse and Spark types + * Add “Prerequisites” section describing different aspects of connecting to Clickhouse + * Separate documentation of `DBReader` and `.sql()` / `.pipeline(...)` + * Add examples for `.fetch()` and `.execute()` ([#211](https://github.com/MobileTeleSystems/onetl/pull/211), [#228](https://github.com/MobileTeleSystems/onetl/pull/228), [#229](https://github.com/MobileTeleSystems/onetl/pull/229), [#233](https://github.com/MobileTeleSystems/onetl/pull/233), [#234](https://github.com/MobileTeleSystems/onetl/pull/234), [#235](https://github.com/MobileTeleSystems/onetl/pull/235), [#236](https://github.com/MobileTeleSystems/onetl/pull/236), [#240](https://github.com/MobileTeleSystems/onetl/pull/240)) +- Add notes to Greenplum documentation about issues with IP resolution and building `gpfdist` URL ([#228](https://github.com/MobileTeleSystems/onetl/pull/228)) +- Allow calling `MongoDB.pipeline(...)` with passing just collection name, without explicit aggregation pipeline. ([#237](https://github.com/MobileTeleSystems/onetl/pull/237)) +- Update default `Postgres(extra={...})` to include `{"stringtype": "unspecified"}` option. + This allows to write text data to non-text column (or vice versa), relying to Postgres cast capabilities. + + For example, now it is possible to read column of type `money` as Spark’s `StringType()`, and write it back to the same column, + without using intermediate columns or tables. ([#229](https://github.com/MobileTeleSystems/onetl/pull/229)) + +## Bug Fixes + +- Return back handling of `DBReader(columns="string")`. This was a valid syntax up to v0.10 release, but it was removed because + most of users neved used it. It looks that we were wrong, returning this behavior back, but with deprecation warning. ([#238](https://github.com/MobileTeleSystems/onetl/pull/238)) +- Downgrade Greenplum package version from `2.3.0` to `2.2.0`. ([#239](https://github.com/MobileTeleSystems/onetl/pull/239)) + + This is because version 2.3.0 introduced issues with writing data to Greenplum 6.x. + Connector can open transaction with `SELECT * FROM table LIMIT 0` query, but does not close it, which leads to deadlocks. + + For using this connector with Greenplum 7.x, please pass package version explicitly: + ```python + maven_packages = Greenplum.get_packages(package_version="2.3.0", ...) + ``` diff --git a/mddocs/docs/_build/markdown/changelog/0.11.0.md b/mddocs/docs/_build/markdown/changelog/0.11.0.md new file mode 100644 index 000000000..2069d081f --- /dev/null +++ b/mddocs/docs/_build/markdown/changelog/0.11.0.md @@ -0,0 +1,202 @@ +# 0.11.0 (2024-05-27) + +## Breaking Changes + +There can be some changes in connection behavior, related to version upgrades. So we mark these changes as **breaking** although +most of users will not see any differences. + +- Update Clickhouse JDBC driver to latest version ([#249](https://github.com/MobileTeleSystems/onetl/pull/249)): + : * Package was renamed `ru.yandex.clickhouse:clickhouse-jdbc` → `com.clickhouse:clickhouse-jdbc`. + * Package version changed `0.3.2` → `0.6.0-patch5`. + * Driver name changed `ru.yandex.clickhouse.ClickHouseDriver` → `com.clickhouse.jdbc.ClickHouseDriver`. + + This brings up several fixes for Spark <-> Clickhouse type compatibility, and also Clickhouse clusters support. +- Update other JDBC drivers to latest versions: + : * MSSQL `12.2.0` → `12.6.2` ([#254](https://github.com/MobileTeleSystems/onetl/pull/254)). + * MySQL `8.0.33` → `8.4.0` ([#253](https://github.com/MobileTeleSystems/onetl/pull/253), [#285](https://github.com/MobileTeleSystems/onetl/pull/285)). + * Oracle `23.2.0.0` → `23.4.0.24.05` ([#252](https://github.com/MobileTeleSystems/onetl/pull/252), [#284](https://github.com/MobileTeleSystems/onetl/pull/284)). + * Postgres `42.6.0` → `42.7.3` ([#251](https://github.com/MobileTeleSystems/onetl/pull/251)). +- Update MongoDB connector to latest version: `10.1.1` → `10.3.0` ([#255](https://github.com/MobileTeleSystems/onetl/pull/255), [#283](https://github.com/MobileTeleSystems/onetl/pull/283)). + + This brings up Spark 3.5 support. +- Update `XML` package to latest version: `0.17.0` → `0.18.0` ([#259](https://github.com/MobileTeleSystems/onetl/pull/259)). + + This brings few bugfixes with datetime format handling. +- For JDBC connections add new `SQLOptions` class for `DB.sql(query, options=...)` method ([#272](https://github.com/MobileTeleSystems/onetl/pull/272)). + + Firsly, to keep naming more consistent. + + Secondly, some of options are not supported by `DB.sql(...)` method, but supported by `DBReader`. + For example, `SQLOptions` do not support `partitioning_mode` and require explicit definition of `lower_bound` and `upper_bound` when `num_partitions` is greater than 1. + `ReadOptions` does support `partitioning_mode` and allows skipping `lower_bound` and `upper_bound` values. + + This require some code changes. Before: + ```python + from onetl.connection import Postgres + + postgres = Postgres(...) + df = postgres.sql( + """ + SELECT * + FROM some.mytable + WHERE key = 'something' + """, + options=Postgres.ReadOptions( + partitioning_mode="range", + partition_column="id", + num_partitions=10, + ), + ) + ``` + + After: + ```python + from onetl.connection import Postgres + + postgres = Postgres(...) + df = postgres.sql( + """ + SELECT * + FROM some.mytable + WHERE key = 'something' + """, + options=Postgres.SQLOptions( + # partitioning_mode is not supported! + partition_column="id", + num_partitions=10, + lower_bound=0, # <-- set explicitly + upper_bound=1000, # <-- set explicitly + ), + ) + ``` + + For now, `DB.sql(query, options=...)` can accept `ReadOptions` to keep backward compatibility, but emits deprecation warning. + The support will be removed in `v1.0.0`. +- Split up `JDBCOptions` class into `FetchOptions` and `ExecuteOptions` ([#274](https://github.com/MobileTeleSystems/onetl/pull/274)). + + New classes are used by `DB.fetch(query, options=...)` and `DB.execute(query, options=...)` methods respectively. + This is mostly to keep naming more consistent. + + This require some code changes. Before: + ```python + from onetl.connection import Postgres + + postgres = Postgres(...) + df = postgres.fetch( + "SELECT * FROM some.mytable WHERE key = 'something'", + options=Postgres.JDBCOptions( + fetchsize=1000, + query_timeout=30, + ), + ) + + postgres.execute( + "UPDATE some.mytable SET value = 'new' WHERE key = 'something'", + options=Postgres.JDBCOptions(query_timeout=30), + ) + ``` + + After: + ```python + from onetl.connection import Postgres + + # Using FetchOptions for fetching data + postgres = Postgres(...) + df = postgres.fetch( + "SELECT * FROM some.mytable WHERE key = 'something'", + options=Postgres.FetchOptions( # <-- change class name + fetchsize=1000, + query_timeout=30, + ), + ) + + # Using ExecuteOptions for executing statements + postgres.execute( + "UPDATE some.mytable SET value = 'new' WHERE key = 'something'", + options=Postgres.ExecuteOptions(query_timeout=30), # <-- change class name + ) + ``` + + For now, `DB.fetch(query, options=...)` and `DB.execute(query, options=...)` can accept `JDBCOptions`, to keep backward compatibility, + but emit a deprecation warning. The old class will be removed in `v1.0.0`. +- Serialize `ColumnDatetimeHWM` to Clickhouse’s `DateTime64(6)` (precision up to microseconds) instead of `DateTime` (precision up to seconds) ([#267](https://github.com/MobileTeleSystems/onetl/pull/267)). + + In previous onETL versions, `ColumnDatetimeHWM` value was rounded to the second, and thus reading some rows that were read in previous runs, + producing duplicates. + + For Clickhouse versions below 21.1 comparing column of type `DateTime` with a value of type `DateTime64` is not supported, returning an empty dataframe. + To avoid this, replace: + ```python + DBReader( + ..., + hwm=DBReader.AutoDetectHWM( + name="my_hwm", + expression="hwm_column", # <-- + ), + ) + ``` + + with: + ```python + DBReader( + ..., + hwm=DBReader.AutoDetectHWM( + name="my_hwm", + expression="CAST(hwm_column AS DateTime64)", # <-- add explicit CAST + ), + ) + ``` +- Pass JDBC connection extra params as `properties` dict instead of URL with query part ([#268](https://github.com/MobileTeleSystems/onetl/pull/268)). + + This allows passing custom connection parameters like `Clickhouse(extra={"custom_http_options": "option1=value1,option2=value2"})` + without need to apply urlencode to parameter value, like `option1%3Dvalue1%2Coption2%3Dvalue2`. + +## Features + +Improve user experience with Kafka messages and Database tables with serialized columns, like JSON/XML. + +- Allow passing custom package version as argument for `DB.get_packages(...)` method of several DB connectors: + : * `Clickhouse.get_packages(package_version=..., apache_http_client_version=...)` ([#249](https://github.com/MobileTeleSystems/onetl/pull/249)). + * `MongoDB.get_packages(scala_version=..., spark_version=..., package_version=...)` ([#255](https://github.com/MobileTeleSystems/onetl/pull/255)). + * `MySQL.get_packages(package_version=...)` ([#253](https://github.com/MobileTeleSystems/onetl/pull/253)). + * `MSSQL.get_packages(java_version=..., package_version=...)` ([#254](https://github.com/MobileTeleSystems/onetl/pull/254)). + * `Oracle.get_packages(java_version=..., package_version=...)` ([#252](https://github.com/MobileTeleSystems/onetl/pull/252)). + * `Postgres.get_packages(package_version=...)` ([#251](https://github.com/MobileTeleSystems/onetl/pull/251)). + * `Teradata.get_packages(package_version=...)` ([#256](https://github.com/MobileTeleSystems/onetl/pull/256)). + + Now users can downgrade or upgrade connection without waiting for next onETL release. Previously only `Kafka` and `Greenplum` supported this feature. +- Add `FileFormat.parse_column(...)` method to several classes: + : * `Avro.parse_column(col)` ([#265](https://github.com/MobileTeleSystems/onetl/pull/265)). + * `JSON.parse_column(col, schema=...)` ([#257](https://github.com/MobileTeleSystems/onetl/pull/257)). + * `CSV.parse_column(col, schema=...)` ([#258](https://github.com/MobileTeleSystems/onetl/pull/258)). + * `XML.parse_column(col, schema=...)` ([#269](https://github.com/MobileTeleSystems/onetl/pull/269)). + + This allows parsing data in `value` field of Kafka message or string/binary column of some table as a nested Spark structure. +- Add `FileFormat.serialize_column(...)` method to several classes: + : * `Avro.serialize_column(col)` ([#265](https://github.com/MobileTeleSystems/onetl/pull/265)). + * `JSON.serialize_column(col)` ([#257](https://github.com/MobileTeleSystems/onetl/pull/257)). + * `CSV.serialize_column(col)` ([#258](https://github.com/MobileTeleSystems/onetl/pull/258)). + + This allows saving Spark nested structures or arrays to `value` field of Kafka message or string/binary column of some table. + +## Improvements + +Few documentation improvements. + +- Replace all `assert` in documentation with doctest syntax. This should make documentation more readable ([#273](https://github.com/MobileTeleSystems/onetl/pull/273)). +- Add generic `Troubleshooting` guide ([#275](https://github.com/MobileTeleSystems/onetl/pull/275)). +- Improve Kafka documentation: + : * Add “Prerequisites” page describing different aspects of connecting to Kafka. + * Improve “Reading from” and “Writing to” page of Kafka documentation, add more examples and usage notes. + * Add “Troubleshooting” page ([#276](https://github.com/MobileTeleSystems/onetl/pull/276)). +- Improve Hive documentation: + : * Add “Prerequisites” page describing different aspects of connecting to Hive. + * Improve “Reading from” and “Writing to” page of Hive documentation, add more examples and recommendations. + * Improve “Executing statements in Hive” page of Hive documentation. ([#278](https://github.com/MobileTeleSystems/onetl/pull/278)). +- Add “Prerequisites” page describing different aspects of using SparkHDFS and SparkS3 connectors. ([#279](https://github.com/MobileTeleSystems/onetl/pull/279)). +- Add note about connecting to Clickhouse cluster. ([#280](https://github.com/MobileTeleSystems/onetl/pull/280)). +- Add notes about versions when specific class/method/attribute/argument was added, renamed or changed behavior ([#282](https://github.com/MobileTeleSystems/onetl/pull/282)). + +## Bug Fixes + +- Fix missing `pysmb` package after installing `pip install onetl[files]` . diff --git a/mddocs/docs/_build/markdown/changelog/0.11.1.md b/mddocs/docs/_build/markdown/changelog/0.11.1.md new file mode 100644 index 000000000..823afe3be --- /dev/null +++ b/mddocs/docs/_build/markdown/changelog/0.11.1.md @@ -0,0 +1,9 @@ +# 0.11.1 (2024-05-29) + +## Features + +- Change `MSSQL.port` default from `1433` to `None`, allowing use of `instanceName` to detect port number. ([#287](https://github.com/MobileTeleSystems/onetl/pull/287)) + +## Bug Fixes + +- Remove `fetchsize` from `JDBC.WriteOptions`. ([#288](https://github.com/MobileTeleSystems/onetl/pull/288)) diff --git a/mddocs/docs/_build/markdown/changelog/0.11.2.md b/mddocs/docs/_build/markdown/changelog/0.11.2.md new file mode 100644 index 000000000..9278d22f8 --- /dev/null +++ b/mddocs/docs/_build/markdown/changelog/0.11.2.md @@ -0,0 +1,5 @@ +# 0.11.2 (2024-09-02) + +## Bug Fixes + +- Fix passing `Greenplum(extra={"options": ...})` during read/write operations. ([#308](https://github.com/MobileTeleSystems/onetl/pull/308)) diff --git a/mddocs/docs/_build/markdown/changelog/0.12.0.md b/mddocs/docs/_build/markdown/changelog/0.12.0.md new file mode 100644 index 000000000..d212c0062 --- /dev/null +++ b/mddocs/docs/_build/markdown/changelog/0.12.0.md @@ -0,0 +1,48 @@ +# 0.12.0 (2024-09-03) + +## Breaking Changes + +- Change connection URL used for generating HWM names of S3 and Samba sources: + : * `smb://host:port` -> `smb://host:port/share` + * `s3://host:port` -> `s3://host:port/bucket` ([#304](https://github.com/MobileTeleSystems/onetl/pull/304)) +- Update DB connectors/drivers to latest versions: + : * Clickhouse `0.6.0-patch5` → `0.6.5` + * MongoDB `10.3.0` → `10.4.0` + * MSSQL `12.6.2` → `12.8.1` + * MySQL `8.4.0` → `9.0.0` + * Oracle `23.4.0.24.05` → `23.5.0.24.07` + * Postgres `42.7.3` → `42.7.4` +- Update `Excel` package from `0.20.3` to `0.20.4`, to include Spark 3.5.1 support. ([#306](https://github.com/MobileTeleSystems/onetl/pull/306)) + +## Features + +- Add support for specifying file formats (`ORC`, `Parquet`, `CSV`, etc.) in `HiveWriteOptions.format` ([#292](https://github.com/MobileTeleSystems/onetl/pull/292)): + ```python + Hive.WriteOptions(format=ORC(compression="snappy")) + ``` +- Collect Spark execution metrics in following methods, and log then in DEBUG mode: + : * `DBWriter.run()` + * `FileDFWriter.run()` + * `Hive.sql()` + * `Hive.execute()` + + This is implemented using custom `SparkListener` which wraps the entire method call, and + then report collected metrics. But these metrics sometimes may be missing due to Spark architecture, + so they are not reliable source of information. That’s why logs are printed only in DEBUG mode, and + are not returned as method call result. ([#303](https://github.com/MobileTeleSystems/onetl/pull/303)) +- Generate default `jobDescription` based on currently executed method. Examples: + : * `DBWriter.run(schema.table) -> Postgres[host:5432/database]` + * `MongoDB[localhost:27017/admin] -> DBReader.has_data(mycollection)` + * `Hive[cluster].execute()` + + If user already set custom `jobDescription`, it will left intact. ([#304](https://github.com/MobileTeleSystems/onetl/pull/304)) +- Add log.info about JDBC dialect usage ([#305](https://github.com/MobileTeleSystems/onetl/pull/305)): + ```text + |MySQL| Detected dialect: 'org.apache.spark.sql.jdbc.MySQLDialect' + ``` +- Log estimated size of in-memory dataframe created by `JDBC.fetch` and `JDBC.execute` methods. ([#303](https://github.com/MobileTeleSystems/onetl/pull/303)) + +## Bug Fixes + +- Fix passing `Greenplum(extra={"options": ...})` during read/write operations. ([#308](https://github.com/MobileTeleSystems/onetl/pull/308)) +- Do not raise exception if yield-based hook whas something past (and only one) `yield`. diff --git a/mddocs/docs/_build/markdown/changelog/0.12.1.md b/mddocs/docs/_build/markdown/changelog/0.12.1.md new file mode 100644 index 000000000..3575634d5 --- /dev/null +++ b/mddocs/docs/_build/markdown/changelog/0.12.1.md @@ -0,0 +1,17 @@ +# 0.12.1 (2024-10-28) + +## Features + +- Log detected JDBC dialect while using `DBWriter`. + +## Bug Fixes + +- Fix `SparkMetricsRecorder` failing when receiving `SparkListenerTaskEnd` without `taskMetrics` (e.g. executor was killed by OOM). ([#313](https://github.com/MobileTeleSystems/onetl/pull/313)) +- Call `kinit` before checking for HDFS active namenode. +- Wrap `kinit` with `threading.Lock` to avoid multithreading issues. +- Immediately show `kinit` errors to user, instead of hiding them. +- Use `AttributeError` instead of `ImportError` in module’s `__getattr__` method, to make code compliant with Python spec. + +## Doc only Changes + +- Add note about [spark-dialect-extension](https://github.com/MobileTeleSystems/spark-dialect-extension) package to Clickhouse connector documentation. ([#310](https://github.com/MobileTeleSystems/onetl/pull/310)) diff --git a/mddocs/docs/_build/markdown/changelog/0.12.2.md b/mddocs/docs/_build/markdown/changelog/0.12.2.md new file mode 100644 index 000000000..23a8d383d --- /dev/null +++ b/mddocs/docs/_build/markdown/changelog/0.12.2.md @@ -0,0 +1,18 @@ +# 0.12.2 (2024-11-12) + +## Improvements + +- Change Spark `jobDescription` for DBReader & FileDFReader from `DBReader.run() -> Connection` to `Connection -> DBReader.run()`. + +## Bug Fixes + +- Fix `log_hwm` result for `KeyValueIntHWM` (used by Kafka). ([#316](https://github.com/MobileTeleSystems/onetl/pull/316)) +- Fix `log_collection` hiding values of `Kafka.addresses` in logs with `INFO` level. ([#316](https://github.com/MobileTeleSystems/onetl/pull/316)) + +## Dependencies + +- Allow using [etl-entities==2.4.0](https://github.com/MobileTeleSystems/etl-entities/releases/tag/2.4.0). + +## Doc only Changes + +- Fix links to MSSQL date & time type documentation. diff --git a/mddocs/docs/_build/markdown/changelog/0.12.3.md b/mddocs/docs/_build/markdown/changelog/0.12.3.md new file mode 100644 index 000000000..741c74e1f --- /dev/null +++ b/mddocs/docs/_build/markdown/changelog/0.12.3.md @@ -0,0 +1,5 @@ +# 0.12.3 (2024-11-22) + +## Bug Fixes + +- Allow passing table names in format `schema."table.with.dots"` to `DBReader(source=...)` and `DBWriter(target=...)`. diff --git a/mddocs/docs/_build/markdown/changelog/0.12.4.md b/mddocs/docs/_build/markdown/changelog/0.12.4.md new file mode 100644 index 000000000..3ebc57a87 --- /dev/null +++ b/mddocs/docs/_build/markdown/changelog/0.12.4.md @@ -0,0 +1,5 @@ +# 0.12.4 (2024-11-27) + +## Bug Fixes + +- Fix `DBReader(conn=oracle, options={"partitioning_mode": "hash"})` lead to data skew in last partition due to wrong `ora_hash` usage. ([#319](https://github.com/MobileTeleSystems/onetl/pull/319)) diff --git a/mddocs/docs/_build/markdown/changelog/0.12.5.md b/mddocs/docs/_build/markdown/changelog/0.12.5.md new file mode 100644 index 000000000..c542a50fd --- /dev/null +++ b/mddocs/docs/_build/markdown/changelog/0.12.5.md @@ -0,0 +1,13 @@ +# 0.12.5 (2024-12-03) + +## Improvements + +- Use `sipHash64` instead of `md5` in Clickhouse for reading data with `{"partitioning_mode": "hash"}`, as it is 5 times faster. +- Use `hashtext` instead of `md5` in Postgres for reading data with `{"partitioning_mode": "hash"}`, as it is 3-5 times faster. +- Use `BINARY_CHECKSUM` instead of `HASHBYTES` in MSSQL for reading data with `{"partitioning_mode": "hash"}`, as it is 5 times faster. + +## Big fixes + +- In JDBC sources wrap `MOD(partitionColumn, numPartitions)` with `ABS(...)` to make al returned values positive. This prevents data skew. +- Fix reading table data from MSSQL using `{"partitioning_mode": "hash"}` with `partitionColumn` of integer type. +- Fix reading table data from Postgres using `{"partitioning_mode": "hash"}` lead to data skew (all the data was read into one Spark partition). diff --git a/mddocs/docs/_build/markdown/changelog/0.13.0.md b/mddocs/docs/_build/markdown/changelog/0.13.0.md new file mode 100644 index 000000000..0b7256299 --- /dev/null +++ b/mddocs/docs/_build/markdown/changelog/0.13.0.md @@ -0,0 +1,197 @@ +# 0.13.0 (2025-02-24) + +🎉 3 years since first release 0.1.0 🎉 + +## Breaking Changes + +- Add Python 3.13. support. ([#298](https://github.com/MobileTeleSystems/onetl/pull/298)) +- Change the logic of `FileConnection.walk` and `FileConnection.list_dir`. ([#327](https://github.com/MobileTeleSystems/onetl/pull/327)) + + Previously `limits.stops_at(path) == True` considered as “return current file and stop”, and could lead to exceeding some limit. + Not it means “stop immediately”. +- Change default value for `FileDFWriter.Options(if_exists=...)` from `error` to `append`, + to make it consistent with other `.Options()` classes within onETL. ([#343](https://github.com/MobileTeleSystems/onetl/pull/343)) + +## Features + +- Add support for `FileModifiedTimeHWM` HWM class (see [etl-entities 2.5.0](https://github.com/MobileTeleSystems/etl-entities/releases/tag/2.5.0)): + ```python + from etl_entitites.hwm import FileModifiedTimeHWM + from onetl.file import FileDownloader + from onetl.strategy import IncrementalStrategy + + downloader = FileDownloader( + ..., + hwm=FileModifiedTimeHWM(name="somename"), + ) + + with IncrementalStrategy(): + downloader.run() + ``` +- Introduce `FileSizeRange(min=..., max=...)` filter class. ([#325](https://github.com/MobileTeleSystems/onetl/pull/325)) + + Now users can set `FileDownloader` / `FileMover` to download/move only files with specific file size range: + ```python + from onetl.file import FileDownloader + from onetl.file.filter import FileSizeRange + + downloader = FileDownloader( + ..., + filters=[FileSizeRange(min="10KiB", max="1GiB")], + ) + ``` +- Introduce `TotalFilesSize(...)` limit class. ([#326](https://github.com/MobileTeleSystems/onetl/pull/326)) + + Now users can set `FileDownloader` / `FileMover` to stop downloading/moving files after reaching a certain amount of data: + ```python + from datetime import datetime, timedelta + from onetl.file import FileDownloader + from onetl.file.limit import TotalFilesSize + + downloader = FileDownloader( + ..., + limits=[TotalFilesSize("1GiB")], + ) + ``` +- Implement `FileModifiedTime(since=..., until=...)` file filter. ([#330](https://github.com/MobileTeleSystems/onetl/pull/330)) + + Now users can set `FileDownloader` / `FileMover` to download/move only files with specific file modification time: + ```python + from datetime import datetime, timedelta + from onetl.file import FileDownloader + from onetl.file.filter import FileModifiedTime + + downloader = FileDownloader( + ..., + filters=[FileModifiedTime(before=datetime.now() - timedelta(hours=1))], + ) + ``` +- Add `SparkS3.get_exclude_packages()` and `Kafka.get_exclude_packages()` methods. ([#341](https://github.com/MobileTeleSystems/onetl/pull/341)) + + Using them allows to skip downloading dependencies not required by this specific connector, or which are already a part of Spark/PySpark: + ```python + from onetl.connection import SparkS3, Kafka + + maven_packages = [ + *SparkS3.get_packages(spark_version="3.5.4"), + *Kafka.get_packages(spark_version="3.5.4"), + ] + exclude_packages = SparkS3.get_exclude_packages() + Kafka.get_exclude_packages() + spark = ( + SparkSession.builder.appName("spark_app_onetl_demo") + .config("spark.jars.packages", ",".join(maven_packages)) + .config("spark.jars.excludes", ",".join(exclude_packages)) + .getOrCreate() + ) + ``` + +## Improvements + +- All DB connections opened by `JDBC.fetch(...)`, `JDBC.execute(...)` or `JDBC.check()` + are immediately closed after the statements is executed. ([#334](https://github.com/MobileTeleSystems/onetl/pull/334)) + + Previously Spark session with `master=local[3]` actually opened up to 5 connections to target DB - one for `JDBC.check()`, + another for Spark driver interaction with DB to create tables, and one for each Spark executor. Now only max 4 connections are opened, + as `JDBC.check()` does not hold opened connection. + + This is important for RDBMS like Postgres or Greenplum where number of connections is strictly limited and limit is usually quite low. +- Set up `ApplicationName` (client info) for Clickhouse, MongoDB, MSSQL, MySQL and Oracle. ([#339](https://github.com/MobileTeleSystems/onetl/pull/339), [#248](https://github.com/MobileTeleSystems/onetl/pull/248)) + + Also update `ApplicationName` format for Greenplum, Postgres, Kafka and SparkS3. + Now all connectors have the same `ApplicationName` format: `${spark.applicationId} ${spark.appName} onETL/${onetl.version} Spark/${spark.version}` + + The only connections not sending `ApplicationName` are Teradata and FileConnection implementations. +- Now `DB.check()` will test connection availability not only on Spark driver, but also from some Spark executor. ([#346](https://github.com/MobileTeleSystems/onetl/pull/346)) + + This allows to fail immediately if Spark driver host has network access to target DB, but Spark executors have not. + + #### NOTE + Now `Greenplum.check()` requires the same user grants as `DBReader(connection=greenplum)`: + ```sql + -- yes, "writable" for reading data from GP, it's not a mistake + ALTER USER username CREATEEXTTABLE(type = 'writable', protocol = 'gpfdist'); + + -- for both reading and writing to GP + -- ALTER USER username CREATEEXTTABLE(type = 'readable', protocol = 'gpfdist') CREATEEXTTABLE(type = 'writable', protocol = 'gpfdist'); + ``` + + Please ask your Greenplum administrators to provide these grants. + +## Bug Fixes + +- Avoid suppressing Hive Metastore errors while using `DBWriter`. ([#329](https://github.com/MobileTeleSystems/onetl/pull/329)) + + Previously this was implemented as: + ```python + try: + spark.sql(f"SELECT * FROM {table}") + table_exists = True + except Exception: + table_exists = False + ``` + + If Hive Metastore was overloaded and responded with an exception, it was considered as non-existing table, resulting + to full table override instead of append or override only partitions subset. +- Fix using onETL to write data to PostgreSQL or Greenplum instances behind *pgbouncer* with `pool_mode=transaction`. ([#336](https://github.com/MobileTeleSystems/onetl/pull/336)) + + Previously `Postgres.check()` opened a read-only transaction, pgbouncer changed the entire connection type from read-write to read-only, + and when `DBWriter.run(df)` executed in read-only connection, producing errors like: + ```default + org.postgresql.util.PSQLException: ERROR: cannot execute INSERT in a read-only transaction + org.postgresql.util.PSQLException: ERROR: cannot execute TRUNCATE TABLE in a read-only transaction + ``` + + Added a workaround by passing `readOnly=True` to JDBC params for read-only connections, so pgbouncer may differ read-only and read-write connections properly. + + After upgrading onETL 0.13.x or higher the same error still may appear of pgbouncer still holds read-only connections and returns them for DBWriter. + To this this, user can manually convert read-only connection to read-write: + ```python + postgres.execute("BEGIN READ WRITE;") # <-- add this line + DBWriter(...).run() + ``` + + After all connections in pgbouncer pool were converted from read-only to read-write, and error fixed, this additional line could be removed. + + See [Postgres JDBC driver documentation](https://jdbc.postgresql.org/documentation/use/). +- Fix `MSSQL.fetch(...)` and `MySQL.fetch(...)` opened a read-write connection instead of read-only. ([#337](https://github.com/MobileTeleSystems/onetl/pull/337)) + + Now this is fixed: + : * `MSSQL.fetch(...)` establishes connection with `ApplicationIntent=ReadOnly`. + * `MySQL.fetch(...)` calls `SET SESSION TRANSACTION READ ONLY` statement. +- Fixed passing multiple filters to `FileDownloader` and `FileMover`. ([#338](https://github.com/MobileTeleSystems/onetl/pull/338)) + If was caused by sorting filters list in internal logging method, but `FileFilter` subclasses are not sortable. +- Fix a false warning about a lof of parallel connections to Grenplum. ([#342](https://github.com/MobileTeleSystems/onetl/pull/342)) + + Creating Spark session with `.master("local[5]")` may open up to 6 connections to Greenplum (=number of Spark executors + 1 for driver), + but onETL instead used number of *CPU cores* on the host as a number of parallel connections. + + This lead to showing a false warning that number of Greenplum connections is too high, + which actually should be the case only if number of executors is higher than 30. +- Fix MongoDB trying to use current database name as `authSource`. ([#347](https://github.com/MobileTeleSystems/onetl/pull/347)) + + Use default connector value which is `admin` database. Previous onETL versions could be fixed by: + ```python + from onetl.connection import MongoDB + + mongodb = MongoDB( + ..., + database="mydb", + extra={ + "authSource": "admin", + }, + ) + ``` + +## Dependencies + +- Minimal `etl-entities` version is now [2.5.0](https://github.com/MobileTeleSystems/etl-entities/releases/tag/2.5.0). ([#331](https://github.com/MobileTeleSystems/onetl/pull/331)) +- Update DB connectors/drivers to latest versions: ([#345](https://github.com/MobileTeleSystems/onetl/pull/345)) + : * Clickhouse `0.6.5` → `0.7.2` + * MongoDB `10.4.0` → `10.4.1` + * MySQL `9.0.0` → `9.2.0` + * Oracle `23.5.0.24.07` → `23.7.0.25.01` + * Postgres `42.7.4` → `42.7.5` + +## Doc only Changes + +- Split large code examples to tabs. ([#344](https://github.com/MobileTeleSystems/onetl/pull/344)) diff --git a/mddocs/docs/_build/markdown/changelog/0.13.1.md b/mddocs/docs/_build/markdown/changelog/0.13.1.md new file mode 100644 index 000000000..e8eb120d2 --- /dev/null +++ b/mddocs/docs/_build/markdown/changelog/0.13.1.md @@ -0,0 +1,9 @@ +# 0.13.1 (2025-03-06) + +## Bug Fixes + +In 0.13.0, using `DBWriter(connection=hive, target="SOMEDB.SOMETABLE")` lead to executing `df.write.saveAsTable()` +instead of `df.write.insertInto()` if target table `somedb.sometable` already exist. + +This is caused by table name normalization (Hive uses lower-case names), which wasn’t properly handled by method used for checking table existence. +([#350](https://github.com/MobileTeleSystems/onetl/pull/350)) diff --git a/mddocs/docs/_build/markdown/changelog/0.13.3.md b/mddocs/docs/_build/markdown/changelog/0.13.3.md new file mode 100644 index 000000000..1aa289b49 --- /dev/null +++ b/mddocs/docs/_build/markdown/changelog/0.13.3.md @@ -0,0 +1,5 @@ +# 0.13.3 (2025-03-11) + +## Dependencies + +Allow using [etl-entities 2.6.0](https://github.com/MobileTeleSystems/etl-entities/releases/tag/2.6.0). diff --git a/mddocs/docs/_build/markdown/changelog/0.13.4.md b/mddocs/docs/_build/markdown/changelog/0.13.4.md new file mode 100644 index 000000000..10f695e0c --- /dev/null +++ b/mddocs/docs/_build/markdown/changelog/0.13.4.md @@ -0,0 +1,10 @@ +# 0.13.4 (2025-03-20) + +## Doc only Changes + +- Prefer `ReadOptions(partitionColumn=..., numPartitions=..., queryTimeout=...)` + instead of `ReadOptions(partition_column=..., num_partitions=..., query_timeout=...)`, + to match Spark documentation. ([#352](https://github.com/MobileTeleSystems/onetl/pull/352)) +- Prefer `WriteOptions(if_exists=...)` instead of `WriteOptions(mode=...)` for IDE suggestions. ([#354](https://github.com/MobileTeleSystems/onetl/pull/354)) +- Document all options of supported file formats. + ([#355](https://github.com/MobileTeleSystems/onetl/pull/355), [#356](https://github.com/MobileTeleSystems/onetl/pull/356), [#357](https://github.com/MobileTeleSystems/onetl/pull/357), [#358](https://github.com/MobileTeleSystems/onetl/pull/358), [#359](https://github.com/MobileTeleSystems/onetl/pull/359), [#360](https://github.com/MobileTeleSystems/onetl/pull/360), [#361](https://github.com/MobileTeleSystems/onetl/pull/361), [#362](https://github.com/MobileTeleSystems/onetl/pull/362)) diff --git a/mddocs/docs/_build/markdown/changelog/0.7.0.md b/mddocs/docs/_build/markdown/changelog/0.7.0.md new file mode 100644 index 000000000..3f896b82b --- /dev/null +++ b/mddocs/docs/_build/markdown/changelog/0.7.0.md @@ -0,0 +1,211 @@ +# 0.7.0 (2023-05-15) + +## 🎉 onETL is now open source 🎉 + +That was long road, but we finally did it! + +## Breaking Changes + +* Changed installation method. + + **TL;DR What should I change to restore previous behavior** + + Simple way: + + | onETL < 0.7.0 | onETL >= 0.7.0 | + |-------------------|-----------------------------------| + | pip install onetl | pip install onetl[files,kerberos] | + + Right way - enumerate connectors should be installed: + ```bash + pip install onetl[hdfs,ftp,kerberos] # except DB connections + ``` + + **Details** + + In onetl<0.7 the package installation looks like: + ```bash + pip install onetl + ``` + + But this includes all dependencies for all connectors, even if user does not use them. + This caused some issues, for example user had to install Kerberos libraries to be able to install onETL, even if user uses only S3 (without Kerberos support). + + Since 0.7.0 installation process was changed: + ```bash + pip install onetl # minimal installation, only onETL core + # there is no extras for DB connections because they are using Java packages which are installed in runtime + + pip install onetl[ftp,ftps,hdfs,sftp,s3,webdav] # install dependencies for specified file connections + pip install onetl[files] # install dependencies for all file connections + + pip install onetl[kerberos] # Kerberos auth support + pip install onetl[spark] # install PySpark to use DB connections + + pip install onetl[spark,kerberos,files] # all file connections + Kerberos + PySpark + pip install onetl[all] # alias for previous case + ``` + + There are corresponding documentation items for each extras. + + Also onETL checks that some requirements are missing, and raises exception with recommendation how to install them: + ```text + Cannot import module "pyspark". + + Since onETL v0.7.0 you should install package as follows: + pip install onetl[spark] + + or inject PySpark to sys.path in some other way BEFORE creating MongoDB instance. + ``` + + ```text + Cannot import module "ftputil". + + Since onETL v0.7.0 you should install package as follows: + pip install onetl[ftp] + + or + pip install onetl[files] + ``` +* Added new `cluster` argument to `Hive` and `HDFS` connections. + + `Hive` qualified name (used in HWM) contains cluster name. But in onETL<0.7.0 cluster name had hard coded value `rnd-dwh` which was not OK for some users. + + `HDFS` connection qualified name contains host (active namenode of Hadoop cluster), but its value can change over time, leading to creating of new HWM. + + Since onETL 0.7.0 both `Hive` and `HDFS` connections have `cluster` attribute which can be set to a specific cluster name. + For `Hive` it is mandatory, for `HDFS` it can be omitted (using host as a fallback). + + But passing cluster name every time could lead to errors. + + Now `Hive` and `HDFS` have nested class named `slots` with methods: + * `normalize_cluster_name` + * `get_known_clusters` + * `get_current_cluster` + * `normalize_namenode_host` (only `HDFS`) + * `get_cluster_namenodes` (only `HDFS`) + * `get_webhdfs_port` (only `HDFS`) + * `is_namenode_active` (only `HDFS`) + + And new method `HDFS.get_current` / `Hive.get_current`. + + Developers can implement hooks validating user input or substituting values for automatic cluster detection. + This should improve user experience while using these connectors. + + See slots documentation. +* Update JDBC connection drivers. + * Greenplum `2.1.3` → `2.1.4`. + * MSSQL `10.2.1.jre8` → `12.2.0.jre8`. Minimal supported version of MSSQL is now 2014 instead 2021. + * MySQL `8.0.30` → `8.0.33`: + \* Package was renamed `mysql:mysql-connector-java` → `com.mysql:mysql-connector-j`. + \* Driver class was renamed `com.mysql.jdbc.Driver` → `com.mysql.cj.jdbc.Driver`. + * Oracle `21.6.0.0.1` → `23.2.0.0`. + * Postgres `42.4.0` → `42.6.0`. + * Teradata `17.20.00.08` → `17.20.00.15`: + \* Package was renamed `com.teradata.jdbc:terajdbc4` → `com.teradata.jdbc:terajdbc`. + \* Teradata driver is now published to Maven. + + See [#31](https://github.com/MobileTeleSystems/onetl/pull/31). + +## Features + +* Added MongoDB connection. + + Using official [MongoDB connector for Spark v10](https://www.mongodb.com/docs/spark-connector/current/). Only Spark 3.2+ is supported. + + There are some differences between MongoDB and other database sources: + * Instead of `mongodb.sql` method there is `mongodb.pipeline`. + * No methods `mongodb.fetch` and `mongodb.execute`. + * `DBReader.hint` and `DBReader.where` have different types than in SQL databases: + + ```python + where = { + "col1": { + "$eq": 10, + }, + } + + hint = { + "col1": 1, + } + ``` + + * Because MongoDB does not have schemas of collections, but Spark cannot create dataframe with dynamic schema, new option `DBReader.df_schema` was introduced. + It is mandatory for MongoDB, but optional for other sources. + * Currently DBReader cannot be used with MongoDB and hwm expression, e.g. `hwm_column=("mycolumn", {"$cast": {"col1": "date"}})` + + Because there are no tables in MongoDB, some options were renamed in core classes: + * `DBReader(table=...)` → `DBReader(source=...)` + * `DBWriter(table=...)` → `DBWriter(target=...)` + + Old names can be used too, they are not deprecated ([#30](https://github.com/MobileTeleSystems/onetl/pull/30)). +* Added option for disabling some plugins during import. + + Previously if some plugin were failing during the import, the only way to import onETL would be to disable all plugins + using environment variable. + + Now there are several variables with different behavior: + * `ONETL_PLUGINS_ENABLED=false` - disable all plugins autoimport. Previously it was named `ONETL_ENABLE_PLUGINS`. + * `ONETL_PLUGINS_BLACKLIST=plugin-name,another-plugin` - set list of plugins which should NOT be imported automatically. + * `ONETL_PLUGINS_WHITELIST=plugin-name,another-plugin` - set list of plugins which should ONLY be imported automatically. + + Also we improved exception message with recommendation how to disable a failing plugin: + ```text + Error while importing plugin 'mtspark' from package 'mtspark' v4.0.0. + + Statement: + import mtspark.onetl + + Check if plugin is compatible with current onETL version 0.7.0. + + You can disable loading this plugin by setting environment variable: + ONETL_PLUGINS_BLACKLIST='mtspark,failing-plugin' + + You can also define a whitelist of packages which can be loaded by onETL: + ONETL_PLUGINS_WHITELIST='not-failing-plugin1,not-failing-plugin2' + + Please take into account that plugin name may differ from package or module name. + See package metadata for more details + ``` + +## Improvements + +* Added compatibility with Python 3.11 and PySpark 3.4.0. + + File connections were OK, but `jdbc.fetch` and `jdbc.execute` were failing. Fixed in [#28](https://github.com/MobileTeleSystems/onetl/pull/28). +* Added check for missing Java packages. + + Previously if DB connection tried to use some Java class which were not loaded into Spark version, it raised an exception + with long Java stacktrace. Most users failed to interpret this trace. + + Now onETL shows the following error message: + ```text + |Spark| Cannot import Java class 'com.mongodb.spark.sql.connector.MongoTableProvider'. + + It looks like you've created Spark session without this option: + SparkSession.builder.config("spark.jars.packages", MongoDB.package_spark_3_2) + + Please call `spark.stop()`, restart the interpreter, + and then create new SparkSession with proper options. + ``` +* Documentation improvements. + * Changed documentation site theme - using [furo](https://github.com/pradyunsg/furo) + instead of default [ReadTheDocs](https://github.com/readthedocs/sphinx_rtd_theme). + + New theme supports wide screens and dark mode. + See [#10](https://github.com/MobileTeleSystems/onetl/pull/10). + * Now each connection class have compatibility table for Spark + Java + Python. + * Added global compatibility table for Spark + Java + Python + Scala. + +## Bug Fixes + +* Fixed several SFTP issues. + * If SSH config file `~/.ssh/config` contains some options not recognized by Paramiko (unknown syntax, unknown option name), + previous versions were raising exception until fixing or removing this file. Since 0.7.0 exception is replaced with warning. + * If user passed `host_key_check=False` but server changed SSH keys, previous versions raised exception until new key is accepted. + Since 0.7.0 exception is replaced with warning if option value is `False`. + + Fixed in [#19](https://github.com/MobileTeleSystems/onetl/pull/19). +* Fixed several S3 issues. + + There was a bug in S3 connection which prevented handling files in the root of a bucket - they were invisible for the connector. Fixed in [#29](https://github.com/MobileTeleSystems/onetl/pull/29). diff --git a/mddocs/docs/_build/markdown/changelog/0.7.1.md b/mddocs/docs/_build/markdown/changelog/0.7.1.md new file mode 100644 index 000000000..fc423f756 --- /dev/null +++ b/mddocs/docs/_build/markdown/changelog/0.7.1.md @@ -0,0 +1,31 @@ +# 0.7.1 (2023-05-23) + +## Bug Fixes + +* Fixed `setup_logging` function. + + In onETL==0.7.0 calling `onetl.log.setup_logging()` broke the logging: + ```text + Traceback (most recent call last): + File "/opt/anaconda/envs/py39/lib/python3.9/logging/__init__.py", line 434, in format + return self._format(record) + File "/opt/anaconda/envs/py39/lib/python3.9/logging/__init__.py", line 430, in _format + return self._fmt % record.dict + KeyError: 'levelname:8s' + ``` +* Fixed installation examples. + + In onETL==0.7.0 there are examples of installing onETL with extras: + ```bash + pip install onetl[files, kerberos, spark] + ``` + + But pip fails to install such package: + ```text + ERROR: Invalid requirement: 'onet[files,' + ``` + + This is because of spaces in extras clause. Fixed: + ```bash + pip install onetl[files,kerberos,spark] + ``` diff --git a/mddocs/docs/_build/markdown/changelog/0.7.2.md b/mddocs/docs/_build/markdown/changelog/0.7.2.md new file mode 100644 index 000000000..80bb2cfc5 --- /dev/null +++ b/mddocs/docs/_build/markdown/changelog/0.7.2.md @@ -0,0 +1,33 @@ +# 0.7.2 (2023-05-24) + +## Dependencies + +* Limited `typing-extensions` version. + + `typing-extensions==4.6.0` release contains some breaking changes causing errors like: + ```text + Traceback (most recent call last): + File "/Users/project/lib/python3.9/typing.py", line 852, in __subclasscheck__ + return issubclass(cls, self.__origin__) + TypeError: issubclass() arg 1 must be a class + ``` + + `typing-extensions==4.6.1` was causing another error: + ```text + Traceback (most recent call last): + File "/home/maxim/Repo/typing_extensions/1.py", line 33, in + isinstance(file, ContainsException) + File "/home/maxim/Repo/typing_extensions/src/typing_extensions.py", line 599, in __instancecheck__ + if super().__instancecheck__(instance): + File "/home/maxim/.pyenv/versions/3.7.8/lib/python3.7/abc.py", line 139, in __instancecheck__ + return _abc_instancecheck(cls, instance) + File "/home/maxim/Repo/typing_extensions/src/typing_extensions.py", line 583, in __subclasscheck__ + return super().__subclasscheck__(other) + File "/home/maxim/.pyenv/versions/3.7.8/lib/python3.7/abc.py", line 143, in __subclasscheck__ + return _abc_subclasscheck(cls, subclass) + File "/home/maxim/Repo/typing_extensions/src/typing_extensions.py", line 661, in _proto_hook + and other._is_protocol + AttributeError: type object 'PathWithFailure' has no attribute '_is_protocol' + ``` + + We updated requirements with `typing-extensions<4.6` until fixing compatibility issues. diff --git a/mddocs/docs/_build/markdown/changelog/0.8.0.md b/mddocs/docs/_build/markdown/changelog/0.8.0.md new file mode 100644 index 000000000..3fe438549 --- /dev/null +++ b/mddocs/docs/_build/markdown/changelog/0.8.0.md @@ -0,0 +1,137 @@ +# 0.8.0 (2023-05-31) + +## Breaking Changes + +- Rename methods of `FileConnection` classes: + * `get_directory` → `resolve_dir` + * `get_file` → `resolve_file` + * `listdir` → `list_dir` + * `mkdir` → `create_dir` + * `rmdir` → `remove_dir` + + New naming should be more consistent. + + They were undocumented in previous versions, but someone could use these methods, so this is a breaking change. ([#36](https://github.com/MobileTeleSystems/onetl/pull/36)) +- Deprecate `onetl.core.FileFilter` class, replace it with new classes: + * `onetl.file.filter.Glob` + * `onetl.file.filter.Regexp` + * `onetl.file.filter.ExcludeDir` + + Old class will be removed in v1.0.0. ([#43](https://github.com/MobileTeleSystems/onetl/pull/43)) +- Deprecate `onetl.core.FileLimit` class, replace it with new class `onetl.file.limit.MaxFilesCount`. + + Old class will be removed in v1.0.0. ([#44](https://github.com/MobileTeleSystems/onetl/pull/44)) +- Change behavior of `BaseFileLimit.reset` method. + + This method should now return `self` instead of `None`. + Return value could be the same limit object or a copy, this is an implementation detail. ([#44](https://github.com/MobileTeleSystems/onetl/pull/44)) +- Replaced `FileDownloader.filter` and `.limit` with new options `.filters` and `.limits`: + ```python + FileDownloader( + ..., + filter=FileFilter(glob="*.txt", exclude_dir="/path"), + limit=FileLimit(count_limit=10), + ) + ``` + + ```python + FileDownloader( + ..., + filters=[Glob("*.txt"), ExcludeDir("/path")], + limits=[MaxFilesCount(10)], + ) + ``` + + This allows to developers to implement their own filter and limit classes, and combine them with existing ones. + + Old behavior still supported, but it will be removed in v1.0.0. ([#45](https://github.com/MobileTeleSystems/onetl/pull/45)) +- Removed default value for `FileDownloader.limits`, user should pass limits list explicitly. ([#45](https://github.com/MobileTeleSystems/onetl/pull/45)) +- Move classes from module `onetl.core`: + ```python + from onetl.core import DBReader + from onetl.core import DBWriter + from onetl.core import FileDownloader + from onetl.core import FileUploader + ``` + + with new modules `onetl.db` and `onetl.file`: + ```python + from onetl.db import DBReader + from onetl.db import DBWriter + + from onetl.file import FileDownloader + from onetl.file import FileUploader + ``` + + Imports from old module `onetl.core` still can be used, but marked as deprecated. Module will be removed in v1.0.0. ([#46](https://github.com/MobileTeleSystems/onetl/pull/46)) + +## Features + +- Add `rename_dir` method. + + Method was added to following connections: + * `FTP` + * `FTPS` + * `HDFS` + * `SFTP` + * `WebDAV` + + It allows to rename/move directory to new path with all its content. + + `S3` does not have directories, so there is no such method in that class. ([#40](https://github.com/MobileTeleSystems/onetl/pull/40)) +- Add `onetl.file.FileMover` class. + + It allows to move files between directories of remote file system. + Signature is almost the same as in `FileDownloader`, but without HWM support. ([#42](https://github.com/MobileTeleSystems/onetl/pull/42)) + +## Improvements + +- Document all public methods in `FileConnection` classes: + * `download_file` + * `resolve_dir` + * `resolve_file` + * `get_stat` + * `is_dir` + * `is_file` + * `list_dir` + * `create_dir` + * `path_exists` + * `remove_file` + * `rename_file` + * `remove_dir` + * `upload_file` + * `walk` ([#39](https://github.com/MobileTeleSystems/onetl/pull/39)) +- Update documentation of `check` method of all connections - add usage example and document result type. ([#39](https://github.com/MobileTeleSystems/onetl/pull/39)) +- Add new exception type `FileSizeMismatchError`. + + Methods `connection.download_file` and `connection.upload_file` now raise new exception type instead of `RuntimeError`, + if target file after download/upload has different size than source. ([#39](https://github.com/MobileTeleSystems/onetl/pull/39)) +- Add new exception type `DirectoryExistsError` - it is raised if target directory already exists. ([#40](https://github.com/MobileTeleSystems/onetl/pull/40)) +- Improved `FileDownloader` / `FileUploader` exception logging. + + If `DEBUG` logging is enabled, print exception with stacktrace instead of + printing only exception message. ([#42](https://github.com/MobileTeleSystems/onetl/pull/42)) +- Updated documentation of `FileUploader`. + * Class does not support read strategies, added note to documentation. + * Added examples of using `run` method with explicit files list passing, both absolute and relative paths. + * Fix outdated imports and class names in examples. ([#42](https://github.com/MobileTeleSystems/onetl/pull/42)) +- Updated documentation of `DownloadResult` class - fix outdated imports and class names. ([#42](https://github.com/MobileTeleSystems/onetl/pull/42)) +- Improved file filters documentation section. + + Document interface class `onetl.base.BaseFileFilter` and function `match_all_filters`. ([#43](https://github.com/MobileTeleSystems/onetl/pull/43)) +- Improved file limits documentation section. + + Document interface class `onetl.base.BaseFileLimit` and functions `limits_stop_at` / `limits_reached` / `reset_limits`. ([#44](https://github.com/MobileTeleSystems/onetl/pull/44)) +- Added changelog. + + Changelog is generated from separated news files using [towncrier](https://pypi.org/project/towncrier/). ([#47](https://github.com/MobileTeleSystems/onetl/pull/47)) + +## Misc + +- Improved CI workflow for tests. + * If developer haven’t changed source core of a specific connector or its dependencies, + run tests only against maximum supported versions of Spark, Python, Java and db/file server. + * If developed made some changes in a specific connector, or in core classes, or in dependencies, + run tests for both minimal and maximum versions. + * Once a week run all aganst for minimal and latest versions to detect breaking changes in dependencies + * Minimal tested Spark version is 2.3.1 instead on 2.4.8. ([#32](https://github.com/MobileTeleSystems/onetl/pull/32)) diff --git a/mddocs/docs/_build/markdown/changelog/0.8.1.md b/mddocs/docs/_build/markdown/changelog/0.8.1.md new file mode 100644 index 000000000..d3bd32564 --- /dev/null +++ b/mddocs/docs/_build/markdown/changelog/0.8.1.md @@ -0,0 +1,34 @@ +# 0.8.1 (2023-07-10) + +## Features + +- Add `@slot` decorator to public methods of: + * `DBConnection` + * `FileConnection` + * `DBReader` + * `DBWriter` + * `FileDownloader` + * `FileUploader` + * `FileMover` ([#49](https://github.com/MobileTeleSystems/onetl/pull/49)) +- Add `workers` field to `FileDownloader` / `FileUploader` / `FileMover`. `Options` classes. + + This allows to speed up all file operations using parallel threads. ([#57](https://github.com/MobileTeleSystems/onetl/pull/57)) + +## Improvements + +- Add documentation for HWM store `.get` and `.save` methods. ([#49](https://github.com/MobileTeleSystems/onetl/pull/49)) +- Improve Readme: + * Move `Quick start` section from documentation + * Add `Non-goals` section + * Fix code blocks indentation ([#50](https://github.com/MobileTeleSystems/onetl/pull/50)) +- Improve Contributing guide: + * Move `Develop` section from Readme + * Move `docs/changelog/README.rst` content + * Add `Limitations` section + * Add instruction of creating a fork and building documentation ([#50](https://github.com/MobileTeleSystems/onetl/pull/50)) +- Remove duplicated checks for source file existence in `FileDownloader` / `FileMover`. ([#57](https://github.com/MobileTeleSystems/onetl/pull/57)) +- Update default logging format to include thread name. ([#57](https://github.com/MobileTeleSystems/onetl/pull/57)) + +## Bug Fixes + +- Fix `S3.list_dir('/')` returns empty list on latest Minio version. ([#58](https://github.com/MobileTeleSystems/onetl/pull/58)) diff --git a/mddocs/docs/_build/markdown/changelog/0.9.0.md b/mddocs/docs/_build/markdown/changelog/0.9.0.md new file mode 100644 index 000000000..b2d10f257 --- /dev/null +++ b/mddocs/docs/_build/markdown/changelog/0.9.0.md @@ -0,0 +1,107 @@ +# 0.9.0 (2023-08-17) + +## Breaking Changes + +- Rename methods: + * `DBConnection.read_df` → `DBConnection.read_source_as_df` + * `DBConnection.write_df` → `DBConnection.write_df_to_target` ([#66](https://github.com/MobileTeleSystems/onetl/pull/66)) +- Rename classes: + * `HDFS.slots` → `HDFS.Slots` + * `Hive.slots` → `Hive.Slots` + + Old names are left intact, but will be removed in v1.0.0 ([#103](https://github.com/MobileTeleSystems/onetl/pull/103)) +- Rename options to make them self-explanatory: + * `Hive.WriteOptions(mode="append")` → `Hive.WriteOptions(if_exists="append")` + * `Hive.WriteOptions(mode="overwrite_table")` → `Hive.WriteOptions(if_exists="replace_entire_table")` + * `Hive.WriteOptions(mode="overwrite_partitions")` → `Hive.WriteOptions(if_exists="replace_overlapping_partitions")` + * `JDBC.WriteOptions(mode="append")` → `JDBC.WriteOptions(if_exists="append")` + * `JDBC.WriteOptions(mode="overwrite")` → `JDBC.WriteOptions(if_exists="replace_entire_table")` + * `Greenplum.WriteOptions(mode="append")` → `Greenplum.WriteOptions(if_exists="append")` + * `Greenplum.WriteOptions(mode="overwrite")` → `Greenplum.WriteOptions(if_exists="replace_entire_table")` + * `MongoDB.WriteOptions(mode="append")` → `Greenplum.WriteOptions(if_exists="append")` + * `MongoDB.WriteOptions(mode="overwrite")` → `Greenplum.WriteOptions(if_exists="replace_entire_collection")` + * `FileDownloader.Options(mode="error")` → `FileDownloader.Options(if_exists="error")` + * `FileDownloader.Options(mode="ignore")` → `FileDownloader.Options(if_exists="ignore")` + * `FileDownloader.Options(mode="overwrite")` → `FileDownloader.Options(if_exists="replace_file")` + * `FileDownloader.Options(mode="delete_all")` → `FileDownloader.Options(if_exists="replace_entire_directory")` + * `FileUploader.Options(mode="error")` → `FileUploader.Options(if_exists="error")` + * `FileUploader.Options(mode="ignore")` → `FileUploader.Options(if_exists="ignore")` + * `FileUploader.Options(mode="overwrite")` → `FileUploader.Options(if_exists="replace_file")` + * `FileUploader.Options(mode="delete_all")` → `FileUploader.Options(if_exists="replace_entire_directory")` + * `FileMover.Options(mode="error")` → `FileMover.Options(if_exists="error")` + * `FileMover.Options(mode="ignore")` → `FileMover.Options(if_exists="ignore")` + * `FileMover.Options(mode="overwrite")` → `FileMover.Options(if_exists="replace_file")` + * `FileMover.Options(mode="delete_all")` → `FileMover.Options(if_exists="replace_entire_directory")` + + Old names are left intact, but will be removed in v1.0.0 ([#108](https://github.com/MobileTeleSystems/onetl/pull/108)) +- Rename `onetl.log.disable_clients_logging()` to `onetl.log.setup_clients_logging()`. ([#120](https://github.com/MobileTeleSystems/onetl/pull/120)) + +## Features + +- Add new methods returning Maven packages for specific connection class: + * `Clickhouse.get_packages()` + * `MySQL.get_packages()` + * `Postgres.get_packages()` + * `Teradata.get_packages()` + * `MSSQL.get_packages(java_version="8")` + * `Oracle.get_packages(java_version="8")` + * `Greenplum.get_packages(scala_version="2.12")` + * `MongoDB.get_packages(scala_version="2.12")` + * `Kafka.get_packages(spark_version="3.4.1", scala_version="2.12")` + + Deprecate old syntax: + * `Clickhouse.package` + * `MySQL.package` + * `Postgres.package` + * `Teradata.package` + * `MSSQL.package` + * `Oracle.package` + * `Greenplum.package_spark_2_3` + * `Greenplum.package_spark_2_4` + * `Greenplum.package_spark_3_2` + * `MongoDB.package_spark_3_2` + * `MongoDB.package_spark_3_3` + * `MongoDB.package_spark_3_4` ([#87](https://github.com/MobileTeleSystems/onetl/pull/87)) +- Allow to set client modules log level in `onetl.log.setup_clients_logging()`. + + Allow to enable underlying client modules logging in `onetl.log.setup_logging()` by providing additional argument `enable_clients=True`. + This is useful for debug. ([#120](https://github.com/MobileTeleSystems/onetl/pull/120)) +- Added support for reading and writing data to Kafka topics. + + For these operations, new classes were added. + * `Kafka` ([#54](https://github.com/MobileTeleSystems/onetl/pull/54), [#60](https://github.com/MobileTeleSystems/onetl/pull/60), [#72](https://github.com/MobileTeleSystems/onetl/pull/72), [#84](https://github.com/MobileTeleSystems/onetl/pull/84), [#87](https://github.com/MobileTeleSystems/onetl/pull/87), [#89](https://github.com/MobileTeleSystems/onetl/pull/89), [#93](https://github.com/MobileTeleSystems/onetl/pull/93), [#96](https://github.com/MobileTeleSystems/onetl/pull/96), [#102](https://github.com/MobileTeleSystems/onetl/pull/102), [#104](https://github.com/MobileTeleSystems/onetl/pull/104)) + * `Kafka.PlaintextProtocol` ([#79](https://github.com/MobileTeleSystems/onetl/pull/79)) + * `Kafka.SSLProtocol` ([#118](https://github.com/MobileTeleSystems/onetl/pull/118)) + * `Kafka.BasicAuth` ([#63](https://github.com/MobileTeleSystems/onetl/pull/63), [#77](https://github.com/MobileTeleSystems/onetl/pull/77)) + * `Kafka.KerberosAuth` ([#63](https://github.com/MobileTeleSystems/onetl/pull/63), [#77](https://github.com/MobileTeleSystems/onetl/pull/77), [#110](https://github.com/MobileTeleSystems/onetl/pull/110)) + * `Kafka.ScramAuth` ([#115](https://github.com/MobileTeleSystems/onetl/pull/115)) + * `Kafka.Slots` ([#109](https://github.com/MobileTeleSystems/onetl/pull/109)) + * `Kafka.ReadOptions` ([#68](https://github.com/MobileTeleSystems/onetl/pull/68)) + * `Kafka.WriteOptions` ([#68](https://github.com/MobileTeleSystems/onetl/pull/68)) + + Currently, Kafka does not support incremental read strategies, this will be implemented in future releases. +- Added support for reading files as Spark DataFrame and saving DataFrame as Files. + + For these operations, new classes were added. + + FileDFConnections: + * `SparkHDFS` ([#98](https://github.com/MobileTeleSystems/onetl/pull/98)) + * `SparkS3` ([#94](https://github.com/MobileTeleSystems/onetl/pull/94), [#100](https://github.com/MobileTeleSystems/onetl/pull/100), [#124](https://github.com/MobileTeleSystems/onetl/pull/124)) + * `SparkLocalFS` ([#67](https://github.com/MobileTeleSystems/onetl/pull/67)) + + High-level classes: + * `FileDFReader` ([#73](https://github.com/MobileTeleSystems/onetl/pull/73)) + * `FileDFWriter` ([#81](https://github.com/MobileTeleSystems/onetl/pull/81)) + + File formats: + * `Avro` ([#69](https://github.com/MobileTeleSystems/onetl/pull/69)) + * `CSV` ([#92](https://github.com/MobileTeleSystems/onetl/pull/92)) + * `JSON` ([#83](https://github.com/MobileTeleSystems/onetl/pull/83)) + * `JSONLine` ([#83](https://github.com/MobileTeleSystems/onetl/pull/83)) + * `ORC` ([#86](https://github.com/MobileTeleSystems/onetl/pull/86)) + * `Parquet` ([#88](https://github.com/MobileTeleSystems/onetl/pull/88)) + +## Improvements + +- Remove redundant checks for driver availability in Greenplum and MongoDB connections. ([#67](https://github.com/MobileTeleSystems/onetl/pull/67)) +- Check of Java class availability moved from `.check()` method to connection constructor. ([#97](https://github.com/MobileTeleSystems/onetl/pull/97)) diff --git a/mddocs/docs/_build/markdown/changelog/0.9.1.md b/mddocs/docs/_build/markdown/changelog/0.9.1.md new file mode 100644 index 000000000..1779274b1 --- /dev/null +++ b/mddocs/docs/_build/markdown/changelog/0.9.1.md @@ -0,0 +1,7 @@ +# 0.9.1 (2023-08-17) + +## Bug Fixes + +- Fixed bug then number of threads created by `FileDownloader` / `FileUploader` / `FileMover` was + not `min(workers, len(files))`, but `max(workers, len(files))`. leading to create too much workers + on large files list. diff --git a/mddocs/docs/_build/markdown/changelog/0.9.2.md b/mddocs/docs/_build/markdown/changelog/0.9.2.md new file mode 100644 index 000000000..4ca4fcff9 --- /dev/null +++ b/mddocs/docs/_build/markdown/changelog/0.9.2.md @@ -0,0 +1,21 @@ +# 0.9.2 (2023-09-06) + +## Features + +- Add `if_exists="ignore"` and `error` to `Greenplum.WriteOptions` ([#142](https://github.com/MobileTeleSystems/onetl/pull/142)) + +## Improvements + +- Improve validation messages while writing dataframe to Kafka. ([#131](https://github.com/MobileTeleSystems/onetl/pull/131)) +- Improve documentation: + * Add notes about reading and writing to database connections documentation + * Add notes about executing statements in JDBC and Greenplum connections + +## Bug Fixes + +- Fixed validation of `headers` column is written to Kafka with default `Kafka.WriteOptions()` - default value was `False`, + but instead of raising an exception, column value was just ignored. ([#131](https://github.com/MobileTeleSystems/onetl/pull/131)) +- Fix reading data from Oracle with `partitioningMode="range"` without explicitly set `lowerBound` / `upperBound`. ([#133](https://github.com/MobileTeleSystems/onetl/pull/133)) +- Update Kafka documentation with SSLProtocol usage. ([#136](https://github.com/MobileTeleSystems/onetl/pull/136)) +- Raise exception if someone tries to read data from Kafka topic which does not exist. ([#138](https://github.com/MobileTeleSystems/onetl/pull/138)) +- Allow to pass Kafka topics with name like `some.topic.name` to DBReader. Same for MongoDB collections. ([#139](https://github.com/MobileTeleSystems/onetl/pull/139)) diff --git a/mddocs/docs/_build/markdown/changelog/0.9.3.md b/mddocs/docs/_build/markdown/changelog/0.9.3.md new file mode 100644 index 000000000..1a8c25d4d --- /dev/null +++ b/mddocs/docs/_build/markdown/changelog/0.9.3.md @@ -0,0 +1,5 @@ +# 0.9.3 (2023-09-06) + +## Bug Fixes + +- Fix documentation build diff --git a/mddocs/docs/_build/markdown/changelog/0.9.4.md b/mddocs/docs/_build/markdown/changelog/0.9.4.md new file mode 100644 index 000000000..cf9288760 --- /dev/null +++ b/mddocs/docs/_build/markdown/changelog/0.9.4.md @@ -0,0 +1,24 @@ +# 0.9.4 (2023-09-26) + +## Features + +- Add `Excel` file format support. ([#148](https://github.com/MobileTeleSystems/onetl/pull/148)) +- Add `Samba` file connection. + It is now possible to download and upload files to Samba shared folders using `FileDownloader`/`FileUploader`. ([#150](https://github.com/MobileTeleSystems/onetl/pull/150)) +- Add `if_exists="ignore"` and `error` to `Hive.WriteOptions` ([#143](https://github.com/MobileTeleSystems/onetl/pull/143)) +- Add `if_exists="ignore"` and `error` to `JDBC.WriteOptions` ([#144](https://github.com/MobileTeleSystems/onetl/pull/144)) +- Add `if_exists="ignore"` and `error` to `MongoDB.WriteOptions` ([#145](https://github.com/MobileTeleSystems/onetl/pull/145)) + +## Improvements + +- Add documentation about different ways of passing packages to Spark session. ([#151](https://github.com/MobileTeleSystems/onetl/pull/151)) +- Drastically improve `Greenplum` documentation: + : * Added information about network ports, grants, `pg_hba.conf` and so on. + * Added interaction schemas for reading, writing and executing statements in Greenplum. + * Added recommendations about reading data from views and `JOIN` results from Greenplum. ([#154](https://github.com/MobileTeleSystems/onetl/pull/154)) +- Make `.fetch` and `.execute` methods of DB connections thread-safe. Each thread works with its own connection. ([#156](https://github.com/MobileTeleSystems/onetl/pull/156)) +- Call `.close()` on `FileConnection` then it is removed by garbage collector. ([#156](https://github.com/MobileTeleSystems/onetl/pull/156)) + +## Bug Fixes + +- Fix issue when stopping Python interpreter calls `JDBCMixin.close()`, but it is finished with exceptions. ([#156](https://github.com/MobileTeleSystems/onetl/pull/156)) diff --git a/mddocs/docs/_build/markdown/changelog/0.9.5.md b/mddocs/docs/_build/markdown/changelog/0.9.5.md new file mode 100644 index 000000000..1d7358c0b --- /dev/null +++ b/mddocs/docs/_build/markdown/changelog/0.9.5.md @@ -0,0 +1,14 @@ +# 0.9.5 (2023-10-10) + +## Features + +- Add `XML` file format support. ([#163](https://github.com/MobileTeleSystems/onetl/pull/163)) +- Tested compatibility with Spark 3.5.0. `MongoDB` and `Excel` are not supported yet, but other packages do. ([#159](https://github.com/MobileTeleSystems/onetl/pull/159)) + +## Improvements + +- Add check to all DB and FileDF connections that Spark session is alive. ([#164](https://github.com/MobileTeleSystems/onetl/pull/164)) + +## Bug Fixes + +- Fix `Hive.check()` behavior when Hive Metastore is not available. ([#164](https://github.com/MobileTeleSystems/onetl/pull/164)) diff --git a/mddocs/docs/_build/markdown/changelog/DRAFT.md b/mddocs/docs/_build/markdown/changelog/DRAFT.md new file mode 100644 index 000000000..e69de29bb diff --git a/mddocs/docs/_build/markdown/changelog/NEXT_RELEASE.md b/mddocs/docs/_build/markdown/changelog/NEXT_RELEASE.md new file mode 100644 index 000000000..8cf66aa33 --- /dev/null +++ b/mddocs/docs/_build/markdown/changelog/NEXT_RELEASE.md @@ -0,0 +1 @@ + diff --git a/mddocs/docs/_build/markdown/changelog/index.md b/mddocs/docs/_build/markdown/changelog/index.md new file mode 100644 index 000000000..37d5d85ae --- /dev/null +++ b/mddocs/docs/_build/markdown/changelog/index.md @@ -0,0 +1,29 @@ +Changelog + +* [0.13.4 (2025-03-20)](0.13.4.md) +* [0.13.3 (2025-03-11)](0.13.3.md) +* [0.13.1 (2025-03-06)](0.13.1.md) +* [0.13.0 (2025-02-24)](0.13.0.md) +* [0.12.5 (2024-12-03)](0.12.5.md) +* [0.12.4 (2024-11-27)](0.12.4.md) +* [0.12.3 (2024-11-22)](0.12.3.md) +* [0.12.2 (2024-11-12)](0.12.2.md) +* [0.12.1 (2024-10-28)](0.12.1.md) +* [0.12.0 (2024-09-03)](0.12.0.md) +* [0.11.2 (2024-09-02)](0.11.2.md) +* [0.11.1 (2024-05-29)](0.11.1.md) +* [0.11.0 (2024-05-27)](0.11.0.md) +* [0.10.2 (2024-03-21)](0.10.2.md) +* [0.10.1 (2024-02-05)](0.10.1.md) +* [0.10.0 (2023-12-18)](0.10.0.md) +* [0.9.5 (2023-10-10)](0.9.5.md) +* [0.9.4 (2023-09-26)](0.9.4.md) +* [0.9.3 (2023-09-06)](0.9.3.md) +* [0.9.2 (2023-09-06)](0.9.2.md) +* [0.9.1 (2023-08-17)](0.9.1.md) +* [0.9.0 (2023-08-17)](0.9.0.md) +* [0.8.1 (2023-07-10)](0.8.1.md) +* [0.8.0 (2023-05-31)](0.8.0.md) +* [0.7.2 (2023-05-24)](0.7.2.md) +* [0.7.1 (2023-05-23)](0.7.1.md) +* [0.7.0 (2023-05-15)](0.7.0.md) diff --git a/mddocs/docs/_build/markdown/concepts.md b/mddocs/docs/_build/markdown/concepts.md new file mode 100644 index 000000000..cf74e9eaa --- /dev/null +++ b/mddocs/docs/_build/markdown/concepts.md @@ -0,0 +1,340 @@ +# Concepts + +Here you can find detailed documentation about each one of the onETL concepts and how to use them. + +## Connection + +### Basics + +onETL is used to pull and push data into other systems, and so it has a first-class `Connection` concept for storing credentials that are used to communicate with external systems. + +A `Connection` is essentially a set of parameters, such as username, password, hostname. + +To create a connection to a specific storage type, you must use a class that matches the storage type. The class name is the same as the storage type name (`Oracle`, `MSSQL`, `SFTP`, etc): + +```python +from onetl.connection import SFTP + +sftp = SFTP( + host="sftp.test.com", + user="onetl", + password="onetl", +) +``` + +All connection types are inherited from the parent class `BaseConnection`. + +### Class diagram + +### DBConnection + +Classes inherited from `DBConnection` could be used for accessing databases. + +A `DBConnection` could be instantiated as follows: + +```python +from onetl.connection import MSSQL + +mssql = MSSQL( + host="mssqldb.demo.com", + user="onetl", + password="onetl", + database="Telecom", + spark=spark, +) +``` + +where **spark** is the current SparkSession. +`onETL` uses `Spark` and specific Java connectors under the hood to work with databases. + +For a description of other parameters, see the documentation for the [available DBConnections](connection/db_connection/index.md#db-connections). + +### FileConnection + +Classes inherited from `FileConnection` could be used to access files stored on the different file systems/file servers + +A `FileConnection` could be instantiated as follows: + +```python +from onetl.connection import SFTP + +sftp = SFTP( + host="sftp.test.com", + user="onetl", + password="onetl", +) +``` + +For a description of other parameters, see the documentation for the [available FileConnections](connection/file_connection/index.md#file-connections). + +### FileDFConnection + +Classes inherited from `FileDFConnection` could be used for accessing files as Spark DataFrames. + +A `FileDFConnection` could be instantiated as follows: + +```python +from onetl.connection import SparkHDFS + +spark_hdfs = SparkHDFS( + host="namenode1.domain.com", + cluster="mycluster", + spark=spark, +) +``` + +where **spark** is the current SparkSession. +`onETL` uses `Spark` and specific Java connectors under the hood to work with DataFrames. + +For a description of other parameters, see the documentation for the [available FileDFConnections](connection/file_df_connection/index.md#file-df-connections). + +### Checking connection availability + +Once you have created a connection, you can check the database/filesystem availability using the method `check()`: + +```python +mssql.check() +sftp.check() +spark_hdfs.check() +``` + +It will raise an exception if database/filesystem cannot be accessed. + +This method returns connection itself, so you can create connection and immediately check its availability: + +```Python +mssql = MSSQL( + host="mssqldb.demo.com", + user="onetl", + password="onetl", + database="Telecom", + spark=spark, +).check() # <-- +``` + +## Extract/Load data + +### Basics + +As we said above, onETL is used to extract data from and load data into remote systems. + +onETL provides several classes for this: + +> * [DBReader](db/db_reader.md#db-reader) +> * [DBWriter](db/db_writer.md#db-writer) +> * [FileDFReader](file_df/file_df_reader/file_df_reader.md#file-df-reader) +> * [FileDFWriter](file_df/file_df_writer/file_df_writer.md#file-df-writer) +> * [FileDownloader](file/file_downloader/file_downloader.md#file-downloader) +> * [FileUploader](file/file_uploader/file_uploader.md#file-uploader) +> * [FileMover](file/file_mover/file_mover.md#file-mover) + +All of these classes have a method `run()` that starts extracting/loading the data: + +```python +from onetl.db import DBReader, DBWriter + +reader = DBReader( + connection=mssql, + source="dbo.demo_table", + columns=["column_1", "column_2"], +) + +# Read data as Spark DataFrame +df = reader.run() + +db_writer = DBWriter( + connection=hive, + target="dl_sb.demo_table", +) + +# Save Spark DataFrame to Hive table +writer.run(df) +``` + +### Extract data + +To extract data you can use classes: + +| | Use case | Connection | `run()` gets | `run()` returns | +|-------------------------------------------------------------------------|-------------------------------------------|------------------------------------------------------------------------------------|---------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------| +| [DBReader](db/db_reader.md#db-reader) | Reading data from a database | Any [DBConnection](connection/db_connection/index.md#db-connections) | - | [Spark DataFrame](https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.sql.DataFrame.html#pyspark.sql.DataFrame) | +| [FileDFReader](file_df/file_df_reader/file_df_reader.md#file-df-reader) | Read data from a file or set of files | Any [FileDFConnection](connection/file_df_connection/index.md#file-df-connections) | No input, or List[File path on FileSystem] | [Spark DataFrame](https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.sql.DataFrame.html#pyspark.sql.DataFrame) | +| [FileDownloader](db/db_reader.md#db-reader) | Download files from remote FS to local FS | Any [FileConnection](connection/file_connection/index.md#file-connections) | No input, or List[File path on remote FileSystem] | [DownloadResult](file/file_downloader/result.md#file-downloader-result) | + +### Load data + +To load data you can use classes: + +| | Use case | Connection | `run()` gets | `run()` returns | +|-------------------------------------------------------------------|----------------------------------------------|------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------| +| [DBWriter](db/db_writer.md#db-writer) | Writing data from a DataFrame to a database | Any [DBConnection](connection/db_connection/index.md#db-connections) | [Spark DataFrame](https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.sql.DataFrame.html#pyspark.sql.DataFrame) | None | +| [FileDFWriter](db/db_writer.md#db-writer) | Writing data from a DataFrame to a folder | Any [FileDFConnection](connection/file_df_connection/index.md#file-df-connections) | [Spark DataFrame](https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.sql.DataFrame.html#pyspark.sql.DataFrame) | None | +| [FileUploader](file/file_uploader/file_uploader.md#file-uploader) | Uploading files from a local FS to remote FS | Any [FileConnection](connection/file_connection/index.md#file-connections) | List[File path on local FileSystem] | [UploadResult](file/file_uploader/result.md#file-uploader-result) | + +### Manipulate data + +To manipulate data you can use classes: + +| | Use case | Connection | `run()` gets | `run()` returns | +|-------------------------------------------------------|---------------------------------------------|----------------------------------------------------------------------------|--------------------------------------|-----------------------------------------------------------| +| [FileMover](file/file_mover/file_mover.md#file-mover) | Move files between directories in remote FS | Any [FileConnection](connection/file_connection/index.md#file-connections) | List[File path on remote FileSystem] | [MoveResult](file/file_mover/result.md#file-mover-result) | + +### Options + +Extract and load classes have a `options` parameter, which has a special meaning: + +> * all other parameters - *WHAT* we extract / *WHERE* we load to +> * `options` parameter - *HOW* we extract/load data +```python +db_reader = DBReader( + # WHAT do we read: + connection=mssql, + source="dbo.demo_table", # some table from MSSQL + columns=["column_1", "column_2"], # but only specific set of columns + where="column_2 > 1000", # only rows matching the clause + # HOW do we read: + options=MSSQL.ReadOptions( + numPartitions=10, # read in 10 parallel jobs + partitionColumn="id", # balance data read by assigning each job a part of data using `hash(id) mod N` expression + partitioningMode="hash", + fetchsize=1000, # each job will fetch block of 1000 rows each on every read attempt + ), +) + +db_writer = DBWriter( + # WHERE do we write to - to some table in Hive + connection=hive, + target="dl_sb.demo_table", + # HOW do we write - overwrite all the data in the existing table + options=Hive.WriteOptions(if_exists="replace_entire_table"), +) + +file_downloader = FileDownloader( + # WHAT do we download - files from some dir in SFTP + connection=sftp, + source_path="/source", + filters=[Glob("*.csv")], # only CSV files + limits=[MaxFilesCount(1000)], # 1000 files max + # WHERE do we download to - a specific dir on local FS + local_path="/some", + # HOW do we download: + options=FileDownloader.Options( + delete_source=True, # after downloading each file remove it from source_path + if_exists="replace_file", # replace existing files in the local_path + ), +) + +file_uploader = FileUploader( + # WHAT do we upload - files from some local dir + local_path="/source", + # WHERE do we upload to- specific remote dir in HDFS + connection=hdfs, + target_path="/some", + # HOW do we upload: + options=FileUploader.Options( + delete_local=True, # after uploading each file remove it from local_path + if_exists="replace_file", # replace existing files in the target_path + ), +) + +file_mover = FileMover( + # WHAT do we move - files in some remote dir in HDFS + source_path="/source", + connection=hdfs, + # WHERE do we move files to + target_path="/some", # a specific remote dir within the same HDFS connection + # HOW do we load - replace existing files in the target_path + options=FileMover.Options(if_exists="replace_file"), +) + +file_df_reader = FileDFReader( + # WHAT do we read - *.csv files from some dir in S3 + connection=s3, + source_path="/source", + file_format=CSV(), + # HOW do we read - load files from /source/*.csv, not from /source/nested/*.csv + options=FileDFReader.Options(recursive=False), +) + +file_df_writer = FileDFWriter( + # WHERE do we write to - as .csv files in some dir in S3 + connection=s3, + target_path="/target", + file_format=CSV(), + # HOW do we write - replace all existing files in /target, if exists + options=FileDFWriter.Options(if_exists="replace_entire_directory"), +) +``` + +More information about `options` could be found on [DB connection](connection/db_connection/index.md#db-connections). and +[File Downloader](file/file_downloader/file_downloader.md#file-downloader) / [File Uploader](file/file_uploader/file_uploader.md#file-uploader) / [File Mover](file/file_mover/file_mover.md#file-mover) / [FileDF Reader](file_df/file_df_reader/file_df_reader.md#file-df-reader) / [FileDF Writer](file_df/file_df_writer/file_df_writer.md#file-df-writer) documentation + +### Read Strategies + +onETL have several builtin strategies for reading data: + +1. [Snapshot strategy](strategy/snapshot_strategy.html) (default strategy) +2. [Incremental strategy](strategy/incremental_strategy.html) +3. [Snapshot batch strategy](strategy/snapshot_batch_strategy.html) +4. [Incremental batch strategy](strategy/incremental_batch_strategy.html) + +For example, an incremental strategy allows you to get only new data from the table: + +```python +from onetl.strategy import IncrementalStrategy + +reader = DBReader( + connection=mssql, + source="dbo.demo_table", + hwm_column="id", # detect new data based on value of "id" column +) + +# first run +with IncrementalStrategy(): + df = reader.run() + +sleep(3600) + +# second run +with IncrementalStrategy(): + # only rows, that appeared in the source since previous run + df = reader.run() +``` + +or get only files which were not downloaded before: + +```python +from onetl.strategy import IncrementalStrategy + +file_downloader = FileDownloader( + connection=sftp, + source_path="/remote", + local_path="/local", + hwm_type="file_list", # save all downloaded files to a list, and exclude files already present in this list +) + +# first run +with IncrementalStrategy(): + files = file_downloader.run() + +sleep(3600) + +# second run +with IncrementalStrategy(): + # only files, that appeared in the source since previous run + files = file_downloader.run() +``` + +Most of strategies are based on [HWM](hwm_store/index.md#hwm), Please check each strategy documentation for more details + +### Why just not use Connection class for extract/load? + +Connections are very simple, they have only a set of some basic operations, +like `mkdir`, `remove_file`, `get_table_schema`, and so on. + +High-level operations, like +: * [Read Strategies](strategy/index.md#strategy) support + * Handling metadata push/pull + * Handling different options, like `if_exists="replace_file"` in case of file download/upload + +is moved to a separate class which calls the connection object methods to perform some complex logic. diff --git a/mddocs/docs/_build/markdown/connection/db_connection/clickhouse/connection.md b/mddocs/docs/_build/markdown/connection/db_connection/clickhouse/connection.md new file mode 100644 index 000000000..8a13aad02 --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/clickhouse/connection.md @@ -0,0 +1,3 @@ + + +# Clickhouse connection diff --git a/mddocs/docs/_build/markdown/connection/db_connection/clickhouse/execute.md b/mddocs/docs/_build/markdown/connection/db_connection/clickhouse/execute.md new file mode 100644 index 000000000..c766d6d8c --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/clickhouse/execute.md @@ -0,0 +1,98 @@ + + +# Executing statements in Clickhouse + +#### WARNING +Methods below **read all the rows** returned from DB **to Spark driver memory**, and then convert them to DataFrame. + +Do **NOT** use them to read large amounts of data. Use [DBReader](read.md#clickhouse-read) or [Clickhouse.sql](sql.md#clickhouse-sql) instead. + +## How to + +There are 2 ways to execute some statement in Clickhouse + +### Use `Clickhouse.fetch` + +Use this method to perform some `SELECT` query which returns **small number or rows**, like reading +Clickhouse config, or reading data from some reference table. Method returns Spark DataFrame. + +Method accepts `Clickhouse.FetchOptions`. + +Connection opened using this method should be then closed with `connection.close()` or `with connection:`. + +#### WARNING +Please take into account [Clickhouse <-> Spark type mapping](types.md#clickhouse-types). + +#### Syntax support + +This method supports **any** query syntax supported by Clickhouse, like: + +* ✅︎ `SELECT ... FROM ...` +* ✅︎ `WITH alias AS (...) SELECT ...` +* ✅︎ `SELECT func(arg1, arg2)` - call function +* ✅︎ `SHOW ...` +* ❌ `SET ...; SELECT ...;` - multiple statements not supported + +#### Examples + +```python +from onetl.connection import Clickhouse + +clickhouse = Clickhouse(...) + +df = clickhouse.fetch( + "SELECT value FROM some.reference_table WHERE key = 'some_constant'", + options=Clickhouse.FetchOptions(queryTimeout=10), +) +clickhouse.close() +value = df.collect()[0][0] # get value from first row and first column +``` + +### Use `Clickhouse.execute` + +Use this method to execute DDL and DML operations. Each method call runs operation in a separated transaction, and then commits it. + +Method accepts `Clickhouse.ExecuteOptions`. + +Connection opened using this method should be then closed with `connection.close()` or `with connection:`. + +#### Syntax support + +This method supports **any** query syntax supported by Clickhouse, like: + +* ✅︎ `CREATE TABLE ...`, `CREATE VIEW ...`, and so on +* ✅︎ `ALTER ...` +* ✅︎ `INSERT INTO ... SELECT ...`, `UPDATE ...`, `DELETE ...`, and so on +* ✅︎ `DROP TABLE ...`, `DROP VIEW ...`, `TRUNCATE TABLE`, and so on +* ✅︎ other statements not mentioned here +* ❌ `SET ...; SELECT ...;` - multiple statements not supported + +#### Examples + +```python +from onetl.connection import Clickhouse + +clickhouse = Clickhouse(...) + +clickhouse.execute("DROP TABLE schema.table") +clickhouse.execute( + """ + CREATE TABLE schema.table ( + id UInt8, + key String, + value Float32 + ) + ENGINE = MergeTree() + ORDER BY id + """, + options=Clickhouse.ExecuteOptions(queryTimeout=10), +) +``` + +## Notes + +These methods **read all the rows** returned from DB **to Spark driver memory**, and then convert them to DataFrame. + +So it should **NOT** be used to read large amounts of data. Use [DBReader](read.md#clickhouse-read) or [Clickhouse.sql](sql.md#clickhouse-sql) instead. + +## Options diff --git a/mddocs/docs/_build/markdown/connection/db_connection/clickhouse/index.md b/mddocs/docs/_build/markdown/connection/db_connection/clickhouse/index.md new file mode 100644 index 000000000..3b226910d --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/clickhouse/index.md @@ -0,0 +1,19 @@ + + +# Clickhouse + +# Connection + +* [Prerequisites](prerequisites.md) +* [Clickhouse connection](connection.md) + +# Operations + +* [Reading from Clickhouse using `DBReader`](read.md) +* [Reading from Clickhouse using `Clickhouse.sql`](sql.md) +* [Writing to Clickhouse using `DBWriter`](write.md) +* [Executing statements in Clickhouse](execute.md) + +# Troubleshooting + +* [Clickhouse <-> Spark type mapping](types.md) diff --git a/mddocs/docs/_build/markdown/connection/db_connection/clickhouse/prerequisites.md b/mddocs/docs/_build/markdown/connection/db_connection/clickhouse/prerequisites.md new file mode 100644 index 000000000..86551b41a --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/clickhouse/prerequisites.md @@ -0,0 +1,73 @@ + + +# Prerequisites + +## Version Compatibility + +* Clickhouse server versions: + : * Officially declared: 22.8 or higher + * Actually tested: 21.1, 25.1 +* Spark versions: 2.3.x - 3.5.x +* Java versions: 8 - 20 + +See [official documentation](https://clickhouse.com/docs/en/integrations/java#jdbc-driver). + +## Installing PySpark + +To use Clickhouse connector you should have PySpark installed (or injected to `sys.path`) +BEFORE creating the connector instance. + +See [Spark](../../../install/spark.md#install-spark) installation instruction for more details. + +## Connecting to Clickhouse + +### Connection port + +Connector can only use **HTTP** (usually `8123` port) or **HTTPS** (usually `8443` port) protocol. + +TCP and GRPC protocols are NOT supported. + +### Connecting to cluster + +It is possible to connect to Clickhouse cluster, and use it’s load balancing capabilities to read or write data in parallel. +Each Spark executor can connect to random Clickhouse nodes, instead of sending all the data to a node specified in connection params. + +This requires all Clickhouse servers to run on different hosts, and **listen the same HTTP port**. +Set `auto_discovery=True` to enable this feature (disabled by default): + +```python +Clickhouse( + host="node1.of.cluster", + port=8123, + extra={ + "auto_discovery": True, + "load_balancing_policy": "roundRobin", + }, +) +``` + +See [official documentation](https://clickhouse.com/docs/en/integrations/java#configuring-node-discovery-load-balancing-and-failover). + +### Required grants + +Ask your Clickhouse cluster administrator to set following grants for a user, +used for creating a connection: + +Read + Write + +```sql +-- allow creating tables in the target schema +GRANT CREATE TABLE ON myschema.* TO username; + +-- allow read & write access to specific table +GRANT SELECT, INSERT ON myschema.mytable TO username; +``` + +Read only + +```sql +-- allow read access to specific table +GRANT SELECT ON myschema.mytable TO username; +``` + +More details can be found in [official documentation](https://clickhouse.com/docs/en/sql-reference/statements/grant). diff --git a/mddocs/docs/_build/markdown/connection/db_connection/clickhouse/read.md b/mddocs/docs/_build/markdown/connection/db_connection/clickhouse/read.md new file mode 100644 index 000000000..0f0492035 --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/clickhouse/read.md @@ -0,0 +1,78 @@ + + +# Reading from Clickhouse using `DBReader` + +`DBReader` supports [Read Strategies](../../../strategy/index.md#strategy) for incremental data reading, +but does not support custom queries, like `JOIN`. + +#### WARNING +Please take into account [Clickhouse <-> Spark type mapping](types.md#clickhouse-types) + +## Supported DBReader features + +* ✅︎ `columns` +* ✅︎ `where` +* ✅︎ `hwm`, supported strategies: +* * ✅︎ [Snapshot Strategy](../../../strategy/snapshot_strategy.md#snapshot-strategy) +* * ✅︎ [Incremental Strategy](../../../strategy/incremental_strategy.md#incremental-strategy) +* * ✅︎ [Snapshot Batch Strategy](../../../strategy/snapshot_batch_strategy.md#snapshot-batch-strategy) +* * ✅︎ [Incremental Batch Strategy](../../../strategy/incremental_batch_strategy.md#incremental-batch-strategy) +* ❌ `hint` (is not supported by Clickhouse) +* ❌ `df_schema` +* ✅︎ `options` (see `Clickhouse.ReadOptions`) + +## Examples + +Snapshot strategy: + +```python +from onetl.connection import Clickhouse +from onetl.db import DBReader + +clickhouse = Clickhouse(...) + +reader = DBReader( + connection=clickhouse, + source="schema.table", + columns=["id", "key", "CAST(value AS String) value", "updated_dt"], + where="key = 'something'", + options=Clickhouse.ReadOptions(partitionColumn="id", numPartitions=10), +) +df = reader.run() +``` + +Incremental strategy: + +```python +from onetl.connection import Clickhouse +from onetl.db import DBReader +from onetl.strategy import IncrementalStrategy + +clickhouse = Clickhouse(...) + +reader = DBReader( + connection=clickhouse, + source="schema.table", + columns=["id", "key", "CAST(value AS String) value", "updated_dt"], + where="key = 'something'", + hwm=DBReader.AutoDetectHWM(name="clickhouse_hwm", expression="updated_dt"), + options=Clickhouse.ReadOptions(partitionColumn="id", numPartitions=10), +) + +with IncrementalStrategy(): + df = reader.run() +``` + +## Recommendations + +### Select only required columns + +Instead of passing `"*"` in `DBReader(columns=[...])` prefer passing exact column names. This reduces the amount of data passed from Clickhouse to Spark. + +### Pay attention to `where` value + +Instead of filtering data on Spark side using `df.filter(df.column == 'value')` pass proper `DBReader(where="column = 'value'")` clause. +This both reduces the amount of data send from Clickhouse to Spark, and may also improve performance of the query. +Especially if there are indexes or partitions for columns used in `where` clause. + +## Options diff --git a/mddocs/docs/_build/markdown/connection/db_connection/clickhouse/sql.md b/mddocs/docs/_build/markdown/connection/db_connection/clickhouse/sql.md new file mode 100644 index 000000000..98c17143e --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/clickhouse/sql.md @@ -0,0 +1,62 @@ + + +# Reading from Clickhouse using `Clickhouse.sql` + +`Clickhouse.sql` allows passing custom SQL query, but does not support incremental strategies. + +#### WARNING +Please take into account [Clickhouse <-> Spark type mapping](types.md#clickhouse-types) + +#### WARNING +Statement is executed in **read-write** connection, so if you’re calling some functions/procedures with DDL/DML statements inside, +they can change data in your database. + +## Syntax support + +Only queries with the following syntax are supported: + +* ✅︎ `SELECT ... FROM ...` +* ✅︎ `WITH alias AS (...) SELECT ...` +* ❌ `SET ...; SELECT ...;` - multiple statements not supported + +## Examples + +```python +from onetl.connection import Clickhouse + +clickhouse = Clickhouse(...) +df = clickhouse.sql( + """ + SELECT + id, + key, + CAST(value AS String) value, + updated_at + FROM + some.mytable + WHERE + key = 'something' + """, + options=Clickhouse.SQLOptions( + partitionColumn="id", + numPartitions=10, + lowerBound=0, + upperBound=1000, + ), +) +``` + +## Recommendations + +### Select only required columns + +Instead of passing `SELECT * FROM ...` prefer passing exact column names `SELECT col1, col2, ...`. +This reduces the amount of data passed from Clickhouse to Spark. + +### Pay attention to `where` value + +Instead of filtering data on Spark side using `df.filter(df.column == 'value')` pass proper `WHERE column = 'value'` clause. +This both reduces the amount of data send from Clickhouse to Spark, and may also improve performance of the query. +Especially if there are indexes or partitions for columns used in `where` clause. + +## Options diff --git a/mddocs/docs/_build/markdown/connection/db_connection/clickhouse/types.md b/mddocs/docs/_build/markdown/connection/db_connection/clickhouse/types.md new file mode 100644 index 000000000..d75e28142 --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/clickhouse/types.md @@ -0,0 +1,347 @@ + + +# Clickhouse <-> Spark type mapping + +#### NOTE +The results below are valid for Spark 3.5.5, and may differ on other Spark versions. + +#### NOTE +It is recommended to use [spark-dialect-extension](https://github.com/MobileTeleSystems/spark-dialect-extension) package, +which implements writing Arrays from Spark to Clickhouse, fixes dropping fractions of seconds in `TimestampType`, +and fixes other type conversion issues. + +## Type detection & casting + +Spark’s DataFrames always have a `schema` which is a list of columns with corresponding Spark types. All operations on a column are performed using column type. + +### Reading from Clickhouse + +This is how Clickhouse connector performs this: + +* For each column in query result (`SELECT column1, column2, ... FROM table ...`) get column name and Clickhouse type. +* Find corresponding `Clickhouse type (read)` → `Spark type` combination (see below) for each DataFrame column. If no combination is found, raise exception. +* Create DataFrame from query with specific column names and Spark types. + +### Writing to some existing Clickhouse table + +This is how Clickhouse connector performs this: + +* Get names of columns in DataFrame. [1](#id3) +* Perform `SELECT * FROM table LIMIT 0` query. +* Take only columns present in DataFrame (by name, case insensitive). For each found column get Clickhouse type. +* **Find corresponding** `Clickhouse type (read)` → `Spark type` **combination** (see below) for each DataFrame column. If no combination is found, raise exception. [2](#id4) +* Find corresponding `Spark type` → `Clickhousetype (write)` combination (see below) for each DataFrame column. If no combination is found, raise exception. +* If `Clickhousetype (write)` match `Clickhouse type (read)`, no additional casts will be performed, DataFrame column will be written to Clickhouse as is. +* If `Clickhousetype (write)` does not match `Clickhouse type (read)`, DataFrame column will be casted to target column type **on Clickhouse side**. For example, you can write column with text data to `Int32` column, if column contains valid integer values within supported value range and precision. + +* **[1]** This allows to write data to tables with `DEFAULT` columns - if DataFrame has no such column, it will be populated by Clickhouse. +* **[2]** Yes, this is weird. + +### Create new table using Spark + +#### WARNING +ABSOLUTELY NOT RECOMMENDED! + +This is how Clickhouse connector performs this: + +* Find corresponding `Spark type` → `Clickhouse type (create)` combination (see below) for each DataFrame column. If no combination is found, raise exception. +* Generate DDL for creating table in Clickhouse, like `CREATE TABLE (col1 ...)`, and run it. +* Write DataFrame to created table as is. + +But Spark does not have specific dialect for Clickhouse, so Generic JDBC dialect is used. +Generic dialect is using SQL ANSI type names while creating tables in target database, not database-specific types. + +If some cases this may lead to using wrong column type. For example, Spark creates column of type `TIMESTAMP` +which corresponds to Clickhouse type `DateTime32` (precision up to seconds) +instead of more precise `DateTime64` (precision up to nanoseconds). +This may lead to incidental precision loss, or sometimes data cannot be written to created table at all. + +So instead of relying on Spark to create tables: + +### See example + +```python +writer = DBWriter( + connection=clickhouse, + target="default.target_tbl", + options=Clickhouse.WriteOptions( + if_exists="append", + # ENGINE is required by Clickhouse + createTableOptions="ENGINE = MergeTree() ORDER BY id", + ), +) +writer.run(df) +``` + +Always prefer creating tables with specific types **BEFORE WRITING DATA**: + +### See example + +```python +clickhouse.execute( + """ + CREATE TABLE default.target_tbl ( + id UInt8, + value DateTime64(6) -- specific type and precision + ) + ENGINE = MergeTree() + ORDER BY id + """, +) + +writer = DBWriter( + connection=clickhouse, + target="default.target_tbl", + options=Clickhouse.WriteOptions(if_exists="append"), +) +writer.run(df) +``` + +### References + +Here you can find source code with type conversions: + +* [Clickhouse -> JDBC](https://github.com/ClickHouse/clickhouse-java/blob/0.3.2/clickhouse-jdbc/src/main/java/com/clickhouse/jdbc/JdbcTypeMapping.java#L39-L176) +* [JDBC -> Spark](https://github.com/apache/spark/blob/v3.5.5/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JdbcUtils.scala#L307) +* [Spark -> JDBC](https://github.com/apache/spark/blob/v3.5.5/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JdbcUtils.scala#L141-L164) +* [JDBC -> Clickhouse](https://github.com/ClickHouse/clickhouse-java/blob/0.3.2/clickhouse-jdbc/src/main/java/com/clickhouse/jdbc/JdbcTypeMapping.java#L185-L311) + +## Supported types + +See [official documentation](https://clickhouse.com/docs/en/sql-reference/data-types) + +### Generic types + +* `LowCardinality(T)` is same as `T` +* `Nullable(T)` is same as `T`, but Spark column is inferred as `nullable=True` + +### Numeric types + +| Clickhouse type (read) | Spark type | Clickhouse type (write) | Clickhouse type (create) | +|------------------------------|----------------------------------|-----------------------------|-----------------------------| +| `Bool` | `BooleanType()` | `Bool` | `UInt64` | +| `Decimal` | `DecimalType(P=10, S=0)` | `Decimal(P=10, S=0)` | `Decimal(P=10, S=0)` | +| `Decimal(P=0..38)` | `DecimalType(P=0..38, S=0)` | `Decimal(P=0..38, S=0)` | `Decimal(P=0..38, S=0)` | +| `Decimal(P=0..38, S=0..38)` | `DecimalType(P=0..38, S=0..38)` | `Decimal(P=0..38, S=0..38)` | `Decimal(P=0..38, S=0..38)` | +| `Decimal(P=39..76, S=0..76)` | unsupported [3](#id9) | | | +| `Decimal32(P=0..9)` | `DecimalType(P=9, S=0..9)` | `Decimal(P=9, S=0..9)` | `Decimal(P=9, S=0..9)` | +| `Decimal64(S=0..18)` | `DecimalType(P=18, S=0..18)` | `Decimal(P=18, S=0..18)` | `Decimal(P=18, S=0..18)` | +| `Decimal128(S=0..38)` | `DecimalType(P=38, S=0..38)` | `Decimal(P=38, S=0..38)` | `Decimal(P=38, S=0..38)` | +| `Decimal256(S=0..76)` | unsupported [3](#id9) | | | +| `Float32` | `FloatType()` | `Float32` | `Float32` | +| `Float64` | `DoubleType()` | `Float64` | `Float64` | +| `Int8` | `IntegerType()` | `Int32` | `Int32` | +| `Int16` | | | | +| `Int32` | | | | +| `Int64` | `LongType()` | `Int64` | `Int64` | +| `Int128` | unsupported [3](#id9) | | | +| `Int256` | | | | +| `-` | `ByteType()` | `Int8` | `Int8` | +| `-` | `ShortType()` | `Int32` | `Int32` | +| `UInt8` | `IntegerType()` | `Int32` | `Int32` | +| `UInt16` | `LongType()` | `Int64` | `Int64` | +| `UInt32` | `DecimalType(20,0)` | `Decimal(20,0)` | `Decimal(20,0)` | +| `UInt64` | | | | +| `UInt128` | unsupported [3](#id9) | | | +| `UInt256` | | | | +* **[3]** Clickhouse support numeric types up to 256 bit - `Int256`, `UInt256`, `Decimal256(S)`, `Decimal(P=39..76, S=0..76)`. But Spark’s `DecimalType(P, S)` supports maximum `P=38` (128 bit). It is impossible to read, write or operate with values of larger precision, this leads to an exception. + +### Temporal types + +Notes: +: * Datetime with timezone has the same precision as without timezone + * `DateTime` is alias for `DateTime32` + * `TIMESTAMP` is alias for `DateTime32`, but `TIMESTAMP(N)` is alias for `DateTime64(N)` + +| Clickhouse type (read) | Spark type | Clickhouse type (write) | Clickhouse type (create) | +|---------------------------------|-------------------------------------------------------------------------------|-------------------------------|---------------------------------------------------------------------| +| `Date` | `DateType()` | `Date` | `Date` | +| `Date32` | `DateType()` | `Date` | `Date`,
**cannot insert data** [4](#id15) | +| `DateTime32`, seconds | `TimestampType()`, microseconds | `DateTime64(6)`, microseconds | `DateTime32`, seconds | +| `DateTime64(3)`, milliseconds | `TimestampType()`, microseconds | `DateTime64(6)`, microseconds | `DateTime32`, seconds,
**precision loss** [5](#id16) | +| `DateTime64(6)`, microseconds | `TimestampType()`, microseconds | | `DateTime32`, seconds,
**precision loss** [7](#id18) | +| `DateTime64(7..9)`, nanoseconds | `TimestampType()`, microseconds,
**precision loss** [6](#id17) | | | +| `-` | `TimestampNTZType()`, microseconds | | | +| `DateTime32(TZ)` | unsupported [7](#id18) | | | +| `DateTime64(P, TZ)` | | | | +| `IntervalNanosecond` | `LongType()` | `Int64` | `Int64` | +| `IntervalMicrosecond` | | | | +| `IntervalMillisecond` | | | | +| `IntervalSecond` | | | | +| `IntervalMinute` | | | | +| `IntervalHour` | | | | +| `IntervalDay` | | | | +| `IntervalMonth` | | | | +| `IntervalQuarter` | | | | +| `IntervalWeek` | | | | +| `IntervalYear` | | | | + +#### WARNING +Note that types in Clickhouse and Spark have different value ranges: + +| Clickhouse type | Min value | Max value | Spark type | Min value | Max value | +|----------------------|---------------------------------|---------------------------------|-------------------|------------------------------|------------------------------| +| `Date` | `1970-01-01` | `2149-06-06` | `DateType()` | `0001-01-01` | `9999-12-31` | +| `DateTime32` | `1970-01-01 00:00:00` | `2106-02-07 06:28:15` | `TimestampType()` | `0001-01-01 00:00:00.000000` | `9999-12-31 23:59:59.999999` | +| `DateTime64(P=0..8)` | `1900-01-01 00:00:00.00000000` | `2299-12-31 23:59:59.99999999` | | | | +| `DateTime64(P=9)` | `1900-01-01 00:00:00.000000000` | `2262-04-11 23:47:16.999999999` | | | | + +So not all of values in Spark DataFrame can be written to Clickhouse. + +References: +: * [Clickhouse Date documentation](https://clickhouse.com/docs/en/sql-reference/data-types/date) + * [Clickhouse Datetime32 documentation](https://clickhouse.com/docs/en/sql-reference/data-types/datetime) + * [Clickhouse Datetime64 documentation](https://clickhouse.com/docs/en/sql-reference/data-types/datetime64) + * [Spark DateType documentation](https://spark.apache.org/docs/latest/api/java/org/apache/spark/sql/types/DateType.html) + * [Spark TimestampType documentation](https://spark.apache.org/docs/latest/api/java/org/apache/spark/sql/types/TimestampType.html) + +* **[4]** `Date32` has different bytes representation than `Date`, and inserting value of type `Date32` to `Date` column leads to errors on Clickhouse side, e.g. `Date(106617) should be between 0 and 65535 inclusive of both values`. Although Spark does properly read the `Date32` column as `DateType()`, and there should be no difference at all. Probably this is some bug in Clickhouse driver. +* **[5]** Generic JDBC dialect generates DDL with Clickhouse type `TIMESTAMP` which is alias for `DateTime32` with precision up to seconds (`23:59:59`). Inserting data with milliseconds precision (`23:59:59.999`) will lead to **throwing away milliseconds**. Solution: create table manually, with proper column type. +* **[6]** Clickhouse support datetime up to nanoseconds precision (`23:59:59.999999999`), but Spark `TimestampType()` supports datetime up to microseconds precision (`23:59:59.999999`). Nanoseconds will be lost during read or write operations. Solution: create table manually, with proper column type. +* **[7]** Clickhouse will raise an exception that data in format `2001-01-01 23:59:59.999999` has data `.999999` which does not match format `YYYY-MM-DD hh:mm:ss` of `DateTime32` column type (see [5](#id16)). So Spark can create Clickhouse table, but cannot write data to column of this type. Solution: create table manually, with proper column type. + +### String types + +| Clickhouse type (read) | Spark type | Clickhousetype (write) | Clickhouse type (create) | +|--------------------------|----------------|--------------------------|----------------------------| +| `FixedString(N)` | `StringType()` | `String` | `String` | +| `String` | | | | +| `Enum8` | | | | +| `Enum16` | | | | +| `IPv4` | | | | +| `IPv6` | | | | +| `UUID` | | | | +| `-` | `BinaryType()` | | | + +## Unsupported types + +Columns of these Clickhouse types cannot be read by Spark: +: * `AggregateFunction(func, T)` + * `Array(T)` + * `JSON` + * `Map(K, V)` + * `MultiPolygon` + * `Nested(field1 T1, ...)` + * `Nothing` + * `Point` + * `Polygon` + * `Ring` + * `SimpleAggregateFunction(func, T)` + * `Tuple(T1, T2, ...)` + +Dataframe with these Spark types cannot be written to Clickhouse: +: * `ArrayType(T)` + * `BinaryType()` + * `CharType(N)` + * `DayTimeIntervalType(P, S)` + * `MapType(K, V)` + * `NullType()` + * `StructType([...])` + * `TimestampNTZType()` + * `VarcharType(N)` + +This is because Spark does not have dedicated Clickhouse dialect, and uses Generic JDBC dialect instead. +This dialect does not have type conversion between some types, like Clickhouse `Array` -> Spark `ArrayType()`, and vice versa. + +The is a way to avoid this - just cast everything to `String`. + +## Explicit type cast + +### `DBReader` + +Use `CAST` or `toJSONString` to get column data as string in JSON format, + +For parsing JSON columns in ClickHouse, `JSON.parse_column` method. + +```python +from pyspark.sql.types import ArrayType, IntegerType + +from onetl.file.format import JSON +from onetl.connection import ClickHouse +from onetl.db import DBReader + +reader = DBReader( + connection=clickhouse, + target="default.source_tbl", + columns=[ + "id", + "toJSONString(array_column) array_column", + ], +) +df = reader.run() + +# Spark requires all columns to have some specific type, describe it +column_type = ArrayType(IntegerType()) + +json = JSON() +df = df.select( + df.id, + json.parse_column("array_column", column_type), +) +``` + +### `DBWriter` + +For writing JSON data to ClickHouse, use the `JSON.serialize_column` method to convert a DataFrame column to JSON format efficiently and write it as a `String` column in Clickhouse. + +```python +from onetl.file.format import JSON +from onetl.connection import ClickHouse +from onetl.db import DBWriter + +clickhouse = ClickHouse(...) + +clickhouse.execute( + """ + CREATE TABLE default.target_tbl ( + id Int32, + array_column_json String, + ) + ENGINE = MergeTree() + ORDER BY id + """, +) + +json = JSON() +df = df.select( + df.id, + json.serialize_column(df.array_column).alias("array_column_json"), +) + +writer.run(df) +``` + +Then you can parse this column on Clickhouse side - for example, by creating a view: + +```sql +SELECT + id, + JSONExtract(json_column, 'Array(String)') AS array_column +FROM target_tbl +``` + +You can also use [ALIAS](https://clickhouse.com/docs/en/sql-reference/statements/create/table#alias) +or [MATERIALIZED](https://clickhouse.com/docs/en/sql-reference/statements/create/table#materialized) columns +to avoid writing such expression in every `SELECT` clause all the time: + +```sql +CREATE TABLE default.target_tbl ( + id Int32, + array_column_json String, + -- computed column + array_column Array(String) ALIAS JSONExtract(json_column, 'Array(String)') + -- or materialized column + -- array_column Array(String) MATERIALIZED JSONExtract(json_column, 'Array(String)') +) +ENGINE = MergeTree() +ORDER BY id +``` + +Downsides: + +* Using `SELECT JSONExtract(...)` or `ALIAS` column can be expensive, because value is calculated on every row access. This can be especially harmful if such column is used in `WHERE` clause. +* `ALIAS` and `MATERIALIZED` columns are not included in `SELECT *` clause, they should be added explicitly: `SELECT *, calculated_column FROM table`. + +#### WARNING +[EPHEMERAL](https://clickhouse.com/docs/en/sql-reference/statements/create/table#ephemeral) columns are not supported by Spark +because they cannot be selected to determine target column type. diff --git a/mddocs/docs/_build/markdown/connection/db_connection/clickhouse/write.md b/mddocs/docs/_build/markdown/connection/db_connection/clickhouse/write.md new file mode 100644 index 000000000..28d5f4676 --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/clickhouse/write.md @@ -0,0 +1,42 @@ + + +# Writing to Clickhouse using `DBWriter` + +For writing data to Clickhouse, use `DBWriter`. + +#### WARNING +Please take into account [Clickhouse <-> Spark type mapping](types.md#clickhouse-types) + +#### WARNING +It is always recommended to create table explicitly using [Clickhouse.execute](execute.md#clickhouse-execute) +instead of relying on Spark’s table DDL generation. + +This is because Spark’s DDL generator can create columns with different precision and types than it is expected, +causing precision loss or other issues. + +## Examples + +```python +from onetl.connection import Clickhouse +from onetl.db import DBWriter + +clickhouse = Clickhouse(...) + +df = ... # data is here + +writer = DBWriter( + connection=clickhouse, + target="schema.table", + options=Clickhouse.WriteOptions( + if_exists="append", + # ENGINE is required by Clickhouse + createTableOptions="ENGINE = MergeTree() ORDER BY id", + ), +) + +writer.run(df) +``` + +## Options + +Method above accepts `Clickhouse.WriteOptions` diff --git a/mddocs/docs/_build/markdown/connection/db_connection/greenplum/connection.md b/mddocs/docs/_build/markdown/connection/db_connection/greenplum/connection.md new file mode 100644 index 000000000..8f3d32d76 --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/greenplum/connection.md @@ -0,0 +1,3 @@ + + +# Greenplum connection diff --git a/mddocs/docs/_build/markdown/connection/db_connection/greenplum/execute.md b/mddocs/docs/_build/markdown/connection/db_connection/greenplum/execute.md new file mode 100644 index 000000000..715addad7 --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/greenplum/execute.md @@ -0,0 +1,102 @@ + + +# Executing statements in Greenplum + +#### WARNING +Methods below **read all the rows** returned from DB **to Spark driver memory**, and then convert them to DataFrame. + +Do **NOT** use them to read large amounts of data. Use [DBReader](read.md#greenplum-read) instead. + +## How to + +There are 2 ways to execute some statement in Greenplum + +### Use `Greenplum.fetch` + +Use this method to perform some `SELECT` query which returns **small number or rows**, like reading +Greenplum config, or reading data from some reference table. Method returns Spark DataFrame. + +Method accepts `Greenplum.FetchOptions`. + +Connection opened using this method should be then closed with `connection.close()` or `with connection:`. + +#### WARNING +`Greenplum.fetch` is implemented using Postgres JDBC connection, +so types are handled a bit differently than in `DBReader`. See [Postgres <-> Spark type mapping](../postgres/types.md#postgres-types). + +#### Syntax support + +This method supports **any** query syntax supported by Greenplum, like: + +* ✅︎ `SELECT ... FROM ...` +* ✅︎ `WITH alias AS (...) SELECT ...` +* ✅︎ `SELECT func(arg1, arg2)` or `{call func(arg1, arg2)}` - special syntax for calling functions +* ❌ `SET ...; SELECT ...;` - multiple statements not supported + +#### Examples + +```python +from onetl.connection import Greenplum + +greenplum = Greenplum(...) + +df = greenplum.fetch( + "SELECT value FROM some.reference_table WHERE key = 'some_constant'", + options=Greenplum.FetchOptions(queryTimeout=10), +) +greenplum.close() +value = df.collect()[0][0] # get value from first row and first column +``` + +### Use `Greenplum.execute` + +Use this method to execute DDL and DML operations. Each method call runs operation in a separated transaction, and then commits it. + +Method accepts `Greenplum.ExecuteOptions`. + +Connection opened using this method should be then closed with `connection.close()` or `with connection:`. + +#### Syntax support + +This method supports **any** query syntax supported by Greenplum, like: + +* ✅︎ `CREATE TABLE ...`, `CREATE VIEW ...`, and so on +* ✅︎ `ALTER ...` +* ✅︎ `INSERT INTO ... SELECT ...`, `UPDATE ...`, `DELETE ...`, and so on +* ✅︎ `DROP TABLE ...`, `DROP VIEW ...`, `TRUNCATE TABLE`, and so on +* ✅︎ `CALL procedure(arg1, arg2) ...` +* ✅︎ `SELECT func(arg1, arg2)` or `{call func(arg1, arg2)}` - special syntax for calling functions +* ✅︎ other statements not mentioned here +* ❌ `SET ...; SELECT ...;` - multiple statements not supported + +#### Examples + +```python +from onetl.connection import Greenplum + +greenplum = Greenplum(...) + +greenplum.execute("DROP TABLE schema.table") +greenplum.execute( + """ + CREATE TABLE schema.table ( + id int, + key text, + value real + ) + DISTRIBUTED BY id + """, + options=Greenplum.ExecuteOptions(queryTimeout=10), +) +``` + +## Interaction schema + +Unlike reading & writing, executing statements in Greenplum is done **only** through Greenplum master node, +without any interaction between Greenplum segments and Spark executors. More than that, Spark executors are not used in this case. + +The only port used while interacting with Greenplum in this case is `5432` (Greenplum master port). + +### Spark <-> Greenplum interaction during Greenplum.execute()/Greenplum.fetch() + +## Options diff --git a/mddocs/docs/_build/markdown/connection/db_connection/greenplum/index.md b/mddocs/docs/_build/markdown/connection/db_connection/greenplum/index.md new file mode 100644 index 000000000..9fcc68f66 --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/greenplum/index.md @@ -0,0 +1,18 @@ + + +# Greenplum + +# Connection + +* [Prerequisites](prerequisites.md) +* [Greenplum connection](connection.md) + +# Operations + +* [Reading from Greenplum using `DBReader`](read.md) +* [Writing to Greenplum using `DBWriter`](write.md) +* [Executing statements in Greenplum](execute.md) + +# Troubleshooting + +* [Greenplum <-> Spark type mapping](types.md) diff --git a/mddocs/docs/_build/markdown/connection/db_connection/greenplum/prerequisites.md b/mddocs/docs/_build/markdown/connection/db_connection/greenplum/prerequisites.md new file mode 100644 index 000000000..6c0b92846 --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/greenplum/prerequisites.md @@ -0,0 +1,358 @@ + + +# Prerequisites + +## Version Compatibility + +* Greenplum server versions: + : * Officially declared: 5.x, 6.x, and 7.x (which requires `Greenplum.get_packages(package_version="2.3.0")` or higher) + * Actually tested: 6.23, 7.0 +* Spark versions: 2.3.x - 3.2.x (Spark 3.3+ is not supported yet) +* Java versions: 8 - 11 + +See [official documentation](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.2/greenplum-connector-spark/release_notes.html). + +## Installing PySpark + +To use Greenplum connector you should have PySpark installed (or injected to `sys.path`) +BEFORE creating the connector instance. + +See [Spark](../../../install/spark.md#install-spark) installation instruction for more details. + +## Downloading VMware package + +To use Greenplum connector you should download connector `.jar` file from +[VMware website](https://network.tanzu.vmware.com/products/vmware-greenplum#/releases/1413479/file_groups/16966) +and then pass it to Spark session. + +#### WARNING +Please pay attention to [Spark & Scala version compatibility](../../../install/spark.md#spark-compatibility-matrix). + +#### WARNING +There are issues with using package of version 2.3.0/2.3.1 with Greenplum 6.x - connector can +open transaction with `SELECT * FROM table LIMIT 0` query, but does not close it, which leads to deadlocks +during write. + +There are several ways to do that. See [Injecting Java packages](../../../install/spark.md#java-packages) for details. + +#### NOTE +If you’re uploading package to private package repo, use `groupId=io.pivotal` and `artifactoryId=greenplum-spark_2.12` +(`2.12` is Scala version) to give uploaded package a proper name. + +## Connecting to Greenplum + +### Interaction schema + +Spark executors open ports to listen incoming requests. +Greenplum segments are initiating connections to Spark executors using [EXTERNAL TABLE](https://docs.vmware.com/en/VMware-Greenplum/7/greenplum-database/ref_guide-sql_commands-CREATE_EXTERNAL_TABLE.html) +functionality, and send/read data using [gpfdist protocol](https://docs.vmware.com/en/VMware-Greenplum/7/greenplum-database/admin_guide-external-g-using-the-greenplum-parallel-file-server--gpfdist-.html#about-gpfdist-setup-and-performance-1). + +Data is **not** send through Greenplum master. +Greenplum master only receives commands to start reading/writing process, and manages all the metadata (external table location, schema and so on). + +More details can be found in [official documentation](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/overview.html). + +### Set number of connections + +#### WARNING +This is very important!!! + +If you don’t limit number of connections, you can exceed the [max_connections](https://docs.vmware.com/en/VMware-Greenplum/7/greenplum-database/admin_guide-client_auth.html#limiting-concurrent-connections#limiting-concurrent-connections-2) +limit set on the Greenplum side. It’s usually not so high, e.g. 500-1000 connections max, +depending on your Greenplum instance settings and using connection balancers like `pgbouncer`. + +Consuming all available connections means **nobody** (even admin users) can connect to Greenplum. + +Each job on the Spark executor makes its own connection to Greenplum master node, +so you need to limit number of connections to avoid opening too many of them. + +* Reading about `5-10Gb` of data requires about `3-5` parallel connections. +* Reading about `20-30Gb` of data requires about `5-10` parallel connections. +* Reading about `50Gb` of data requires ~ `10-20` parallel connections. +* Reading about `100+Gb` of data requires `20-30` parallel connections. +* Opening more than `30-50` connections is not recommended. + +Number of connections can be limited by 2 ways: + +* By limiting number of Spark executors and number of cores per-executor. Max number of parallel jobs is `executors * cores`. + +Spark with master=local + +```py +spark = ( + SparkSession.builder + # Spark will run with 5 threads in local mode, allowing up to 5 parallel tasks + .config("spark.master", "local[5]") + .config("spark.executor.cores", 1) +).getOrCreate() +``` + +Spark with master=yarn or master=k8s, dynamic allocation + +```py +spark = ( + SparkSession.builder + .config("spark.master", "yarn") + # Spark will start MAX 10 executors with 1 core each (dynamically), so max number of parallel jobs is 10 + .config("spark.dynamicAllocation.maxExecutors", 10) + .config("spark.executor.cores", 1) +).getOrCreate() +``` + +Spark with master=yarn or master=k8s, static allocation + +```py +spark = ( + SparkSession.builder + .config("spark.master", "yarn") + # Spark will start EXACTLY 10 executors with 1 core each, so max number of parallel jobs is 10 + .config("spark.executor.instances", 10) + .config("spark.executor.cores", 1) +).getOrCreate() +``` + +* By limiting connection pool size user by Spark (**only** for Spark with `master=local`): + +```python +spark = SparkSession.builder.config("spark.master", "local[*]").getOrCreate() + +# No matter how many executors are started and how many cores they have, +# number of connections cannot exceed pool size: +Greenplum( + ..., + extra={ + "pool.maxSize": 10, + }, +) +``` + +See [connection pooling](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/using_the_connector.html#jdbcconnpool) +documentation. + +* By setting `num_partitions` + and `partition_column` (not recommended). + +### Allowing connection to Greenplum master + +Ask your Greenplum cluster administrator to allow your user to connect to Greenplum master node, +e.g. by updating `pg_hba.conf` file. + +More details can be found in [official documentation](https://docs.vmware.com/en/VMware-Greenplum/7/greenplum-database/admin_guide-client_auth.html#limiting-concurrent-connections#allowing-connections-to-greenplum-database-0). + +### Set connection port + +#### Spark with `master=k8s` + +Please follow [the official documentation](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/configure.html#k8scfg) + +#### Spark with `master=yarn` or `master=local` + +To read data from Greenplum using Spark, following ports should be opened in firewall between Spark and Greenplum: + +* Spark driver and all Spark executors -> port `5432` on Greenplum master node. + + This port number should be set while connecting to Greenplum: + ```python + greenplum = Greenplum(host="master.host", port=5432, ...) + ``` +* Greenplum segments -> some port range (e.g. `41000-42000`) **listened by Spark executors**. + + This range should be set in `extra` option: + ```python + greenplum = Greenplum( + ..., + extra={ + "server.port": "41000-42000", + }, + ) + ``` + + Number of ports in this range is `number of parallel running Spark sessions` \* `number of parallel connections per session`. + + Number of connections per session (see below) is usually less than `30` (see above). + + Number of session depends on your environment: + : * For `master=local` only few ones-tens sessions can be started on the same host, depends on available RAM and CPU. + * For `master=yarn` hundreds or thousands of sessions can be started simultaneously, + but they are executing on different cluster nodes, so one port can be opened on different nodes at the same time. + +More details can be found in official documentation: +: * [port requirements](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/sys_reqs.html#network-port-requirements) + * [format of server.port value](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/options.html#server.port) + * [port troubleshooting](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/troubleshooting.html#port-errors) + +### Set connection host + +#### Spark with `master=k8s` + +Please follow [the official documentation](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/configure.html#k8scfg) + +#### Spark with `master=local` + +By default, Greenplum connector tries to resolve IP of current host, and then pass it as `gpfdist` URL to Greenplum segment. +This may fail in some cases. + +For example, IP can be resolved using `/etc/hosts` content like this: + +```text +127.0.0.1 localhost real-host-name +``` + +```bash +$ hostname -f +localhost + +$ hostname -i +127.0.0.1 +``` + +Reading/writing data to Greenplum will fail with following exception: + +```text +org.postgresql.util.PSQLException: ERROR: connection with gpfdist failed for +"gpfdist://127.0.0.1:49152/local-1709739764667/exec/driver", +effective url: "http://127.0.0.1:49152/local-1709739764667/exec/driver": +error code = 111 (Connection refused); (seg3 slice1 12.34.56.78:10003 pid=123456) +``` + +There are 2 ways to fix that: + +* Explicitly pass your host IP address to connector, like this + ```python + import os + + # pass here real host IP (accessible from GP segments) + os.environ["HOST_IP"] = "192.168.1.1" + + greenplum = Greenplum( + ..., + extra={ + # connector will read IP from this environment variable + "server.hostEnv": "env.HOST_IP", + }, + spark=spark, + ) + ``` + + More details can be found in [official documentation](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/options.html#server.hostenv). +* Update `/etc/hosts` file to include real host IP: + ```text + 127.0.0.1 localhost + # this IP should be accessible from GP segments + 192.168.1.1 driver-host-name + ``` + + So Greenplum connector will properly resolve host IP. + +#### Spark with `master=yarn` + +The same issue with resolving IP address can occur on Hadoop cluster node, but it’s tricky to fix, because each node has a different IP. + +There are 3 ways to fix that: + +* Pass node hostname to `gpfdist` URL. So IP will be resolved on segment side: + ```python + greenplum = Greenplum( + ..., + extra={ + "server.useHostname": "true", + }, + ) + ``` + + But this may fail if Hadoop cluster node hostname cannot be resolved from Greenplum segment side. + + More details can be found in [official documentation](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/options.html#server.usehostname). +* Set specific network interface to get IP address from: + ```python + greenplum = Greenplum( + ..., + extra={ + "server.nic": "eth0", + }, + ) + ``` + + You can get list of network interfaces using this command. + + #### NOTE + This command should be executed on Hadoop cluster node, **not** Spark driver host! + + ```bash + $ ip address + 1: lo: mtu 65536 qdisc noqueue state UNKNOWN group default qlen 1000 + inet 127.0.0.1/8 scope host lo + valid_lft forever preferred_lft forever + 2: eth0: mtu 1500 qdisc fq_codel state UP group default qlen 1000 + inet 192.168.1.1/24 brd 192.168.1.255 scope global dynamic noprefixroute eth0 + valid_lft 83457sec preferred_lft 83457sec + ``` + + Note that in this case **each** Hadoop cluster node node should have network interface with name `eth0`. + + More details can be found in [official documentation](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/options.html#server.nic). +* Update `/etc/hosts` on each Hadoop cluster node to include real node IP: + ```text + 127.0.0.1 localhost + # this IP should be accessible from GP segments + 192.168.1.1 cluster-node-name + ``` + + So Greenplum connector will properly resolve node IP. + +### Set required grants + +Ask your Greenplum cluster administrator to set following grants for a user, +used for creating a connection: + +Read + Write + +```sql +-- get access to get tables metadata & cluster information +GRANT SELECT ON information_schema.tables TO username; +GRANT SELECT ON pg_attribute TO username; +GRANT SELECT ON pg_class TO username; +GRANT SELECT ON pg_namespace TO username; +GRANT SELECT ON pg_settings TO username; +GRANT SELECT ON pg_stats TO username; +GRANT SELECT ON gp_distributed_xacts TO username; +GRANT SELECT ON gp_segment_configuration TO username; +-- Greenplum 5.x only +GRANT SELECT ON gp_distribution_policy TO username; + +-- allow creating external tables in the same schema as source/target table +GRANT USAGE ON SCHEMA myschema TO username; +GRANT CREATE ON SCHEMA myschema TO username; +ALTER USER username CREATEEXTTABLE(type = 'readable', protocol = 'gpfdist') CREATEEXTTABLE(type = 'writable', protocol = 'gpfdist'); + +-- allow read access to specific table (to get column types) +-- allow write access to specific table +GRANT SELECT, INSERT ON myschema.mytable TO username; +``` + +Read only + +```sql +-- get access to get tables metadata & cluster information +GRANT SELECT ON information_schema.tables TO username; +GRANT SELECT ON pg_attribute TO username; +GRANT SELECT ON pg_class TO username; +GRANT SELECT ON pg_namespace TO username; +GRANT SELECT ON pg_settings TO username; +GRANT SELECT ON pg_stats TO username; +GRANT SELECT ON gp_distributed_xacts TO username; +GRANT SELECT ON gp_segment_configuration TO username; +-- Greenplum 5.x only +GRANT SELECT ON gp_distribution_policy TO username; + +-- allow creating external tables in the same schema as source table +GRANT USAGE ON SCHEMA schema_to_read TO username; +GRANT CREATE ON SCHEMA schema_to_read TO username; +-- yes, ``writable`` for reading from GP, because data is written from Greenplum to Spark executor. +ALTER USER username CREATEEXTTABLE(type = 'writable', protocol = 'gpfdist'); + +-- allow read access to specific table +GRANT SELECT ON schema_to_read.table_to_read TO username; +``` + +More details can be found in [official documentation](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/install_cfg.html#role-privileges). diff --git a/mddocs/docs/_build/markdown/connection/db_connection/greenplum/read.md b/mddocs/docs/_build/markdown/connection/db_connection/greenplum/read.md new file mode 100644 index 000000000..64af11703 --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/greenplum/read.md @@ -0,0 +1,285 @@ + + +# Reading from Greenplum using `DBReader` + +Data can be read from Greenplum to Spark using `DBReader`. +It also supports [Read Strategies](../../../strategy/index.md#strategy) for incremental data reading. + +#### WARNING +Please take into account [Greenplum <-> Spark type mapping](types.md#greenplum-types). + +#### NOTE +Unlike JDBC connectors, *Greenplum connector for Spark* does not support +executing **custom** SQL queries using `.sql` method. Connector can be used to only read data from a table or view. + +## Supported DBReader features + +* ✅︎ `columns` (see note below) +* ✅︎ `where` (see note below) +* ✅︎ `hwm` (see note below), supported strategies: +* * ✅︎ [Snapshot Strategy](../../../strategy/snapshot_strategy.md#snapshot-strategy) +* * ✅︎ [Incremental Strategy](../../../strategy/incremental_strategy.md#incremental-strategy) +* * ✅︎ [Snapshot Batch Strategy](../../../strategy/snapshot_batch_strategy.md#snapshot-batch-strategy) +* * ✅︎ [Incremental Batch Strategy](../../../strategy/incremental_batch_strategy.md#incremental-batch-strategy) +* ❌ `hint` (is not supported by Greenplum) +* ❌ `df_schema` +* ✅︎ `options` (see `Greenplum.ReadOptions`) + +#### WARNING +In case of Greenplum connector, `DBReader` does not generate raw `SELECT` query. Instead it relies on Spark SQL syntax +which in some cases (using column projection and predicate pushdown) can be converted to Greenplum SQL. + +So `columns`, `where` and `hwm.expression` should be specified in [Spark SQL](https://spark.apache.org/docs/latest/sql-ref-syntax.html) syntax, +not Greenplum SQL. + +This is OK: + +```python +DBReader( + columns=[ + "some_column", + # this cast is executed on Spark side + "CAST(another_column AS STRING)", + ], + # this predicate is parsed by Spark, and can be pushed down to Greenplum + where="some_column LIKE 'val1%'", +) +``` + +This is will fail: + +```python +DBReader( + columns=[ + "some_column", + # Spark does not have `text` type + "CAST(another_column AS text)", + ], + # Spark does not support ~ syntax for regexp matching + where="some_column ~ 'val1.*'", +) +``` + +## Examples + +Snapshot strategy: + +```python +from onetl.connection import Greenplum +from onetl.db import DBReader + +greenplum = Greenplum(...) + +reader = DBReader( + connection=greenplum, + source="schema.table", + columns=["id", "key", "CAST(value AS string) value", "updated_dt"], + where="key = 'something'", +) +df = reader.run() +``` + +Incremental strategy: + +```python +from onetl.connection import Greenplum +from onetl.db import DBReader +from onetl.strategy import IncrementalStrategy + +greenplum = Greenplum(...) + +reader = DBReader( + connection=greenplum, + source="schema.table", + columns=["id", "key", "CAST(value AS string) value", "updated_dt"], + where="key = 'something'", + hwm=DBReader.AutoDetectHWM(name="greenplum_hwm", expression="updated_dt"), +) + +with IncrementalStrategy(): + df = reader.run() +``` + +## Interaction schema + +High-level schema is described in [Prerequisites](prerequisites.md#greenplum-prerequisites). You can find detailed interaction schema below. + +### Spark <-> Greenplum interaction during DBReader.run() + +## Recommendations + +### Select only required columns + +Instead of passing `"*"` in `DBReader(columns=[...])` prefer passing exact column names. This reduces the amount of data passed from Greenplum to Spark. + +### Pay attention to `where` value + +Instead of filtering data on Spark side using `df.filter(df.column == 'value')` pass proper `DBReader(where="column = 'value'")` clause. +This both reduces the amount of data send from Greenplum to Spark, and may also improve performance of the query. +Especially if there are indexes or partitions for columns used in `where` clause. + +### Read data in parallel + +`DBReader` in case of Greenplum connector requires view or table to have a column which is used by Spark +for parallel reads. + +Choosing proper column allows each Spark executor to read only part of data stored in the specified segment, +avoiding moving large amounts of data between segments, which improves reading performance. + +#### Using `gp_segment_id` + +By default, `DBReader` will use [gp_segment_id](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/troubleshooting.html#reading-from-a-view) +column for parallel data reading. Each DataFrame partition will contain data of a specific Greenplum segment. + +This allows each Spark executor read only data from specific Greenplum segment, avoiding moving large amounts of data between segments. + +If view is used, it is recommended to include `gp_segment_id` column to this view: + +### Reading from view with gp_segment_id column + +```python +from onetl.connection import Greenplum +from onetl.db import DBReader + +greenplum = Greenplum(...) + +greenplum.execute( + """ + CREATE VIEW schema.view_with_gp_segment_id AS + SELECT + id, + some_column, + another_column, + gp_segment_id -- IMPORTANT + FROM schema.some_table + """, +) + +reader = DBReader( + connection=greenplum, + source="schema.view_with_gp_segment_id", +) +df = reader.run() +``` + +#### Using custom `partition_column` + +Sometimes table or view is lack of `gp_segment_id` column, but there is some column +with value range correlated with Greenplum segment distribution. + +In this case, custom column can be used instead: + +### Reading from view with custom partition_column + +```python +from onetl.connection import Greenplum +from onetl.db import DBReader + +greenplum = Greenplum(...) + +greenplum.execute( + """ + CREATE VIEW schema.view_with_partition_column AS + SELECT + id, + some_column, + part_column -- correlated to greenplum segment ID + FROM schema.some_table + """, +) + +reader = DBReader( + connection=greenplum, + source="schema.view_with_partition_column", + options=Greenplum.ReadOptions( + # parallelize data using specified column + partitionColumn="part_column", + # create 10 Spark tasks, each will read only part of table data + partitions=10, + ), +) +df = reader.run() +``` + +#### Reading `DISTRIBUTED REPLICATED` tables + +Replicated tables do not have `gp_segment_id` column at all, so you need to set `partition_column` to some column name +of type integer/bigint/smallint. + +### Parallel `JOIN` execution + +In case of using views which require some data motion between Greenplum segments, like `JOIN` queries, another approach should be used. + +Each Spark executor N will run the same query, so each of N query will start its own JOIN process, leading to really heavy load on Greenplum segments. +**This should be avoided**. + +Instead is recommended to run `JOIN` query on Greenplum side, save the result to an intermediate table, +and then read this table using `DBReader`: + +### Reading from view using intermediate table + +```python +from onetl.connection import Greenplum +from onetl.db import DBReader + +greenplum = Greenplum(...) + +greenplum.execute( + """ + CREATE UNLOGGED TABLE schema.intermediate_table AS + SELECT + id, + tbl1.col1, + tbl1.data, + tbl2.another_data + FROM + schema.table1 as tbl1 + JOIN + schema.table2 as tbl2 + ON + tbl1.col1 = tbl2.col2 + WHERE ... + """, +) + +reader = DBReader( + connection=greenplum, + source="schema.intermediate_table", +) +df = reader.run() + +# write dataframe somethere + +greenplum.execute( + """ + DROP TABLE schema.intermediate_table + """, +) +``` + +#### WARNING +**NEVER** do that: + +```python +df1 = DBReader(connection=greenplum, target="public.table1", ...).run() +df2 = DBReader(connection=greenplum, target="public.table2", ...).run() + +joined_df = df1.join(df2, on="col") +``` + +This will lead to sending all the data from both `table1` and `table2` to Spark executor memory, and then `JOIN` +will be performed on Spark side, not inside Greenplum. This is **VERY** inefficient. + +#### `TEMPORARY` tables notice + +Someone could think that writing data from view or result of `JOIN` to `TEMPORARY` table, +and then passing it to `DBReader`, is an efficient way to read data from Greenplum. This is because temp tables are not generating WAL files, +and are automatically deleted after finishing the transaction. + +That will **NOT** work. Each Spark executor establishes its own connection to Greenplum. +And each connection starts its own transaction which means that every executor will read empty temporary table. + +You should use [UNLOGGED](https://docs.vmware.com/en/VMware-Greenplum/7/greenplum-database/ref_guide-sql_commands-CREATE_TABLE.html) tables +to write data to intermediate table without generating WAL logs. + +## Options diff --git a/mddocs/docs/_build/markdown/connection/db_connection/greenplum/types.md b/mddocs/docs/_build/markdown/connection/db_connection/greenplum/types.md new file mode 100644 index 000000000..755f86751 --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/greenplum/types.md @@ -0,0 +1,318 @@ + + +# Greenplum <-> Spark type mapping + +#### NOTE +The results below are valid for Spark 3.2.4, and may differ on other Spark versions. + +## Type detection & casting + +Spark’s DataFrames always have a `schema` which is a list of columns with corresponding Spark types. All operations on a column are performed using column type. + +### Reading from Greenplum + +This is how Greenplum connector performs this: + +* Execute query `SELECT * FROM table LIMIT 0` [1](#id2). +* For each column in query result get column name and Greenplum type. +* Find corresponding `Greenplum type (read)` → `Spark type` combination (see below) for each DataFrame column. If no combination is found, raise exception. +* Use Spark column projection and predicate pushdown features to build a final query. +* Create DataFrame from generated query with inferred schema. + +* **[1]** Yes, **all columns of a table**, not just selected ones. This means that if source table **contains** columns with unsupported type, the entire table cannot be read. + +### Writing to some existing Greenplum table + +This is how Greenplum connector performs this: + +* Get names of columns in DataFrame. +* Perform `SELECT * FROM table LIMIT 0` query. +* For each column in query result get column name and Greenplum type. +* Match table columns with DataFrame columns (by name, case insensitive). + If some column is present only in target table, but not in DataFrame (like `DEFAULT` or `SERIAL` column), and vice versa, raise an exception. + See [Explicit type cast](). +* Find corresponding `Spark type` → `Greenplumtype (write)` combination (see below) for each DataFrame column. If no combination is found, raise exception. +* If `Greenplumtype (write)` match `Greenplum type (read)`, no additional casts will be performed, DataFrame column will be written to Greenplum as is. +* If `Greenplumtype (write)` does not match `Greenplum type (read)`, DataFrame column will be casted to target column type **on Greenplum side**. For example, you can write column with text data to `json` column which Greenplum connector currently does not support. + +### Create new table using Spark + +#### WARNING +ABSOLUTELY NOT RECOMMENDED! + +This is how Greenplum connector performs this: + +* Find corresponding `Spark type` → `Greenplum type (create)` combination (see below) for each DataFrame column. If no combination is found, raise exception. +* Generate DDL for creating table in Greenplum, like `CREATE TABLE (col1 ...)`, and run it. +* Write DataFrame to created table as is. + +More details [can be found here](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/write_to_gpdb.html). + +But Greenplum connector support only limited number of types and almost no custom clauses (like `PARTITION BY`). +So instead of relying on Spark to create tables: + +### See example + +```python +writer = DBWriter( + connection=greenplum, + target="public.table", + options=Greenplum.WriteOptions( + if_exists="append", + # by default distribution is random + distributedBy="id", + # partitionBy is not supported + ), +) +writer.run(df) +``` + +Always prefer creating table with desired DDL **BEFORE WRITING DATA**: + +### See example + +```python +greenplum.execute( + """ + CREATE TABLE public.table ( + id int32, + business_dt timestamp(6), + value json + ) + PARTITION BY RANGE (business_dt) + DISTRIBUTED BY id + """, +) + +writer = DBWriter( + connection=greenplum, + target="public.table", + options=Greenplum.WriteOptions(if_exists="append"), +) +writer.run(df) +``` + +See Greenplum [CREATE TABLE](https://docs.vmware.com/en/VMware-Greenplum/7/greenplum-database/ref_guide-sql_commands-CREATE_TABLE.html) documentation. + +## Supported types + +See: +: * [official connector documentation](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/reference-datatype_mapping.html) + * [list of Greenplum types](https://docs.vmware.com/en/VMware-Greenplum/7/greenplum-database/ref_guide-data_types.html) + +### Numeric types + +| Greenplum type (read) | Spark type | Greenplumtype (write) | Greenplum type (create) | +|-----------------------------|----------------------------------|-----------------------------|---------------------------| +| `decimal` | `DecimalType(P=38, S=18)` | `decimal(P=38, S=18)` | `decimal` (unbounded) | +| `decimal(P=0..38)` | `DecimalType(P=0..38, S=0)` | `decimal(P=0..38, S=0)` | | +| `decimal(P=0..38, S=0..38)` | `DecimalType(P=0..38, S=0..38)` | `decimal(P=0..38, S=0..38)` | | +| `decimal(P=39.., S=0..)` | unsupported [2](#id4) | | | +| `real` | `FloatType()` | `real` | `real` | +| `double precision` | `DoubleType()` | `double precision` | `double precision` | +| `-` | `ByteType()` | unsupported | unsupported | +| `smallint` | `ShortType()` | `smallint` | `smallint` | +| `integer` | `IntegerType()` | `integer` | `integer` | +| `bigint` | `LongType()` | `bigint` | `bigint` | +| `money` | unsupported | | | +| `int4range` | | | | +| `int8range` | | | | +| `numrange` | | | | +| `int2vector` | | | | +* **[2]** Greenplum support decimal types with unlimited precision. But Spark’s `DecimalType(P, S)` supports maximum `P=38` (128 bit). It is impossible to read, write or operate with values of larger precision, this leads to an exception. + +### Temporal types + +| Greenplum type (read) | Spark type | Greenplumtype (write) | Greenplum type (create) | +|----------------------------------|----------------------------------------------------------------|-------------------------|---------------------------| +| `date` | `DateType()` | `date` | `date` | +| `time` | `TimestampType()`,
time format quirks [3](#id6) | `timestamp` | `timestamp` | +| `time(0..6)` | | | | +| `time with time zone` | | | | +| `time(0..6) with time zone` | | | | +| `timestamp` | `TimestampType()` | `timestamp` | `timestamp` | +| `timestamp(0..6)` | | | | +| `timestamp with time zone` | | | | +| `timestamp(0..6) with time zone` | | | | +| `interval` or any precision | unsupported | | | +| `daterange` | | | | +| `tsrange` | | | | +| `tstzrange` | | | | + +#### WARNING +Note that types in Greenplum and Spark have different value ranges: + +| Greenplum type | Min value | Max value | Spark type | Min value | Max value | +|------------------|-------------------------------|--------------------------------|-------------------|------------------------------|------------------------------| +| `date` | `-4713-01-01` | `5874897-01-01` | `DateType()` | `0001-01-01` | `9999-12-31` | +| `timestamp` | `-4713-01-01 00:00:00.000000` | `294276-12-31 23:59:59.999999` | `TimestampType()` | `0001-01-01 00:00:00.000000` | `9999-12-31 23:59:59.999999` | +| `time` | `00:00:00.000000` | `24:00:00.000000` | | | | + +So not all of values can be read from Greenplum to Spark. + +References: +: * [Greenplum types documentation](https://docs.vmware.com/en/VMware-Greenplum/7/greenplum-database/ref_guide-data_types.html) + * [Spark DateType documentation](https://spark.apache.org/docs/latest/api/java/org/apache/spark/sql/types/DateType.html) + * [Spark TimestampType documentation](https://spark.apache.org/docs/latest/api/java/org/apache/spark/sql/types/TimestampType.html) + +* **[3]** `time` type is the same as `timestamp` with date `1970-01-01`. So instead of reading data from Postgres like `23:59:59` it is actually read `1970-01-01 23:59:59`, and vice versa. + +### String types + +| Greenplum type (read) | Spark type | Greenplumtype (write) | Greenplum type (create) | +|---------------------------|----------------|-------------------------|---------------------------| +| `character` | `StringType()` | `text` | `text` | +| `character(N)` | | | | +| `character varying` | | | | +| `character varying(N)` | | | | +| `text` | | | | +| `xml` | | | | +| `CREATE TYPE ... AS ENUM` | | | | +| `json` | unsupported | | | +| `jsonb` | | | | + +### Binary types + +| Greenplum type (read) | Spark type | Greenplumtype (write) | Greenplum type (create) | +|-------------------------|----------------------------------|-------------------------|---------------------------| +| `boolean` | `BooleanType()` | `boolean` | `boolean` | +| `bit` | unsupported | | | +| `bit(N)` | | | | +| `bit varying` | | | | +| `bit varying(N)` | | | | +| `bytea` | unsupported [4](#id8) | | | +| `-` | `BinaryType()` | `bytea` | `bytea` | +* **[4]** Yes, that’s weird. + +### Struct types + +| Greenplum type (read) | Spark type | Greenplumtype (write) | Greenplum type (create) | +|------------------------------|----------------|-------------------------|---------------------------| +| `T[]` | unsupported | | | +| `-` | `ArrayType()` | unsupported | | +| `CREATE TYPE sometype (...)` | `StringType()` | `text` | `text` | +| `-` | `StructType()` | unsupported | | +| `-` | `MapType()` | | | + +## Unsupported types + +Columns of these types cannot be read/written by Spark: +: * `cidr` + * `inet` + * `macaddr` + * `macaddr8` + * `circle` + * `box` + * `line` + * `lseg` + * `path` + * `point` + * `polygon` + * `tsvector` + * `tsquery` + * `uuid` + +The is a way to avoid this - just cast unsupported types to `text`. But the way this can be done is not a straightforward. + +## Explicit type cast + +### `DBReader` + +Direct casting of Greenplum types is not supported by DBReader due to the connector’s implementation specifics. + +```python +reader = DBReader( + connection=greenplum, + # will fail + columns=["CAST(unsupported_column AS text)"], +) +``` + +But there is a workaround - create a view with casting unsupported column to text (or any other supported type). +For example, you can use [to_json](https://www.postgresql.org/docs/current/functions-json.html) Postgres function to convert column of any type to string representation and then parse this column on Spark side using `JSON.parse_column` method. + +```python +from pyspark.sql.types import ArrayType, IntegerType + +from onetl.connection import Greenplum +from onetl.db import DBReader +from onetl.file.format import JSON + +greenplum = Greenplum(...) + +greenplum.execute( + """ + CREATE VIEW schema.view_with_json_column AS + SELECT + id, + supported_column, + to_json(array_column) array_column_as_json, + gp_segment_id -- ! important ! + FROM + schema.table_with_unsupported_columns + """, +) + +# create dataframe using this view +reader = DBReader( + connection=greenplum, + source="schema.view_with_json_column", +) +df = reader.run() + +# Define the schema for the JSON data +json_scheme = ArrayType(IntegerType()) + +df = df.select( + df.id, + df.supported_column, + JSON().parse_column(df.array_column_as_json, json_scheme).alias("array_column"), +) +``` + +### `DBWriter` + +To write data to a `text` or `json` column in a Greenplum table, use `JSON.serialize_column` method. + +```python +from onetl.connection import Greenplum +from onetl.db import DBWriter +from onetl.file.format import JSON + +greenplum = Greenplum(...) + +greenplum.execute( + """ + CREATE TABLE schema.target_table ( + id int, + supported_column timestamp, + array_column_as_json jsonb, -- or text + ) + DISTRIBUTED BY id + """, +) + +write_df = df.select( + df.id, + df.supported_column, + JSON().serialize_column(df.array_column).alias("array_column_json"), +) + +writer = DBWriter( + connection=greenplum, + target="schema.target_table", +) +writer.run(write_df) +``` + +Then you can parse this column on Greenplum side: + +```sql +SELECT + id, + supported_column, + -- access first item of an array + array_column_as_json->0 +FROM + schema.target_table +``` diff --git a/mddocs/docs/_build/markdown/connection/db_connection/greenplum/write.md b/mddocs/docs/_build/markdown/connection/db_connection/greenplum/write.md new file mode 100644 index 000000000..fe25f225f --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/greenplum/write.md @@ -0,0 +1,47 @@ + + +# Writing to Greenplum using `DBWriter` + +For writing data to Greenplum, use `DBWriter` +with `GreenplumWriteOptions`. + +#### WARNING +Please take into account [Greenplum <-> Spark type mapping](types.md#greenplum-types). + +#### WARNING +It is always recommended to create table explicitly using [Greenplum.execute](execute.md#greenplum-execute) +instead of relying on Spark’s table DDL generation. + +This is because Spark’s DDL generator can create columns with different types than it is expected. + +## Examples + +```python +from onetl.connection import Greenplum +from onetl.db import DBWriter + +greenplum = Greenplum(...) + +df = ... # data is here + +writer = DBWriter( + connection=greenplum, + target="schema.table", + options=Greenplum.WriteOptions( + if_exists="append", + # by default distribution is random + distributedBy="id", + # partitionBy is not supported + ), +) + +writer.run(df) +``` + +## Interaction schema + +High-level schema is described in [Prerequisites](prerequisites.md#greenplum-prerequisites). You can find detailed interaction schema below. + +### Spark <-> Greenplum interaction during DBWriter.run() + +## Options diff --git a/mddocs/docs/_build/markdown/connection/db_connection/hive/connection.md b/mddocs/docs/_build/markdown/connection/db_connection/hive/connection.md new file mode 100644 index 000000000..a9e0744d3 --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/hive/connection.md @@ -0,0 +1,3 @@ + + +# Hive Connection diff --git a/mddocs/docs/_build/markdown/connection/db_connection/hive/execute.md b/mddocs/docs/_build/markdown/connection/db_connection/hive/execute.md new file mode 100644 index 000000000..33544a205 --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/hive/execute.md @@ -0,0 +1,44 @@ + + +# Executing statements in Hive + +Use `Hive.execute(...)` to execute DDL and DML operations. + +## Syntax support + +This method supports **any** query syntax supported by Hive, like: + +* ✅︎ `CREATE TABLE ...`, `CREATE VIEW ...`, and so on +* ✅︎ `LOAD DATA ...`, and so on +* ✅︎ `ALTER ...` +* ✅︎ `INSERT INTO ... SELECT ...`, and so on +* ✅︎ `DROP TABLE ...`, `DROP VIEW ...`, and so on +* ✅︎ `MSCK REPAIR TABLE ...`, and so on +* ✅︎ other statements not mentioned here +* ❌ `SET ...; SELECT ...;` - multiple statements not supported + +#### WARNING +Actually, query should be written using [SparkSQL](https://spark.apache.org/docs/latest/sql-ref-syntax.html#ddl-statements) syntax, not HiveQL. + +## Examples + +```python +from onetl.connection import Hive + +hive = Hive(...) + +hive.execute("DROP TABLE schema.table") +hive.execute( + """ + CREATE TABLE schema.table ( + id NUMBER, + key VARCHAR, + value DOUBLE + ) + PARTITION BY (business_date DATE) + STORED AS orc + """ +) +``` + +### Details diff --git a/mddocs/docs/_build/markdown/connection/db_connection/hive/index.md b/mddocs/docs/_build/markdown/connection/db_connection/hive/index.md new file mode 100644 index 000000000..bc551b745 --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/hive/index.md @@ -0,0 +1,19 @@ + + +# Hive + +# Connection + +* [Prerequisites](prerequisites.md) +* [Hive Connection](connection.md) + +# Operations + +* [Reading from Hive using `DBReader`](read.md) +* [Reading from Hive using `Hive.sql`](sql.md) +* [Writing to Hive using `DBWriter`](write.md) +* [Executing statements in Hive](execute.md) + +# For developers + +* [Hive Slots](slots.md) diff --git a/mddocs/docs/_build/markdown/connection/db_connection/hive/prerequisites.md b/mddocs/docs/_build/markdown/connection/db_connection/hive/prerequisites.md new file mode 100644 index 000000000..aa0fb49c7 --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/hive/prerequisites.md @@ -0,0 +1,128 @@ + + +# Prerequisites + +#### NOTE +onETL’s Hive connection is actually SparkSession with access to [Hive Thrift Metastore](https://docs.cloudera.com/cdw-runtime/1.5.0/hive-hms-overview/topics/hive-hms-introduction.html) +and HDFS/S3. +All data motion is made using Spark. Hive Metastore is used only to store tables and partitions metadata. + +This connector does **NOT** require Hive server. It also does **NOT** use Hive JDBC connector. + +## Version Compatibility + +* Hive Metastore version: + : * Officially declared: 0.12 - 3.1.3 (may require to add proper .jar file explicitly) + * Actually tested: 1.2.100, 2.3.10, 3.1.3 +* Spark versions: 2.3.x - 3.5.x +* Java versions: 8 - 20 + +See [official documentation](https://spark.apache.org/docs/latest/sql-data-sources-hive-tables.html). + +## Installing PySpark + +To use Hive connector you should have PySpark installed (or injected to `sys.path`) +BEFORE creating the connector instance. + +See [Spark](../../../install/spark.md#install-spark) installation instruction for more details. + +## Connecting to Hive Metastore + +#### NOTE +If you’re using managed Hadoop cluster, skip this step, as all Spark configs are should already present on the host. + +Create `$SPARK_CONF_DIR/hive-site.xml` with Hive Metastore URL: + +```xml + + + + + hive.metastore.uris + thrift://metastore.host.name:9083 + + +``` + +Create `$SPARK_CONF_DIR/core-site.xml` with warehouse location ,e.g. HDFS IPC port of Hadoop namenode, or S3 bucket address & credentials: + +HDFS + +```xml + + + + + fs.defaultFS + hdfs://myhadoopcluster:9820 + + +``` + +S3 + +```xml + + + + !-- See https://hadoop.apache.org/docs/current/hadoop-aws/tools/hadoop-aws/index.html#General_S3A_Client_configuration + + fs.defaultFS + s3a://mys3bucket/ + + + fs.s3a.bucket.mybucket.endpoint + http://s3.somain + + + fs.s3a.bucket.mybucket.connection.ssl.enabled + false + + + fs.s3a.bucket.mybucket.path.style.access + true + + + fs.s3a.bucket.mybucket.aws.credentials.provider + org.apache.hadoop.fs.s3a.SimpleAWSCredentialsProvider + + + fs.s3a.bucket.mybucket.access.key + some-user + + + fs.s3a.bucket.mybucket.secret.key + mysecrettoken + + +``` + +## Using Kerberos + +Some of Hadoop managed clusters use Kerberos authentication. In this case, you should call [kinit](https://web.mit.edu/kerberos/krb5-1.12/doc/user/user_commands/kinit.html) command +**BEFORE** starting Spark session to generate Kerberos ticket. See [Kerberos support](../../../install/kerberos.md#install-kerberos). + +Sometimes it is also required to pass keytab file to Spark config, allowing Spark executors to generate own Kerberos tickets: + +Spark 3 + +```python +SparkSession.builder + .option("spark.kerberos.access.hadoopFileSystems", "hdfs://namenode1.domain.com:9820,hdfs://namenode2.domain.com:9820") + .option("spark.kerberos.principal", "user") + .option("spark.kerberos.keytab", "/path/to/keytab") + .gerOrCreate() +``` + +Spark 2 + +```python +SparkSession.builder + .option("spark.yarn.access.hadoopFileSystems", "hdfs://namenode1.domain.com:9820,hdfs://namenode2.domain.com:9820") + .option("spark.yarn.principal", "user") + .option("spark.yarn.keytab", "/path/to/keytab") + .gerOrCreate() +``` + +See [Spark security documentation](https://spark.apache.org/docs/latest/security.html#kerberos) +for more details. diff --git a/mddocs/docs/_build/markdown/connection/db_connection/hive/read.md b/mddocs/docs/_build/markdown/connection/db_connection/hive/read.md new file mode 100644 index 000000000..2caa0ba73 --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/hive/read.md @@ -0,0 +1,92 @@ + + +# Reading from Hive using `DBReader` + +`DBReader` supports [Read Strategies](../../../strategy/index.md#strategy) for incremental data reading, +but does not support custom queries, like `JOIN`. + +## Supported DBReader features + +* ✅︎ `columns` +* ✅︎ `where` +* ✅︎ `hwm`, supported strategies: +* * ✅︎ [Snapshot Strategy](../../../strategy/snapshot_strategy.md#snapshot-strategy) +* * ✅︎ [Incremental Strategy](../../../strategy/incremental_strategy.md#incremental-strategy) +* * ✅︎ [Snapshot Batch Strategy](../../../strategy/snapshot_batch_strategy.md#snapshot-batch-strategy) +* * ✅︎ [Incremental Batch Strategy](../../../strategy/incremental_batch_strategy.md#incremental-batch-strategy) +* ❌ `hint` (is not supported by Hive) +* ❌ `df_schema` +* ❌ `options` (only Spark config params are used) + +#### WARNING +Actually, `columns`, `where` and `hwm.expression` should be written using [SparkSQL](https://spark.apache.org/docs/latest/sql-ref-syntax.html#data-retrieval-statements) syntax, +not HiveQL. + +## Examples + +Snapshot strategy: + +```python +from onetl.connection import Hive +from onetl.db import DBReader + +hive = Hive(...) + +reader = DBReader( + connection=hive, + source="schema.table", + columns=["id", "key", "CAST(value AS text) value", "updated_dt"], + where="key = 'something'", +) +df = reader.run() +``` + +Incremental strategy: + +```python +from onetl.connection import Hive +from onetl.db import DBReader +from onetl.strategy import IncrementalStrategy + +hive = Hive(...) + +reader = DBReader( + connection=hive, + source="schema.table", + columns=["id", "key", "CAST(value AS text) value", "updated_dt"], + where="key = 'something'", + hwm=DBReader.AutoDetectHWM(name="hive_hwm", expression="updated_dt"), +) + +with IncrementalStrategy(): + df = reader.run() +``` + +## Recommendations + +### Use column-based write formats + +Prefer these write formats: +: * [ORC](https://spark.apache.org/docs/latest/sql-data-sources-orc.html) + * [Parquet](https://spark.apache.org/docs/latest/sql-data-sources-parquet.html) + * [Iceberg](https://iceberg.apache.org/spark-quickstart/) + * [Hudi](https://hudi.apache.org/docs/quick-start-guide/) + * [Delta](https://docs.delta.io/latest/quick-start.html#set-up-apache-spark-with-delta-lake) + +For colum-based write formats, each file contains separated sections there column data is stored. The file footer contains +location of each column section/group. Spark can use this information to load only sections required by specific query, e.g. only selected columns, +to drastically speed up the query. + +Another advantage is high compression ratio, e.g. 10x-100x in comparison to JSON or CSV. + +### Select only required columns + +Instead of passing `"*"` in `DBReader(columns=[...])` prefer passing exact column names. +This drastically reduces the amount of data read by Spark, **if column-based file formats are used**. + +### Use partition columns in `where` clause + +Queries should include `WHERE` clause with filters on Hive partitioning columns. +This allows Spark to read only small set of files (*partition pruning*) instead of scanning the entire table, so this drastically increases performance. + +Supported operators are: `=`, `>`, `<` and `BETWEEN`, and only against some **static** value. diff --git a/mddocs/docs/_build/markdown/connection/db_connection/hive/slots.md b/mddocs/docs/_build/markdown/connection/db_connection/hive/slots.md new file mode 100644 index 000000000..1ecf2bfd0 --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/hive/slots.md @@ -0,0 +1,3 @@ + + +# Hive Slots diff --git a/mddocs/docs/_build/markdown/connection/db_connection/hive/sql.md b/mddocs/docs/_build/markdown/connection/db_connection/hive/sql.md new file mode 100644 index 000000000..13a8aa158 --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/hive/sql.md @@ -0,0 +1,68 @@ + + +# Reading from Hive using `Hive.sql` + +`Hive.sql` allows passing custom SQL query, but does not support incremental strategies. + +## Syntax support + +Only queries with the following syntax are supported: + +* ✅︎ `SELECT ... FROM ...` +* ✅︎ `WITH alias AS (...) SELECT ...` +* ❌ `SET ...; SELECT ...;` - multiple statements not supported + +#### WARNING +Actually, query should be written using [SparkSQL](https://spark.apache.org/docs/latest/sql-ref-syntax.html#data-retrieval-statements) syntax, not HiveQL. + +## Examples + +```python +from onetl.connection import Hive + +hive = Hive(...) +df = hive.sql( + """ + SELECT + id, + key, + CAST(value AS text) value, + updated_at + FROM + some.mytable + WHERE + key = 'something' + """ +) +``` + +## Recommendations + +### Use column-based write formats + +Prefer these write formats: +: * [ORC](https://spark.apache.org/docs/latest/sql-data-sources-orc.html) + * [Parquet](https://spark.apache.org/docs/latest/sql-data-sources-parquet.html) + * [Iceberg](https://iceberg.apache.org/spark-quickstart/) + * [Hudi](https://hudi.apache.org/docs/quick-start-guide/) + * [Delta](https://docs.delta.io/latest/quick-start.html#set-up-apache-spark-with-delta-lake) + +For colum-based write formats, each file contains separated sections there column data is stored. The file footer contains +location of each column section/group. Spark can use this information to load only sections required by specific query, e.g. only selected columns, +to drastically speed up the query. + +Another advantage is high compression ratio, e.g. 10x-100x in comparison to JSON or CSV. + +### Select only required columns + +Instead of passing `SELECT * FROM ...` prefer passing exact column names `SELECT col1, col2, ...`. +This drastically reduces the amount of data read by Spark, **if column-based file formats are used**. + +### Use partition columns in `where` clause + +Queries should include `WHERE` clause with filters on Hive partitioning columns. +This allows Spark to read only small set of files (*partition pruning*) instead of scanning the entire table, so this drastically increases performance. + +Supported operators are: `=`, `>`, `<` and `BETWEEN`, and only against some **static** value. + +## Details diff --git a/mddocs/docs/_build/markdown/connection/db_connection/hive/write.md b/mddocs/docs/_build/markdown/connection/db_connection/hive/write.md new file mode 100644 index 000000000..8e513e41a --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/hive/write.md @@ -0,0 +1,167 @@ + + +# Writing to Hive using `DBWriter` + +For writing data to Hive, use `DBWriter`. + +## Examples + +```python +from onetl.connection import Hive +from onetl.db import DBWriter + +hive = Hive(...) + +df = ... # data is here + +# Create dataframe with specific number of Spark partitions. +# Use the Hive partitioning columns to group the data. Create max 20 files per Hive partition. +# Also sort the data by column which most data is correlated with (e.g. user_id), reducing files size. + +num_files_per_partition = 20 +partition_columns = ["country", "business_date"] +sort_columns = ["user_id"] +write_df = df.repartition( + num_files_per_partition, + *partition_columns, + *sort_columns, +).sortWithinPartitions(*partition_columns, *sort_columns) + +writer = DBWriter( + connection=hive, + target="schema.table", + options=Hive.WriteOptions( + if_exists="append", + # Hive partitioning columns. + partitionBy=partition_columns, + ), +) + +writer.run(write_df) +``` + +## Recommendations + +### Use column-based write formats + +Prefer these write formats: +: * [ORC](https://spark.apache.org/docs/latest/sql-data-sources-orc.html) (**default**) + * [Parquet](https://spark.apache.org/docs/latest/sql-data-sources-parquet.html) + * [Iceberg](https://iceberg.apache.org/spark-quickstart/) + * [Hudi](https://hudi.apache.org/docs/quick-start-guide/) + * [Delta](https://docs.delta.io/latest/quick-start.html#set-up-apache-spark-with-delta-lake) + +#### WARNING +When using `DBWriter`, the default spark data format configured in `spark.sql.sources.default` is ignored, as `Hive.WriteOptions(format=...)` default value is explicitly set to `orc`. + +For column-based write formats, each file contains separated sections where column data is stored. The file footer contains +location of each column section/group. Spark can use this information to load only sections required by specific query, e.g. only selected columns, +to drastically speed up the query. + +Another advantage is high compression ratio, e.g. 10x-100x in comparison to JSON or CSV. + +### Use partitioning + +#### How does it work + +Hive support splitting data to partitions, which are different directories in filesystem with names like `some_col=value1/another_col=value2`. + +For example, dataframe with content like this: + +| country: string | business_date: date | user_id: int | bytes: long | +|-------------------|-----------------------|----------------|---------------| +| RU | 2024-01-01 | 1234 | 25325253525 | +| RU | 2024-01-01 | 2345 | 23234535243 | +| RU | 2024-01-02 | 1234 | 62346634564 | +| US | 2024-01-01 | 5678 | 4252345354 | +| US | 2024-01-02 | 5678 | 5474575745 | +| US | 2024-01-03 | 5678 | 3464574567 | + +With `partitionBy=["country", "business_dt"]` data will be stored as files in the following subfolders: +: * `/country=RU/business_date=2024-01-01/` + * `/country=RU/business_date=2024-01-02/` + * `/country=US/business_date=2024-01-01/` + * `/country=US/business_date=2024-01-02/` + * `/country=US/business_date=2024-01-03/` + +A separated subdirectory is created for each distinct combination of column values in the dataframe. + +Please do not confuse Spark dataframe partitions (a.k.a batches of data handled by Spark executors, usually in parallel) +and Hive partitioning (store data in different subdirectories). +Number of Spark dataframe partitions is correlated the number of files created in **each** Hive partition. +For example, Spark dataframe with 10 partitions and 5 distinct values of Hive partition columns will be saved as 5 subfolders with 10 files each = 50 files in total. +Without Hive partitioning, all the files are placed into one flat directory. + +#### But why? + +Queries which has `WHERE` clause with filters on Hive partitioning columns, like `WHERE country = 'RU' AND business_date='2024-01-01'`, will +read only files from this exact partitions, like `/country=RU/business_date=2024-01-01/`, and skip files from other partitions. + +This drastically increases performance and reduces the amount of memory used by Spark. +Consider using Hive partitioning in all tables. + +#### Which columns should I use? + +Usually Hive partitioning columns are based on event date or location, like `country: string`, `business_date: date`, `run_date: date` and so on. + +**Partition columns should contain data with low cardinality.** +Dates, small integers, strings with low number of possible values are OK. +But timestamp, float, decimals, longs (like user id), strings with lots oj unique values (like user name or email) should **NOT** be used as Hive partitioning columns. +Unlike some other databases, range and hash-based partitions are not supported. + +Partition column should be a part of a dataframe. If you want to partition values by date component of `business_dt: timestamp` column, +add a new column to dataframe like this: `df.withColumn("business_date", date(df.business_dt))`. + +### Use compression + +Using compression algorithms like `snappy`, `lz4` or `zstd` can reduce the size of files (up to 10x). + +### Prefer creating large files + +Storing millions of small files is not that HDFS and S3 are designed for. Minimal file size should be at least 10Mb, but usually it is like 128Mb+ or 256Mb+ (HDFS block size). +**NEVER** create files with few Kbytes in size. + +Number of files can be different in different cases. +On one hand, Spark Adaptive Query Execution (AQE) can merge small Spark dataframe partitions into one larger. +On the other hand, dataframes with skewed data can produce a larger number of files than expected. + +To create small amount of large files, you can reduce number of Spark dataframe partitions. +Use [df.repartition(N, columns…)](https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.DataFrame.repartition.html) function, +like this: `df.repartition(20, "col1", "col2")`. +This creates new Spark dataframe with partitions using `hash(df.col1 + df.col2) mod 20` expression, avoiding data skew. + +Note: larger dataframe partitions requires more resources (CPU, RAM) on Spark executor. The exact number of partitions +should be determined empirically, as it depends on the amount of data and available resources. + +### Sort data before writing + +Dataframe with sorted content: + +| country: string | business_date: date | user_id: int | business_dt: timestamp | bytes: long | +|-------------------|-----------------------|----------------|--------------------------|---------------| +| RU | 2024-01-01 | 1234 | 2024-01-01T11:22:33.456 | 25325253525 | +| RU | 2024-01-01 | 1234 | 2024-01-01T12:23:44.567 | 25325253525 | +| RU | 2024-01-02 | 1234 | 2024-01-01T13:25:56.789 | 34335645635 | +| US | 2024-01-01 | 2345 | 2024-01-01T10:00:00.000 | 12341 | +| US | 2024-01-02 | 2345 | 2024-01-01T15:11:22.345 | 13435 | +| US | 2024-01-03 | 2345 | 2024-01-01T20:22:33.567 | 14564 | + +Has a much better compression rate than unsorted one, e.g. 2x or even higher: + +| country: string | business_date: date | user_id: int | business_dt: timestamp | bytes: long | +|-------------------|-----------------------|----------------|--------------------------|---------------| +| RU | 2024-01-01 | 1234 | 2024-01-01T11:22:33.456 | 25325253525 | +| RU | 2024-01-01 | 6345 | 2024-12-01T23:03:44.567 | 25365 | +| RU | 2024-01-02 | 5234 | 2024-07-01T06:10:56.789 | 45643456747 | +| US | 2024-01-01 | 4582 | 2024-04-01T17:59:00.000 | 362546475 | +| US | 2024-01-02 | 2345 | 2024-09-01T04:24:22.345 | 3235 | +| US | 2024-01-03 | 3575 | 2024-03-01T21:37:33.567 | 346345764 | + +Choosing columns to sort data by is really depends on the data. If data is correlated with some specific +column, like in example above the amount of traffic is correlated with both `user_id` and `timestamp`, +use `df.sortWithinPartitions("user_id", "timestamp")` before writing the data. + +If `df.repartition(N, repartition_columns...)` is used in combination with `df.sortWithinPartitions(sort_columns...)`, +then `sort_columns` should start with `repartition_columns` or be equal to it. + +## Options diff --git a/mddocs/docs/_build/markdown/connection/db_connection/index.md b/mddocs/docs/_build/markdown/connection/db_connection/index.md new file mode 100644 index 000000000..cd8f76477 --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/index.md @@ -0,0 +1,16 @@ + + +# DB Connections + +# DB Connections + +* [Clickhouse](clickhouse/index.md) +* [Greenplum](greenplum/index.md) +* [Kafka](kafka/index.md) +* [Hive](hive/index.md) +* [MongoDB](mongodb/index.md) +* [MSSQL](mssql/index.md) +* [MySQL](mysql/index.md) +* [Oracle](oracle/index.md) +* [Postgres](postgres/index.md) +* [Teradata](teradata/index.md) diff --git a/mddocs/docs/_build/markdown/connection/db_connection/kafka/auth.md b/mddocs/docs/_build/markdown/connection/db_connection/kafka/auth.md new file mode 100644 index 000000000..000cfa210 --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/kafka/auth.md @@ -0,0 +1,3 @@ + + +# Kafka Auth diff --git a/mddocs/docs/_build/markdown/connection/db_connection/kafka/basic_auth.md b/mddocs/docs/_build/markdown/connection/db_connection/kafka/basic_auth.md new file mode 100644 index 000000000..d6153e780 --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/kafka/basic_auth.md @@ -0,0 +1,3 @@ + + +# Kafka BasicAuth diff --git a/mddocs/docs/_build/markdown/connection/db_connection/kafka/connection.md b/mddocs/docs/_build/markdown/connection/db_connection/kafka/connection.md new file mode 100644 index 000000000..12c48de50 --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/kafka/connection.md @@ -0,0 +1,3 @@ + + +# Kafka Connection diff --git a/mddocs/docs/_build/markdown/connection/db_connection/kafka/index.md b/mddocs/docs/_build/markdown/connection/db_connection/kafka/index.md new file mode 100644 index 000000000..5e5f45ba1 --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/kafka/index.md @@ -0,0 +1,31 @@ + + +# Kafka + +# Connection + +* [Prerequisites](prerequisites.md) +* [Kafka Connection](connection.md) +* [Kafka Troubleshooting](troubleshooting.md) + +# Protocols + +* [Kafka PlaintextProtocol](plaintext_protocol.md) +* [Kafka SSLProtocol](ssl_protocol.md) + +# Auth methods + +* [Kafka BasicAuth](basic_auth.md) +* [Kafka KerberosAuth](kerberos_auth.md) +* [Kafka ScramAuth](scram_auth.md) + +# Operations + +* [Reading from Kafka](read.md) +* [Writing to Kafka](write.md) + +# For developers + +* [Kafka Auth](auth.md) +* [Kafka Protocol](protocol.md) +* [Kafka Slots](slots.md) diff --git a/mddocs/docs/_build/markdown/connection/db_connection/kafka/kerberos_auth.md b/mddocs/docs/_build/markdown/connection/db_connection/kafka/kerberos_auth.md new file mode 100644 index 000000000..f2a2a540b --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/kafka/kerberos_auth.md @@ -0,0 +1,3 @@ + + +# Kafka KerberosAuth diff --git a/mddocs/docs/_build/markdown/connection/db_connection/kafka/plaintext_protocol.md b/mddocs/docs/_build/markdown/connection/db_connection/kafka/plaintext_protocol.md new file mode 100644 index 000000000..aa2c7fa78 --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/kafka/plaintext_protocol.md @@ -0,0 +1,3 @@ + + +# Kafka PlaintextProtocol diff --git a/mddocs/docs/_build/markdown/connection/db_connection/kafka/prerequisites.md b/mddocs/docs/_build/markdown/connection/db_connection/kafka/prerequisites.md new file mode 100644 index 000000000..3299560cf --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/kafka/prerequisites.md @@ -0,0 +1,65 @@ + + +# Prerequisites + +## Version Compatibility + +* Kafka server versions: + : * Officially declared: 0.10 or higher + * Actually tested: 3.2.3, 3.9.0 (only Kafka 3.x supports message headers) +* Spark versions: 2.4.x - 3.5.x +* Java versions: 8 - 17 + +See [official documentation](https://spark.apache.org/docs/latest/structured-streaming-kafka-integration.html). + +## Installing PySpark + +To use Kafka connector you should have PySpark installed (or injected to `sys.path`) +BEFORE creating the connector instance. + +See [Spark](../../../install/spark.md#install-spark) installation instruction for more details. + +## Connecting to Kafka + +### Connection address + +Kafka is a distributed service, and usually has a list of brokers you can connect to (unlike other connectors, there only one host+port can be set). +Please contact your Kafka administrator to get addresses of these brokers, as there are no defaults. + +Also Kafka has a feature called *advertised listeners* - client connects to one broker, and received list of other brokers in the clusters. +So you don’t have to pass all brokers to `addresses`, it can be some subset. Other broker addresses will be fetched directly from the cluster. + +### Connection protocol + +Kafka can support different connection protocols. List of currently supported protocols: +: * `PLAINTEXT` (not secure) + * `SSL` (secure, recommended) + +Note that specific port can listen for only one of these protocols, so it is important to set +proper port number + protocol combination. + +### Authentication mechanism + +Kafka can support different authentication mechanism (also known as [SASL](https://en.wikipedia.org/wiki/Simple_Authentication_and_Security_Layer)). + +List of currently supported mechanisms: +: * `PLAIN`. To no confuse this with `PLAINTEXT` connection protocol, onETL uses name `BasicAuth`. + * `GSSAPI`. To simplify naming, onETL uses name `KerberosAuth`. + * `SCRAM-SHA-256 or SCRAM-SHA-512` (recommended). + +Different mechanisms use different types of credentials (login + password, keytab file, and so on). + +Note that connection protocol and auth mechanism are set in pairs: +: * If you see `SASL_PLAINTEXT` this means `PLAINTEXT` connection protocol + some auth mechanism. + * If you see `SASL_SSL` this means `SSL` connection protocol + some auth mechanism. + * If you see just `PLAINTEXT` or `SSL` (**no** `SASL`), this means that authentication is disabled (anonymous access). + +Please contact your Kafka administrator to get details about enabled auth mechanism in a specific Kafka instance. + +### Required grants + +Ask your Kafka administrator to set following grants for a user, *if Kafka instance uses ACL*: +: * `Describe` + `Read` for reading data from Kafka (Consumer). + * `Describe` + `Write` for writing data from Kafka (Producer). + +More details can be found in [documentation](https://kafka.apache.org/documentation/#operations_in_kafka). diff --git a/mddocs/docs/_build/markdown/connection/db_connection/kafka/protocol.md b/mddocs/docs/_build/markdown/connection/db_connection/kafka/protocol.md new file mode 100644 index 000000000..135322bfb --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/kafka/protocol.md @@ -0,0 +1,3 @@ + + +# Kafka Protocol diff --git a/mddocs/docs/_build/markdown/connection/db_connection/kafka/read.md b/mddocs/docs/_build/markdown/connection/db_connection/kafka/read.md new file mode 100644 index 000000000..2836aaa1f --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/kafka/read.md @@ -0,0 +1,123 @@ + + +# Reading from Kafka + +Data can be read from Kafka to Spark using `DBReader`. +It also supports [Read Strategies](../../../strategy/index.md#strategy) for incremental data reading. + +## Supported DBReader features + +* ❌ `columns` (is not supported by Kafka) +* ❌ `where` (is not supported by Kafka) +* ✅︎ `hwm`, supported strategies: +* * ✅︎ [Snapshot Strategy](../../../strategy/snapshot_strategy.md#snapshot-strategy) +* * ✅︎ [Incremental Strategy](../../../strategy/incremental_strategy.md#incremental-strategy) +* * ❌ [Snapshot Batch Strategy](../../../strategy/snapshot_batch_strategy.md#snapshot-batch-strategy) +* * ❌ [Incremental Batch Strategy](../../../strategy/incremental_batch_strategy.md#incremental-batch-strategy) +* ❌ `hint` (is not supported by Kafka) +* ❌ `df_schema` (see note below) +* ✅︎ `options` (see `Kafka.ReadOptions`) + +## Dataframe schema + +Unlike other DB connections, Kafka does not have concept of columns. +All the topics messages have the same set of fields, see structure below: + +```text +root +|-- key: binary (nullable = true) +|-- value: binary (nullable = true) +|-- topic: string (nullable = false) +|-- partition: integer (nullable = false) +|-- offset: integer (nullable = false) +|-- timestamp: timestamp (nullable = false) +|-- timestampType: integer (nullable = false) +|-- headers: struct (nullable = true) + |-- key: string (nullable = false) + |-- value: binary (nullable = true) +``` + +`headers` field is present in the dataframe only if `Kafka.ReadOptions(include_headers=True)` is passed (compatibility with Kafka 1.x). + +## Value deserialization + +To read `value` or `key` of other type than bytes (e.g. struct or integer), users have to deserialize values manually. + +This could be done using following methods: +: * `Avro.parse_column` + * `JSON.parse_column` + * `CSV.parse_column` + * `XML.parse_column` + +## Examples + +Snapshot strategy, `value` is Avro binary data: + +```python +from onetl.connection import Kafka +from onetl.db import DBReader, DBWriter +from onetl.file.format import Avro +from pyspark.sql.functions import decode + +# read all topic data from Kafka +kafka = Kafka(...) +reader = DBReader(connection=kafka, source="avro_topic") +read_df = reader.run() + +# parse Avro format to Spark struct +avro = Avro( + schema_dict={ + "type": "record", + "name": "Person", + "fields": [ + {"name": "name", "type": "string"}, + {"name": "age", "type": "int"}, + ], + } +) +deserialized_df = read_df.select( + # cast binary key to string + decode("key", "UTF-8").alias("key"), + avro.parse_column("value"), +) +``` + +Incremental strategy, `value` is JSON string: + +#### NOTE +Currently Kafka connector does support only HWMs based on `offset` field. Other fields, like `timestamp`, are not yet supported. + +```python +from onetl.connection import Kafka +from onetl.db import DBReader, DBWriter +from onetl.file.format import JSON +from pyspark.sql.functions import decode + +kafka = Kafka(...) + +# read only new data from Kafka topic +reader = DBReader( + connection=kafka, + source="topic_name", + hwm=DBReader.AutoDetectHWM(name="kafka_hwm", expression="offset"), +) + +with IncrementalStrategy(): + read_df = reader.run() + +# parse JSON format to Spark struct +json = JSON() +schema = StructType( + [ + StructField("name", StringType(), nullable=True), + StructField("age", IntegerType(), nullable=True), + ], +) +deserialized_df = read_df.select( + # cast binary key to string + decode("key", "UTF-8").alias("key"), + json.parse_column("value", json), +) +``` + +## Options diff --git a/mddocs/docs/_build/markdown/connection/db_connection/kafka/scram_auth.md b/mddocs/docs/_build/markdown/connection/db_connection/kafka/scram_auth.md new file mode 100644 index 000000000..a850d28ad --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/kafka/scram_auth.md @@ -0,0 +1,3 @@ + + +# Kafka ScramAuth diff --git a/mddocs/docs/_build/markdown/connection/db_connection/kafka/slots.md b/mddocs/docs/_build/markdown/connection/db_connection/kafka/slots.md new file mode 100644 index 000000000..2dc47acf7 --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/kafka/slots.md @@ -0,0 +1,3 @@ + + +# Kafka Slots diff --git a/mddocs/docs/_build/markdown/connection/db_connection/kafka/ssl_protocol.md b/mddocs/docs/_build/markdown/connection/db_connection/kafka/ssl_protocol.md new file mode 100644 index 000000000..35e661a6a --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/kafka/ssl_protocol.md @@ -0,0 +1,3 @@ + + +# Kafka SSLProtocol diff --git a/mddocs/docs/_build/markdown/connection/db_connection/kafka/troubleshooting.md b/mddocs/docs/_build/markdown/connection/db_connection/kafka/troubleshooting.md new file mode 100644 index 000000000..cd15b5ba7 --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/kafka/troubleshooting.md @@ -0,0 +1,10 @@ + + +# Kafka Troubleshooting + +#### NOTE +General guide: [Troubleshooting](../../../troubleshooting/index.md#troubleshooting). + +## Cannot connect using `SSL` protocol + +Please check that certificate files are not Base-64 encoded. diff --git a/mddocs/docs/_build/markdown/connection/db_connection/kafka/write.md b/mddocs/docs/_build/markdown/connection/db_connection/kafka/write.md new file mode 100644 index 000000000..48e6e2b3e --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/kafka/write.md @@ -0,0 +1,64 @@ + + +# Writing to Kafka + +For writing data to Kafka, use `DBWriter` with specific options (see below). + +## Dataframe schema + +Unlike other DB connections, Kafka does not have concept of columns. +All the topics messages have the same set of fields. Only some of them can be written: + +```text +root +|-- key: binary (nullable = true) +|-- value: binary (nullable = true) +|-- headers: struct (nullable = true) + |-- key: string (nullable = false) + |-- value: binary (nullable = true) +``` + +`headers` can be passed only with `Kafka.WriteOptions(include_headers=True)` (compatibility with Kafka 1.x). + +Field `topic` should not be present in the dataframe, as it is passed to `DBWriter(target=...)`. + +Other fields, like `partition`, `offset`, `timestamp` are set by Kafka, and cannot be passed explicitly. + +## Value serialization + +To write `value` or `key` of other type than bytes (e.g. struct or integer), users have to serialize values manually. + +This could be done using following methods: +: * `Avro.serialize_column` + * `JSON.serialize_column` + * `CSV.serialize_column` + +## Examples + +Convert `value` to JSON string, and write to Kafka: + +```python +from onetl.connection import Kafka +from onetl.db import DBWriter +from onetl.file.format import JSON + +df = ... # original data is here + +# serialize struct data as JSON +json = JSON() +write_df = df.select( + df.key, + json.serialize_column(df.value), +) + +# write data to Kafka +kafka = Kafka(...) + +writer = DBWriter( + connection=kafka, + target="topic_name", +) +writer.run(write_df) +``` + +## Options diff --git a/mddocs/docs/_build/markdown/connection/db_connection/mongodb/connection.md b/mddocs/docs/_build/markdown/connection/db_connection/mongodb/connection.md new file mode 100644 index 000000000..c4af27d58 --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/mongodb/connection.md @@ -0,0 +1,3 @@ + + +# MongoDB Connection diff --git a/mddocs/docs/_build/markdown/connection/db_connection/mongodb/index.md b/mddocs/docs/_build/markdown/connection/db_connection/mongodb/index.md new file mode 100644 index 000000000..45ab522a3 --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/mongodb/index.md @@ -0,0 +1,18 @@ + + +# MongoDB + +# Connection + +* [Prerequisites](prerequisites.md) +* [MongoDB Connection](connection.md) + +# Operations + +* [Reading from MongoDB using `DBReader`](read.md) +* [Reading from MongoDB using `MongoDB.pipeline`](pipeline.md) +* [Writing to MongoDB using `DBWriter`](write.md) + +# Troubleshooting + +* [MongoDB <-> Spark type mapping](types.md) diff --git a/mddocs/docs/_build/markdown/connection/db_connection/mongodb/pipeline.md b/mddocs/docs/_build/markdown/connection/db_connection/mongodb/pipeline.md new file mode 100644 index 000000000..02a77f855 --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/mongodb/pipeline.md @@ -0,0 +1,19 @@ + + +# Reading from MongoDB using `MongoDB.pipeline` + +`MongoDB.sql` allows passing custom pipeline, +but does not support incremental strategies. + +#### WARNING +Please take into account [MongoDB <-> Spark type mapping](types.md#mongodb-types) + +## Recommendations + +### Pay attention to `pipeline` value + +Instead of filtering data on Spark side using `df.filter(df.column == 'value')` pass proper `mongodb.pipeline(..., pipeline={"$match": {"column": {"$eq": "value"}}})` value. +This both reduces the amount of data send from MongoDB to Spark, and may also improve performance of the query. +Especially if there are indexes for columns used in `pipeline` value. + +## References diff --git a/mddocs/docs/_build/markdown/connection/db_connection/mongodb/prerequisites.md b/mddocs/docs/_build/markdown/connection/db_connection/mongodb/prerequisites.md new file mode 100644 index 000000000..4572fb4f6 --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/mongodb/prerequisites.md @@ -0,0 +1,72 @@ + + +# Prerequisites + +## Version Compatibility + +* MongoDB server versions: + : * Officially declared: 4.0 or higher + * Actually tested: 4.0.0, 8.0.4 +* Spark versions: 3.2.x - 3.5.x +* Java versions: 8 - 20 + +See [official documentation](https://www.mongodb.com/docs/spark-connector/). + +## Installing PySpark + +To use MongoDB connector you should have PySpark installed (or injected to `sys.path`) +BEFORE creating the connector instance. + +See [Spark](../../../install/spark.md#install-spark) installation instruction for more details. + +## Connecting to MongoDB + +### Connection host + +It is possible to connect to MongoDB host by using either DNS name of host or it’s IP address. + +It is also possible to connect to MongoDB shared cluster: + +```python +mongo = MongoDB( + host="master.host.or.ip", + user="user", + password="*****", + database="target_database", + spark=spark, + extra={ + # read data from secondary cluster node, switch to primary if not available + "readPreference": "secondaryPreferred", + }, +) +``` + +Supported `readPreference` values are described in [official documentation](https://www.mongodb.com/docs/manual/core/read-preference/). + +### Connection port + +Connection is usually performed to port `27017`. Port may differ for different MongoDB instances. +Please ask your MongoDB administrator to provide required information. + +### Required grants + +Ask your MongoDB cluster administrator to set following grants for a user, +used for creating a connection: + +Read + Write + +```js +// allow writing data to specific database +db.grantRolesToUser("username", [{db: "somedb", role: "readWrite"}]) +``` + +Read only + +```js +// allow reading data from specific database +db.grantRolesToUser("username", [{db: "somedb", role: "read"}]) +``` + +See: +: * [db.grantRolesToUser documentation](https://www.mongodb.com/docs/manual/reference/method/db.grantRolesToUser) + * [MongoDB builtin roles](https://www.mongodb.com/docs/manual/reference/built-in-roles) diff --git a/mddocs/docs/_build/markdown/connection/db_connection/mongodb/read.md b/mddocs/docs/_build/markdown/connection/db_connection/mongodb/read.md new file mode 100644 index 000000000..f51f362e7 --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/mongodb/read.md @@ -0,0 +1,127 @@ + + +# Reading from MongoDB using `DBReader` + +`DBReader` supports [Read Strategies](../../../strategy/index.md#strategy) for incremental data reading, +but does not support custom pipelines, e.g. aggregation. + +#### WARNING +Please take into account [MongoDB <-> Spark type mapping](types.md#mongodb-types) + +## Supported DBReader features + +* ❌ `columns` (for now, all document fields are read) +* ✅︎ `where` (passed to `{"$match": ...}` aggregation pipeline) +* ✅︎ `hwm`, supported strategies: +* * ✅︎ [Snapshot Strategy](../../../strategy/snapshot_strategy.md#snapshot-strategy) +* * ✅︎ [Incremental Strategy](../../../strategy/incremental_strategy.md#incremental-strategy) +* * ✅︎ [Snapshot Batch Strategy](../../../strategy/snapshot_batch_strategy.md#snapshot-batch-strategy) +* * ✅︎ [Incremental Batch Strategy](../../../strategy/incremental_batch_strategy.md#incremental-batch-strategy) +* * Note that `expression` field of HWM can only be a field name, not a custom expression +* ✅︎ `hint` (see [official documentation](https://www.mongodb.com/docs/v5.0/reference/operator/meta/hint/)) +* ✅︎ `df_schema` (mandatory) +* ✅︎ `options` (see `MongoDB.ReadOptions`) + +## Examples + +Snapshot strategy: + +```python +from onetl.connection import MongoDB +from onetl.db import DBReader + +from pyspark.sql.types import ( + StructType, + StructField, + IntegerType, + StringType, + TimestampType, +) + +mongodb = MongoDB(...) + +# mandatory +df_schema = StructType( + [ + StructField("_id", StringType()), + StructField("some", StringType()), + StructField( + "field", + StructType( + [ + StructField("nested", IntegerType()), + ], + ), + ), + StructField("updated_dt", TimestampType()), + ] +) + +reader = DBReader( + connection=mongodb, + source="some_collection", + df_schema=df_schema, + where={"field": {"$eq": 123}}, + hint={"field": 1}, + options=MongoDBReadOptions(batchSize=10000), +) +df = reader.run() +``` + +Incremental strategy: + +```python +from onetl.connection import MongoDB +from onetl.db import DBReader +from onetl.strategy import IncrementalStrategy + +from pyspark.sql.types import ( + StructType, + StructField, + IntegerType, + StringType, + TimestampType, +) + +mongodb = MongoDB(...) + +# mandatory +df_schema = StructType( + [ + StructField("_id", StringType()), + StructField("some", StringType()), + StructField( + "field", + StructType( + [ + StructField("nested", IntegerType()), + ], + ), + ), + StructField("updated_dt", TimestampType()), + ] +) + +reader = DBReader( + connection=mongodb, + source="some_collection", + df_schema=df_schema, + where={"field": {"$eq": 123}}, + hint={"field": 1}, + hwm=DBReader.AutoDetectHWM(name="mongodb_hwm", expression="updated_dt"), + options=MongoDBReadOptions(batchSize=10000), +) + +with IncrementalStrategy(): + df = reader.run() +``` + +## Recommendations + +### Pay attention to `where` value + +Instead of filtering data on Spark side using `df.filter(df.column == 'value')` pass proper `DBReader(where={"column": {"$eq": "value"}})` clause. +This both reduces the amount of data send from MongoDB to Spark, and may also improve performance of the query. +Especially if there are indexes for columns used in `where` clause. + +## Read options diff --git a/mddocs/docs/_build/markdown/connection/db_connection/mongodb/types.md b/mddocs/docs/_build/markdown/connection/db_connection/mongodb/types.md new file mode 100644 index 000000000..2d75b5e52 --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/mongodb/types.md @@ -0,0 +1,214 @@ + + +# MongoDB <-> Spark type mapping + +#### NOTE +The results below are valid for Spark 3.5.5, and may differ on other Spark versions. + +## Type detection & casting + +Spark’s DataFrames always have a `schema` which is a list of fields with corresponding Spark types. All operations on a field are performed using field type. + +MongoDB is, by design, \_\_schemaless_\_. So there are 2 ways how this can be handled: + +* User provides DataFrame schema explicitly: + + ### See example + + ```python + from onetl.connection import MongoDB + from onetl.db import DBReader + + from pyspark.sql.types import ( + StructType, + StructField, + IntegerType, + StringType, + TimestampType, + ) + + mongodb = MongoDB(...) + + df_schema = StructType( + [ + StructField("_id", StringType()), + StructField("some", StringType()), + StructField( + "field", + StructType( + [ + StructField("nested", IntegerType()), + ] + ), + ), + ] + ) + + reader = DBReader( + connection=mongodb, + source="some_collection", + df_schema=df_schema, + ) + df = reader.run() + + # or + + df = mongodb.pipeline( + collection="some_collection", + df_schema=df_schema, + ) + ``` +* Rely on MongoDB connector schema infer: + ```python + df = mongodb.pipeline(collection="some_collection") + ``` + + In this case MongoDB connector read a sample of collection documents, and build DataFrame schema based on document fields and values. + +It is highly recommended to pass `df_schema` explicitly, to avoid type conversion issues. + +### References + +Here you can find source code with type conversions: + +* [MongoDB -> Spark](https://github.com/mongodb/mongo-spark/blob/r10.4.1/src/main/java/com/mongodb/spark/sql/connector/schema/InferSchema.java#L214-L260) +* [Spark -> MongoDB](https://github.com/mongodb/mongo-spark/blob/r10.4.1/src/main/java/com/mongodb/spark/sql/connector/schema/RowToBsonDocumentConverter.java#L157-L260) + +## Supported types + +See [official documentation](https://www.mongodb.com/docs/manual/reference/bson-types/) + +### Numeric types + +| MongoDB type (read) | Spark type | MongoDB type (write) | +|-----------------------|---------------------------|------------------------| +| `Decimal128` | `DecimalType(P=34, S=32)` | `Decimal128` | +| `-` | `FloatType()` | `Double` | +| `Double` | `DoubleType()` | | +| `-` | `ByteType()` | `Int32` | +| `-` | `ShortType()` | | +| `Int32` | `IntegerType()` | | +| `Int64` | `LongType()` | `Int64` | + +### Temporal types + +| MongoDB type (read) | Spark type | MongoDB type (write) | +|-----------------------|---------------------------------|-------------------------------------------------------------------| +| `-` | `DateType()`, days | `Date`, milliseconds | +| `Date`, milliseconds | `TimestampType()`, microseconds | `Date`, milliseconds,
**precision loss** [2](#id2) | +| `Timestamp`, seconds | `TimestampType()`, microseconds | `Date`, milliseconds | +| `-` | `TimestampNTZType()` | unsupported | +| `-` | `DayTimeIntervalType()` | | + +#### WARNING +Note that types in MongoDB and Spark have different value ranges: + +| MongoDB type | Min value | Max value | Spark type | Min value | Max value | +|----------------|-----------------------|-----------------------|-------------------|------------------------------|------------------------------| +| `Date` | -290 million years | 290 million years | `TimestampType()` | `0001-01-01 00:00:00.000000` | `9999-12-31 23:59:59.999999` | +| `Timestamp` | `1970-01-01 00:00:00` | `2106-02-07 09:28:16` | | | | + +So not all values can be read from MongoDB to Spark, and can written from Spark DataFrame to MongoDB. + +References: +: * [MongoDB Date type documentation](https://www.mongodb.com/docs/manual/reference/bson-types/#date) + * [MongoDB Timestamp documentation](https://www.mongodb.com/docs/manual/reference/bson-types/#timestamps) + * [Spark DateType documentation](https://spark.apache.org/docs/latest/api/java/org/apache/spark/sql/types/DateType.html) + * [Spark TimestampType documentation](https://spark.apache.org/docs/latest/api/java/org/apache/spark/sql/types/TimestampType.html) + +* **[2]** MongoDB `Date` type has precision up to milliseconds (`23:59:59.999`). Inserting data with microsecond precision (`23:59:59.999999`) will lead to **throwing away microseconds**. + +### String types + +Note: fields of deprecated MongoDB type `Symbol` are excluded during read. + +| MongoDB type (read) | Spark type | MongoDB type (write) | +|-----------------------|----------------|------------------------| +| `String` | `StringType()` | `String` | +| `Code` | | | +| `RegExp` | | | + +### Binary types + +| MongoDB type (read) | Spark type | MongoDB type (write) | +|-----------------------|-----------------|------------------------| +| `Boolean` | `BooleanType()` | `Boolean` | +| `Binary` | `BinaryType()` | `Binary` | + +### Struct types + +| MongoDB type (read) | Spark type | MongoDB type (write) | +|-----------------------|---------------------|------------------------| +| `Array[T]` | `ArrayType(T)` | `Array[T]` | +| `Object[...]` | `StructType([...])` | `Object[...]` | +| `-` | `MapType(...)` | | + +### Special types + +| MongoDB type (read) | Spark type | MongoDB type (write) | +|-----------------------|-------------------------------------------------------|-------------------------------------| +| `ObjectId` | `StringType()` | `String` | +| `MaxKey` | | | +| `MinKey` | | | +| `Null` | `NullType()` | `Null` | +| `Undefined` | | | +| `DBRef` | `StructType([$ref: StringType(), $id: StringType()])` | `Object[$ref: String, $id: String]` | + +## Explicit type cast + +### `DBReader` + +Currently it is not possible to cast field types using `DBReader`. But this can be done using `MongoDB.pipeline`. + +### `MongoDB.pipeline` + +You can use `$project` aggregation to cast field types: + +```python +from pyspark.sql.types import IntegerType, StructField, StructType + +from onetl.connection import MongoDB +from onetl.db import DBReader + +mongodb = MongoDB(...) + +df = mongodb.pipeline( + collection="my_collection", + pipeline=[ + { + "$project": { + # convert unsupported_field to string + "unsupported_field_str": { + "$convert": { + "input": "$unsupported_field", + "to": "string", + }, + }, + # skip unsupported_field from result + "unsupported_field": 0, + } + } + ], +) + +# cast field content to proper Spark type +df = df.select( + df.id, + df.supported_field, + # explicit cast + df.unsupported_field_str.cast("integer").alias("parsed_integer"), +) +``` + +### `DBWriter` + +Convert dataframe field to string on Spark side, and then write it to MongoDB: + +```python +df = df.select( + df.id, + df.unsupported_field.cast("string").alias("array_field_json"), +) + +writer.run(df) +``` diff --git a/mddocs/docs/_build/markdown/connection/db_connection/mongodb/write.md b/mddocs/docs/_build/markdown/connection/db_connection/mongodb/write.md new file mode 100644 index 000000000..14703402f --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/mongodb/write.md @@ -0,0 +1,33 @@ + + +# Writing to MongoDB using `DBWriter` + +For writing data to MongoDB, use `DBWriter`. + +#### WARNING +Please take into account [MongoDB <-> Spark type mapping](types.md#mongodb-types) + +## Examples + +```python +from onetl.connection import MongoDB +from onetl.db import DBWriter + +mongodb = MongoDB(...) + +df = ... # data is here + +writer = DBWriter( + connection=mongodb, + target="schema.table", + options=MongoDB.WriteOptions( + if_exists="append", + ), +) + +writer.run(df) +``` + +## Write options + +Method above accepts `MongoDB.WriteOptions` diff --git a/mddocs/docs/_build/markdown/connection/db_connection/mssql/connection.md b/mddocs/docs/_build/markdown/connection/db_connection/mssql/connection.md new file mode 100644 index 000000000..9148ad994 --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/mssql/connection.md @@ -0,0 +1,3 @@ + + +# MSSQL connection diff --git a/mddocs/docs/_build/markdown/connection/db_connection/mssql/execute.md b/mddocs/docs/_build/markdown/connection/db_connection/mssql/execute.md new file mode 100644 index 000000000..f0f40a5f6 --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/mssql/execute.md @@ -0,0 +1,91 @@ + + +# Executing statements in MSSQL + +#### WARNING +Methods below **read all the rows** returned from DB **to Spark driver memory**, and then convert them to DataFrame. + +Do **NOT** use them to read large amounts of data. Use [DBReader](read.md#mssql-read) or [MSSQL.sql](sql.md#mssql-sql) instead. + +## How to + +There are 2 ways to execute some statement in MSSQL + +### Use `MSSQL.fetch` + +Use this method to perform some `SELECT` query which returns **small number or rows**, like reading +MSSQL config, or reading data from some reference table. Method returns Spark DataFrame. + +Method accepts `MSSQL.FetchOptions`. + +Connection opened using this method should be then closed with `connection.close()` or `with connection:`. + +#### WARNING +Please take into account [MSSQL <-> Spark type mapping](types.md#mssql-types). + +#### Syntax support + +This method supports **any** query syntax supported by MSSQL, like: + +* ✅︎ `SELECT ... FROM ...` +* ✅︎ `WITH alias AS (...) SELECT ...` +* ✅︎ `SELECT func(arg1, arg2) FROM DUAL` - call function +* ❌ `SET ...; SELECT ...;` - multiple statements not supported + +#### Examples + +```python +from onetl.connection import MSSQL + +mssql = MSSQL(...) + +df = mssql.fetch( + "SELECT value FROM some.reference_table WHERE key = 'some_constant'", + options=MSSQL.FetchOptions(queryTimeout=10), +) +mssql.close() +value = df.collect()[0][0] # get value from first row and first column +``` + +### Use `MSSQL.execute` + +Use this method to execute DDL and DML operations. Each method call runs operation in a separated transaction, and then commits it. + +Method accepts `MSSQL.ExecuteOptions`. + +Connection opened using this method should be then closed with `connection.close()` or `with connection:`. + +#### Syntax support + +This method supports **any** query syntax supported by MSSQL, like: + +* ✅︎ `CREATE TABLE ...`, `CREATE VIEW ...` +* ✅︎ `ALTER ...` +* ✅︎ `INSERT INTO ... AS SELECT ...` +* ✅︎ `DROP TABLE ...`, `DROP VIEW ...`, `TRUNCATE TABLE`, and so on +* ✅︎ `EXEC procedure(arg1, arg2) ...` or `{call procedure(arg1, arg2)}` - special syntax for calling procedure +* ✅︎ `DECLARE ... BEGIN ... END` - execute PL/SQL statement +* ✅︎ other statements not mentioned here +* ❌ `SET ...; SELECT ...;` - multiple statements not supported + +#### Examples + +```python +from onetl.connection import MSSQL + +mssql = MSSQL(...) + +mssql.execute("DROP TABLE schema.table") +mssql.execute( + """ + CREATE TABLE schema.table ( + id bigint GENERATED ALWAYS AS IDENTITY, + key VARCHAR2(4000), + value NUMBER + ) + """, + options=MSSQL.ExecuteOptions(queryTimeout=10), +) +``` + +## Options diff --git a/mddocs/docs/_build/markdown/connection/db_connection/mssql/index.md b/mddocs/docs/_build/markdown/connection/db_connection/mssql/index.md new file mode 100644 index 000000000..a35b4acbb --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/mssql/index.md @@ -0,0 +1,19 @@ + + +# MSSQL + +# Connection + +* [Prerequisites](prerequisites.md) +* [MSSQL connection](connection.md) + +# Operations + +* [Reading from MSSQL using `DBReader`](read.md) +* [Reading from MSSQL using `MSSQL.sql`](sql.md) +* [Writing to MSSQL using `DBWriter`](write.md) +* [Executing statements in MSSQL](execute.md) + +# Troubleshooting + +* [MSSQL <-> Spark type mapping](types.md) diff --git a/mddocs/docs/_build/markdown/connection/db_connection/mssql/prerequisites.md b/mddocs/docs/_build/markdown/connection/db_connection/mssql/prerequisites.md new file mode 100644 index 000000000..e103ced3e --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/mssql/prerequisites.md @@ -0,0 +1,78 @@ + + +# Prerequisites + +## Version Compatibility + +* SQL Server versions: + : * Officially declared: 2016 - 2022 + * Actually tested: 2017, 2022 +* Spark versions: 2.3.x - 3.5.x +* Java versions: 8 - 20 + +See [official documentation](https://learn.microsoft.com/en-us/sql/connect/jdbc/system-requirements-for-the-jdbc-driver) +and [official compatibility matrix](https://learn.microsoft.com/en-us/sql/connect/jdbc/microsoft-jdbc-driver-for-sql-server-support-matrix). + +## Installing PySpark + +To use MSSQL connector you should have PySpark installed (or injected to `sys.path`) +BEFORE creating the connector instance. + +See [Spark](../../../install/spark.md#install-spark) installation instruction for more details. + +## Connecting to MSSQL + +### Connection port + +Connection is usually performed to port 1433. Port may differ for different MSSQL instances. +Please ask your MSSQL administrator to provide required information. + +For named MSSQL instances (`instanceName` option), [port number is optional](https://learn.microsoft.com/en-us/sql/connect/jdbc/building-the-connection-url?view=sql-server-ver16#named-and-multiple-sql-server-instances), and could be omitted. + +### Connection host + +It is possible to connect to MSSQL by using either DNS name of host or it’s IP address. + +If you’re using MSSQL cluster, it is currently possible to connect only to **one specific node**. +Connecting to multiple nodes to perform load balancing, as well as automatic failover to new master/replica are not supported. + +### Required grants + +Ask your MSSQL cluster administrator to set following grants for a user, +used for creating a connection: + +Read + Write (schema is owned by user) + +```sql +-- allow creating tables for user +GRANT CREATE TABLE TO username; + +-- allow read & write access to specific table +GRANT SELECT, INSERT ON username.mytable TO username; + +-- only if if_exists="replace_entire_table" is used: +-- allow dropping/truncating tables in any schema +GRANT ALTER ON username.mytable TO username; +``` + +Read + Write (schema is not owned by user) + +```sql +-- allow creating tables for user +GRANT CREATE TABLE TO username; + +-- allow managing tables in specific schema, and inserting data to tables +GRANT ALTER, SELECT, INSERT ON SCHEMA::someschema TO username; +``` + +Read only + +```sql +-- allow read access to specific table +GRANT SELECT ON someschema.mytable TO username; +``` + +More details can be found in official documentation: +: * [GRANT ON DATABASE](https://learn.microsoft.com/en-us/sql/t-sql/statements/grant-database-permissions-transact-sql) + * [GRANT ON OBJECT](https://learn.microsoft.com/en-us/sql/t-sql/statements/grant-object-permissions-transact-sql) + * [GRANT ON SCHEMA](https://learn.microsoft.com/en-us/sql/t-sql/statements/grant-schema-permissions-transact-sql) diff --git a/mddocs/docs/_build/markdown/connection/db_connection/mssql/read.md b/mddocs/docs/_build/markdown/connection/db_connection/mssql/read.md new file mode 100644 index 000000000..2572b365c --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/mssql/read.md @@ -0,0 +1,78 @@ + + +# Reading from MSSQL using `DBReader` + +`DBReader` supports [Read Strategies](../../../strategy/index.md#strategy) for incremental data reading, +but does not support custom queries, like `JOIN`. + +#### WARNING +Please take into account [MSSQL <-> Spark type mapping](types.md#mssql-types) + +## Supported DBReader features + +* ✅︎ `columns` +* ✅︎ `where` +* ✅︎ `hwm`, supported strategies: +* * ✅︎ [Snapshot Strategy](../../../strategy/snapshot_strategy.md#snapshot-strategy) +* * ✅︎ [Incremental Strategy](../../../strategy/incremental_strategy.md#incremental-strategy) +* * ✅︎ [Snapshot Batch Strategy](../../../strategy/snapshot_batch_strategy.md#snapshot-batch-strategy) +* * ✅︎ [Incremental Batch Strategy](../../../strategy/incremental_batch_strategy.md#incremental-batch-strategy) +* ❌ `hint` (MSSQL does support hints, but DBReader not, at least for now) +* ❌ `df_schema` +* ✅︎ `options` (see `MSSQL.ReadOptions`) + +## Examples + +Snapshot strategy: + +```python +from onetl.connection import MSSQL +from onetl.db import DBReader + +mssql = MSSQL(...) + +reader = DBReader( + connection=mssql, + source="schema.table", + columns=["id", "key", "CAST(value AS text) value", "updated_dt"], + where="key = 'something'", + options=MSSQL.ReadOptions(partitionColumn="id", numPartitions=10), +) +df = reader.run() +``` + +Incremental strategy: + +```python +from onetl.connection import MSSQL +from onetl.db import DBReader +from onetl.strategy import IncrementalStrategy + +mssql = MSSQL(...) + +reader = DBReader( + connection=mssql, + source="schema.table", + columns=["id", "key", "CAST(value AS text) value", "updated_dt"], + where="key = 'something'", + hwm=DBReader.AutoDetectHWM(name="mssql_hwm", expression="updated_dt"), + options=MSSQL.ReadOptions(partitionColumn="id", numPartitions=10), +) + +with IncrementalStrategy(): + df = reader.run() +``` + +## Recommendations + +### Select only required columns + +Instead of passing `"*"` in `DBReader(columns=[...])` prefer passing exact column names. This reduces the amount of data passed from MSSQL to Spark. + +### Pay attention to `where` value + +Instead of filtering data on Spark side using `df.filter(df.column == 'value')` pass proper `DBReader(where="column = 'value'")` clause. +This both reduces the amount of data send from MSSQL to Spark, and may also improve performance of the query. +Especially if there are indexes or partitions for columns used in `where` clause. + +## Options diff --git a/mddocs/docs/_build/markdown/connection/db_connection/mssql/sql.md b/mddocs/docs/_build/markdown/connection/db_connection/mssql/sql.md new file mode 100644 index 000000000..c0a1ce365 --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/mssql/sql.md @@ -0,0 +1,62 @@ + + +# Reading from MSSQL using `MSSQL.sql` + +`MSSQL.sql` allows passing custom SQL query, but does not support incremental strategies. + +#### WARNING +Please take into account [MSSQL <-> Spark type mapping](types.md#mssql-types) + +#### WARNING +Statement is executed in **read-write** connection, so if you’re calling some functions/procedures with DDL/DML statements inside, +they can change data in your database. + +## Syntax support + +Only queries with the following syntax are supported: + +* ✅︎ `SELECT ... FROM ...` +* ❌ `WITH alias AS (...) SELECT ...` +* ❌ `SET ...; SELECT ...;` - multiple statements not supported + +## Examples + +```python +from onetl.connection import MSSQL + +mssql = MSSQL(...) +df = mssql.sql( + """ + SELECT + id, + key, + CAST(value AS text) value, + updated_at + FROM + some.mytable + WHERE + key = 'something' + """, + options=MSSQL.SQLOptions( + partitionColumn="id", + numPartitions=10, + lowerBound=0, + upperBound=1000, + ), +) +``` + +## Recommendations + +### Select only required columns + +Instead of passing `SELECT * FROM ...` prefer passing exact column names `SELECT col1, col2, ...`. +This reduces the amount of data passed from MSSQL to Spark. + +### Pay attention to `where` value + +Instead of filtering data on Spark side using `df.filter(df.column == 'value')` pass proper `WHERE column = 'value'` clause. +This both reduces the amount of data send from MSSQL to Spark, and may also improve performance of the query. +Especially if there are indexes or partitions for columns used in `where` clause. + +## Options diff --git a/mddocs/docs/_build/markdown/connection/db_connection/mssql/types.md b/mddocs/docs/_build/markdown/connection/db_connection/mssql/types.md new file mode 100644 index 000000000..5981107ad --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/mssql/types.md @@ -0,0 +1,281 @@ + + +# MSSQL <-> Spark type mapping + +#### NOTE +The results below are valid for Spark 3.5.5, and may differ on other Spark versions. + +## Type detection & casting + +Spark’s DataFrames always have a `schema` which is a list of columns with corresponding Spark types. All operations on a column are performed using column type. + +### Reading from MSSQL + +This is how MSSQL connector performs this: + +* For each column in query result (`SELECT column1, column2, ... FROM table ...`) get column name and MSSQL type. +* Find corresponding `MSSQL type (read)` → `Spark type` combination (see below) for each DataFrame column. If no combination is found, raise exception. +* Create DataFrame from query with specific column names and Spark types. + +### Writing to some existing MSSQL table + +This is how MSSQL connector performs this: + +* Get names of columns in DataFrame. [1](#id3) +* Perform `SELECT * FROM table LIMIT 0` query. +* Take only columns present in DataFrame (by name, case insensitive). For each found column get MSSQL type. +* Find corresponding `Spark type` → `MSSQL type (write)` combination (see below) for each DataFrame column. If no combination is found, raise exception. +* If `MSSQL type (write)` match `MSSQL type (read)`, no additional casts will be performed, DataFrame column will be written to MSSQL as is. +* If `MSSQL type (write)` does not match `MSSQL type (read)`, DataFrame column will be casted to target column type **on MSSQL side**. + For example, you can write column with text data to `int` column, if column contains valid integer values within supported value range and precision [2](#id4). + +* **[1]** This allows to write data to tables with `DEFAULT` and `GENERATED` columns - if DataFrame has no such column, it will be populated by MSSQL. +* **[2]** This is true only if DataFrame column is a `StringType()`, because text value is parsed automatically to target column type. But other types cannot be silently converted, like `int -> text`. This requires explicit casting, see [DBWriter](). + +### Create new table using Spark + +#### WARNING +ABSOLUTELY NOT RECOMMENDED! + +This is how MSSQL connector performs this: + +* Find corresponding `Spark type` → `MSSQL type (create)` combination (see below) for each DataFrame column. If no combination is found, raise exception. +* Generate DDL for creating table in MSSQL, like `CREATE TABLE (col1 ...)`, and run it. +* Write DataFrame to created table as is. + +But some cases this may lead to using wrong column type. For example, Spark creates column of type `timestamp` +which corresponds to MSSQL’s type `timestamp(0)` (precision up to seconds) +instead of more precise `timestamp(6)` (precision up to nanoseconds). +This may lead to incidental precision loss, or sometimes data cannot be written to created table at all. + +So instead of relying on Spark to create tables: + +### See example + +```python +writer = DBWriter( + connection=mssql, + target="myschema.target_tbl", + options=MSSQL.WriteOptions( + if_exists="append", + ), +) +writer.run(df) +``` + +Always prefer creating tables with specific types **BEFORE WRITING DATA**: + +### See example + +```python +mssql.execute( + """ + CREATE TABLE schema.table ( + id bigint, + key text, + value datetime2(6) -- specific type and precision + ) + """, +) + +writer = DBWriter( + connection=mssql, + target="myschema.target_tbl", + options=MSSQL.WriteOptions(if_exists="append"), +) +writer.run(df) +``` + +### References + +Here you can find source code with type conversions: + +* [MSSQL -> JDBC](https://github.com/microsoft/mssql-jdbc/blob/v12.2.0/src/main/java/com/microsoft/sqlserver/jdbc/SQLServerResultSetMetaData.java#L117-L170) +* [JDBC -> Spark](https://github.com/apache/spark/blob/v3.5.5/sql/core/src/main/scala/org/apache/spark/sql/jdbc/MsSqlServerDialect.scala#L135-L152) +* [Spark -> JDBC](https://github.com/apache/spark/blob/v3.5.5/sql/core/src/main/scala/org/apache/spark/sql/jdbc/MsSqlServerDialect.scala#L154-L163) +* [JDBC -> MSSQL](https://github.com/microsoft/mssql-jdbc/blob/v12.2.0/src/main/java/com/microsoft/sqlserver/jdbc/DataTypes.java#L625-L676) + +## Supported types + +See [official documentation](https://learn.microsoft.com/en-us/sql/t-sql/data-types/data-types-transact-sql) + +### Numeric types + +| MSSQL type (read) | Spark type | MSSQL type (write) | MSSQL type (create) | +|-----------------------------|---------------------------------|-----------------------------|-----------------------------| +| `decimal` | `DecimalType(P=18, S=0)` | `decimal(P=18, S=0)` | `decimal(P=18, S=0)` | +| `decimal(P=0..38)` | `DecimalType(P=0..38, S=0)` | `decimal(P=0..38, S=0)` | `decimal(P=0..38, S=0)` | +| `decimal(P=0..38, S=0..38)` | `DecimalType(P=0..38, S=0..38)` | `decimal(P=0..38, S=0..38)` | `decimal(P=0..38, S=0..38)` | +| `real` | `FloatType()` | `real` | `real` | +| `float` | `DoubleType()` | `float` | `float` | +| `smallint` | `ShortType()` | `smallint` | `smallint` | +| `tinyint` | `IntegerType()` | `int` | `int` | +| `int` | | | | +| `bigint` | `LongType()` | `bigint` | `bigint` | + +### Temporal types + +#### NOTE +MSSQL `timestamp` type is alias for `rowversion` (see [Special types]()). It is not a temporal type! + +| MSSQL type (read) | Spark type | MSSQL type (write) | MSSQL type (create) | +|-------------------------------------|----------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------|------------------------------------------------------------------------| +| `date` | `DateType()` | `date` | `date` | +| `smalldatetime`, minutes | `TimestampType()`, microseconds | `datetime2(6)`, microseconds | `datetime`, milliseconds | +| `datetime`, milliseconds | | | | +| `datetime2(0)`, seconds | | | | +| `datetime2(3)`, milliseconds | | | | +| `datetime2(6)`, microseconds | `TimestampType()`, microseconds | `datetime2(6)`, microseconds | `datetime`, milliseconds,
**precision loss** [3](#id14) | +| `datetime2(7)`, 100s of nanoseconds | `TimestampType()`, microseconds,
**precision loss** [4](#id15) | `datetime2(6)`, microseconds,
**precision loss** [4](#id15) | | +| `time(0)`, seconds | `TimestampType()`, microseconds,
with time format quirks [5](#id16) | `datetime2(6)`, microseconds | `datetime`, milliseconds | +| `time(3)`, milliseconds | | | | +| `time(6)`, microseconds | `TimestampType()`, microseconds,
with time format quirks [5](#id16) | `datetime2(6)`, microseconds | `datetime`, milliseconds,
**precision loss** [3](#id14) | +| | | | | +| `time`, 100s of nanoseconds | `TimestampType()`, microseconds,
**precision loss** [4](#id15),
with time format quirks [5](#id16) | `datetime2(6)`, microseconds
**precision loss** [3](#id14) | | +| `time(7)`, 100s of nanoseconds | | | | +| `datetimeoffset` | `StringType()` | `nvarchar` | `nvarchar` | + +#### WARNING +Note that types in MSSQL and Spark have different value ranges: + +| MySQL type | Min value | Max value | Spark type | Min value | Max value | +|-----------------|------------------------------|------------------------------|-------------------|------------------------------|------------------------------| +| `smalldatetime` | `1900-01-01 00:00:00` | `2079-06-06 23:59:00` | `TimestampType()` | `0001-01-01 00:00:00.000000` | `9999-12-31 23:59:59.999999` | +| `datetime` | `1753-01-01 00:00:00.000` | `9999-12-31 23:59:59.997` | | | | +| `datetime2` | `0001-01-01 00:00:00.000000` | `9999-12-31 23:59:59.999999` | | | | +| `time` | `00:00:00.0000000` | `23:59:59.9999999` | | | | + +So not all of values in Spark DataFrame can be written to MSSQL. + +References: +: * [MSSQL date & time types documentation](https://learn.microsoft.com/en-us/sql/t-sql/data-types/date-and-time-types) + * [Spark DateType documentation](https://spark.apache.org/docs/latest/api/java/org/apache/spark/sql/types/DateType.html) + * [Spark TimestampType documentation](https://spark.apache.org/docs/latest/api/java/org/apache/spark/sql/types/TimestampType.html) + +* **[3]** MSSQL dialect for Spark generates DDL with type `datetime` which has precision up to milliseconds (`23:59:59.999`, 10-3 seconds). Inserting data with microsecond and higher precision (`23:59:59.999999` .. `23.59:59.9999999`, 10-6 .. 10-7 seconds) will lead to **throwing away microseconds**. +* **[4]** MSSQL support timestamp up to 100s of nanoseconds precision (`23:59:59.9999999999`, 10-7 seconds), but Spark `TimestampType()` supports datetime up to microseconds precision (`23:59:59.999999`, 10-6 seconds). Last digit will be lost during read or write operations. +* **[5]** `time` type is the same as `datetime2` with date `1970-01-01`. So instead of reading data from MSSQL like `23:59:59.999999` it is actually read `1970-01-01 23:59:59.999999`, and vice versa. + +### String types + +| MSSQL type (read) | Spark type | MSSQL type (write) | MSSQL type (create) | +|---------------------|----------------|----------------------|-----------------------| +| `char` | `StringType()` | `nvarchar` | `nvarchar` | +| `char(N)` | | | | +| `nchar` | | | | +| `nchar(N)` | | | | +| `varchar` | | | | +| `varchar(N)` | | | | +| `nvarchar` | | | | +| `nvarchar(N)` | | | | +| `mediumtext` | | | | +| `text` | | | | +| `ntext` | | | | +| `xml` | | | | + +### Binary types + +| MSSQL type (read) | Spark type | MSSQL type (write) | MSSQL type (create) | +|---------------------|-----------------|----------------------|-----------------------| +| `bit` | `BooleanType()` | `bit` | `bit` | +| `binary` | `BinaryType()` | `varbinary` | `varbinary` | +| `binary(N)` | | | | +| `varbinary` | | | | +| `varbinary(N)` | | | | +| `image` | | | | + +### Special types + +| MSSQL type (read) | Spark type | MSSQL type (write) | MSSQL type (create) | +|---------------------|----------------|----------------------|-----------------------| +| `geography` | `BinaryType()` | `varbinary` | `varbinary` | +| `geometry` | | | | +| `hierarchyid` | | | | +| `rowversion` | | | | +| `sql_variant` | unsupported | | | +| `sysname` | `StringType()` | `nvarchar` | `nvarchar` | +| `uniqueidentifier` | | | | + +## Explicit type cast + +### `DBReader` + +It is possible to explicitly cast column type using `DBReader(columns=...)` syntax. + +For example, you can use `CAST(column AS text)` to convert data to string representation on MSSQL side, and so it will be read as Spark’s `StringType()`: + +```python +from onetl.connection import MSSQL +from onetl.db import DBReader + +mssql = MSSQL(...) + +DBReader( + connection=mssql, + columns=[ + "id", + "supported_column", + "CAST(unsupported_column AS text) unsupported_column_str", + ], +) +df = reader.run() + +# cast column content to proper Spark type +df = df.select( + df.id, + df.supported_column, + # explicit cast + df.unsupported_column_str.cast("integer").alias("parsed_integer"), +) +``` + +### `DBWriter` + +Convert dataframe column to JSON using [to_json](https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.functions.to_json.html), +and write it as `text` column in MSSQL: + +```python +mssql.execute( + """ + CREATE TABLE schema.target_tbl ( + id bigint, + struct_column_json text -- any string type, actually + ) + """, +) + +from pyspark.sql.functions import to_json + +df = df.select( + df.id, + to_json(df.struct_column).alias("struct_column_json"), +) + +writer.run(df) +``` + +Then you can parse this column on MSSQL side - for example, by creating a view: + +```sql +SELECT + id, + JSON_VALUE(struct_column_json, "$.nested.field") AS nested_field +FROM target_tbl +``` + +Or by using [computed column](https://learn.microsoft.com/en-us/sql/relational-databases/tables/specify-computed-columns-in-a-table): + +```sql +CREATE TABLE schema.target_table ( + id bigint, + supported_column datetime2(6), + struct_column_json text, -- any string type, actually + -- computed column + nested_field AS (JSON_VALUE(struct_column_json, "$.nested.field")) + -- or persisted column + -- nested_field AS (JSON_VALUE(struct_column_json, "$.nested.field")) PERSISTED +) +``` + +By default, column value is calculated on every table read. +Column marked as `PERSISTED` is calculated during insert, but this require additional space. diff --git a/mddocs/docs/_build/markdown/connection/db_connection/mssql/write.md b/mddocs/docs/_build/markdown/connection/db_connection/mssql/write.md new file mode 100644 index 000000000..cae7470e8 --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/mssql/write.md @@ -0,0 +1,38 @@ + + +# Writing to MSSQL using `DBWriter` + +For writing data to MSSQL, use `DBWriter`. + +#### WARNING +Please take into account [MSSQL <-> Spark type mapping](types.md#mssql-types) + +#### WARNING +It is always recommended to create table explicitly using [MSSQL.execute](execute.md#mssql-execute) +instead of relying on Spark’s table DDL generation. + +This is because Spark’s DDL generator can create columns with different precision and types than it is expected, +causing precision loss or other issues. + +## Examples + +```python +from onetl.connection import MSSQL +from onetl.db import DBWriter + +mssql = MSSQL(...) + +df = ... # data is here + +writer = DBWriter( + connection=mssql, + target="schema.table", + options=MSSQL.WriteOptions(if_exists="append"), +) + +writer.run(df) +``` + +## Options + +Method above accepts `MSSQL.WriteOptions` diff --git a/mddocs/docs/_build/markdown/connection/db_connection/mysql/connection.md b/mddocs/docs/_build/markdown/connection/db_connection/mysql/connection.md new file mode 100644 index 000000000..3cfb0cc04 --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/mysql/connection.md @@ -0,0 +1,3 @@ + + +# MySQL connection diff --git a/mddocs/docs/_build/markdown/connection/db_connection/mysql/execute.md b/mddocs/docs/_build/markdown/connection/db_connection/mysql/execute.md new file mode 100644 index 000000000..de7c953b0 --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/mysql/execute.md @@ -0,0 +1,92 @@ + + +# Executing statements in MySQL + +#### WARNING +Methods below **read all the rows** returned from DB **to Spark driver memory**, and then convert them to DataFrame. + +Do **NOT** use them to read large amounts of data. Use [DBReader](read.md#mysql-read) or [MySQL.sql](sql.md#mysql-sql) instead. + +## How to + +There are 2 ways to execute some statement in MySQL + +### Use `MySQL.fetch` + +Use this method to perform some `SELECT` query which returns **small number or rows**, like reading +MySQL config, or reading data from some reference table. Method returns Spark DataFrame. + +Method accepts `MySQL.FetchOptions`. + +Connection opened using this method should be then closed with `connection.close()` or `with connection:`. + +#### WARNING +Please take into account [MySQL <-> Spark type mapping](types.md#mysql-types). + +#### Syntax support + +This method supports **any** query syntax supported by MySQL, like: + +* ✅︎ `SELECT ... FROM ...` +* ✅︎ `WITH alias AS (...) SELECT ...` +* ✅︎ `SELECT func(arg1, arg2)` or `{?= call func(arg1, arg2)}` - special syntax for calling function +* ✅︎ `SHOW ...` +* ❌ `SET ...; SELECT ...;` - multiple statements not supported + +#### Examples + +```python +from onetl.connection import MySQL + +mysql = MySQL(...) + +df = mysql.fetch( + "SELECT value FROM some.reference_table WHERE key = 'some_constant'", + options=MySQL.FetchOptions(queryTimeout=10), +) +mysql.close() +value = df.collect()[0][0] # get value from first row and first column +``` + +### Use `MySQL.execute` + +Use this method to execute DDL and DML operations. Each method call runs operation in a separated transaction, and then commits it. + +Method accepts `MySQL.ExecuteOptions`. + +Connection opened using this method should be then closed with `connection.close()` or `with connection:`. + +#### Syntax support + +This method supports **any** query syntax supported by MySQL, like: + +* ✅︎ `CREATE TABLE ...`, `CREATE VIEW ...`, and so on +* ✅︎ `ALTER ...` +* ✅︎ `INSERT INTO ... SELECT ...`, `UPDATE ...`, `DELETE ...`, and so on +* ✅︎ `DROP TABLE ...`, `DROP VIEW ...`, and so on +* ✅︎ `CALL procedure(arg1, arg2) ...` or `{call procedure(arg1, arg2)}` - special syntax for calling procedure +* ✅︎ other statements not mentioned here +* ❌ `SET ...; SELECT ...;` - multiple statements not supported + +#### Examples + +```python +from onetl.connection import MySQL + +mysql = MySQL(...) + +mysql.execute("DROP TABLE schema.table") +mysql.execute( + """ + CREATE TABLE schema.table ( + id bigint, + key text, + value float + ) + ENGINE = InnoDB + """, + options=MySQL.ExecuteOptions(queryTimeout=10), +) +``` + +## Options diff --git a/mddocs/docs/_build/markdown/connection/db_connection/mysql/index.md b/mddocs/docs/_build/markdown/connection/db_connection/mysql/index.md new file mode 100644 index 000000000..6722ca77d --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/mysql/index.md @@ -0,0 +1,19 @@ + + +# MySQL + +# Connection + +* [Prerequisites](prerequisites.md) +* [MySQL connection](connection.md) + +# Operations + +* [Reading from MySQL using `DBReader`](read.md) +* [Reading from MySQL using `MySQL.sql`](sql.md) +* [Writing to MySQL using `DBWriter`](write.md) +* [Executing statements in MySQL](execute.md) + +# Troubleshooting + +* [MySQL <-> Spark type mapping](types.md) diff --git a/mddocs/docs/_build/markdown/connection/db_connection/mysql/prerequisites.md b/mddocs/docs/_build/markdown/connection/db_connection/mysql/prerequisites.md new file mode 100644 index 000000000..d0e551443 --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/mysql/prerequisites.md @@ -0,0 +1,61 @@ + + +# Prerequisites + +## Version Compatibility + +* MySQL server versions: + : * Officially declared: 8.0 - 9.2 + * Actually tested: 5.7.13, 9.2.0 +* Spark versions: 2.3.x - 3.5.x +* Java versions: 8 - 20 + +See [official documentation](https://dev.mysql.com/doc/connector-j/en/connector-j-versions.html). + +## Installing PySpark + +To use MySQL connector you should have PySpark installed (or injected to `sys.path`) +BEFORE creating the connector instance. + +See [Spark](../../../install/spark.md#install-spark) installation instruction for more details. + +## Connecting to MySQL + +### Connection host + +It is possible to connect to MySQL by using either DNS name of host or it’s IP address. + +If you’re using MySQL cluster, it is currently possible to connect only to **one specific node**. +Connecting to multiple nodes to perform load balancing, as well as automatic failover to new master/replica are not supported. + +### Connection port + +Connection is usually performed to port 3306. Port may differ for different MySQL instances. +Please ask your MySQL administrator to provide required information. + +### Required grants + +Ask your MySQL cluster administrator to set following grants for a user, +used for creating a connection: + +Read + Write + +```sql +-- allow creating tables in the target schema +GRANT CREATE ON myschema.* TO username@'192.168.1.%'; + +-- allow read & write access to specific table +GRANT SELECT, INSERT ON myschema.mytable TO username@'192.168.1.%'; +``` + +Read only + +```sql +-- allow read access to specific table +GRANT SELECT ON myschema.mytable TO username@'192.168.1.%'; +``` + +In example above `'192.168.1.%''` is a network subnet `192.168.1.0 - 192.168.1.255` +where Spark driver and executors are running. To allow connecting user from any IP, use `'%'` (not secure!). + +More details can be found in [official documentation](https://dev.mysql.com/doc/refman/en/grant.html). diff --git a/mddocs/docs/_build/markdown/connection/db_connection/mysql/read.md b/mddocs/docs/_build/markdown/connection/db_connection/mysql/read.md new file mode 100644 index 000000000..85204c1da --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/mysql/read.md @@ -0,0 +1,80 @@ + + +# Reading from MySQL using `DBReader` + +`DBReader` supports [Read Strategies](../../../strategy/index.md#strategy) for incremental data reading, +but does not support custom queries, like `JOIN`. + +#### WARNING +Please take into account [MySQL <-> Spark type mapping](types.md#mysql-types) + +## Supported DBReader features + +* ✅︎ `columns` +* ✅︎ `where` +* ✅︎ `hwm`, supported strategies: +* * ✅︎ [Snapshot Strategy](../../../strategy/snapshot_strategy.md#snapshot-strategy) +* * ✅︎ [Incremental Strategy](../../../strategy/incremental_strategy.md#incremental-strategy) +* * ✅︎ [Snapshot Batch Strategy](../../../strategy/snapshot_batch_strategy.md#snapshot-batch-strategy) +* * ✅︎ [Incremental Batch Strategy](../../../strategy/incremental_batch_strategy.md#incremental-batch-strategy) +* ✅︎ `hint` (see [official documentation](https://dev.mysql.com/doc/refman/en/optimizer-hints.html)) +* ❌ `df_schema` +* ✅︎ `options` (see `MySQL.ReadOptions`) + +## Examples + +Snapshot strategy: + +```python +from onetl.connection import MySQL +from onetl.db import DBReader + +mysql = MySQL(...) + +reader = DBReader( + connection=mysql, + source="schema.table", + columns=["id", "key", "CAST(value AS text) value", "updated_dt"], + where="key = 'something'", + hint="SKIP_SCAN(schema.table key_index)", + options=MySQL.ReadOptions(partitionColumn="id", numPartitions=10), +) +df = reader.run() +``` + +Incremental strategy: + +```python +from onetl.connection import MySQL +from onetl.db import DBReader +from onetl.strategy import IncrementalStrategy + +mysql = MySQL(...) + +reader = DBReader( + connection=mysql, + source="schema.table", + columns=["id", "key", "CAST(value AS text) value", "updated_dt"], + where="key = 'something'", + hint="SKIP_SCAN(schema.table key_index)", + hwm=DBReader.AutoDetectHWM(name="mysql_hwm", expression="updated_dt"), + options=MySQL.ReadOptions(partitionColumn="id", numPartitions=10), +) + +with IncrementalStrategy(): + df = reader.run() +``` + +## Recommendations + +### Select only required columns + +Instead of passing `"*"` in `DBReader(columns=[...])` prefer passing exact column names. This reduces the amount of data passed from Oracle to Spark. + +### Pay attention to `where` value + +Instead of filtering data on Spark side using `df.filter(df.column == 'value')` pass proper `DBReader(where="column = 'value'")` clause. +This both reduces the amount of data send from Oracle to Spark, and may also improve performance of the query. +Especially if there are indexes for columns used in `where` clause. + +## Options diff --git a/mddocs/docs/_build/markdown/connection/db_connection/mysql/sql.md b/mddocs/docs/_build/markdown/connection/db_connection/mysql/sql.md new file mode 100644 index 000000000..c4f4f8bbf --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/mysql/sql.md @@ -0,0 +1,63 @@ + + +# Reading from MySQL using `MySQL.sql` + +`MySQL.sql` allows passing custom SQL query, but does not support incremental strategies. + +#### WARNING +Please take into account [MySQL <-> Spark type mapping](types.md#mysql-types) + +#### WARNING +Statement is executed in **read-write** connection, so if you’re calling some functions/procedures with DDL/DML statements inside, +they can change data in your database. + +## Syntax support + +Only queries with the following syntax are supported: + +* ✅︎ `SELECT ... FROM ...` +* ✅︎ `WITH alias AS (...) SELECT ...` +* ❌ `SHOW ...` +* ❌ `SET ...; SELECT ...;` - multiple statements not supported + +## Examples + +```python +from onetl.connection import MySQL + +mysql = MySQL(...) +df = mysql.sql( + """ + SELECT + id, + key, + CAST(value AS text) value, + updated_at + FROM + some.mytable + WHERE + key = 'something' + """, + options=MySQL.SQLOptions( + partitionColumn="id", + numPartitions=10, + lowerBound=0, + upperBound=1000, + ), +) +``` + +## Recommendations + +### Select only required columns + +Instead of passing `SELECT * FROM ...` prefer passing exact column names `SELECT col1, col2, ...`. +This reduces the amount of data passed from MySQL to Spark. + +### Pay attention to `where` value + +Instead of filtering data on Spark side using `df.filter(df.column == 'value')` pass proper `WHERE column = 'value'` clause. +This both reduces the amount of data send from MySQL to Spark, and may also improve performance of the query. +Especially if there are indexes or partitions for columns used in `where` clause. + +## Options diff --git a/mddocs/docs/_build/markdown/connection/db_connection/mysql/types.md b/mddocs/docs/_build/markdown/connection/db_connection/mysql/types.md new file mode 100644 index 000000000..6e49eb2a4 --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/mysql/types.md @@ -0,0 +1,292 @@ + + +# MySQL <-> Spark type mapping + +#### NOTE +The results below are valid for Spark 3.5.5, and may differ on other Spark versions. + +## Type detection & casting + +Spark’s DataFrames always have a `schema` which is a list of columns with corresponding Spark types. All operations on a column are performed using column type. + +### Reading from MySQL + +This is how MySQL connector performs this: + +* For each column in query result (`SELECT column1, column2, ... FROM table ...`) get column name and MySQL type. +* Find corresponding `MySQL type (read)` → `Spark type` combination (see below) for each DataFrame column. If no combination is found, raise exception. +* Create DataFrame from query with specific column names and Spark types. + +### Writing to some existing MySQL table + +This is how MySQL connector performs this: + +* Get names of columns in DataFrame. [1](#id2) +* Perform `SELECT * FROM table LIMIT 0` query. +* Take only columns present in DataFrame (by name, case insensitive). For each found column get MySQL type. +* Find corresponding `Spark type` → `MySQL type (write)` combination (see below) for each DataFrame column. If no combination is found, raise exception. +* If `MySQL type (write)` match `MySQL type (read)`, no additional casts will be performed, DataFrame column will be written to MySQL as is. +* If `MySQL type (write)` does not match `MySQL type (read)`, DataFrame column will be casted to target column type **on MySQL side**. For example, you can write column with text data to `int` column, if column contains valid integer values within supported value range and precision. + +* **[1]** This allows to write data to tables with `DEFAULT` and `GENERATED` columns - if DataFrame has no such column, it will be populated by MySQL. + +### Create new table using Spark + +#### WARNING +ABSOLUTELY NOT RECOMMENDED! + +This is how MySQL connector performs this: + +* Find corresponding `Spark type` → `MySQL type (create)` combination (see below) for each DataFrame column. If no combination is found, raise exception. +* Generate DDL for creating table in MySQL, like `CREATE TABLE (col1 ...)`, and run it. +* Write DataFrame to created table as is. + +But some cases this may lead to using wrong column type. For example, Spark creates column of type `timestamp` +which corresponds to MySQL type `timestamp(0)` (precision up to seconds) +instead of more precise `timestamp(6)` (precision up to nanoseconds). +This may lead to incidental precision loss, or sometimes data cannot be written to created table at all. + +So instead of relying on Spark to create tables: + +### See example + +```python +writer = DBWriter( + connection=mysql, + target="myschema.target_tbl", + options=MySQL.WriteOptions( + if_exists="append", + createTableOptions="ENGINE = InnoDB", + ), +) +writer.run(df) +``` + +Always prefer creating tables with specific types **BEFORE WRITING DATA**: + +### See example + +```python +mysql.execute( + """ + CREATE TABLE schema.table ( + id bigint, + key text, + value timestamp(6) -- specific type and precision + ) + ENGINE = InnoDB + """, +) + +writer = DBWriter( + connection=mysql, + target="myschema.target_tbl", + options=MySQL.WriteOptions(if_exists="append"), +) +writer.run(df) +``` + +### References + +Here you can find source code with type conversions: + +* [MySQL -> JDBC](https://github.com/mysql/mysql-connector-j/blob/8.0.33/src/main/core-api/java/com/mysql/cj/MysqlType.java#L44-L623) +* [JDBC -> Spark](https://github.com/apache/spark/blob/v3.5.5/sql/core/src/main/scala/org/apache/spark/sql/jdbc/MySQLDialect.scala#L104-L132) +* [Spark -> JDBC](https://github.com/apache/spark/blob/v3.5.5/sql/core/src/main/scala/org/apache/spark/sql/jdbc/MySQLDialect.scala#L204-L211) +* [JDBC -> MySQL](https://github.com/mysql/mysql-connector-j/blob/8.0.33/src/main/core-api/java/com/mysql/cj/MysqlType.java#L625-L867) + +## Supported types + +See [official documentation](https://dev.mysql.com/doc/refman/en/data-types.html) + +### Numeric types + +| MySQL type (read) | Spark type | MySQL type (write) | MySQL type (create) | +|-----------------------------|----------------------------------|-----------------------------|-----------------------------| +| `decimal` | `DecimalType(P=10, S=0)` | `decimal(P=10, S=0)` | `decimal(P=10, S=0)` | +| `decimal(P=0..38)` | `DecimalType(P=0..38, S=0)` | `decimal(P=0..38, S=0)` | `decimal(P=0..38, S=0)` | +| `decimal(P=0..38, S=0..30)` | `DecimalType(P=0..38, S=0..30)` | `decimal(P=0..38, S=0..30)` | `decimal(P=0..38, S=0..30)` | +| `decimal(P=39..65, S=...)` | unsupported [2](#id4) | | | +| `float` | `DoubleType()` | `double` | `double` | +| `double` | | | | +| `tinyint` | `IntegerType()` | `int` | `int` | +| `smallint` | | | | +| `mediumint` | | | | +| `int` | | | | +| `bigint` | `LongType()` | `bigint` | `bigint` | +* **[2]** MySQL support decimal types with precision `P` up to 65. But Spark’s `DecimalType(P, S)` supports maximum `P=38`. It is impossible to read, write or operate with values of larger precision, this leads to an exception. + +### Temporal types + +| MySQL type (read) | Spark type | MySQL type (write) | MySQL type (create) | +|------------------------------|------------------------------------------------------------------------------------|------------------------------|-----------------------------------------------------------------------| +| `year` | `DateType()` | `date` | `date` | +| `date` | | | | +| `datetime`, seconds | `TimestampType()`, microseconds | `timestamp(6)`, microseconds | `timestamp(0)`, seconds | +| `timestamp`, seconds | | | | +| `datetime(0)`, seconds | | | | +| `timestamp(0)`, seconds | | | | +| `datetime(3)`, milliseconds | `TimestampType()`, microseconds | `timestamp(6)`, microseconds | `timestamp(0)`, seconds,
**precision loss** [4](#id9), | +| `timestamp(3)`, milliseconds | | | | +| `datetime(6)`, microseconds | | | | +| `timestamp(6)`, microseconds | | | | +| `time`, seconds | `TimestampType()`, microseconds,
with time format quirks [5](#id10) | `timestamp(6)`, microseconds | `timestamp(0)`, seconds | +| `time(0)`, seconds | | | | +| `time(3)`, milliseconds | `TimestampType()`, microseconds
with time format quirks [5](#id10) | `timestamp(6)`, microseconds | `timestamp(0)`, seconds,
**precision loss** [4](#id9), | +| `time(6)`, microseconds | | | | + +#### WARNING +Note that types in MySQL and Spark have different value ranges: + +| MySQL type | Min value | Max value | Spark type | Min value | Max value | +|--------------|------------------------------|------------------------------|-------------------|------------------------------|------------------------------| +| `year` | `1901` | `2155` | `DateType()` | `0001-01-01` | `9999-12-31` | +| `date` | `1000-01-01` | `9999-12-31` | | | | +| `datetime` | `1000-01-01 00:00:00.000000` | `9999-12-31 23:59:59.499999` | `TimestampType()` | `0001-01-01 00:00:00.000000` | `9999-12-31 23:59:59.999999` | +| `timestamp` | `1970-01-01 00:00:01.000000` | `9999-12-31 23:59:59.499999` | | | | +| `time` | `-838:59:59.000000` | `838:59:59.000000` | | | | + +So Spark can read all the values from MySQL, but not all of values in Spark DataFrame can be written to MySQL. + +References: +: * [MySQL year documentation](https://dev.mysql.com/doc/refman/en/year.html) + * [MySQL date, datetime & timestamp documentation](https://dev.mysql.com/doc/refman/en/datetime.html) + * [MySQL time documentation](https://dev.mysql.com/doc/refman/en/time.html) + * [Spark DateType documentation](https://spark.apache.org/docs/latest/api/java/org/apache/spark/sql/types/DateType.html) + * [Spark TimestampType documentation](https://spark.apache.org/docs/latest/api/java/org/apache/spark/sql/types/TimestampType.html) + +* **[4]** MySQL dialect generates DDL with MySQL type `timestamp` which is alias for `timestamp(0)` with precision up to seconds (`23:59:59`). Inserting data with microseconds precision (`23:59:59.999999`) will lead to **throwing away microseconds**. +* **[5]** `time` type is the same as `timestamp` with date `1970-01-01`. So instead of reading data from MySQL like `23:59:59` it is actually read `1970-01-01 23:59:59`, and vice versa. + +### String types + +| MySQL type (read) | Spark type | MySQL type (write) | MySQL type (create) | +|-----------------------------|----------------|----------------------|-----------------------| +| `char` | `StringType()` | `longtext` | `longtext` | +| `char(N)` | | | | +| `varchar(N)` | | | | +| `mediumtext` | | | | +| `text` | | | | +| `longtext` | | | | +| `json` | | | | +| `enum("val1", "val2", ...)` | | | | +| `set("val1", "val2", ...)` | | | | + +### Binary types + +| MySQL type (read) | Spark type | MySQL type (write) | MySQL type (create) | +|---------------------|----------------|----------------------|-----------------------| +| `binary` | `BinaryType()` | `blob` | `blob` | +| `binary(N)` | | | | +| `varbinary(N)` | | | | +| `mediumblob` | | | | +| `blob` | | | | +| `longblob` | | | | + +### Geometry types + +| MySQL type (read) | Spark type | MySQL type (write) | MySQL type (create) | +|----------------------|----------------|----------------------|-----------------------| +| `point` | `BinaryType()` | `blob` | `blob` | +| `linestring` | | | | +| `polygon` | | | | +| `geometry` | | | | +| `multipoint` | | | | +| `multilinestring` | | | | +| `multipolygon` | | | | +| `geometrycollection` | | | | + +## Explicit type cast + +### `DBReader` + +It is possible to explicitly cast column type using `DBReader(columns=...)` syntax. + +For example, you can use `CAST(column AS text)` to convert data to string representation on MySQL side, and so it will be read as Spark’s `StringType()`. + +It is also possible to use [JSON_OBJECT](https://dev.mysql.com/doc/refman/en/json.html) MySQL function and parse JSON columns in MySQL with the `JSON.parse_column` method. + +```python +from pyspark.sql.types import IntegerType, StructType, StructField + +from onetl.connection import MySQL +from onetl.db import DBReader +from onetl.file.format import JSON + +mysql = MySQL(...) + +DBReader( + connection=mysql, + columns=[ + "id", + "supported_column", + "CAST(unsupported_column AS text) unsupported_column_str", + # or + "JSON_OBJECT('key', value_column) json_column", + ], +) +df = reader.run() + +json_scheme = StructType([StructField("key", IntegerType())]) + +df = df.select( + df.id, + df.supported_column, + # explicit cast + df.unsupported_column_str.cast("integer").alias("parsed_integer"), + JSON().parse_column("json_column", json_scheme).alias("struct_column"), +) +``` + +### `DBWriter` + +To write JSON data to a `json` or `text` column in a MySQL table, use the `JSON.serialize_column` method. + +```python +from onetl.connection import MySQL +from onetl.db import DBWriter +from onetl.file.format import JSON + +mysql.execute( + """ + CREATE TABLE schema.target_tbl ( + id bigint, + array_column_json json -- any string type, actually + ) + ENGINE = InnoDB + """, +) + +df = df.select( + df.id, + JSON().serialize_column(df.array_column).alias("array_column_json"), +) + +writer.run(df) +``` + +Then you can parse this column on MySQL side - for example, by creating a view: + +```sql +SELECT + id, + array_column_json->"$[0]" AS array_item +FROM target_tbl +``` + +Or by using [GENERATED column](https://dev.mysql.com/doc/refman/en/create-table-generated-columns.html): + +```sql +CREATE TABLE schema.target_table ( + id bigint, + supported_column timestamp, + array_column_json json, -- any string type, actually + -- virtual column + array_item_0 GENERATED ALWAYS AS (array_column_json->"$[0]")) VIRTUAL + -- or stired column + -- array_item_0 GENERATED ALWAYS AS (array_column_json->"$[0]")) STORED +) +``` + +`VIRTUAL` column value is calculated on every table read. +`STORED` column value is calculated during insert, but this require additional space. diff --git a/mddocs/docs/_build/markdown/connection/db_connection/mysql/write.md b/mddocs/docs/_build/markdown/connection/db_connection/mysql/write.md new file mode 100644 index 000000000..aac236e57 --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/mysql/write.md @@ -0,0 +1,42 @@ + + +# Writing to MySQL using `DBWriter` + +For writing data to MySQL, use `DBWriter`. + +#### WARNING +Please take into account [MySQL <-> Spark type mapping](types.md#mysql-types) + +#### WARNING +It is always recommended to create table explicitly using [MySQL.execute](execute.md#mysql-execute) +instead of relying on Spark’s table DDL generation. + +This is because Spark’s DDL generator can create columns with different precision and types than it is expected, +causing precision loss or other issues. + +## Examples + +```python +from onetl.connection import MySQL +from onetl.db import DBWriter + +mysql = MySQL(...) + +df = ... # data is here + +writer = DBWriter( + connection=mysql, + target="schema.table", + options=MySQL.WriteOptions( + if_exists="append", + # ENGINE is required by MySQL + createTableOptions="ENGINE = MergeTree() ORDER BY id", + ), +) + +writer.run(df) +``` + +## Options + +Method above accepts `MySQL.WriteOptions` diff --git a/mddocs/docs/_build/markdown/connection/db_connection/oracle/connection.md b/mddocs/docs/_build/markdown/connection/db_connection/oracle/connection.md new file mode 100644 index 000000000..cdbad7c09 --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/oracle/connection.md @@ -0,0 +1,3 @@ + + +# Oracle connection diff --git a/mddocs/docs/_build/markdown/connection/db_connection/oracle/execute.md b/mddocs/docs/_build/markdown/connection/db_connection/oracle/execute.md new file mode 100644 index 000000000..92d3c73c9 --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/oracle/execute.md @@ -0,0 +1,92 @@ + + +# Executing statements in Oracle + +#### WARNING +Methods below **read all the rows** returned from DB **to Spark driver memory**, and then convert them to DataFrame. + +Do **NOT** use them to read large amounts of data. Use [DBReader](read.md#oracle-read) or [Oracle.sql](sql.md#oracle-sql) instead. + +## How to + +There are 2 ways to execute some statement in Oracle + +### Use `Oracle.fetch` + +Use this method to execute some `SELECT` query which returns **small number or rows**, like reading +Oracle config, or reading data from some reference table. Method returns Spark DataFrame. + +Method accepts `Oracle.FetchOptions`. + +Connection opened using this method should be then closed with `connection.close()` or `with connection:`. + +#### WARNING +Please take into account [Oracle <-> Spark type mapping](types.md#oracle-types). + +#### Syntax support + +This method supports **any** query syntax supported by Oracle, like: + +* ✅︎ `SELECT ... FROM ...` +* ✅︎ `WITH alias AS (...) SELECT ...` +* ✅︎ `SELECT func(arg1, arg2) FROM DUAL` - call function +* ✅︎ `SHOW ...` +* ❌ `SET ...; SELECT ...;` - multiple statements not supported + +#### Examples + +```python +from onetl.connection import Oracle + +oracle = Oracle(...) + +df = oracle.fetch( + "SELECT value FROM some.reference_table WHERE key = 'some_constant'", + options=Oracle.FetchOptions(queryTimeout=10), +) +oracle.close() +value = df.collect()[0][0] # get value from first row and first column +``` + +### Use `Oracle.execute` + +Use this method to execute DDL and DML operations. Each method call runs operation in a separated transaction, and then commits it. + +Method accepts `Oracle.ExecuteOptions`. + +Connection opened using this method should be then closed with `connection.close()` or `with connection:`. + +#### Syntax support + +This method supports **any** query syntax supported by Oracle, like: + +* ✅︎ `CREATE TABLE ...`, `CREATE VIEW ...` +* ✅︎ `ALTER ...` +* ✅︎ `INSERT INTO ... SELECT ...`, `UPDATE ...`, `DELETE ...`, and so on +* ✅︎ `DROP TABLE ...`, `DROP VIEW ...`, `TRUNCATE TABLE`, and so on +* ✅︎ `CALL procedure(arg1, arg2) ...` or `{call procedure(arg1, arg2)}` - special syntax for calling procedure +* ✅︎ `DECLARE ... BEGIN ... END` - execute PL/SQL statement +* ✅︎ other statements not mentioned here +* ❌ `SET ...; SELECT ...;` - multiple statements not supported + +#### Examples + +```python +from onetl.connection import Oracle + +oracle = Oracle(...) + +oracle.execute("DROP TABLE schema.table") +oracle.execute( + """ + CREATE TABLE schema.table ( + id bigint GENERATED ALWAYS AS IDENTITY, + key VARCHAR2(4000), + value NUMBER + ) + """, + options=Oracle.ExecuteOptions(queryTimeout=10), +) +``` + +## Options diff --git a/mddocs/docs/_build/markdown/connection/db_connection/oracle/index.md b/mddocs/docs/_build/markdown/connection/db_connection/oracle/index.md new file mode 100644 index 000000000..39987fb24 --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/oracle/index.md @@ -0,0 +1,19 @@ + + +# Oracle + +# Connection + +* [Prerequisites](prerequisites.md) +* [Oracle connection](connection.md) + +# Operations + +* [Reading from Oracle using `DBReader`](read.md) +* [Reading from Oracle using `Oracle.sql`](sql.md) +* [Writing to Oracle using `DBWriter`](write.md) +* [Executing statements in Oracle](execute.md) + +# Troubleshooting + +* [Oracle <-> Spark type mapping](types.md) diff --git a/mddocs/docs/_build/markdown/connection/db_connection/oracle/prerequisites.md b/mddocs/docs/_build/markdown/connection/db_connection/oracle/prerequisites.md new file mode 100644 index 000000000..6dff632ce --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/oracle/prerequisites.md @@ -0,0 +1,110 @@ + + +# Prerequisites + +## Version Compatibility + +* Oracle Server versions: + : * Officially declared: 19c, 21c, 23ai + * Actually tested: 11.2, 23.5 +* Spark versions: 2.3.x - 3.5.x +* Java versions: 8 - 20 + +See [official documentation](https://www.oracle.com/cis/database/technologies/appdev/jdbc-downloads.html). + +## Installing PySpark + +To use Oracle connector you should have PySpark installed (or injected to `sys.path`) +BEFORE creating the connector instance. + +See [Spark](../../../install/spark.md#install-spark) installation instruction for more details. + +## Connecting to Oracle + +### Connection port + +Connection is usually performed to port 1521. Port may differ for different Oracle instances. +Please ask your Oracle administrator to provide required information. + +### Connection host + +It is possible to connect to Oracle by using either DNS name of host or it’s IP address. + +If you’re using Oracle cluster, it is currently possible to connect only to **one specific node**. +Connecting to multiple nodes to perform load balancing, as well as automatic failover to new master/replica are not supported. + +### Connect as proxy user + +It is possible to connect to database as another user without knowing this user password. + +This can be enabled by granting user a special `CONNECT THROUGH` permission: + +```sql +ALTER USER schema_owner GRANT CONNECT THROUGH proxy_user; +``` + +Then you can connect to Oracle using credentials of `proxy_user` but specify that you need permissions of `schema_owner`: + +```python +oracle = Oracle( + ..., + user="proxy_user[schema_owner]", + password="proxy_user password", +) +``` + +See [official documentation](https://oracle-base.com/articles/misc/proxy-users-and-connect-through). + +### Required grants + +Ask your Oracle cluster administrator to set following grants for a user, +used for creating a connection: + +Read + Write (schema is owned by user) + +```sql +-- allow user to log in +GRANT CREATE SESSION TO username; + +-- allow creating tables in user schema +GRANT CREATE TABLE TO username; + +-- allow read & write access to specific table +GRANT SELECT, INSERT ON username.mytable TO username; +``` + +Read + Write (schema is not owned by user) + +```sql +-- allow user to log in +GRANT CREATE SESSION TO username; + +-- allow creating tables in any schema, +-- as Oracle does not support specifying exact schema name +GRANT CREATE ANY TABLE TO username; + +-- allow read & write access to specific table +GRANT SELECT, INSERT ON someschema.mytable TO username; + +-- only if if_exists="replace_entire_table" is used: +-- allow dropping/truncating tables in any schema, +-- as Oracle does not support specifying exact schema name +GRANT DROP ANY TABLE TO username; +``` + +Read only + +```sql +-- allow user to log in +GRANT CREATE SESSION TO username; + +-- allow read access to specific table +GRANT SELECT ON someschema.mytable TO username; +``` + +More details can be found in official documentation: +: * [GRANT](https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/GRANT.html) + * [SELECT](https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/SELECT.html) + * [CREATE TABLE](https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/SELECT.html) + * [INSERT](https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/INSERT.html) + * [TRUNCATE TABLE](https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/TRUNCATE-TABLE.html) diff --git a/mddocs/docs/_build/markdown/connection/db_connection/oracle/read.md b/mddocs/docs/_build/markdown/connection/db_connection/oracle/read.md new file mode 100644 index 000000000..6b32f12f5 --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/oracle/read.md @@ -0,0 +1,80 @@ + + +# Reading from Oracle using `DBReader` + +`DBReader` supports [Read Strategies](../../../strategy/index.md#strategy) for incremental data reading, +but does not support custom queries, like `JOIN`. + +#### WARNING +Please take into account [Oracle <-> Spark type mapping](types.md#oracle-types) + +## Supported DBReader features + +* ✅︎ `columns` +* ✅︎ `where` +* ✅︎ `hwm`, supported strategies: +* * ✅︎ [Snapshot Strategy](../../../strategy/snapshot_strategy.md#snapshot-strategy) +* * ✅︎ [Incremental Strategy](../../../strategy/incremental_strategy.md#incremental-strategy) +* * ✅︎ [Snapshot Batch Strategy](../../../strategy/snapshot_batch_strategy.md#snapshot-batch-strategy) +* * ✅︎ [Incremental Batch Strategy](../../../strategy/incremental_batch_strategy.md#incremental-batch-strategy) +* ✅︎ `hint` (see [official documentation](https://docs.oracle.com/cd/B10500_01/server.920/a96533/hintsref.htm)) +* ❌ `df_schema` +* ✅︎ `options` (see `Oracle.ReadOptions`) + +## Examples + +Snapshot strategy: + +```python +from onetl.connection import Oracle +from onetl.db import DBReader + +oracle = Oracle(...) + +reader = DBReader( + connection=oracle, + source="schema.table", + columns=["id", "key", "CAST(value AS VARCHAR2(4000)) value", "updated_dt"], + where="key = 'something'", + hint="INDEX(schema.table key_index)", + options=Oracle.ReadOptions(partitionColumn="id", numPartitions=10), +) +df = reader.run() +``` + +Incremental strategy: + +```python +from onetl.connection import Oracle +from onetl.db import DBReader +from onetl.strategy import IncrementalStrategy + +oracle = Oracle(...) + +reader = DBReader( + connection=oracle, + source="schema.table", + columns=["id", "key", "CAST(value AS VARCHAR2(4000)) value", "updated_dt"], + where="key = 'something'", + hint="INDEX(schema.table key_index)", + hwm=DBReader.AutoDetectHWM(name="oracle_hwm", expression="updated_dt"), + options=Oracle.ReadOptions(partitionColumn="id", numPartitions=10), +) + +with IncrementalStrategy(): + df = reader.run() +``` + +## Recommendations + +### Select only required columns + +Instead of passing `"*"` in `DBReader(columns=[...])` prefer passing exact column names. This reduces the amount of data passed from Oracle to Spark. + +### Pay attention to `where` value + +Instead of filtering data on Spark side using `df.filter(df.column == 'value')` pass proper `DBReader(where="column = 'value'")` clause. +This both reduces the amount of data send from Oracle to Spark, and may also improve performance of the query. +Especially if there are indexes or partitions for columns used in `where` clause. + +## Options diff --git a/mddocs/docs/_build/markdown/connection/db_connection/oracle/sql.md b/mddocs/docs/_build/markdown/connection/db_connection/oracle/sql.md new file mode 100644 index 000000000..b98dd6952 --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/oracle/sql.md @@ -0,0 +1,63 @@ + + +# Reading from Oracle using `Oracle.sql` + +`Oracle.sql` allows passing custom SQL query, but does not support incremental strategies. + +#### WARNING +Please take into account [Oracle <-> Spark type mapping](types.md#oracle-types) + +#### WARNING +Statement is executed in **read-write** connection, so if you’re calling some functions/procedures with DDL/DML statements inside, +they can change data in your database. + +## Syntax support + +Only queries with the following syntax are supported: + +* ✅︎ `SELECT ... FROM ...` +* ✅︎ `WITH alias AS (...) SELECT ...` +* ❌ `SHOW ...` +* ❌ `SET ...; SELECT ...;` - multiple statements not supported + +## Examples + +```python +from onetl.connection import Oracle + +oracle = Oracle(...) +df = oracle.sql( + """ + SELECT + id, + key, + CAST(value AS VARCHAR2(4000)) value, + updated_at + FROM + some.mytable + WHERE + key = 'something' + """, + options=Oracle.SQLOptions( + partitionColumn="id", + numPartitions=10, + lowerBound=0, + upperBound=1000, + ), +) +``` + +## Recommendations + +### Select only required columns + +Instead of passing `SELECT * FROM ...` prefer passing exact column names `SELECT col1, col2, ...`. +This reduces the amount of data passed from Oracle to Spark. + +### Pay attention to `where` value + +Instead of filtering data on Spark side using `df.filter(df.column == 'value')` pass proper `WHERE column = 'value'` clause. +This both reduces the amount of data send from Oracle to Spark, and may also improve performance of the query. +Especially if there are indexes or partitions for columns used in `where` clause. + +## Options diff --git a/mddocs/docs/_build/markdown/connection/db_connection/oracle/types.md b/mddocs/docs/_build/markdown/connection/db_connection/oracle/types.md new file mode 100644 index 000000000..9f4f2e13e --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/oracle/types.md @@ -0,0 +1,303 @@ + + +# Oracle <-> Spark type mapping + +#### NOTE +The results below are valid for Spark 3.5.5, and may differ on other Spark versions. + +## Type detection & casting + +Spark’s DataFrames always have a `schema` which is a list of columns with corresponding Spark types. All operations on a column are performed using column type. + +### Reading from Oracle + +This is how Oracle connector performs this: + +* For each column in query result (`SELECT column1, column2, ... FROM table ...`) get column name and Oracle type. +* Find corresponding `Oracle type (read)` → `Spark type` combination (see below) for each DataFrame column. If no combination is found, raise exception. +* Create DataFrame from query with specific column names and Spark types. + +### Writing to some existing Oracle table + +This is how Oracle connector performs this: + +* Get names of columns in DataFrame. [1](#id3) +* Perform `SELECT * FROM table LIMIT 0` query. +* Take only columns present in DataFrame (by name, case insensitive). For each found column get Clickhouse type. +* **Find corresponding** `Oracle type (read)` → `Spark type` **combination** (see below) for each DataFrame column. If no combination is found, raise exception. [2](#id4) +* Find corresponding `Spark type` → `Oracle type (write)` combination (see below) for each DataFrame column. If no combination is found, raise exception. +* If `Oracle type (write)` match `Oracle type (read)`, no additional casts will be performed, DataFrame column will be written to Oracle as is. +* If `Oracle type (write)` does not match `Oracle type (read)`, DataFrame column will be casted to target column type **on Oracle side**. + For example, you can write column with text data to `int` column, if column contains valid integer values within supported value range and precision. + +* **[1]** This allows to write data to tables with `DEFAULT` and `GENERATED` columns - if DataFrame has no such column, it will be populated by Oracle. +* **[2]** Yes, this is weird. + +### Create new table using Spark + +#### WARNING +ABSOLUTELY NOT RECOMMENDED! + +This is how Oracle connector performs this: + +* Find corresponding `Spark type` → `Oracle type (create)` combination (see below) for each DataFrame column. If no combination is found, raise exception. +* Generate DDL for creating table in Oracle, like `CREATE TABLE (col1 ...)`, and run it. +* Write DataFrame to created table as is. + +But Oracle connector support only limited number of types and almost no custom clauses (like `PARTITION BY`, `INDEX`, etc). +So instead of relying on Spark to create tables: + +### See example + +```python +writer = DBWriter( + connection=oracle, + target="public.table", + options=Oracle.WriteOptions(if_exists="append"), +) +writer.run(df) +``` + +Always prefer creating table with desired DDL **BEFORE WRITING DATA**: + +### See example + +```python +oracle.execute( + """ + CREATE TABLE username.table ( + id NUMBER, + business_dt TIMESTAMP(6), + value VARCHAR2(2000) + ) + """, +) + +writer = DBWriter( + connection=oracle, + target="public.table", + options=Oracle.WriteOptions(if_exists="append"), +) +writer.run(df) +``` + +See Oracle [CREATE TABLE](https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/SELECT.html) documentation. + +## Supported types + +### References + +See [List of Oracle types](https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/Data-Types.html). + +Here you can find source code with type conversions: + +* [JDBC -> Spark](https://github.com/apache/spark/blob/v3.5.5/sql/core/src/main/scala/org/apache/spark/sql/jdbc/OracleDialect.scala#L83-L109) +* [Spark -> JDBC](https://github.com/apache/spark/blob/v3.5.5/sql/core/src/main/scala/org/apache/spark/sql/jdbc/OracleDialect.scala#L111-L123) + +### Numeric types + +| Oracle type (read) | Spark type | Oracle type (write) | Oracle type (create) | +|-----------------------------|----------------------------------|----------------------------|-------------------------| +| `NUMBER` | `DecimalType(P=38, S=10)` | `NUMBER(P=38, S=10)` | `NUMBER(P=38, S=10)` | +| `NUMBER(P=0..38)` | `DecimalType(P=0..38, S=0)` | `NUMBER(P=0..38, S=0)` | `NUMBER(P=38, S=0)` | +| `NUMBER(P=0..38, S=0..38)` | `DecimalType(P=0..38, S=0..38)` | `NUMBER(P=0..38, S=0..38)` | `NUMBER(P=38, S=0..38)` | +| `NUMBER(P=..., S=-127..-1)` | unsupported [3](#id6) | | | +| `FLOAT` | `DecimalType(P=38, S=10)` | `NUMBER(P=38, S=10)` | `NUMBER(P=38, S=10)` | +| `FLOAT(N)` | | | | +| `REAL` | | | | +| `DOUBLE PRECISION` | | | | +| `BINARY_FLOAT` | `FloatType()` | `NUMBER(P=19, S=4)` | `NUMBER(P=19, S=4)` | +| `BINARY_DOUBLE` | `DoubleType()` | | | +| `SMALLINT` | `DecimalType(P=38, S=0)` | `NUMBER(P=38, S=0)` | `NUMBER(P=38, S=0)` | +| `INTEGER` | | | | +| `LONG` | `StringType()` | `CLOB` | `CLOB` | +* **[3]** Oracle support decimal types with negative scale, like `NUMBER(38, -10)`. Spark doesn’t. + +### Temporal types + +| Oracle type (read) | Spark type | Oracle type (write) | Oracle type (create) | +|-------------------------------------|------------------------------------------------------------------------------|------------------------------------------------------|------------------------------------------------------| +| `DATE`, days | `TimestampType()`, microseconds | `TIMESTAMP(6)`, microseconds | `TIMESTAMP(6)`, microseconds | +| `TIMESTAMP`, microseconds | `TimestampType()`, microseconds | `TIMESTAMP(6)`, microseconds | `TIMESTAMP(6)`, microseconds | +| `TIMESTAMP(0)`, seconds | | | | +| `TIMESTAMP(3)`, milliseconds | | | | +| `TIMESTAMP(6)`, microseconds | | | | +| `TIMESTAMP(9)`, nanoseconds | `TimestampType()`, microseconds,
**precision loss** [4](#id8) | `TIMESTAMP(6)`, microseconds,
**precision loss** | `TIMESTAMP(6)`, microseconds,
**precision loss** | +| `TIMESTAMP WITH TIME ZONE` | unsupported | | | +| `TIMESTAMP(N) WITH TIME ZONE` | | | | +| `TIMESTAMP WITH LOCAL TIME ZONE` | | | | +| `TIMESTAMP(N) WITH LOCAL TIME ZONE` | | | | +| `INTERVAL YEAR TO MONTH` | | | | +| `INTERVAL DAY TO SECOND` | | | | + +#### WARNING +Note that types in Oracle and Spark have different value ranges: + +| Oracle type | Min value | Max value | Spark type | Min value | Max value | +|---------------|----------------------------------|---------------------------------|-------------------|------------------------------|------------------------------| +| `date` | `-4712-01-01` | `9999-01-01` | `DateType()` | `0001-01-01` | `9999-12-31` | +| `timestamp` | `-4712-01-01 00:00:00.000000000` | `9999-12-31 23:59:59.999999999` | `TimestampType()` | `0001-01-01 00:00:00.000000` | `9999-12-31 23:59:59.999999` | + +So not all of values can be read from Oracle to Spark. + +References: +: * [Oracle date, timestamp and intervals documentation](https://oracle-base.com/articles/misc/oracle-dates-timestamps-and-intervals#DATE) + * [Spark DateType documentation](https://spark.apache.org/docs/latest/api/java/org/apache/spark/sql/types/DateType.html) + * [Spark TimestampType documentation](https://spark.apache.org/docs/latest/api/java/org/apache/spark/sql/types/TimestampType.html) + +* **[4]** Oracle support timestamp up to nanoseconds precision (`23:59:59.999999999`), but Spark `TimestampType()` supports datetime up to microseconds precision (`23:59:59.999999`). Nanoseconds will be lost during read or write operations. + +### String types + +| Oracle type (read) | Spark type | Oracle type (write) | Oracle type (create) | +|----------------------|----------------|-----------------------|------------------------| +| `CHAR` | `StringType()` | `CLOB` | `CLOB` | +| `CHAR(N CHAR)` | | | | +| `CHAR(N BYTE)` | | | | +| `NCHAR` | | | | +| `NCHAR(N)` | | | | +| `VARCHAR(N)` | | | | +| `LONG VARCHAR` | | | | +| `VARCHAR2(N CHAR)` | | | | +| `VARCHAR2(N BYTE)` | | | | +| `NVARCHAR2(N)` | | | | +| `CLOB` | | | | +| `NCLOB` | | | | + +### Binary types + +| Oracle type (read) | Spark type | Oracle type (write) | Oracle type (create) | +|----------------------|----------------|-----------------------|------------------------| +| `RAW(N)` | `BinaryType()` | `BLOB` | `BLOB` | +| `LONG RAW` | | | | +| `BLOB` | | | | +| `BFILE` | unsupported | | | + +### Struct types + +| Oracle type (read) | Spark type | Oracle type (write) | Oracle type (create) | +|-----------------------------------|----------------|-----------------------|------------------------| +| `XMLType` | `StringType()` | `CLOB` | `CLOB` | +| `URIType` | | | | +| `DBURIType` | | | | +| `XDBURIType` | | | | +| `HTTPURIType` | | | | +| `CREATE TYPE ... AS OBJECT (...)` | | | | +| `JSON` | unsupported | | | +| `CREATE TYPE ... AS VARRAY ...` | | | | +| `CREATE TYPE ... AS TABLE OF ...` | | | | + +### Special types + +| Oracle type (read) | Spark type | Oracle type (write) | Oracle type (create) | +|----------------------|-----------------|-----------------------|------------------------| +| `BOOLEAN` | `BooleanType()` | `BOOLEAN` | `NUMBER(P=1, S=0)` | +| `ROWID` | `StringType()` | `CLOB` | `CLOB` | +| `UROWID` | | | | +| `UROWID(N)` | | | | +| `ANYTYPE` | unsupported | | | +| `ANYDATA` | | | | +| `ANYDATASET` | | | | + +## Explicit type cast + +### `DBReader` + +It is possible to explicitly cast column of unsupported type using `DBReader(columns=...)` syntax. + +For example, you can use `CAST(column AS CLOB)` to convert data to string representation on Oracle side, and so it will be read as Spark’s `StringType()`. + +It is also possible to use [JSON_ARRAY](https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/JSON_ARRAY.html) +or [JSON_OBJECT](https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/JSON_OBJECT.html) Oracle functions +to convert column of any type to string representation. Then this JSON string can then be effectively parsed using the `JSON.parse_column` method. + +```python +from onetl.file.format import JSON +from pyspark.sql.types import IntegerType, StructType, StructField + +from onetl.connection import Oracle +from onetl.db import DBReader + +oracle = Oracle(...) + +DBReader( + connection=oracle, + columns=[ + "id", + "supported_column", + "CAST(unsupported_column AS VARCHAR2(4000)) unsupported_column_str", + # or + "JSON_ARRAY(array_column) array_column_json", + ], +) +df = reader.run() + +json_scheme = StructType([StructField("key", IntegerType())]) + +df = df.select( + df.id, + df.supported_column, + df.unsupported_column_str.cast("integer").alias("parsed_integer"), + JSON().parse_column("array_column_json", json_scheme).alias("array_column"), +) +``` + +### `DBWriter` + +It is always possible to convert data on Spark side to string, and then write it to text column in Oracle table. + +To serialize and write JSON data to a `text` or `json` column in an Oracle table use the `JSON.serialize_column` method. + +```python +from onetl.connection import Oracle +from onetl.db import DBWriter +from onetl.file.format import JSON + +oracle = Oracle(...) + +oracle.execute( + """ + CREATE TABLE schema.target_table ( + id INTEGER, + supported_column TIMESTAMP, + array_column_json VARCHAR2(4000) -- any string type, actually + ) + """, +) + +write_df = df.select( + df.id, + df.supported_column, + JSON().serialize_column(df.unsupported_column).alias("array_column_json"), +) + +writer = DBWriter( + connection=oracle, + target="schema.target_table", +) +writer.run(write_df) +``` + +Then you can parse this column on Oracle side - for example, by creating a view: + +```sql +SELECT + id, + supported_column, + JSON_VALUE(array_column_json, '$[0]' RETURNING NUMBER) AS array_item_0 +FROM + schema.target_table +``` + +Or by using [VIRTUAL column](https://oracle-base.com/articles/11g/virtual-columns-11gr1): + +```sql +CREATE TABLE schema.target_table ( + id INTEGER, + supported_column TIMESTAMP, + array_column_json VARCHAR2(4000), -- any string type, actually + array_item_0 GENERATED ALWAYS AS (JSON_VALUE(array_column_json, '$[0]' RETURNING NUMBER)) VIRTUAL +) +``` + +But data will be parsed on each table read in any case, as Oracle does no support `GENERATED ALWAYS AS (...) STORED` columns. diff --git a/mddocs/docs/_build/markdown/connection/db_connection/oracle/write.md b/mddocs/docs/_build/markdown/connection/db_connection/oracle/write.md new file mode 100644 index 000000000..202aa831d --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/oracle/write.md @@ -0,0 +1,38 @@ + + +# Writing to Oracle using `DBWriter` + +For writing data to Oracle, use `DBWriter`. + +#### WARNING +Please take into account [Oracle <-> Spark type mapping](types.md#oracle-types) + +#### WARNING +It is always recommended to create table explicitly using [Oracle.execute](execute.md#oracle-execute) +instead of relying on Spark’s table DDL generation. + +This is because Spark’s DDL generator can create columns with different precision and types than it is expected, +causing precision loss or other issues. + +## Examples + +```python +from onetl.connection import Oracle +from onetl.db import DBWriter + +oracle = Oracle(...) + +df = ... # data is here + +writer = DBWriter( + connection=oracle, + target="schema.table", + options=Oracle.WriteOptions(if_exists="append"), +) + +writer.run(df) +``` + +## Options + +Method above accepts `OracleWriteOptions` diff --git a/mddocs/docs/_build/markdown/connection/db_connection/postgres/connection.md b/mddocs/docs/_build/markdown/connection/db_connection/postgres/connection.md new file mode 100644 index 000000000..655ec9480 --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/postgres/connection.md @@ -0,0 +1,3 @@ + + +# Postgres connection diff --git a/mddocs/docs/_build/markdown/connection/db_connection/postgres/execute.md b/mddocs/docs/_build/markdown/connection/db_connection/postgres/execute.md new file mode 100644 index 000000000..cee848800 --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/postgres/execute.md @@ -0,0 +1,90 @@ + + +# Executing statements in Postgres + +#### WARNING +Methods below **read all the rows** returned from DB **to Spark driver memory**, and then convert them to DataFrame. + +Do **NOT** use them to read large amounts of data. Use [DBReader](read.md#postgres-read) or [Postgres.sql](sql.md#postgres-sql) instead. + +## How to + +There are 2 ways to execute some statement in Postgres + +### Use `Postgres.fetch` + +Use this method to execute some `SELECT` query which returns **small number or rows**, like reading +Postgres config, or reading data from some reference table. Method returns Spark DataFrame. + +Method accepts `Postgres.FetchOptions`. + +Connection opened using this method should be then closed with `connection.close()` or `with connection:`. + +#### WARNING +Please take into account [Postgres <-> Spark type mapping](types.md#postgres-types). + +#### Syntax support + +This method supports **any** query syntax supported by Postgres, like: + +* ✅︎ `SELECT ... FROM ...` +* ✅︎ `WITH alias AS (...) SELECT ...` +* ❌ `SET ...; SELECT ...;` - multiple statements not supported + +#### Examples + +```python +from onetl.connection import Postgres + +postgres = Postgres(...) + +df = postgres.fetch( + "SELECT value FROM some.reference_table WHERE key = 'some_constant'", + options=Postgres.FetchOptions(queryTimeout=10), +) +postgres.close() +value = df.collect()[0][0] # get value from first row and first column +``` + +### Use `Postgres.execute` + +Use this method to execute DDL and DML operations. Each method call runs operation in a separated transaction, and then commits it. + +Method accepts `Postgres.ExecuteOptions`. + +Connection opened using this method should be then closed with `connection.close()` or `with connection:`. + +#### Syntax support + +This method supports **any** query syntax supported by Postgres, like: + +* ✅︎ `CREATE TABLE ...`, `CREATE VIEW ...`, and so on +* ✅︎ `ALTER ...` +* ✅︎ `INSERT INTO ... SELECT ...`, `UPDATE ...`, `DELETE ...`, and so on +* ✅︎ `DROP TABLE ...`, `DROP VIEW ...`, `TRUNCATE TABLE`, and so on +* ✅︎ `CALL procedure(arg1, arg2) ...` +* ✅︎ `SELECT func(arg1, arg2)` or `{call func(arg1, arg2)}` - special syntax for calling functions +* ✅︎ other statements not mentioned here +* ❌ `SET ...; SELECT ...;` - multiple statements not supported + +#### Examples + +```python +from onetl.connection import Postgres + +postgres = Postgres(...) + +postgres.execute("DROP TABLE schema.table") +postgres.execute( + """ + CREATE TABLE schema.table ( + id bigint GENERATED ALWAYS AS IDENTITY, + key text, + value real + ) + """, + options=Postgres.ExecuteOptions(queryTimeout=10), +) +``` + +## Options diff --git a/mddocs/docs/_build/markdown/connection/db_connection/postgres/index.md b/mddocs/docs/_build/markdown/connection/db_connection/postgres/index.md new file mode 100644 index 000000000..e1a8f76eb --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/postgres/index.md @@ -0,0 +1,19 @@ + + +# Postgres + +# Connection + +* [Prerequisites](prerequisites.md) +* [Postgres connection](connection.md) + +# Operations + +* [Reading from Postgres using `DBReader`](read.md) +* [Reading from Postgres using `Postgres.sql`](sql.md) +* [Writing to Postgres using `DBWriter`](write.md) +* [Executing statements in Postgres](execute.md) + +# Troubleshooting + +* [Postgres <-> Spark type mapping](types.md) diff --git a/mddocs/docs/_build/markdown/connection/db_connection/postgres/prerequisites.md b/mddocs/docs/_build/markdown/connection/db_connection/postgres/prerequisites.md new file mode 100644 index 000000000..2bfb04e0f --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/postgres/prerequisites.md @@ -0,0 +1,71 @@ + + +# Prerequisites + +## Version Compatibility + +* PostgreSQL server versions: + : * Officially declared: 8.2 - 17 + * Actually tested: 9.4.26, 17.3 +* Spark versions: 2.3.x - 3.5.x +* Java versions: 8 - 20 + +See [official documentation](https://jdbc.postgresql.org/). + +## Installing PySpark + +To use Postgres connector you should have PySpark installed (or injected to `sys.path`) +BEFORE creating the connector instance. + +See [Spark](../../../install/spark.md#install-spark) installation instruction for more details. + +## Connecting to Postgres + +### Allowing connection to Postgres instance + +Ask your Postgres administrator to allow your user (and probably IP) to connect to instance, +e.g. by updating `pg_hba.conf` file. + +See [official documentation](https://www.postgresql.org/docs/current/auth-pg-hba-conf.html). + +### Connection port + +Connection is usually performed to port 5432. Port may differ for different Postgres instances. +Please ask your Postgres administrator to provide required information. + +### Connection host + +It is possible to connect to Postgres by using either DNS name of host or it’s IP address. + +If you’re using Postgres cluster, it is currently possible to connect only to **one specific node**. +Connecting to multiple nodes to perform load balancing, as well as automatic failover to new master/replica are not supported. + +### Required grants + +Ask your Postgres cluster administrator to set following grants for a user, +used for creating a connection: + +Read + Write + +```sql +-- allow creating tables in specific schema +GRANT USAGE, CREATE ON SCHEMA myschema TO username; + +-- allow read & write access to specific table +GRANT SELECT, INSERT ON myschema.mytable TO username; + +-- only if if_exists="replace_entire_table" is used: +GRANT TRUNCATE ON myschema.mytable TO username; +``` + +Read only + +```sql +-- allow creating tables in specific schema +GRANT USAGE ON SCHEMA myschema TO username; + +-- allow read access to specific table +GRANT SELECT ON myschema.mytable TO username; +``` + +More details can be found in [official documentation](https://www.postgresql.org/docs/current/sql-grant.html). diff --git a/mddocs/docs/_build/markdown/connection/db_connection/postgres/read.md b/mddocs/docs/_build/markdown/connection/db_connection/postgres/read.md new file mode 100644 index 000000000..81565aeec --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/postgres/read.md @@ -0,0 +1,78 @@ + + +# Reading from Postgres using `DBReader` + +`DBReader` supports [Read Strategies](../../../strategy/index.md#strategy) for incremental data reading, +but does not support custom queries, like `JOIN`. + +#### WARNING +Please take into account [Postgres <-> Spark type mapping](types.md#postgres-types) + +## Supported DBReader features + +* ✅︎ `columns` +* ✅︎ `where` +* ✅︎ `hwm`, supported strategies: +* * ✅︎ [Snapshot Strategy](../../../strategy/snapshot_strategy.md#snapshot-strategy) +* * ✅︎ [Incremental Strategy](../../../strategy/incremental_strategy.md#incremental-strategy) +* * ✅︎ [Snapshot Batch Strategy](../../../strategy/snapshot_batch_strategy.md#snapshot-batch-strategy) +* * ✅︎ [Incremental Batch Strategy](../../../strategy/incremental_batch_strategy.md#incremental-batch-strategy) +* ❌ `hint` (is not supported by Postgres) +* ❌ `df_schema` +* ✅︎ `options` (see `Postgres.ReadOptions`) + +## Examples + +Snapshot strategy: + +```python +from onetl.connection import Postgres +from onetl.db import DBReader + +postgres = Postgres(...) + +reader = DBReader( + connection=postgres, + source="schema.table", + columns=["id", "key", "CAST(value AS text) value", "updated_dt"], + where="key = 'something'", + options=Postgres.ReadOptions(partitionColumn="id", numPartitions=10), +) +df = reader.run() +``` + +Incremental strategy: + +```python +from onetl.connection import Postgres +from onetl.db import DBReader +from onetl.strategy import IncrementalStrategy + +postgres = Postgres(...) + +reader = DBReader( + connection=postgres, + source="schema.table", + columns=["id", "key", "CAST(value AS text) value", "updated_dt"], + where="key = 'something'", + hwm=DBReader.AutoDetectHWM(name="postgres_hwm", expression="updated_dt"), + options=Postgres.ReadOptions(partitionColumn="id", numPartitions=10), +) + +with IncrementalStrategy(): + df = reader.run() +``` + +## Recommendations + +### Select only required columns + +Instead of passing `"*"` in `DBReader(columns=[...])` prefer passing exact column names. This reduces the amount of data passed from Postgres to Spark. + +### Pay attention to `where` value + +Instead of filtering data on Spark side using `df.filter(df.column == 'value')` pass proper `DBReader(where="column = 'value'")` clause. +This both reduces the amount of data send from Postgres to Spark, and may also improve performance of the query. +Especially if there are indexes or partitions for columns used in `where` clause. + +## Options diff --git a/mddocs/docs/_build/markdown/connection/db_connection/postgres/sql.md b/mddocs/docs/_build/markdown/connection/db_connection/postgres/sql.md new file mode 100644 index 000000000..a5b648f9a --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/postgres/sql.md @@ -0,0 +1,62 @@ + + +# Reading from Postgres using `Postgres.sql` + +`Postgres.sql` allows passing custom SQL query, but does not support incremental strategies. + +#### WARNING +Please take into account [Postgres <-> Spark type mapping](types.md#postgres-types) + +#### WARNING +Statement is executed in **read-write** connection, so if you’re calling some functions/procedures with DDL/DML statements inside, +they can change data in your database. + +## Syntax support + +Only queries with the following syntax are supported: + +* ✅︎ `SELECT ... FROM ...` +* ✅︎ `WITH alias AS (...) SELECT ...` +* ❌ `SET ...; SELECT ...;` - multiple statements not supported + +## Examples + +```python +from onetl.connection import Postgres + +postgres = Postgres(...) +df = postgres.sql( + """ + SELECT + id, + key, + CAST(value AS text) value, + updated_at + FROM + some.mytable + WHERE + key = 'something' + """, + options=Postgres.SQLOptions( + partitionColumn="id", + numPartitions=10, + lowerBound=0, + upperBound=1000, + ), +) +``` + +## Recommendations + +### Select only required columns + +Instead of passing `SELECT * FROM ...` prefer passing exact column names `SELECT col1, col2, ...`. +This reduces the amount of data passed from Postgres to Spark. + +### Pay attention to `where` value + +Instead of filtering data on Spark side using `df.filter(df.column == 'value')` pass proper `WHERE column = 'value'` clause. +This both reduces the amount of data send from Postgres to Spark, and may also improve performance of the query. +Especially if there are indexes or partitions for columns used in `where` clause. + +## Options diff --git a/mddocs/docs/_build/markdown/connection/db_connection/postgres/types.md b/mddocs/docs/_build/markdown/connection/db_connection/postgres/types.md new file mode 100644 index 000000000..5fda197e5 --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/postgres/types.md @@ -0,0 +1,372 @@ + + +# Postgres <-> Spark type mapping + +#### NOTE +The results below are valid for Spark 3.5.5, and may differ on other Spark versions. + +## Type detection & casting + +Spark’s DataFrames always have a `schema` which is a list of columns with corresponding Spark types. All operations on a column are performed using column type. + +### Reading from Postgres + +This is how Postgres connector performs this: + +* For each column in query result (`SELECT column1, column2, ... FROM table ...`) get column name and Postgres type. +* Find corresponding `Postgres type (read)` → `Spark type` combination (see below) for each DataFrame column [1](#id2). If no combination is found, raise exception. +* Create DataFrame from query with specific column names and Spark types. + +* **[1]** All Postgres types that doesn’t have corresponding Java type are converted to `String`. + +### Writing to some existing Postgres table + +This is how Postgres connector performs this: + +* Get names of columns in DataFrame. [1](#id2) +* Perform `SELECT * FROM table LIMIT 0` query. +* Take only columns present in DataFrame (by name, case insensitive) [2](#id6). For each found column get Postgres type. +* Find corresponding `Spark type` → `Postgres type (write)` combination (see below) for each DataFrame column. If no combination is found, raise exception. +* If `Postgres type (write)` match `Postgres type (read)`, no additional casts will be performed, DataFrame column will be written to Postgres as is. +* If `Postgres type (write)` does not match `Postgres type (read)`, DataFrame column will be casted to target column type **on Postgres side**. + For example, you can write column with text data to `int` column, if column contains valid integer values within supported value range and precision [3](#id7). + +* **[2]** This allows to write data to tables with `DEFAULT` and `GENERATED` columns - if DataFrame has no such column, it will be populated by Postgres. +* **[3]** This is true only if either DataFrame column is a `StringType()`, or target column is `text` type. But other types cannot be silently converted, like `bytea -> bit(N)`. This requires explicit casting, see [Manual conversion to string](). + +### Create new table using Spark + +#### WARNING +ABSOLUTELY NOT RECOMMENDED! + +This is how Postgres connector performs this: + +* Find corresponding `Spark type` → `Postgres type (create)` combination (see below) for each DataFrame column. If no combination is found, raise exception. +* Generate DDL for creating table in Postgres, like `CREATE TABLE (col1 ...)`, and run it. +* Write DataFrame to created table as is. + +But Postgres connector support only limited number of types and almost no custom clauses (like `PARTITION BY`, `INDEX`, etc). +So instead of relying on Spark to create tables: + +### See example + +```python +writer = DBWriter( + connection=postgres, + target="public.table", + options=Postgres.WriteOptions( + if_exists="append", + createTableOptions="PARTITION BY RANGE (id)", + ), +) +writer.run(df) +``` + +Always prefer creating table with desired DDL **BEFORE WRITING DATA**: + +### See example + +```python +postgres.execute( + """ + CREATE TABLE public.table ( + id bigint, + business_dt timestamp(6), + value json + ) + PARTITION BY RANGE (Id) + """, +) + +writer = DBWriter( + connection=postgres, + target="public.table", + options=Postgres.WriteOptions(if_exists="append"), +) +writer.run(df) +``` + +See Postgres [CREATE TABLE](https://www.postgresql.org/docs/current/sql-createtable.html) documentation. + +## Supported types + +### References + +See [List of Postgres types](https://www.postgresql.org/docs/current/datatype.html). + +Here you can find source code with type conversions: + +* [Postgres <-> JDBC](https://github.com/pgjdbc/pgjdbc/blob/REL42.6.0/pgjdbc/src/main/java/org/postgresql/jdbc/TypeInfoCache.java#L78-L112) +* [JDBC -> Spark](https://github.com/apache/spark/blob/v3.5.5/sql/core/src/main/scala/org/apache/spark/sql/jdbc/PostgresDialect.scala#L52-L108) +* [Spark -> JDBC](https://github.com/apache/spark/blob/v3.5.5/sql/core/src/main/scala/org/apache/spark/sql/jdbc/PostgresDialect.scala#L118-L132) + +### Numeric types + +| Postgres type (read) | Spark type | Postgres type (write) | Postgres type (create) | +|-----------------------------|-------------------------------------|-----------------------------|--------------------------| +| `decimal` | `DecimalType(P=38, S=18)` | `decimal(P=38, S=18)` | `decimal` (unbounded) | +| `decimal(P=0..38)` | `DecimalType(P=0..38, S=0)` | `decimal(P=0..38, S=0)` | | +| `decimal(P=0..38, S=0..38)` | `DecimalType(P=0..38, S=0..38)` | `decimal(P=0..38, S=0..38)` | | +| `decimal(P=39.., S=0..)` | unsupported [4](#id11) | | | +| `decimal(P=.., S=..-1)` | unsupported [5](#id12) | | | +| `real` | `FloatType()` | `real` | `real` | +| `double precision` | `DoubleType()` | `double precision` | `double precision` | +| `smallint` | `ShortType()` | `smallint` | `smallint` | +| `-` | `ByteType()` | | | +| `integer` | `IntegerType()` | `integer` | `integer` | +| `bigint` | `LongType()` | `bigint` | `bigint` | +| `money` | `StringType()` [1](#id2) | `text` | `text` | +| `int4range` | | | | +| `int8range` | | | | +| `numrange` | | | | +| `int2vector` | | | | +* **[4]** Postgres support decimal types with unlimited precision. But Spark’s `DecimalType(P, S)` supports maximum `P=38` (128 bit). It is impossible to read, write or operate with values of larger precision, this leads to an exception. +* **[5]** Postgres support decimal types with negative scale, like `decimal(38, -10)`. Spark doesn’t. + +### Temporal types + +| Postgres type (read) | Spark type | Postgres type (write) | Postgres type (create) | +|----------------------------------|----------------------------------------------------------------------|-------------------------|--------------------------| +| `date` | `DateType()` | `date` | `date` | +| `time` | `TimestampType()`,
with time format quirks [6](#id16) | `timestamp(6)` | `timestamp(6)` | +| `time(0..6)` | | | | +| `time with time zone` | | | | +| `time(0..6) with time zone` | | | | +| `timestamp` | `TimestampType()` | `timestamp(6)` | `timestamp(6)` | +| `timestamp(0..6)` | | | | +| `timestamp with time zone` | | | | +| `timestamp(0..6) with time zone` | | | | +| `-` | `TimestampNTZType()` | `timestamp(6)` | `timestamp(6)` | +| `interval` of any precision | `StringType()` [1](#id2) | `text` | `text` | +| `-` | `DayTimeIntervalType()` | unsupported | unsupported | +| `-` | `YearMonthIntervalType()` | unsupported | unsupported | +| `daterange` | `StringType()` [1](#id2) | `text` | `text` | +| `tsrange` | | | | +| `tstzrange` | | | | + +#### WARNING +Note that types in Postgres and Spark have different value ranges: + +| Postgres type | Min value | Max value | Spark type | Min value | Max value | +|-----------------|-------------------------------|--------------------------------|-------------------|------------------------------|------------------------------| +| `date` | `-4713-01-01` | `5874897-01-01` | `DateType()` | `0001-01-01` | `9999-12-31` | +| `timestamp` | `-4713-01-01 00:00:00.000000` | `294276-12-31 23:59:59.999999` | `TimestampType()` | `0001-01-01 00:00:00.000000` | `9999-12-31 23:59:59.999999` | +| `time` | `00:00:00.000000` | `24:00:00.000000` | | | | + +So not all of values can be read from Postgres to Spark. + +References: +: * [Postgres date/time types documentation](https://www.postgresql.org/docs/current/datatype-datetime.html) + * [Spark DateType documentation](https://spark.apache.org/docs/latest/api/java/org/apache/spark/sql/types/DateType.html) + * [Spark TimestampType documentation](https://spark.apache.org/docs/latest/api/java/org/apache/spark/sql/types/TimestampType.html) + +* **[6]** `time` type is the same as `timestamp` with date `1970-01-01`. So instead of reading data from Postgres like `23:59:59` it is actually read `1970-01-01 23:59:59`, and vice versa. + +### String types + +| Postgres type (read) | Spark type | Postgres type (write) | Postgres type (create) | +|---------------------------|-------------------------------------|-------------------------|--------------------------| +| `character` | `StringType()` | `text` | `text` | +| `character(N)` | | | | +| `character varying` | | | | +| `character varying(N)` | | | | +| `text` | | | | +| `json` | | | | +| `jsonb` | | | | +| `xml` | | | | +| `CREATE TYPE ... AS ENUM` | `StringType()` [1](#id2) | | | +| `tsvector` | | | | +| `tsquery` | | | | +| `-` | `CharType()` | `unsupported` | `unsupported` | +| `-` | `VarcharType()` | `unsupported` | `unsupported` | + +### Binary types + +| Postgres type (read) | Spark type | Postgres type (write) | Postgres type (create) | +|------------------------|-------------------------------------|----------------------------------------------------------|--------------------------| +| `boolean` | `BooleanType()` | `boolean` | `boolean` | +| `bit` | `BooleanType()` | `bool`,
**cannot insert data** [3](#id7) | `bool` | +| `bit(N=1)` | | | | +| `bit(N=2..)` | `ByteType()` | `bytea`,
**cannot insert data** [3](#id7) | `bytea` | +| `bit varying` | `StringType()` [1](#id2) | `text` | `text` | +| `bit varying(N)` | | | | +| `bytea` | `BinaryType()` | `bytea` | `bytea` | + +### Struct types + +| Postgres type (read) | Spark type | Postgres type (write) | Postgres type (create) | +|------------------------------|-------------------------------------|-------------------------|--------------------------| +| `T[]` | `ArrayType(T)` | `T[]` | `T[]` | +| `T[][]` | unsupported | | | +| `CREATE TYPE sometype (...)` | `StringType()` [1](#id2) | `text` | `text` | +| `-` | `StructType()` | unsupported | | +| `-` | `MapType()` | | | + +### Network types + +| Postgres type (read) | Spark type | Postgres type (write) | Postgres type (create) | +|------------------------|-------------------------------------|-------------------------|--------------------------| +| `cidr` | `StringType()` [1](#id2) | `text` | `text` | +| `inet` | | | | +| `macaddr` | | | | +| `macaddr8` | | | | + +### Geo types + +| Postgres type (read) | Spark type | Postgres type (write) | Postgres type (create) | +|------------------------|-------------------------------------|-------------------------|--------------------------| +| `circle` | `StringType()` [1](#id2) | `text` | `text` | +| `box` | | | | +| `line` | | | | +| `lseg` | | | | +| `path` | | | | +| `point` | | | | +| `polygon` | | | | +| `polygon` | | | | + +## Explicit type cast + +### `DBReader` + +It is possible to explicitly cast column of unsupported type using `DBReader(columns=...)` syntax. + +For example, you can use `CAST(column AS text)` to convert data to string representation on Postgres side, and so it will be read as Spark’s `StringType()`. + +It is also possible to use [to_json](https://www.postgresql.org/docs/current/functions-json.html) Postgres function to convert column of any type to string representation, and then parse this column on Spark side you can use the `JSON.parse_column` method: + +```python +from pyspark.sql.types import IntegerType + +from onetl.connection import Postgres +from onetl.db import DBReader +from onetl.file.format import JSON + +postgres = Postgres(...) + +DBReader( + connection=postgres, + columns=[ + "id", + "supported_column", + "CAST(unsupported_column AS text) unsupported_column_str", + # or + "to_json(unsupported_column) array_column_json", + ], +) +df = reader.run() + +json_schema = StructType( + [ + StructField("id", IntegerType(), nullable=True), + StructField("name", StringType(), nullable=True), + ..., + ] +) +df = df.select( + df.id, + df.supported_column, + # explicit cast + df.unsupported_column_str.cast("integer").alias("parsed_integer"), + JSON().parse_column("array_column_json", json_schema).alias("json_string"), +) +``` + +### `DBWriter` + +It is always possible to convert data on the Spark side to a string, and then write it to a text column in a Postgres table. + +#### Using JSON.serialize_column + +You can use the `JSON.serialize_column` method for data serialization: + +```python +from onetl.file.format import JSON +from pyspark.sql.functions import col + +from onetl.connection import Postgres +from onetl.db import DBWriter + +postgres = Postgres(...) + +postgres.execute( + """ + CREATE TABLE schema.target_table ( + id int, + supported_column timestamp, + array_column_json jsonb -- any column type, actually + ) + """, +) + +write_df = df.select( + df.id, + df.supported_column, + JSON().serialize_column(df.unsupported_column).alias("array_column_json"), +) + +writer = DBWriter( + connection=postgres, + target="schema.target_table", +) +writer.run(write_df) +``` + +Then you can parse this column on the Postgres side (for example, by creating a view): + +```sql +SELECT + id, + supported_column, + array_column_json->'0' AS array_item_0 +FROM + schema.target_table +``` + +To avoid casting the value on every table read you can use [GENERATED ALWAYS STORED](https://www.postgresql.org/docs/current/ddl-generated-columns.html) column, but this requires 2x space (for original and parsed value). + +#### Manual conversion to string + +Postgres connector also supports conversion text value directly to target column type, if this value has a proper format. + +For example, you can write data like `[123, 345)` to `int8range` type because Postgres allows cast `'[123, 345)'::int8range'`: + +```python +from pyspark.sql.ftypes import StringType +from pyspark.sql.functions import udf + +from onetl.connection import Postgres +from onetl.db import DBReader + +postgres = Postgres(...) + +postgres.execute( + """ + CREATE TABLE schema.target_table ( + id int, + range_column int8range -- any column type, actually + ) + """, +) + + +@udf(returnType=StringType()) +def array_to_range(value: tuple): + """This UDF allows to convert tuple[start, end] to Postgres' range format""" + start, end = value + return f"[{start},{end})" + + +write_df = df.select( + df.id, + array_to_range(df.range_column).alias("range_column"), +) + +writer = DBWriter( + connection=postgres, + target="schema.target_table", +) +writer.run(write_df) +``` + +This can be tricky to implement and may lead to longer write process. +But this does not require extra space on Postgres side, and allows to avoid explicit value cast on every table read. diff --git a/mddocs/docs/_build/markdown/connection/db_connection/postgres/write.md b/mddocs/docs/_build/markdown/connection/db_connection/postgres/write.md new file mode 100644 index 000000000..823b98c9d --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/postgres/write.md @@ -0,0 +1,38 @@ + + +# Writing to Postgres using `DBWriter` + +For writing data to Postgres, use `DBWriter`. + +#### WARNING +Please take into account [Postgres <-> Spark type mapping](types.md#postgres-types) + +#### WARNING +It is always recommended to create table explicitly using [Postgres.execute](execute.md#postgres-execute) +instead of relying on Spark’s table DDL generation. + +This is because Spark’s DDL generator can create columns with different precision and types than it is expected, +causing precision loss or other issues. + +## Examples + +```python +from onetl.connection import Postgres +from onetl.db import DBWriter + +postgres = Postgres(...) + +df = ... # data is here + +writer = DBWriter( + connection=postgres, + target="schema.table", + options=Postgres.WriteOptions(if_exists="append"), +) + +writer.run(df) +``` + +## Options + +Method above accepts `Postgres.WriteOptions` diff --git a/mddocs/docs/_build/markdown/connection/db_connection/teradata/connection.md b/mddocs/docs/_build/markdown/connection/db_connection/teradata/connection.md new file mode 100644 index 000000000..3f4d2e832 --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/teradata/connection.md @@ -0,0 +1,3 @@ + + +# Teradata connection diff --git a/mddocs/docs/_build/markdown/connection/db_connection/teradata/execute.md b/mddocs/docs/_build/markdown/connection/db_connection/teradata/execute.md new file mode 100644 index 000000000..cf5e6d40c --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/teradata/execute.md @@ -0,0 +1,90 @@ + + +# Executing statements in Teradata + +#### WARNING +Methods below **read all the rows** returned from DB **to Spark driver memory**, and then convert them to DataFrame. + +Do **NOT** use them to read large amounts of data. Use [DBReader](read.md#teradata-read) or [Teradata.sql](sql.md#teradata-sql) instead. + +## How to + +There are 2 ways to execute some statement in Teradata + +### Use `Teradata.fetch` + +Use this method to execute some `SELECT` query which returns **small number or rows**, like reading +Teradata config, or reading data from some reference table. Method returns Spark DataFrame. + +Method accepts `Teradata.FetchOptions`. + +Connection opened using this method should be then closed with `connection.close()` or `with connection:`. + +#### Syntax support + +This method supports **any** query syntax supported by Teradata, like: + +* ✅︎ `SELECT ... FROM ...` +* ✅︎ `WITH alias AS (...) SELECT ...` +* ✅︎ `SHOW ...` +* ❌ `SET ...; SELECT ...;` - multiple statements not supported + +#### Examples + +```python +from onetl.connection import Teradata + +teradata = Teradata(...) + +df = teradata.fetch( + "SELECT value FROM some.reference_table WHERE key = 'some_constant'", + options=Teradata.FetchOptions(queryTimeout=10), +) +teradata.close() +value = df.collect()[0][0] # get value from first row and first column +``` + +### Use `Teradata.execute` + +Use this method to execute DDL and DML operations. Each method call runs operation in a separated transaction, and then commits it. + +Method accepts `Teradata.ExecuteOptions`. + +Connection opened using this method should be then closed with `connection.close()` or `with connection:`. + +#### Syntax support + +This method supports **any** query syntax supported by Teradata, like: + +* ✅︎ `CREATE TABLE ...`, `CREATE VIEW ...`, and so on +* ✅︎ `ALTER ...` +* ✅︎ `INSERT INTO ... SELECT ...`, `UPDATE ...`, `DELETE ...`, and so on +* ✅︎ `DROP TABLE ...`, `DROP VIEW ...`, `TRUNCATE TABLE`, and so on +* ✅︎ `CALL procedure(arg1, arg2) ...` or `{call procedure(arg1, arg2)}` - special syntax for calling procedure +* ✅︎ `EXECUTE macro(arg1, arg2)` +* ✅︎ `EXECUTE FUNCTION ...` +* ✅︎ other statements not mentioned here +* ❌ `SET ...; SELECT ...;` - multiple statements not supported + +#### Examples + +```python +from onetl.connection import Teradata + +teradata = Teradata(...) + +teradata.execute("DROP TABLE database.table") +teradata.execute( + """ + CREATE MULTISET TABLE database.table AS ( + id BIGINT, + key VARCHAR, + value REAL + ) + NO PRIMARY INDEX + """, + options=Teradata.ExecuteOptions(queryTimeout=10), +) +``` + +## Options diff --git a/mddocs/docs/_build/markdown/connection/db_connection/teradata/index.md b/mddocs/docs/_build/markdown/connection/db_connection/teradata/index.md new file mode 100644 index 000000000..f35e86981 --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/teradata/index.md @@ -0,0 +1,15 @@ + + +# Teradata + +# Connection + +* [Prerequisites](prerequisites.md) +* [Teradata connection](connection.md) + +# Operations + +* [Reading from Teradata using `DBReader`](read.md) +* [Reading from Teradata using `Teradata.sql`](sql.md) +* [Writing to Teradata using `DBWriter`](write.md) +* [Executing statements in Teradata](execute.md) diff --git a/mddocs/docs/_build/markdown/connection/db_connection/teradata/prerequisites.md b/mddocs/docs/_build/markdown/connection/db_connection/teradata/prerequisites.md new file mode 100644 index 000000000..3217390e2 --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/teradata/prerequisites.md @@ -0,0 +1,57 @@ + + +# Prerequisites + +## Version Compatibility + +* Teradata server versions: + : * Officially declared: 16.10 - 20.0 + * Actually tested: 16.10 +* Spark versions: 2.3.x - 3.5.x +* Java versions: 8 - 20 + +See [official documentation](https://teradata-docs.s3.amazonaws.com/doc/connectivity/jdbc/reference/current/platformMatrix.html). + +## Installing PySpark + +To use Teradata connector you should have PySpark installed (or injected to `sys.path`) +BEFORE creating the connector instance. + +See [Spark](../../../install/spark.md#install-spark) installation instruction for more details. + +## Connecting to Teradata + +### Connection host + +It is possible to connect to Teradata by using either DNS name Parsing Engine (PE) host, or it’s IP address. + +### Connection port + +Connection is usually performed to port `1025`. Port may differ for different Teradata instances. +Please ask your Teradata administrator to provide required information. + +### Required grants + +Ask your Teradata cluster administrator to set following grants for a user, +used for creating a connection: + +Read + Write + +```sql +-- allow creating tables in the target schema +GRANT CREATE TABLE ON database TO username; + +-- allow read & write access to specific table +GRANT SELECT, INSERT ON database.mytable TO username; +``` + +Read only + +```sql +-- allow read access to specific table +GRANT SELECT ON database.mytable TO username; +``` + +See: +: * [Teradata access rights](https://www.dwhpro.com/teradata-access-rights/) + * [GRANT documentation](https://teradata.github.io/presto/docs/0.167-t/sql/grant.html) diff --git a/mddocs/docs/_build/markdown/connection/db_connection/teradata/read.md b/mddocs/docs/_build/markdown/connection/db_connection/teradata/read.md new file mode 100644 index 000000000..e06d2cebc --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/teradata/read.md @@ -0,0 +1,107 @@ + + +# Reading from Teradata using `DBReader` + +`DBReader` supports [Read Strategies](../../../strategy/index.md#strategy) for incremental data reading, +but does not support custom queries, like `JOIN`. + +## Supported DBReader features + +* ✅︎ `columns` +* ✅︎ `where` +* ✅︎ `hwm`, supported strategies: +* * ✅︎ [Snapshot Strategy](../../../strategy/snapshot_strategy.md#snapshot-strategy) +* * ✅︎ [Incremental Strategy](../../../strategy/incremental_strategy.md#incremental-strategy) +* * ✅︎ [Snapshot Batch Strategy](../../../strategy/snapshot_batch_strategy.md#snapshot-batch-strategy) +* * ✅︎ [Incremental Batch Strategy](../../../strategy/incremental_batch_strategy.md#incremental-batch-strategy) +* ❌ `hint` (is not supported by Teradata) +* ❌ `df_schema` +* ✅︎ `options` (see `Teradata.ReadOptions`) + +## Examples + +Snapshot strategy: + +```python +from onetl.connection import Teradata +from onetl.db import DBReader + +teradata = Teradata(...) + +reader = DBReader( + connection=teradata, + source="database.table", + columns=["id", "key", "CAST(value AS VARCHAR) value", "updated_dt"], + where="key = 'something'", + options=Teradata.ReadOptions( + partitioning_mode="hash", + partitionColumn="id", + numPartitions=10, + ), +) +df = reader.run() +``` + +Incremental strategy: + +```python +from onetl.connection import Teradata +from onetl.db import DBReader +from onetl.strategy import IncrementalStrategy + +teradata = Teradata(...) + +reader = DBReader( + connection=teradata, + source="database.table", + columns=["id", "key", "CAST(value AS VARCHAR) value", "updated_dt"], + where="key = 'something'", + hwm=DBReader.AutoDetectHWM(name="teradata_hwm", expression="updated_dt"), + options=Teradata.ReadOptions( + partitioning_mode="hash", + partitionColumn="id", + numPartitions=10, + ), +) + +with IncrementalStrategy(): + df = reader.run() +``` + +## Recommendations + +### Select only required columns + +Instead of passing `"*"` in `DBReader(columns=[...])` prefer passing exact column names. This reduces the amount of data passed from Teradata to Spark. + +### Pay attention to `where` value + +Instead of filtering data on Spark side using `df.filter(df.column == 'value')` pass proper `DBReader(where="column = 'value'")` clause. +This both reduces the amount of data send from Teradata to Spark, and may also improve performance of the query. +Especially if there are indexes or partitions for columns used in `where` clause. + +### Read data in parallel + +`DBReader` can read data in multiple parallel connections by passing `Teradata.ReadOptions(numPartitions=..., partitionColumn=...)`. + +In the example above, Spark opens 10 parallel connections, and data is evenly distributed between all these connections using expression +`HASHAMP(HASHBUCKET(HASHROW({partition_column}))) MOD {num_partitions}`. +This allows sending each Spark worker only some piece of data, reducing resource consumption. +`partition_column` here can be table column of any type. + +It is also possible to use `partitioning_mode="mod"` or `partitioning_mode="range"`, but in this case +`partition_column` have to be an integer, should not contain `NULL`, and values to be uniformly distributed. +It is also less performant than `partitioning_mode="hash"` due to Teradata `HASHAMP` implementation. + +### Do **NOT** use `TYPE=FASTEXPORT` + +Teradata supports several [different connection types](https://teradata-docs.s3.amazonaws.com/doc/connectivity/jdbc/reference/current/jdbcug_chapter_2.html#BABFGFAF): +: * `TYPE=DEFAULT` - perform plain `SELECT` queries + * `TYPE=FASTEXPORT` - uses special FastExport protocol for select queries + +But `TYPE=FASTEXPORT` uses exclusive lock on the source table, so it is impossible to use multiple Spark workers parallel data read. +This leads to sending all the data to just one Spark worker, which is slow and takes a lot of RAM. + +Prefer using `partitioning_mode="hash"` from example above. + +## Options diff --git a/mddocs/docs/_build/markdown/connection/db_connection/teradata/sql.md b/mddocs/docs/_build/markdown/connection/db_connection/teradata/sql.md new file mode 100644 index 000000000..d52c3f407 --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/teradata/sql.md @@ -0,0 +1,61 @@ + + +# Reading from Teradata using `Teradata.sql` + +`Teradata.sql` allows passing custom SQL query, but does not support incremental strategies. + +#### WARNING +Statement is executed in **read-write** connection, so if you’re calling some functions/procedures with DDL/DML statements inside, +they can change data in your database. + +## Syntax support + +Only queries with the following syntax are supported: + +* ✅︎ `SELECT ... FROM ...` +* ✅︎ `WITH alias AS (...) SELECT ...` +* ❌ `SHOW ...` +* ❌ `SET ...; SELECT ...;` - multiple statements not supported + +## Examples + +```python +from onetl.connection import Teradata + +teradata = Teradata(...) +df = teradata.sql( + """ + SELECT + id, + key, + CAST(value AS VARCHAR) AS value, + updated_at, + HASHAMP(HASHBUCKET(HASHROW(id))) MOD 10 AS part_column + FROM + database.mytable + WHERE + key = 'something' + """, + options=Teradata.SQLOptions( + partitionColumn="id", + numPartitions=10, + lowerBound=0, + upperBound=1000, + ), +) +``` + +## Recommendations + +### Select only required columns + +Instead of passing `SELECT * FROM ...` prefer passing exact column names `SELECT col1, col2, ...`. +This reduces the amount of data passed from Teradata to Spark. + +### Pay attention to `where` value + +Instead of filtering data on Spark side using `df.filter(df.column == 'value')` pass proper `WHERE column = 'value'` clause. +This both reduces the amount of data send from Teradata to Spark, and may also improve performance of the query. +Especially if there are indexes or partitions for columns used in `where` clause. + +## Options diff --git a/mddocs/docs/_build/markdown/connection/db_connection/teradata/write.md b/mddocs/docs/_build/markdown/connection/db_connection/teradata/write.md new file mode 100644 index 000000000..9ee2f5c6e --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/db_connection/teradata/write.md @@ -0,0 +1,105 @@ + + +# Writing to Teradata using `DBWriter` + +For writing data to Teradata, use `DBWriter`. + +#### WARNING +It is always recommended to create table explicitly using [Teradata.execute](execute.md#teradata-execute) +instead of relying on Spark’s table DDL generation. + +This is because Spark’s DDL generator can create columns with different precision and types than it is expected, +causing precision loss or other issues. + +## Examples + +```python +from onetl.connection import Teradata +from onetl.db import DBWriter + +teradata = Teradata( + ..., + extra={"TYPE": "FASTLOAD", "TMODE": "TERA"}, +) + +df = ... # data is here + +writer = DBWriter( + connection=teradata, + target="database.table", + options=Teradata.WriteOptions( + if_exists="append", + # avoid creating SET table, use MULTISET + createTableOptions="NO PRIMARY INDEX", + ), +) + +writer.run(df.repartition(1)) +``` + +## Recommendations + +### Number of connections + +Teradata is not MVCC based, so write operations take exclusive lock on the entire table. +So **it is impossible to write data to Teradata table in multiple parallel connections**, no exceptions. + +The only way to write to Teradata without making deadlocks is write dataframe with exactly 1 partition. + +It can be implemented using `df.repartition(1)`: + +```python +# do NOT use df.coalesce(1) as it can freeze +writer.run(df.repartition(1)) +``` + +This moves all the data to just one Spark worker, so it may consume a lot of RAM. It is usually require to increase `spark.executor.memory` to handle this. + +Another way is to write all dataframe partitions one-by-one: + +```python +from pyspark.sql.functions import spark_partition_id + +# get list of all partitions in the dataframe +partitions = sorted(df.select(spark_partition_id()).distinct().collect()) + +for partition in partitions: + # get only part of data within this exact partition + part_df = df.where(**partition.asDict()).coalesce(1) + + writer.run(part_df) +``` + +This require even data distribution for all partitions to avoid data skew and spikes of RAM consuming. + +### Choosing connection type + +Teradata supports several [different connection types](https://teradata-docs.s3.amazonaws.com/doc/connectivity/jdbc/reference/current/jdbcug_chapter_2.html#BABFGFAF): +: * `TYPE=DEFAULT` - perform plain `INSERT` queries + * `TYPE=FASTLOAD` - uses special FastLoad protocol for insert queries + +It is always recommended to use `TYPE=FASTLOAD` because: +: * It provides higher performance + * It properly handles inserting `NULL` values (`TYPE=DEFAULT` raises an exception) + +But it can be used only during write, not read. + +### Choosing transaction mode + +Teradata supports [2 different transaction modes](https://teradata-docs.s3.amazonaws.com/doc/connectivity/jdbc/reference/current/jdbcug_chapter_2.html#TMODESEC): +: * `TMODE=ANSI` + * `TMODE=TERA` + +Choosing one of the modes can alter connector behavior. For example: +: * Inserting data which exceeds table column length, like insert `CHAR(25)` to column with type `CHAR(24)`: + * * `TMODE=ANSI` - raises exception + * * `TMODE=TERA` - truncates input string to 24 symbols + * Creating table using Spark: + * * `TMODE=ANSI` - creates `MULTISET` table + * * `TMODE=TERA` - creates `SET` table with `PRIMARY KEY` is a first column in dataframe. + This can lead to slower insert time, because each row will be checked against a unique index. + Fortunately, this can be disabled by passing custom `createTableOptions`. + +## Options + +Method above accepts `Teradata.WriteOptions` diff --git a/mddocs/docs/_build/markdown/connection/file_connection/ftp.md b/mddocs/docs/_build/markdown/connection/file_connection/ftp.md new file mode 100644 index 000000000..30502b16f --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/file_connection/ftp.md @@ -0,0 +1,3 @@ + + +# FTP connection diff --git a/mddocs/docs/_build/markdown/connection/file_connection/ftps.md b/mddocs/docs/_build/markdown/connection/file_connection/ftps.md new file mode 100644 index 000000000..d880128ed --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/file_connection/ftps.md @@ -0,0 +1,3 @@ + + +# FTPS connection diff --git a/mddocs/docs/_build/markdown/connection/file_connection/hdfs/connection.md b/mddocs/docs/_build/markdown/connection/file_connection/hdfs/connection.md new file mode 100644 index 000000000..cec7cfdc3 --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/file_connection/hdfs/connection.md @@ -0,0 +1,3 @@ + + +# HDFS connection diff --git a/mddocs/docs/_build/markdown/connection/file_connection/hdfs/index.md b/mddocs/docs/_build/markdown/connection/file_connection/hdfs/index.md new file mode 100644 index 000000000..a65c43ea3 --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/file_connection/hdfs/index.md @@ -0,0 +1,11 @@ + + +# HDFS + +# Connection + +* [HDFS connection](connection.md) + +# For developers + +* [HDFS Slots](slots.md) diff --git a/mddocs/docs/_build/markdown/connection/file_connection/hdfs/slots.md b/mddocs/docs/_build/markdown/connection/file_connection/hdfs/slots.md new file mode 100644 index 000000000..598fca6e2 --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/file_connection/hdfs/slots.md @@ -0,0 +1,3 @@ + + +# HDFS Slots diff --git a/mddocs/docs/_build/markdown/connection/file_connection/index.md b/mddocs/docs/_build/markdown/connection/file_connection/index.md new file mode 100644 index 000000000..874845c78 --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/file_connection/index.md @@ -0,0 +1,13 @@ + + +# File Connections + +# File Connections + +* [FTP](ftp.md) +* [FTPS](ftps.md) +* [HDFS](hdfs/index.md) +* [Samba](samba.md) +* [SFTP](sftp.md) +* [S3](s3.md) +* [Webdav](webdav.md) diff --git a/mddocs/docs/_build/markdown/connection/file_connection/s3.md b/mddocs/docs/_build/markdown/connection/file_connection/s3.md new file mode 100644 index 000000000..1d1fddc84 --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/file_connection/s3.md @@ -0,0 +1,3 @@ + + +# S3 connection diff --git a/mddocs/docs/_build/markdown/connection/file_connection/samba.md b/mddocs/docs/_build/markdown/connection/file_connection/samba.md new file mode 100644 index 000000000..47ec6a315 --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/file_connection/samba.md @@ -0,0 +1,3 @@ + + +# Samba connection diff --git a/mddocs/docs/_build/markdown/connection/file_connection/sftp.md b/mddocs/docs/_build/markdown/connection/file_connection/sftp.md new file mode 100644 index 000000000..28af73f44 --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/file_connection/sftp.md @@ -0,0 +1,3 @@ + + +# SFTP connection diff --git a/mddocs/docs/_build/markdown/connection/file_connection/webdav.md b/mddocs/docs/_build/markdown/connection/file_connection/webdav.md new file mode 100644 index 000000000..f3bc2daf7 --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/file_connection/webdav.md @@ -0,0 +1,3 @@ + + +# WebDAV connection diff --git a/mddocs/docs/_build/markdown/connection/file_df_connection/base.md b/mddocs/docs/_build/markdown/connection/file_df_connection/base.md new file mode 100644 index 000000000..00056fbf0 --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/file_df_connection/base.md @@ -0,0 +1,3 @@ + + +# Base interface diff --git a/mddocs/docs/_build/markdown/connection/file_df_connection/index.md b/mddocs/docs/_build/markdown/connection/file_df_connection/index.md new file mode 100644 index 000000000..423e1c82a --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/file_df_connection/index.md @@ -0,0 +1,19 @@ + + +# File DataFrame Connections + +# File DataFrame Connections + +* [Spark LocalFS](spark_local_fs.md) +* [Spark HDFS](spark_hdfs/index.md) + * [Prerequisites](spark_hdfs/prerequisites.md) + * [Connection](spark_hdfs/connection.md) + * [Slots](spark_hdfs/slots.md) +* [Spark S3](spark_s3/index.md) + * [Prerequisites](spark_s3/prerequisites.md) + * [Connection](spark_s3/connection.md) + * [Troubleshooting](spark_s3/troubleshooting.md) + +# For developers + +* [Base interface](base.md) diff --git a/mddocs/docs/_build/markdown/connection/file_df_connection/spark_hdfs/connection.md b/mddocs/docs/_build/markdown/connection/file_df_connection/spark_hdfs/connection.md new file mode 100644 index 000000000..f18513c1c --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/file_df_connection/spark_hdfs/connection.md @@ -0,0 +1,3 @@ + + +# Spark HDFS Connection diff --git a/mddocs/docs/_build/markdown/connection/file_df_connection/spark_hdfs/index.md b/mddocs/docs/_build/markdown/connection/file_df_connection/spark_hdfs/index.md new file mode 100644 index 000000000..d5eae4c27 --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/file_df_connection/spark_hdfs/index.md @@ -0,0 +1,12 @@ + + +# Spark HDFS + +# Connection + +* [Prerequisites](prerequisites.md) +* [Connection](connection.md) + +# For developers + +* [Slots](slots.md) diff --git a/mddocs/docs/_build/markdown/connection/file_df_connection/spark_hdfs/prerequisites.md b/mddocs/docs/_build/markdown/connection/file_df_connection/spark_hdfs/prerequisites.md new file mode 100644 index 000000000..2011410da --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/file_df_connection/spark_hdfs/prerequisites.md @@ -0,0 +1,46 @@ + + +# Prerequisites + +## Version Compatibility + +* Hadoop versions: 2.x, 3.x (only with Hadoop 3.x libraries) +* Spark versions: 2.3.x - 3.5.x +* Java versions: 8 - 20 + +## Installing PySpark + +To use SparkHDFS connector you should have PySpark installed (or injected to `sys.path`) +BEFORE creating the connector instance. + +See [Spark](../../../install/spark.md#install-spark) installation instruction for more details. + +## Using Kerberos + +Some of Hadoop managed clusters use Kerberos authentication. In this case, you should call [kinit](https://web.mit.edu/kerberos/krb5-1.12/doc/user/user_commands/kinit.html) command +**BEFORE** starting Spark session to generate Kerberos ticket. See [Kerberos support](../../../install/kerberos.md#install-kerberos). + +Sometimes it is also required to pass keytab file to Spark config, allowing Spark executors to generate own Kerberos tickets: + +Spark 3 + +```python +SparkSession.builder + .option("spark.kerberos.access.hadoopFileSystems", "hdfs://namenode1.domain.com:9820,hdfs://namenode2.domain.com:9820") + .option("spark.kerberos.principal", "user") + .option("spark.kerberos.keytab", "/path/to/keytab") + .gerOrCreate() +``` + +Spark 2 + +```python +SparkSession.builder + .option("spark.yarn.access.hadoopFileSystems", "hdfs://namenode1.domain.com:9820,hdfs://namenode2.domain.com:9820") + .option("spark.yarn.principal", "user") + .option("spark.yarn.keytab", "/path/to/keytab") + .gerOrCreate() +``` + +See [Spark security documentation](https://spark.apache.org/docs/latest/security.html#kerberos) +for more details. diff --git a/mddocs/docs/_build/markdown/connection/file_df_connection/spark_hdfs/slots.md b/mddocs/docs/_build/markdown/connection/file_df_connection/spark_hdfs/slots.md new file mode 100644 index 000000000..295e641da --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/file_df_connection/spark_hdfs/slots.md @@ -0,0 +1,3 @@ + + +# Spark HDFS Slots diff --git a/mddocs/docs/_build/markdown/connection/file_df_connection/spark_local_fs.md b/mddocs/docs/_build/markdown/connection/file_df_connection/spark_local_fs.md new file mode 100644 index 000000000..b16a60faa --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/file_df_connection/spark_local_fs.md @@ -0,0 +1,3 @@ + + +# Spark LocalFS diff --git a/mddocs/docs/_build/markdown/connection/file_df_connection/spark_s3/connection.md b/mddocs/docs/_build/markdown/connection/file_df_connection/spark_s3/connection.md new file mode 100644 index 000000000..fdb762c4f --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/file_df_connection/spark_s3/connection.md @@ -0,0 +1,3 @@ + + +# Spark S3 Connection diff --git a/mddocs/docs/_build/markdown/connection/file_df_connection/spark_s3/index.md b/mddocs/docs/_build/markdown/connection/file_df_connection/spark_s3/index.md new file mode 100644 index 000000000..be66a0adc --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/file_df_connection/spark_s3/index.md @@ -0,0 +1,9 @@ + + +# Spark S3 + +# Connection + +* [Prerequisites](prerequisites.md) +* [Connection](connection.md) +* [Troubleshooting](troubleshooting.md) diff --git a/mddocs/docs/_build/markdown/connection/file_df_connection/spark_s3/prerequisites.md b/mddocs/docs/_build/markdown/connection/file_df_connection/spark_s3/prerequisites.md new file mode 100644 index 000000000..1bfd0846a --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/file_df_connection/spark_s3/prerequisites.md @@ -0,0 +1,61 @@ + + +# Prerequisites + +## Version Compatibility + +* Spark versions: 3.2.x - 3.5.x (only with Hadoop 3.x libraries) +* Java versions: 8 - 20 + +## Installing PySpark + +To use SparkS3 connector you should have PySpark installed (or injected to `sys.path`) +BEFORE creating the connector instance. + +See [Spark](../../../install/spark.md#install-spark) installation instruction for more details. + +## Connecting to S3 + +### Bucket access style + +AWS and some other S3 cloud providers allows bucket access using domain style only, e.g. `https://mybucket.s3provider.com`. + +Other implementations, like Minio, by default allows path style access only, e.g. `https://s3provider.com/mybucket` +(see [MINIO_DOMAIN](https://min.io/docs/minio/linux/reference/minio-server/minio-server.html#envvar.MINIO_DOMAIN)). + +You should set `path.style.access` to `True` or `False`, to choose the preferred style. + +### Authentication + +Different S3 instances can use different authentication methods, like: +: * `access_key + secret_key` (or username + password) + * `access_key + secret_key + session_token` + +Usually these are just passed to SparkS3 constructor: + +```python +SparkS3( + access_key=..., + secret_key=..., + session_token=..., +) +``` + +But some S3 cloud providers, like AWS, may require custom credential providers. You can pass them like: + +```python +SparkS3( + extra={ + # provider class + "aws.credentials.provider": "org.apache.hadoop.fs.s3a.auth.AssumedRoleCredentialProvider", + # other options, if needed + "assumed.role.arn": "arn:aws:iam::90066806600238:role/s3-restricted", + }, +) +``` + +See [Hadoop-AWS](https://hadoop.apache.org/docs/stable/hadoop-aws/tools/hadoop-aws/index.html#Changing_Authentication_Providers) documentation. + +## Troubleshooting + +See [Spark S3 Troubleshooting](troubleshooting.md#spark-s3-troubleshooting). diff --git a/mddocs/docs/_build/markdown/connection/file_df_connection/spark_s3/troubleshooting.md b/mddocs/docs/_build/markdown/connection/file_df_connection/spark_s3/troubleshooting.md new file mode 100644 index 000000000..32ea15667 --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/file_df_connection/spark_s3/troubleshooting.md @@ -0,0 +1,366 @@ + + +# Spark S3 Troubleshooting + +#### NOTE +General guide: [Troubleshooting](../../../troubleshooting/index.md#troubleshooting). + +More details: + +* [Hadoop AWS Troubleshooting Guide](https://hadoop.apache.org/docs/stable/hadoop-aws/tools/hadoop-aws/troubleshooting_s3a.html) +* [Hadoop AWS Performance Guide](https://hadoop.apache.org/docs/stable/hadoop-aws/tools/hadoop-aws/performance.html) +* [Spark integration with Cloud Infrastructures](https://spark.apache.org/docs/latest/cloud-integration.html) + +## `SparkS3.check()` and other methods hang + +### Details + +S3 may not respond for connection attempts for a long time if it’s under heavy load. +To handle this, Hadoop AWS library has retry mechanism. By default it retries 7 times with 500ms interval. + +Hadoop AWS is based on AWS SDK library, which also has retry mechanism. This mechanism is not disabled because it handles different +errors than Hadoop AWS, so they complement each other. Default number of attempts in AWS SDK is 20 with minimal 5s interval, +which is exponentially increasing with each failed attempt. + +It is not a problem if S3 source is not accessible at all, like hostname cannot be resolved, or port is not opened. +These errors are not recoverable, and retry mechanism is not activated. + +But errors like SSL issues, are considered recoverable, and this causing retry of retry over increasing interval. +So user is waiting for [almost 15 minutes](https://issues.apache.org/jira/browse/HADOOP-18839) just to get exception message. + +### How to determine reason + +#### Make logging more verbose + +Change Spark session log level to [DEBUG](../../../troubleshooting/spark.md#troubleshooting-spark) to print result of each attempt. +Resulting logs will look like this + +### See log + +```text +23/08/03 11:25:10 DEBUG S3AFileSystem: Using S3ABlockOutputStream with buffer = disk; block=67108864; queue limit=4 +23/08/03 11:25:10 DEBUG S3Guard: Metastore option source [core-default.xml] +23/08/03 11:25:10 DEBUG S3Guard: Using NullMetadataStore metadata store for s3a filesystem +23/08/03 11:25:10 DEBUG S3AFileSystem: S3Guard is disabled on this bucket: test-bucket +23/08/03 11:25:10 DEBUG DirectoryPolicyImpl: Directory markers will be deleted +23/08/03 11:25:10 DEBUG S3AFileSystem: Directory marker retention policy is DirectoryMarkerRetention{policy='delete'} +23/08/03 11:25:10 DEBUG S3AUtils: Value of fs.s3a.multipart.purge.age is 86400 +23/08/03 11:25:10 DEBUG S3AUtils: Value of fs.s3a.bulk.delete.page.size is 250 +23/08/03 11:25:10 DEBUG FileSystem: Creating FS s3a://test-bucket/fake: duration 0:01.029s +23/08/03 11:25:10 DEBUG IOStatisticsStoreImpl: Incrementing counter op_is_directory by 1 with final value 1 +23/08/03 11:25:10 DEBUG S3AFileSystem: Getting path status for s3a://test-bucket/fake (fake); needEmptyDirectory=false +23/08/03 11:25:10 DEBUG S3AFileSystem: S3GetFileStatus s3a://test-bucket/fake +23/08/03 11:25:10 DEBUG S3AFileSystem: LIST List test-bucket:/fake/ delimiter=/ keys=2 requester pays=false +23/08/03 11:25:10 DEBUG S3AFileSystem: Starting: LIST +23/08/03 11:25:10 DEBUG IOStatisticsStoreImpl: Incrementing counter object_list_request by 1 with final value 1 +23/08/03 11:25:10 DEBUG AWSCredentialProviderList: Using credentials from SimpleAWSCredentialsProvider +23/08/03 11:25:10 DEBUG request: Sending Request: GET https://test-bucket.localhost:9000 / Parameters: ({"list-type":["2"],"delimiter":["/"],"max-keys":["2"],"prefix":["fake/"],"fetch-owner":["false"]}Headers: (amz-sdk-invocation-id: e6d62603-96e4-a80f-10a1-816e0822bc71, Content-Type: application/octet-stream, User-Agent: Hadoop 3.3.4, aws-sdk-java/1.12.262 Linux/6.4.7-1-MANJARO OpenJDK_64-Bit_Server_VM/25.292-b10 java/1.8.0_292 scala/2.12.17 vendor/AdoptOpenJDK cfg/retry-mode/legacy, ) +23/08/03 11:25:10 DEBUG AWS4Signer: AWS4 Canonical Request: '"GET +/ +delimiter=%2F&fetch-owner=false&list-type=2&max-keys=2&prefix=fake%2F +amz-sdk-invocation-id:e6d62603-96e4-a80f-10a1-816e0822bc71 +amz-sdk-request:attempt=1;max=21 +amz-sdk-retry:0/0/500 +content-type:application/octet-stream +host:test-bucket.localhost:9000 +user-agent:Hadoop 3.3.4, aws-sdk-java/1.12.262 Linux/6.4.7-1-MANJARO OpenJDK_64-Bit_Server_VM/25.292-b10 java/1.8.0_292 scala/2.12.17 vendor/AdoptOpenJDK cfg/retry-mode/legacy +x-amz-content-sha256:UNSIGNED-PAYLOAD +x-amz-date:20230803T112510Z + +amz-sdk-invocation-id;amz-sdk-request;amz-sdk-retry;content-type;host;user-agent;x-amz-content-sha256;x-amz-date +UNSIGNED-PAYLOAD" +23/08/03 11:25:10 DEBUG AWS4Signer: AWS4 String to Sign: '"AWS4-HMAC-SHA256 +20230803T112510Z +20230803/us-east-1/s3/aws4_request +31a317bb7f6d97248dd0cf03429d701cbb3e29ce889cfbb98ba7a34c57a3bfba" +23/08/03 11:25:10 DEBUG AWS4Signer: Generating a new signing key as the signing key not available in the cache for the date 1691020800000 +23/08/03 11:25:10 DEBUG RequestAddCookies: CookieSpec selected: default +23/08/03 11:25:10 DEBUG RequestAuthCache: Auth cache not set in the context +23/08/03 11:25:10 DEBUG PoolingHttpClientConnectionManager: Connection request: [route: {s}->https://test-bucket.localhost:9000][total available: 0; route allocated: 0 of 96; total allocated: 0 of 96] +23/08/03 11:25:10 DEBUG PoolingHttpClientConnectionManager: Connection leased: [id: 0][route: {s}->https://test-bucket.localhost:9000][total available: 0; route allocated: 1 of 96; total allocated: 1 of 96] +23/08/03 11:25:10 DEBUG MainClientExec: Opening connection {s}->https://test-bucket.localhost:9000 +23/08/03 11:25:10 DEBUG DefaultHttpClientConnectionOperator: Connecting to test-bucket.localhost/127.0.0.1:9000 +23/08/03 11:25:10 DEBUG SSLConnectionSocketFactory: Connecting socket to test-bucket.localhost/127.0.0.1:9000 with timeout 5000 +23/08/03 11:25:10 DEBUG SSLConnectionSocketFactory: Enabled protocols: [TLSv1.2] +23/08/03 11:25:10 DEBUG SSLConnectionSocketFactory: Enabled cipher suites:[TLS_ECDHE_ECDSA_WITH_AES_256_CBC_SHA384, TLS_ECDHE_RSA_WITH_AES_256_CBC_SHA384, TLS_RSA_WITH_AES_256_CBC_SHA256, TLS_ECDH_ECDSA_WITH_AES_256_CBC_SHA384, TLS_ECDH_RSA_WITH_AES_256_CBC_SHA384, TLS_DHE_RSA_WITH_AES_256_CBC_SHA256, TLS_DHE_DSS_WITH_AES_256_CBC_SHA256, TLS_ECDHE_ECDSA_WITH_AES_256_CBC_SHA, TLS_ECDHE_RSA_WITH_AES_256_CBC_SHA, TLS_RSA_WITH_AES_256_CBC_SHA, TLS_ECDH_ECDSA_WITH_AES_256_CBC_SHA, TLS_ECDH_RSA_WITH_AES_256_CBC_SHA, TLS_DHE_RSA_WITH_AES_256_CBC_SHA, TLS_DHE_DSS_WITH_AES_256_CBC_SHA, TLS_ECDHE_ECDSA_WITH_AES_128_CBC_SHA256, TLS_ECDHE_RSA_WITH_AES_128_CBC_SHA256, TLS_RSA_WITH_AES_128_CBC_SHA256, TLS_ECDH_ECDSA_WITH_AES_128_CBC_SHA256, TLS_ECDH_RSA_WITH_AES_128_CBC_SHA256, TLS_DHE_RSA_WITH_AES_128_CBC_SHA256, TLS_DHE_DSS_WITH_AES_128_CBC_SHA256, TLS_ECDHE_ECDSA_WITH_AES_128_CBC_SHA, TLS_ECDHE_RSA_WITH_AES_128_CBC_SHA, TLS_RSA_WITH_AES_128_CBC_SHA, TLS_ECDH_ECDSA_WITH_AES_128_CBC_SHA, TLS_ECDH_RSA_WITH_AES_128_CBC_SHA, TLS_DHE_RSA_WITH_AES_128_CBC_SHA, TLS_DHE_DSS_WITH_AES_128_CBC_SHA, TLS_EMPTY_RENEGOTIATION_INFO_SCSV] +23/08/03 11:25:10 DEBUG SSLConnectionSocketFactory: Starting handshake +23/08/03 11:25:10 DEBUG ClientConnectionManagerFactory: +java.lang.reflect.InvocationTargetException + at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) + at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62) + at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) + at java.lang.reflect.Method.invoke(Method.java:498) + at com.amazonaws.http.conn.ClientConnectionManagerFactory$Handler.invoke(ClientConnectionManagerFactory.java:76) + at com.amazonaws.http.conn.$Proxy32.connect(Unknown Source) + at com.amazonaws.thirdparty.apache.http.impl.execchain.MainClientExec.establishRoute(MainClientExec.java:393) + at com.amazonaws.thirdparty.apache.http.impl.execchain.MainClientExec.execute(MainClientExec.java:236) + at com.amazonaws.thirdparty.apache.http.impl.execchain.ProtocolExec.execute(ProtocolExec.java:186) + at com.amazonaws.thirdparty.apache.http.impl.client.InternalHttpClient.doExecute(InternalHttpClient.java:185) + at com.amazonaws.thirdparty.apache.http.impl.client.CloseableHttpClient.execute(CloseableHttpClient.java:83) + at com.amazonaws.thirdparty.apache.http.impl.client.CloseableHttpClient.execute(CloseableHttpClient.java:56) + at com.amazonaws.http.apache.client.impl.SdkHttpClient.execute(SdkHttpClient.java:72) + at com.amazonaws.http.AmazonHttpClient$RequestExecutor.executeOneRequest(AmazonHttpClient.java:1346) + at com.amazonaws.http.AmazonHttpClient$RequestExecutor.executeHelper(AmazonHttpClient.java:1157) + at com.amazonaws.http.AmazonHttpClient$RequestExecutor.doExecute(AmazonHttpClient.java:814) + at com.amazonaws.http.AmazonHttpClient$RequestExecutor.executeWithTimer(AmazonHttpClient.java:781) + at com.amazonaws.http.AmazonHttpClient$RequestExecutor.execute(AmazonHttpClient.java:755) + at com.amazonaws.http.AmazonHttpClient$RequestExecutor.access$500(AmazonHttpClient.java:715) + at com.amazonaws.http.AmazonHttpClient$RequestExecutionBuilderImpl.execute(AmazonHttpClient.java:697) + at com.amazonaws.http.AmazonHttpClient.execute(AmazonHttpClient.java:561) + at com.amazonaws.http.AmazonHttpClient.execute(AmazonHttpClient.java:541) + at com.amazonaws.services.s3.AmazonS3Client.invoke(AmazonS3Client.java:5456) + at com.amazonaws.services.s3.AmazonS3Client.invoke(AmazonS3Client.java:5403) + at com.amazonaws.services.s3.AmazonS3Client.invoke(AmazonS3Client.java:5397) + at com.amazonaws.services.s3.AmazonS3Client.listObjectsV2(AmazonS3Client.java:971) + at org.apache.hadoop.fs.s3a.S3AFileSystem.lambda$listObjects$11(S3AFileSystem.java:2595) + at org.apache.hadoop.fs.statistics.impl.IOStatisticsBinding.lambda$trackDurationOfOperation$5(IOStatisticsBinding.java:499) + at org.apache.hadoop.fs.s3a.Invoker.retryUntranslated(Invoker.java:414) + at org.apache.hadoop.fs.s3a.Invoker.retryUntranslated(Invoker.java:377) + at org.apache.hadoop.fs.s3a.S3AFileSystem.listObjects(S3AFileSystem.java:2586) + at org.apache.hadoop.fs.s3a.S3AFileSystem.s3GetFileStatus(S3AFileSystem.java:3832) + at org.apache.hadoop.fs.s3a.S3AFileSystem.innerGetFileStatus(S3AFileSystem.java:3688) + at org.apache.hadoop.fs.s3a.S3AFileSystem.lambda$isDirectory$35(S3AFileSystem.java:4724) + at org.apache.hadoop.fs.statistics.impl.IOStatisticsBinding.lambda$trackDurationOfOperation$5(IOStatisticsBinding.java:499) + at org.apache.hadoop.fs.statistics.impl.IOStatisticsBinding.trackDuration(IOStatisticsBinding.java:444) + at org.apache.hadoop.fs.s3a.S3AFileSystem.trackDurationAndSpan(S3AFileSystem.java:2337) + at org.apache.hadoop.fs.s3a.S3AFileSystem.trackDurationAndSpan(S3AFileSystem.java:2356) + at org.apache.hadoop.fs.s3a.S3AFileSystem.isDirectory(S3AFileSystem.java:4722) + at org.apache.spark.sql.execution.streaming.FileStreamSink$.hasMetadata(FileStreamSink.scala:54) + at org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:366) + at org.apache.spark.sql.DataFrameReader.loadV1Source(DataFrameReader.scala:229) + at org.apache.spark.sql.DataFrameReader.$anonfun$load$2(DataFrameReader.scala:211) + at scala.Option.getOrElse(Option.scala:189) + at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:211) + at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:186) + at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) + at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62) + at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) + at java.lang.reflect.Method.invoke(Method.java:498) + at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244) + at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:374) + at py4j.Gateway.invoke(Gateway.java:282) + at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132) + at py4j.commands.CallCommand.execute(CallCommand.java:79) + at py4j.ClientServerConnection.waitForCommands(ClientServerConnection.java:182) + at py4j.ClientServerConnection.run(ClientServerConnection.java:106) + at java.lang.Thread.run(Thread.java:748) +Caused by: javax.net.ssl.SSLException: Unsupported or unrecognized SSL message + at sun.security.ssl.SSLSocketInputRecord.handleUnknownRecord(SSLSocketInputRecord.java:448) + at sun.security.ssl.SSLSocketInputRecord.decode(SSLSocketInputRecord.java:184) + at sun.security.ssl.SSLTransport.decode(SSLTransport.java:109) + at sun.security.ssl.SSLSocketImpl.decode(SSLSocketImpl.java:1383) + at sun.security.ssl.SSLSocketImpl.readHandshakeRecord(SSLSocketImpl.java:1291) + at sun.security.ssl.SSLSocketImpl.startHandshake(SSLSocketImpl.java:435) + at com.amazonaws.thirdparty.apache.http.conn.ssl.SSLConnectionSocketFactory.createLayeredSocket(SSLConnectionSocketFactory.java:436) + at com.amazonaws.thirdparty.apache.http.conn.ssl.SSLConnectionSocketFactory.connectSocket(SSLConnectionSocketFactory.java:384) + at com.amazonaws.thirdparty.apache.http.impl.conn.DefaultHttpClientConnectionOperator.connect(DefaultHttpClientConnectionOperator.java:142) + at com.amazonaws.thirdparty.apache.http.impl.conn.PoolingHttpClientConnectionManager.connect(PoolingHttpClientConnectionManager.java:376) + ... 58 more +23/08/03 11:25:10 DEBUG DefaultManagedHttpClientConnection: http-outgoing-0: Shutdown connection +23/08/03 11:25:10 DEBUG MainClientExec: Connection discarded +23/08/03 11:25:10 DEBUG PoolingHttpClientConnectionManager: Connection released: [id: 0][route: {s}->https://test-bucket.localhost:9000][total available: 0; route allocated: 0 of 96; total allocated: 0 of 96] +23/08/03 11:25:10 DEBUG AmazonHttpClient: Unable to execute HTTP request: Unsupported or unrecognized SSL message Request will be retried. +23/08/03 11:25:10 DEBUG request: Retrying Request: GET https://test-bucket.localhost:9000 / Parameters: ({"list-type":["2"],"delimiter":["/"],"max-keys":["2"],"prefix":["fake/"],"fetch-owner":["false"]}Headers: (amz-sdk-invocation-id: e6d62603-96e4-a80f-10a1-816e0822bc71, Content-Type: application/octet-stream, User-Agent: Hadoop 3.3.4, aws-sdk-java/1.12.262 Linux/6.4.7-1-MANJARO OpenJDK_64-Bit_Server_VM/25.292-b10 java/1.8.0_292 scala/2.12.17 vendor/AdoptOpenJDK cfg/retry-mode/legacy, ) +23/08/03 11:25:10 DEBUG AmazonHttpClient: Retriable error detected, will retry in 49ms, attempt number: 0 +``` + +#### Change number of retries + +You can also change number of retries performed by both libraries using `extra` parameter: + +```python +spark_s3 = SparkS3( + ..., + extra={ + "attempts.maximum": 1, + "retry.limit": 1, + }, +) +``` + +So accessing S3 will fail almost immediately if there is any error. + +### Most common mistakes + +#### No network access + +```text +Caused by: java.net.ConnectException: Connection refused +``` + +Mostly caused by: + +* Trying to access port number which S3 server does not listen +* You’re trying to access host which is unreachable from your network (e.g. running behind some proxy or VPN) +* There are some firewall restrictions for accessing specific host or port + +#### Using HTTPS protocol for HTTP port + +```text +Caused by: javax.net.ssl.SSLException: Unsupported or unrecognized SSL message +``` + +By default, SparkS3 uses HTTPS protocol for connection. +If you change port number, this does not lead to changing protocol: + +```python +spark_s3 = SparkS3(host="s3provider.com", port=8080, ...) +``` + +You should pass protocol explicitly: + +```python +spark_s3 = SparkS3(host="s3provider.com", port=8080, protocol="http", ...) +``` + +#### SSL certificate is self-signed + +```text +sun.security.provider.certpath.SunCertPathBuilderException: unable to find valid certification path to requested target +``` + +To connect to HTTPS port with self-signed certificate, you should +[add certificate chain to Java TrustedStore](https://stackoverflow.com/questions/373295/digital-certificate-how-to-import-cer-file-in-to-truststore-file-using). + +Another option is to disable SSL check: + +```python +spark_s3 = SparkS3( + ..., + extra={ + "connection.ssl.enabled": False, + }, +) +``` + +But is is **NOT** recommended. + +#### Accessing S3 without domain-style access style support + +```text +Caused by: java.net.UnknownHostException: my-bucket.s3provider.com +``` + +To use path-style access, use option below: + +```python +spark_s3 = SparkS3( + host="s3provider.com", + bucket="my-bucket", + ..., + extra={ + "path.style.access": True, + }, +) +``` + +## Slow or unstable writing to S3 + +Hadoop AWS allows to use different writing strategies for different S3 implementations, depending +on list of supported features by server. + +These strategies are called [committers](https://hadoop.apache.org/docs/stable/hadoop-aws/tools/hadoop-aws/committers.html). +There are [different types of committers](https://hadoop.apache.org/docs/stable/hadoop-aws/tools/hadoop-aws/committers.html#Switching_to_an_S3A_Committer): + +* `file` (default) +* `directory` +* `partitioned` +* `magic` + +### `file` committer + +This committer is quite slow and unstable, so it is not recommended to use: + +```text +WARN AbstractS3ACommitterFactory: Using standard FileOutputCommitter to commit work. This is slow and potentially unsafe. +``` + +This is caused by the fact it creates files in the temp directory on remote filesystem, and after all of them are written successfully, +they are moved to target directory on same remote filesystem. + +This is not an issue for HDFS which does support file move operations and also support renaming directory +as atomic operation with `O(1)` time complexity. + +But S3 does support only file copying, so moving is performed via copy + delete. +Also it does not support atomic directory rename operation. Instead, renaming files with the same prefix has time complexity `O(n)`. + +### `directory` and `partitioned` committers + +These are [staging committers](https://hadoop.apache.org/docs/stable/hadoop-aws/tools/hadoop-aws/committer_architecture.html), +meaning that they create temp directories on local filesystem, and after all files are written successfully, +they will be uploaded to S3. Local filesystems do support file moving and directory renaming, +so these committers does not have issues that `file` committer has. + +But they both require free space on local filesystem, and this may be an issue if user need to write large amount of data. +Also this can be an issue for container environment, like Kubernetes, there resources should be allocated before starting a container. + +### `magic` committer + +This committer uses multipart upload feature of S3 API, allowing to create multiple files +and after all of them were written successfully finish the transaction. Before transaction is finished, +files will not be accessible by other clients. + +Because it does not require neither file moving operations, nor directory atomic rename, +upload process is done in most efficient way S3 support. +This [drastically increases writing performance](https://spot.io/blog/improve-apache-spark-performance-with-the-s3-magic-committer/). + +To use this committer, set [following properties](https://github.com/apache/spark/pull/32518) while creating Spark session. + +S3 your main distributed filesystem (Spark on Kubernetes) + +```py +# https://issues.apache.org/jira/browse/SPARK-23977 +# https://spark.apache.org/docs/latest/cloud-integration.html#committing-work-into-cloud-storage-safely-and-fast +spark = ( + SparkSession.builder.appName("spark-app-name") + .config("spark.hadoop.fs.s3a.committer.magic.enabled", "true") + .config("spark.hadoop.fs.s3a.committer.name", "magic") + .config("spark.hadoop.mapreduce.outputcommitter.factory.scheme.s3a", "org.apache.hadoop.fs.s3a.commit.S3ACommitterFactory") + .config("spark.sql.parquet.output.committer.class", "org.apache.spark.internal.io.cloud.BindingParquetOutputCommitter") + .config("spark.sql.sources.commitProtocolClass", "org.apache.spark.internal.io.cloud.PathOutputCommitProtocol") + .getOrCreate() +) +``` + +HDFS is your main distributed filesystem (Spark on Hadoop) + +```py +# https://community.cloudera.com/t5/Support-Questions/spark-sql-sources-partitionOverwriteMode-dynamic-quot-not/m-p/343483/highlight/true +spark = ( + SparkSession.builder.appName("spark-app-name") + .config("spark.hadoop.fs.s3a.committer.magic.enabled", "true") + .config("spark.hadoop.fs.s3a.committer.name", "magic") + .getOrCreate() +) +``` + +#### WARNING +`magic` committer requires S3 implementation to have strong consistency - file upload API return response only +if it was written on enough number of cluster nodes, and any cluster node error does not lead to missing or corrupting files. + +Some S3 implementations does have strong consistency +(like [AWS S3](https://aws.amazon.com/ru/blogs/aws/amazon-s3-update-strong-read-after-write-consistency/) and +[MinIO](https://blog.min.io/migrating-hdfs-to-object-storage/)), some not. Please contact your S3 provider +to get information about S3 implementation consistency. + +#### WARNING +`magic` committer does not support `if_exists="replace_overlapping_partitions"`. +Either use another `if_exists` value, or use `partitioned` committer. + +### See also + +* [directory.marker.retention=”keep”](https://hadoop.apache.org/docs/stable/hadoop-aws/tools/hadoop-aws/directory_markers.html) + +## Slow reading from S3 + +Please read following documentation: + +* [prefetch.enabled](https://hadoop.apache.org/docs/stable/hadoop-aws/tools/hadoop-aws/prefetching.html) +* [experimental.input.fadvise](https://hadoop.apache.org/docs/stable/hadoop-aws/tools/hadoop-aws/performance.html#Improving_data_input_performance_through_fadvise) +* [Parquet and ORC I/O settings](https://spark.apache.org/docs/latest/cloud-integration.html#parquet-io-settings) + +If you’re reading data from row-based formats, like [CSV](../../../file_df/file_formats/csv.md#csv-file-format), prefer +[experimental.input.fadvise=”sequential” with increased readahead.range](https://issues.apache.org/jira/browse/HADOOP-17789?focusedCommentId=17383559#comment-17383559). + +But for other file formats, especially using compression, prefer +[experimental.input.fadvise=”normal”](https://issues.apache.org/jira/browse/HADOOP-17789?focusedCommentId=17383743#comment-17383743) diff --git a/mddocs/docs/_build/markdown/connection/index.md b/mddocs/docs/_build/markdown/connection/index.md new file mode 100644 index 000000000..5ad28edef --- /dev/null +++ b/mddocs/docs/_build/markdown/connection/index.md @@ -0,0 +1,34 @@ + + + DB Connection + +* [DB Connections](db_connection/index.md) + * [Clickhouse](db_connection/clickhouse/index.md) + * [Greenplum](db_connection/greenplum/index.md) + * [Kafka](db_connection/kafka/index.md) + * [Hive](db_connection/hive/index.md) + * [MongoDB](db_connection/mongodb/index.md) + * [MSSQL](db_connection/mssql/index.md) + * [MySQL](db_connection/mysql/index.md) + * [Oracle](db_connection/oracle/index.md) + * [Postgres](db_connection/postgres/index.md) + * [Teradata](db_connection/teradata/index.md) + + File Connection + +* [File Connections](file_connection/index.md) + * [FTP](file_connection/ftp.md) + * [FTPS](file_connection/ftps.md) + * [HDFS](file_connection/hdfs/index.md) + * [Samba](file_connection/samba.md) + * [SFTP](file_connection/sftp.md) + * [S3](file_connection/s3.md) + * [Webdav](file_connection/webdav.md) + + File DataFrame Connection + +* [File DataFrame Connections](file_df_connection/index.md) + * [Spark LocalFS](file_df_connection/spark_local_fs.md) + * [Spark HDFS](file_df_connection/spark_hdfs/index.md) + * [Spark S3](file_df_connection/spark_s3/index.md) + * [Base interface](file_df_connection/base.md) diff --git a/mddocs/docs/_build/markdown/contributing.md b/mddocs/docs/_build/markdown/contributing.md new file mode 100644 index 000000000..739798897 --- /dev/null +++ b/mddocs/docs/_build/markdown/contributing.md @@ -0,0 +1,391 @@ +# Contributing Guide + +Welcome! There are many ways to contribute, including submitting bug +reports, improving documentation, submitting feature requests, reviewing +new submissions, or contributing code that can be incorporated into the +project. + +## Limitations + +We should keep close to these items during development: + +* Some companies still use old Spark versions, like 2.3.1. So it is required to keep compatibility if possible, e.g. adding branches for different Spark versions. +* Different users uses onETL in different ways - some uses only DB connectors, some only files. Connector-specific dependencies should be optional. +* Instead of creating classes with a lot of different options, prefer splitting them into smaller classes, e.g. options class, context manager, etc, and using composition. + +## Initial setup for local development + +### Install Git + +Please follow [instruction](https://docs.github.com/en/get-started/quickstart/set-up-git). + +### Create a fork + +If you are not a member of a development team building onETL, you should create a fork before making any changes. + +Please follow [instruction](https://docs.github.com/en/get-started/quickstart/fork-a-repo). + +### Clone the repo + +Open terminal and run these commands: + +```bash +git clone git@github.com:myuser/onetl.git -b develop + +cd onetl +``` + +### Setup environment + +Create virtualenv and install dependencies: + +```bash +python -m venv venv +source venv/bin/activate +pip install -U wheel +pip install -U pip setuptools +pip install -U \ + -r requirements/core.txt \ + -r requirements/ftp.txt \ + -r requirements/hdfs.txt \ + -r requirements/kerberos.txt \ + -r requirements/s3.txt \ + -r requirements/sftp.txt \ + -r requirements/webdav.txt \ + -r requirements/dev.txt \ + -r requirements/docs.txt \ + -r requirements/tests/base.txt \ + -r requirements/tests/clickhouse.txt \ + -r requirements/tests/kafka.txt \ + -r requirements/tests/mongodb.txt \ + -r requirements/tests/mssql.txt \ + -r requirements/tests/mysql.txt \ + -r requirements/tests/postgres.txt \ + -r requirements/tests/oracle.txt \ + -r requirements/tests/pydantic-2.txt \ + -r requirements/tests/spark-3.5.5.txt + +# TODO: remove after https://github.com/zqmillet/sphinx-plantuml/pull/4 +pip install sphinx-plantuml --no-deps +``` + +### Enable pre-commit hooks + +Install pre-commit hooks: + +```bash +pre-commit install --install-hooks +``` + +Test pre-commit hooks run: + +```bash +pre-commit run +``` + +## How to + +### Run tests locally + +#### Using docker-compose + +Build image for running tests: + +```bash +docker-compose build +``` + +Start all containers with dependencies: + +```bash +docker-compose --profile all up -d +``` + +You can run limited set of dependencies: + +```bash +docker-compose --profile mongodb up -d +``` + +Run tests: + +```bash +docker-compose run --rm onetl ./run_tests.sh +``` + +You can pass additional arguments, they will be passed to pytest: + +```bash +docker-compose run --rm onetl ./run_tests.sh -m mongodb -lsx -vvvv --log-cli-level=INFO +``` + +You can run interactive bash session and use it: + +```bash +docker-compose run --rm onetl bash + +./run_tests.sh -m mongodb -lsx -vvvv --log-cli-level=INFO +``` + +See logs of test container: + +```bash +docker-compose logs -f onetl +``` + +Stop all containers and remove created volumes: + +```bash +docker-compose --profile all down -v +``` + +#### Without docker-compose + +#### WARNING +To run HDFS tests locally you should add the following line to your `/etc/hosts` (file path depends on OS): + +```default +# HDFS server returns container hostname as connection address, causing error in DNS resolution +127.0.0.1 hdfs +``` + +#### NOTE +To run Oracle tests you need to install [Oracle instantclient](https://www.oracle.com/database/technologies/instant-client.html), +and pass its path to `ONETL_ORA_CLIENT_PATH` and `LD_LIBRARY_PATH` environment variables, +e.g. `ONETL_ORA_CLIENT_PATH=/path/to/client64/lib`. + +It may also require to add the same path into `LD_LIBRARY_PATH` environment variable + +#### NOTE +To run Greenplum tests, you should: + +* Download [VMware Greenplum connector for Spark](https://onetl.readthedocs.io/en/latest/connection/db_connection/greenplum/prerequisites.html) +* Either move it to `~/.ivy2/jars/`, or pass file path to `CLASSPATH` +* Set environment variable `ONETL_GP_PACKAGE_VERSION=local`. + +Start all containers with dependencies: + +```bash +docker-compose --profile all up -d +``` + +You can run limited set of dependencies: + +```bash +docker-compose --profile mongodb up -d +``` + +Load environment variables with connection properties: + +```bash +source .env.local +``` + +Run tests: + +```bash +./run_tests.sh +``` + +You can pass additional arguments, they will be passed to pytest: + +```bash +./run_tests.sh -m mongodb -lsx -vvvv --log-cli-level=INFO +``` + +Stop all containers and remove created volumes: + +```bash +docker-compose --profile all down -v +``` + +### Build documentation + +Build documentation using Sphinx: + +```bash +cd docs +make html +``` + +Then open in browser `docs/_build/index.html`. + +## Review process + +Please create a new GitHub issue for any significant changes and +enhancements that you wish to make. Provide the feature you would like +to see, why you need it, and how it will work. Discuss your ideas +transparently and get community feedback before proceeding. + +Significant Changes that you wish to contribute to the project should be +discussed first in a GitHub issue that clearly outlines the changes and +benefits of the feature. + +Small Changes can directly be crafted and submitted to the GitHub +Repository as a Pull Request. + +### Create pull request + +Commit your changes: + +```bash +git commit -m "Commit message" +git push +``` + +Then open Github interface and [create pull request](https://docs.github.com/en/get-started/quickstart/contributing-to-projects#making-a-pull-request). +Please follow guide from PR body template. + +After pull request is created, it get a corresponding number, e.g. 123 (`pr_number`). + +### Write release notes + +`onETL` uses [towncrier](https://pypi.org/project/towncrier/) +for changelog management. + +To submit a change note about your PR, add a text file into the +[docs/changelog/next_release](./next_release) folder. It should contain an +explanation of what applying this PR will change in the way +end-users interact with the project. One sentence is usually +enough but feel free to add as many details as you feel necessary +for the users to understand what it means. + +**Use the past tense** for the text in your fragment because, +combined with others, it will be a part of the “news digest” +telling the readers **what changed** in a specific version of +the library *since the previous version*. + +You should also use +reStructuredText syntax for highlighting code (inline or block), +linking parts of the docs or external sites. +If you wish to sign your change, feel free to add `-- by +:user:`github-username`` at the end (replace `github-username` +with your own!). + +Finally, name your file following the convention that Towncrier +understands: it should start with the number of an issue or a +PR followed by a dot, then add a patch type, like `feature`, +`doc`, `misc` etc., and add `.rst` as a suffix. If you +need to add more than one fragment, you may add an optional +sequence number (delimited with another period) between the type +and the suffix. + +In general the name will follow `..rst` pattern, +where the categories are: + +- `feature`: Any new feature +- `bugfix`: A bug fix +- `improvement`: An improvement +- `doc`: A change to the documentation +- `dependency`: Dependency-related changes +- `misc`: Changes internal to the repo like CI, test and build changes + +A pull request may have more than one of these components, for example +a code change may introduce a new feature that deprecates an old +feature, in which case two fragments should be added. It is not +necessary to make a separate documentation fragment for documentation +changes accompanying the relevant code changes. + +#### Examples for adding changelog entries to your Pull Requests + +```rst +Added a ``:github:user:`` role to Sphinx config -- by :github:user:`someuser` +``` + +```rst +Fixed behavior of ``WebDAV`` connector -- by :github:user:`someuser` +``` + +```rst +Added support of ``timeout`` in ``S3`` connector +-- by :github:user:`someuser`, :github:user:`anotheruser` and :github:user:`otheruser` +``` + +#### How to skip change notes check? + +Just add `ci:skip-changelog` label to pull request. + +#### Release Process + +Before making a release from the `develop` branch, follow these steps: + +1. Checkout to `develop` branch and update it to the actual state + +```bash +git checkout develop +git pull -p +``` + +1. Backup `NEXT_RELEASE.rst` + +```bash +cp "docs/changelog/NEXT_RELEASE.rst" "docs/changelog/temp_NEXT_RELEASE.rst" +``` + +1. Build the Release notes with Towncrier + +```bash +VERSION=$(cat onetl/VERSION) +towncrier build "--version=${VERSION}" --yes +``` + +1. Change file with changelog to release version number + +```bash +mv docs/changelog/NEXT_RELEASE.rst "docs/changelog/${VERSION}.rst" +``` + +1. Remove content above the version number heading in the `${VERSION}.rst` file + +```bash +awk '!/^.*towncrier release notes start/' "docs/changelog/${VERSION}.rst" > temp && mv temp "docs/changelog/${VERSION}.rst" +``` + +1. Update Changelog Index + +```bash +awk -v version=${VERSION} '/DRAFT/{print;print " " version;next}1' docs/changelog/index.rst > temp && mv temp docs/changelog/index.rst +``` + +1. Restore `NEXT_RELEASE.rst` file from backup + +```bash +mv "docs/changelog/temp_NEXT_RELEASE.rst" "docs/changelog/NEXT_RELEASE.rst" +``` + +1. Commit and push changes to `develop` branch + +```bash +git add . +git commit -m "Prepare for release ${VERSION}" +git push +``` + +1. Merge `develop` branch to `master`, **WITHOUT** squashing + +```bash +git checkout master +git pull +git merge develop +git push +``` + +1. Add git tag to the latest commit in `master` branch + +```bash +git tag "$VERSION" +git push origin "$VERSION" +``` + +1. Update version in `develop` branch **after release**: + +```bash +git checkout develop + +NEXT_VERSION=$(echo "$VERSION" | awk -F. '/[0-9]+\./{$NF++;print}' OFS=.) +echo "$NEXT_VERSION" > onetl/VERSION + +git add . +git commit -m "Bump version" +git push +``` diff --git a/mddocs/docs/_build/markdown/db/db_reader.md b/mddocs/docs/_build/markdown/db/db_reader.md new file mode 100644 index 000000000..0c7be1def --- /dev/null +++ b/mddocs/docs/_build/markdown/db/db_reader.md @@ -0,0 +1,3 @@ + + +# DB Reader diff --git a/mddocs/docs/_build/markdown/db/db_writer.md b/mddocs/docs/_build/markdown/db/db_writer.md new file mode 100644 index 000000000..313801b96 --- /dev/null +++ b/mddocs/docs/_build/markdown/db/db_writer.md @@ -0,0 +1,3 @@ + + +# DB Writer diff --git a/mddocs/docs/_build/markdown/db/index.md b/mddocs/docs/_build/markdown/db/index.md new file mode 100644 index 000000000..861a539ff --- /dev/null +++ b/mddocs/docs/_build/markdown/db/index.md @@ -0,0 +1,6 @@ + + + DB classes + +* [DB Reader](db_reader.md) +* [DB Writer](db_writer.md) diff --git a/mddocs/docs/_build/markdown/file/file_downloader/file_downloader.md b/mddocs/docs/_build/markdown/file/file_downloader/file_downloader.md new file mode 100644 index 000000000..fd9de1c6a --- /dev/null +++ b/mddocs/docs/_build/markdown/file/file_downloader/file_downloader.md @@ -0,0 +1,3 @@ + + +# File Downloader diff --git a/mddocs/docs/_build/markdown/file/file_downloader/index.md b/mddocs/docs/_build/markdown/file/file_downloader/index.md new file mode 100644 index 000000000..3a7b4164b --- /dev/null +++ b/mddocs/docs/_build/markdown/file/file_downloader/index.md @@ -0,0 +1,9 @@ + + +# File Downloader + +# File Downloader + +* [File Downloader](file_downloader.md) +* [File Downloader Options](options.md) +* [File Downloader Result](result.md) diff --git a/mddocs/docs/_build/markdown/file/file_downloader/options.md b/mddocs/docs/_build/markdown/file/file_downloader/options.md new file mode 100644 index 000000000..1699758c8 --- /dev/null +++ b/mddocs/docs/_build/markdown/file/file_downloader/options.md @@ -0,0 +1,3 @@ + + +# File Downloader Options diff --git a/mddocs/docs/_build/markdown/file/file_downloader/result.md b/mddocs/docs/_build/markdown/file/file_downloader/result.md new file mode 100644 index 000000000..56d7f76a0 --- /dev/null +++ b/mddocs/docs/_build/markdown/file/file_downloader/result.md @@ -0,0 +1,3 @@ + + +# File Downloader Result diff --git a/mddocs/docs/_build/markdown/file/file_filters/base.md b/mddocs/docs/_build/markdown/file/file_filters/base.md new file mode 100644 index 000000000..168e50ede --- /dev/null +++ b/mddocs/docs/_build/markdown/file/file_filters/base.md @@ -0,0 +1,3 @@ + + +# Base interface diff --git a/mddocs/docs/_build/markdown/file/file_filters/exclude_dir.md b/mddocs/docs/_build/markdown/file/file_filters/exclude_dir.md new file mode 100644 index 000000000..1a1d5c327 --- /dev/null +++ b/mddocs/docs/_build/markdown/file/file_filters/exclude_dir.md @@ -0,0 +1,3 @@ + + +# ExcludeDir diff --git a/mddocs/docs/_build/markdown/file/file_filters/file_filter.md b/mddocs/docs/_build/markdown/file/file_filters/file_filter.md new file mode 100644 index 000000000..9e2ff94a4 --- /dev/null +++ b/mddocs/docs/_build/markdown/file/file_filters/file_filter.md @@ -0,0 +1,3 @@ + + +# File Filter (legacy) diff --git a/mddocs/docs/_build/markdown/file/file_filters/file_mtime_filter.md b/mddocs/docs/_build/markdown/file/file_filters/file_mtime_filter.md new file mode 100644 index 000000000..1b09868e0 --- /dev/null +++ b/mddocs/docs/_build/markdown/file/file_filters/file_mtime_filter.md @@ -0,0 +1,3 @@ + + +# FileModifiedTime diff --git a/mddocs/docs/_build/markdown/file/file_filters/file_size_filter.md b/mddocs/docs/_build/markdown/file/file_filters/file_size_filter.md new file mode 100644 index 000000000..92260d249 --- /dev/null +++ b/mddocs/docs/_build/markdown/file/file_filters/file_size_filter.md @@ -0,0 +1,3 @@ + + +# FileSizeRange diff --git a/mddocs/docs/_build/markdown/file/file_filters/glob.md b/mddocs/docs/_build/markdown/file/file_filters/glob.md new file mode 100644 index 000000000..115269b2f --- /dev/null +++ b/mddocs/docs/_build/markdown/file/file_filters/glob.md @@ -0,0 +1,3 @@ + + +# Glob diff --git a/mddocs/docs/_build/markdown/file/file_filters/index.md b/mddocs/docs/_build/markdown/file/file_filters/index.md new file mode 100644 index 000000000..40155ae9a --- /dev/null +++ b/mddocs/docs/_build/markdown/file/file_filters/index.md @@ -0,0 +1,20 @@ + + +# File Filters + +# File filters + +* [Glob](glob.md) +* [Regexp](regexp.md) +* [ExcludeDir](exclude_dir.md) +* [FileSizeRange](file_size_filter.md) +* [FileModifiedTime](file_mtime_filter.md) + +# Legacy + +* [File Filter (legacy)](file_filter.md) + +# For developers + +* [Base interface](base.md) +* [match_all_filters](match_all_filters.md) diff --git a/mddocs/docs/_build/markdown/file/file_filters/match_all_filters.md b/mddocs/docs/_build/markdown/file/file_filters/match_all_filters.md new file mode 100644 index 000000000..aa9d094ce --- /dev/null +++ b/mddocs/docs/_build/markdown/file/file_filters/match_all_filters.md @@ -0,0 +1,3 @@ + + +# match_all_filters diff --git a/mddocs/docs/_build/markdown/file/file_filters/regexp.md b/mddocs/docs/_build/markdown/file/file_filters/regexp.md new file mode 100644 index 000000000..80fe7c2a5 --- /dev/null +++ b/mddocs/docs/_build/markdown/file/file_filters/regexp.md @@ -0,0 +1,3 @@ + + +# Regexp diff --git a/mddocs/docs/_build/markdown/file/file_limits/base.md b/mddocs/docs/_build/markdown/file/file_limits/base.md new file mode 100644 index 000000000..cdf6d9b1b --- /dev/null +++ b/mddocs/docs/_build/markdown/file/file_limits/base.md @@ -0,0 +1,3 @@ + + +# Base interface diff --git a/mddocs/docs/_build/markdown/file/file_limits/file_limit.md b/mddocs/docs/_build/markdown/file/file_limits/file_limit.md new file mode 100644 index 000000000..ddf6ecf6e --- /dev/null +++ b/mddocs/docs/_build/markdown/file/file_limits/file_limit.md @@ -0,0 +1,3 @@ + + +# File Limit (legacy) diff --git a/mddocs/docs/_build/markdown/file/file_limits/index.md b/mddocs/docs/_build/markdown/file/file_limits/index.md new file mode 100644 index 000000000..be23f82da --- /dev/null +++ b/mddocs/docs/_build/markdown/file/file_limits/index.md @@ -0,0 +1,19 @@ + + +# File Limits + +# File limits + +* [MaxFilesCount](max_files_count.md) +* [TotalFilesSize](total_files_size.md) + +# Legacy + +* [File Limit (legacy)](file_limit.md) + +# For developers + +* [Base interface](base.md) +* [limits_stop_at](limits_stop_at.md) +* [limits_reached](limits_reached.md) +* [reset_limits](reset_limits.md) diff --git a/mddocs/docs/_build/markdown/file/file_limits/limits_reached.md b/mddocs/docs/_build/markdown/file/file_limits/limits_reached.md new file mode 100644 index 000000000..5d04fbf86 --- /dev/null +++ b/mddocs/docs/_build/markdown/file/file_limits/limits_reached.md @@ -0,0 +1,3 @@ + + +# limits_reached diff --git a/mddocs/docs/_build/markdown/file/file_limits/limits_stop_at.md b/mddocs/docs/_build/markdown/file/file_limits/limits_stop_at.md new file mode 100644 index 000000000..8fc53c1b0 --- /dev/null +++ b/mddocs/docs/_build/markdown/file/file_limits/limits_stop_at.md @@ -0,0 +1,3 @@ + + +# limits_stop_at diff --git a/mddocs/docs/_build/markdown/file/file_limits/max_files_count.md b/mddocs/docs/_build/markdown/file/file_limits/max_files_count.md new file mode 100644 index 000000000..e01ca0a84 --- /dev/null +++ b/mddocs/docs/_build/markdown/file/file_limits/max_files_count.md @@ -0,0 +1,3 @@ + + +# MaxFilesCount diff --git a/mddocs/docs/_build/markdown/file/file_limits/reset_limits.md b/mddocs/docs/_build/markdown/file/file_limits/reset_limits.md new file mode 100644 index 000000000..80ce0389c --- /dev/null +++ b/mddocs/docs/_build/markdown/file/file_limits/reset_limits.md @@ -0,0 +1,3 @@ + + +# reset_limits diff --git a/mddocs/docs/_build/markdown/file/file_limits/total_files_size.md b/mddocs/docs/_build/markdown/file/file_limits/total_files_size.md new file mode 100644 index 000000000..33f96fe55 --- /dev/null +++ b/mddocs/docs/_build/markdown/file/file_limits/total_files_size.md @@ -0,0 +1,3 @@ + + +# TotalFilesSize diff --git a/mddocs/docs/_build/markdown/file/file_mover/file_mover.md b/mddocs/docs/_build/markdown/file/file_mover/file_mover.md new file mode 100644 index 000000000..eb25aaf6d --- /dev/null +++ b/mddocs/docs/_build/markdown/file/file_mover/file_mover.md @@ -0,0 +1,3 @@ + + +# File Mover diff --git a/mddocs/docs/_build/markdown/file/file_mover/index.md b/mddocs/docs/_build/markdown/file/file_mover/index.md new file mode 100644 index 000000000..c8b55c7d0 --- /dev/null +++ b/mddocs/docs/_build/markdown/file/file_mover/index.md @@ -0,0 +1,9 @@ + + +# File Mover + +# File Mover + +* [File Mover](file_mover.md) +* [File Mover Options](options.md) +* [File Mover Result](result.md) diff --git a/mddocs/docs/_build/markdown/file/file_mover/options.md b/mddocs/docs/_build/markdown/file/file_mover/options.md new file mode 100644 index 000000000..1bbc40fc8 --- /dev/null +++ b/mddocs/docs/_build/markdown/file/file_mover/options.md @@ -0,0 +1,3 @@ + + +# File Mover Options diff --git a/mddocs/docs/_build/markdown/file/file_mover/result.md b/mddocs/docs/_build/markdown/file/file_mover/result.md new file mode 100644 index 000000000..34121e2f4 --- /dev/null +++ b/mddocs/docs/_build/markdown/file/file_mover/result.md @@ -0,0 +1,3 @@ + + +# File Mover Result diff --git a/mddocs/docs/_build/markdown/file/file_uploader/file_uploader.md b/mddocs/docs/_build/markdown/file/file_uploader/file_uploader.md new file mode 100644 index 000000000..1a0488c5b --- /dev/null +++ b/mddocs/docs/_build/markdown/file/file_uploader/file_uploader.md @@ -0,0 +1,3 @@ + + +# File Uploader diff --git a/mddocs/docs/_build/markdown/file/file_uploader/index.md b/mddocs/docs/_build/markdown/file/file_uploader/index.md new file mode 100644 index 000000000..e11581a9c --- /dev/null +++ b/mddocs/docs/_build/markdown/file/file_uploader/index.md @@ -0,0 +1,9 @@ + + +# File Uploader + +# File Uploader + +* [File Uploader](file_uploader.md) +* [File Uploader Options](options.md) +* [File Uploader Result](result.md) diff --git a/mddocs/docs/_build/markdown/file/file_uploader/options.md b/mddocs/docs/_build/markdown/file/file_uploader/options.md new file mode 100644 index 000000000..4cf5cf73b --- /dev/null +++ b/mddocs/docs/_build/markdown/file/file_uploader/options.md @@ -0,0 +1,3 @@ + + +# File Uploader Options diff --git a/mddocs/docs/_build/markdown/file/file_uploader/result.md b/mddocs/docs/_build/markdown/file/file_uploader/result.md new file mode 100644 index 000000000..8050abb92 --- /dev/null +++ b/mddocs/docs/_build/markdown/file/file_uploader/result.md @@ -0,0 +1,3 @@ + + +# File Uploader Result diff --git a/mddocs/docs/_build/markdown/file/index.md b/mddocs/docs/_build/markdown/file/index.md new file mode 100644 index 000000000..2da812bab --- /dev/null +++ b/mddocs/docs/_build/markdown/file/index.md @@ -0,0 +1,9 @@ + + + File classes + +* [File Downloader](file_downloader/index.md) +* [File Uploader](file_uploader/index.md) +* [File Mover](file_mover/index.md) +* [File Filters](file_filters/index.md) +* [File Limits](file_limits/index.md) diff --git a/mddocs/docs/_build/markdown/file_df/file_df_reader/file_df_reader.md b/mddocs/docs/_build/markdown/file_df/file_df_reader/file_df_reader.md new file mode 100644 index 000000000..f0099864b --- /dev/null +++ b/mddocs/docs/_build/markdown/file_df/file_df_reader/file_df_reader.md @@ -0,0 +1,3 @@ + + +# FileDF Reader diff --git a/mddocs/docs/_build/markdown/file_df/file_df_reader/index.md b/mddocs/docs/_build/markdown/file_df/file_df_reader/index.md new file mode 100644 index 000000000..0087e74ac --- /dev/null +++ b/mddocs/docs/_build/markdown/file_df/file_df_reader/index.md @@ -0,0 +1,8 @@ + + +# FileDF Reader + +# FileDF Reader + +* [FileDF Reader](file_df_reader.md) +* [Options](options.md) diff --git a/mddocs/docs/_build/markdown/file_df/file_df_reader/options.md b/mddocs/docs/_build/markdown/file_df/file_df_reader/options.md new file mode 100644 index 000000000..126620300 --- /dev/null +++ b/mddocs/docs/_build/markdown/file_df/file_df_reader/options.md @@ -0,0 +1,3 @@ + + +# Options diff --git a/mddocs/docs/_build/markdown/file_df/file_df_writer/file_df_writer.md b/mddocs/docs/_build/markdown/file_df/file_df_writer/file_df_writer.md new file mode 100644 index 000000000..e3a9f5ad0 --- /dev/null +++ b/mddocs/docs/_build/markdown/file_df/file_df_writer/file_df_writer.md @@ -0,0 +1,3 @@ + + +# FileDF Writer diff --git a/mddocs/docs/_build/markdown/file_df/file_df_writer/index.md b/mddocs/docs/_build/markdown/file_df/file_df_writer/index.md new file mode 100644 index 000000000..9813ed1d4 --- /dev/null +++ b/mddocs/docs/_build/markdown/file_df/file_df_writer/index.md @@ -0,0 +1,8 @@ + + +# FileDF Writer + +# FileDF Writer + +* [FileDF Writer](file_df_writer.md) +* [Options](options.md) diff --git a/mddocs/docs/_build/markdown/file_df/file_df_writer/options.md b/mddocs/docs/_build/markdown/file_df/file_df_writer/options.md new file mode 100644 index 000000000..d53a2df29 --- /dev/null +++ b/mddocs/docs/_build/markdown/file_df/file_df_writer/options.md @@ -0,0 +1,3 @@ + + +# Options diff --git a/mddocs/docs/_build/markdown/file_df/file_formats/avro.md b/mddocs/docs/_build/markdown/file_df/file_formats/avro.md new file mode 100644 index 000000000..80f484f01 --- /dev/null +++ b/mddocs/docs/_build/markdown/file_df/file_formats/avro.md @@ -0,0 +1,3 @@ + + +# Avro diff --git a/mddocs/docs/_build/markdown/file_df/file_formats/base.md b/mddocs/docs/_build/markdown/file_df/file_formats/base.md new file mode 100644 index 000000000..e7522ea8b --- /dev/null +++ b/mddocs/docs/_build/markdown/file_df/file_formats/base.md @@ -0,0 +1,3 @@ + + +# Base interface diff --git a/mddocs/docs/_build/markdown/file_df/file_formats/csv.md b/mddocs/docs/_build/markdown/file_df/file_formats/csv.md new file mode 100644 index 000000000..6a52871e2 --- /dev/null +++ b/mddocs/docs/_build/markdown/file_df/file_formats/csv.md @@ -0,0 +1,3 @@ + + +# CSV diff --git a/mddocs/docs/_build/markdown/file_df/file_formats/excel.md b/mddocs/docs/_build/markdown/file_df/file_formats/excel.md new file mode 100644 index 000000000..3d71a09d0 --- /dev/null +++ b/mddocs/docs/_build/markdown/file_df/file_formats/excel.md @@ -0,0 +1,3 @@ + + +# Excel diff --git a/mddocs/docs/_build/markdown/file_df/file_formats/index.md b/mddocs/docs/_build/markdown/file_df/file_formats/index.md new file mode 100644 index 000000000..a6500bdf3 --- /dev/null +++ b/mddocs/docs/_build/markdown/file_df/file_formats/index.md @@ -0,0 +1,18 @@ + + +# File Formats + +# File formats + +* [Avro](avro.md) +* [CSV](csv.md) +* [Excel](excel.md) +* [JSON](json.md) +* [JSONLine](jsonline.md) +* [ORC](orc.md) +* [Parquet](parquet.md) +* [XML](xml.md) + +# For developers + +* [Base interface](base.md) diff --git a/mddocs/docs/_build/markdown/file_df/file_formats/json.md b/mddocs/docs/_build/markdown/file_df/file_formats/json.md new file mode 100644 index 000000000..7ce67ce99 --- /dev/null +++ b/mddocs/docs/_build/markdown/file_df/file_formats/json.md @@ -0,0 +1,3 @@ + + +# JSON diff --git a/mddocs/docs/_build/markdown/file_df/file_formats/jsonline.md b/mddocs/docs/_build/markdown/file_df/file_formats/jsonline.md new file mode 100644 index 000000000..f65f59bdd --- /dev/null +++ b/mddocs/docs/_build/markdown/file_df/file_formats/jsonline.md @@ -0,0 +1,3 @@ + + +# JSONLine diff --git a/mddocs/docs/_build/markdown/file_df/file_formats/orc.md b/mddocs/docs/_build/markdown/file_df/file_formats/orc.md new file mode 100644 index 000000000..bbcdbbf34 --- /dev/null +++ b/mddocs/docs/_build/markdown/file_df/file_formats/orc.md @@ -0,0 +1,3 @@ + + +# ORC diff --git a/mddocs/docs/_build/markdown/file_df/file_formats/parquet.md b/mddocs/docs/_build/markdown/file_df/file_formats/parquet.md new file mode 100644 index 000000000..e831eb1bd --- /dev/null +++ b/mddocs/docs/_build/markdown/file_df/file_formats/parquet.md @@ -0,0 +1,3 @@ + + +# Parquet diff --git a/mddocs/docs/_build/markdown/file_df/file_formats/xml.md b/mddocs/docs/_build/markdown/file_df/file_formats/xml.md new file mode 100644 index 000000000..0853f499f --- /dev/null +++ b/mddocs/docs/_build/markdown/file_df/file_formats/xml.md @@ -0,0 +1,3 @@ + + +# XML diff --git a/mddocs/docs/_build/markdown/file_df/index.md b/mddocs/docs/_build/markdown/file_df/index.md new file mode 100644 index 000000000..c225567d6 --- /dev/null +++ b/mddocs/docs/_build/markdown/file_df/index.md @@ -0,0 +1,7 @@ + + + File DataFrame classes + +* [FileDF Reader](file_df_reader/index.md) +* [FileDF Writer](file_df_writer/index.md) +* [File Formats](file_formats/index.md) diff --git a/mddocs/docs/_build/markdown/hooks/design.md b/mddocs/docs/_build/markdown/hooks/design.md new file mode 100644 index 000000000..e4f786f65 --- /dev/null +++ b/mddocs/docs/_build/markdown/hooks/design.md @@ -0,0 +1,642 @@ + + +# High level design + +## What are hooks? + +Hook mechanism is a part of onETL which allows to inject some additional behavior into +existing methods of (almost) any class. + +### Features + +Hooks mechanism allows to: + +* Inspect and validate input arguments and output results of method call +* Access, modify or replace method call result (but NOT input arguments) +* Wrap method calls with a context manager and catch raised exceptions + +Hooks can be placed into [Plugins](../plugins.md#plugins), allowing to modify onETL behavior by installing some additional package. + +### Limitations + +* Hooks can be bound to methods of a class only (not functions). +* Only methods decorated with [@slot decorator](slot.md#slot-decorator) implement hooks mechanism. These class and methods are marked as [![support_hooks](https://img.shields.io/badge/%20-support%20hooks-blue)](https://onetl.readthedocs.io/en/0.13.5/hooks/index.html). +* Hooks can be bound to public methods only. + +## Terms + +* [@slot decorator](slot.md#slot-decorator) - method of a class with a special decorator +* `Callback` - function which implements some additional logic which modifies slot behavior +* [@hook decorator](hook.md#hook-decorator) - wrapper around callback which stores hook state, priority and some useful methods +* `Hooks mechanism` - calling `Slot()` will call all enabled hooks which are bound to the slot. Implemented by [@support_hooks decorator](support_hooks.md#support-hooks-decorator). + +## How to implement hooks? + +### TL;DR + +```python +from onetl.hooks import support_hooks, slot, hook + + +@support_hooks # enabling hook mechanism for the class +class MyClass: + def __init__(self, data): + self.data = data + + # this is slot + @slot + def method(self, arg): + pass + + +@MyClass.method.bind # bound hook to the slot +@hook # this is hook +def callback(obj, arg): # this is callback + print(obj.data, arg) + + +obj = MyClass(1) +obj.method(2) # will call callback(obj, 1) + +# prints "1 2" +``` + +#### Define a slot + +* Create a class with a method: + +```python +class MyClass: + def __init__(self, data): + self.data = data + + def method(self, arg): + return self.data, arg +``` + +* Add [@slot decorator](slot.md#slot-decorator) to the method: + +```python +from onetl.hooks import support_hooks, slot, hook + + +class MyClass: + @slot + def method(self, arg): + return self.data, arg +``` + +If method has other decorators like `@classmethod` or `@staticmethod`, `@slot` should be placed on the top: + +```python +from onetl.hooks import support_hooks, slot, hook + + +class MyClass: + @slot + @classmethod + def class_method(cls, arg): + return cls, arg + + @slot + @staticmethod + def static_method(arg): + return arg +``` + +* Add [@support_hooks decorator](support_hooks.md#support-hooks-decorator) to the class: + +```python +from onetl.hooks import support_hooks, slot, hook + + +@support_hooks +class MyClass: + @slot + def method(self, arg): + return self.data, arg +``` + +Slot is created. + +#### Define a callback + +Define some function (a.k.a callback): + +```python +def callback(self, arg): + print(self.data, arg) +``` + +It should have signature *compatible* with `MyClass.method`. *Compatible* does not mean *exactly the same* - +for example, you can rename positional arguments: + +```python +def callback(obj, arg): + print(obj.data, arg) +``` + +Use `*args` and `**kwargs` to omit arguments you don’t care about: + +```python +def callback(obj, *args, **kwargs): + print(obj.data, args, kwargs) +``` + +There is also an argument `method_name` which has a special meaning - +the method name which the callback is bound to is passed into this argument: + +```python +def callback(obj, *args, method_name: str, **kwargs): + print(obj.data, args, method_name, kwargs) +``` + +#### NOTE +`method_name` should always be a keyword argument, **NOT** positional. + +#### WARNING +If callback signature is not compatible with slot signature, an exception will be raised, +but **ONLY** while slot is called. + +#### Define a hook + +Add [@hook decorator](hook.md#hook-decorator) to create a hook from your callback: + +```python +@hook +def callback(obj, arg): + print(obj.data, arg) +``` + +You can pass more options to the `@hook` decorator, like state or priority. +See decorator documentation for more details. + +#### Bind hook to the slot + +Use `Slot.bind` method to bind hook to the slot: + +```python +@MyClass.method.bind +@hook +def callback(obj, arg): + print(obj, arg) +``` + +You can bind more than one hook to the same slot, and bind same hook to multiple slots: + +```python +@MyClass.method1.bind +@MyClass.method2.bind +@hook +def callback1(obj, arg): + "Will be called by both MyClass.method1 and MyClass.method2" + + +@MyClass.method1.bind +@hook +def callback2(obj, arg): + "Will be called by MyClass.method1 too" +``` + +## How hooks are called? + +### General + +Just call the method decorated by `@slot` to trigger the hook: + +```python +obj = MyClass(1) +obj.method(2) # will call callback(obj, 2) + +# prints "1 2" +``` + +There are some special callback types that has a slightly different behavior. + +### Context managers + +`@hook` decorator can be placed on a context manager class: + +```python +@hook +class ContextManager: + def __init__(self, obj, arg): + self.obj = obj + self.arg = arg + + def __enter__(self): + # do something on enter + print(obj.data, arg) + return self + + def __exit__(self, exc_type, exc_value, traceback): + # do something on exit + return False +``` + +Context manager is entered while calling the `Slot()`, and exited then the call is finished. + +If present, method `process_result` has a special meaning - +it can receive `MyClass.method` call result, and also modify/replace it: + +```python +@hook +class ContextManager: + def __init__(self, obj, arg): + self.obj = obj + self.arg = arg + + def __enter__(self): + # do something on enter + print(obj.data, arg) + return self + + def __exit__(self, exc_type, exc_value, traceback): + # do something on exit + return False + + def process_result(self, result): + # do something with method call result + return modified(result) +``` + +See examples below for more information. + +### Generator function + +`@hook` decorator can be placed on a generator function: + +```python +@hook +def callback(obj, arg): + print(obj.data, arg) + # this is called before original method body + + yield # method is called here + + # this is called after original method body +``` + +It is converted to a context manager, in the same manner as +[contextlib.contextmanager](https://docs.python.org/3/library/contextlib.html#contextlib.contextmanager). + +Generator body can be wrapped with `try..except..finally` to catch exceptions: + +```python +@hook +def callback(obj, arg): + print(obj.data, arg) + + try: + # this is called before original method body + + yield # method is called here + except Exception as e: + process_exception(a) + finally: + # this is called after original method body + finalizer() +``` + +There is also a special syntax which allows generator to access and modify/replace method call result: + +```default +@hook +def callback(obj, arg): + original_result = yield # method is called here + + new_result = do_something(original_result) + + yield new_result # modify/replace the result +``` + +### Calling hooks in details + +* The callback will be called with the same arguments as the original method. + * If slot is a regular method: + ```python + callback_result = callback(self, *args, **kwargs) + ``` + + Here `self` is a class instance (`obj`). + * If slot is a class method: + ```python + callback_result = callback(cls, *args, **kwargs) + ``` + + Here `cls` is the class itself (`MyClass`). + * If slot is a static method: + ```python + callback_result = callback(*args, **kwargs) + ``` + + Neither object not class are passed to the callback in this case. +* If `callback_result` is a context manager, enter the context. Context manager can catch all the exceptions raised. + > If there are multiple hooks bound the the slot, every context manager will be entered. +* Then call the original method wrapped by `@slot`: + ```python + original_result = method(*args, **kwargs) + ``` +* Process `original_result`: + * If `callback_result` object has method `process_result`, or is a generator wrapped with `@hook`, call it: + ```python + new_result = callback_result.process_result(original_result) + ``` + * Otherwise set `new_result = callback_result`. + * If there are multiple hooks bound the the method, pass `new_result` through the chain: + ```python + new_result = callback1_result.process_result(original_result) + new_result = callback2_result.process_result(new_result or original_result) + new_result = callback3_result.process_result(new_result or original_result) + ``` +* Finally return: + ```python + return new_result or original_result + ``` + + All `None` values are ignored on every step above. +* Exit all the context managers entered during the slot call. + +### Hooks priority + +Hooks are executed in the following order: + +1. Parent class slot + `FIRST` +2. Inherited class slot + `FIRST` +3. Parent class slot + `NORMAL` +4. Inherited class slot + `NORMAL` +5. Parent class slot + `LAST` +6. Inherited class slot + `LAST` + +Hooks with the same priority and inheritance will be executed in the same order they were registered (`Slot.bind` call). + +#### NOTE +Calls of `super()` inside inherited class methods does not trigger hooks call. +Hooks are triggered only if method is called explicitly. + +This allow to wrap with a hook the entire slot call without influencing its internal logic. + +### Hook types + +Here are several examples of using hooks. These types are not exceptional, they can be mixed - for example, +hook can both modify method result and catch exceptions. + +#### Before hook + +Can be used for inspecting or validating input args of the original function: + +```python +@hook +def before1(obj, arg): + print(obj, arg) + # original method is called after exiting this function + + +@hook +def before2(obj, arg): + if arg == 1: + raise ValueError("arg=1 is not allowed") + return None # return None is the same as no return statement +``` + +Executed before calling the original method wrapped by `@slot`. +If hook raises an exception, method will not be called at all. + +#### After hook + +Can be used for performing some actions after original method was successfully executed: + +```python +@hook +def after1(obj, arg): + yield # original method is called here + print(obj, arg) + + +@hook +def after2(obj, arg): + yield None # yielding None is the same as empty yield + if arg == 1: + raise ValueError("arg=1 is not allowed") +``` + +If original method raises an exception, the block of code after `yield` will not be called. + +#### Context hook + +Can be used for catching and handling some exceptions, or to determine that there was no exception during slot call: + +Generator syntax + +```py +# This is just the same as using @contextlib.contextmanager + +@hook +def context_generator(obj, arg): + try: + yield # original method is called here + print(obj, arg) # <-- this line will not be called if method raised an exception + except SomeException as e: + magic(e) + finally: + finalizer() +``` + +Context manager syntax + +```py +@hook +class ContextManager: + def __init__(self, obj, args): + self.obj = obj + self.args = args + + def __enter__(self): + return self + + # original method is called between __enter__ and __exit__ + + def __exit__(self, exc_type, exc_value, traceback): + result = False + if exc_type is not None and isinstance(exc_value, SomeException): + magic(exc_value) + result = True # suppress exception + else: + print(self.obj, self.arg) + finalizer() + return result +``` + +#### NOTE +Contexts are exited in the reverse order of the hook calls. +So if some hook raised an exception, it will be passed into the previous hook, not the next one. + +It is recommended to specify the proper priority for the hook, e.g. `FIRST` + +#### Replacing result hook + +Replaces the output result of the original method. + +Can be used for delegating some implementation details for third-party extensions. +See [Hive](../connection/db_connection/hive/index.md#hive) and [HDFS](../connection/file_connection/hdfs/index.md#hdfs) as an example. + +```python +@hook +def replace1(obj, arg): + result = arg + 10 # any non-None return result + + # original method call result is ignored, output will always be arg + 10 + return result + + +@hook +def replace2(obj, arg): + yield arg + 10 # same as above +``` + +#### NOTE +If there are multiple hooks bound to the same slot, the result of last hook will be used. +It is recommended to specify the proper priority for the hook, e.g. `LAST` + +#### Accessing result hook + +Can access output result of the original method and inspect or validate it: + +Generator syntax + +```py +@hook +def access_result(obj, arg): + result = yield # original method is called here, and result can be used in the hook + print(result) + yield # does not modify result +``` + +Context manager syntax + +```py +@hook +class ModifiesResult: + def __init__(self, obj, args): + self.obj = obj + self.args = args + + def __enter__(self): + return self + + # original method is called between __enter__ and __exit__ + # result is passed into process_result method of context manager, if present + + def process_result(self, result): + print(result) # result can be used in the hook + return None # does not modify result. same as no return statement in the method + + def __exit__(self, exc_type, exc_value, traceback): + return False +``` + +#### Modifying result hook + +Can access output result of the original method, and return the modified one: + +Generator syntax + +```py +@hook +def modifies_result(obj, arg): + result = yield # original method is called here, and result can be used in the hook + yield result + 10 # modify output result. None values are ignored +``` + +Context manager syntax + +```py +@hook +class ModifiesResult: + def __init__(self, obj, args): + self.obj = obj + self.args = args + + def __enter__(self): + return self + + # original method is called between __enter__ and __exit__ + # result is passed into process_result method of context manager, if present + + def process_result(self, result): + print(result) # result can be used in the hook + return result + 10 # modify output result. None values are ignored + + def __exit__(self, exc_type, exc_value, traceback): + return False +``` + +#### NOTE +If there are multiple hooks bound to the same slot, the result of last hook will be used. +It is recommended to specify the proper priority for the hook, e.g. `LAST` + +## How to enable/disable hooks? + +You can enable/disable/temporary disable hooks on 4 different levels: + +* Manage global hooks state (level 1): + > * `onetl.hooks.hooks_state.stop_all_hooks` + > * `onetl.hooks.hooks_state.resume_all_hooks` + > * `onetl.hooks.hooks_state.skip_all_hooks` +* Manage all hooks bound to a specific class (level 2): + > * `onetl.hooks.support_hooks.suspend_hooks` + > * `onetl.hooks.support_hooks.resume_hooks` + > * `onetl.hooks.support_hooks.skip_hooks` +* Manage all hooks bound to a specific slot (level 3): + > * `onetl.hooks.slot.Slot.suspend_hooks` + > * `onetl.hooks.slot.Slot.resume_hooks` + > * `onetl.hooks.slot.Slot.skip_hooks` +* Manage state of a specific hook (level 4): + > * `onetl.hooks.hook.Hook.enable` + > * `onetl.hooks.hook.Hook.disable` + +More details in the documentation above. + +#### NOTE +All of these levels are independent. + +Calling `stop` on the level 1 has higher priority than level 2, and so on. +But calling `resume` on the level 1 does not automatically resume hooks stopped in the level 2, +they should be resumed explicitly. + +## How to see logs of the hook mechanism? + +Hooks registration emits logs with `DEBUG` level: + +```python +from onetl.logs import setup_logging + +setup_logging() +``` + +```text +DEBUG |onETL| Registered hook 'mymodule.callback1' for 'MyClass.method' (enabled=True, priority=HookPriority.NORMAL) +DEBUG |onETL| Registered hook 'mymodule.callback2' for 'MyClass.method' (enabled=True, priority=HookPriority.NORMAL) +DEBUG |onETL| Registered hook 'mymodule.callback3' for 'MyClass.method' (enabled=False, priority=HookPriority.NORMAL) +``` + +But most of logs are emitted with even lower level `NOTICE`, to make output less verbose: + +```python +from onetl.logs import NOTICE, setup_logging + +setup_logging(level=NOTICE) +``` + +```default +NOTICE |Hooks| 2 hooks registered for 'MyClass.method' +NOTICE |Hooks| Calling hook 'mymodule.callback1' (1/2) +NOTICE |Hooks| Hook is finished with returning non-None result +NOTICE |Hooks| Calling hook 'mymodule.callback2' (2/2) +NOTICE |Hooks| This is a context manager, entering ... +NOTICE |Hooks| Calling original method 'MyClass.method' +NOTICE |Hooks| Method call is finished +NOTICE |Hooks| Method call result (*NOT* None) will be replaced with result of hook 'mymodule.callback1' +NOTICE |Hooks| Passing result to 'process_result' method of context manager 'mymodule.callback2' +NOTICE |Hooks| Method call result (*NOT* None) is modified by hook! +``` diff --git a/mddocs/docs/_build/markdown/hooks/global_state.md b/mddocs/docs/_build/markdown/hooks/global_state.md new file mode 100644 index 000000000..fbebfae6a --- /dev/null +++ b/mddocs/docs/_build/markdown/hooks/global_state.md @@ -0,0 +1,3 @@ + + +# Hooks global state diff --git a/mddocs/docs/_build/markdown/hooks/hook.md b/mddocs/docs/_build/markdown/hooks/hook.md new file mode 100644 index 000000000..2b94b2235 --- /dev/null +++ b/mddocs/docs/_build/markdown/hooks/hook.md @@ -0,0 +1,3 @@ + + +# `@hook` decorator diff --git a/mddocs/docs/_build/markdown/hooks/index.md b/mddocs/docs/_build/markdown/hooks/index.md new file mode 100644 index 000000000..8e1ba7fb9 --- /dev/null +++ b/mddocs/docs/_build/markdown/hooks/index.md @@ -0,0 +1,14 @@ + + +# Hooks + +#### Versionadded +Added in version 0.6.0. + +# Hooks + +* [High level design](design.md) +* [@hook decorator](hook.md) +* [@slot decorator](slot.md) +* [@support_hooks decorator](support_hooks.md) +* [Hooks global state](global_state.md) diff --git a/mddocs/docs/_build/markdown/hooks/slot.md b/mddocs/docs/_build/markdown/hooks/slot.md new file mode 100644 index 000000000..3bfe4d730 --- /dev/null +++ b/mddocs/docs/_build/markdown/hooks/slot.md @@ -0,0 +1,3 @@ + + +# `@slot` decorator diff --git a/mddocs/docs/_build/markdown/hooks/support_hooks.md b/mddocs/docs/_build/markdown/hooks/support_hooks.md new file mode 100644 index 000000000..2fddf4442 --- /dev/null +++ b/mddocs/docs/_build/markdown/hooks/support_hooks.md @@ -0,0 +1,3 @@ + + +# `@support_hooks` decorator diff --git a/mddocs/docs/_build/markdown/hwm_store/index.md b/mddocs/docs/_build/markdown/hwm_store/index.md new file mode 100644 index 000000000..c59cc0096 --- /dev/null +++ b/mddocs/docs/_build/markdown/hwm_store/index.md @@ -0,0 +1,9 @@ + + +# HWM + +Since onETL v0.10.0, the `HWMStore` and `HWM` classes have been moved to a separate library [etl-entities](https://etl-entities.readthedocs.io/en/stable/). + +The only class was left intact is [YAMLHWMStore](yaml_hwm_store.md#yaml-hwm-store), **which is default** in onETL. + +Other known implementation is [HorizonHWMStore](https://horizon-hwm-store.readthedocs.io/). diff --git a/mddocs/docs/_build/markdown/hwm_store/yaml_hwm_store.md b/mddocs/docs/_build/markdown/hwm_store/yaml_hwm_store.md new file mode 100644 index 000000000..44a228144 --- /dev/null +++ b/mddocs/docs/_build/markdown/hwm_store/yaml_hwm_store.md @@ -0,0 +1,3 @@ + + +# YAMLHWMStore diff --git a/mddocs/docs/_build/markdown/index.md b/mddocs/docs/_build/markdown/index.md new file mode 100644 index 000000000..436e5eb85 --- /dev/null +++ b/mddocs/docs/_build/markdown/index.md @@ -0,0 +1,59 @@ + + +# onETL + +[![Repo status - Active](https://www.repostatus.org/badges/latest/active.svg)](https://github.com/MobileTeleSystems/onetl) [![PyPI - Latest Release](https://img.shields.io/pypi/v/onetl)](https://pypi.org/project/onetl/) [![PyPI - License](https://img.shields.io/pypi/l/onetl.svg)](https://github.com/MobileTeleSystems/onetl/blob/develop/LICENSE.txt) [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/onetl.svg)](https://pypi.org/project/onetl/) [![PyPI - Downloads](https://img.shields.io/pypi/dm/onetl)](https://pypi.org/project/onetl/) +[![Documentation - ReadTheDocs](https://readthedocs.org/projects/onetl/badge/?version=stable)](https://onetl.readthedocs.io/) [![Github Actions - latest CI build status](https://github.com/MobileTeleSystems/onetl/workflows/Tests/badge.svg)](https://github.com/MobileTeleSystems/onetl/actions) [![Test coverage - percent](https://codecov.io/gh/MobileTeleSystems/onetl/branch/develop/graph/badge.svg?token=RIO8URKNZJ)](https://codecov.io/gh/MobileTeleSystems/onetl) [![pre-commit.ci - status](https://results.pre-commit.ci/badge/github/MobileTeleSystems/onetl/develop.svg)](https://results.pre-commit.ci/latest/github/MobileTeleSystems/onetl/develop) + +![onETL logo](_static/logo_wide.svg) + +## What is onETL? + +Python ETL/ELT library powered by [Apache Spark](https://spark.apache.org/) & other open-source tools. + +## Goals + +* Provide unified classes to extract data from (**E**) & load data to (**L**) various stores. +* Provides [Spark DataFrame API](https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.DataFrame.html) for performing transformations (**T**) in terms of *ETL*. +* Provide direct assess to database, allowing to execute SQL queries, as well as DDL, DML, and call functions/procedures. This can be used for building up *ELT* pipelines. +* Support different [read strategies](https://onetl.readthedocs.io/en/stable/strategy/index.html) for incremental and batch data fetching. +* Provide [hooks](https://onetl.readthedocs.io/en/stable/hooks/index.html) & [plugins](https://onetl.readthedocs.io/en/stable/plugins.html) mechanism for altering behavior of internal classes. + +## Non-goals + +* onETL is not a Spark replacement. It just provides additional functionality that Spark does not have, and improves UX for end users. +* onETL is not a framework, as it does not have requirements to project structure, naming, the way of running ETL/ELT processes, configuration, etc. All of that should be implemented in some other tool. +* onETL is deliberately developed without any integration with scheduling software like Apache Airflow. All integrations should be implemented as separated tools. +* Only batch operations, no streaming. For streaming prefer [Apache Flink](https://flink.apache.org/). + +## Requirements + +* **Python 3.7 - 3.13** +* PySpark 2.3.x - 3.5.x (depends on used connector) +* Java 8+ (required by Spark, see below) +* Kerberos libs & GCC (required by `Hive`, `HDFS` and `SparkHDFS` connectors) + +## Supported storages + +| Type | Storage | Powered by | +|--------------------|-----------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------| +| Database | Clickhouse | Apache Spark [JDBC Data Source](https://spark.apache.org/docs/latest/sql-data-sources-jdbc.html) | +| MSSQL | | | +| MySQL | | | +| Postgres | | | +| Oracle | | | +| Teradata | | | +| Hive | Apache Spark [Hive integration](https://spark.apache.org/docs/latest/sql-data-sources-hive-tables.html) | | +| Kafka | Apache Spark [Kafka integration](https://spark.apache.org/docs/latest/structured-streaming-kafka-integration.html) | | +| Greenplum | VMware [Greenplum Spark connector](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/index.html) | | +| MongoDB | [MongoDB Spark connector](https://www.mongodb.com/docs/spark-connector/current) | | +| File | HDFS | [HDFS Python client](https://pypi.org/project/hdfs/) | +| S3 | [minio-py client](https://pypi.org/project/minio/) | | +| SFTP | [Paramiko library](https://pypi.org/project/paramiko/) | | +| FTP | [FTPUtil library](https://pypi.org/project/ftputil/) | | +| FTPS | | | +| WebDAV | [WebdavClient3 library](https://pypi.org/project/webdavclient3/) | | +| Samba | [pysmb library](https://pypi.org/project/pysmb/) | | +| Files as DataFrame | SparkLocalFS | Apache Spark [File Data Source](https://spark.apache.org/docs/latest/sql-data-sources-generic-options.html) | +| SparkHDFS | | | +| SparkS3 | [Hadoop AWS](https://hadoop.apache.org/docs/current3/hadoop-aws/tools/hadoop-aws/index.html) library | | diff --git a/mddocs/docs/_build/markdown/install/files.md b/mddocs/docs/_build/markdown/install/files.md new file mode 100644 index 000000000..60c747462 --- /dev/null +++ b/mddocs/docs/_build/markdown/install/files.md @@ -0,0 +1,20 @@ + + +# File connections + +All File (but not *FileDF*) connection classes (`FTP`, `SFTP`, `HDFS` and so on) requires specific Python clients to be installed. + +Each client can be installed explicitly by passing connector name (in lowercase) to `extras`: + +```bash +pip install onetl[ftp] # specific connector +pip install onetl[ftp,ftps,sftp,hdfs,s3,webdav,samba] # multiple connectors +``` + +To install all file connectors at once you can pass `files` to `extras`: + +```bash +pip install onetl[files] +``` + +**Otherwise class import will fail.** diff --git a/mddocs/docs/_build/markdown/install/full.md b/mddocs/docs/_build/markdown/install/full.md new file mode 100644 index 000000000..889f6c401 --- /dev/null +++ b/mddocs/docs/_build/markdown/install/full.md @@ -0,0 +1,15 @@ + + +# Full bundle + +To install all connectors and dependencies, you can pass `all` into `extras`: + +```bash +pip install onetl[all] + +# this is just the same as +pip install onetl[spark,files,kerberos] +``` + +#### WARNING +This method consumes a lot of disk space, and requires for Java & Kerberos libraries to be installed into your OS. diff --git a/mddocs/docs/_build/markdown/install/index.md b/mddocs/docs/_build/markdown/install/index.md new file mode 100644 index 000000000..1a5fb63cc --- /dev/null +++ b/mddocs/docs/_build/markdown/install/index.md @@ -0,0 +1,33 @@ + + +# How to install + +Base `onetl` package contains: + +* `DBReader`, `DBWriter` and related classes +* `FileDownloader`, `FileUploader`, `FileMover` and related classes, like file filters & limits +* `FileDFReader`, `FileDFWriter` and related classes, like file formats +* Read Strategies & HWM classes +* Plugins support + +It can be installed via: + +```bash +pip install onetl +``` + +#### WARNING +This method does NOT include any connections. + +This method is recommended for use in third-party libraries which require for `onetl` to be installed, +but do not use its connection classes. + +## Installation in details + +## How to install + +* [How to install]() +* [Spark](spark.md) +* [File connections](files.md) +* [Kerberos support](kerberos.md) +* [Full bundle](full.md) diff --git a/mddocs/docs/_build/markdown/install/kerberos.md b/mddocs/docs/_build/markdown/install/kerberos.md new file mode 100644 index 000000000..1826124c2 --- /dev/null +++ b/mddocs/docs/_build/markdown/install/kerberos.md @@ -0,0 +1,32 @@ + + +# Kerberos support + +Most of Hadoop instances set up with Kerberos support, +so some connections require additional setup to work properly. + +* `HDFS` + Uses [requests-kerberos](https://pypi.org/project/requests-kerberos/) and + [GSSApi](https://pypi.org/project/gssapi/) for authentication. + It also uses `kinit` executable to generate Kerberos ticket. +* `Hive` and `SparkHDFS` + require Kerberos ticket to exist before creating Spark session. + +So you need to install OS packages with: + +* `krb5` libs +* Headers for `krb5` +* `gcc` or other compiler for C sources + +The exact installation instruction depends on your OS, here are some examples: + +```bash +apt install libkrb5-dev krb5-user gcc # Debian-based +dnf install krb5-devel krb5-libs krb5-workstation gcc # CentOS, OracleLinux +``` + +Also you should pass `kerberos` to `extras` to install required Python packages: + +```bash +pip install onetl[kerberos] +``` diff --git a/mddocs/docs/_build/markdown/install/spark.md b/mddocs/docs/_build/markdown/install/spark.md new file mode 100644 index 000000000..914f71611 --- /dev/null +++ b/mddocs/docs/_build/markdown/install/spark.md @@ -0,0 +1,342 @@ + + +# Spark + +All DB connection classes (`Clickhouse`, `Greenplum`, `Hive` and others) +and all FileDF connection classes (`SparkHDFS`, `SparkLocalFS`, `SparkS3`) +require Spark to be installed. + +## Installing Java + +Firstly, you should install JDK. The exact installation instruction depends on your OS, here are some examples: + +```bash +yum install java-1.8.0-openjdk-devel # CentOS 7 + Spark 2 +dnf install java-11-openjdk-devel # CentOS 8 + Spark 3 +apt-get install openjdk-11-jdk # Debian-based + Spark 3 +``` + + + +### Compatibility matrix + +| Spark | Python | Java | Scala | +|-----------------------------------------------------------|------------|------------|---------| +| [2.3.x](https://spark.apache.org/docs/2.3.1/#downloading) | 3.7 only | 8 only | 2.11 | +| [2.4.x](https://spark.apache.org/docs/2.4.8/#downloading) | 3.7 only | 8 only | 2.11 | +| [3.2.x](https://spark.apache.org/docs/3.2.4/#downloading) | 3.7 - 3.10 | 8u201 - 11 | 2.12 | +| [3.3.x](https://spark.apache.org/docs/3.3.4/#downloading) | 3.7 - 3.12 | 8u201 - 17 | 2.12 | +| [3.4.x](https://spark.apache.org/docs/3.4.4/#downloading) | 3.7 - 3.12 | 8u362 - 20 | 2.12 | +| [3.5.x](https://spark.apache.org/docs/3.5.5/#downloading) | 3.8 - 3.13 | 8u371 - 20 | 2.12 | + +## Installing PySpark + +Then you should install PySpark via passing `spark` to `extras`: + +```bash +pip install onetl[spark] # install latest PySpark +``` + +or install PySpark explicitly: + +```bash +pip install onetl pyspark==3.5.5 # install a specific PySpark version +``` + +or inject PySpark to `sys.path` in some other way BEFORE creating a class instance. +**Otherwise connection object cannot be created.** + + + +## Injecting Java packages + +Some DB and FileDF connection classes require specific packages to be inserted to `CLASSPATH` of Spark session, +like JDBC drivers. + +This is usually done by setting up `spark.jars.packages` option while creating Spark session: + +```python +# here is a list of packages to be downloaded: +maven_packages = ( + Greenplum.get_packages(spark_version="3.2") + + MySQL.get_packages() + + Teradata.get_packages() +) + +spark = ( + SparkSession.builder.config("spark.app.name", "onetl") + .config("spark.jars.packages", ",".join(maven_packages)) + .getOrCreate() +) +``` + +Spark automatically resolves package and all its dependencies, download them and inject to Spark session +(both driver and all executors). + +This requires internet access, because package metadata and `.jar` files are fetched from [Maven Repository](https://mvnrepository.com/). + +But sometimes it is required to: + +* Install package without direct internet access (isolated network) +* Install package which is not available in Maven + +There are several ways to do that. + +### Using `spark.jars` + +The most simple solution, but this requires to store raw `.jar` files somewhere on filesystem or web server. + +* Download `package.jar` files (it’s usually something like `some-package_1.0.0.jar`). Local file name does not matter, but it should be unique. +* (For `spark.submit.deployMode=cluster`) place downloaded files to HDFS or deploy to any HTTP web server serving static files. See [official documentation](https://spark.apache.org/docs/latest/submitting-applications.html#advanced-dependency-management) for more details. +* Create Spark session with passing `.jar` absolute file path to `spark.jars` Spark config option: + +for spark.submit.deployMode=client (default) + +```py +jar_files = ["/path/to/package.jar"] + +# do not pass spark.jars.packages +spark = ( + SparkSession.builder.config("spark.app.name", "onetl") + .config("spark.jars", ",".join(jar_files)) + .getOrCreate() +) +``` + +for spark.submit.deployMode=cluster + +```py +# you can also pass URLs like http://domain.com/path/to/downloadable/package.jar +jar_files = ["hdfs:///path/to/package.jar"] + +# do not pass spark.jars.packages +spark = ( + SparkSession.builder.config("spark.app.name", "onetl") + .config("spark.jars", ",".join(jar_files)) + .getOrCreate() +) +``` + +### Using `spark.jars.repositories` + +#### NOTE +In this case Spark still will try to fetch packages from the internet, so if you don’t have internet access, +Spark session will be created with significant delay because of all attempts to fetch packages. + +Can be used if you have access both to public repos (like Maven) and a private Artifactory/Nexus repo. + +* Setup private Maven repository in [JFrog Artifactory](https://jfrog.com/artifactory/) or [Sonatype Nexus](https://www.sonatype.com/products/sonatype-nexus-repository). +* Download `package.jar` file (it’s usually something like `some-package_1.0.0.jar`). Local file name does not matter. +* Upload `package.jar` file to private repository (with same `groupId` and `artifactoryId` as in source package in Maven). +* Pass repo URL to `spark.jars.repositories` Spark config option. +* Create Spark session with passing Package name to `spark.jars.packages` Spark config option: + +```python +maven_packages = ( + Greenplum.get_packages(spark_version="3.2") + + MySQL.get_packages() + + Teradata.get_packages() +) + +spark = ( + SparkSession.builder.config("spark.app.name", "onetl") + .config("spark.jars.repositories", "http://nexus.mydomain.com/private-repo/") + .config("spark.jars.packages", ",".join(maven_packages)) + .getOrCreate() +) +``` + +### Using `spark.jars.ivySettings` + +Same as above, but can be used even if there is no network access to public repos like Maven. + +* Setup private Maven repository in [JFrog Artifactory](https://jfrog.com/artifactory/) or [Sonatype Nexus](https://www.sonatype.com/products/sonatype-nexus-repository). +* Download `package.jar` file (it’s usually something like `some-package_1.0.0.jar`). Local file name does not matter. +* Upload `package.jar` file to [private repository](https://help.sonatype.com/repomanager3/nexus-repository-administration/repository-management#RepositoryManagement-HostedRepository) (with same `groupId` and `artifactoryId` as in source package in Maven). +* Create `ivysettings.xml` file (see below). +* Add here a resolver with repository URL (and credentials, if required). +* Pass `ivysettings.xml` absolute path to `spark.jars.ivySettings` Spark config option. +* Create Spark session with passing package name to `spark.jars.packages` Spark config option: + +ivysettings-all-packages-uploaded-to-nexus.xml + +```xml + + + + + + + + + + + + + +``` + +ivysettings-private-packages-in-nexus-public-in-maven.xml + +```xml + + + + + + + + + + + + + + + + + +``` + +ivysettings-private-packages-in-nexus-public-fetched-using-proxy-repo.xml + +```xml + + + + + + + + + + + + + + + +``` + +ivysettings-nexus-with-auth-required.xml + +```xml + + + + + + + + + + + + + + + + + + + +``` + +```python +maven_packages = ( + Greenplum.get_packages(spark_version="3.2") + + MySQL.get_packages() + + Teradata.get_packages() +) + +spark = ( + SparkSession.builder.config("spark.app.name", "onetl") + .config("spark.jars.ivySettings", "/path/to/ivysettings.xml") + .config("spark.jars.packages", ",".join(maven_packages)) + .getOrCreate() +) +``` + +### Place `.jar` file to `-/.ivy2/jars/` + +Can be used to pass already downloaded file to Ivy, and skip resolving package from Maven. + +* Download `package.jar` file (it’s usually something like `some-package_1.0.0.jar`). Local file name does not matter, but it should be unique. +* Move it to `-/.ivy2/jars/` folder. +* Create Spark session with passing package name to `spark.jars.packages` Spark config option: + +```python +maven_packages = ( + Greenplum.get_packages(spark_version="3.2") + + MySQL.get_packages() + + Teradata.get_packages() +) + +spark = ( + SparkSession.builder.config("spark.app.name", "onetl") + .config("spark.jars.packages", ",".join(maven_packages)) + .getOrCreate() +) +``` + +### Place `.jar` file to Spark jars folder + +#### NOTE +Package file should be placed on all hosts/containers Spark is running, +both driver and all executors. + +Usually this is used only with either: +: * `spark.master=local` (driver and executors are running on the same host), + * `spark.master=k8s://...` (`.jar` files are added to image or to volume mounted to all pods). + +Can be used to embed `.jar` files to a default Spark classpath. + +* Download `package.jar` file (it’s usually something like `some-package_1.0.0.jar`). Local file name does not matter, but it should be unique. +* Move it to `$SPARK_HOME/jars/` folder, e.g. `^/.local/lib/python3.7/site-packages/pyspark/jars/` or `/opt/spark/3.2.3/jars/`. +* Create Spark session **WITHOUT** passing Package name to `spark.jars.packages` + +```python +# no need to set spark.jars.packages or any other spark.jars.* option +# all jars already present in CLASSPATH, and loaded automatically + +spark = SparkSession.builder.config("spark.app.name", "onetl").getOrCreate() +``` + +### Manually adding `.jar` files to `CLASSPATH` + +#### NOTE +Package file should be placed on all hosts/containers Spark is running, +both driver and all executors. + +Usually this is used only with either: +: * `spark.master=local` (driver and executors are running on the same host), + * `spark.master=k8s://...` (`.jar` files are added to image or to volume mounted to all pods). + +Can be used to embed `.jar` files to a default Java classpath. + +* Download `package.jar` file (it’s usually something like `some-package_1.0.0.jar`). Local file name does not matter. +* Set environment variable `CLASSPATH` to `/path/to/package.jar`. You can set multiple file paths +* Create Spark session **WITHOUT** passing Package name to `spark.jars.packages` + +```python +# no need to set spark.jars.packages or any other spark.jars.* option +# all jars already present in CLASSPATH, and loaded automatically + +import os + +jar_files = ["/path/to/package.jar"] +# different delimiters for Windows and Linux +delimiter = ";" if os.name == "nt" else ":" +spark = ( + SparkSession.builder.config("spark.app.name", "onetl") + .config("spark.driver.extraClassPath", delimiter.join(jar_files)) + .config("spark.executor.extraClassPath", delimiter.join(jar_files)) + .getOrCreate() +) +``` diff --git a/mddocs/docs/_build/markdown/logging.md b/mddocs/docs/_build/markdown/logging.md new file mode 100644 index 000000000..27918f939 --- /dev/null +++ b/mddocs/docs/_build/markdown/logging.md @@ -0,0 +1,153 @@ + + +# Logging + +Logging is quite important to understand what’s going on under the hood of onETL. + +Default logging level for Python interpreters is `WARNING`, +but most of onETL logs are in `INFO` level, so users usually don’t see much. + +To change logging level, there is a function `setup_logging` +which should be called at the top of the script: + +```python +from onetl.log import setup_logging +from other.lib import some, more, imports + +setup_logging() + +# rest of code +... +``` + +This changes both log level and log formatting to something like this: + +### See logs + +```text +2024-04-12 10:12:10,834 [INFO ] MainThread: |onETL| Using IncrementalStrategy as a strategy +2024-04-12 10:12:10,835 [INFO ] MainThread: =================================== DBReader.run() starts =================================== +2024-04-12 10:12:10,835 [INFO ] MainThread: |DBReader| Getting Spark type for HWM expression: 'updated_at' +2024-04-12 10:12:10,836 [INFO ] MainThread: |MSSQL| Fetching schema of table 'source_schema.table' ... +2024-04-12 10:12:11,636 [INFO ] MainThread: |MSSQL| Schema fetched. +2024-04-12 10:12:11,642 [INFO ] MainThread: |DBReader| Got Spark field: StructField('updated_at', TimestampType(), True) +2024-04-12 10:12:11,642 [INFO ] MainThread: |DBReader| Detected HWM type: 'ColumnDateTimeHWM' +2024-04-12 10:12:11,643 [INFO ] MainThread: |IncrementalStrategy| Fetching HWM from HorizonHWMStore: +2024-04-12 10:12:11,643 [INFO ] MainThread: name = 'updated_at#source_schema.table@mssql:/mssql.host:1433/somedb' +2024-04-12 10:12:12,181 [INFO ] MainThread: |IncrementalStrategy| Fetched HWM: +2024-04-12 10:12:12,182 [INFO ] MainThread: hwm = ColumnDateTimeHWM( +2024-04-12 10:12:12,182 [INFO ] MainThread: name = 'updated_at#source_schema.table@mssql:/mssql.host:1433/somedb', +2024-04-12 10:12:12,182 [INFO ] MainThread: entity = 'source_schema.table', +2024-04-12 10:12:12,182 [INFO ] MainThread: expression = 'updated_at', +2024-04-12 10:12:12,184 [INFO ] MainThread: value = datetime.datetime(2024, 4, 11, 18, 10, 2, 120000), +2024-04-12 10:12:12,184 [INFO ] MainThread: ) +2024-04-12 10:12:12,184 [INFO ] MainThread: |MSSQL| -> |Spark| Reading DataFrame from source using parameters: +2024-04-12 10:12:12,185 [INFO ] MainThread: source = 'source_schema.table' +2024-04-12 10:12:12,185 [INFO ] MainThread: columns = [ +2024-04-12 10:12:12,185 [INFO ] MainThread: 'id', +2024-04-12 10:12:12,186 [INFO ] MainThread: 'new_value', +2024-04-12 10:12:12,186 [INFO ] MainThread: 'old_value', +2024-04-12 10:12:12,186 [INFO ] MainThread: 'updated_at', +2024-04-12 10:12:12,186 [INFO ] MainThread: ] +2024-04-12 10:12:12,187 [INFO ] MainThread: where = "field = 'some'" +2024-04-12 10:12:12,187 [INFO ] MainThread: hwm = AutoDetectHWM( +2024-04-12 10:12:12,187 [INFO ] MainThread: name = 'updated_at#source_schema.table@mssql:/mssql.host:1433/somedb', +2024-04-12 10:12:12,187 [INFO ] MainThread: entity = 'source_schema.table', +2024-04-12 10:12:12,187 [INFO ] MainThread: expression = 'updated_at', +2024-04-12 10:12:12,188 [INFO ] MainThread: ) +2024-04-12 10:12:12,188 [INFO ] MainThread: options = { +2024-04-12 10:12:12,188 [INFO ] MainThread: 'fetchsize': 100000, +2024-04-12 10:12:12,188 [INFO ] MainThread: 'numPartitions': 1, +2024-04-12 10:12:12,189 [INFO ] MainThread: 'partitioningMode': 'range', +2024-04-12 10:12:12,189 [INFO ] MainThread: } +2024-04-12 10:12:12,189 [INFO ] MainThread: |MSSQL| Checking connection availability... +2024-04-12 10:12:12,189 [INFO ] MainThread: |MSSQL| Using connection parameters: +2024-04-12 10:12:12,190 [INFO ] MainThread: user = 'db_user' +2024-04-12 10:12:12,190 [INFO ] MainThread: password = SecretStr('**********') +2024-04-12 10:12:12,190 [INFO ] MainThread: host = 'mssql.host' +2024-04-12 10:12:12,190 [INFO ] MainThread: port = 1433 +2024-04-12 10:12:12,191 [INFO ] MainThread: database = 'somedb' +2024-04-12 10:12:12,191 [INFO ] MainThread: extra = {'applicationIntent': 'ReadOnly', 'trustServerCertificate': 'true'} +2024-04-12 10:12:12,191 [INFO ] MainThread: jdbc_url = 'jdbc:sqlserver:/mssql.host:1433' +2024-04-12 10:12:12,579 [INFO ] MainThread: |MSSQL| Connection is available. +2024-04-12 10:12:12,581 [INFO ] MainThread: |MSSQL| Executing SQL query (on driver): +2024-04-12 10:12:12,581 [INFO ] MainThread: SELECT +2024-04-12 10:12:12,581 [INFO ] MainThread: MIN(updated_at) AS "min", +2024-04-12 10:12:12,582 [INFO ] MainThread: MAX(updated_at) AS "max" +2024-04-12 10:12:12,582 [INFO ] MainThread: FROM +2024-04-12 10:12:12,582 [INFO ] MainThread: source_schema.table +2024-04-12 10:12:12,582 [INFO ] MainThread: WHERE +2024-04-12 10:12:12,582 [INFO ] MainThread: (field = 'some') +2024-04-12 10:12:12,583 [INFO ] MainThread: AND +2024-04-12 10:12:12,583 [INFO ] MainThread: (updated_at >= CAST('2024-04-11T18:10:02.120000' AS datetime2)) +2024-04-12 10:16:22,537 [INFO ] MainThread: |MSSQL| Received values: +2024-04-12 10:16:22,538 [INFO ] MainThread: MIN(updated_at) = datetime.datetime(2024, 4, 11, 21, 10, 7, 397000) +2024-04-12 10:16:22,538 [INFO ] MainThread: MAX(updated_at) = datetime.datetime(2024, 4, 12, 13, 12, 2, 123000) +2024-04-12 10:16:22,540 [INFO ] MainThread: |MSSQL| Executing SQL query (on executor): +2024-04-12 10:16:22,540 [INFO ] MainThread: SELECT +2024-04-12 10:16:22,540 [INFO ] MainThread: id, +2024-04-12 10:16:22,541 [INFO ] MainThread: new_value, +2024-04-12 10:16:22,541 [INFO ] MainThread: old_value, +2024-04-12 10:16:22,541 [INFO ] MainThread: updated_at +2024-04-12 10:16:22,541 [INFO ] MainThread: FROM +2024-04-12 10:16:22,541 [INFO ] MainThread: source_schema.table +2024-04-12 10:16:22,542 [INFO ] MainThread: WHERE +2024-04-12 10:16:22,542 [INFO ] MainThread: (field = 'some') +2024-04-12 10:16:22,542 [INFO ] MainThread: AND +2024-04-12 10:16:22,542 [INFO ] MainThread: (updated_at > CAST('2024-04-11T18:10:02.120000' AS datetime2)) +2024-04-12 10:16:22,542 [INFO ] MainThread: AND +2024-04-12 10:16:22,542 [INFO ] MainThread: (updated_at <= CAST('2024-04-12T13:12:02.123000' AS datetime2)) +2024-04-12 10:16:22,892 [INFO ] MainThread: |Spark| DataFrame successfully created from SQL statement +2024-04-12 10:16:22,892 [INFO ] MainThread: ------------------------------------ DBReader.run() ends ------------------------------------ +2024-04-12 10:40:42,409 [INFO ] MainThread: =================================== DBWriter.run() starts =================================== +2024-04-12 10:40:42,409 [INFO ] MainThread: |Spark| -> |Hive| Writing DataFrame to target using parameters: +2024-04-12 10:40:42,410 [INFO ] MainThread: target = 'target_source_schema.table' +2024-04-12 10:40:42,410 [INFO ] MainThread: options = { +2024-04-12 10:40:42,410 [INFO ] MainThread: 'mode': 'append', +2024-04-12 10:40:42,410 [INFO ] MainThread: 'format': 'orc', +2024-04-12 10:40:42,410 [INFO ] MainThread: 'partitionBy': 'part_dt', +2024-04-12 10:40:42,410 [INFO ] MainThread: } +2024-04-12 10:40:42,411 [INFO ] MainThread: df_schema: +2024-04-12 10:40:42,412 [INFO ] MainThread: root +2024-04-12 10:40:42,412 [INFO ] MainThread: |-- id: integer (nullable = true) +2024-04-12 10:40:42,413 [INFO ] MainThread: |-- new_value: string (nullable = true) +2024-04-12 10:40:42,413 [INFO ] MainThread: |-- old_value: string (nullable = true) +2024-04-12 10:40:42,413 [INFO ] MainThread: |-- updated_at: timestamp (nullable = true) +2024-04-12 10:40:42,413 [INFO ] MainThread: |-- part_dt: date (nullable = true) +2024-04-12 10:40:42,414 [INFO ] MainThread: +2024-04-12 10:40:42,421 [INFO ] MainThread: |Hive| Checking connection availability... +2024-04-12 10:40:42,421 [INFO ] MainThread: |Hive| Using connection parameters: +2024-04-12 10:40:42,421 [INFO ] MainThread: cluster = 'dwh' +2024-04-12 10:40:42,475 [INFO ] MainThread: |Hive| Connection is available. +2024-04-12 10:40:42,476 [INFO ] MainThread: |Hive| Fetching schema of table 'target_source_schema.table' ... +2024-04-12 10:40:43,518 [INFO ] MainThread: |Hive| Schema fetched. +2024-04-12 10:40:43,521 [INFO ] MainThread: |Hive| Table 'target_source_schema.table' already exists +2024-04-12 10:40:43,521 [WARNING ] MainThread: |Hive| User-specified options {'partitionBy': 'part_dt'} are ignored while inserting into existing table. Using only table parameters from Hive metastore +2024-04-12 10:40:43,782 [INFO ] MainThread: |Hive| Inserting data into existing table 'target_source_schema.table' ... +2024-04-12 11:06:07,396 [INFO ] MainThread: |Hive| Data is successfully inserted into table 'target_source_schema.table'. +2024-04-12 11:06:07,397 [INFO ] MainThread: ------------------------------------ DBWriter.run() ends ------------------------------------ +2024-04-12 11:06:07,397 [INFO ] MainThread: |onETL| Exiting IncrementalStrategy +2024-04-12 11:06:07,397 [INFO ] MainThread: |IncrementalStrategy| Saving HWM to 'HorizonHWMStore': +2024-04-12 11:06:07,397 [INFO ] MainThread: hwm = ColumnDateTimeHWM( +2024-04-12 11:06:07,397 [INFO ] MainThread: name = 'updated_at#source_schema.table@mssql:/mssql.host:1433/somedb', +2024-04-12 11:06:07,397 [INFO ] MainThread: entity = 'source_source_schema.table', +2024-04-12 11:06:07,397 [INFO ] MainThread: expression = 'updated_at', +2024-04-12 11:06:07,397 [INFO ] MainThread: value = datetime.datetime(2024, 4, 12, 13, 12, 2, 123000), +2024-04-12 11:06:07,397 [INFO ] MainThread: ) +2024-04-12 11:06:07,495 [INFO ] MainThread: |IncrementalStrategy| HWM has been saved +``` + +Each step performed by onETL is extensively logged, which should help with debugging. + +You can make logs even more verbose by changing level to `DEBUG`: + +```python +from onetl.log import setup_logging + +setup_logging(level="DEBUG", enable_clients=True) + +# rest of code +... +``` + +This also changes log level for all underlying Python libraries, e.g. showing each HTTP request being made, and so on. diff --git a/mddocs/docs/_build/markdown/plugins.md b/mddocs/docs/_build/markdown/plugins.md new file mode 100644 index 000000000..d42ded034 --- /dev/null +++ b/mddocs/docs/_build/markdown/plugins.md @@ -0,0 +1,147 @@ + + +# Plugins + +#### Versionadded +Added in version 0.6.0. + +## What are plugins? + +### Terms + +* `Plugin` - some Python package which implements some extra functionality for onETL, like [Hooks](hooks/index.md#hooks) +* `Plugin autoimport` - onETL behavior which allows to automatically import this package if it contains proper metadata (`entry_points`) + +### Features + +Plugins mechanism allows to: + +* Automatically register [Hooks](hooks/index.md#hooks) which can alter onETL behavior +* Automatically register new classes, like HWM type, HWM stores and so on + +### Limitations + +Unlike other projects (like *Airflow 1.x*), plugins does not inject imported classes or functions to `onetl.*` namespace. +Users should import classes from the plugin package **explicitly** to avoid name collisions. + +## How to implement plugin? + +Create a Python package `some-plugin` with a file `some_plugin/setup.py`: + +```python +# some_plugin/setup.py +from setuptools import setup + +setup( + # if you want to import something from onETL, add it to requirements list + install_requires=["onetl"], + entry_points={ + # this key enables plugins autoimport functionality + "onetl.plugins": [ + "some-plugin-name=some_plugin.module", # automatically import all module content + "some-plugin-class=some_plugin.module.internals:MyClass", # import a specific class + "some-plugin-function=some_plugin.module.internals:my_function", # import a specific function + ], + }, +) +``` + +See [setuptools documentation for entry_points](https://setuptools.pypa.io/en/latest/userguide/entry_point.html) + +## How plugins are imported? + +* User should install a package implementing the plugin: + +```bash +pip install some-package +``` + +* Then user should import something from `onetl` module or its submodules: + +```python +import onetl +from onetl.connection import SomeConnection + +# and so on +``` + +* This import automatically executes something like: + +```python +import some_plugin.module +from some_plugin.module.internals import MyClass +from some_plugin.module.internals import my_function +``` + +If specific module/class/function uses some registration capabilities of onETL, +like [@hook decorator](hooks/hook.md#hook-decorator), it will be executed during this import. + +## How to enable/disable plugins? + +#### Versionadded +Added in version 0.7.0. + +### Disable/enable all plugins + +By default plugins are enabled. + +To disabled them, you can set environment variable `ONETL_PLUGINS_ENABLED` to `false` BEFORE +importing onETL. This will disable all plugins autoimport. + +But user is still be able to explicitly import `some_plugin.module`, executing +all decorators and registration capabilities of onETL. + +### Disable a specific plugin (blacklist) + +If some plugin is failing during import, you can disable it by setting up environment variable +`ONETL_PLUGINS_BLACKLIST=some-failing-plugin`. Multiple plugin names could be passed with `,` as delimiter. + +Again, this environment variable should be set BEFORE importing onETL. + +### Disable all plugins except a specific one (whitelist) + +You can also disable all plugins except a specific one by setting up environment variable +`ONETL_PLUGINS_WHITELIST=some-not-failing-plugin`. Multiple plugin names could be passed with `,` as delimiter. + +Again, this environment variable should be set BEFORE importing onETL. + +If both whitelist and blacklist environment variables are set, blacklist has a higher priority. + +## How to see logs of the plugins mechanism? + +Plugins registration emits logs with `INFO` level: + +```python +import logging + +logging.basicConfig(level=logging.INFO) +``` + +```text +INFO |onETL| Found 2 plugins +INFO |onETL| Loading plugin 'my-plugin' +INFO |onETL| Skipping plugin 'failing' because it is in a blacklist +``` + +More detailed logs are emitted with `DEBUG` level, to make output less verbose: + +```python +import logging + +logging.basicConfig(level=logging.DEBUG) +``` + +```text +DEBUG |onETL| Searching for plugins with group 'onetl.plugins' +DEBUG |Plugins| Plugins whitelist: [] +DEBUG |Plugins| Plugins blacklist: ['failing-plugin'] +INFO |Plugins| Found 2 plugins +INFO |onETL| Loading plugin (1/2): +DEBUG name: 'my-plugin' +DEBUG package: 'my-package' +DEBUG version: '0.1.0' +DEBUG importing: 'my_package.my_module:MyClass' +DEBUG |onETL| Successfully loaded plugin 'my-plugin' +DEBUG source: '/usr/lib/python3.11/site-packages/my_package/my_module/my_class.py' +INFO |onETL| Skipping plugin 'failing' because it is in a blacklist +``` diff --git a/mddocs/docs/_build/markdown/quickstart.md b/mddocs/docs/_build/markdown/quickstart.md new file mode 100644 index 000000000..7a9bd0652 --- /dev/null +++ b/mddocs/docs/_build/markdown/quickstart.md @@ -0,0 +1,365 @@ +: + +# Quick start + +## MSSQL → Hive + +Read data from MSSQL, transform & write to Hive. + +```bash +# install onETL and PySpark +pip install onetl[spark] +``` + +```python +# Import pyspark to initialize the SparkSession +from pyspark.sql import SparkSession + +# import function to setup onETL logging +from onetl.log import setup_logging + +# Import required connections +from onetl.connection import MSSQL, Hive + +# Import onETL classes to read & write data +from onetl.db import DBReader, DBWriter + +# change logging level to INFO, and set up default logging format and handler +setup_logging() + +# Initialize new SparkSession with MSSQL driver loaded +maven_packages = MSSQL.get_packages() +spark = ( + SparkSession.builder.appName("spark_app_onetl_demo") + .config("spark.jars.packages", ",".join(maven_packages)) + .enableHiveSupport() # for Hive + .getOrCreate() +) + +# Initialize MSSQL connection and check if database is accessible +mssql = MSSQL( + host="mssqldb.demo.com", + user="onetl", + password="onetl", + database="Telecom", + spark=spark, + # These options are passed to MSSQL JDBC Driver: + extra={"applicationIntent": "ReadOnly"}, +).check() + +# >>> INFO:|MSSQL| Connection is available + +# Initialize DBReader +reader = DBReader( + connection=mssql, + source="dbo.demo_table", + columns=["on", "etl"], + # Set some MSSQL read options: + options=MSSQL.ReadOptions(fetchsize=10000), +) + +# checks that there is data in the table, otherwise raises exception +reader.raise_if_no_data() + +# Read data to DataFrame +df = reader.run() +df.printSchema() +# root +# |-- id: integer (nullable = true) +# |-- phone_number: string (nullable = true) +# |-- region: string (nullable = true) +# |-- birth_date: date (nullable = true) +# |-- registered_at: timestamp (nullable = true) +# |-- account_balance: double (nullable = true) + +# Apply any PySpark transformations +from pyspark.sql.functions import lit + +df_to_write = df.withColumn("engine", lit("onetl")) +df_to_write.printSchema() +# root +# |-- id: integer (nullable = true) +# |-- phone_number: string (nullable = true) +# |-- region: string (nullable = true) +# |-- birth_date: date (nullable = true) +# |-- registered_at: timestamp (nullable = true) +# |-- account_balance: double (nullable = true) +# |-- engine: string (nullable = false) + +# Initialize Hive connection +hive = Hive(cluster="rnd-dwh", spark=spark) + +# Initialize DBWriter +db_writer = DBWriter( + connection=hive, + target="dl_sb.demo_table", + # Set some Hive write options: + options=Hive.WriteOptions(if_exists="replace_entire_table"), +) + +# Write data from DataFrame to Hive +db_writer.run(df_to_write) + +# Success! +``` + +## SFTP → HDFS + +Download files from SFTP & upload them to HDFS. + +```bash +# install onETL with SFTP and HDFS clients, and Kerberos support +pip install onetl[hdfs,sftp,kerberos] +``` + +```python +# import function to setup onETL logging +from onetl.log import setup_logging + +# Import required connections +from onetl.connection import SFTP, HDFS + +# Import onETL classes to download & upload files +from onetl.file import FileDownloader, FileUploader + +# import filter & limit classes +from onetl.file.filter import Glob, ExcludeDir +from onetl.file.limit import MaxFilesCount + +# change logging level to INFO, and set up default logging format and handler +setup_logging() + +# Initialize SFTP connection and check it +sftp = SFTP( + host="sftp.test.com", + user="someuser", + password="somepassword", +).check() + +# >>> INFO:|SFTP| Connection is available + +# Initialize downloader +file_downloader = FileDownloader( + connection=sftp, + source_path="/remote/tests/Report", # path on SFTP + local_path="/local/onetl/Report", # local fs path + filters=[ + # download only files matching the glob + Glob("*.csv"), + # exclude files from this directory + ExcludeDir("/remote/tests/Report/exclude_dir/"), + ], + limits=[ + # download max 1000 files per run + MaxFilesCount(1000), + ], + options=FileDownloader.Options( + # delete files from SFTP after successful download + delete_source=True, + # mark file as failed if it already exist in local_path + if_exists="error", + ), +) + +# Download files to local filesystem +download_result = downloader.run() + +# Method run returns a DownloadResult object, +# which contains collection of downloaded files, divided to 4 categories +download_result + +# DownloadResult( +# successful=[ +# LocalPath('/local/onetl/Report/file_1.json'), +# LocalPath('/local/onetl/Report/file_2.json'), +# ], +# failed=[FailedRemoteFile('/remote/onetl/Report/file_3.json')], +# ignored=[RemoteFile('/remote/onetl/Report/file_4.json')], +# missing=[], +# ) + +# Raise exception if there are failed files, or there were no files in the remote filesystem +download_result.raise_if_failed() or download_result.raise_if_empty() + +# Do any kind of magic with files: rename files, remove header for csv files, ... +renamed_files = my_rename_function(download_result.success) + +# function removed "_" from file names +# [ +# LocalPath('/home/onetl/Report/file1.json'), +# LocalPath('/home/onetl/Report/file2.json'), +# ] + +# Initialize HDFS connection +hdfs = HDFS( + host="my.name.node", + user="someuser", + password="somepassword", # or keytab +) + +# Initialize uploader +file_uploader = FileUploader( + connection=hdfs, + target_path="/user/onetl/Report/", # hdfs path +) + +# Upload files from local fs to HDFS +upload_result = file_uploader.run(renamed_files) + +# Method run returns a UploadResult object, +# which contains collection of uploaded files, divided to 4 categories +upload_result + +# UploadResult( +# successful=[RemoteFile('/user/onetl/Report/file1.json')], +# failed=[FailedLocalFile('/local/onetl/Report/file2.json')], +# ignored=[], +# missing=[], +# ) + +# Raise exception if there are failed files, or there were no files in the local filesystem, or some input file is missing +upload_result.raise_if_failed() or upload_result.raise_if_empty() or upload_result.raise_if_missing() + +# Success! +``` + +## S3 → Postgres + +Read files directly from S3 path, convert them to dataframe, transform it and then write to a database. + +```bash +# install onETL and PySpark +pip install onetl[spark] +``` + +```python +# Import pyspark to initialize the SparkSession +from pyspark.sql import SparkSession + +# import function to setup onETL logging +from onetl.log import setup_logging + +# Import required connections +from onetl.connection import Postgres, SparkS3 + +# Import onETL classes to read files +from onetl.file import FileDFReader +from onetl.file.format import CSV + +# Import onETL classes to write data +from onetl.db import DBWriter + +# change logging level to INFO, and set up default logging format and handler +setup_logging() + +# Initialize new SparkSession with Hadoop AWS libraries and Postgres driver loaded +maven_packages = SparkS3.get_packages(spark_version="3.5.5") + Postgres.get_packages() +exclude_packages = SparkS3.get_exclude_packages() +spark = ( + SparkSession.builder.appName("spark_app_onetl_demo") + .config("spark.jars.packages", ",".join(maven_packages)) + .config("spark.jars.excludes", ",".join(exclude_packages)) + .getOrCreate() +) + +# Initialize S3 connection and check it +spark_s3 = SparkS3( + host="s3.test.com", + protocol="https", + bucket="my-bucket", + access_key="somekey", + secret_key="somesecret", + # Access bucket as s3.test.com/my-bucket + extra={"path.style.access": True}, + spark=spark, +).check() + +# >>> INFO:|SparkS3| Connection is available + +# Describe file format and parsing options +csv = CSV( + delimiter=";", + header=True, + encoding="utf-8", +) + +# Describe DataFrame schema of files +from pyspark.sql.types import ( + DateType, + DoubleType, + IntegerType, + StringType, + StructField, + StructType, + TimestampType, +) + +df_schema = StructType( + [ + StructField("id", IntegerType()), + StructField("phone_number", StringType()), + StructField("region", StringType()), + StructField("birth_date", DateType()), + StructField("registered_at", TimestampType()), + StructField("account_balance", DoubleType()), + ], +) + +# Initialize file df reader +reader = FileDFReader( + connection=spark_s3, + source_path="/remote/tests/Report", # path on S3 there *.csv files are located + format=csv, # file format with specific parsing options + df_schema=df_schema, # columns & types +) + +# Read files directly from S3 as Spark DataFrame +df = reader.run() + +# Check that DataFrame schema is same as expected +df.printSchema() +# root +# |-- id: integer (nullable = true) +# |-- phone_number: string (nullable = true) +# |-- region: string (nullable = true) +# |-- birth_date: date (nullable = true) +# |-- registered_at: timestamp (nullable = true) +# |-- account_balance: double (nullable = true) + +# Apply any PySpark transformations +from pyspark.sql.functions import lit + +df_to_write = df.withColumn("engine", lit("onetl")) +df_to_write.printSchema() +# root +# |-- id: integer (nullable = true) +# |-- phone_number: string (nullable = true) +# |-- region: string (nullable = true) +# |-- birth_date: date (nullable = true) +# |-- registered_at: timestamp (nullable = true) +# |-- account_balance: double (nullable = true) +# |-- engine: string (nullable = false) + +# Initialize Postgres connection +postgres = Postgres( + host="192.169.11.23", + user="onetl", + password="somepassword", + database="mydb", + spark=spark, +) + +# Initialize DBWriter +db_writer = DBWriter( + connection=postgres, + # write to specific table + target="public.my_table", + # with some writing options + options=Postgres.WriteOptions(if_exists="append"), +) + +# Write DataFrame to Postgres table +db_writer.run(df_to_write) + +# Success! +``` diff --git a/mddocs/docs/_build/markdown/security.md b/mddocs/docs/_build/markdown/security.md new file mode 100644 index 000000000..3048c040b --- /dev/null +++ b/mddocs/docs/_build/markdown/security.md @@ -0,0 +1,25 @@ +# Security + +## Supported Python versions + +3.7 or above + +## Product development security recommendations + +1. Update dependencies to last stable version +2. Build SBOM for the project +3. Perform SAST (Static Application Security Testing) where possible + +## Product development security requirements + +1. No binaries in repository +2. No passwords, keys, access tokens in source code +3. No “Critical” and/or “High” vulnerabilities in contributed source code + +## Vulnerability reports + +Please, use email [mailto:onetools@mts.ru](mailto:onetools@mts.ru) for reporting security issues or anything that can cause any consequences for security. + +Please avoid any public disclosure (including registering issues) at least until it is fixed. + +Thank you in advance for understanding. diff --git a/mddocs/docs/_build/markdown/strategy/incremental_batch_strategy.md b/mddocs/docs/_build/markdown/strategy/incremental_batch_strategy.md new file mode 100644 index 000000000..9c10d9e59 --- /dev/null +++ b/mddocs/docs/_build/markdown/strategy/incremental_batch_strategy.md @@ -0,0 +1,3 @@ + + +# Incremental Batch Strategy diff --git a/mddocs/docs/_build/markdown/strategy/incremental_strategy.md b/mddocs/docs/_build/markdown/strategy/incremental_strategy.md new file mode 100644 index 000000000..540dbdad8 --- /dev/null +++ b/mddocs/docs/_build/markdown/strategy/incremental_strategy.md @@ -0,0 +1,3 @@ + + +# Incremental Strategy diff --git a/mddocs/docs/_build/markdown/strategy/index.md b/mddocs/docs/_build/markdown/strategy/index.md new file mode 100644 index 000000000..7791fed74 --- /dev/null +++ b/mddocs/docs/_build/markdown/strategy/index.md @@ -0,0 +1,10 @@ + + +# Read Strategies + +onETL have several builtin strategies for reading data: + +1. [Snapshot Strategy](snapshot_strategy.md) +2. [Incremental Strategy](incremental_strategy.md) +3. [Snapshot Batch Strategy](snapshot_batch_strategy.md) +4. [Incremental Batch Strategy](incremental_batch_strategy.md) diff --git a/mddocs/docs/_build/markdown/strategy/snapshot_batch_strategy.md b/mddocs/docs/_build/markdown/strategy/snapshot_batch_strategy.md new file mode 100644 index 000000000..bc86cab8f --- /dev/null +++ b/mddocs/docs/_build/markdown/strategy/snapshot_batch_strategy.md @@ -0,0 +1,3 @@ + + +# Snapshot Batch Strategy diff --git a/mddocs/docs/_build/markdown/strategy/snapshot_strategy.md b/mddocs/docs/_build/markdown/strategy/snapshot_strategy.md new file mode 100644 index 000000000..d775c15b0 --- /dev/null +++ b/mddocs/docs/_build/markdown/strategy/snapshot_strategy.md @@ -0,0 +1,3 @@ + + +# Snapshot Strategy diff --git a/mddocs/docs/_build/markdown/troubleshooting/index.md b/mddocs/docs/_build/markdown/troubleshooting/index.md new file mode 100644 index 000000000..847f426e9 --- /dev/null +++ b/mddocs/docs/_build/markdown/troubleshooting/index.md @@ -0,0 +1,17 @@ + + +# Troubleshooting + +In case of error please follow instructions below: + +* Read the logs or exception messages you’ve faced with. + : * If Python logs are note verbose enough, [increase the log level](../logging.md#logging). + * If Spark logs are note verbose enough, [increase the log level](spark.md#troubleshooting-spark). +* Read documentation related to a class or method you are using. +* [Google](https://google.com) the error message, and carefully read the search result: + : * [StackOverflow](https://stackoverflow.com/) answers. + * [Spark](https://spark.apache.org/docs/latest/) documentation. + * Documentation of database or filesystem you are connecting to. + * Documentation of underlying connector. +* Search for known [issues](https://github.com/MobileTeleSystems/onetl/issues), or create a new one. +* Always use the most resent versions of onETL, PySpark and connector packages, [compatible with your environment](../install/spark.md#install-spark). diff --git a/mddocs/docs/_build/markdown/troubleshooting/spark.md b/mddocs/docs/_build/markdown/troubleshooting/spark.md new file mode 100644 index 000000000..812b38acc --- /dev/null +++ b/mddocs/docs/_build/markdown/troubleshooting/spark.md @@ -0,0 +1,72 @@ + + +# Spark Troubleshooting + +## Restarting Spark session + +Sometimes it is required to stop current Spark session and start a new one, e.g. to add some .jar packages, or change session config. +But PySpark not only starts Spark session, but also starts Java virtual machine (JVM) process in the background, +which. So calling `sparkSession.stop()` [does not shutdown JVM](https://issues.apache.org/jira/browse/SPARK-47740), +and this can cause some issue. + +Also apart from JVM properties, stopping Spark session does not clear Spark context, which is a global object. So new +Spark sessions are created using the same context object, and thus using the same Spark config options. + +To properly stop Spark session, it is **required** to: +\* Stop Spark session by calling `sparkSession.stop()`. +\* **STOP PYTHON INTERPRETER**, e.g. by calling `sys.exit()`. +\* Start new Python interpreter. +\* Start new Spark session with config options you need. + +Skipping some of these steps can lead to issues with creating new Spark session. + +## Driver log level + +Default logging level for Spark session is `WARN`. To show more verbose logs, use: + +```python +spark.sparkContext.setLogLevel("INFO") +``` + +or increase verbosity even more: + +```python +spark.sparkContext.setLogLevel("DEBUG") +``` + +After getting all information you need, you can return back the previous log level: + +```python +spark.sparkContext.setLogLevel("WARN") +``` + +## Executors log level + +`sparkContext.setLogLevel` changes only log level of Spark session on Spark **driver**. +To make Spark executor logs more verbose, perform following steps: + +* Create `log4j.properties` file with content like this: + ```jproperties + log4j.rootCategory=DEBUG, console + + log4j.appender.console=org.apache.log4j.ConsoleAppender + log4j.appender.console.target=System.err + log4j.appender.console.layout=org.apache.log4j.PatternLayout + log4j.appender.console.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss} %p %c{1}: %m%n + ``` +* Stop existing Spark session and create a new one with following options: + ```python + from pyspark.sql import SparkSession + + spark = ( + SparkSesion.builder.config("spark.files", "file:log4j.properties").config( + "spark.executor.extraJavaOptions", "-Dlog4j.configuration=file:log4j.properties" + ) + # you can apply the same logging settings to Spark driver, by uncommenting the line below + # .config("spark.driver.extraJavaOptions", "-Dlog4j.configuration=file:log4j.properties") + .getOrCreate() + ) + ``` + +Each Spark executor will receive a copy of `log4j.properties` file during start, and load it to change own log level. +Same approach can be used for Spark driver as well, to investigate issue when Spark session cannot properly start. diff --git a/mddocs/docs/_static/icon.svg b/mddocs/docs/_static/icon.svg new file mode 100644 index 000000000..a4d737f81 --- /dev/null +++ b/mddocs/docs/_static/icon.svg @@ -0,0 +1,11 @@ + + + + + + + + + + + diff --git a/mddocs/docs/_static/logo.svg b/mddocs/docs/_static/logo.svg new file mode 100644 index 000000000..76527ebf1 --- /dev/null +++ b/mddocs/docs/_static/logo.svg @@ -0,0 +1,214 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/mddocs/docs/_static/logo_wide.svg b/mddocs/docs/_static/logo_wide.svg new file mode 100644 index 000000000..981bf0148 --- /dev/null +++ b/mddocs/docs/_static/logo_wide.svg @@ -0,0 +1,329 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/mddocs/docs/changelog.md b/mddocs/docs/changelog.md new file mode 100644 index 000000000..b66b72579 --- /dev/null +++ b/mddocs/docs/changelog.md @@ -0,0 +1,8 @@ +# Changelog + +```{toctree} +:caption: Changelog +:maxdepth: 1 + +changelog/index +``` diff --git a/mddocs/docs/changelog/0.10.0.md b/mddocs/docs/changelog/0.10.0.md new file mode 100644 index 000000000..68a609eb6 --- /dev/null +++ b/mddocs/docs/changelog/0.10.0.md @@ -0,0 +1,543 @@ +# 0.10.0 (2023-12-18) + +## Breaking Changes + +- Upgrade `etl-entities` from v1 to v2 ([#172](https://github.com/MobileTeleSystems/onetl/pull/172)). + + This implies that `HWM` classes are now have different internal structure than they used to. + + Before: + + ```python + from etl_entities.old_hwm import IntHWM as OldIntHWM + from etl_entities.source import Column, Table + from etl_entities.process import Process + + hwm = OldIntHWM( + process=Process(name="myprocess", task="abc", dag="cde", host="myhost"), + source=Table(name="schema.table", instance="postgres://host:5432/db"), + column=Column(name="col1"), + value=123, + ) + ``` + + After: + + ```python + from etl_entities.hwm import ColumnIntHWM + + hwm = ColumnIntHWM( + name="some_unique_name", + description="any value you want", + source="schema.table", + expression="col1", + value=123, + ) + ``` + + **Breaking change:** If you used HWM classes from `etl_entities` module, you should rewrite your code to make it compatible with new version. + +??? "More details" + + - ``HWM`` classes used by previous onETL versions were moved from ``etl_entities`` to ``etl_entities.old_hwm`` submodule. They are here for compatibility reasons, but are planned to be removed in ``etl-entities`` v3 release. + - New ``HWM`` classes have flat structure instead of nested. + - New ``HWM`` classes have mandatory ``name`` attribute (it was known as ``qualified_name`` before). + - Type aliases used while serializing and deserializing ``HWM`` objects to ``dict`` representation were changed too: ``int`` → ``column_int``. + + + To make migration simpler, you can use new method: + + ```python + old_hwm = OldIntHWM(...) + new_hwm = old_hwm.as_new_hwm() + ``` + + Which automatically converts all fields from old structure to new one, including `qualified_name` → `name`. + +- **Breaking changes:** + + - Methods `BaseHWMStore.get()` and `BaseHWMStore.save()` were renamed to `get_hwm()` and `set_hwm()`. + - They now can be used only with new HWM classes from `etl_entities.hwm`, **old HWM classes are not supported**. + + If you used them in your code, please update it accordingly. + +- YAMLHWMStore **CANNOT read files created by older onETL versions** (0.9.x or older). + +??? "Update procedure" + + ```python + # pip install onetl==0.9.5 + + # Get qualified_name for HWM + + + # Option 1. HWM is built manually + from etl_entities import IntHWM, FileListHWM + from etl_entities.source import Column, Table, RemoteFolder + from etl_entities.process import Process + + # for column HWM + old_column_hwm = IntHWM( + process=Process(name="myprocess", task="abc", dag="cde", host="myhost"), + source=Table(name="schema.table", instance="postgres://host:5432/db"), + column=Column(name="col1"), + ) + qualified_name = old_column_hwm.qualified_name + # "col1#schema.table@postgres://host:5432/db#cde.abc.myprocess@myhost" + + # for file HWM + old_file_hwm = FileListHWM( + process=Process(name="myprocess", task="abc", dag="cde", host="myhost"), + source=RemoteFolder(name="/absolute/path", instance="ftp://ftp.server:21"), + ) + qualified_name = old_file_hwm.qualified_name + # "file_list#/absolute/path@ftp://ftp.server:21#cde.abc.myprocess@myhost" + + + # Option 2. HWM is generated automatically (by DBReader/FileDownloader) + # See onETL logs and search for string like qualified_name = '...' + + qualified_name = "col1#schema.table@postgres://host:5432/db#cde.abc.myprocess@myhost" + + + # Get .yml file path by qualified_name + + import os + from pathlib import PurePosixPath + from onetl.hwm.store import YAMLHWMStore + + # here you should pass the same arguments as used on production, if any + yaml_hwm_store = YAMLHWMStore() + hwm_path = yaml_hwm_store.get_file_path(qualified_name) + print(hwm_path) + + # for column HWM + # LocalPosixPath('/home/maxim/.local/share/onETL/yml_hwm_store/col1__schema.table__postgres_host_5432_db__cde.abc.myprocess__myhost.yml') + + # for file HWM + # LocalPosixPath('/home/maxim/.local/share/onETL/yml_hwm_store/file_list__absolute_path__ftp_ftp.server_21__cde.abc.myprocess__myhost.yml') + + + # Read raw .yml file content + + from yaml import safe_load, dump + + raw_old_hwm_items = safe_load(hwm_path.read_text()) + print(raw_old_hwm_items) + + # for column HWM + # [ + # { + # "column": { "name": "col1", "partition": {} }, + # "modified_time": "2023-12-18T10: 39: 47.377378", + # "process": { "dag": "cde", "host": "myhost", "name": "myprocess", "task": "abc" }, + # "source": { "instance": "postgres: //host:5432/db", "name": "schema.table" }, + # "type": "int", + # "value": "123", + # }, + # ] + + # for file HWM + # [ + # { + # "modified_time": "2023-12-18T11:15:36.478462", + # "process": { "dag": "cde", "host": "myhost", "name": "myprocess", "task": "abc" }, + # "source": { "instance": "ftp://ftp.server:21", "name": "/absolute/path" }, + # "type": "file_list", + # "value": ["file1.txt", "file2.txt"], + # }, + # ] + + + # Convert file content to new structure, compatible with onETL 0.10.x + raw_new_hwm_items = [] + for old_hwm in raw_old_hwm_items: + new_hwm = {"name": qualified_name, "modified_time": old_hwm["modified_time"]} + + if "column" in old_hwm: + new_hwm["expression"] = old_hwm["column"]["name"] + new_hwm["entity"] = old_hwm["source"]["name"] + old_hwm.pop("process", None) + + if old_hwm["type"] == "int": + new_hwm["type"] = "column_int" + new_hwm["value"] = old_hwm["value"] + + elif old_hwm["type"] == "date": + new_hwm["type"] = "column_date" + new_hwm["value"] = old_hwm["value"] + + elif old_hwm["type"] == "datetime": + new_hwm["type"] = "column_datetime" + new_hwm["value"] = old_hwm["value"] + + elif old_hwm["type"] == "file_list": + new_hwm["type"] = "file_list" + new_hwm["value"] = [ + os.fspath(PurePosixPath(old_hwm["source"]["name"]).joinpath(path)) + for path in old_hwm["value"] + ] + + else: + raise ValueError("WAT?") + + raw_new_hwm_items.append(new_hwm) + + + print(raw_new_hwm_items) + # for column HWM + # [ + # { + # "name": "col1#schema.table@postgres://host:5432/db#cde.abc.myprocess@myhost", + # "modified_time": "2023-12-18T10:39:47.377378", + # "expression": "col1", + # "source": "schema.table", + # "type": "column_int", + # "value": 123, + # }, + # ] + + # for file HWM + # [ + # { + # "name": "file_list#/absolute/path@ftp://ftp.server:21#cde.abc.myprocess@myhost", + # "modified_time": "2023-12-18T11:15:36.478462", + # "entity": "/absolute/path", + # "type": "file_list", + # "value": ["/absolute/path/file1.txt", "/absolute/path/file2.txt"], + # }, + # ] + + + # Save file with new content + with open(hwm_path, "w") as file: + dump(raw_new_hwm_items, file) + + + # Stop Python interpreter and update onETL + # pip install onetl==0.10.0 + # Check that new .yml file can be read + + from onetl.hwm.store import YAMLHWMStore + + qualified_name = ... + + # here you should pass the same arguments as used on production, if any + yaml_hwm_store = YAMLHWMStore() + yaml_hwm_store.get_hwm(qualified_name) + + # for column HWM + # ColumnIntHWM( + # name='col1#schema.table@postgres://host:5432/db#cde.abc.myprocess@myhost', + # description='', + # entity='schema.table', + # value=123, + # expression='col1', + # modified_time=datetime.datetime(2023, 12, 18, 10, 39, 47, 377378), + # ) + + # for file HWM + # FileListHWM( + # name='file_list#/absolute/path@ftp://ftp.server:21#cde.abc.myprocess@myhost', + # description='', + # entity=AbsolutePath('/absolute/path'), + # value=frozenset({AbsolutePath('/absolute/path/file1.txt'), AbsolutePath('/absolute/path/file2.txt')}), + # expression=None, + # modified_time=datetime.datetime(2023, 12, 18, 11, 15, 36, 478462) + # ) + + + # That's all! + ``` + +But most of users use other HWM store implementations which do not have such issues. + +- Several classes and functions were moved from `onetl` to `etl_entities`: + +=== "onETL ``0.9.x`` and older" + + ```python + + from onetl.hwm.store import ( + detect_hwm_store, + BaseHWMStore, + HWMStoreClassRegistry, + register_hwm_store_class, + HWMStoreManager, + MemoryHWMStore, + ) + ``` + +=== "nETL ``0.10.x`` and newer" + + ```python + + from etl_entities.hwm_store import ( + detect_hwm_store, + BaseHWMStore, + HWMStoreClassRegistry, + register_hwm_store_class, + HWMStoreManager, + MemoryHWMStore, + ) + ``` + + They still can be imported from old module, but this is deprecated and will be removed in v1.0.0 release. + +- Change the way of passing `HWM` to `DBReader` and `FileDownloader` classes: + +=== "onETL ``0.9.x`` and older" + + ```python linenums="1" hl_lines="12-21" + # Simple + reader = DBReader( + connection=..., + source=..., + hwm_column="col1", + ) + + + + + + # Complex + reader = DBReader( + connection=..., + source=..., + hwm_column=( + "col1", + "cast(col1 as date)", + ), + ) + + + # Files + downloader = FileDownloader( + connection=..., + source_path=..., + target_path=..., + hwm_type="file_list", + ) + ``` + +=== "onETL ``0.10.x`` and newer" + + ```python linenums="1" hl_lines="12-21" + # Simple + reader = DBReader( + connection=..., + source=..., + hwm=DBReader.AutoDetectHWM( + # name is mandatory now! + name="my_unique_hwm_name", + expression="col1", + ), + ) + + # Complex + reader = DBReader( + connection=..., + source=..., + hwm=DBReader.AutoDetectHWM( + # name is mandatory now! + name="my_unique_hwm_name", + expression="cast(col1 as date)", + ), + ) + + # Files + downloader = FileDownloader( + connection=..., + source_path=..., + target_path=..., + hwm=FileListHWM( + # name is mandatory now! + name="another_unique_hwm_name", + ), + ) + ``` + + + New HWM classes have **mandatory** `name` attribute which should be passed explicitly, + instead of generating if automatically under the hood. + + Automatic `name` generation using the old `DBReader.hwm_column` / `FileDownloader.hwm_type` + syntax is still supported, but will be removed in v1.0.0 release. ([#179](https://github.com/MobileTeleSystems/onetl/pull/179)) + +- Performance of read Incremental and Batch strategies has been drastically improved. ([#182](https://github.com/MobileTeleSystems/onetl/pull/182)). + +??? "Before and after in details" + + ``DBReader.run()`` + incremental/batch strategy behavior in versions 0.9.x and older: + + - Get table schema by making query ``SELECT * FROM table WHERE 1=0`` (if ``DBReader.columns`` has ``*``) + - Expand ``*`` to real column names from table, add here ``hwm_column``, remove duplicates (as some RDBMS does not allow that). + - Create dataframe from query like ``SELECT hwm_expression AS hwm_column, ...other table columns... FROM table WHERE hwm_expression > prev_hwm.value``. + - Determine HWM class using dataframe schema: ``df.schema[hwm_column].dataType``. + - Determine x HWM column value using Spark: ``df.select(max(hwm_column)).collect()``. + - Use ``max(hwm_column)`` as next HWM value, and save it to HWM Store. + - Return dataframe to user. + + This was far from ideal: + + - Dataframe content (all rows or just changed ones) was loaded from the source to Spark only to get min/max values of specific column. + + - Step of fetching table schema and then substituting column names in the next query caused some unexpected errors. + + For example, source contains columns with mixed name case, like ``"CamelColumn"`` or ``"spaced column"``. + + Column names were *not* escaped during query generation, leading to queries that cannot be executed by database. + + So users have to *explicitly* pass column names ``DBReader``, wrapping columns with mixed naming with ``"``: + + ```python + reader = DBReader( + connection=..., + source=..., + columns=[ # passing '*' here leads to wrong SQL query generation + "normal_column", + '"CamelColumn"', + '"spaced column"', + ..., + ], + ) + ``` + - Using ``DBReader`` with ``IncrementalStrategy`` could lead to reading rows already read before. + + Dataframe was created from query with WHERE clause like ``hwm.expression > prev_hwm.value``, + not ``hwm.expression > prev_hwm.value AND hwm.expression <= current_hwm.value``. + + So if new rows appeared in the source **after** HWM value is determined, + they can be read by accessing dataframe content (because Spark dataframes are lazy), + leading to inconsistencies between HWM value and dataframe content. + + This may lead to issues then ``DBReader.run()`` read some data, updated HWM value, and next call of ``DBReader.run()`` + will read rows that were already read in previous run. + + ``DBReader.run()`` + incremental/batch strategy behavior in versions 0.10.x and newer: + + - Detect type of HWM expression: ``SELECT hwm.expression FROM table WHERE 1=0``. + - Determine corresponding Spark type ``df.schema[0]`` and when determine matching HWM class (if ``DReader.AutoDetectHWM`` is used). + - Get min/max values by querying the source: ``SELECT MAX(hwm.expression) FROM table WHERE hwm.expression >= prev_hwm.value``. + - Use ``max(hwm.expression)`` as next HWM value, and save it to HWM Store. + - Create dataframe from query ``SELECT ... table columns ... FROM table WHERE hwm.expression > prev_hwm.value AND hwm.expression <= current_hwm.value``, baking new HWM value into the query. + - Return dataframe to user. + + Improvements: + + - Allow source to calculate min/max instead of loading everything to Spark. This should be **faster** on large amounts of data (**up to x2**), because we do not transfer all the data from the source to Spark. This can be even faster if source have indexes for HWM column. + - Columns list is passed to source as-is, without any resolving on `DBReader` side. So you can pass `DBReader(columns=["*"])` to read tables with mixed columns naming. + - Restrict dataframe content to always match HWM values, which leads to never reading the same row twice. + + **Breaking change**: HWM column is not being implicitly added to dataframe. It was a part of `SELECT` clause, but now it is mentioned only in `WHERE` clause. + + So if you had code like this, you have to rewrite it: + +=== "onETL ``0.9.x`` and older" + + ```python linenums="1" hl_lines="1-16" + reader = DBReader( + connection=..., + source=..., + columns=[ + "col1", + "col2", + ], + hwm_column="hwm_col", + ) + + df = reader.run() + # hwm_column value is in the dataframe + assert df.columns == ["col1", "col2", "hwm_col"] + + + + + reader = DBReader( + connection=..., + source=..., + columns=[ + "col1", + "col2", + ], + hwm_column=( + "hwm_col", + "cast(hwm_col as int)", + ), + ) + + df = reader.run() + # hwm_expression value is in the dataframe + assert df.columns == ["col1", "col2", "hwm_col"] + + ``` + +=== "onETL ``0.10.x`` and newer" + + ```python linenums="1" hl_lines="1-16" + reader = DBReader( + connection=..., + source=..., + columns=[ + "col1", + "col2", + # add hwm_column explicitly + "hwm_col", + ], + hwm_column="hwm_col", + ) + + df = reader.run() + # if columns list is not updated, + # this fill fail + assert df.columns == ["col1", "col2", "hwm_col"] + + reader = DBReader( + connection=..., + source=..., + columns=[ + "col1", + "col2", + # add hwm_expression explicitly + "cast(hwm_col as int) as hwm_col", + ], + hwm_column=( + "hwm_col", + "cast(hwm_col as int)", + ), + ) + df = reader.run() + # if columns list is not updated, + # this fill fail + assert df.columns == ["col1", "col2", "hwm_col"] + ``` + + But most users just use `columns=["*"]` anyway, they won't see any changes. + +- `FileDownloader.run()` now updates HWM in HWM Store not after each file is being successfully downloaded, + but after all files were handled. + + This is because: + + - FileDownloader can be used with `DownloadOptions(workers=N)`, which could lead to race condition - one thread can save to HWM store one HWM value when another thread will save different value. + - FileDownloader can download hundreds and thousands of files, and issuing a request to HWM Store for each file could potentially DDoS HWM Store. ([#189](https://github.com/MobileTeleSystems/onetl/pull/189)) + + There is a exception handler which tries to save HWM to HWM store if download process was interrupted. But if it was interrupted by force, like sending `SIGKILL` event, + HWM will not be saved to HWM store, so some already downloaded files may be downloaded again next time. + + But unexpected process kill may produce other negative impact, like some file will be downloaded partially, so this is an expected behavior. + +## Features + +- Add Python 3.12 compatibility. ([#167](https://github.com/MobileTeleSystems/onetl/pull/167)) +- `Excel` file format now can be used with Spark 3.5.0. ([#187](https://github.com/MobileTeleSystems/onetl/pull/187)) +- `SnapshotBatchStagy` and `IncrementalBatchStrategy` does no raise exceptions if source does not contain any data. + Instead they stop at first iteration and return empty dataframe. ([#188](https://github.com/MobileTeleSystems/onetl/pull/188)) +- Cache result of `connection.check()` in high-level classes like `DBReader`, `FileDownloader` and so on. This makes logs less verbose. ([#190](https://github.com/MobileTeleSystems/onetl/pull/190)) + +## Bug Fixes + +- Fix `@slot` and `@hook` decorators returning methods with missing arguments in signature (Pylance, VS Code). ([#183](https://github.com/MobileTeleSystems/onetl/pull/183)) +- Kafka connector documentation said that it does support reading topic data incrementally by passing `group.id` or `groupIdPrefix`. + Actually, this is not true, because Spark does not send information to Kafka which messages were consumed. + So currently users can only read the whole topic, no incremental reads are supported. diff --git a/mddocs/docs/changelog/0.10.1.md b/mddocs/docs/changelog/0.10.1.md new file mode 100644 index 000000000..ba3ed40bb --- /dev/null +++ b/mddocs/docs/changelog/0.10.1.md @@ -0,0 +1,29 @@ +# 0.10.1 (2024-02-05) + +## Features + +- Add support of `Incremental Strategies` for `Kafka` connection: + + ```python + reader = DBReader( + connection=Kafka(...), + source="topic_name", + hwm=DBReader.AutoDetectHWM(name="some_hwm_name", expression="offset"), + ) + + with IncrementalStrategy(): + df = reader.run() + ``` + + This lets you resume reading data from a Kafka topic starting at the last committed offset from your previous run. ([#202](https://github.com/MobileTeleSystems/onetl/pull/202)) + +- Add `has_data`, `raise_if_no_data` methods to `DBReader` class. ([#203](https://github.com/MobileTeleSystems/onetl/pull/203)) + +- Updare VMware Greenplum connector from `2.1.4` to `2.3.0`. This implies: + : - Greenplum 7.x support + - [Kubernetes support](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/configure.html#k8scfg) + - New read option [gpdb.matchDistributionPolicy](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/options.html#distpolmotion) + which allows to match each Spark executor with specific Greenplum segment, avoiding redundant data transfer between Greenplum segments + - Allows overriding [Greenplum optimizer parameters](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/options.html#greenplum-gucs) in read/write operations ([#208](https://github.com/MobileTeleSystems/onetl/pull/208)) + +- `Greenplum.get_packages()` method now accepts optional arg `package_version` which allows to override version of Greenplum connector package. ([#208](https://github.com/MobileTeleSystems/onetl/pull/208)) diff --git a/mddocs/docs/changelog/0.10.2.md b/mddocs/docs/changelog/0.10.2.md new file mode 100644 index 000000000..3ae7ad822 --- /dev/null +++ b/mddocs/docs/changelog/0.10.2.md @@ -0,0 +1,39 @@ +# 0.10.2 (2024-03-21) + +## Features + +- Add support of Pydantic v2. ([#230](https://github.com/MobileTeleSystems/onetl/pull/230)) + +## Improvements + +- Improve database connections documentation: + - Add "Types" section describing mapping between Clickhouse and Spark types + - Add "Prerequisites" section describing different aspects of connecting to Clickhouse + - Separate documentation of `DBReader` and `.sql()` / `.pipeline(...)` + - Add examples for `.fetch()` and `.execute()` ([#211](https://github.com/MobileTeleSystems/onetl/pull/211), [#228](https://github.com/MobileTeleSystems/onetl/pull/228), [#229](https://github.com/MobileTeleSystems/onetl/pull/229), [#233](https://github.com/MobileTeleSystems/onetl/pull/233), [#234](https://github.com/MobileTeleSystems/onetl/pull/234), [#235](https://github.com/MobileTeleSystems/onetl/pull/235), [#236](https://github.com/MobileTeleSystems/onetl/pull/236), [#240](https://github.com/MobileTeleSystems/onetl/pull/240)) + +- Add notes to Greenplum documentation about issues with IP resolution and building `gpfdist` URL ([#228](https://github.com/MobileTeleSystems/onetl/pull/228)) + +- Allow calling `MongoDB.pipeline(...)` with passing just collection name, without explicit aggregation pipeline. ([#237](https://github.com/MobileTeleSystems/onetl/pull/237)) + +- Update default `Postgres(extra={...})` to include `{"stringtype": "unspecified"}` option. + This allows to write text data to non-text column (or vice versa), relying to Postgres cast capabilities. + + For example, now it is possible to read column of type `money` as Spark's `StringType()`, and write it back to the same column, + without using intermediate columns or tables. ([#229](https://github.com/MobileTeleSystems/onetl/pull/229)) + +## Bug Fixes + +- Return back handling of `DBReader(columns="string")`. This was a valid syntax up to v0.10 release, but it was removed because + most of users neved used it. It looks that we were wrong, returning this behavior back, but with deprecation warning. ([#238](https://github.com/MobileTeleSystems/onetl/pull/238)) + +- Downgrade Greenplum package version from `2.3.0` to `2.2.0`. ([#239](https://github.com/MobileTeleSystems/onetl/pull/239)) + + This is because version 2.3.0 introduced issues with writing data to Greenplum 6.x. + Connector can open transaction with `SELECT * FROM table LIMIT 0` query, but does not close it, which leads to deadlocks. + + For using this connector with Greenplum 7.x, please pass package version explicitly: + + ```python + maven_packages = Greenplum.get_packages(package_version="2.3.0", ...) + ``` diff --git a/mddocs/docs/changelog/0.11.0.md b/mddocs/docs/changelog/0.11.0.md new file mode 100644 index 000000000..c77c977b0 --- /dev/null +++ b/mddocs/docs/changelog/0.11.0.md @@ -0,0 +1,212 @@ +# 0.11.0 (2024-05-27) + +## Breaking Changes + +There can be some changes in connection behavior, related to version upgrades. So we mark these changes as **breaking** although +most of users will not see any differences. + +- Update Clickhouse JDBC driver to latest version ([#249](https://github.com/MobileTeleSystems/onetl/pull/249)): + - Package was renamed `ru.yandex.clickhouse:clickhouse-jdbc` → `com.clickhouse:clickhouse-jdbc`. + - Package version changed `0.3.2` → `0.6.0-patch5`. + - Driver name changed `ru.yandex.clickhouse.ClickHouseDriver` → `com.clickhouse.jdbc.ClickHouseDriver`. + + This brings up several fixes for Spark \<-> Clickhouse type compatibility, and also Clickhouse clusters support. + +- Update other JDBC drivers to latest versions: + - MSSQL `12.2.0` → `12.6.2` ([#254](https://github.com/MobileTeleSystems/onetl/pull/254)). + - MySQL `8.0.33` → `8.4.0` ([#253](https://github.com/MobileTeleSystems/onetl/pull/253), [#285](https://github.com/MobileTeleSystems/onetl/pull/285)). + - Oracle `23.2.0.0` → `23.4.0.24.05` ([#252](https://github.com/MobileTeleSystems/onetl/pull/252), [#284](https://github.com/MobileTeleSystems/onetl/pull/284)). + - Postgres `42.6.0` → `42.7.3` ([#251](https://github.com/MobileTeleSystems/onetl/pull/251)). + +- Update MongoDB connector to latest version: `10.1.1` → `10.3.0` ([#255](https://github.com/MobileTeleSystems/onetl/pull/255), [#283](https://github.com/MobileTeleSystems/onetl/pull/283)). + + This brings up Spark 3.5 support. + +- Update `XML` package to latest version: `0.17.0` → `0.18.0` ([#259](https://github.com/MobileTeleSystems/onetl/pull/259)). + + This brings few bugfixes with datetime format handling. + +- For JDBC connections add new `SQLOptions` class for `DB.sql(query, options=...)` method ([#272](https://github.com/MobileTeleSystems/onetl/pull/272)). + + Firsly, to keep naming more consistent. + + Secondly, some of options are not supported by `DB.sql(...)` method, but supported by `DBReader`. + For example, `SQLOptions` do not support `partitioning_mode` and require explicit definition of `lower_bound` and `upper_bound` when `num_partitions` is greater than 1. + `ReadOptions` does support `partitioning_mode` and allows skipping `lower_bound` and `upper_bound` values. + + This require some code changes. Before: + + ```python + from onetl.connection import Postgres + + postgres = Postgres(...) + df = postgres.sql( + """ + SELECT * + FROM some.mytable + WHERE key = 'something' + """, + options=Postgres.ReadOptions( + partitioning_mode="range", + partition_column="id", + num_partitions=10, + ), + ) + ``` + + After: + + ```python + from onetl.connection import Postgres + + postgres = Postgres(...) + df = postgres.sql( + """ + SELECT * + FROM some.mytable + WHERE key = 'something' + """, + options=Postgres.SQLOptions( + # partitioning_mode is not supported! + partition_column="id", + num_partitions=10, + lower_bound=0, # <-- set explicitly + upper_bound=1000, # <-- set explicitly + ), + ) + ``` + + For now, `DB.sql(query, options=...)` can accept `ReadOptions` to keep backward compatibility, but emits deprecation warning. + The support will be removed in `v1.0.0`. + +- Split up `JDBCOptions` class into `FetchOptions` and `ExecuteOptions` ([#274](https://github.com/MobileTeleSystems/onetl/pull/274)). + + New classes are used by `DB.fetch(query, options=...)` and `DB.execute(query, options=...)` methods respectively. + This is mostly to keep naming more consistent. + + This require some code changes. Before: + + ```python + from onetl.connection import Postgres + + postgres = Postgres(...) + df = postgres.fetch( + "SELECT * FROM some.mytable WHERE key = 'something'", + options=Postgres.JDBCOptions( + fetchsize=1000, + query_timeout=30, + ), + ) + + postgres.execute( + "UPDATE some.mytable SET value = 'new' WHERE key = 'something'", + options=Postgres.JDBCOptions(query_timeout=30), + ) + ``` + + After: + + ```python + from onetl.connection import Postgres + + # Using FetchOptions for fetching data + postgres = Postgres(...) + df = postgres.fetch( + "SELECT * FROM some.mytable WHERE key = 'something'", + options=Postgres.FetchOptions( # <-- change class name + fetchsize=1000, + query_timeout=30, + ), + ) + + # Using ExecuteOptions for executing statements + postgres.execute( + "UPDATE some.mytable SET value = 'new' WHERE key = 'something'", + options=Postgres.ExecuteOptions(query_timeout=30), # <-- change class name + ) + ``` + + For now, `DB.fetch(query, options=...)` and `DB.execute(query, options=...)` can accept `JDBCOptions`, to keep backward compatibility, + but emit a deprecation warning. The old class will be removed in `v1.0.0`. + +- Serialize `ColumnDatetimeHWM` to Clickhouse's `DateTime64(6)` (precision up to microseconds) instead of `DateTime` (precision up to seconds) ([#267](https://github.com/MobileTeleSystems/onetl/pull/267)). + + In previous onETL versions, `ColumnDatetimeHWM` value was rounded to the second, and thus reading some rows that were read in previous runs, + producing duplicates. + + For Clickhouse versions below 21.1 comparing column of type `DateTime` with a value of type `DateTime64` is not supported, returning an empty dataframe. + To avoid this, replace: + + ```python + DBReader( + ..., + hwm=DBReader.AutoDetectHWM( + name="my_hwm", + expression="hwm_column", # <-- + ), + ) + ``` + + with: + + ```python + DBReader( + ..., + hwm=DBReader.AutoDetectHWM( + name="my_hwm", + expression="CAST(hwm_column AS DateTime64)", # <-- add explicit CAST + ), + ) + ``` + +- Pass JDBC connection extra params as `properties` dict instead of URL with query part ([#268](https://github.com/MobileTeleSystems/onetl/pull/268)). + + This allows passing custom connection parameters like `Clickhouse(extra={"custom_http_options": "option1=value1,option2=value2"})` + without need to apply urlencode to parameter value, like `option1%3Dvalue1%2Coption2%3Dvalue2`. + +## Features + +Improve user experience with Kafka messages and Database tables with serialized columns, like JSON/XML. + +- Allow passing custom package version as argument for `DB.get_packages(...)` method of several DB connectors: + - `Clickhouse.get_packages(package_version=..., apache_http_client_version=...)` ([#249](https://github.com/MobileTeleSystems/onetl/pull/249)). + - `MongoDB.get_packages(scala_version=..., spark_version=..., package_version=...)` ([#255](https://github.com/MobileTeleSystems/onetl/pull/255)). + - `MySQL.get_packages(package_version=...)` ([#253](https://github.com/MobileTeleSystems/onetl/pull/253)). + - `MSSQL.get_packages(java_version=..., package_version=...)` ([#254](https://github.com/MobileTeleSystems/onetl/pull/254)). + - `Oracle.get_packages(java_version=..., package_version=...)` ([#252](https://github.com/MobileTeleSystems/onetl/pull/252)). + - `Postgres.get_packages(package_version=...)` ([#251](https://github.com/MobileTeleSystems/onetl/pull/251)). + - `Teradata.get_packages(package_version=...)` ([#256](https://github.com/MobileTeleSystems/onetl/pull/256)). + Now users can downgrade or upgrade connection without waiting for next onETL release. Previously only `Kafka` and `Greenplum` supported this feature. +- Add `FileFormat.parse_column(...)` method to several classes: + - `Avro.parse_column(col)` ([#265](https://github.com/MobileTeleSystems/onetl/pull/265)). + - `JSON.parse_column(col, schema=...)` ([#257](https://github.com/MobileTeleSystems/onetl/pull/257)). + - `CSV.parse_column(col, schema=...)` ([#258](https://github.com/MobileTeleSystems/onetl/pull/258)). + - `XML.parse_column(col, schema=...)` ([#269](https://github.com/MobileTeleSystems/onetl/pull/269)). + This allows parsing data in `value` field of Kafka message or string/binary column of some table as a nested Spark structure. +- Add `FileFormat.serialize_column(...)` method to several classes: + - `Avro.serialize_column(col)` ([#265](https://github.com/MobileTeleSystems/onetl/pull/265)). + - `JSON.serialize_column(col)` ([#257](https://github.com/MobileTeleSystems/onetl/pull/257)). + - `CSV.serialize_column(col)` ([#258](https://github.com/MobileTeleSystems/onetl/pull/258)). + This allows saving Spark nested structures or arrays to `value` field of Kafka message or string/binary column of some table. + +## Improvements + +Few documentation improvements. + +- Replace all `assert` in documentation with doctest syntax. This should make documentation more readable ([#273](https://github.com/MobileTeleSystems/onetl/pull/273)). +- Add generic `Troubleshooting` guide ([#275](https://github.com/MobileTeleSystems/onetl/pull/275)). +- Improve Kafka documentation: + - Add "Prerequisites" page describing different aspects of connecting to Kafka. + - Improve "Reading from" and "Writing to" page of Kafka documentation, add more examples and usage notes. + - Add "Troubleshooting" page ([#276](https://github.com/MobileTeleSystems/onetl/pull/276)). +- Improve Hive documentation: + - Add "Prerequisites" page describing different aspects of connecting to Hive. + - Improve "Reading from" and "Writing to" page of Hive documentation, add more examples and recommendations. + - Improve "Executing statements in Hive" page of Hive documentation. ([#278](https://github.com/MobileTeleSystems/onetl/pull/278)). +- Add "Prerequisites" page describing different aspects of using SparkHDFS and SparkS3 connectors. ([#279](https://github.com/MobileTeleSystems/onetl/pull/279)). +- Add note about connecting to Clickhouse cluster. ([#280](https://github.com/MobileTeleSystems/onetl/pull/280)). +- Add notes about versions when specific class/method/attribute/argument was added, renamed or changed behavior ([#282](https://github.com/MobileTeleSystems/onetl/pull/282)). + +## Bug Fixes + +- Fix missing `pysmb` package after installing `pip install onetl[files]` . diff --git a/mddocs/docs/changelog/0.11.1.md b/mddocs/docs/changelog/0.11.1.md new file mode 100644 index 000000000..823afe3be --- /dev/null +++ b/mddocs/docs/changelog/0.11.1.md @@ -0,0 +1,9 @@ +# 0.11.1 (2024-05-29) + +## Features + +- Change `MSSQL.port` default from `1433` to `None`, allowing use of `instanceName` to detect port number. ([#287](https://github.com/MobileTeleSystems/onetl/pull/287)) + +## Bug Fixes + +- Remove `fetchsize` from `JDBC.WriteOptions`. ([#288](https://github.com/MobileTeleSystems/onetl/pull/288)) diff --git a/mddocs/docs/changelog/0.11.2.md b/mddocs/docs/changelog/0.11.2.md new file mode 100644 index 000000000..9278d22f8 --- /dev/null +++ b/mddocs/docs/changelog/0.11.2.md @@ -0,0 +1,5 @@ +# 0.11.2 (2024-09-02) + +## Bug Fixes + +- Fix passing `Greenplum(extra={"options": ...})` during read/write operations. ([#308](https://github.com/MobileTeleSystems/onetl/pull/308)) diff --git a/mddocs/docs/changelog/0.12.0.md b/mddocs/docs/changelog/0.12.0.md new file mode 100644 index 000000000..aeaca1d23 --- /dev/null +++ b/mddocs/docs/changelog/0.12.0.md @@ -0,0 +1,54 @@ +# 0.12.0 (2024-09-03) + +## Breaking Changes + +- Change connection URL used for generating HWM names of S3 and Samba sources: + - `smb://host:port` -> `smb://host:port/share` + - `s3://host:port` -> `s3://host:port/bucket` ([#304](https://github.com/MobileTeleSystems/onetl/pull/304)) +- Update DB connectors/drivers to latest versions: + - Clickhouse `0.6.0-patch5` → `0.6.5` + - MongoDB `10.3.0` → `10.4.0` + - MSSQL `12.6.2` → `12.8.1` + - MySQL `8.4.0` → `9.0.0` + - Oracle `23.4.0.24.05` → `23.5.0.24.07` + - Postgres `42.7.3` → `42.7.4` +- Update `Excel` package from `0.20.3` to `0.20.4`, to include Spark 3.5.1 support. ([#306](https://github.com/MobileTeleSystems/onetl/pull/306)) + +## Features + +- Add support for specifying file formats (`ORC`, `Parquet`, `CSV`, etc.) in `HiveWriteOptions.format` ([#292](https://github.com/MobileTeleSystems/onetl/pull/292)): + + ```python + Hive.WriteOptions(format=ORC(compression="snappy")) + ``` + +- Collect Spark execution metrics in following methods, and log then in DEBUG mode: + - `DBWriter.run()` + - `FileDFWriter.run()` + - `Hive.sql()` + - `Hive.execute()` + + This is implemented using custom `SparkListener` which wraps the entire method call, and + then report collected metrics. But these metrics sometimes may be missing due to Spark architecture, + so they are not reliable source of information. That's why logs are printed only in DEBUG mode, and + are not returned as method call result. ([#303](https://github.com/MobileTeleSystems/onetl/pull/303)) + +- Generate default `jobDescription` based on currently executed method. Examples: + - `DBWriter.run(schema.table) -> Postgres[host:5432/database]` + - `MongoDB[localhost:27017/admin] -> DBReader.has_data(mycollection)` + - `Hive[cluster].execute()` + + If user already set custom `jobDescription`, it will left intact. ([#304](https://github.com/MobileTeleSystems/onetl/pull/304)) + +- Add log.info about JDBC dialect usage ([#305](https://github.com/MobileTeleSystems/onetl/pull/305)): + + ```text + |MySQL| Detected dialect: 'org.apache.spark.sql.jdbc.MySQLDialect' + ``` + +- Log estimated size of in-memory dataframe created by `JDBC.fetch` and `JDBC.execute` methods. ([#303](https://github.com/MobileTeleSystems/onetl/pull/303)) + +## Bug Fixes + +- Fix passing `Greenplum(extra={"options": ...})` during read/write operations. ([#308](https://github.com/MobileTeleSystems/onetl/pull/308)) +- Do not raise exception if yield-based hook whas something past (and only one) `yield`. diff --git a/mddocs/docs/changelog/0.12.1.md b/mddocs/docs/changelog/0.12.1.md new file mode 100644 index 000000000..d32401517 --- /dev/null +++ b/mddocs/docs/changelog/0.12.1.md @@ -0,0 +1,17 @@ +# 0.12.1 (2024-10-28) + +## Features + +- Log detected JDBC dialect while using `DBWriter`. + +## Bug Fixes + +- Fix `SparkMetricsRecorder` failing when receiving `SparkListenerTaskEnd` without `taskMetrics` (e.g. executor was killed by OOM). ([#313](https://github.com/MobileTeleSystems/onetl/pull/313)) +- Call `kinit` before checking for HDFS active namenode. +- Wrap `kinit` with `threading.Lock` to avoid multithreading issues. +- Immediately show `kinit` errors to user, instead of hiding them. +- Use `AttributeError` instead of `ImportError` in module's `__getattr__` method, to make code compliant with Python spec. + +## Doc only Changes + +- Add note about [spark-dialect-extension](https://github.com/MobileTeleSystems/spark-dialect-extension) package to Clickhouse connector documentation. ([#310](https://github.com/MobileTeleSystems/onetl/pull/310)) diff --git a/mddocs/docs/changelog/0.12.2.md b/mddocs/docs/changelog/0.12.2.md new file mode 100644 index 000000000..23a8d383d --- /dev/null +++ b/mddocs/docs/changelog/0.12.2.md @@ -0,0 +1,18 @@ +# 0.12.2 (2024-11-12) + +## Improvements + +- Change Spark `jobDescription` for DBReader & FileDFReader from `DBReader.run() -> Connection` to `Connection -> DBReader.run()`. + +## Bug Fixes + +- Fix `log_hwm` result for `KeyValueIntHWM` (used by Kafka). ([#316](https://github.com/MobileTeleSystems/onetl/pull/316)) +- Fix `log_collection` hiding values of `Kafka.addresses` in logs with `INFO` level. ([#316](https://github.com/MobileTeleSystems/onetl/pull/316)) + +## Dependencies + +- Allow using [etl-entities==2.4.0](https://github.com/MobileTeleSystems/etl-entities/releases/tag/2.4.0). + +## Doc only Changes + +- Fix links to MSSQL date & time type documentation. diff --git a/mddocs/docs/changelog/0.12.3.md b/mddocs/docs/changelog/0.12.3.md new file mode 100644 index 000000000..741c74e1f --- /dev/null +++ b/mddocs/docs/changelog/0.12.3.md @@ -0,0 +1,5 @@ +# 0.12.3 (2024-11-22) + +## Bug Fixes + +- Allow passing table names in format `schema."table.with.dots"` to `DBReader(source=...)` and `DBWriter(target=...)`. diff --git a/mddocs/docs/changelog/0.12.4.md b/mddocs/docs/changelog/0.12.4.md new file mode 100644 index 000000000..3ebc57a87 --- /dev/null +++ b/mddocs/docs/changelog/0.12.4.md @@ -0,0 +1,5 @@ +# 0.12.4 (2024-11-27) + +## Bug Fixes + +- Fix `DBReader(conn=oracle, options={"partitioning_mode": "hash"})` lead to data skew in last partition due to wrong `ora_hash` usage. ([#319](https://github.com/MobileTeleSystems/onetl/pull/319)) diff --git a/mddocs/docs/changelog/0.12.5.md b/mddocs/docs/changelog/0.12.5.md new file mode 100644 index 000000000..c542a50fd --- /dev/null +++ b/mddocs/docs/changelog/0.12.5.md @@ -0,0 +1,13 @@ +# 0.12.5 (2024-12-03) + +## Improvements + +- Use `sipHash64` instead of `md5` in Clickhouse for reading data with `{"partitioning_mode": "hash"}`, as it is 5 times faster. +- Use `hashtext` instead of `md5` in Postgres for reading data with `{"partitioning_mode": "hash"}`, as it is 3-5 times faster. +- Use `BINARY_CHECKSUM` instead of `HASHBYTES` in MSSQL for reading data with `{"partitioning_mode": "hash"}`, as it is 5 times faster. + +## Big fixes + +- In JDBC sources wrap `MOD(partitionColumn, numPartitions)` with `ABS(...)` to make al returned values positive. This prevents data skew. +- Fix reading table data from MSSQL using `{"partitioning_mode": "hash"}` with `partitionColumn` of integer type. +- Fix reading table data from Postgres using `{"partitioning_mode": "hash"}` lead to data skew (all the data was read into one Spark partition). diff --git a/mddocs/docs/changelog/0.13.0.md b/mddocs/docs/changelog/0.13.0.md new file mode 100644 index 000000000..37ae8368a --- /dev/null +++ b/mddocs/docs/changelog/0.13.0.md @@ -0,0 +1,222 @@ +# 0.13.0 (2025-02-24) + +🎉 3 years since first release 0.1.0 🎉 + +## Breaking Changes + +- Add Python 3.13. support. ([#298](https://github.com/MobileTeleSystems/onetl/pull/298)) + +- Change the logic of `FileConnection.walk` and `FileConnection.list_dir`. ([#327](https://github.com/MobileTeleSystems/onetl/pull/327)) + + Previously `limits.stops_at(path) == True` considered as "return current file and stop", and could lead to exceeding some limit. + Not it means "stop immediately". + +- Change default value for `FileDFWriter.Options(if_exists=...)` from `error` to `append`, + to make it consistent with other `.Options()` classes within onETL. ([#343](https://github.com/MobileTeleSystems/onetl/pull/343)) + +## Features + +- Add support for `FileModifiedTimeHWM` HWM class (see [etl-entities 2.5.0](https://github.com/MobileTeleSystems/etl-entities/releases/tag/2.5.0)): + + ```python + from etl_entitites.hwm import FileModifiedTimeHWM + from onetl.file import FileDownloader + from onetl.strategy import IncrementalStrategy + + downloader = FileDownloader( + ..., + hwm=FileModifiedTimeHWM(name="somename"), + ) + + with IncrementalStrategy(): + downloader.run() + ``` + +- Introduce `FileSizeRange(min=..., max=...)` filter class. ([#325](https://github.com/MobileTeleSystems/onetl/pull/325)) + + Now users can set `FileDownloader` / `FileMover` to download/move only files with specific file size range: + + ```python + from onetl.file import FileDownloader + from onetl.file.filter import FileSizeRange + + downloader = FileDownloader( + ..., + filters=[FileSizeRange(min="10KiB", max="1GiB")], + ) + ``` + +- Introduce `TotalFilesSize(...)` limit class. ([#326](https://github.com/MobileTeleSystems/onetl/pull/326)) + + Now users can set `FileDownloader` / `FileMover` to stop downloading/moving files after reaching a certain amount of data: + + ```python + from datetime import datetime, timedelta + from onetl.file import FileDownloader + from onetl.file.limit import TotalFilesSize + + downloader = FileDownloader( + ..., + limits=[TotalFilesSize("1GiB")], + ) + ``` + +- Implement `FileModifiedTime(since=..., until=...)` file filter. ([#330](https://github.com/MobileTeleSystems/onetl/pull/330)) + + Now users can set `FileDownloader` / `FileMover` to download/move only files with specific file modification time: + + ```python + from datetime import datetime, timedelta + from onetl.file import FileDownloader + from onetl.file.filter import FileModifiedTime + + downloader = FileDownloader( + ..., + filters=[FileModifiedTime(before=datetime.now() - timedelta(hours=1))], + ) + ``` + +- Add `SparkS3.get_exclude_packages()` and `Kafka.get_exclude_packages()` methods. ([#341](https://github.com/MobileTeleSystems/onetl/pull/341)) + + Using them allows to skip downloading dependencies not required by this specific connector, or which are already a part of Spark/PySpark: + + ```python + from onetl.connection import SparkS3, Kafka + + maven_packages = [ + *SparkS3.get_packages(spark_version="3.5.4"), + *Kafka.get_packages(spark_version="3.5.4"), + ] + exclude_packages = SparkS3.get_exclude_packages() + Kafka.get_exclude_packages() + spark = ( + SparkSession.builder.appName("spark_app_onetl_demo") + .config("spark.jars.packages", ",".join(maven_packages)) + .config("spark.jars.excludes", ",".join(exclude_packages)) + .getOrCreate() + ) + ``` + +## Improvements + +- All DB connections opened by `JDBC.fetch(...)`, `JDBC.execute(...)` or `JDBC.check()` + are immediately closed after the statements is executed. ([#334](https://github.com/MobileTeleSystems/onetl/pull/334)) + + Previously Spark session with `master=local[3]` actually opened up to 5 connections to target DB - one for `JDBC.check()`, + another for Spark driver interaction with DB to create tables, and one for each Spark executor. Now only max 4 connections are opened, + as `JDBC.check()` does not hold opened connection. + + This is important for RDBMS like Postgres or Greenplum where number of connections is strictly limited and limit is usually quite low. + +- Set up `ApplicationName` (client info) for Clickhouse, MongoDB, MSSQL, MySQL and Oracle. ([#339](https://github.com/MobileTeleSystems/onetl/pull/339), [#248](https://github.com/MobileTeleSystems/onetl/pull/248)) + + Also update `ApplicationName` format for Greenplum, Postgres, Kafka and SparkS3. + Now all connectors have the same `ApplicationName` format: `${spark.applicationId} ${spark.appName} onETL/${onetl.version} Spark/${spark.version}` + + The only connections not sending `ApplicationName` are Teradata and FileConnection implementations. + +- Now `DB.check()` will test connection availability not only on Spark driver, but also from some Spark executor. ([#346](https://github.com/MobileTeleSystems/onetl/pull/346)) + + This allows to fail immediately if Spark driver host has network access to target DB, but Spark executors have not. + +!!! note + + Now ``Greenplum.check()`` requires the same user grants as ``DBReader(connection=greenplum)``: + + ```sql + -- yes, "writable" for reading data from GP, it's not a mistake + ALTER USER username CREATEEXTTABLE(type = 'writable', protocol = 'gpfdist'); + + -- for both reading and writing to GP + -- ALTER USER username CREATEEXTTABLE(type = 'readable', protocol = 'gpfdist') CREATEEXTTABLE(type = 'writable', protocol = 'gpfdist'); + ``` + + Please ask your Greenplum administrators to provide these grants. + + +## Bug Fixes + +- Avoid suppressing Hive Metastore errors while using `DBWriter`. ([#329](https://github.com/MobileTeleSystems/onetl/pull/329)) + + Previously this was implemented as: + + ```python + try: + spark.sql(f"SELECT * FROM {table}") + table_exists = True + except Exception: + table_exists = False + ``` + + If Hive Metastore was overloaded and responded with an exception, it was considered as non-existing table, resulting + to full table override instead of append or override only partitions subset. + +- Fix using onETL to write data to PostgreSQL or Greenplum instances behind *pgbouncer* with `pool_mode=transaction`. ([#336](https://github.com/MobileTeleSystems/onetl/pull/336)) + + Previously `Postgres.check()` opened a read-only transaction, pgbouncer changed the entire connection type from read-write to read-only, + and when `DBWriter.run(df)` executed in read-only connection, producing errors like: + + ``` + org.postgresql.util.PSQLException: ERROR: cannot execute INSERT in a read-only transaction + org.postgresql.util.PSQLException: ERROR: cannot execute TRUNCATE TABLE in a read-only transaction + ``` + + Added a workaround by passing `readOnly=True` to JDBC params for read-only connections, so pgbouncer may differ read-only and read-write connections properly. + + After upgrading onETL 0.13.x or higher the same error still may appear of pgbouncer still holds read-only connections and returns them for DBWriter. + To this this, user can manually convert read-only connection to read-write: + + ```python + postgres.execute("BEGIN READ WRITE;") # <-- add this line + DBWriter(...).run() + ``` + + After all connections in pgbouncer pool were converted from read-only to read-write, and error fixed, this additional line could be removed. + + See [Postgres JDBC driver documentation](https://jdbc.postgresql.org/documentation/use/). + +- Fix `MSSQL.fetch(...)` and `MySQL.fetch(...)` opened a read-write connection instead of read-only. ([#337](https://github.com/MobileTeleSystems/onetl/pull/337)) + + Now this is fixed: + : - `MSSQL.fetch(...)` establishes connection with `ApplicationIntent=ReadOnly`. + - `MySQL.fetch(...)` calls `SET SESSION TRANSACTION READ ONLY` statement. + +- Fixed passing multiple filters to `FileDownloader` and `FileMover`. ([#338](https://github.com/MobileTeleSystems/onetl/pull/338)) + If was caused by sorting filters list in internal logging method, but `FileFilter` subclasses are not sortable. + +- Fix a false warning about a lof of parallel connections to Grenplum. ([#342](https://github.com/MobileTeleSystems/onetl/pull/342)) + + Creating Spark session with `.master("local[5]")` may open up to 6 connections to Greenplum (=number of Spark executors + 1 for driver), + but onETL instead used number of *CPU cores* on the host as a number of parallel connections. + + This lead to showing a false warning that number of Greenplum connections is too high, + which actually should be the case only if number of executors is higher than 30. + +- Fix MongoDB trying to use current database name as `authSource`. ([#347](https://github.com/MobileTeleSystems/onetl/pull/347)) + + Use default connector value which is `admin` database. Previous onETL versions could be fixed by: + + ```python + from onetl.connection import MongoDB + + mongodb = MongoDB( + ..., + database="mydb", + extra={ + "authSource": "admin", + }, + ) + ``` + +## Dependencies + +- Minimal `etl-entities` version is now [2.5.0](https://github.com/MobileTeleSystems/etl-entities/releases/tag/2.5.0). ([#331](https://github.com/MobileTeleSystems/onetl/pull/331)) +- Update DB connectors/drivers to latest versions: ([#345](https://github.com/MobileTeleSystems/onetl/pull/345)) + : - Clickhouse `0.6.5` → `0.7.2` + - MongoDB `10.4.0` → `10.4.1` + - MySQL `9.0.0` → `9.2.0` + - Oracle `23.5.0.24.07` → `23.7.0.25.01` + - Postgres `42.7.4` → `42.7.5` + +## Doc only Changes + +- Split large code examples to tabs. ([#344](https://github.com/MobileTeleSystems/onetl/pull/344)) diff --git a/mddocs/docs/changelog/0.13.1.md b/mddocs/docs/changelog/0.13.1.md new file mode 100644 index 000000000..4045c7cbf --- /dev/null +++ b/mddocs/docs/changelog/0.13.1.md @@ -0,0 +1,9 @@ +# 0.13.1 (2025-03-06) + +## Bug Fixes + +In 0.13.0, using `DBWriter(connection=hive, target="SOMEDB.SOMETABLE")` lead to executing `df.write.saveAsTable()` +instead of `df.write.insertInto()` if target table `somedb.sometable` already exist. + +This is caused by table name normalization (Hive uses lower-case names), which wasn't properly handled by method used for checking table existence. +([#350](https://github.com/MobileTeleSystems/onetl/pull/350)) diff --git a/mddocs/docs/changelog/0.13.3.md b/mddocs/docs/changelog/0.13.3.md new file mode 100644 index 000000000..1aa289b49 --- /dev/null +++ b/mddocs/docs/changelog/0.13.3.md @@ -0,0 +1,5 @@ +# 0.13.3 (2025-03-11) + +## Dependencies + +Allow using [etl-entities 2.6.0](https://github.com/MobileTeleSystems/etl-entities/releases/tag/2.6.0). diff --git a/mddocs/docs/changelog/0.13.4.md b/mddocs/docs/changelog/0.13.4.md new file mode 100644 index 000000000..10f695e0c --- /dev/null +++ b/mddocs/docs/changelog/0.13.4.md @@ -0,0 +1,10 @@ +# 0.13.4 (2025-03-20) + +## Doc only Changes + +- Prefer `ReadOptions(partitionColumn=..., numPartitions=..., queryTimeout=...)` + instead of `ReadOptions(partition_column=..., num_partitions=..., query_timeout=...)`, + to match Spark documentation. ([#352](https://github.com/MobileTeleSystems/onetl/pull/352)) +- Prefer `WriteOptions(if_exists=...)` instead of `WriteOptions(mode=...)` for IDE suggestions. ([#354](https://github.com/MobileTeleSystems/onetl/pull/354)) +- Document all options of supported file formats. + ([#355](https://github.com/MobileTeleSystems/onetl/pull/355), [#356](https://github.com/MobileTeleSystems/onetl/pull/356), [#357](https://github.com/MobileTeleSystems/onetl/pull/357), [#358](https://github.com/MobileTeleSystems/onetl/pull/358), [#359](https://github.com/MobileTeleSystems/onetl/pull/359), [#360](https://github.com/MobileTeleSystems/onetl/pull/360), [#361](https://github.com/MobileTeleSystems/onetl/pull/361), [#362](https://github.com/MobileTeleSystems/onetl/pull/362)) diff --git a/mddocs/docs/changelog/0.7.0.md b/mddocs/docs/changelog/0.7.0.md new file mode 100644 index 000000000..7325faa96 --- /dev/null +++ b/mddocs/docs/changelog/0.7.0.md @@ -0,0 +1,239 @@ +# 0.7.0 (2023-05-15) + +## 🎉 onETL is now open source 🎉 + +That was long road, but we finally did it! + +## Breaking Changes + +- Changed installation method. + + **TL;DR What should I change to restore previous behavior** + + Simple way: + + | onETL < 0.7.0 | onETL >= 0.7.0 | + | ----------------- | --------------------------------- | + | pip install onetl | pip install onetl[files,kerberos] | + + Right way - enumerate connectors should be installed: + + ```bash + pip install onetl[hdfs,ftp,kerberos] # except DB connections + ``` + + **Details** + + In onetl<0.7 the package installation looks like: + + ```bash title="before" + + pip install onetl + ``` + + But this includes all dependencies for all connectors, even if user does not use them. + This caused some issues, for example user had to install Kerberos libraries to be able to install onETL, even if user uses only S3 (without Kerberos support). + + Since 0.7.0 installation process was changed: + + ``` bash title="after" + + pip install onetl # minimal installation, only onETL core + # there is no extras for DB connections because they are using Java packages which are installed in runtime + + pip install onetl[ftp,ftps,hdfs,sftp,s3,webdav] # install dependencies for specified file connections + pip install onetl[files] # install dependencies for all file connections + + pip install onetl[kerberos] # Kerberos auth support + pip install onetl[spark] # install PySpark to use DB connections + + pip install onetl[spark,kerberos,files] # all file connections + Kerberos + PySpark + pip install onetl[all] # alias for previous case + ``` + + There are corresponding documentation items for each extras. + + Also onETL checks that some requirements are missing, and raises exception with recommendation how to install them: + + ``` text title="exception while import Clickhouse connection" + + Cannot import module "pyspark". + + Since onETL v0.7.0 you should install package as follows: + pip install onetl[spark] + + or inject PySpark to sys.path in some other way BEFORE creating MongoDB instance. + ``` + + ``` text title="exception while import FTP connection" + + Cannot import module "ftputil". + + Since onETL v0.7.0 you should install package as follows: + pip install onetl[ftp] + + or + pip install onetl[files] + ``` + +- Added new `cluster` argument to `Hive` and `HDFS` connections. + + `Hive` qualified name (used in HWM) contains cluster name. But in onETL\<0.7.0 cluster name had hard coded value `rnd-dwh` which was not OK for some users. + + `HDFS` connection qualified name contains host (active namenode of Hadoop cluster), but its value can change over time, leading to creating of new HWM. + + Since onETL 0.7.0 both `Hive` and `HDFS` connections have `cluster` attribute which can be set to a specific cluster name. + For `Hive` it is mandatory, for `HDFS` it can be omitted (using host as a fallback). + + But passing cluster name every time could lead to errors. + + Now `Hive` and `HDFS` have nested class named `slots` with methods: + + - `normalize_cluster_name` + - `get_known_clusters` + - `get_current_cluster` + - `normalize_namenode_host` (only `HDFS`) + - `get_cluster_namenodes` (only `HDFS`) + - `get_webhdfs_port` (only `HDFS`) + - `is_namenode_active` (only `HDFS`) + + And new method `HDFS.get_current` / `Hive.get_current`. + + Developers can implement hooks validating user input or substituting values for automatic cluster detection. + This should improve user experience while using these connectors. + + See slots documentation. + +- Update JDBC connection drivers. + + - Greenplum `2.1.3` → `2.1.4`. + - MSSQL `10.2.1.jre8` → `12.2.0.jre8`. Minimal supported version of MSSQL is now 2014 instead 2021. + - MySQL `8.0.30` → `8.0.33`: + - Package was renamed `mysql:mysql-connector-java` → `com.mysql:mysql-connector-j`. + - Driver class was renamed `com.mysql.jdbc.Driver` → `com.mysql.cj.jdbc.Driver`. + - Oracle `21.6.0.0.1` → `23.2.0.0`. + - Postgres `42.4.0` → `42.6.0`. + - Teradata `17.20.00.08` → `17.20.00.15`: + - Package was renamed `com.teradata.jdbc:terajdbc4` → `com.teradata.jdbc:terajdbc`. + - Teradata driver is now published to Maven. + + See [#31](https://github.com/MobileTeleSystems/onetl/pull/31). + +## Features + +- Added MongoDB connection. + + Using official [MongoDB connector for Spark v10](https://www.mongodb.com/docs/spark-connector/current/). Only Spark 3.2+ is supported. + + There are some differences between MongoDB and other database sources: + + - Instead of `mongodb.sql` method there is `mongodb.pipeline`. + - No methods `mongodb.fetch` and `mongodb.execute`. + - `DBReader.hint` and `DBReader.where` have different types than in SQL databases: + + ```python + where = { + "col1": { + "$eq": 10, + }, + } + + hint = { + "col1": 1, + } + ``` + + - Because MongoDB does not have schemas of collections, but Spark cannot create dataframe with dynamic schema, new option `DBReader.df_schema` was introduced. + It is mandatory for MongoDB, but optional for other sources. + - Currently DBReader cannot be used with MongoDB and hwm expression, e.g. `hwm_column=("mycolumn", {"$cast": {"col1": "date"}})` + + Because there are no tables in MongoDB, some options were renamed in core classes: + + - `DBReader(table=...)` → `DBReader(source=...)` + - `DBWriter(table=...)` → `DBWriter(target=...)` + + Old names can be used too, they are not deprecated ([#30](https://github.com/MobileTeleSystems/onetl/pull/30)). + +- Added option for disabling some plugins during import. + + Previously if some plugin were failing during the import, the only way to import onETL would be to disable all plugins + using environment variable. + + Now there are several variables with different behavior: + + - `ONETL_PLUGINS_ENABLED=false` - disable all plugins autoimport. Previously it was named `ONETL_ENABLE_PLUGINS`. + - `ONETL_PLUGINS_BLACKLIST=plugin-name,another-plugin` - set list of plugins which should NOT be imported automatically. + - `ONETL_PLUGINS_WHITELIST=plugin-name,another-plugin` - set list of plugins which should ONLY be imported automatically. + + Also we improved exception message with recommendation how to disable a failing plugin: + + ``` text title="exception message example" + + Error while importing plugin 'mtspark' from package 'mtspark' v4.0.0. + + Statement: + import mtspark.onetl + + Check if plugin is compatible with current onETL version 0.7.0. + + You can disable loading this plugin by setting environment variable: + ONETL_PLUGINS_BLACKLIST='mtspark,failing-plugin' + + You can also define a whitelist of packages which can be loaded by onETL: + ONETL_PLUGINS_WHITELIST='not-failing-plugin1,not-failing-plugin2' + + Please take into account that plugin name may differ from package or module name. + See package metadata for more details + ``` + +## Improvements + +- Added compatibility with Python 3.11 and PySpark 3.4.0. + + File connections were OK, but `jdbc.fetch` and `jdbc.execute` were failing. Fixed in [#28](https://github.com/MobileTeleSystems/onetl/pull/28). + +- Added check for missing Java packages. + + Previously if DB connection tried to use some Java class which were not loaded into Spark version, it raised an exception + with long Java stacktrace. Most users failed to interpret this trace. + + Now onETL shows the following error message: + + ``` text title="exception message example" + + |Spark| Cannot import Java class 'com.mongodb.spark.sql.connector.MongoTableProvider'. + + It looks like you've created Spark session without this option: + SparkSession.builder.config("spark.jars.packages", MongoDB.package_spark_3_2) + + Please call `spark.stop()`, restart the interpreter, + and then create new SparkSession with proper options. + ``` + +- Documentation improvements. + + - Changed documentation site theme - using [furo](https://github.com/pradyunsg/furo) + instead of default [ReadTheDocs](https://github.com/readthedocs/sphinx_rtd_theme). + + New theme supports wide screens and dark mode. + See [#10](https://github.com/MobileTeleSystems/onetl/pull/10). + + - Now each connection class have compatibility table for Spark + Java + Python. + + - Added global compatibility table for Spark + Java + Python + Scala. + +## Bug Fixes + +- Fixed several SFTP issues. + + - If SSH config file `~/.ssh/config` contains some options not recognized by Paramiko (unknown syntax, unknown option name), + previous versions were raising exception until fixing or removing this file. Since 0.7.0 exception is replaced with warning. + + - If user passed `host_key_check=False` but server changed SSH keys, previous versions raised exception until new key is accepted. + Since 0.7.0 exception is replaced with warning if option value is `False`. + + Fixed in [#19](https://github.com/MobileTeleSystems/onetl/pull/19). + +- Fixed several S3 issues. + + There was a bug in S3 connection which prevented handling files in the root of a bucket - they were invisible for the connector. Fixed in [#29](https://github.com/MobileTeleSystems/onetl/pull/29). diff --git a/mddocs/docs/changelog/0.7.1.md b/mddocs/docs/changelog/0.7.1.md new file mode 100644 index 000000000..9eac47a1a --- /dev/null +++ b/mddocs/docs/changelog/0.7.1.md @@ -0,0 +1,40 @@ +# 0.7.1 (2023-05-23) + +## Bug Fixes + +- Fixed `setup_logging` function. + + In onETL==0.7.0 calling `onetl.log.setup_logging()` broke the logging: + + ``` text title="exception message" + + Traceback (most recent call last): + File "/opt/anaconda/envs/py39/lib/python3.9/logging/__init__.py", line 434, in format + return self._format(record) + File "/opt/anaconda/envs/py39/lib/python3.9/logging/__init__.py", line 430, in _format + return self._fmt % record.dict + KeyError: 'levelname:8s' + ``` + +- Fixed installation examples. + + In onETL==0.7.0 there are examples of installing onETL with extras: + + ``` bash title="before" + + pip install onetl[files, kerberos, spark] + ``` + + But pip fails to install such package: + + ``` text title="exception" + + ERROR: Invalid requirement: 'onet[files,' + ``` + + This is because of spaces in extras clause. Fixed: + + ``` bash title="after" + + pip install onetl[files,kerberos,spark] + ``` diff --git a/mddocs/docs/changelog/0.7.2.md b/mddocs/docs/changelog/0.7.2.md new file mode 100644 index 000000000..505b03725 --- /dev/null +++ b/mddocs/docs/changelog/0.7.2.md @@ -0,0 +1,37 @@ +# 0.7.2 (2023-05-24) + +## Dependencies + +- Limited `typing-extensions` version. + + `typing-extensions==4.6.0` release contains some breaking changes causing errors like: + + ``` text title="typing-extensions 4.6.0" + + Traceback (most recent call last): + File "/Users/project/lib/python3.9/typing.py", line 852, in __subclasscheck__ + return issubclass(cls, self.__origin__) + TypeError: issubclass() arg 1 must be a class + ``` + + `typing-extensions==4.6.1` was causing another error: + + ``` text title="typing-extensions 4.6.1" + + Traceback (most recent call last): + File "/home/maxim/Repo/typing_extensions/1.py", line 33, in + isinstance(file, ContainsException) + File "/home/maxim/Repo/typing_extensions/src/typing_extensions.py", line 599, in __instancecheck__ + if super().__instancecheck__(instance): + File "/home/maxim/.pyenv/versions/3.7.8/lib/python3.7/abc.py", line 139, in __instancecheck__ + return _abc_instancecheck(cls, instance) + File "/home/maxim/Repo/typing_extensions/src/typing_extensions.py", line 583, in __subclasscheck__ + return super().__subclasscheck__(other) + File "/home/maxim/.pyenv/versions/3.7.8/lib/python3.7/abc.py", line 143, in __subclasscheck__ + return _abc_subclasscheck(cls, subclass) + File "/home/maxim/Repo/typing_extensions/src/typing_extensions.py", line 661, in _proto_hook + and other._is_protocol + AttributeError: type object 'PathWithFailure' has no attribute '_is_protocol' + ``` + + We updated requirements with `typing-extensions<4.6` until fixing compatibility issues. diff --git a/mddocs/docs/changelog/0.8.0.md b/mddocs/docs/changelog/0.8.0.md new file mode 100644 index 000000000..49d6015f9 --- /dev/null +++ b/mddocs/docs/changelog/0.8.0.md @@ -0,0 +1,166 @@ +# 0.8.0 (2023-05-31) + +## Breaking Changes + +- Rename methods of `FileConnection` classes: + + - `get_directory` → `resolve_dir` + - `get_file` → `resolve_file` + - `listdir` → `list_dir` + - `mkdir` → `create_dir` + - `rmdir` → `remove_dir` + + New naming should be more consistent. + + They were undocumented in previous versions, but someone could use these methods, so this is a breaking change. ([#36](https://github.com/MobileTeleSystems/onetl/pull/36)) + +- Deprecate `onetl.core.FileFilter` class, replace it with new classes: + + - `onetl.file.filter.Glob` + - `onetl.file.filter.Regexp` + - `onetl.file.filter.ExcludeDir` + + Old class will be removed in v1.0.0. ([#43](https://github.com/MobileTeleSystems/onetl/pull/43)) + +- Deprecate `onetl.core.FileLimit` class, replace it with new class `onetl.file.limit.MaxFilesCount`. + + Old class will be removed in v1.0.0. ([#44](https://github.com/MobileTeleSystems/onetl/pull/44)) + +- Change behavior of `BaseFileLimit.reset` method. + + This method should now return `self` instead of `None`. + Return value could be the same limit object or a copy, this is an implementation detail. ([#44](https://github.com/MobileTeleSystems/onetl/pull/44)) + +- Replaced `FileDownloader.filter` and `.limit` with new options `.filters` and `.limits`: + + ``` python title="onETL < 0.8.0" + + FileDownloader( + ..., + filter=FileFilter(glob="*.txt", exclude_dir="/path"), + limit=FileLimit(count_limit=10), + ) + ``` + + ``` python title="onETL >= 0.8.0" + + FileDownloader( + ..., + filters=[Glob("*.txt"), ExcludeDir("/path")], + limits=[MaxFilesCount(10)], + ) + ``` + + This allows to developers to implement their own filter and limit classes, and combine them with existing ones. + + Old behavior still supported, but it will be removed in v1.0.0. ([#45](https://github.com/MobileTeleSystems/onetl/pull/45)) + +- Removed default value for `FileDownloader.limits`, user should pass limits list explicitly. ([#45](https://github.com/MobileTeleSystems/onetl/pull/45)) + +- Move classes from module `onetl.core`: + + ``` python title="before" + + from onetl.core import DBReader + from onetl.core import DBWriter + from onetl.core import FileDownloader + from onetl.core import FileUploader + ``` + + with new modules `onetl.db` and `onetl.file`: + + ``` python title="after" + + from onetl.db import DBReader + from onetl.db import DBWriter + + from onetl.file import FileDownloader + from onetl.file import FileUploader + ``` + + Imports from old module `onetl.core` still can be used, but marked as deprecated. Module will be removed in v1.0.0. ([#46](https://github.com/MobileTeleSystems/onetl/pull/46)) + +## Features + +- Add `rename_dir` method. + + Method was added to following connections: + + - `FTP` + - `FTPS` + - `HDFS` + - `SFTP` + - `WebDAV` + + It allows to rename/move directory to new path with all its content. + + `S3` does not have directories, so there is no such method in that class. ([#40](https://github.com/MobileTeleSystems/onetl/pull/40)) + +- Add `onetl.file.FileMover` class. + + It allows to move files between directories of remote file system. + Signature is almost the same as in `FileDownloader`, but without HWM support. ([#42](https://github.com/MobileTeleSystems/onetl/pull/42)) + +## Improvements + +- Document all public methods in `FileConnection` classes: + + - `download_file` + - `resolve_dir` + - `resolve_file` + - `get_stat` + - `is_dir` + - `is_file` + - `list_dir` + - `create_dir` + - `path_exists` + - `remove_file` + - `rename_file` + - `remove_dir` + - `upload_file` + - `walk` ([#39](https://github.com/MobileTeleSystems/onetl/pull/39)) + +- Update documentation of `check` method of all connections - add usage example and document result type. ([#39](https://github.com/MobileTeleSystems/onetl/pull/39)) + +- Add new exception type `FileSizeMismatchError`. + + Methods `connection.download_file` and `connection.upload_file` now raise new exception type instead of `RuntimeError`, + if target file after download/upload has different size than source. ([#39](https://github.com/MobileTeleSystems/onetl/pull/39)) + +- Add new exception type `DirectoryExistsError` - it is raised if target directory already exists. ([#40](https://github.com/MobileTeleSystems/onetl/pull/40)) + +- Improved `FileDownloader` / `FileUploader` exception logging. + + If `DEBUG` logging is enabled, print exception with stacktrace instead of + printing only exception message. ([#42](https://github.com/MobileTeleSystems/onetl/pull/42)) + +- Updated documentation of `FileUploader`. + + - Class does not support read strategies, added note to documentation. + - Added examples of using `run` method with explicit files list passing, both absolute and relative paths. + - Fix outdated imports and class names in examples. ([#42](https://github.com/MobileTeleSystems/onetl/pull/42)) + +- Updated documentation of `DownloadResult` class - fix outdated imports and class names. ([#42](https://github.com/MobileTeleSystems/onetl/pull/42)) + +- Improved file filters documentation section. + + Document interface class `onetl.base.BaseFileFilter` and function `match_all_filters`. ([#43](https://github.com/MobileTeleSystems/onetl/pull/43)) + +- Improved file limits documentation section. + + Document interface class `onetl.base.BaseFileLimit` and functions `limits_stop_at` / `limits_reached` / `reset_limits`. ([#44](https://github.com/MobileTeleSystems/onetl/pull/44)) + +- Added changelog. + + Changelog is generated from separated news files using [towncrier](https://pypi.org/project/towncrier/). ([#47](https://github.com/MobileTeleSystems/onetl/pull/47)) + +## Misc + +- Improved CI workflow for tests. + + - If developer haven't changed source core of a specific connector or its dependencies, + run tests only against maximum supported versions of Spark, Python, Java and db/file server. + - If developed made some changes in a specific connector, or in core classes, or in dependencies, + run tests for both minimal and maximum versions. + - Once a week run all aganst for minimal and latest versions to detect breaking changes in dependencies + - Minimal tested Spark version is 2.3.1 instead on 2.4.8. ([#32](https://github.com/MobileTeleSystems/onetl/pull/32)) diff --git a/mddocs/docs/changelog/0.8.1.md b/mddocs/docs/changelog/0.8.1.md new file mode 100644 index 000000000..aaf777091 --- /dev/null +++ b/mddocs/docs/changelog/0.8.1.md @@ -0,0 +1,42 @@ +# 0.8.1 (2023-07-10) + +## Features + +- Add `@slot` decorator to public methods of: + + - `DBConnection` + - `FileConnection` + - `DBReader` + - `DBWriter` + - `FileDownloader` + - `FileUploader` + - `FileMover` ([#49](https://github.com/MobileTeleSystems/onetl/pull/49)) + +- Add `workers` field to `FileDownloader` / `FileUploader` / `FileMover`. `Options` classes. + + This allows to speed up all file operations using parallel threads. ([#57](https://github.com/MobileTeleSystems/onetl/pull/57)) + +## Improvements + +- Add documentation for HWM store `.get` and `.save` methods. ([#49](https://github.com/MobileTeleSystems/onetl/pull/49)) + +- Improve Readme: + + - Move `Quick start` section from documentation + - Add `Non-goals` section + - Fix code blocks indentation ([#50](https://github.com/MobileTeleSystems/onetl/pull/50)) + +- Improve Contributing guide: + + - Move `Develop` section from Readme + - Move `docs/changelog/README.rst` content + - Add `Limitations` section + - Add instruction of creating a fork and building documentation ([#50](https://github.com/MobileTeleSystems/onetl/pull/50)) + +- Remove duplicated checks for source file existence in `FileDownloader` / `FileMover`. ([#57](https://github.com/MobileTeleSystems/onetl/pull/57)) + +- Update default logging format to include thread name. ([#57](https://github.com/MobileTeleSystems/onetl/pull/57)) + +## Bug Fixes + +- Fix `S3.list_dir('/')` returns empty list on latest Minio version. ([#58](https://github.com/MobileTeleSystems/onetl/pull/58)) diff --git a/mddocs/docs/changelog/0.9.0.md b/mddocs/docs/changelog/0.9.0.md new file mode 100644 index 000000000..8aee9a3eb --- /dev/null +++ b/mddocs/docs/changelog/0.9.0.md @@ -0,0 +1,122 @@ +# 0.9.0 (2023-08-17) + +## Breaking Changes + +- Rename methods: + + - `DBConnection.read_df` → `DBConnection.read_source_as_df` + - `DBConnection.write_df` → `DBConnection.write_df_to_target` ([#66](https://github.com/MobileTeleSystems/onetl/pull/66)) + +- Rename classes: + + - `HDFS.slots` → `HDFS.Slots` + - `Hive.slots` → `Hive.Slots` + + Old names are left intact, but will be removed in v1.0.0 ([#103](https://github.com/MobileTeleSystems/onetl/pull/103)) + +- Rename options to make them self-explanatory: + + - `Hive.WriteOptions(mode="append")` → `Hive.WriteOptions(if_exists="append")` + - `Hive.WriteOptions(mode="overwrite_table")` → `Hive.WriteOptions(if_exists="replace_entire_table")` + - `Hive.WriteOptions(mode="overwrite_partitions")` → `Hive.WriteOptions(if_exists="replace_overlapping_partitions")` + - `JDBC.WriteOptions(mode="append")` → `JDBC.WriteOptions(if_exists="append")` + - `JDBC.WriteOptions(mode="overwrite")` → `JDBC.WriteOptions(if_exists="replace_entire_table")` + - `Greenplum.WriteOptions(mode="append")` → `Greenplum.WriteOptions(if_exists="append")` + - `Greenplum.WriteOptions(mode="overwrite")` → `Greenplum.WriteOptions(if_exists="replace_entire_table")` + - `MongoDB.WriteOptions(mode="append")` → `Greenplum.WriteOptions(if_exists="append")` + - `MongoDB.WriteOptions(mode="overwrite")` → `Greenplum.WriteOptions(if_exists="replace_entire_collection")` + - `FileDownloader.Options(mode="error")` → `FileDownloader.Options(if_exists="error")` + - `FileDownloader.Options(mode="ignore")` → `FileDownloader.Options(if_exists="ignore")` + - `FileDownloader.Options(mode="overwrite")` → `FileDownloader.Options(if_exists="replace_file")` + - `FileDownloader.Options(mode="delete_all")` → `FileDownloader.Options(if_exists="replace_entire_directory")` + - `FileUploader.Options(mode="error")` → `FileUploader.Options(if_exists="error")` + - `FileUploader.Options(mode="ignore")` → `FileUploader.Options(if_exists="ignore")` + - `FileUploader.Options(mode="overwrite")` → `FileUploader.Options(if_exists="replace_file")` + - `FileUploader.Options(mode="delete_all")` → `FileUploader.Options(if_exists="replace_entire_directory")` + - `FileMover.Options(mode="error")` → `FileMover.Options(if_exists="error")` + - `FileMover.Options(mode="ignore")` → `FileMover.Options(if_exists="ignore")` + - `FileMover.Options(mode="overwrite")` → `FileMover.Options(if_exists="replace_file")` + - `FileMover.Options(mode="delete_all")` → `FileMover.Options(if_exists="replace_entire_directory")` + + Old names are left intact, but will be removed in v1.0.0 ([#108](https://github.com/MobileTeleSystems/onetl/pull/108)) + +- Rename `onetl.log.disable_clients_logging()` to `onetl.log.setup_clients_logging()`. ([#120](https://github.com/MobileTeleSystems/onetl/pull/120)) + +## Features + +- Add new methods returning Maven packages for specific connection class: + + - `Clickhouse.get_packages()` + - `MySQL.get_packages()` + - `Postgres.get_packages()` + - `Teradata.get_packages()` + - `MSSQL.get_packages(java_version="8")` + - `Oracle.get_packages(java_version="8")` + - `Greenplum.get_packages(scala_version="2.12")` + - `MongoDB.get_packages(scala_version="2.12")` + - `Kafka.get_packages(spark_version="3.4.1", scala_version="2.12")` + + Deprecate old syntax: + + - `Clickhouse.package` + - `MySQL.package` + - `Postgres.package` + - `Teradata.package` + - `MSSQL.package` + - `Oracle.package` + - `Greenplum.package_spark_2_3` + - `Greenplum.package_spark_2_4` + - `Greenplum.package_spark_3_2` + - `MongoDB.package_spark_3_2` + - `MongoDB.package_spark_3_3` + - `MongoDB.package_spark_3_4` ([#87](https://github.com/MobileTeleSystems/onetl/pull/87)) + +- Allow to set client modules log level in `onetl.log.setup_clients_logging()`. + + Allow to enable underlying client modules logging in `onetl.log.setup_logging()` by providing additional argument `enable_clients=True`. + This is useful for debug. ([#120](https://github.com/MobileTeleSystems/onetl/pull/120)) + +- Added support for reading and writing data to Kafka topics. + + For these operations, new classes were added. + + - `Kafka` ([#54](https://github.com/MobileTeleSystems/onetl/pull/54), [#60](https://github.com/MobileTeleSystems/onetl/pull/60), [#72](https://github.com/MobileTeleSystems/onetl/pull/72), [#84](https://github.com/MobileTeleSystems/onetl/pull/84), [#87](https://github.com/MobileTeleSystems/onetl/pull/87), [#89](https://github.com/MobileTeleSystems/onetl/pull/89), [#93](https://github.com/MobileTeleSystems/onetl/pull/93), [#96](https://github.com/MobileTeleSystems/onetl/pull/96), [#102](https://github.com/MobileTeleSystems/onetl/pull/102), [#104](https://github.com/MobileTeleSystems/onetl/pull/104)) + - `Kafka.PlaintextProtocol` ([#79](https://github.com/MobileTeleSystems/onetl/pull/79)) + - `Kafka.SSLProtocol` ([#118](https://github.com/MobileTeleSystems/onetl/pull/118)) + - `Kafka.BasicAuth` ([#63](https://github.com/MobileTeleSystems/onetl/pull/63), [#77](https://github.com/MobileTeleSystems/onetl/pull/77)) + - `Kafka.KerberosAuth` ([#63](https://github.com/MobileTeleSystems/onetl/pull/63), [#77](https://github.com/MobileTeleSystems/onetl/pull/77), [#110](https://github.com/MobileTeleSystems/onetl/pull/110)) + - `Kafka.ScramAuth` ([#115](https://github.com/MobileTeleSystems/onetl/pull/115)) + - `Kafka.Slots` ([#109](https://github.com/MobileTeleSystems/onetl/pull/109)) + - `Kafka.ReadOptions` ([#68](https://github.com/MobileTeleSystems/onetl/pull/68)) + - `Kafka.WriteOptions` ([#68](https://github.com/MobileTeleSystems/onetl/pull/68)) + + Currently, Kafka does not support incremental read strategies, this will be implemented in future releases. + +- Added support for reading files as Spark DataFrame and saving DataFrame as Files. + + For these operations, new classes were added. + + FileDFConnections: + + - `SparkHDFS` ([#98](https://github.com/MobileTeleSystems/onetl/pull/98)) + - `SparkS3` ([#94](https://github.com/MobileTeleSystems/onetl/pull/94), [#100](https://github.com/MobileTeleSystems/onetl/pull/100), [#124](https://github.com/MobileTeleSystems/onetl/pull/124)) + - `SparkLocalFS` ([#67](https://github.com/MobileTeleSystems/onetl/pull/67)) + + High-level classes: + + - `FileDFReader` ([#73](https://github.com/MobileTeleSystems/onetl/pull/73)) + - `FileDFWriter` ([#81](https://github.com/MobileTeleSystems/onetl/pull/81)) + + File formats: + + - `Avro` ([#69](https://github.com/MobileTeleSystems/onetl/pull/69)) + - `CSV` ([#92](https://github.com/MobileTeleSystems/onetl/pull/92)) + - `JSON` ([#83](https://github.com/MobileTeleSystems/onetl/pull/83)) + - `JSONLine` ([#83](https://github.com/MobileTeleSystems/onetl/pull/83)) + - `ORC` ([#86](https://github.com/MobileTeleSystems/onetl/pull/86)) + - `Parquet` ([#88](https://github.com/MobileTeleSystems/onetl/pull/88)) + +## Improvements + +- Remove redundant checks for driver availability in Greenplum and MongoDB connections. ([#67](https://github.com/MobileTeleSystems/onetl/pull/67)) +- Check of Java class availability moved from `.check()` method to connection constructor. ([#97](https://github.com/MobileTeleSystems/onetl/pull/97)) diff --git a/mddocs/docs/changelog/0.9.1.md b/mddocs/docs/changelog/0.9.1.md new file mode 100644 index 000000000..1779274b1 --- /dev/null +++ b/mddocs/docs/changelog/0.9.1.md @@ -0,0 +1,7 @@ +# 0.9.1 (2023-08-17) + +## Bug Fixes + +- Fixed bug then number of threads created by `FileDownloader` / `FileUploader` / `FileMover` was + not `min(workers, len(files))`, but `max(workers, len(files))`. leading to create too much workers + on large files list. diff --git a/mddocs/docs/changelog/0.9.2.md b/mddocs/docs/changelog/0.9.2.md new file mode 100644 index 000000000..9c34d16a0 --- /dev/null +++ b/mddocs/docs/changelog/0.9.2.md @@ -0,0 +1,23 @@ +# 0.9.2 (2023-09-06) + +## Features + +- Add `if_exists="ignore"` and `error` to `Greenplum.WriteOptions` ([#142](https://github.com/MobileTeleSystems/onetl/pull/142)) + +## Improvements + +- Improve validation messages while writing dataframe to Kafka. ([#131](https://github.com/MobileTeleSystems/onetl/pull/131)) + +- Improve documentation: + + - Add notes about reading and writing to database connections documentation + - Add notes about executing statements in JDBC and Greenplum connections + +## Bug Fixes + +- Fixed validation of `headers` column is written to Kafka with default `Kafka.WriteOptions()` - default value was `False`, + but instead of raising an exception, column value was just ignored. ([#131](https://github.com/MobileTeleSystems/onetl/pull/131)) +- Fix reading data from Oracle with `partitioningMode="range"` without explicitly set `lowerBound` / `upperBound`. ([#133](https://github.com/MobileTeleSystems/onetl/pull/133)) +- Update Kafka documentation with SSLProtocol usage. ([#136](https://github.com/MobileTeleSystems/onetl/pull/136)) +- Raise exception if someone tries to read data from Kafka topic which does not exist. ([#138](https://github.com/MobileTeleSystems/onetl/pull/138)) +- Allow to pass Kafka topics with name like `some.topic.name` to DBReader. Same for MongoDB collections. ([#139](https://github.com/MobileTeleSystems/onetl/pull/139)) diff --git a/mddocs/docs/changelog/0.9.3.md b/mddocs/docs/changelog/0.9.3.md new file mode 100644 index 000000000..1a8c25d4d --- /dev/null +++ b/mddocs/docs/changelog/0.9.3.md @@ -0,0 +1,5 @@ +# 0.9.3 (2023-09-06) + +## Bug Fixes + +- Fix documentation build diff --git a/mddocs/docs/changelog/0.9.4.md b/mddocs/docs/changelog/0.9.4.md new file mode 100644 index 000000000..a330de366 --- /dev/null +++ b/mddocs/docs/changelog/0.9.4.md @@ -0,0 +1,24 @@ +# 0.9.4 (2023-09-26) + +## Features + +- Add `Excel` file format support. ([#148](https://github.com/MobileTeleSystems/onetl/pull/148)) +- Add `Samba` file connection. + It is now possible to download and upload files to Samba shared folders using `FileDownloader`/`FileUploader`. ([#150](https://github.com/MobileTeleSystems/onetl/pull/150)) +- Add `if_exists="ignore"` and `error` to `Hive.WriteOptions` ([#143](https://github.com/MobileTeleSystems/onetl/pull/143)) +- Add `if_exists="ignore"` and `error` to `JDBC.WriteOptions` ([#144](https://github.com/MobileTeleSystems/onetl/pull/144)) +- Add `if_exists="ignore"` and `error` to `MongoDB.WriteOptions` ([#145](https://github.com/MobileTeleSystems/onetl/pull/145)) + +## Improvements + +- Add documentation about different ways of passing packages to Spark session. ([#151](https://github.com/MobileTeleSystems/onetl/pull/151)) +- Drastically improve `Greenplum` documentation: + : - Added information about network ports, grants, `pg_hba.conf` and so on. + - Added interaction schemas for reading, writing and executing statements in Greenplum. + - Added recommendations about reading data from views and `JOIN` results from Greenplum. ([#154](https://github.com/MobileTeleSystems/onetl/pull/154)) +- Make `.fetch` and `.execute` methods of DB connections thread-safe. Each thread works with its own connection. ([#156](https://github.com/MobileTeleSystems/onetl/pull/156)) +- Call `.close()` on `FileConnection` then it is removed by garbage collector. ([#156](https://github.com/MobileTeleSystems/onetl/pull/156)) + +## Bug Fixes + +- Fix issue when stopping Python interpreter calls `JDBCMixin.close()`, but it is finished with exceptions. ([#156](https://github.com/MobileTeleSystems/onetl/pull/156)) diff --git a/mddocs/docs/changelog/0.9.5.md b/mddocs/docs/changelog/0.9.5.md new file mode 100644 index 000000000..1d7358c0b --- /dev/null +++ b/mddocs/docs/changelog/0.9.5.md @@ -0,0 +1,14 @@ +# 0.9.5 (2023-10-10) + +## Features + +- Add `XML` file format support. ([#163](https://github.com/MobileTeleSystems/onetl/pull/163)) +- Tested compatibility with Spark 3.5.0. `MongoDB` and `Excel` are not supported yet, but other packages do. ([#159](https://github.com/MobileTeleSystems/onetl/pull/159)) + +## Improvements + +- Add check to all DB and FileDF connections that Spark session is alive. ([#164](https://github.com/MobileTeleSystems/onetl/pull/164)) + +## Bug Fixes + +- Fix `Hive.check()` behavior when Hive Metastore is not available. ([#164](https://github.com/MobileTeleSystems/onetl/pull/164)) diff --git a/mddocs/docs/changelog/DRAFT.md b/mddocs/docs/changelog/DRAFT.md new file mode 100644 index 000000000..912b7d7f7 --- /dev/null +++ b/mddocs/docs/changelog/DRAFT.md @@ -0,0 +1,3 @@ +```{eval-rst} +.. towncrier-draft-entries:: |release| [UNRELEASED] +``` diff --git a/mddocs/docs/changelog/NEXT_RELEASE.md b/mddocs/docs/changelog/NEXT_RELEASE.md new file mode 100644 index 000000000..a9831f9d1 --- /dev/null +++ b/mddocs/docs/changelog/NEXT_RELEASE.md @@ -0,0 +1 @@ +% towncrier release notes start diff --git a/mddocs/docs/changelog/index.md b/mddocs/docs/changelog/index.md new file mode 100644 index 000000000..04704744d --- /dev/null +++ b/mddocs/docs/changelog/index.md @@ -0,0 +1,29 @@ +# Changelog + +- [0.13.4](../changelog/0.13.4) +- [0.13.3](../changelog/0.13.3) +- [0.13.1](../changelog/0.13.1) +- [0.13.0](../changelog/0.13.0) +- [0.12.5](../changelog/0.12.5) +- [0.12.4](../changelog/0.12.4) +- [0.12.3](../changelog/0.12.3) +- [0.12.2](../changelog/0.12.2) +- [0.12.1](../changelog/0.12.1) +- [0.12.0](../changelog/0.12.0) +- [0.11.2](../changelog/0.11.2) +- [0.11.1](../changelog/0.11.1) +- [0.11.0](../changelog/0.11.0) +- [0.10.2](../changelog/0.10.2) +- [0.10.1](../changelog/0.10.1) +- [0.10.0](../changelog/0.10.0) +- [0.9.5](../changelog/0.9.5) +- [0.9.4](../changelog/0.9.4) +- [0.9.3](../changelog/0.9.3) +- [0.9.2](../changelog/0.9.2) +- [0.9.1](../changelog/0.9.1) +- [0.9.0](../changelog/0.9.0) +- [0.8.1](../changelog/0.8.1) +- [0.8.0](../changelog/0.8.0) +- [0.7.2](../changelog/0.7.2) +- [0.7.1](../changelog/0.7.1) +- [0.7.0](../changelog/0.7.0) diff --git a/mddocs/docs/changelog/next_release/.keep b/mddocs/docs/changelog/next_release/.keep new file mode 100644 index 000000000..e69de29bb diff --git a/mddocs/docs/concepts.md b/mddocs/docs/concepts.md new file mode 100644 index 000000000..b8b0baf4a --- /dev/null +++ b/mddocs/docs/concepts.md @@ -0,0 +1,458 @@ +# Concepts + +Here you can find detailed documentation about each one of the onETL concepts and how to use them. + +## Connection + +### Basics + +onETL is used to pull and push data into other systems, and so it has a first-class `Connection` concept for storing credentials that are used to communicate with external systems. + +A `Connection` is essentially a set of parameters, such as username, password, hostname. + +To create a connection to a specific storage type, you must use a class that matches the storage type. The class name is the same as the storage type name (`Oracle`, `MSSQL`, `SFTP`, etc): + +```python +from onetl.connection import SFTP + +sftp = SFTP( + host="sftp.test.com", + user="onetl", + password="onetl", +) +``` + +All connection types are inherited from the parent class `BaseConnection`. + +### Class diagram + +```{eval-rst} +.. plantuml:: + + @startuml + left to right direction + skinparam classFontSize 20 + skinparam class { + BackgroundColor<> LightGreen + BackgroundColor<> Khaki + BackgroundColor<> LightBlue + StereotypeFontColor<> Transparent + StereotypeFontColor<> Transparent + StereotypeFontColor<> Transparent + } + + class BaseConnection { + } + + class DBConnection <>{ + } + DBConnection --|> BaseConnection + + class Hive <>{ + } + Hive --|> DBConnection + + class Greenplum <>{ + } + Greenplum --|> DBConnection + + class MongoDB <>{ + } + MongoDB --|> DBConnection + + class Kafka <>{ + } + Kafka --|> DBConnection + + class JDBCConnection <>{ + } + JDBCConnection --|> DBConnection + + class Clickhouse <>{ + } + Clickhouse --|> JDBCConnection + + class MSSQL <>{ + } + MSSQL --|> JDBCConnection + + class MySQL <>{ + } + MySQL --|> JDBCConnection + + class Postgres <>{ + } + Postgres --|> JDBCConnection + + class Oracle <>{ + } + Oracle --|> JDBCConnection + + class Teradata <>{ + } + Teradata --|> JDBCConnection + + class FileConnection <>{ + } + FileConnection --|> BaseConnection + + class FTP <>{ + } + FTP --|> FileConnection + + class FTPS <>{ + } + FTPS --|> FileConnection + + class HDFS <>{ + } + HDFS --|> FileConnection + + class WebDAV <>{ + } + WebDAV --|> FileConnection + + class Samba <>{ + } + Samba --|> FileConnection + + class SFTP <>{ + } + SFTP --|> FileConnection + + class S3 <>{ + } + S3 --|> FileConnection + + class FileDFConnection <>{ + } + FileDFConnection --|> BaseConnection + + class SparkHDFS <>{ + } + SparkHDFS --|> FileDFConnection + + class SparkLocalFS <>{ + } + SparkLocalFS --|> FileDFConnection + + class SparkS3 <>{ + } + SparkS3 --|> FileDFConnection + + @enduml +``` + +### DBConnection + +Classes inherited from `DBConnection` could be used for accessing databases. + +A `DBConnection` could be instantiated as follows: + +```python +from onetl.connection import MSSQL + +mssql = MSSQL( + host="mssqldb.demo.com", + user="onetl", + password="onetl", + database="Telecom", + spark=spark, +) +``` + +where **spark** is the current SparkSession. +`onETL` uses `Spark` and specific Java connectors under the hood to work with databases. + +For a description of other parameters, see the documentation for the {ref}`available DBConnections `. + +### FileConnection + +Classes inherited from `FileConnection` could be used to access files stored on the different file systems/file servers + +A `FileConnection` could be instantiated as follows: + +```python +from onetl.connection import SFTP + +sftp = SFTP( + host="sftp.test.com", + user="onetl", + password="onetl", +) +``` + +For a description of other parameters, see the documentation for the {ref}`available FileConnections `. + +### FileDFConnection + +Classes inherited from `FileDFConnection` could be used for accessing files as Spark DataFrames. + +A `FileDFConnection` could be instantiated as follows: + +```python +from onetl.connection import SparkHDFS + +spark_hdfs = SparkHDFS( + host="namenode1.domain.com", + cluster="mycluster", + spark=spark, +) +``` + +where **spark** is the current SparkSession. +`onETL` uses `Spark` and specific Java connectors under the hood to work with DataFrames. + +For a description of other parameters, see the documentation for the {ref}`available FileDFConnections `. + +### Checking connection availability + +Once you have created a connection, you can check the database/filesystem availability using the method `check()`: + +```python +mssql.check() +sftp.check() +spark_hdfs.check() +``` + +It will raise an exception if database/filesystem cannot be accessed. + +This method returns connection itself, so you can create connection and immediately check its availability: + +```Python +mssql = MSSQL( + host="mssqldb.demo.com", + user="onetl", + password="onetl", + database="Telecom", + spark=spark, +).check() # <-- +``` + +## Extract/Load data + +### Basics + +As we said above, onETL is used to extract data from and load data into remote systems. + +onETL provides several classes for this: + +> - {ref}`DBReader ` +> - {ref}`DBWriter ` +> - {ref}`FileDFReader ` +> - {ref}`FileDFWriter ` +> - {ref}`FileDownloader ` +> - {ref}`FileUploader ` +> - {ref}`FileMover ` + +All of these classes have a method `run()` that starts extracting/loading the data: + +```python +from onetl.db import DBReader, DBWriter + +reader = DBReader( + connection=mssql, + source="dbo.demo_table", + columns=["column_1", "column_2"], +) + +# Read data as Spark DataFrame +df = reader.run() + +db_writer = DBWriter( + connection=hive, + target="dl_sb.demo_table", +) + +# Save Spark DataFrame to Hive table +writer.run(df) +``` + +### Extract data + +To extract data you can use classes: + +| | Use case | Connection | `run()` gets | `run()` returns | +| ------------------------------------ | ----------------------------------------- | ------------------------------------------------- | ------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------- | +| {ref}`DBReader ` | Reading data from a database | Any {ref}`DBConnection ` | - | [Spark DataFrame](https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.sql.DataFrame.html#pyspark.sql.DataFrame) | +| {ref}`FileDFReader ` | Read data from a file or set of files | Any {ref}`FileDFConnection ` | No input, or List[File path on FileSystem] | [Spark DataFrame](https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.sql.DataFrame.html#pyspark.sql.DataFrame) | +| {ref}`FileDownloader ` | Download files from remote FS to local FS | Any {ref}`FileConnection ` | No input, or List[File path on remote FileSystem] | {ref}`DownloadResult ` | + +### Load data + +To load data you can use classes: + +| | Use case | Connection | `run()` gets | `run()` returns | +| ----------------------------------- | -------------------------------------------- | ------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------ | +| {ref}`DBWriter ` | Writing data from a DataFrame to a database | Any {ref}`DBConnection ` | [Spark DataFrame](https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.sql.DataFrame.html#pyspark.sql.DataFrame) | None | +| {ref}`FileDFWriter ` | Writing data from a DataFrame to a folder | Any {ref}`FileDFConnection ` | [Spark DataFrame](https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.sql.DataFrame.html#pyspark.sql.DataFrame) | None | +| {ref}`FileUploader ` | Uploading files from a local FS to remote FS | Any {ref}`FileConnection ` | List[File path on local FileSystem] | {ref}`UploadResult ` | + +### Manipulate data + +To manipulate data you can use classes: + +| | Use case | Connection | `run()` gets | `run()` returns | +| ----------------------------- | ------------------------------------------- | -------------------------------------------- | ------------------------------------ | ------------------------------------- | +| {ref}`FileMover ` | Move files between directories in remote FS | Any {ref}`FileConnection ` | List[File path on remote FileSystem] | {ref}`MoveResult ` | + +### Options + +Extract and load classes have a `options` parameter, which has a special meaning: + +> - all other parameters - *WHAT* we extract / *WHERE* we load to +> - `options` parameter - *HOW* we extract/load data + +```python +db_reader = DBReader( + # WHAT do we read: + connection=mssql, + source="dbo.demo_table", # some table from MSSQL + columns=["column_1", "column_2"], # but only specific set of columns + where="column_2 > 1000", # only rows matching the clause + # HOW do we read: + options=MSSQL.ReadOptions( + numPartitions=10, # read in 10 parallel jobs + partitionColumn="id", # balance data read by assigning each job a part of data using `hash(id) mod N` expression + partitioningMode="hash", + fetchsize=1000, # each job will fetch block of 1000 rows each on every read attempt + ), +) + +db_writer = DBWriter( + # WHERE do we write to - to some table in Hive + connection=hive, + target="dl_sb.demo_table", + # HOW do we write - overwrite all the data in the existing table + options=Hive.WriteOptions(if_exists="replace_entire_table"), +) + +file_downloader = FileDownloader( + # WHAT do we download - files from some dir in SFTP + connection=sftp, + source_path="/source", + filters=[Glob("*.csv")], # only CSV files + limits=[MaxFilesCount(1000)], # 1000 files max + # WHERE do we download to - a specific dir on local FS + local_path="/some", + # HOW do we download: + options=FileDownloader.Options( + delete_source=True, # after downloading each file remove it from source_path + if_exists="replace_file", # replace existing files in the local_path + ), +) + +file_uploader = FileUploader( + # WHAT do we upload - files from some local dir + local_path="/source", + # WHERE do we upload to- specific remote dir in HDFS + connection=hdfs, + target_path="/some", + # HOW do we upload: + options=FileUploader.Options( + delete_local=True, # after uploading each file remove it from local_path + if_exists="replace_file", # replace existing files in the target_path + ), +) + +file_mover = FileMover( + # WHAT do we move - files in some remote dir in HDFS + source_path="/source", + connection=hdfs, + # WHERE do we move files to + target_path="/some", # a specific remote dir within the same HDFS connection + # HOW do we load - replace existing files in the target_path + options=FileMover.Options(if_exists="replace_file"), +) + +file_df_reader = FileDFReader( + # WHAT do we read - *.csv files from some dir in S3 + connection=s3, + source_path="/source", + file_format=CSV(), + # HOW do we read - load files from /source/*.csv, not from /source/nested/*.csv + options=FileDFReader.Options(recursive=False), +) + +file_df_writer = FileDFWriter( + # WHERE do we write to - as .csv files in some dir in S3 + connection=s3, + target_path="/target", + file_format=CSV(), + # HOW do we write - replace all existing files in /target, if exists + options=FileDFWriter.Options(if_exists="replace_entire_directory"), +) +``` + +More information about `options` could be found on {ref}`DB connection `. and +{ref}`file-downloader` / {ref}`file-uploader` / {ref}`file-mover` / {ref}`file-df-reader` / {ref}`file-df-writer` documentation + +### Read Strategies + +onETL have several builtin strategies for reading data: + +1. [Snapshot strategy](strategy/snapshot_strategy.html) (default strategy) +2. [Incremental strategy](strategy/incremental_strategy.html) +3. [Snapshot batch strategy](strategy/snapshot_batch_strategy.html) +4. [Incremental batch strategy](strategy/incremental_batch_strategy.html) + +For example, an incremental strategy allows you to get only new data from the table: + +```python +from onetl.strategy import IncrementalStrategy + +reader = DBReader( + connection=mssql, + source="dbo.demo_table", + hwm_column="id", # detect new data based on value of "id" column +) + +# first run +with IncrementalStrategy(): + df = reader.run() + +sleep(3600) + +# second run +with IncrementalStrategy(): + # only rows, that appeared in the source since previous run + df = reader.run() +``` + +or get only files which were not downloaded before: + +```python +from onetl.strategy import IncrementalStrategy + +file_downloader = FileDownloader( + connection=sftp, + source_path="/remote", + local_path="/local", + hwm_type="file_list", # save all downloaded files to a list, and exclude files already present in this list +) + +# first run +with IncrementalStrategy(): + files = file_downloader.run() + +sleep(3600) + +# second run +with IncrementalStrategy(): + # only files, that appeared in the source since previous run + files = file_downloader.run() +``` + +Most of strategies are based on {ref}`hwm`, Please check each strategy documentation for more details + +### Why just not use Connection class for extract/load? + +Connections are very simple, they have only a set of some basic operations, +like `mkdir`, `remove_file`, `get_table_schema`, and so on. + +High-level operations, like +: - {ref}`strategy` support + - Handling metadata push/pull + - Handling different options, like `if_exists="replace_file"` in case of file download/upload + +is moved to a separate class which calls the connection object methods to perform some complex logic. diff --git a/mddocs/docs/conf.py b/mddocs/docs/conf.py new file mode 100644 index 000000000..018e6e870 --- /dev/null +++ b/mddocs/docs/conf.py @@ -0,0 +1,169 @@ +# Configuration file for the Sphinx documentation builder. +# +# This file only contains a selection of the most common options. For a full +# list see the documentation: +# https://www.sphinx-doc.org/en/master/usage/configuration.html + +# -- Path setup -------------------------------------------------------------- + +# If extensions (or modules to document with autodoc) are in another directory, +# add these directories to sys.path here. If the directory is relative to the +# documentation root, use os.path.abspath to make it absolute, like shown here. + + +import os +import subprocess +import sys +from pathlib import Path + +from packaging.version import Version + +PROJECT_ROOT_DIR = Path(__file__).parent.parent.resolve() + +sys.path.insert(0, os.fspath(PROJECT_ROOT_DIR)) + +# -- Project information ----------------------------------------------------- + +project = "onETL" +copyright = "2021-2025 MTS PJSC" +author = "MWS Data Bridge" + +# The version info for the project you're documenting, acts as replacement for +# |version| and |release|, also used in various other places throughout the +# built documents. +# +# The short X.Y version. + +ver = Version(subprocess.check_output("python ../setup.py --version", shell=True, text=True).strip()) +version = ver.base_version +# The full version, including alpha/beta/rc tags. +release = ver.public + +# -- General configuration --------------------------------------------------- + +# Add any Sphinx extension module names here, as strings. They can be +# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom +# ones. +extensions = [ + "numpydoc", + "sphinx_design", + "sphinx_substitution_extensions", + "sphinx_tabs.tabs", + "sphinx_toolbox.more_autodoc.autoprotocol", + "sphinx_toolbox.github", + "sphinx_copybutton", + "sphinx.ext.autodoc", + "sphinx.ext.autosummary", + "sphinxcontrib.towncrier", # provides `towncrier-draft-entries` directive + "sphinxcontrib.plantuml", + "sphinx.ext.extlinks", + "sphinx_favicon", + "sphinxcontrib.autodoc_pydantic", + "sphinx_last_updated_by_git", + "myst_parser", +] + + +myst_enable_extensions = [ + "amsmath", + "attrs_inline", + "colon_fence", + "deflist", + "dollarmath", + "fieldlist", + "html_admonition", + "html_image", + "linkify", + "replacements", + "smartquotes", + "strikethrough", + "substitution", + "tasklist", +] + + +numpydoc_show_class_members = False +autodoc_pydantic_model_show_config = False +autodoc_pydantic_model_show_config_summary = False +autodoc_pydantic_model_show_config_member = False +autodoc_pydantic_model_show_json = False +autodoc_pydantic_model_show_validator_summary = False +autodoc_pydantic_model_show_validator_members = False +autodoc_pydantic_field_list_validators = False +sphinx_tabs_disable_tab_closing = True + +# prevent >>>, ... and doctest outputs from copying +copybutton_prompt_text = r">>> |\.\.\. |\$ |In \[\d*\]: | {2,5}\.\.\.: | {5,8}: " +copybutton_prompt_is_regexp = True +copybutton_copy_empty_lines = False +copybutton_only_copy_prompt_lines = True + +towncrier_draft_autoversion_mode = "draft" +towncrier_draft_include_empty = False +towncrier_draft_working_directory = PROJECT_ROOT_DIR + +github_username = "MobileTeleSystems" +github_repository = "onetl" + +rst_prolog = f""" +.. |support_hooks| image:: https://img.shields.io/badge/%20-support%20hooks-blue + :target: https://onetl.readthedocs.io/en/{ver}/hooks/index.html +""" + +# Add any paths that contain templates here, relative to this directory. +templates_path = ["_templates"] + +# List of patterns, relative to source directory, that match files and +# directories to ignore when looking for source files. +# This pattern also affects html_static_path and html_extra_path. +exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"] + + +# -- Options for HTML output ------------------------------------------------- + +# The theme to use for HTML and HTML Help pages. See the documentation for +# a list of builtin themes. + +html_theme = "furo" + +# Theme options are theme-specific and customize the look and feel of a theme +# further. For a list of options available for each theme, see the +# documentation. +html_theme_options = {} + +# Add any paths that contain custom static files (such as style sheets) here, +# relative to this directory. They are copied after the builtin static files, +# so a file named "default.css" will overwrite the builtin "default.css". +html_static_path = ["_static"] +html_extra_path = ["robots.txt"] +html_logo = "./_static/logo.svg" +favicons = [ + {"rel": "icon", "href": "icon.svg", "type": "image/svg+xml"}, +] + +# The master toctree document. +master_doc = "index" + +# The language for content autogenerated by Sphinx. Refer to documentation +# for a list of supported languages. +# +# This is also used if you do content translation via gettext catalogs. +# Usually you set "language" from the command line for these cases. +language = "en" + +# The name of the Pygments (syntax highlighting) style to use. +pygments_style = "sphinx" + +# Create an alias for etl-entities lib in onetl documentation +extlinks = { + "etl-entities": ("https://etl-entities.readthedocs.io/en/stable/%s", None), +} + + +# If true, `todo` and `todoList` produce output, else they produce nothing. +todo_include_todos = False + +# -- Options for HTMLHelp output ------------------------------------------ + +# Output file base name for HTML help builder. +htmlhelp_basename = "onetl-doc" diff --git a/mddocs/docs/connection/db_connection/clickhouse/connection.md b/mddocs/docs/connection/db_connection/clickhouse/connection.md new file mode 100644 index 000000000..06fa5a462 --- /dev/null +++ b/mddocs/docs/connection/db_connection/clickhouse/connection.md @@ -0,0 +1,12 @@ +(clickhouse-connection)= + +# Clickhouse connection + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.clickhouse.connection +``` + +```{eval-rst} +.. autoclass:: Clickhouse + :members: get_packages, check +``` diff --git a/mddocs/docs/connection/db_connection/clickhouse/execute.md b/mddocs/docs/connection/db_connection/clickhouse/execute.md new file mode 100644 index 000000000..501fed530 --- /dev/null +++ b/mddocs/docs/connection/db_connection/clickhouse/execute.md @@ -0,0 +1,125 @@ +(clickhouse-execute)= + +# Executing statements in Clickhouse + +```{eval-rst} +.. warning:: + + Methods below **read all the rows** returned from DB **to Spark driver memory**, and then convert them to DataFrame. + + Do **NOT** use them to read large amounts of data. Use :ref:`DBReader ` or :ref:`Clickhouse.sql ` instead. +``` + +## How to + +There are 2 ways to execute some statement in Clickhouse + +### Use `Clickhouse.fetch` + +Use this method to perform some `SELECT` query which returns **small number or rows**, like reading +Clickhouse config, or reading data from some reference table. Method returns Spark DataFrame. + +Method accepts {obj}`Clickhouse.FetchOptions `. + +Connection opened using this method should be then closed with `connection.close()` or `with connection:`. + +```{eval-rst} +.. warning:: + + Please take into account :ref:`clickhouse-types`. +``` + +#### Syntax support + +This method supports **any** query syntax supported by Clickhouse, like: + +- ✅︎ `SELECT ... FROM ...` +- ✅︎ `WITH alias AS (...) SELECT ...` +- ✅︎ `SELECT func(arg1, arg2)` - call function +- ✅︎ `SHOW ...` +- ❌ `SET ...; SELECT ...;` - multiple statements not supported + +#### Examples + +```python +from onetl.connection import Clickhouse + +clickhouse = Clickhouse(...) + +df = clickhouse.fetch( + "SELECT value FROM some.reference_table WHERE key = 'some_constant'", + options=Clickhouse.FetchOptions(queryTimeout=10), +) +clickhouse.close() +value = df.collect()[0][0] # get value from first row and first column +``` + +### Use `Clickhouse.execute` + +Use this method to execute DDL and DML operations. Each method call runs operation in a separated transaction, and then commits it. + +Method accepts {obj}`Clickhouse.ExecuteOptions `. + +Connection opened using this method should be then closed with `connection.close()` or `with connection:`. + +#### Syntax support + +This method supports **any** query syntax supported by Clickhouse, like: + +- ✅︎ `CREATE TABLE ...`, `CREATE VIEW ...`, and so on +- ✅︎ `ALTER ...` +- ✅︎ `INSERT INTO ... SELECT ...`, `UPDATE ...`, `DELETE ...`, and so on +- ✅︎ `DROP TABLE ...`, `DROP VIEW ...`, `TRUNCATE TABLE`, and so on +- ✅︎ other statements not mentioned here +- ❌ `SET ...; SELECT ...;` - multiple statements not supported + +#### Examples + +```python +from onetl.connection import Clickhouse + +clickhouse = Clickhouse(...) + +clickhouse.execute("DROP TABLE schema.table") +clickhouse.execute( + """ + CREATE TABLE schema.table ( + id UInt8, + key String, + value Float32 + ) + ENGINE = MergeTree() + ORDER BY id + """, + options=Clickhouse.ExecuteOptions(queryTimeout=10), +) +``` + +## Notes + +These methods **read all the rows** returned from DB **to Spark driver memory**, and then convert them to DataFrame. + +So it should **NOT** be used to read large amounts of data. Use {ref}`DBReader ` or {ref}`Clickhouse.sql ` instead. + +## Options + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.clickhouse.options +``` + +```{eval-rst} +.. autopydantic_model:: ClickhouseFetchOptions + :inherited-members: GenericOptions + :member-order: bysource + :model-show-field-summary: false + :field-show-constraints: false + +``` + +```{eval-rst} +.. autopydantic_model:: ClickhouseExecuteOptions + :inherited-members: GenericOptions + :member-order: bysource + :model-show-field-summary: false + :field-show-constraints: false +``` diff --git a/mddocs/docs/connection/db_connection/clickhouse/index.md b/mddocs/docs/connection/db_connection/clickhouse/index.md new file mode 100644 index 000000000..28c20dec1 --- /dev/null +++ b/mddocs/docs/connection/db_connection/clickhouse/index.md @@ -0,0 +1,28 @@ +(clickhouse)= + +# Clickhouse + +```{toctree} +:caption: Connection +:maxdepth: 1 + +prerequisites +connection +``` + +```{toctree} +:caption: Operations +:maxdepth: 1 + +read +sql +write +execute +``` + +```{toctree} +:caption: Troubleshooting +:maxdepth: 1 + +types +``` diff --git a/mddocs/docs/connection/db_connection/clickhouse/prerequisites.md b/mddocs/docs/connection/db_connection/clickhouse/prerequisites.md new file mode 100644 index 000000000..563dbad3f --- /dev/null +++ b/mddocs/docs/connection/db_connection/clickhouse/prerequisites.md @@ -0,0 +1,73 @@ +(clickhouse-prerequisites)= + +# Prerequisites + +## Version Compatibility + +- Clickhouse server versions: + : - Officially declared: 22.8 or higher + - Actually tested: 21.1, 25.1 +- Spark versions: 2.3.x - 3.5.x +- Java versions: 8 - 20 + +See [official documentation](https://clickhouse.com/docs/en/integrations/java#jdbc-driver). + +## Installing PySpark + +To use Clickhouse connector you should have PySpark installed (or injected to `sys.path`) +BEFORE creating the connector instance. + +See {ref}`install-spark` installation instruction for more details. + +## Connecting to Clickhouse + +### Connection port + +Connector can only use **HTTP** (usually `8123` port) or **HTTPS** (usually `8443` port) protocol. + +TCP and GRPC protocols are NOT supported. + +### Connecting to cluster + +It is possible to connect to Clickhouse cluster, and use it's load balancing capabilities to read or write data in parallel. +Each Spark executor can connect to random Clickhouse nodes, instead of sending all the data to a node specified in connection params. + +This requires all Clickhouse servers to run on different hosts, and **listen the same HTTP port**. +Set `auto_discovery=True` to enable this feature (disabled by default): + +```python +Clickhouse( + host="node1.of.cluster", + port=8123, + extra={ + "auto_discovery": True, + "load_balancing_policy": "roundRobin", + }, +) +``` + +See [official documentation](https://clickhouse.com/docs/en/integrations/java#configuring-node-discovery-load-balancing-and-failover). + +### Required grants + +Ask your Clickhouse cluster administrator to set following grants for a user, +used for creating a connection: + +```{eval-rst} +.. tabs:: + + .. code-tab:: sql Read + Write + + -- allow creating tables in the target schema + GRANT CREATE TABLE ON myschema.* TO username; + + -- allow read & write access to specific table + GRANT SELECT, INSERT ON myschema.mytable TO username; + + .. code-tab:: sql Read only + + -- allow read access to specific table + GRANT SELECT ON myschema.mytable TO username; +``` + +More details can be found in [official documentation](https://clickhouse.com/docs/en/sql-reference/statements/grant). diff --git a/mddocs/docs/connection/db_connection/clickhouse/read.md b/mddocs/docs/connection/db_connection/clickhouse/read.md new file mode 100644 index 000000000..2c62132ed --- /dev/null +++ b/mddocs/docs/connection/db_connection/clickhouse/read.md @@ -0,0 +1,93 @@ +(clickhouse-read)= + +# Reading from Clickhouse using `DBReader` + +{obj}`DBReader ` supports {ref}`strategy` for incremental data reading, +but does not support custom queries, like `JOIN`. + +```{eval-rst} +.. warning:: + + Please take into account :ref:`clickhouse-types` +``` + +## Supported DBReader features + +- ✅︎ `columns` +- ✅︎ `where` +- ✅︎ `hwm`, supported strategies: +- - ✅︎ {ref}`snapshot-strategy` +- - ✅︎ {ref}`incremental-strategy` +- - ✅︎ {ref}`snapshot-batch-strategy` +- - ✅︎ {ref}`incremental-batch-strategy` +- ❌ `hint` (is not supported by Clickhouse) +- ❌ `df_schema` +- ✅︎ `options` (see {obj}`Clickhouse.ReadOptions `) + +## Examples + +Snapshot strategy: + +```python +from onetl.connection import Clickhouse +from onetl.db import DBReader + +clickhouse = Clickhouse(...) + +reader = DBReader( + connection=clickhouse, + source="schema.table", + columns=["id", "key", "CAST(value AS String) value", "updated_dt"], + where="key = 'something'", + options=Clickhouse.ReadOptions(partitionColumn="id", numPartitions=10), +) +df = reader.run() +``` + +Incremental strategy: + +```python +from onetl.connection import Clickhouse +from onetl.db import DBReader +from onetl.strategy import IncrementalStrategy + +clickhouse = Clickhouse(...) + +reader = DBReader( + connection=clickhouse, + source="schema.table", + columns=["id", "key", "CAST(value AS String) value", "updated_dt"], + where="key = 'something'", + hwm=DBReader.AutoDetectHWM(name="clickhouse_hwm", expression="updated_dt"), + options=Clickhouse.ReadOptions(partitionColumn="id", numPartitions=10), +) + +with IncrementalStrategy(): + df = reader.run() +``` + +## Recommendations + +### Select only required columns + +Instead of passing `"*"` in `DBReader(columns=[...])` prefer passing exact column names. This reduces the amount of data passed from Clickhouse to Spark. + +### Pay attention to `where` value + +Instead of filtering data on Spark side using `df.filter(df.column == 'value')` pass proper `DBReader(where="column = 'value'")` clause. +This both reduces the amount of data send from Clickhouse to Spark, and may also improve performance of the query. +Especially if there are indexes or partitions for columns used in `where` clause. + +## Options + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.clickhouse.options +``` + +```{eval-rst} +.. autopydantic_model:: ClickhouseReadOptions + :inherited-members: GenericOptions + :member-order: bysource + :model-show-field-summary: false + :field-show-constraints: false +``` diff --git a/mddocs/docs/connection/db_connection/clickhouse/sql.md b/mddocs/docs/connection/db_connection/clickhouse/sql.md new file mode 100644 index 000000000..bbe62adaa --- /dev/null +++ b/mddocs/docs/connection/db_connection/clickhouse/sql.md @@ -0,0 +1,80 @@ +(clickhouse-sql)= + +# Reading from Clickhouse using `Clickhouse.sql` + +`Clickhouse.sql` allows passing custom SQL query, but does not support incremental strategies. + +```{eval-rst} +.. warning:: + + Please take into account :ref:`clickhouse-types` +``` + +```{eval-rst} +.. warning:: + + Statement is executed in **read-write** connection, so if you're calling some functions/procedures with DDL/DML statements inside, + they can change data in your database. +``` + +## Syntax support + +Only queries with the following syntax are supported: + +- ✅︎ `SELECT ... FROM ...` +- ✅︎ `WITH alias AS (...) SELECT ...` +- ❌ `SET ...; SELECT ...;` - multiple statements not supported + +## Examples + +```python +from onetl.connection import Clickhouse + +clickhouse = Clickhouse(...) +df = clickhouse.sql( + """ + SELECT + id, + key, + CAST(value AS String) value, + updated_at + FROM + some.mytable + WHERE + key = 'something' + """, + options=Clickhouse.SQLOptions( + partitionColumn="id", + numPartitions=10, + lowerBound=0, + upperBound=1000, + ), +) +``` + +## Recommendations + +### Select only required columns + +Instead of passing `SELECT * FROM ...` prefer passing exact column names `SELECT col1, col2, ...`. +This reduces the amount of data passed from Clickhouse to Spark. + +### Pay attention to `where` value + +Instead of filtering data on Spark side using `df.filter(df.column == 'value')` pass proper `WHERE column = 'value'` clause. +This both reduces the amount of data send from Clickhouse to Spark, and may also improve performance of the query. +Especially if there are indexes or partitions for columns used in `where` clause. + +## Options + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.clickhouse.options +``` + +```{eval-rst} +.. autopydantic_model:: ClickhouseSQLOptions + :inherited-members: GenericOptions + :member-order: bysource + :model-show-field-summary: false + :field-show-constraints: false +``` diff --git a/mddocs/docs/connection/db_connection/clickhouse/types.md b/mddocs/docs/connection/db_connection/clickhouse/types.md new file mode 100644 index 000000000..f288bfb21 --- /dev/null +++ b/mddocs/docs/connection/db_connection/clickhouse/types.md @@ -0,0 +1,457 @@ +(clickhouse-types)= + +# Clickhouse \<-> Spark type mapping + +```{eval-rst} +.. note:: + + The results below are valid for Spark 3.5.5, and may differ on other Spark versions. +``` + +```{eval-rst} +.. note:: + + It is recommended to use `spark-dialect-extension `_ package, + which implements writing Arrays from Spark to Clickhouse, fixes dropping fractions of seconds in ``TimestampType``, + and fixes other type conversion issues. +``` + +## Type detection & casting + +Spark's DataFrames always have a `schema` which is a list of columns with corresponding Spark types. All operations on a column are performed using column type. + +### Reading from Clickhouse + +This is how Clickhouse connector performs this: + +- For each column in query result (`SELECT column1, column2, ... FROM table ...`) get column name and Clickhouse type. +- Find corresponding `Clickhouse type (read)` → `Spark type` combination (see below) for each DataFrame column. If no combination is found, raise exception. +- Create DataFrame from query with specific column names and Spark types. + +### Writing to some existing Clickhouse table + +This is how Clickhouse connector performs this: + +- Get names of columns in DataFrame. [^footnote-1] +- Perform `SELECT * FROM table LIMIT 0` query. +- Take only columns present in DataFrame (by name, case insensitive). For each found column get Clickhouse type. +- **Find corresponding** `Clickhouse type (read)` → `Spark type` **combination** (see below) for each DataFrame column. If no combination is found, raise exception. [^footnote-2] +- Find corresponding `Spark type` → `Clickhousetype (write)` combination (see below) for each DataFrame column. If no combination is found, raise exception. +- If `Clickhousetype (write)` match `Clickhouse type (read)`, no additional casts will be performed, DataFrame column will be written to Clickhouse as is. +- If `Clickhousetype (write)` does not match `Clickhouse type (read)`, DataFrame column will be casted to target column type **on Clickhouse side**. For example, you can write column with text data to `Int32` column, if column contains valid integer values within supported value range and precision. + +[^footnote-1]: This allows to write data to tables with `DEFAULT` columns - if DataFrame has no such column, + it will be populated by Clickhouse. + +[^footnote-2]: Yes, this is weird. + +### Create new table using Spark + +```{eval-rst} +.. warning:: + + ABSOLUTELY NOT RECOMMENDED! +``` + +This is how Clickhouse connector performs this: + +- Find corresponding `Spark type` → `Clickhouse type (create)` combination (see below) for each DataFrame column. If no combination is found, raise exception. +- Generate DDL for creating table in Clickhouse, like `CREATE TABLE (col1 ...)`, and run it. +- Write DataFrame to created table as is. + +But Spark does not have specific dialect for Clickhouse, so Generic JDBC dialect is used. +Generic dialect is using SQL ANSI type names while creating tables in target database, not database-specific types. + +If some cases this may lead to using wrong column type. For example, Spark creates column of type `TIMESTAMP` +which corresponds to Clickhouse type `DateTime32` (precision up to seconds) +instead of more precise `DateTime64` (precision up to nanoseconds). +This may lead to incidental precision loss, or sometimes data cannot be written to created table at all. + +So instead of relying on Spark to create tables: + +```{eval-rst} +.. dropdown:: See example + + .. code:: python + + writer = DBWriter( + connection=clickhouse, + target="default.target_tbl", + options=Clickhouse.WriteOptions( + if_exists="append", + # ENGINE is required by Clickhouse + createTableOptions="ENGINE = MergeTree() ORDER BY id", + ), + ) + writer.run(df) +``` + +Always prefer creating tables with specific types **BEFORE WRITING DATA**: + +```{eval-rst} +.. dropdown:: See example + + .. code:: python + + clickhouse.execute( + """ + CREATE TABLE default.target_tbl ( + id UInt8, + value DateTime64(6) -- specific type and precision + ) + ENGINE = MergeTree() + ORDER BY id + """, + ) + + writer = DBWriter( + connection=clickhouse, + target="default.target_tbl", + options=Clickhouse.WriteOptions(if_exists="append"), + ) + writer.run(df) +``` + +### References + +Here you can find source code with type conversions: + +- [Clickhouse -> JDBC](https://github.com/ClickHouse/clickhouse-java/blob/0.3.2/clickhouse-jdbc/src/main/java/com/clickhouse/jdbc/JdbcTypeMapping.java#L39-L176) +- [JDBC -> Spark](https://github.com/apache/spark/blob/v3.5.5/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JdbcUtils.scala#L307) +- [Spark -> JDBC](https://github.com/apache/spark/blob/v3.5.5/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JdbcUtils.scala#L141-L164) +- [JDBC -> Clickhouse](https://github.com/ClickHouse/clickhouse-java/blob/0.3.2/clickhouse-jdbc/src/main/java/com/clickhouse/jdbc/JdbcTypeMapping.java#L185-L311) + +## Supported types + +See [official documentation](https://clickhouse.com/docs/en/sql-reference/data-types) + +### Generic types + +- `LowCardinality(T)` is same as `T` +- `Nullable(T)` is same as `T`, but Spark column is inferred as `nullable=True` + +### Numeric types + +```{eval-rst} ++--------------------------------+-----------------------------------+-------------------------------+-------------------------------+ +| Clickhouse type (read) | Spark type | Clickhouse type (write) | Clickhouse type (create) | ++================================+===================================+===============================+===============================+ +| ``Bool`` | ``BooleanType()`` | ``Bool`` | ``UInt64`` | ++--------------------------------+-----------------------------------+-------------------------------+-------------------------------+ +| ``Decimal`` | ``DecimalType(P=10, S=0)`` | ``Decimal(P=10, S=0)`` | ``Decimal(P=10, S=0)`` | ++--------------------------------+-----------------------------------+-------------------------------+-------------------------------+ +| ``Decimal(P=0..38)`` | ``DecimalType(P=0..38, S=0)`` | ``Decimal(P=0..38, S=0)`` | ``Decimal(P=0..38, S=0)`` | ++--------------------------------+-----------------------------------+-------------------------------+-------------------------------+ +| ``Decimal(P=0..38, S=0..38)`` | ``DecimalType(P=0..38, S=0..38)`` | ``Decimal(P=0..38, S=0..38)`` | ``Decimal(P=0..38, S=0..38)`` | ++--------------------------------+-----------------------------------+-------------------------------+-------------------------------+ +| ``Decimal(P=39..76, S=0..76)`` | unsupported [3]_ | | | ++--------------------------------+-----------------------------------+-------------------------------+-------------------------------+ +| ``Decimal32(P=0..9)`` | ``DecimalType(P=9, S=0..9)`` | ``Decimal(P=9, S=0..9)`` | ``Decimal(P=9, S=0..9)`` | ++--------------------------------+-----------------------------------+-------------------------------+-------------------------------+ +| ``Decimal64(S=0..18)`` | ``DecimalType(P=18, S=0..18)`` | ``Decimal(P=18, S=0..18)`` | ``Decimal(P=18, S=0..18)`` | ++--------------------------------+-----------------------------------+-------------------------------+-------------------------------+ +| ``Decimal128(S=0..38)`` | ``DecimalType(P=38, S=0..38)`` | ``Decimal(P=38, S=0..38)`` | ``Decimal(P=38, S=0..38)`` | ++--------------------------------+-----------------------------------+-------------------------------+-------------------------------+ +| ``Decimal256(S=0..76)`` | unsupported [3]_ | | | ++--------------------------------+-----------------------------------+-------------------------------+-------------------------------+ +| ``Float32`` | ``FloatType()`` | ``Float32`` | ``Float32`` | ++--------------------------------+-----------------------------------+-------------------------------+-------------------------------+ +| ``Float64`` | ``DoubleType()`` | ``Float64`` | ``Float64`` | ++--------------------------------+-----------------------------------+-------------------------------+-------------------------------+ +| ``Int8`` | ``IntegerType()`` | ``Int32`` | ``Int32`` | ++--------------------------------+ | | | +| ``Int16`` | | | | ++--------------------------------+ | | | +| ``Int32`` | | | | ++--------------------------------+-----------------------------------+-------------------------------+-------------------------------+ +| ``Int64`` | ``LongType()`` | ``Int64`` | ``Int64`` | ++--------------------------------+-----------------------------------+-------------------------------+-------------------------------+ +| ``Int128`` | unsupported [3]_ | | | ++--------------------------------+ | | | +| ``Int256`` | | | | ++--------------------------------+-----------------------------------+-------------------------------+-------------------------------+ +| ``-`` | ``ByteType()`` | ``Int8`` | ``Int8`` | ++--------------------------------+-----------------------------------+-------------------------------+-------------------------------+ +| ``-`` | ``ShortType()`` | ``Int32`` | ``Int32`` | ++--------------------------------+-----------------------------------+-------------------------------+-------------------------------+ +| ``UInt8`` | ``IntegerType()`` | ``Int32`` | ``Int32`` | ++--------------------------------+-----------------------------------+-------------------------------+-------------------------------+ +| ``UInt16`` | ``LongType()`` | ``Int64`` | ``Int64`` | ++--------------------------------+-----------------------------------+-------------------------------+-------------------------------+ +| ``UInt32`` | ``DecimalType(20,0)`` | ``Decimal(20,0)`` | ``Decimal(20,0)`` | ++--------------------------------+ | | | +| ``UInt64`` | | | | ++--------------------------------+-----------------------------------+-------------------------------+-------------------------------+ +| ``UInt128`` | unsupported [3]_ | | | ++--------------------------------+ | | | +| ``UInt256`` | | | | ++--------------------------------+-----------------------------------+-------------------------------+-------------------------------+ +``` + +[^footnote-3]: Clickhouse support numeric types up to 256 bit - `Int256`, `UInt256`, `Decimal256(S)`, `Decimal(P=39..76, S=0..76)`. + + But Spark's `DecimalType(P, S)` supports maximum `P=38` (128 bit). It is impossible to read, write or operate with values of larger precision, + this leads to an exception. + +### Temporal types + +Notes: +: - Datetime with timezone has the same precision as without timezone + - `DateTime` is alias for `DateTime32` + - `TIMESTAMP` is alias for `DateTime32`, but `TIMESTAMP(N)` is alias for `DateTime64(N)` + +```{eval-rst} ++-----------------------------------+--------------------------------------+----------------------------------+-------------------------------+ +| Clickhouse type (read) | Spark type | Clickhouse type (write) | Clickhouse type (create) | ++===================================+======================================+==================================+===============================+ +| ``Date`` | ``DateType()`` | ``Date`` | ``Date`` | ++-----------------------------------+--------------------------------------+----------------------------------+-------------------------------+ +| ``Date32`` | ``DateType()`` | ``Date`` | ``Date``, | +| | | | **cannot insert data** [4]_ | ++-----------------------------------+--------------------------------------+----------------------------------+-------------------------------+ +| ``DateTime32``, seconds | ``TimestampType()``, microseconds | ``DateTime64(6)``, microseconds | ``DateTime32``, seconds | ++-----------------------------------+--------------------------------------+----------------------------------+-------------------------------+ +| ``DateTime64(3)``, milliseconds | ``TimestampType()``, microseconds | ``DateTime64(6)``, microseconds | ``DateTime32``, seconds, | +| | | | **precision loss** [5]_ | ++-----------------------------------+--------------------------------------+----------------------------------+-------------------------------+ +| ``DateTime64(6)``, microseconds | ``TimestampType()``, microseconds | | ``DateTime32``, seconds, | ++-----------------------------------+--------------------------------------+ | **precision loss** [7]_ | +| ``DateTime64(7..9)``, nanoseconds | ``TimestampType()``, microseconds, | | | +| | **precision loss** [6]_ | | | +| | | | | ++-----------------------------------+--------------------------------------+ | | +| ``-`` | ``TimestampNTZType()``, microseconds | | | ++-----------------------------------+--------------------------------------+----------------------------------+-------------------------------+ +| ``DateTime32(TZ)`` | unsupported [7]_ | | | ++-----------------------------------+ | | | +| ``DateTime64(P, TZ)`` | | | | ++-----------------------------------+--------------------------------------+----------------------------------+-------------------------------+ +| ``IntervalNanosecond`` | ``LongType()`` | ``Int64`` | ``Int64`` | ++-----------------------------------+ | | | +| ``IntervalMicrosecond`` | | | | ++-----------------------------------+ | | | +| ``IntervalMillisecond`` | | | | ++-----------------------------------+ | | | +| ``IntervalSecond`` | | | | ++-----------------------------------+ | | | +| ``IntervalMinute`` | | | | ++-----------------------------------+ | | | +| ``IntervalHour`` | | | | ++-----------------------------------+ | | | +| ``IntervalDay`` | | | | ++-----------------------------------+ | | | +| ``IntervalMonth`` | | | | ++-----------------------------------+ | | | +| ``IntervalQuarter`` | | | | ++-----------------------------------+ | | | +| ``IntervalWeek`` | | | | ++-----------------------------------+ | | | +| ``IntervalYear`` | | | | ++-----------------------------------+--------------------------------------+----------------------------------+-------------------------------+ +``` + +```{eval-rst} +.. warning:: + + Note that types in Clickhouse and Spark have different value ranges: + + +------------------------+-----------------------------------+-----------------------------------+---------------------+--------------------------------+--------------------------------+ + | Clickhouse type | Min value | Max value | Spark type | Min value | Max value | + +========================+===================================+===================================+=====================+================================+================================+ + | ``Date`` | ``1970-01-01`` | ``2149-06-06`` | ``DateType()`` | ``0001-01-01`` | ``9999-12-31`` | + +------------------------+-----------------------------------+-----------------------------------+---------------------+--------------------------------+--------------------------------+ + | ``DateTime32`` | ``1970-01-01 00:00:00`` | ``2106-02-07 06:28:15`` | ``TimestampType()`` | ``0001-01-01 00:00:00.000000`` | ``9999-12-31 23:59:59.999999`` | + +------------------------+-----------------------------------+-----------------------------------+ | | | + | ``DateTime64(P=0..8)`` | ``1900-01-01 00:00:00.00000000`` | ``2299-12-31 23:59:59.99999999`` | | | | + +------------------------+-----------------------------------+-----------------------------------+ | | | + | ``DateTime64(P=9)`` | ``1900-01-01 00:00:00.000000000`` | ``2262-04-11 23:47:16.999999999`` | | | | + +------------------------+-----------------------------------+-----------------------------------+---------------------+--------------------------------+--------------------------------+ + + So not all of values in Spark DataFrame can be written to Clickhouse. + + References: + * `Clickhouse Date documentation `_ + * `Clickhouse Datetime32 documentation `_ + * `Clickhouse Datetime64 documentation `_ + * `Spark DateType documentation `_ + * `Spark TimestampType documentation `_ +``` + +[^footnote-4]: `Date32` has different bytes representation than `Date`, and inserting value of type `Date32` to `Date` column + leads to errors on Clickhouse side, e.g. `Date(106617) should be between 0 and 65535 inclusive of both values`. + Although Spark does properly read the `Date32` column as `DateType()`, and there should be no difference at all. + Probably this is some bug in Clickhouse driver. + +[^footnote-5]: Generic JDBC dialect generates DDL with Clickhouse type `TIMESTAMP` which is alias for `DateTime32` with precision up to seconds (`23:59:59`). + Inserting data with milliseconds precision (`23:59:59.999`) will lead to **throwing away milliseconds**. + Solution: create table manually, with proper column type. + +[^footnote-6]: Clickhouse support datetime up to nanoseconds precision (`23:59:59.999999999`), + but Spark `TimestampType()` supports datetime up to microseconds precision (`23:59:59.999999`). + Nanoseconds will be lost during read or write operations. + Solution: create table manually, with proper column type. + +[^footnote-7]: Clickhouse will raise an exception that data in format `2001-01-01 23:59:59.999999` has data `.999999` which does not match format `YYYY-MM-DD hh:mm:ss` + of `DateTime32` column type (see [^footnote-5]). + So Spark can create Clickhouse table, but cannot write data to column of this type. + Solution: create table manually, with proper column type. + +### String types + +```{eval-rst} ++--------------------------------------+------------------+------------------------+--------------------------+ +| Clickhouse type (read) | Spark type | Clickhousetype (write) | Clickhouse type (create) | ++======================================+==================+========================+==========================+ +| ``FixedString(N)`` | ``StringType()`` | ``String`` | ``String`` | ++--------------------------------------+ | | | +| ``String`` | | | | ++--------------------------------------+ | | | +| ``Enum8`` | | | | ++--------------------------------------+ | | | +| ``Enum16`` | | | | ++--------------------------------------+ | | | +| ``IPv4`` | | | | ++--------------------------------------+ | | | +| ``IPv6`` | | | | ++--------------------------------------+ | | | +| ``UUID`` | | | | ++--------------------------------------+------------------+ | | +| ``-`` | ``BinaryType()`` | | | ++--------------------------------------+------------------+------------------------+--------------------------+ +``` + +## Unsupported types + +Columns of these Clickhouse types cannot be read by Spark: +: - `AggregateFunction(func, T)` + - `Array(T)` + - `JSON` + - `Map(K, V)` + - `MultiPolygon` + - `Nested(field1 T1, ...)` + - `Nothing` + - `Point` + - `Polygon` + - `Ring` + - `SimpleAggregateFunction(func, T)` + - `Tuple(T1, T2, ...)` + +Dataframe with these Spark types cannot be written to Clickhouse: +: - `ArrayType(T)` + - `BinaryType()` + - `CharType(N)` + - `DayTimeIntervalType(P, S)` + - `MapType(K, V)` + - `NullType()` + - `StructType([...])` + - `TimestampNTZType()` + - `VarcharType(N)` + +This is because Spark does not have dedicated Clickhouse dialect, and uses Generic JDBC dialect instead. +This dialect does not have type conversion between some types, like Clickhouse `Array` -> Spark `ArrayType()`, and vice versa. + +The is a way to avoid this - just cast everything to `String`. + +## Explicit type cast + +### `DBReader` + +Use `CAST` or `toJSONString` to get column data as string in JSON format, + +For parsing JSON columns in ClickHouse, {obj}`JSON.parse_column ` method. + +```python +from pyspark.sql.types import ArrayType, IntegerType + +from onetl.file.format import JSON +from onetl.connection import ClickHouse +from onetl.db import DBReader + +reader = DBReader( + connection=clickhouse, + target="default.source_tbl", + columns=[ + "id", + "toJSONString(array_column) array_column", + ], +) +df = reader.run() + +# Spark requires all columns to have some specific type, describe it +column_type = ArrayType(IntegerType()) + +json = JSON() +df = df.select( + df.id, + json.parse_column("array_column", column_type), +) +``` + +### `DBWriter` + +For writing JSON data to ClickHouse, use the {obj}`JSON.serialize_column ` method to convert a DataFrame column to JSON format efficiently and write it as a `String` column in Clickhouse. + +```python +from onetl.file.format import JSON +from onetl.connection import ClickHouse +from onetl.db import DBWriter + +clickhouse = ClickHouse(...) + +clickhouse.execute( + """ + CREATE TABLE default.target_tbl ( + id Int32, + array_column_json String, + ) + ENGINE = MergeTree() + ORDER BY id + """, +) + +json = JSON() +df = df.select( + df.id, + json.serialize_column(df.array_column).alias("array_column_json"), +) + +writer.run(df) +``` + +Then you can parse this column on Clickhouse side - for example, by creating a view: + +```sql +SELECT + id, + JSONExtract(json_column, 'Array(String)') AS array_column +FROM target_tbl +``` + +You can also use [ALIAS](https://clickhouse.com/docs/en/sql-reference/statements/create/table#alias) +or [MATERIALIZED](https://clickhouse.com/docs/en/sql-reference/statements/create/table#materialized) columns +to avoid writing such expression in every `SELECT` clause all the time: + +```sql +CREATE TABLE default.target_tbl ( + id Int32, + array_column_json String, + -- computed column + array_column Array(String) ALIAS JSONExtract(json_column, 'Array(String)') + -- or materialized column + -- array_column Array(String) MATERIALIZED JSONExtract(json_column, 'Array(String)') +) +ENGINE = MergeTree() +ORDER BY id +``` + +Downsides: + +- Using `SELECT JSONExtract(...)` or `ALIAS` column can be expensive, because value is calculated on every row access. This can be especially harmful if such column is used in `WHERE` clause. +- `ALIAS` and `MATERIALIZED` columns are not included in `SELECT *` clause, they should be added explicitly: `SELECT *, calculated_column FROM table`. + +```{eval-rst} +.. warning:: + + `EPHEMERAL `_ columns are not supported by Spark + because they cannot be selected to determine target column type. +``` diff --git a/mddocs/docs/connection/db_connection/clickhouse/write.md b/mddocs/docs/connection/db_connection/clickhouse/write.md new file mode 100644 index 000000000..fa39d81a2 --- /dev/null +++ b/mddocs/docs/connection/db_connection/clickhouse/write.md @@ -0,0 +1,60 @@ +(clickhouse-write)= + +# Writing to Clickhouse using `DBWriter` + +For writing data to Clickhouse, use {obj}`DBWriter `. + +```{eval-rst} +.. warning:: + + Please take into account :ref:`clickhouse-types` +``` + +```{eval-rst} +.. warning:: + + It is always recommended to create table explicitly using :ref:`Clickhouse.execute ` + instead of relying on Spark's table DDL generation. + + This is because Spark's DDL generator can create columns with different precision and types than it is expected, + causing precision loss or other issues. +``` + +## Examples + +```python +from onetl.connection import Clickhouse +from onetl.db import DBWriter + +clickhouse = Clickhouse(...) + +df = ... # data is here + +writer = DBWriter( + connection=clickhouse, + target="schema.table", + options=Clickhouse.WriteOptions( + if_exists="append", + # ENGINE is required by Clickhouse + createTableOptions="ENGINE = MergeTree() ORDER BY id", + ), +) + +writer.run(df) +``` + +## Options + +Method above accepts {obj}`Clickhouse.WriteOptions ` + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.clickhouse.options +``` + +```{eval-rst} +.. autopydantic_model:: ClickhouseWriteOptions + :inherited-members: GenericOptions + :member-order: bysource + :model-show-field-summary: false + :field-show-constraints: false +``` diff --git a/mddocs/docs/connection/db_connection/greenplum/connection.md b/mddocs/docs/connection/db_connection/greenplum/connection.md new file mode 100644 index 000000000..e95a4e6a8 --- /dev/null +++ b/mddocs/docs/connection/db_connection/greenplum/connection.md @@ -0,0 +1,12 @@ +(greenplum-connection)= + +# Greenplum connection + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.greenplum.connection +``` + +```{eval-rst} +.. autoclass:: Greenplum + :members: get_packages, check +``` diff --git a/mddocs/docs/connection/db_connection/greenplum/execute.md b/mddocs/docs/connection/db_connection/greenplum/execute.md new file mode 100644 index 000000000..0b10c0bc7 --- /dev/null +++ b/mddocs/docs/connection/db_connection/greenplum/execute.md @@ -0,0 +1,159 @@ +(greenplum-execute)= + +# Executing statements in Greenplum + +```{eval-rst} +.. warning:: + + Methods below **read all the rows** returned from DB **to Spark driver memory**, and then convert them to DataFrame. + + Do **NOT** use them to read large amounts of data. Use :ref:`DBReader ` instead. +``` + +## How to + +There are 2 ways to execute some statement in Greenplum + +### Use `Greenplum.fetch` + +Use this method to perform some `SELECT` query which returns **small number or rows**, like reading +Greenplum config, or reading data from some reference table. Method returns Spark DataFrame. + +Method accepts {obj}`Greenplum.FetchOptions `. + +Connection opened using this method should be then closed with `connection.close()` or `with connection:`. + +```{eval-rst} +.. warning:: + + ``Greenplum.fetch`` is implemented using Postgres JDBC connection, + so types are handled a bit differently than in ``DBReader``. See :ref:`postgres-types`. +``` + +#### Syntax support + +This method supports **any** query syntax supported by Greenplum, like: + +- ✅︎ `SELECT ... FROM ...` +- ✅︎ `WITH alias AS (...) SELECT ...` +- ✅︎ `SELECT func(arg1, arg2)` or `{call func(arg1, arg2)}` - special syntax for calling functions +- ❌ `SET ...; SELECT ...;` - multiple statements not supported + +#### Examples + +```python +from onetl.connection import Greenplum + +greenplum = Greenplum(...) + +df = greenplum.fetch( + "SELECT value FROM some.reference_table WHERE key = 'some_constant'", + options=Greenplum.FetchOptions(queryTimeout=10), +) +greenplum.close() +value = df.collect()[0][0] # get value from first row and first column +``` + +### Use `Greenplum.execute` + +Use this method to execute DDL and DML operations. Each method call runs operation in a separated transaction, and then commits it. + +Method accepts {obj}`Greenplum.ExecuteOptions `. + +Connection opened using this method should be then closed with `connection.close()` or `with connection:`. + +#### Syntax support + +This method supports **any** query syntax supported by Greenplum, like: + +- ✅︎ `CREATE TABLE ...`, `CREATE VIEW ...`, and so on +- ✅︎ `ALTER ...` +- ✅︎ `INSERT INTO ... SELECT ...`, `UPDATE ...`, `DELETE ...`, and so on +- ✅︎ `DROP TABLE ...`, `DROP VIEW ...`, `TRUNCATE TABLE`, and so on +- ✅︎ `CALL procedure(arg1, arg2) ...` +- ✅︎ `SELECT func(arg1, arg2)` or `{call func(arg1, arg2)}` - special syntax for calling functions +- ✅︎ other statements not mentioned here +- ❌ `SET ...; SELECT ...;` - multiple statements not supported + +#### Examples + +```python +from onetl.connection import Greenplum + +greenplum = Greenplum(...) + +greenplum.execute("DROP TABLE schema.table") +greenplum.execute( + """ + CREATE TABLE schema.table ( + id int, + key text, + value real + ) + DISTRIBUTED BY id + """, + options=Greenplum.ExecuteOptions(queryTimeout=10), +) +``` + +## Interaction schema + +Unlike reading & writing, executing statements in Greenplum is done **only** through Greenplum master node, +without any interaction between Greenplum segments and Spark executors. More than that, Spark executors are not used in this case. + +The only port used while interacting with Greenplum in this case is `5432` (Greenplum master port). + +```{eval-rst} +.. dropdown:: Spark <-> Greenplum interaction during Greenplum.execute()/Greenplum.fetch() + + .. plantuml:: + + @startuml + title Greenplum master <-> Spark driver + box "Spark" + participant "Spark driver" + end box + + box "Greenplum" + participant "Greenplum master" + end box + + == Greenplum.check() == + + activate "Spark driver" + "Spark driver" -> "Greenplum master" ++ : CONNECT + + == Greenplum.execute(statement) == + "Spark driver" --> "Greenplum master" : EXECUTE statement + "Greenplum master" -> "Spark driver" : RETURN result + + == Greenplum.close() == + "Spark driver" --> "Greenplum master" : CLOSE CONNECTION + + deactivate "Greenplum master" + deactivate "Spark driver" + @enduml +``` + +## Options + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.greenplum.options +``` + +```{eval-rst} +.. autopydantic_model:: GreenplumFetchOptions + :inherited-members: GenericOptions + :member-order: bysource + :model-show-field-summary: false + :field-show-constraints: false + +``` + +```{eval-rst} +.. autopydantic_model:: GreenplumExecuteOptions + :inherited-members: GenericOptions + :member-order: bysource + :model-show-field-summary: false + :field-show-constraints: false +``` diff --git a/mddocs/docs/connection/db_connection/greenplum/index.md b/mddocs/docs/connection/db_connection/greenplum/index.md new file mode 100644 index 000000000..1707fc960 --- /dev/null +++ b/mddocs/docs/connection/db_connection/greenplum/index.md @@ -0,0 +1,27 @@ +(greenplum)= + +# Greenplum + +```{toctree} +:caption: Connection +:maxdepth: 1 + +prerequisites +connection +``` + +```{toctree} +:caption: Operations +:maxdepth: 1 + +read +write +execute +``` + +```{toctree} +:caption: Troubleshooting +:maxdepth: 1 + +types +``` diff --git a/mddocs/docs/connection/db_connection/greenplum/prerequisites.md b/mddocs/docs/connection/db_connection/greenplum/prerequisites.md new file mode 100644 index 000000000..a2e55b950 --- /dev/null +++ b/mddocs/docs/connection/db_connection/greenplum/prerequisites.md @@ -0,0 +1,382 @@ +(greenplum-prerequisites)= + +# Prerequisites + +## Version Compatibility + +- Greenplum server versions: + : - Officially declared: 5.x, 6.x, and 7.x (which requires `Greenplum.get_packages(package_version="2.3.0")` or higher) + - Actually tested: 6.23, 7.0 +- Spark versions: 2.3.x - 3.2.x (Spark 3.3+ is not supported yet) +- Java versions: 8 - 11 + +See [official documentation](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.2/greenplum-connector-spark/release_notes.html). + +## Installing PySpark + +To use Greenplum connector you should have PySpark installed (or injected to `sys.path`) +BEFORE creating the connector instance. + +See {ref}`install-spark` installation instruction for more details. + +## Downloading VMware package + +To use Greenplum connector you should download connector `.jar` file from +[VMware website](https://network.tanzu.vmware.com/products/vmware-greenplum#/releases/1413479/file_groups/16966) +and then pass it to Spark session. + +```{eval-rst} +.. warning:: + + Please pay attention to :ref:`Spark & Scala version compatibility `. +``` + +```{eval-rst} +.. warning:: + + There are issues with using package of version 2.3.0/2.3.1 with Greenplum 6.x - connector can + open transaction with ``SELECT * FROM table LIMIT 0`` query, but does not close it, which leads to deadlocks + during write. +``` + +There are several ways to do that. See {ref}`java-packages` for details. + +```{eval-rst} +.. note:: + + If you're uploading package to private package repo, use ``groupId=io.pivotal`` and ``artifactoryId=greenplum-spark_2.12`` + (``2.12`` is Scala version) to give uploaded package a proper name. +``` + +## Connecting to Greenplum + +### Interaction schema + +Spark executors open ports to listen incoming requests. +Greenplum segments are initiating connections to Spark executors using [EXTERNAL TABLE](https://docs.vmware.com/en/VMware-Greenplum/7/greenplum-database/ref_guide-sql_commands-CREATE_EXTERNAL_TABLE.html) +functionality, and send/read data using [gpfdist protocol](https://docs.vmware.com/en/VMware-Greenplum/7/greenplum-database/admin_guide-external-g-using-the-greenplum-parallel-file-server--gpfdist-.html#about-gpfdist-setup-and-performance-1). + +Data is **not** send through Greenplum master. +Greenplum master only receives commands to start reading/writing process, and manages all the metadata (external table location, schema and so on). + +More details can be found in [official documentation](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/overview.html). + +### Set number of connections + +```{eval-rst} +.. warning:: + + This is very important!!! + + If you don't limit number of connections, you can exceed the `max_connections `_ + limit set on the Greenplum side. It's usually not so high, e.g. 500-1000 connections max, + depending on your Greenplum instance settings and using connection balancers like ``pgbouncer``. + + Consuming all available connections means **nobody** (even admin users) can connect to Greenplum. +``` + +Each job on the Spark executor makes its own connection to Greenplum master node, +so you need to limit number of connections to avoid opening too many of them. + +- Reading about `5-10Gb` of data requires about `3-5` parallel connections. +- Reading about `20-30Gb` of data requires about `5-10` parallel connections. +- Reading about `50Gb` of data requires ~ `10-20` parallel connections. +- Reading about `100+Gb` of data requires `20-30` parallel connections. +- Opening more than `30-50` connections is not recommended. + +Number of connections can be limited by 2 ways: + +- By limiting number of Spark executors and number of cores per-executor. Max number of parallel jobs is `executors * cores`. + +```{eval-rst} +.. tabs:: + + .. code-tab:: py Spark with master=local + + spark = ( + SparkSession.builder + # Spark will run with 5 threads in local mode, allowing up to 5 parallel tasks + .config("spark.master", "local[5]") + .config("spark.executor.cores", 1) + ).getOrCreate() + + .. code-tab:: py Spark with master=yarn or master=k8s, dynamic allocation + + spark = ( + SparkSession.builder + .config("spark.master", "yarn") + # Spark will start MAX 10 executors with 1 core each (dynamically), so max number of parallel jobs is 10 + .config("spark.dynamicAllocation.maxExecutors", 10) + .config("spark.executor.cores", 1) + ).getOrCreate() + + .. code-tab:: py Spark with master=yarn or master=k8s, static allocation + + spark = ( + SparkSession.builder + .config("spark.master", "yarn") + # Spark will start EXACTLY 10 executors with 1 core each, so max number of parallel jobs is 10 + .config("spark.executor.instances", 10) + .config("spark.executor.cores", 1) + ).getOrCreate() +``` + +- By limiting connection pool size user by Spark (**only** for Spark with `master=local`): + +```python +spark = SparkSession.builder.config("spark.master", "local[*]").getOrCreate() + +# No matter how many executors are started and how many cores they have, +# number of connections cannot exceed pool size: +Greenplum( + ..., + extra={ + "pool.maxSize": 10, + }, +) +``` + +See [connection pooling](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/using_the_connector.html#jdbcconnpool) +documentation. + +- By setting {obj}`num_partitions ` + and {obj}`partition_column ` (not recommended). + +### Allowing connection to Greenplum master + +Ask your Greenplum cluster administrator to allow your user to connect to Greenplum master node, +e.g. by updating `pg_hba.conf` file. + +More details can be found in [official documentation](https://docs.vmware.com/en/VMware-Greenplum/7/greenplum-database/admin_guide-client_auth.html#limiting-concurrent-connections#allowing-connections-to-greenplum-database-0). + +### Set connection port + +#### Spark with `master=k8s` + +Please follow [the official documentation](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/configure.html#k8scfg) + +#### Spark with `master=yarn` or `master=local` + +To read data from Greenplum using Spark, following ports should be opened in firewall between Spark and Greenplum: + +- Spark driver and all Spark executors -> port `5432` on Greenplum master node. + + This port number should be set while connecting to Greenplum: + + ```python + greenplum = Greenplum(host="master.host", port=5432, ...) + ``` + +- Greenplum segments -> some port range (e.g. `41000-42000`) **listened by Spark executors**. + + This range should be set in `extra` option: + + ```python + greenplum = Greenplum( + ..., + extra={ + "server.port": "41000-42000", + }, + ) + ``` + + Number of ports in this range is `number of parallel running Spark sessions` * `number of parallel connections per session`. + + Number of connections per session (see below) is usually less than `30` (see above). + + Number of session depends on your environment: + : - For `master=local` only few ones-tens sessions can be started on the same host, depends on available RAM and CPU. + - For `master=yarn` hundreds or thousands of sessions can be started simultaneously, + but they are executing on different cluster nodes, so one port can be opened on different nodes at the same time. + +More details can be found in official documentation: +: - [port requirements](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/sys_reqs.html#network-port-requirements) + - [format of server.port value](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/options.html#server.port) + - [port troubleshooting](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/troubleshooting.html#port-errors) + +### Set connection host + +#### Spark with `master=k8s` + +Please follow [the official documentation](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/configure.html#k8scfg) + +#### Spark with `master=local` + +By default, Greenplum connector tries to resolve IP of current host, and then pass it as `gpfdist` URL to Greenplum segment. +This may fail in some cases. + +For example, IP can be resolved using `/etc/hosts` content like this: + +```text +127.0.0.1 localhost real-host-name +``` + +```bash +$ hostname -f +localhost + +$ hostname -i +127.0.0.1 +``` + +Reading/writing data to Greenplum will fail with following exception: + +```text +org.postgresql.util.PSQLException: ERROR: connection with gpfdist failed for +"gpfdist://127.0.0.1:49152/local-1709739764667/exec/driver", +effective url: "http://127.0.0.1:49152/local-1709739764667/exec/driver": +error code = 111 (Connection refused); (seg3 slice1 12.34.56.78:10003 pid=123456) +``` + +There are 2 ways to fix that: + +- Explicitly pass your host IP address to connector, like this + + ```python + import os + + # pass here real host IP (accessible from GP segments) + os.environ["HOST_IP"] = "192.168.1.1" + + greenplum = Greenplum( + ..., + extra={ + # connector will read IP from this environment variable + "server.hostEnv": "env.HOST_IP", + }, + spark=spark, + ) + ``` + + More details can be found in [official documentation](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/options.html#server.hostenv). + +- Update `/etc/hosts` file to include real host IP: + + ```text + 127.0.0.1 localhost + # this IP should be accessible from GP segments + 192.168.1.1 driver-host-name + ``` + + So Greenplum connector will properly resolve host IP. + +#### Spark with `master=yarn` + +The same issue with resolving IP address can occur on Hadoop cluster node, but it's tricky to fix, because each node has a different IP. + +There are 3 ways to fix that: + +- Pass node hostname to `gpfdist` URL. So IP will be resolved on segment side: + + ```python + greenplum = Greenplum( + ..., + extra={ + "server.useHostname": "true", + }, + ) + ``` + + But this may fail if Hadoop cluster node hostname cannot be resolved from Greenplum segment side. + + More details can be found in [official documentation](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/options.html#server.usehostname). + +- Set specific network interface to get IP address from: + + ```python + greenplum = Greenplum( + ..., + extra={ + "server.nic": "eth0", + }, + ) + ``` + + You can get list of network interfaces using this command. + + ```{eval-rst} + .. note:: + + This command should be executed on Hadoop cluster node, **not** Spark driver host! + ``` + + ```bash + $ ip address + 1: lo: mtu 65536 qdisc noqueue state UNKNOWN group default qlen 1000 + inet 127.0.0.1/8 scope host lo + valid_lft forever preferred_lft forever + 2: eth0: mtu 1500 qdisc fq_codel state UP group default qlen 1000 + inet 192.168.1.1/24 brd 192.168.1.255 scope global dynamic noprefixroute eth0 + valid_lft 83457sec preferred_lft 83457sec + ``` + + Note that in this case **each** Hadoop cluster node node should have network interface with name `eth0`. + + More details can be found in [official documentation](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/options.html#server.nic). + +- Update `/etc/hosts` on each Hadoop cluster node to include real node IP: + + ```text + 127.0.0.1 localhost + # this IP should be accessible from GP segments + 192.168.1.1 cluster-node-name + ``` + + So Greenplum connector will properly resolve node IP. + +### Set required grants + +Ask your Greenplum cluster administrator to set following grants for a user, +used for creating a connection: + +```{eval-rst} +.. tabs:: + + .. code-tab:: sql Read + Write + + -- get access to get tables metadata & cluster information + GRANT SELECT ON information_schema.tables TO username; + GRANT SELECT ON pg_attribute TO username; + GRANT SELECT ON pg_class TO username; + GRANT SELECT ON pg_namespace TO username; + GRANT SELECT ON pg_settings TO username; + GRANT SELECT ON pg_stats TO username; + GRANT SELECT ON gp_distributed_xacts TO username; + GRANT SELECT ON gp_segment_configuration TO username; + -- Greenplum 5.x only + GRANT SELECT ON gp_distribution_policy TO username; + + -- allow creating external tables in the same schema as source/target table + GRANT USAGE ON SCHEMA myschema TO username; + GRANT CREATE ON SCHEMA myschema TO username; + ALTER USER username CREATEEXTTABLE(type = 'readable', protocol = 'gpfdist') CREATEEXTTABLE(type = 'writable', protocol = 'gpfdist'); + + -- allow read access to specific table (to get column types) + -- allow write access to specific table + GRANT SELECT, INSERT ON myschema.mytable TO username; + + .. code-tab:: sql Read only + + -- get access to get tables metadata & cluster information + GRANT SELECT ON information_schema.tables TO username; + GRANT SELECT ON pg_attribute TO username; + GRANT SELECT ON pg_class TO username; + GRANT SELECT ON pg_namespace TO username; + GRANT SELECT ON pg_settings TO username; + GRANT SELECT ON pg_stats TO username; + GRANT SELECT ON gp_distributed_xacts TO username; + GRANT SELECT ON gp_segment_configuration TO username; + -- Greenplum 5.x only + GRANT SELECT ON gp_distribution_policy TO username; + + -- allow creating external tables in the same schema as source table + GRANT USAGE ON SCHEMA schema_to_read TO username; + GRANT CREATE ON SCHEMA schema_to_read TO username; + -- yes, ``writable`` for reading from GP, because data is written from Greenplum to Spark executor. + ALTER USER username CREATEEXTTABLE(type = 'writable', protocol = 'gpfdist'); + + -- allow read access to specific table + GRANT SELECT ON schema_to_read.table_to_read TO username; +``` + +More details can be found in [official documentation](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/install_cfg.html#role-privileges). diff --git a/mddocs/docs/connection/db_connection/greenplum/read.md b/mddocs/docs/connection/db_connection/greenplum/read.md new file mode 100644 index 000000000..502bbb915 --- /dev/null +++ b/mddocs/docs/connection/db_connection/greenplum/read.md @@ -0,0 +1,386 @@ +(greenplum-read)= + +# Reading from Greenplum using `DBReader` + +Data can be read from Greenplum to Spark using {obj}`DBReader `. +It also supports {ref}`strategy` for incremental data reading. + +```{eval-rst} +.. warning:: + + Please take into account :ref:`greenplum-types`. +``` + +```{eval-rst} +.. note:: + + Unlike JDBC connectors, *Greenplum connector for Spark* does not support + executing **custom** SQL queries using ``.sql`` method. Connector can be used to only read data from a table or view. +``` + +## Supported DBReader features + +- ✅︎ `columns` (see note below) +- ✅︎ `where` (see note below) +- ✅︎ `hwm` (see note below), supported strategies: +- - ✅︎ {ref}`snapshot-strategy` +- - ✅︎ {ref}`incremental-strategy` +- - ✅︎ {ref}`snapshot-batch-strategy` +- - ✅︎ {ref}`incremental-batch-strategy` +- ❌ `hint` (is not supported by Greenplum) +- ❌ `df_schema` +- ✅︎ `options` (see {obj}`Greenplum.ReadOptions `) + +```{eval-rst} +.. warning:: + + In case of Greenplum connector, ``DBReader`` does not generate raw ``SELECT`` query. Instead it relies on Spark SQL syntax + which in some cases (using column projection and predicate pushdown) can be converted to Greenplum SQL. + + So ``columns``, ``where`` and ``hwm.expression`` should be specified in `Spark SQL `_ syntax, + not Greenplum SQL. + + This is OK: + + .. code-block:: python + + DBReader( + columns=[ + "some_column", + # this cast is executed on Spark side + "CAST(another_column AS STRING)", + ], + # this predicate is parsed by Spark, and can be pushed down to Greenplum + where="some_column LIKE 'val1%'", + ) + + This is will fail: + + .. code-block:: python + + DBReader( + columns=[ + "some_column", + # Spark does not have `text` type + "CAST(another_column AS text)", + ], + # Spark does not support ~ syntax for regexp matching + where="some_column ~ 'val1.*'", + ) +``` + +## Examples + +Snapshot strategy: + +```python +from onetl.connection import Greenplum +from onetl.db import DBReader + +greenplum = Greenplum(...) + +reader = DBReader( + connection=greenplum, + source="schema.table", + columns=["id", "key", "CAST(value AS string) value", "updated_dt"], + where="key = 'something'", +) +df = reader.run() +``` + +Incremental strategy: + +```python +from onetl.connection import Greenplum +from onetl.db import DBReader +from onetl.strategy import IncrementalStrategy + +greenplum = Greenplum(...) + +reader = DBReader( + connection=greenplum, + source="schema.table", + columns=["id", "key", "CAST(value AS string) value", "updated_dt"], + where="key = 'something'", + hwm=DBReader.AutoDetectHWM(name="greenplum_hwm", expression="updated_dt"), +) + +with IncrementalStrategy(): + df = reader.run() +``` + +## Interaction schema + +High-level schema is described in {ref}`greenplum-prerequisites`. You can find detailed interaction schema below. + +```{eval-rst} +.. dropdown:: Spark <-> Greenplum interaction during DBReader.run() + + .. plantuml:: + + @startuml + title Greenplum master <-> Spark driver + box "Spark" + participant "Spark driver" + participant "Spark executor1" + participant "Spark executor2" + participant "Spark executorN" + end box + + box "Greenplum" + participant "Greenplum master" + participant "Greenplum segment1" + participant "Greenplum segment2" + participant "Greenplum segmentN" + end box + + == Greenplum.check() == + + activate "Spark driver" + "Spark driver" -> "Greenplum master" ++ : CONNECT + + "Spark driver" --> "Greenplum master" : CHECK IF TABLE EXISTS gp_table + "Greenplum master" --> "Spark driver" : TABLE EXISTS + "Spark driver" -> "Greenplum master" : SHOW SCHEMA FOR gp_table + "Greenplum master" --> "Spark driver" : (id bigint, col1 int, col2 text, ...) + + == DBReader.run() == + + "Spark driver" -> "Spark executor1" ++ : START EXECUTOR FOR df(id bigint, col1 int, col2 text, ...) PARTITION 1 + "Spark driver" -> "Spark executor2" ++ : START EXECUTOR FOR df(id bigint, col1 int, col2 text, ...) PARTITION 2 + "Spark driver" -> "Spark executorN" ++ : START EXECUTOR FOR df(id bigint, col1 int, col2 text, ...) PARTITION N + + note right of "Spark driver" : This is done in parallel,\nexecutors are independent\n|\n|\n|\nV + "Spark executor1" -> "Greenplum master" ++ : CREATE WRITABLE EXTERNAL TABLE spark_executor1 (id bigint, col1 int, col2 text, ...) USING address=executor1_host:executor1_port;\nINSERT INTO EXTERNAL TABLE spark_executor1 FROM gp_table WHERE gp_segment_id = 1 + note right of "Greenplum master" : Each white vertical line here is a opened connection to master.\nUsually, **N+1** connections are created from Spark to Greenplum master + "Greenplum master" --> "Greenplum segment1" ++ : SELECT DATA FROM gp_table_data_on_segment1 TO spark_executor1 + note right of "Greenplum segment1" : No direct requests between Greenplum segments & Spark.\nData transfer is always initiated by Greenplum segments. + + "Spark executor2" -> "Greenplum master" ++ : CREATE WRITABLE EXTERNAL TABLE spark_executor2 (id bigint, col1 int, col2 text, ...) USING address=executor2_host:executor2_port;\nINSERT INTO EXTERNAL TABLE spark_executor2 FROM gp_table WHERE gp_segment_id = 2 + "Greenplum master" --> "Greenplum segment2" ++ : SELECT DATA FROM gp_table_data_on_segment2 TO spark_executor2 + + "Spark executorN" -> "Greenplum master" ++ : CREATE WRITABLE EXTERNAL TABLE spark_executorN (id bigint, col1 int, col2 text, ...) USING address=executorN_host:executorN_port;\nINSERT INTO EXTERNAL TABLE spark_executorN FROM gp_table WHERE gp_segment_id = N + "Greenplum master" --> "Greenplum segmentN" ++ : SELECT DATA FROM gp_table_data_on_segmentN TO spark_executorN + + "Greenplum segment1" ->o "Spark executor1" -- : INITIALIZE CONNECTION TO Spark executor1\nPUSH DATA TO Spark executor1 + note left of "Spark executor1" : Circle is an open GPFDIST port,\nlistened by executor + + "Greenplum segment2" ->o "Spark executor2" -- : INITIALIZE CONNECTION TO Spark executor2\nPUSH DATA TO Spark executor2 + "Greenplum segmentN" ->o "Spark executorN" -- : INITIALIZE CONNECTION TO Spark executorN\nPUSH DATA TO Spark executorN + + == Spark.stop() == + + "Spark executor1" --> "Greenplum master" : DROP TABLE spark_executor1 + deactivate "Greenplum master" + "Spark executor2" --> "Greenplum master" : DROP TABLE spark_executor2 + deactivate "Greenplum master" + "Spark executorN" --> "Greenplum master" : DROP TABLE spark_executorN + deactivate "Greenplum master" + + "Spark executor1" --> "Spark driver" -- : DONE + "Spark executor2" --> "Spark driver" -- : DONE + "Spark executorN" --> "Spark driver" -- : DONE + + "Spark driver" --> "Greenplum master" : CLOSE CONNECTION + deactivate "Greenplum master" + deactivate "Spark driver" + @enduml +``` + +## Recommendations + +### Select only required columns + +Instead of passing `"*"` in `DBReader(columns=[...])` prefer passing exact column names. This reduces the amount of data passed from Greenplum to Spark. + +### Pay attention to `where` value + +Instead of filtering data on Spark side using `df.filter(df.column == 'value')` pass proper `DBReader(where="column = 'value'")` clause. +This both reduces the amount of data send from Greenplum to Spark, and may also improve performance of the query. +Especially if there are indexes or partitions for columns used in `where` clause. + +### Read data in parallel + +`DBReader` in case of Greenplum connector requires view or table to have a column which is used by Spark +for parallel reads. + +Choosing proper column allows each Spark executor to read only part of data stored in the specified segment, +avoiding moving large amounts of data between segments, which improves reading performance. + +#### Using `gp_segment_id` + +By default, `DBReader` will use [gp_segment_id](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/troubleshooting.html#reading-from-a-view) +column for parallel data reading. Each DataFrame partition will contain data of a specific Greenplum segment. + +This allows each Spark executor read only data from specific Greenplum segment, avoiding moving large amounts of data between segments. + +If view is used, it is recommended to include `gp_segment_id` column to this view: + +```{eval-rst} +.. dropdown:: Reading from view with gp_segment_id column + + .. code-block:: python + + from onetl.connection import Greenplum + from onetl.db import DBReader + + greenplum = Greenplum(...) + + greenplum.execute( + """ + CREATE VIEW schema.view_with_gp_segment_id AS + SELECT + id, + some_column, + another_column, + gp_segment_id -- IMPORTANT + FROM schema.some_table + """, + ) + + reader = DBReader( + connection=greenplum, + source="schema.view_with_gp_segment_id", + ) + df = reader.run() +``` + +#### Using custom `partition_column` + +Sometimes table or view is lack of `gp_segment_id` column, but there is some column +with value range correlated with Greenplum segment distribution. + +In this case, custom column can be used instead: + +```{eval-rst} +.. dropdown:: Reading from view with custom partition_column + + .. code-block:: python + + from onetl.connection import Greenplum + from onetl.db import DBReader + + greenplum = Greenplum(...) + + greenplum.execute( + """ + CREATE VIEW schema.view_with_partition_column AS + SELECT + id, + some_column, + part_column -- correlated to greenplum segment ID + FROM schema.some_table + """, + ) + + reader = DBReader( + connection=greenplum, + source="schema.view_with_partition_column", + options=Greenplum.ReadOptions( + # parallelize data using specified column + partitionColumn="part_column", + # create 10 Spark tasks, each will read only part of table data + partitions=10, + ), + ) + df = reader.run() +``` + +#### Reading `DISTRIBUTED REPLICATED` tables + +Replicated tables do not have `gp_segment_id` column at all, so you need to set `partition_column` to some column name +of type integer/bigint/smallint. + +### Parallel `JOIN` execution + +In case of using views which require some data motion between Greenplum segments, like `JOIN` queries, another approach should be used. + +Each Spark executor N will run the same query, so each of N query will start its own JOIN process, leading to really heavy load on Greenplum segments. +**This should be avoided**. + +Instead is recommended to run `JOIN` query on Greenplum side, save the result to an intermediate table, +and then read this table using `DBReader`: + +```{eval-rst} +.. dropdown:: Reading from view using intermediate table + + .. code-block:: python + + from onetl.connection import Greenplum + from onetl.db import DBReader + + greenplum = Greenplum(...) + + greenplum.execute( + """ + CREATE UNLOGGED TABLE schema.intermediate_table AS + SELECT + id, + tbl1.col1, + tbl1.data, + tbl2.another_data + FROM + schema.table1 as tbl1 + JOIN + schema.table2 as tbl2 + ON + tbl1.col1 = tbl2.col2 + WHERE ... + """, + ) + + reader = DBReader( + connection=greenplum, + source="schema.intermediate_table", + ) + df = reader.run() + + # write dataframe somethere + + greenplum.execute( + """ + DROP TABLE schema.intermediate_table + """, + ) +``` + +```{eval-rst} +.. warning:: + + **NEVER** do that: + + .. code-block:: python + + df1 = DBReader(connection=greenplum, target="public.table1", ...).run() + df2 = DBReader(connection=greenplum, target="public.table2", ...).run() + + joined_df = df1.join(df2, on="col") + + This will lead to sending all the data from both ``table1`` and ``table2`` to Spark executor memory, and then ``JOIN`` + will be performed on Spark side, not inside Greenplum. This is **VERY** inefficient. +``` + +#### `TEMPORARY` tables notice + +Someone could think that writing data from view or result of `JOIN` to `TEMPORARY` table, +and then passing it to `DBReader`, is an efficient way to read data from Greenplum. This is because temp tables are not generating WAL files, +and are automatically deleted after finishing the transaction. + +That will **NOT** work. Each Spark executor establishes its own connection to Greenplum. +And each connection starts its own transaction which means that every executor will read empty temporary table. + +You should use [UNLOGGED](https://docs.vmware.com/en/VMware-Greenplum/7/greenplum-database/ref_guide-sql_commands-CREATE_TABLE.html) tables +to write data to intermediate table without generating WAL logs. + +## Options + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.greenplum.options +``` + +```{eval-rst} +.. autopydantic_model:: GreenplumReadOptions + :member-order: bysource + :model-show-field-summary: false + :field-show-constraints: false +``` diff --git a/mddocs/docs/connection/db_connection/greenplum/types.md b/mddocs/docs/connection/db_connection/greenplum/types.md new file mode 100644 index 000000000..1f9d83627 --- /dev/null +++ b/mddocs/docs/connection/db_connection/greenplum/types.md @@ -0,0 +1,406 @@ +(greenplum-types)= + +# Greenplum \<-> Spark type mapping + +```{eval-rst} +.. note:: + + The results below are valid for Spark 3.2.4, and may differ on other Spark versions. +``` + +## Type detection & casting + +Spark's DataFrames always have a `schema` which is a list of columns with corresponding Spark types. All operations on a column are performed using column type. + +### Reading from Greenplum + +This is how Greenplum connector performs this: + +- Execute query `SELECT * FROM table LIMIT 0` [^footnote-1]. +- For each column in query result get column name and Greenplum type. +- Find corresponding `Greenplum type (read)` → `Spark type` combination (see below) for each DataFrame column. If no combination is found, raise exception. +- Use Spark column projection and predicate pushdown features to build a final query. +- Create DataFrame from generated query with inferred schema. + +[^footnote-1]: Yes, **all columns of a table**, not just selected ones. + This means that if source table **contains** columns with unsupported type, the entire table cannot be read. + +### Writing to some existing Greenplum table + +This is how Greenplum connector performs this: + +- Get names of columns in DataFrame. +- Perform `SELECT * FROM table LIMIT 0` query. +- For each column in query result get column name and Greenplum type. +- Match table columns with DataFrame columns (by name, case insensitive). + If some column is present only in target table, but not in DataFrame (like `DEFAULT` or `SERIAL` column), and vice versa, raise an exception. + See [Explicit type cast]. +- Find corresponding `Spark type` → `Greenplumtype (write)` combination (see below) for each DataFrame column. If no combination is found, raise exception. +- If `Greenplumtype (write)` match `Greenplum type (read)`, no additional casts will be performed, DataFrame column will be written to Greenplum as is. +- If `Greenplumtype (write)` does not match `Greenplum type (read)`, DataFrame column will be casted to target column type **on Greenplum side**. For example, you can write column with text data to `json` column which Greenplum connector currently does not support. + +### Create new table using Spark + +```{eval-rst} +.. warning:: + + ABSOLUTELY NOT RECOMMENDED! +``` + +This is how Greenplum connector performs this: + +- Find corresponding `Spark type` → `Greenplum type (create)` combination (see below) for each DataFrame column. If no combination is found, raise exception. +- Generate DDL for creating table in Greenplum, like `CREATE TABLE (col1 ...)`, and run it. +- Write DataFrame to created table as is. + +More details [can be found here](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/write_to_gpdb.html). + +But Greenplum connector support only limited number of types and almost no custom clauses (like `PARTITION BY`). +So instead of relying on Spark to create tables: + +```{eval-rst} +.. dropdown:: See example + + .. code:: python + + writer = DBWriter( + connection=greenplum, + target="public.table", + options=Greenplum.WriteOptions( + if_exists="append", + # by default distribution is random + distributedBy="id", + # partitionBy is not supported + ), + ) + writer.run(df) +``` + +Always prefer creating table with desired DDL **BEFORE WRITING DATA**: + +```{eval-rst} +.. dropdown:: See example + + .. code:: python + + greenplum.execute( + """ + CREATE TABLE public.table ( + id int32, + business_dt timestamp(6), + value json + ) + PARTITION BY RANGE (business_dt) + DISTRIBUTED BY id + """, + ) + + writer = DBWriter( + connection=greenplum, + target="public.table", + options=Greenplum.WriteOptions(if_exists="append"), + ) + writer.run(df) +``` + +See Greenplum [CREATE TABLE](https://docs.vmware.com/en/VMware-Greenplum/7/greenplum-database/ref_guide-sql_commands-CREATE_TABLE.html) documentation. + +## Supported types + +See: +: - [official connector documentation](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/reference-datatype_mapping.html) + - [list of Greenplum types](https://docs.vmware.com/en/VMware-Greenplum/7/greenplum-database/ref_guide-data_types.html) + +### Numeric types + +```{eval-rst} ++----------------------------------+-----------------------------------+-------------------------------+-------------------------+ +| Greenplum type (read) | Spark type | Greenplumtype (write) | Greenplum type (create) | ++==================================+===================================+===============================+=========================+ +| ``decimal`` | ``DecimalType(P=38, S=18)`` | ``decimal(P=38, S=18)`` | ``decimal`` (unbounded) | ++----------------------------------+-----------------------------------+-------------------------------+ | +| ``decimal(P=0..38)`` | ``DecimalType(P=0..38, S=0)`` | ``decimal(P=0..38, S=0)`` | | ++----------------------------------+-----------------------------------+-------------------------------+ | +| ``decimal(P=0..38, S=0..38)`` | ``DecimalType(P=0..38, S=0..38)`` | ``decimal(P=0..38, S=0..38)`` | | ++----------------------------------+-----------------------------------+-------------------------------+-------------------------+ +| ``decimal(P=39.., S=0..)`` | unsupported [2]_ | | | ++----------------------------------+-----------------------------------+-------------------------------+-------------------------+ +| ``real`` | ``FloatType()`` | ``real`` | ``real`` | ++----------------------------------+-----------------------------------+-------------------------------+-------------------------+ +| ``double precision`` | ``DoubleType()`` | ``double precision`` | ``double precision`` | ++----------------------------------+-----------------------------------+-------------------------------+-------------------------+ +| ``-`` | ``ByteType()`` | unsupported | unsupported | ++----------------------------------+-----------------------------------+-------------------------------+-------------------------+ +| ``smallint`` | ``ShortType()`` | ``smallint`` | ``smallint`` | ++----------------------------------+-----------------------------------+-------------------------------+-------------------------+ +| ``integer`` | ``IntegerType()`` | ``integer`` | ``integer`` | ++----------------------------------+-----------------------------------+-------------------------------+-------------------------+ +| ``bigint`` | ``LongType()`` | ``bigint`` | ``bigint`` | ++----------------------------------+-----------------------------------+-------------------------------+-------------------------+ +| ``money`` | unsupported | | | ++----------------------------------+ | | | +| ``int4range`` | | | | ++----------------------------------+ | | | +| ``int8range`` | | | | ++----------------------------------+ | | | +| ``numrange`` | | | | ++----------------------------------+ | | | +| ``int2vector`` | | | | ++----------------------------------+-----------------------------------+-------------------------------+-------------------------+ +``` + +[^footnote-2]: Greenplum support decimal types with unlimited precision. + + But Spark's `DecimalType(P, S)` supports maximum `P=38` (128 bit). It is impossible to read, write or operate with values of larger precision, + this leads to an exception. + +### Temporal types + +```{eval-rst} ++------------------------------------+-------------------------+-----------------------+-------------------------+ +| Greenplum type (read) | Spark type | Greenplumtype (write) | Greenplum type (create) | ++====================================+=========================+=======================+=========================+ +| ``date`` | ``DateType()`` | ``date`` | ``date`` | ++------------------------------------+-------------------------+-----------------------+-------------------------+ +| ``time`` | ``TimestampType()``, | ``timestamp`` | ``timestamp`` | ++------------------------------------+ time format quirks [3]_ | | | +| ``time(0..6)`` | | | | ++------------------------------------+ | | | +| ``time with time zone`` | | | | ++------------------------------------+ | | | +| ``time(0..6) with time zone`` | | | | ++------------------------------------+-------------------------+-----------------------+-------------------------+ +| ``timestamp`` | ``TimestampType()`` | ``timestamp`` | ``timestamp`` | ++------------------------------------+ | | | +| ``timestamp(0..6)`` | | | | ++------------------------------------+ | | | +| ``timestamp with time zone`` | | | | ++------------------------------------+ | | | +| ``timestamp(0..6) with time zone`` | | | | ++------------------------------------+-------------------------+-----------------------+-------------------------+ +| ``interval`` or any precision | unsupported | | | ++------------------------------------+ | | | +| ``daterange`` | | | | ++------------------------------------+ | | | +| ``tsrange`` | | | | ++------------------------------------+ | | | +| ``tstzrange`` | | | | ++------------------------------------+-------------------------+-----------------------+-------------------------+ +``` + +```{eval-rst} +.. warning:: + + Note that types in Greenplum and Spark have different value ranges: + + +----------------+---------------------------------+----------------------------------+---------------------+--------------------------------+--------------------------------+ + | Greenplum type | Min value | Max value | Spark type | Min value | Max value | + +================+=================================+==================================+=====================+================================+================================+ + | ``date`` | ``-4713-01-01`` | ``5874897-01-01`` | ``DateType()`` | ``0001-01-01`` | ``9999-12-31`` | + +----------------+---------------------------------+----------------------------------+---------------------+--------------------------------+--------------------------------+ + | ``timestamp`` | ``-4713-01-01 00:00:00.000000`` | ``294276-12-31 23:59:59.999999`` | ``TimestampType()`` | ``0001-01-01 00:00:00.000000`` | ``9999-12-31 23:59:59.999999`` | + +----------------+---------------------------------+----------------------------------+ | | | + | ``time`` | ``00:00:00.000000`` | ``24:00:00.000000`` | | | | + +----------------+---------------------------------+----------------------------------+---------------------+--------------------------------+--------------------------------+ + + So not all of values can be read from Greenplum to Spark. + + References: + * `Greenplum types documentation `_ + * `Spark DateType documentation `_ + * `Spark TimestampType documentation `_ +``` + +[^footnote-3]: `time` type is the same as `timestamp` with date `1970-01-01`. So instead of reading data from Postgres like `23:59:59` + it is actually read `1970-01-01 23:59:59`, and vice versa. + +### String types + +```{eval-rst} ++-----------------------------+------------------+-----------------------+-------------------------+ +| Greenplum type (read) | Spark type | Greenplumtype (write) | Greenplum type (create) | ++=============================+==================+=======================+=========================+ +| ``character`` | ``StringType()`` | ``text`` | ``text`` | ++-----------------------------+ | | | +| ``character(N)`` | | | | ++-----------------------------+ | | | +| ``character varying`` | | | | ++-----------------------------+ | | | +| ``character varying(N)`` | | | | ++-----------------------------+ | | | +| ``text`` | | | | ++-----------------------------+ | | | +| ``xml`` | | | | ++-----------------------------+ | | | +| ``CREATE TYPE ... AS ENUM`` | | | | ++-----------------------------+------------------+-----------------------+-------------------------+ +| ``json`` | unsupported | | | ++-----------------------------+ | | | +| ``jsonb`` | | | | ++-----------------------------+------------------+-----------------------+-------------------------+ +``` + +### Binary types + +```{eval-rst} ++--------------------------+-------------------+-----------------------+-------------------------+ +| Greenplum type (read) | Spark type | Greenplumtype (write) | Greenplum type (create) | ++==========================+===================+=======================+=========================+ +| ``boolean`` | ``BooleanType()`` | ``boolean`` | ``boolean`` | ++--------------------------+-------------------+-----------------------+-------------------------+ +| ``bit`` | unsupported | | | ++--------------------------+ | | | +| ``bit(N)`` | | | | ++--------------------------+ | | | +| ``bit varying`` | | | | ++--------------------------+ | | | +| ``bit varying(N)`` | | | | ++--------------------------+-------------------+-----------------------+-------------------------+ +| ``bytea`` | unsupported [4]_ | | | ++--------------------------+-------------------+-----------------------+-------------------------+ +| ``-`` | ``BinaryType()`` | ``bytea`` | ``bytea`` | ++--------------------------+-------------------+-----------------------+-------------------------+ +``` + +[^footnote-4]: Yes, that's weird. + +### Struct types + +```{eval-rst} ++--------------------------------+------------------+-----------------------+-------------------------+ +| Greenplum type (read) | Spark type | Greenplumtype (write) | Greenplum type (create) | ++================================+==================+=======================+=========================+ +| ``T[]`` | unsupported | | | ++--------------------------------+------------------+-----------------------+-------------------------+ +| ``-`` | ``ArrayType()`` | unsupported | | ++--------------------------------+------------------+-----------------------+-------------------------+ +| ``CREATE TYPE sometype (...)`` | ``StringType()`` | ``text`` | ``text`` | ++--------------------------------+------------------+-----------------------+-------------------------+ +| ``-`` | ``StructType()`` | unsupported | | ++--------------------------------+------------------+ | | +| ``-`` | ``MapType()`` | | | ++--------------------------------+------------------+-----------------------+-------------------------+ +``` + +## Unsupported types + +Columns of these types cannot be read/written by Spark: +: - `cidr` + - `inet` + - `macaddr` + - `macaddr8` + - `circle` + - `box` + - `line` + - `lseg` + - `path` + - `point` + - `polygon` + - `tsvector` + - `tsquery` + - `uuid` + +The is a way to avoid this - just cast unsupported types to `text`. But the way this can be done is not a straightforward. + +## Explicit type cast + +### `DBReader` + +Direct casting of Greenplum types is not supported by DBReader due to the connector’s implementation specifics. + +```python +reader = DBReader( + connection=greenplum, + # will fail + columns=["CAST(unsupported_column AS text)"], +) +``` + +But there is a workaround - create a view with casting unsupported column to text (or any other supported type). +For example, you can use [to_json](https://www.postgresql.org/docs/current/functions-json.html) Postgres function to convert column of any type to string representation and then parse this column on Spark side using {obj}`JSON.parse_column ` method. + +```python +from pyspark.sql.types import ArrayType, IntegerType + +from onetl.connection import Greenplum +from onetl.db import DBReader +from onetl.file.format import JSON + +greenplum = Greenplum(...) + +greenplum.execute( + """ + CREATE VIEW schema.view_with_json_column AS + SELECT + id, + supported_column, + to_json(array_column) array_column_as_json, + gp_segment_id -- ! important ! + FROM + schema.table_with_unsupported_columns + """, +) + +# create dataframe using this view +reader = DBReader( + connection=greenplum, + source="schema.view_with_json_column", +) +df = reader.run() + +# Define the schema for the JSON data +json_scheme = ArrayType(IntegerType()) + +df = df.select( + df.id, + df.supported_column, + JSON().parse_column(df.array_column_as_json, json_scheme).alias("array_column"), +) +``` + +### `DBWriter` + +To write data to a `text` or `json` column in a Greenplum table, use {obj}`JSON.serialize_column ` method. + +```python +from onetl.connection import Greenplum +from onetl.db import DBWriter +from onetl.file.format import JSON + +greenplum = Greenplum(...) + +greenplum.execute( + """ + CREATE TABLE schema.target_table ( + id int, + supported_column timestamp, + array_column_as_json jsonb, -- or text + ) + DISTRIBUTED BY id + """, +) + +write_df = df.select( + df.id, + df.supported_column, + JSON().serialize_column(df.array_column).alias("array_column_json"), +) + +writer = DBWriter( + connection=greenplum, + target="schema.target_table", +) +writer.run(write_df) +``` + +Then you can parse this column on Greenplum side: + +```sql +SELECT + id, + supported_column, + -- access first item of an array + array_column_as_json->0 +FROM + schema.target_table +``` diff --git a/mddocs/docs/connection/db_connection/greenplum/write.md b/mddocs/docs/connection/db_connection/greenplum/write.md new file mode 100644 index 000000000..af367ad75 --- /dev/null +++ b/mddocs/docs/connection/db_connection/greenplum/write.md @@ -0,0 +1,140 @@ +(greenplum-write)= + +# Writing to Greenplum using `DBWriter` + +For writing data to Greenplum, use {obj}`DBWriter ` +with {obj}`GreenplumWriteOptions `. + +```{eval-rst} +.. warning:: + + Please take into account :ref:`greenplum-types`. +``` + +```{eval-rst} +.. warning:: + + It is always recommended to create table explicitly using :ref:`Greenplum.execute ` + instead of relying on Spark's table DDL generation. + + This is because Spark's DDL generator can create columns with different types than it is expected. +``` + +## Examples + +```python +from onetl.connection import Greenplum +from onetl.db import DBWriter + +greenplum = Greenplum(...) + +df = ... # data is here + +writer = DBWriter( + connection=greenplum, + target="schema.table", + options=Greenplum.WriteOptions( + if_exists="append", + # by default distribution is random + distributedBy="id", + # partitionBy is not supported + ), +) + +writer.run(df) +``` + +## Interaction schema + +High-level schema is described in {ref}`greenplum-prerequisites`. You can find detailed interaction schema below. + +```{eval-rst} +.. dropdown:: Spark <-> Greenplum interaction during DBWriter.run() + + .. plantuml:: + + @startuml + title Greenplum master <-> Spark driver + box "Spark" + participant "Spark driver" + participant "Spark executor1" + participant "Spark executor2" + participant "Spark executorN" + end box + + box "Greenplum" + participant "Greenplum master" + participant "Greenplum segment1" + participant "Greenplum segment2" + participant "Greenplum segmentN" + end box + + == Greenplum.check() == + + activate "Spark driver" + "Spark driver" -> "Greenplum master" ++ : CONNECT + "Spark driver" --> "Greenplum master" ++ : CHECK IF TABLE EXISTS gp_table + "Greenplum master" --> "Spark driver" : TABLE NOT EXISTS + + == DBWriter.run(df) == + + "Spark driver" -> "Spark executor1" ++ : START EXECUTOR FOR df(id bigint, col1 int, col2 text, ...) PARTITION 1 + "Spark driver" -> "Spark executor2" ++ : START EXECUTOR FOR df(id bigint, col1 int, col2 text, ...) PARTITION 2 + "Spark driver" -> "Spark executorN" ++ : START EXECUTOR FOR df(id bigint, col1 int, col2 text, ...) PARTITION N + + note right of "Spark driver" : This is done in parallel,\nexecutors are independent\n|\n|\n|\nV + "Spark executor1" -> "Greenplum master" ++ : CREATE READABLE EXTERNAL TABLE spark_executor1 (id bigint, col1 int, col2 text, ...) USING address=executor1_host:executor1_port;\nINSERT INTO gp_table FROM spark_executor1 + note right of "Greenplum master" : Each white vertical line here is a opened connection to master.\nUsually, **N+1** connections are created from Spark to Greenplum master + "Greenplum master" --> "Greenplum segment1" ++ : SELECT DATA FROM spark_executor1 TO gp_table_data_on_segment1 + note right of "Greenplum segment1" : No direct requests between Greenplum segments & Spark.\nData transfer is always initiated by Greenplum segments. + + "Spark executor2" -> "Greenplum master" ++ : CREATE READABLE EXTERNAL TABLE spark_executor2 (id bigint, col1 int, col2 text, ...) USING address=executor2_host:executor2_port;\nINSERT INTO gp_table FROM spark_executor2 + "Greenplum master" --> "Greenplum segment2" ++ : SELECT DATA FROM spark_executor2 TO gp_table_data_on_segment2 + + "Spark executorN" -> "Greenplum master" ++ : CREATE READABLE EXTERNAL TABLE spark_executorN (id bigint, col1 int, col2 text, ...) USING address=executorN_host:executorN_port;\nINSERT INTO gp_table FROM spark_executorN + "Greenplum master" --> "Greenplum segmentN" ++ : SELECT DATA FROM spark_executorN TO gp_table_data_on_segmentN + + "Greenplum segment1" -->o "Spark executor1" : INITIALIZE CONNECTION TO Spark executor1 + "Spark executor1" -> "Greenplum segment1" : READ DATA FROM Spark executor1 + note left of "Spark executor1" : Circle is an open GPFDIST port,\nlistened by executor + deactivate "Greenplum segment1" + + "Greenplum segment2" -->o "Spark executor2" : INITIALIZE CONNECTION TO Spark executor2 + "Spark executor2" -> "Greenplum segment2" : READ DATA FROM Spark executor2 + deactivate "Greenplum segment2" + + "Greenplum segmentN" -->o "Spark executorN" : INITIALIZE CONNECTION TO Spark executorN + "Spark executorN" -> "Greenplum segmentN" : READ DATA FROM Spark executorN + deactivate "Greenplum segmentN" + + == Finished == + + "Spark executor1" --> "Greenplum master" : DROP TABLE spark_executor1 + deactivate "Greenplum master" + "Spark executor2" --> "Greenplum master" : DROP TABLE spark_executor2 + deactivate "Greenplum master" + "Spark executorN" --> "Greenplum master" : DROP TABLE spark_executorN + deactivate "Greenplum master" + + "Spark executor1" --> "Spark driver" -- : DONE + "Spark executor2" --> "Spark driver" -- : DONE + "Spark executorN" --> "Spark driver" -- : DONE + + "Spark driver" --> "Greenplum master" : CLOSE CONNECTION + deactivate "Greenplum master" + deactivate "Spark driver" + @enduml +``` + +## Options + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.greenplum.options +``` + +```{eval-rst} +.. autopydantic_model:: GreenplumWriteOptions + :member-order: bysource + :model-show-field-summary: false + :field-show-constraints: false +``` diff --git a/mddocs/docs/connection/db_connection/hive/connection.md b/mddocs/docs/connection/db_connection/hive/connection.md new file mode 100644 index 000000000..a9a0cfc55 --- /dev/null +++ b/mddocs/docs/connection/db_connection/hive/connection.md @@ -0,0 +1,13 @@ +(hive-connection)= + +# Hive Connection + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.hive.connection +``` + +```{eval-rst} +.. autoclass:: Hive + :members: get_current, check + :member-order: bysource +``` diff --git a/mddocs/docs/connection/db_connection/hive/execute.md b/mddocs/docs/connection/db_connection/hive/execute.md new file mode 100644 index 000000000..d7b0694c4 --- /dev/null +++ b/mddocs/docs/connection/db_connection/hive/execute.md @@ -0,0 +1,55 @@ +(hive-execute)= + +# Executing statements in Hive + +Use `Hive.execute(...)` to execute DDL and DML operations. + +## Syntax support + +This method supports **any** query syntax supported by Hive, like: + +- ✅︎ `CREATE TABLE ...`, `CREATE VIEW ...`, and so on +- ✅︎ `LOAD DATA ...`, and so on +- ✅︎ `ALTER ...` +- ✅︎ `INSERT INTO ... SELECT ...`, and so on +- ✅︎ `DROP TABLE ...`, `DROP VIEW ...`, and so on +- ✅︎ `MSCK REPAIR TABLE ...`, and so on +- ✅︎ other statements not mentioned here +- ❌ `SET ...; SELECT ...;` - multiple statements not supported + +```{eval-rst} +.. warning:: + + Actually, query should be written using `SparkSQL `_ syntax, not HiveQL. +``` + +## Examples + +```python +from onetl.connection import Hive + +hive = Hive(...) + +hive.execute("DROP TABLE schema.table") +hive.execute( + """ + CREATE TABLE schema.table ( + id NUMBER, + key VARCHAR, + value DOUBLE + ) + PARTITION BY (business_date DATE) + STORED AS orc + """ +) +``` + +### Details + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.hive.connection +``` + +```{eval-rst} +.. automethod:: Hive.execute +``` diff --git a/mddocs/docs/connection/db_connection/hive/index.md b/mddocs/docs/connection/db_connection/hive/index.md new file mode 100644 index 000000000..bb8e5ebcc --- /dev/null +++ b/mddocs/docs/connection/db_connection/hive/index.md @@ -0,0 +1,28 @@ +(hive)= + +# Hive + +```{toctree} +:caption: Connection +:maxdepth: 1 + +prerequisites +connection +``` + +```{toctree} +:caption: Operations +:maxdepth: 1 + +read +sql +write +execute +``` + +```{toctree} +:caption: For developers +:maxdepth: 1 + +slots +``` diff --git a/mddocs/docs/connection/db_connection/hive/prerequisites.md b/mddocs/docs/connection/db_connection/hive/prerequisites.md new file mode 100644 index 000000000..79ff0e5dc --- /dev/null +++ b/mddocs/docs/connection/db_connection/hive/prerequisites.md @@ -0,0 +1,134 @@ +(hive-prerequisites)= + +# Prerequisites + +```{eval-rst} +.. note:: + + onETL's Hive connection is actually SparkSession with access to `Hive Thrift Metastore `_ + and HDFS/S3. + All data motion is made using Spark. Hive Metastore is used only to store tables and partitions metadata. + + This connector does **NOT** require Hive server. It also does **NOT** use Hive JDBC connector. +``` + +## Version Compatibility + +- Hive Metastore version: + : - Officially declared: 0.12 - 3.1.3 (may require to add proper .jar file explicitly) + - Actually tested: 1.2.100, 2.3.10, 3.1.3 +- Spark versions: 2.3.x - 3.5.x +- Java versions: 8 - 20 + +See [official documentation](https://spark.apache.org/docs/latest/sql-data-sources-hive-tables.html). + +## Installing PySpark + +To use Hive connector you should have PySpark installed (or injected to `sys.path`) +BEFORE creating the connector instance. + +See {ref}`install-spark` installation instruction for more details. + +## Connecting to Hive Metastore + +```{eval-rst} +.. note:: + + If you're using managed Hadoop cluster, skip this step, as all Spark configs are should already present on the host. +``` + +Create `$SPARK_CONF_DIR/hive-site.xml` with Hive Metastore URL: + +```xml + + + + + hive.metastore.uris + thrift://metastore.host.name:9083 + + +``` + +Create `$SPARK_CONF_DIR/core-site.xml` with warehouse location ,e.g. HDFS IPC port of Hadoop namenode, or S3 bucket address & credentials: + +```{eval-rst} +.. tabs:: + + .. code-tab:: xml HDFS + + + + + + fs.defaultFS + hdfs://myhadoopcluster:9820 + + + + .. code-tab:: xml S3 + + + + + !-- See https://hadoop.apache.org/docs/current/hadoop-aws/tools/hadoop-aws/index.html#General_S3A_Client_configuration + + fs.defaultFS + s3a://mys3bucket/ + + + fs.s3a.bucket.mybucket.endpoint + http://s3.somain + + + fs.s3a.bucket.mybucket.connection.ssl.enabled + false + + + fs.s3a.bucket.mybucket.path.style.access + true + + + fs.s3a.bucket.mybucket.aws.credentials.provider + org.apache.hadoop.fs.s3a.SimpleAWSCredentialsProvider + + + fs.s3a.bucket.mybucket.access.key + some-user + + + fs.s3a.bucket.mybucket.secret.key + mysecrettoken + + +``` + +## Using Kerberos + +Some of Hadoop managed clusters use Kerberos authentication. In this case, you should call [kinit](https://web.mit.edu/kerberos/krb5-1.12/doc/user/user_commands/kinit.html) command +**BEFORE** starting Spark session to generate Kerberos ticket. See {ref}`install-kerberos`. + +Sometimes it is also required to pass keytab file to Spark config, allowing Spark executors to generate own Kerberos tickets: + +```{eval-rst} +.. tabs:: + + .. code-tab:: python Spark 3 + + SparkSession.builder + .option("spark.kerberos.access.hadoopFileSystems", "hdfs://namenode1.domain.com:9820,hdfs://namenode2.domain.com:9820") + .option("spark.kerberos.principal", "user") + .option("spark.kerberos.keytab", "/path/to/keytab") + .gerOrCreate() + + .. code-tab:: python Spark 2 + + SparkSession.builder + .option("spark.yarn.access.hadoopFileSystems", "hdfs://namenode1.domain.com:9820,hdfs://namenode2.domain.com:9820") + .option("spark.yarn.principal", "user") + .option("spark.yarn.keytab", "/path/to/keytab") + .gerOrCreate() +``` + +See [Spark security documentation](https://spark.apache.org/docs/latest/security.html#kerberos) +for more details. diff --git a/mddocs/docs/connection/db_connection/hive/read.md b/mddocs/docs/connection/db_connection/hive/read.md new file mode 100644 index 000000000..82433c988 --- /dev/null +++ b/mddocs/docs/connection/db_connection/hive/read.md @@ -0,0 +1,95 @@ +(hive-read)= + +# Reading from Hive using `DBReader` + +{obj}`DBReader ` supports {ref}`strategy` for incremental data reading, +but does not support custom queries, like `JOIN`. + +## Supported DBReader features + +- ✅︎ `columns` +- ✅︎ `where` +- ✅︎ `hwm`, supported strategies: +- - ✅︎ {ref}`snapshot-strategy` +- - ✅︎ {ref}`incremental-strategy` +- - ✅︎ {ref}`snapshot-batch-strategy` +- - ✅︎ {ref}`incremental-batch-strategy` +- ❌ `hint` (is not supported by Hive) +- ❌ `df_schema` +- ❌ `options` (only Spark config params are used) + +```{eval-rst} +.. warning:: + + Actually, ``columns``, ``where`` and ``hwm.expression`` should be written using `SparkSQL `_ syntax, + not HiveQL. +``` + +## Examples + +Snapshot strategy: + +```python +from onetl.connection import Hive +from onetl.db import DBReader + +hive = Hive(...) + +reader = DBReader( + connection=hive, + source="schema.table", + columns=["id", "key", "CAST(value AS text) value", "updated_dt"], + where="key = 'something'", +) +df = reader.run() +``` + +Incremental strategy: + +```python +from onetl.connection import Hive +from onetl.db import DBReader +from onetl.strategy import IncrementalStrategy + +hive = Hive(...) + +reader = DBReader( + connection=hive, + source="schema.table", + columns=["id", "key", "CAST(value AS text) value", "updated_dt"], + where="key = 'something'", + hwm=DBReader.AutoDetectHWM(name="hive_hwm", expression="updated_dt"), +) + +with IncrementalStrategy(): + df = reader.run() +``` + +## Recommendations + +### Use column-based write formats + +Prefer these write formats: +: - [ORC](https://spark.apache.org/docs/latest/sql-data-sources-orc.html) + - [Parquet](https://spark.apache.org/docs/latest/sql-data-sources-parquet.html) + - [Iceberg](https://iceberg.apache.org/spark-quickstart/) + - [Hudi](https://hudi.apache.org/docs/quick-start-guide/) + - [Delta](https://docs.delta.io/latest/quick-start.html#set-up-apache-spark-with-delta-lake) + +For colum-based write formats, each file contains separated sections there column data is stored. The file footer contains +location of each column section/group. Spark can use this information to load only sections required by specific query, e.g. only selected columns, +to drastically speed up the query. + +Another advantage is high compression ratio, e.g. 10x-100x in comparison to JSON or CSV. + +### Select only required columns + +Instead of passing `"*"` in `DBReader(columns=[...])` prefer passing exact column names. +This drastically reduces the amount of data read by Spark, **if column-based file formats are used**. + +### Use partition columns in `where` clause + +Queries should include `WHERE` clause with filters on Hive partitioning columns. +This allows Spark to read only small set of files (*partition pruning*) instead of scanning the entire table, so this drastically increases performance. + +Supported operators are: `=`, `>`, `<` and `BETWEEN`, and only against some **static** value. diff --git a/mddocs/docs/connection/db_connection/hive/slots.md b/mddocs/docs/connection/db_connection/hive/slots.md new file mode 100644 index 000000000..8db1f7610 --- /dev/null +++ b/mddocs/docs/connection/db_connection/hive/slots.md @@ -0,0 +1,13 @@ +(hive-slots)= + +# Hive Slots + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.hive.slots +``` + +```{eval-rst} +.. autoclass:: HiveSlots + :members: normalize_cluster_name, get_known_clusters, get_current_cluster + :member-order: bysource +``` diff --git a/mddocs/docs/connection/db_connection/hive/sql.md b/mddocs/docs/connection/db_connection/hive/sql.md new file mode 100644 index 000000000..9b035f8ff --- /dev/null +++ b/mddocs/docs/connection/db_connection/hive/sql.md @@ -0,0 +1,79 @@ +(hive-sql)= + +# Reading from Hive using `Hive.sql` + +`Hive.sql` allows passing custom SQL query, but does not support incremental strategies. + +## Syntax support + +Only queries with the following syntax are supported: + +- ✅︎ `SELECT ... FROM ...` +- ✅︎ `WITH alias AS (...) SELECT ...` +- ❌ `SET ...; SELECT ...;` - multiple statements not supported + +```{eval-rst} +.. warning:: + + Actually, query should be written using `SparkSQL `_ syntax, not HiveQL. +``` + +## Examples + +```python +from onetl.connection import Hive + +hive = Hive(...) +df = hive.sql( + """ + SELECT + id, + key, + CAST(value AS text) value, + updated_at + FROM + some.mytable + WHERE + key = 'something' + """ +) +``` + +## Recommendations + +### Use column-based write formats + +Prefer these write formats: +: - [ORC](https://spark.apache.org/docs/latest/sql-data-sources-orc.html) + - [Parquet](https://spark.apache.org/docs/latest/sql-data-sources-parquet.html) + - [Iceberg](https://iceberg.apache.org/spark-quickstart/) + - [Hudi](https://hudi.apache.org/docs/quick-start-guide/) + - [Delta](https://docs.delta.io/latest/quick-start.html#set-up-apache-spark-with-delta-lake) + +For colum-based write formats, each file contains separated sections there column data is stored. The file footer contains +location of each column section/group. Spark can use this information to load only sections required by specific query, e.g. only selected columns, +to drastically speed up the query. + +Another advantage is high compression ratio, e.g. 10x-100x in comparison to JSON or CSV. + +### Select only required columns + +Instead of passing `SELECT * FROM ...` prefer passing exact column names `SELECT col1, col2, ...`. +This drastically reduces the amount of data read by Spark, **if column-based file formats are used**. + +### Use partition columns in `where` clause + +Queries should include `WHERE` clause with filters on Hive partitioning columns. +This allows Spark to read only small set of files (*partition pruning*) instead of scanning the entire table, so this drastically increases performance. + +Supported operators are: `=`, `>`, `<` and `BETWEEN`, and only against some **static** value. + +## Details + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.hive.connection +``` + +```{eval-rst} +.. automethod:: Hive.sql +``` diff --git a/mddocs/docs/connection/db_connection/hive/write.md b/mddocs/docs/connection/db_connection/hive/write.md new file mode 100644 index 000000000..8e6761146 --- /dev/null +++ b/mddocs/docs/connection/db_connection/hive/write.md @@ -0,0 +1,180 @@ +(hive-write)= + +# Writing to Hive using `DBWriter` + +For writing data to Hive, use {obj}`DBWriter `. + +## Examples + +```python +from onetl.connection import Hive +from onetl.db import DBWriter + +hive = Hive(...) + +df = ... # data is here + +# Create dataframe with specific number of Spark partitions. +# Use the Hive partitioning columns to group the data. Create max 20 files per Hive partition. +# Also sort the data by column which most data is correlated with (e.g. user_id), reducing files size. + +num_files_per_partition = 20 +partition_columns = ["country", "business_date"] +sort_columns = ["user_id"] +write_df = df.repartition( + num_files_per_partition, + *partition_columns, + *sort_columns, +).sortWithinPartitions(*partition_columns, *sort_columns) + +writer = DBWriter( + connection=hive, + target="schema.table", + options=Hive.WriteOptions( + if_exists="append", + # Hive partitioning columns. + partitionBy=partition_columns, + ), +) + +writer.run(write_df) +``` + +## Recommendations + +### Use column-based write formats + +Prefer these write formats: +: - [ORC](https://spark.apache.org/docs/latest/sql-data-sources-orc.html) (**default**) + - [Parquet](https://spark.apache.org/docs/latest/sql-data-sources-parquet.html) + - [Iceberg](https://iceberg.apache.org/spark-quickstart/) + - [Hudi](https://hudi.apache.org/docs/quick-start-guide/) + - [Delta](https://docs.delta.io/latest/quick-start.html#set-up-apache-spark-with-delta-lake) + +```{eval-rst} +.. warning:: + When using ``DBWriter``, the default spark data format configured in ``spark.sql.sources.default`` is ignored, as ``Hive.WriteOptions(format=...)`` default value is explicitly set to ``orc``. +``` + +For column-based write formats, each file contains separated sections where column data is stored. The file footer contains +location of each column section/group. Spark can use this information to load only sections required by specific query, e.g. only selected columns, +to drastically speed up the query. + +Another advantage is high compression ratio, e.g. 10x-100x in comparison to JSON or CSV. + +### Use partitioning + +#### How does it work + +Hive support splitting data to partitions, which are different directories in filesystem with names like `some_col=value1/another_col=value2`. + +For example, dataframe with content like this: + +| country: string | business_date: date | user_id: int | bytes: long | +| --------------- | ------------------- | ------------ | ----------- | +| RU | 2024-01-01 | 1234 | 25325253525 | +| RU | 2024-01-01 | 2345 | 23234535243 | +| RU | 2024-01-02 | 1234 | 62346634564 | +| US | 2024-01-01 | 5678 | 4252345354 | +| US | 2024-01-02 | 5678 | 5474575745 | +| US | 2024-01-03 | 5678 | 3464574567 | + +With `partitionBy=["country", "business_dt"]` data will be stored as files in the following subfolders: +: - `/country=RU/business_date=2024-01-01/` + - `/country=RU/business_date=2024-01-02/` + - `/country=US/business_date=2024-01-01/` + - `/country=US/business_date=2024-01-02/` + - `/country=US/business_date=2024-01-03/` + +A separated subdirectory is created for each distinct combination of column values in the dataframe. + +Please do not confuse Spark dataframe partitions (a.k.a batches of data handled by Spark executors, usually in parallel) +and Hive partitioning (store data in different subdirectories). +Number of Spark dataframe partitions is correlated the number of files created in **each** Hive partition. +For example, Spark dataframe with 10 partitions and 5 distinct values of Hive partition columns will be saved as 5 subfolders with 10 files each = 50 files in total. +Without Hive partitioning, all the files are placed into one flat directory. + +#### But why? + +Queries which has `WHERE` clause with filters on Hive partitioning columns, like `WHERE country = 'RU' AND business_date='2024-01-01'`, will +read only files from this exact partitions, like `/country=RU/business_date=2024-01-01/`, and skip files from other partitions. + +This drastically increases performance and reduces the amount of memory used by Spark. +Consider using Hive partitioning in all tables. + +#### Which columns should I use? + +Usually Hive partitioning columns are based on event date or location, like `country: string`, `business_date: date`, `run_date: date` and so on. + +**Partition columns should contain data with low cardinality.** +Dates, small integers, strings with low number of possible values are OK. +But timestamp, float, decimals, longs (like user id), strings with lots oj unique values (like user name or email) should **NOT** be used as Hive partitioning columns. +Unlike some other databases, range and hash-based partitions are not supported. + +Partition column should be a part of a dataframe. If you want to partition values by date component of `business_dt: timestamp` column, +add a new column to dataframe like this: `df.withColumn("business_date", date(df.business_dt))`. + +### Use compression + +Using compression algorithms like `snappy`, `lz4` or `zstd` can reduce the size of files (up to 10x). + +### Prefer creating large files + +Storing millions of small files is not that HDFS and S3 are designed for. Minimal file size should be at least 10Mb, but usually it is like 128Mb+ or 256Mb+ (HDFS block size). +**NEVER** create files with few Kbytes in size. + +Number of files can be different in different cases. +On one hand, Spark Adaptive Query Execution (AQE) can merge small Spark dataframe partitions into one larger. +On the other hand, dataframes with skewed data can produce a larger number of files than expected. + +To create small amount of large files, you can reduce number of Spark dataframe partitions. +Use [df.repartition(N, columns...)](https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.DataFrame.repartition.html) function, +like this: `df.repartition(20, "col1", "col2")`. +This creates new Spark dataframe with partitions using `hash(df.col1 + df.col2) mod 20` expression, avoiding data skew. + +Note: larger dataframe partitions requires more resources (CPU, RAM) on Spark executor. The exact number of partitions +should be determined empirically, as it depends on the amount of data and available resources. + +### Sort data before writing + +Dataframe with sorted content: + +| country: string | business_date: date | user_id: int | business_dt: timestamp | bytes: long | +| --------------- | ------------------- | ------------ | ----------------------- | ----------- | +| RU | 2024-01-01 | 1234 | 2024-01-01T11:22:33.456 | 25325253525 | +| RU | 2024-01-01 | 1234 | 2024-01-01T12:23:44.567 | 25325253525 | +| RU | 2024-01-02 | 1234 | 2024-01-01T13:25:56.789 | 34335645635 | +| US | 2024-01-01 | 2345 | 2024-01-01T10:00:00.000 | 12341 | +| US | 2024-01-02 | 2345 | 2024-01-01T15:11:22.345 | 13435 | +| US | 2024-01-03 | 2345 | 2024-01-01T20:22:33.567 | 14564 | + +Has a much better compression rate than unsorted one, e.g. 2x or even higher: + +| country: string | business_date: date | user_id: int | business_dt: timestamp | bytes: long | +| --------------- | ------------------- | ------------ | ----------------------- | ----------- | +| RU | 2024-01-01 | 1234 | 2024-01-01T11:22:33.456 | 25325253525 | +| RU | 2024-01-01 | 6345 | 2024-12-01T23:03:44.567 | 25365 | +| RU | 2024-01-02 | 5234 | 2024-07-01T06:10:56.789 | 45643456747 | +| US | 2024-01-01 | 4582 | 2024-04-01T17:59:00.000 | 362546475 | +| US | 2024-01-02 | 2345 | 2024-09-01T04:24:22.345 | 3235 | +| US | 2024-01-03 | 3575 | 2024-03-01T21:37:33.567 | 346345764 | + +Choosing columns to sort data by is really depends on the data. If data is correlated with some specific +column, like in example above the amount of traffic is correlated with both `user_id` and `timestamp`, +use `df.sortWithinPartitions("user_id", "timestamp")` before writing the data. + +If `df.repartition(N, repartition_columns...)` is used in combination with `df.sortWithinPartitions(sort_columns...)`, +then `sort_columns` should start with `repartition_columns` or be equal to it. + +## Options + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.hive.options +``` + +```{eval-rst} +.. autopydantic_model:: HiveWriteOptions + :member-order: bysource + :model-show-field-summary: false + :field-show-constraints: false +``` diff --git a/mddocs/docs/connection/db_connection/index.md b/mddocs/docs/connection/db_connection/index.md new file mode 100644 index 000000000..4977cf5d5 --- /dev/null +++ b/mddocs/docs/connection/db_connection/index.md @@ -0,0 +1,19 @@ +(db-connections)= + +# DB Connections + +```{toctree} +:caption: DB Connections +:maxdepth: 1 + +Clickhouse +Greenplum +Kafka +Hive +MongoDB +MSSQL +MySQL +Oracle +Postgres +Teradata +``` diff --git a/mddocs/docs/connection/db_connection/kafka/auth.md b/mddocs/docs/connection/db_connection/kafka/auth.md new file mode 100644 index 000000000..7d112d59a --- /dev/null +++ b/mddocs/docs/connection/db_connection/kafka/auth.md @@ -0,0 +1,13 @@ +(kafka-auth)= + +# Kafka Auth + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.kafka.kafka_auth +``` + +```{eval-rst} +.. autoclass:: KafkaAuth + :members: get_options, cleanup + :member-order: bysource +``` diff --git a/mddocs/docs/connection/db_connection/kafka/basic_auth.md b/mddocs/docs/connection/db_connection/kafka/basic_auth.md new file mode 100644 index 000000000..cb3cded81 --- /dev/null +++ b/mddocs/docs/connection/db_connection/kafka/basic_auth.md @@ -0,0 +1,14 @@ +(kafka-basic-auth)= + +# Kafka BasicAuth + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.kafka.kafka_basic_auth +``` + +```{eval-rst} +.. autopydantic_model:: KafkaBasicAuth + :member-order: bysource + :model-show-field-summary: false + :field-show-constraints: false +``` diff --git a/mddocs/docs/connection/db_connection/kafka/connection.md b/mddocs/docs/connection/db_connection/kafka/connection.md new file mode 100644 index 000000000..169b5b701 --- /dev/null +++ b/mddocs/docs/connection/db_connection/kafka/connection.md @@ -0,0 +1,12 @@ +(kafka-connection)= + +# Kafka Connection + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.kafka.connection +``` + +```{eval-rst} +.. autoclass:: Kafka + :members: get_packages, get_exclude_packages, check, close +``` diff --git a/mddocs/docs/connection/db_connection/kafka/index.md b/mddocs/docs/connection/db_connection/kafka/index.md new file mode 100644 index 000000000..903feea96 --- /dev/null +++ b/mddocs/docs/connection/db_connection/kafka/index.md @@ -0,0 +1,46 @@ +(kafka)= + +# Kafka + +```{toctree} +:caption: Connection +:maxdepth: 1 + +prerequisites +connection +troubleshooting +``` + +```{toctree} +:caption: Protocols +:maxdepth: 1 + +plaintext_protocol +ssl_protocol +``` + +```{toctree} +:caption: Auth methods +:maxdepth: 1 + +basic_auth +kerberos_auth +scram_auth +``` + +```{toctree} +:caption: Operations +:maxdepth: 1 + +read +write +``` + +```{toctree} +:caption: For developers +:maxdepth: 1 + +auth +protocol +slots +``` diff --git a/mddocs/docs/connection/db_connection/kafka/kerberos_auth.md b/mddocs/docs/connection/db_connection/kafka/kerberos_auth.md new file mode 100644 index 000000000..637c9e16e --- /dev/null +++ b/mddocs/docs/connection/db_connection/kafka/kerberos_auth.md @@ -0,0 +1,14 @@ +(kafka-kerberos-auth)= + +# Kafka KerberosAuth + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.kafka.kafka_kerberos_auth +``` + +```{eval-rst} +.. autopydantic_model:: KafkaKerberosAuth + :member-order: bysource + :model-show-field-summary: false + :field-show-constraints: false +``` diff --git a/mddocs/docs/connection/db_connection/kafka/plaintext_protocol.md b/mddocs/docs/connection/db_connection/kafka/plaintext_protocol.md new file mode 100644 index 000000000..5bc9ae035 --- /dev/null +++ b/mddocs/docs/connection/db_connection/kafka/plaintext_protocol.md @@ -0,0 +1,14 @@ +(kafka-plaintext-protocol)= + +# Kafka PlaintextProtocol + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.kafka.kafka_plaintext_protocol +``` + +```{eval-rst} +.. autopydantic_model:: KafkaPlaintextProtocol + :member-order: bysource + :model-show-field-summary: false + :field-show-constraints: false +``` diff --git a/mddocs/docs/connection/db_connection/kafka/prerequisites.md b/mddocs/docs/connection/db_connection/kafka/prerequisites.md new file mode 100644 index 000000000..3020648e4 --- /dev/null +++ b/mddocs/docs/connection/db_connection/kafka/prerequisites.md @@ -0,0 +1,65 @@ +(kafka-prerequisites)= + +# Prerequisites + +## Version Compatibility + +- Kafka server versions: + : - Officially declared: 0.10 or higher + - Actually tested: 3.2.3, 3.9.0 (only Kafka 3.x supports message headers) +- Spark versions: 2.4.x - 3.5.x +- Java versions: 8 - 17 + +See [official documentation](https://spark.apache.org/docs/latest/structured-streaming-kafka-integration.html). + +## Installing PySpark + +To use Kafka connector you should have PySpark installed (or injected to `sys.path`) +BEFORE creating the connector instance. + +See {ref}`install-spark` installation instruction for more details. + +## Connecting to Kafka + +### Connection address + +Kafka is a distributed service, and usually has a list of brokers you can connect to (unlike other connectors, there only one host+port can be set). +Please contact your Kafka administrator to get addresses of these brokers, as there are no defaults. + +Also Kafka has a feature called *advertised listeners* - client connects to one broker, and received list of other brokers in the clusters. +So you don't have to pass all brokers to `addresses`, it can be some subset. Other broker addresses will be fetched directly from the cluster. + +### Connection protocol + +Kafka can support different connection protocols. List of currently supported protocols: +: - {obj}`PLAINTEXT ` (not secure) + - {obj}`SSL ` (secure, recommended) + +Note that specific port can listen for only one of these protocols, so it is important to set +proper port number + protocol combination. + +### Authentication mechanism + +Kafka can support different authentication mechanism (also known as [SASL](https://en.wikipedia.org/wiki/Simple_Authentication_and_Security_Layer)). + +List of currently supported mechanisms: +: - {obj}`PLAIN `. To no confuse this with `PLAINTEXT` connection protocol, onETL uses name `BasicAuth`. + - {obj}`GSSAPI `. To simplify naming, onETL uses name `KerberosAuth`. + - {obj}`SCRAM-SHA-256 or SCRAM-SHA-512 ` (recommended). + +Different mechanisms use different types of credentials (login + password, keytab file, and so on). + +Note that connection protocol and auth mechanism are set in pairs: +: - If you see `SASL_PLAINTEXT` this means `PLAINTEXT` connection protocol + some auth mechanism. + - If you see `SASL_SSL` this means `SSL` connection protocol + some auth mechanism. + - If you see just `PLAINTEXT` or `SSL` (**no** `SASL`), this means that authentication is disabled (anonymous access). + +Please contact your Kafka administrator to get details about enabled auth mechanism in a specific Kafka instance. + +### Required grants + +Ask your Kafka administrator to set following grants for a user, *if Kafka instance uses ACL*: +: - `Describe` + `Read` for reading data from Kafka (Consumer). + - `Describe` + `Write` for writing data from Kafka (Producer). + +More details can be found in [documentation](https://kafka.apache.org/documentation/#operations_in_kafka). diff --git a/mddocs/docs/connection/db_connection/kafka/protocol.md b/mddocs/docs/connection/db_connection/kafka/protocol.md new file mode 100644 index 000000000..b9278d009 --- /dev/null +++ b/mddocs/docs/connection/db_connection/kafka/protocol.md @@ -0,0 +1,13 @@ +(kafka-protocol)= + +# Kafka Protocol + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.kafka.kafka_protocol +``` + +```{eval-rst} +.. autoclass:: KafkaProtocol + :members: get_options, cleanup + :member-order: bysource +``` diff --git a/mddocs/docs/connection/db_connection/kafka/read.md b/mddocs/docs/connection/db_connection/kafka/read.md new file mode 100644 index 000000000..be707f260 --- /dev/null +++ b/mddocs/docs/connection/db_connection/kafka/read.md @@ -0,0 +1,137 @@ +(kafka-read)= + +# Reading from Kafka + +Data can be read from Kafka to Spark using {obj}`DBReader `. +It also supports {ref}`strategy` for incremental data reading. + +## Supported DBReader features + +- ❌ `columns` (is not supported by Kafka) +- ❌ `where` (is not supported by Kafka) +- ✅︎ `hwm`, supported strategies: +- - ✅︎ {ref}`snapshot-strategy` +- - ✅︎ {ref}`incremental-strategy` +- - ❌ {ref}`snapshot-batch-strategy` +- - ❌ {ref}`incremental-batch-strategy` +- ❌ `hint` (is not supported by Kafka) +- ❌ `df_schema` (see note below) +- ✅︎ `options` (see {obj}`Kafka.ReadOptions `) + +## Dataframe schema + +Unlike other DB connections, Kafka does not have concept of columns. +All the topics messages have the same set of fields, see structure below: + +```text +root +|-- key: binary (nullable = true) +|-- value: binary (nullable = true) +|-- topic: string (nullable = false) +|-- partition: integer (nullable = false) +|-- offset: integer (nullable = false) +|-- timestamp: timestamp (nullable = false) +|-- timestampType: integer (nullable = false) +|-- headers: struct (nullable = true) + |-- key: string (nullable = false) + |-- value: binary (nullable = true) +``` + +`headers` field is present in the dataframe only if `Kafka.ReadOptions(include_headers=True)` is passed (compatibility with Kafka 1.x). + +## Value deserialization + +To read `value` or `key` of other type than bytes (e.g. struct or integer), users have to deserialize values manually. + +This could be done using following methods: +: - {obj}`Avro.parse_column ` + - {obj}`JSON.parse_column ` + - {obj}`CSV.parse_column ` + - {obj}`XML.parse_column ` + +## Examples + +Snapshot strategy, `value` is Avro binary data: + +```python +from onetl.connection import Kafka +from onetl.db import DBReader, DBWriter +from onetl.file.format import Avro +from pyspark.sql.functions import decode + +# read all topic data from Kafka +kafka = Kafka(...) +reader = DBReader(connection=kafka, source="avro_topic") +read_df = reader.run() + +# parse Avro format to Spark struct +avro = Avro( + schema_dict={ + "type": "record", + "name": "Person", + "fields": [ + {"name": "name", "type": "string"}, + {"name": "age", "type": "int"}, + ], + } +) +deserialized_df = read_df.select( + # cast binary key to string + decode("key", "UTF-8").alias("key"), + avro.parse_column("value"), +) +``` + +Incremental strategy, `value` is JSON string: + +```{eval-rst} +.. note:: + + Currently Kafka connector does support only HWMs based on ``offset`` field. Other fields, like ``timestamp``, are not yet supported. +``` + +```python +from onetl.connection import Kafka +from onetl.db import DBReader, DBWriter +from onetl.file.format import JSON +from pyspark.sql.functions import decode + +kafka = Kafka(...) + +# read only new data from Kafka topic +reader = DBReader( + connection=kafka, + source="topic_name", + hwm=DBReader.AutoDetectHWM(name="kafka_hwm", expression="offset"), +) + +with IncrementalStrategy(): + read_df = reader.run() + +# parse JSON format to Spark struct +json = JSON() +schema = StructType( + [ + StructField("name", StringType(), nullable=True), + StructField("age", IntegerType(), nullable=True), + ], +) +deserialized_df = read_df.select( + # cast binary key to string + decode("key", "UTF-8").alias("key"), + json.parse_column("value", json), +) +``` + +## Options + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.kafka.options +``` + +```{eval-rst} +.. autopydantic_model:: KafkaReadOptions + :member-order: bysource + :model-show-field-summary: false + :field-show-constraints: false +``` diff --git a/mddocs/docs/connection/db_connection/kafka/scram_auth.md b/mddocs/docs/connection/db_connection/kafka/scram_auth.md new file mode 100644 index 000000000..5a27a46ea --- /dev/null +++ b/mddocs/docs/connection/db_connection/kafka/scram_auth.md @@ -0,0 +1,14 @@ +(kafka-scram-auth)= + +# Kafka ScramAuth + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.kafka.kafka_scram_auth +``` + +```{eval-rst} +.. autopydantic_model:: KafkaScramAuth + :member-order: bysource + :model-show-field-summary: false + :field-show-constraints: false +``` diff --git a/mddocs/docs/connection/db_connection/kafka/slots.md b/mddocs/docs/connection/db_connection/kafka/slots.md new file mode 100644 index 000000000..0c58413fb --- /dev/null +++ b/mddocs/docs/connection/db_connection/kafka/slots.md @@ -0,0 +1,13 @@ +(kafka-slots)= + +# Kafka Slots + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.kafka.slots +``` + +```{eval-rst} +.. autoclass:: KafkaSlots + :members: normalize_cluster_name, get_known_clusters, normalize_address, get_cluster_addresses + :member-order: bysource +``` diff --git a/mddocs/docs/connection/db_connection/kafka/ssl_protocol.md b/mddocs/docs/connection/db_connection/kafka/ssl_protocol.md new file mode 100644 index 000000000..6e1a7641d --- /dev/null +++ b/mddocs/docs/connection/db_connection/kafka/ssl_protocol.md @@ -0,0 +1,14 @@ +(kafka-ssl-protocol)= + +# Kafka SSLProtocol + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.kafka.kafka_ssl_protocol +``` + +```{eval-rst} +.. autopydantic_model:: KafkaSSLProtocol + :member-order: bysource + :model-show-field-summary: false + :field-show-constraints: false +``` diff --git a/mddocs/docs/connection/db_connection/kafka/troubleshooting.md b/mddocs/docs/connection/db_connection/kafka/troubleshooting.md new file mode 100644 index 000000000..7a3536fbf --- /dev/null +++ b/mddocs/docs/connection/db_connection/kafka/troubleshooting.md @@ -0,0 +1,13 @@ +(kafka-troubleshooting)= + +# Kafka Troubleshooting + +```{eval-rst} +.. note:: + + General guide: :ref:`troubleshooting`. +``` + +## Cannot connect using `SSL` protocol + +Please check that certificate files are not Base-64 encoded. diff --git a/mddocs/docs/connection/db_connection/kafka/write.md b/mddocs/docs/connection/db_connection/kafka/write.md new file mode 100644 index 000000000..cb3e3019a --- /dev/null +++ b/mddocs/docs/connection/db_connection/kafka/write.md @@ -0,0 +1,75 @@ +(kafka-write)= + +# Writing to Kafka + +For writing data to Kafka, use {obj}`DBWriter ` with specific options (see below). + +## Dataframe schema + +Unlike other DB connections, Kafka does not have concept of columns. +All the topics messages have the same set of fields. Only some of them can be written: + +```text +root +|-- key: binary (nullable = true) +|-- value: binary (nullable = true) +|-- headers: struct (nullable = true) + |-- key: string (nullable = false) + |-- value: binary (nullable = true) +``` + +`headers` can be passed only with `Kafka.WriteOptions(include_headers=True)` (compatibility with Kafka 1.x). + +Field `topic` should not be present in the dataframe, as it is passed to `DBWriter(target=...)`. + +Other fields, like `partition`, `offset`, `timestamp` are set by Kafka, and cannot be passed explicitly. + +## Value serialization + +To write `value` or `key` of other type than bytes (e.g. struct or integer), users have to serialize values manually. + +This could be done using following methods: +: - {obj}`Avro.serialize_column ` + - {obj}`JSON.serialize_column ` + - {obj}`CSV.serialize_column ` + +## Examples + +Convert `value` to JSON string, and write to Kafka: + +```python +from onetl.connection import Kafka +from onetl.db import DBWriter +from onetl.file.format import JSON + +df = ... # original data is here + +# serialize struct data as JSON +json = JSON() +write_df = df.select( + df.key, + json.serialize_column(df.value), +) + +# write data to Kafka +kafka = Kafka(...) + +writer = DBWriter( + connection=kafka, + target="topic_name", +) +writer.run(write_df) +``` + +## Options + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.kafka.options +``` + +```{eval-rst} +.. autopydantic_model:: KafkaWriteOptions + :member-order: bysource + :model-show-field-summary: false + :field-show-constraints: false +``` diff --git a/mddocs/docs/connection/db_connection/mongodb/connection.md b/mddocs/docs/connection/db_connection/mongodb/connection.md new file mode 100644 index 000000000..b81c56ffc --- /dev/null +++ b/mddocs/docs/connection/db_connection/mongodb/connection.md @@ -0,0 +1,13 @@ +(mongodb-connection)= + +# MongoDB Connection + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.mongodb.connection +``` + +```{eval-rst} +.. autoclass:: MongoDB + :members: get_packages, check + :member-order: bysource +``` diff --git a/mddocs/docs/connection/db_connection/mongodb/index.md b/mddocs/docs/connection/db_connection/mongodb/index.md new file mode 100644 index 000000000..fb9656117 --- /dev/null +++ b/mddocs/docs/connection/db_connection/mongodb/index.md @@ -0,0 +1,27 @@ +(mongodb)= + +# MongoDB + +```{toctree} +:caption: Connection +:maxdepth: 1 + +prerequisites +connection +``` + +```{toctree} +:caption: Operations +:maxdepth: 1 + +read +pipeline +write +``` + +```{toctree} +:caption: Troubleshooting +:maxdepth: 1 + +types +``` diff --git a/mddocs/docs/connection/db_connection/mongodb/pipeline.md b/mddocs/docs/connection/db_connection/mongodb/pipeline.md new file mode 100644 index 000000000..2cc30458c --- /dev/null +++ b/mddocs/docs/connection/db_connection/mongodb/pipeline.md @@ -0,0 +1,41 @@ +(mongodb-sql)= + +# Reading from MongoDB using `MongoDB.pipeline` + +{obj}`MongoDB.sql ` allows passing custom pipeline, +but does not support incremental strategies. + +```{eval-rst} +.. warning:: + + Please take into account :ref:`mongodb-types` +``` + +## Recommendations + +### Pay attention to `pipeline` value + +Instead of filtering data on Spark side using `df.filter(df.column == 'value')` pass proper `mongodb.pipeline(..., pipeline={"$match": {"column": {"$eq": "value"}}})` value. +This both reduces the amount of data send from MongoDB to Spark, and may also improve performance of the query. +Especially if there are indexes for columns used in `pipeline` value. + +## References + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.mongodb.connection +``` + +```{eval-rst} +.. automethod:: MongoDB.pipeline +``` + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.mongodb.options +``` + +```{eval-rst} +.. autopydantic_model:: MongoDBPipelineOptions + :member-order: bysource + :model-show-field-summary: false + :field-show-constraints: false +``` diff --git a/mddocs/docs/connection/db_connection/mongodb/prerequisites.md b/mddocs/docs/connection/db_connection/mongodb/prerequisites.md new file mode 100644 index 000000000..f491ca10e --- /dev/null +++ b/mddocs/docs/connection/db_connection/mongodb/prerequisites.md @@ -0,0 +1,72 @@ +(mongodb-prerequisites)= + +# Prerequisites + +## Version Compatibility + +- MongoDB server versions: + : - Officially declared: 4.0 or higher + - Actually tested: 4.0.0, 8.0.4 +- Spark versions: 3.2.x - 3.5.x +- Java versions: 8 - 20 + +See [official documentation](https://www.mongodb.com/docs/spark-connector/). + +## Installing PySpark + +To use MongoDB connector you should have PySpark installed (or injected to `sys.path`) +BEFORE creating the connector instance. + +See {ref}`install-spark` installation instruction for more details. + +## Connecting to MongoDB + +### Connection host + +It is possible to connect to MongoDB host by using either DNS name of host or it's IP address. + +It is also possible to connect to MongoDB shared cluster: + +```python +mongo = MongoDB( + host="master.host.or.ip", + user="user", + password="*****", + database="target_database", + spark=spark, + extra={ + # read data from secondary cluster node, switch to primary if not available + "readPreference": "secondaryPreferred", + }, +) +``` + +Supported `readPreference` values are described in [official documentation](https://www.mongodb.com/docs/manual/core/read-preference/). + +### Connection port + +Connection is usually performed to port `27017`. Port may differ for different MongoDB instances. +Please ask your MongoDB administrator to provide required information. + +### Required grants + +Ask your MongoDB cluster administrator to set following grants for a user, +used for creating a connection: + +```{eval-rst} +.. tabs:: + + .. code-tab:: js Read + Write + + // allow writing data to specific database + db.grantRolesToUser("username", [{db: "somedb", role: "readWrite"}]) + + .. code-tab:: js Read only + + // allow reading data from specific database + db.grantRolesToUser("username", [{db: "somedb", role: "read"}]) +``` + +See: +: - [db.grantRolesToUser documentation](https://www.mongodb.com/docs/manual/reference/method/db.grantRolesToUser) + - [MongoDB builtin roles](https://www.mongodb.com/docs/manual/reference/built-in-roles) diff --git a/mddocs/docs/connection/db_connection/mongodb/read.md b/mddocs/docs/connection/db_connection/mongodb/read.md new file mode 100644 index 000000000..15cf34bb1 --- /dev/null +++ b/mddocs/docs/connection/db_connection/mongodb/read.md @@ -0,0 +1,141 @@ +(mongodb-read)= + +# Reading from MongoDB using `DBReader` + +{obj}`DBReader ` supports {ref}`strategy` for incremental data reading, +but does not support custom pipelines, e.g. aggregation. + +```{eval-rst} +.. warning:: + + Please take into account :ref:`mongodb-types` +``` + +## Supported DBReader features + +- ❌ `columns` (for now, all document fields are read) +- ✅︎ `where` (passed to `{"$match": ...}` aggregation pipeline) +- ✅︎ `hwm`, supported strategies: +- - ✅︎ {ref}`snapshot-strategy` +- - ✅︎ {ref}`incremental-strategy` +- - ✅︎ {ref}`snapshot-batch-strategy` +- - ✅︎ {ref}`incremental-batch-strategy` +- - Note that `expression` field of HWM can only be a field name, not a custom expression +- ✅︎ `hint` (see [official documentation](https://www.mongodb.com/docs/v5.0/reference/operator/meta/hint/)) +- ✅︎ `df_schema` (mandatory) +- ✅︎ `options` (see {obj}`MongoDB.ReadOptions `) + +## Examples + +Snapshot strategy: + +```python +from onetl.connection import MongoDB +from onetl.db import DBReader + +from pyspark.sql.types import ( + StructType, + StructField, + IntegerType, + StringType, + TimestampType, +) + +mongodb = MongoDB(...) + +# mandatory +df_schema = StructType( + [ + StructField("_id", StringType()), + StructField("some", StringType()), + StructField( + "field", + StructType( + [ + StructField("nested", IntegerType()), + ], + ), + ), + StructField("updated_dt", TimestampType()), + ] +) + +reader = DBReader( + connection=mongodb, + source="some_collection", + df_schema=df_schema, + where={"field": {"$eq": 123}}, + hint={"field": 1}, + options=MongoDBReadOptions(batchSize=10000), +) +df = reader.run() +``` + +Incremental strategy: + +```python +from onetl.connection import MongoDB +from onetl.db import DBReader +from onetl.strategy import IncrementalStrategy + +from pyspark.sql.types import ( + StructType, + StructField, + IntegerType, + StringType, + TimestampType, +) + +mongodb = MongoDB(...) + +# mandatory +df_schema = StructType( + [ + StructField("_id", StringType()), + StructField("some", StringType()), + StructField( + "field", + StructType( + [ + StructField("nested", IntegerType()), + ], + ), + ), + StructField("updated_dt", TimestampType()), + ] +) + +reader = DBReader( + connection=mongodb, + source="some_collection", + df_schema=df_schema, + where={"field": {"$eq": 123}}, + hint={"field": 1}, + hwm=DBReader.AutoDetectHWM(name="mongodb_hwm", expression="updated_dt"), + options=MongoDBReadOptions(batchSize=10000), +) + +with IncrementalStrategy(): + df = reader.run() +``` + +## Recommendations + +### Pay attention to `where` value + +Instead of filtering data on Spark side using `df.filter(df.column == 'value')` pass proper `DBReader(where={"column": {"$eq": "value"}})` clause. +This both reduces the amount of data send from MongoDB to Spark, and may also improve performance of the query. +Especially if there are indexes for columns used in `where` clause. + +## Read options + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.mongodb.options +``` + +```{eval-rst} +.. autopydantic_model:: MongoDBReadOptions + :member-order: bysource + :model-show-field-summary: false + :field-show-constraints: false +``` diff --git a/mddocs/docs/connection/db_connection/mongodb/types.md b/mddocs/docs/connection/db_connection/mongodb/types.md new file mode 100644 index 000000000..5c6c506d0 --- /dev/null +++ b/mddocs/docs/connection/db_connection/mongodb/types.md @@ -0,0 +1,269 @@ +(mongodb-types)= + +# MongoDB \<-> Spark type mapping + +```{eval-rst} +.. note:: + + The results below are valid for Spark 3.5.5, and may differ on other Spark versions. +``` + +## Type detection & casting + +Spark's DataFrames always have a `schema` which is a list of fields with corresponding Spark types. All operations on a field are performed using field type. + +MongoDB is, by design, \_\_schemaless\_\_. So there are 2 ways how this can be handled: + +- User provides DataFrame schema explicitly: + + ```{eval-rst} + .. dropdown:: See example + + .. code-block:: python + + from onetl.connection import MongoDB + from onetl.db import DBReader + + from pyspark.sql.types import ( + StructType, + StructField, + IntegerType, + StringType, + TimestampType, + ) + + mongodb = MongoDB(...) + + df_schema = StructType( + [ + StructField("_id", StringType()), + StructField("some", StringType()), + StructField( + "field", + StructType( + [ + StructField("nested", IntegerType()), + ] + ), + ), + ] + ) + + reader = DBReader( + connection=mongodb, + source="some_collection", + df_schema=df_schema, + ) + df = reader.run() + + # or + + df = mongodb.pipeline( + collection="some_collection", + df_schema=df_schema, + ) + ``` + +- Rely on MongoDB connector schema infer: + + ```python + df = mongodb.pipeline(collection="some_collection") + ``` + + In this case MongoDB connector read a sample of collection documents, and build DataFrame schema based on document fields and values. + +It is highly recommended to pass `df_schema` explicitly, to avoid type conversion issues. + +### References + +Here you can find source code with type conversions: + +- [MongoDB -> Spark](https://github.com/mongodb/mongo-spark/blob/r10.4.1/src/main/java/com/mongodb/spark/sql/connector/schema/InferSchema.java#L214-L260) +- [Spark -> MongoDB](https://github.com/mongodb/mongo-spark/blob/r10.4.1/src/main/java/com/mongodb/spark/sql/connector/schema/RowToBsonDocumentConverter.java#L157-L260) + +## Supported types + +See [official documentation](https://www.mongodb.com/docs/manual/reference/bson-types/) + +### Numeric types + +```{eval-rst} ++---------------------+-----------------------------+----------------------+ +| MongoDB type (read) | Spark type | MongoDB type (write) | ++=====================+=============================+======================+ +| ``Decimal128`` | ``DecimalType(P=34, S=32)`` | ``Decimal128`` | ++---------------------+-----------------------------+----------------------+ +| ``-`` | ``FloatType()`` | ``Double`` | ++---------------------+-----------------------------+ | +| ``Double`` | ``DoubleType()`` | | ++---------------------+-----------------------------+----------------------+ +| ``-`` | ``ByteType()`` | ``Int32`` | ++---------------------+-----------------------------+ | +| ``-`` | ``ShortType()`` | | ++---------------------+-----------------------------+ | +| ``Int32`` | ``IntegerType()`` | | ++---------------------+-----------------------------+----------------------+ +| ``Int64`` | ``LongType()`` | ``Int64`` | ++---------------------+-----------------------------+----------------------+ +``` + +### Temporal types + +```{eval-rst} ++------------------------+-----------------------------------+-------------------------+ +| MongoDB type (read) | Spark type | MongoDB type (write) | ++========================+===================================+=========================+ +| ``-`` | ``DateType()``, days | ``Date``, milliseconds | ++------------------------+-----------------------------------+-------------------------+ +| ``Date``, milliseconds | ``TimestampType()``, microseconds | ``Date``, milliseconds, | +| | | **precision loss** [2]_ | ++------------------------+-----------------------------------+-------------------------+ +| ``Timestamp``, seconds | ``TimestampType()``, microseconds | ``Date``, milliseconds | ++------------------------+-----------------------------------+-------------------------+ +| ``-`` | ``TimestampNTZType()`` | unsupported | ++------------------------+-----------------------------------+ | +| ``-`` | ``DayTimeIntervalType()`` | | ++------------------------+-----------------------------------+-------------------------+ +``` + +```{eval-rst} +.. warning:: + + Note that types in MongoDB and Spark have different value ranges: + + +---------------+--------------------------------+--------------------------------+---------------------+--------------------------------+--------------------------------+ + | MongoDB type | Min value | Max value | Spark type | Min value | Max value | + +===============+================================+================================+=====================+================================+================================+ + | ``Date`` | -290 million years | 290 million years | ``TimestampType()`` | ``0001-01-01 00:00:00.000000`` | ``9999-12-31 23:59:59.999999`` | + +---------------+--------------------------------+--------------------------------+ | | | + | ``Timestamp`` | ``1970-01-01 00:00:00`` | ``2106-02-07 09:28:16`` | | | | + +---------------+--------------------------------+--------------------------------+---------------------+--------------------------------+--------------------------------+ + + So not all values can be read from MongoDB to Spark, and can written from Spark DataFrame to MongoDB. + + References: + * `MongoDB Date type documentation `_ + * `MongoDB Timestamp documentation `_ + * `Spark DateType documentation `_ + * `Spark TimestampType documentation `_ +``` + +[^footnote-1]: MongoDB `Date` type has precision up to milliseconds (`23:59:59.999`). + Inserting data with microsecond precision (`23:59:59.999999`) + will lead to **throwing away microseconds**. + +### String types + +Note: fields of deprecated MongoDB type `Symbol` are excluded during read. + +```{eval-rst} ++---------------------+------------------+----------------------+ +| MongoDB type (read) | Spark type | MongoDB type (write) | ++=====================+==================+======================+ +| ``String`` | ``StringType()`` | ``String`` | ++---------------------+ | | +| ``Code`` | | | ++---------------------+ | | +| ``RegExp`` | | | ++---------------------+------------------+----------------------+ +``` + +### Binary types + +| MongoDB type (read) | Spark type | MongoDB type (write) | +| ------------------- | --------------- | -------------------- | +| `Boolean` | `BooleanType()` | `Boolean` | +| `Binary` | `BinaryType()` | `Binary` | + +### Struct types + +```{eval-rst} ++---------------------+-----------------------+----------------------+ +| MongoDB type (read) | Spark type | MongoDB type (write) | ++=====================+=======================+======================+ +| ``Array[T]`` | ``ArrayType(T)`` | ``Array[T]`` | ++---------------------+-----------------------+----------------------+ +| ``Object[...]`` | ``StructType([...])`` | ``Object[...]`` | ++---------------------+-----------------------+ | +| ``-`` | ``MapType(...)`` | | ++---------------------+-----------------------+----------------------+ +``` + +### Special types + +```{eval-rst} ++---------------------+---------------------------------------------------------+---------------------------------------+ +| MongoDB type (read) | Spark type | MongoDB type (write) | ++=====================+=========================================================+=======================================+ +| ``ObjectId`` | ``StringType()`` | ``String`` | ++---------------------+ | | +| ``MaxKey`` | | | ++---------------------+ | | +| ``MinKey`` | | | ++---------------------+---------------------------------------------------------+---------------------------------------+ +| ``Null`` | ``NullType()`` | ``Null`` | ++---------------------+ | | +| ``Undefined`` | | | ++---------------------+---------------------------------------------------------+---------------------------------------+ +| ``DBRef`` | ``StructType([$ref: StringType(), $id: StringType()])`` | ``Object[$ref: String, $id: String]`` | ++---------------------+---------------------------------------------------------+---------------------------------------+ +``` + +## Explicit type cast + +### `DBReader` + +Currently it is not possible to cast field types using `DBReader`. But this can be done using `MongoDB.pipeline`. + +### `MongoDB.pipeline` + +You can use `$project` aggregation to cast field types: + +```python +from pyspark.sql.types import IntegerType, StructField, StructType + +from onetl.connection import MongoDB +from onetl.db import DBReader + +mongodb = MongoDB(...) + +df = mongodb.pipeline( + collection="my_collection", + pipeline=[ + { + "$project": { + # convert unsupported_field to string + "unsupported_field_str": { + "$convert": { + "input": "$unsupported_field", + "to": "string", + }, + }, + # skip unsupported_field from result + "unsupported_field": 0, + } + } + ], +) + +# cast field content to proper Spark type +df = df.select( + df.id, + df.supported_field, + # explicit cast + df.unsupported_field_str.cast("integer").alias("parsed_integer"), +) +``` + +### `DBWriter` + +Convert dataframe field to string on Spark side, and then write it to MongoDB: + +```python +df = df.select( + df.id, + df.unsupported_field.cast("string").alias("array_field_json"), +) + +writer.run(df) +``` diff --git a/mddocs/docs/connection/db_connection/mongodb/write.md b/mddocs/docs/connection/db_connection/mongodb/write.md new file mode 100644 index 000000000..5b44d8613 --- /dev/null +++ b/mddocs/docs/connection/db_connection/mongodb/write.md @@ -0,0 +1,47 @@ +(mongodb-write)= + +# Writing to MongoDB using `DBWriter` + +For writing data to MongoDB, use {obj}`DBWriter `. + +```{eval-rst} +.. warning:: + + Please take into account :ref:`mongodb-types` +``` + +## Examples + +```python +from onetl.connection import MongoDB +from onetl.db import DBWriter + +mongodb = MongoDB(...) + +df = ... # data is here + +writer = DBWriter( + connection=mongodb, + target="schema.table", + options=MongoDB.WriteOptions( + if_exists="append", + ), +) + +writer.run(df) +``` + +## Write options + +Method above accepts {obj}`MongoDB.WriteOptions ` + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.mongodb.options +``` + +```{eval-rst} +.. autopydantic_model:: MongoDBWriteOptions + :member-order: bysource + :model-show-field-summary: false + :field-show-constraints: false +``` diff --git a/mddocs/docs/connection/db_connection/mssql/connection.md b/mddocs/docs/connection/db_connection/mssql/connection.md new file mode 100644 index 000000000..e35ef6ed9 --- /dev/null +++ b/mddocs/docs/connection/db_connection/mssql/connection.md @@ -0,0 +1,12 @@ +(mssql-connection)= + +# MSSQL connection + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.mssql.connection +``` + +```{eval-rst} +.. autoclass:: MSSQL + :members: get_packages, check +``` diff --git a/mddocs/docs/connection/db_connection/mssql/execute.md b/mddocs/docs/connection/db_connection/mssql/execute.md new file mode 100644 index 000000000..278d84229 --- /dev/null +++ b/mddocs/docs/connection/db_connection/mssql/execute.md @@ -0,0 +1,117 @@ +(mssql-execute)= + +# Executing statements in MSSQL + +```{eval-rst} +.. warning:: + + Methods below **read all the rows** returned from DB **to Spark driver memory**, and then convert them to DataFrame. + + Do **NOT** use them to read large amounts of data. Use :ref:`DBReader ` or :ref:`MSSQL.sql ` instead. +``` + +## How to + +There are 2 ways to execute some statement in MSSQL + +### Use `MSSQL.fetch` + +Use this method to perform some `SELECT` query which returns **small number or rows**, like reading +MSSQL config, or reading data from some reference table. Method returns Spark DataFrame. + +Method accepts {obj}`MSSQL.FetchOptions `. + +Connection opened using this method should be then closed with `connection.close()` or `with connection:`. + +```{eval-rst} +.. warning:: + + Please take into account :ref:`mssql-types`. +``` + +#### Syntax support + +This method supports **any** query syntax supported by MSSQL, like: + +- ✅︎ `SELECT ... FROM ...` +- ✅︎ `WITH alias AS (...) SELECT ...` +- ✅︎ `SELECT func(arg1, arg2) FROM DUAL` - call function +- ❌ `SET ...; SELECT ...;` - multiple statements not supported + +#### Examples + +```python +from onetl.connection import MSSQL + +mssql = MSSQL(...) + +df = mssql.fetch( + "SELECT value FROM some.reference_table WHERE key = 'some_constant'", + options=MSSQL.FetchOptions(queryTimeout=10), +) +mssql.close() +value = df.collect()[0][0] # get value from first row and first column +``` + +### Use `MSSQL.execute` + +Use this method to execute DDL and DML operations. Each method call runs operation in a separated transaction, and then commits it. + +Method accepts {obj}`MSSQL.ExecuteOptions `. + +Connection opened using this method should be then closed with `connection.close()` or `with connection:`. + +#### Syntax support + +This method supports **any** query syntax supported by MSSQL, like: + +- ✅︎ `CREATE TABLE ...`, `CREATE VIEW ...` +- ✅︎ `ALTER ...` +- ✅︎ `INSERT INTO ... AS SELECT ...` +- ✅︎ `DROP TABLE ...`, `DROP VIEW ...`, `TRUNCATE TABLE`, and so on +- ✅︎ `EXEC procedure(arg1, arg2) ...` or `{call procedure(arg1, arg2)}` - special syntax for calling procedure +- ✅︎ `DECLARE ... BEGIN ... END` - execute PL/SQL statement +- ✅︎ other statements not mentioned here +- ❌ `SET ...; SELECT ...;` - multiple statements not supported + +#### Examples + +```python +from onetl.connection import MSSQL + +mssql = MSSQL(...) + +mssql.execute("DROP TABLE schema.table") +mssql.execute( + """ + CREATE TABLE schema.table ( + id bigint GENERATED ALWAYS AS IDENTITY, + key VARCHAR2(4000), + value NUMBER + ) + """, + options=MSSQL.ExecuteOptions(queryTimeout=10), +) +``` + +## Options + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.mssql.options +``` + +```{eval-rst} +.. autopydantic_model:: MSSQLFetchOptions + :inherited-members: GenericOptions + :member-order: bysource + :model-show-field-summary: false + :field-show-constraints: false +``` + +```{eval-rst} +.. autopydantic_model:: MSSQLExecuteOptions + :inherited-members: GenericOptions + :member-order: bysource + :model-show-field-summary: false + :field-show-constraints: false +``` diff --git a/mddocs/docs/connection/db_connection/mssql/index.md b/mddocs/docs/connection/db_connection/mssql/index.md new file mode 100644 index 000000000..16448c6a1 --- /dev/null +++ b/mddocs/docs/connection/db_connection/mssql/index.md @@ -0,0 +1,28 @@ +(mssql)= + +# MSSQL + +```{toctree} +:caption: Connection +:maxdepth: 1 + +prerequisites +connection +``` + +```{toctree} +:caption: Operations +:maxdepth: 1 + +read +sql +write +execute +``` + +```{toctree} +:caption: Troubleshooting +:maxdepth: 1 + +types +``` diff --git a/mddocs/docs/connection/db_connection/mssql/prerequisites.md b/mddocs/docs/connection/db_connection/mssql/prerequisites.md new file mode 100644 index 000000000..89e62f322 --- /dev/null +++ b/mddocs/docs/connection/db_connection/mssql/prerequisites.md @@ -0,0 +1,76 @@ +(mssql-prerequisites)= + +# Prerequisites + +## Version Compatibility + +- SQL Server versions: + : - Officially declared: 2016 - 2022 + - Actually tested: 2017, 2022 +- Spark versions: 2.3.x - 3.5.x +- Java versions: 8 - 20 + +See [official documentation](https://learn.microsoft.com/en-us/sql/connect/jdbc/system-requirements-for-the-jdbc-driver) +and [official compatibility matrix](https://learn.microsoft.com/en-us/sql/connect/jdbc/microsoft-jdbc-driver-for-sql-server-support-matrix). + +## Installing PySpark + +To use MSSQL connector you should have PySpark installed (or injected to `sys.path`) +BEFORE creating the connector instance. + +See {ref}`install-spark` installation instruction for more details. + +## Connecting to MSSQL + +### Connection port + +Connection is usually performed to port 1433. Port may differ for different MSSQL instances. +Please ask your MSSQL administrator to provide required information. + +For named MSSQL instances (`instanceName` option), [port number is optional](https://learn.microsoft.com/en-us/sql/connect/jdbc/building-the-connection-url?view=sql-server-ver16#named-and-multiple-sql-server-instances), and could be omitted. + +### Connection host + +It is possible to connect to MSSQL by using either DNS name of host or it's IP address. + +If you're using MSSQL cluster, it is currently possible to connect only to **one specific node**. +Connecting to multiple nodes to perform load balancing, as well as automatic failover to new master/replica are not supported. + +### Required grants + +Ask your MSSQL cluster administrator to set following grants for a user, +used for creating a connection: + +```{eval-rst} +.. tabs:: + + .. code-tab:: sql Read + Write (schema is owned by user) + + -- allow creating tables for user + GRANT CREATE TABLE TO username; + + -- allow read & write access to specific table + GRANT SELECT, INSERT ON username.mytable TO username; + + -- only if if_exists="replace_entire_table" is used: + -- allow dropping/truncating tables in any schema + GRANT ALTER ON username.mytable TO username; + + .. code-tab:: sql Read + Write (schema is not owned by user) + + -- allow creating tables for user + GRANT CREATE TABLE TO username; + + -- allow managing tables in specific schema, and inserting data to tables + GRANT ALTER, SELECT, INSERT ON SCHEMA::someschema TO username; + + .. code-tab:: sql Read only + + -- allow read access to specific table + GRANT SELECT ON someschema.mytable TO username; +``` + +More details can be found in official documentation: +: - [GRANT ON DATABASE](https://learn.microsoft.com/en-us/sql/t-sql/statements/grant-database-permissions-transact-sql) + - [GRANT ON OBJECT](https://learn.microsoft.com/en-us/sql/t-sql/statements/grant-object-permissions-transact-sql) + - [GRANT ON SCHEMA](https://learn.microsoft.com/en-us/sql/t-sql/statements/grant-schema-permissions-transact-sql) diff --git a/mddocs/docs/connection/db_connection/mssql/read.md b/mddocs/docs/connection/db_connection/mssql/read.md new file mode 100644 index 000000000..924d10e2f --- /dev/null +++ b/mddocs/docs/connection/db_connection/mssql/read.md @@ -0,0 +1,93 @@ +(mssql-read)= + +# Reading from MSSQL using `DBReader` + +{obj}`DBReader ` supports {ref}`strategy` for incremental data reading, +but does not support custom queries, like `JOIN`. + +```{eval-rst} +.. warning:: + + Please take into account :ref:`mssql-types` +``` + +## Supported DBReader features + +- ✅︎ `columns` +- ✅︎ `where` +- ✅︎ `hwm`, supported strategies: +- - ✅︎ {ref}`snapshot-strategy` +- - ✅︎ {ref}`incremental-strategy` +- - ✅︎ {ref}`snapshot-batch-strategy` +- - ✅︎ {ref}`incremental-batch-strategy` +- ❌ `hint` (MSSQL does support hints, but DBReader not, at least for now) +- ❌ `df_schema` +- ✅︎ `options` (see {obj}`MSSQL.ReadOptions `) + +## Examples + +Snapshot strategy: + +```python +from onetl.connection import MSSQL +from onetl.db import DBReader + +mssql = MSSQL(...) + +reader = DBReader( + connection=mssql, + source="schema.table", + columns=["id", "key", "CAST(value AS text) value", "updated_dt"], + where="key = 'something'", + options=MSSQL.ReadOptions(partitionColumn="id", numPartitions=10), +) +df = reader.run() +``` + +Incremental strategy: + +```python +from onetl.connection import MSSQL +from onetl.db import DBReader +from onetl.strategy import IncrementalStrategy + +mssql = MSSQL(...) + +reader = DBReader( + connection=mssql, + source="schema.table", + columns=["id", "key", "CAST(value AS text) value", "updated_dt"], + where="key = 'something'", + hwm=DBReader.AutoDetectHWM(name="mssql_hwm", expression="updated_dt"), + options=MSSQL.ReadOptions(partitionColumn="id", numPartitions=10), +) + +with IncrementalStrategy(): + df = reader.run() +``` + +## Recommendations + +### Select only required columns + +Instead of passing `"*"` in `DBReader(columns=[...])` prefer passing exact column names. This reduces the amount of data passed from MSSQL to Spark. + +### Pay attention to `where` value + +Instead of filtering data on Spark side using `df.filter(df.column == 'value')` pass proper `DBReader(where="column = 'value'")` clause. +This both reduces the amount of data send from MSSQL to Spark, and may also improve performance of the query. +Especially if there are indexes or partitions for columns used in `where` clause. + +## Options + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.mssql.options +``` + +```{eval-rst} +.. autopydantic_model:: MSSQLReadOptions + :inherited-members: GenericOptions + :member-order: bysource + :model-show-field-summary: false + :field-show-constraints: false +``` diff --git a/mddocs/docs/connection/db_connection/mssql/sql.md b/mddocs/docs/connection/db_connection/mssql/sql.md new file mode 100644 index 000000000..de932e2d3 --- /dev/null +++ b/mddocs/docs/connection/db_connection/mssql/sql.md @@ -0,0 +1,80 @@ +(mssql-sql)= + +# Reading from MSSQL using `MSSQL.sql` + +`MSSQL.sql` allows passing custom SQL query, but does not support incremental strategies. + +```{eval-rst} +.. warning:: + + Please take into account :ref:`mssql-types` +``` + +```{eval-rst} +.. warning:: + + Statement is executed in **read-write** connection, so if you're calling some functions/procedures with DDL/DML statements inside, + they can change data in your database. +``` + +## Syntax support + +Only queries with the following syntax are supported: + +- ✅︎ `SELECT ... FROM ...` +- ❌ `WITH alias AS (...) SELECT ...` +- ❌ `SET ...; SELECT ...;` - multiple statements not supported + +## Examples + +```python +from onetl.connection import MSSQL + +mssql = MSSQL(...) +df = mssql.sql( + """ + SELECT + id, + key, + CAST(value AS text) value, + updated_at + FROM + some.mytable + WHERE + key = 'something' + """, + options=MSSQL.SQLOptions( + partitionColumn="id", + numPartitions=10, + lowerBound=0, + upperBound=1000, + ), +) +``` + +## Recommendations + +### Select only required columns + +Instead of passing `SELECT * FROM ...` prefer passing exact column names `SELECT col1, col2, ...`. +This reduces the amount of data passed from MSSQL to Spark. + +### Pay attention to `where` value + +Instead of filtering data on Spark side using `df.filter(df.column == 'value')` pass proper `WHERE column = 'value'` clause. +This both reduces the amount of data send from MSSQL to Spark, and may also improve performance of the query. +Especially if there are indexes or partitions for columns used in `where` clause. + +## Options + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.mssql.options +``` + +```{eval-rst} +.. autopydantic_model:: MSSQLSQLOptions + :inherited-members: GenericOptions + :member-order: bysource + :model-show-field-summary: false + :field-show-constraints: false +``` diff --git a/mddocs/docs/connection/db_connection/mssql/types.md b/mddocs/docs/connection/db_connection/mssql/types.md new file mode 100644 index 000000000..23e4972c9 --- /dev/null +++ b/mddocs/docs/connection/db_connection/mssql/types.md @@ -0,0 +1,376 @@ +(mssql-types)= + +# MSSQL \<-> Spark type mapping + +```{eval-rst} +.. note:: + + The results below are valid for Spark 3.5.5, and may differ on other Spark versions. +``` + +## Type detection & casting + +Spark's DataFrames always have a `schema` which is a list of columns with corresponding Spark types. All operations on a column are performed using column type. + +### Reading from MSSQL + +This is how MSSQL connector performs this: + +- For each column in query result (`SELECT column1, column2, ... FROM table ...`) get column name and MSSQL type. +- Find corresponding `MSSQL type (read)` → `Spark type` combination (see below) for each DataFrame column. If no combination is found, raise exception. +- Create DataFrame from query with specific column names and Spark types. + +### Writing to some existing MSSQL table + +This is how MSSQL connector performs this: + +- Get names of columns in DataFrame. [^footnote-1] +- Perform `SELECT * FROM table LIMIT 0` query. +- Take only columns present in DataFrame (by name, case insensitive). For each found column get MSSQL type. +- Find corresponding `Spark type` → `MSSQL type (write)` combination (see below) for each DataFrame column. If no combination is found, raise exception. +- If `MSSQL type (write)` match `MSSQL type (read)`, no additional casts will be performed, DataFrame column will be written to MSSQL as is. +- If `MSSQL type (write)` does not match `MSSQL type (read)`, DataFrame column will be casted to target column type **on MSSQL side**. + For example, you can write column with text data to `int` column, if column contains valid integer values within supported value range and precision [^footnote-2]. + +[^footnote-1]: This allows to write data to tables with `DEFAULT` and `GENERATED` columns - if DataFrame has no such column, + it will be populated by MSSQL. + +[^footnote-2]: This is true only if DataFrame column is a `StringType()`, because text value is parsed automatically to target column type. + + But other types cannot be silently converted, like `int -> text`. This requires explicit casting, see [DBWriter]. + +### Create new table using Spark + +```{eval-rst} +.. warning:: + + ABSOLUTELY NOT RECOMMENDED! +``` + +This is how MSSQL connector performs this: + +- Find corresponding `Spark type` → `MSSQL type (create)` combination (see below) for each DataFrame column. If no combination is found, raise exception. +- Generate DDL for creating table in MSSQL, like `CREATE TABLE (col1 ...)`, and run it. +- Write DataFrame to created table as is. + +But some cases this may lead to using wrong column type. For example, Spark creates column of type `timestamp` +which corresponds to MSSQL's type `timestamp(0)` (precision up to seconds) +instead of more precise `timestamp(6)` (precision up to nanoseconds). +This may lead to incidental precision loss, or sometimes data cannot be written to created table at all. + +So instead of relying on Spark to create tables: + +```{eval-rst} +.. dropdown:: See example + + .. code:: python + + writer = DBWriter( + connection=mssql, + target="myschema.target_tbl", + options=MSSQL.WriteOptions( + if_exists="append", + ), + ) + writer.run(df) +``` + +Always prefer creating tables with specific types **BEFORE WRITING DATA**: + +```{eval-rst} +.. dropdown:: See example + + .. code:: python + + mssql.execute( + """ + CREATE TABLE schema.table ( + id bigint, + key text, + value datetime2(6) -- specific type and precision + ) + """, + ) + + writer = DBWriter( + connection=mssql, + target="myschema.target_tbl", + options=MSSQL.WriteOptions(if_exists="append"), + ) + writer.run(df) +``` + +### References + +Here you can find source code with type conversions: + +- [MSSQL -> JDBC](https://github.com/microsoft/mssql-jdbc/blob/v12.2.0/src/main/java/com/microsoft/sqlserver/jdbc/SQLServerResultSetMetaData.java#L117-L170) +- [JDBC -> Spark](https://github.com/apache/spark/blob/v3.5.5/sql/core/src/main/scala/org/apache/spark/sql/jdbc/MsSqlServerDialect.scala#L135-L152) +- [Spark -> JDBC](https://github.com/apache/spark/blob/v3.5.5/sql/core/src/main/scala/org/apache/spark/sql/jdbc/MsSqlServerDialect.scala#L154-L163) +- [JDBC -> MSSQL](https://github.com/microsoft/mssql-jdbc/blob/v12.2.0/src/main/java/com/microsoft/sqlserver/jdbc/DataTypes.java#L625-L676) + +## Supported types + +See [official documentation](https://learn.microsoft.com/en-us/sql/t-sql/data-types/data-types-transact-sql) + +### Numeric types + +```{eval-rst} ++-------------------------------+-----------------------------------+-------------------------------+-------------------------------+ +| MSSQL type (read) | Spark type | MSSQL type (write) | MSSQL type (create) | ++===============================+===================================+===============================+===============================+ +| ``decimal`` | ``DecimalType(P=18, S=0)`` | ``decimal(P=18, S=0)`` | ``decimal(P=18, S=0)`` | ++-------------------------------+-----------------------------------+-------------------------------+-------------------------------+ +| ``decimal(P=0..38)`` | ``DecimalType(P=0..38, S=0)`` | ``decimal(P=0..38, S=0)`` | ``decimal(P=0..38, S=0)`` | ++-------------------------------+-----------------------------------+-------------------------------+-------------------------------+ +| ``decimal(P=0..38, S=0..38)`` | ``DecimalType(P=0..38, S=0..38)`` | ``decimal(P=0..38, S=0..38)`` | ``decimal(P=0..38, S=0..38)`` | ++-------------------------------+-----------------------------------+-------------------------------+-------------------------------+ +| ``real`` | ``FloatType()`` | ``real`` | ``real`` | ++-------------------------------+-----------------------------------+-------------------------------+-------------------------------+ +| ``float`` | ``DoubleType()`` | ``float`` | ``float`` | ++-------------------------------+-----------------------------------+-------------------------------+-------------------------------+ +| ``smallint`` | ``ShortType()`` | ``smallint`` | ``smallint`` | ++-------------------------------+-----------------------------------+-------------------------------+-------------------------------+ +| ``tinyint`` | ``IntegerType()`` | ``int`` | ``int`` | ++-------------------------------+ | | | +| ``int`` | | | | ++-------------------------------+-----------------------------------+-------------------------------+-------------------------------+ +| ``bigint`` | ``LongType()`` | ``bigint`` | ``bigint`` | ++-------------------------------+-----------------------------------+-------------------------------+-------------------------------+ +``` + +### Temporal types + +```{eval-rst} +.. note:: + + MSSQL ``timestamp`` type is alias for ``rowversion`` (see `Special types`_). It is not a temporal type! +``` + +```{eval-rst} ++------------------------------------------+--------------------------------------+-----------------------------------+-------------------------------+ +| MSSQL type (read) | Spark type | MSSQL type (write) | MSSQL type (create) | ++==========================================+======================================+===================================+===============================+ +| ``date`` | ``DateType()`` | ``date`` | ``date`` | ++------------------------------------------+--------------------------------------+-----------------------------------+-------------------------------+ +| ``smalldatetime``, minutes | ``TimestampType()``, microseconds | ``datetime2(6)``, microseconds | ``datetime``, milliseconds | ++------------------------------------------+ | | | +| ``datetime``, milliseconds | | | | ++------------------------------------------+ | | | +| ``datetime2(0)``, seconds | | | | ++------------------------------------------+ | | | +| ``datetime2(3)``, milliseconds | | | | ++------------------------------------------+--------------------------------------+-----------------------------------+-------------------------------+ +| ``datetime2(6)``, microseconds | ``TimestampType()``, microseconds | ``datetime2(6)``, microseconds | ``datetime``, milliseconds, | ++------------------------------------------+--------------------------------------+-----------------------------------+ **precision loss** [3]_ | +| ``datetime2(7)``, 100s of nanoseconds | ``TimestampType()``, microseconds, | ``datetime2(6)``, microseconds, | | +| | **precision loss** [4]_ | **precision loss** [4]_ | | ++------------------------------------------+--------------------------------------+-----------------------------------+-------------------------------+ +| ``time(0)``, seconds | ``TimestampType()``, microseconds, | ``datetime2(6)``, microseconds | ``datetime``, milliseconds | ++------------------------------------------+ with time format quirks [5]_ | | | +| ``time(3)``, milliseconds | | | | ++------------------------------------------+--------------------------------------+-----------------------------------+-------------------------------+ +| ``time(6)``, microseconds | ``TimestampType()``, microseconds, | ``datetime2(6)``, microseconds | ``datetime``, milliseconds, | ++ | with time format quirks [5]_ | | **precision loss** [3]_ | ++------------------------------------------+--------------------------------------+-----------------------------------+ + +| ``time``, 100s of nanoseconds | ``TimestampType()``, microseconds, | ``datetime2(6)``, microseconds | | ++------------------------------------------+ **precision loss** [4]_, | **precision loss** [3]_ | | +| ``time(7)``, 100s of nanoseconds | with time format quirks [5]_ | | | ++------------------------------------------+--------------------------------------+-----------------------------------+-------------------------------+ +| ``datetimeoffset`` | ``StringType()`` | ``nvarchar`` | ``nvarchar`` | ++------------------------------------------+--------------------------------------+-----------------------------------+-------------------------------+ +``` + +```{eval-rst} +.. warning:: + + Note that types in MSSQL and Spark have different value ranges: + + +-------------------+--------------------------------+--------------------------------+---------------------+--------------------------------+--------------------------------+ + | MySQL type | Min value | Max value | Spark type | Min value | Max value | + +===================+================================+================================+=====================+================================+================================+ + | ``smalldatetime`` | ``1900-01-01 00:00:00`` | ``2079-06-06 23:59:00`` | ``TimestampType()`` | ``0001-01-01 00:00:00.000000`` | ``9999-12-31 23:59:59.999999`` | + +-------------------+--------------------------------+--------------------------------+ | | | + | ``datetime`` | ``1753-01-01 00:00:00.000`` | ``9999-12-31 23:59:59.997`` | | | | + +-------------------+--------------------------------+--------------------------------+ | | | + | ``datetime2`` | ``0001-01-01 00:00:00.000000`` | ``9999-12-31 23:59:59.999999`` | | | | + +-------------------+--------------------------------+--------------------------------+ | | | + | ``time`` | ``00:00:00.0000000`` | ``23:59:59.9999999`` | | | | + +-------------------+--------------------------------+--------------------------------+---------------------+--------------------------------+--------------------------------+ + + So not all of values in Spark DataFrame can be written to MSSQL. + + References: + * `MSSQL date & time types documentation `_ + * `Spark DateType documentation `_ + * `Spark TimestampType documentation `_ +``` + +[^footnote-3]: MSSQL dialect for Spark generates DDL with type `datetime` which has precision up to milliseconds (`23:59:59.999`, 10{superscript}`-3` seconds). + Inserting data with microsecond and higher precision (`23:59:59.999999` .. `23.59:59.9999999`, 10{superscript}`-6` .. 10{superscript}`-7` seconds) + will lead to **throwing away microseconds**. + +[^footnote-4]: MSSQL support timestamp up to 100s of nanoseconds precision (`23:59:59.9999999999`, 10{superscript}`-7` seconds), + but Spark `TimestampType()` supports datetime up to microseconds precision (`23:59:59.999999`, 10{superscript}`-6` seconds). + Last digit will be lost during read or write operations. + +[^footnote-5]: `time` type is the same as `datetime2` with date `1970-01-01`. So instead of reading data from MSSQL like `23:59:59.999999` + it is actually read `1970-01-01 23:59:59.999999`, and vice versa. + +### String types + +```{eval-rst} ++-------------------+------------------+--------------------+---------------------+ +| MSSQL type (read) | Spark type | MSSQL type (write) | MSSQL type (create) | ++===================+==================+====================+=====================+ +| ``char`` | ``StringType()`` | ``nvarchar`` | ``nvarchar`` | ++-------------------+ | | | +| ``char(N)`` | | | | ++-------------------+ | | | +| ``nchar`` | | | | ++-------------------+ | | | +| ``nchar(N)`` | | | | ++-------------------+ | | | +| ``varchar`` | | | | ++-------------------+ | | | +| ``varchar(N)`` | | | | ++-------------------+ | | | +| ``nvarchar`` | | | | ++-------------------+ | | | +| ``nvarchar(N)`` | | | | ++-------------------+ | | | +| ``mediumtext`` | | | | ++-------------------+ | | | +| ``text`` | | | | ++-------------------+ | | | +| ``ntext`` | | | | ++-------------------+ | | | +| ``xml`` | | | | ++-------------------+------------------+--------------------+---------------------+ +``` + +### Binary types + +```{eval-rst} ++--------------------+-------------------+--------------------+---------------------+ +| MSSQL type (read) | Spark type | MSSQL type (write) | MSSQL type (create) | ++====================+===================+====================+=====================+ +| ``bit`` | ``BooleanType()`` | ``bit`` | ``bit`` | ++--------------------+-------------------+--------------------+---------------------+ +| ``binary`` | ``BinaryType()`` | ``varbinary`` | ``varbinary`` | ++--------------------+ | | | +| ``binary(N)`` | | | | ++--------------------+ | | | +| ``varbinary`` | | | | ++--------------------+ | | | +| ``varbinary(N)`` | | | | ++--------------------+ | | | +| ``image`` | | | | ++--------------------+-------------------+--------------------+---------------------+ +``` + +### Special types + +```{eval-rst} ++---------------------------+------------------+--------------------+---------------------+ +| MSSQL type (read) | Spark type | MSSQL type (write) | MSSQL type (create) | ++===========================+==================+====================+=====================+ +| ``geography`` | ``BinaryType()`` | ``varbinary`` | ``varbinary`` | ++---------------------------+ | | | +| ``geometry`` | | | | ++---------------------------+ | | | +| ``hierarchyid`` | | | | ++---------------------------+ | | | +| ``rowversion`` | | | | ++---------------------------+------------------+--------------------+---------------------+ +| ``sql_variant`` | unsupported | | | ++---------------------------+------------------+--------------------+---------------------+ +| ``sysname`` | ``StringType()`` | ``nvarchar`` | ``nvarchar`` | ++---------------------------+ | | | +| ``uniqueidentifier`` | | | | ++---------------------------+------------------+--------------------+---------------------+ +``` + +## Explicit type cast + +### `DBReader` + +It is possible to explicitly cast column type using `DBReader(columns=...)` syntax. + +For example, you can use `CAST(column AS text)` to convert data to string representation on MSSQL side, and so it will be read as Spark's `StringType()`: + +```python +from onetl.connection import MSSQL +from onetl.db import DBReader + +mssql = MSSQL(...) + +DBReader( + connection=mssql, + columns=[ + "id", + "supported_column", + "CAST(unsupported_column AS text) unsupported_column_str", + ], +) +df = reader.run() + +# cast column content to proper Spark type +df = df.select( + df.id, + df.supported_column, + # explicit cast + df.unsupported_column_str.cast("integer").alias("parsed_integer"), +) +``` + +### `DBWriter` + +Convert dataframe column to JSON using [to_json](https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.functions.to_json.html), +and write it as `text` column in MSSQL: + +```python +mssql.execute( + """ + CREATE TABLE schema.target_tbl ( + id bigint, + struct_column_json text -- any string type, actually + ) + """, +) + +from pyspark.sql.functions import to_json + +df = df.select( + df.id, + to_json(df.struct_column).alias("struct_column_json"), +) + +writer.run(df) +``` + +Then you can parse this column on MSSQL side - for example, by creating a view: + +```sql +SELECT + id, + JSON_VALUE(struct_column_json, "$.nested.field") AS nested_field +FROM target_tbl +``` + +Or by using [computed column](https://learn.microsoft.com/en-us/sql/relational-databases/tables/specify-computed-columns-in-a-table): + +```sql +CREATE TABLE schema.target_table ( + id bigint, + supported_column datetime2(6), + struct_column_json text, -- any string type, actually + -- computed column + nested_field AS (JSON_VALUE(struct_column_json, "$.nested.field")) + -- or persisted column + -- nested_field AS (JSON_VALUE(struct_column_json, "$.nested.field")) PERSISTED +) +``` + +By default, column value is calculated on every table read. +Column marked as `PERSISTED` is calculated during insert, but this require additional space. diff --git a/mddocs/docs/connection/db_connection/mssql/write.md b/mddocs/docs/connection/db_connection/mssql/write.md new file mode 100644 index 000000000..0b6d8db19 --- /dev/null +++ b/mddocs/docs/connection/db_connection/mssql/write.md @@ -0,0 +1,56 @@ +(mssql-write)= + +# Writing to MSSQL using `DBWriter` + +For writing data to MSSQL, use {obj}`DBWriter `. + +```{eval-rst} +.. warning:: + + Please take into account :ref:`mssql-types` +``` + +```{eval-rst} +.. warning:: + + It is always recommended to create table explicitly using :ref:`MSSQL.execute ` + instead of relying on Spark's table DDL generation. + + This is because Spark's DDL generator can create columns with different precision and types than it is expected, + causing precision loss or other issues. +``` + +## Examples + +```python +from onetl.connection import MSSQL +from onetl.db import DBWriter + +mssql = MSSQL(...) + +df = ... # data is here + +writer = DBWriter( + connection=mssql, + target="schema.table", + options=MSSQL.WriteOptions(if_exists="append"), +) + +writer.run(df) +``` + +## Options + +Method above accepts {obj}`MSSQL.WriteOptions ` + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.mssql.options +``` + +```{eval-rst} +.. autopydantic_model:: MSSQLWriteOptions + :inherited-members: GenericOptions + :member-order: bysource + :model-show-field-summary: false + :field-show-constraints: false +``` diff --git a/mddocs/docs/connection/db_connection/mysql/connection.md b/mddocs/docs/connection/db_connection/mysql/connection.md new file mode 100644 index 000000000..1b2ba94c3 --- /dev/null +++ b/mddocs/docs/connection/db_connection/mysql/connection.md @@ -0,0 +1,12 @@ +(mysql-connection)= + +# MySQL connection + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.mysql.connection +``` + +```{eval-rst} +.. autoclass:: MySQL + :members: get_packages, check +``` diff --git a/mddocs/docs/connection/db_connection/mysql/execute.md b/mddocs/docs/connection/db_connection/mysql/execute.md new file mode 100644 index 000000000..99c86be5b --- /dev/null +++ b/mddocs/docs/connection/db_connection/mysql/execute.md @@ -0,0 +1,115 @@ +(mysql-execute)= + +# Executing statements in MySQL + +```{eval-rst} +.. warning:: + + Methods below **read all the rows** returned from DB **to Spark driver memory**, and then convert them to DataFrame. + + Do **NOT** use them to read large amounts of data. Use :ref:`DBReader ` or :ref:`MySQL.sql ` instead. +``` + +## How to + +There are 2 ways to execute some statement in MySQL + +### Use `MySQL.fetch` + +Use this method to perform some `SELECT` query which returns **small number or rows**, like reading +MySQL config, or reading data from some reference table. Method returns Spark DataFrame. + +Method accepts {obj}`MySQL.FetchOptions `. + +Connection opened using this method should be then closed with `connection.close()` or `with connection:`. + +```{eval-rst} +.. warning:: + + Please take into account :ref:`mysql-types`. +``` + +#### Syntax support + +This method supports **any** query syntax supported by MySQL, like: + +- ✅︎ `SELECT ... FROM ...` +- ✅︎ `WITH alias AS (...) SELECT ...` +- ✅︎ `SELECT func(arg1, arg2)` or `{?= call func(arg1, arg2)}` - special syntax for calling function +- ✅︎ `SHOW ...` +- ❌ `SET ...; SELECT ...;` - multiple statements not supported + +#### Examples + +```python +from onetl.connection import MySQL + +mysql = MySQL(...) + +df = mysql.fetch( + "SELECT value FROM some.reference_table WHERE key = 'some_constant'", + options=MySQL.FetchOptions(queryTimeout=10), +) +mysql.close() +value = df.collect()[0][0] # get value from first row and first column +``` + +### Use `MySQL.execute` + +Use this method to execute DDL and DML operations. Each method call runs operation in a separated transaction, and then commits it. + +Method accepts {obj}`MySQL.ExecuteOptions `. + +Connection opened using this method should be then closed with `connection.close()` or `with connection:`. + +#### Syntax support + +This method supports **any** query syntax supported by MySQL, like: + +- ✅︎ `CREATE TABLE ...`, `CREATE VIEW ...`, and so on +- ✅︎ `ALTER ...` +- ✅︎ `INSERT INTO ... SELECT ...`, `UPDATE ...`, `DELETE ...`, and so on +- ✅︎ `DROP TABLE ...`, `DROP VIEW ...`, and so on +- ✅︎ `CALL procedure(arg1, arg2) ...` or `{call procedure(arg1, arg2)}` - special syntax for calling procedure +- ✅︎ other statements not mentioned here +- ❌ `SET ...; SELECT ...;` - multiple statements not supported + +#### Examples + +```python +from onetl.connection import MySQL + +mysql = MySQL(...) + +mysql.execute("DROP TABLE schema.table") +mysql.execute( + """ + CREATE TABLE schema.table ( + id bigint, + key text, + value float + ) + ENGINE = InnoDB + """, + options=MySQL.ExecuteOptions(queryTimeout=10), +) +``` + +## Options + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.mysql.options +``` + +```{eval-rst} +.. autopydantic_model:: MySQLFetchOptions + :inherited-members: GenericOptions + :member-order: bysource + +``` + +```{eval-rst} +.. autopydantic_model:: MySQLExecuteOptions + :inherited-members: GenericOptions + :member-order: bysource +``` diff --git a/mddocs/docs/connection/db_connection/mysql/index.md b/mddocs/docs/connection/db_connection/mysql/index.md new file mode 100644 index 000000000..6b69e1c1b --- /dev/null +++ b/mddocs/docs/connection/db_connection/mysql/index.md @@ -0,0 +1,28 @@ +(mysql)= + +# MySQL + +```{toctree} +:caption: Connection +:maxdepth: 1 + +prerequisites +connection +``` + +```{toctree} +:caption: Operations +:maxdepth: 1 + +read +sql +write +execute +``` + +```{toctree} +:caption: Troubleshooting +:maxdepth: 1 + +types +``` diff --git a/mddocs/docs/connection/db_connection/mysql/prerequisites.md b/mddocs/docs/connection/db_connection/mysql/prerequisites.md new file mode 100644 index 000000000..6292e7761 --- /dev/null +++ b/mddocs/docs/connection/db_connection/mysql/prerequisites.md @@ -0,0 +1,61 @@ +(mysql-prerequisites)= + +# Prerequisites + +## Version Compatibility + +- MySQL server versions: + : - Officially declared: 8.0 - 9.2 + - Actually tested: 5.7.13, 9.2.0 +- Spark versions: 2.3.x - 3.5.x +- Java versions: 8 - 20 + +See [official documentation](https://dev.mysql.com/doc/connector-j/en/connector-j-versions.html). + +## Installing PySpark + +To use MySQL connector you should have PySpark installed (or injected to `sys.path`) +BEFORE creating the connector instance. + +See {ref}`install-spark` installation instruction for more details. + +## Connecting to MySQL + +### Connection host + +It is possible to connect to MySQL by using either DNS name of host or it's IP address. + +If you're using MySQL cluster, it is currently possible to connect only to **one specific node**. +Connecting to multiple nodes to perform load balancing, as well as automatic failover to new master/replica are not supported. + +### Connection port + +Connection is usually performed to port 3306. Port may differ for different MySQL instances. +Please ask your MySQL administrator to provide required information. + +### Required grants + +Ask your MySQL cluster administrator to set following grants for a user, +used for creating a connection: + +```{eval-rst} +.. tabs:: + + .. code-tab:: sql Read + Write + + -- allow creating tables in the target schema + GRANT CREATE ON myschema.* TO username@'192.168.1.%'; + + -- allow read & write access to specific table + GRANT SELECT, INSERT ON myschema.mytable TO username@'192.168.1.%'; + + .. code-tab:: sql Read only + + -- allow read access to specific table + GRANT SELECT ON myschema.mytable TO username@'192.168.1.%'; +``` + +In example above `'192.168.1.%''` is a network subnet `192.168.1.0 - 192.168.1.255` +where Spark driver and executors are running. To allow connecting user from any IP, use `'%'` (not secure!). + +More details can be found in [official documentation](https://dev.mysql.com/doc/refman/en/grant.html). diff --git a/mddocs/docs/connection/db_connection/mysql/read.md b/mddocs/docs/connection/db_connection/mysql/read.md new file mode 100644 index 000000000..c3751dca2 --- /dev/null +++ b/mddocs/docs/connection/db_connection/mysql/read.md @@ -0,0 +1,93 @@ +(mysql-read)= + +# Reading from MySQL using `DBReader` + +{obj}`DBReader ` supports {ref}`strategy` for incremental data reading, +but does not support custom queries, like `JOIN`. + +```{eval-rst} +.. warning:: + + Please take into account :ref:`mysql-types` +``` + +## Supported DBReader features + +- ✅︎ `columns` +- ✅︎ `where` +- ✅︎ `hwm`, supported strategies: +- - ✅︎ {ref}`snapshot-strategy` +- - ✅︎ {ref}`incremental-strategy` +- - ✅︎ {ref}`snapshot-batch-strategy` +- - ✅︎ {ref}`incremental-batch-strategy` +- ✅︎ `hint` (see [official documentation](https://dev.mysql.com/doc/refman/en/optimizer-hints.html)) +- ❌ `df_schema` +- ✅︎ `options` (see {obj}`MySQL.ReadOptions `) + +## Examples + +Snapshot strategy: + +```python +from onetl.connection import MySQL +from onetl.db import DBReader + +mysql = MySQL(...) + +reader = DBReader( + connection=mysql, + source="schema.table", + columns=["id", "key", "CAST(value AS text) value", "updated_dt"], + where="key = 'something'", + hint="SKIP_SCAN(schema.table key_index)", + options=MySQL.ReadOptions(partitionColumn="id", numPartitions=10), +) +df = reader.run() +``` + +Incremental strategy: + +```python +from onetl.connection import MySQL +from onetl.db import DBReader +from onetl.strategy import IncrementalStrategy + +mysql = MySQL(...) + +reader = DBReader( + connection=mysql, + source="schema.table", + columns=["id", "key", "CAST(value AS text) value", "updated_dt"], + where="key = 'something'", + hint="SKIP_SCAN(schema.table key_index)", + hwm=DBReader.AutoDetectHWM(name="mysql_hwm", expression="updated_dt"), + options=MySQL.ReadOptions(partitionColumn="id", numPartitions=10), +) + +with IncrementalStrategy(): + df = reader.run() +``` + +## Recommendations + +### Select only required columns + +Instead of passing `"*"` in `DBReader(columns=[...])` prefer passing exact column names. This reduces the amount of data passed from Oracle to Spark. + +### Pay attention to `where` value + +Instead of filtering data on Spark side using `df.filter(df.column == 'value')` pass proper `DBReader(where="column = 'value'")` clause. +This both reduces the amount of data send from Oracle to Spark, and may also improve performance of the query. +Especially if there are indexes for columns used in `where` clause. + +## Options + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.mysql.options +``` + +```{eval-rst} +.. autopydantic_model:: MySQLReadOptions + :inherited-members: GenericOptions + :member-order: bysource +``` diff --git a/mddocs/docs/connection/db_connection/mysql/sql.md b/mddocs/docs/connection/db_connection/mysql/sql.md new file mode 100644 index 000000000..7d2dd4e51 --- /dev/null +++ b/mddocs/docs/connection/db_connection/mysql/sql.md @@ -0,0 +1,81 @@ +(mysql-sql)= + +# Reading from MySQL using `MySQL.sql` + +`MySQL.sql` allows passing custom SQL query, but does not support incremental strategies. + +```{eval-rst} +.. warning:: + + Please take into account :ref:`mysql-types` +``` + +```{eval-rst} +.. warning:: + + Statement is executed in **read-write** connection, so if you're calling some functions/procedures with DDL/DML statements inside, + they can change data in your database. +``` + +## Syntax support + +Only queries with the following syntax are supported: + +- ✅︎ `SELECT ... FROM ...` +- ✅︎ `WITH alias AS (...) SELECT ...` +- ❌ `SHOW ...` +- ❌ `SET ...; SELECT ...;` - multiple statements not supported + +## Examples + +```python +from onetl.connection import MySQL + +mysql = MySQL(...) +df = mysql.sql( + """ + SELECT + id, + key, + CAST(value AS text) value, + updated_at + FROM + some.mytable + WHERE + key = 'something' + """, + options=MySQL.SQLOptions( + partitionColumn="id", + numPartitions=10, + lowerBound=0, + upperBound=1000, + ), +) +``` + +## Recommendations + +### Select only required columns + +Instead of passing `SELECT * FROM ...` prefer passing exact column names `SELECT col1, col2, ...`. +This reduces the amount of data passed from MySQL to Spark. + +### Pay attention to `where` value + +Instead of filtering data on Spark side using `df.filter(df.column == 'value')` pass proper `WHERE column = 'value'` clause. +This both reduces the amount of data send from MySQL to Spark, and may also improve performance of the query. +Especially if there are indexes or partitions for columns used in `where` clause. + +## Options + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.mysql.options +``` + +```{eval-rst} +.. autopydantic_model:: MySQLSQLOptions + :inherited-members: GenericOptions + :member-order: bysource + :model-show-field-summary: false + :field-show-constraints: false +``` diff --git a/mddocs/docs/connection/db_connection/mysql/types.md b/mddocs/docs/connection/db_connection/mysql/types.md new file mode 100644 index 000000000..df3d6849b --- /dev/null +++ b/mddocs/docs/connection/db_connection/mysql/types.md @@ -0,0 +1,382 @@ +(mysql-types)= + +# MySQL \<-> Spark type mapping + +```{eval-rst} +.. note:: + + The results below are valid for Spark 3.5.5, and may differ on other Spark versions. +``` + +## Type detection & casting + +Spark's DataFrames always have a `schema` which is a list of columns with corresponding Spark types. All operations on a column are performed using column type. + +### Reading from MySQL + +This is how MySQL connector performs this: + +- For each column in query result (`SELECT column1, column2, ... FROM table ...`) get column name and MySQL type. +- Find corresponding `MySQL type (read)` → `Spark type` combination (see below) for each DataFrame column. If no combination is found, raise exception. +- Create DataFrame from query with specific column names and Spark types. + +### Writing to some existing MySQL table + +This is how MySQL connector performs this: + +- Get names of columns in DataFrame. [^footnote-1] +- Perform `SELECT * FROM table LIMIT 0` query. +- Take only columns present in DataFrame (by name, case insensitive). For each found column get MySQL type. +- Find corresponding `Spark type` → `MySQL type (write)` combination (see below) for each DataFrame column. If no combination is found, raise exception. +- If `MySQL type (write)` match `MySQL type (read)`, no additional casts will be performed, DataFrame column will be written to MySQL as is. +- If `MySQL type (write)` does not match `MySQL type (read)`, DataFrame column will be casted to target column type **on MySQL side**. For example, you can write column with text data to `int` column, if column contains valid integer values within supported value range and precision. + +[^footnote-1]: This allows to write data to tables with `DEFAULT` and `GENERATED` columns - if DataFrame has no such column, + it will be populated by MySQL. + +### Create new table using Spark + +```{eval-rst} +.. warning:: + + ABSOLUTELY NOT RECOMMENDED! +``` + +This is how MySQL connector performs this: + +- Find corresponding `Spark type` → `MySQL type (create)` combination (see below) for each DataFrame column. If no combination is found, raise exception. +- Generate DDL for creating table in MySQL, like `CREATE TABLE (col1 ...)`, and run it. +- Write DataFrame to created table as is. + +But some cases this may lead to using wrong column type. For example, Spark creates column of type `timestamp` +which corresponds to MySQL type `timestamp(0)` (precision up to seconds) +instead of more precise `timestamp(6)` (precision up to nanoseconds). +This may lead to incidental precision loss, or sometimes data cannot be written to created table at all. + +So instead of relying on Spark to create tables: + +```{eval-rst} +.. dropdown:: See example + + .. code:: python + + writer = DBWriter( + connection=mysql, + target="myschema.target_tbl", + options=MySQL.WriteOptions( + if_exists="append", + createTableOptions="ENGINE = InnoDB", + ), + ) + writer.run(df) +``` + +Always prefer creating tables with specific types **BEFORE WRITING DATA**: + +```{eval-rst} +.. dropdown:: See example + + .. code:: python + + mysql.execute( + """ + CREATE TABLE schema.table ( + id bigint, + key text, + value timestamp(6) -- specific type and precision + ) + ENGINE = InnoDB + """, + ) + + writer = DBWriter( + connection=mysql, + target="myschema.target_tbl", + options=MySQL.WriteOptions(if_exists="append"), + ) + writer.run(df) +``` + +### References + +Here you can find source code with type conversions: + +- [MySQL -> JDBC](https://github.com/mysql/mysql-connector-j/blob/8.0.33/src/main/core-api/java/com/mysql/cj/MysqlType.java#L44-L623) +- [JDBC -> Spark](https://github.com/apache/spark/blob/v3.5.5/sql/core/src/main/scala/org/apache/spark/sql/jdbc/MySQLDialect.scala#L104-L132) +- [Spark -> JDBC](https://github.com/apache/spark/blob/v3.5.5/sql/core/src/main/scala/org/apache/spark/sql/jdbc/MySQLDialect.scala#L204-L211) +- [JDBC -> MySQL](https://github.com/mysql/mysql-connector-j/blob/8.0.33/src/main/core-api/java/com/mysql/cj/MysqlType.java#L625-L867) + +## Supported types + +See [official documentation](https://dev.mysql.com/doc/refman/en/data-types.html) + +### Numeric types + +```{eval-rst} ++-------------------------------+-----------------------------------+-------------------------------+-------------------------------+ +| MySQL type (read) | Spark type | MySQL type (write) | MySQL type (create) | ++===============================+===================================+===============================+===============================+ +| ``decimal`` | ``DecimalType(P=10, S=0)`` | ``decimal(P=10, S=0)`` | ``decimal(P=10, S=0)`` | ++-------------------------------+-----------------------------------+-------------------------------+-------------------------------+ +| ``decimal(P=0..38)`` | ``DecimalType(P=0..38, S=0)`` | ``decimal(P=0..38, S=0)`` | ``decimal(P=0..38, S=0)`` | ++-------------------------------+-----------------------------------+-------------------------------+-------------------------------+ +| ``decimal(P=0..38, S=0..30)`` | ``DecimalType(P=0..38, S=0..30)`` | ``decimal(P=0..38, S=0..30)`` | ``decimal(P=0..38, S=0..30)`` | ++-------------------------------+-----------------------------------+-------------------------------+-------------------------------+ +| ``decimal(P=39..65, S=...)`` | unsupported [2]_ | | | ++-------------------------------+-----------------------------------+-------------------------------+-------------------------------+ +| ``float`` | ``DoubleType()`` | ``double`` | ``double`` | ++-------------------------------+ | | | +| ``double`` | | | | ++-------------------------------+-----------------------------------+-------------------------------+-------------------------------+ +| ``tinyint`` | ``IntegerType()`` | ``int`` | ``int`` | ++-------------------------------+ | | | +| ``smallint`` | | | | ++-------------------------------+ | | | +| ``mediumint`` | | | | ++-------------------------------+ | | | +| ``int`` | | | | ++-------------------------------+-----------------------------------+-------------------------------+-------------------------------+ +| ``bigint`` | ``LongType()`` | ``bigint`` | ``bigint`` | ++-------------------------------+-----------------------------------+-------------------------------+-------------------------------+ +``` + +[^footnote-2]: MySQL support decimal types with precision `P` up to 65. + + But Spark's `DecimalType(P, S)` supports maximum `P=38`. It is impossible to read, write or operate with values of larger precision, + this leads to an exception. + +### Temporal types + +```{eval-rst} ++-----------------------------------+--------------------------------------+-----------------------------------+-------------------------------+ +| MySQL type (read) | Spark type | MySQL type (write) | MySQL type (create) | ++===================================+======================================+===================================+===============================+ +| ``year`` | ``DateType()`` | ``date`` | ``date`` | ++-----------------------------------+ | | | +| ``date`` | | | | ++-----------------------------------+--------------------------------------+-----------------------------------+-------------------------------+ +| ``datetime``, seconds | ``TimestampType()``, microseconds | ``timestamp(6)``, microseconds | ``timestamp(0)``, seconds | ++-----------------------------------+ | | | +| ``timestamp``, seconds | | | | ++-----------------------------------+ | | | +| ``datetime(0)``, seconds | | | | ++-----------------------------------+ | | | +| ``timestamp(0)``, seconds | | | | ++-----------------------------------+--------------------------------------+-----------------------------------+-------------------------------+ +| ``datetime(3)``, milliseconds | ``TimestampType()``, microseconds | ``timestamp(6)``, microseconds | ``timestamp(0)``, seconds, | ++-----------------------------------+ | | **precision loss** [4]_, | +| ``timestamp(3)``, milliseconds | | | | ++-----------------------------------+ | | | +| ``datetime(6)``, microseconds | | | | ++-----------------------------------+ | | | +| ``timestamp(6)``, microseconds | | | | ++-----------------------------------+--------------------------------------+-----------------------------------+-------------------------------+ +| ``time``, seconds | ``TimestampType()``, microseconds, | ``timestamp(6)``, microseconds | ``timestamp(0)``, seconds | ++-----------------------------------+ with time format quirks [5]_ | | | +| ``time(0)``, seconds | | | | ++-----------------------------------+--------------------------------------+-----------------------------------+-------------------------------+ +| ``time(3)``, milliseconds | ``TimestampType()``, microseconds | ``timestamp(6)``, microseconds | ``timestamp(0)``, seconds, | ++-----------------------------------+ with time format quirks [5]_ | | **precision loss** [4]_, | +| ``time(6)``, microseconds | | | | ++-----------------------------------+--------------------------------------+-----------------------------------+-------------------------------+ +``` + +```{eval-rst} +.. warning:: + + Note that types in MySQL and Spark have different value ranges: + + +---------------+--------------------------------+--------------------------------+---------------------+--------------------------------+--------------------------------+ + | MySQL type | Min value | Max value | Spark type | Min value | Max value | + +===============+================================+================================+=====================+================================+================================+ + | ``year`` | ``1901`` | ``2155`` | ``DateType()`` | ``0001-01-01`` | ``9999-12-31`` | + +---------------+--------------------------------+--------------------------------+ | | | + | ``date`` | ``1000-01-01`` | ``9999-12-31`` | | | | + +---------------+--------------------------------+--------------------------------+---------------------+--------------------------------+--------------------------------+ + | ``datetime`` | ``1000-01-01 00:00:00.000000`` | ``9999-12-31 23:59:59.499999`` | ``TimestampType()`` | ``0001-01-01 00:00:00.000000`` | ``9999-12-31 23:59:59.999999`` | + +---------------+--------------------------------+--------------------------------+ | | | + | ``timestamp`` | ``1970-01-01 00:00:01.000000`` | ``9999-12-31 23:59:59.499999`` | | | | + +---------------+--------------------------------+--------------------------------+ | | | + | ``time`` | ``-838:59:59.000000`` | ``838:59:59.000000`` | | | | + +---------------+--------------------------------+--------------------------------+---------------------+--------------------------------+--------------------------------+ + + So Spark can read all the values from MySQL, but not all of values in Spark DataFrame can be written to MySQL. + + References: + * `MySQL year documentation `_ + * `MySQL date, datetime & timestamp documentation `_ + * `MySQL time documentation `_ + * `Spark DateType documentation `_ + * `Spark TimestampType documentation `_ +``` + +[^footnote-3]: MySQL dialect generates DDL with MySQL type `timestamp` which is alias for `timestamp(0)` with precision up to seconds (`23:59:59`). + Inserting data with microseconds precision (`23:59:59.999999`) will lead to **throwing away microseconds**. + +[^footnote-4]: `time` type is the same as `timestamp` with date `1970-01-01`. So instead of reading data from MySQL like `23:59:59` + it is actually read `1970-01-01 23:59:59`, and vice versa. + +### String types + +```{eval-rst} ++-------------------------------+------------------+--------------------+---------------------+ +| MySQL type (read) | Spark type | MySQL type (write) | MySQL type (create) | ++===============================+==================+====================+=====================+ +| ``char`` | ``StringType()`` | ``longtext`` | ``longtext`` | ++-------------------------------+ | | | +| ``char(N)`` | | | | ++-------------------------------+ | | | +| ``varchar(N)`` | | | | ++-------------------------------+ | | | +| ``mediumtext`` | | | | ++-------------------------------+ | | | +| ``text`` | | | | ++-------------------------------+ | | | +| ``longtext`` | | | | ++-------------------------------+ | | | +| ``json`` | | | | ++-------------------------------+ | | | +| ``enum("val1", "val2", ...)`` | | | | ++-------------------------------+ | | | +| ``set("val1", "val2", ...)`` | | | | ++-------------------------------+------------------+--------------------+---------------------+ +``` + +### Binary types + +```{eval-rst} ++-------------------+------------------+--------------------+---------------------+ +| MySQL type (read) | Spark type | MySQL type (write) | MySQL type (create) | ++===================+==================+====================+=====================+ +| ``binary`` | ``BinaryType()`` | ``blob`` | ``blob`` | ++-------------------+ | | | +| ``binary(N)`` | | | | ++-------------------+ | | | +| ``varbinary(N)`` | | | | ++-------------------+ | | | +| ``mediumblob`` | | | | ++-------------------+ | | | +| ``blob`` | | | | ++-------------------+ | | | +| ``longblob`` | | | | ++-------------------+------------------+--------------------+---------------------+ +``` + +### Geometry types + +```{eval-rst} ++------------------------+------------------+--------------------+---------------------+ +| MySQL type (read) | Spark type | MySQL type (write) | MySQL type (create) | ++========================+==================+====================+=====================+ +| ``point`` | ``BinaryType()`` | ``blob`` | ``blob`` | ++------------------------+ | | | +| ``linestring`` | | | | ++------------------------+ | | | +| ``polygon`` | | | | ++------------------------+ | | | +| ``geometry`` | | | | ++------------------------+ | | | +| ``multipoint`` | | | | ++------------------------+ | | | +| ``multilinestring`` | | | | ++------------------------+ | | | +| ``multipolygon`` | | | | ++------------------------+ | | | +| ``geometrycollection`` | | | | ++------------------------+------------------+--------------------+---------------------+ +``` + +## Explicit type cast + +### `DBReader` + +It is possible to explicitly cast column type using `DBReader(columns=...)` syntax. + +For example, you can use `CAST(column AS text)` to convert data to string representation on MySQL side, and so it will be read as Spark's `StringType()`. + +It is also possible to use [JSON_OBJECT](https://dev.mysql.com/doc/refman/en/json.html) MySQL function and parse JSON columns in MySQL with the {obj}`JSON.parse_column ` method. + +```python +from pyspark.sql.types import IntegerType, StructType, StructField + +from onetl.connection import MySQL +from onetl.db import DBReader +from onetl.file.format import JSON + +mysql = MySQL(...) + +DBReader( + connection=mysql, + columns=[ + "id", + "supported_column", + "CAST(unsupported_column AS text) unsupported_column_str", + # or + "JSON_OBJECT('key', value_column) json_column", + ], +) +df = reader.run() + +json_scheme = StructType([StructField("key", IntegerType())]) + +df = df.select( + df.id, + df.supported_column, + # explicit cast + df.unsupported_column_str.cast("integer").alias("parsed_integer"), + JSON().parse_column("json_column", json_scheme).alias("struct_column"), +) +``` + +### `DBWriter` + +To write JSON data to a `json` or `text` column in a MySQL table, use the {obj}`JSON.serialize_column ` method. + +```python +from onetl.connection import MySQL +from onetl.db import DBWriter +from onetl.file.format import JSON + +mysql.execute( + """ + CREATE TABLE schema.target_tbl ( + id bigint, + array_column_json json -- any string type, actually + ) + ENGINE = InnoDB + """, +) + +df = df.select( + df.id, + JSON().serialize_column(df.array_column).alias("array_column_json"), +) + +writer.run(df) +``` + +Then you can parse this column on MySQL side - for example, by creating a view: + +```sql +SELECT + id, + array_column_json->"$[0]" AS array_item +FROM target_tbl +``` + +Or by using [GENERATED column](https://dev.mysql.com/doc/refman/en/create-table-generated-columns.html): + +```sql +CREATE TABLE schema.target_table ( + id bigint, + supported_column timestamp, + array_column_json json, -- any string type, actually + -- virtual column + array_item_0 GENERATED ALWAYS AS (array_column_json->"$[0]")) VIRTUAL + -- or stired column + -- array_item_0 GENERATED ALWAYS AS (array_column_json->"$[0]")) STORED +) +``` + +`VIRTUAL` column value is calculated on every table read. +`STORED` column value is calculated during insert, but this require additional space. diff --git a/mddocs/docs/connection/db_connection/mysql/write.md b/mddocs/docs/connection/db_connection/mysql/write.md new file mode 100644 index 000000000..7f7f2a5d2 --- /dev/null +++ b/mddocs/docs/connection/db_connection/mysql/write.md @@ -0,0 +1,60 @@ +(mysql-write)= + +# Writing to MySQL using `DBWriter` + +For writing data to MySQL, use {obj}`DBWriter `. + +```{eval-rst} +.. warning:: + + Please take into account :ref:`mysql-types` +``` + +```{eval-rst} +.. warning:: + + It is always recommended to create table explicitly using :ref:`MySQL.execute ` + instead of relying on Spark's table DDL generation. + + This is because Spark's DDL generator can create columns with different precision and types than it is expected, + causing precision loss or other issues. +``` + +## Examples + +```python +from onetl.connection import MySQL +from onetl.db import DBWriter + +mysql = MySQL(...) + +df = ... # data is here + +writer = DBWriter( + connection=mysql, + target="schema.table", + options=MySQL.WriteOptions( + if_exists="append", + # ENGINE is required by MySQL + createTableOptions="ENGINE = MergeTree() ORDER BY id", + ), +) + +writer.run(df) +``` + +## Options + +Method above accepts {obj}`MySQL.WriteOptions ` + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.mysql.options +``` + +```{eval-rst} +.. autopydantic_model:: MySQLWriteOptions + :inherited-members: GenericOptions + :member-order: bysource + :model-show-field-summary: false + :field-show-constraints: false +``` diff --git a/mddocs/docs/connection/db_connection/oracle/connection.md b/mddocs/docs/connection/db_connection/oracle/connection.md new file mode 100644 index 000000000..26fc8a01e --- /dev/null +++ b/mddocs/docs/connection/db_connection/oracle/connection.md @@ -0,0 +1,12 @@ +(oracle-connection)= + +# Oracle connection + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.oracle.connection +``` + +```{eval-rst} +.. autoclass:: Oracle + :members: get_packages, check +``` diff --git a/mddocs/docs/connection/db_connection/oracle/execute.md b/mddocs/docs/connection/db_connection/oracle/execute.md new file mode 100644 index 000000000..28b5baa39 --- /dev/null +++ b/mddocs/docs/connection/db_connection/oracle/execute.md @@ -0,0 +1,115 @@ +(oracle-execute)= + +# Executing statements in Oracle + +```{eval-rst} +.. warning:: + + Methods below **read all the rows** returned from DB **to Spark driver memory**, and then convert them to DataFrame. + + Do **NOT** use them to read large amounts of data. Use :ref:`DBReader ` or :ref:`Oracle.sql ` instead. +``` + +## How to + +There are 2 ways to execute some statement in Oracle + +### Use `Oracle.fetch` + +Use this method to execute some `SELECT` query which returns **small number or rows**, like reading +Oracle config, or reading data from some reference table. Method returns Spark DataFrame. + +Method accepts {obj}`Oracle.FetchOptions `. + +Connection opened using this method should be then closed with `connection.close()` or `with connection:`. + +```{eval-rst} +.. warning:: + + Please take into account :ref:`oracle-types`. +``` + +#### Syntax support + +This method supports **any** query syntax supported by Oracle, like: + +- ✅︎ `SELECT ... FROM ...` +- ✅︎ `WITH alias AS (...) SELECT ...` +- ✅︎ `SELECT func(arg1, arg2) FROM DUAL` - call function +- ✅︎ `SHOW ...` +- ❌ `SET ...; SELECT ...;` - multiple statements not supported + +#### Examples + +```python +from onetl.connection import Oracle + +oracle = Oracle(...) + +df = oracle.fetch( + "SELECT value FROM some.reference_table WHERE key = 'some_constant'", + options=Oracle.FetchOptions(queryTimeout=10), +) +oracle.close() +value = df.collect()[0][0] # get value from first row and first column +``` + +### Use `Oracle.execute` + +Use this method to execute DDL and DML operations. Each method call runs operation in a separated transaction, and then commits it. + +Method accepts {obj}`Oracle.ExecuteOptions `. + +Connection opened using this method should be then closed with `connection.close()` or `with connection:`. + +#### Syntax support + +This method supports **any** query syntax supported by Oracle, like: + +- ✅︎ `CREATE TABLE ...`, `CREATE VIEW ...` +- ✅︎ `ALTER ...` +- ✅︎ `INSERT INTO ... SELECT ...`, `UPDATE ...`, `DELETE ...`, and so on +- ✅︎ `DROP TABLE ...`, `DROP VIEW ...`, `TRUNCATE TABLE`, and so on +- ✅︎ `CALL procedure(arg1, arg2) ...` or `{call procedure(arg1, arg2)}` - special syntax for calling procedure +- ✅︎ `DECLARE ... BEGIN ... END` - execute PL/SQL statement +- ✅︎ other statements not mentioned here +- ❌ `SET ...; SELECT ...;` - multiple statements not supported + +#### Examples + +```python +from onetl.connection import Oracle + +oracle = Oracle(...) + +oracle.execute("DROP TABLE schema.table") +oracle.execute( + """ + CREATE TABLE schema.table ( + id bigint GENERATED ALWAYS AS IDENTITY, + key VARCHAR2(4000), + value NUMBER + ) + """, + options=Oracle.ExecuteOptions(queryTimeout=10), +) +``` + +## Options + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.oracle.options +``` + +```{eval-rst} +.. autopydantic_model:: OracleFetchOptions + :inherited-members: GenericOptions + :member-order: bysource + +``` + +```{eval-rst} +.. autopydantic_model:: OracleExecuteOptions + :inherited-members: GenericOptions + :member-order: bysource +``` diff --git a/mddocs/docs/connection/db_connection/oracle/index.md b/mddocs/docs/connection/db_connection/oracle/index.md new file mode 100644 index 000000000..5218cc287 --- /dev/null +++ b/mddocs/docs/connection/db_connection/oracle/index.md @@ -0,0 +1,28 @@ +(oracle)= + +# Oracle + +```{toctree} +:caption: Connection +:maxdepth: 1 + +prerequisites +connection +``` + +```{toctree} +:caption: Operations +:maxdepth: 1 + +read +sql +write +execute +``` + +```{toctree} +:caption: Troubleshooting +:maxdepth: 1 + +types +``` diff --git a/mddocs/docs/connection/db_connection/oracle/prerequisites.md b/mddocs/docs/connection/db_connection/oracle/prerequisites.md new file mode 100644 index 000000000..5a350937b --- /dev/null +++ b/mddocs/docs/connection/db_connection/oracle/prerequisites.md @@ -0,0 +1,108 @@ +(oracle-prerequisites)= + +# Prerequisites + +## Version Compatibility + +- Oracle Server versions: + : - Officially declared: 19c, 21c, 23ai + - Actually tested: 11.2, 23.5 +- Spark versions: 2.3.x - 3.5.x +- Java versions: 8 - 20 + +See [official documentation](https://www.oracle.com/cis/database/technologies/appdev/jdbc-downloads.html). + +## Installing PySpark + +To use Oracle connector you should have PySpark installed (or injected to `sys.path`) +BEFORE creating the connector instance. + +See {ref}`install-spark` installation instruction for more details. + +## Connecting to Oracle + +### Connection port + +Connection is usually performed to port 1521. Port may differ for different Oracle instances. +Please ask your Oracle administrator to provide required information. + +### Connection host + +It is possible to connect to Oracle by using either DNS name of host or it's IP address. + +If you're using Oracle cluster, it is currently possible to connect only to **one specific node**. +Connecting to multiple nodes to perform load balancing, as well as automatic failover to new master/replica are not supported. + +### Connect as proxy user + +It is possible to connect to database as another user without knowing this user password. + +This can be enabled by granting user a special `CONNECT THROUGH` permission: + +```sql +ALTER USER schema_owner GRANT CONNECT THROUGH proxy_user; +``` + +Then you can connect to Oracle using credentials of `proxy_user` but specify that you need permissions of `schema_owner`: + +```python +oracle = Oracle( + ..., + user="proxy_user[schema_owner]", + password="proxy_user password", +) +``` + +See [official documentation](https://oracle-base.com/articles/misc/proxy-users-and-connect-through). + +### Required grants + +Ask your Oracle cluster administrator to set following grants for a user, +used for creating a connection: + +```{eval-rst} +.. tabs:: + + .. code-tab:: sql Read + Write (schema is owned by user) + + -- allow user to log in + GRANT CREATE SESSION TO username; + + -- allow creating tables in user schema + GRANT CREATE TABLE TO username; + + -- allow read & write access to specific table + GRANT SELECT, INSERT ON username.mytable TO username; + + .. code-tab:: sql Read + Write (schema is not owned by user) + + -- allow user to log in + GRANT CREATE SESSION TO username; + + -- allow creating tables in any schema, + -- as Oracle does not support specifying exact schema name + GRANT CREATE ANY TABLE TO username; + + -- allow read & write access to specific table + GRANT SELECT, INSERT ON someschema.mytable TO username; + + -- only if if_exists="replace_entire_table" is used: + -- allow dropping/truncating tables in any schema, + -- as Oracle does not support specifying exact schema name + GRANT DROP ANY TABLE TO username; + + .. code-tab:: sql Read only + + -- allow user to log in + GRANT CREATE SESSION TO username; + + -- allow read access to specific table + GRANT SELECT ON someschema.mytable TO username; +``` + +More details can be found in official documentation: +: - [GRANT](https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/GRANT.html) + - [SELECT](https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/SELECT.html) + - [CREATE TABLE](https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/SELECT.html) + - [INSERT](https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/INSERT.html) + - [TRUNCATE TABLE](https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/TRUNCATE-TABLE.html) diff --git a/mddocs/docs/connection/db_connection/oracle/read.md b/mddocs/docs/connection/db_connection/oracle/read.md new file mode 100644 index 000000000..4f11e86e7 --- /dev/null +++ b/mddocs/docs/connection/db_connection/oracle/read.md @@ -0,0 +1,93 @@ +(oracle-read)= + +# Reading from Oracle using `DBReader` + +{obj}`DBReader ` supports {ref}`strategy` for incremental data reading, +but does not support custom queries, like `JOIN`. + +```{eval-rst} +.. warning:: + + Please take into account :ref:`oracle-types` +``` + +## Supported DBReader features + +- ✅︎ `columns` +- ✅︎ `where` +- ✅︎ `hwm`, supported strategies: +- - ✅︎ {ref}`snapshot-strategy` +- - ✅︎ {ref}`incremental-strategy` +- - ✅︎ {ref}`snapshot-batch-strategy` +- - ✅︎ {ref}`incremental-batch-strategy` +- ✅︎ `hint` (see [official documentation](https://docs.oracle.com/cd/B10500_01/server.920/a96533/hintsref.htm)) +- ❌ `df_schema` +- ✅︎ `options` (see {obj}`Oracle.ReadOptions `) + +## Examples + +Snapshot strategy: + +```python +from onetl.connection import Oracle +from onetl.db import DBReader + +oracle = Oracle(...) + +reader = DBReader( + connection=oracle, + source="schema.table", + columns=["id", "key", "CAST(value AS VARCHAR2(4000)) value", "updated_dt"], + where="key = 'something'", + hint="INDEX(schema.table key_index)", + options=Oracle.ReadOptions(partitionColumn="id", numPartitions=10), +) +df = reader.run() +``` + +Incremental strategy: + +```python +from onetl.connection import Oracle +from onetl.db import DBReader +from onetl.strategy import IncrementalStrategy + +oracle = Oracle(...) + +reader = DBReader( + connection=oracle, + source="schema.table", + columns=["id", "key", "CAST(value AS VARCHAR2(4000)) value", "updated_dt"], + where="key = 'something'", + hint="INDEX(schema.table key_index)", + hwm=DBReader.AutoDetectHWM(name="oracle_hwm", expression="updated_dt"), + options=Oracle.ReadOptions(partitionColumn="id", numPartitions=10), +) + +with IncrementalStrategy(): + df = reader.run() +``` + +## Recommendations + +### Select only required columns + +Instead of passing `"*"` in `DBReader(columns=[...])` prefer passing exact column names. This reduces the amount of data passed from Oracle to Spark. + +### Pay attention to `where` value + +Instead of filtering data on Spark side using `df.filter(df.column == 'value')` pass proper `DBReader(where="column = 'value'")` clause. +This both reduces the amount of data send from Oracle to Spark, and may also improve performance of the query. +Especially if there are indexes or partitions for columns used in `where` clause. + +## Options + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.oracle.options +``` + +```{eval-rst} +.. autopydantic_model:: OracleReadOptions + :inherited-members: GenericOptions + :member-order: bysource +``` diff --git a/mddocs/docs/connection/db_connection/oracle/sql.md b/mddocs/docs/connection/db_connection/oracle/sql.md new file mode 100644 index 000000000..73d82e739 --- /dev/null +++ b/mddocs/docs/connection/db_connection/oracle/sql.md @@ -0,0 +1,81 @@ +(oracle-sql)= + +# Reading from Oracle using `Oracle.sql` + +`Oracle.sql` allows passing custom SQL query, but does not support incremental strategies. + +```{eval-rst} +.. warning:: + + Please take into account :ref:`oracle-types` +``` + +```{eval-rst} +.. warning:: + + Statement is executed in **read-write** connection, so if you're calling some functions/procedures with DDL/DML statements inside, + they can change data in your database. +``` + +## Syntax support + +Only queries with the following syntax are supported: + +- ✅︎ `SELECT ... FROM ...` +- ✅︎ `WITH alias AS (...) SELECT ...` +- ❌ `SHOW ...` +- ❌ `SET ...; SELECT ...;` - multiple statements not supported + +## Examples + +```python +from onetl.connection import Oracle + +oracle = Oracle(...) +df = oracle.sql( + """ + SELECT + id, + key, + CAST(value AS VARCHAR2(4000)) value, + updated_at + FROM + some.mytable + WHERE + key = 'something' + """, + options=Oracle.SQLOptions( + partitionColumn="id", + numPartitions=10, + lowerBound=0, + upperBound=1000, + ), +) +``` + +## Recommendations + +### Select only required columns + +Instead of passing `SELECT * FROM ...` prefer passing exact column names `SELECT col1, col2, ...`. +This reduces the amount of data passed from Oracle to Spark. + +### Pay attention to `where` value + +Instead of filtering data on Spark side using `df.filter(df.column == 'value')` pass proper `WHERE column = 'value'` clause. +This both reduces the amount of data send from Oracle to Spark, and may also improve performance of the query. +Especially if there are indexes or partitions for columns used in `where` clause. + +## Options + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.oracle.options +``` + +```{eval-rst} +.. autopydantic_model:: OracleSQLOptions + :inherited-members: GenericOptions + :member-order: bysource + :model-show-field-summary: false + :field-show-constraints: false +``` diff --git a/mddocs/docs/connection/db_connection/oracle/types.md b/mddocs/docs/connection/db_connection/oracle/types.md new file mode 100644 index 000000000..7097bd439 --- /dev/null +++ b/mddocs/docs/connection/db_connection/oracle/types.md @@ -0,0 +1,400 @@ +(oracle-types)= + +# Oracle \<-> Spark type mapping + +```{eval-rst} +.. note:: + + The results below are valid for Spark 3.5.5, and may differ on other Spark versions. +``` + +## Type detection & casting + +Spark's DataFrames always have a `schema` which is a list of columns with corresponding Spark types. All operations on a column are performed using column type. + +### Reading from Oracle + +This is how Oracle connector performs this: + +- For each column in query result (`SELECT column1, column2, ... FROM table ...`) get column name and Oracle type. +- Find corresponding `Oracle type (read)` → `Spark type` combination (see below) for each DataFrame column. If no combination is found, raise exception. +- Create DataFrame from query with specific column names and Spark types. + +### Writing to some existing Oracle table + +This is how Oracle connector performs this: + +- Get names of columns in DataFrame. [^footnote-1] +- Perform `SELECT * FROM table LIMIT 0` query. +- Take only columns present in DataFrame (by name, case insensitive). For each found column get Clickhouse type. +- **Find corresponding** `Oracle type (read)` → `Spark type` **combination** (see below) for each DataFrame column. If no combination is found, raise exception. [^footnote-2] +- Find corresponding `Spark type` → `Oracle type (write)` combination (see below) for each DataFrame column. If no combination is found, raise exception. +- If `Oracle type (write)` match `Oracle type (read)`, no additional casts will be performed, DataFrame column will be written to Oracle as is. +- If `Oracle type (write)` does not match `Oracle type (read)`, DataFrame column will be casted to target column type **on Oracle side**. + For example, you can write column with text data to `int` column, if column contains valid integer values within supported value range and precision. + +[^footnote-1]: This allows to write data to tables with `DEFAULT` and `GENERATED` columns - if DataFrame has no such column, + it will be populated by Oracle. + +[^footnote-2]: Yes, this is weird. + +### Create new table using Spark + +```{eval-rst} +.. warning:: + + ABSOLUTELY NOT RECOMMENDED! +``` + +This is how Oracle connector performs this: + +- Find corresponding `Spark type` → `Oracle type (create)` combination (see below) for each DataFrame column. If no combination is found, raise exception. +- Generate DDL for creating table in Oracle, like `CREATE TABLE (col1 ...)`, and run it. +- Write DataFrame to created table as is. + +But Oracle connector support only limited number of types and almost no custom clauses (like `PARTITION BY`, `INDEX`, etc). +So instead of relying on Spark to create tables: + +```{eval-rst} +.. dropdown:: See example + + .. code:: python + + writer = DBWriter( + connection=oracle, + target="public.table", + options=Oracle.WriteOptions(if_exists="append"), + ) + writer.run(df) +``` + +Always prefer creating table with desired DDL **BEFORE WRITING DATA**: + +```{eval-rst} +.. dropdown:: See example + + .. code:: python + + oracle.execute( + """ + CREATE TABLE username.table ( + id NUMBER, + business_dt TIMESTAMP(6), + value VARCHAR2(2000) + ) + """, + ) + + writer = DBWriter( + connection=oracle, + target="public.table", + options=Oracle.WriteOptions(if_exists="append"), + ) + writer.run(df) +``` + +See Oracle [CREATE TABLE](https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/SELECT.html) documentation. + +## Supported types + +### References + +See [List of Oracle types](https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/Data-Types.html). + +Here you can find source code with type conversions: + +- [JDBC -> Spark](https://github.com/apache/spark/blob/v3.5.5/sql/core/src/main/scala/org/apache/spark/sql/jdbc/OracleDialect.scala#L83-L109) +- [Spark -> JDBC](https://github.com/apache/spark/blob/v3.5.5/sql/core/src/main/scala/org/apache/spark/sql/jdbc/OracleDialect.scala#L111-L123) + +### Numeric types + +```{eval-rst} ++----------------------------------+-----------------------------------+-------------------------------+---------------------------+ +| Oracle type (read) | Spark type | Oracle type (write) | Oracle type (create) | ++==================================+===================================+===============================+===========================+ +| ``NUMBER`` | ``DecimalType(P=38, S=10)`` | ``NUMBER(P=38, S=10)`` | ``NUMBER(P=38, S=10)`` | ++----------------------------------+-----------------------------------+-------------------------------+---------------------------+ +| ``NUMBER(P=0..38)`` | ``DecimalType(P=0..38, S=0)`` | ``NUMBER(P=0..38, S=0)`` | ``NUMBER(P=38, S=0)`` | ++----------------------------------+-----------------------------------+-------------------------------+---------------------------+ +| ``NUMBER(P=0..38, S=0..38)`` | ``DecimalType(P=0..38, S=0..38)`` | ``NUMBER(P=0..38, S=0..38)`` | ``NUMBER(P=38, S=0..38)`` | ++----------------------------------+-----------------------------------+-------------------------------+---------------------------+ +| ``NUMBER(P=..., S=-127..-1)`` | unsupported [3]_ | | | ++----------------------------------+-----------------------------------+-------------------------------+---------------------------+ +| ``FLOAT`` | ``DecimalType(P=38, S=10)`` | ``NUMBER(P=38, S=10)`` | ``NUMBER(P=38, S=10)`` | ++----------------------------------+ | | | +| ``FLOAT(N)`` | | | | ++----------------------------------+ | | | +| ``REAL`` | | | | ++----------------------------------+ | | | +| ``DOUBLE PRECISION`` | | | | ++----------------------------------+-----------------------------------+-------------------------------+---------------------------+ +| ``BINARY_FLOAT`` | ``FloatType()`` | ``NUMBER(P=19, S=4)`` | ``NUMBER(P=19, S=4)`` | ++----------------------------------+-----------------------------------+ | | +| ``BINARY_DOUBLE`` | ``DoubleType()`` | | | ++----------------------------------+-----------------------------------+-------------------------------+---------------------------+ +| ``SMALLINT`` | ``DecimalType(P=38, S=0)`` | ``NUMBER(P=38, S=0)`` | ``NUMBER(P=38, S=0)`` | ++----------------------------------+ | | | +| ``INTEGER`` | | | | ++----------------------------------+-----------------------------------+-------------------------------+---------------------------+ +| ``LONG`` | ``StringType()`` | ``CLOB`` | ``CLOB`` | ++----------------------------------+-----------------------------------+-------------------------------+---------------------------+ +``` + +[^footnote-3]: Oracle support decimal types with negative scale, like `NUMBER(38, -10)`. Spark doesn't. + +### Temporal types + +```{eval-rst} ++--------------------------------------------+------------------------------------+---------------------------------+---------------------------------+ +| Oracle type (read) | Spark type | Oracle type (write) | Oracle type (create) | ++============================================+====================================+=================================+=================================+ +| ``DATE``, days | ``TimestampType()``, microseconds | ``TIMESTAMP(6)``, microseconds | ``TIMESTAMP(6)``, microseconds | ++--------------------------------------------+------------------------------------+---------------------------------+---------------------------------+ +| ``TIMESTAMP``, microseconds | ``TimestampType()``, microseconds | ``TIMESTAMP(6)``, microseconds | ``TIMESTAMP(6)``, microseconds | ++--------------------------------------------+ | | | +| ``TIMESTAMP(0)``, seconds | | | | ++--------------------------------------------+ | | | +| ``TIMESTAMP(3)``, milliseconds | | | | ++--------------------------------------------+ | | | +| ``TIMESTAMP(6)``, microseconds | | | | ++--------------------------------------------+------------------------------------+---------------------------------+---------------------------------+ +| ``TIMESTAMP(9)``, nanoseconds | ``TimestampType()``, microseconds, | ``TIMESTAMP(6)``, microseconds, | ``TIMESTAMP(6)``, microseconds, | +| | **precision loss** [4]_ | **precision loss** | **precision loss** | ++--------------------------------------------+------------------------------------+---------------------------------+---------------------------------+ +| ``TIMESTAMP WITH TIME ZONE`` | unsupported | | | ++--------------------------------------------+ | | | +| ``TIMESTAMP(N) WITH TIME ZONE`` | | | | ++--------------------------------------------+ | | | +| ``TIMESTAMP WITH LOCAL TIME ZONE`` | | | | ++--------------------------------------------+ | | | +| ``TIMESTAMP(N) WITH LOCAL TIME ZONE`` | | | | ++--------------------------------------------+ | | | +| ``INTERVAL YEAR TO MONTH`` | | | | ++--------------------------------------------+ | | | +| ``INTERVAL DAY TO SECOND`` | | | | ++--------------------------------------------+------------------------------------+---------------------------------+---------------------------------+ +``` + +```{eval-rst} +.. warning:: + + Note that types in Oracle and Spark have different value ranges: + + +---------------+------------------------------------+-----------------------------------+---------------------+--------------------------------+--------------------------------+ + | Oracle type | Min value | Max value | Spark type | Min value | Max value | + +===============+====================================+===================================+=====================+================================+================================+ + | ``date`` | ``-4712-01-01`` | ``9999-01-01`` | ``DateType()`` | ``0001-01-01`` | ``9999-12-31`` | + +---------------+------------------------------------+-----------------------------------+---------------------+--------------------------------+--------------------------------+ + | ``timestamp`` | ``-4712-01-01 00:00:00.000000000`` | ``9999-12-31 23:59:59.999999999`` | ``TimestampType()`` | ``0001-01-01 00:00:00.000000`` | ``9999-12-31 23:59:59.999999`` | + +---------------+------------------------------------+-----------------------------------+---------------------+--------------------------------+--------------------------------+ + + So not all of values can be read from Oracle to Spark. + + References: + * `Oracle date, timestamp and intervals documentation `_ + * `Spark DateType documentation `_ + * `Spark TimestampType documentation `_ +``` + +[^footnote-4]: Oracle support timestamp up to nanoseconds precision (`23:59:59.999999999`), + but Spark `TimestampType()` supports datetime up to microseconds precision (`23:59:59.999999`). + Nanoseconds will be lost during read or write operations. + +### String types + +```{eval-rst} ++-----------------------------+------------------+---------------------+----------------------+ +| Oracle type (read) | Spark type | Oracle type (write) | Oracle type (create) | ++=============================+==================+=====================+======================+ +| ``CHAR`` | ``StringType()`` | ``CLOB`` | ``CLOB`` | ++-----------------------------+ | | | +| ``CHAR(N CHAR)`` | | | | ++-----------------------------+ | | | +| ``CHAR(N BYTE)`` | | | | ++-----------------------------+ | | | +| ``NCHAR`` | | | | ++-----------------------------+ | | | +| ``NCHAR(N)`` | | | | ++-----------------------------+ | | | +| ``VARCHAR(N)`` | | | | ++-----------------------------+ | | | +| ``LONG VARCHAR`` | | | | ++-----------------------------+ | | | +| ``VARCHAR2(N CHAR)`` | | | | ++-----------------------------+ | | | +| ``VARCHAR2(N BYTE)`` | | | | ++-----------------------------+ | | | +| ``NVARCHAR2(N)`` | | | | ++-----------------------------+ | | | +| ``CLOB`` | | | | ++-----------------------------+ | | | +| ``NCLOB`` | | | | ++-----------------------------+------------------+---------------------+----------------------+ +``` + +### Binary types + +```{eval-rst} ++--------------------------+------------------+---------------------+----------------------+ +| Oracle type (read) | Spark type | Oracle type (write) | Oracle type (create) | ++==========================+==================+=====================+======================+ +| ``RAW(N)`` | ``BinaryType()`` | ``BLOB`` | ``BLOB`` | ++--------------------------+ | | | +| ``LONG RAW`` | | | | ++--------------------------+ | | | +| ``BLOB`` | | | | ++--------------------------+------------------+---------------------+----------------------+ +| ``BFILE`` | unsupported | | | ++--------------------------+------------------+---------------------+----------------------+ +``` + +### Struct types + +```{eval-rst} ++-------------------------------------+------------------+---------------------+----------------------+ +| Oracle type (read) | Spark type | Oracle type (write) | Oracle type (create) | ++=====================================+==================+=====================+======================+ +| ``XMLType`` | ``StringType()`` | ``CLOB`` | ``CLOB`` | ++-------------------------------------+ | | | +| ``URIType`` | | | | ++-------------------------------------+ | | | +| ``DBURIType`` | | | | ++-------------------------------------+ | | | +| ``XDBURIType`` | | | | ++-------------------------------------+ | | | +| ``HTTPURIType`` | | | | ++-------------------------------------+ | | | +| ``CREATE TYPE ... AS OBJECT (...)`` | | | | ++-------------------------------------+------------------+---------------------+----------------------+ +| ``JSON`` | unsupported | | | ++-------------------------------------+ | | | +| ``CREATE TYPE ... AS VARRAY ...`` | | | | ++-------------------------------------+ | | | +| ``CREATE TYPE ... AS TABLE OF ...`` | | | | ++-------------------------------------+------------------+---------------------+----------------------+ +``` + +### Special types + +```{eval-rst} ++--------------------+-------------------+---------------------+----------------------+ +| Oracle type (read) | Spark type | Oracle type (write) | Oracle type (create) | ++====================+===================+=====================+======================+ +| ``BOOLEAN`` | ``BooleanType()`` | ``BOOLEAN`` | ``NUMBER(P=1, S=0)`` | ++--------------------+-------------------+---------------------+----------------------+ +| ``ROWID`` | ``StringType()`` | ``CLOB`` | ``CLOB`` | ++--------------------+ | | | +| ``UROWID`` | | | | ++--------------------+ | | | +| ``UROWID(N)`` | | | | ++--------------------+-------------------+---------------------+----------------------+ +| ``ANYTYPE`` | unsupported | | | ++--------------------+ | | | +| ``ANYDATA`` | | | | ++--------------------+ | | | +| ``ANYDATASET`` | | | | ++--------------------+-------------------+---------------------+----------------------+ +``` + +## Explicit type cast + +### `DBReader` + +It is possible to explicitly cast column of unsupported type using `DBReader(columns=...)` syntax. + +For example, you can use `CAST(column AS CLOB)` to convert data to string representation on Oracle side, and so it will be read as Spark's `StringType()`. + +It is also possible to use [JSON_ARRAY](https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/JSON_ARRAY.html) +or [JSON_OBJECT](https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/JSON_OBJECT.html) Oracle functions +to convert column of any type to string representation. Then this JSON string can then be effectively parsed using the {obj}`JSON.parse_column ` method. + +```python +from onetl.file.format import JSON +from pyspark.sql.types import IntegerType, StructType, StructField + +from onetl.connection import Oracle +from onetl.db import DBReader + +oracle = Oracle(...) + +DBReader( + connection=oracle, + columns=[ + "id", + "supported_column", + "CAST(unsupported_column AS VARCHAR2(4000)) unsupported_column_str", + # or + "JSON_ARRAY(array_column) array_column_json", + ], +) +df = reader.run() + +json_scheme = StructType([StructField("key", IntegerType())]) + +df = df.select( + df.id, + df.supported_column, + df.unsupported_column_str.cast("integer").alias("parsed_integer"), + JSON().parse_column("array_column_json", json_scheme).alias("array_column"), +) +``` + +### `DBWriter` + +It is always possible to convert data on Spark side to string, and then write it to text column in Oracle table. + +To serialize and write JSON data to a `text` or `json` column in an Oracle table use the {obj}`JSON.serialize_column ` method. + +```python +from onetl.connection import Oracle +from onetl.db import DBWriter +from onetl.file.format import JSON + +oracle = Oracle(...) + +oracle.execute( + """ + CREATE TABLE schema.target_table ( + id INTEGER, + supported_column TIMESTAMP, + array_column_json VARCHAR2(4000) -- any string type, actually + ) + """, +) + +write_df = df.select( + df.id, + df.supported_column, + JSON().serialize_column(df.unsupported_column).alias("array_column_json"), +) + +writer = DBWriter( + connection=oracle, + target="schema.target_table", +) +writer.run(write_df) +``` + +Then you can parse this column on Oracle side - for example, by creating a view: + +```sql +SELECT + id, + supported_column, + JSON_VALUE(array_column_json, '$[0]' RETURNING NUMBER) AS array_item_0 +FROM + schema.target_table +``` + +Or by using [VIRTUAL column](https://oracle-base.com/articles/11g/virtual-columns-11gr1): + +```sql +CREATE TABLE schema.target_table ( + id INTEGER, + supported_column TIMESTAMP, + array_column_json VARCHAR2(4000), -- any string type, actually + array_item_0 GENERATED ALWAYS AS (JSON_VALUE(array_column_json, '$[0]' RETURNING NUMBER)) VIRTUAL +) +``` + +But data will be parsed on each table read in any case, as Oracle does no support `GENERATED ALWAYS AS (...) STORED` columns. diff --git a/mddocs/docs/connection/db_connection/oracle/write.md b/mddocs/docs/connection/db_connection/oracle/write.md new file mode 100644 index 000000000..09e021c79 --- /dev/null +++ b/mddocs/docs/connection/db_connection/oracle/write.md @@ -0,0 +1,56 @@ +(oracle-write)= + +# Writing to Oracle using `DBWriter` + +For writing data to Oracle, use {obj}`DBWriter `. + +```{eval-rst} +.. warning:: + + Please take into account :ref:`oracle-types` +``` + +```{eval-rst} +.. warning:: + + It is always recommended to create table explicitly using :ref:`Oracle.execute ` + instead of relying on Spark's table DDL generation. + + This is because Spark's DDL generator can create columns with different precision and types than it is expected, + causing precision loss or other issues. +``` + +## Examples + +```python +from onetl.connection import Oracle +from onetl.db import DBWriter + +oracle = Oracle(...) + +df = ... # data is here + +writer = DBWriter( + connection=oracle, + target="schema.table", + options=Oracle.WriteOptions(if_exists="append"), +) + +writer.run(df) +``` + +## Options + +Method above accepts {obj}`OracleWriteOptions ` + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.oracle.options +``` + +```{eval-rst} +.. autopydantic_model:: OracleWriteOptions + :inherited-members: GenericOptions + :member-order: bysource + :model-show-field-summary: false + :field-show-constraints: false +``` diff --git a/mddocs/docs/connection/db_connection/postgres/connection.md b/mddocs/docs/connection/db_connection/postgres/connection.md new file mode 100644 index 000000000..80c59021c --- /dev/null +++ b/mddocs/docs/connection/db_connection/postgres/connection.md @@ -0,0 +1,12 @@ +(postgres-connection)= + +# Postgres connection + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.postgres.connection +``` + +```{eval-rst} +.. autoclass:: Postgres + :members: get_packages, check +``` diff --git a/mddocs/docs/connection/db_connection/postgres/execute.md b/mddocs/docs/connection/db_connection/postgres/execute.md new file mode 100644 index 000000000..6964b43e9 --- /dev/null +++ b/mddocs/docs/connection/db_connection/postgres/execute.md @@ -0,0 +1,113 @@ +(postgres-execute)= + +# Executing statements in Postgres + +```{eval-rst} +.. warning:: + + Methods below **read all the rows** returned from DB **to Spark driver memory**, and then convert them to DataFrame. + + Do **NOT** use them to read large amounts of data. Use :ref:`DBReader ` or :ref:`Postgres.sql ` instead. +``` + +## How to + +There are 2 ways to execute some statement in Postgres + +### Use `Postgres.fetch` + +Use this method to execute some `SELECT` query which returns **small number or rows**, like reading +Postgres config, or reading data from some reference table. Method returns Spark DataFrame. + +Method accepts {obj}`Postgres.FetchOptions `. + +Connection opened using this method should be then closed with `connection.close()` or `with connection:`. + +```{eval-rst} +.. warning:: + + Please take into account :ref:`postgres-types`. +``` + +#### Syntax support + +This method supports **any** query syntax supported by Postgres, like: + +- ✅︎ `SELECT ... FROM ...` +- ✅︎ `WITH alias AS (...) SELECT ...` +- ❌ `SET ...; SELECT ...;` - multiple statements not supported + +#### Examples + +```python +from onetl.connection import Postgres + +postgres = Postgres(...) + +df = postgres.fetch( + "SELECT value FROM some.reference_table WHERE key = 'some_constant'", + options=Postgres.FetchOptions(queryTimeout=10), +) +postgres.close() +value = df.collect()[0][0] # get value from first row and first column +``` + +### Use `Postgres.execute` + +Use this method to execute DDL and DML operations. Each method call runs operation in a separated transaction, and then commits it. + +Method accepts {obj}`Postgres.ExecuteOptions `. + +Connection opened using this method should be then closed with `connection.close()` or `with connection:`. + +#### Syntax support + +This method supports **any** query syntax supported by Postgres, like: + +- ✅︎ `CREATE TABLE ...`, `CREATE VIEW ...`, and so on +- ✅︎ `ALTER ...` +- ✅︎ `INSERT INTO ... SELECT ...`, `UPDATE ...`, `DELETE ...`, and so on +- ✅︎ `DROP TABLE ...`, `DROP VIEW ...`, `TRUNCATE TABLE`, and so on +- ✅︎ `CALL procedure(arg1, arg2) ...` +- ✅︎ `SELECT func(arg1, arg2)` or `{call func(arg1, arg2)}` - special syntax for calling functions +- ✅︎ other statements not mentioned here +- ❌ `SET ...; SELECT ...;` - multiple statements not supported + +#### Examples + +```python +from onetl.connection import Postgres + +postgres = Postgres(...) + +postgres.execute("DROP TABLE schema.table") +postgres.execute( + """ + CREATE TABLE schema.table ( + id bigint GENERATED ALWAYS AS IDENTITY, + key text, + value real + ) + """, + options=Postgres.ExecuteOptions(queryTimeout=10), +) +``` + +## Options + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.postgres.options +``` + +```{eval-rst} +.. autopydantic_model:: PostgresFetchOptions + :inherited-members: GenericOptions + :member-order: bysource + +``` + +```{eval-rst} +.. autopydantic_model:: PostgresExecuteOptions + :inherited-members: GenericOptions + :member-order: bysource +``` diff --git a/mddocs/docs/connection/db_connection/postgres/index.md b/mddocs/docs/connection/db_connection/postgres/index.md new file mode 100644 index 000000000..56442bfb0 --- /dev/null +++ b/mddocs/docs/connection/db_connection/postgres/index.md @@ -0,0 +1,28 @@ +(postgres)= + +# Postgres + +```{toctree} +:caption: Connection +:maxdepth: 1 + +prerequisites +connection +``` + +```{toctree} +:caption: Operations +:maxdepth: 1 + +read +sql +write +execute +``` + +```{toctree} +:caption: Troubleshooting +:maxdepth: 1 + +types +``` diff --git a/mddocs/docs/connection/db_connection/postgres/prerequisites.md b/mddocs/docs/connection/db_connection/postgres/prerequisites.md new file mode 100644 index 000000000..d2221552d --- /dev/null +++ b/mddocs/docs/connection/db_connection/postgres/prerequisites.md @@ -0,0 +1,71 @@ +(postgres-prerequisites)= + +# Prerequisites + +## Version Compatibility + +- PostgreSQL server versions: + : - Officially declared: 8.2 - 17 + - Actually tested: 9.4.26, 17.3 +- Spark versions: 2.3.x - 3.5.x +- Java versions: 8 - 20 + +See [official documentation](https://jdbc.postgresql.org/). + +## Installing PySpark + +To use Postgres connector you should have PySpark installed (or injected to `sys.path`) +BEFORE creating the connector instance. + +See {ref}`install-spark` installation instruction for more details. + +## Connecting to Postgres + +### Allowing connection to Postgres instance + +Ask your Postgres administrator to allow your user (and probably IP) to connect to instance, +e.g. by updating `pg_hba.conf` file. + +See [official documentation](https://www.postgresql.org/docs/current/auth-pg-hba-conf.html). + +### Connection port + +Connection is usually performed to port 5432. Port may differ for different Postgres instances. +Please ask your Postgres administrator to provide required information. + +### Connection host + +It is possible to connect to Postgres by using either DNS name of host or it's IP address. + +If you're using Postgres cluster, it is currently possible to connect only to **one specific node**. +Connecting to multiple nodes to perform load balancing, as well as automatic failover to new master/replica are not supported. + +### Required grants + +Ask your Postgres cluster administrator to set following grants for a user, +used for creating a connection: + +```{eval-rst} +.. tabs:: + + .. code-tab:: sql Read + Write + + -- allow creating tables in specific schema + GRANT USAGE, CREATE ON SCHEMA myschema TO username; + + -- allow read & write access to specific table + GRANT SELECT, INSERT ON myschema.mytable TO username; + + -- only if if_exists="replace_entire_table" is used: + GRANT TRUNCATE ON myschema.mytable TO username; + + .. code-tab:: sql Read only + + -- allow creating tables in specific schema + GRANT USAGE ON SCHEMA myschema TO username; + + -- allow read access to specific table + GRANT SELECT ON myschema.mytable TO username; +``` + +More details can be found in [official documentation](https://www.postgresql.org/docs/current/sql-grant.html). diff --git a/mddocs/docs/connection/db_connection/postgres/read.md b/mddocs/docs/connection/db_connection/postgres/read.md new file mode 100644 index 000000000..71db1908c --- /dev/null +++ b/mddocs/docs/connection/db_connection/postgres/read.md @@ -0,0 +1,91 @@ +(postgres-read)= + +# Reading from Postgres using `DBReader` + +{obj}`DBReader ` supports {ref}`strategy` for incremental data reading, +but does not support custom queries, like `JOIN`. + +```{eval-rst} +.. warning:: + + Please take into account :ref:`postgres-types` +``` + +## Supported DBReader features + +- ✅︎ `columns` +- ✅︎ `where` +- ✅︎ `hwm`, supported strategies: +- - ✅︎ {ref}`snapshot-strategy` +- - ✅︎ {ref}`incremental-strategy` +- - ✅︎ {ref}`snapshot-batch-strategy` +- - ✅︎ {ref}`incremental-batch-strategy` +- ❌ `hint` (is not supported by Postgres) +- ❌ `df_schema` +- ✅︎ `options` (see {obj}`Postgres.ReadOptions `) + +## Examples + +Snapshot strategy: + +```python +from onetl.connection import Postgres +from onetl.db import DBReader + +postgres = Postgres(...) + +reader = DBReader( + connection=postgres, + source="schema.table", + columns=["id", "key", "CAST(value AS text) value", "updated_dt"], + where="key = 'something'", + options=Postgres.ReadOptions(partitionColumn="id", numPartitions=10), +) +df = reader.run() +``` + +Incremental strategy: + +```python +from onetl.connection import Postgres +from onetl.db import DBReader +from onetl.strategy import IncrementalStrategy + +postgres = Postgres(...) + +reader = DBReader( + connection=postgres, + source="schema.table", + columns=["id", "key", "CAST(value AS text) value", "updated_dt"], + where="key = 'something'", + hwm=DBReader.AutoDetectHWM(name="postgres_hwm", expression="updated_dt"), + options=Postgres.ReadOptions(partitionColumn="id", numPartitions=10), +) + +with IncrementalStrategy(): + df = reader.run() +``` + +## Recommendations + +### Select only required columns + +Instead of passing `"*"` in `DBReader(columns=[...])` prefer passing exact column names. This reduces the amount of data passed from Postgres to Spark. + +### Pay attention to `where` value + +Instead of filtering data on Spark side using `df.filter(df.column == 'value')` pass proper `DBReader(where="column = 'value'")` clause. +This both reduces the amount of data send from Postgres to Spark, and may also improve performance of the query. +Especially if there are indexes or partitions for columns used in `where` clause. + +## Options + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.postgres.options +``` + +```{eval-rst} +.. autopydantic_model:: PostgresReadOptions + :inherited-members: GenericOptions + :member-order: bysource +``` diff --git a/mddocs/docs/connection/db_connection/postgres/sql.md b/mddocs/docs/connection/db_connection/postgres/sql.md new file mode 100644 index 000000000..adceab0dc --- /dev/null +++ b/mddocs/docs/connection/db_connection/postgres/sql.md @@ -0,0 +1,80 @@ +(postgres-sql)= + +# Reading from Postgres using `Postgres.sql` + +`Postgres.sql` allows passing custom SQL query, but does not support incremental strategies. + +```{eval-rst} +.. warning:: + + Please take into account :ref:`postgres-types` +``` + +```{eval-rst} +.. warning:: + + Statement is executed in **read-write** connection, so if you're calling some functions/procedures with DDL/DML statements inside, + they can change data in your database. +``` + +## Syntax support + +Only queries with the following syntax are supported: + +- ✅︎ `SELECT ... FROM ...` +- ✅︎ `WITH alias AS (...) SELECT ...` +- ❌ `SET ...; SELECT ...;` - multiple statements not supported + +## Examples + +```python +from onetl.connection import Postgres + +postgres = Postgres(...) +df = postgres.sql( + """ + SELECT + id, + key, + CAST(value AS text) value, + updated_at + FROM + some.mytable + WHERE + key = 'something' + """, + options=Postgres.SQLOptions( + partitionColumn="id", + numPartitions=10, + lowerBound=0, + upperBound=1000, + ), +) +``` + +## Recommendations + +### Select only required columns + +Instead of passing `SELECT * FROM ...` prefer passing exact column names `SELECT col1, col2, ...`. +This reduces the amount of data passed from Postgres to Spark. + +### Pay attention to `where` value + +Instead of filtering data on Spark side using `df.filter(df.column == 'value')` pass proper `WHERE column = 'value'` clause. +This both reduces the amount of data send from Postgres to Spark, and may also improve performance of the query. +Especially if there are indexes or partitions for columns used in `where` clause. + +## Options + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.postgres.options +``` + +```{eval-rst} +.. autopydantic_model:: PostgresSQLOptions + :inherited-members: GenericOptions + :member-order: bysource + :model-show-field-summary: false + :field-show-constraints: false +``` diff --git a/mddocs/docs/connection/db_connection/postgres/types.md b/mddocs/docs/connection/db_connection/postgres/types.md new file mode 100644 index 000000000..3b3540383 --- /dev/null +++ b/mddocs/docs/connection/db_connection/postgres/types.md @@ -0,0 +1,490 @@ +(postgres-types)= + +# Postgres \<-> Spark type mapping + +```{eval-rst} +.. note:: + + The results below are valid for Spark 3.5.5, and may differ on other Spark versions. +``` + +## Type detection & casting + +Spark's DataFrames always have a `schema` which is a list of columns with corresponding Spark types. All operations on a column are performed using column type. + +### Reading from Postgres + +This is how Postgres connector performs this: + +- For each column in query result (`SELECT column1, column2, ... FROM table ...`) get column name and Postgres type. +- Find corresponding `Postgres type (read)` → `Spark type` combination (see below) for each DataFrame column [^footnote-1]. If no combination is found, raise exception. +- Create DataFrame from query with specific column names and Spark types. + +[^footnote-1]: All Postgres types that doesn't have corresponding Java type are converted to `String`. + +### Writing to some existing Postgres table + +This is how Postgres connector performs this: + +- Get names of columns in DataFrame. [^footnote-1] +- Perform `SELECT * FROM table LIMIT 0` query. +- Take only columns present in DataFrame (by name, case insensitive) [^footnote-2]. For each found column get Postgres type. +- Find corresponding `Spark type` → `Postgres type (write)` combination (see below) for each DataFrame column. If no combination is found, raise exception. +- If `Postgres type (write)` match `Postgres type (read)`, no additional casts will be performed, DataFrame column will be written to Postgres as is. +- If `Postgres type (write)` does not match `Postgres type (read)`, DataFrame column will be casted to target column type **on Postgres side**. + For example, you can write column with text data to `int` column, if column contains valid integer values within supported value range and precision [^footnote-3]. + +[^footnote-2]: This allows to write data to tables with `DEFAULT` and `GENERATED` columns - if DataFrame has no such column, + it will be populated by Postgres. + +[^footnote-3]: This is true only if either DataFrame column is a `StringType()`, or target column is `text` type. + + But other types cannot be silently converted, like `bytea -> bit(N)`. This requires explicit casting, see [Manual conversion to string]. + +### Create new table using Spark + +```{eval-rst} +.. warning:: + + ABSOLUTELY NOT RECOMMENDED! +``` + +This is how Postgres connector performs this: + +- Find corresponding `Spark type` → `Postgres type (create)` combination (see below) for each DataFrame column. If no combination is found, raise exception. +- Generate DDL for creating table in Postgres, like `CREATE TABLE (col1 ...)`, and run it. +- Write DataFrame to created table as is. + +But Postgres connector support only limited number of types and almost no custom clauses (like `PARTITION BY`, `INDEX`, etc). +So instead of relying on Spark to create tables: + +```{eval-rst} +.. dropdown:: See example + + .. code:: python + + writer = DBWriter( + connection=postgres, + target="public.table", + options=Postgres.WriteOptions( + if_exists="append", + createTableOptions="PARTITION BY RANGE (id)", + ), + ) + writer.run(df) +``` + +Always prefer creating table with desired DDL **BEFORE WRITING DATA**: + +```{eval-rst} +.. dropdown:: See example + + .. code:: python + + postgres.execute( + """ + CREATE TABLE public.table ( + id bigint, + business_dt timestamp(6), + value json + ) + PARTITION BY RANGE (Id) + """, + ) + + writer = DBWriter( + connection=postgres, + target="public.table", + options=Postgres.WriteOptions(if_exists="append"), + ) + writer.run(df) +``` + +See Postgres [CREATE TABLE](https://www.postgresql.org/docs/current/sql-createtable.html) documentation. + +## Supported types + +### References + +See [List of Postgres types](https://www.postgresql.org/docs/current/datatype.html). + +Here you can find source code with type conversions: + +- [Postgres \<-> JDBC](https://github.com/pgjdbc/pgjdbc/blob/REL42.6.0/pgjdbc/src/main/java/org/postgresql/jdbc/TypeInfoCache.java#L78-L112) +- [JDBC -> Spark](https://github.com/apache/spark/blob/v3.5.5/sql/core/src/main/scala/org/apache/spark/sql/jdbc/PostgresDialect.scala#L52-L108) +- [Spark -> JDBC](https://github.com/apache/spark/blob/v3.5.5/sql/core/src/main/scala/org/apache/spark/sql/jdbc/PostgresDialect.scala#L118-L132) + +### Numeric types + +```{eval-rst} ++----------------------------------+-----------------------------------+-------------------------------+-------------------------+ +| Postgres type (read) | Spark type | Postgres type (write) | Postgres type (create) | ++==================================+===================================+===============================+=========================+ +| ``decimal`` | ``DecimalType(P=38, S=18)`` | ``decimal(P=38, S=18)`` | ``decimal`` (unbounded) | ++----------------------------------+-----------------------------------+-------------------------------+ | +| ``decimal(P=0..38)`` | ``DecimalType(P=0..38, S=0)`` | ``decimal(P=0..38, S=0)`` | | ++----------------------------------+-----------------------------------+-------------------------------+ | +| ``decimal(P=0..38, S=0..38)`` | ``DecimalType(P=0..38, S=0..38)`` | ``decimal(P=0..38, S=0..38)`` | | ++----------------------------------+-----------------------------------+-------------------------------+-------------------------+ +| ``decimal(P=39.., S=0..)`` | unsupported [4]_ | | | ++----------------------------------+-----------------------------------+-------------------------------+-------------------------+ +| ``decimal(P=.., S=..-1)`` | unsupported [5]_ | | | ++----------------------------------+-----------------------------------+-------------------------------+-------------------------+ +| ``real`` | ``FloatType()`` | ``real`` | ``real`` | ++----------------------------------+-----------------------------------+-------------------------------+-------------------------+ +| ``double precision`` | ``DoubleType()`` | ``double precision`` | ``double precision`` | ++----------------------------------+-----------------------------------+-------------------------------+-------------------------+ +| ``smallint`` | ``ShortType()`` | ``smallint`` | ``smallint`` | ++----------------------------------+-----------------------------------+ | | +| ``-`` | ``ByteType()`` | | | ++----------------------------------+-----------------------------------+-------------------------------+-------------------------+ +| ``integer`` | ``IntegerType()`` | ``integer`` | ``integer`` | ++----------------------------------+-----------------------------------+-------------------------------+-------------------------+ +| ``bigint`` | ``LongType()`` | ``bigint`` | ``bigint`` | ++----------------------------------+-----------------------------------+-------------------------------+-------------------------+ +| ``money`` | ``StringType()`` [1]_ | ``text`` | ``text`` | ++----------------------------------+ | | | +| ``int4range`` | | | | ++----------------------------------+ | | | +| ``int8range`` | | | | ++----------------------------------+ | | | +| ``numrange`` | | | | ++----------------------------------+ | | | +| ``int2vector`` | | | | ++----------------------------------+-----------------------------------+-------------------------------+-------------------------+ +``` + +[^footnote-4]: Postgres support decimal types with unlimited precision. + + But Spark's `DecimalType(P, S)` supports maximum `P=38` (128 bit). It is impossible to read, write or operate with values of larger precision, + this leads to an exception. + +[^footnote-5]: Postgres support decimal types with negative scale, like `decimal(38, -10)`. Spark doesn't. + +### Temporal types + +```{eval-rst} ++------------------------------------+------------------------------+-----------------------+-------------------------+ +| Postgres type (read) | Spark type | Postgres type (write) | Postgres type (create) | ++====================================+==============================+=======================+=========================+ +| ``date`` | ``DateType()`` | ``date`` | ``date`` | ++------------------------------------+------------------------------+-----------------------+-------------------------+ +| ``time`` | ``TimestampType()``, | ``timestamp(6)`` | ``timestamp(6)`` | ++------------------------------------+ with time format quirks [6]_ | | | +| ``time(0..6)`` | | | | ++------------------------------------+ | | | +| ``time with time zone`` | | | | ++------------------------------------+ | | | +| ``time(0..6) with time zone`` | | | | ++------------------------------------+------------------------------+-----------------------+-------------------------+ +| ``timestamp`` | ``TimestampType()`` | ``timestamp(6)`` | ``timestamp(6)`` | ++------------------------------------+ | | | +| ``timestamp(0..6)`` | | | | ++------------------------------------+ | | | +| ``timestamp with time zone`` | | | | ++------------------------------------+ | | | +| ``timestamp(0..6) with time zone`` | | | | ++------------------------------------+------------------------------+-----------------------+-------------------------+ +| ``-`` | ``TimestampNTZType()`` | ``timestamp(6)`` | ``timestamp(6)`` | ++------------------------------------+------------------------------+-----------------------+-------------------------+ +| ``interval`` of any precision | ``StringType()`` [1]_ | ``text`` | ``text`` | ++------------------------------------+------------------------------+-----------------------+-------------------------+ +| ``-`` | ``DayTimeIntervalType()`` | unsupported | unsupported | ++------------------------------------+------------------------------+-----------------------+-------------------------+ +| ``-`` | ``YearMonthIntervalType()`` | unsupported | unsupported | ++------------------------------------+------------------------------+-----------------------+-------------------------+ +| ``daterange`` | ``StringType()`` [1]_ | ``text`` | ``text`` | ++------------------------------------+ | | | +| ``tsrange`` | | | | ++------------------------------------+ | | | +| ``tstzrange`` | | | | ++------------------------------------+------------------------------+-----------------------+-------------------------+ +``` + +```{eval-rst} +.. warning:: + + Note that types in Postgres and Spark have different value ranges: + + +---------------+---------------------------------+----------------------------------+---------------------+--------------------------------+--------------------------------+ + | Postgres type | Min value | Max value | Spark type | Min value | Max value | + +===============+=================================+==================================+=====================+================================+================================+ + | ``date`` | ``-4713-01-01`` | ``5874897-01-01`` | ``DateType()`` | ``0001-01-01`` | ``9999-12-31`` | + +---------------+---------------------------------+----------------------------------+---------------------+--------------------------------+--------------------------------+ + | ``timestamp`` | ``-4713-01-01 00:00:00.000000`` | ``294276-12-31 23:59:59.999999`` | ``TimestampType()`` | ``0001-01-01 00:00:00.000000`` | ``9999-12-31 23:59:59.999999`` | + +---------------+---------------------------------+----------------------------------+ | | | + | ``time`` | ``00:00:00.000000`` | ``24:00:00.000000`` | | | | + +---------------+---------------------------------+----------------------------------+---------------------+--------------------------------+--------------------------------+ + + So not all of values can be read from Postgres to Spark. + + References: + * `Postgres date/time types documentation `_ + * `Spark DateType documentation `_ + * `Spark TimestampType documentation `_ +``` + +[^footnote-6]: `time` type is the same as `timestamp` with date `1970-01-01`. So instead of reading data from Postgres like `23:59:59` + it is actually read `1970-01-01 23:59:59`, and vice versa. + +### String types + +```{eval-rst} ++-----------------------------+-----------------------+-----------------------+-------------------------+ +| Postgres type (read) | Spark type | Postgres type (write) | Postgres type (create) | ++=============================+=======================+=======================+=========================+ +| ``character`` | ``StringType()`` | ``text`` | ``text`` | ++-----------------------------+ | | | +| ``character(N)`` | | | | ++-----------------------------+ | | | +| ``character varying`` | | | | ++-----------------------------+ | | | +| ``character varying(N)`` | | | | ++-----------------------------+ | | | +| ``text`` | | | | ++-----------------------------+ | | | +| ``json`` | | | | ++-----------------------------+ | | | +| ``jsonb`` | | | | ++-----------------------------+ | | | +| ``xml`` | | | | ++-----------------------------+-----------------------+ | | +| ``CREATE TYPE ... AS ENUM`` | ``StringType()`` [1]_ | | | ++-----------------------------+ | | | +| ``tsvector`` | | | | ++-----------------------------+ | | | +| ``tsquery`` | | | | ++-----------------------------+-----------------------+-----------------------+-------------------------+ +| ``-`` | ``CharType()`` | ``unsupported`` | ``unsupported`` | ++-----------------------------+-----------------------+-----------------------+-------------------------+ +| ``-`` | ``VarcharType()`` | ``unsupported`` | ``unsupported`` | ++-----------------------------+-----------------------+-----------------------+-------------------------+ +``` + +### Binary types + +```{eval-rst} ++--------------------------+-----------------------+-----------------------------+-------------------------+ +| Postgres type (read) | Spark type | Postgres type (write) | Postgres type (create) | ++==========================+=======================+=============================+=========================+ +| ``boolean`` | ``BooleanType()`` | ``boolean`` | ``boolean`` | ++--------------------------+-----------------------+-----------------------------+-------------------------+ +| ``bit`` | ``BooleanType()`` | ``bool``, | ``bool`` | ++--------------------------+ | **cannot insert data** [3]_ | | +| ``bit(N=1)`` | | | | ++--------------------------+-----------------------+-----------------------------+-------------------------+ +| ``bit(N=2..)`` | ``ByteType()`` | ``bytea``, | ``bytea`` | +| | | **cannot insert data** [3]_ | | ++--------------------------+-----------------------+-----------------------------+-------------------------+ +| ``bit varying`` | ``StringType()`` [1]_ | ``text`` | ``text`` | ++--------------------------+ | | | +| ``bit varying(N)`` | | | | ++--------------------------+-----------------------+-----------------------------+-------------------------+ +| ``bytea`` | ``BinaryType()`` | ``bytea`` | ``bytea`` | ++--------------------------+-----------------------+-----------------------------+-------------------------+ +``` + +### Struct types + +```{eval-rst} ++--------------------------------+-----------------------+-----------------------+-------------------------+ +| Postgres type (read) | Spark type | Postgres type (write) | Postgres type (create) | ++================================+=======================+=======================+=========================+ +| ``T[]`` | ``ArrayType(T)`` | ``T[]`` | ``T[]`` | ++--------------------------------+-----------------------+-----------------------+-------------------------+ +| ``T[][]`` | unsupported | | | ++--------------------------------+-----------------------+-----------------------+-------------------------+ +| ``CREATE TYPE sometype (...)`` | ``StringType()`` [1]_ | ``text`` | ``text`` | ++--------------------------------+-----------------------+-----------------------+-------------------------+ +| ``-`` | ``StructType()`` | unsupported | | ++--------------------------------+-----------------------+ | | +| ``-`` | ``MapType()`` | | | ++--------------------------------+-----------------------+-----------------------+-------------------------+ +``` + +### Network types + +```{eval-rst} ++----------------------+-----------------------+-----------------------+-------------------------+ +| Postgres type (read) | Spark type | Postgres type (write) | Postgres type (create) | ++======================+=======================+=======================+=========================+ +| ``cidr`` | ``StringType()`` [1]_ | ``text`` | ``text`` | ++----------------------+ | | | +| ``inet`` | | | | ++----------------------+ | | | +| ``macaddr`` | | | | ++----------------------+ | | | +| ``macaddr8`` | | | | ++----------------------+-----------------------+-----------------------+-------------------------+ +``` + +### Geo types + +```{eval-rst} ++----------------------+-----------------------+-----------------------+-------------------------+ +| Postgres type (read) | Spark type | Postgres type (write) | Postgres type (create) | ++======================+=======================+=======================+=========================+ +| ``circle`` | ``StringType()`` [1]_ | ``text`` | ``text`` | ++----------------------+ | | | +| ``box`` | | | | ++----------------------+ | | | +| ``line`` | | | | ++----------------------+ | | | +| ``lseg`` | | | | ++----------------------+ | | | +| ``path`` | | | | ++----------------------+ | | | +| ``point`` | | | | ++----------------------+ | | | +| ``polygon`` | | | | ++----------------------+ | | | +| ``polygon`` | | | | ++----------------------+-----------------------+-----------------------+-------------------------+ +``` + +## Explicit type cast + +### `DBReader` + +It is possible to explicitly cast column of unsupported type using `DBReader(columns=...)` syntax. + +For example, you can use `CAST(column AS text)` to convert data to string representation on Postgres side, and so it will be read as Spark's `StringType()`. + +It is also possible to use [to_json](https://www.postgresql.org/docs/current/functions-json.html) Postgres function to convert column of any type to string representation, and then parse this column on Spark side you can use the {obj}`JSON.parse_column ` method: + +```python +from pyspark.sql.types import IntegerType + +from onetl.connection import Postgres +from onetl.db import DBReader +from onetl.file.format import JSON + +postgres = Postgres(...) + +DBReader( + connection=postgres, + columns=[ + "id", + "supported_column", + "CAST(unsupported_column AS text) unsupported_column_str", + # or + "to_json(unsupported_column) array_column_json", + ], +) +df = reader.run() + +json_schema = StructType( + [ + StructField("id", IntegerType(), nullable=True), + StructField("name", StringType(), nullable=True), + ..., + ] +) +df = df.select( + df.id, + df.supported_column, + # explicit cast + df.unsupported_column_str.cast("integer").alias("parsed_integer"), + JSON().parse_column("array_column_json", json_schema).alias("json_string"), +) +``` + +### `DBWriter` + +It is always possible to convert data on the Spark side to a string, and then write it to a text column in a Postgres table. + +#### Using JSON.serialize_column + +You can use the {obj}`JSON.serialize_column ` method for data serialization: + +```python +from onetl.file.format import JSON +from pyspark.sql.functions import col + +from onetl.connection import Postgres +from onetl.db import DBWriter + +postgres = Postgres(...) + +postgres.execute( + """ + CREATE TABLE schema.target_table ( + id int, + supported_column timestamp, + array_column_json jsonb -- any column type, actually + ) + """, +) + +write_df = df.select( + df.id, + df.supported_column, + JSON().serialize_column(df.unsupported_column).alias("array_column_json"), +) + +writer = DBWriter( + connection=postgres, + target="schema.target_table", +) +writer.run(write_df) +``` + +Then you can parse this column on the Postgres side (for example, by creating a view): + +```sql +SELECT + id, + supported_column, + array_column_json->'0' AS array_item_0 +FROM + schema.target_table +``` + +To avoid casting the value on every table read you can use [GENERATED ALWAYS STORED](https://www.postgresql.org/docs/current/ddl-generated-columns.html) column, but this requires 2x space (for original and parsed value). + +#### Manual conversion to string + +Postgres connector also supports conversion text value directly to target column type, if this value has a proper format. + +For example, you can write data like `[123, 345)` to `int8range` type because Postgres allows cast `'[123, 345)'::int8range'`: + +```python +from pyspark.sql.ftypes import StringType +from pyspark.sql.functions import udf + +from onetl.connection import Postgres +from onetl.db import DBReader + +postgres = Postgres(...) + +postgres.execute( + """ + CREATE TABLE schema.target_table ( + id int, + range_column int8range -- any column type, actually + ) + """, +) + + +@udf(returnType=StringType()) +def array_to_range(value: tuple): + """This UDF allows to convert tuple[start, end] to Postgres' range format""" + start, end = value + return f"[{start},{end})" + + +write_df = df.select( + df.id, + array_to_range(df.range_column).alias("range_column"), +) + +writer = DBWriter( + connection=postgres, + target="schema.target_table", +) +writer.run(write_df) +``` + +This can be tricky to implement and may lead to longer write process. +But this does not require extra space on Postgres side, and allows to avoid explicit value cast on every table read. diff --git a/mddocs/docs/connection/db_connection/postgres/write.md b/mddocs/docs/connection/db_connection/postgres/write.md new file mode 100644 index 000000000..8f01de26a --- /dev/null +++ b/mddocs/docs/connection/db_connection/postgres/write.md @@ -0,0 +1,56 @@ +(postgres-write)= + +# Writing to Postgres using `DBWriter` + +For writing data to Postgres, use {obj}`DBWriter `. + +```{eval-rst} +.. warning:: + + Please take into account :ref:`postgres-types` +``` + +```{eval-rst} +.. warning:: + + It is always recommended to create table explicitly using :ref:`Postgres.execute ` + instead of relying on Spark's table DDL generation. + + This is because Spark's DDL generator can create columns with different precision and types than it is expected, + causing precision loss or other issues. +``` + +## Examples + +```python +from onetl.connection import Postgres +from onetl.db import DBWriter + +postgres = Postgres(...) + +df = ... # data is here + +writer = DBWriter( + connection=postgres, + target="schema.table", + options=Postgres.WriteOptions(if_exists="append"), +) + +writer.run(df) +``` + +## Options + +Method above accepts {obj}`Postgres.WriteOptions ` + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.postgres.options +``` + +```{eval-rst} +.. autopydantic_model:: PostgresWriteOptions + :inherited-members: GenericOptions + :member-order: bysource + :model-show-field-summary: false + :field-show-constraints: false +``` diff --git a/mddocs/docs/connection/db_connection/teradata/connection.md b/mddocs/docs/connection/db_connection/teradata/connection.md new file mode 100644 index 000000000..0804437cf --- /dev/null +++ b/mddocs/docs/connection/db_connection/teradata/connection.md @@ -0,0 +1,12 @@ +(teradata-connection)= + +# Teradata connection + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.teradata.connection +``` + +```{eval-rst} +.. autoclass:: Teradata + :members: get_packages, check +``` diff --git a/mddocs/docs/connection/db_connection/teradata/execute.md b/mddocs/docs/connection/db_connection/teradata/execute.md new file mode 100644 index 000000000..de1b018fe --- /dev/null +++ b/mddocs/docs/connection/db_connection/teradata/execute.md @@ -0,0 +1,110 @@ +(teradata-execute)= + +# Executing statements in Teradata + +```{eval-rst} +.. warning:: + + Methods below **read all the rows** returned from DB **to Spark driver memory**, and then convert them to DataFrame. + + Do **NOT** use them to read large amounts of data. Use :ref:`DBReader ` or :ref:`Teradata.sql ` instead. +``` + +## How to + +There are 2 ways to execute some statement in Teradata + +### Use `Teradata.fetch` + +Use this method to execute some `SELECT` query which returns **small number or rows**, like reading +Teradata config, or reading data from some reference table. Method returns Spark DataFrame. + +Method accepts {obj}`Teradata.FetchOptions `. + +Connection opened using this method should be then closed with `connection.close()` or `with connection:`. + +#### Syntax support + +This method supports **any** query syntax supported by Teradata, like: + +- ✅︎ `SELECT ... FROM ...` +- ✅︎ `WITH alias AS (...) SELECT ...` +- ✅︎ `SHOW ...` +- ❌ `SET ...; SELECT ...;` - multiple statements not supported + +#### Examples + +```python +from onetl.connection import Teradata + +teradata = Teradata(...) + +df = teradata.fetch( + "SELECT value FROM some.reference_table WHERE key = 'some_constant'", + options=Teradata.FetchOptions(queryTimeout=10), +) +teradata.close() +value = df.collect()[0][0] # get value from first row and first column +``` + +### Use `Teradata.execute` + +Use this method to execute DDL and DML operations. Each method call runs operation in a separated transaction, and then commits it. + +Method accepts {obj}`Teradata.ExecuteOptions `. + +Connection opened using this method should be then closed with `connection.close()` or `with connection:`. + +#### Syntax support + +This method supports **any** query syntax supported by Teradata, like: + +- ✅︎ `CREATE TABLE ...`, `CREATE VIEW ...`, and so on +- ✅︎ `ALTER ...` +- ✅︎ `INSERT INTO ... SELECT ...`, `UPDATE ...`, `DELETE ...`, and so on +- ✅︎ `DROP TABLE ...`, `DROP VIEW ...`, `TRUNCATE TABLE`, and so on +- ✅︎ `CALL procedure(arg1, arg2) ...` or `{call procedure(arg1, arg2)}` - special syntax for calling procedure +- ✅︎ `EXECUTE macro(arg1, arg2)` +- ✅︎ `EXECUTE FUNCTION ...` +- ✅︎ other statements not mentioned here +- ❌ `SET ...; SELECT ...;` - multiple statements not supported + +#### Examples + +```python +from onetl.connection import Teradata + +teradata = Teradata(...) + +teradata.execute("DROP TABLE database.table") +teradata.execute( + """ + CREATE MULTISET TABLE database.table AS ( + id BIGINT, + key VARCHAR, + value REAL + ) + NO PRIMARY INDEX + """, + options=Teradata.ExecuteOptions(queryTimeout=10), +) +``` + +## Options + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.teradata.options +``` + +```{eval-rst} +.. autopydantic_model:: TeradataFetchOptions + :inherited-members: GenericOptions + :member-order: bysource + +``` + +```{eval-rst} +.. autopydantic_model:: TeradataExecuteOptions + :inherited-members: GenericOptions + :member-order: bysource +``` diff --git a/mddocs/docs/connection/db_connection/teradata/index.md b/mddocs/docs/connection/db_connection/teradata/index.md new file mode 100644 index 000000000..f86e25726 --- /dev/null +++ b/mddocs/docs/connection/db_connection/teradata/index.md @@ -0,0 +1,21 @@ +(teradata)= + +# Teradata + +```{toctree} +:caption: Connection +:maxdepth: 1 + +prerequisites +connection +``` + +```{toctree} +:caption: Operations +:maxdepth: 1 + +read +sql +write +execute +``` diff --git a/mddocs/docs/connection/db_connection/teradata/prerequisites.md b/mddocs/docs/connection/db_connection/teradata/prerequisites.md new file mode 100644 index 000000000..d180ffdeb --- /dev/null +++ b/mddocs/docs/connection/db_connection/teradata/prerequisites.md @@ -0,0 +1,57 @@ +(teradata-prerequisites)= + +# Prerequisites + +## Version Compatibility + +- Teradata server versions: + : - Officially declared: 16.10 - 20.0 + - Actually tested: 16.10 +- Spark versions: 2.3.x - 3.5.x +- Java versions: 8 - 20 + +See [official documentation](https://teradata-docs.s3.amazonaws.com/doc/connectivity/jdbc/reference/current/platformMatrix.html). + +## Installing PySpark + +To use Teradata connector you should have PySpark installed (or injected to `sys.path`) +BEFORE creating the connector instance. + +See {ref}`install-spark` installation instruction for more details. + +## Connecting to Teradata + +### Connection host + +It is possible to connect to Teradata by using either DNS name Parsing Engine (PE) host, or it's IP address. + +### Connection port + +Connection is usually performed to port `1025`. Port may differ for different Teradata instances. +Please ask your Teradata administrator to provide required information. + +### Required grants + +Ask your Teradata cluster administrator to set following grants for a user, +used for creating a connection: + +```{eval-rst} +.. tabs:: + + .. code-tab:: sql Read + Write + + -- allow creating tables in the target schema + GRANT CREATE TABLE ON database TO username; + + -- allow read & write access to specific table + GRANT SELECT, INSERT ON database.mytable TO username; + + .. code-tab:: sql Read only + + -- allow read access to specific table + GRANT SELECT ON database.mytable TO username; +``` + +See: +: - [Teradata access rights](https://www.dwhpro.com/teradata-access-rights/) + - [GRANT documentation](https://teradata.github.io/presto/docs/0.167-t/sql/grant.html) diff --git a/mddocs/docs/connection/db_connection/teradata/read.md b/mddocs/docs/connection/db_connection/teradata/read.md new file mode 100644 index 000000000..c57b09119 --- /dev/null +++ b/mddocs/docs/connection/db_connection/teradata/read.md @@ -0,0 +1,117 @@ +(teradata-read)= + +# Reading from Teradata using `DBReader` + +{obj}`DBReader ` supports {ref}`strategy` for incremental data reading, +but does not support custom queries, like `JOIN`. + +## Supported DBReader features + +- ✅︎ `columns` +- ✅︎ `where` +- ✅︎ `hwm`, supported strategies: +- - ✅︎ {ref}`snapshot-strategy` +- - ✅︎ {ref}`incremental-strategy` +- - ✅︎ {ref}`snapshot-batch-strategy` +- - ✅︎ {ref}`incremental-batch-strategy` +- ❌ `hint` (is not supported by Teradata) +- ❌ `df_schema` +- ✅︎ `options` (see {obj}`Teradata.ReadOptions `) + +## Examples + +Snapshot strategy: + +```python +from onetl.connection import Teradata +from onetl.db import DBReader + +teradata = Teradata(...) + +reader = DBReader( + connection=teradata, + source="database.table", + columns=["id", "key", "CAST(value AS VARCHAR) value", "updated_dt"], + where="key = 'something'", + options=Teradata.ReadOptions( + partitioning_mode="hash", + partitionColumn="id", + numPartitions=10, + ), +) +df = reader.run() +``` + +Incremental strategy: + +```python +from onetl.connection import Teradata +from onetl.db import DBReader +from onetl.strategy import IncrementalStrategy + +teradata = Teradata(...) + +reader = DBReader( + connection=teradata, + source="database.table", + columns=["id", "key", "CAST(value AS VARCHAR) value", "updated_dt"], + where="key = 'something'", + hwm=DBReader.AutoDetectHWM(name="teradata_hwm", expression="updated_dt"), + options=Teradata.ReadOptions( + partitioning_mode="hash", + partitionColumn="id", + numPartitions=10, + ), +) + +with IncrementalStrategy(): + df = reader.run() +``` + +## Recommendations + +### Select only required columns + +Instead of passing `"*"` in `DBReader(columns=[...])` prefer passing exact column names. This reduces the amount of data passed from Teradata to Spark. + +### Pay attention to `where` value + +Instead of filtering data on Spark side using `df.filter(df.column == 'value')` pass proper `DBReader(where="column = 'value'")` clause. +This both reduces the amount of data send from Teradata to Spark, and may also improve performance of the query. +Especially if there are indexes or partitions for columns used in `where` clause. + +### Read data in parallel + +`DBReader` can read data in multiple parallel connections by passing `Teradata.ReadOptions(numPartitions=..., partitionColumn=...)`. + +In the example above, Spark opens 10 parallel connections, and data is evenly distributed between all these connections using expression +`HASHAMP(HASHBUCKET(HASHROW({partition_column}))) MOD {num_partitions}`. +This allows sending each Spark worker only some piece of data, reducing resource consumption. +`partition_column` here can be table column of any type. + +It is also possible to use `partitioning_mode="mod"` or `partitioning_mode="range"`, but in this case +`partition_column` have to be an integer, should not contain `NULL`, and values to be uniformly distributed. +It is also less performant than `partitioning_mode="hash"` due to Teradata `HASHAMP` implementation. + +### Do **NOT** use `TYPE=FASTEXPORT` + +Teradata supports several [different connection types](https://teradata-docs.s3.amazonaws.com/doc/connectivity/jdbc/reference/current/jdbcug_chapter_2.html#BABFGFAF): +: - `TYPE=DEFAULT` - perform plain `SELECT` queries + - `TYPE=FASTEXPORT` - uses special FastExport protocol for select queries + +But `TYPE=FASTEXPORT` uses exclusive lock on the source table, so it is impossible to use multiple Spark workers parallel data read. +This leads to sending all the data to just one Spark worker, which is slow and takes a lot of RAM. + +Prefer using `partitioning_mode="hash"` from example above. + +## Options + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.teradata.options +``` + +```{eval-rst} +.. autopydantic_model:: TeradataReadOptions + :inherited-members: GenericOptions + :member-order: bysource +``` diff --git a/mddocs/docs/connection/db_connection/teradata/sql.md b/mddocs/docs/connection/db_connection/teradata/sql.md new file mode 100644 index 000000000..ad0919dce --- /dev/null +++ b/mddocs/docs/connection/db_connection/teradata/sql.md @@ -0,0 +1,76 @@ +(teradata-sql)= + +# Reading from Teradata using `Teradata.sql` + +`Teradata.sql` allows passing custom SQL query, but does not support incremental strategies. + +```{eval-rst} +.. warning:: + + Statement is executed in **read-write** connection, so if you're calling some functions/procedures with DDL/DML statements inside, + they can change data in your database. +``` + +## Syntax support + +Only queries with the following syntax are supported: + +- ✅︎ `SELECT ... FROM ...` +- ✅︎ `WITH alias AS (...) SELECT ...` +- ❌ `SHOW ...` +- ❌ `SET ...; SELECT ...;` - multiple statements not supported + +## Examples + +```python +from onetl.connection import Teradata + +teradata = Teradata(...) +df = teradata.sql( + """ + SELECT + id, + key, + CAST(value AS VARCHAR) AS value, + updated_at, + HASHAMP(HASHBUCKET(HASHROW(id))) MOD 10 AS part_column + FROM + database.mytable + WHERE + key = 'something' + """, + options=Teradata.SQLOptions( + partitionColumn="id", + numPartitions=10, + lowerBound=0, + upperBound=1000, + ), +) +``` + +## Recommendations + +### Select only required columns + +Instead of passing `SELECT * FROM ...` prefer passing exact column names `SELECT col1, col2, ...`. +This reduces the amount of data passed from Teradata to Spark. + +### Pay attention to `where` value + +Instead of filtering data on Spark side using `df.filter(df.column == 'value')` pass proper `WHERE column = 'value'` clause. +This both reduces the amount of data send from Teradata to Spark, and may also improve performance of the query. +Especially if there are indexes or partitions for columns used in `where` clause. + +## Options + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.teradata.options +``` + +```{eval-rst} +.. autopydantic_model:: TeradataSQLOptions + :inherited-members: GenericOptions + :member-order: bysource + :model-show-field-summary: false + :field-show-constraints: false +``` diff --git a/mddocs/docs/connection/db_connection/teradata/write.md b/mddocs/docs/connection/db_connection/teradata/write.md new file mode 100644 index 000000000..6d1f6644f --- /dev/null +++ b/mddocs/docs/connection/db_connection/teradata/write.md @@ -0,0 +1,120 @@ +(teradata-write)= + +# Writing to Teradata using `DBWriter` + +For writing data to Teradata, use {obj}`DBWriter `. + +```{eval-rst} +.. warning:: + + It is always recommended to create table explicitly using :ref:`Teradata.execute ` + instead of relying on Spark's table DDL generation. + + This is because Spark's DDL generator can create columns with different precision and types than it is expected, + causing precision loss or other issues. +``` + +## Examples + +```python +from onetl.connection import Teradata +from onetl.db import DBWriter + +teradata = Teradata( + ..., + extra={"TYPE": "FASTLOAD", "TMODE": "TERA"}, +) + +df = ... # data is here + +writer = DBWriter( + connection=teradata, + target="database.table", + options=Teradata.WriteOptions( + if_exists="append", + # avoid creating SET table, use MULTISET + createTableOptions="NO PRIMARY INDEX", + ), +) + +writer.run(df.repartition(1)) +``` + +## Recommendations + +### Number of connections + +Teradata is not MVCC based, so write operations take exclusive lock on the entire table. +So **it is impossible to write data to Teradata table in multiple parallel connections**, no exceptions. + +The only way to write to Teradata without making deadlocks is write dataframe with exactly 1 partition. + +It can be implemented using `df.repartition(1)`: + +```python +# do NOT use df.coalesce(1) as it can freeze +writer.run(df.repartition(1)) +``` + +This moves all the data to just one Spark worker, so it may consume a lot of RAM. It is usually require to increase `spark.executor.memory` to handle this. + +Another way is to write all dataframe partitions one-by-one: + +```python +from pyspark.sql.functions import spark_partition_id + +# get list of all partitions in the dataframe +partitions = sorted(df.select(spark_partition_id()).distinct().collect()) + +for partition in partitions: + # get only part of data within this exact partition + part_df = df.where(**partition.asDict()).coalesce(1) + + writer.run(part_df) +``` + +This require even data distribution for all partitions to avoid data skew and spikes of RAM consuming. + +### Choosing connection type + +Teradata supports several [different connection types](https://teradata-docs.s3.amazonaws.com/doc/connectivity/jdbc/reference/current/jdbcug_chapter_2.html#BABFGFAF): +: - `TYPE=DEFAULT` - perform plain `INSERT` queries + - `TYPE=FASTLOAD` - uses special FastLoad protocol for insert queries + +It is always recommended to use `TYPE=FASTLOAD` because: +: - It provides higher performance + - It properly handles inserting `NULL` values (`TYPE=DEFAULT` raises an exception) + +But it can be used only during write, not read. + +### Choosing transaction mode + +Teradata supports [2 different transaction modes](https://teradata-docs.s3.amazonaws.com/doc/connectivity/jdbc/reference/current/jdbcug_chapter_2.html#TMODESEC): +: - `TMODE=ANSI` + - `TMODE=TERA` + +Choosing one of the modes can alter connector behavior. For example: +: - Inserting data which exceeds table column length, like insert `CHAR(25)` to column with type `CHAR(24)`: + - - `TMODE=ANSI` - raises exception + - - `TMODE=TERA` - truncates input string to 24 symbols + - Creating table using Spark: + - - `TMODE=ANSI` - creates `MULTISET` table + - - `TMODE=TERA` - creates `SET` table with `PRIMARY KEY` is a first column in dataframe. + This can lead to slower insert time, because each row will be checked against a unique index. + Fortunately, this can be disabled by passing custom `createTableOptions`. + +## Options + +Method above accepts {obj}`Teradata.WriteOptions ` + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.teradata.options +``` + +```{eval-rst} +.. autopydantic_model:: TeradataWriteOptions + :inherited-members: GenericOptions + :member-order: bysource + :model-show-field-summary: false + :field-show-constraints: false +``` diff --git a/mddocs/docs/connection/file_connection/ftp.md b/mddocs/docs/connection/file_connection/ftp.md new file mode 100644 index 000000000..8877f479e --- /dev/null +++ b/mddocs/docs/connection/file_connection/ftp.md @@ -0,0 +1,12 @@ +(ftp)= + +# FTP connection + +```{eval-rst} +.. currentmodule:: onetl.connection.file_connection.ftp +``` + +```{eval-rst} +.. autoclass:: FTP + :members: __init__, check, path_exists, is_file, is_dir, get_stat, resolve_dir, resolve_file, create_dir, remove_file, remove_dir, rename_dir, rename_file, list_dir, walk, download_file, upload_file +``` diff --git a/mddocs/docs/connection/file_connection/ftps.md b/mddocs/docs/connection/file_connection/ftps.md new file mode 100644 index 000000000..668be45b5 --- /dev/null +++ b/mddocs/docs/connection/file_connection/ftps.md @@ -0,0 +1,12 @@ +(ftps)= + +# FTPS connection + +```{eval-rst} +.. currentmodule:: onetl.connection.file_connection.ftps +``` + +```{eval-rst} +.. autoclass:: FTPS + :members: __init__, check, path_exists, is_file, is_dir, get_stat, resolve_dir, resolve_file, create_dir, remove_file, remove_dir, rename_dir, rename_file, list_dir, walk, download_file, upload_file +``` diff --git a/mddocs/docs/connection/file_connection/hdfs/connection.md b/mddocs/docs/connection/file_connection/hdfs/connection.md new file mode 100644 index 000000000..b1cffa679 --- /dev/null +++ b/mddocs/docs/connection/file_connection/hdfs/connection.md @@ -0,0 +1,12 @@ +(hdfs-connection)= + +# HDFS connection + +```{eval-rst} +.. currentmodule:: onetl.connection.file_connection.hdfs.connection +``` + +```{eval-rst} +.. autoclass:: HDFS + :members: get_current, check, path_exists, is_file, is_dir, get_stat, resolve_dir, resolve_file, create_dir, remove_file, remove_dir, rename_dir, rename_file, list_dir, walk, download_file, upload_file +``` diff --git a/mddocs/docs/connection/file_connection/hdfs/index.md b/mddocs/docs/connection/file_connection/hdfs/index.md new file mode 100644 index 000000000..a4c9fa148 --- /dev/null +++ b/mddocs/docs/connection/file_connection/hdfs/index.md @@ -0,0 +1,17 @@ +(hdfs)= + +# HDFS + +```{toctree} +:caption: Connection +:maxdepth: 1 + +connection +``` + +```{toctree} +:caption: For developers +:maxdepth: 1 + +slots +``` diff --git a/mddocs/docs/connection/file_connection/hdfs/slots.md b/mddocs/docs/connection/file_connection/hdfs/slots.md new file mode 100644 index 000000000..88f7e2943 --- /dev/null +++ b/mddocs/docs/connection/file_connection/hdfs/slots.md @@ -0,0 +1,13 @@ +(hdfs-slots)= + +# HDFS Slots + +```{eval-rst} +.. currentmodule:: onetl.connection.file_connection.hdfs.slots +``` + +```{eval-rst} +.. autoclass:: HDFSSlots + :members: normalize_cluster_name, normalize_namenode_host, get_known_clusters, get_cluster_namenodes, get_current_cluster, get_webhdfs_port, is_namenode_active + :member-order: bysource +``` diff --git a/mddocs/docs/connection/file_connection/index.md b/mddocs/docs/connection/file_connection/index.md new file mode 100644 index 000000000..4b581e0fd --- /dev/null +++ b/mddocs/docs/connection/file_connection/index.md @@ -0,0 +1,16 @@ +(file-connections)= + +# File Connections + +```{toctree} +:caption: File Connections +:maxdepth: 1 + +FTP +FTPS +HDFS +Samba +SFTP +S3 +Webdav +``` diff --git a/mddocs/docs/connection/file_connection/s3.md b/mddocs/docs/connection/file_connection/s3.md new file mode 100644 index 000000000..c69a3ed8b --- /dev/null +++ b/mddocs/docs/connection/file_connection/s3.md @@ -0,0 +1,12 @@ +(s3)= + +# S3 connection + +```{eval-rst} +.. currentmodule:: onetl.connection.file_connection.s3 +``` + +```{eval-rst} +.. autoclass:: S3 + :members: __init__, check, path_exists, is_file, is_dir, get_stat, resolve_dir, resolve_file, create_dir, remove_file, remove_dir, rename_file, list_dir, walk, download_file, upload_file +``` diff --git a/mddocs/docs/connection/file_connection/samba.md b/mddocs/docs/connection/file_connection/samba.md new file mode 100644 index 000000000..29f9f3f81 --- /dev/null +++ b/mddocs/docs/connection/file_connection/samba.md @@ -0,0 +1,12 @@ +(samba)= + +# Samba connection + +```{eval-rst} +.. currentmodule:: onetl.connection.file_connection.samba +``` + +```{eval-rst} +.. autoclass:: Samba + :members: __init__, check, path_exists, is_file, is_dir, get_stat, resolve_dir, resolve_file, create_dir, remove_file, remove_dir, rename_file, list_dir, download_file, upload_file +``` diff --git a/mddocs/docs/connection/file_connection/sftp.md b/mddocs/docs/connection/file_connection/sftp.md new file mode 100644 index 000000000..45c7affa6 --- /dev/null +++ b/mddocs/docs/connection/file_connection/sftp.md @@ -0,0 +1,12 @@ +(sftp)= + +# SFTP connection + +```{eval-rst} +.. currentmodule:: onetl.connection.file_connection.sftp +``` + +```{eval-rst} +.. autoclass:: SFTP + :members: __init__, check, path_exists, is_file, is_dir, get_stat, resolve_dir, resolve_file, create_dir, remove_file, remove_dir, rename_dir, rename_file, list_dir, walk, download_file, upload_file +``` diff --git a/mddocs/docs/connection/file_connection/webdav.md b/mddocs/docs/connection/file_connection/webdav.md new file mode 100644 index 000000000..5fd5ba006 --- /dev/null +++ b/mddocs/docs/connection/file_connection/webdav.md @@ -0,0 +1,12 @@ +(webdav)= + +# WebDAV connection + +```{eval-rst} +.. currentmodule:: onetl.connection.file_connection.webdav +``` + +```{eval-rst} +.. autoclass:: WebDAV + :members: __init__, check, path_exists, is_file, is_dir, get_stat, resolve_dir, resolve_file, create_dir, remove_file, remove_dir, rename_file, list_dir, walk, download_file, upload_file +``` diff --git a/mddocs/docs/connection/file_df_connection/base.md b/mddocs/docs/connection/file_df_connection/base.md new file mode 100644 index 000000000..4b3935bf7 --- /dev/null +++ b/mddocs/docs/connection/file_df_connection/base.md @@ -0,0 +1,12 @@ +(base-file-df-connection)= + +# Base interface + +```{eval-rst} +.. currentmodule:: onetl.base.base_file_df_connection +``` + +```{eval-rst} +.. autoclass:: BaseFileDFConnection + :members: check, check_if_format_supported, read_files_as_df, write_df_as_files +``` diff --git a/mddocs/docs/connection/file_df_connection/index.md b/mddocs/docs/connection/file_df_connection/index.md new file mode 100644 index 000000000..8f8d24bf0 --- /dev/null +++ b/mddocs/docs/connection/file_df_connection/index.md @@ -0,0 +1,19 @@ +(file-df-connections)= + +# File DataFrame Connections + +```{toctree} +:caption: File DataFrame Connections +:maxdepth: 2 + +Spark LocalFS +Spark HDFS +Spark S3 +``` + +```{toctree} +:caption: For developers +:maxdepth: 1 + +base +``` diff --git a/mddocs/docs/connection/file_df_connection/spark_hdfs/connection.md b/mddocs/docs/connection/file_df_connection/spark_hdfs/connection.md new file mode 100644 index 000000000..08b8e4c3b --- /dev/null +++ b/mddocs/docs/connection/file_df_connection/spark_hdfs/connection.md @@ -0,0 +1,12 @@ +(spark-hdfs-connection)= + +# Spark HDFS Connection + +```{eval-rst} +.. currentmodule:: onetl.connection.file_df_connection.spark_hdfs.connection +``` + +```{eval-rst} +.. autoclass:: SparkHDFS + :members: check, get_current +``` diff --git a/mddocs/docs/connection/file_df_connection/spark_hdfs/index.md b/mddocs/docs/connection/file_df_connection/spark_hdfs/index.md new file mode 100644 index 000000000..77d347d7a --- /dev/null +++ b/mddocs/docs/connection/file_df_connection/spark_hdfs/index.md @@ -0,0 +1,18 @@ +(spark-hdfs)= + +# Spark HDFS + +```{toctree} +:caption: Connection +:maxdepth: 1 + +prerequisites +Connection +``` + +```{toctree} +:caption: For developers +:maxdepth: 1 + +Slots +``` diff --git a/mddocs/docs/connection/file_df_connection/spark_hdfs/prerequisites.md b/mddocs/docs/connection/file_df_connection/spark_hdfs/prerequisites.md new file mode 100644 index 000000000..f946b3a70 --- /dev/null +++ b/mddocs/docs/connection/file_df_connection/spark_hdfs/prerequisites.md @@ -0,0 +1,46 @@ +(spark-hdfs-prerequisites)= + +# Prerequisites + +## Version Compatibility + +- Hadoop versions: 2.x, 3.x (only with Hadoop 3.x libraries) +- Spark versions: 2.3.x - 3.5.x +- Java versions: 8 - 20 + +## Installing PySpark + +To use SparkHDFS connector you should have PySpark installed (or injected to `sys.path`) +BEFORE creating the connector instance. + +See {ref}`install-spark` installation instruction for more details. + +## Using Kerberos + +Some of Hadoop managed clusters use Kerberos authentication. In this case, you should call [kinit](https://web.mit.edu/kerberos/krb5-1.12/doc/user/user_commands/kinit.html) command +**BEFORE** starting Spark session to generate Kerberos ticket. See {ref}`install-kerberos`. + +Sometimes it is also required to pass keytab file to Spark config, allowing Spark executors to generate own Kerberos tickets: + +```{eval-rst} +.. tabs:: + + .. code-tab:: python Spark 3 + + SparkSession.builder + .option("spark.kerberos.access.hadoopFileSystems", "hdfs://namenode1.domain.com:9820,hdfs://namenode2.domain.com:9820") + .option("spark.kerberos.principal", "user") + .option("spark.kerberos.keytab", "/path/to/keytab") + .gerOrCreate() + + .. code-tab:: python Spark 2 + + SparkSession.builder + .option("spark.yarn.access.hadoopFileSystems", "hdfs://namenode1.domain.com:9820,hdfs://namenode2.domain.com:9820") + .option("spark.yarn.principal", "user") + .option("spark.yarn.keytab", "/path/to/keytab") + .gerOrCreate() +``` + +See [Spark security documentation](https://spark.apache.org/docs/latest/security.html#kerberos) +for more details. diff --git a/mddocs/docs/connection/file_df_connection/spark_hdfs/slots.md b/mddocs/docs/connection/file_df_connection/spark_hdfs/slots.md new file mode 100644 index 000000000..715578579 --- /dev/null +++ b/mddocs/docs/connection/file_df_connection/spark_hdfs/slots.md @@ -0,0 +1,13 @@ +(spark-hdfs-slots)= + +# Spark HDFS Slots + +```{eval-rst} +.. currentmodule:: onetl.connection.file_df_connection.spark_hdfs.slots +``` + +```{eval-rst} +.. autoclass:: SparkHDFSSlots + :members: normalize_cluster_name, normalize_namenode_host, get_known_clusters, get_cluster_namenodes, get_current_cluster, get_ipc_port, is_namenode_active + :member-order: bysource +``` diff --git a/mddocs/docs/connection/file_df_connection/spark_local_fs.md b/mddocs/docs/connection/file_df_connection/spark_local_fs.md new file mode 100644 index 000000000..524d1c676 --- /dev/null +++ b/mddocs/docs/connection/file_df_connection/spark_local_fs.md @@ -0,0 +1,12 @@ +(spark-local-fs)= + +# Spark LocalFS + +```{eval-rst} +.. currentmodule:: onetl.connection.file_df_connection.spark_local_fs +``` + +```{eval-rst} +.. autoclass:: SparkLocalFS + :members: check +``` diff --git a/mddocs/docs/connection/file_df_connection/spark_s3/connection.md b/mddocs/docs/connection/file_df_connection/spark_s3/connection.md new file mode 100644 index 000000000..82de3999f --- /dev/null +++ b/mddocs/docs/connection/file_df_connection/spark_s3/connection.md @@ -0,0 +1,12 @@ +(spark-s3-connection)= + +# Spark S3 Connection + +```{eval-rst} +.. currentmodule:: onetl.connection.file_df_connection.spark_s3.connection +``` + +```{eval-rst} +.. autoclass:: SparkS3 + :members: check, close, get_packages, get_exclude_packages +``` diff --git a/mddocs/docs/connection/file_df_connection/spark_s3/index.md b/mddocs/docs/connection/file_df_connection/spark_s3/index.md new file mode 100644 index 000000000..1ecf94737 --- /dev/null +++ b/mddocs/docs/connection/file_df_connection/spark_s3/index.md @@ -0,0 +1,12 @@ +(spark-s3)= + +# Spark S3 + +```{toctree} +:caption: Connection +:maxdepth: 1 + +prerequisites +Connection +Troubleshooting +``` diff --git a/mddocs/docs/connection/file_df_connection/spark_s3/prerequisites.md b/mddocs/docs/connection/file_df_connection/spark_s3/prerequisites.md new file mode 100644 index 000000000..9135275f3 --- /dev/null +++ b/mddocs/docs/connection/file_df_connection/spark_s3/prerequisites.md @@ -0,0 +1,61 @@ +(spark-s3-prerequisites)= + +# Prerequisites + +## Version Compatibility + +- Spark versions: 3.2.x - 3.5.x (only with Hadoop 3.x libraries) +- Java versions: 8 - 20 + +## Installing PySpark + +To use SparkS3 connector you should have PySpark installed (or injected to `sys.path`) +BEFORE creating the connector instance. + +See {ref}`install-spark` installation instruction for more details. + +## Connecting to S3 + +### Bucket access style + +AWS and some other S3 cloud providers allows bucket access using domain style only, e.g. `https://mybucket.s3provider.com`. + +Other implementations, like Minio, by default allows path style access only, e.g. `https://s3provider.com/mybucket` +(see [MINIO_DOMAIN](https://min.io/docs/minio/linux/reference/minio-server/minio-server.html#envvar.MINIO_DOMAIN)). + +You should set `path.style.access` to `True` or `False`, to choose the preferred style. + +### Authentication + +Different S3 instances can use different authentication methods, like: +: - `access_key + secret_key` (or username + password) + - `access_key + secret_key + session_token` + +Usually these are just passed to SparkS3 constructor: + +```python +SparkS3( + access_key=..., + secret_key=..., + session_token=..., +) +``` + +But some S3 cloud providers, like AWS, may require custom credential providers. You can pass them like: + +```python +SparkS3( + extra={ + # provider class + "aws.credentials.provider": "org.apache.hadoop.fs.s3a.auth.AssumedRoleCredentialProvider", + # other options, if needed + "assumed.role.arn": "arn:aws:iam::90066806600238:role/s3-restricted", + }, +) +``` + +See [Hadoop-AWS](https://hadoop.apache.org/docs/stable/hadoop-aws/tools/hadoop-aws/index.html#Changing_Authentication_Providers) documentation. + +## Troubleshooting + +See {ref}`spark-s3-troubleshooting`. diff --git a/mddocs/docs/connection/file_df_connection/spark_s3/troubleshooting.md b/mddocs/docs/connection/file_df_connection/spark_s3/troubleshooting.md new file mode 100644 index 000000000..5f998779d --- /dev/null +++ b/mddocs/docs/connection/file_df_connection/spark_s3/troubleshooting.md @@ -0,0 +1,377 @@ +(spark-s3-troubleshooting)= + +# Spark S3 Troubleshooting + +```{eval-rst} +.. note:: + + General guide: :ref:`troubleshooting`. +``` + +More details: + +- [Hadoop AWS Troubleshooting Guide](https://hadoop.apache.org/docs/stable/hadoop-aws/tools/hadoop-aws/troubleshooting_s3a.html) +- [Hadoop AWS Performance Guide](https://hadoop.apache.org/docs/stable/hadoop-aws/tools/hadoop-aws/performance.html) +- [Spark integration with Cloud Infrastructures](https://spark.apache.org/docs/latest/cloud-integration.html) + +## `SparkS3.check()` and other methods hang + +### Details + +S3 may not respond for connection attempts for a long time if it's under heavy load. +To handle this, Hadoop AWS library has retry mechanism. By default it retries 7 times with 500ms interval. + +Hadoop AWS is based on AWS SDK library, which also has retry mechanism. This mechanism is not disabled because it handles different +errors than Hadoop AWS, so they complement each other. Default number of attempts in AWS SDK is 20 with minimal 5s interval, +which is exponentially increasing with each failed attempt. + +It is not a problem if S3 source is not accessible at all, like hostname cannot be resolved, or port is not opened. +These errors are not recoverable, and retry mechanism is not activated. + +But errors like SSL issues, are considered recoverable, and this causing retry of retry over increasing interval. +So user is waiting for [almost 15 minutes](https://issues.apache.org/jira/browse/HADOOP-18839) just to get exception message. + +### How to determine reason + +#### Make logging more verbose + +Change Spark session log level to {ref}`DEBUG ` to print result of each attempt. +Resulting logs will look like this + +```{eval-rst} +.. dropdown:: See log + + .. code:: text + + 23/08/03 11:25:10 DEBUG S3AFileSystem: Using S3ABlockOutputStream with buffer = disk; block=67108864; queue limit=4 + 23/08/03 11:25:10 DEBUG S3Guard: Metastore option source [core-default.xml] + 23/08/03 11:25:10 DEBUG S3Guard: Using NullMetadataStore metadata store for s3a filesystem + 23/08/03 11:25:10 DEBUG S3AFileSystem: S3Guard is disabled on this bucket: test-bucket + 23/08/03 11:25:10 DEBUG DirectoryPolicyImpl: Directory markers will be deleted + 23/08/03 11:25:10 DEBUG S3AFileSystem: Directory marker retention policy is DirectoryMarkerRetention{policy='delete'} + 23/08/03 11:25:10 DEBUG S3AUtils: Value of fs.s3a.multipart.purge.age is 86400 + 23/08/03 11:25:10 DEBUG S3AUtils: Value of fs.s3a.bulk.delete.page.size is 250 + 23/08/03 11:25:10 DEBUG FileSystem: Creating FS s3a://test-bucket/fake: duration 0:01.029s + 23/08/03 11:25:10 DEBUG IOStatisticsStoreImpl: Incrementing counter op_is_directory by 1 with final value 1 + 23/08/03 11:25:10 DEBUG S3AFileSystem: Getting path status for s3a://test-bucket/fake (fake); needEmptyDirectory=false + 23/08/03 11:25:10 DEBUG S3AFileSystem: S3GetFileStatus s3a://test-bucket/fake + 23/08/03 11:25:10 DEBUG S3AFileSystem: LIST List test-bucket:/fake/ delimiter=/ keys=2 requester pays=false + 23/08/03 11:25:10 DEBUG S3AFileSystem: Starting: LIST + 23/08/03 11:25:10 DEBUG IOStatisticsStoreImpl: Incrementing counter object_list_request by 1 with final value 1 + 23/08/03 11:25:10 DEBUG AWSCredentialProviderList: Using credentials from SimpleAWSCredentialsProvider + 23/08/03 11:25:10 DEBUG request: Sending Request: GET https://test-bucket.localhost:9000 / Parameters: ({"list-type":["2"],"delimiter":["/"],"max-keys":["2"],"prefix":["fake/"],"fetch-owner":["false"]}Headers: (amz-sdk-invocation-id: e6d62603-96e4-a80f-10a1-816e0822bc71, Content-Type: application/octet-stream, User-Agent: Hadoop 3.3.4, aws-sdk-java/1.12.262 Linux/6.4.7-1-MANJARO OpenJDK_64-Bit_Server_VM/25.292-b10 java/1.8.0_292 scala/2.12.17 vendor/AdoptOpenJDK cfg/retry-mode/legacy, ) + 23/08/03 11:25:10 DEBUG AWS4Signer: AWS4 Canonical Request: '"GET + / + delimiter=%2F&fetch-owner=false&list-type=2&max-keys=2&prefix=fake%2F + amz-sdk-invocation-id:e6d62603-96e4-a80f-10a1-816e0822bc71 + amz-sdk-request:attempt=1;max=21 + amz-sdk-retry:0/0/500 + content-type:application/octet-stream + host:test-bucket.localhost:9000 + user-agent:Hadoop 3.3.4, aws-sdk-java/1.12.262 Linux/6.4.7-1-MANJARO OpenJDK_64-Bit_Server_VM/25.292-b10 java/1.8.0_292 scala/2.12.17 vendor/AdoptOpenJDK cfg/retry-mode/legacy + x-amz-content-sha256:UNSIGNED-PAYLOAD + x-amz-date:20230803T112510Z + + amz-sdk-invocation-id;amz-sdk-request;amz-sdk-retry;content-type;host;user-agent;x-amz-content-sha256;x-amz-date + UNSIGNED-PAYLOAD" + 23/08/03 11:25:10 DEBUG AWS4Signer: AWS4 String to Sign: '"AWS4-HMAC-SHA256 + 20230803T112510Z + 20230803/us-east-1/s3/aws4_request + 31a317bb7f6d97248dd0cf03429d701cbb3e29ce889cfbb98ba7a34c57a3bfba" + 23/08/03 11:25:10 DEBUG AWS4Signer: Generating a new signing key as the signing key not available in the cache for the date 1691020800000 + 23/08/03 11:25:10 DEBUG RequestAddCookies: CookieSpec selected: default + 23/08/03 11:25:10 DEBUG RequestAuthCache: Auth cache not set in the context + 23/08/03 11:25:10 DEBUG PoolingHttpClientConnectionManager: Connection request: [route: {s}->https://test-bucket.localhost:9000][total available: 0; route allocated: 0 of 96; total allocated: 0 of 96] + 23/08/03 11:25:10 DEBUG PoolingHttpClientConnectionManager: Connection leased: [id: 0][route: {s}->https://test-bucket.localhost:9000][total available: 0; route allocated: 1 of 96; total allocated: 1 of 96] + 23/08/03 11:25:10 DEBUG MainClientExec: Opening connection {s}->https://test-bucket.localhost:9000 + 23/08/03 11:25:10 DEBUG DefaultHttpClientConnectionOperator: Connecting to test-bucket.localhost/127.0.0.1:9000 + 23/08/03 11:25:10 DEBUG SSLConnectionSocketFactory: Connecting socket to test-bucket.localhost/127.0.0.1:9000 with timeout 5000 + 23/08/03 11:25:10 DEBUG SSLConnectionSocketFactory: Enabled protocols: [TLSv1.2] + 23/08/03 11:25:10 DEBUG SSLConnectionSocketFactory: Enabled cipher suites:[TLS_ECDHE_ECDSA_WITH_AES_256_CBC_SHA384, TLS_ECDHE_RSA_WITH_AES_256_CBC_SHA384, TLS_RSA_WITH_AES_256_CBC_SHA256, TLS_ECDH_ECDSA_WITH_AES_256_CBC_SHA384, TLS_ECDH_RSA_WITH_AES_256_CBC_SHA384, TLS_DHE_RSA_WITH_AES_256_CBC_SHA256, TLS_DHE_DSS_WITH_AES_256_CBC_SHA256, TLS_ECDHE_ECDSA_WITH_AES_256_CBC_SHA, TLS_ECDHE_RSA_WITH_AES_256_CBC_SHA, TLS_RSA_WITH_AES_256_CBC_SHA, TLS_ECDH_ECDSA_WITH_AES_256_CBC_SHA, TLS_ECDH_RSA_WITH_AES_256_CBC_SHA, TLS_DHE_RSA_WITH_AES_256_CBC_SHA, TLS_DHE_DSS_WITH_AES_256_CBC_SHA, TLS_ECDHE_ECDSA_WITH_AES_128_CBC_SHA256, TLS_ECDHE_RSA_WITH_AES_128_CBC_SHA256, TLS_RSA_WITH_AES_128_CBC_SHA256, TLS_ECDH_ECDSA_WITH_AES_128_CBC_SHA256, TLS_ECDH_RSA_WITH_AES_128_CBC_SHA256, TLS_DHE_RSA_WITH_AES_128_CBC_SHA256, TLS_DHE_DSS_WITH_AES_128_CBC_SHA256, TLS_ECDHE_ECDSA_WITH_AES_128_CBC_SHA, TLS_ECDHE_RSA_WITH_AES_128_CBC_SHA, TLS_RSA_WITH_AES_128_CBC_SHA, TLS_ECDH_ECDSA_WITH_AES_128_CBC_SHA, TLS_ECDH_RSA_WITH_AES_128_CBC_SHA, TLS_DHE_RSA_WITH_AES_128_CBC_SHA, TLS_DHE_DSS_WITH_AES_128_CBC_SHA, TLS_EMPTY_RENEGOTIATION_INFO_SCSV] + 23/08/03 11:25:10 DEBUG SSLConnectionSocketFactory: Starting handshake + 23/08/03 11:25:10 DEBUG ClientConnectionManagerFactory: + java.lang.reflect.InvocationTargetException + at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) + at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62) + at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) + at java.lang.reflect.Method.invoke(Method.java:498) + at com.amazonaws.http.conn.ClientConnectionManagerFactory$Handler.invoke(ClientConnectionManagerFactory.java:76) + at com.amazonaws.http.conn.$Proxy32.connect(Unknown Source) + at com.amazonaws.thirdparty.apache.http.impl.execchain.MainClientExec.establishRoute(MainClientExec.java:393) + at com.amazonaws.thirdparty.apache.http.impl.execchain.MainClientExec.execute(MainClientExec.java:236) + at com.amazonaws.thirdparty.apache.http.impl.execchain.ProtocolExec.execute(ProtocolExec.java:186) + at com.amazonaws.thirdparty.apache.http.impl.client.InternalHttpClient.doExecute(InternalHttpClient.java:185) + at com.amazonaws.thirdparty.apache.http.impl.client.CloseableHttpClient.execute(CloseableHttpClient.java:83) + at com.amazonaws.thirdparty.apache.http.impl.client.CloseableHttpClient.execute(CloseableHttpClient.java:56) + at com.amazonaws.http.apache.client.impl.SdkHttpClient.execute(SdkHttpClient.java:72) + at com.amazonaws.http.AmazonHttpClient$RequestExecutor.executeOneRequest(AmazonHttpClient.java:1346) + at com.amazonaws.http.AmazonHttpClient$RequestExecutor.executeHelper(AmazonHttpClient.java:1157) + at com.amazonaws.http.AmazonHttpClient$RequestExecutor.doExecute(AmazonHttpClient.java:814) + at com.amazonaws.http.AmazonHttpClient$RequestExecutor.executeWithTimer(AmazonHttpClient.java:781) + at com.amazonaws.http.AmazonHttpClient$RequestExecutor.execute(AmazonHttpClient.java:755) + at com.amazonaws.http.AmazonHttpClient$RequestExecutor.access$500(AmazonHttpClient.java:715) + at com.amazonaws.http.AmazonHttpClient$RequestExecutionBuilderImpl.execute(AmazonHttpClient.java:697) + at com.amazonaws.http.AmazonHttpClient.execute(AmazonHttpClient.java:561) + at com.amazonaws.http.AmazonHttpClient.execute(AmazonHttpClient.java:541) + at com.amazonaws.services.s3.AmazonS3Client.invoke(AmazonS3Client.java:5456) + at com.amazonaws.services.s3.AmazonS3Client.invoke(AmazonS3Client.java:5403) + at com.amazonaws.services.s3.AmazonS3Client.invoke(AmazonS3Client.java:5397) + at com.amazonaws.services.s3.AmazonS3Client.listObjectsV2(AmazonS3Client.java:971) + at org.apache.hadoop.fs.s3a.S3AFileSystem.lambda$listObjects$11(S3AFileSystem.java:2595) + at org.apache.hadoop.fs.statistics.impl.IOStatisticsBinding.lambda$trackDurationOfOperation$5(IOStatisticsBinding.java:499) + at org.apache.hadoop.fs.s3a.Invoker.retryUntranslated(Invoker.java:414) + at org.apache.hadoop.fs.s3a.Invoker.retryUntranslated(Invoker.java:377) + at org.apache.hadoop.fs.s3a.S3AFileSystem.listObjects(S3AFileSystem.java:2586) + at org.apache.hadoop.fs.s3a.S3AFileSystem.s3GetFileStatus(S3AFileSystem.java:3832) + at org.apache.hadoop.fs.s3a.S3AFileSystem.innerGetFileStatus(S3AFileSystem.java:3688) + at org.apache.hadoop.fs.s3a.S3AFileSystem.lambda$isDirectory$35(S3AFileSystem.java:4724) + at org.apache.hadoop.fs.statistics.impl.IOStatisticsBinding.lambda$trackDurationOfOperation$5(IOStatisticsBinding.java:499) + at org.apache.hadoop.fs.statistics.impl.IOStatisticsBinding.trackDuration(IOStatisticsBinding.java:444) + at org.apache.hadoop.fs.s3a.S3AFileSystem.trackDurationAndSpan(S3AFileSystem.java:2337) + at org.apache.hadoop.fs.s3a.S3AFileSystem.trackDurationAndSpan(S3AFileSystem.java:2356) + at org.apache.hadoop.fs.s3a.S3AFileSystem.isDirectory(S3AFileSystem.java:4722) + at org.apache.spark.sql.execution.streaming.FileStreamSink$.hasMetadata(FileStreamSink.scala:54) + at org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:366) + at org.apache.spark.sql.DataFrameReader.loadV1Source(DataFrameReader.scala:229) + at org.apache.spark.sql.DataFrameReader.$anonfun$load$2(DataFrameReader.scala:211) + at scala.Option.getOrElse(Option.scala:189) + at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:211) + at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:186) + at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) + at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62) + at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) + at java.lang.reflect.Method.invoke(Method.java:498) + at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244) + at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:374) + at py4j.Gateway.invoke(Gateway.java:282) + at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132) + at py4j.commands.CallCommand.execute(CallCommand.java:79) + at py4j.ClientServerConnection.waitForCommands(ClientServerConnection.java:182) + at py4j.ClientServerConnection.run(ClientServerConnection.java:106) + at java.lang.Thread.run(Thread.java:748) + Caused by: javax.net.ssl.SSLException: Unsupported or unrecognized SSL message + at sun.security.ssl.SSLSocketInputRecord.handleUnknownRecord(SSLSocketInputRecord.java:448) + at sun.security.ssl.SSLSocketInputRecord.decode(SSLSocketInputRecord.java:184) + at sun.security.ssl.SSLTransport.decode(SSLTransport.java:109) + at sun.security.ssl.SSLSocketImpl.decode(SSLSocketImpl.java:1383) + at sun.security.ssl.SSLSocketImpl.readHandshakeRecord(SSLSocketImpl.java:1291) + at sun.security.ssl.SSLSocketImpl.startHandshake(SSLSocketImpl.java:435) + at com.amazonaws.thirdparty.apache.http.conn.ssl.SSLConnectionSocketFactory.createLayeredSocket(SSLConnectionSocketFactory.java:436) + at com.amazonaws.thirdparty.apache.http.conn.ssl.SSLConnectionSocketFactory.connectSocket(SSLConnectionSocketFactory.java:384) + at com.amazonaws.thirdparty.apache.http.impl.conn.DefaultHttpClientConnectionOperator.connect(DefaultHttpClientConnectionOperator.java:142) + at com.amazonaws.thirdparty.apache.http.impl.conn.PoolingHttpClientConnectionManager.connect(PoolingHttpClientConnectionManager.java:376) + ... 58 more + 23/08/03 11:25:10 DEBUG DefaultManagedHttpClientConnection: http-outgoing-0: Shutdown connection + 23/08/03 11:25:10 DEBUG MainClientExec: Connection discarded + 23/08/03 11:25:10 DEBUG PoolingHttpClientConnectionManager: Connection released: [id: 0][route: {s}->https://test-bucket.localhost:9000][total available: 0; route allocated: 0 of 96; total allocated: 0 of 96] + 23/08/03 11:25:10 DEBUG AmazonHttpClient: Unable to execute HTTP request: Unsupported or unrecognized SSL message Request will be retried. + 23/08/03 11:25:10 DEBUG request: Retrying Request: GET https://test-bucket.localhost:9000 / Parameters: ({"list-type":["2"],"delimiter":["/"],"max-keys":["2"],"prefix":["fake/"],"fetch-owner":["false"]}Headers: (amz-sdk-invocation-id: e6d62603-96e4-a80f-10a1-816e0822bc71, Content-Type: application/octet-stream, User-Agent: Hadoop 3.3.4, aws-sdk-java/1.12.262 Linux/6.4.7-1-MANJARO OpenJDK_64-Bit_Server_VM/25.292-b10 java/1.8.0_292 scala/2.12.17 vendor/AdoptOpenJDK cfg/retry-mode/legacy, ) + 23/08/03 11:25:10 DEBUG AmazonHttpClient: Retriable error detected, will retry in 49ms, attempt number: 0 +``` + +#### Change number of retries + +You can also change number of retries performed by both libraries using `extra` parameter: + +```python +spark_s3 = SparkS3( + ..., + extra={ + "attempts.maximum": 1, + "retry.limit": 1, + }, +) +``` + +So accessing S3 will fail almost immediately if there is any error. + +### Most common mistakes + +#### No network access + +```text +Caused by: java.net.ConnectException: Connection refused +``` + +Mostly caused by: + +- Trying to access port number which S3 server does not listen +- You're trying to access host which is unreachable from your network (e.g. running behind some proxy or VPN) +- There are some firewall restrictions for accessing specific host or port + +#### Using HTTPS protocol for HTTP port + +```text +Caused by: javax.net.ssl.SSLException: Unsupported or unrecognized SSL message +``` + +By default, SparkS3 uses HTTPS protocol for connection. +If you change port number, this does not lead to changing protocol: + +```python +spark_s3 = SparkS3(host="s3provider.com", port=8080, ...) +``` + +You should pass protocol explicitly: + +```python +spark_s3 = SparkS3(host="s3provider.com", port=8080, protocol="http", ...) +``` + +#### SSL certificate is self-signed + +```text +sun.security.provider.certpath.SunCertPathBuilderException: unable to find valid certification path to requested target +``` + +To connect to HTTPS port with self-signed certificate, you should +[add certificate chain to Java TrustedStore](https://stackoverflow.com/questions/373295/digital-certificate-how-to-import-cer-file-in-to-truststore-file-using). + +Another option is to disable SSL check: + +```python +spark_s3 = SparkS3( + ..., + extra={ + "connection.ssl.enabled": False, + }, +) +``` + +But is is **NOT** recommended. + +#### Accessing S3 without domain-style access style support + +```text +Caused by: java.net.UnknownHostException: my-bucket.s3provider.com +``` + +To use path-style access, use option below: + +```python +spark_s3 = SparkS3( + host="s3provider.com", + bucket="my-bucket", + ..., + extra={ + "path.style.access": True, + }, +) +``` + +## Slow or unstable writing to S3 + +Hadoop AWS allows to use different writing strategies for different S3 implementations, depending +on list of supported features by server. + +These strategies are called [committers](https://hadoop.apache.org/docs/stable/hadoop-aws/tools/hadoop-aws/committers.html). +There are [different types of committers](https://hadoop.apache.org/docs/stable/hadoop-aws/tools/hadoop-aws/committers.html#Switching_to_an_S3A_Committer): + +- `file` (default) +- `directory` +- `partitioned` +- `magic` + +### `file` committer + +This committer is quite slow and unstable, so it is not recommended to use: + +```text +WARN AbstractS3ACommitterFactory: Using standard FileOutputCommitter to commit work. This is slow and potentially unsafe. +``` + +This is caused by the fact it creates files in the temp directory on remote filesystem, and after all of them are written successfully, +they are moved to target directory on same remote filesystem. + +This is not an issue for HDFS which does support file move operations and also support renaming directory +as atomic operation with `O(1)` time complexity. + +But S3 does support only file copying, so moving is performed via copy + delete. +Also it does not support atomic directory rename operation. Instead, renaming files with the same prefix has time complexity `O(n)`. + +### `directory` and `partitioned` committers + +These are [staging committers](https://hadoop.apache.org/docs/stable/hadoop-aws/tools/hadoop-aws/committer_architecture.html), +meaning that they create temp directories on local filesystem, and after all files are written successfully, +they will be uploaded to S3. Local filesystems do support file moving and directory renaming, +so these committers does not have issues that `file` committer has. + +But they both require free space on local filesystem, and this may be an issue if user need to write large amount of data. +Also this can be an issue for container environment, like Kubernetes, there resources should be allocated before starting a container. + +### `magic` committer + +This committer uses multipart upload feature of S3 API, allowing to create multiple files +and after all of them were written successfully finish the transaction. Before transaction is finished, +files will not be accessible by other clients. + +Because it does not require neither file moving operations, nor directory atomic rename, +upload process is done in most efficient way S3 support. +This [drastically increases writing performance](https://spot.io/blog/improve-apache-spark-performance-with-the-s3-magic-committer/). + +To use this committer, set [following properties](https://github.com/apache/spark/pull/32518) while creating Spark session. + +```{eval-rst} +.. tabs:: + + .. code-tab:: py S3 your main distributed filesystem (Spark on Kubernetes) + + # https://issues.apache.org/jira/browse/SPARK-23977 + # https://spark.apache.org/docs/latest/cloud-integration.html#committing-work-into-cloud-storage-safely-and-fast + spark = ( + SparkSession.builder.appName("spark-app-name") + .config("spark.hadoop.fs.s3a.committer.magic.enabled", "true") + .config("spark.hadoop.fs.s3a.committer.name", "magic") + .config("spark.hadoop.mapreduce.outputcommitter.factory.scheme.s3a", "org.apache.hadoop.fs.s3a.commit.S3ACommitterFactory") + .config("spark.sql.parquet.output.committer.class", "org.apache.spark.internal.io.cloud.BindingParquetOutputCommitter") + .config("spark.sql.sources.commitProtocolClass", "org.apache.spark.internal.io.cloud.PathOutputCommitProtocol") + .getOrCreate() + ) + + .. code-tab:: py HDFS is your main distributed filesystem (Spark on Hadoop) + + # https://community.cloudera.com/t5/Support-Questions/spark-sql-sources-partitionOverwriteMode-dynamic-quot-not/m-p/343483/highlight/true + spark = ( + SparkSession.builder.appName("spark-app-name") + .config("spark.hadoop.fs.s3a.committer.magic.enabled", "true") + .config("spark.hadoop.fs.s3a.committer.name", "magic") + .getOrCreate() + ) +``` + +```{eval-rst} +.. warning:: + + ``magic`` committer requires S3 implementation to have strong consistency - file upload API return response only + if it was written on enough number of cluster nodes, and any cluster node error does not lead to missing or corrupting files. + + Some S3 implementations does have strong consistency + (like `AWS S3 `_ and + `MinIO `_), some not. Please contact your S3 provider + to get information about S3 implementation consistency. +``` + +```{eval-rst} +.. warning:: + + ``magic`` committer does not support ``if_exists="replace_overlapping_partitions"``. + Either use another ``if_exists`` value, or use ``partitioned`` committer. +``` + +### See also + +- [directory.marker.retention="keep"](https://hadoop.apache.org/docs/stable/hadoop-aws/tools/hadoop-aws/directory_markers.html) + +## Slow reading from S3 + +Please read following documentation: + +- [prefetch.enabled](https://hadoop.apache.org/docs/stable/hadoop-aws/tools/hadoop-aws/prefetching.html) +- [experimental.input.fadvise](https://hadoop.apache.org/docs/stable/hadoop-aws/tools/hadoop-aws/performance.html#Improving_data_input_performance_through_fadvise) +- [Parquet and ORC I/O settings](https://spark.apache.org/docs/latest/cloud-integration.html#parquet-io-settings) + +If you're reading data from row-based formats, like {ref}`csv-file-format`, prefer +[experimental.input.fadvise="sequential" with increased readahead.range](https://issues.apache.org/jira/browse/HADOOP-17789?focusedCommentId=17383559#comment-17383559). + +But for other file formats, especially using compression, prefer +[experimental.input.fadvise="normal"](https://issues.apache.org/jira/browse/HADOOP-17789?focusedCommentId=17383743#comment-17383743) diff --git a/mddocs/docs/connection/index.md b/mddocs/docs/connection/index.md new file mode 100644 index 000000000..00cbfc41a --- /dev/null +++ b/mddocs/docs/connection/index.md @@ -0,0 +1,22 @@ +(connection)= + +```{toctree} +:caption: DB Connection +:maxdepth: 2 + +db_connection/index +``` + +```{toctree} +:caption: File Connection +:maxdepth: 2 + +file_connection/index +``` + +```{toctree} +:caption: File DataFrame Connection +:maxdepth: 2 + +file_df_connection/index +``` diff --git a/mddocs/docs/contributing.md b/mddocs/docs/contributing.md new file mode 100644 index 000000000..cc9ee4419 --- /dev/null +++ b/mddocs/docs/contributing.md @@ -0,0 +1,3 @@ +```{eval-rst} +.. include:: ../CONTRIBUTING.rst +``` diff --git a/mddocs/docs/db/db_reader.md b/mddocs/docs/db/db_reader.md new file mode 100644 index 000000000..5368093b1 --- /dev/null +++ b/mddocs/docs/db/db_reader.md @@ -0,0 +1,21 @@ +(db-reader)= + +# DB Reader + +```{eval-rst} +.. currentmodule:: onetl.db.db_reader.db_reader +``` + +```{eval-rst} +.. autosummary:: + + DBReader + DBReader.run + DBReader.has_data + DBReader.raise_if_no_data +``` + +```{eval-rst} +.. autoclass:: DBReader + :members: run, has_data, raise_if_no_data +``` diff --git a/mddocs/docs/db/db_writer.md b/mddocs/docs/db/db_writer.md new file mode 100644 index 000000000..d2d789c40 --- /dev/null +++ b/mddocs/docs/db/db_writer.md @@ -0,0 +1,19 @@ +(db-writer)= + +# DB Writer + +```{eval-rst} +.. currentmodule:: onetl.db.db_writer.db_writer +``` + +```{eval-rst} +.. autosummary:: + + DBWriter + DBWriter.run +``` + +```{eval-rst} +.. autoclass:: DBWriter + :members: run +``` diff --git a/mddocs/docs/db/index.md b/mddocs/docs/db/index.md new file mode 100644 index 000000000..baf632bf9 --- /dev/null +++ b/mddocs/docs/db/index.md @@ -0,0 +1,9 @@ +(db-root)= + +```{toctree} +:caption: DB classes +:maxdepth: 1 + +db_reader +db_writer +``` diff --git a/mddocs/docs/file/file_downloader/file_downloader.md b/mddocs/docs/file/file_downloader/file_downloader.md new file mode 100644 index 000000000..46fa1b406 --- /dev/null +++ b/mddocs/docs/file/file_downloader/file_downloader.md @@ -0,0 +1,21 @@ +(file-downloader)= + +# File Downloader + +```{eval-rst} +.. currentmodule:: onetl.file.file_downloader.file_downloader +``` + +```{eval-rst} +.. autosummary:: + + FileDownloader + FileDownloader.run + FileDownloader.view_files +``` + +```{eval-rst} +.. autoclass:: FileDownloader + :members: run, view_files + :member-order: bysource +``` diff --git a/mddocs/docs/file/file_downloader/index.md b/mddocs/docs/file/file_downloader/index.md new file mode 100644 index 000000000..4fd38db2c --- /dev/null +++ b/mddocs/docs/file/file_downloader/index.md @@ -0,0 +1,12 @@ +(file-downloader-root)= + +# File Downloader + +```{toctree} +:caption: File Downloader +:maxdepth: 1 + +file_downloader +options +result +``` diff --git a/mddocs/docs/file/file_downloader/options.md b/mddocs/docs/file/file_downloader/options.md new file mode 100644 index 000000000..2189236b1 --- /dev/null +++ b/mddocs/docs/file/file_downloader/options.md @@ -0,0 +1,14 @@ +(file-downloader-options)= + +# File Downloader Options + +```{eval-rst} +.. currentmodule:: onetl.file.file_downloader.options +``` + +```{eval-rst} +.. autopydantic_model:: FileDownloaderOptions + :member-order: bysource + :model-show-field-summary: false + :field-show-constraints: false +``` diff --git a/mddocs/docs/file/file_downloader/result.md b/mddocs/docs/file/file_downloader/result.md new file mode 100644 index 000000000..fe6a165c9 --- /dev/null +++ b/mddocs/docs/file/file_downloader/result.md @@ -0,0 +1,12 @@ +(file-downloader-result)= + +# File Downloader Result + +```{eval-rst} +.. currentmodule:: onetl.file.file_downloader.result +``` + +```{eval-rst} +.. autoclass:: DownloadResult + :members: successful, failed, skipped, missing, successful_count, failed_count, skipped_count, missing_count, total_count, successful_size, failed_size, skipped_size, total_size, raise_if_failed, raise_if_missing, raise_if_skipped, raise_if_empty, is_empty, raise_if_contains_zero_size, details, summary, dict, json +``` diff --git a/mddocs/docs/file/file_filters/base.md b/mddocs/docs/file/file_filters/base.md new file mode 100644 index 000000000..375bb9a9c --- /dev/null +++ b/mddocs/docs/file/file_filters/base.md @@ -0,0 +1,19 @@ +(base-file-filter)= + +# Base interface + +```{eval-rst} +.. currentmodule:: onetl.base.base_file_filter +``` + +```{eval-rst} +.. autosummary:: + + BaseFileFilter + BaseFileFilter.match +``` + +```{eval-rst} +.. autoclass:: BaseFileFilter + :members: match +``` diff --git a/mddocs/docs/file/file_filters/exclude_dir.md b/mddocs/docs/file/file_filters/exclude_dir.md new file mode 100644 index 000000000..6b9aaf80e --- /dev/null +++ b/mddocs/docs/file/file_filters/exclude_dir.md @@ -0,0 +1,12 @@ +(exclude-dir-filter)= + +# ExcludeDir + +```{eval-rst} +.. currentmodule:: onetl.file.filter.exclude_dir +``` + +```{eval-rst} +.. autoclass:: ExcludeDir + :members: match +``` diff --git a/mddocs/docs/file/file_filters/file_filter.md b/mddocs/docs/file/file_filters/file_filter.md new file mode 100644 index 000000000..6ef16e7b1 --- /dev/null +++ b/mddocs/docs/file/file_filters/file_filter.md @@ -0,0 +1,12 @@ +(file-filter)= + +# File Filter (legacy) + +```{eval-rst} +.. currentmodule:: onetl.core.file_filter.file_filter +``` + +```{eval-rst} +.. autoclass:: FileFilter + :members: match +``` diff --git a/mddocs/docs/file/file_filters/file_mtime_filter.md b/mddocs/docs/file/file_filters/file_mtime_filter.md new file mode 100644 index 000000000..4414d9bac --- /dev/null +++ b/mddocs/docs/file/file_filters/file_mtime_filter.md @@ -0,0 +1,12 @@ +(file-modificatiom-time)= + +# FileModifiedTime + +```{eval-rst} +.. currentmodule:: onetl.file.filter.file_mtime +``` + +```{eval-rst} +.. autoclass:: FileModifiedTime + :members: match +``` diff --git a/mddocs/docs/file/file_filters/file_size_filter.md b/mddocs/docs/file/file_filters/file_size_filter.md new file mode 100644 index 000000000..a8eed5e22 --- /dev/null +++ b/mddocs/docs/file/file_filters/file_size_filter.md @@ -0,0 +1,12 @@ +(file-size-range)= + +# FileSizeRange + +```{eval-rst} +.. currentmodule:: onetl.file.filter.file_size +``` + +```{eval-rst} +.. autoclass:: FileSizeRange + :members: match +``` diff --git a/mddocs/docs/file/file_filters/glob.md b/mddocs/docs/file/file_filters/glob.md new file mode 100644 index 000000000..9234b7d3c --- /dev/null +++ b/mddocs/docs/file/file_filters/glob.md @@ -0,0 +1,12 @@ +(glob-filter)= + +# Glob + +```{eval-rst} +.. currentmodule:: onetl.file.filter.glob +``` + +```{eval-rst} +.. autoclass:: Glob + :members: match +``` diff --git a/mddocs/docs/file/file_filters/index.md b/mddocs/docs/file/file_filters/index.md new file mode 100644 index 000000000..d6b12f0dd --- /dev/null +++ b/mddocs/docs/file/file_filters/index.md @@ -0,0 +1,29 @@ +(file-filters)= + +# File Filters + +```{toctree} +:caption: File filters +:maxdepth: 1 + +glob +regexp +exclude_dir +file_size_filter +file_mtime_filter +``` + +```{toctree} +:caption: Legacy +:maxdepth: 1 + +file_filter +``` + +```{toctree} +:caption: For developers +:maxdepth: 1 + +base +match_all_filters +``` diff --git a/mddocs/docs/file/file_filters/match_all_filters.md b/mddocs/docs/file/file_filters/match_all_filters.md new file mode 100644 index 000000000..26818a46d --- /dev/null +++ b/mddocs/docs/file/file_filters/match_all_filters.md @@ -0,0 +1,11 @@ +(match-all-filters)= + +# match_all_filters + +```{eval-rst} +.. currentmodule:: onetl.file.filter.match_all_filters +``` + +```{eval-rst} +.. autofunction:: match_all_filters +``` diff --git a/mddocs/docs/file/file_filters/regexp.md b/mddocs/docs/file/file_filters/regexp.md new file mode 100644 index 000000000..e62fe9345 --- /dev/null +++ b/mddocs/docs/file/file_filters/regexp.md @@ -0,0 +1,12 @@ +(regexp-filter)= + +# Regexp + +```{eval-rst} +.. currentmodule:: onetl.file.filter.regexp +``` + +```{eval-rst} +.. autoclass:: Regexp + :members: match +``` diff --git a/mddocs/docs/file/file_limits/base.md b/mddocs/docs/file/file_limits/base.md new file mode 100644 index 000000000..7990c28a9 --- /dev/null +++ b/mddocs/docs/file/file_limits/base.md @@ -0,0 +1,21 @@ +(base-limit)= + +# Base interface + +```{eval-rst} +.. currentmodule:: onetl.base.base_file_limit +``` + +```{eval-rst} +.. autosummary:: + + BaseFileLimit + BaseFileLimit.reset + BaseFileLimit.stops_at + BaseFileLimit.is_reached +``` + +```{eval-rst} +.. autoclass:: BaseFileLimit + :members: reset, stops_at, is_reached +``` diff --git a/mddocs/docs/file/file_limits/file_limit.md b/mddocs/docs/file/file_limits/file_limit.md new file mode 100644 index 000000000..5034bf0f5 --- /dev/null +++ b/mddocs/docs/file/file_limits/file_limit.md @@ -0,0 +1,12 @@ +(file-limit)= + +# File Limit (legacy) + +```{eval-rst} +.. currentmodule:: onetl.core.file_limit.file_limit +``` + +```{eval-rst} +.. autoclass:: FileLimit + :members: reset, stops_at, is_reached +``` diff --git a/mddocs/docs/file/file_limits/index.md b/mddocs/docs/file/file_limits/index.md new file mode 100644 index 000000000..52d226b3e --- /dev/null +++ b/mddocs/docs/file/file_limits/index.md @@ -0,0 +1,28 @@ +(file-limits)= + +# File Limits + +```{toctree} +:caption: File limits +:maxdepth: 1 + +max_files_count +total_files_size +``` + +```{toctree} +:caption: Legacy +:maxdepth: 1 + +file_limit +``` + +```{toctree} +:caption: For developers +:maxdepth: 1 + +base +limits_stop_at +limits_reached +reset_limits +``` diff --git a/mddocs/docs/file/file_limits/limits_reached.md b/mddocs/docs/file/file_limits/limits_reached.md new file mode 100644 index 000000000..2d9a837b9 --- /dev/null +++ b/mddocs/docs/file/file_limits/limits_reached.md @@ -0,0 +1,11 @@ +(limits-reached)= + +# limits_reached + +```{eval-rst} +.. currentmodule:: onetl.file.limit.limits_reached +``` + +```{eval-rst} +.. autofunction:: limits_reached +``` diff --git a/mddocs/docs/file/file_limits/limits_stop_at.md b/mddocs/docs/file/file_limits/limits_stop_at.md new file mode 100644 index 000000000..a53aba6d7 --- /dev/null +++ b/mddocs/docs/file/file_limits/limits_stop_at.md @@ -0,0 +1,11 @@ +(limits-stop-at)= + +# limits_stop_at + +```{eval-rst} +.. currentmodule:: onetl.file.limit.limits_stop_at +``` + +```{eval-rst} +.. autofunction:: limits_stop_at +``` diff --git a/mddocs/docs/file/file_limits/max_files_count.md b/mddocs/docs/file/file_limits/max_files_count.md new file mode 100644 index 000000000..b82896718 --- /dev/null +++ b/mddocs/docs/file/file_limits/max_files_count.md @@ -0,0 +1,12 @@ +(max-files-count)= + +# MaxFilesCount + +```{eval-rst} +.. currentmodule:: onetl.file.limit.max_files_count +``` + +```{eval-rst} +.. autoclass:: MaxFilesCount + :members: reset, stops_at, is_reached +``` diff --git a/mddocs/docs/file/file_limits/reset_limits.md b/mddocs/docs/file/file_limits/reset_limits.md new file mode 100644 index 000000000..a55769041 --- /dev/null +++ b/mddocs/docs/file/file_limits/reset_limits.md @@ -0,0 +1,11 @@ +(reset-limits)= + +# reset_limits + +```{eval-rst} +.. currentmodule:: onetl.file.limit.reset_limits +``` + +```{eval-rst} +.. autofunction:: reset_limits +``` diff --git a/mddocs/docs/file/file_limits/total_files_size.md b/mddocs/docs/file/file_limits/total_files_size.md new file mode 100644 index 000000000..bc06c4f76 --- /dev/null +++ b/mddocs/docs/file/file_limits/total_files_size.md @@ -0,0 +1,12 @@ +(total-files-size-limit)= + +# TotalFilesSize + +```{eval-rst} +.. currentmodule:: onetl.file.limit.total_files_size +``` + +```{eval-rst} +.. autoclass:: TotalFilesSize + :members: reset, stops_at, is_reached +``` diff --git a/mddocs/docs/file/file_mover/file_mover.md b/mddocs/docs/file/file_mover/file_mover.md new file mode 100644 index 000000000..39e2cb1b7 --- /dev/null +++ b/mddocs/docs/file/file_mover/file_mover.md @@ -0,0 +1,21 @@ +(file-mover)= + +# File Mover + +```{eval-rst} +.. currentmodule:: onetl.file.file_mover.file_mover +``` + +```{eval-rst} +.. autosummary:: + + FileMover + FileMover.run + FileMover.view_files +``` + +```{eval-rst} +.. autoclass:: FileMover + :members: run, view_files + :member-order: bysource +``` diff --git a/mddocs/docs/file/file_mover/index.md b/mddocs/docs/file/file_mover/index.md new file mode 100644 index 000000000..6e9b44d39 --- /dev/null +++ b/mddocs/docs/file/file_mover/index.md @@ -0,0 +1,12 @@ +(file-mover-root)= + +# File Mover + +```{toctree} +:caption: File Mover +:maxdepth: 1 + +file_mover +options +result +``` diff --git a/mddocs/docs/file/file_mover/options.md b/mddocs/docs/file/file_mover/options.md new file mode 100644 index 000000000..57d42d500 --- /dev/null +++ b/mddocs/docs/file/file_mover/options.md @@ -0,0 +1,14 @@ +(file-mover-options)= + +# File Mover Options + +```{eval-rst} +.. currentmodule:: onetl.file.file_mover.options +``` + +```{eval-rst} +.. autopydantic_model:: FileMoverOptions + :member-order: bysource + :model-show-field-summary: false + :field-show-constraints: false +``` diff --git a/mddocs/docs/file/file_mover/result.md b/mddocs/docs/file/file_mover/result.md new file mode 100644 index 000000000..f0fec0fb7 --- /dev/null +++ b/mddocs/docs/file/file_mover/result.md @@ -0,0 +1,12 @@ +(file-mover-result)= + +# File Mover Result + +```{eval-rst} +.. currentmodule:: onetl.file.file_mover.result +``` + +```{eval-rst} +.. autoclass:: MoveResult + :members: successful, failed, skipped, missing, successful_count, failed_count, skipped_count, missing_count, total_count, successful_size, failed_size, skipped_size, total_size, raise_if_failed, raise_if_missing, raise_if_skipped, raise_if_empty, is_empty, raise_if_contains_zero_size, details, summary, dict, json +``` diff --git a/mddocs/docs/file/file_uploader/file_uploader.md b/mddocs/docs/file/file_uploader/file_uploader.md new file mode 100644 index 000000000..770ae8660 --- /dev/null +++ b/mddocs/docs/file/file_uploader/file_uploader.md @@ -0,0 +1,21 @@ +(file-uploader)= + +# File Uploader + +```{eval-rst} +.. currentmodule:: onetl.file.file_uploader.file_uploader +``` + +```{eval-rst} +.. autosummary:: + + FileUploader + FileUploader.run + FileUploader.view_files +``` + +```{eval-rst} +.. autoclass:: FileUploader + :members: run, view_files + :member-order: bysource +``` diff --git a/mddocs/docs/file/file_uploader/index.md b/mddocs/docs/file/file_uploader/index.md new file mode 100644 index 000000000..ada740442 --- /dev/null +++ b/mddocs/docs/file/file_uploader/index.md @@ -0,0 +1,12 @@ +(file-uploader-root)= + +# File Uploader + +```{toctree} +:caption: File Uploader +:maxdepth: 1 + +file_uploader +options +result +``` diff --git a/mddocs/docs/file/file_uploader/options.md b/mddocs/docs/file/file_uploader/options.md new file mode 100644 index 000000000..8b987e1d8 --- /dev/null +++ b/mddocs/docs/file/file_uploader/options.md @@ -0,0 +1,14 @@ +(file-uploader-options)= + +# File Uploader Options + +```{eval-rst} +.. currentmodule:: onetl.file.file_uploader.options +``` + +```{eval-rst} +.. autopydantic_model:: FileUploaderOptions + :member-order: bysource + :model-show-field-summary: false + :field-show-constraints: false +``` diff --git a/mddocs/docs/file/file_uploader/result.md b/mddocs/docs/file/file_uploader/result.md new file mode 100644 index 000000000..5a8137636 --- /dev/null +++ b/mddocs/docs/file/file_uploader/result.md @@ -0,0 +1,12 @@ +(file-uploader-result)= + +# File Uploader Result + +```{eval-rst} +.. currentmodule:: onetl.file.file_uploader.result +``` + +```{eval-rst} +.. autoclass:: UploadResult + :members: successful, failed, skipped, missing, successful_count, failed_count, skipped_count, missing_count, total_count, successful_size, failed_size, skipped_size, total_size, raise_if_failed, raise_if_missing, raise_if_skipped, raise_if_empty, is_empty, raise_if_contains_zero_size, details, summary, dict, json +``` diff --git a/mddocs/docs/file/index.md b/mddocs/docs/file/index.md new file mode 100644 index 000000000..49365dce5 --- /dev/null +++ b/mddocs/docs/file/index.md @@ -0,0 +1,12 @@ +(file-root)= + +```{toctree} +:caption: File classes +:maxdepth: 1 + +file_downloader/index +file_uploader/index +file_mover/index +file_filters/index +file_limits/index +``` diff --git a/mddocs/docs/file_df/file_df_reader/file_df_reader.md b/mddocs/docs/file_df/file_df_reader/file_df_reader.md new file mode 100644 index 000000000..713916f77 --- /dev/null +++ b/mddocs/docs/file_df/file_df_reader/file_df_reader.md @@ -0,0 +1,13 @@ +(file-df-reader)= + +# FileDF Reader + +```{eval-rst} +.. currentmodule:: onetl.file.file_df_reader.file_df_reader +``` + +```{eval-rst} +.. autoclass:: FileDFReader + :members: run + :member-order: bysource +``` diff --git a/mddocs/docs/file_df/file_df_reader/index.md b/mddocs/docs/file_df/file_df_reader/index.md new file mode 100644 index 000000000..99ac82d09 --- /dev/null +++ b/mddocs/docs/file_df/file_df_reader/index.md @@ -0,0 +1,11 @@ +(file-df-reader-root)= + +# FileDF Reader + +```{toctree} +:caption: FileDF Reader +:maxdepth: 1 + +file_df_reader +options +``` diff --git a/mddocs/docs/file_df/file_df_reader/options.md b/mddocs/docs/file_df/file_df_reader/options.md new file mode 100644 index 000000000..4318d288a --- /dev/null +++ b/mddocs/docs/file_df/file_df_reader/options.md @@ -0,0 +1,13 @@ +(file-df-reader-options)= + +# Options + +```{eval-rst} +.. currentmodule:: onetl.file.file_df_reader.options +``` + +```{eval-rst} +.. autoclass:: FileDFReaderOptions + :members: recursive + :member-order: bysource +``` diff --git a/mddocs/docs/file_df/file_df_writer/file_df_writer.md b/mddocs/docs/file_df/file_df_writer/file_df_writer.md new file mode 100644 index 000000000..6d53044e9 --- /dev/null +++ b/mddocs/docs/file_df/file_df_writer/file_df_writer.md @@ -0,0 +1,13 @@ +(file-df-writer)= + +# FileDF Writer + +```{eval-rst} +.. currentmodule:: onetl.file.file_df_writer.file_df_writer +``` + +```{eval-rst} +.. autoclass:: FileDFWriter + :members: run + :member-order: bysource +``` diff --git a/mddocs/docs/file_df/file_df_writer/index.md b/mddocs/docs/file_df/file_df_writer/index.md new file mode 100644 index 000000000..473031415 --- /dev/null +++ b/mddocs/docs/file_df/file_df_writer/index.md @@ -0,0 +1,11 @@ +(file-df-writer-root)= + +# FileDF Writer + +```{toctree} +:caption: FileDF Writer +:maxdepth: 1 + +file_df_writer +options +``` diff --git a/mddocs/docs/file_df/file_df_writer/options.md b/mddocs/docs/file_df/file_df_writer/options.md new file mode 100644 index 000000000..7afee67c7 --- /dev/null +++ b/mddocs/docs/file_df/file_df_writer/options.md @@ -0,0 +1,13 @@ +(file-df-writer-options)= + +# Options + +```{eval-rst} +.. currentmodule:: onetl.file.file_df_writer.options +``` + +```{eval-rst} +.. autoclass:: FileDFWriterOptions + :members: if_exists, partition_by + :member-order: bysource +``` diff --git a/mddocs/docs/file_df/file_formats/avro.md b/mddocs/docs/file_df/file_formats/avro.md new file mode 100644 index 000000000..528e3810a --- /dev/null +++ b/mddocs/docs/file_df/file_formats/avro.md @@ -0,0 +1,13 @@ +(avro-file-format)= + +# Avro + +```{eval-rst} +.. currentmodule:: onetl.file.format.avro +``` + +```{eval-rst} +.. autoclass:: Avro + :members: get_packages, parse_column, serialize_column, schema_dict, schema_url, recordName,recordNamespace,compression,mode,datetimeRebaseMode,positionalFieldMatching,enableStableIdentifiersForUnionType + :member-order: bysource +``` diff --git a/mddocs/docs/file_df/file_formats/base.md b/mddocs/docs/file_df/file_formats/base.md new file mode 100644 index 000000000..91a175871 --- /dev/null +++ b/mddocs/docs/file_df/file_formats/base.md @@ -0,0 +1,19 @@ +(base-file-format)= + +# Base interface + +```{eval-rst} +.. currentmodule:: onetl.base.base_file_format +``` + +```{eval-rst} +.. autoclass:: BaseReadableFileFormat + :members: check_if_supported, apply_to_reader + :member-order: bysource +``` + +```{eval-rst} +.. autoclass:: BaseWritableFileFormat + :members: check_if_supported, apply_to_writer + :member-order: bysource +``` diff --git a/mddocs/docs/file_df/file_formats/csv.md b/mddocs/docs/file_df/file_formats/csv.md new file mode 100644 index 000000000..30098ea66 --- /dev/null +++ b/mddocs/docs/file_df/file_formats/csv.md @@ -0,0 +1,13 @@ +(csv-file-format)= + +# CSV + +```{eval-rst} +.. currentmodule:: onetl.file.format.csv +``` + +```{eval-rst} +.. autoclass:: CSV + :members: __init__, parse_column, serialize_column, charToEscapeQuoteEscaping,columnNameOfCorruptRecord,comment,compression,dateFormat,delimiter,emptyValue,enforceSchema,escapeQuotes,header,ignoreLeadingWhiteSpace,ignoreTrailingWhiteSpace,inferSchema,locale,maxCharsPerColumn,mode,multiLine,nanValue,negativeInf,nullValue,positiveInf,preferDate,quote,quoteAll,samplingRatio,timestampFormat,timestampNTZFormat,unescapedQuoteHandling + :member-order: bysource +``` diff --git a/mddocs/docs/file_df/file_formats/excel.md b/mddocs/docs/file_df/file_formats/excel.md new file mode 100644 index 000000000..98e96003e --- /dev/null +++ b/mddocs/docs/file_df/file_formats/excel.md @@ -0,0 +1,13 @@ +(excel-file-format)= + +# Excel + +```{eval-rst} +.. currentmodule:: onetl.file.format.excel +``` + +```{eval-rst} +.. autoclass:: Excel + :members: get_packages,header,dataAddress,treatEmptyValuesAsNulls,setErrorCellsToFallbackValues,usePlainNumberFormat,inferSchema,timestampFormat,maxRowsInMemory,maxByteArraySize,tempFileThreshold,excerptSize,workbookPassword + :member-order: bysource +``` diff --git a/mddocs/docs/file_df/file_formats/index.md b/mddocs/docs/file_df/file_formats/index.md new file mode 100644 index 000000000..155cb7121 --- /dev/null +++ b/mddocs/docs/file_df/file_formats/index.md @@ -0,0 +1,24 @@ +(file-formats)= + +# File Formats + +```{toctree} +:caption: File formats +:maxdepth: 1 + +avro +csv +excel +json +jsonline +orc +parquet +xml +``` + +```{toctree} +:caption: For developers +:maxdepth: 1 + +base +``` diff --git a/mddocs/docs/file_df/file_formats/json.md b/mddocs/docs/file_df/file_formats/json.md new file mode 100644 index 000000000..976855782 --- /dev/null +++ b/mddocs/docs/file_df/file_formats/json.md @@ -0,0 +1,13 @@ +(json-file-format)= + +# JSON + +```{eval-rst} +.. currentmodule:: onetl.file.format.json +``` + +```{eval-rst} +.. autoclass:: JSON + :members: __init__, parse_column, serialize_column, allowBackslashEscapingAnyCharacter,allowComments,allowNonNumericNumbers,allowNumericLeadingZeros,allowSingleQuotes,allowUnquotedControlChars,allowUnquotedFieldNames,columnNameOfCorruptRecord,dateFormat,dropFieldIfAllNull,encoding,lineSep,locale,mode,prefersDecimal,primitivesAsString,samplingRatio,timestampFormat,timestampNTZFormat,timezone + :member-order: bysource +``` diff --git a/mddocs/docs/file_df/file_formats/jsonline.md b/mddocs/docs/file_df/file_formats/jsonline.md new file mode 100644 index 000000000..6a3794de7 --- /dev/null +++ b/mddocs/docs/file_df/file_formats/jsonline.md @@ -0,0 +1,13 @@ +(jsonline-file-format)= + +# JSONLine + +```{eval-rst} +.. currentmodule:: onetl.file.format.jsonline +``` + +```{eval-rst} +.. autoclass:: JSONLine + :members: __init__, allowBackslashEscapingAnyCharacter,allowComments,allowNonNumericNumbers,allowNumericLeadingZeros,allowSingleQuotes,allowUnquotedControlChars,allowUnquotedFieldNames,columnNameOfCorruptRecord,compression,dateFormat,dropFieldIfAllNull,encoding,ignoreNullFields,lineSep,locale,mode,prefersDecimal,primitivesAsString,samplingRatio,timestampFormat,timestampNTZFormat,timezone + :member-order: bysource +``` diff --git a/mddocs/docs/file_df/file_formats/orc.md b/mddocs/docs/file_df/file_formats/orc.md new file mode 100644 index 000000000..be830b88f --- /dev/null +++ b/mddocs/docs/file_df/file_formats/orc.md @@ -0,0 +1,13 @@ +(orc-file-format)= + +# ORC + +```{eval-rst} +.. currentmodule:: onetl.file.format.orc +``` + +```{eval-rst} +.. autoclass:: ORC + :members: __init__, mergeSchema,compression + :member-order: bysource +``` diff --git a/mddocs/docs/file_df/file_formats/parquet.md b/mddocs/docs/file_df/file_formats/parquet.md new file mode 100644 index 000000000..2d9b91bbc --- /dev/null +++ b/mddocs/docs/file_df/file_formats/parquet.md @@ -0,0 +1,13 @@ +(parquet-file-format)= + +# Parquet + +```{eval-rst} +.. currentmodule:: onetl.file.format.parquet +``` + +```{eval-rst} +.. autoclass:: Parquet + :members: __init__, mergeSchema,compression + :member-order: bysource +``` diff --git a/mddocs/docs/file_df/file_formats/xml.md b/mddocs/docs/file_df/file_formats/xml.md new file mode 100644 index 000000000..92b127bce --- /dev/null +++ b/mddocs/docs/file_df/file_formats/xml.md @@ -0,0 +1,13 @@ +(xml-file-format)= + +# XML + +```{eval-rst} +.. currentmodule:: onetl.file.format.xml +``` + +```{eval-rst} +.. autoclass:: XML + :members: get_packages, parse_column,arrayElementName,attributePrefix,charset,columnNameOfCorruptRecord,compression,dateFormat,declaration,excludeAttribute,ignoreNamespace,ignoreSurroundingSpaces,inferSchema,mode,nullValue,rootTag,row_tag,rowValidationXSDPath,samplingRatio,timestampFormat,valueTag,wildcardColName + :member-order: bysource +``` diff --git a/mddocs/docs/file_df/index.md b/mddocs/docs/file_df/index.md new file mode 100644 index 000000000..a955dcca5 --- /dev/null +++ b/mddocs/docs/file_df/index.md @@ -0,0 +1,10 @@ +(file-df-root)= + +```{toctree} +:caption: File DataFrame classes +:maxdepth: 1 + +file_df_reader/index +file_df_writer/index +file_formats/index +``` diff --git a/mddocs/docs/hooks/design.md b/mddocs/docs/hooks/design.md new file mode 100644 index 000000000..94e0f6188 --- /dev/null +++ b/mddocs/docs/hooks/design.md @@ -0,0 +1,694 @@ +(hooks-design)= + +# High level design + +## What are hooks? + +Hook mechanism is a part of onETL which allows to inject some additional behavior into +existing methods of (almost) any class. + +### Features + +Hooks mechanism allows to: + +- Inspect and validate input arguments and output results of method call +- Access, modify or replace method call result (but NOT input arguments) +- Wrap method calls with a context manager and catch raised exceptions + +Hooks can be placed into {ref}`plugins`, allowing to modify onETL behavior by installing some additional package. + +### Limitations + +- Hooks can be bound to methods of a class only (not functions). +- Only methods decorated with {ref}`slot-decorator` implement hooks mechanism. These class and methods are marked as {{ support_hooks }}. +- Hooks can be bound to public methods only. + +## Terms + +- {ref}`slot-decorator` - method of a class with a special decorator +- `Callback` - function which implements some additional logic which modifies slot behavior +- {ref}`hook-decorator` - wrapper around callback which stores hook state, priority and some useful methods +- `Hooks mechanism` - calling `Slot()` will call all enabled hooks which are bound to the slot. Implemented by {ref}`support-hooks-decorator`. + +## How to implement hooks? + +### TL;DR + +```python +from onetl.hooks import support_hooks, slot, hook + + +@support_hooks # enabling hook mechanism for the class +class MyClass: + def __init__(self, data): + self.data = data + + # this is slot + @slot + def method(self, arg): + pass + + +@MyClass.method.bind # bound hook to the slot +@hook # this is hook +def callback(obj, arg): # this is callback + print(obj.data, arg) + + +obj = MyClass(1) +obj.method(2) # will call callback(obj, 1) + +# prints "1 2" +``` + +#### Define a slot + +- Create a class with a method: + +```python +class MyClass: + def __init__(self, data): + self.data = data + + def method(self, arg): + return self.data, arg +``` + +- Add {ref}`slot-decorator` to the method: + +```python +from onetl.hooks import support_hooks, slot, hook + + +class MyClass: + @slot + def method(self, arg): + return self.data, arg +``` + +If method has other decorators like `@classmethod` or `@staticmethod`, `@slot` should be placed on the top: + +```python +from onetl.hooks import support_hooks, slot, hook + + +class MyClass: + @slot + @classmethod + def class_method(cls, arg): + return cls, arg + + @slot + @staticmethod + def static_method(arg): + return arg +``` + +- Add {ref}`support-hooks-decorator` to the class: + +```python +from onetl.hooks import support_hooks, slot, hook + + +@support_hooks +class MyClass: + @slot + def method(self, arg): + return self.data, arg +``` + +Slot is created. + +#### Define a callback + +Define some function (a.k.a callback): + +```python +def callback(self, arg): + print(self.data, arg) +``` + +It should have signature *compatible* with `MyClass.method`. *Compatible* does not mean *exactly the same* - +for example, you can rename positional arguments: + +```python +def callback(obj, arg): + print(obj.data, arg) +``` + +Use `*args` and `**kwargs` to omit arguments you don't care about: + +```python +def callback(obj, *args, **kwargs): + print(obj.data, args, kwargs) +``` + +There is also an argument `method_name` which has a special meaning - +the method name which the callback is bound to is passed into this argument: + +```python +def callback(obj, *args, method_name: str, **kwargs): + print(obj.data, args, method_name, kwargs) +``` + +```{eval-rst} +.. note:: + + ``method_name`` should always be a keyword argument, **NOT** positional. +``` + +```{eval-rst} +.. warning:: + + If callback signature is not compatible with slot signature, an exception will be raised, + but **ONLY** while slot is called. +``` + +#### Define a hook + +Add {ref}`hook-decorator` to create a hook from your callback: + +```python +@hook +def callback(obj, arg): + print(obj.data, arg) +``` + +You can pass more options to the `@hook` decorator, like state or priority. +See decorator documentation for more details. + +#### Bind hook to the slot + +Use `Slot.bind` method to bind hook to the slot: + +```python +@MyClass.method.bind +@hook +def callback(obj, arg): + print(obj, arg) +``` + +You can bind more than one hook to the same slot, and bind same hook to multiple slots: + +```python +@MyClass.method1.bind +@MyClass.method2.bind +@hook +def callback1(obj, arg): + "Will be called by both MyClass.method1 and MyClass.method2" + + +@MyClass.method1.bind +@hook +def callback2(obj, arg): + "Will be called by MyClass.method1 too" +``` + +## How hooks are called? + +### General + +Just call the method decorated by `@slot` to trigger the hook: + +```python +obj = MyClass(1) +obj.method(2) # will call callback(obj, 2) + +# prints "1 2" +``` + +There are some special callback types that has a slightly different behavior. + +### Context managers + +`@hook` decorator can be placed on a context manager class: + +```python +@hook +class ContextManager: + def __init__(self, obj, arg): + self.obj = obj + self.arg = arg + + def __enter__(self): + # do something on enter + print(obj.data, arg) + return self + + def __exit__(self, exc_type, exc_value, traceback): + # do something on exit + return False +``` + +Context manager is entered while calling the `Slot()`, and exited then the call is finished. + +If present, method `process_result` has a special meaning - +it can receive `MyClass.method` call result, and also modify/replace it: + +```python +@hook +class ContextManager: + def __init__(self, obj, arg): + self.obj = obj + self.arg = arg + + def __enter__(self): + # do something on enter + print(obj.data, arg) + return self + + def __exit__(self, exc_type, exc_value, traceback): + # do something on exit + return False + + def process_result(self, result): + # do something with method call result + return modified(result) +``` + +See examples below for more information. + +### Generator function + +`@hook` decorator can be placed on a generator function: + +```python +@hook +def callback(obj, arg): + print(obj.data, arg) + # this is called before original method body + + yield # method is called here + + # this is called after original method body +``` + +It is converted to a context manager, in the same manner as +[contextlib.contextmanager](https://docs.python.org/3/library/contextlib.html#contextlib.contextmanager). + +Generator body can be wrapped with `try..except..finally` to catch exceptions: + +```python +@hook +def callback(obj, arg): + print(obj.data, arg) + + try: + # this is called before original method body + + yield # method is called here + except Exception as e: + process_exception(a) + finally: + # this is called after original method body + finalizer() +``` + +There is also a special syntax which allows generator to access and modify/replace method call result: + +``` +@hook +def callback(obj, arg): + original_result = yield # method is called here + + new_result = do_something(original_result) + + yield new_result # modify/replace the result +``` + +### Calling hooks in details + +- The callback will be called with the same arguments as the original method. + + - If slot is a regular method: + + ```python + callback_result = callback(self, *args, **kwargs) + ``` + + Here `self` is a class instance (`obj`). + + - If slot is a class method: + + ```python + callback_result = callback(cls, *args, **kwargs) + ``` + + Here `cls` is the class itself (`MyClass`). + + - If slot is a static method: + + ```python + callback_result = callback(*args, **kwargs) + ``` + + Neither object not class are passed to the callback in this case. + +- If `callback_result` is a context manager, enter the context. Context manager can catch all the exceptions raised. + + > If there are multiple hooks bound the the slot, every context manager will be entered. + +- Then call the original method wrapped by `@slot`: + + ```python + original_result = method(*args, **kwargs) + ``` + +- Process `original_result`: + + - If `callback_result` object has method `process_result`, or is a generator wrapped with `@hook`, call it: + + ```python + new_result = callback_result.process_result(original_result) + ``` + + - Otherwise set `new_result = callback_result`. + + - If there are multiple hooks bound the the method, pass `new_result` through the chain: + + ```python + new_result = callback1_result.process_result(original_result) + new_result = callback2_result.process_result(new_result or original_result) + new_result = callback3_result.process_result(new_result or original_result) + ``` + +- Finally return: + + ```python + return new_result or original_result + ``` + + All `None` values are ignored on every step above. + +- Exit all the context managers entered during the slot call. + +### Hooks priority + +Hooks are executed in the following order: + +1. Parent class slot + {obj}`FIRST ` +2. Inherited class slot + {obj}`FIRST ` +3. Parent class slot + {obj}`NORMAL ` +4. Inherited class slot + {obj}`NORMAL ` +5. Parent class slot + {obj}`LAST ` +6. Inherited class slot + {obj}`LAST ` + +Hooks with the same priority and inheritance will be executed in the same order they were registered (`Slot.bind` call). + +```{eval-rst} +.. note:: + + Calls of ``super()`` inside inherited class methods does not trigger hooks call. + Hooks are triggered only if method is called explicitly. + + This allow to wrap with a hook the entire slot call without influencing its internal logic. + +``` + +### Hook types + +Here are several examples of using hooks. These types are not exceptional, they can be mixed - for example, +hook can both modify method result and catch exceptions. + +#### Before hook + +Can be used for inspecting or validating input args of the original function: + +```python +@hook +def before1(obj, arg): + print(obj, arg) + # original method is called after exiting this function + + +@hook +def before2(obj, arg): + if arg == 1: + raise ValueError("arg=1 is not allowed") + return None # return None is the same as no return statement +``` + +Executed before calling the original method wrapped by `@slot`. +If hook raises an exception, method will not be called at all. + +#### After hook + +Can be used for performing some actions after original method was successfully executed: + +```python +@hook +def after1(obj, arg): + yield # original method is called here + print(obj, arg) + + +@hook +def after2(obj, arg): + yield None # yielding None is the same as empty yield + if arg == 1: + raise ValueError("arg=1 is not allowed") +``` + +If original method raises an exception, the block of code after `yield` will not be called. + +#### Context hook + +Can be used for catching and handling some exceptions, or to determine that there was no exception during slot call: + +```{eval-rst} +.. tabs:: + + .. code-tab:: py Generator syntax + + # This is just the same as using @contextlib.contextmanager + + @hook + def context_generator(obj, arg): + try: + yield # original method is called here + print(obj, arg) # <-- this line will not be called if method raised an exception + except SomeException as e: + magic(e) + finally: + finalizer() + + .. code-tab:: py Context manager syntax + + @hook + class ContextManager: + def __init__(self, obj, args): + self.obj = obj + self.args = args + + def __enter__(self): + return self + + # original method is called between __enter__ and __exit__ + + def __exit__(self, exc_type, exc_value, traceback): + result = False + if exc_type is not None and isinstance(exc_value, SomeException): + magic(exc_value) + result = True # suppress exception + else: + print(self.obj, self.arg) + finalizer() + return result +``` + +```{eval-rst} +.. note:: + + Contexts are exited in the reverse order of the hook calls. + So if some hook raised an exception, it will be passed into the previous hook, not the next one. + + It is recommended to specify the proper priority for the hook, e.g. :obj:`FIRST ` +``` + +#### Replacing result hook + +Replaces the output result of the original method. + +Can be used for delegating some implementation details for third-party extensions. +See {ref}`hive` and {ref}`hdfs` as an example. + +```python +@hook +def replace1(obj, arg): + result = arg + 10 # any non-None return result + + # original method call result is ignored, output will always be arg + 10 + return result + + +@hook +def replace2(obj, arg): + yield arg + 10 # same as above +``` + +```{eval-rst} +.. note:: + + If there are multiple hooks bound to the same slot, the result of last hook will be used. + It is recommended to specify the proper priority for the hook, e.g. :obj:`LAST ` + +``` + +#### Accessing result hook + +Can access output result of the original method and inspect or validate it: + +```{eval-rst} +.. tabs:: + + .. code-tab:: py Generator syntax + + @hook + def access_result(obj, arg): + result = yield # original method is called here, and result can be used in the hook + print(result) + yield # does not modify result + + .. code-tab:: py Context manager syntax + + @hook + class ModifiesResult: + def __init__(self, obj, args): + self.obj = obj + self.args = args + + def __enter__(self): + return self + + # original method is called between __enter__ and __exit__ + # result is passed into process_result method of context manager, if present + + def process_result(self, result): + print(result) # result can be used in the hook + return None # does not modify result. same as no return statement in the method + + def __exit__(self, exc_type, exc_value, traceback): + return False + +``` + +#### Modifying result hook + +Can access output result of the original method, and return the modified one: + +```{eval-rst} +.. tabs:: + + .. code-tab:: py Generator syntax + + @hook + def modifies_result(obj, arg): + result = yield # original method is called here, and result can be used in the hook + yield result + 10 # modify output result. None values are ignored + + .. code-tab:: py Context manager syntax + + @hook + class ModifiesResult: + def __init__(self, obj, args): + self.obj = obj + self.args = args + + def __enter__(self): + return self + + # original method is called between __enter__ and __exit__ + # result is passed into process_result method of context manager, if present + + def process_result(self, result): + print(result) # result can be used in the hook + return result + 10 # modify output result. None values are ignored + + def __exit__(self, exc_type, exc_value, traceback): + return False +``` + +```{eval-rst} +.. note:: + + If there are multiple hooks bound to the same slot, the result of last hook will be used. + It is recommended to specify the proper priority for the hook, e.g. :obj:`LAST ` + +``` + +## How to enable/disable hooks? + +You can enable/disable/temporary disable hooks on 4 different levels: + +- Manage global hooks state (level 1): + + > - {obj}`onetl.hooks.hooks_state.stop_all_hooks` + > - {obj}`onetl.hooks.hooks_state.resume_all_hooks` + > - {obj}`onetl.hooks.hooks_state.skip_all_hooks` + +- Manage all hooks bound to a specific class (level 2): + + > - {obj}`onetl.hooks.support_hooks.suspend_hooks` + > - {obj}`onetl.hooks.support_hooks.resume_hooks` + > - {obj}`onetl.hooks.support_hooks.skip_hooks` + +- Manage all hooks bound to a specific slot (level 3): + + > - {obj}`onetl.hooks.slot.Slot.suspend_hooks` + > - {obj}`onetl.hooks.slot.Slot.resume_hooks` + > - {obj}`onetl.hooks.slot.Slot.skip_hooks` + +- Manage state of a specific hook (level 4): + + > - {obj}`onetl.hooks.hook.Hook.enable` + > - {obj}`onetl.hooks.hook.Hook.disable` + +More details in the documentation above. + +```{eval-rst} +.. note:: + + All of these levels are independent. + + Calling ``stop`` on the level 1 has higher priority than level 2, and so on. + But calling ``resume`` on the level 1 does not automatically resume hooks stopped in the level 2, + they should be resumed explicitly. + +``` + +## How to see logs of the hook mechanism? + +Hooks registration emits logs with `DEBUG` level: + +```python +from onetl.logs import setup_logging + +setup_logging() +``` + +```text +DEBUG |onETL| Registered hook 'mymodule.callback1' for 'MyClass.method' (enabled=True, priority=HookPriority.NORMAL) +DEBUG |onETL| Registered hook 'mymodule.callback2' for 'MyClass.method' (enabled=True, priority=HookPriority.NORMAL) +DEBUG |onETL| Registered hook 'mymodule.callback3' for 'MyClass.method' (enabled=False, priority=HookPriority.NORMAL) +``` + +But most of logs are emitted with even lower level `NOTICE`, to make output less verbose: + +```python +from onetl.logs import NOTICE, setup_logging + +setup_logging(level=NOTICE) +``` + +``` +NOTICE |Hooks| 2 hooks registered for 'MyClass.method' +NOTICE |Hooks| Calling hook 'mymodule.callback1' (1/2) +NOTICE |Hooks| Hook is finished with returning non-None result +NOTICE |Hooks| Calling hook 'mymodule.callback2' (2/2) +NOTICE |Hooks| This is a context manager, entering ... +NOTICE |Hooks| Calling original method 'MyClass.method' +NOTICE |Hooks| Method call is finished +NOTICE |Hooks| Method call result (*NOT* None) will be replaced with result of hook 'mymodule.callback1' +NOTICE |Hooks| Passing result to 'process_result' method of context manager 'mymodule.callback2' +NOTICE |Hooks| Method call result (*NOT* None) is modified by hook! +``` diff --git a/mddocs/docs/hooks/global_state.md b/mddocs/docs/hooks/global_state.md new file mode 100644 index 000000000..f4fd50a43 --- /dev/null +++ b/mddocs/docs/hooks/global_state.md @@ -0,0 +1,27 @@ +(hooks-global-state)= + +# Hooks global state + +```{eval-rst} +.. currentmodule:: onetl.hooks.hooks_state +``` + +```{eval-rst} +.. autosummary:: + + skip_all_hooks + stop_all_hooks + resume_all_hooks +``` + +```{eval-rst} +.. autofunction:: skip_all_hooks +``` + +```{eval-rst} +.. autofunction:: stop_all_hooks +``` + +```{eval-rst} +.. autofunction:: resume_all_hooks +``` diff --git a/mddocs/docs/hooks/hook.md b/mddocs/docs/hooks/hook.md new file mode 100644 index 000000000..8dc54cf56 --- /dev/null +++ b/mddocs/docs/hooks/hook.md @@ -0,0 +1,34 @@ +(hook-decorator)= + +# `@hook` decorator + +```{eval-rst} +.. currentmodule:: onetl.hooks.hook +``` + +```{eval-rst} +.. autosummary:: + + hook + HookPriority + Hook + Hook.enable + Hook.disable + Hook.skip +``` + +```{eval-rst} +.. autodecorator:: hook +``` + +```{eval-rst} +.. autoclass:: HookPriority + :members: FIRST, NORMAL, LAST + :member-order: bysource +``` + +```{eval-rst} +.. autoclass:: Hook + :members: enable, disable, skip + :member-order: bysource +``` diff --git a/mddocs/docs/hooks/index.md b/mddocs/docs/hooks/index.md new file mode 100644 index 000000000..9b489afe6 --- /dev/null +++ b/mddocs/docs/hooks/index.md @@ -0,0 +1,17 @@ +(hooks)= + +# Hooks + +:::{versionadded} 0.6.0 +::: + +```{toctree} +:caption: Hooks +:maxdepth: 1 + +High level design +@hook decorator +@slot decorator +@support_hooks decorator +Hooks global state +``` diff --git a/mddocs/docs/hooks/slot.md b/mddocs/docs/hooks/slot.md new file mode 100644 index 000000000..ac3c8d74b --- /dev/null +++ b/mddocs/docs/hooks/slot.md @@ -0,0 +1,26 @@ +(slot-decorator)= + +# `@slot` decorator + +```{eval-rst} +.. currentmodule:: onetl.hooks.slot +``` + +```{eval-rst} +.. autosummary:: + + slot + Slot + Slot.bind + Slot.skip_hooks + Slot.suspend_hooks + Slot.resume_hooks +``` + +```{eval-rst} +.. autodecorator:: slot +``` + +```{eval-rst} +.. autoprotocol:: Slot +``` diff --git a/mddocs/docs/hooks/support_hooks.md b/mddocs/docs/hooks/support_hooks.md new file mode 100644 index 000000000..a2fd9fb60 --- /dev/null +++ b/mddocs/docs/hooks/support_hooks.md @@ -0,0 +1,32 @@ +(support-hooks-decorator)= + +# `@support_hooks` decorator + +```{eval-rst} +.. currentmodule:: onetl.hooks.support_hooks +``` + +```{eval-rst} +.. autosummary:: + + support_hooks + skip_hooks + suspend_hooks + resume_hooks +``` + +```{eval-rst} +.. autodecorator:: support_hooks +``` + +```{eval-rst} +.. autofunction:: skip_hooks +``` + +```{eval-rst} +.. autofunction:: suspend_hooks +``` + +```{eval-rst} +.. autofunction:: resume_hooks +``` diff --git a/mddocs/docs/hwm_store/index.md b/mddocs/docs/hwm_store/index.md new file mode 100644 index 000000000..099f56154 --- /dev/null +++ b/mddocs/docs/hwm_store/index.md @@ -0,0 +1,16 @@ +(hwm)= + +# HWM + +Since onETL v0.10.0, the `HWMStore` and `HWM` classes have been moved to a separate library {etl-entities}`etl-entities <>`. + +The only class was left intact is {ref}`yaml-hwm-store`, **which is default** in onETL. + +Other known implementation is [HorizonHWMStore](https://horizon-hwm-store.readthedocs.io/). + +```{toctree} +:hidden: true +:maxdepth: 2 + +yaml_hwm_store +``` diff --git a/mddocs/docs/hwm_store/yaml_hwm_store.md b/mddocs/docs/hwm_store/yaml_hwm_store.md new file mode 100644 index 000000000..63beb672e --- /dev/null +++ b/mddocs/docs/hwm_store/yaml_hwm_store.md @@ -0,0 +1,12 @@ +(yaml-hwm-store)= + +# YAMLHWMStore + +```{eval-rst} +.. currentmodule:: onetl.hwm.store.yaml_hwm_store +``` + +```{eval-rst} +.. autoclass:: YAMLHWMStore + :members: get_hwm, set_hwm, __enter__ +``` diff --git a/mddocs/docs/index.md b/mddocs/docs/index.md new file mode 100644 index 000000000..46642bf4e --- /dev/null +++ b/mddocs/docs/index.md @@ -0,0 +1,87 @@ +```{eval-rst} +.. include:: ../README.rst + :end-before: |Logo| +``` + +```{image} _static/logo_wide.svg +:alt: onETL logo +``` + +```{eval-rst} +.. include:: ../README.rst + :start-after: |Logo| + :end-before: documentation +``` + +```{toctree} +:caption: How to +:hidden: true +:maxdepth: 2 + +self +install/index +quickstart +concepts +logging +troubleshooting/index +``` + +```{toctree} +:caption: Connection +:hidden: true +:maxdepth: 3 + +connection/index +``` + +```{toctree} +:caption: DB classes +:hidden: true +:maxdepth: 3 + +db/index +``` + +```{toctree} +:caption: File classes +:hidden: true +:maxdepth: 3 + +file/index +``` + +```{toctree} +:caption: File DataFrame classes +:hidden: true +:maxdepth: 3 + +file_df/index +``` + +```{toctree} +:caption: Read strategies and HWM +:hidden: true +:maxdepth: 2 + +strategy/index +hwm_store/index +``` + +```{toctree} +:caption: Hooks & plugins +:hidden: true +:maxdepth: 2 + +hooks/index +plugins +``` + +```{toctree} +:caption: Development +:hidden: true +:maxdepth: 2 + +changelog +contributing +security +``` diff --git a/mddocs/docs/install/files.md b/mddocs/docs/install/files.md new file mode 100644 index 000000000..7a9867f53 --- /dev/null +++ b/mddocs/docs/install/files.md @@ -0,0 +1,9 @@ +(install-files)= + +# File connections + +```{eval-rst} +.. include:: ../../README.rst + :start-after: .. _files-install: + :end-before: With Kerberos support +``` diff --git a/mddocs/docs/install/full.md b/mddocs/docs/install/full.md new file mode 100644 index 000000000..f6d1cc107 --- /dev/null +++ b/mddocs/docs/install/full.md @@ -0,0 +1,9 @@ +(install-full)= + +# Full bundle + +```{eval-rst} +.. include:: ../../README.rst + :start-after: .. _full-bundle: + :end-before: .. _quick-start: +``` diff --git a/mddocs/docs/install/index.md b/mddocs/docs/install/index.md new file mode 100644 index 000000000..d71a13de9 --- /dev/null +++ b/mddocs/docs/install/index.md @@ -0,0 +1,22 @@ +(install)= + +# How to install + +```{eval-rst} +.. include:: ../../README.rst + :start-after: .. _minimal-install: + :end-before: With DB and FileDF connections +``` + +## Installation in details + +```{toctree} +:caption: How to install +:maxdepth: 1 + +self +spark +files +kerberos +full +``` diff --git a/mddocs/docs/install/kerberos.md b/mddocs/docs/install/kerberos.md new file mode 100644 index 000000000..7c772b94a --- /dev/null +++ b/mddocs/docs/install/kerberos.md @@ -0,0 +1,9 @@ +(install-kerberos)= + +# Kerberos support + +```{eval-rst} +.. include:: ../../README.rst + :start-after: .. _kerberos-install: + :end-before: Full bundle +``` diff --git a/mddocs/docs/install/spark.md b/mddocs/docs/install/spark.md new file mode 100644 index 000000000..40c15766a --- /dev/null +++ b/mddocs/docs/install/spark.md @@ -0,0 +1,328 @@ +(install-spark)= + +# Spark + +```{eval-rst} +.. include:: ../../README.rst + :start-after: .. _spark-install: + :end-before: .. _java-install: +``` + +## Installing Java + +```{eval-rst} +.. include:: ../../README.rst + :start-after: .. _java-install: + :end-before: .. _pyspark-install: +``` + +## Installing PySpark + +```{eval-rst} +.. include:: ../../README.rst + :start-after: .. _pyspark-install: + :end-before: With File connections +``` + +(java-packages)= + +## Injecting Java packages + +Some DB and FileDF connection classes require specific packages to be inserted to `CLASSPATH` of Spark session, +like JDBC drivers. + +This is usually done by setting up `spark.jars.packages` option while creating Spark session: + +```python +# here is a list of packages to be downloaded: +maven_packages = ( + Greenplum.get_packages(spark_version="3.2") + + MySQL.get_packages() + + Teradata.get_packages() +) + +spark = ( + SparkSession.builder.config("spark.app.name", "onetl") + .config("spark.jars.packages", ",".join(maven_packages)) + .getOrCreate() +) +``` + +Spark automatically resolves package and all its dependencies, download them and inject to Spark session +(both driver and all executors). + +This requires internet access, because package metadata and `.jar` files are fetched from [Maven Repository](https://mvnrepository.com/). + +But sometimes it is required to: + +- Install package without direct internet access (isolated network) +- Install package which is not available in Maven + +There are several ways to do that. + +### Using `spark.jars` + +The most simple solution, but this requires to store raw `.jar` files somewhere on filesystem or web server. + +- Download `package.jar` files (it's usually something like `some-package_1.0.0.jar`). Local file name does not matter, but it should be unique. +- (For `spark.submit.deployMode=cluster`) place downloaded files to HDFS or deploy to any HTTP web server serving static files. See [official documentation](https://spark.apache.org/docs/latest/submitting-applications.html#advanced-dependency-management) for more details. +- Create Spark session with passing `.jar` absolute file path to `spark.jars` Spark config option: + +```{eval-rst} +.. tabs:: + + .. code-tab:: py for spark.submit.deployMode=client (default) + + jar_files = ["/path/to/package.jar"] + + # do not pass spark.jars.packages + spark = ( + SparkSession.builder.config("spark.app.name", "onetl") + .config("spark.jars", ",".join(jar_files)) + .getOrCreate() + ) + + .. code-tab:: py for spark.submit.deployMode=cluster + + # you can also pass URLs like http://domain.com/path/to/downloadable/package.jar + jar_files = ["hdfs:///path/to/package.jar"] + + # do not pass spark.jars.packages + spark = ( + SparkSession.builder.config("spark.app.name", "onetl") + .config("spark.jars", ",".join(jar_files)) + .getOrCreate() + ) +``` + +### Using `spark.jars.repositories` + +```{eval-rst} +.. note:: + + In this case Spark still will try to fetch packages from the internet, so if you don't have internet access, + Spark session will be created with significant delay because of all attempts to fetch packages. +``` + +Can be used if you have access both to public repos (like Maven) and a private Artifactory/Nexus repo. + +- Setup private Maven repository in [JFrog Artifactory](https://jfrog.com/artifactory/) or [Sonatype Nexus](https://www.sonatype.com/products/sonatype-nexus-repository). +- Download `package.jar` file (it's usually something like `some-package_1.0.0.jar`). Local file name does not matter. +- Upload `package.jar` file to private repository (with same `groupId` and `artifactoryId` as in source package in Maven). +- Pass repo URL to `spark.jars.repositories` Spark config option. +- Create Spark session with passing Package name to `spark.jars.packages` Spark config option: + +```python +maven_packages = ( + Greenplum.get_packages(spark_version="3.2") + + MySQL.get_packages() + + Teradata.get_packages() +) + +spark = ( + SparkSession.builder.config("spark.app.name", "onetl") + .config("spark.jars.repositories", "http://nexus.mydomain.com/private-repo/") + .config("spark.jars.packages", ",".join(maven_packages)) + .getOrCreate() +) +``` + +### Using `spark.jars.ivySettings` + +Same as above, but can be used even if there is no network access to public repos like Maven. + +- Setup private Maven repository in [JFrog Artifactory](https://jfrog.com/artifactory/) or [Sonatype Nexus](https://www.sonatype.com/products/sonatype-nexus-repository). +- Download `package.jar` file (it's usually something like `some-package_1.0.0.jar`). Local file name does not matter. +- Upload `package.jar` file to [private repository](https://help.sonatype.com/repomanager3/nexus-repository-administration/repository-management#RepositoryManagement-HostedRepository) (with same `groupId` and `artifactoryId` as in source package in Maven). +- Create `ivysettings.xml` file (see below). +- Add here a resolver with repository URL (and credentials, if required). +- Pass `ivysettings.xml` absolute path to `spark.jars.ivySettings` Spark config option. +- Create Spark session with passing package name to `spark.jars.packages` Spark config option: + +```{eval-rst} +.. tabs:: + + .. code-tab:: xml ivysettings-all-packages-uploaded-to-nexus.xml + + + + + + + + + + + + + + + + .. code-tab:: xml ivysettings-private-packages-in-nexus-public-in-maven.xml + + + + + + + + + + + + + + + + + + + + .. code-tab:: xml ivysettings-private-packages-in-nexus-public-fetched-using-proxy-repo.xml + + + + + + + + + + + + + + + + + + .. code-tab:: xml ivysettings-nexus-with-auth-required.xml + + + + + + + + + + + + + + + + + + + + + +``` + +```{code-block} python +:caption: script.py + +maven_packages = ( + Greenplum.get_packages(spark_version="3.2") + + MySQL.get_packages() + + Teradata.get_packages() +) + +spark = ( + SparkSession.builder.config("spark.app.name", "onetl") + .config("spark.jars.ivySettings", "/path/to/ivysettings.xml") + .config("spark.jars.packages", ",".join(maven_packages)) + .getOrCreate() +) +``` + +### Place `.jar` file to `-/.ivy2/jars/` + +Can be used to pass already downloaded file to Ivy, and skip resolving package from Maven. + +- Download `package.jar` file (it's usually something like `some-package_1.0.0.jar`). Local file name does not matter, but it should be unique. +- Move it to `-/.ivy2/jars/` folder. +- Create Spark session with passing package name to `spark.jars.packages` Spark config option: + +```python +maven_packages = ( + Greenplum.get_packages(spark_version="3.2") + + MySQL.get_packages() + + Teradata.get_packages() +) + +spark = ( + SparkSession.builder.config("spark.app.name", "onetl") + .config("spark.jars.packages", ",".join(maven_packages)) + .getOrCreate() +) +``` + +### Place `.jar` file to Spark jars folder + +```{eval-rst} +.. note:: + + Package file should be placed on all hosts/containers Spark is running, + both driver and all executors. + + Usually this is used only with either: + * ``spark.master=local`` (driver and executors are running on the same host), + * ``spark.master=k8s://...`` (``.jar`` files are added to image or to volume mounted to all pods). +``` + +Can be used to embed `.jar` files to a default Spark classpath. + +- Download `package.jar` file (it's usually something like `some-package_1.0.0.jar`). Local file name does not matter, but it should be unique. +- Move it to `$SPARK_HOME/jars/` folder, e.g. `^/.local/lib/python3.7/site-packages/pyspark/jars/` or `/opt/spark/3.2.3/jars/`. +- Create Spark session **WITHOUT** passing Package name to `spark.jars.packages` + +```python +# no need to set spark.jars.packages or any other spark.jars.* option +# all jars already present in CLASSPATH, and loaded automatically + +spark = SparkSession.builder.config("spark.app.name", "onetl").getOrCreate() +``` + +### Manually adding `.jar` files to `CLASSPATH` + +```{eval-rst} +.. note:: + + Package file should be placed on all hosts/containers Spark is running, + both driver and all executors. + + Usually this is used only with either: + * ``spark.master=local`` (driver and executors are running on the same host), + * ``spark.master=k8s://...`` (``.jar`` files are added to image or to volume mounted to all pods). +``` + +Can be used to embed `.jar` files to a default Java classpath. + +- Download `package.jar` file (it's usually something like `some-package_1.0.0.jar`). Local file name does not matter. +- Set environment variable `CLASSPATH` to `/path/to/package.jar`. You can set multiple file paths +- Create Spark session **WITHOUT** passing Package name to `spark.jars.packages` + +```python +# no need to set spark.jars.packages or any other spark.jars.* option +# all jars already present in CLASSPATH, and loaded automatically + +import os + +jar_files = ["/path/to/package.jar"] +# different delimiters for Windows and Linux +delimiter = ";" if os.name == "nt" else ":" +spark = ( + SparkSession.builder.config("spark.app.name", "onetl") + .config("spark.driver.extraClassPath", delimiter.join(jar_files)) + .config("spark.executor.extraClassPath", delimiter.join(jar_files)) + .getOrCreate() +) +``` diff --git a/mddocs/docs/logging.md b/mddocs/docs/logging.md new file mode 100644 index 000000000..dae7a9d73 --- /dev/null +++ b/mddocs/docs/logging.md @@ -0,0 +1,171 @@ +(logging)= + +# Logging + +Logging is quite important to understand what's going on under the hood of onETL. + +Default logging level for Python interpreters is `WARNING`, +but most of onETL logs are in `INFO` level, so users usually don't see much. + +To change logging level, there is a function {obj}`setup_logging ` +which should be called at the top of the script: + +```python +from onetl.log import setup_logging +from other.lib import some, more, imports + +setup_logging() + +# rest of code +... +``` + +This changes both log level and log formatting to something like this: + +```{eval-rst} +.. dropdown:: See logs + + .. code:: text + + 2024-04-12 10:12:10,834 [INFO ] MainThread: |onETL| Using IncrementalStrategy as a strategy + 2024-04-12 10:12:10,835 [INFO ] MainThread: =================================== DBReader.run() starts =================================== + 2024-04-12 10:12:10,835 [INFO ] MainThread: |DBReader| Getting Spark type for HWM expression: 'updated_at' + 2024-04-12 10:12:10,836 [INFO ] MainThread: |MSSQL| Fetching schema of table 'source_schema.table' ... + 2024-04-12 10:12:11,636 [INFO ] MainThread: |MSSQL| Schema fetched. + 2024-04-12 10:12:11,642 [INFO ] MainThread: |DBReader| Got Spark field: StructField('updated_at', TimestampType(), True) + 2024-04-12 10:12:11,642 [INFO ] MainThread: |DBReader| Detected HWM type: 'ColumnDateTimeHWM' + 2024-04-12 10:12:11,643 [INFO ] MainThread: |IncrementalStrategy| Fetching HWM from HorizonHWMStore: + 2024-04-12 10:12:11,643 [INFO ] MainThread: name = 'updated_at#source_schema.table@mssql:/mssql.host:1433/somedb' + 2024-04-12 10:12:12,181 [INFO ] MainThread: |IncrementalStrategy| Fetched HWM: + 2024-04-12 10:12:12,182 [INFO ] MainThread: hwm = ColumnDateTimeHWM( + 2024-04-12 10:12:12,182 [INFO ] MainThread: name = 'updated_at#source_schema.table@mssql:/mssql.host:1433/somedb', + 2024-04-12 10:12:12,182 [INFO ] MainThread: entity = 'source_schema.table', + 2024-04-12 10:12:12,182 [INFO ] MainThread: expression = 'updated_at', + 2024-04-12 10:12:12,184 [INFO ] MainThread: value = datetime.datetime(2024, 4, 11, 18, 10, 2, 120000), + 2024-04-12 10:12:12,184 [INFO ] MainThread: ) + 2024-04-12 10:12:12,184 [INFO ] MainThread: |MSSQL| -> |Spark| Reading DataFrame from source using parameters: + 2024-04-12 10:12:12,185 [INFO ] MainThread: source = 'source_schema.table' + 2024-04-12 10:12:12,185 [INFO ] MainThread: columns = [ + 2024-04-12 10:12:12,185 [INFO ] MainThread: 'id', + 2024-04-12 10:12:12,186 [INFO ] MainThread: 'new_value', + 2024-04-12 10:12:12,186 [INFO ] MainThread: 'old_value', + 2024-04-12 10:12:12,186 [INFO ] MainThread: 'updated_at', + 2024-04-12 10:12:12,186 [INFO ] MainThread: ] + 2024-04-12 10:12:12,187 [INFO ] MainThread: where = "field = 'some'" + 2024-04-12 10:12:12,187 [INFO ] MainThread: hwm = AutoDetectHWM( + 2024-04-12 10:12:12,187 [INFO ] MainThread: name = 'updated_at#source_schema.table@mssql:/mssql.host:1433/somedb', + 2024-04-12 10:12:12,187 [INFO ] MainThread: entity = 'source_schema.table', + 2024-04-12 10:12:12,187 [INFO ] MainThread: expression = 'updated_at', + 2024-04-12 10:12:12,188 [INFO ] MainThread: ) + 2024-04-12 10:12:12,188 [INFO ] MainThread: options = { + 2024-04-12 10:12:12,188 [INFO ] MainThread: 'fetchsize': 100000, + 2024-04-12 10:12:12,188 [INFO ] MainThread: 'numPartitions': 1, + 2024-04-12 10:12:12,189 [INFO ] MainThread: 'partitioningMode': 'range', + 2024-04-12 10:12:12,189 [INFO ] MainThread: } + 2024-04-12 10:12:12,189 [INFO ] MainThread: |MSSQL| Checking connection availability... + 2024-04-12 10:12:12,189 [INFO ] MainThread: |MSSQL| Using connection parameters: + 2024-04-12 10:12:12,190 [INFO ] MainThread: user = 'db_user' + 2024-04-12 10:12:12,190 [INFO ] MainThread: password = SecretStr('**********') + 2024-04-12 10:12:12,190 [INFO ] MainThread: host = 'mssql.host' + 2024-04-12 10:12:12,190 [INFO ] MainThread: port = 1433 + 2024-04-12 10:12:12,191 [INFO ] MainThread: database = 'somedb' + 2024-04-12 10:12:12,191 [INFO ] MainThread: extra = {'applicationIntent': 'ReadOnly', 'trustServerCertificate': 'true'} + 2024-04-12 10:12:12,191 [INFO ] MainThread: jdbc_url = 'jdbc:sqlserver:/mssql.host:1433' + 2024-04-12 10:12:12,579 [INFO ] MainThread: |MSSQL| Connection is available. + 2024-04-12 10:12:12,581 [INFO ] MainThread: |MSSQL| Executing SQL query (on driver): + 2024-04-12 10:12:12,581 [INFO ] MainThread: SELECT + 2024-04-12 10:12:12,581 [INFO ] MainThread: MIN(updated_at) AS "min", + 2024-04-12 10:12:12,582 [INFO ] MainThread: MAX(updated_at) AS "max" + 2024-04-12 10:12:12,582 [INFO ] MainThread: FROM + 2024-04-12 10:12:12,582 [INFO ] MainThread: source_schema.table + 2024-04-12 10:12:12,582 [INFO ] MainThread: WHERE + 2024-04-12 10:12:12,582 [INFO ] MainThread: (field = 'some') + 2024-04-12 10:12:12,583 [INFO ] MainThread: AND + 2024-04-12 10:12:12,583 [INFO ] MainThread: (updated_at >= CAST('2024-04-11T18:10:02.120000' AS datetime2)) + 2024-04-12 10:16:22,537 [INFO ] MainThread: |MSSQL| Received values: + 2024-04-12 10:16:22,538 [INFO ] MainThread: MIN(updated_at) = datetime.datetime(2024, 4, 11, 21, 10, 7, 397000) + 2024-04-12 10:16:22,538 [INFO ] MainThread: MAX(updated_at) = datetime.datetime(2024, 4, 12, 13, 12, 2, 123000) + 2024-04-12 10:16:22,540 [INFO ] MainThread: |MSSQL| Executing SQL query (on executor): + 2024-04-12 10:16:22,540 [INFO ] MainThread: SELECT + 2024-04-12 10:16:22,540 [INFO ] MainThread: id, + 2024-04-12 10:16:22,541 [INFO ] MainThread: new_value, + 2024-04-12 10:16:22,541 [INFO ] MainThread: old_value, + 2024-04-12 10:16:22,541 [INFO ] MainThread: updated_at + 2024-04-12 10:16:22,541 [INFO ] MainThread: FROM + 2024-04-12 10:16:22,541 [INFO ] MainThread: source_schema.table + 2024-04-12 10:16:22,542 [INFO ] MainThread: WHERE + 2024-04-12 10:16:22,542 [INFO ] MainThread: (field = 'some') + 2024-04-12 10:16:22,542 [INFO ] MainThread: AND + 2024-04-12 10:16:22,542 [INFO ] MainThread: (updated_at > CAST('2024-04-11T18:10:02.120000' AS datetime2)) + 2024-04-12 10:16:22,542 [INFO ] MainThread: AND + 2024-04-12 10:16:22,542 [INFO ] MainThread: (updated_at <= CAST('2024-04-12T13:12:02.123000' AS datetime2)) + 2024-04-12 10:16:22,892 [INFO ] MainThread: |Spark| DataFrame successfully created from SQL statement + 2024-04-12 10:16:22,892 [INFO ] MainThread: ------------------------------------ DBReader.run() ends ------------------------------------ + 2024-04-12 10:40:42,409 [INFO ] MainThread: =================================== DBWriter.run() starts =================================== + 2024-04-12 10:40:42,409 [INFO ] MainThread: |Spark| -> |Hive| Writing DataFrame to target using parameters: + 2024-04-12 10:40:42,410 [INFO ] MainThread: target = 'target_source_schema.table' + 2024-04-12 10:40:42,410 [INFO ] MainThread: options = { + 2024-04-12 10:40:42,410 [INFO ] MainThread: 'mode': 'append', + 2024-04-12 10:40:42,410 [INFO ] MainThread: 'format': 'orc', + 2024-04-12 10:40:42,410 [INFO ] MainThread: 'partitionBy': 'part_dt', + 2024-04-12 10:40:42,410 [INFO ] MainThread: } + 2024-04-12 10:40:42,411 [INFO ] MainThread: df_schema: + 2024-04-12 10:40:42,412 [INFO ] MainThread: root + 2024-04-12 10:40:42,412 [INFO ] MainThread: |-- id: integer (nullable = true) + 2024-04-12 10:40:42,413 [INFO ] MainThread: |-- new_value: string (nullable = true) + 2024-04-12 10:40:42,413 [INFO ] MainThread: |-- old_value: string (nullable = true) + 2024-04-12 10:40:42,413 [INFO ] MainThread: |-- updated_at: timestamp (nullable = true) + 2024-04-12 10:40:42,413 [INFO ] MainThread: |-- part_dt: date (nullable = true) + 2024-04-12 10:40:42,414 [INFO ] MainThread: + 2024-04-12 10:40:42,421 [INFO ] MainThread: |Hive| Checking connection availability... + 2024-04-12 10:40:42,421 [INFO ] MainThread: |Hive| Using connection parameters: + 2024-04-12 10:40:42,421 [INFO ] MainThread: cluster = 'dwh' + 2024-04-12 10:40:42,475 [INFO ] MainThread: |Hive| Connection is available. + 2024-04-12 10:40:42,476 [INFO ] MainThread: |Hive| Fetching schema of table 'target_source_schema.table' ... + 2024-04-12 10:40:43,518 [INFO ] MainThread: |Hive| Schema fetched. + 2024-04-12 10:40:43,521 [INFO ] MainThread: |Hive| Table 'target_source_schema.table' already exists + 2024-04-12 10:40:43,521 [WARNING ] MainThread: |Hive| User-specified options {'partitionBy': 'part_dt'} are ignored while inserting into existing table. Using only table parameters from Hive metastore + 2024-04-12 10:40:43,782 [INFO ] MainThread: |Hive| Inserting data into existing table 'target_source_schema.table' ... + 2024-04-12 11:06:07,396 [INFO ] MainThread: |Hive| Data is successfully inserted into table 'target_source_schema.table'. + 2024-04-12 11:06:07,397 [INFO ] MainThread: ------------------------------------ DBWriter.run() ends ------------------------------------ + 2024-04-12 11:06:07,397 [INFO ] MainThread: |onETL| Exiting IncrementalStrategy + 2024-04-12 11:06:07,397 [INFO ] MainThread: |IncrementalStrategy| Saving HWM to 'HorizonHWMStore': + 2024-04-12 11:06:07,397 [INFO ] MainThread: hwm = ColumnDateTimeHWM( + 2024-04-12 11:06:07,397 [INFO ] MainThread: name = 'updated_at#source_schema.table@mssql:/mssql.host:1433/somedb', + 2024-04-12 11:06:07,397 [INFO ] MainThread: entity = 'source_source_schema.table', + 2024-04-12 11:06:07,397 [INFO ] MainThread: expression = 'updated_at', + 2024-04-12 11:06:07,397 [INFO ] MainThread: value = datetime.datetime(2024, 4, 12, 13, 12, 2, 123000), + 2024-04-12 11:06:07,397 [INFO ] MainThread: ) + 2024-04-12 11:06:07,495 [INFO ] MainThread: |IncrementalStrategy| HWM has been saved +``` + +Each step performed by onETL is extensively logged, which should help with debugging. + +You can make logs even more verbose by changing level to `DEBUG`: + +```python +from onetl.log import setup_logging + +setup_logging(level="DEBUG", enable_clients=True) + +# rest of code +... +``` + +This also changes log level for all underlying Python libraries, e.g. showing each HTTP request being made, and so on. + +```{eval-rst} +.. currentmodule:: onetl.log +``` + +```{eval-rst} +.. autofunction:: setup_logging +``` + +```{eval-rst} +.. autofunction:: setup_clients_logging +``` + +```{eval-rst} +.. autofunction:: set_default_logging_format +``` diff --git a/mddocs/docs/make.bat b/mddocs/docs/make.bat new file mode 100644 index 000000000..53ad1e82c --- /dev/null +++ b/mddocs/docs/make.bat @@ -0,0 +1,35 @@ +@ECHO OFF + +pushd %~dp0 + +REM Command file for Sphinx documentation + +if "%SPHINXBUILD%" == "" ( + set SPHINXBUILD=sphinx-build +) +set SOURCEDIR=. +set BUILDDIR=_build + +if "%1" == "" goto help + +%SPHINXBUILD% >NUL 2>NUL +if errorlevel 9009 ( + echo. + echo.The 'sphinx-build' command was not found. Make sure you have Sphinx + echo.installed, then set the SPHINXBUILD environment variable to point + echo.to the full path of the 'sphinx-build' executable. Alternatively you + echo.may add the Sphinx directory to PATH. + echo. + echo.If you don't have Sphinx installed, grab it from + echo.https://www.sphinx-doc.org/ + exit /b 1 +) + +%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% +goto end + +:help +%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% + +:end +popd diff --git a/mddocs/docs/plugins.md b/mddocs/docs/plugins.md new file mode 100644 index 000000000..a7904b076 --- /dev/null +++ b/mddocs/docs/plugins.md @@ -0,0 +1,147 @@ +(plugins)= + +# Plugins + +:::{versionadded} 0.6.0 +::: + +## What are plugins? + +### Terms + +- `Plugin` - some Python package which implements some extra functionality for onETL, like {ref}`hooks` +- `Plugin autoimport` - onETL behavior which allows to automatically import this package if it contains proper metadata (`entry_points`) + +### Features + +Plugins mechanism allows to: + +- Automatically register {ref}`hooks` which can alter onETL behavior +- Automatically register new classes, like HWM type, HWM stores and so on + +### Limitations + +Unlike other projects (like *Airflow 1.x*), plugins does not inject imported classes or functions to `onetl.*` namespace. +Users should import classes from the plugin package **explicitly** to avoid name collisions. + +## How to implement plugin? + +Create a Python package `some-plugin` with a file `some_plugin/setup.py`: + +```python +# some_plugin/setup.py +from setuptools import setup + +setup( + # if you want to import something from onETL, add it to requirements list + install_requires=["onetl"], + entry_points={ + # this key enables plugins autoimport functionality + "onetl.plugins": [ + "some-plugin-name=some_plugin.module", # automatically import all module content + "some-plugin-class=some_plugin.module.internals:MyClass", # import a specific class + "some-plugin-function=some_plugin.module.internals:my_function", # import a specific function + ], + }, +) +``` + +See [setuptools documentation for entry_points](https://setuptools.pypa.io/en/latest/userguide/entry_point.html) + +## How plugins are imported? + +- User should install a package implementing the plugin: + +```bash +pip install some-package +``` + +- Then user should import something from `onetl` module or its submodules: + +```python +import onetl +from onetl.connection import SomeConnection + +# and so on +``` + +- This import automatically executes something like: + +```python +import some_plugin.module +from some_plugin.module.internals import MyClass +from some_plugin.module.internals import my_function +``` + +If specific module/class/function uses some registration capabilities of onETL, +like {ref}`hook-decorator`, it will be executed during this import. + +## How to enable/disable plugins? + +:::{versionadded} 0.7.0 +::: + +### Disable/enable all plugins + +By default plugins are enabled. + +To disabled them, you can set environment variable `ONETL_PLUGINS_ENABLED` to `false` BEFORE +importing onETL. This will disable all plugins autoimport. + +But user is still be able to explicitly import `some_plugin.module`, executing +all decorators and registration capabilities of onETL. + +### Disable a specific plugin (blacklist) + +If some plugin is failing during import, you can disable it by setting up environment variable +`ONETL_PLUGINS_BLACKLIST=some-failing-plugin`. Multiple plugin names could be passed with `,` as delimiter. + +Again, this environment variable should be set BEFORE importing onETL. + +### Disable all plugins except a specific one (whitelist) + +You can also disable all plugins except a specific one by setting up environment variable +`ONETL_PLUGINS_WHITELIST=some-not-failing-plugin`. Multiple plugin names could be passed with `,` as delimiter. + +Again, this environment variable should be set BEFORE importing onETL. + +If both whitelist and blacklist environment variables are set, blacklist has a higher priority. + +## How to see logs of the plugins mechanism? + +Plugins registration emits logs with `INFO` level: + +```python +import logging + +logging.basicConfig(level=logging.INFO) +``` + +```text +INFO |onETL| Found 2 plugins +INFO |onETL| Loading plugin 'my-plugin' +INFO |onETL| Skipping plugin 'failing' because it is in a blacklist +``` + +More detailed logs are emitted with `DEBUG` level, to make output less verbose: + +```python +import logging + +logging.basicConfig(level=logging.DEBUG) +``` + +```text +DEBUG |onETL| Searching for plugins with group 'onetl.plugins' +DEBUG |Plugins| Plugins whitelist: [] +DEBUG |Plugins| Plugins blacklist: ['failing-plugin'] +INFO |Plugins| Found 2 plugins +INFO |onETL| Loading plugin (1/2): +DEBUG name: 'my-plugin' +DEBUG package: 'my-package' +DEBUG version: '0.1.0' +DEBUG importing: 'my_package.my_module:MyClass' +DEBUG |onETL| Successfully loaded plugin 'my-plugin' +DEBUG source: '/usr/lib/python3.11/site-packages/my_package/my_module/my_class.py' +INFO |onETL| Skipping plugin 'failing' because it is in a blacklist +``` diff --git a/mddocs/docs/quickstart.md b/mddocs/docs/quickstart.md new file mode 100644 index 000000000..d2367a3de --- /dev/null +++ b/mddocs/docs/quickstart.md @@ -0,0 +1,4 @@ +```{eval-rst} +.. include:: ../README.rst + :start-after: quick-start +``` diff --git a/mddocs/docs/robots.txt b/mddocs/docs/robots.txt new file mode 100644 index 000000000..dcab3dc9e --- /dev/null +++ b/mddocs/docs/robots.txt @@ -0,0 +1,5 @@ +User-agent: * +Allow: /*/stable/ +Allow: /en/stable/ # Fallback for bots that don't understand wildcards +Disallow: / +Sitemap: https://onel.readthedocs.io/sitemap.xml diff --git a/mddocs/docs/security.md b/mddocs/docs/security.md new file mode 100644 index 000000000..05a7bef5a --- /dev/null +++ b/mddocs/docs/security.md @@ -0,0 +1,3 @@ +```{eval-rst} +.. include:: ../SECURITY.rst +``` diff --git a/mddocs/docs/strategy/incremental_batch_strategy.md b/mddocs/docs/strategy/incremental_batch_strategy.md new file mode 100644 index 000000000..8884066a2 --- /dev/null +++ b/mddocs/docs/strategy/incremental_batch_strategy.md @@ -0,0 +1,12 @@ +(incremental-batch-strategy)= + +# Incremental Batch Strategy + +```{eval-rst} +.. currentmodule:: onetl.strategy.incremental_strategy +``` + +```{eval-rst} +.. autoclass:: IncrementalBatchStrategy + :members: __init__ +``` diff --git a/mddocs/docs/strategy/incremental_strategy.md b/mddocs/docs/strategy/incremental_strategy.md new file mode 100644 index 000000000..35c4fff36 --- /dev/null +++ b/mddocs/docs/strategy/incremental_strategy.md @@ -0,0 +1,12 @@ +(incremental-strategy)= + +# Incremental Strategy + +```{eval-rst} +.. currentmodule:: onetl.strategy.incremental_strategy +``` + +```{eval-rst} +.. autoclass:: IncrementalStrategy + :members: __init__ +``` diff --git a/mddocs/docs/strategy/index.md b/mddocs/docs/strategy/index.md new file mode 100644 index 000000000..eebe727fe --- /dev/null +++ b/mddocs/docs/strategy/index.md @@ -0,0 +1,21 @@ +(strategy)= + +# Read Strategies + +```{toctree} +:caption: Read Strategies +:hidden: true +:maxdepth: 3 + +snapshot_strategy +incremental_strategy +snapshot_batch_strategy +incremental_batch_strategy +``` + +onETL have several builtin strategies for reading data: + +1. {doc}`snapshot_strategy` +2. {doc}`incremental_strategy` +3. {doc}`snapshot_batch_strategy` +4. {doc}`incremental_batch_strategy` diff --git a/mddocs/docs/strategy/snapshot_batch_strategy.md b/mddocs/docs/strategy/snapshot_batch_strategy.md new file mode 100644 index 000000000..dcff0670d --- /dev/null +++ b/mddocs/docs/strategy/snapshot_batch_strategy.md @@ -0,0 +1,12 @@ +(snapshot-batch-strategy)= + +# Snapshot Batch Strategy + +```{eval-rst} +.. currentmodule:: onetl.strategy.snapshot_strategy +``` + +```{eval-rst} +.. autoclass:: SnapshotBatchStrategy + :members: __init__ +``` diff --git a/mddocs/docs/strategy/snapshot_strategy.md b/mddocs/docs/strategy/snapshot_strategy.md new file mode 100644 index 000000000..e5d973831 --- /dev/null +++ b/mddocs/docs/strategy/snapshot_strategy.md @@ -0,0 +1,12 @@ +(snapshot-strategy)= + +# Snapshot Strategy + +```{eval-rst} +.. currentmodule:: onetl.strategy.snapshot_strategy +``` + +```{eval-rst} +.. autoclass:: SnapshotStrategy + :members: __init__ +``` diff --git a/mddocs/docs/troubleshooting/index.md b/mddocs/docs/troubleshooting/index.md new file mode 100644 index 000000000..de93ac11e --- /dev/null +++ b/mddocs/docs/troubleshooting/index.md @@ -0,0 +1,25 @@ +(troubleshooting)= + +# Troubleshooting + +In case of error please follow instructions below: + +- Read the logs or exception messages you've faced with. + : - If Python logs are note verbose enough, {ref}`increase the log level `. + - If Spark logs are note verbose enough, {ref}`increase the log level `. +- Read documentation related to a class or method you are using. +- [Google](https://google.com) the error message, and carefully read the search result: + : - [StackOverflow](https://stackoverflow.com/) answers. + - [Spark](https://spark.apache.org/docs/latest/) documentation. + - Documentation of database or filesystem you are connecting to. + - Documentation of underlying connector. +- Search for known [issues](https://github.com/MobileTeleSystems/onetl/issues), or create a new one. +- Always use the most resent versions of onETL, PySpark and connector packages, {ref}`compatible with your environment `. + +```{toctree} +:caption: Troubleshooting +:hidden: true +:maxdepth: 3 + +spark +``` diff --git a/mddocs/docs/troubleshooting/spark.md b/mddocs/docs/troubleshooting/spark.md new file mode 100644 index 000000000..a71d8b732 --- /dev/null +++ b/mddocs/docs/troubleshooting/spark.md @@ -0,0 +1,75 @@ +(troubleshooting-spark)= + +# Spark Troubleshooting + +## Restarting Spark session + +Sometimes it is required to stop current Spark session and start a new one, e.g. to add some .jar packages, or change session config. +But PySpark not only starts Spark session, but also starts Java virtual machine (JVM) process in the background, +which. So calling `sparkSession.stop()` [does not shutdown JVM](https://issues.apache.org/jira/browse/SPARK-47740), +and this can cause some issue. + +Also apart from JVM properties, stopping Spark session does not clear Spark context, which is a global object. So new +Spark sessions are created using the same context object, and thus using the same Spark config options. + +To properly stop Spark session, it is **required** to: +\* Stop Spark session by calling `sparkSession.stop()`. +\* **STOP PYTHON INTERPRETER**, e.g. by calling `sys.exit()`. +\* Start new Python interpreter. +\* Start new Spark session with config options you need. + +Skipping some of these steps can lead to issues with creating new Spark session. + +## Driver log level + +Default logging level for Spark session is `WARN`. To show more verbose logs, use: + +```python +spark.sparkContext.setLogLevel("INFO") +``` + +or increase verbosity even more: + +```python +spark.sparkContext.setLogLevel("DEBUG") +``` + +After getting all information you need, you can return back the previous log level: + +```python +spark.sparkContext.setLogLevel("WARN") +``` + +## Executors log level + +`sparkContext.setLogLevel` changes only log level of Spark session on Spark **driver**. +To make Spark executor logs more verbose, perform following steps: + +- Create `log4j.properties` file with content like this: + + ```jproperties + log4j.rootCategory=DEBUG, console + + log4j.appender.console=org.apache.log4j.ConsoleAppender + log4j.appender.console.target=System.err + log4j.appender.console.layout=org.apache.log4j.PatternLayout + log4j.appender.console.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss} %p %c{1}: %m%n + ``` + +- Stop existing Spark session and create a new one with following options: + + ```python + from pyspark.sql import SparkSession + + spark = ( + SparkSesion.builder.config("spark.files", "file:log4j.properties").config( + "spark.executor.extraJavaOptions", "-Dlog4j.configuration=file:log4j.properties" + ) + # you can apply the same logging settings to Spark driver, by uncommenting the line below + # .config("spark.driver.extraJavaOptions", "-Dlog4j.configuration=file:log4j.properties") + .getOrCreate() + ) + ``` + +Each Spark executor will receive a copy of `log4j.properties` file during start, and load it to change own log level. +Same approach can be used for Spark driver as well, to investigate issue when Spark session cannot properly start. diff --git a/mddocs/mkdocs.yml b/mddocs/mkdocs.yml new file mode 100644 index 000000000..6dcd08d5a --- /dev/null +++ b/mddocs/mkdocs.yml @@ -0,0 +1,46 @@ +site_name: onETL Docs + +theme: + name: material + features: + - navigation.indexes + - content.tabs.link + palette: + - scheme: default + toggle: + icon: material/weather-night + name: Switch to dark mode + - scheme: slate + toggle: + icon: material/weather-sunny + name: Switch to light mode + locale: ru + highlightjs: true + hljs_languages: + - yaml + +markdown_extensions: + - attr_list + - md_in_html + - admonition + - pymdownx.details + - pymdownx.critic + - mdx_gh_links + - pymdownx.tabbed: + alternate_style: true + - pymdownx.emoji: + emoji_index: !!python/name:material.extensions.emoji.twemoji + emoji_generator: !!python/name:material.extensions.emoji.to_svg + - pymdownx.superfences: + custom_fences: + - name: mermaid + class: mermaid + format: !!python/name:pymdownx.superfences.fence_code_format + +extension_configs: + - mdx_gh_links: + - user: "foo" + - repo: "bar" + +nav: + - Test: changelog/0.7.0.md \ No newline at end of file From bf69d94c2aa7360178af4304893936a44c7cb812 Mon Sep 17 00:00:00 2001 From: Sattar Gyulmamedov Date: Mon, 9 Jun 2025 18:31:09 +0300 Subject: [PATCH 07/22] added changelog translation from scratch --- mddocs/docs/changelog/0.7.0.md | 2 +- mddocs/ru/changelog/0.10.0.md | 543 +++++++++++++++++++++++++++++++++ mddocs/ru/changelog/0.10.1.md | 29 ++ mddocs/ru/changelog/0.10.2.md | 39 +++ mddocs/ru/changelog/0.11.0.md | 212 +++++++++++++ mddocs/ru/changelog/0.11.1.md | 9 + mddocs/ru/changelog/0.11.2.md | 5 + mddocs/ru/changelog/0.12.0.md | 54 ++++ mddocs/ru/changelog/0.12.1.md | 17 ++ mddocs/ru/changelog/0.12.2.md | 18 ++ mddocs/ru/changelog/0.12.3.md | 5 + mddocs/ru/changelog/0.12.4.md | 5 + mddocs/ru/changelog/0.12.5.md | 13 + mddocs/ru/changelog/0.13.0.md | 222 ++++++++++++++ mddocs/ru/changelog/0.13.1.md | 9 + mddocs/ru/changelog/0.13.3.md | 5 + mddocs/ru/changelog/0.13.4.md | 10 + mddocs/ru/changelog/0.7.0.md | 237 ++++++++++++++ mddocs/ru/changelog/0.7.1.md | 40 +++ mddocs/ru/changelog/0.7.2.md | 37 +++ mddocs/ru/changelog/0.8.0.md | 162 ++++++++++ mddocs/ru/changelog/0.8.1.md | 42 +++ mddocs/ru/changelog/0.9.0.md | 122 ++++++++ mddocs/ru/changelog/0.9.1.md | 7 + mddocs/ru/changelog/0.9.2.md | 23 ++ mddocs/ru/changelog/0.9.3.md | 5 + mddocs/ru/changelog/0.9.4.md | 23 ++ mddocs/ru/changelog/0.9.5.md | 14 + 28 files changed, 1908 insertions(+), 1 deletion(-) create mode 100644 mddocs/ru/changelog/0.10.0.md create mode 100644 mddocs/ru/changelog/0.10.1.md create mode 100644 mddocs/ru/changelog/0.10.2.md create mode 100644 mddocs/ru/changelog/0.11.0.md create mode 100644 mddocs/ru/changelog/0.11.1.md create mode 100644 mddocs/ru/changelog/0.11.2.md create mode 100644 mddocs/ru/changelog/0.12.0.md create mode 100644 mddocs/ru/changelog/0.12.1.md create mode 100644 mddocs/ru/changelog/0.12.2.md create mode 100644 mddocs/ru/changelog/0.12.3.md create mode 100644 mddocs/ru/changelog/0.12.4.md create mode 100644 mddocs/ru/changelog/0.12.5.md create mode 100644 mddocs/ru/changelog/0.13.0.md create mode 100644 mddocs/ru/changelog/0.13.1.md create mode 100644 mddocs/ru/changelog/0.13.3.md create mode 100644 mddocs/ru/changelog/0.13.4.md create mode 100644 mddocs/ru/changelog/0.7.0.md create mode 100644 mddocs/ru/changelog/0.7.1.md create mode 100644 mddocs/ru/changelog/0.7.2.md create mode 100644 mddocs/ru/changelog/0.8.0.md create mode 100644 mddocs/ru/changelog/0.8.1.md create mode 100644 mddocs/ru/changelog/0.9.0.md create mode 100644 mddocs/ru/changelog/0.9.1.md create mode 100644 mddocs/ru/changelog/0.9.2.md create mode 100644 mddocs/ru/changelog/0.9.3.md create mode 100644 mddocs/ru/changelog/0.9.4.md create mode 100644 mddocs/ru/changelog/0.9.5.md diff --git a/mddocs/docs/changelog/0.7.0.md b/mddocs/docs/changelog/0.7.0.md index 7325faa96..2140962fa 100644 --- a/mddocs/docs/changelog/0.7.0.md +++ b/mddocs/docs/changelog/0.7.0.md @@ -78,7 +78,7 @@ That was long road, but we finally did it! - Added new `cluster` argument to `Hive` and `HDFS` connections. - `Hive` qualified name (used in HWM) contains cluster name. But in onETL\<0.7.0 cluster name had hard coded value `rnd-dwh` which was not OK for some users. + `Hive` qualified name (used in HWM) contains cluster name. But in onETL<0.7.0 cluster name had hard coded value `rnd-dwh` which was not OK for some users. `HDFS` connection qualified name contains host (active namenode of Hadoop cluster), but its value can change over time, leading to creating of new HWM. diff --git a/mddocs/ru/changelog/0.10.0.md b/mddocs/ru/changelog/0.10.0.md new file mode 100644 index 000000000..68a609eb6 --- /dev/null +++ b/mddocs/ru/changelog/0.10.0.md @@ -0,0 +1,543 @@ +# 0.10.0 (2023-12-18) + +## Breaking Changes + +- Upgrade `etl-entities` from v1 to v2 ([#172](https://github.com/MobileTeleSystems/onetl/pull/172)). + + This implies that `HWM` classes are now have different internal structure than they used to. + + Before: + + ```python + from etl_entities.old_hwm import IntHWM as OldIntHWM + from etl_entities.source import Column, Table + from etl_entities.process import Process + + hwm = OldIntHWM( + process=Process(name="myprocess", task="abc", dag="cde", host="myhost"), + source=Table(name="schema.table", instance="postgres://host:5432/db"), + column=Column(name="col1"), + value=123, + ) + ``` + + After: + + ```python + from etl_entities.hwm import ColumnIntHWM + + hwm = ColumnIntHWM( + name="some_unique_name", + description="any value you want", + source="schema.table", + expression="col1", + value=123, + ) + ``` + + **Breaking change:** If you used HWM classes from `etl_entities` module, you should rewrite your code to make it compatible with new version. + +??? "More details" + + - ``HWM`` classes used by previous onETL versions were moved from ``etl_entities`` to ``etl_entities.old_hwm`` submodule. They are here for compatibility reasons, but are planned to be removed in ``etl-entities`` v3 release. + - New ``HWM`` classes have flat structure instead of nested. + - New ``HWM`` classes have mandatory ``name`` attribute (it was known as ``qualified_name`` before). + - Type aliases used while serializing and deserializing ``HWM`` objects to ``dict`` representation were changed too: ``int`` → ``column_int``. + + + To make migration simpler, you can use new method: + + ```python + old_hwm = OldIntHWM(...) + new_hwm = old_hwm.as_new_hwm() + ``` + + Which automatically converts all fields from old structure to new one, including `qualified_name` → `name`. + +- **Breaking changes:** + + - Methods `BaseHWMStore.get()` and `BaseHWMStore.save()` were renamed to `get_hwm()` and `set_hwm()`. + - They now can be used only with new HWM classes from `etl_entities.hwm`, **old HWM classes are not supported**. + + If you used them in your code, please update it accordingly. + +- YAMLHWMStore **CANNOT read files created by older onETL versions** (0.9.x or older). + +??? "Update procedure" + + ```python + # pip install onetl==0.9.5 + + # Get qualified_name for HWM + + + # Option 1. HWM is built manually + from etl_entities import IntHWM, FileListHWM + from etl_entities.source import Column, Table, RemoteFolder + from etl_entities.process import Process + + # for column HWM + old_column_hwm = IntHWM( + process=Process(name="myprocess", task="abc", dag="cde", host="myhost"), + source=Table(name="schema.table", instance="postgres://host:5432/db"), + column=Column(name="col1"), + ) + qualified_name = old_column_hwm.qualified_name + # "col1#schema.table@postgres://host:5432/db#cde.abc.myprocess@myhost" + + # for file HWM + old_file_hwm = FileListHWM( + process=Process(name="myprocess", task="abc", dag="cde", host="myhost"), + source=RemoteFolder(name="/absolute/path", instance="ftp://ftp.server:21"), + ) + qualified_name = old_file_hwm.qualified_name + # "file_list#/absolute/path@ftp://ftp.server:21#cde.abc.myprocess@myhost" + + + # Option 2. HWM is generated automatically (by DBReader/FileDownloader) + # See onETL logs and search for string like qualified_name = '...' + + qualified_name = "col1#schema.table@postgres://host:5432/db#cde.abc.myprocess@myhost" + + + # Get .yml file path by qualified_name + + import os + from pathlib import PurePosixPath + from onetl.hwm.store import YAMLHWMStore + + # here you should pass the same arguments as used on production, if any + yaml_hwm_store = YAMLHWMStore() + hwm_path = yaml_hwm_store.get_file_path(qualified_name) + print(hwm_path) + + # for column HWM + # LocalPosixPath('/home/maxim/.local/share/onETL/yml_hwm_store/col1__schema.table__postgres_host_5432_db__cde.abc.myprocess__myhost.yml') + + # for file HWM + # LocalPosixPath('/home/maxim/.local/share/onETL/yml_hwm_store/file_list__absolute_path__ftp_ftp.server_21__cde.abc.myprocess__myhost.yml') + + + # Read raw .yml file content + + from yaml import safe_load, dump + + raw_old_hwm_items = safe_load(hwm_path.read_text()) + print(raw_old_hwm_items) + + # for column HWM + # [ + # { + # "column": { "name": "col1", "partition": {} }, + # "modified_time": "2023-12-18T10: 39: 47.377378", + # "process": { "dag": "cde", "host": "myhost", "name": "myprocess", "task": "abc" }, + # "source": { "instance": "postgres: //host:5432/db", "name": "schema.table" }, + # "type": "int", + # "value": "123", + # }, + # ] + + # for file HWM + # [ + # { + # "modified_time": "2023-12-18T11:15:36.478462", + # "process": { "dag": "cde", "host": "myhost", "name": "myprocess", "task": "abc" }, + # "source": { "instance": "ftp://ftp.server:21", "name": "/absolute/path" }, + # "type": "file_list", + # "value": ["file1.txt", "file2.txt"], + # }, + # ] + + + # Convert file content to new structure, compatible with onETL 0.10.x + raw_new_hwm_items = [] + for old_hwm in raw_old_hwm_items: + new_hwm = {"name": qualified_name, "modified_time": old_hwm["modified_time"]} + + if "column" in old_hwm: + new_hwm["expression"] = old_hwm["column"]["name"] + new_hwm["entity"] = old_hwm["source"]["name"] + old_hwm.pop("process", None) + + if old_hwm["type"] == "int": + new_hwm["type"] = "column_int" + new_hwm["value"] = old_hwm["value"] + + elif old_hwm["type"] == "date": + new_hwm["type"] = "column_date" + new_hwm["value"] = old_hwm["value"] + + elif old_hwm["type"] == "datetime": + new_hwm["type"] = "column_datetime" + new_hwm["value"] = old_hwm["value"] + + elif old_hwm["type"] == "file_list": + new_hwm["type"] = "file_list" + new_hwm["value"] = [ + os.fspath(PurePosixPath(old_hwm["source"]["name"]).joinpath(path)) + for path in old_hwm["value"] + ] + + else: + raise ValueError("WAT?") + + raw_new_hwm_items.append(new_hwm) + + + print(raw_new_hwm_items) + # for column HWM + # [ + # { + # "name": "col1#schema.table@postgres://host:5432/db#cde.abc.myprocess@myhost", + # "modified_time": "2023-12-18T10:39:47.377378", + # "expression": "col1", + # "source": "schema.table", + # "type": "column_int", + # "value": 123, + # }, + # ] + + # for file HWM + # [ + # { + # "name": "file_list#/absolute/path@ftp://ftp.server:21#cde.abc.myprocess@myhost", + # "modified_time": "2023-12-18T11:15:36.478462", + # "entity": "/absolute/path", + # "type": "file_list", + # "value": ["/absolute/path/file1.txt", "/absolute/path/file2.txt"], + # }, + # ] + + + # Save file with new content + with open(hwm_path, "w") as file: + dump(raw_new_hwm_items, file) + + + # Stop Python interpreter and update onETL + # pip install onetl==0.10.0 + # Check that new .yml file can be read + + from onetl.hwm.store import YAMLHWMStore + + qualified_name = ... + + # here you should pass the same arguments as used on production, if any + yaml_hwm_store = YAMLHWMStore() + yaml_hwm_store.get_hwm(qualified_name) + + # for column HWM + # ColumnIntHWM( + # name='col1#schema.table@postgres://host:5432/db#cde.abc.myprocess@myhost', + # description='', + # entity='schema.table', + # value=123, + # expression='col1', + # modified_time=datetime.datetime(2023, 12, 18, 10, 39, 47, 377378), + # ) + + # for file HWM + # FileListHWM( + # name='file_list#/absolute/path@ftp://ftp.server:21#cde.abc.myprocess@myhost', + # description='', + # entity=AbsolutePath('/absolute/path'), + # value=frozenset({AbsolutePath('/absolute/path/file1.txt'), AbsolutePath('/absolute/path/file2.txt')}), + # expression=None, + # modified_time=datetime.datetime(2023, 12, 18, 11, 15, 36, 478462) + # ) + + + # That's all! + ``` + +But most of users use other HWM store implementations which do not have such issues. + +- Several classes and functions were moved from `onetl` to `etl_entities`: + +=== "onETL ``0.9.x`` and older" + + ```python + + from onetl.hwm.store import ( + detect_hwm_store, + BaseHWMStore, + HWMStoreClassRegistry, + register_hwm_store_class, + HWMStoreManager, + MemoryHWMStore, + ) + ``` + +=== "nETL ``0.10.x`` and newer" + + ```python + + from etl_entities.hwm_store import ( + detect_hwm_store, + BaseHWMStore, + HWMStoreClassRegistry, + register_hwm_store_class, + HWMStoreManager, + MemoryHWMStore, + ) + ``` + + They still can be imported from old module, but this is deprecated and will be removed in v1.0.0 release. + +- Change the way of passing `HWM` to `DBReader` and `FileDownloader` classes: + +=== "onETL ``0.9.x`` and older" + + ```python linenums="1" hl_lines="12-21" + # Simple + reader = DBReader( + connection=..., + source=..., + hwm_column="col1", + ) + + + + + + # Complex + reader = DBReader( + connection=..., + source=..., + hwm_column=( + "col1", + "cast(col1 as date)", + ), + ) + + + # Files + downloader = FileDownloader( + connection=..., + source_path=..., + target_path=..., + hwm_type="file_list", + ) + ``` + +=== "onETL ``0.10.x`` and newer" + + ```python linenums="1" hl_lines="12-21" + # Simple + reader = DBReader( + connection=..., + source=..., + hwm=DBReader.AutoDetectHWM( + # name is mandatory now! + name="my_unique_hwm_name", + expression="col1", + ), + ) + + # Complex + reader = DBReader( + connection=..., + source=..., + hwm=DBReader.AutoDetectHWM( + # name is mandatory now! + name="my_unique_hwm_name", + expression="cast(col1 as date)", + ), + ) + + # Files + downloader = FileDownloader( + connection=..., + source_path=..., + target_path=..., + hwm=FileListHWM( + # name is mandatory now! + name="another_unique_hwm_name", + ), + ) + ``` + + + New HWM classes have **mandatory** `name` attribute which should be passed explicitly, + instead of generating if automatically under the hood. + + Automatic `name` generation using the old `DBReader.hwm_column` / `FileDownloader.hwm_type` + syntax is still supported, but will be removed in v1.0.0 release. ([#179](https://github.com/MobileTeleSystems/onetl/pull/179)) + +- Performance of read Incremental and Batch strategies has been drastically improved. ([#182](https://github.com/MobileTeleSystems/onetl/pull/182)). + +??? "Before and after in details" + + ``DBReader.run()`` + incremental/batch strategy behavior in versions 0.9.x and older: + + - Get table schema by making query ``SELECT * FROM table WHERE 1=0`` (if ``DBReader.columns`` has ``*``) + - Expand ``*`` to real column names from table, add here ``hwm_column``, remove duplicates (as some RDBMS does not allow that). + - Create dataframe from query like ``SELECT hwm_expression AS hwm_column, ...other table columns... FROM table WHERE hwm_expression > prev_hwm.value``. + - Determine HWM class using dataframe schema: ``df.schema[hwm_column].dataType``. + - Determine x HWM column value using Spark: ``df.select(max(hwm_column)).collect()``. + - Use ``max(hwm_column)`` as next HWM value, and save it to HWM Store. + - Return dataframe to user. + + This was far from ideal: + + - Dataframe content (all rows or just changed ones) was loaded from the source to Spark only to get min/max values of specific column. + + - Step of fetching table schema and then substituting column names in the next query caused some unexpected errors. + + For example, source contains columns with mixed name case, like ``"CamelColumn"`` or ``"spaced column"``. + + Column names were *not* escaped during query generation, leading to queries that cannot be executed by database. + + So users have to *explicitly* pass column names ``DBReader``, wrapping columns with mixed naming with ``"``: + + ```python + reader = DBReader( + connection=..., + source=..., + columns=[ # passing '*' here leads to wrong SQL query generation + "normal_column", + '"CamelColumn"', + '"spaced column"', + ..., + ], + ) + ``` + - Using ``DBReader`` with ``IncrementalStrategy`` could lead to reading rows already read before. + + Dataframe was created from query with WHERE clause like ``hwm.expression > prev_hwm.value``, + not ``hwm.expression > prev_hwm.value AND hwm.expression <= current_hwm.value``. + + So if new rows appeared in the source **after** HWM value is determined, + they can be read by accessing dataframe content (because Spark dataframes are lazy), + leading to inconsistencies between HWM value and dataframe content. + + This may lead to issues then ``DBReader.run()`` read some data, updated HWM value, and next call of ``DBReader.run()`` + will read rows that were already read in previous run. + + ``DBReader.run()`` + incremental/batch strategy behavior in versions 0.10.x and newer: + + - Detect type of HWM expression: ``SELECT hwm.expression FROM table WHERE 1=0``. + - Determine corresponding Spark type ``df.schema[0]`` and when determine matching HWM class (if ``DReader.AutoDetectHWM`` is used). + - Get min/max values by querying the source: ``SELECT MAX(hwm.expression) FROM table WHERE hwm.expression >= prev_hwm.value``. + - Use ``max(hwm.expression)`` as next HWM value, and save it to HWM Store. + - Create dataframe from query ``SELECT ... table columns ... FROM table WHERE hwm.expression > prev_hwm.value AND hwm.expression <= current_hwm.value``, baking new HWM value into the query. + - Return dataframe to user. + + Improvements: + + - Allow source to calculate min/max instead of loading everything to Spark. This should be **faster** on large amounts of data (**up to x2**), because we do not transfer all the data from the source to Spark. This can be even faster if source have indexes for HWM column. + - Columns list is passed to source as-is, without any resolving on `DBReader` side. So you can pass `DBReader(columns=["*"])` to read tables with mixed columns naming. + - Restrict dataframe content to always match HWM values, which leads to never reading the same row twice. + + **Breaking change**: HWM column is not being implicitly added to dataframe. It was a part of `SELECT` clause, but now it is mentioned only in `WHERE` clause. + + So if you had code like this, you have to rewrite it: + +=== "onETL ``0.9.x`` and older" + + ```python linenums="1" hl_lines="1-16" + reader = DBReader( + connection=..., + source=..., + columns=[ + "col1", + "col2", + ], + hwm_column="hwm_col", + ) + + df = reader.run() + # hwm_column value is in the dataframe + assert df.columns == ["col1", "col2", "hwm_col"] + + + + + reader = DBReader( + connection=..., + source=..., + columns=[ + "col1", + "col2", + ], + hwm_column=( + "hwm_col", + "cast(hwm_col as int)", + ), + ) + + df = reader.run() + # hwm_expression value is in the dataframe + assert df.columns == ["col1", "col2", "hwm_col"] + + ``` + +=== "onETL ``0.10.x`` and newer" + + ```python linenums="1" hl_lines="1-16" + reader = DBReader( + connection=..., + source=..., + columns=[ + "col1", + "col2", + # add hwm_column explicitly + "hwm_col", + ], + hwm_column="hwm_col", + ) + + df = reader.run() + # if columns list is not updated, + # this fill fail + assert df.columns == ["col1", "col2", "hwm_col"] + + reader = DBReader( + connection=..., + source=..., + columns=[ + "col1", + "col2", + # add hwm_expression explicitly + "cast(hwm_col as int) as hwm_col", + ], + hwm_column=( + "hwm_col", + "cast(hwm_col as int)", + ), + ) + df = reader.run() + # if columns list is not updated, + # this fill fail + assert df.columns == ["col1", "col2", "hwm_col"] + ``` + + But most users just use `columns=["*"]` anyway, they won't see any changes. + +- `FileDownloader.run()` now updates HWM in HWM Store not after each file is being successfully downloaded, + but after all files were handled. + + This is because: + + - FileDownloader can be used with `DownloadOptions(workers=N)`, which could lead to race condition - one thread can save to HWM store one HWM value when another thread will save different value. + - FileDownloader can download hundreds and thousands of files, and issuing a request to HWM Store for each file could potentially DDoS HWM Store. ([#189](https://github.com/MobileTeleSystems/onetl/pull/189)) + + There is a exception handler which tries to save HWM to HWM store if download process was interrupted. But if it was interrupted by force, like sending `SIGKILL` event, + HWM will not be saved to HWM store, so some already downloaded files may be downloaded again next time. + + But unexpected process kill may produce other negative impact, like some file will be downloaded partially, so this is an expected behavior. + +## Features + +- Add Python 3.12 compatibility. ([#167](https://github.com/MobileTeleSystems/onetl/pull/167)) +- `Excel` file format now can be used with Spark 3.5.0. ([#187](https://github.com/MobileTeleSystems/onetl/pull/187)) +- `SnapshotBatchStagy` and `IncrementalBatchStrategy` does no raise exceptions if source does not contain any data. + Instead they stop at first iteration and return empty dataframe. ([#188](https://github.com/MobileTeleSystems/onetl/pull/188)) +- Cache result of `connection.check()` in high-level classes like `DBReader`, `FileDownloader` and so on. This makes logs less verbose. ([#190](https://github.com/MobileTeleSystems/onetl/pull/190)) + +## Bug Fixes + +- Fix `@slot` and `@hook` decorators returning methods with missing arguments in signature (Pylance, VS Code). ([#183](https://github.com/MobileTeleSystems/onetl/pull/183)) +- Kafka connector documentation said that it does support reading topic data incrementally by passing `group.id` or `groupIdPrefix`. + Actually, this is not true, because Spark does not send information to Kafka which messages were consumed. + So currently users can only read the whole topic, no incremental reads are supported. diff --git a/mddocs/ru/changelog/0.10.1.md b/mddocs/ru/changelog/0.10.1.md new file mode 100644 index 000000000..ba3ed40bb --- /dev/null +++ b/mddocs/ru/changelog/0.10.1.md @@ -0,0 +1,29 @@ +# 0.10.1 (2024-02-05) + +## Features + +- Add support of `Incremental Strategies` for `Kafka` connection: + + ```python + reader = DBReader( + connection=Kafka(...), + source="topic_name", + hwm=DBReader.AutoDetectHWM(name="some_hwm_name", expression="offset"), + ) + + with IncrementalStrategy(): + df = reader.run() + ``` + + This lets you resume reading data from a Kafka topic starting at the last committed offset from your previous run. ([#202](https://github.com/MobileTeleSystems/onetl/pull/202)) + +- Add `has_data`, `raise_if_no_data` methods to `DBReader` class. ([#203](https://github.com/MobileTeleSystems/onetl/pull/203)) + +- Updare VMware Greenplum connector from `2.1.4` to `2.3.0`. This implies: + : - Greenplum 7.x support + - [Kubernetes support](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/configure.html#k8scfg) + - New read option [gpdb.matchDistributionPolicy](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/options.html#distpolmotion) + which allows to match each Spark executor with specific Greenplum segment, avoiding redundant data transfer between Greenplum segments + - Allows overriding [Greenplum optimizer parameters](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/options.html#greenplum-gucs) in read/write operations ([#208](https://github.com/MobileTeleSystems/onetl/pull/208)) + +- `Greenplum.get_packages()` method now accepts optional arg `package_version` which allows to override version of Greenplum connector package. ([#208](https://github.com/MobileTeleSystems/onetl/pull/208)) diff --git a/mddocs/ru/changelog/0.10.2.md b/mddocs/ru/changelog/0.10.2.md new file mode 100644 index 000000000..3ae7ad822 --- /dev/null +++ b/mddocs/ru/changelog/0.10.2.md @@ -0,0 +1,39 @@ +# 0.10.2 (2024-03-21) + +## Features + +- Add support of Pydantic v2. ([#230](https://github.com/MobileTeleSystems/onetl/pull/230)) + +## Improvements + +- Improve database connections documentation: + - Add "Types" section describing mapping between Clickhouse and Spark types + - Add "Prerequisites" section describing different aspects of connecting to Clickhouse + - Separate documentation of `DBReader` and `.sql()` / `.pipeline(...)` + - Add examples for `.fetch()` and `.execute()` ([#211](https://github.com/MobileTeleSystems/onetl/pull/211), [#228](https://github.com/MobileTeleSystems/onetl/pull/228), [#229](https://github.com/MobileTeleSystems/onetl/pull/229), [#233](https://github.com/MobileTeleSystems/onetl/pull/233), [#234](https://github.com/MobileTeleSystems/onetl/pull/234), [#235](https://github.com/MobileTeleSystems/onetl/pull/235), [#236](https://github.com/MobileTeleSystems/onetl/pull/236), [#240](https://github.com/MobileTeleSystems/onetl/pull/240)) + +- Add notes to Greenplum documentation about issues with IP resolution and building `gpfdist` URL ([#228](https://github.com/MobileTeleSystems/onetl/pull/228)) + +- Allow calling `MongoDB.pipeline(...)` with passing just collection name, without explicit aggregation pipeline. ([#237](https://github.com/MobileTeleSystems/onetl/pull/237)) + +- Update default `Postgres(extra={...})` to include `{"stringtype": "unspecified"}` option. + This allows to write text data to non-text column (or vice versa), relying to Postgres cast capabilities. + + For example, now it is possible to read column of type `money` as Spark's `StringType()`, and write it back to the same column, + without using intermediate columns or tables. ([#229](https://github.com/MobileTeleSystems/onetl/pull/229)) + +## Bug Fixes + +- Return back handling of `DBReader(columns="string")`. This was a valid syntax up to v0.10 release, but it was removed because + most of users neved used it. It looks that we were wrong, returning this behavior back, but with deprecation warning. ([#238](https://github.com/MobileTeleSystems/onetl/pull/238)) + +- Downgrade Greenplum package version from `2.3.0` to `2.2.0`. ([#239](https://github.com/MobileTeleSystems/onetl/pull/239)) + + This is because version 2.3.0 introduced issues with writing data to Greenplum 6.x. + Connector can open transaction with `SELECT * FROM table LIMIT 0` query, but does not close it, which leads to deadlocks. + + For using this connector with Greenplum 7.x, please pass package version explicitly: + + ```python + maven_packages = Greenplum.get_packages(package_version="2.3.0", ...) + ``` diff --git a/mddocs/ru/changelog/0.11.0.md b/mddocs/ru/changelog/0.11.0.md new file mode 100644 index 000000000..c77c977b0 --- /dev/null +++ b/mddocs/ru/changelog/0.11.0.md @@ -0,0 +1,212 @@ +# 0.11.0 (2024-05-27) + +## Breaking Changes + +There can be some changes in connection behavior, related to version upgrades. So we mark these changes as **breaking** although +most of users will not see any differences. + +- Update Clickhouse JDBC driver to latest version ([#249](https://github.com/MobileTeleSystems/onetl/pull/249)): + - Package was renamed `ru.yandex.clickhouse:clickhouse-jdbc` → `com.clickhouse:clickhouse-jdbc`. + - Package version changed `0.3.2` → `0.6.0-patch5`. + - Driver name changed `ru.yandex.clickhouse.ClickHouseDriver` → `com.clickhouse.jdbc.ClickHouseDriver`. + + This brings up several fixes for Spark \<-> Clickhouse type compatibility, and also Clickhouse clusters support. + +- Update other JDBC drivers to latest versions: + - MSSQL `12.2.0` → `12.6.2` ([#254](https://github.com/MobileTeleSystems/onetl/pull/254)). + - MySQL `8.0.33` → `8.4.0` ([#253](https://github.com/MobileTeleSystems/onetl/pull/253), [#285](https://github.com/MobileTeleSystems/onetl/pull/285)). + - Oracle `23.2.0.0` → `23.4.0.24.05` ([#252](https://github.com/MobileTeleSystems/onetl/pull/252), [#284](https://github.com/MobileTeleSystems/onetl/pull/284)). + - Postgres `42.6.0` → `42.7.3` ([#251](https://github.com/MobileTeleSystems/onetl/pull/251)). + +- Update MongoDB connector to latest version: `10.1.1` → `10.3.0` ([#255](https://github.com/MobileTeleSystems/onetl/pull/255), [#283](https://github.com/MobileTeleSystems/onetl/pull/283)). + + This brings up Spark 3.5 support. + +- Update `XML` package to latest version: `0.17.0` → `0.18.0` ([#259](https://github.com/MobileTeleSystems/onetl/pull/259)). + + This brings few bugfixes with datetime format handling. + +- For JDBC connections add new `SQLOptions` class for `DB.sql(query, options=...)` method ([#272](https://github.com/MobileTeleSystems/onetl/pull/272)). + + Firsly, to keep naming more consistent. + + Secondly, some of options are not supported by `DB.sql(...)` method, but supported by `DBReader`. + For example, `SQLOptions` do not support `partitioning_mode` and require explicit definition of `lower_bound` and `upper_bound` when `num_partitions` is greater than 1. + `ReadOptions` does support `partitioning_mode` and allows skipping `lower_bound` and `upper_bound` values. + + This require some code changes. Before: + + ```python + from onetl.connection import Postgres + + postgres = Postgres(...) + df = postgres.sql( + """ + SELECT * + FROM some.mytable + WHERE key = 'something' + """, + options=Postgres.ReadOptions( + partitioning_mode="range", + partition_column="id", + num_partitions=10, + ), + ) + ``` + + After: + + ```python + from onetl.connection import Postgres + + postgres = Postgres(...) + df = postgres.sql( + """ + SELECT * + FROM some.mytable + WHERE key = 'something' + """, + options=Postgres.SQLOptions( + # partitioning_mode is not supported! + partition_column="id", + num_partitions=10, + lower_bound=0, # <-- set explicitly + upper_bound=1000, # <-- set explicitly + ), + ) + ``` + + For now, `DB.sql(query, options=...)` can accept `ReadOptions` to keep backward compatibility, but emits deprecation warning. + The support will be removed in `v1.0.0`. + +- Split up `JDBCOptions` class into `FetchOptions` and `ExecuteOptions` ([#274](https://github.com/MobileTeleSystems/onetl/pull/274)). + + New classes are used by `DB.fetch(query, options=...)` and `DB.execute(query, options=...)` methods respectively. + This is mostly to keep naming more consistent. + + This require some code changes. Before: + + ```python + from onetl.connection import Postgres + + postgres = Postgres(...) + df = postgres.fetch( + "SELECT * FROM some.mytable WHERE key = 'something'", + options=Postgres.JDBCOptions( + fetchsize=1000, + query_timeout=30, + ), + ) + + postgres.execute( + "UPDATE some.mytable SET value = 'new' WHERE key = 'something'", + options=Postgres.JDBCOptions(query_timeout=30), + ) + ``` + + After: + + ```python + from onetl.connection import Postgres + + # Using FetchOptions for fetching data + postgres = Postgres(...) + df = postgres.fetch( + "SELECT * FROM some.mytable WHERE key = 'something'", + options=Postgres.FetchOptions( # <-- change class name + fetchsize=1000, + query_timeout=30, + ), + ) + + # Using ExecuteOptions for executing statements + postgres.execute( + "UPDATE some.mytable SET value = 'new' WHERE key = 'something'", + options=Postgres.ExecuteOptions(query_timeout=30), # <-- change class name + ) + ``` + + For now, `DB.fetch(query, options=...)` and `DB.execute(query, options=...)` can accept `JDBCOptions`, to keep backward compatibility, + but emit a deprecation warning. The old class will be removed in `v1.0.0`. + +- Serialize `ColumnDatetimeHWM` to Clickhouse's `DateTime64(6)` (precision up to microseconds) instead of `DateTime` (precision up to seconds) ([#267](https://github.com/MobileTeleSystems/onetl/pull/267)). + + In previous onETL versions, `ColumnDatetimeHWM` value was rounded to the second, and thus reading some rows that were read in previous runs, + producing duplicates. + + For Clickhouse versions below 21.1 comparing column of type `DateTime` with a value of type `DateTime64` is not supported, returning an empty dataframe. + To avoid this, replace: + + ```python + DBReader( + ..., + hwm=DBReader.AutoDetectHWM( + name="my_hwm", + expression="hwm_column", # <-- + ), + ) + ``` + + with: + + ```python + DBReader( + ..., + hwm=DBReader.AutoDetectHWM( + name="my_hwm", + expression="CAST(hwm_column AS DateTime64)", # <-- add explicit CAST + ), + ) + ``` + +- Pass JDBC connection extra params as `properties` dict instead of URL with query part ([#268](https://github.com/MobileTeleSystems/onetl/pull/268)). + + This allows passing custom connection parameters like `Clickhouse(extra={"custom_http_options": "option1=value1,option2=value2"})` + without need to apply urlencode to parameter value, like `option1%3Dvalue1%2Coption2%3Dvalue2`. + +## Features + +Improve user experience with Kafka messages and Database tables with serialized columns, like JSON/XML. + +- Allow passing custom package version as argument for `DB.get_packages(...)` method of several DB connectors: + - `Clickhouse.get_packages(package_version=..., apache_http_client_version=...)` ([#249](https://github.com/MobileTeleSystems/onetl/pull/249)). + - `MongoDB.get_packages(scala_version=..., spark_version=..., package_version=...)` ([#255](https://github.com/MobileTeleSystems/onetl/pull/255)). + - `MySQL.get_packages(package_version=...)` ([#253](https://github.com/MobileTeleSystems/onetl/pull/253)). + - `MSSQL.get_packages(java_version=..., package_version=...)` ([#254](https://github.com/MobileTeleSystems/onetl/pull/254)). + - `Oracle.get_packages(java_version=..., package_version=...)` ([#252](https://github.com/MobileTeleSystems/onetl/pull/252)). + - `Postgres.get_packages(package_version=...)` ([#251](https://github.com/MobileTeleSystems/onetl/pull/251)). + - `Teradata.get_packages(package_version=...)` ([#256](https://github.com/MobileTeleSystems/onetl/pull/256)). + Now users can downgrade or upgrade connection without waiting for next onETL release. Previously only `Kafka` and `Greenplum` supported this feature. +- Add `FileFormat.parse_column(...)` method to several classes: + - `Avro.parse_column(col)` ([#265](https://github.com/MobileTeleSystems/onetl/pull/265)). + - `JSON.parse_column(col, schema=...)` ([#257](https://github.com/MobileTeleSystems/onetl/pull/257)). + - `CSV.parse_column(col, schema=...)` ([#258](https://github.com/MobileTeleSystems/onetl/pull/258)). + - `XML.parse_column(col, schema=...)` ([#269](https://github.com/MobileTeleSystems/onetl/pull/269)). + This allows parsing data in `value` field of Kafka message or string/binary column of some table as a nested Spark structure. +- Add `FileFormat.serialize_column(...)` method to several classes: + - `Avro.serialize_column(col)` ([#265](https://github.com/MobileTeleSystems/onetl/pull/265)). + - `JSON.serialize_column(col)` ([#257](https://github.com/MobileTeleSystems/onetl/pull/257)). + - `CSV.serialize_column(col)` ([#258](https://github.com/MobileTeleSystems/onetl/pull/258)). + This allows saving Spark nested structures or arrays to `value` field of Kafka message or string/binary column of some table. + +## Improvements + +Few documentation improvements. + +- Replace all `assert` in documentation with doctest syntax. This should make documentation more readable ([#273](https://github.com/MobileTeleSystems/onetl/pull/273)). +- Add generic `Troubleshooting` guide ([#275](https://github.com/MobileTeleSystems/onetl/pull/275)). +- Improve Kafka documentation: + - Add "Prerequisites" page describing different aspects of connecting to Kafka. + - Improve "Reading from" and "Writing to" page of Kafka documentation, add more examples and usage notes. + - Add "Troubleshooting" page ([#276](https://github.com/MobileTeleSystems/onetl/pull/276)). +- Improve Hive documentation: + - Add "Prerequisites" page describing different aspects of connecting to Hive. + - Improve "Reading from" and "Writing to" page of Hive documentation, add more examples and recommendations. + - Improve "Executing statements in Hive" page of Hive documentation. ([#278](https://github.com/MobileTeleSystems/onetl/pull/278)). +- Add "Prerequisites" page describing different aspects of using SparkHDFS and SparkS3 connectors. ([#279](https://github.com/MobileTeleSystems/onetl/pull/279)). +- Add note about connecting to Clickhouse cluster. ([#280](https://github.com/MobileTeleSystems/onetl/pull/280)). +- Add notes about versions when specific class/method/attribute/argument was added, renamed or changed behavior ([#282](https://github.com/MobileTeleSystems/onetl/pull/282)). + +## Bug Fixes + +- Fix missing `pysmb` package after installing `pip install onetl[files]` . diff --git a/mddocs/ru/changelog/0.11.1.md b/mddocs/ru/changelog/0.11.1.md new file mode 100644 index 000000000..823afe3be --- /dev/null +++ b/mddocs/ru/changelog/0.11.1.md @@ -0,0 +1,9 @@ +# 0.11.1 (2024-05-29) + +## Features + +- Change `MSSQL.port` default from `1433` to `None`, allowing use of `instanceName` to detect port number. ([#287](https://github.com/MobileTeleSystems/onetl/pull/287)) + +## Bug Fixes + +- Remove `fetchsize` from `JDBC.WriteOptions`. ([#288](https://github.com/MobileTeleSystems/onetl/pull/288)) diff --git a/mddocs/ru/changelog/0.11.2.md b/mddocs/ru/changelog/0.11.2.md new file mode 100644 index 000000000..9278d22f8 --- /dev/null +++ b/mddocs/ru/changelog/0.11.2.md @@ -0,0 +1,5 @@ +# 0.11.2 (2024-09-02) + +## Bug Fixes + +- Fix passing `Greenplum(extra={"options": ...})` during read/write operations. ([#308](https://github.com/MobileTeleSystems/onetl/pull/308)) diff --git a/mddocs/ru/changelog/0.12.0.md b/mddocs/ru/changelog/0.12.0.md new file mode 100644 index 000000000..aeaca1d23 --- /dev/null +++ b/mddocs/ru/changelog/0.12.0.md @@ -0,0 +1,54 @@ +# 0.12.0 (2024-09-03) + +## Breaking Changes + +- Change connection URL used for generating HWM names of S3 and Samba sources: + - `smb://host:port` -> `smb://host:port/share` + - `s3://host:port` -> `s3://host:port/bucket` ([#304](https://github.com/MobileTeleSystems/onetl/pull/304)) +- Update DB connectors/drivers to latest versions: + - Clickhouse `0.6.0-patch5` → `0.6.5` + - MongoDB `10.3.0` → `10.4.0` + - MSSQL `12.6.2` → `12.8.1` + - MySQL `8.4.0` → `9.0.0` + - Oracle `23.4.0.24.05` → `23.5.0.24.07` + - Postgres `42.7.3` → `42.7.4` +- Update `Excel` package from `0.20.3` to `0.20.4`, to include Spark 3.5.1 support. ([#306](https://github.com/MobileTeleSystems/onetl/pull/306)) + +## Features + +- Add support for specifying file formats (`ORC`, `Parquet`, `CSV`, etc.) in `HiveWriteOptions.format` ([#292](https://github.com/MobileTeleSystems/onetl/pull/292)): + + ```python + Hive.WriteOptions(format=ORC(compression="snappy")) + ``` + +- Collect Spark execution metrics in following methods, and log then in DEBUG mode: + - `DBWriter.run()` + - `FileDFWriter.run()` + - `Hive.sql()` + - `Hive.execute()` + + This is implemented using custom `SparkListener` which wraps the entire method call, and + then report collected metrics. But these metrics sometimes may be missing due to Spark architecture, + so they are not reliable source of information. That's why logs are printed only in DEBUG mode, and + are not returned as method call result. ([#303](https://github.com/MobileTeleSystems/onetl/pull/303)) + +- Generate default `jobDescription` based on currently executed method. Examples: + - `DBWriter.run(schema.table) -> Postgres[host:5432/database]` + - `MongoDB[localhost:27017/admin] -> DBReader.has_data(mycollection)` + - `Hive[cluster].execute()` + + If user already set custom `jobDescription`, it will left intact. ([#304](https://github.com/MobileTeleSystems/onetl/pull/304)) + +- Add log.info about JDBC dialect usage ([#305](https://github.com/MobileTeleSystems/onetl/pull/305)): + + ```text + |MySQL| Detected dialect: 'org.apache.spark.sql.jdbc.MySQLDialect' + ``` + +- Log estimated size of in-memory dataframe created by `JDBC.fetch` and `JDBC.execute` methods. ([#303](https://github.com/MobileTeleSystems/onetl/pull/303)) + +## Bug Fixes + +- Fix passing `Greenplum(extra={"options": ...})` during read/write operations. ([#308](https://github.com/MobileTeleSystems/onetl/pull/308)) +- Do not raise exception if yield-based hook whas something past (and only one) `yield`. diff --git a/mddocs/ru/changelog/0.12.1.md b/mddocs/ru/changelog/0.12.1.md new file mode 100644 index 000000000..d32401517 --- /dev/null +++ b/mddocs/ru/changelog/0.12.1.md @@ -0,0 +1,17 @@ +# 0.12.1 (2024-10-28) + +## Features + +- Log detected JDBC dialect while using `DBWriter`. + +## Bug Fixes + +- Fix `SparkMetricsRecorder` failing when receiving `SparkListenerTaskEnd` without `taskMetrics` (e.g. executor was killed by OOM). ([#313](https://github.com/MobileTeleSystems/onetl/pull/313)) +- Call `kinit` before checking for HDFS active namenode. +- Wrap `kinit` with `threading.Lock` to avoid multithreading issues. +- Immediately show `kinit` errors to user, instead of hiding them. +- Use `AttributeError` instead of `ImportError` in module's `__getattr__` method, to make code compliant with Python spec. + +## Doc only Changes + +- Add note about [spark-dialect-extension](https://github.com/MobileTeleSystems/spark-dialect-extension) package to Clickhouse connector documentation. ([#310](https://github.com/MobileTeleSystems/onetl/pull/310)) diff --git a/mddocs/ru/changelog/0.12.2.md b/mddocs/ru/changelog/0.12.2.md new file mode 100644 index 000000000..23a8d383d --- /dev/null +++ b/mddocs/ru/changelog/0.12.2.md @@ -0,0 +1,18 @@ +# 0.12.2 (2024-11-12) + +## Improvements + +- Change Spark `jobDescription` for DBReader & FileDFReader from `DBReader.run() -> Connection` to `Connection -> DBReader.run()`. + +## Bug Fixes + +- Fix `log_hwm` result for `KeyValueIntHWM` (used by Kafka). ([#316](https://github.com/MobileTeleSystems/onetl/pull/316)) +- Fix `log_collection` hiding values of `Kafka.addresses` in logs with `INFO` level. ([#316](https://github.com/MobileTeleSystems/onetl/pull/316)) + +## Dependencies + +- Allow using [etl-entities==2.4.0](https://github.com/MobileTeleSystems/etl-entities/releases/tag/2.4.0). + +## Doc only Changes + +- Fix links to MSSQL date & time type documentation. diff --git a/mddocs/ru/changelog/0.12.3.md b/mddocs/ru/changelog/0.12.3.md new file mode 100644 index 000000000..741c74e1f --- /dev/null +++ b/mddocs/ru/changelog/0.12.3.md @@ -0,0 +1,5 @@ +# 0.12.3 (2024-11-22) + +## Bug Fixes + +- Allow passing table names in format `schema."table.with.dots"` to `DBReader(source=...)` and `DBWriter(target=...)`. diff --git a/mddocs/ru/changelog/0.12.4.md b/mddocs/ru/changelog/0.12.4.md new file mode 100644 index 000000000..3ebc57a87 --- /dev/null +++ b/mddocs/ru/changelog/0.12.4.md @@ -0,0 +1,5 @@ +# 0.12.4 (2024-11-27) + +## Bug Fixes + +- Fix `DBReader(conn=oracle, options={"partitioning_mode": "hash"})` lead to data skew in last partition due to wrong `ora_hash` usage. ([#319](https://github.com/MobileTeleSystems/onetl/pull/319)) diff --git a/mddocs/ru/changelog/0.12.5.md b/mddocs/ru/changelog/0.12.5.md new file mode 100644 index 000000000..c542a50fd --- /dev/null +++ b/mddocs/ru/changelog/0.12.5.md @@ -0,0 +1,13 @@ +# 0.12.5 (2024-12-03) + +## Improvements + +- Use `sipHash64` instead of `md5` in Clickhouse for reading data with `{"partitioning_mode": "hash"}`, as it is 5 times faster. +- Use `hashtext` instead of `md5` in Postgres for reading data with `{"partitioning_mode": "hash"}`, as it is 3-5 times faster. +- Use `BINARY_CHECKSUM` instead of `HASHBYTES` in MSSQL for reading data with `{"partitioning_mode": "hash"}`, as it is 5 times faster. + +## Big fixes + +- In JDBC sources wrap `MOD(partitionColumn, numPartitions)` with `ABS(...)` to make al returned values positive. This prevents data skew. +- Fix reading table data from MSSQL using `{"partitioning_mode": "hash"}` with `partitionColumn` of integer type. +- Fix reading table data from Postgres using `{"partitioning_mode": "hash"}` lead to data skew (all the data was read into one Spark partition). diff --git a/mddocs/ru/changelog/0.13.0.md b/mddocs/ru/changelog/0.13.0.md new file mode 100644 index 000000000..37ae8368a --- /dev/null +++ b/mddocs/ru/changelog/0.13.0.md @@ -0,0 +1,222 @@ +# 0.13.0 (2025-02-24) + +🎉 3 years since first release 0.1.0 🎉 + +## Breaking Changes + +- Add Python 3.13. support. ([#298](https://github.com/MobileTeleSystems/onetl/pull/298)) + +- Change the logic of `FileConnection.walk` and `FileConnection.list_dir`. ([#327](https://github.com/MobileTeleSystems/onetl/pull/327)) + + Previously `limits.stops_at(path) == True` considered as "return current file and stop", and could lead to exceeding some limit. + Not it means "stop immediately". + +- Change default value for `FileDFWriter.Options(if_exists=...)` from `error` to `append`, + to make it consistent with other `.Options()` classes within onETL. ([#343](https://github.com/MobileTeleSystems/onetl/pull/343)) + +## Features + +- Add support for `FileModifiedTimeHWM` HWM class (see [etl-entities 2.5.0](https://github.com/MobileTeleSystems/etl-entities/releases/tag/2.5.0)): + + ```python + from etl_entitites.hwm import FileModifiedTimeHWM + from onetl.file import FileDownloader + from onetl.strategy import IncrementalStrategy + + downloader = FileDownloader( + ..., + hwm=FileModifiedTimeHWM(name="somename"), + ) + + with IncrementalStrategy(): + downloader.run() + ``` + +- Introduce `FileSizeRange(min=..., max=...)` filter class. ([#325](https://github.com/MobileTeleSystems/onetl/pull/325)) + + Now users can set `FileDownloader` / `FileMover` to download/move only files with specific file size range: + + ```python + from onetl.file import FileDownloader + from onetl.file.filter import FileSizeRange + + downloader = FileDownloader( + ..., + filters=[FileSizeRange(min="10KiB", max="1GiB")], + ) + ``` + +- Introduce `TotalFilesSize(...)` limit class. ([#326](https://github.com/MobileTeleSystems/onetl/pull/326)) + + Now users can set `FileDownloader` / `FileMover` to stop downloading/moving files after reaching a certain amount of data: + + ```python + from datetime import datetime, timedelta + from onetl.file import FileDownloader + from onetl.file.limit import TotalFilesSize + + downloader = FileDownloader( + ..., + limits=[TotalFilesSize("1GiB")], + ) + ``` + +- Implement `FileModifiedTime(since=..., until=...)` file filter. ([#330](https://github.com/MobileTeleSystems/onetl/pull/330)) + + Now users can set `FileDownloader` / `FileMover` to download/move only files with specific file modification time: + + ```python + from datetime import datetime, timedelta + from onetl.file import FileDownloader + from onetl.file.filter import FileModifiedTime + + downloader = FileDownloader( + ..., + filters=[FileModifiedTime(before=datetime.now() - timedelta(hours=1))], + ) + ``` + +- Add `SparkS3.get_exclude_packages()` and `Kafka.get_exclude_packages()` methods. ([#341](https://github.com/MobileTeleSystems/onetl/pull/341)) + + Using them allows to skip downloading dependencies not required by this specific connector, or which are already a part of Spark/PySpark: + + ```python + from onetl.connection import SparkS3, Kafka + + maven_packages = [ + *SparkS3.get_packages(spark_version="3.5.4"), + *Kafka.get_packages(spark_version="3.5.4"), + ] + exclude_packages = SparkS3.get_exclude_packages() + Kafka.get_exclude_packages() + spark = ( + SparkSession.builder.appName("spark_app_onetl_demo") + .config("spark.jars.packages", ",".join(maven_packages)) + .config("spark.jars.excludes", ",".join(exclude_packages)) + .getOrCreate() + ) + ``` + +## Improvements + +- All DB connections opened by `JDBC.fetch(...)`, `JDBC.execute(...)` or `JDBC.check()` + are immediately closed after the statements is executed. ([#334](https://github.com/MobileTeleSystems/onetl/pull/334)) + + Previously Spark session with `master=local[3]` actually opened up to 5 connections to target DB - one for `JDBC.check()`, + another for Spark driver interaction with DB to create tables, and one for each Spark executor. Now only max 4 connections are opened, + as `JDBC.check()` does not hold opened connection. + + This is important for RDBMS like Postgres or Greenplum where number of connections is strictly limited and limit is usually quite low. + +- Set up `ApplicationName` (client info) for Clickhouse, MongoDB, MSSQL, MySQL and Oracle. ([#339](https://github.com/MobileTeleSystems/onetl/pull/339), [#248](https://github.com/MobileTeleSystems/onetl/pull/248)) + + Also update `ApplicationName` format for Greenplum, Postgres, Kafka and SparkS3. + Now all connectors have the same `ApplicationName` format: `${spark.applicationId} ${spark.appName} onETL/${onetl.version} Spark/${spark.version}` + + The only connections not sending `ApplicationName` are Teradata and FileConnection implementations. + +- Now `DB.check()` will test connection availability not only on Spark driver, but also from some Spark executor. ([#346](https://github.com/MobileTeleSystems/onetl/pull/346)) + + This allows to fail immediately if Spark driver host has network access to target DB, but Spark executors have not. + +!!! note + + Now ``Greenplum.check()`` requires the same user grants as ``DBReader(connection=greenplum)``: + + ```sql + -- yes, "writable" for reading data from GP, it's not a mistake + ALTER USER username CREATEEXTTABLE(type = 'writable', protocol = 'gpfdist'); + + -- for both reading and writing to GP + -- ALTER USER username CREATEEXTTABLE(type = 'readable', protocol = 'gpfdist') CREATEEXTTABLE(type = 'writable', protocol = 'gpfdist'); + ``` + + Please ask your Greenplum administrators to provide these grants. + + +## Bug Fixes + +- Avoid suppressing Hive Metastore errors while using `DBWriter`. ([#329](https://github.com/MobileTeleSystems/onetl/pull/329)) + + Previously this was implemented as: + + ```python + try: + spark.sql(f"SELECT * FROM {table}") + table_exists = True + except Exception: + table_exists = False + ``` + + If Hive Metastore was overloaded and responded with an exception, it was considered as non-existing table, resulting + to full table override instead of append or override only partitions subset. + +- Fix using onETL to write data to PostgreSQL or Greenplum instances behind *pgbouncer* with `pool_mode=transaction`. ([#336](https://github.com/MobileTeleSystems/onetl/pull/336)) + + Previously `Postgres.check()` opened a read-only transaction, pgbouncer changed the entire connection type from read-write to read-only, + and when `DBWriter.run(df)` executed in read-only connection, producing errors like: + + ``` + org.postgresql.util.PSQLException: ERROR: cannot execute INSERT in a read-only transaction + org.postgresql.util.PSQLException: ERROR: cannot execute TRUNCATE TABLE in a read-only transaction + ``` + + Added a workaround by passing `readOnly=True` to JDBC params for read-only connections, so pgbouncer may differ read-only and read-write connections properly. + + After upgrading onETL 0.13.x or higher the same error still may appear of pgbouncer still holds read-only connections and returns them for DBWriter. + To this this, user can manually convert read-only connection to read-write: + + ```python + postgres.execute("BEGIN READ WRITE;") # <-- add this line + DBWriter(...).run() + ``` + + After all connections in pgbouncer pool were converted from read-only to read-write, and error fixed, this additional line could be removed. + + See [Postgres JDBC driver documentation](https://jdbc.postgresql.org/documentation/use/). + +- Fix `MSSQL.fetch(...)` and `MySQL.fetch(...)` opened a read-write connection instead of read-only. ([#337](https://github.com/MobileTeleSystems/onetl/pull/337)) + + Now this is fixed: + : - `MSSQL.fetch(...)` establishes connection with `ApplicationIntent=ReadOnly`. + - `MySQL.fetch(...)` calls `SET SESSION TRANSACTION READ ONLY` statement. + +- Fixed passing multiple filters to `FileDownloader` and `FileMover`. ([#338](https://github.com/MobileTeleSystems/onetl/pull/338)) + If was caused by sorting filters list in internal logging method, but `FileFilter` subclasses are not sortable. + +- Fix a false warning about a lof of parallel connections to Grenplum. ([#342](https://github.com/MobileTeleSystems/onetl/pull/342)) + + Creating Spark session with `.master("local[5]")` may open up to 6 connections to Greenplum (=number of Spark executors + 1 for driver), + but onETL instead used number of *CPU cores* on the host as a number of parallel connections. + + This lead to showing a false warning that number of Greenplum connections is too high, + which actually should be the case only if number of executors is higher than 30. + +- Fix MongoDB trying to use current database name as `authSource`. ([#347](https://github.com/MobileTeleSystems/onetl/pull/347)) + + Use default connector value which is `admin` database. Previous onETL versions could be fixed by: + + ```python + from onetl.connection import MongoDB + + mongodb = MongoDB( + ..., + database="mydb", + extra={ + "authSource": "admin", + }, + ) + ``` + +## Dependencies + +- Minimal `etl-entities` version is now [2.5.0](https://github.com/MobileTeleSystems/etl-entities/releases/tag/2.5.0). ([#331](https://github.com/MobileTeleSystems/onetl/pull/331)) +- Update DB connectors/drivers to latest versions: ([#345](https://github.com/MobileTeleSystems/onetl/pull/345)) + : - Clickhouse `0.6.5` → `0.7.2` + - MongoDB `10.4.0` → `10.4.1` + - MySQL `9.0.0` → `9.2.0` + - Oracle `23.5.0.24.07` → `23.7.0.25.01` + - Postgres `42.7.4` → `42.7.5` + +## Doc only Changes + +- Split large code examples to tabs. ([#344](https://github.com/MobileTeleSystems/onetl/pull/344)) diff --git a/mddocs/ru/changelog/0.13.1.md b/mddocs/ru/changelog/0.13.1.md new file mode 100644 index 000000000..4045c7cbf --- /dev/null +++ b/mddocs/ru/changelog/0.13.1.md @@ -0,0 +1,9 @@ +# 0.13.1 (2025-03-06) + +## Bug Fixes + +In 0.13.0, using `DBWriter(connection=hive, target="SOMEDB.SOMETABLE")` lead to executing `df.write.saveAsTable()` +instead of `df.write.insertInto()` if target table `somedb.sometable` already exist. + +This is caused by table name normalization (Hive uses lower-case names), which wasn't properly handled by method used for checking table existence. +([#350](https://github.com/MobileTeleSystems/onetl/pull/350)) diff --git a/mddocs/ru/changelog/0.13.3.md b/mddocs/ru/changelog/0.13.3.md new file mode 100644 index 000000000..1aa289b49 --- /dev/null +++ b/mddocs/ru/changelog/0.13.3.md @@ -0,0 +1,5 @@ +# 0.13.3 (2025-03-11) + +## Dependencies + +Allow using [etl-entities 2.6.0](https://github.com/MobileTeleSystems/etl-entities/releases/tag/2.6.0). diff --git a/mddocs/ru/changelog/0.13.4.md b/mddocs/ru/changelog/0.13.4.md new file mode 100644 index 000000000..10f695e0c --- /dev/null +++ b/mddocs/ru/changelog/0.13.4.md @@ -0,0 +1,10 @@ +# 0.13.4 (2025-03-20) + +## Doc only Changes + +- Prefer `ReadOptions(partitionColumn=..., numPartitions=..., queryTimeout=...)` + instead of `ReadOptions(partition_column=..., num_partitions=..., query_timeout=...)`, + to match Spark documentation. ([#352](https://github.com/MobileTeleSystems/onetl/pull/352)) +- Prefer `WriteOptions(if_exists=...)` instead of `WriteOptions(mode=...)` for IDE suggestions. ([#354](https://github.com/MobileTeleSystems/onetl/pull/354)) +- Document all options of supported file formats. + ([#355](https://github.com/MobileTeleSystems/onetl/pull/355), [#356](https://github.com/MobileTeleSystems/onetl/pull/356), [#357](https://github.com/MobileTeleSystems/onetl/pull/357), [#358](https://github.com/MobileTeleSystems/onetl/pull/358), [#359](https://github.com/MobileTeleSystems/onetl/pull/359), [#360](https://github.com/MobileTeleSystems/onetl/pull/360), [#361](https://github.com/MobileTeleSystems/onetl/pull/361), [#362](https://github.com/MobileTeleSystems/onetl/pull/362)) diff --git a/mddocs/ru/changelog/0.7.0.md b/mddocs/ru/changelog/0.7.0.md new file mode 100644 index 000000000..e1b285d79 --- /dev/null +++ b/mddocs/ru/changelog/0.7.0.md @@ -0,0 +1,237 @@ +# 0.7.0 (2023-05-15) + +## 🎉 onETL теперь с открытым исходным кодом 🎉 + +Это была долгая дорога, но мы наконец-то преодолели её! + +## Критические изменения + +- Изменен способ установки. + + **TL;DR Что нужно изменить, чтобы восстановить предыдущее поведение** + + Простой способ: + + | onETL < 0.7.0 | onETL >= 0.7.0 | + | ----------------- | --------------------------------- | + | pip install onetl | pip install onetl[files,kerberos] | + + Правильный способ - перечислить коннекторы, которые должны быть установлены: + + ```bash + pip install onetl[hdfs,ftp,kerberos] # кроме DB соединений + ``` + + **Подробности** + + В onetl<0.7 установка пакета выглядит так: + + ```bash title="before" + + pip install onetl + ``` + + Но это включает в себя все зависимости для всех коннекторов, даже если пользователь их не использует. + Это вызывало некоторые проблемы, например, пользователю приходилось устанавливать библиотеки Kerberos, чтобы иметь возможность установить onETL, даже если он использует только S3 (без поддержки Kerberos). + + Начиная с версии 0.7.0 процесс установки был изменен: + + ``` bash title="after" + + pip install onetl # минимальная установка, только ядро onETL + # нет extras для подключений к БД, потому что они используют Java пакеты, которые устанавливаются во время выполнения + + pip install onetl[ftp,ftps,hdfs,sftp,s3,webdav] # установить зависимости для указанных файловых подключений + pip install onetl[files] # установить зависимости для всех файловых подключений + + pip install onetl[kerberos] # Поддержка аутентификации Kerberos + pip install onetl[spark] # установить PySpark для использования подключений к БД + + pip install onetl[spark,kerberos,files] # все файловые подключения + Kerberos + PySpark + pip install onetl[all] # алиас для предыдущего случая + ``` + + Для каждого extras есть соответствующие элементы документации. + + Кроме того, onETL проверяет, что некоторые требования отсутствуют, и выдает исключение с рекомендацией, как их установить: + + ``` text title="exception while import Clickhouse connection" + + Cannot import module "pyspark". + + Since onETL v0.7.0 you should install package as follows: + pip install onetl[spark] + + or inject PySpark to sys.path in some other way BEFORE creating MongoDB instance. + ``` + + ``` text title="exception while import FTP connection" + + Cannot import module "ftputil". + + Since onETL v0.7.0 you should install package as follows: + pip install onetl[ftp] + + or + pip install onetl[files] + ``` + +- Добавлен новый аргумент `cluster` для подключений `Hive` и `HDFS`. + + Полное имя `Hive` (используемое в HWM) содержит имя кластера. Но в onETL<0.7.0 имя кластера имело не изменяемое значение `rnd-dwh`, что не подходило некоторым пользователям. + + Полное имя соединения `HDFS` содержит имя хоста (активной namenode кластера Hadoop), но его значение может меняться со временем, что приводит к созданию нового HWM. + + Начиная с onETL 0.7.0, оба подключения `Hive` и `HDFS` имеют атрибут `cluster`, в котором можно установить определенное имя кластера. + Для `Hive` это обязательно, для `HDFS` это можно опустить (используя хост в качестве запасного варианта). + + Но передача имени кластера каждый раз может привести к ошибкам. + + Теперь `Hive` и `HDFS` имеют вложенный класс с именем `slots` с методами: + + - `normalize_cluster_name` + - `get_known_clusters` + - `get_current_cluster` + - `normalize_namenode_host` (только `HDFS`) + - `get_cluster_namenodes` (только `HDFS`) + - `get_webhdfs_port` (только `HDFS`) + - `is_namenode_active` (только `HDFS`) + + И новый метод `HDFS.get_current` / `Hive.get_current`. + + Разработчики могут реализовать хуки, проверяющие ввод пользователя или подставляющие значения для автоматического определения кластера. + На наш взгляд, описанная функциональность улучшит пользовательский опыт при использовании этих коннекторов. + + См. документацию по slots. + +- Обновление драйверов JDBC соединений. + + - Greenplum `2.1.3` → `2.1.4`. + - MSSQL `10.2.1.jre8` → `12.2.0.jre8`. Минимальная поддерживаемая версия MSSQL теперь 2014 вместо 2021. + - MySQL `8.0.30` → `8.0.33`: + - Переименован пакет `mysql:mysql-connector-java` → `com.mysql:mysql-connector-j`. + - Переименован класс драйвера `com.mysql.jdbc.Driver` → `com.mysql.cj.jdbc.Driver`. + - Oracle `21.6.0.0.1` → `23.2.0.0`. + - Postgres `42.4.0` → `42.6.0`. + - Teradata `17.20.00.08` → `17.20.00.15`: + - Переименован пакет `com.teradata.jdbc:terajdbc4` → `com.teradata.jdbc:terajdbc`. + - Драйвер Teradata теперь опубликован в Maven. + + См. [#31](https://github.com/MobileTeleSystems/onetl/pull/31). + +## Функциональность + +- Добавлено подключение MongoDB. + + Используется официальный [MongoDB connector for Spark v10](https://www.mongodb.com/docs/spark-connector/current/). Поддерживается только Spark 3.2+. + + Есть некоторые различия между MongoDB и другими БД-источниками: + + - Метод `mongodb.pipeline` применяется вместо `mongodb.sql`. + - Нет методов `mongodb.fetch` и `mongodb.execute`. + - Типы `DBReader.hint` и `DBReader.where` отличаются от типов для БД, поддерживающих SQL: + + ```python + where = { + "col1": { + "$eq": 10, + }, + } + + hint = { + "col1": 1, + } + ``` + + - Поскольку MongoDB не имеет схем для коллекций и Spark не может создавать dataframe с динамической схемой, была введена новая опция `DBReader.df_schema`. + Она обязательна для MongoDB, но необязательна для других источников. + - В настоящее время DBReader нельзя использовать с MongoDB и hwm выражения, например, `hwm_column=("mycolumn", {"$cast": {"col1": "date"}})` + + Поскольку в MongoDB нет таблиц, в основных классах были переименованы некоторые опции: + + - `DBReader(table=...)` → `DBReader(source=...)` + - `DBWriter(table=...)` → `DBWriter(target=...)` + + Старые имена также можно использовать, они не устарели ([#30](https://github.com/MobileTeleSystems/onetl/pull/30)). + +- Добавлена опция для отключения некоторых плагинов во время импорта. + + Ранее, если какой-либо плагин не работал во время импорта, единственным способом импортировать onETL было отключить все плагины + с помощью переменной окружения. + + Теперь есть несколько переменных с разным поведением: + + - `ONETL_PLUGINS_ENABLED=false` - отключить автоматический импорт всех плагинов. Ранее она называлась `ONETL_ENABLE_PLUGINS`. + - `ONETL_PLUGINS_BLACKLIST=plugin-name,another-plugin` - установить список плагинов, которые НЕ должны импортироваться автоматически. + - `ONETL_PLUGINS_WHITELIST=plugin-name,another-plugin` - установить список плагинов, которые должны импортироваться ТОЛЬКО автоматически. + + Ещё мы улучшили сообщение об исключении с рекомендацией, как отключить неработающий плагин: + + ``` text title="exception message example" + + Error while importing plugin 'mtspark' from package 'mtspark' v4.0.0. + + Statement: + import mtspark.onetl + + Check if plugin is compatible with current onETL version 0.7.0. + + You can disable loading this plugin by setting environment variable: + ONETL_PLUGINS_BLACKLIST='mtspark,failing-plugin' + + You can also define a whitelist of packages which can be loaded by onETL: + ONETL_PLUGINS_WHITELIST='not-failing-plugin1,not-failing-plugin2' + + Please take into account that plugin name may differ from package or module name. + See package metadata for more details + ``` + +## Улучшения + +- Добавлена совместимость с Python 3.11 и PySpark 3.4.0. + + Файловые подключения работали нормально, но `jdbc.fetch` и `jdbc.execute` не работали. Исправлено в [#28](https://github.com/MobileTeleSystems/onetl/pull/28). + +- Добавлена проверка отсутствующих пакетов Java. + + Ранее, если подключение к БД пыталось использовать какой-либо Java-класс, который не был загружен в версию Spark, возникало исключение + с длинным stacktrace. Большинство пользователей не могли интерпретировать эту информацию. + + Теперь onETL показывает следующее сообщение об ошибке: + + ``` text title="exception message example" + + |Spark| Cannot import Java class 'com.mongodb.spark.sql.connector.MongoTableProvider'. + + It looks like you've created Spark session without this option: + SparkSession.builder.config("spark.jars.packages", MongoDB.package_spark_3_2) + + Please call `spark.stop()`, restart the interpreter, + and then create new SparkSession with proper options. + ``` + +- Улучшения документации. + + - Изменена тема сайта документации - используется [furo](https://github.com/pradyunsg/furo) + вместо темы по умолчанию [ReadTheDocs](https://github.com/readthedocs/sphinx_rtd_theme). + + Новая тема поддерживает широкие экраны и темный режим. + См. [#10](https://github.com/MobileTeleSystems/onetl/pull/10). + + - Теперь каждый класс подключения имеет таблицу совместимости для Spark + Java + Python. + + - Добавлена глобальная таблица совместимости для Spark + Java + Python + Scala. + +## Исправление ошибок + +- Исправлено несколько проблем с SFTP. + + - Если файл конфигурации SSH `~/.ssh/config` содержит некоторые параметры, не распознаваемые Paramiko (неизвестный синтаксис, неизвестное имя параметра), предыдущие версии будут выбрасывать исключение до исправления или удаления этого файла. Начиная с версии 0.7.0 исключение заменено предупреждением. + + - Если пользователь передал `host_key_check=False`, но сервер изменил SSH ключи, предыдущие версии выдавали исключение до принятия нового люча. Начиная с версии 0.7.0 исключение заменено предупреждением, если значение параметра равно `False`. + + Исправлено в [#19](https://github.com/MobileTeleSystems/onetl/pull/19). + +- Исправлено несколько проблем с S3. + + Была ошибка в подключении к S3, которая не позволяла обрабатывать файлы в корне бакета - они были невидимы для коннектора. Исправлено в [#29](https://github.com/MobileTeleSystems/onetl/pull/29). diff --git a/mddocs/ru/changelog/0.7.1.md b/mddocs/ru/changelog/0.7.1.md new file mode 100644 index 000000000..7e0fdad57 --- /dev/null +++ b/mddocs/ru/changelog/0.7.1.md @@ -0,0 +1,40 @@ +# 0.7.1 (2023-05-23) + +## Bug Fixes + +- Исправлена функция `setup_logging`. + + В onETL==0.7.0 вызов `onetl.log.setup_logging()` приводил к ошибке логирования: + + ``` text title="exception message" + + Traceback (most recent call last): + File "/opt/anaconda/envs/py39/lib/python3.9/logging/__init__.py", line 434, in format + return self._format(record) + File "/opt/anaconda/envs/py39/lib/python3.9/logging/__init__.py", line 430, in _format + return self._fmt % record.dict + KeyError: 'levelname:8s' + ``` + +- Исправлены примеры установки. + + В onETL==0.7.0 приводились примеры установки с применением extras: + + ``` bash title="before" + + pip install onetl[files, kerberos, spark] + ``` + + Однако, это приводило к ошибке pip: + + ``` text title="exception" + + ERROR: Invalid requirement: 'onet[files,' + ``` + + Это происходило из-за наличия пробелов в extras. Исправлено: + + ``` bash title="after" + + pip install onetl[files,kerberos,spark] + ``` diff --git a/mddocs/ru/changelog/0.7.2.md b/mddocs/ru/changelog/0.7.2.md new file mode 100644 index 000000000..f92fd9944 --- /dev/null +++ b/mddocs/ru/changelog/0.7.2.md @@ -0,0 +1,37 @@ +# 0.7.2 (2023-05-24) + +## Dependencies + +- Ограничена версия `typing-extensions`. + + Релиз `typing-extensions==4.6.0` содержит критические изменения вызывающие ошибки вида: + + ``` text title="typing-extensions 4.6.0" + + Traceback (most recent call last): + File "/Users/project/lib/python3.9/typing.py", line 852, in __subclasscheck__ + return issubclass(cls, self.__origin__) + TypeError: issubclass() arg 1 must be a class + ``` + + Релиз `typing-extensions==4.6.1` вызывает другую ошибку: + + ``` text title="typing-extensions 4.6.1" + + Traceback (most recent call last): + File "/home/maxim/Repo/typing_extensions/1.py", line 33, in + isinstance(file, ContainsException) + File "/home/maxim/Repo/typing_extensions/src/typing_extensions.py", line 599, in __instancecheck__ + if super().__instancecheck__(instance): + File "/home/maxim/.pyenv/versions/3.7.8/lib/python3.7/abc.py", line 139, in __instancecheck__ + return _abc_instancecheck(cls, instance) + File "/home/maxim/Repo/typing_extensions/src/typing_extensions.py", line 583, in __subclasscheck__ + return super().__subclasscheck__(other) + File "/home/maxim/.pyenv/versions/3.7.8/lib/python3.7/abc.py", line 143, in __subclasscheck__ + return _abc_subclasscheck(cls, subclass) + File "/home/maxim/Repo/typing_extensions/src/typing_extensions.py", line 661, in _proto_hook + and other._is_protocol + AttributeError: type object 'PathWithFailure' has no attribute '_is_protocol' + ``` + + Мы указали в requirements.txt ограничение `typing-extensions<4.6` и будем его придерживаться пока причины ошибок не будут устранены. diff --git a/mddocs/ru/changelog/0.8.0.md b/mddocs/ru/changelog/0.8.0.md new file mode 100644 index 000000000..39501dedf --- /dev/null +++ b/mddocs/ru/changelog/0.8.0.md @@ -0,0 +1,162 @@ +# 0.8.0 (2023-05-31) + +## Критические изменения + +- Переименованы методы классов `FileConnection`: + + - `get_directory` → `resolve_dir` + - `get_file` → `resolve_file` + - `listdir` → `list_dir` + - `mkdir` → `create_dir` + - `rmdir` → `remove_dir` + + Мы считаем, что новое именование будет более последовательным. + + Эти методы были не задокументированы в предыдущих версиях, но кто-то мог использовать их, поэтому это изменение не является обратно совместимым. ([#36](https://github.com/MobileTeleSystems/onetl/pull/36)) + +- Класс `onetl.core.FileFilter` устарел, его заменяют новые классы: + + - `onetl.file.filter.Glob` + - `onetl.file.filter.Regexp` + - `onetl.file.filter.ExcludeDir` + + Старый класс будет удален в v1.0.0. ([#43](https://github.com/MobileTeleSystems/onetl/pull/43)) + +- Класс `onetl.core.FileLimit` также устарел, его заменяет `onetl.file.limit.MaxFilesCount`. + + Старый класс будет удален в v1.0.0. ([#44](https://github.com/MobileTeleSystems/onetl/pull/44)) + +- Изменено поведение метода `BaseFileLimit.reset`. + + Этот метод теперь возвращает `self` вместо `None`. + Возвращаемое значение может быть тем же объектом или его копией, это зависит от конкретной реализации. ([#44](https://github.com/MobileTeleSystems/onetl/pull/44)) + +- `FileDownloader.filter` и `.limit` заменены новыми опциями `.filters` и `.limits`: + + ``` python title="onETL < 0.8.0" + + FileDownloader( + ..., + filter=FileFilter(glob="*.txt", exclude_dir="/path"), + limit=FileLimit(count_limit=10), + ) + ``` + + ``` python title="onETL >= 0.8.0" + + FileDownloader( + ..., + filters=[Glob("*.txt"), ExcludeDir("/path")], + limits=[MaxFilesCount(10)], + ) + ``` + + Это позволяет разработчикам реализовывать свои собственные классы фильтров и лимитов, а также комбинировать их с существующими. + + Старое поведение все еще поддерживается, но будет удалено в v1.0.0. ([#45](https://github.com/MobileTeleSystems/onetl/pull/45)) + +- Удалено значение по умолчанию для `FileDownloader.limits`, теперь пользователь должен явно передавать список лимитов. ([#45](https://github.com/MobileTeleSystems/onetl/pull/45)) + +- Классы из модуля `onetl.core`: + + ``` python title="before" + + from onetl.core import DBReader + from onetl.core import DBWriter + from onetl.core import FileDownloader + from onetl.core import FileUploader + ``` + + перемещены в новые модули `onetl.db` и `onetl.file`: + + ``` python title="after" + + from onetl.db import DBReader + from onetl.db import DBWriter + + from onetl.file import FileDownloader + from onetl.file import FileUploader + ``` + + Импорты из старого модуля `onetl.core` все еще можно использовать, но они помечены как устаревшие. Модуль будет удален в v1.0.0. ([#46](https://github.com/MobileTeleSystems/onetl/pull/46)) + +## Features + +- Добавлен метод `rename_dir`. + + Метод был добавлен к следующим подключениям: + + - `FTP` + - `FTPS` + - `HDFS` + - `SFTP` + - `WebDAV` + + Он позволяет переименовывать/перемещать каталог со всем его содержимым в новое расположение / новый путь. + + `S3` не имеет каталогов, поэтому для этого подключения нет такого метода. ([#40](https://github.com/MobileTeleSystems/onetl/pull/40)) + +- Добавлен класс `onetl.file.FileMover`. + + Он позволяет перемещать файлы между каталогами удаленной файловой системы. + Сигнатура почти такая же, как и в `FileDownloader`, но без поддержки HWM. ([#42](https://github.com/MobileTeleSystems/onetl/pull/42)) + +## Improvements + +- Задокументированы все публичные методы в классах `FileConnection`: + + - `download_file` + - `resolve_dir` + - `resolve_file` + - `get_stat` + - `is_dir` + - `is_file` + - `list_dir` + - `create_dir` + - `path_exists` + - `remove_file` + - `rename_file` + - `remove_dir` + - `upload_file` + - `walk` ([#39](https://github.com/MobileTeleSystems/onetl/pull/39)) + +- Обновлена документация метода `check` для всех подключений - добавлен пример использования и задокументирован тип результата. ([#39](https://github.com/MobileTeleSystems/onetl/pull/39)) + +- Добавлен новый тип исключения `FileSizeMismatchError`. + + Если целевой файл после загрузки/выгрузки имеет размер, отличный от исходного методы `connection.download_file` и `connection.upload_file` теперь вызывают новый тип исключения вместо `RuntimeError`. ([#39](https://github.com/MobileTeleSystems/onetl/pull/39)) + +- Добавлен новый тип исключения `DirectoryExistsError` - он возникает, если целевой каталог уже существует. ([#40](https://github.com/MobileTeleSystems/onetl/pull/40)) + +- Улучшено логирование исключений `FileDownloader` / `FileUploader`. + + Если включено логирование `DEBUG`, то вместо вывода только сообщения об исключении выводится исключение со стектрейсом . ([#42](https://github.com/MobileTeleSystems/onetl/pull/42)) + +- Обновлена документация `FileUploader`. + + - В документацию добавлено примечание, что класс не поддерживает стратегии чтения. + - Добавлены примеры использования метода `run` с явной передачей списка файлов, посредством как абсолютных, так и относительных путей. + - Исправлены устаревшие импорты и имена классов в примерах. ([#42](https://github.com/MobileTeleSystems/onetl/pull/42)) + +- Обновлена документация класса `DownloadResult` - исправлены устаревшие импорты и имена классов. ([#42](https://github.com/MobileTeleSystems/onetl/pull/42)) + +- Улучшен раздел документации по фильтрам файлов. + + Задокументирован интерфейсный класс `onetl.base.BaseFileFilter` и функция `match_all_filters`. ([#43](https://github.com/MobileTeleSystems/onetl/pull/43)) + +- Улучшен раздел документации по лимитам файлов. + + Задокументирован интерфейсный класс `onetl.base.BaseFileLimit` и функции `limits_stop_at` / `limits_reached` / `reset_limits`. ([#44](https://github.com/MobileTeleSystems/onetl/pull/44)) + +- Добавлен changelog. + + Changelog генерируется из отдельных файлов новостей с использованием [towncrier](https://pypi.org/project/towncrier/). ([#47](https://github.com/MobileTeleSystems/onetl/pull/47)) + +## Misc + +- Улучшен CI для тестов. + + - Если разработчик не изменил исходный код конкретного коннектора или его зависимости, запускать тесты только для максимально поддерживаемых версий Spark, Python, Java и поключения к БД или файловому хранилищу. + - Если разработчик внес какие-либо изменения в конкретный коннектор, в основные классы или в зависимости, запускать тесты для минимальной и максимальной версий. + - Раз в неделю запускать все тесты для минимальной и последней версий, чтобы выявить критические изменения в зависимостях. + - Минимальная протестированная версия Spark - 2.3.1 вместо 2.4.8. ([#32](https://github.com/MobileTeleSystems/onetl/pull/32)) diff --git a/mddocs/ru/changelog/0.8.1.md b/mddocs/ru/changelog/0.8.1.md new file mode 100644 index 000000000..db1f17ca6 --- /dev/null +++ b/mddocs/ru/changelog/0.8.1.md @@ -0,0 +1,42 @@ +# 0.8.1 (2023-07-10) + +## Новые возможности + +- Добавлен декоратор `@slot` к публичным методам: + + - `DBConnection` + - `FileConnection` + - `DBReader` + - `DBWriter` + - `FileDownloader` + - `FileUploader` + - `FileMover` ([#49](https://github.com/MobileTeleSystems/onetl/pull/49)) + +- Добавлено поле `workers` в классы `FileDownloader` / `FileUploader` / `FileMover` / `Options`. + + Это позволяет ускорить все файловые операции, используя параллельные потоки. ([#57](https://github.com/MobileTeleSystems/onetl/pull/57)) + +## Улучшения + +- Добавлена документация для методов `.get` и `.save` хранилища HWM. ([#49](https://github.com/MobileTeleSystems/onetl/pull/49)) + +- Улучшен Readme: + + - Раздел `Quick start` вынесен из документации + - Добавлен раздел `Non-goals` + - Исправлен отступ блоков кода ([#50](https://github.com/MobileTeleSystems/onetl/pull/50)) + +- Улучшено руководство по участию в разработке (Contributing guide): + + - Перемещен раздел `Develop` из Readme + - Перемещено содержимое `docs/changelog/README.rst` + - Добавлен раздел `Limitations` + - Добавлена инструкция по созданию форка и сборке документации ([#50](https://github.com/MobileTeleSystems/onetl/pull/50)) + +- Удалены дублирующиеся проверки существования исходного файла в `FileDownloader` / `FileMover`. ([#57](https://github.com/MobileTeleSystems/onetl/pull/57)) + +- Обновлен формат логирования по умолчанию, таким образом, чтобы в нем содержалось имя потока. ([#57](https://github.com/MobileTeleSystems/onetl/pull/57)) + +## Исправления ошибок + +- Исправлена ошибка, из-за которой `S3.list_dir('/')` возвращал пустой список в последней версии Minio. ([#58](https://github.com/MobileTeleSystems/onetl/pull/58)) diff --git a/mddocs/ru/changelog/0.9.0.md b/mddocs/ru/changelog/0.9.0.md new file mode 100644 index 000000000..c5f383e9a --- /dev/null +++ b/mddocs/ru/changelog/0.9.0.md @@ -0,0 +1,122 @@ +# 0.9.0 (2023-08-17) + +## Критические изменения + +- Переименованы методы: + + - `DBConnection.read_df` → `DBConnection.read_source_as_df` + - `DBConnection.write_df` → `DBConnection.write_df_to_target` ([#66](https://github.com/MobileTeleSystems/onetl/pull/66)) + +- Переименованы классы: + + - `HDFS.slots` → `HDFS.Slots` + - `Hive.slots` → `Hive.Slots` + + Старые имена сохранены, но будут удалены в v1.0.0 ([#103](https://github.com/MobileTeleSystems/onetl/pull/103)) + +- Для большей понятности переименованы некоторые опции: + + - `Hive.WriteOptions(mode="append")` → `Hive.WriteOptions(if_exists="append")` + - `Hive.WriteOptions(mode="overwrite_table")` → `Hive.WriteOptions(if_exists="replace_entire_table")` + - `Hive.WriteOptions(mode="overwrite_partitions")` → `Hive.WriteOptions(if_exists="replace_overlapping_partitions")` + - `JDBC.WriteOptions(mode="append")` → `JDBC.WriteOptions(if_exists="append")` + - `JDBC.WriteOptions(mode="overwrite")` → `JDBC.WriteOptions(if_exists="replace_entire_table")` + - `Greenplum.WriteOptions(mode="append")` → `Greenplum.WriteOptions(if_exists="append")` + - `Greenplum.WriteOptions(mode="overwrite")` → `Greenplum.WriteOptions(if_exists="replace_entire_table")` + - `MongoDB.WriteOptions(mode="append")` → `Greenplum.WriteOptions(if_exists="append")` + - `MongoDB.WriteOptions(mode="overwrite")` → `Greenplum.WriteOptions(if_exists="replace_entire_collection")` + - `FileDownloader.Options(mode="error")` → `FileDownloader.Options(if_exists="error")` + - `FileDownloader.Options(mode="ignore")` → `FileDownloader.Options(if_exists="ignore")` + - `FileDownloader.Options(mode="overwrite")` → `FileDownloader.Options(if_exists="replace_file")` + - `FileDownloader.Options(mode="delete_all")` → `FileDownloader.Options(if_exists="replace_entire_directory")` + - `FileUploader.Options(mode="error")` → `FileUploader.Options(if_exists="error")` + - `FileUploader.Options(mode="ignore")` → `FileUploader.Options(if_exists="ignore")` + - `FileUploader.Options(mode="overwrite")` → `FileUploader.Options(if_exists="replace_file")` + - `FileUploader.Options(mode="delete_all")` → `FileUploader.Options(if_exists="replace_entire_directory")` + - `FileMover.Options(mode="error")` → `FileMover.Options(if_exists="error")` + - `FileMover.Options(mode="ignore")` → `FileMover.Options(if_exists="ignore")` + - `FileMover.Options(mode="overwrite")` → `FileMover.Options(if_exists="replace_file")` + - `FileMover.Options(mode="delete_all")` → `FileMover.Options(if_exists="replace_entire_directory")` + + Старые имена сохранены, но будут удалены в v1.0.0 ([#108](https://github.com/MobileTeleSystems/onetl/pull/108)) + +- `onetl.log.disable_clients_logging()` переименован в `onetl.log.setup_clients_logging()`. ([#120](https://github.com/MobileTeleSystems/onetl/pull/120)) + +## Новая функциональность + +- Добавлены новые методы, возвращающие пакеты Maven для определенного подключения: + + - `Clickhouse.get_packages()` + - `MySQL.get_packages()` + - `Postgres.get_packages()` + - `Teradata.get_packages()` + - `MSSQL.get_packages(java_version="8")` + - `Oracle.get_packages(java_version="8")` + - `Greenplum.get_packages(scala_version="2.12")` + - `MongoDB.get_packages(scala_version="2.12")` + - `Kafka.get_packages(spark_version="3.4.1", scala_version="2.12")` + + Устаревший синтаксис: + + - `Clickhouse.package` + - `MySQL.package` + - `Postgres.package` + - `Teradata.package` + - `MSSQL.package` + - `Oracle.package` + - `Greenplum.package_spark_2_3` + - `Greenplum.package_spark_2_4` + - `Greenplum.package_spark_3_2` + - `MongoDB.package_spark_3_2` + - `MongoDB.package_spark_3_3` + - `MongoDB.package_spark_3_4` ([#87](https://github.com/MobileTeleSystems/onetl/pull/87)) + +- Разрешено устанавливать уровень логирования клиентских модулей в `onetl.log.setup_clients_logging()`. + + В `onetl.log.setup_logging()` новый дополнительный аргумент `enable_clients=True` позволяет включать логирование базовых клиентских модулей. + Это полезно для отладки. ([#120](https://github.com/MobileTeleSystems/onetl/pull/120)) + +- Добавлена поддержка чтения и записи данных в топики Kafka. + + Для этих операций были добавлены новые классы. + + - `Kafka` ([#54](https://github.com/MobileTeleSystems/onetl/pull/54), [#60](https://github.com/MobileTeleSystems/onetl/pull/60), [#72](https://github.com/MobileTeleSystems/onetl/pull/72), [#84](https://github.com/MobileTeleSystems/onetl/pull/84), [#87](https://github.com/MobileTeleSystems/onetl/pull/87), [#89](https://github.com/MobileTeleSystems/onetl/pull/89), [#93](https://github.com/MobileTeleSystems/onetl/pull/93), [#96](https://github.com/MobileTeleSystems/onetl/pull/96), [#102](https://github.com/MobileTeleSystems/onetl/pull/102), [#104](https://github.com/MobileTeleSystems/onetl/pull/104)) + - `Kafka.PlaintextProtocol` ([#79](https://github.com/MobileTeleSystems/onetl/pull/79)) + - `Kafka.SSLProtocol` ([#118](https://github.com/MobileTeleSystems/onetl/pull/118)) + - `Kafka.BasicAuth` ([#63](https://github.com/MobileTeleSystems/onetl/pull/63), [#77](https://github.com/MobileTeleSystems/onetl/pull/77)) + - `Kafka.KerberosAuth` ([#63](https://github.com/MobileTeleSystems/onetl/pull/63), [#77](https://github.com/MobileTeleSystems/onetl/pull/77), [#110](https://github.com/MobileTeleSystems/onetl/pull/110)) + - `Kafka.ScramAuth` ([#115](https://github.com/MobileTeleSystems/onetl/pull/115)) + - `Kafka.Slots` ([#109](https://github.com/MobileTeleSystems/onetl/pull/109)) + - `Kafka.ReadOptions` ([#68](https://github.com/MobileTeleSystems/onetl/pull/68)) + - `Kafka.WriteOptions` ([#68](https://github.com/MobileTeleSystems/onetl/pull/68)) + + В настоящее время Kafka не поддерживает инкрементальные стратегии чтения, такая возможность будет реализована в будущих выпусках. + +- Добавлена поддержка чтения файлов в Spark DataFrame и сохранения DataFrame в файлы. + + Для этих операций были добавлены новые классы. + + FileDFConnections: + + - `SparkHDFS` ([#98](https://github.com/MobileTeleSystems/onetl/pull/98)) + - `SparkS3` ([#94](https://github.com/MobileTeleSystems/onetl/pull/94), [#100](https://github.com/MobileTeleSystems/onetl/pull/100), [#124](https://github.com/MobileTeleSystems/onetl/pull/124)) + - `SparkLocalFS` ([#67](https://github.com/MobileTeleSystems/onetl/pull/67)) + + Классы верхнего уровня абстракции: + + - `FileDFReader` ([#73](https://github.com/MobileTeleSystems/onetl/pull/73)) + - `FileDFWriter` ([#81](https://github.com/MobileTeleSystems/onetl/pull/81)) + + Форматы файлов: + + - `Avro` ([#69](https://github.com/MobileTeleSystems/onetl/pull/69)) + - `CSV` ([#92](https://github.com/MobileTeleSystems/onetl/pull/92)) + - `JSON` ([#83](https://github.com/MobileTeleSystems/onetl/pull/83)) + - `JSONLine` ([#83](https://github.com/MobileTeleSystems/onetl/pull/83)) + - `ORC` ([#86](https://github.com/MobileTeleSystems/onetl/pull/86)) + - `Parquet` ([#88](https://github.com/MobileTeleSystems/onetl/pull/88)) + +## Улучшения + +- Удалены избыточные проверки доступности драйвера в соединениях Greenplum и MongoDB. ([#67](https://github.com/MobileTeleSystems/onetl/pull/67)) +- Проверка доступности класса Java перенесена из метода `.check()` в конструктор соединения. ([#97](https://github.com/MobileTeleSystems/onetl/pull/97)) diff --git a/mddocs/ru/changelog/0.9.1.md b/mddocs/ru/changelog/0.9.1.md new file mode 100644 index 000000000..78bf28d7e --- /dev/null +++ b/mddocs/ru/changelog/0.9.1.md @@ -0,0 +1,7 @@ +# 0.9.1 (2023-08-17) + +## Исправления ошибок + +- Исправлена ошибка, из-за которой количество потоков, создаваемых `FileDownloader` / `FileUploader` / `FileMover`, было + не `min(workers, len(files))`, а `max(workers, len(files))`, что приводило к созданию слишком большого количества рабочих потоков + в случаях больших списков файлов. diff --git a/mddocs/ru/changelog/0.9.2.md b/mddocs/ru/changelog/0.9.2.md new file mode 100644 index 000000000..dbd0bf7bf --- /dev/null +++ b/mddocs/ru/changelog/0.9.2.md @@ -0,0 +1,23 @@ +# 0.9.2 (2023-09-06) + +## Новые возможности + +- Добавлены `if_exists="ignore"` и `error` в `Greenplum.WriteOptions` ([#142](https://github.com/MobileTeleSystems/onetl/pull/142)) + +## Улучшения + +- Улучшены сообщения валидации при записи dataframe в Kafka. ([#131](https://github.com/MobileTeleSystems/onetl/pull/131)) + +- Улучшена документация: + + - В документации по подключениям к базам данных добавлены некоторые нюансы о чтении и записи + - Расширена информация о выполнении операторов для подключений JDBC и Greenplum + +## Исправления ошибок + +- Исправлена валидация столбца `headers` при записи в Kafka с `Kafka.WriteOptions()` по умолчанию - значение по умолчанию было `False`, + но вместо того, чтобы вызывать исключение, значение столбца просто игнорировалось. ([#131](https://github.com/MobileTeleSystems/onetl/pull/131)) +- Исправлено чтение данных из Oracle с `partitioningMode="range"` без явно установленных `lowerBound` / `upperBound`. ([#133](https://github.com/MobileTeleSystems/onetl/pull/133)) +- Обновлена документация Kafka об использовании SSLProtocol. ([#136](https://github.com/MobileTeleSystems/onetl/pull/136)) +- Выбрасывается исключение, если кто-то пытается прочитать данные из несуществующей темы Kafka. ([#138](https://github.com/MobileTeleSystems/onetl/pull/138)) +- В DBReader разрешено передавать Kafka имя топик вида `some.topic.name`. Аналогично для коллекций MongoDB. ([#139](https://github.com/MobileTeleSystems/onetl/pull/139)) diff --git a/mddocs/ru/changelog/0.9.3.md b/mddocs/ru/changelog/0.9.3.md new file mode 100644 index 000000000..eb43b4352 --- /dev/null +++ b/mddocs/ru/changelog/0.9.3.md @@ -0,0 +1,5 @@ +# 0.9.3 (2023-09-06) + +## Исправление ошибок + +- Исправлена сборка документации diff --git a/mddocs/ru/changelog/0.9.4.md b/mddocs/ru/changelog/0.9.4.md new file mode 100644 index 000000000..f871309c6 --- /dev/null +++ b/mddocs/ru/changelog/0.9.4.md @@ -0,0 +1,23 @@ +# 0.9.4 (2023-09-26) + +## Функциональность + +- Добавлена поддержка файлов формата `Excel`. ([#148](https://github.com/MobileTeleSystems/onetl/pull/148)) +- Добавлено файловое подключение `Samba`. Теперь можно скачивать и загружать файлы в общие папки Samba с помощью `FileDownloader`/`FileUploader`. ([#150](https://github.com/MobileTeleSystems/onetl/pull/150)) +- Добавлены `if_exists="ignore"` и `error` в `Hive.WriteOptions` ([#143](https://github.com/MobileTeleSystems/onetl/pull/143)) +- Добавлены `if_exists="ignore"` и `error` в `JDBC.WriteOptions` ([#144](https://github.com/MobileTeleSystems/onetl/pull/144)) +- Добавлены `if_exists="ignore"` и `error` в `MongoDB.WriteOptions` ([#145](https://github.com/MobileTeleSystems/onetl/pull/145)) + +## Улучшения + +- Добавлена документация о различных способах передачи пакетов в Spark session. ([#151](https://github.com/MobileTeleSystems/onetl/pull/151)) +- Значительно улучшена документация по `Greenplum`: + - Добавлена информация о сетевых портах, правах доступа, `pg_hba.conf` и т.д. + - Добавлены схемы взаимодействия для чтения, записи и выполнения операторов в Greenplum. + - Добавлены рекомендации по чтению данных из представлений и результатов `JOIN` Greenplum. ([#154](https://github.com/MobileTeleSystems/onetl/pull/154)) +- Методы `.fetch` и `.execute` для подключений к БД стали потокобезопасными. Каждый поток работает со своим собственным экземпляром подключения. ([#156](https://github.com/MobileTeleSystems/onetl/pull/156)) +- Для `FileConnection` вызывается `.close()`, когда первый удаляется сборщиком мусора. ([#156](https://github.com/MobileTeleSystems/onetl/pull/156)) + +## Исправления ошибок + +- Исправлена проблема, когда остановка интерпретатора Python вызывает `JDBCMixin.close()`, но он завершается исключениями. ([#156](https://github.com/MobileTeleSystems/onetl/pull/156)) diff --git a/mddocs/ru/changelog/0.9.5.md b/mddocs/ru/changelog/0.9.5.md new file mode 100644 index 000000000..1d7358c0b --- /dev/null +++ b/mddocs/ru/changelog/0.9.5.md @@ -0,0 +1,14 @@ +# 0.9.5 (2023-10-10) + +## Features + +- Add `XML` file format support. ([#163](https://github.com/MobileTeleSystems/onetl/pull/163)) +- Tested compatibility with Spark 3.5.0. `MongoDB` and `Excel` are not supported yet, but other packages do. ([#159](https://github.com/MobileTeleSystems/onetl/pull/159)) + +## Improvements + +- Add check to all DB and FileDF connections that Spark session is alive. ([#164](https://github.com/MobileTeleSystems/onetl/pull/164)) + +## Bug Fixes + +- Fix `Hive.check()` behavior when Hive Metastore is not available. ([#164](https://github.com/MobileTeleSystems/onetl/pull/164)) From a7808b1c95b6fb2710156cbc298c6ab594d417bb Mon Sep 17 00:00:00 2001 From: Sattar Gyulmamedov Date: Tue, 10 Jun 2025 00:12:47 +0300 Subject: [PATCH 08/22] changelog converted & translated v.1 --- mddocs/docs/changelog/0.10.0.md | 2 +- mddocs/docs/changelog/0.10.1.md | 2 +- mddocs/docs/changelog/0.11.0.md | 2 +- mddocs/docs/changelog/0.13.0.md | 4 +- mddocs/ru/changelog/0.10.0.md | 169 +++++++++++++++----------------- mddocs/ru/changelog/0.10.1.md | 22 ++--- mddocs/ru/changelog/0.10.2.md | 38 ++++--- mddocs/ru/changelog/0.11.0.md | 124 ++++++++++++----------- mddocs/ru/changelog/0.11.1.md | 9 +- mddocs/ru/changelog/0.11.2.md | 4 +- mddocs/ru/changelog/0.12.0.md | 33 +++---- mddocs/ru/changelog/0.12.1.md | 21 ++-- mddocs/ru/changelog/0.12.2.md | 19 ++-- mddocs/ru/changelog/0.12.3.md | 4 +- mddocs/ru/changelog/0.12.4.md | 4 +- mddocs/ru/changelog/0.12.5.md | 16 +-- mddocs/ru/changelog/0.13.0.md | 119 ++++++++++------------ mddocs/ru/changelog/0.13.1.md | 8 +- mddocs/ru/changelog/0.13.3.md | 4 +- mddocs/ru/changelog/0.13.4.md | 11 +-- mddocs/ru/changelog/0.9.5.md | 9 +- 21 files changed, 297 insertions(+), 327 deletions(-) diff --git a/mddocs/docs/changelog/0.10.0.md b/mddocs/docs/changelog/0.10.0.md index 68a609eb6..508aac654 100644 --- a/mddocs/docs/changelog/0.10.0.md +++ b/mddocs/docs/changelog/0.10.0.md @@ -417,7 +417,7 @@ But most of users use other HWM store implementations which do not have such iss ``DBReader.run()`` + incremental/batch strategy behavior in versions 0.10.x and newer: - Detect type of HWM expression: ``SELECT hwm.expression FROM table WHERE 1=0``. - - Determine corresponding Spark type ``df.schema[0]`` and when determine matching HWM class (if ``DReader.AutoDetectHWM`` is used). + - Determine corresponding Spark type ``df.schema[0]`` and when determine matching HWM class (if ``DBReader.AutoDetectHWM`` is used). - Get min/max values by querying the source: ``SELECT MAX(hwm.expression) FROM table WHERE hwm.expression >= prev_hwm.value``. - Use ``max(hwm.expression)`` as next HWM value, and save it to HWM Store. - Create dataframe from query ``SELECT ... table columns ... FROM table WHERE hwm.expression > prev_hwm.value AND hwm.expression <= current_hwm.value``, baking new HWM value into the query. diff --git a/mddocs/docs/changelog/0.10.1.md b/mddocs/docs/changelog/0.10.1.md index ba3ed40bb..6280dc26b 100644 --- a/mddocs/docs/changelog/0.10.1.md +++ b/mddocs/docs/changelog/0.10.1.md @@ -20,7 +20,7 @@ - Add `has_data`, `raise_if_no_data` methods to `DBReader` class. ([#203](https://github.com/MobileTeleSystems/onetl/pull/203)) - Updare VMware Greenplum connector from `2.1.4` to `2.3.0`. This implies: - : - Greenplum 7.x support + - Greenplum 7.x support - [Kubernetes support](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/configure.html#k8scfg) - New read option [gpdb.matchDistributionPolicy](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/options.html#distpolmotion) which allows to match each Spark executor with specific Greenplum segment, avoiding redundant data transfer between Greenplum segments diff --git a/mddocs/docs/changelog/0.11.0.md b/mddocs/docs/changelog/0.11.0.md index c77c977b0..64dee376f 100644 --- a/mddocs/docs/changelog/0.11.0.md +++ b/mddocs/docs/changelog/0.11.0.md @@ -10,7 +10,7 @@ most of users will not see any differences. - Package version changed `0.3.2` → `0.6.0-patch5`. - Driver name changed `ru.yandex.clickhouse.ClickHouseDriver` → `com.clickhouse.jdbc.ClickHouseDriver`. - This brings up several fixes for Spark \<-> Clickhouse type compatibility, and also Clickhouse clusters support. + This brings up several fixes for Spark <-> Clickhouse type compatibility, and also Clickhouse clusters support. - Update other JDBC drivers to latest versions: - MSSQL `12.2.0` → `12.6.2` ([#254](https://github.com/MobileTeleSystems/onetl/pull/254)). diff --git a/mddocs/docs/changelog/0.13.0.md b/mddocs/docs/changelog/0.13.0.md index 37ae8368a..1a39a060a 100644 --- a/mddocs/docs/changelog/0.13.0.md +++ b/mddocs/docs/changelog/0.13.0.md @@ -177,7 +177,7 @@ - Fix `MSSQL.fetch(...)` and `MySQL.fetch(...)` opened a read-write connection instead of read-only. ([#337](https://github.com/MobileTeleSystems/onetl/pull/337)) Now this is fixed: - : - `MSSQL.fetch(...)` establishes connection with `ApplicationIntent=ReadOnly`. + - `MSSQL.fetch(...)` establishes connection with `ApplicationIntent=ReadOnly`. - `MySQL.fetch(...)` calls `SET SESSION TRANSACTION READ ONLY` statement. - Fixed passing multiple filters to `FileDownloader` and `FileMover`. ([#338](https://github.com/MobileTeleSystems/onetl/pull/338)) @@ -211,7 +211,7 @@ - Minimal `etl-entities` version is now [2.5.0](https://github.com/MobileTeleSystems/etl-entities/releases/tag/2.5.0). ([#331](https://github.com/MobileTeleSystems/onetl/pull/331)) - Update DB connectors/drivers to latest versions: ([#345](https://github.com/MobileTeleSystems/onetl/pull/345)) - : - Clickhouse `0.6.5` → `0.7.2` + - Clickhouse `0.6.5` → `0.7.2` - MongoDB `10.4.0` → `10.4.1` - MySQL `9.0.0` → `9.2.0` - Oracle `23.5.0.24.07` → `23.7.0.25.01` diff --git a/mddocs/ru/changelog/0.10.0.md b/mddocs/ru/changelog/0.10.0.md index 68a609eb6..4c5893a68 100644 --- a/mddocs/ru/changelog/0.10.0.md +++ b/mddocs/ru/changelog/0.10.0.md @@ -1,12 +1,12 @@ # 0.10.0 (2023-12-18) -## Breaking Changes +## Критические изменения -- Upgrade `etl-entities` from v1 to v2 ([#172](https://github.com/MobileTeleSystems/onetl/pull/172)). +- Обновление `etl-entities` с v1 до v2 ([#172](https://github.com/MobileTeleSystems/onetl/pull/172)). - This implies that `HWM` classes are now have different internal structure than they used to. + Это означает, что классы `HWM` теперь имеют другую внутреннюю структуру, чем раньше. - Before: + До: ```python from etl_entities.old_hwm import IntHWM as OldIntHWM @@ -21,7 +21,7 @@ ) ``` - After: + После: ```python from etl_entities.hwm import ColumnIntHWM @@ -35,35 +35,35 @@ ) ``` - **Breaking change:** If you used HWM classes from `etl_entities` module, you should rewrite your code to make it compatible with new version. + **Критическое изменение:** Если вы использовали классы HWM из модуля `etl_entities`, вам следует переписать свой код, чтобы сделать его совместимым с новой версией. -??? "More details" +??? "Подробнее" - - ``HWM`` classes used by previous onETL versions were moved from ``etl_entities`` to ``etl_entities.old_hwm`` submodule. They are here for compatibility reasons, but are planned to be removed in ``etl-entities`` v3 release. - - New ``HWM`` classes have flat structure instead of nested. - - New ``HWM`` classes have mandatory ``name`` attribute (it was known as ``qualified_name`` before). - - Type aliases used while serializing and deserializing ``HWM`` objects to ``dict`` representation were changed too: ``int`` → ``column_int``. + - Классы ``HWM``, используемые предыдущими версиями onETL, были перемещены из ``etl_entities`` в подмодуль ``etl_entities.old_hwm``. Они сохранены для обеспечения совместимости, но будут удалены в выпуске ``etl-entities`` v3. + - Новые классы ``HWM`` имеют плоскую структуру вместо вложенной. + - Новые классы ``HWM`` имеют обязательный атрибут ``name`` (ранее известный как ``qualified_name``). + - Типы псевдонимов, используемые при сериализации и десериализации объектов ``HWM`` в представлении ``dict``, также были изменены: ``int`` → ``column_int``. - To make migration simpler, you can use new method: + Чтобы упростить миграцию, вы можете использовать новый метод: ```python old_hwm = OldIntHWM(...) new_hwm = old_hwm.as_new_hwm() ``` - Which automatically converts all fields from old structure to new one, including `qualified_name` → `name`. + Он автоматически преобразует все поля из старой структуры в новую, включая `qualified_name` → `name`. -- **Breaking changes:** +- **Критические изменения:** - - Methods `BaseHWMStore.get()` and `BaseHWMStore.save()` were renamed to `get_hwm()` and `set_hwm()`. - - They now can be used only with new HWM classes from `etl_entities.hwm`, **old HWM classes are not supported**. + - Методы `BaseHWMStore.get()` и `BaseHWMStore.save()` были переименованы в `get_hwm()` и `set_hwm()`. + - Теперь их можно использовать только с новыми классами HWM из `etl_entities.hwm`, **старые классы HWM не поддерживаются**. - If you used them in your code, please update it accordingly. + Если вы использовали их в своем коде, пожалуйста, обновите его соответствующим образом. -- YAMLHWMStore **CANNOT read files created by older onETL versions** (0.9.x or older). +- YAMLHWMStore **НЕ МОЖЕТ читать файлы, созданные более старыми версиями onETL** (0.9.x или старше). -??? "Update procedure" +??? "Процедура обновления" ```python # pip install onetl==0.9.5 @@ -250,11 +250,11 @@ # That's all! ``` -But most of users use other HWM store implementations which do not have such issues. +Большинство пользователей используют другие реализации хранилища HWM, для которых указанная процедура не требуется. -- Several classes and functions were moved from `onetl` to `etl_entities`: +- Некоторые классы и функции были перемещены из `onetl` в `etl_entities`: -=== "onETL ``0.9.x`` and older" +=== "onETL ``0.9.x`` и старше" ```python @@ -268,7 +268,7 @@ But most of users use other HWM store implementations which do not have such iss ) ``` -=== "nETL ``0.10.x`` and newer" +=== "nETL ``0.10.x`` и новее" ```python @@ -282,11 +282,11 @@ But most of users use other HWM store implementations which do not have such iss ) ``` - They still can be imported from old module, but this is deprecated and will be removed in v1.0.0 release. + Их все еще можно импортировать из старого модуля, но они устарели и будут удалены в выпуске v1.0.0. -- Change the way of passing `HWM` to `DBReader` and `FileDownloader` classes: +- Изменился способ передачи `HWM` классам `DBReader` и `FileDownloader`: -=== "onETL ``0.9.x`` and older" +=== "onETL ``0.9.x`` и старше" ```python linenums="1" hl_lines="12-21" # Simple @@ -320,7 +320,7 @@ But most of users use other HWM store implementations which do not have such iss ) ``` -=== "onETL ``0.10.x`` and newer" +=== "onETL ``0.10.x`` и новее" ```python linenums="1" hl_lines="12-21" # Simple @@ -358,37 +358,35 @@ But most of users use other HWM store implementations which do not have such iss ``` - New HWM classes have **mandatory** `name` attribute which should be passed explicitly, - instead of generating if automatically under the hood. + Новые классы HWM имеют **обязательный** атрибут `name`, который должен быть передан явно, вместо автоматической генерации. - Automatic `name` generation using the old `DBReader.hwm_column` / `FileDownloader.hwm_type` - syntax is still supported, but will be removed in v1.0.0 release. ([#179](https://github.com/MobileTeleSystems/onetl/pull/179)) + Автоматическая генерация `name` с использованием старого синтаксиса `DBReader.hwm_column` / `FileDownloader.hwm_type` все еще поддерживается, но будет удалена в выпуске v1.0.0. ([#179](https://github.com/MobileTeleSystems/onetl/pull/179)) -- Performance of read Incremental and Batch strategies has been drastically improved. ([#182](https://github.com/MobileTeleSystems/onetl/pull/182)). +- Значительно улучшена производительность стратегий чтения Incremental и Batch. ([#182](https://github.com/MobileTeleSystems/onetl/pull/182)). -??? "Before and after in details" +??? "Подробнее о том, что было до и после" - ``DBReader.run()`` + incremental/batch strategy behavior in versions 0.9.x and older: + ``DBReader.run()`` + поведение инкрементной/пакетной стратегии в версиях 0.9.x и старше: - - Get table schema by making query ``SELECT * FROM table WHERE 1=0`` (if ``DBReader.columns`` has ``*``) - - Expand ``*`` to real column names from table, add here ``hwm_column``, remove duplicates (as some RDBMS does not allow that). - - Create dataframe from query like ``SELECT hwm_expression AS hwm_column, ...other table columns... FROM table WHERE hwm_expression > prev_hwm.value``. - - Determine HWM class using dataframe schema: ``df.schema[hwm_column].dataType``. - - Determine x HWM column value using Spark: ``df.select(max(hwm_column)).collect()``. - - Use ``max(hwm_column)`` as next HWM value, and save it to HWM Store. - - Return dataframe to user. + - Получить схему таблицы, выполнив запрос ``SELECT * FROM table WHERE 1=0`` (если ``DBReader.columns`` содержит ``*``) + - Развернуть ``*`` в реальные имена столбцов из таблицы, добавить сюда ``hwm_column``, удалить дубликаты (поскольку некоторые СУБД этого не позволяют). + - Создать dataframe из запроса, подобного ``SELECT hwm_expression AS hwm_column, ...other table columns... FROM table WHERE hwm_expression > prev_hwm.value``. + - Определить класс HWM, используя схему dataframe: ``df.schema[hwm_column].dataType``. + - Определить значение столбца x HWM, используя Spark: ``df.select(max(hwm_column)).collect()``. + - Использовать ``max(hwm_column)`` в качестве следующего значения HWM и сохранить его в HWM Store. + - Вернуть dataframe пользователю. - This was far from ideal: + Это было далеко от идеала: - - Dataframe content (all rows or just changed ones) was loaded from the source to Spark only to get min/max values of specific column. + - Содержимое Dataframe (все строки или только измененные) загружалось из источника в Spark только для получения минимальных/максимальных значений определенного столбца. - - Step of fetching table schema and then substituting column names in the next query caused some unexpected errors. + - Шаг получения схемы таблицы, а затем подстановки имен столбцов в следующем запросе вызывал некоторые неожиданные ошибки. - For example, source contains columns with mixed name case, like ``"CamelColumn"`` or ``"spaced column"``. + Например, если источник содержит столбцы со смешанным регистром имен, например ``"CamelColumn"`` или ``"spaced column"``. - Column names were *not* escaped during query generation, leading to queries that cannot be executed by database. + Имена столбцов *не* экранировались во время генерации запроса, что приводило к запросам, которые не могли быть выполнены базой данных. - So users have to *explicitly* pass column names ``DBReader``, wrapping columns with mixed naming with ``"``: + Поэтому пользователи должны были *явно* передавать имена столбцов ``DBReader``, заключая столбцы со смешанным регистром именования в ``"``: ```python reader = DBReader( @@ -402,38 +400,34 @@ But most of users use other HWM store implementations which do not have such iss ], ) ``` - - Using ``DBReader`` with ``IncrementalStrategy`` could lead to reading rows already read before. + - Использование ``DBReader`` с ``IncrementalStrategy`` могло привести к чтению строк, уже прочитанных ранее. - Dataframe was created from query with WHERE clause like ``hwm.expression > prev_hwm.value``, - not ``hwm.expression > prev_hwm.value AND hwm.expression <= current_hwm.value``. + Dataframe был создан из запроса с предложением WHERE, подобным ``hwm.expression > prev_hwm.value``, а не ``hwm.expression > prev_hwm.value AND hwm.expression <= current_hwm.value``. - So if new rows appeared in the source **after** HWM value is determined, - they can be read by accessing dataframe content (because Spark dataframes are lazy), - leading to inconsistencies between HWM value and dataframe content. + Поэтому, если в источнике появились новые строки **после** определения значения HWM, они могли быть прочитаны при доступе к содержимому dataframe (поскольку Spark использует "ленивые" вычисления), и это могло привести к несоответствиям между значением HWM и содержимого dataframe. - This may lead to issues then ``DBReader.run()`` read some data, updated HWM value, and next call of ``DBReader.run()`` - will read rows that were already read in previous run. + Что впоследствии могло привести к проблемам, в случае если ``DBReader.run()`` прочитает некоторые данные, обновит значение HWM, а следующий вызов ``DBReader.run()`` прочитает строки, которые уже были прочитаны в предыдущем запуске. - ``DBReader.run()`` + incremental/batch strategy behavior in versions 0.10.x and newer: + ``DBReader.run()`` + поведение инкрементной/пакетной стратегии в версиях 0.10.x и новее: - - Detect type of HWM expression: ``SELECT hwm.expression FROM table WHERE 1=0``. - - Determine corresponding Spark type ``df.schema[0]`` and when determine matching HWM class (if ``DReader.AutoDetectHWM`` is used). - - Get min/max values by querying the source: ``SELECT MAX(hwm.expression) FROM table WHERE hwm.expression >= prev_hwm.value``. - - Use ``max(hwm.expression)`` as next HWM value, and save it to HWM Store. - - Create dataframe from query ``SELECT ... table columns ... FROM table WHERE hwm.expression > prev_hwm.value AND hwm.expression <= current_hwm.value``, baking new HWM value into the query. - - Return dataframe to user. + - Определить тип выражения HWM: ``SELECT hwm.expression FROM table WHERE 1=0``. + - Определить соответствующий тип Spark ``df.schema[0]`` и затем определить соответствующий класс HWM (если используется ``DBReader.AutoDetectHWM``). + - Получить минимальные/максимальные значения, запросив источник: ``SELECT MAX(hwm.expression) FROM table WHERE hwm.expression >= prev_hwm.value``. + - Использовать ``max(hwm.expression)`` в качестве следующего значения HWM и сохранить его в HWM Store. + - Создать dataframe из запроса ``SELECT ... table columns ... FROM table WHERE hwm.expression > prev_hwm.value AND hwm.expression <= current_hwm.value``, внедрив новое значение HWM в запрос. + - Вернуть dataframe пользователю. - Improvements: + Улучшения: - - Allow source to calculate min/max instead of loading everything to Spark. This should be **faster** on large amounts of data (**up to x2**), because we do not transfer all the data from the source to Spark. This can be even faster if source have indexes for HWM column. - - Columns list is passed to source as-is, without any resolving on `DBReader` side. So you can pass `DBReader(columns=["*"])` to read tables with mixed columns naming. - - Restrict dataframe content to always match HWM values, which leads to never reading the same row twice. + - Разрешение источнику вычислять min/max вместо загрузки всего набора данных в Spark. Это должно быть **быстрее** на больших объемах данных (**до x2**), потому что мы не передаем все данные из источника в Spark. Это может быть даже еще быстрее, если источник имеет индексы для столбца HWM. + - Список столбцов передается в источник как есть, без какой-либо обработки на стороне `DBReader`. Таким образом, вы можете передать `DBReader(columns=["*"])` для чтения таблиц со смешанным именованием столбцов. + - Ограничение содержимого dataframe, чтобы оно всегда соответствовало значениям HWM, гарантирующее, что одна и та же строка никогда не будет прочитана дважды. - **Breaking change**: HWM column is not being implicitly added to dataframe. It was a part of `SELECT` clause, but now it is mentioned only in `WHERE` clause. + **Критическое изменение**: Столбец HWM больше не добавляется неявно в dataframe. Раньше он был частью предложения `SELECT`, но теперь он упоминается только в предложении `WHERE`. - So if you had code like this, you have to rewrite it: + Поэтому, если у вас был код, подобный этому, вам придется его переписать: -=== "onETL ``0.9.x`` and older" +=== "onETL ``0.9.x`` и старше" ```python linenums="1" hl_lines="1-16" reader = DBReader( @@ -472,7 +466,7 @@ But most of users use other HWM store implementations which do not have such iss ``` -=== "onETL ``0.10.x`` and newer" +=== "onETL ``0.10.x`` и новее" ```python linenums="1" hl_lines="1-16" reader = DBReader( @@ -512,32 +506,27 @@ But most of users use other HWM store implementations which do not have such iss assert df.columns == ["col1", "col2", "hwm_col"] ``` - But most users just use `columns=["*"]` anyway, they won't see any changes. + Но поскольку большинство пользователей все равно используют `columns=["*"]`, они не увидят никаких изменений. -- `FileDownloader.run()` now updates HWM in HWM Store not after each file is being successfully downloaded, - but after all files were handled. +- `FileDownloader.run()` теперь обновляет HWM в HWM Store после обработки всех файлов, а не после загрузки каждого отдельного файла. - This is because: + Причины изменений: - - FileDownloader can be used with `DownloadOptions(workers=N)`, which could lead to race condition - one thread can save to HWM store one HWM value when another thread will save different value. - - FileDownloader can download hundreds and thousands of files, and issuing a request to HWM Store for each file could potentially DDoS HWM Store. ([#189](https://github.com/MobileTeleSystems/onetl/pull/189)) + - FileDownloader можно использовать с `DownloadOptions(workers=N)`, что может привести к состоянию гонки - один поток может сохранить в HWM store одно значение HWM, а другой -- другое. + - FileDownloader может загружать сотни и даже тысячи файлов, и отправка запроса в HWM Store для каждого файла может потенциально привести к DDoS HWM Store. ([#189](https://github.com/MobileTeleSystems/onetl/pull/189)) - There is a exception handler which tries to save HWM to HWM store if download process was interrupted. But if it was interrupted by force, like sending `SIGKILL` event, - HWM will not be saved to HWM store, so some already downloaded files may be downloaded again next time. + Обработчик исключений пытается сохранить HWM в HWM store, если процесс загрузки был прерван. Но если он был прерван принудительно, например, отправкой события `SIGKILL`, HWM не будет сохранен в HWM store, и тогда некоторые уже загруженные файлы могут быть загружены снова. - But unexpected process kill may produce other negative impact, like some file will be downloaded partially, so this is an expected behavior. + Кроме того, неожиданное завершение процесса может привести к другим негативным последствиям, например, некоторые файлы будут загружены частично, поэтому мы считаем такую реализацию ожидаемым поведением. -## Features +## Функциональность -- Add Python 3.12 compatibility. ([#167](https://github.com/MobileTeleSystems/onetl/pull/167)) -- `Excel` file format now can be used with Spark 3.5.0. ([#187](https://github.com/MobileTeleSystems/onetl/pull/187)) -- `SnapshotBatchStagy` and `IncrementalBatchStrategy` does no raise exceptions if source does not contain any data. - Instead they stop at first iteration and return empty dataframe. ([#188](https://github.com/MobileTeleSystems/onetl/pull/188)) -- Cache result of `connection.check()` in high-level classes like `DBReader`, `FileDownloader` and so on. This makes logs less verbose. ([#190](https://github.com/MobileTeleSystems/onetl/pull/190)) +- Добавлена совместимость с Python 3.12. ([#167](https://github.com/MobileTeleSystems/onetl/pull/167)) +- Формат файлов `Excel` теперь можно использовать с Spark 3.5.0. ([#187](https://github.com/MobileTeleSystems/onetl/pull/187)) +- `SnapshotBatchStagy` и `IncrementalBatchStrategy` не вызывают исключений, если источник не содержит данных. Вместо этого они останавливаются на первой итерации и возвращают пустой dataframe. ([#188](https://github.com/MobileTeleSystems/onetl/pull/188)) +- Добавлено кэширование результата `connection.check()` в классах высокого уровня, таких как `DBReader`, `FileDownloader` и т. д. Это устраняет избыточную информативность логов. ([#190](https://github.com/MobileTeleSystems/onetl/pull/190)) -## Bug Fixes +## Исправление ошибок -- Fix `@slot` and `@hook` decorators returning methods with missing arguments in signature (Pylance, VS Code). ([#183](https://github.com/MobileTeleSystems/onetl/pull/183)) -- Kafka connector documentation said that it does support reading topic data incrementally by passing `group.id` or `groupIdPrefix`. - Actually, this is not true, because Spark does not send information to Kafka which messages were consumed. - So currently users can only read the whole topic, no incremental reads are supported. +- Исправлены декораторы `@slot` и `@hook`, возвращающие методы с отсутствующими аргументами в сигнатуре (Pylance, VS Code). ([#183](https://github.com/MobileTeleSystems/onetl/pull/183)) +- В документации Kafka connector говорилось, что он поддерживает инкрементное чтение данных топика путем передачи `group.id` или `groupIdPrefix`. На самом деле это не так, потому что Spark не отправляет в Kafka информацию о том, какие сообщения были получены. Поэтому в настоящее время пользователи могут читать весь топик целиком, а инкрементное чтение не поддерживается. diff --git a/mddocs/ru/changelog/0.10.1.md b/mddocs/ru/changelog/0.10.1.md index ba3ed40bb..b4b0d90c2 100644 --- a/mddocs/ru/changelog/0.10.1.md +++ b/mddocs/ru/changelog/0.10.1.md @@ -1,8 +1,8 @@ # 0.10.1 (2024-02-05) -## Features +## Новые возможности -- Add support of `Incremental Strategies` for `Kafka` connection: +- Добавлена поддержка `Incremental Strategies` для подключения `Kafka`: ```python reader = DBReader( @@ -15,15 +15,15 @@ df = reader.run() ``` - This lets you resume reading data from a Kafka topic starting at the last committed offset from your previous run. ([#202](https://github.com/MobileTeleSystems/onetl/pull/202)) + Использование этой стратегии позволяет возобновить чтение данных из топика Kafka, начиная с последнего зафиксированного смещения. ([#202](https://github.com/MobileTeleSystems/onetl/pull/202)) -- Add `has_data`, `raise_if_no_data` methods to `DBReader` class. ([#203](https://github.com/MobileTeleSystems/onetl/pull/203)) +- Добавлены методы `has_data`, `raise_if_no_data` в класс `DBReader`. ([#203](https://github.com/MobileTeleSystems/onetl/pull/203)) -- Updare VMware Greenplum connector from `2.1.4` to `2.3.0`. This implies: - : - Greenplum 7.x support - - [Kubernetes support](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/configure.html#k8scfg) - - New read option [gpdb.matchDistributionPolicy](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/options.html#distpolmotion) - which allows to match each Spark executor with specific Greenplum segment, avoiding redundant data transfer between Greenplum segments - - Allows overriding [Greenplum optimizer parameters](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/options.html#greenplum-gucs) in read/write operations ([#208](https://github.com/MobileTeleSystems/onetl/pull/208)) +- Обновлен коннектор VMware Greenplum с `2.1.4` до `2.3.0`. Что предоставляет: + - Поддержку Greenplum 7.x + - [Поддержку Kubernetes](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/configure.html#k8scfg) + - Новую опцию чтения [gpdb.matchDistributionPolicy](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/options.html#distpolmotion) которая позволяет сопоставить каждый Spark executor с определенным сегментом Greenplum, избегая избыточной передачи данных между сегментами Greenplum + - Возможность переопределять [параметры оптимизатора Greenplum](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/options.html#greenplum-gucs) в операциях чтения/записи ([#208](https://github.com/MobileTeleSystems/onetl/pull/208)) + +- Метод `Greenplum.get_packages()` теперь принимает необязательный аргумент `package_version`, который позволяет переопределить версию пакета коннектора Greenplum. ([#208](https://github.com/MobileTeleSystems/onetl/pull/208)) -- `Greenplum.get_packages()` method now accepts optional arg `package_version` which allows to override version of Greenplum connector package. ([#208](https://github.com/MobileTeleSystems/onetl/pull/208)) diff --git a/mddocs/ru/changelog/0.10.2.md b/mddocs/ru/changelog/0.10.2.md index 3ae7ad822..443356408 100644 --- a/mddocs/ru/changelog/0.10.2.md +++ b/mddocs/ru/changelog/0.10.2.md @@ -1,38 +1,34 @@ # 0.10.2 (2024-03-21) -## Features +## Функциональность -- Add support of Pydantic v2. ([#230](https://github.com/MobileTeleSystems/onetl/pull/230)) +- Добавлена поддержка Pydantic v2. ([#230](https://github.com/MobileTeleSystems/onetl/pull/230)) -## Improvements +## Улучшения -- Improve database connections documentation: - - Add "Types" section describing mapping between Clickhouse and Spark types - - Add "Prerequisites" section describing different aspects of connecting to Clickhouse - - Separate documentation of `DBReader` and `.sql()` / `.pipeline(...)` - - Add examples for `.fetch()` and `.execute()` ([#211](https://github.com/MobileTeleSystems/onetl/pull/211), [#228](https://github.com/MobileTeleSystems/onetl/pull/228), [#229](https://github.com/MobileTeleSystems/onetl/pull/229), [#233](https://github.com/MobileTeleSystems/onetl/pull/233), [#234](https://github.com/MobileTeleSystems/onetl/pull/234), [#235](https://github.com/MobileTeleSystems/onetl/pull/235), [#236](https://github.com/MobileTeleSystems/onetl/pull/236), [#240](https://github.com/MobileTeleSystems/onetl/pull/240)) +- Улучшена документация по подключениям к базам данных: + - Добавлен раздел "Типы" с описанием соответствия типов Clickhouse и Spark + - Добавлен раздел "Prerequisites" с описанием различных аспектов подключения к Clickhouse + - Разделена документация `DBReader` и `.sql()` / `.pipeline(...)` + - Добавлены примеры для `.fetch()` и `.execute()` ([#211](https://github.com/MobileTeleSystems/onetl/pull/211), [#228](https://github.com/MobileTeleSystems/onetl/pull/228), [#229](https://github.com/MobileTeleSystems/onetl/pull/229), [#233](https://github.com/MobileTeleSystems/onetl/pull/233), [#234](https://github.com/MobileTeleSystems/onetl/pull/234), [#235](https://github.com/MobileTeleSystems/onetl/pull/235), [#236](https://github.com/MobileTeleSystems/onetl/pull/236), [#240](https://github.com/MobileTeleSystems/onetl/pull/240)) -- Add notes to Greenplum documentation about issues with IP resolution and building `gpfdist` URL ([#228](https://github.com/MobileTeleSystems/onetl/pull/228)) +- Добавлены примечания в документацию Greenplum о проблемах с разрешением IP-адресов и построением URL `gpfdist` ([#228](https://github.com/MobileTeleSystems/onetl/pull/228)) -- Allow calling `MongoDB.pipeline(...)` with passing just collection name, without explicit aggregation pipeline. ([#237](https://github.com/MobileTeleSystems/onetl/pull/237)) +- Разрешен вызов `MongoDB.pipeline(...)` с передачей только имени коллекции, без явного указания aggregation pipeline. ([#237](https://github.com/MobileTeleSystems/onetl/pull/237)) -- Update default `Postgres(extra={...})` to include `{"stringtype": "unspecified"}` option. - This allows to write text data to non-text column (or vice versa), relying to Postgres cast capabilities. +- Обновлен default `Postgres(extra={...})` для включения опции `{"stringtype": "unspecified"}`. Это позволяет записывать текстовые данные в нетекстовый столбец (или наоборот), полагаясь на возможности приведения типов Postgres. - For example, now it is possible to read column of type `money` as Spark's `StringType()`, and write it back to the same column, - without using intermediate columns or tables. ([#229](https://github.com/MobileTeleSystems/onetl/pull/229)) + Например, теперь можно прочитать столбец типа `money` как тип `StringType()` Apache Spark, и записать его обратно в тот же столбец, без использования промежуточных столбцов или таблиц. ([#229](https://github.com/MobileTeleSystems/onetl/pull/229)) -## Bug Fixes +## Исправление ошибок -- Return back handling of `DBReader(columns="string")`. This was a valid syntax up to v0.10 release, but it was removed because - most of users neved used it. It looks that we were wrong, returning this behavior back, but with deprecation warning. ([#238](https://github.com/MobileTeleSystems/onetl/pull/238)) +- Возвращена обработка `DBReader(columns="string")`. Это был валидный синтаксис до версии v0.10, но он был удален, потому что большинство пользователей им не пользовались. Похоже, мы ошибались, возвращаем это поведение обратно, но с предупреждением об устаревании. ([#238](https://github.com/MobileTeleSystems/onetl/pull/238)) -- Downgrade Greenplum package version from `2.3.0` to `2.2.0`. ([#239](https://github.com/MobileTeleSystems/onetl/pull/239)) +- Понижена версия пакета Greenplum с `2.3.0` до `2.2.0`. ([#239](https://github.com/MobileTeleSystems/onetl/pull/239)) - This is because version 2.3.0 introduced issues with writing data to Greenplum 6.x. - Connector can open transaction with `SELECT * FROM table LIMIT 0` query, but does not close it, which leads to deadlocks. + Это связано с тем, что версия 2.3.0 вызвала проблемы с записью данных в Greenplum 6.x. Коннектор может открыть транзакцию с запросом `SELECT * FROM table LIMIT 0`, но не закрывает ее, что приводит к deadlock'ам. - For using this connector with Greenplum 7.x, please pass package version explicitly: + Для использования этого коннектора с Greenplum 7.x, пожалуйста, передайте версию пакета явно: ```python maven_packages = Greenplum.get_packages(package_version="2.3.0", ...) diff --git a/mddocs/ru/changelog/0.11.0.md b/mddocs/ru/changelog/0.11.0.md index c77c977b0..4479d91ce 100644 --- a/mddocs/ru/changelog/0.11.0.md +++ b/mddocs/ru/changelog/0.11.0.md @@ -1,40 +1,41 @@ # 0.11.0 (2024-05-27) -## Breaking Changes +## Критические изменения -There can be some changes in connection behavior, related to version upgrades. So we mark these changes as **breaking** although -most of users will not see any differences. +Могут быть некоторые изменения в поведении подключения, связанные с обновлениями версий. Поэтому мы отмечаем такие изменения как **breaking**, хотя большинство пользователей не увидят никаких различий. -- Update Clickhouse JDBC driver to latest version ([#249](https://github.com/MobileTeleSystems/onetl/pull/249)): - - Package was renamed `ru.yandex.clickhouse:clickhouse-jdbc` → `com.clickhouse:clickhouse-jdbc`. - - Package version changed `0.3.2` → `0.6.0-patch5`. - - Driver name changed `ru.yandex.clickhouse.ClickHouseDriver` → `com.clickhouse.jdbc.ClickHouseDriver`. +- Обновление JDBC драйвера Clickhouse до последней версии ([#249](https://github.com/MobileTeleSystems/onetl/pull/249)): + - Пакет был переименован `ru.yandex.clickhouse:clickhouse-jdbc` → `com.clickhouse:clickhouse-jdbc`. + - Версия пакета изменена `0.3.2` → `0.6.0-patch5`. + - Имя драйвера изменено `ru.yandex.clickhouse.ClickHouseDriver` → `com.clickhouse.jdbc.ClickHouseDriver`. - This brings up several fixes for Spark \<-> Clickhouse type compatibility, and also Clickhouse clusters support. + Это приводит к необходимости нескольких изменений для совместимости типов Spark <-> Clickhouse, а также поддержки кластеров Clickhouse. -- Update other JDBC drivers to latest versions: +- Обновление других JDBC драйверов до последних версий: - MSSQL `12.2.0` → `12.6.2` ([#254](https://github.com/MobileTeleSystems/onetl/pull/254)). - MySQL `8.0.33` → `8.4.0` ([#253](https://github.com/MobileTeleSystems/onetl/pull/253), [#285](https://github.com/MobileTeleSystems/onetl/pull/285)). - Oracle `23.2.0.0` → `23.4.0.24.05` ([#252](https://github.com/MobileTeleSystems/onetl/pull/252), [#284](https://github.com/MobileTeleSystems/onetl/pull/284)). - Postgres `42.6.0` → `42.7.3` ([#251](https://github.com/MobileTeleSystems/onetl/pull/251)). -- Update MongoDB connector to latest version: `10.1.1` → `10.3.0` ([#255](https://github.com/MobileTeleSystems/onetl/pull/255), [#283](https://github.com/MobileTeleSystems/onetl/pull/283)). +- Обновление коннектора MongoDB до последней версии: `10.1.1` → `10.3.0` ([#255](https://github.com/MobileTeleSystems/onetl/pull/255), [#283](https://github.com/MobileTeleSystems/onetl/pull/283)). - This brings up Spark 3.5 support. + Благодаря которому появляется поддержка Spark 3.5. -- Update `XML` package to latest version: `0.17.0` → `0.18.0` ([#259](https://github.com/MobileTeleSystems/onetl/pull/259)). +- Обновление пакета `XML` до последней версии: `0.17.0` → `0.18.0` ([#259](https://github.com/MobileTeleSystems/onetl/pull/259)). - This brings few bugfixes with datetime format handling. + Которое устраняет несколько ошибок обработки формата datetime. -- For JDBC connections add new `SQLOptions` class for `DB.sql(query, options=...)` method ([#272](https://github.com/MobileTeleSystems/onetl/pull/272)). +- Для JDBC соединений добавлен новый класс `SQLOptions` для метода `DB.sql(query, options=...)` ([#272](https://github.com/MobileTeleSystems/onetl/pull/272)). - Firsly, to keep naming more consistent. + Благодаря этому: - Secondly, some of options are not supported by `DB.sql(...)` method, but supported by `DBReader`. - For example, `SQLOptions` do not support `partitioning_mode` and require explicit definition of `lower_bound` and `upper_bound` when `num_partitions` is greater than 1. - `ReadOptions` does support `partitioning_mode` and allows skipping `lower_bound` and `upper_bound` values. + Во-первых, лучше поддерживается согласованность именования. - This require some code changes. Before: + Во-вторых, некоторые опции не поддерживаются методом `DB.sql(...)`, но поддерживаются `DBReader`. + Например, `SQLOptions` не поддерживает `partitioning_mode` и требует явного определения `lower_bound` и `upper_bound`, когда `num_partitions` больше 1. + `ReadOptions` поддерживает `partitioning_mode` и позволяет пропускать значения `lower_bound` и `upper_bound`. + + Это требует некоторых изменений в коде. Раньше: ```python from onetl.connection import Postgres @@ -54,7 +55,7 @@ most of users will not see any differences. ) ``` - After: + Теперь: ```python from onetl.connection import Postgres @@ -76,15 +77,15 @@ most of users will not see any differences. ) ``` - For now, `DB.sql(query, options=...)` can accept `ReadOptions` to keep backward compatibility, but emits deprecation warning. - The support will be removed in `v1.0.0`. + На данный момент `DB.sql(query, options=...)` может принимать `ReadOptions` для сохранения обратной совместимости, но выдает предупреждение об устаревании. + Поддержка будет удалена в `v1.0.0`. -- Split up `JDBCOptions` class into `FetchOptions` and `ExecuteOptions` ([#274](https://github.com/MobileTeleSystems/onetl/pull/274)). +- Разделение класса `JDBCOptions` на `FetchOptions` и `ExecuteOptions` ([#274](https://github.com/MobileTeleSystems/onetl/pull/274)). - New classes are used by `DB.fetch(query, options=...)` and `DB.execute(query, options=...)` methods respectively. - This is mostly to keep naming more consistent. + Новые классы используются методами `DB.fetch(query, options=...)` и `DB.execute(query, options=...)` соответственно. + Это изменение необходимо в основном для большей консистентности именования. - This require some code changes. Before: + Оно также требует некоторых изменений в коде. Раньше: ```python from onetl.connection import Postgres @@ -104,7 +105,7 @@ most of users will not see any differences. ) ``` - After: + Теперь: ```python from onetl.connection import Postgres @@ -126,16 +127,14 @@ most of users will not see any differences. ) ``` - For now, `DB.fetch(query, options=...)` and `DB.execute(query, options=...)` can accept `JDBCOptions`, to keep backward compatibility, - but emit a deprecation warning. The old class will be removed in `v1.0.0`. + На данный момент `DB.fetch(query, options=...)` и `DB.execute(query, options=...)` могут принимать `JDBCOptions` для сохранения обратной совместимости, но выдают предупреждение об устаревании. Старый класс будет удален в `v1.0.0`. -- Serialize `ColumnDatetimeHWM` to Clickhouse's `DateTime64(6)` (precision up to microseconds) instead of `DateTime` (precision up to seconds) ([#267](https://github.com/MobileTeleSystems/onetl/pull/267)). +- Сериализация `ColumnDatetimeHWM` в тип `DateTime64(6)` Clickhouse (точность до микросекунд) вместо `DateTime` (точность до секунд) ([#267](https://github.com/MobileTeleSystems/onetl/pull/267)). - In previous onETL versions, `ColumnDatetimeHWM` value was rounded to the second, and thus reading some rows that were read in previous runs, - producing duplicates. + В предыдущих версиях onETL значение `ColumnDatetimeHWM` округлялось до секунды, и, таким образом, чтение некоторых строк, которые были прочитаны раньше, приводило к дублированию данных. - For Clickhouse versions below 21.1 comparing column of type `DateTime` with a value of type `DateTime64` is not supported, returning an empty dataframe. - To avoid this, replace: + Для версий Clickhouse ниже 21.1 сравнение столбца типа `DateTime` со значением типа `DateTime64` не поддерживается, возвращая пустой dataframe. + Чтобы избежать этого, замените: ```python DBReader( @@ -147,7 +146,7 @@ most of users will not see any differences. ) ``` - with: + на: ```python DBReader( @@ -159,16 +158,15 @@ most of users will not see any differences. ) ``` -- Pass JDBC connection extra params as `properties` dict instead of URL with query part ([#268](https://github.com/MobileTeleSystems/onetl/pull/268)). +- Передача дополнительных параметров подключения JDBC как словаря `properties` вместо URL с "query part" ([#268](https://github.com/MobileTeleSystems/onetl/pull/268)). - This allows passing custom connection parameters like `Clickhouse(extra={"custom_http_options": "option1=value1,option2=value2"})` - without need to apply urlencode to parameter value, like `option1%3Dvalue1%2Coption2%3Dvalue2`. + Это позволяет передавать пользовательские параметры соединения, такие как `Clickhouse(extra={"custom_http_options": "option1=value1,option2=value2"})` без необходимости применять urlencode к значению параметра, например `option1%3Dvalue1%2Coption2%3Dvalue2`. -## Features +## Функциональность -Improve user experience with Kafka messages and Database tables with serialized columns, like JSON/XML. +Улучшение пользовательского опыта при работе с сообщениями Kafka и таблицами баз данных, содержащими сериализованные столбцы, такие как JSON или XML. -- Allow passing custom package version as argument for `DB.get_packages(...)` method of several DB connectors: +- Разрешена передача пользовательской версии пакета в качестве аргумента для метода `DB.get_packages(...)` нескольких коннекторах к БД: - `Clickhouse.get_packages(package_version=..., apache_http_client_version=...)` ([#249](https://github.com/MobileTeleSystems/onetl/pull/249)). - `MongoDB.get_packages(scala_version=..., spark_version=..., package_version=...)` ([#255](https://github.com/MobileTeleSystems/onetl/pull/255)). - `MySQL.get_packages(package_version=...)` ([#253](https://github.com/MobileTeleSystems/onetl/pull/253)). @@ -176,37 +174,37 @@ Improve user experience with Kafka messages and Database tables with serialized - `Oracle.get_packages(java_version=..., package_version=...)` ([#252](https://github.com/MobileTeleSystems/onetl/pull/252)). - `Postgres.get_packages(package_version=...)` ([#251](https://github.com/MobileTeleSystems/onetl/pull/251)). - `Teradata.get_packages(package_version=...)` ([#256](https://github.com/MobileTeleSystems/onetl/pull/256)). - Now users can downgrade or upgrade connection without waiting for next onETL release. Previously only `Kafka` and `Greenplum` supported this feature. -- Add `FileFormat.parse_column(...)` method to several classes: + Теперь пользователи могут понижать или повышать версию подключения, не дожидаясь следующего релиза onETL. Раньше такую возможность поддерживали только `Kafka` и `Greenplum`. +- Добавлен метод `FileFormat.parse_column(...)` в несколько классов: - `Avro.parse_column(col)` ([#265](https://github.com/MobileTeleSystems/onetl/pull/265)). - `JSON.parse_column(col, schema=...)` ([#257](https://github.com/MobileTeleSystems/onetl/pull/257)). - `CSV.parse_column(col, schema=...)` ([#258](https://github.com/MobileTeleSystems/onetl/pull/258)). - `XML.parse_column(col, schema=...)` ([#269](https://github.com/MobileTeleSystems/onetl/pull/269)). - This allows parsing data in `value` field of Kafka message or string/binary column of some table as a nested Spark structure. -- Add `FileFormat.serialize_column(...)` method to several classes: + Это позволяет анализировать данные в поле `value` сообщения Kafka или строковый/двоичный столбец некоторой таблицы как вложенную структуру Spark. +- Добавлен метод `FileFormat.serialize_column(...)` в несколько классов: - `Avro.serialize_column(col)` ([#265](https://github.com/MobileTeleSystems/onetl/pull/265)). - `JSON.serialize_column(col)` ([#257](https://github.com/MobileTeleSystems/onetl/pull/257)). - `CSV.serialize_column(col)` ([#258](https://github.com/MobileTeleSystems/onetl/pull/258)). - This allows saving Spark nested structures or arrays to `value` field of Kafka message or string/binary column of some table. + Это позволяет сохранять вложенные структуры Spark или массивы в поле `value` сообщения Kafka или строковый/двоичный столбец некоторой таблицы. -## Improvements +## Улучшения -Few documentation improvements. +Несколько улучшений документации. -- Replace all `assert` in documentation with doctest syntax. This should make documentation more readable ([#273](https://github.com/MobileTeleSystems/onetl/pull/273)). -- Add generic `Troubleshooting` guide ([#275](https://github.com/MobileTeleSystems/onetl/pull/275)). -- Improve Kafka documentation: - - Add "Prerequisites" page describing different aspects of connecting to Kafka. - - Improve "Reading from" and "Writing to" page of Kafka documentation, add more examples and usage notes. - - Add "Troubleshooting" page ([#276](https://github.com/MobileTeleSystems/onetl/pull/276)). -- Improve Hive documentation: - - Add "Prerequisites" page describing different aspects of connecting to Hive. - - Improve "Reading from" and "Writing to" page of Hive documentation, add more examples and recommendations. - - Improve "Executing statements in Hive" page of Hive documentation. ([#278](https://github.com/MobileTeleSystems/onetl/pull/278)). -- Add "Prerequisites" page describing different aspects of using SparkHDFS and SparkS3 connectors. ([#279](https://github.com/MobileTeleSystems/onetl/pull/279)). -- Add note about connecting to Clickhouse cluster. ([#280](https://github.com/MobileTeleSystems/onetl/pull/280)). -- Add notes about versions when specific class/method/attribute/argument was added, renamed or changed behavior ([#282](https://github.com/MobileTeleSystems/onetl/pull/282)). +- `assert` заменены синтаксисом doctest. Это должно сделать документацию более читаемой ([#273](https://github.com/MobileTeleSystems/onetl/pull/273)). +- Добавлено общее руководство `Troubleshooting` ([#275](https://github.com/MobileTeleSystems/onetl/pull/275)). +- Улучшена документация Kafka: + - Добавлена страница "Prerequisites", описывающая различные аспекты подключения к Kafka. + - Дополнительные примеры и примечания добавлены на страницы "Reading from" и "Writing to" документации Kafka. + - Добавлена страница "Troubleshooting" ([#276](https://github.com/MobileTeleSystems/onetl/pull/276)). +- Улучшена документация Hive: + - Добавлена страница "Prerequisites", описывающая различные аспекты подключения к Hive. + - Дополнительные примеры и примечания добавлены на страницы "Reading from" и "Writing to" документации Hive. + - Страница "Executing statements in Hive" документации Hive стала более содержательной. ([#278](https://github.com/MobileTeleSystems/onetl/pull/278)). +- Добавлена страница "Prerequisites", описывающая различные аспекты использования коннекторов SparkHDFS и SparkS3. ([#279](https://github.com/MobileTeleSystems/onetl/pull/279)). +- Добавлены примечания о подключении к кластеру Clickhouse. ([#280](https://github.com/MobileTeleSystems/onetl/pull/280)). +- Добавлены примечания о версиях, когда был добавлен, переименован или изменен класс/метод/атрибут/аргумент ([#282](https://github.com/MobileTeleSystems/onetl/pull/282)). -## Bug Fixes +## Исправление ошибок -- Fix missing `pysmb` package after installing `pip install onetl[files]` . +- После установки `pip install onetl[files]` ранее отсутствовал пакет `pysmb`. Теперь этот недостаток устранен. diff --git a/mddocs/ru/changelog/0.11.1.md b/mddocs/ru/changelog/0.11.1.md index 823afe3be..2aebc25e1 100644 --- a/mddocs/ru/changelog/0.11.1.md +++ b/mddocs/ru/changelog/0.11.1.md @@ -1,9 +1,10 @@ # 0.11.1 (2024-05-29) -## Features +## Функциональность -- Change `MSSQL.port` default from `1433` to `None`, allowing use of `instanceName` to detect port number. ([#287](https://github.com/MobileTeleSystems/onetl/pull/287)) +- Изменено значение по умолчанию атрибута `MSSQL.port` с `1433` на `None`, что позволяет использовать `instanceName` для определения номера порта. ([#287](https://github.com/MobileTeleSystems/onetl/pull/287)) -## Bug Fixes +## Исправление ошибок + +- Удален `fetchsize` из `JDBC.WriteOptions`. ([#288](https://github.com/MobileTeleSystems/onetl/pull/288)) -- Remove `fetchsize` from `JDBC.WriteOptions`. ([#288](https://github.com/MobileTeleSystems/onetl/pull/288)) diff --git a/mddocs/ru/changelog/0.11.2.md b/mddocs/ru/changelog/0.11.2.md index 9278d22f8..bf7e1cc2d 100644 --- a/mddocs/ru/changelog/0.11.2.md +++ b/mddocs/ru/changelog/0.11.2.md @@ -1,5 +1,5 @@ # 0.11.2 (2024-09-02) -## Bug Fixes +## Исправление ошибок -- Fix passing `Greenplum(extra={"options": ...})` during read/write operations. ([#308](https://github.com/MobileTeleSystems/onetl/pull/308)) +- Исправлена передача `Greenplum(extra={"options": ...})` во время операций чтения/записи. ([#308](https://github.com/MobileTeleSystems/onetl/pull/308)) diff --git a/mddocs/ru/changelog/0.12.0.md b/mddocs/ru/changelog/0.12.0.md index aeaca1d23..10def116b 100644 --- a/mddocs/ru/changelog/0.12.0.md +++ b/mddocs/ru/changelog/0.12.0.md @@ -1,54 +1,51 @@ # 0.12.0 (2024-09-03) -## Breaking Changes +## Критические изменения -- Change connection URL used for generating HWM names of S3 and Samba sources: +- Изменен URL-адрес подключения, используемый для генерации HWM-имен источников S3 и Samba: - `smb://host:port` -> `smb://host:port/share` - `s3://host:port` -> `s3://host:port/bucket` ([#304](https://github.com/MobileTeleSystems/onetl/pull/304)) -- Update DB connectors/drivers to latest versions: +- Обновлены коннекторы/драйверы БД до последних версий: - Clickhouse `0.6.0-patch5` → `0.6.5` - MongoDB `10.3.0` → `10.4.0` - MSSQL `12.6.2` → `12.8.1` - MySQL `8.4.0` → `9.0.0` - Oracle `23.4.0.24.05` → `23.5.0.24.07` - Postgres `42.7.3` → `42.7.4` -- Update `Excel` package from `0.20.3` to `0.20.4`, to include Spark 3.5.1 support. ([#306](https://github.com/MobileTeleSystems/onetl/pull/306)) +- Обновлен пакет `Excel` с `0.20.3` до `0.20.4`, для включения поддержки Spark 3.5.1. ([#306](https://github.com/MobileTeleSystems/onetl/pull/306)) -## Features +## Функциональность -- Add support for specifying file formats (`ORC`, `Parquet`, `CSV`, etc.) in `HiveWriteOptions.format` ([#292](https://github.com/MobileTeleSystems/onetl/pull/292)): +- Добавлена поддержка указания форматов файлов (`ORC`, `Parquet`, `CSV` и т.д.) в `HiveWriteOptions.format` ([#292](https://github.com/MobileTeleSystems/onetl/pull/292)): ```python Hive.WriteOptions(format=ORC(compression="snappy")) ``` -- Collect Spark execution metrics in following methods, and log then in DEBUG mode: +- Сбор метрик выполнения Spark и их логирование в режиме DEBUG в следующих методах: - `DBWriter.run()` - `FileDFWriter.run()` - `Hive.sql()` - `Hive.execute()` - This is implemented using custom `SparkListener` which wraps the entire method call, and - then report collected metrics. But these metrics sometimes may be missing due to Spark architecture, - so they are not reliable source of information. That's why logs are printed only in DEBUG mode, and - are not returned as method call result. ([#303](https://github.com/MobileTeleSystems/onetl/pull/303)) + Это реализовано с использованием пользовательского `SparkListener`, который оборачивает весь вызов метода, и затем сообщает собранные метрики. Но эти метрики иногда могут отсутствовать из-за архитектуры Spark, и таким образом они не являются надежным источником информации. Поэтому логи выводятся только в режиме DEBUG, и не возвращаются как результат вызова метода. ([#303](https://github.com/MobileTeleSystems/onetl/pull/303)) -- Generate default `jobDescription` based on currently executed method. Examples: +- Генерация `jobDescription` по умолчанию на основе текущего выполняемого метода. Примеры: - `DBWriter.run(schema.table) -> Postgres[host:5432/database]` - `MongoDB[localhost:27017/admin] -> DBReader.has_data(mycollection)` - `Hive[cluster].execute()` - If user already set custom `jobDescription`, it will left intact. ([#304](https://github.com/MobileTeleSystems/onetl/pull/304)) + Если пользователь уже установил пользовательский `jobDescription`, он останется нетронутым. ([#304](https://github.com/MobileTeleSystems/onetl/pull/304)) -- Add log.info about JDBC dialect usage ([#305](https://github.com/MobileTeleSystems/onetl/pull/305)): +- Добавлен log.info об использовании диалекта JDBC ([#305](https://github.com/MobileTeleSystems/onetl/pull/305)): ```text |MySQL| Detected dialect: 'org.apache.spark.sql.jdbc.MySQLDialect' ``` -- Log estimated size of in-memory dataframe created by `JDBC.fetch` and `JDBC.execute` methods. ([#303](https://github.com/MobileTeleSystems/onetl/pull/303)) +- Логирование оценочного размера dataframe в памяти, созданного методами `JDBC.fetch` и `JDBC.execute`. ([#303](https://github.com/MobileTeleSystems/onetl/pull/303)) -## Bug Fixes +## Исправление ошибок -- Fix passing `Greenplum(extra={"options": ...})` during read/write operations. ([#308](https://github.com/MobileTeleSystems/onetl/pull/308)) -- Do not raise exception if yield-based hook whas something past (and only one) `yield`. +- Исправлена передача `Greenplum(extra={"options": ...})` во время операций чтения/записи. ([#308](https://github.com/MobileTeleSystems/onetl/pull/308)) +- Не вызывается исключение, если хук на базе yield выдал что-то единственное после `yield`. diff --git a/mddocs/ru/changelog/0.12.1.md b/mddocs/ru/changelog/0.12.1.md index d32401517..4e97ad677 100644 --- a/mddocs/ru/changelog/0.12.1.md +++ b/mddocs/ru/changelog/0.12.1.md @@ -1,17 +1,18 @@ # 0.12.1 (2024-10-28) -## Features +## Функциональность -- Log detected JDBC dialect while using `DBWriter`. +- При использовании `DBWriter` логируется обнаруженный диалект JDBC. -## Bug Fixes +## Исправление ошибок -- Fix `SparkMetricsRecorder` failing when receiving `SparkListenerTaskEnd` without `taskMetrics` (e.g. executor was killed by OOM). ([#313](https://github.com/MobileTeleSystems/onetl/pull/313)) -- Call `kinit` before checking for HDFS active namenode. -- Wrap `kinit` with `threading.Lock` to avoid multithreading issues. -- Immediately show `kinit` errors to user, instead of hiding them. -- Use `AttributeError` instead of `ImportError` in module's `__getattr__` method, to make code compliant with Python spec. +- Исправлена ошибка `SparkMetricsRecorder`, возникающая при получении `SparkListenerTaskEnd` без `taskMetrics` (например, executor был убит из-за OOM). ([#313](https://github.com/MobileTeleSystems/onetl/pull/313)) +- Вызывается `kinit` перед проверкой активного namenode HDFS. +- `kinit` обернут в `threading.Lock`, чтобы избежать проблем с многопоточностью. +- Ошибки `kinit` немедленно отображаюся пользователю, вместо того, чтобы скрывать их. +- `AttributeError` используется вместо `ImportError` в методе `__getattr__` модуля, чтобы код соответствовал спецификации Python. -## Doc only Changes +## Изменения в документации (только) + +- Добавлено примечание о пакете [spark-dialect-extension](https://github.com/MobileTeleSystems/spark-dialect-extension) в документацию коннектора Clickhouse. ([#310](https://github.com/MobileTeleSystems/onetl/pull/310)) -- Add note about [spark-dialect-extension](https://github.com/MobileTeleSystems/spark-dialect-extension) package to Clickhouse connector documentation. ([#310](https://github.com/MobileTeleSystems/onetl/pull/310)) diff --git a/mddocs/ru/changelog/0.12.2.md b/mddocs/ru/changelog/0.12.2.md index 23a8d383d..28f9edb84 100644 --- a/mddocs/ru/changelog/0.12.2.md +++ b/mddocs/ru/changelog/0.12.2.md @@ -1,18 +1,19 @@ # 0.12.2 (2024-11-12) -## Improvements +## Улучшения -- Change Spark `jobDescription` for DBReader & FileDFReader from `DBReader.run() -> Connection` to `Connection -> DBReader.run()`. +- Изменен Spark `jobDescription` для DBReader и FileDFReader с `DBReader.run() -> Connection` на `Connection -> DBReader.run()`. -## Bug Fixes +## Исправления ошибок -- Fix `log_hwm` result for `KeyValueIntHWM` (used by Kafka). ([#316](https://github.com/MobileTeleSystems/onetl/pull/316)) -- Fix `log_collection` hiding values of `Kafka.addresses` in logs with `INFO` level. ([#316](https://github.com/MobileTeleSystems/onetl/pull/316)) +- Исправлен результат `log_hwm` для `KeyValueIntHWM` (используется Kafka). ([#316](https://github.com/MobileTeleSystems/onetl/pull/316)) +- Исправлено сокрытие значений `Kafka.addresses` в логах с уровнем `INFO` в `log_collection`. ([#316](https://github.com/MobileTeleSystems/onetl/pull/316)) -## Dependencies +## Зависимости -- Allow using [etl-entities==2.4.0](https://github.com/MobileTeleSystems/etl-entities/releases/tag/2.4.0). +- Разрешено использование [etl-entities==2.4.0](https://github.com/MobileTeleSystems/etl-entities/releases/tag/2.4.0). -## Doc only Changes +## Изменения только в документации + +- Исправлены ссылки на документацию по типам даты и времени MSSQL. -- Fix links to MSSQL date & time type documentation. diff --git a/mddocs/ru/changelog/0.12.3.md b/mddocs/ru/changelog/0.12.3.md index 741c74e1f..ff44131b8 100644 --- a/mddocs/ru/changelog/0.12.3.md +++ b/mddocs/ru/changelog/0.12.3.md @@ -1,5 +1,5 @@ # 0.12.3 (2024-11-22) -## Bug Fixes +## Исправление ошибок -- Allow passing table names in format `schema."table.with.dots"` to `DBReader(source=...)` and `DBWriter(target=...)`. +- Разрешена передача имен таблиц в формате `schema."table.with.dots"` для `DBReader(source=...)` и `DBWriter(target=...)`. diff --git a/mddocs/ru/changelog/0.12.4.md b/mddocs/ru/changelog/0.12.4.md index 3ebc57a87..9ab18b4d7 100644 --- a/mddocs/ru/changelog/0.12.4.md +++ b/mddocs/ru/changelog/0.12.4.md @@ -1,5 +1,5 @@ # 0.12.4 (2024-11-27) -## Bug Fixes +## Исправление ошибок -- Fix `DBReader(conn=oracle, options={"partitioning_mode": "hash"})` lead to data skew in last partition due to wrong `ora_hash` usage. ([#319](https://github.com/MobileTeleSystems/onetl/pull/319)) +- Исправлен `DBReader(conn=oracle, options={"partitioning_mode": "hash"})` приводивший к перекосу данных в последней партиции из-за некорректного использования `ora_hash`. ([#319](https://github.com/MobileTeleSystems/onetl/pull/319)) diff --git a/mddocs/ru/changelog/0.12.5.md b/mddocs/ru/changelog/0.12.5.md index c542a50fd..2e4855a8d 100644 --- a/mddocs/ru/changelog/0.12.5.md +++ b/mddocs/ru/changelog/0.12.5.md @@ -1,13 +1,13 @@ # 0.12.5 (2024-12-03) -## Improvements +## Улучшения -- Use `sipHash64` instead of `md5` in Clickhouse for reading data with `{"partitioning_mode": "hash"}`, as it is 5 times faster. -- Use `hashtext` instead of `md5` in Postgres for reading data with `{"partitioning_mode": "hash"}`, as it is 3-5 times faster. -- Use `BINARY_CHECKSUM` instead of `HASHBYTES` in MSSQL for reading data with `{"partitioning_mode": "hash"}`, as it is 5 times faster. +- `sipHash64` используется вместо `md5` в Clickhouse для чтения данных с `{"partitioning_mode": "hash"}`, так как это в 5 раз быстрее. +- `hashtext` используется вместо `md5` в Postgres для чтения данных с `{"partitioning_mode": "hash"}`, так как это в 3-5 раз быстрее. +- `BINARY_CHECKSUM` используется вместо `HASHBYTES` в MSSQL для чтения данных с `{"partitioning_mode": "hash"}`, так как это в 5 раз быстрее. -## Big fixes +## Исправления -- In JDBC sources wrap `MOD(partitionColumn, numPartitions)` with `ABS(...)` to make al returned values positive. This prevents data skew. -- Fix reading table data from MSSQL using `{"partitioning_mode": "hash"}` with `partitionColumn` of integer type. -- Fix reading table data from Postgres using `{"partitioning_mode": "hash"}` lead to data skew (all the data was read into one Spark partition). +- Для JDBC источников `MOD(partitionColumn, numPartitions)` оборачивается в `ABS(...)`, чтобы все возвращаемые значения были положительными. Это предотвращает перекос данных. +- Исправлено чтение данных из MSSQL с использованием `{"partitioning_mode": "hash"}` в случае `partitionColumn` целочисленного типа. +- Исправлено чтение данных из Postgres с использованием `{"partitioning_mode": "hash"}` приводившее к перекосу данных (все данные считывались в один раздел Spark). diff --git a/mddocs/ru/changelog/0.13.0.md b/mddocs/ru/changelog/0.13.0.md index 37ae8368a..951019bf7 100644 --- a/mddocs/ru/changelog/0.13.0.md +++ b/mddocs/ru/changelog/0.13.0.md @@ -1,22 +1,20 @@ # 0.13.0 (2025-02-24) -🎉 3 years since first release 0.1.0 🎉 +🎉 3 года с первого релиза 0.1.0 🎉 -## Breaking Changes +## Критические изменения -- Add Python 3.13. support. ([#298](https://github.com/MobileTeleSystems/onetl/pull/298)) +- Добавлена поддержка Python 3.13. ([#298](https://github.com/MobileTeleSystems/onetl/pull/298)) -- Change the logic of `FileConnection.walk` and `FileConnection.list_dir`. ([#327](https://github.com/MobileTeleSystems/onetl/pull/327)) +- Изменена логика `FileConnection.walk` и `FileConnection.list_dir`. ([#327](https://github.com/MobileTeleSystems/onetl/pull/327)) - Previously `limits.stops_at(path) == True` considered as "return current file and stop", and could lead to exceeding some limit. - Not it means "stop immediately". + Ранее `limits.stops_at(path) == True` выполнялось как "вернуть текущий файл и остановиться", и могло привести к превышению некоторого лимита. Теперь это означает "остановиться немедленно". -- Change default value for `FileDFWriter.Options(if_exists=...)` from `error` to `append`, - to make it consistent with other `.Options()` classes within onETL. ([#343](https://github.com/MobileTeleSystems/onetl/pull/343)) +- Изменено значение по умолчанию для `FileDFWriter.Options(if_exists=...)` с `error` на `append`, чтобы соответствовать другим классам `.Options()` внутри onETL. ([#343](https://github.com/MobileTeleSystems/onetl/pull/343)) -## Features +## Функциональность -- Add support for `FileModifiedTimeHWM` HWM class (see [etl-entities 2.5.0](https://github.com/MobileTeleSystems/etl-entities/releases/tag/2.5.0)): +- Добавлена поддержка для класса HWM `FileModifiedTimeHWM` (см. [etl-entities 2.5.0](https://github.com/MobileTeleSystems/etl-entities/releases/tag/2.5.0)): ```python from etl_entitites.hwm import FileModifiedTimeHWM @@ -32,9 +30,9 @@ downloader.run() ``` -- Introduce `FileSizeRange(min=..., max=...)` filter class. ([#325](https://github.com/MobileTeleSystems/onetl/pull/325)) +- Добавлен класс фильтра `FileSizeRange(min=..., max=...)`. ([#325](https://github.com/MobileTeleSystems/onetl/pull/325)) - Now users can set `FileDownloader` / `FileMover` to download/move only files with specific file size range: + Теперь пользователи могут настроить `FileDownloader` / `FileMover` для скачивания/перемещения только файлов с определенным диапазоном их размеров: ```python from onetl.file import FileDownloader @@ -46,9 +44,9 @@ ) ``` -- Introduce `TotalFilesSize(...)` limit class. ([#326](https://github.com/MobileTeleSystems/onetl/pull/326)) +- Добавлен класс лимита `TotalFilesSize(...)`. ([#326](https://github.com/MobileTeleSystems/onetl/pull/326)) - Now users can set `FileDownloader` / `FileMover` to stop downloading/moving files after reaching a certain amount of data: + Теперь пользователи могут настроить `FileDownloader` / `FileMover` для остановки скачивания/перемещения файлов после достижения определенного объема обработанных данных: ```python from datetime import datetime, timedelta @@ -61,9 +59,9 @@ ) ``` -- Implement `FileModifiedTime(since=..., until=...)` file filter. ([#330](https://github.com/MobileTeleSystems/onetl/pull/330)) +- Реализован фильтр файлов `FileModifiedTime(since=..., until=...)`. ([#330](https://github.com/MobileTeleSystems/onetl/pull/330)) - Now users can set `FileDownloader` / `FileMover` to download/move only files with specific file modification time: + Теперь пользователи могут настроить `FileDownloader` / `FileMover` для скачивания/перемещения только файлов с определенным временем изменения: ```python from datetime import datetime, timedelta @@ -76,9 +74,9 @@ ) ``` -- Add `SparkS3.get_exclude_packages()` and `Kafka.get_exclude_packages()` methods. ([#341](https://github.com/MobileTeleSystems/onetl/pull/341)) +- Добавлены методы `SparkS3.get_exclude_packages()` и `Kafka.get_exclude_packages()`. ([#341](https://github.com/MobileTeleSystems/onetl/pull/341)) - Using them allows to skip downloading dependencies not required by this specific connector, or which are already a part of Spark/PySpark: + Их использование позволяет пропустить скачивание зависимостей, не требующихся этим конкретным коннекторам, или таких, которые уже являются частью Spark/PySpark: ```python from onetl.connection import SparkS3, Kafka @@ -96,31 +94,28 @@ ) ``` -## Improvements +## Улучшения -- All DB connections opened by `JDBC.fetch(...)`, `JDBC.execute(...)` or `JDBC.check()` - are immediately closed after the statements is executed. ([#334](https://github.com/MobileTeleSystems/onetl/pull/334)) +- Все подключения к БД, открытые `JDBC.fetch(...)`, `JDBC.execute(...)` или `JDBC.check()`, немедленно закрываются после выполнения операторов. ([#334](https://github.com/MobileTeleSystems/onetl/pull/334)) - Previously Spark session with `master=local[3]` actually opened up to 5 connections to target DB - one for `JDBC.check()`, - another for Spark driver interaction with DB to create tables, and one for each Spark executor. Now only max 4 connections are opened, - as `JDBC.check()` does not hold opened connection. + Ранее Spark сессия с `master=local[3]` фактически открывала до 5 соединений: одно для `JDBC.check()`, другое для взаимодействия драйвера Spark с БД для создания таблиц, и по одному для каждого Spark executor. Теперь открывается максимум 4 соединения, так как `JDBC.check()` не удерживает открытое соединение. - This is important for RDBMS like Postgres or Greenplum where number of connections is strictly limited and limit is usually quite low. + Это важно для СУБД, таких как Postgres или Greenplum, где количество соединений строго ограничено, и лимит обычно довольно низкий. -- Set up `ApplicationName` (client info) for Clickhouse, MongoDB, MSSQL, MySQL and Oracle. ([#339](https://github.com/MobileTeleSystems/onetl/pull/339), [#248](https://github.com/MobileTeleSystems/onetl/pull/248)) +- Настроен `ApplicationName` (client info) для Clickhouse, MongoDB, MSSQL, MySQL и Oracle. ([#339](https://github.com/MobileTeleSystems/onetl/pull/339), [#248](https://github.com/MobileTeleSystems/onetl/pull/248)) - Also update `ApplicationName` format for Greenplum, Postgres, Kafka and SparkS3. - Now all connectors have the same `ApplicationName` format: `${spark.applicationId} ${spark.appName} onETL/${onetl.version} Spark/${spark.version}` + Обновлен формат `ApplicationName` для Greenplum, Postgres, Kafka и SparkS3. + Теперь все коннекторы имеют одинаковый формат `ApplicationName`: `${spark.applicationId} ${spark.appName} onETL/${onetl.version} Spark/${spark.version}` - The only connections not sending `ApplicationName` are Teradata and FileConnection implementations. + Единственные подключения, не отправляющие `ApplicationName`, - это реализации Teradata и FileConnection. -- Now `DB.check()` will test connection availability not only on Spark driver, but also from some Spark executor. ([#346](https://github.com/MobileTeleSystems/onetl/pull/346)) +- Теперь `DB.check()` будет проверять доступность соединения не только на драйвере Spark, но и на одном из экзекуторов Spark. ([#346](https://github.com/MobileTeleSystems/onetl/pull/346)) - This allows to fail immediately if Spark driver host has network access to target DB, but Spark executors have not. + Это позволяет немедленно завершиться с ошибкой, если хост драйвера Spark имеет сетевой доступ к целевой БД, а экзекутор Spark - нет. !!! note - Now ``Greenplum.check()`` requires the same user grants as ``DBReader(connection=greenplum)``: + Теперь ``Greenplum.check()`` требует тех же прав пользователя, что и ``DBReader(connection=greenplum)``: ```sql -- yes, "writable" for reading data from GP, it's not a mistake @@ -130,14 +125,14 @@ -- ALTER USER username CREATEEXTTABLE(type = 'readable', protocol = 'gpfdist') CREATEEXTTABLE(type = 'writable', protocol = 'gpfdist'); ``` - Please ask your Greenplum administrators to provide these grants. + Пожалуйста, попросите ваших администраторов Greenplum предоставить эти права. -## Bug Fixes +## Исправление ошибок -- Avoid suppressing Hive Metastore errors while using `DBWriter`. ([#329](https://github.com/MobileTeleSystems/onetl/pull/329)) +- Предотвращено подавление ошибок Hive Metastore при использовании `DBWriter`. ([#329](https://github.com/MobileTeleSystems/onetl/pull/329)) - Previously this was implemented as: + Ранее это было реализовано как: ```python try: @@ -147,53 +142,47 @@ table_exists = False ``` - If Hive Metastore was overloaded and responded with an exception, it was considered as non-existing table, resulting - to full table override instead of append or override only partitions subset. + Если Hive Metastore был перегружен и отвечал исключением, считалось, что таблица не существует и это приводило к ее полному переопределению, вместо добавления или переопределения только подмножества разделов. -- Fix using onETL to write data to PostgreSQL or Greenplum instances behind *pgbouncer* with `pool_mode=transaction`. ([#336](https://github.com/MobileTeleSystems/onetl/pull/336)) +- Исправлено использование onETL для записи данных в экземпляры PostgreSQL или Greenplum, использующих *pgbouncer* с `pool_mode=transaction`. ([#336](https://github.com/MobileTeleSystems/onetl/pull/336)) - Previously `Postgres.check()` opened a read-only transaction, pgbouncer changed the entire connection type from read-write to read-only, - and when `DBWriter.run(df)` executed in read-only connection, producing errors like: + Ранее `Postgres.check()` открывал транзакцию только для чтения, pgbouncer изменял весь тип соединения с "чтения-записи" на "только для чтения", и когда `DBWriter.run(df)` выполнялся в соединении только для чтения, возникали ошибки, такие как: ``` org.postgresql.util.PSQLException: ERROR: cannot execute INSERT in a read-only transaction org.postgresql.util.PSQLException: ERROR: cannot execute TRUNCATE TABLE in a read-only transaction ``` - Added a workaround by passing `readOnly=True` to JDBC params for read-only connections, so pgbouncer may differ read-only and read-write connections properly. + Добавлено обходное решение путем передачи `readOnly=True` в параметры JDBC для соединений только для чтения, чтобы pgbouncer мог правильно различать соединения "только для чтения" и "чтения-записи". - After upgrading onETL 0.13.x or higher the same error still may appear of pgbouncer still holds read-only connections and returns them for DBWriter. - To this this, user can manually convert read-only connection to read-write: + После обновления onETL 0.13.x или выше та же ошибка все еще может появляться, если pgbouncer все еще удерживает соединения "только для чтения" и возвращает их для DBWriter. Чтобы это исправить, пользователь может вручную преобразовать соединение "только для чтения" в соединение "чтения-записи": ```python postgres.execute("BEGIN READ WRITE;") # <-- add this line DBWriter(...).run() ``` - After all connections in pgbouncer pool were converted from read-only to read-write, and error fixed, this additional line could be removed. + После того, как все соединения в пуле pgbouncer были преобразованы из "только для чтения" в "чтение-запись", и ошибка исправлена, эту дополнительную строку можно удалить. - See [Postgres JDBC driver documentation](https://jdbc.postgresql.org/documentation/use/). + См. [Документацию драйвера Postgres JDBC](https://jdbc.postgresql.org/documentation/use/). -- Fix `MSSQL.fetch(...)` and `MySQL.fetch(...)` opened a read-write connection instead of read-only. ([#337](https://github.com/MobileTeleSystems/onetl/pull/337)) +- Исправлены `MSSQL.fetch(...)` и `MySQL.fetch(...)`, открывавшие соединение "чтения-записи" вместо соединения "только для чтения". ([#337](https://github.com/MobileTeleSystems/onetl/pull/337)) - Now this is fixed: - : - `MSSQL.fetch(...)` establishes connection with `ApplicationIntent=ReadOnly`. - - `MySQL.fetch(...)` calls `SET SESSION TRANSACTION READ ONLY` statement. + Теперь: + - `MSSQL.fetch(...)` устанавливает соединение с `ApplicationIntent=ReadOnly`. + - `MySQL.fetch(...)` вызывает оператор `SET SESSION TRANSACTION READ ONLY`. -- Fixed passing multiple filters to `FileDownloader` and `FileMover`. ([#338](https://github.com/MobileTeleSystems/onetl/pull/338)) - If was caused by sorting filters list in internal logging method, but `FileFilter` subclasses are not sortable. +- Исправлена передача нескольких фильтров в `FileDownloader` и `FileMover`. ([#338](https://github.com/MobileTeleSystems/onetl/pull/338)) Необходимость этого была вызвана сортировкой списка фильтров во внутреннем методе ведения журнала, но подклассы `FileFilter` не поддерживают сортировку. -- Fix a false warning about a lof of parallel connections to Grenplum. ([#342](https://github.com/MobileTeleSystems/onetl/pull/342)) +- Исправлено ложное предупреждение о большом количестве параллельных соединений с Grenplum. ([#342](https://github.com/MobileTeleSystems/onetl/pull/342)) - Creating Spark session with `.master("local[5]")` may open up to 6 connections to Greenplum (=number of Spark executors + 1 for driver), - but onETL instead used number of *CPU cores* on the host as a number of parallel connections. + Создание сессии Spark с `.master("local[5]")` может открыть до 6 соединений с Greenplum (количество экзекуторов Spark + 1 для драйвера), но onETL вместо этого использовала количество *ядер CPU* на хосте в качестве количества параллельных соединений. - This lead to showing a false warning that number of Greenplum connections is too high, - which actually should be the case only if number of executors is higher than 30. + Это приводило к отображению ложного предупреждения о том, что количество соединений Greenplum слишком велико, что на самом деле должно быть только в том случае, если количество экзекуторов превышает 30. -- Fix MongoDB trying to use current database name as `authSource`. ([#347](https://github.com/MobileTeleSystems/onetl/pull/347)) +- Исправлена ошибка, из-за которой подключение к MongoDB пыталось использовать имя указанной базы данных в качестве `authSource`. ([#347](https://github.com/MobileTeleSystems/onetl/pull/347)) - Use default connector value which is `admin` database. Previous onETL versions could be fixed by: + Теперь используется значение коннектора по умолчанию, т.е. `admin`. Предыдущие версии onETL можно исправить следующим образом: ```python from onetl.connection import MongoDB @@ -207,16 +196,16 @@ ) ``` -## Dependencies +## Зависимости -- Minimal `etl-entities` version is now [2.5.0](https://github.com/MobileTeleSystems/etl-entities/releases/tag/2.5.0). ([#331](https://github.com/MobileTeleSystems/onetl/pull/331)) -- Update DB connectors/drivers to latest versions: ([#345](https://github.com/MobileTeleSystems/onetl/pull/345)) - : - Clickhouse `0.6.5` → `0.7.2` +- Минимальная версия `etl-entities` теперь [2.5.0](https://github.com/MobileTeleSystems/etl-entities/releases/tag/2.5.0). ([#331](https://github.com/MobileTeleSystems/onetl/pull/331)) +- До последних версий обновлены коннекторы/драйверы БД: ([#345](https://github.com/MobileTeleSystems/onetl/pull/345)) + - Clickhouse `0.6.5` → `0.7.2` - MongoDB `10.4.0` → `10.4.1` - MySQL `9.0.0` → `9.2.0` - Oracle `23.5.0.24.07` → `23.7.0.25.01` - Postgres `42.7.4` → `42.7.5` -## Doc only Changes +## Изменения только в документации -- Split large code examples to tabs. ([#344](https://github.com/MobileTeleSystems/onetl/pull/344)) +- Большие примеры кода разделены на вкладки. ([#344](https://github.com/MobileTeleSystems/onetl/pull/344)) diff --git a/mddocs/ru/changelog/0.13.1.md b/mddocs/ru/changelog/0.13.1.md index 4045c7cbf..cda08f438 100644 --- a/mddocs/ru/changelog/0.13.1.md +++ b/mddocs/ru/changelog/0.13.1.md @@ -1,9 +1,9 @@ # 0.13.1 (2025-03-06) -## Bug Fixes +## Исправления ошибок -In 0.13.0, using `DBWriter(connection=hive, target="SOMEDB.SOMETABLE")` lead to executing `df.write.saveAsTable()` -instead of `df.write.insertInto()` if target table `somedb.sometable` already exist. +В версии 0.13.0 использование `DBWriter(connection=hive, target="SOMEDB.SOMETABLE")` приводило к выполнению `df.write.saveAsTable()` вместо `df.write.insertInto()`, если целевая таблица `somedb.sometable` уже существовала. -This is caused by table name normalization (Hive uses lower-case names), which wasn't properly handled by method used for checking table existence. +Это происходило из-за нормализации имени таблицы (Hive использует имена в нижнем регистре), которое неправильно обрабатывалась методом, использующимся для проверки существования таблицы. ([#350](https://github.com/MobileTeleSystems/onetl/pull/350)) + diff --git a/mddocs/ru/changelog/0.13.3.md b/mddocs/ru/changelog/0.13.3.md index 1aa289b49..0ad3a100d 100644 --- a/mddocs/ru/changelog/0.13.3.md +++ b/mddocs/ru/changelog/0.13.3.md @@ -1,5 +1,5 @@ # 0.13.3 (2025-03-11) -## Dependencies +## Зависимости -Allow using [etl-entities 2.6.0](https://github.com/MobileTeleSystems/etl-entities/releases/tag/2.6.0). +Используется [etl-entities 2.6.0](https://github.com/MobileTeleSystems/etl-entities/releases/tag/2.6.0). diff --git a/mddocs/ru/changelog/0.13.4.md b/mddocs/ru/changelog/0.13.4.md index 10f695e0c..59a04350a 100644 --- a/mddocs/ru/changelog/0.13.4.md +++ b/mddocs/ru/changelog/0.13.4.md @@ -1,10 +1,7 @@ # 0.13.4 (2025-03-20) -## Doc only Changes +## Изменения только в документации -- Prefer `ReadOptions(partitionColumn=..., numPartitions=..., queryTimeout=...)` - instead of `ReadOptions(partition_column=..., num_partitions=..., query_timeout=...)`, - to match Spark documentation. ([#352](https://github.com/MobileTeleSystems/onetl/pull/352)) -- Prefer `WriteOptions(if_exists=...)` instead of `WriteOptions(mode=...)` for IDE suggestions. ([#354](https://github.com/MobileTeleSystems/onetl/pull/354)) -- Document all options of supported file formats. - ([#355](https://github.com/MobileTeleSystems/onetl/pull/355), [#356](https://github.com/MobileTeleSystems/onetl/pull/356), [#357](https://github.com/MobileTeleSystems/onetl/pull/357), [#358](https://github.com/MobileTeleSystems/onetl/pull/358), [#359](https://github.com/MobileTeleSystems/onetl/pull/359), [#360](https://github.com/MobileTeleSystems/onetl/pull/360), [#361](https://github.com/MobileTeleSystems/onetl/pull/361), [#362](https://github.com/MobileTeleSystems/onetl/pull/362)) +- Предпочитается использование `ReadOptions(partitionColumn=..., numPartitions=..., queryTimeout=...)` вместо `ReadOptions(partition_column=..., num_partitions=..., query_timeout=...)`, чтобы лучше соответствовать документации Spark. ([#352](https://github.com/MobileTeleSystems/onetl/pull/352)) +- Предпочитается `WriteOptions(if_exists=...)` вместо `WriteOptions(mode=...)` для предложений IDE. ([#354](https://github.com/MobileTeleSystems/onetl/pull/354)) +- Документированы все опции поддерживаемых форматов файлов. ([#355](https://github.com/MobileTeleSystems/onetl/pull/355), [#356](https://github.com/MobileTeleSystems/onetl/pull/356), [#357](https://github.com/MobileTeleSystems/onetl/pull/357), [#358](https://github.com/MobileTeleSystems/onetl/pull/358), [#359](https://github.com/MobileTeleSystems/onetl/pull/359), [#360](https://github.com/MobileTeleSystems/onetl/pull/360), [#361](https://github.com/MobileTeleSystems/onetl/pull/361), [#362](https://github.com/MobileTeleSystems/onetl/pull/362)) diff --git a/mddocs/ru/changelog/0.9.5.md b/mddocs/ru/changelog/0.9.5.md index 1d7358c0b..e44185e85 100644 --- a/mddocs/ru/changelog/0.9.5.md +++ b/mddocs/ru/changelog/0.9.5.md @@ -2,13 +2,14 @@ ## Features -- Add `XML` file format support. ([#163](https://github.com/MobileTeleSystems/onetl/pull/163)) -- Tested compatibility with Spark 3.5.0. `MongoDB` and `Excel` are not supported yet, but other packages do. ([#159](https://github.com/MobileTeleSystems/onetl/pull/159)) +- Добавлена поддержка файлов формата `XML`. ([#163](https://github.com/MobileTeleSystems/onetl/pull/163)) +- Протестирована совместимость со Spark 3.5.0. Поддерживаются все пакеты, кроме `MongoDB` и `Excel`. ([#159](https://github.com/MobileTeleSystems/onetl/pull/159)) ## Improvements -- Add check to all DB and FileDF connections that Spark session is alive. ([#164](https://github.com/MobileTeleSystems/onetl/pull/164)) +- Добавлена проверка активности сессии Spark для всех подключений к БД и объектов чтения/записи файлов в DataFrame (FileDF). ([#164](https://github.com/MobileTeleSystems/onetl/pull/164)) ## Bug Fixes -- Fix `Hive.check()` behavior when Hive Metastore is not available. ([#164](https://github.com/MobileTeleSystems/onetl/pull/164)) +- Исправлено поведение `Hive.check()`, когда Hive Metastore недоступен. ([#164](https://github.com/MobileTeleSystems/onetl/pull/164)) + From 52836a0b27f9d2ab67840c98e1e88ca506057398 Mon Sep 17 00:00:00 2001 From: Sattar Gyulmamedov Date: Tue, 10 Jun 2025 00:46:11 +0300 Subject: [PATCH 09/22] add common doc files --- mddocs/common_rst/CONTRIBUTING.md | 415 +++++++++++++++++++ mddocs/common_rst/README.md | 660 ++++++++++++++++++++++++++++++ mddocs/common_rst/SECURITY.md | 25 ++ mddocs/ru/CONTRIBUTING.md | 370 +++++++++++++++++ mddocs/ru/README.md | 660 ++++++++++++++++++++++++++++++ mddocs/ru/SECURITY.md | 25 ++ mddocs/ru/concepts.md | 458 +++++++++++++++++++++ mddocs/ru/contributing.md | 3 + mddocs/ru/index.md | 87 ++++ mddocs/ru/logging.md | 171 ++++++++ mddocs/ru/plugins.md | 147 +++++++ mddocs/ru/quickstart.md | 4 + mddocs/ru/security.md | 3 + 13 files changed, 3028 insertions(+) create mode 100644 mddocs/common_rst/CONTRIBUTING.md create mode 100644 mddocs/common_rst/README.md create mode 100644 mddocs/common_rst/SECURITY.md create mode 100644 mddocs/ru/CONTRIBUTING.md create mode 100644 mddocs/ru/README.md create mode 100644 mddocs/ru/SECURITY.md create mode 100644 mddocs/ru/concepts.md create mode 100644 mddocs/ru/contributing.md create mode 100644 mddocs/ru/index.md create mode 100644 mddocs/ru/logging.md create mode 100644 mddocs/ru/plugins.md create mode 100644 mddocs/ru/quickstart.md create mode 100644 mddocs/ru/security.md diff --git a/mddocs/common_rst/CONTRIBUTING.md b/mddocs/common_rst/CONTRIBUTING.md new file mode 100644 index 000000000..eb33c2be5 --- /dev/null +++ b/mddocs/common_rst/CONTRIBUTING.md @@ -0,0 +1,415 @@ +# Contributing Guide + +Welcome! There are many ways to contribute, including submitting bug +reports, improving documentation, submitting feature requests, reviewing +new submissions, or contributing code that can be incorporated into the +project. + +## Limitations + +We should keep close to these items during development: + +- Some companies still use old Spark versions, like 2.3.1. So it is required to keep compatibility if possible, e.g. adding branches for different Spark versions. +- Different users uses onETL in different ways - some uses only DB connectors, some only files. Connector-specific dependencies should be optional. +- Instead of creating classes with a lot of different options, prefer splitting them into smaller classes, e.g. options class, context manager, etc, and using composition. + +## Initial setup for local development + +### Install Git + +Please follow [instruction](https://docs.github.com/en/get-started/quickstart/set-up-git). + +### Create a fork + +If you are not a member of a development team building onETL, you should create a fork before making any changes. + +Please follow [instruction](https://docs.github.com/en/get-started/quickstart/fork-a-repo). + +### Clone the repo + +Open terminal and run these commands: + +```bash +git clone git@github.com:myuser/onetl.git -b develop + +cd onetl +``` + +### Setup environment + +Create virtualenv and install dependencies: + +```bash +python -m venv venv +source venv/bin/activate +pip install -U wheel +pip install -U pip setuptools +pip install -U \ + -r requirements/core.txt \ + -r requirements/ftp.txt \ + -r requirements/hdfs.txt \ + -r requirements/kerberos.txt \ + -r requirements/s3.txt \ + -r requirements/sftp.txt \ + -r requirements/webdav.txt \ + -r requirements/dev.txt \ + -r requirements/docs.txt \ + -r requirements/tests/base.txt \ + -r requirements/tests/clickhouse.txt \ + -r requirements/tests/kafka.txt \ + -r requirements/tests/mongodb.txt \ + -r requirements/tests/mssql.txt \ + -r requirements/tests/mysql.txt \ + -r requirements/tests/postgres.txt \ + -r requirements/tests/oracle.txt \ + -r requirements/tests/pydantic-2.txt \ + -r requirements/tests/spark-3.5.5.txt + +# TODO: remove after https://github.com/zqmillet/sphinx-plantuml/pull/4 +pip install sphinx-plantuml --no-deps +``` + +### Enable pre-commit hooks + +Install pre-commit hooks: + +```bash +pre-commit install --install-hooks +``` + +Test pre-commit hooks run: + +```bash +pre-commit run +``` + +## How to + +### Run tests locally + +#### Using docker-compose + +Build image for running tests: + +```bash +docker-compose build +``` + +Start all containers with dependencies: + +```bash +docker-compose --profile all up -d +``` + +You can run limited set of dependencies: + +```bash +docker-compose --profile mongodb up -d +``` + +Run tests: + +```bash +docker-compose run --rm onetl ./run_tests.sh +``` + +You can pass additional arguments, they will be passed to pytest: + +```bash +docker-compose run --rm onetl ./run_tests.sh -m mongodb -lsx -vvvv --log-cli-level=INFO +``` + +You can run interactive bash session and use it: + +```bash +docker-compose run --rm onetl bash + +./run_tests.sh -m mongodb -lsx -vvvv --log-cli-level=INFO +``` + +See logs of test container: + +```bash +docker-compose logs -f onetl +``` + +Stop all containers and remove created volumes: + +```bash +docker-compose --profile all down -v +``` + +#### Without docker-compose + +```{eval-rst} +.. warning:: + + To run HDFS tests locally you should add the following line to your ``/etc/hosts`` (file path depends on OS): + + .. code:: + + # HDFS server returns container hostname as connection address, causing error in DNS resolution + 127.0.0.1 hdfs +``` + +```{eval-rst} +.. note:: + + To run Oracle tests you need to install `Oracle instantclient `__, + and pass its path to ``ONETL_ORA_CLIENT_PATH`` and ``LD_LIBRARY_PATH`` environment variables, + e.g. ``ONETL_ORA_CLIENT_PATH=/path/to/client64/lib``. + + It may also require to add the same path into ``LD_LIBRARY_PATH`` environment variable +``` + +```{eval-rst} +.. note:: + + To run Greenplum tests, you should: + + * Download `VMware Greenplum connector for Spark `_ + * Either move it to ``~/.ivy2/jars/``, or pass file path to ``CLASSPATH`` + * Set environment variable ``ONETL_GP_PACKAGE_VERSION=local``. +``` + +Start all containers with dependencies: + +```bash +docker-compose --profile all up -d +``` + +You can run limited set of dependencies: + +```bash +docker-compose --profile mongodb up -d +``` + +Load environment variables with connection properties: + +```bash +source .env.local +``` + +Run tests: + +```bash +./run_tests.sh +``` + +You can pass additional arguments, they will be passed to pytest: + +```bash +./run_tests.sh -m mongodb -lsx -vvvv --log-cli-level=INFO +``` + +Stop all containers and remove created volumes: + +```bash +docker-compose --profile all down -v +``` + +### Build documentation + +Build documentation using Sphinx: + +```bash +cd docs +make html +``` + +Then open in browser `docs/_build/index.html`. + +## Review process + +Please create a new GitHub issue for any significant changes and +enhancements that you wish to make. Provide the feature you would like +to see, why you need it, and how it will work. Discuss your ideas +transparently and get community feedback before proceeding. + +Significant Changes that you wish to contribute to the project should be +discussed first in a GitHub issue that clearly outlines the changes and +benefits of the feature. + +Small Changes can directly be crafted and submitted to the GitHub +Repository as a Pull Request. + +### Create pull request + +Commit your changes: + +```bash +git commit -m "Commit message" +git push +``` + +Then open Github interface and [create pull request](https://docs.github.com/en/get-started/quickstart/contributing-to-projects#making-a-pull-request). +Please follow guide from PR body template. + +After pull request is created, it get a corresponding number, e.g. 123 (`pr_number`). + +### Write release notes + +`onETL` uses [towncrier](https://pypi.org/project/towncrier/) +for changelog management. + +To submit a change note about your PR, add a text file into the +[docs/changelog/next_release](./next_release) folder. It should contain an +explanation of what applying this PR will change in the way +end-users interact with the project. One sentence is usually +enough but feel free to add as many details as you feel necessary +for the users to understand what it means. + +**Use the past tense** for the text in your fragment because, +combined with others, it will be a part of the "news digest" +telling the readers **what changed** in a specific version of +the library *since the previous version*. + +You should also use +reStructuredText syntax for highlighting code (inline or block), +linking parts of the docs or external sites. +If you wish to sign your change, feel free to add `` -- by +\:user:`github-username` `` at the end (replace `github-username` +with your own!). + +Finally, name your file following the convention that Towncrier +understands: it should start with the number of an issue or a +PR followed by a dot, then add a patch type, like `feature`, +`doc`, `misc` etc., and add `.rst` as a suffix. If you +need to add more than one fragment, you may add an optional +sequence number (delimited with another period) between the type +and the suffix. + +In general the name will follow `..rst` pattern, +where the categories are: + +- `feature`: Any new feature +- `bugfix`: A bug fix +- `improvement`: An improvement +- `doc`: A change to the documentation +- `dependency`: Dependency-related changes +- `misc`: Changes internal to the repo like CI, test and build changes + +A pull request may have more than one of these components, for example +a code change may introduce a new feature that deprecates an old +feature, in which case two fragments should be added. It is not +necessary to make a separate documentation fragment for documentation +changes accompanying the relevant code changes. + +#### Examples for adding changelog entries to your Pull Requests + +```{code-block} rst +:caption: docs/changelog/next_release/1234.doc.1.rst + +Added a ``:github:user:`` role to Sphinx config -- by :github:user:`someuser` +``` + +```{code-block} rst +:caption: docs/changelog/next_release/2345.bugfix.rst + +Fixed behavior of ``WebDAV`` connector -- by :github:user:`someuser` +``` + +```{code-block} rst +:caption: docs/changelog/next_release/3456.feature.rst + +Added support of ``timeout`` in ``S3`` connector +-- by :github:user:`someuser`, :github:user:`anotheruser` and :github:user:`otheruser` +``` + +```{eval-rst} +.. tip:: + + See `pyproject.toml `_ for all available categories + (``tool.towncrier.type``). +``` + +#### How to skip change notes check? + +Just add `ci:skip-changelog` label to pull request. + +#### Release Process + +Before making a release from the `develop` branch, follow these steps: + +0. Checkout to `develop` branch and update it to the actual state + +```bash +git checkout develop +git pull -p +``` + +1. Backup `NEXT_RELEASE.rst` + +```bash +cp "docs/changelog/NEXT_RELEASE.rst" "docs/changelog/temp_NEXT_RELEASE.rst" +``` + +2. Build the Release notes with Towncrier + +```bash +VERSION=$(cat onetl/VERSION) +towncrier build "--version=${VERSION}" --yes +``` + +3. Change file with changelog to release version number + +```bash +mv docs/changelog/NEXT_RELEASE.rst "docs/changelog/${VERSION}.rst" +``` + +4. Remove content above the version number heading in the `${VERSION}.rst` file + +```bash +awk '!/^.*towncrier release notes start/' "docs/changelog/${VERSION}.rst" > temp && mv temp "docs/changelog/${VERSION}.rst" +``` + +5. Update Changelog Index + +```bash +awk -v version=${VERSION} '/DRAFT/{print;print " " version;next}1' docs/changelog/index.rst > temp && mv temp docs/changelog/index.rst +``` + +6. Restore `NEXT_RELEASE.rst` file from backup + +```bash +mv "docs/changelog/temp_NEXT_RELEASE.rst" "docs/changelog/NEXT_RELEASE.rst" +``` + +7. Commit and push changes to `develop` branch + +```bash +git add . +git commit -m "Prepare for release ${VERSION}" +git push +``` + +8. Merge `develop` branch to `master`, **WITHOUT** squashing + +```bash +git checkout master +git pull +git merge develop +git push +``` + +9. Add git tag to the latest commit in `master` branch + +```bash +git tag "$VERSION" +git push origin "$VERSION" +``` + +10. Update version in `develop` branch **after release**: + +```bash +git checkout develop + +NEXT_VERSION=$(echo "$VERSION" | awk -F. '/[0-9]+\./{$NF++;print}' OFS=.) +echo "$NEXT_VERSION" > onetl/VERSION + +git add . +git commit -m "Bump version" +git push +``` + +[towncrier philosophy]: https://towncrier.readthedocs.io/en/stable/#philosophy diff --git a/mddocs/common_rst/README.md b/mddocs/common_rst/README.md new file mode 100644 index 000000000..bed7994b8 --- /dev/null +++ b/mddocs/common_rst/README.md @@ -0,0 +1,660 @@ +--- +substitutions: + CI Status: |- + ```{image} https://github.com/MobileTeleSystems/onetl/workflows/Tests/badge.svg + :alt: Github Actions - latest CI build status + :target: https://github.com/MobileTeleSystems/onetl/actions + ``` + Documentation: |- + ```{image} https://readthedocs.org/projects/onetl/badge/?version=stable + :alt: Documentation - ReadTheDocs + :target: https://onetl.readthedocs.io/ + ``` + Logo: |- + ```{image} docs/_static/logo_wide.svg + :alt: onETL logo + :target: https://github.com/MobileTeleSystems/onetl + ``` + PyPI Downloads: |- + ```{image} https://img.shields.io/pypi/dm/onetl + :alt: PyPI - Downloads + :target: https://pypi.org/project/onetl/ + ``` + PyPI Latest Release: |- + ```{image} https://img.shields.io/pypi/v/onetl + :alt: PyPI - Latest Release + :target: https://pypi.org/project/onetl/ + ``` + PyPI License: |- + ```{image} https://img.shields.io/pypi/l/onetl.svg + :alt: PyPI - License + :target: https://github.com/MobileTeleSystems/onetl/blob/develop/LICENSE.txt + ``` + PyPI Python Version: |- + ```{image} https://img.shields.io/pypi/pyversions/onetl.svg + :alt: PyPI - Python Version + :target: https://pypi.org/project/onetl/ + ``` + Repo Status: |- + ```{image} https://www.repostatus.org/badges/latest/active.svg + :alt: Repo status - Active + :target: https://github.com/MobileTeleSystems/onetl + ``` + Test Coverage: |- + ```{image} https://codecov.io/gh/MobileTeleSystems/onetl/branch/develop/graph/badge.svg?token=RIO8URKNZJ + :alt: Test coverage - percent + :target: https://codecov.io/gh/MobileTeleSystems/onetl + ``` + pre-commit.ci Status: |- + ```{image} https://results.pre-commit.ci/badge/github/MobileTeleSystems/onetl/develop.svg + :alt: pre-commit.ci - status + :target: https://results.pre-commit.ci/latest/github/MobileTeleSystems/onetl/develop + ``` +--- + +(readme)= + +# onETL + +{{ Repo Status }} {{ PyPI Latest Release }} {{ PyPI License }} {{ PyPI Python Version }} {{ PyPI Downloads }} +{{ Documentation }} {{ CI Status }} {{ Test Coverage }} {{ pre-commit.ci Status }} + +{{ Logo }} + +## What is onETL? + +Python ETL/ELT library powered by [Apache Spark](https://spark.apache.org/) & other open-source tools. + +## Goals + +- Provide unified classes to extract data from (**E**) & load data to (**L**) various stores. +- Provides [Spark DataFrame API](https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.DataFrame.html) for performing transformations (**T**) in terms of *ETL*. +- Provide direct assess to database, allowing to execute SQL queries, as well as DDL, DML, and call functions/procedures. This can be used for building up *ELT* pipelines. +- Support different [read strategies](https://onetl.readthedocs.io/en/stable/strategy/index.html) for incremental and batch data fetching. +- Provide [hooks](https://onetl.readthedocs.io/en/stable/hooks/index.html) & [plugins](https://onetl.readthedocs.io/en/stable/plugins.html) mechanism for altering behavior of internal classes. + +## Non-goals + +- onETL is not a Spark replacement. It just provides additional functionality that Spark does not have, and improves UX for end users. +- onETL is not a framework, as it does not have requirements to project structure, naming, the way of running ETL/ELT processes, configuration, etc. All of that should be implemented in some other tool. +- onETL is deliberately developed without any integration with scheduling software like Apache Airflow. All integrations should be implemented as separated tools. +- Only batch operations, no streaming. For streaming prefer [Apache Flink](https://flink.apache.org/). + +## Requirements + +- **Python 3.7 - 3.13** +- PySpark 2.3.x - 3.5.x (depends on used connector) +- Java 8+ (required by Spark, see below) +- Kerberos libs & GCC (required by `Hive`, `HDFS` and `SparkHDFS` connectors) + +## Supported storages + +```{eval-rst} ++--------------------+--------------+-------------------------------------------------------------------------------------------------------------------------+ +| Type | Storage | Powered by | ++====================+==============+=========================================================================================================================+ +| Database | Clickhouse | Apache Spark `JDBC Data Source `_ | ++ +--------------+ + +| | MSSQL | | ++ +--------------+ + +| | MySQL | | ++ +--------------+ + +| | Postgres | | ++ +--------------+ + +| | Oracle | | ++ +--------------+ + +| | Teradata | | ++ +--------------+-------------------------------------------------------------------------------------------------------------------------+ +| | Hive | Apache Spark `Hive integration `_ | ++ +--------------+-------------------------------------------------------------------------------------------------------------------------+ +| | Kafka | Apache Spark `Kafka integration `_ | ++ +--------------+-------------------------------------------------------------------------------------------------------------------------+ +| | Greenplum | VMware `Greenplum Spark connector `_ | ++ +--------------+-------------------------------------------------------------------------------------------------------------------------+ +| | MongoDB | `MongoDB Spark connector `_ | ++--------------------+--------------+-------------------------------------------------------------------------------------------------------------------------+ +| File | HDFS | `HDFS Python client `_ | ++ +--------------+-------------------------------------------------------------------------------------------------------------------------+ +| | S3 | `minio-py client `_ | ++ +--------------+-------------------------------------------------------------------------------------------------------------------------+ +| | SFTP | `Paramiko library `_ | ++ +--------------+-------------------------------------------------------------------------------------------------------------------------+ +| | FTP | `FTPUtil library `_ | ++ +--------------+ + +| | FTPS | | ++ +--------------+-------------------------------------------------------------------------------------------------------------------------+ +| | WebDAV | `WebdavClient3 library `_ | ++ +--------------+-------------------------------------------------------------------------------------------------------------------------+ +| | Samba | `pysmb library `_ | ++--------------------+--------------+-------------------------------------------------------------------------------------------------------------------------+ +| Files as DataFrame | SparkLocalFS | Apache Spark `File Data Source `_ | +| +--------------+ + +| | SparkHDFS | | +| +--------------+-------------------------------------------------------------------------------------------------------------------------+ +| | SparkS3 | `Hadoop AWS `_ library | ++--------------------+--------------+-------------------------------------------------------------------------------------------------------------------------+ +``` + +% documentation + +## Documentation + +See + +## How to install + +(install)= + +### Minimal installation + +(minimal-install)= + +Base `onetl` package contains: + +- `DBReader`, `DBWriter` and related classes +- `FileDownloader`, `FileUploader`, `FileMover` and related classes, like file filters & limits +- `FileDFReader`, `FileDFWriter` and related classes, like file formats +- Read Strategies & HWM classes +- Plugins support + +It can be installed via: + +```bash +pip install onetl +``` + +```{eval-rst} +.. warning:: + + This method does NOT include any connections. + + This method is recommended for use in third-party libraries which require for ``onetl`` to be installed, + but do not use its connection classes. +``` + +### With DB and FileDF connections + +(spark-install)= + +All DB connection classes (`Clickhouse`, `Greenplum`, `Hive` and others) +and all FileDF connection classes (`SparkHDFS`, `SparkLocalFS`, `SparkS3`) +require Spark to be installed. + +(java-install)= + +Firstly, you should install JDK. The exact installation instruction depends on your OS, here are some examples: + +```bash +yum install java-1.8.0-openjdk-devel # CentOS 7 + Spark 2 +dnf install java-11-openjdk-devel # CentOS 8 + Spark 3 +apt-get install openjdk-11-jdk # Debian-based + Spark 3 +``` + +(spark-compatibility-matrix)= + +#### Compatibility matrix + +| Spark | Python | Java | Scala | +| --------------------------------------------------------- | ---------- | ---------- | ----- | +| [2.3.x](https://spark.apache.org/docs/2.3.1/#downloading) | 3.7 only | 8 only | 2.11 | +| [2.4.x](https://spark.apache.org/docs/2.4.8/#downloading) | 3.7 only | 8 only | 2.11 | +| [3.2.x](https://spark.apache.org/docs/3.2.4/#downloading) | 3.7 - 3.10 | 8u201 - 11 | 2.12 | +| [3.3.x](https://spark.apache.org/docs/3.3.4/#downloading) | 3.7 - 3.12 | 8u201 - 17 | 2.12 | +| [3.4.x](https://spark.apache.org/docs/3.4.4/#downloading) | 3.7 - 3.12 | 8u362 - 20 | 2.12 | +| [3.5.x](https://spark.apache.org/docs/3.5.5/#downloading) | 3.8 - 3.13 | 8u371 - 20 | 2.12 | + +(pyspark-install)= + +Then you should install PySpark via passing `spark` to `extras`: + +```bash +pip install onetl[spark] # install latest PySpark +``` + +or install PySpark explicitly: + +```bash +pip install onetl pyspark==3.5.5 # install a specific PySpark version +``` + +or inject PySpark to `sys.path` in some other way BEFORE creating a class instance. +**Otherwise connection object cannot be created.** + +### With File connections + +(files-install)= + +All File (but not *FileDF*) connection classes (`FTP`, `SFTP`, `HDFS` and so on) requires specific Python clients to be installed. + +Each client can be installed explicitly by passing connector name (in lowercase) to `extras`: + +```bash +pip install onetl[ftp] # specific connector +pip install onetl[ftp,ftps,sftp,hdfs,s3,webdav,samba] # multiple connectors +``` + +To install all file connectors at once you can pass `files` to `extras`: + +```bash +pip install onetl[files] +``` + +**Otherwise class import will fail.** + +### With Kerberos support + +(kerberos-install)= + +Most of Hadoop instances set up with Kerberos support, +so some connections require additional setup to work properly. + +- `HDFS` + Uses [requests-kerberos](https://pypi.org/project/requests-kerberos/) and + [GSSApi](https://pypi.org/project/gssapi/) for authentication. + It also uses `kinit` executable to generate Kerberos ticket. +- `Hive` and `SparkHDFS` + require Kerberos ticket to exist before creating Spark session. + +So you need to install OS packages with: + +- `krb5` libs +- Headers for `krb5` +- `gcc` or other compiler for C sources + +The exact installation instruction depends on your OS, here are some examples: + +```bash +apt install libkrb5-dev krb5-user gcc # Debian-based +dnf install krb5-devel krb5-libs krb5-workstation gcc # CentOS, OracleLinux +``` + +Also you should pass `kerberos` to `extras` to install required Python packages: + +```bash +pip install onetl[kerberos] +``` + +### Full bundle + +(full-bundle-1)= + +To install all connectors and dependencies, you can pass `all` into `extras`: + +```bash +pip install onetl[all] + +# this is just the same as +pip install onetl[spark,files,kerberos] +``` + +```{eval-rst} +.. warning:: + + This method consumes a lot of disk space, and requires for Java & Kerberos libraries to be installed into your OS. +``` + +(quick-start)= + +## Quick start + +### MSSQL → Hive + +Read data from MSSQL, transform & write to Hive. + +```bash +# install onETL and PySpark +pip install onetl[spark] +``` + +```python +# Import pyspark to initialize the SparkSession +from pyspark.sql import SparkSession + +# import function to setup onETL logging +from onetl.log import setup_logging + +# Import required connections +from onetl.connection import MSSQL, Hive + +# Import onETL classes to read & write data +from onetl.db import DBReader, DBWriter + +# change logging level to INFO, and set up default logging format and handler +setup_logging() + +# Initialize new SparkSession with MSSQL driver loaded +maven_packages = MSSQL.get_packages() +spark = ( + SparkSession.builder.appName("spark_app_onetl_demo") + .config("spark.jars.packages", ",".join(maven_packages)) + .enableHiveSupport() # for Hive + .getOrCreate() +) + +# Initialize MSSQL connection and check if database is accessible +mssql = MSSQL( + host="mssqldb.demo.com", + user="onetl", + password="onetl", + database="Telecom", + spark=spark, + # These options are passed to MSSQL JDBC Driver: + extra={"applicationIntent": "ReadOnly"}, +).check() + +# >>> INFO:|MSSQL| Connection is available + +# Initialize DBReader +reader = DBReader( + connection=mssql, + source="dbo.demo_table", + columns=["on", "etl"], + # Set some MSSQL read options: + options=MSSQL.ReadOptions(fetchsize=10000), +) + +# checks that there is data in the table, otherwise raises exception +reader.raise_if_no_data() + +# Read data to DataFrame +df = reader.run() +df.printSchema() +# root +# |-- id: integer (nullable = true) +# |-- phone_number: string (nullable = true) +# |-- region: string (nullable = true) +# |-- birth_date: date (nullable = true) +# |-- registered_at: timestamp (nullable = true) +# |-- account_balance: double (nullable = true) + +# Apply any PySpark transformations +from pyspark.sql.functions import lit + +df_to_write = df.withColumn("engine", lit("onetl")) +df_to_write.printSchema() +# root +# |-- id: integer (nullable = true) +# |-- phone_number: string (nullable = true) +# |-- region: string (nullable = true) +# |-- birth_date: date (nullable = true) +# |-- registered_at: timestamp (nullable = true) +# |-- account_balance: double (nullable = true) +# |-- engine: string (nullable = false) + +# Initialize Hive connection +hive = Hive(cluster="rnd-dwh", spark=spark) + +# Initialize DBWriter +db_writer = DBWriter( + connection=hive, + target="dl_sb.demo_table", + # Set some Hive write options: + options=Hive.WriteOptions(if_exists="replace_entire_table"), +) + +# Write data from DataFrame to Hive +db_writer.run(df_to_write) + +# Success! +``` + +### SFTP → HDFS + +Download files from SFTP & upload them to HDFS. + +```bash +# install onETL with SFTP and HDFS clients, and Kerberos support +pip install onetl[hdfs,sftp,kerberos] +``` + +```python +# import function to setup onETL logging +from onetl.log import setup_logging + +# Import required connections +from onetl.connection import SFTP, HDFS + +# Import onETL classes to download & upload files +from onetl.file import FileDownloader, FileUploader + +# import filter & limit classes +from onetl.file.filter import Glob, ExcludeDir +from onetl.file.limit import MaxFilesCount + +# change logging level to INFO, and set up default logging format and handler +setup_logging() + +# Initialize SFTP connection and check it +sftp = SFTP( + host="sftp.test.com", + user="someuser", + password="somepassword", +).check() + +# >>> INFO:|SFTP| Connection is available + +# Initialize downloader +file_downloader = FileDownloader( + connection=sftp, + source_path="/remote/tests/Report", # path on SFTP + local_path="/local/onetl/Report", # local fs path + filters=[ + # download only files matching the glob + Glob("*.csv"), + # exclude files from this directory + ExcludeDir("/remote/tests/Report/exclude_dir/"), + ], + limits=[ + # download max 1000 files per run + MaxFilesCount(1000), + ], + options=FileDownloader.Options( + # delete files from SFTP after successful download + delete_source=True, + # mark file as failed if it already exist in local_path + if_exists="error", + ), +) + +# Download files to local filesystem +download_result = downloader.run() + +# Method run returns a DownloadResult object, +# which contains collection of downloaded files, divided to 4 categories +download_result + +# DownloadResult( +# successful=[ +# LocalPath('/local/onetl/Report/file_1.json'), +# LocalPath('/local/onetl/Report/file_2.json'), +# ], +# failed=[FailedRemoteFile('/remote/onetl/Report/file_3.json')], +# ignored=[RemoteFile('/remote/onetl/Report/file_4.json')], +# missing=[], +# ) + +# Raise exception if there are failed files, or there were no files in the remote filesystem +download_result.raise_if_failed() or download_result.raise_if_empty() + +# Do any kind of magic with files: rename files, remove header for csv files, ... +renamed_files = my_rename_function(download_result.success) + +# function removed "_" from file names +# [ +# LocalPath('/home/onetl/Report/file1.json'), +# LocalPath('/home/onetl/Report/file2.json'), +# ] + +# Initialize HDFS connection +hdfs = HDFS( + host="my.name.node", + user="someuser", + password="somepassword", # or keytab +) + +# Initialize uploader +file_uploader = FileUploader( + connection=hdfs, + target_path="/user/onetl/Report/", # hdfs path +) + +# Upload files from local fs to HDFS +upload_result = file_uploader.run(renamed_files) + +# Method run returns a UploadResult object, +# which contains collection of uploaded files, divided to 4 categories +upload_result + +# UploadResult( +# successful=[RemoteFile('/user/onetl/Report/file1.json')], +# failed=[FailedLocalFile('/local/onetl/Report/file2.json')], +# ignored=[], +# missing=[], +# ) + +# Raise exception if there are failed files, or there were no files in the local filesystem, or some input file is missing +upload_result.raise_if_failed() or upload_result.raise_if_empty() or upload_result.raise_if_missing() + +# Success! +``` + +### S3 → Postgres + +Read files directly from S3 path, convert them to dataframe, transform it and then write to a database. + +```bash +# install onETL and PySpark +pip install onetl[spark] +``` + +```python +# Import pyspark to initialize the SparkSession +from pyspark.sql import SparkSession + +# import function to setup onETL logging +from onetl.log import setup_logging + +# Import required connections +from onetl.connection import Postgres, SparkS3 + +# Import onETL classes to read files +from onetl.file import FileDFReader +from onetl.file.format import CSV + +# Import onETL classes to write data +from onetl.db import DBWriter + +# change logging level to INFO, and set up default logging format and handler +setup_logging() + +# Initialize new SparkSession with Hadoop AWS libraries and Postgres driver loaded +maven_packages = SparkS3.get_packages(spark_version="3.5.5") + Postgres.get_packages() +exclude_packages = SparkS3.get_exclude_packages() +spark = ( + SparkSession.builder.appName("spark_app_onetl_demo") + .config("spark.jars.packages", ",".join(maven_packages)) + .config("spark.jars.excludes", ",".join(exclude_packages)) + .getOrCreate() +) + +# Initialize S3 connection and check it +spark_s3 = SparkS3( + host="s3.test.com", + protocol="https", + bucket="my-bucket", + access_key="somekey", + secret_key="somesecret", + # Access bucket as s3.test.com/my-bucket + extra={"path.style.access": True}, + spark=spark, +).check() + +# >>> INFO:|SparkS3| Connection is available + +# Describe file format and parsing options +csv = CSV( + delimiter=";", + header=True, + encoding="utf-8", +) + +# Describe DataFrame schema of files +from pyspark.sql.types import ( + DateType, + DoubleType, + IntegerType, + StringType, + StructField, + StructType, + TimestampType, +) + +df_schema = StructType( + [ + StructField("id", IntegerType()), + StructField("phone_number", StringType()), + StructField("region", StringType()), + StructField("birth_date", DateType()), + StructField("registered_at", TimestampType()), + StructField("account_balance", DoubleType()), + ], +) + +# Initialize file df reader +reader = FileDFReader( + connection=spark_s3, + source_path="/remote/tests/Report", # path on S3 there *.csv files are located + format=csv, # file format with specific parsing options + df_schema=df_schema, # columns & types +) + +# Read files directly from S3 as Spark DataFrame +df = reader.run() + +# Check that DataFrame schema is same as expected +df.printSchema() +# root +# |-- id: integer (nullable = true) +# |-- phone_number: string (nullable = true) +# |-- region: string (nullable = true) +# |-- birth_date: date (nullable = true) +# |-- registered_at: timestamp (nullable = true) +# |-- account_balance: double (nullable = true) + +# Apply any PySpark transformations +from pyspark.sql.functions import lit + +df_to_write = df.withColumn("engine", lit("onetl")) +df_to_write.printSchema() +# root +# |-- id: integer (nullable = true) +# |-- phone_number: string (nullable = true) +# |-- region: string (nullable = true) +# |-- birth_date: date (nullable = true) +# |-- registered_at: timestamp (nullable = true) +# |-- account_balance: double (nullable = true) +# |-- engine: string (nullable = false) + +# Initialize Postgres connection +postgres = Postgres( + host="192.169.11.23", + user="onetl", + password="somepassword", + database="mydb", + spark=spark, +) + +# Initialize DBWriter +db_writer = DBWriter( + connection=postgres, + # write to specific table + target="public.my_table", + # with some writing options + options=Postgres.WriteOptions(if_exists="append"), +) + +# Write DataFrame to Postgres table +db_writer.run(df_to_write) + +# Success! +``` diff --git a/mddocs/common_rst/SECURITY.md b/mddocs/common_rst/SECURITY.md new file mode 100644 index 000000000..5d9dce52b --- /dev/null +++ b/mddocs/common_rst/SECURITY.md @@ -0,0 +1,25 @@ +# Security + +## Supported Python versions + +3.7 or above + +## Product development security recommendations + +1. Update dependencies to last stable version +2. Build SBOM for the project +3. Perform SAST (Static Application Security Testing) where possible + +## Product development security requirements + +1. No binaries in repository +2. No passwords, keys, access tokens in source code +3. No "Critical" and/or "High" vulnerabilities in contributed source code + +## Vulnerability reports + +Please, use email [mailto:onetools@mts.ru](mailto:onetools@mts.ru) for reporting security issues or anything that can cause any consequences for security. + +Please avoid any public disclosure (including registering issues) at least until it is fixed. + +Thank you in advance for understanding. diff --git a/mddocs/ru/CONTRIBUTING.md b/mddocs/ru/CONTRIBUTING.md new file mode 100644 index 000000000..cb3d79377 --- /dev/null +++ b/mddocs/ru/CONTRIBUTING.md @@ -0,0 +1,370 @@ +# Руководство по участию + +Добро пожаловать! Существует множество способов внести свой вклад, включая отправку отчетов об ошибках, улучшение документации, отправку запросов на добавление функций, рецензирование новых материалов или внесение кода, который можно включить в проект. + +## Ограничения + +При разработке следует придерживаться следующих пунктов: + +- Некоторые компании до сих пор используют старые версии Spark, например 2.3.1. Поэтому, по возможности, необходимо поддерживать такую совместимость, например, добавлять ветки для разных версий Spark. +- Разные пользователи используют onETL по-разному - некоторые используют только коннекторы к БД, другие только файловые подключения. Зависимости, специфичные для коннекторов, должны быть необязательными. +- Вместо создания классов с большим количеством различных опций, лучше разделить их на более мелкие классы, например, класс опций, контекстный менеджер и т.д., и использовать композицию. + +## Начальная настройка для локальной разработки + +### Установите Git + +Пожалуйста, следуйте [инструкции](https://docs.github.com/en/get-started/quickstart/set-up-git). + +### Создайте форк + +Если вы не являетесь членом команды разработчиков, создающей onETL, вам следует создать форк, прежде чем вносить какие-либо изменения. + +Пожалуйста, следуйте [инструкции](https://docs.github.com/en/get-started/quickstart/fork-a-repo). + +### Клонируйте репозиторий + +Откройте терминал и выполните следующие команды: + +```bash +git clone git@github.com:myuser/onetl.git -b develop + +cd onetl +``` + +### Настройте окружение + +Создайте virtualenv и установите зависимости: + +```bash +python -m venv venv +source venv/bin/activate +pip install -U wheel +pip install -U pip setuptools +pip install -U \ + -r requirements/core.txt \ + -r requirements/ftp.txt \ + -r requirements/hdfs.txt \ + -r requirements/kerberos.txt \ + -r requirements/s3.txt \ + -r requirements/sftp.txt \ + -r requirements/webdav.txt \ + -r requirements/dev.txt \ + -r requirements/docs.txt \ + -r requirements/tests/base.txt \ + -r requirements/tests/clickhouse.txt \ + -r requirements/tests/kafka.txt \ + -r requirements/tests/mongodb.txt \ + -r requirements/tests/mssql.txt \ + -r requirements/tests/mysql.txt \ + -r requirements/tests/postgres.txt \ + -r requirements/tests/oracle.txt \ + -r requirements/tests/pydantic-2.txt \ + -r requirements/tests/spark-3.5.5.txt + +# TODO: remove after https://github.com/zqmillet/sphinx-plantuml/pull/4 +pip install sphinx-plantuml --no-deps +``` + +### Включите pre-commit hooks + +Установите pre-commit hooks: + +```bash +pre-commit install --install-hooks +``` + +Проверьте запуск pre-commit hooks: + +```bash +pre-commit run +``` + +## Как + +### Запустите тесты локально + +#### Используя docker-compose + +Соберите образ для запуска тестов: + +```bash +docker-compose build +``` + +Запустите все контейнеры с зависимостями: + +```bash +docker-compose --profile all up -d +``` + +Вы можете запустить ограниченный набор зависимостей: + +```bash +docker-compose --profile mongodb up -d +``` + +Запустите тесты: + +```bash +docker-compose run --rm onetl ./run_tests.sh +``` + +Вы можете передать дополнительные аргументы и они будут переданы в pytest: + +```bash +docker-compose run --rm onetl ./run_tests.sh -m mongodb -lsx -vvvv --log-cli-level=INFO +``` + +Вы можете запустить интерактивную bash сессию и использовать ее: + +```bash +docker-compose run --rm onetl bash + +./run_tests.sh -m mongodb -lsx -vvvv --log-cli-level=INFO +``` + +Посмотрите логи тестового контейнера: + +```bash +docker-compose logs -f onetl +``` + +Остановите все контейнеры и удалите созданные тома: + +```bash +docker-compose --profile all down -v +``` + +#### Без docker-compose + +!!! warning + + Для локального запуска тестов HDFS необходимо добавить следующую строку в ``/etc/hosts`` (путь к файлу зависит от ОС): + + ```text + # HDFS server returns container hostname as connection address, causing error in DNS resolution + 127.0.0.1 hdfs + ``` + +!!! note + + Для запуска тестов Oracle необходимо установить `Oracle instantclient `__, + и передать его путь в переменные окружения ``ONETL_ORA_CLIENT_PATH`` и ``LD_LIBRARY_PATH``, + например, ``ONETL_ORA_CLIENT_PATH=/path/to/client64/lib``. + + Также может потребоваться добавить этот же путь в переменную окружения ``LD_LIBRARY_PATH`` + + +!!! note + + Для запуска тестов Greenplum необходимо: + + * Скачать `VMware Greenplum connector for Spark `_ + * Либо переместить его в ``~/.ivy2/jars/``, либо передать путь к файлу в ``CLASSPATH`` + * Установить переменную окружения ``ONETL_GP_PACKAGE_VERSION=local``. + + +Запустите все контейнеры с зависимостями: + +```bash +docker-compose --profile all up -d +``` + +Вы можете запустить ограниченный набор зависимостей: + +```bash +docker-compose --profile mongodb up -d +``` + +Загрузите переменные окружения со свойствами подключения: + +```bash +source .env.local +``` + +Запустите тесты: + +```bash +./run_tests.sh +``` + +Вы можете передать дополнительные аргументы, они будут переданы в pytest: + +```bash +./run_tests.sh -m mongodb -lsx -vvvv --log-cli-level=INFO +``` + +Остановите все контейнеры и удалите созданные тома: + +```bash +docker-compose --profile all down -v +``` + +### Соберите документацию + +Соберите документацию с помощью Sphinx: + +```bash +cd docs +make html +``` + +Затем откройте в браузере `docs/_build/index.html`. + +## Процесс рецензирования + +Пожалуйста, создайте новую задачу GitHub для любых значительных изменений и улучшений, которые вы хотите внести. Опишите функцию, которую вы хотели бы видеть, зачем она вам нужна и как она будет работать. Обсудите свои идеи прозрачно и получите отзывы сообщества, прежде чем продолжить. + +Значительные изменения, которые вы хотите внести в проект, должны быть предварительно обсуждены в задаче GitHub, в которой четко изложены изменения и преимущества этой функции. + +Небольшие изменения можно сразу же создать и отправить в репозиторий GitHub в виде запроса на включение изменений (Pull Request). + +### Создайте pull request + +Зафиксируйте свои изменения: + +```bash +git commit -m "Commit message" +git push +``` + +Затем откройте интерфейс Github и [создайте pull request](https://docs.github.com/en/get-started/quickstart/contributing-to-projects#making-a-pull-request). Пожалуйста, следуйте руководству из шаблона тела PR. + +После создания pull request он получает соответствующий номер, например, 123 (`pr_number`). + +### Напишите release notes + +`onETL` использует [towncrier](https://pypi.org/project/towncrier/) для управления журналом изменений. + +Чтобы отправить заметку об изменении для вашего PR, добавьте текстовый файл в папку [docs/changelog/next_release](./next_release). Он должен содержать объяснение того, как применение этого PR изменит способ взаимодействия конечных пользователей с проектом. Одного предложения обычно достаточно, но не стесняйтесь добавлять столько деталей, сколько считаете необходимым для того, чтобы пользователи поняли, что это значит. + +**Используйте прошедшее время** для текста в вашем фрагменте, потому что, в сочетании с другими, он станет частью "сводки новостей", сообщающей читателям **что изменилось** в конкретной версии библиотеки *с момента предыдущей версии*. + +Вам также следует использовать синтаксис reStructuredText для выделения кода (встроенного или блочного), связывания частей документации или внешних сайтов. Если вы хотите подписать свое изменение, не стесняйтесь добавить `` -- by\:user:`github-username` `` в конце (замените `github-username` на свое собственное!). + +Наконец, назовите свой файл в соответствии с соглашением, которое понимает Towncrier: он должен начинаться с номера задачи или PR, за которым следует точка, затем добавьте тип патча, например, `feature`, `doc`, `misc` и т.д., и добавьте `.rst` в качестве суффикса. Если вам нужно добавить более одного фрагмента, вы можете добавить необязательный порядковый номер (разделенный другой точкой) между типом и суффиксом. + +В общем, имя будет соответствовать шаблону `..rst`, где категории: + +- `feature`: Любая новая функция +- `bugfix`: Исправление ошибки +- `improvement`: Улучшение +- `doc`: Изменение в документации +- `dependency`: Изменения, связанные с зависимостями +- `misc`: Внутренние изменения в репозитории, такие как CI, тесты и изменения сборки + +Pull request может иметь более одного из этих компонентов, например, изменение кода может представить новую функцию, которая устаревает старую функцию, в этом случае следует добавить два фрагмента. Нет необходимо делать отдельный фрагмент документации для изменений документации, сопровождающих соответствующие изменения кода. + +#### Примеры добавления записей в журнал изменений в ваши Pull Requests + +```rst title="docs/changelog/next_release/1234.doc.1.rst" + +Added a ``:github:user:`` role to Sphinx config -- by :github:user:`someuser` +``` + +```rst title="docs/changelog/next_release/2345.bugfix.rst" + +Fixed behavior of ``WebDAV`` connector -- by :github:user:`someuser` +``` + +```rst title="docs/changelog/next_release/3456.feature.rst" + +Added support of ``timeout`` in ``S3`` connector +-- by :github:user:`someuser`, :github:user:`anotheruser` and :github:user:`otheruser` +``` + +!!! tip + + См. `pyproject.toml `_ для всех доступных категорий (``tool.towncrier.type``). + +#### Как пропустить проверку change notes? + +Просто добавьте метку `ci:skip-changelog` к pull request. + +#### Процесс релиза + +Перед тем, как делать релиз из ветки `develop`, выполните следующие шаги: + +0. Перейдите в ветку `develop` и обновите ее до актуального состояния + +```bash +git checkout develop +git pull -p +``` + +1. Сделайте резервную копию `NEXT_RELEASE.rst` + +```bash +cp "docs/changelog/NEXT_RELEASE.rst" "docs/changelog/temp_NEXT_RELEASE.rst" +``` + +2. Соберите Release notes с помощью Towncrier + +```bash +VERSION=$(cat onetl/VERSION) +towncrier build "--version=${VERSION}" --yes +``` + +3. Измените файл с журналом изменений на номер версии релиза + +```bash +mv docs/changelog/NEXT_RELEASE.rst "docs/changelog/${VERSION}.rst" +``` + +4. Удалите содержимое над заголовком номера версии в файле `${VERSION}.rst` + +```bash +awk '!/^.*towncrier release notes start/' "docs/changelog/${VERSION}.rst" > temp && mv temp "docs/changelog/${VERSION}.rst" +``` + +5. Обновите индекс журнала изменений + +```bash +awk -v version=${VERSION} '/DRAFT/{print;print " " version;next}1' docs/changelog/index.rst > temp && mv temp docs/changelog/index.rst +``` + +6. Восстановите файл `NEXT_RELEASE.rst` из резервной копии + +```bash +mv "docs/changelog/temp_NEXT_RELEASE.rst" "docs/changelog/NEXT_RELEASE.rst" +``` + +7. Зафиксируйте и отправьте изменения в ветку `develop` + +```bash +git add . +git commit -m "Prepare for release ${VERSION}" +git push +``` + +8. Объедините ветку `develop` с `master`, **БЕЗ** squashing + +```bash +git checkout master +git pull +git merge develop +git push +``` + +9. Добавьте git tag к последнему коммиту в ветке `master` + +```bash +git tag "$VERSION" +git push origin "$VERSION" +``` + +10. Обновите версию в ветке `develop` **после релиза**: + +```bash +git checkout develop + +NEXT_VERSION=$(echo "$VERSION" | awk -F. '/[0-9]+\./{$NF++;print}' OFS=.) +echo "$NEXT_VERSION" > onetl/VERSION + +git add . +git commit -m "Bump version" +git push +``` + +[towncrier philosophy](https://towncrier.readthedocs.io/en/stable/#philosophy) diff --git a/mddocs/ru/README.md b/mddocs/ru/README.md new file mode 100644 index 000000000..bed7994b8 --- /dev/null +++ b/mddocs/ru/README.md @@ -0,0 +1,660 @@ +--- +substitutions: + CI Status: |- + ```{image} https://github.com/MobileTeleSystems/onetl/workflows/Tests/badge.svg + :alt: Github Actions - latest CI build status + :target: https://github.com/MobileTeleSystems/onetl/actions + ``` + Documentation: |- + ```{image} https://readthedocs.org/projects/onetl/badge/?version=stable + :alt: Documentation - ReadTheDocs + :target: https://onetl.readthedocs.io/ + ``` + Logo: |- + ```{image} docs/_static/logo_wide.svg + :alt: onETL logo + :target: https://github.com/MobileTeleSystems/onetl + ``` + PyPI Downloads: |- + ```{image} https://img.shields.io/pypi/dm/onetl + :alt: PyPI - Downloads + :target: https://pypi.org/project/onetl/ + ``` + PyPI Latest Release: |- + ```{image} https://img.shields.io/pypi/v/onetl + :alt: PyPI - Latest Release + :target: https://pypi.org/project/onetl/ + ``` + PyPI License: |- + ```{image} https://img.shields.io/pypi/l/onetl.svg + :alt: PyPI - License + :target: https://github.com/MobileTeleSystems/onetl/blob/develop/LICENSE.txt + ``` + PyPI Python Version: |- + ```{image} https://img.shields.io/pypi/pyversions/onetl.svg + :alt: PyPI - Python Version + :target: https://pypi.org/project/onetl/ + ``` + Repo Status: |- + ```{image} https://www.repostatus.org/badges/latest/active.svg + :alt: Repo status - Active + :target: https://github.com/MobileTeleSystems/onetl + ``` + Test Coverage: |- + ```{image} https://codecov.io/gh/MobileTeleSystems/onetl/branch/develop/graph/badge.svg?token=RIO8URKNZJ + :alt: Test coverage - percent + :target: https://codecov.io/gh/MobileTeleSystems/onetl + ``` + pre-commit.ci Status: |- + ```{image} https://results.pre-commit.ci/badge/github/MobileTeleSystems/onetl/develop.svg + :alt: pre-commit.ci - status + :target: https://results.pre-commit.ci/latest/github/MobileTeleSystems/onetl/develop + ``` +--- + +(readme)= + +# onETL + +{{ Repo Status }} {{ PyPI Latest Release }} {{ PyPI License }} {{ PyPI Python Version }} {{ PyPI Downloads }} +{{ Documentation }} {{ CI Status }} {{ Test Coverage }} {{ pre-commit.ci Status }} + +{{ Logo }} + +## What is onETL? + +Python ETL/ELT library powered by [Apache Spark](https://spark.apache.org/) & other open-source tools. + +## Goals + +- Provide unified classes to extract data from (**E**) & load data to (**L**) various stores. +- Provides [Spark DataFrame API](https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.DataFrame.html) for performing transformations (**T**) in terms of *ETL*. +- Provide direct assess to database, allowing to execute SQL queries, as well as DDL, DML, and call functions/procedures. This can be used for building up *ELT* pipelines. +- Support different [read strategies](https://onetl.readthedocs.io/en/stable/strategy/index.html) for incremental and batch data fetching. +- Provide [hooks](https://onetl.readthedocs.io/en/stable/hooks/index.html) & [plugins](https://onetl.readthedocs.io/en/stable/plugins.html) mechanism for altering behavior of internal classes. + +## Non-goals + +- onETL is not a Spark replacement. It just provides additional functionality that Spark does not have, and improves UX for end users. +- onETL is not a framework, as it does not have requirements to project structure, naming, the way of running ETL/ELT processes, configuration, etc. All of that should be implemented in some other tool. +- onETL is deliberately developed without any integration with scheduling software like Apache Airflow. All integrations should be implemented as separated tools. +- Only batch operations, no streaming. For streaming prefer [Apache Flink](https://flink.apache.org/). + +## Requirements + +- **Python 3.7 - 3.13** +- PySpark 2.3.x - 3.5.x (depends on used connector) +- Java 8+ (required by Spark, see below) +- Kerberos libs & GCC (required by `Hive`, `HDFS` and `SparkHDFS` connectors) + +## Supported storages + +```{eval-rst} ++--------------------+--------------+-------------------------------------------------------------------------------------------------------------------------+ +| Type | Storage | Powered by | ++====================+==============+=========================================================================================================================+ +| Database | Clickhouse | Apache Spark `JDBC Data Source `_ | ++ +--------------+ + +| | MSSQL | | ++ +--------------+ + +| | MySQL | | ++ +--------------+ + +| | Postgres | | ++ +--------------+ + +| | Oracle | | ++ +--------------+ + +| | Teradata | | ++ +--------------+-------------------------------------------------------------------------------------------------------------------------+ +| | Hive | Apache Spark `Hive integration `_ | ++ +--------------+-------------------------------------------------------------------------------------------------------------------------+ +| | Kafka | Apache Spark `Kafka integration `_ | ++ +--------------+-------------------------------------------------------------------------------------------------------------------------+ +| | Greenplum | VMware `Greenplum Spark connector `_ | ++ +--------------+-------------------------------------------------------------------------------------------------------------------------+ +| | MongoDB | `MongoDB Spark connector `_ | ++--------------------+--------------+-------------------------------------------------------------------------------------------------------------------------+ +| File | HDFS | `HDFS Python client `_ | ++ +--------------+-------------------------------------------------------------------------------------------------------------------------+ +| | S3 | `minio-py client `_ | ++ +--------------+-------------------------------------------------------------------------------------------------------------------------+ +| | SFTP | `Paramiko library `_ | ++ +--------------+-------------------------------------------------------------------------------------------------------------------------+ +| | FTP | `FTPUtil library `_ | ++ +--------------+ + +| | FTPS | | ++ +--------------+-------------------------------------------------------------------------------------------------------------------------+ +| | WebDAV | `WebdavClient3 library `_ | ++ +--------------+-------------------------------------------------------------------------------------------------------------------------+ +| | Samba | `pysmb library `_ | ++--------------------+--------------+-------------------------------------------------------------------------------------------------------------------------+ +| Files as DataFrame | SparkLocalFS | Apache Spark `File Data Source `_ | +| +--------------+ + +| | SparkHDFS | | +| +--------------+-------------------------------------------------------------------------------------------------------------------------+ +| | SparkS3 | `Hadoop AWS `_ library | ++--------------------+--------------+-------------------------------------------------------------------------------------------------------------------------+ +``` + +% documentation + +## Documentation + +See + +## How to install + +(install)= + +### Minimal installation + +(minimal-install)= + +Base `onetl` package contains: + +- `DBReader`, `DBWriter` and related classes +- `FileDownloader`, `FileUploader`, `FileMover` and related classes, like file filters & limits +- `FileDFReader`, `FileDFWriter` and related classes, like file formats +- Read Strategies & HWM classes +- Plugins support + +It can be installed via: + +```bash +pip install onetl +``` + +```{eval-rst} +.. warning:: + + This method does NOT include any connections. + + This method is recommended for use in third-party libraries which require for ``onetl`` to be installed, + but do not use its connection classes. +``` + +### With DB and FileDF connections + +(spark-install)= + +All DB connection classes (`Clickhouse`, `Greenplum`, `Hive` and others) +and all FileDF connection classes (`SparkHDFS`, `SparkLocalFS`, `SparkS3`) +require Spark to be installed. + +(java-install)= + +Firstly, you should install JDK. The exact installation instruction depends on your OS, here are some examples: + +```bash +yum install java-1.8.0-openjdk-devel # CentOS 7 + Spark 2 +dnf install java-11-openjdk-devel # CentOS 8 + Spark 3 +apt-get install openjdk-11-jdk # Debian-based + Spark 3 +``` + +(spark-compatibility-matrix)= + +#### Compatibility matrix + +| Spark | Python | Java | Scala | +| --------------------------------------------------------- | ---------- | ---------- | ----- | +| [2.3.x](https://spark.apache.org/docs/2.3.1/#downloading) | 3.7 only | 8 only | 2.11 | +| [2.4.x](https://spark.apache.org/docs/2.4.8/#downloading) | 3.7 only | 8 only | 2.11 | +| [3.2.x](https://spark.apache.org/docs/3.2.4/#downloading) | 3.7 - 3.10 | 8u201 - 11 | 2.12 | +| [3.3.x](https://spark.apache.org/docs/3.3.4/#downloading) | 3.7 - 3.12 | 8u201 - 17 | 2.12 | +| [3.4.x](https://spark.apache.org/docs/3.4.4/#downloading) | 3.7 - 3.12 | 8u362 - 20 | 2.12 | +| [3.5.x](https://spark.apache.org/docs/3.5.5/#downloading) | 3.8 - 3.13 | 8u371 - 20 | 2.12 | + +(pyspark-install)= + +Then you should install PySpark via passing `spark` to `extras`: + +```bash +pip install onetl[spark] # install latest PySpark +``` + +or install PySpark explicitly: + +```bash +pip install onetl pyspark==3.5.5 # install a specific PySpark version +``` + +or inject PySpark to `sys.path` in some other way BEFORE creating a class instance. +**Otherwise connection object cannot be created.** + +### With File connections + +(files-install)= + +All File (but not *FileDF*) connection classes (`FTP`, `SFTP`, `HDFS` and so on) requires specific Python clients to be installed. + +Each client can be installed explicitly by passing connector name (in lowercase) to `extras`: + +```bash +pip install onetl[ftp] # specific connector +pip install onetl[ftp,ftps,sftp,hdfs,s3,webdav,samba] # multiple connectors +``` + +To install all file connectors at once you can pass `files` to `extras`: + +```bash +pip install onetl[files] +``` + +**Otherwise class import will fail.** + +### With Kerberos support + +(kerberos-install)= + +Most of Hadoop instances set up with Kerberos support, +so some connections require additional setup to work properly. + +- `HDFS` + Uses [requests-kerberos](https://pypi.org/project/requests-kerberos/) and + [GSSApi](https://pypi.org/project/gssapi/) for authentication. + It also uses `kinit` executable to generate Kerberos ticket. +- `Hive` and `SparkHDFS` + require Kerberos ticket to exist before creating Spark session. + +So you need to install OS packages with: + +- `krb5` libs +- Headers for `krb5` +- `gcc` or other compiler for C sources + +The exact installation instruction depends on your OS, here are some examples: + +```bash +apt install libkrb5-dev krb5-user gcc # Debian-based +dnf install krb5-devel krb5-libs krb5-workstation gcc # CentOS, OracleLinux +``` + +Also you should pass `kerberos` to `extras` to install required Python packages: + +```bash +pip install onetl[kerberos] +``` + +### Full bundle + +(full-bundle-1)= + +To install all connectors and dependencies, you can pass `all` into `extras`: + +```bash +pip install onetl[all] + +# this is just the same as +pip install onetl[spark,files,kerberos] +``` + +```{eval-rst} +.. warning:: + + This method consumes a lot of disk space, and requires for Java & Kerberos libraries to be installed into your OS. +``` + +(quick-start)= + +## Quick start + +### MSSQL → Hive + +Read data from MSSQL, transform & write to Hive. + +```bash +# install onETL and PySpark +pip install onetl[spark] +``` + +```python +# Import pyspark to initialize the SparkSession +from pyspark.sql import SparkSession + +# import function to setup onETL logging +from onetl.log import setup_logging + +# Import required connections +from onetl.connection import MSSQL, Hive + +# Import onETL classes to read & write data +from onetl.db import DBReader, DBWriter + +# change logging level to INFO, and set up default logging format and handler +setup_logging() + +# Initialize new SparkSession with MSSQL driver loaded +maven_packages = MSSQL.get_packages() +spark = ( + SparkSession.builder.appName("spark_app_onetl_demo") + .config("spark.jars.packages", ",".join(maven_packages)) + .enableHiveSupport() # for Hive + .getOrCreate() +) + +# Initialize MSSQL connection and check if database is accessible +mssql = MSSQL( + host="mssqldb.demo.com", + user="onetl", + password="onetl", + database="Telecom", + spark=spark, + # These options are passed to MSSQL JDBC Driver: + extra={"applicationIntent": "ReadOnly"}, +).check() + +# >>> INFO:|MSSQL| Connection is available + +# Initialize DBReader +reader = DBReader( + connection=mssql, + source="dbo.demo_table", + columns=["on", "etl"], + # Set some MSSQL read options: + options=MSSQL.ReadOptions(fetchsize=10000), +) + +# checks that there is data in the table, otherwise raises exception +reader.raise_if_no_data() + +# Read data to DataFrame +df = reader.run() +df.printSchema() +# root +# |-- id: integer (nullable = true) +# |-- phone_number: string (nullable = true) +# |-- region: string (nullable = true) +# |-- birth_date: date (nullable = true) +# |-- registered_at: timestamp (nullable = true) +# |-- account_balance: double (nullable = true) + +# Apply any PySpark transformations +from pyspark.sql.functions import lit + +df_to_write = df.withColumn("engine", lit("onetl")) +df_to_write.printSchema() +# root +# |-- id: integer (nullable = true) +# |-- phone_number: string (nullable = true) +# |-- region: string (nullable = true) +# |-- birth_date: date (nullable = true) +# |-- registered_at: timestamp (nullable = true) +# |-- account_balance: double (nullable = true) +# |-- engine: string (nullable = false) + +# Initialize Hive connection +hive = Hive(cluster="rnd-dwh", spark=spark) + +# Initialize DBWriter +db_writer = DBWriter( + connection=hive, + target="dl_sb.demo_table", + # Set some Hive write options: + options=Hive.WriteOptions(if_exists="replace_entire_table"), +) + +# Write data from DataFrame to Hive +db_writer.run(df_to_write) + +# Success! +``` + +### SFTP → HDFS + +Download files from SFTP & upload them to HDFS. + +```bash +# install onETL with SFTP and HDFS clients, and Kerberos support +pip install onetl[hdfs,sftp,kerberos] +``` + +```python +# import function to setup onETL logging +from onetl.log import setup_logging + +# Import required connections +from onetl.connection import SFTP, HDFS + +# Import onETL classes to download & upload files +from onetl.file import FileDownloader, FileUploader + +# import filter & limit classes +from onetl.file.filter import Glob, ExcludeDir +from onetl.file.limit import MaxFilesCount + +# change logging level to INFO, and set up default logging format and handler +setup_logging() + +# Initialize SFTP connection and check it +sftp = SFTP( + host="sftp.test.com", + user="someuser", + password="somepassword", +).check() + +# >>> INFO:|SFTP| Connection is available + +# Initialize downloader +file_downloader = FileDownloader( + connection=sftp, + source_path="/remote/tests/Report", # path on SFTP + local_path="/local/onetl/Report", # local fs path + filters=[ + # download only files matching the glob + Glob("*.csv"), + # exclude files from this directory + ExcludeDir("/remote/tests/Report/exclude_dir/"), + ], + limits=[ + # download max 1000 files per run + MaxFilesCount(1000), + ], + options=FileDownloader.Options( + # delete files from SFTP after successful download + delete_source=True, + # mark file as failed if it already exist in local_path + if_exists="error", + ), +) + +# Download files to local filesystem +download_result = downloader.run() + +# Method run returns a DownloadResult object, +# which contains collection of downloaded files, divided to 4 categories +download_result + +# DownloadResult( +# successful=[ +# LocalPath('/local/onetl/Report/file_1.json'), +# LocalPath('/local/onetl/Report/file_2.json'), +# ], +# failed=[FailedRemoteFile('/remote/onetl/Report/file_3.json')], +# ignored=[RemoteFile('/remote/onetl/Report/file_4.json')], +# missing=[], +# ) + +# Raise exception if there are failed files, or there were no files in the remote filesystem +download_result.raise_if_failed() or download_result.raise_if_empty() + +# Do any kind of magic with files: rename files, remove header for csv files, ... +renamed_files = my_rename_function(download_result.success) + +# function removed "_" from file names +# [ +# LocalPath('/home/onetl/Report/file1.json'), +# LocalPath('/home/onetl/Report/file2.json'), +# ] + +# Initialize HDFS connection +hdfs = HDFS( + host="my.name.node", + user="someuser", + password="somepassword", # or keytab +) + +# Initialize uploader +file_uploader = FileUploader( + connection=hdfs, + target_path="/user/onetl/Report/", # hdfs path +) + +# Upload files from local fs to HDFS +upload_result = file_uploader.run(renamed_files) + +# Method run returns a UploadResult object, +# which contains collection of uploaded files, divided to 4 categories +upload_result + +# UploadResult( +# successful=[RemoteFile('/user/onetl/Report/file1.json')], +# failed=[FailedLocalFile('/local/onetl/Report/file2.json')], +# ignored=[], +# missing=[], +# ) + +# Raise exception if there are failed files, or there were no files in the local filesystem, or some input file is missing +upload_result.raise_if_failed() or upload_result.raise_if_empty() or upload_result.raise_if_missing() + +# Success! +``` + +### S3 → Postgres + +Read files directly from S3 path, convert them to dataframe, transform it and then write to a database. + +```bash +# install onETL and PySpark +pip install onetl[spark] +``` + +```python +# Import pyspark to initialize the SparkSession +from pyspark.sql import SparkSession + +# import function to setup onETL logging +from onetl.log import setup_logging + +# Import required connections +from onetl.connection import Postgres, SparkS3 + +# Import onETL classes to read files +from onetl.file import FileDFReader +from onetl.file.format import CSV + +# Import onETL classes to write data +from onetl.db import DBWriter + +# change logging level to INFO, and set up default logging format and handler +setup_logging() + +# Initialize new SparkSession with Hadoop AWS libraries and Postgres driver loaded +maven_packages = SparkS3.get_packages(spark_version="3.5.5") + Postgres.get_packages() +exclude_packages = SparkS3.get_exclude_packages() +spark = ( + SparkSession.builder.appName("spark_app_onetl_demo") + .config("spark.jars.packages", ",".join(maven_packages)) + .config("spark.jars.excludes", ",".join(exclude_packages)) + .getOrCreate() +) + +# Initialize S3 connection and check it +spark_s3 = SparkS3( + host="s3.test.com", + protocol="https", + bucket="my-bucket", + access_key="somekey", + secret_key="somesecret", + # Access bucket as s3.test.com/my-bucket + extra={"path.style.access": True}, + spark=spark, +).check() + +# >>> INFO:|SparkS3| Connection is available + +# Describe file format and parsing options +csv = CSV( + delimiter=";", + header=True, + encoding="utf-8", +) + +# Describe DataFrame schema of files +from pyspark.sql.types import ( + DateType, + DoubleType, + IntegerType, + StringType, + StructField, + StructType, + TimestampType, +) + +df_schema = StructType( + [ + StructField("id", IntegerType()), + StructField("phone_number", StringType()), + StructField("region", StringType()), + StructField("birth_date", DateType()), + StructField("registered_at", TimestampType()), + StructField("account_balance", DoubleType()), + ], +) + +# Initialize file df reader +reader = FileDFReader( + connection=spark_s3, + source_path="/remote/tests/Report", # path on S3 there *.csv files are located + format=csv, # file format with specific parsing options + df_schema=df_schema, # columns & types +) + +# Read files directly from S3 as Spark DataFrame +df = reader.run() + +# Check that DataFrame schema is same as expected +df.printSchema() +# root +# |-- id: integer (nullable = true) +# |-- phone_number: string (nullable = true) +# |-- region: string (nullable = true) +# |-- birth_date: date (nullable = true) +# |-- registered_at: timestamp (nullable = true) +# |-- account_balance: double (nullable = true) + +# Apply any PySpark transformations +from pyspark.sql.functions import lit + +df_to_write = df.withColumn("engine", lit("onetl")) +df_to_write.printSchema() +# root +# |-- id: integer (nullable = true) +# |-- phone_number: string (nullable = true) +# |-- region: string (nullable = true) +# |-- birth_date: date (nullable = true) +# |-- registered_at: timestamp (nullable = true) +# |-- account_balance: double (nullable = true) +# |-- engine: string (nullable = false) + +# Initialize Postgres connection +postgres = Postgres( + host="192.169.11.23", + user="onetl", + password="somepassword", + database="mydb", + spark=spark, +) + +# Initialize DBWriter +db_writer = DBWriter( + connection=postgres, + # write to specific table + target="public.my_table", + # with some writing options + options=Postgres.WriteOptions(if_exists="append"), +) + +# Write DataFrame to Postgres table +db_writer.run(df_to_write) + +# Success! +``` diff --git a/mddocs/ru/SECURITY.md b/mddocs/ru/SECURITY.md new file mode 100644 index 000000000..5d9dce52b --- /dev/null +++ b/mddocs/ru/SECURITY.md @@ -0,0 +1,25 @@ +# Security + +## Supported Python versions + +3.7 or above + +## Product development security recommendations + +1. Update dependencies to last stable version +2. Build SBOM for the project +3. Perform SAST (Static Application Security Testing) where possible + +## Product development security requirements + +1. No binaries in repository +2. No passwords, keys, access tokens in source code +3. No "Critical" and/or "High" vulnerabilities in contributed source code + +## Vulnerability reports + +Please, use email [mailto:onetools@mts.ru](mailto:onetools@mts.ru) for reporting security issues or anything that can cause any consequences for security. + +Please avoid any public disclosure (including registering issues) at least until it is fixed. + +Thank you in advance for understanding. diff --git a/mddocs/ru/concepts.md b/mddocs/ru/concepts.md new file mode 100644 index 000000000..b8b0baf4a --- /dev/null +++ b/mddocs/ru/concepts.md @@ -0,0 +1,458 @@ +# Concepts + +Here you can find detailed documentation about each one of the onETL concepts and how to use them. + +## Connection + +### Basics + +onETL is used to pull and push data into other systems, and so it has a first-class `Connection` concept for storing credentials that are used to communicate with external systems. + +A `Connection` is essentially a set of parameters, such as username, password, hostname. + +To create a connection to a specific storage type, you must use a class that matches the storage type. The class name is the same as the storage type name (`Oracle`, `MSSQL`, `SFTP`, etc): + +```python +from onetl.connection import SFTP + +sftp = SFTP( + host="sftp.test.com", + user="onetl", + password="onetl", +) +``` + +All connection types are inherited from the parent class `BaseConnection`. + +### Class diagram + +```{eval-rst} +.. plantuml:: + + @startuml + left to right direction + skinparam classFontSize 20 + skinparam class { + BackgroundColor<> LightGreen + BackgroundColor<> Khaki + BackgroundColor<> LightBlue + StereotypeFontColor<> Transparent + StereotypeFontColor<> Transparent + StereotypeFontColor<> Transparent + } + + class BaseConnection { + } + + class DBConnection <>{ + } + DBConnection --|> BaseConnection + + class Hive <>{ + } + Hive --|> DBConnection + + class Greenplum <>{ + } + Greenplum --|> DBConnection + + class MongoDB <>{ + } + MongoDB --|> DBConnection + + class Kafka <>{ + } + Kafka --|> DBConnection + + class JDBCConnection <>{ + } + JDBCConnection --|> DBConnection + + class Clickhouse <>{ + } + Clickhouse --|> JDBCConnection + + class MSSQL <>{ + } + MSSQL --|> JDBCConnection + + class MySQL <>{ + } + MySQL --|> JDBCConnection + + class Postgres <>{ + } + Postgres --|> JDBCConnection + + class Oracle <>{ + } + Oracle --|> JDBCConnection + + class Teradata <>{ + } + Teradata --|> JDBCConnection + + class FileConnection <>{ + } + FileConnection --|> BaseConnection + + class FTP <>{ + } + FTP --|> FileConnection + + class FTPS <>{ + } + FTPS --|> FileConnection + + class HDFS <>{ + } + HDFS --|> FileConnection + + class WebDAV <>{ + } + WebDAV --|> FileConnection + + class Samba <>{ + } + Samba --|> FileConnection + + class SFTP <>{ + } + SFTP --|> FileConnection + + class S3 <>{ + } + S3 --|> FileConnection + + class FileDFConnection <>{ + } + FileDFConnection --|> BaseConnection + + class SparkHDFS <>{ + } + SparkHDFS --|> FileDFConnection + + class SparkLocalFS <>{ + } + SparkLocalFS --|> FileDFConnection + + class SparkS3 <>{ + } + SparkS3 --|> FileDFConnection + + @enduml +``` + +### DBConnection + +Classes inherited from `DBConnection` could be used for accessing databases. + +A `DBConnection` could be instantiated as follows: + +```python +from onetl.connection import MSSQL + +mssql = MSSQL( + host="mssqldb.demo.com", + user="onetl", + password="onetl", + database="Telecom", + spark=spark, +) +``` + +where **spark** is the current SparkSession. +`onETL` uses `Spark` and specific Java connectors under the hood to work with databases. + +For a description of other parameters, see the documentation for the {ref}`available DBConnections `. + +### FileConnection + +Classes inherited from `FileConnection` could be used to access files stored on the different file systems/file servers + +A `FileConnection` could be instantiated as follows: + +```python +from onetl.connection import SFTP + +sftp = SFTP( + host="sftp.test.com", + user="onetl", + password="onetl", +) +``` + +For a description of other parameters, see the documentation for the {ref}`available FileConnections `. + +### FileDFConnection + +Classes inherited from `FileDFConnection` could be used for accessing files as Spark DataFrames. + +A `FileDFConnection` could be instantiated as follows: + +```python +from onetl.connection import SparkHDFS + +spark_hdfs = SparkHDFS( + host="namenode1.domain.com", + cluster="mycluster", + spark=spark, +) +``` + +where **spark** is the current SparkSession. +`onETL` uses `Spark` and specific Java connectors under the hood to work with DataFrames. + +For a description of other parameters, see the documentation for the {ref}`available FileDFConnections `. + +### Checking connection availability + +Once you have created a connection, you can check the database/filesystem availability using the method `check()`: + +```python +mssql.check() +sftp.check() +spark_hdfs.check() +``` + +It will raise an exception if database/filesystem cannot be accessed. + +This method returns connection itself, so you can create connection and immediately check its availability: + +```Python +mssql = MSSQL( + host="mssqldb.demo.com", + user="onetl", + password="onetl", + database="Telecom", + spark=spark, +).check() # <-- +``` + +## Extract/Load data + +### Basics + +As we said above, onETL is used to extract data from and load data into remote systems. + +onETL provides several classes for this: + +> - {ref}`DBReader ` +> - {ref}`DBWriter ` +> - {ref}`FileDFReader ` +> - {ref}`FileDFWriter ` +> - {ref}`FileDownloader ` +> - {ref}`FileUploader ` +> - {ref}`FileMover ` + +All of these classes have a method `run()` that starts extracting/loading the data: + +```python +from onetl.db import DBReader, DBWriter + +reader = DBReader( + connection=mssql, + source="dbo.demo_table", + columns=["column_1", "column_2"], +) + +# Read data as Spark DataFrame +df = reader.run() + +db_writer = DBWriter( + connection=hive, + target="dl_sb.demo_table", +) + +# Save Spark DataFrame to Hive table +writer.run(df) +``` + +### Extract data + +To extract data you can use classes: + +| | Use case | Connection | `run()` gets | `run()` returns | +| ------------------------------------ | ----------------------------------------- | ------------------------------------------------- | ------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------- | +| {ref}`DBReader ` | Reading data from a database | Any {ref}`DBConnection ` | - | [Spark DataFrame](https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.sql.DataFrame.html#pyspark.sql.DataFrame) | +| {ref}`FileDFReader ` | Read data from a file or set of files | Any {ref}`FileDFConnection ` | No input, or List[File path on FileSystem] | [Spark DataFrame](https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.sql.DataFrame.html#pyspark.sql.DataFrame) | +| {ref}`FileDownloader ` | Download files from remote FS to local FS | Any {ref}`FileConnection ` | No input, or List[File path on remote FileSystem] | {ref}`DownloadResult ` | + +### Load data + +To load data you can use classes: + +| | Use case | Connection | `run()` gets | `run()` returns | +| ----------------------------------- | -------------------------------------------- | ------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------ | +| {ref}`DBWriter ` | Writing data from a DataFrame to a database | Any {ref}`DBConnection ` | [Spark DataFrame](https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.sql.DataFrame.html#pyspark.sql.DataFrame) | None | +| {ref}`FileDFWriter ` | Writing data from a DataFrame to a folder | Any {ref}`FileDFConnection ` | [Spark DataFrame](https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.sql.DataFrame.html#pyspark.sql.DataFrame) | None | +| {ref}`FileUploader ` | Uploading files from a local FS to remote FS | Any {ref}`FileConnection ` | List[File path on local FileSystem] | {ref}`UploadResult ` | + +### Manipulate data + +To manipulate data you can use classes: + +| | Use case | Connection | `run()` gets | `run()` returns | +| ----------------------------- | ------------------------------------------- | -------------------------------------------- | ------------------------------------ | ------------------------------------- | +| {ref}`FileMover ` | Move files between directories in remote FS | Any {ref}`FileConnection ` | List[File path on remote FileSystem] | {ref}`MoveResult ` | + +### Options + +Extract and load classes have a `options` parameter, which has a special meaning: + +> - all other parameters - *WHAT* we extract / *WHERE* we load to +> - `options` parameter - *HOW* we extract/load data + +```python +db_reader = DBReader( + # WHAT do we read: + connection=mssql, + source="dbo.demo_table", # some table from MSSQL + columns=["column_1", "column_2"], # but only specific set of columns + where="column_2 > 1000", # only rows matching the clause + # HOW do we read: + options=MSSQL.ReadOptions( + numPartitions=10, # read in 10 parallel jobs + partitionColumn="id", # balance data read by assigning each job a part of data using `hash(id) mod N` expression + partitioningMode="hash", + fetchsize=1000, # each job will fetch block of 1000 rows each on every read attempt + ), +) + +db_writer = DBWriter( + # WHERE do we write to - to some table in Hive + connection=hive, + target="dl_sb.demo_table", + # HOW do we write - overwrite all the data in the existing table + options=Hive.WriteOptions(if_exists="replace_entire_table"), +) + +file_downloader = FileDownloader( + # WHAT do we download - files from some dir in SFTP + connection=sftp, + source_path="/source", + filters=[Glob("*.csv")], # only CSV files + limits=[MaxFilesCount(1000)], # 1000 files max + # WHERE do we download to - a specific dir on local FS + local_path="/some", + # HOW do we download: + options=FileDownloader.Options( + delete_source=True, # after downloading each file remove it from source_path + if_exists="replace_file", # replace existing files in the local_path + ), +) + +file_uploader = FileUploader( + # WHAT do we upload - files from some local dir + local_path="/source", + # WHERE do we upload to- specific remote dir in HDFS + connection=hdfs, + target_path="/some", + # HOW do we upload: + options=FileUploader.Options( + delete_local=True, # after uploading each file remove it from local_path + if_exists="replace_file", # replace existing files in the target_path + ), +) + +file_mover = FileMover( + # WHAT do we move - files in some remote dir in HDFS + source_path="/source", + connection=hdfs, + # WHERE do we move files to + target_path="/some", # a specific remote dir within the same HDFS connection + # HOW do we load - replace existing files in the target_path + options=FileMover.Options(if_exists="replace_file"), +) + +file_df_reader = FileDFReader( + # WHAT do we read - *.csv files from some dir in S3 + connection=s3, + source_path="/source", + file_format=CSV(), + # HOW do we read - load files from /source/*.csv, not from /source/nested/*.csv + options=FileDFReader.Options(recursive=False), +) + +file_df_writer = FileDFWriter( + # WHERE do we write to - as .csv files in some dir in S3 + connection=s3, + target_path="/target", + file_format=CSV(), + # HOW do we write - replace all existing files in /target, if exists + options=FileDFWriter.Options(if_exists="replace_entire_directory"), +) +``` + +More information about `options` could be found on {ref}`DB connection `. and +{ref}`file-downloader` / {ref}`file-uploader` / {ref}`file-mover` / {ref}`file-df-reader` / {ref}`file-df-writer` documentation + +### Read Strategies + +onETL have several builtin strategies for reading data: + +1. [Snapshot strategy](strategy/snapshot_strategy.html) (default strategy) +2. [Incremental strategy](strategy/incremental_strategy.html) +3. [Snapshot batch strategy](strategy/snapshot_batch_strategy.html) +4. [Incremental batch strategy](strategy/incremental_batch_strategy.html) + +For example, an incremental strategy allows you to get only new data from the table: + +```python +from onetl.strategy import IncrementalStrategy + +reader = DBReader( + connection=mssql, + source="dbo.demo_table", + hwm_column="id", # detect new data based on value of "id" column +) + +# first run +with IncrementalStrategy(): + df = reader.run() + +sleep(3600) + +# second run +with IncrementalStrategy(): + # only rows, that appeared in the source since previous run + df = reader.run() +``` + +or get only files which were not downloaded before: + +```python +from onetl.strategy import IncrementalStrategy + +file_downloader = FileDownloader( + connection=sftp, + source_path="/remote", + local_path="/local", + hwm_type="file_list", # save all downloaded files to a list, and exclude files already present in this list +) + +# first run +with IncrementalStrategy(): + files = file_downloader.run() + +sleep(3600) + +# second run +with IncrementalStrategy(): + # only files, that appeared in the source since previous run + files = file_downloader.run() +``` + +Most of strategies are based on {ref}`hwm`, Please check each strategy documentation for more details + +### Why just not use Connection class for extract/load? + +Connections are very simple, they have only a set of some basic operations, +like `mkdir`, `remove_file`, `get_table_schema`, and so on. + +High-level operations, like +: - {ref}`strategy` support + - Handling metadata push/pull + - Handling different options, like `if_exists="replace_file"` in case of file download/upload + +is moved to a separate class which calls the connection object methods to perform some complex logic. diff --git a/mddocs/ru/contributing.md b/mddocs/ru/contributing.md new file mode 100644 index 000000000..cc9ee4419 --- /dev/null +++ b/mddocs/ru/contributing.md @@ -0,0 +1,3 @@ +```{eval-rst} +.. include:: ../CONTRIBUTING.rst +``` diff --git a/mddocs/ru/index.md b/mddocs/ru/index.md new file mode 100644 index 000000000..46642bf4e --- /dev/null +++ b/mddocs/ru/index.md @@ -0,0 +1,87 @@ +```{eval-rst} +.. include:: ../README.rst + :end-before: |Logo| +``` + +```{image} _static/logo_wide.svg +:alt: onETL logo +``` + +```{eval-rst} +.. include:: ../README.rst + :start-after: |Logo| + :end-before: documentation +``` + +```{toctree} +:caption: How to +:hidden: true +:maxdepth: 2 + +self +install/index +quickstart +concepts +logging +troubleshooting/index +``` + +```{toctree} +:caption: Connection +:hidden: true +:maxdepth: 3 + +connection/index +``` + +```{toctree} +:caption: DB classes +:hidden: true +:maxdepth: 3 + +db/index +``` + +```{toctree} +:caption: File classes +:hidden: true +:maxdepth: 3 + +file/index +``` + +```{toctree} +:caption: File DataFrame classes +:hidden: true +:maxdepth: 3 + +file_df/index +``` + +```{toctree} +:caption: Read strategies and HWM +:hidden: true +:maxdepth: 2 + +strategy/index +hwm_store/index +``` + +```{toctree} +:caption: Hooks & plugins +:hidden: true +:maxdepth: 2 + +hooks/index +plugins +``` + +```{toctree} +:caption: Development +:hidden: true +:maxdepth: 2 + +changelog +contributing +security +``` diff --git a/mddocs/ru/logging.md b/mddocs/ru/logging.md new file mode 100644 index 000000000..dae7a9d73 --- /dev/null +++ b/mddocs/ru/logging.md @@ -0,0 +1,171 @@ +(logging)= + +# Logging + +Logging is quite important to understand what's going on under the hood of onETL. + +Default logging level for Python interpreters is `WARNING`, +but most of onETL logs are in `INFO` level, so users usually don't see much. + +To change logging level, there is a function {obj}`setup_logging ` +which should be called at the top of the script: + +```python +from onetl.log import setup_logging +from other.lib import some, more, imports + +setup_logging() + +# rest of code +... +``` + +This changes both log level and log formatting to something like this: + +```{eval-rst} +.. dropdown:: See logs + + .. code:: text + + 2024-04-12 10:12:10,834 [INFO ] MainThread: |onETL| Using IncrementalStrategy as a strategy + 2024-04-12 10:12:10,835 [INFO ] MainThread: =================================== DBReader.run() starts =================================== + 2024-04-12 10:12:10,835 [INFO ] MainThread: |DBReader| Getting Spark type for HWM expression: 'updated_at' + 2024-04-12 10:12:10,836 [INFO ] MainThread: |MSSQL| Fetching schema of table 'source_schema.table' ... + 2024-04-12 10:12:11,636 [INFO ] MainThread: |MSSQL| Schema fetched. + 2024-04-12 10:12:11,642 [INFO ] MainThread: |DBReader| Got Spark field: StructField('updated_at', TimestampType(), True) + 2024-04-12 10:12:11,642 [INFO ] MainThread: |DBReader| Detected HWM type: 'ColumnDateTimeHWM' + 2024-04-12 10:12:11,643 [INFO ] MainThread: |IncrementalStrategy| Fetching HWM from HorizonHWMStore: + 2024-04-12 10:12:11,643 [INFO ] MainThread: name = 'updated_at#source_schema.table@mssql:/mssql.host:1433/somedb' + 2024-04-12 10:12:12,181 [INFO ] MainThread: |IncrementalStrategy| Fetched HWM: + 2024-04-12 10:12:12,182 [INFO ] MainThread: hwm = ColumnDateTimeHWM( + 2024-04-12 10:12:12,182 [INFO ] MainThread: name = 'updated_at#source_schema.table@mssql:/mssql.host:1433/somedb', + 2024-04-12 10:12:12,182 [INFO ] MainThread: entity = 'source_schema.table', + 2024-04-12 10:12:12,182 [INFO ] MainThread: expression = 'updated_at', + 2024-04-12 10:12:12,184 [INFO ] MainThread: value = datetime.datetime(2024, 4, 11, 18, 10, 2, 120000), + 2024-04-12 10:12:12,184 [INFO ] MainThread: ) + 2024-04-12 10:12:12,184 [INFO ] MainThread: |MSSQL| -> |Spark| Reading DataFrame from source using parameters: + 2024-04-12 10:12:12,185 [INFO ] MainThread: source = 'source_schema.table' + 2024-04-12 10:12:12,185 [INFO ] MainThread: columns = [ + 2024-04-12 10:12:12,185 [INFO ] MainThread: 'id', + 2024-04-12 10:12:12,186 [INFO ] MainThread: 'new_value', + 2024-04-12 10:12:12,186 [INFO ] MainThread: 'old_value', + 2024-04-12 10:12:12,186 [INFO ] MainThread: 'updated_at', + 2024-04-12 10:12:12,186 [INFO ] MainThread: ] + 2024-04-12 10:12:12,187 [INFO ] MainThread: where = "field = 'some'" + 2024-04-12 10:12:12,187 [INFO ] MainThread: hwm = AutoDetectHWM( + 2024-04-12 10:12:12,187 [INFO ] MainThread: name = 'updated_at#source_schema.table@mssql:/mssql.host:1433/somedb', + 2024-04-12 10:12:12,187 [INFO ] MainThread: entity = 'source_schema.table', + 2024-04-12 10:12:12,187 [INFO ] MainThread: expression = 'updated_at', + 2024-04-12 10:12:12,188 [INFO ] MainThread: ) + 2024-04-12 10:12:12,188 [INFO ] MainThread: options = { + 2024-04-12 10:12:12,188 [INFO ] MainThread: 'fetchsize': 100000, + 2024-04-12 10:12:12,188 [INFO ] MainThread: 'numPartitions': 1, + 2024-04-12 10:12:12,189 [INFO ] MainThread: 'partitioningMode': 'range', + 2024-04-12 10:12:12,189 [INFO ] MainThread: } + 2024-04-12 10:12:12,189 [INFO ] MainThread: |MSSQL| Checking connection availability... + 2024-04-12 10:12:12,189 [INFO ] MainThread: |MSSQL| Using connection parameters: + 2024-04-12 10:12:12,190 [INFO ] MainThread: user = 'db_user' + 2024-04-12 10:12:12,190 [INFO ] MainThread: password = SecretStr('**********') + 2024-04-12 10:12:12,190 [INFO ] MainThread: host = 'mssql.host' + 2024-04-12 10:12:12,190 [INFO ] MainThread: port = 1433 + 2024-04-12 10:12:12,191 [INFO ] MainThread: database = 'somedb' + 2024-04-12 10:12:12,191 [INFO ] MainThread: extra = {'applicationIntent': 'ReadOnly', 'trustServerCertificate': 'true'} + 2024-04-12 10:12:12,191 [INFO ] MainThread: jdbc_url = 'jdbc:sqlserver:/mssql.host:1433' + 2024-04-12 10:12:12,579 [INFO ] MainThread: |MSSQL| Connection is available. + 2024-04-12 10:12:12,581 [INFO ] MainThread: |MSSQL| Executing SQL query (on driver): + 2024-04-12 10:12:12,581 [INFO ] MainThread: SELECT + 2024-04-12 10:12:12,581 [INFO ] MainThread: MIN(updated_at) AS "min", + 2024-04-12 10:12:12,582 [INFO ] MainThread: MAX(updated_at) AS "max" + 2024-04-12 10:12:12,582 [INFO ] MainThread: FROM + 2024-04-12 10:12:12,582 [INFO ] MainThread: source_schema.table + 2024-04-12 10:12:12,582 [INFO ] MainThread: WHERE + 2024-04-12 10:12:12,582 [INFO ] MainThread: (field = 'some') + 2024-04-12 10:12:12,583 [INFO ] MainThread: AND + 2024-04-12 10:12:12,583 [INFO ] MainThread: (updated_at >= CAST('2024-04-11T18:10:02.120000' AS datetime2)) + 2024-04-12 10:16:22,537 [INFO ] MainThread: |MSSQL| Received values: + 2024-04-12 10:16:22,538 [INFO ] MainThread: MIN(updated_at) = datetime.datetime(2024, 4, 11, 21, 10, 7, 397000) + 2024-04-12 10:16:22,538 [INFO ] MainThread: MAX(updated_at) = datetime.datetime(2024, 4, 12, 13, 12, 2, 123000) + 2024-04-12 10:16:22,540 [INFO ] MainThread: |MSSQL| Executing SQL query (on executor): + 2024-04-12 10:16:22,540 [INFO ] MainThread: SELECT + 2024-04-12 10:16:22,540 [INFO ] MainThread: id, + 2024-04-12 10:16:22,541 [INFO ] MainThread: new_value, + 2024-04-12 10:16:22,541 [INFO ] MainThread: old_value, + 2024-04-12 10:16:22,541 [INFO ] MainThread: updated_at + 2024-04-12 10:16:22,541 [INFO ] MainThread: FROM + 2024-04-12 10:16:22,541 [INFO ] MainThread: source_schema.table + 2024-04-12 10:16:22,542 [INFO ] MainThread: WHERE + 2024-04-12 10:16:22,542 [INFO ] MainThread: (field = 'some') + 2024-04-12 10:16:22,542 [INFO ] MainThread: AND + 2024-04-12 10:16:22,542 [INFO ] MainThread: (updated_at > CAST('2024-04-11T18:10:02.120000' AS datetime2)) + 2024-04-12 10:16:22,542 [INFO ] MainThread: AND + 2024-04-12 10:16:22,542 [INFO ] MainThread: (updated_at <= CAST('2024-04-12T13:12:02.123000' AS datetime2)) + 2024-04-12 10:16:22,892 [INFO ] MainThread: |Spark| DataFrame successfully created from SQL statement + 2024-04-12 10:16:22,892 [INFO ] MainThread: ------------------------------------ DBReader.run() ends ------------------------------------ + 2024-04-12 10:40:42,409 [INFO ] MainThread: =================================== DBWriter.run() starts =================================== + 2024-04-12 10:40:42,409 [INFO ] MainThread: |Spark| -> |Hive| Writing DataFrame to target using parameters: + 2024-04-12 10:40:42,410 [INFO ] MainThread: target = 'target_source_schema.table' + 2024-04-12 10:40:42,410 [INFO ] MainThread: options = { + 2024-04-12 10:40:42,410 [INFO ] MainThread: 'mode': 'append', + 2024-04-12 10:40:42,410 [INFO ] MainThread: 'format': 'orc', + 2024-04-12 10:40:42,410 [INFO ] MainThread: 'partitionBy': 'part_dt', + 2024-04-12 10:40:42,410 [INFO ] MainThread: } + 2024-04-12 10:40:42,411 [INFO ] MainThread: df_schema: + 2024-04-12 10:40:42,412 [INFO ] MainThread: root + 2024-04-12 10:40:42,412 [INFO ] MainThread: |-- id: integer (nullable = true) + 2024-04-12 10:40:42,413 [INFO ] MainThread: |-- new_value: string (nullable = true) + 2024-04-12 10:40:42,413 [INFO ] MainThread: |-- old_value: string (nullable = true) + 2024-04-12 10:40:42,413 [INFO ] MainThread: |-- updated_at: timestamp (nullable = true) + 2024-04-12 10:40:42,413 [INFO ] MainThread: |-- part_dt: date (nullable = true) + 2024-04-12 10:40:42,414 [INFO ] MainThread: + 2024-04-12 10:40:42,421 [INFO ] MainThread: |Hive| Checking connection availability... + 2024-04-12 10:40:42,421 [INFO ] MainThread: |Hive| Using connection parameters: + 2024-04-12 10:40:42,421 [INFO ] MainThread: cluster = 'dwh' + 2024-04-12 10:40:42,475 [INFO ] MainThread: |Hive| Connection is available. + 2024-04-12 10:40:42,476 [INFO ] MainThread: |Hive| Fetching schema of table 'target_source_schema.table' ... + 2024-04-12 10:40:43,518 [INFO ] MainThread: |Hive| Schema fetched. + 2024-04-12 10:40:43,521 [INFO ] MainThread: |Hive| Table 'target_source_schema.table' already exists + 2024-04-12 10:40:43,521 [WARNING ] MainThread: |Hive| User-specified options {'partitionBy': 'part_dt'} are ignored while inserting into existing table. Using only table parameters from Hive metastore + 2024-04-12 10:40:43,782 [INFO ] MainThread: |Hive| Inserting data into existing table 'target_source_schema.table' ... + 2024-04-12 11:06:07,396 [INFO ] MainThread: |Hive| Data is successfully inserted into table 'target_source_schema.table'. + 2024-04-12 11:06:07,397 [INFO ] MainThread: ------------------------------------ DBWriter.run() ends ------------------------------------ + 2024-04-12 11:06:07,397 [INFO ] MainThread: |onETL| Exiting IncrementalStrategy + 2024-04-12 11:06:07,397 [INFO ] MainThread: |IncrementalStrategy| Saving HWM to 'HorizonHWMStore': + 2024-04-12 11:06:07,397 [INFO ] MainThread: hwm = ColumnDateTimeHWM( + 2024-04-12 11:06:07,397 [INFO ] MainThread: name = 'updated_at#source_schema.table@mssql:/mssql.host:1433/somedb', + 2024-04-12 11:06:07,397 [INFO ] MainThread: entity = 'source_source_schema.table', + 2024-04-12 11:06:07,397 [INFO ] MainThread: expression = 'updated_at', + 2024-04-12 11:06:07,397 [INFO ] MainThread: value = datetime.datetime(2024, 4, 12, 13, 12, 2, 123000), + 2024-04-12 11:06:07,397 [INFO ] MainThread: ) + 2024-04-12 11:06:07,495 [INFO ] MainThread: |IncrementalStrategy| HWM has been saved +``` + +Each step performed by onETL is extensively logged, which should help with debugging. + +You can make logs even more verbose by changing level to `DEBUG`: + +```python +from onetl.log import setup_logging + +setup_logging(level="DEBUG", enable_clients=True) + +# rest of code +... +``` + +This also changes log level for all underlying Python libraries, e.g. showing each HTTP request being made, and so on. + +```{eval-rst} +.. currentmodule:: onetl.log +``` + +```{eval-rst} +.. autofunction:: setup_logging +``` + +```{eval-rst} +.. autofunction:: setup_clients_logging +``` + +```{eval-rst} +.. autofunction:: set_default_logging_format +``` diff --git a/mddocs/ru/plugins.md b/mddocs/ru/plugins.md new file mode 100644 index 000000000..a7904b076 --- /dev/null +++ b/mddocs/ru/plugins.md @@ -0,0 +1,147 @@ +(plugins)= + +# Plugins + +:::{versionadded} 0.6.0 +::: + +## What are plugins? + +### Terms + +- `Plugin` - some Python package which implements some extra functionality for onETL, like {ref}`hooks` +- `Plugin autoimport` - onETL behavior which allows to automatically import this package if it contains proper metadata (`entry_points`) + +### Features + +Plugins mechanism allows to: + +- Automatically register {ref}`hooks` which can alter onETL behavior +- Automatically register new classes, like HWM type, HWM stores and so on + +### Limitations + +Unlike other projects (like *Airflow 1.x*), plugins does not inject imported classes or functions to `onetl.*` namespace. +Users should import classes from the plugin package **explicitly** to avoid name collisions. + +## How to implement plugin? + +Create a Python package `some-plugin` with a file `some_plugin/setup.py`: + +```python +# some_plugin/setup.py +from setuptools import setup + +setup( + # if you want to import something from onETL, add it to requirements list + install_requires=["onetl"], + entry_points={ + # this key enables plugins autoimport functionality + "onetl.plugins": [ + "some-plugin-name=some_plugin.module", # automatically import all module content + "some-plugin-class=some_plugin.module.internals:MyClass", # import a specific class + "some-plugin-function=some_plugin.module.internals:my_function", # import a specific function + ], + }, +) +``` + +See [setuptools documentation for entry_points](https://setuptools.pypa.io/en/latest/userguide/entry_point.html) + +## How plugins are imported? + +- User should install a package implementing the plugin: + +```bash +pip install some-package +``` + +- Then user should import something from `onetl` module or its submodules: + +```python +import onetl +from onetl.connection import SomeConnection + +# and so on +``` + +- This import automatically executes something like: + +```python +import some_plugin.module +from some_plugin.module.internals import MyClass +from some_plugin.module.internals import my_function +``` + +If specific module/class/function uses some registration capabilities of onETL, +like {ref}`hook-decorator`, it will be executed during this import. + +## How to enable/disable plugins? + +:::{versionadded} 0.7.0 +::: + +### Disable/enable all plugins + +By default plugins are enabled. + +To disabled them, you can set environment variable `ONETL_PLUGINS_ENABLED` to `false` BEFORE +importing onETL. This will disable all plugins autoimport. + +But user is still be able to explicitly import `some_plugin.module`, executing +all decorators and registration capabilities of onETL. + +### Disable a specific plugin (blacklist) + +If some plugin is failing during import, you can disable it by setting up environment variable +`ONETL_PLUGINS_BLACKLIST=some-failing-plugin`. Multiple plugin names could be passed with `,` as delimiter. + +Again, this environment variable should be set BEFORE importing onETL. + +### Disable all plugins except a specific one (whitelist) + +You can also disable all plugins except a specific one by setting up environment variable +`ONETL_PLUGINS_WHITELIST=some-not-failing-plugin`. Multiple plugin names could be passed with `,` as delimiter. + +Again, this environment variable should be set BEFORE importing onETL. + +If both whitelist and blacklist environment variables are set, blacklist has a higher priority. + +## How to see logs of the plugins mechanism? + +Plugins registration emits logs with `INFO` level: + +```python +import logging + +logging.basicConfig(level=logging.INFO) +``` + +```text +INFO |onETL| Found 2 plugins +INFO |onETL| Loading plugin 'my-plugin' +INFO |onETL| Skipping plugin 'failing' because it is in a blacklist +``` + +More detailed logs are emitted with `DEBUG` level, to make output less verbose: + +```python +import logging + +logging.basicConfig(level=logging.DEBUG) +``` + +```text +DEBUG |onETL| Searching for plugins with group 'onetl.plugins' +DEBUG |Plugins| Plugins whitelist: [] +DEBUG |Plugins| Plugins blacklist: ['failing-plugin'] +INFO |Plugins| Found 2 plugins +INFO |onETL| Loading plugin (1/2): +DEBUG name: 'my-plugin' +DEBUG package: 'my-package' +DEBUG version: '0.1.0' +DEBUG importing: 'my_package.my_module:MyClass' +DEBUG |onETL| Successfully loaded plugin 'my-plugin' +DEBUG source: '/usr/lib/python3.11/site-packages/my_package/my_module/my_class.py' +INFO |onETL| Skipping plugin 'failing' because it is in a blacklist +``` diff --git a/mddocs/ru/quickstart.md b/mddocs/ru/quickstart.md new file mode 100644 index 000000000..d2367a3de --- /dev/null +++ b/mddocs/ru/quickstart.md @@ -0,0 +1,4 @@ +```{eval-rst} +.. include:: ../README.rst + :start-after: quick-start +``` diff --git a/mddocs/ru/security.md b/mddocs/ru/security.md new file mode 100644 index 000000000..05a7bef5a --- /dev/null +++ b/mddocs/ru/security.md @@ -0,0 +1,3 @@ +```{eval-rst} +.. include:: ../SECURITY.rst +``` From d48ddfc7e5b867a7e7a13c9a590ca062b2b194fd Mon Sep 17 00:00:00 2001 From: Sattar Gyulmamedov Date: Thu, 19 Jun 2025 16:30:47 +0300 Subject: [PATCH 10/22] mddocs working version updated --- mddocs/config/en/mkdocs.yml | 141 + 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sphinx_build/markdown}/changelog/0.12.2.md (100%) rename mddocs/{docs => sphinx_build/markdown}/changelog/0.12.3.md (100%) rename mddocs/{docs => sphinx_build/markdown}/changelog/0.12.4.md (100%) rename mddocs/{docs => sphinx_build/markdown}/changelog/0.12.5.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/changelog/0.13.0.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/changelog/0.13.1.md (100%) rename mddocs/{docs => sphinx_build/markdown}/changelog/0.13.3.md (100%) rename mddocs/{docs => sphinx_build/markdown}/changelog/0.13.4.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/changelog/0.7.0.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/changelog/0.7.1.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/changelog/0.7.2.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/changelog/0.8.0.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/changelog/0.8.1.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/changelog/0.9.0.md (100%) rename mddocs/{docs => sphinx_build/markdown}/changelog/0.9.1.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/changelog/0.9.2.md (100%) rename mddocs/{docs => sphinx_build/markdown}/changelog/0.9.3.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/changelog/0.9.4.md (100%) rename mddocs/{docs => sphinx_build/markdown}/changelog/0.9.5.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/changelog/DRAFT.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/changelog/NEXT_RELEASE.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/changelog/index.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/concepts.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/clickhouse/connection.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/clickhouse/execute.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/clickhouse/index.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/clickhouse/prerequisites.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/clickhouse/read.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/clickhouse/sql.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/clickhouse/types.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/clickhouse/write.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/greenplum/connection.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/greenplum/execute.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/greenplum/index.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/greenplum/prerequisites.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/greenplum/read.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/greenplum/types.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/greenplum/write.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/hive/connection.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/hive/execute.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/hive/index.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/hive/prerequisites.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/hive/read.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/hive/slots.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/hive/sql.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/hive/write.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/index.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/kafka/auth.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/kafka/basic_auth.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/kafka/connection.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/kafka/index.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/kafka/kerberos_auth.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/kafka/plaintext_protocol.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/kafka/prerequisites.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/kafka/protocol.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/kafka/read.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/kafka/scram_auth.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/kafka/slots.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/kafka/ssl_protocol.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/kafka/troubleshooting.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/kafka/write.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/mongodb/connection.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/mongodb/index.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/mongodb/pipeline.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/mongodb/prerequisites.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/mongodb/read.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/mongodb/types.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/mongodb/write.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/mssql/connection.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/mssql/execute.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/mssql/index.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/mssql/prerequisites.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/mssql/read.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/mssql/sql.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/mssql/types.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/mssql/write.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/mysql/connection.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/mysql/execute.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/mysql/index.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/mysql/prerequisites.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/mysql/read.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/mysql/sql.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/mysql/types.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/mysql/write.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/oracle/connection.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/oracle/execute.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/oracle/index.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/oracle/prerequisites.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/oracle/read.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/oracle/sql.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/oracle/types.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/oracle/write.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/postgres/connection.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/postgres/execute.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/postgres/index.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/postgres/prerequisites.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/postgres/read.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/postgres/sql.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/postgres/types.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/postgres/write.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/teradata/connection.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/teradata/execute.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/teradata/index.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/teradata/prerequisites.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/teradata/read.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/teradata/sql.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/db_connection/teradata/write.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/file_connection/ftp.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/file_connection/ftps.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/file_connection/hdfs/connection.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/file_connection/hdfs/index.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/file_connection/hdfs/slots.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/file_connection/index.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/file_connection/s3.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/file_connection/samba.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/file_connection/sftp.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/file_connection/webdav.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/file_df_connection/base.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/file_df_connection/index.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/file_df_connection/spark_hdfs/connection.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/file_df_connection/spark_hdfs/index.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/file_df_connection/spark_hdfs/prerequisites.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/file_df_connection/spark_hdfs/slots.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/file_df_connection/spark_local_fs.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/file_df_connection/spark_s3/connection.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/file_df_connection/spark_s3/index.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/file_df_connection/spark_s3/prerequisites.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/file_df_connection/spark_s3/troubleshooting.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/connection/index.md (100%) create mode 100644 mddocs/sphinx_build/markdown/contributing.md rename mddocs/{docs/_build => sphinx_build}/markdown/db/db_reader.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/db/db_writer.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/db/index.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/file/file_downloader/file_downloader.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/file/file_downloader/index.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/file/file_downloader/options.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/file/file_downloader/result.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/file/file_filters/base.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/file/file_filters/exclude_dir.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/file/file_filters/file_filter.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/file/file_filters/file_mtime_filter.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/file/file_filters/file_size_filter.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/file/file_filters/glob.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/file/file_filters/index.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/file/file_filters/match_all_filters.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/file/file_filters/regexp.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/file/file_limits/base.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/file/file_limits/file_limit.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/file/file_limits/index.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/file/file_limits/limits_reached.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/file/file_limits/limits_stop_at.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/file/file_limits/max_files_count.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/file/file_limits/reset_limits.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/file/file_limits/total_files_size.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/file/file_mover/file_mover.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/file/file_mover/index.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/file/file_mover/options.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/file/file_mover/result.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/file/file_uploader/file_uploader.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/file/file_uploader/index.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/file/file_uploader/options.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/file/file_uploader/result.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/file/index.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/file_df/file_df_reader/file_df_reader.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/file_df/file_df_reader/index.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/file_df/file_df_reader/options.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/file_df/file_df_writer/file_df_writer.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/file_df/file_df_writer/index.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/file_df/file_df_writer/options.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/file_df/file_formats/avro.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/file_df/file_formats/base.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/file_df/file_formats/csv.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/file_df/file_formats/excel.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/file_df/file_formats/index.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/file_df/file_formats/json.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/file_df/file_formats/jsonline.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/file_df/file_formats/orc.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/file_df/file_formats/parquet.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/file_df/file_formats/xml.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/file_df/index.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/hooks/design.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/hooks/global_state.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/hooks/hook.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/hooks/index.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/hooks/slot.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/hooks/support_hooks.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/hwm_store/index.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/hwm_store/yaml_hwm_store.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/index.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/install/files.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/install/full.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/install/index.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/install/kerberos.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/install/spark.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/logging.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/plugins.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/quickstart.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/security.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/strategy/incremental_batch_strategy.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/strategy/incremental_strategy.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/strategy/index.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/strategy/snapshot_batch_strategy.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/strategy/snapshot_strategy.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/troubleshooting/index.md (100%) rename mddocs/{docs/_build => sphinx_build}/markdown/troubleshooting/spark.md (100%) diff --git a/mddocs/config/en/mkdocs.yml b/mddocs/config/en/mkdocs.yml new file mode 100644 index 000000000..33818c23c --- /dev/null +++ b/mddocs/config/en/mkdocs.yml @@ -0,0 +1,141 @@ +site_name: onETL Docs +docs_dir: '../../docs/en' # Where to find the English markdown files +site_dir: '../../generated/en' # Where to put the English HTML files + +theme: + name: material + custom_dir: '../../overrides/' # This is where the customization of the theme lives + logo: assets/images/logo.svg # The logo is shared by all languages + favicon: assets/images/icon.svg # The favicon is shared by all languages + language: en + features: + - navigation.indexes + - content.tabs.link + - content.code.copy + - content.code.select + palette: + - scheme: default + toggle: + icon: material/weather-night + name: Switch to dark mode + - scheme: slate + toggle: + icon: material/weather-sunny + name: Switch to light mode + locale: en + highlightjs: true + hljs_languages: + - yaml + +extra_css: + - assets/stylesheets/autodoc_pydantic.css # CSS is shared by all languages + +extra: # Language Selection + onetl_logo_wide: "[![onETL logo](../en/assets/images/logo_wide.svg)](https://github.com/MobileTeleSystems/onetl)" + repo_status_badge: "[![Repo status - Active](https://www.repostatus.org/badges/latest/active.svg)](https://github.com/MobileTeleSystems/onetl)" + pypi_release_bage: "[![PyPI - Latest Release](https://img.shields.io/pypi/v/onetl)](https://pypi.org/project/onetl/)" + pypi_license_bage: "[![PyPI - License](https://img.shields.io/pypi/l/onetl.svg)](https://github.com/MobileTeleSystems/onetl/blob/develop/LICENSE.txt)" + pypi_pyversion_bage: "[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/onetl.svg)](https://pypi.org/project/onetl/)" + pypi_downloads_bage: "[![PyPI - Downloads](https://img.shields.io/pypi/dm/onetl)](https://pypi.org/project/onetl/)" + docs_status_badge: "[![Documentation - ReadTheDocs](https://readthedocs.org/projects/onetl/badge/?version=stable)](https://onetl.readthedocs.io/)" + ci_status_badge: "[![Github Actions - latest CI build status](https://github.com/MobileTeleSystems/onetl/workflows/Tests/badge.svg)](https://github.com/MobileTeleSystems/onetl/actions)" + precommit_badge: "[![pre-commit.ci Status](https://results.pre-commit.ci/badge/github/MobileTeleSystems/onetl/develop.svg)](https://results.pre-commit.ci/latest/github/MobileTeleSystems/onetl/develop)" + test_cov_badge: "[![Test coverage - percent](https://codecov.io/gh/MobileTeleSystems/onetl/branch/develop/graph/badge.svg?token=RIO8URKNZJ)](https://codecov.io/gh/MobileTeleSystems/onetl)" + alternate: + + # Switch to English + - name: English + link: /en/ + lang: en + + # Switch to Russian + - name: Русский + link: /ru/ + lang: ru + +plugins: + - autorefs + - mkdocstrings: + default_handler: python + handlers: + python: + options: + show_source: false + show_root_heading: false + show_root_toc_entry: false + - macros + - plantuml: + puml_url: https://www.plantuml.com/plantuml/ + puml_keyword: plantuml + # - i18n: + # docs_structure: folder + # languages: + # - locale: en + # default: true + # name: English + # build: true + # - locale: ru + # name: Русский + # build: false + +markdown_extensions: + - attr_list + - md_in_html + - admonition + - pymdownx.details + - pymdownx.critic + - pymdownx.snippets: + base_path: ["."] + check_paths: true + - toc: + permalink: true + - pymdownx.tabbed: + alternate_style: true + - pymdownx.emoji: + emoji_index: !!python/name:material.extensions.emoji.twemoji + emoji_generator: !!python/name:material.extensions.emoji.to_svg + - pymdownx.superfences: + custom_fences: + - name: mermaid + class: mermaid + format: !!python/name:pymdownx.superfences.fence_code_format + +nav: + - "Concepts": concepts + - "Quickstart": quickstart + - "Logging": logging + - "Security": security + - "Contributing Guide": contributing + - changelog: + - changelog/index.md + - "0.13.4": changelog/0.13.4 + - "0.13.3": changelog/0.13.3 + - "0.13.1": changelog/0.13.1 + - "0.13.0": changelog/0.13.0 + - "0.12.5": changelog/0.12.5 + - "0.12.4": changelog/0.12.4 + - "0.12.3": changelog/0.12.3 + - "0.12.2": changelog/0.12.2 + - "0.12.1": changelog/0.12.1 + - "0.12.0": changelog/0.12.0 + - "0.11.2": changelog/0.11.2 + - "0.11.1": changelog/0.11.1 + - "0.11.0": changelog/0.11.0 + - "0.10.2": changelog/0.10.2 + - "0.10.1": changelog/0.10.1 + - "0.10.0": changelog/0.10.0 + - "0.9.5": changelog/0.9.5 + - "0.9.4": changelog/0.9.4 + - "0.9.3": changelog/0.9.3 + - "0.9.2": changelog/0.9.2 + - "0.9.1": changelog/0.9.1 + - "0.9.0": changelog/0.9.0 + - "0.8.1": changelog/0.8.1 + - "0.8.0": changelog/0.8.0 + - "0.7.2": changelog/0.7.2 + - "0.7.1": changelog/0.7.1 + - "0.7.0": changelog/0.7.0 + - "DB": + - db_/index.md + - "DBReader": db_/reader + - "DBWriter": db_/writer \ No newline at end of file diff --git a/mddocs/config/ru/mkdocs.yml b/mddocs/config/ru/mkdocs.yml new file mode 100644 index 000000000..96dd0b9ac --- /dev/null +++ b/mddocs/config/ru/mkdocs.yml @@ -0,0 +1,138 @@ +site_name: Документация onETL +docs_dir: '../../docs/ru' # Where to find the English markdown files +site_dir: '../../generated/ru' # Where to put the English HTML files + +theme: + name: material + custom_dir: '../../overrides/' # This is where the customization of the theme lives + logo: assets/images/logo.svg # The logo is shared by all languages + favicon: assets/images/icon.svg # The favicon is shared by all languages + language: ru + features: + - navigation.indexes + - content.tabs.link + - content.code.copy + - content.code.select + palette: + - scheme: default + toggle: + icon: material/weather-night + name: Switch to dark mode + - scheme: slate + toggle: + icon: material/weather-sunny + name: Switch to light mode + locale: ru + highlightjs: true + hljs_languages: + - yaml + +extra_css: + - assets/stylesheets/autodoc_pydantic.css # CSS is shared by all languages + +extra: + onetl_logo_wide: "[![onETL logo](../ru/assets/images/logo_wide.svg)](https://github.com/MobileTeleSystems/onetl)" + repo_status_badge: "[![Repo status - Active](https://www.repostatus.org/badges/latest/active.svg)](https://github.com/MobileTeleSystems/onetl)" + pypi_release_bage: "[![PyPI - Latest Release](https://img.shields.io/pypi/v/onetl)](https://pypi.org/project/onetl/)" + pypi_license_bage: "[![PyPI - License](https://img.shields.io/pypi/l/onetl.svg)](https://github.com/MobileTeleSystems/onetl/blob/develop/LICENSE.txt)" + pypi_pyversion_bage: "[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/onetl.svg)](https://pypi.org/project/onetl/)" + pypi_downloads_bage: "[![PyPI - Downloads](https://img.shields.io/pypi/dm/onetl)](https://pypi.org/project/onetl/)" + docs_status_badge: "[![Documentation - ReadTheDocs](https://readthedocs.org/projects/onetl/badge/?version=stable)](https://onetl.readthedocs.io/)" + ci_status_badge: "[![Github Actions - latest CI build status](https://github.com/MobileTeleSystems/onetl/workflows/Tests/badge.svg)](https://github.com/MobileTeleSystems/onetl/actions)" + precommit_badge: "[![pre-commit.ci Status](https://results.pre-commit.ci/badge/github/MobileTeleSystems/onetl/develop.svg)](https://results.pre-commit.ci/latest/github/MobileTeleSystems/onetl/develop)" + test_cov_badge: "[![Test coverage - percent](https://codecov.io/gh/MobileTeleSystems/onetl/branch/develop/graph/badge.svg?token=RIO8URKNZJ)](https://codecov.io/gh/MobileTeleSystems/onetl)" + alternate: # Language Selection + + # Switch to English + - name: English + link: /en/ + lang: en + + # Switch to Russian + - name: Русский + link: /ru/ + lang: ru + +plugins: + - autorefs + - mkdocstrings: + default_handler: python + handlers: + python: + options: + show_source: false + show_root_heading: false + show_root_toc_entry: false + - macros + - plantuml: + puml_url: https://www.plantuml.com/plantuml/ + puml_keyword: plantuml + # - i18n: + # docs_structure: folder + # languages: + # - locale: en + # name: English + # build: false + # - locale: ru + # default: true + # name: Русский + # build: true + +markdown_extensions: + - attr_list + - md_in_html + - admonition + - pymdownx.details + - pymdownx.critic + - pymdownx.snippets: + base_path: ["."] + check_paths: true + - toc: + permalink: true + - pymdownx.tabbed: + alternate_style: true + - pymdownx.emoji: + emoji_index: !!python/name:material.extensions.emoji.twemoji + emoji_generator: !!python/name:material.extensions.emoji.to_svg + - pymdownx.superfences: + custom_fences: + - name: mermaid + class: mermaid + format: !!python/name:pymdownx.superfences.fence_code_format + +nav: + - "Архитектура библиотеки": concepts + - "Быстрый старт": quickstart + - "Руководство по участию": contributing + - "Безопасность": security + - "Логирование": logging + - "Плагины": plugins + - changelog: + - changelog/index.md + - "0.13.4": changelog/0.13.4 + - "0.13.3": changelog/0.13.3 + - "0.13.1": changelog/0.13.1 + - "0.13.0": changelog/0.13.0 + - "0.12.5": changelog/0.12.5 + - "0.12.4": changelog/0.12.4 + - "0.12.3": changelog/0.12.3 + - "0.12.2": changelog/0.12.2 + - "0.12.1": changelog/0.12.1 + - "0.12.0": changelog/0.12.0 + - "0.11.2": changelog/0.11.2 + - "0.11.1": changelog/0.11.1 + - "0.11.0": changelog/0.11.0 + - "0.10.2": changelog/0.10.2 + - "0.10.1": changelog/0.10.1 + - "0.10.0": changelog/0.10.0 + - "0.9.5": changelog/0.9.5 + - "0.9.4": changelog/0.9.4 + - "0.9.3": changelog/0.9.3 + - "0.9.2": changelog/0.9.2 + - "0.9.1": changelog/0.9.1 + - "0.9.0": changelog/0.9.0 + - "0.8.1": changelog/0.8.1 + - "0.8.0": changelog/0.8.0 + - "0.7.2": changelog/0.7.2 + - "0.7.1": changelog/0.7.1 + - "0.7.0": changelog/0.7.0 \ No newline at end of file diff --git a/mddocs/docs/Makefile b/mddocs/docs/Makefile deleted file mode 100644 index d4bb2cbb9..000000000 --- a/mddocs/docs/Makefile +++ /dev/null @@ -1,20 +0,0 @@ -# Minimal makefile for Sphinx documentation -# - -# You can set these variables from the command line, and also -# from the environment for the first two. -SPHINXOPTS ?= -SPHINXBUILD ?= sphinx-build -SOURCEDIR = . -BUILDDIR = _build - -# Put it first so that "make" without argument is like "make help". -help: - @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) - -.PHONY: help Makefile - -# Catch-all target: route all unknown targets to Sphinx using the new -# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). -%: Makefile - @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/mddocs/docs/concepts.md b/mddocs/docs/concepts.md deleted file mode 100644 index b8b0baf4a..000000000 --- a/mddocs/docs/concepts.md +++ /dev/null @@ -1,458 +0,0 @@ -# Concepts - -Here you can find detailed documentation about each one of the onETL concepts and how to use them. - -## Connection - -### Basics - -onETL is used to pull and push data into other systems, and so it has a first-class `Connection` concept for storing credentials that are used to communicate with external systems. - -A `Connection` is essentially a set of parameters, such as username, password, hostname. - -To create a connection to a specific storage type, you must use a class that matches the storage type. The class name is the same as the storage type name (`Oracle`, `MSSQL`, `SFTP`, etc): - -```python -from onetl.connection import SFTP - -sftp = SFTP( - host="sftp.test.com", - user="onetl", - password="onetl", -) -``` - -All connection types are inherited from the parent class `BaseConnection`. - -### Class diagram - -```{eval-rst} -.. plantuml:: - - @startuml - left to right direction - skinparam classFontSize 20 - skinparam class { - BackgroundColor<> LightGreen - BackgroundColor<> Khaki - BackgroundColor<> LightBlue - StereotypeFontColor<> Transparent - StereotypeFontColor<> Transparent - StereotypeFontColor<> Transparent - } - - class BaseConnection { - } - - class DBConnection <>{ - } - DBConnection --|> BaseConnection - - class Hive <>{ - } - Hive --|> DBConnection - - class Greenplum <>{ - } - Greenplum --|> DBConnection - - class MongoDB <>{ - } - MongoDB --|> DBConnection - - class Kafka <>{ - } - Kafka --|> DBConnection - - class JDBCConnection <>{ - } - JDBCConnection --|> DBConnection - - class Clickhouse <>{ - } - Clickhouse --|> JDBCConnection - - class MSSQL <>{ - } - MSSQL --|> JDBCConnection - - class MySQL <>{ - } - MySQL --|> JDBCConnection - - class Postgres <>{ - } - Postgres --|> JDBCConnection - - class Oracle <>{ - } - Oracle --|> JDBCConnection - - class Teradata <>{ - } - Teradata --|> JDBCConnection - - class FileConnection <>{ - } - FileConnection --|> BaseConnection - - class FTP <>{ - } - FTP --|> FileConnection - - class FTPS <>{ - } - FTPS --|> FileConnection - - class HDFS <>{ - } - HDFS --|> FileConnection - - class WebDAV <>{ - } - WebDAV --|> FileConnection - - class Samba <>{ - } - Samba --|> FileConnection - - class SFTP <>{ - } - SFTP --|> FileConnection - - class S3 <>{ - } - S3 --|> FileConnection - - class FileDFConnection <>{ - } - FileDFConnection --|> BaseConnection - - class SparkHDFS <>{ - } - SparkHDFS --|> FileDFConnection - - class SparkLocalFS <>{ - } - SparkLocalFS --|> FileDFConnection - - class SparkS3 <>{ - } - SparkS3 --|> FileDFConnection - - @enduml -``` - -### DBConnection - -Classes inherited from `DBConnection` could be used for accessing databases. - -A `DBConnection` could be instantiated as follows: - -```python -from onetl.connection import MSSQL - -mssql = MSSQL( - host="mssqldb.demo.com", - user="onetl", - password="onetl", - database="Telecom", - spark=spark, -) -``` - -where **spark** is the current SparkSession. -`onETL` uses `Spark` and specific Java connectors under the hood to work with databases. - -For a description of other parameters, see the documentation for the {ref}`available DBConnections `. - -### FileConnection - -Classes inherited from `FileConnection` could be used to access files stored on the different file systems/file servers - -A `FileConnection` could be instantiated as follows: - -```python -from onetl.connection import SFTP - -sftp = SFTP( - host="sftp.test.com", - user="onetl", - password="onetl", -) -``` - -For a description of other parameters, see the documentation for the {ref}`available FileConnections `. - -### FileDFConnection - -Classes inherited from `FileDFConnection` could be used for accessing files as Spark DataFrames. - -A `FileDFConnection` could be instantiated as follows: - -```python -from onetl.connection import SparkHDFS - -spark_hdfs = SparkHDFS( - host="namenode1.domain.com", - cluster="mycluster", - spark=spark, -) -``` - -where **spark** is the current SparkSession. -`onETL` uses `Spark` and specific Java connectors under the hood to work with DataFrames. - -For a description of other parameters, see the documentation for the {ref}`available FileDFConnections `. - -### Checking connection availability - -Once you have created a connection, you can check the database/filesystem availability using the method `check()`: - -```python -mssql.check() -sftp.check() -spark_hdfs.check() -``` - -It will raise an exception if database/filesystem cannot be accessed. - -This method returns connection itself, so you can create connection and immediately check its availability: - -```Python -mssql = MSSQL( - host="mssqldb.demo.com", - user="onetl", - password="onetl", - database="Telecom", - spark=spark, -).check() # <-- -``` - -## Extract/Load data - -### Basics - -As we said above, onETL is used to extract data from and load data into remote systems. - -onETL provides several classes for this: - -> - {ref}`DBReader ` -> - {ref}`DBWriter ` -> - {ref}`FileDFReader ` -> - {ref}`FileDFWriter ` -> - {ref}`FileDownloader ` -> - {ref}`FileUploader ` -> - {ref}`FileMover ` - -All of these classes have a method `run()` that starts extracting/loading the data: - -```python -from onetl.db import DBReader, DBWriter - -reader = DBReader( - connection=mssql, - source="dbo.demo_table", - columns=["column_1", "column_2"], -) - -# Read data as Spark DataFrame -df = reader.run() - -db_writer = DBWriter( - connection=hive, - target="dl_sb.demo_table", -) - -# Save Spark DataFrame to Hive table -writer.run(df) -``` - -### Extract data - -To extract data you can use classes: - -| | Use case | Connection | `run()` gets | `run()` returns | -| ------------------------------------ | ----------------------------------------- | ------------------------------------------------- | ------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------- | -| {ref}`DBReader ` | Reading data from a database | Any {ref}`DBConnection ` | - | [Spark DataFrame](https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.sql.DataFrame.html#pyspark.sql.DataFrame) | -| {ref}`FileDFReader ` | Read data from a file or set of files | Any {ref}`FileDFConnection ` | No input, or List[File path on FileSystem] | [Spark DataFrame](https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.sql.DataFrame.html#pyspark.sql.DataFrame) | -| {ref}`FileDownloader ` | Download files from remote FS to local FS | Any {ref}`FileConnection ` | No input, or List[File path on remote FileSystem] | {ref}`DownloadResult ` | - -### Load data - -To load data you can use classes: - -| | Use case | Connection | `run()` gets | `run()` returns | -| ----------------------------------- | -------------------------------------------- | ------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------ | -| {ref}`DBWriter ` | Writing data from a DataFrame to a database | Any {ref}`DBConnection ` | [Spark DataFrame](https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.sql.DataFrame.html#pyspark.sql.DataFrame) | None | -| {ref}`FileDFWriter ` | Writing data from a DataFrame to a folder | Any {ref}`FileDFConnection ` | [Spark DataFrame](https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.sql.DataFrame.html#pyspark.sql.DataFrame) | None | -| {ref}`FileUploader ` | Uploading files from a local FS to remote FS | Any {ref}`FileConnection ` | List[File path on local FileSystem] | {ref}`UploadResult ` | - -### Manipulate data - -To manipulate data you can use classes: - -| | Use case | Connection | `run()` gets | `run()` returns | -| ----------------------------- | ------------------------------------------- | -------------------------------------------- | ------------------------------------ | ------------------------------------- | -| {ref}`FileMover ` | Move files between directories in remote FS | Any {ref}`FileConnection ` | List[File path on remote FileSystem] | {ref}`MoveResult ` | - -### Options - -Extract and load classes have a `options` parameter, which has a special meaning: - -> - all other parameters - *WHAT* we extract / *WHERE* we load to -> - `options` parameter - *HOW* we extract/load data - -```python -db_reader = DBReader( - # WHAT do we read: - connection=mssql, - source="dbo.demo_table", # some table from MSSQL - columns=["column_1", "column_2"], # but only specific set of columns - where="column_2 > 1000", # only rows matching the clause - # HOW do we read: - options=MSSQL.ReadOptions( - numPartitions=10, # read in 10 parallel jobs - partitionColumn="id", # balance data read by assigning each job a part of data using `hash(id) mod N` expression - partitioningMode="hash", - fetchsize=1000, # each job will fetch block of 1000 rows each on every read attempt - ), -) - -db_writer = DBWriter( - # WHERE do we write to - to some table in Hive - connection=hive, - target="dl_sb.demo_table", - # HOW do we write - overwrite all the data in the existing table - options=Hive.WriteOptions(if_exists="replace_entire_table"), -) - -file_downloader = FileDownloader( - # WHAT do we download - files from some dir in SFTP - connection=sftp, - source_path="/source", - filters=[Glob("*.csv")], # only CSV files - limits=[MaxFilesCount(1000)], # 1000 files max - # WHERE do we download to - a specific dir on local FS - local_path="/some", - # HOW do we download: - options=FileDownloader.Options( - delete_source=True, # after downloading each file remove it from source_path - if_exists="replace_file", # replace existing files in the local_path - ), -) - -file_uploader = FileUploader( - # WHAT do we upload - files from some local dir - local_path="/source", - # WHERE do we upload to- specific remote dir in HDFS - connection=hdfs, - target_path="/some", - # HOW do we upload: - options=FileUploader.Options( - delete_local=True, # after uploading each file remove it from local_path - if_exists="replace_file", # replace existing files in the target_path - ), -) - -file_mover = FileMover( - # WHAT do we move - files in some remote dir in HDFS - source_path="/source", - connection=hdfs, - # WHERE do we move files to - target_path="/some", # a specific remote dir within the same HDFS connection - # HOW do we load - replace existing files in the target_path - options=FileMover.Options(if_exists="replace_file"), -) - -file_df_reader = FileDFReader( - # WHAT do we read - *.csv files from some dir in S3 - connection=s3, - source_path="/source", - file_format=CSV(), - # HOW do we read - load files from /source/*.csv, not from /source/nested/*.csv - options=FileDFReader.Options(recursive=False), -) - -file_df_writer = FileDFWriter( - # WHERE do we write to - as .csv files in some dir in S3 - connection=s3, - target_path="/target", - file_format=CSV(), - # HOW do we write - replace all existing files in /target, if exists - options=FileDFWriter.Options(if_exists="replace_entire_directory"), -) -``` - -More information about `options` could be found on {ref}`DB connection `. and -{ref}`file-downloader` / {ref}`file-uploader` / {ref}`file-mover` / {ref}`file-df-reader` / {ref}`file-df-writer` documentation - -### Read Strategies - -onETL have several builtin strategies for reading data: - -1. [Snapshot strategy](strategy/snapshot_strategy.html) (default strategy) -2. [Incremental strategy](strategy/incremental_strategy.html) -3. [Snapshot batch strategy](strategy/snapshot_batch_strategy.html) -4. [Incremental batch strategy](strategy/incremental_batch_strategy.html) - -For example, an incremental strategy allows you to get only new data from the table: - -```python -from onetl.strategy import IncrementalStrategy - -reader = DBReader( - connection=mssql, - source="dbo.demo_table", - hwm_column="id", # detect new data based on value of "id" column -) - -# first run -with IncrementalStrategy(): - df = reader.run() - -sleep(3600) - -# second run -with IncrementalStrategy(): - # only rows, that appeared in the source since previous run - df = reader.run() -``` - -or get only files which were not downloaded before: - -```python -from onetl.strategy import IncrementalStrategy - -file_downloader = FileDownloader( - connection=sftp, - source_path="/remote", - local_path="/local", - hwm_type="file_list", # save all downloaded files to a list, and exclude files already present in this list -) - -# first run -with IncrementalStrategy(): - files = file_downloader.run() - -sleep(3600) - -# second run -with IncrementalStrategy(): - # only files, that appeared in the source since previous run - files = file_downloader.run() -``` - -Most of strategies are based on {ref}`hwm`, Please check each strategy documentation for more details - -### Why just not use Connection class for extract/load? - -Connections are very simple, they have only a set of some basic operations, -like `mkdir`, `remove_file`, `get_table_schema`, and so on. - -High-level operations, like -: - {ref}`strategy` support - - Handling metadata push/pull - - Handling different options, like `if_exists="replace_file"` in case of file download/upload - -is moved to a separate class which calls the connection object methods to perform some complex logic. diff --git a/mddocs/docs/conf.py b/mddocs/docs/conf.py deleted file mode 100644 index 018e6e870..000000000 --- a/mddocs/docs/conf.py +++ /dev/null @@ -1,169 +0,0 @@ -# Configuration file for the Sphinx documentation builder. -# -# This file only contains a selection of the most common options. For a full -# list see the documentation: -# https://www.sphinx-doc.org/en/master/usage/configuration.html - -# -- Path setup -------------------------------------------------------------- - -# If extensions (or modules to document with autodoc) are in another directory, -# add these directories to sys.path here. If the directory is relative to the -# documentation root, use os.path.abspath to make it absolute, like shown here. - - -import os -import subprocess -import sys -from pathlib import Path - -from packaging.version import Version - -PROJECT_ROOT_DIR = Path(__file__).parent.parent.resolve() - -sys.path.insert(0, os.fspath(PROJECT_ROOT_DIR)) - -# -- Project information ----------------------------------------------------- - -project = "onETL" -copyright = "2021-2025 MTS PJSC" -author = "MWS Data Bridge" - -# The version info for the project you're documenting, acts as replacement for -# |version| and |release|, also used in various other places throughout the -# built documents. -# -# The short X.Y version. - -ver = Version(subprocess.check_output("python ../setup.py --version", shell=True, text=True).strip()) -version = ver.base_version -# The full version, including alpha/beta/rc tags. -release = ver.public - -# -- General configuration --------------------------------------------------- - -# Add any Sphinx extension module names here, as strings. They can be -# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom -# ones. -extensions = [ - "numpydoc", - "sphinx_design", - "sphinx_substitution_extensions", - "sphinx_tabs.tabs", - "sphinx_toolbox.more_autodoc.autoprotocol", - "sphinx_toolbox.github", - "sphinx_copybutton", - "sphinx.ext.autodoc", - "sphinx.ext.autosummary", - "sphinxcontrib.towncrier", # provides `towncrier-draft-entries` directive - "sphinxcontrib.plantuml", - "sphinx.ext.extlinks", - "sphinx_favicon", - "sphinxcontrib.autodoc_pydantic", - "sphinx_last_updated_by_git", - "myst_parser", -] - - -myst_enable_extensions = [ - "amsmath", - "attrs_inline", - "colon_fence", - "deflist", - "dollarmath", - "fieldlist", - "html_admonition", - "html_image", - "linkify", - "replacements", - "smartquotes", - "strikethrough", - "substitution", - "tasklist", -] - - -numpydoc_show_class_members = False -autodoc_pydantic_model_show_config = False -autodoc_pydantic_model_show_config_summary = False -autodoc_pydantic_model_show_config_member = False -autodoc_pydantic_model_show_json = False -autodoc_pydantic_model_show_validator_summary = False -autodoc_pydantic_model_show_validator_members = False -autodoc_pydantic_field_list_validators = False -sphinx_tabs_disable_tab_closing = True - -# prevent >>>, ... and doctest outputs from copying -copybutton_prompt_text = r">>> |\.\.\. |\$ |In \[\d*\]: | {2,5}\.\.\.: | {5,8}: " -copybutton_prompt_is_regexp = True -copybutton_copy_empty_lines = False -copybutton_only_copy_prompt_lines = True - -towncrier_draft_autoversion_mode = "draft" -towncrier_draft_include_empty = False -towncrier_draft_working_directory = PROJECT_ROOT_DIR - -github_username = "MobileTeleSystems" -github_repository = "onetl" - -rst_prolog = f""" -.. |support_hooks| image:: https://img.shields.io/badge/%20-support%20hooks-blue - :target: https://onetl.readthedocs.io/en/{ver}/hooks/index.html -""" - -# Add any paths that contain templates here, relative to this directory. -templates_path = ["_templates"] - -# List of patterns, relative to source directory, that match files and -# directories to ignore when looking for source files. -# This pattern also affects html_static_path and html_extra_path. -exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"] - - -# -- Options for HTML output ------------------------------------------------- - -# The theme to use for HTML and HTML Help pages. See the documentation for -# a list of builtin themes. - -html_theme = "furo" - -# Theme options are theme-specific and customize the look and feel of a theme -# further. For a list of options available for each theme, see the -# documentation. -html_theme_options = {} - -# Add any paths that contain custom static files (such as style sheets) here, -# relative to this directory. They are copied after the builtin static files, -# so a file named "default.css" will overwrite the builtin "default.css". -html_static_path = ["_static"] -html_extra_path = ["robots.txt"] -html_logo = "./_static/logo.svg" -favicons = [ - {"rel": "icon", "href": "icon.svg", "type": "image/svg+xml"}, -] - -# The master toctree document. -master_doc = "index" - -# The language for content autogenerated by Sphinx. Refer to documentation -# for a list of supported languages. -# -# This is also used if you do content translation via gettext catalogs. -# Usually you set "language" from the command line for these cases. -language = "en" - -# The name of the Pygments (syntax highlighting) style to use. -pygments_style = "sphinx" - -# Create an alias for etl-entities lib in onetl documentation -extlinks = { - "etl-entities": ("https://etl-entities.readthedocs.io/en/stable/%s", None), -} - - -# If true, `todo` and `todoList` produce output, else they produce nothing. -todo_include_todos = False - -# -- Options for HTMLHelp output ------------------------------------------ - -# Output file base name for HTML help builder. -htmlhelp_basename = "onetl-doc" diff --git a/mddocs/docs/connection/file_df_connection/index.md b/mddocs/docs/connection/file_df_connection/index.md deleted file mode 100644 index 8f8d24bf0..000000000 --- a/mddocs/docs/connection/file_df_connection/index.md +++ /dev/null @@ -1,19 +0,0 @@ -(file-df-connections)= - -# File DataFrame Connections - -```{toctree} -:caption: File DataFrame Connections -:maxdepth: 2 - -Spark LocalFS -Spark HDFS -Spark S3 -``` - -```{toctree} -:caption: For developers -:maxdepth: 1 - -base -``` diff --git a/mddocs/docs/connection/index.md b/mddocs/docs/connection/index.md deleted file mode 100644 index 00cbfc41a..000000000 --- a/mddocs/docs/connection/index.md +++ /dev/null @@ -1,22 +0,0 @@ -(connection)= - -```{toctree} -:caption: DB Connection -:maxdepth: 2 - -db_connection/index -``` - -```{toctree} -:caption: File Connection -:maxdepth: 2 - -file_connection/index -``` - -```{toctree} -:caption: File DataFrame Connection -:maxdepth: 2 - -file_df_connection/index -``` diff --git a/mddocs/docs/contributing.md b/mddocs/docs/contributing.md deleted file mode 100644 index cc9ee4419..000000000 --- a/mddocs/docs/contributing.md +++ /dev/null @@ -1,3 +0,0 @@ -```{eval-rst} -.. include:: ../CONTRIBUTING.rst -``` diff --git 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used to communicate with external systems. + +A `Connection` is essentially a set of parameters, such as username, password, hostname. + +To create a connection to a specific storage type, you must use a class that matches the storage type. The class name is the same as the storage type name (`Oracle`, `MSSQL`, `SFTP`, etc): + +```python +from onetl.connection import SFTP + +sftp = SFTP( + host="sftp.test.com", + user="onetl", + password="onetl", +) +``` + +All connection types are inherited from the parent class `BaseConnection`. + +### Class diagram + +```plantuml + + @startuml + left to right direction + skinparam classFontSize 20 + skinparam class { + BackgroundColor<> LightGreen + BackgroundColor<> Khaki + BackgroundColor<> LightBlue + StereotypeFontColor<> Transparent + StereotypeFontColor<> Transparent + StereotypeFontColor<> Transparent + } + + class BaseConnection { + } + + class DBConnection <>{ + } + DBConnection --|> BaseConnection + + class Hive <>{ + } + Hive --|> DBConnection + + class Greenplum <>{ + } + Greenplum --|> DBConnection + + class MongoDB <>{ + } + MongoDB --|> DBConnection + + class Kafka <>{ + } + Kafka --|> DBConnection + + class JDBCConnection <>{ + } + JDBCConnection --|> DBConnection + + class Clickhouse <>{ + } + Clickhouse --|> JDBCConnection + + class MSSQL <>{ + } + MSSQL --|> JDBCConnection + + class MySQL <>{ + } + MySQL --|> JDBCConnection + + class Postgres <>{ + } + Postgres --|> JDBCConnection + + class Oracle <>{ + } + Oracle --|> JDBCConnection + + class Teradata <>{ + } + Teradata --|> JDBCConnection + + class FileConnection <>{ + } + FileConnection --|> BaseConnection + + class FTP <>{ + } + FTP --|> FileConnection + + class FTPS <>{ + } + FTPS --|> FileConnection + + class HDFS <>{ + } + HDFS --|> FileConnection + + class WebDAV <>{ + } + WebDAV --|> FileConnection + + class Samba <>{ + } + Samba --|> FileConnection + + class SFTP <>{ + } + SFTP --|> FileConnection + + class S3 <>{ + } + S3 --|> FileConnection + + class FileDFConnection <>{ + } + FileDFConnection --|> BaseConnection + + class SparkHDFS <>{ + } + SparkHDFS --|> FileDFConnection + + class SparkLocalFS <>{ + } + SparkLocalFS --|> FileDFConnection + + class SparkS3 <>{ + } + SparkS3 --|> FileDFConnection + + @enduml +``` + +```mermaid +classDiagram + BaseConnection <|-- DBConnection + DBConnection <|-- Hive + DBConnection <|-- Greenplum + DBConnection <|-- MongoDB + DBConnection <|-- Kafka + DBConnection <|-- JDBCConnection + JDBCConnection <|-- Clickhouse + JDBCConnection <|-- MSSQL + JDBCConnection <|-- MySQL + JDBCConnection <|-- Postgres + JDBCConnection <|-- Oracle + JDBCConnection <|-- Teradata + BaseConnection <|-- FileConnection + FileConnection <|-- FTP + FileConnection <|-- FTPS + FileConnection <|-- HDFS + FileConnection <|-- WebDAV + FileConnection <|-- Samba + FileConnection <|-- SFTP + FileConnection <|-- S3 + BaseConnection <|-- FileDFConnection + FileDFConnection <|-- SparkHDFS + FileDFConnection <|-- SparkLocalFS + FileDFConnection <|-- SparkS3 +``` + +### DBConnection + +Classes inherited from `DBConnection` could be used for accessing databases. + +A `DBConnection` could be instantiated as follows: + +```python +from onetl.connection import MSSQL + +mssql = MSSQL( + host="mssqldb.demo.com", + user="onetl", + password="onetl", + database="Telecom", + spark=spark, +) +``` + +where **spark** is the current SparkSession. +`onETL` uses `Spark` and specific Java connectors under the hood to work with databases. + +For a description of other parameters, see the documentation for the [available DBConnections](../connection/file_df_connection/). + +### FileConnection + +Classes inherited from `FileConnection` could be used to access files stored on the different file systems/file servers + +A `FileConnection` could be instantiated as follows: + +```python +from onetl.connection import SFTP + +sftp = SFTP( + host="sftp.test.com", + user="onetl", + password="onetl", +) +``` + +For a description of other parameters, see the documentation for the {ref}`available FileConnections `. + +### FileDFConnection + +Classes inherited from `FileDFConnection` could be used for accessing files as Spark DataFrames. + +A `FileDFConnection` could be instantiated as follows: + +```python +from onetl.connection import SparkHDFS + +spark_hdfs = SparkHDFS( + host="namenode1.domain.com", + cluster="mycluster", + spark=spark, +) +``` + +where **spark** is the current SparkSession. +`onETL` uses `Spark` and specific Java connectors under the hood to work with DataFrames. + +For a description of other parameters, see the documentation for the {ref}`available FileDFConnections `. + +### Checking connection availability + +Once you have created a connection, you can check the database/filesystem availability using the method `check()`: + +```python +mssql.check() +sftp.check() +spark_hdfs.check() +``` + +It will raise an exception if database/filesystem cannot be accessed. + +This method returns connection itself, so you can create connection and immediately check its availability: + +```Python +mssql = MSSQL( + host="mssqldb.demo.com", + user="onetl", + password="onetl", + database="Telecom", + spark=spark, +).check() # <-- +``` + +## Extract/Load data + +### Basics + +As we said above, onETL is used to extract data from and load data into remote systems. + +onETL provides several classes for this: + +> * [DBReader](../db_/reader) +> * [DBWriter](../db_/writer) +> * [FileDFReader](../file_df/file_df_reader/file_df_reader) +> * [FileDFWriter](../file_df/file_df_writer/file_df_writer) +> * [FileDownloader](../file/file_downloader/file_downloader) +> * [FileUploader](../file/file_uploader/file_uploader) +> * [FileMover](../file/file_mover/file_mover) + +All of these classes have a method `run()` that starts extracting/loading the data: + +```python +from onetl.db import DBReader, DBWriter + +reader = DBReader( + connection=mssql, + source="dbo.demo_table", + columns=["column_1", "column_2"], +) + +# Read data as Spark DataFrame +df = reader.run() + +db_writer = DBWriter( + connection=hive, + target="dl_sb.demo_table", +) + +# Save Spark DataFrame to Hive table +writer.run(df) +``` + +### Extract data + +To extract data you can use classes: + +| | Use case | Connection | `run()` gets | `run()` returns | +| -- | - | - | - | --- | +| [`DBReader`](../db_/reader) | Reading data from a database | Any [`DBConnection`](../connection/db_connection) | - | [Spark DataFrame](https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/dataframe.html#dataframe) | +| [`FileDFReader`](../file_df/file_df_reader/file_df_reader) | Read data from a file or set of files | Any [`FileDFConnection`](../connection/file_df_connection) | No input, or List[File path on FileSystem] | [Spark DataFrame](https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/dataframe.html#dataframe) | +| [`FileDownloader`](../file/file_downloader/file_downloader) | Download files from remote FS to local FS | Any [`FileConnection`](../connection/file_connection) | No input, or List[File path on remote FileSystem] | [`DownloadResult`](../file/file_downloader/result) | + +### Load data + +To load data you can use classes: + +| | Use case | Connection | `run()` gets | `run()` returns | +| - | -- | - | --- | -- | +| [`DBWriter`](../db_/writer) | Writing data from a DataFrame to a database | Any [`DBConnection`](../connection/db_connection) | [Spark DataFrame](https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/dataframe.html#dataframe) | None | +| [`FileDFWriter`](../file_df/file_df_writer/file_df_writer) | Writing data from a DataFrame to a folder | Any [`FileDFConnection`](../connection/file_df_connection) | [Spark DataFrame](https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/dataframe.html#dataframe) | None | +| [`FileUploader`](../file/file_uploader/file_uploader) | Uploading files from a local FS to remote FS | Any [`FileConnection`](../connection/file_connection) | List[File path on local FileSystem] | [`UploadResult`](../file/file_uploader/result) | + +### Manipulate data + +To manipulate data you can use classes: + +| | Use case | Connection | `run()` gets | `run()` returns | +| - | - | -- | -- | - | +| [`FileMover`](../file/file_mover/file_mover) | Move files between directories in remote FS | Any [`FileConnection`](../connection/file_connection) | List[File path on remote FileSystem] | [`MoveResult`](../file/file_mover/result) | + +### Options + +Extract and load classes have a `options` parameter, which has a special meaning: + +> - all other parameters - *WHAT* we extract / *WHERE* we load to +> - `options` parameter - *HOW* we extract/load data + +```python +db_reader = DBReader( + # WHAT do we read: + connection=mssql, + source="dbo.demo_table", # some table from MSSQL + columns=["column_1", "column_2"], # but only specific set of columns + where="column_2 > 1000", # only rows matching the clause + # HOW do we read: + options=MSSQL.ReadOptions( + numPartitions=10, # read in 10 parallel jobs + partitionColumn="id", # balance data read by assigning each job a part of data using `hash(id) mod N` expression + partitioningMode="hash", + fetchsize=1000, # each job will fetch block of 1000 rows each on every read attempt + ), +) + +db_writer = DBWriter( + # WHERE do we write to - to some table in Hive + connection=hive, + target="dl_sb.demo_table", + # HOW do we write - overwrite all the data in the existing table + options=Hive.WriteOptions(if_exists="replace_entire_table"), +) + +file_downloader = FileDownloader( + # WHAT do we download - files from some dir in SFTP + connection=sftp, + source_path="/source", + filters=[Glob("*.csv")], # only CSV files + limits=[MaxFilesCount(1000)], # 1000 files max + # WHERE do we download to - a specific dir on local FS + local_path="/some", + # HOW do we download: + options=FileDownloader.Options( + delete_source=True, # after downloading each file remove it from source_path + if_exists="replace_file", # replace existing files in the local_path + ), +) + +file_uploader = FileUploader( + # WHAT do we upload - files from some local dir + local_path="/source", + # WHERE do we upload to- specific remote dir in HDFS + connection=hdfs, + target_path="/some", + # HOW do we upload: + options=FileUploader.Options( + delete_local=True, # after uploading each file remove it from local_path + if_exists="replace_file", # replace existing files in the target_path + ), +) + +file_mover = FileMover( + # WHAT do we move - files in some remote dir in HDFS + source_path="/source", + connection=hdfs, + # WHERE do we move files to + target_path="/some", # a specific remote dir within the same HDFS connection + # HOW do we load - replace existing files in the target_path + options=FileMover.Options(if_exists="replace_file"), +) + +file_df_reader = FileDFReader( + # WHAT do we read - *.csv files from some dir in S3 + connection=s3, + source_path="/source", + file_format=CSV(), + # HOW do we read - load files from /source/*.csv, not from /source/nested/*.csv + options=FileDFReader.Options(recursive=False), +) + +file_df_writer = FileDFWriter( + # WHERE do we write to - as .csv files in some dir in S3 + connection=s3, + target_path="/target", + file_format=CSV(), + # HOW do we write - replace all existing files in /target, if exists + options=FileDFWriter.Options(if_exists="replace_entire_directory"), +) +``` + +More information about `options` could be found on [`DBConnection`](../connection/db_connection) and [`FileDownloader`](../file/file_downloader/file_downloader) / [`FileUploader`](../file/file_uploader/file_uploader) / [`FileMover`](../file/file_mover/file_mover) / [`FileDFReader`](../file_df/file_df_reader/file_df_reader) / [`FileDFWriter`](../file_df/file_df_writer/file_df_writer) documentation. + + +### Read Strategies + +onETL have several builtin strategies for reading data: + +1. [Snapshot strategy](../strategy/snapshot_strategy) (default strategy) +2. [Incremental strategy](../strategy/incremental_strategy) +3. [Snapshot batch strategy](../strategy/snapshot_batch_strategy) +4. [Incremental batch strategy](../strategy/incremental_batch_strategy) + +For example, an incremental strategy allows you to get only new data from the table: + +```python +from onetl.strategy import IncrementalStrategy + +reader = DBReader( + connection=mssql, + source="dbo.demo_table", + hwm_column="id", # detect new data based on value of "id" column +) + +# first run +with IncrementalStrategy(): + df = reader.run() + +sleep(3600) + +# second run +with IncrementalStrategy(): + # only rows, that appeared in the source since previous run + df = reader.run() +``` + +or get only files which were not downloaded before: + +```python +from onetl.strategy import IncrementalStrategy + +file_downloader = FileDownloader( + connection=sftp, + source_path="/remote", + local_path="/local", + hwm_type="file_list", # save all downloaded files to a list, and exclude files already present in this list +) + +# first run +with IncrementalStrategy(): + files = file_downloader.run() + +sleep(3600) + +# second run +with IncrementalStrategy(): + # only files, that appeared in the source since previous run + files = file_downloader.run() +``` + +Most of strategies are based on [`HWM`](../hwm_store/), Please check each strategy documentation for more details + +### Why just not use Connection class for extract/load? + +Connections are very simple, they have only a set of some basic operations, +like `mkdir`, `remove_file`, `get_table_schema`, and so on. + +High-level operations, like + + * [`strategy`](../strategy/) support + * Handling metadata push/pull + * Handling different options, like `if_exists="replace_file"` in case of file download/upload + +is moved to a separate class which calls the connection object methods to perform some complex logic. diff --git a/mddocs/docs/connection/db_connection/clickhouse/connection.md b/mddocs/docs/en/connection/db_connection/clickhouse/connection.md similarity index 100% rename from mddocs/docs/connection/db_connection/clickhouse/connection.md 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b/mddocs/docs/en/connection/file_connection/s3.md similarity index 100% rename from mddocs/docs/connection/file_connection/s3.md rename to mddocs/docs/en/connection/file_connection/s3.md diff --git a/mddocs/docs/connection/file_connection/samba.md b/mddocs/docs/en/connection/file_connection/samba.md similarity index 100% rename from mddocs/docs/connection/file_connection/samba.md rename to mddocs/docs/en/connection/file_connection/samba.md diff --git a/mddocs/docs/connection/file_connection/sftp.md b/mddocs/docs/en/connection/file_connection/sftp.md similarity index 100% rename from mddocs/docs/connection/file_connection/sftp.md rename to mddocs/docs/en/connection/file_connection/sftp.md diff --git a/mddocs/docs/connection/file_connection/webdav.md b/mddocs/docs/en/connection/file_connection/webdav.md similarity index 100% rename from mddocs/docs/connection/file_connection/webdav.md rename to mddocs/docs/en/connection/file_connection/webdav.md diff --git a/mddocs/docs/connection/file_df_connection/base.md b/mddocs/docs/en/connection/file_df_connection/base.md similarity index 100% rename from mddocs/docs/connection/file_df_connection/base.md rename to mddocs/docs/en/connection/file_df_connection/base.md diff --git a/mddocs/docs/en/connection/file_df_connection/index.md b/mddocs/docs/en/connection/file_df_connection/index.md new file mode 100644 index 000000000..4b0829450 --- /dev/null +++ b/mddocs/docs/en/connection/file_df_connection/index.md @@ -0,0 +1,15 @@ +# File DataFrame Connections + +* [Spark LocalFS](spark_local_fs.md) +* [Spark HDFS](spark_hdfs/index.md) + * [Prerequisites](spark_hdfs/prerequisites.md) + * [Connection](spark_hdfs/connection.md) + * [Slots](spark_hdfs/slots.md) +* [Spark S3](spark_s3/index.md) + * [Prerequisites](spark_s3/prerequisites.md) + * [Connection](spark_s3/connection.md) + * [Troubleshooting](spark_s3/troubleshooting.md) + +# For developers + +* [Base interface](base.md) \ No newline at end of file diff --git a/mddocs/docs/connection/file_df_connection/spark_hdfs/connection.md b/mddocs/docs/en/connection/file_df_connection/spark_hdfs/connection.md similarity index 100% rename from mddocs/docs/connection/file_df_connection/spark_hdfs/connection.md rename to mddocs/docs/en/connection/file_df_connection/spark_hdfs/connection.md diff --git a/mddocs/docs/connection/file_df_connection/spark_hdfs/index.md b/mddocs/docs/en/connection/file_df_connection/spark_hdfs/index.md similarity index 100% rename from mddocs/docs/connection/file_df_connection/spark_hdfs/index.md rename to mddocs/docs/en/connection/file_df_connection/spark_hdfs/index.md diff --git a/mddocs/docs/connection/file_df_connection/spark_hdfs/prerequisites.md b/mddocs/docs/en/connection/file_df_connection/spark_hdfs/prerequisites.md similarity index 100% rename from mddocs/docs/connection/file_df_connection/spark_hdfs/prerequisites.md rename to mddocs/docs/en/connection/file_df_connection/spark_hdfs/prerequisites.md diff --git a/mddocs/docs/connection/file_df_connection/spark_hdfs/slots.md b/mddocs/docs/en/connection/file_df_connection/spark_hdfs/slots.md similarity index 100% rename from mddocs/docs/connection/file_df_connection/spark_hdfs/slots.md rename to mddocs/docs/en/connection/file_df_connection/spark_hdfs/slots.md diff --git a/mddocs/docs/connection/file_df_connection/spark_local_fs.md b/mddocs/docs/en/connection/file_df_connection/spark_local_fs.md similarity index 100% rename from mddocs/docs/connection/file_df_connection/spark_local_fs.md rename to mddocs/docs/en/connection/file_df_connection/spark_local_fs.md diff --git a/mddocs/docs/connection/file_df_connection/spark_s3/connection.md b/mddocs/docs/en/connection/file_df_connection/spark_s3/connection.md similarity index 100% rename from mddocs/docs/connection/file_df_connection/spark_s3/connection.md rename to mddocs/docs/en/connection/file_df_connection/spark_s3/connection.md diff --git a/mddocs/docs/connection/file_df_connection/spark_s3/index.md b/mddocs/docs/en/connection/file_df_connection/spark_s3/index.md similarity index 100% rename from mddocs/docs/connection/file_df_connection/spark_s3/index.md rename to mddocs/docs/en/connection/file_df_connection/spark_s3/index.md diff --git a/mddocs/docs/connection/file_df_connection/spark_s3/prerequisites.md b/mddocs/docs/en/connection/file_df_connection/spark_s3/prerequisites.md similarity index 100% rename from mddocs/docs/connection/file_df_connection/spark_s3/prerequisites.md rename to mddocs/docs/en/connection/file_df_connection/spark_s3/prerequisites.md diff --git a/mddocs/docs/connection/file_df_connection/spark_s3/troubleshooting.md b/mddocs/docs/en/connection/file_df_connection/spark_s3/troubleshooting.md similarity index 100% rename from mddocs/docs/connection/file_df_connection/spark_s3/troubleshooting.md rename to mddocs/docs/en/connection/file_df_connection/spark_s3/troubleshooting.md diff --git a/mddocs/docs/en/connection/index.md b/mddocs/docs/en/connection/index.md new file mode 100644 index 000000000..8f5eef7d7 --- /dev/null +++ b/mddocs/docs/en/connection/index.md @@ -0,0 +1,34 @@ +# Connection + + DB Connection + +* [DB Connections](db_connection/index.md) + * [Clickhouse](db_connection/clickhouse/index.md) + * [Greenplum](db_connection/greenplum/index.md) + * [Kafka](db_connection/kafka/index.md) + * [Hive](db_connection/hive/index.md) + * [MongoDB](db_connection/mongodb/index.md) + * [MSSQL](db_connection/mssql/index.md) + * [MySQL](db_connection/mysql/index.md) + * [Oracle](db_connection/oracle/index.md) + * [Postgres](db_connection/postgres/index.md) + * [Teradata](db_connection/teradata/index.md) + + File Connection + +* [File Connections](file_connection/index.md) + * [FTP](file_connection/ftp.md) + * [FTPS](file_connection/ftps.md) + * [HDFS](file_connection/hdfs/index.md) + * [Samba](file_connection/samba.md) + * [SFTP](file_connection/sftp.md) + * [S3](file_connection/s3.md) + * [Webdav](file_connection/webdav.md) + + File DataFrame Connection + +* [File DataFrame Connections](file_df_connection/index.md) + * [Spark LocalFS](file_df_connection/spark_local_fs.md) + * [Spark HDFS](file_df_connection/spark_hdfs/index.md) + * [Spark S3](file_df_connection/spark_s3/index.md) + * [Base interface](file_df_connection/base.md) \ No newline at end of file diff --git a/mddocs/docs/_build/markdown/contributing.md b/mddocs/docs/en/contributing.md similarity index 100% rename from mddocs/docs/_build/markdown/contributing.md rename to mddocs/docs/en/contributing.md diff --git a/mddocs/docs/db/index.md b/mddocs/docs/en/db_/index.md similarity index 85% rename from mddocs/docs/db/index.md rename to mddocs/docs/en/db_/index.md index baf632bf9..494a026b1 100644 --- a/mddocs/docs/db/index.md +++ b/mddocs/docs/en/db_/index.md @@ -1,4 +1,6 @@ -(db-root)= +# DB + +lslsv ```{toctree} :caption: DB classes diff --git a/mddocs/docs/db/db_reader.md b/mddocs/docs/en/db_/reader.md similarity index 95% rename from mddocs/docs/db/db_reader.md rename to mddocs/docs/en/db_/reader.md index 5368093b1..912e3d127 100644 --- a/mddocs/docs/db/db_reader.md +++ b/mddocs/docs/en/db_/reader.md @@ -1,7 +1,8 @@ -(db-reader)= - # DB Reader + +ыыыыы + ```{eval-rst} .. currentmodule:: onetl.db.db_reader.db_reader ``` diff --git a/mddocs/docs/db/db_writer.md b/mddocs/docs/en/db_/writer.md similarity index 94% rename from mddocs/docs/db/db_writer.md rename to mddocs/docs/en/db_/writer.md index d2d789c40..ea66d2834 100644 --- a/mddocs/docs/db/db_writer.md +++ b/mddocs/docs/en/db_/writer.md @@ -1,7 +1,7 @@ -(db-writer)= - # DB Writer + +бубуб ```{eval-rst} .. currentmodule:: onetl.db.db_writer.db_writer ``` diff --git a/mddocs/docs/file/file_downloader/file_downloader.md b/mddocs/docs/en/file/file_downloader/file_downloader.md similarity index 100% rename from mddocs/docs/file/file_downloader/file_downloader.md rename to 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similarity index 100% rename from mddocs/docs/file_df/index.md rename to mddocs/docs/en/file_df/index.md diff --git a/mddocs/docs/hooks/design.md b/mddocs/docs/en/hooks/design.md similarity index 100% rename from mddocs/docs/hooks/design.md rename to mddocs/docs/en/hooks/design.md diff --git a/mddocs/docs/hooks/global_state.md b/mddocs/docs/en/hooks/global_state.md similarity index 100% rename from mddocs/docs/hooks/global_state.md rename to mddocs/docs/en/hooks/global_state.md diff --git a/mddocs/docs/hooks/hook.md b/mddocs/docs/en/hooks/hook.md similarity index 100% rename from mddocs/docs/hooks/hook.md rename to mddocs/docs/en/hooks/hook.md diff --git a/mddocs/docs/hooks/index.md b/mddocs/docs/en/hooks/index.md similarity index 81% rename from mddocs/docs/hooks/index.md rename to mddocs/docs/en/hooks/index.md index 9b489afe6..6d8218027 100644 --- a/mddocs/docs/hooks/index.md +++ b/mddocs/docs/en/hooks/index.md @@ -1,9 +1,7 @@ -(hooks)= - # Hooks -:::{versionadded} 0.6.0 -::: +:octicons-versions-16: **version added 0.6.0** + ```{toctree} :caption: Hooks diff --git a/mddocs/docs/hooks/slot.md b/mddocs/docs/en/hooks/slot.md similarity index 100% rename from mddocs/docs/hooks/slot.md rename to mddocs/docs/en/hooks/slot.md diff --git a/mddocs/docs/hooks/support_hooks.md b/mddocs/docs/en/hooks/support_hooks.md similarity index 100% rename from mddocs/docs/hooks/support_hooks.md rename to mddocs/docs/en/hooks/support_hooks.md diff --git a/mddocs/docs/hwm_store/index.md b/mddocs/docs/en/hwm_store/index.md similarity index 100% rename from mddocs/docs/hwm_store/index.md rename to mddocs/docs/en/hwm_store/index.md diff --git a/mddocs/docs/hwm_store/yaml_hwm_store.md b/mddocs/docs/en/hwm_store/yaml_hwm_store.md similarity index 100% rename from mddocs/docs/hwm_store/yaml_hwm_store.md rename to mddocs/docs/en/hwm_store/yaml_hwm_store.md diff --git a/mddocs/docs/en/index.md b/mddocs/docs/en/index.md new file mode 100644 index 000000000..d5db83211 --- /dev/null +++ b/mddocs/docs/en/index.md @@ -0,0 +1,18 @@ +# onETL + +{{ repo_status_badge }} +{{ pypi_release_bage }} +{{ pypi_license_bage }} +{{ pypi_pyversion_bage }} +{{ pypi_downloads_bage }} + +{{ docs_status_badge }} +{{ ci_status_badge }} +{{ precommit_badge }} + + +{{ onetl_logo_wide }} + +----8<---- +../mddocs/docs/en/snippet_0.md +----8<---- \ No newline at end of file diff --git a/mddocs/docs/install/files.md b/mddocs/docs/en/install/files.md similarity index 100% rename from mddocs/docs/install/files.md rename to mddocs/docs/en/install/files.md diff --git a/mddocs/docs/install/full.md b/mddocs/docs/en/install/full.md similarity index 100% rename from mddocs/docs/install/full.md rename to mddocs/docs/en/install/full.md diff --git a/mddocs/docs/install/index.md b/mddocs/docs/en/install/index.md similarity index 100% rename from mddocs/docs/install/index.md rename to mddocs/docs/en/install/index.md diff --git a/mddocs/docs/install/kerberos.md b/mddocs/docs/en/install/kerberos.md similarity index 100% rename from mddocs/docs/install/kerberos.md rename to mddocs/docs/en/install/kerberos.md diff --git a/mddocs/docs/install/spark.md b/mddocs/docs/en/install/spark.md similarity index 100% rename from mddocs/docs/install/spark.md rename to mddocs/docs/en/install/spark.md diff --git a/mddocs/docs/logging.md b/mddocs/docs/en/logging.md similarity index 95% rename from mddocs/docs/logging.md rename to mddocs/docs/en/logging.md index dae7a9d73..2a36b0a75 100644 --- a/mddocs/docs/logging.md +++ b/mddocs/docs/en/logging.md @@ -1,14 +1,10 @@ -(logging)= - # Logging Logging is quite important to understand what's going on under the hood of onETL. -Default logging level for Python interpreters is `WARNING`, -but most of onETL logs are in `INFO` level, so users usually don't see much. +Default logging level for Python interpreters is `WARNING`, but most of onETL logs are in `INFO` level, so users usually don't see much. -To change logging level, there is a function {obj}`setup_logging ` -which should be called at the top of the script: +To change logging level, there is a function [setup_logging](../logging/#onetl.log.setup_logging) which should be called at the top of the script: ```python from onetl.log import setup_logging @@ -22,10 +18,9 @@ setup_logging() This changes both log level and log formatting to something like this: -```{eval-rst} -.. dropdown:: See logs +??? "See logs" - .. code:: text + ```text 2024-04-12 10:12:10,834 [INFO ] MainThread: |onETL| Using IncrementalStrategy as a strategy 2024-04-12 10:12:10,835 [INFO ] MainThread: =================================== DBReader.run() starts =================================== @@ -137,7 +132,7 @@ This changes both log level and log formatting to something like this: 2024-04-12 11:06:07,397 [INFO ] MainThread: value = datetime.datetime(2024, 4, 12, 13, 12, 2, 123000), 2024-04-12 11:06:07,397 [INFO ] MainThread: ) 2024-04-12 11:06:07,495 [INFO ] MainThread: |IncrementalStrategy| HWM has been saved -``` + ``` Each step performed by onETL is extensively logged, which should help with debugging. @@ -154,18 +149,9 @@ setup_logging(level="DEBUG", enable_clients=True) This also changes log level for all underlying Python libraries, e.g. showing each HTTP request being made, and so on. -```{eval-rst} -.. currentmodule:: onetl.log -``` - -```{eval-rst} -.. autofunction:: setup_logging -``` - -```{eval-rst} -.. autofunction:: setup_clients_logging -``` - -```{eval-rst} -.. autofunction:: set_default_logging_format -``` +::: onetl.log + options: + members: + - setup_logging + - setup_clients_logging + - set_default_logging_format diff --git a/mddocs/docs/plugins.md b/mddocs/docs/en/plugins.md similarity index 93% rename from mddocs/docs/plugins.md rename to mddocs/docs/en/plugins.md index a7904b076..d00730747 100644 --- a/mddocs/docs/plugins.md +++ b/mddocs/docs/en/plugins.md @@ -1,22 +1,20 @@ -(plugins)= - # Plugins -:::{versionadded} 0.6.0 -::: +:octicons-versions-16: **version added 0.6.0** + ## What are plugins? ### Terms -- `Plugin` - some Python package which implements some extra functionality for onETL, like {ref}`hooks` +- `Plugin` - some Python package which implements some extra functionality for onETL, like [hooks](../hooks/) - `Plugin autoimport` - onETL behavior which allows to automatically import this package if it contains proper metadata (`entry_points`) ### Features Plugins mechanism allows to: -- Automatically register {ref}`hooks` which can alter onETL behavior +- Automatically register [hooks](../hooks/) which can alter onETL behavior - Automatically register new classes, like HWM type, HWM stores and so on ### Limitations @@ -74,12 +72,11 @@ from some_plugin.module.internals import my_function ``` If specific module/class/function uses some registration capabilities of onETL, -like {ref}`hook-decorator`, it will be executed during this import. +like [hooks](../hooks/), it will be executed during this import. ## How to enable/disable plugins? -:::{versionadded} 0.7.0 -::: +:octicons-versions-16: **version added 0.7.0** ### Disable/enable all plugins diff --git a/mddocs/docs/en/quickstart.md b/mddocs/docs/en/quickstart.md new file mode 100644 index 000000000..39201e990 --- /dev/null +++ b/mddocs/docs/en/quickstart.md @@ -0,0 +1,540 @@ +# onETL + +{{ repo_status_badge }} +{{ pypi_release_bage }} +{{ pypi_license_bage }} +{{ pypi_pyversion_bage }} +{{ pypi_downloads_bage }} + +{{ docs_status_badge }} +{{ ci_status_badge }} +{{ precommit_badge }} + + +{{ onetl_logo_wide }} + +----8<---- +../mddocs/docs/en/snippet_0.md +----8<---- + + + +## Documentation + +See at [ReadTheDocs](https://onetl.readthedocs.io/en/latest/) + +## How to install + + + +### Minimal installation + + + +Base `onetl` package contains: + +- `DBReader`, `DBWriter` and related classes +- `FileDownloader`, `FileUploader`, `FileMover` and related classes, like file filters & limits +- `FileDFReader`, `FileDFWriter` and related classes, like file formats +- Read Strategies & HWM classes +- Plugins support + +It can be installed via: + +```bash +pip install onetl +``` + +!!! warning + + This method does NOT include any connections. + + This method is recommended for use in third-party libraries which require for ``onetl`` to be installed, + but do not use its connection classes. + + +### With DB and FileDF connections + + + +All DB connection classes (`Clickhouse`, `Greenplum`, `Hive` and others) +and all FileDF connection classes (`SparkHDFS`, `SparkLocalFS`, `SparkS3`) +require Spark to be installed. + + + +Firstly, you should install JDK. The exact installation instruction depends on your OS, here are some examples: + +```bash +yum install java-1.8.0-openjdk-devel # CentOS 7 | Spark 2 +dnf install java-11-openjdk-devel # CentOS 8 | Spark 3 +apt-get install openjdk-11-jdk # Debian-based | Spark 3 +``` + + + +#### Compatibility matrix + +| Spark | Python | Java | Scala | +| --------------------------------------------------------- | ---------- | ---------- | ----- | +| [2.3.x](https://spark.apache.org/docs/2.3.1/#downloading) | 3.7 only | 8 only | 2.11 | +| [2.4.x](https://spark.apache.org/docs/2.4.8/#downloading) | 3.7 only | 8 only | 2.11 | +| [3.2.x](https://spark.apache.org/docs/3.2.4/#downloading) | 3.7 - 3.10 | 8u201 - 11 | 2.12 | +| [3.3.x](https://spark.apache.org/docs/3.3.4/#downloading) | 3.7 - 3.12 | 8u201 - 17 | 2.12 | +| [3.4.x](https://spark.apache.org/docs/3.4.4/#downloading) | 3.7 - 3.12 | 8u362 - 20 | 2.12 | +| [3.5.x](https://spark.apache.org/docs/3.5.5/#downloading) | 3.8 - 3.13 | 8u371 - 20 | 2.12 | + + + +Then you should install PySpark via passing `spark` to `extras`: + +```bash +pip install onetl[spark] # install latest PySpark +``` + +or install PySpark explicitly: + +```bash +pip install onetl pyspark==3.5.5 # install a specific PySpark version +``` + +or inject PySpark to `sys.path` in some other way BEFORE creating a class instance. +**Otherwise connection object cannot be created.** + +### With File connections + + + +All File (but not *FileDF*) connection classes (`FTP`, `SFTP`, `HDFS` and so on) requires specific Python clients to be installed. + +Each client can be installed explicitly by passing connector name (in lowercase) to `extras`: + +```bash +pip install onetl[ftp] # specific connector +pip install onetl[ftp,ftps,sftp,hdfs,s3,webdav,samba] # multiple connectors +``` + +To install all file connectors at once you can pass `files` to `extras`: + +```bash +pip install onetl[files] +``` + +**Otherwise class import will fail.** + +### With Kerberos support + + + +Most of Hadoop instances set up with Kerberos support, +so some connections require additional setup to work properly. + +- `HDFS` + Uses [requests-kerberos](https://pypi.org/project/requests-kerberos/) and + [GSSApi](https://pypi.org/project/gssapi/) for authentication. + It also uses `kinit` executable to generate Kerberos ticket. +- `Hive` and `SparkHDFS` + require Kerberos ticket to exist before creating Spark session. + +So you need to install OS packages with: + +- `krb5` libs +- Headers for `krb5` +- `gcc` or other compiler for C sources + +The exact installation instruction depends on your OS, here are some examples: + +```bash +apt install libkrb5-dev krb5-user gcc # Debian-based +dnf install krb5-devel krb5-libs krb5-workstation gcc # CentOS, OracleLinux +``` + +Also you should pass `kerberos` to `extras` to install required Python packages: + +```bash +pip install onetl[kerberos] +``` + +### Full bundle + + + +To install all connectors and dependencies, you can pass `all` into `extras`: + +```bash +pip install onetl[all] + +# this is just the same as +pip install onetl[spark,files,kerberos] +``` + +!!! warning + + This method consumes a lot of disk space, and requires for Java & Kerberos libraries to be installed into your OS. + + + + +## Quick start + +### MSSQL → Hive + +Read data from MSSQL, transform & write to Hive. + +```bash +# install onETL and PySpark +pip install onetl[spark] +``` + +```python +# Import pyspark to initialize the SparkSession +from pyspark.sql import SparkSession + +# import function to setup onETL logging +from onetl.log import setup_logging + +# Import required connections +from onetl.connection import MSSQL, Hive + +# Import onETL classes to read & write data +from onetl.db import DBReader, DBWriter + +# change logging level to INFO, and set up default logging format and handler +setup_logging() + +# Initialize new SparkSession with MSSQL driver loaded +maven_packages = MSSQL.get_packages() +spark = ( + SparkSession.builder.appName("spark_app_onetl_demo") + .config("spark.jars.packages", ",".join(maven_packages)) + .enableHiveSupport() # for Hive + .getOrCreate() +) + +# Initialize MSSQL connection and check if database is accessible +mssql = MSSQL( + host="mssqldb.demo.com", + user="onetl", + password="onetl", + database="Telecom", + spark=spark, + # These options are passed to MSSQL JDBC Driver: + extra={"applicationIntent": "ReadOnly"}, +).check() + +# >>> INFO:|MSSQL| Connection is available + +# Initialize DBReader +reader = DBReader( + connection=mssql, + source="dbo.demo_table", + columns=["on", "etl"], + # Set some MSSQL read options: + options=MSSQL.ReadOptions(fetchsize=10000), +) + +# checks that there is data in the table, otherwise raises exception +reader.raise_if_no_data() + +# Read data to DataFrame +df = reader.run() +df.printSchema() +# root +# |-- id: integer (nullable = true) +# |-- phone_number: string (nullable = true) +# |-- region: string (nullable = true) +# |-- birth_date: date (nullable = true) +# |-- registered_at: timestamp (nullable = true) +# |-- account_balance: double (nullable = true) + +# Apply any PySpark transformations +from pyspark.sql.functions import lit + +df_to_write = df.withColumn("engine", lit("onetl")) +df_to_write.printSchema() +# root +# |-- id: integer (nullable = true) +# |-- phone_number: string (nullable = true) +# |-- region: string (nullable = true) +# |-- birth_date: date (nullable = true) +# |-- registered_at: timestamp (nullable = true) +# |-- account_balance: double (nullable = true) +# |-- engine: string (nullable = false) + +# Initialize Hive connection +hive = Hive(cluster="rnd-dwh", spark=spark) + +# Initialize DBWriter +db_writer = DBWriter( + connection=hive, + target="dl_sb.demo_table", + # Set some Hive write options: + options=Hive.WriteOptions(if_exists="replace_entire_table"), +) + +# Write data from DataFrame to Hive +db_writer.run(df_to_write) + +# Success! +``` + +### SFTP → HDFS + +Download files from SFTP & upload them to HDFS. + +```bash +# install onETL with SFTP and HDFS clients, and Kerberos support +pip install onetl[hdfs,sftp,kerberos] +``` + +```python +# import function to setup onETL logging +from onetl.log import setup_logging + +# Import required connections +from onetl.connection import SFTP, HDFS + +# Import onETL classes to download & upload files +from onetl.file import FileDownloader, FileUploader + +# import filter & limit classes +from onetl.file.filter import Glob, ExcludeDir +from onetl.file.limit import MaxFilesCount + +# change logging level to INFO, and set up default logging format and handler +setup_logging() + +# Initialize SFTP connection and check it +sftp = SFTP( + host="sftp.test.com", + user="someuser", + password="somepassword", +).check() + +# >>> INFO:|SFTP| Connection is available + +# Initialize downloader +file_downloader = FileDownloader( + connection=sftp, + source_path="/remote/tests/Report", # path on SFTP + local_path="/local/onetl/Report", # local fs path + filters=[ + # download only files matching the glob + Glob("*.csv"), + # exclude files from this directory + ExcludeDir("/remote/tests/Report/exclude_dir/"), + ], + limits=[ + # download max 1000 files per run + MaxFilesCount(1000), + ], + options=FileDownloader.Options( + # delete files from SFTP after successful download + delete_source=True, + # mark file as failed if it already exist in local_path + if_exists="error", + ), +) + +# Download files to local filesystem +download_result = downloader.run() + +# Method run returns a DownloadResult object, +# which contains collection of downloaded files, divided to 4 categories +download_result + +# DownloadResult( +# successful=[ +# LocalPath('/local/onetl/Report/file_1.json'), +# LocalPath('/local/onetl/Report/file_2.json'), +# ], +# failed=[FailedRemoteFile('/remote/onetl/Report/file_3.json')], +# ignored=[RemoteFile('/remote/onetl/Report/file_4.json')], +# missing=[], +# ) + +# Raise exception if there are failed files, or there were no files in the remote filesystem +download_result.raise_if_failed() or download_result.raise_if_empty() + +# Do any kind of magic with files: rename files, remove header for csv files, ... +renamed_files = my_rename_function(download_result.success) + +# function removed "_" from file names +# [ +# LocalPath('/home/onetl/Report/file1.json'), +# LocalPath('/home/onetl/Report/file2.json'), +# ] + +# Initialize HDFS connection +hdfs = HDFS( + host="my.name.node", + user="someuser", + password="somepassword", # or keytab +) + +# Initialize uploader +file_uploader = FileUploader( + connection=hdfs, + target_path="/user/onetl/Report/", # hdfs path +) + +# Upload files from local fs to HDFS +upload_result = file_uploader.run(renamed_files) + +# Method run returns a UploadResult object, +# which contains collection of uploaded files, divided to 4 categories +upload_result + +# UploadResult( +# successful=[RemoteFile('/user/onetl/Report/file1.json')], +# failed=[FailedLocalFile('/local/onetl/Report/file2.json')], +# ignored=[], +# missing=[], +# ) + +# Raise exception if there are failed files, or there were no files in the local filesystem, or some input file is missing +upload_result.raise_if_failed() or upload_result.raise_if_empty() or upload_result.raise_if_missing() + +# Success! +``` + +### S3 → Postgres + +Read files directly from S3 path, convert them to dataframe, transform it and then write to a database. + +```bash +# install onETL and PySpark +pip install onetl[spark] +``` + +```python +# Import pyspark to initialize the SparkSession +from pyspark.sql import SparkSession + +# import function to setup onETL logging +from onetl.log import setup_logging + +# Import required connections +from onetl.connection import Postgres, SparkS3 + +# Import onETL classes to read files +from onetl.file import FileDFReader +from onetl.file.format import CSV + +# Import onETL classes to write data +from onetl.db import DBWriter + +# change logging level to INFO, and set up default logging format and handler +setup_logging() + +# Initialize new SparkSession with Hadoop AWS libraries and Postgres driver loaded +maven_packages = SparkS3.get_packages(spark_version="3.5.5") | Postgres.get_packages() +exclude_packages = SparkS3.get_exclude_packages() +spark = ( + SparkSession.builder.appName("spark_app_onetl_demo") + .config("spark.jars.packages", ",".join(maven_packages)) + .config("spark.jars.excludes", ",".join(exclude_packages)) + .getOrCreate() +) + +# Initialize S3 connection and check it +spark_s3 = SparkS3( + host="s3.test.com", + protocol="https", + bucket="my-bucket", + access_key="somekey", + secret_key="somesecret", + # Access bucket as s3.test.com/my-bucket + extra={"path.style.access": True}, + spark=spark, +).check() + +# >>> INFO:|SparkS3| Connection is available + +# Describe file format and parsing options +csv = CSV( + delimiter=";", + header=True, + encoding="utf-8", +) + +# Describe DataFrame schema of files +from pyspark.sql.types import ( + DateType, + DoubleType, + IntegerType, + StringType, + StructField, + StructType, + TimestampType, +) + +df_schema = StructType( + [ + StructField("id", IntegerType()), + StructField("phone_number", StringType()), + StructField("region", StringType()), + StructField("birth_date", DateType()), + StructField("registered_at", TimestampType()), + StructField("account_balance", DoubleType()), + ], +) + +# Initialize file df reader +reader = FileDFReader( + connection=spark_s3, + source_path="/remote/tests/Report", # path on S3 there *.csv files are located + format=csv, # file format with specific parsing options + df_schema=df_schema, # columns & types +) + +# Read files directly from S3 as Spark DataFrame +df = reader.run() + +# Check that DataFrame schema is same as expected +df.printSchema() +# root +# |-- id: integer (nullable = true) +# |-- phone_number: string (nullable = true) +# |-- region: string (nullable = true) +# |-- birth_date: date (nullable = true) +# |-- registered_at: timestamp (nullable = true) +# |-- account_balance: double (nullable = true) + +# Apply any PySpark transformations +from pyspark.sql.functions import lit + +df_to_write = df.withColumn("engine", lit("onetl")) +df_to_write.printSchema() +# root +# |-- id: integer (nullable = true) +# |-- phone_number: string (nullable = true) +# |-- region: string (nullable = true) +# |-- birth_date: date (nullable = true) +# |-- registered_at: timestamp (nullable = true) +# |-- account_balance: double (nullable = true) +# |-- engine: string (nullable = false) + +# Initialize Postgres connection +postgres = Postgres( + host="192.169.11.23", + user="onetl", + password="somepassword", + database="mydb", + spark=spark, +) + +# Initialize DBWriter +db_writer = DBWriter( + connection=postgres, + # write to specific table + target="public.my_table", + # with some writing options + options=Postgres.WriteOptions(if_exists="append"), +) + +# Write DataFrame to Postgres table +db_writer.run(df_to_write) + +# Success! +``` diff --git a/mddocs/ru/SECURITY.md b/mddocs/docs/en/security.md similarity index 72% rename from mddocs/ru/SECURITY.md rename to mddocs/docs/en/security.md index 5d9dce52b..8ca24e077 100644 --- a/mddocs/ru/SECURITY.md +++ b/mddocs/docs/en/security.md @@ -14,12 +14,12 @@ 1. No binaries in repository 2. No passwords, keys, access tokens in source code -3. No "Critical" and/or "High" vulnerabilities in contributed source code +3. No “Critical” and/or “High” vulnerabilities in contributed source code ## Vulnerability reports -Please, use email [mailto:onetools@mts.ru](mailto:onetools@mts.ru) for reporting security issues or anything that can cause any consequences for security. +Please, use email [mailto:onetools@mts.ru](mailto:onetools@mts.ru) for reporting security issues or anything that can cause any consequences for security. Please avoid any public disclosure (including registering issues) at least until it is fixed. -Thank you in advance for understanding. +Thank you in advance for understanding. \ No newline at end of file diff --git a/mddocs/docs/en/snippet_0.md b/mddocs/docs/en/snippet_0.md new file mode 100644 index 000000000..98839961c --- /dev/null +++ b/mddocs/docs/en/snippet_0.md @@ -0,0 +1,44 @@ +## What is onETL? + +Python ETL/ELT library powered by [Apache Spark](https://spark.apache.org/) & other open-source tools. + +## Goals + +- Provide unified classes to extract data from (**E**) & load data to (**L**) various stores. +- Provides [Spark DataFrame API](https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.DataFrame.html) for performing transformations (**T**) in terms of *ETL*. +- Provide direct assess to database, allowing to execute SQL queries, as well as DDL, DML, and call functions/procedures. This can be used for building up *ELT* pipelines. +- Support different [read strategies](../strategy/) for incremental and batch data fetching. +- Provide [hooks](../hooks/) & [plugins](../plugins) mechanism for altering behavior of internal classes. + +## Non-goals + +- onETL is not a Spark replacement. It just provides additional functionality that Spark does not have, and improves UX for end users. +- onETL is not a framework, as it does not have requirements to project structure, naming, the way of running ETL/ELT processes, configuration, etc. All of that should be implemented in some other tool. +- onETL is deliberately developed without any integration with scheduling software like Apache Airflow. All integrations should be implemented as separated tools. +- Only batch operations, no streaming. For streaming prefer [Apache Flink](https://flink.apache.org/). + +## Requirements + +- **Python** 3.7 - 3.13 +- PySpark 2.3.x - 3.5.x (depends on used connector) +- Java 8+ (required by Spark, see below) +- Kerberos libs & GCC (required by `Hive`, `HDFS` and `SparkHDFS` connectors) + +## Supported storages + + +| Type | Storage | Powered by | +|--------------------|--------------|-------------------------------------------------------------------------------------------------------------------------| +| Database {: rowspan=5} | Clickhouse
MSSQL
MySQL
Postgres
Oracle
Teradata |

Apache Spark [JDBC Data Source](https://spark.apache.org/docs/latest/sql-data-sources-jdbc.html) | +| Hive | Apache Spark [Hive integration](https://spark.apache.org/docs/latest/sql-data-sources-hive-tables.html) | +| Kafka | Apache Spark [Kafka integration](https://spark.apache.org/docs/latest/structured-streaming-kafka-integration.html) | +| Greenplum | VMware [Greenplum Spark connector](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/index.html) | +| MongoDB | [MongoDB Spark connector](https://www.mongodb.com/docs/spark-connector/current) | +| File {: rowspan=6} | HDFS | [HDFS Python client](https://pypi.org/project/hdfs/) | +| S3 | [minio-py client](https://pypi.org/project/minio/) | +| SFTP | [Paramiko library](https://pypi.org/project/paramiko/) | +| FTP
FTPS | [FTPUtil library](https://pypi.org/project/ftputil/) | +| WebDAV | [WebdavClient3 library](https://pypi.org/project/webdavclient3/) | +| Samba | [pysmb library](https://pypi.org/project/pysmb/) | +| Files as DataFrame {: rowspan=2} | SparkLocalFS
SparkHDFS | Apache Spark [File Data Source](https://spark.apache.org/docs/latest/sql-data-sources-generic-options.html) | +| SparkS3 | [Hadoop AWS](https://hadoop.apache.org/docs/current3/hadoop-aws/tools/hadoop-aws/index.html) library | \ No newline at end of file diff --git a/mddocs/docs/strategy/incremental_batch_strategy.md b/mddocs/docs/en/strategy/incremental_batch_strategy.md similarity index 100% rename from mddocs/docs/strategy/incremental_batch_strategy.md rename to mddocs/docs/en/strategy/incremental_batch_strategy.md diff --git a/mddocs/docs/strategy/incremental_strategy.md b/mddocs/docs/en/strategy/incremental_strategy.md similarity index 100% rename from mddocs/docs/strategy/incremental_strategy.md rename to mddocs/docs/en/strategy/incremental_strategy.md diff --git a/mddocs/docs/strategy/index.md b/mddocs/docs/en/strategy/index.md similarity index 100% rename from mddocs/docs/strategy/index.md rename to mddocs/docs/en/strategy/index.md diff --git a/mddocs/docs/strategy/snapshot_batch_strategy.md b/mddocs/docs/en/strategy/snapshot_batch_strategy.md similarity index 100% rename from mddocs/docs/strategy/snapshot_batch_strategy.md rename to mddocs/docs/en/strategy/snapshot_batch_strategy.md diff --git a/mddocs/docs/strategy/snapshot_strategy.md b/mddocs/docs/en/strategy/snapshot_strategy.md similarity index 100% rename from mddocs/docs/strategy/snapshot_strategy.md rename to mddocs/docs/en/strategy/snapshot_strategy.md diff --git a/mddocs/docs/troubleshooting/index.md b/mddocs/docs/en/troubleshooting/index.md similarity index 100% rename from mddocs/docs/troubleshooting/index.md rename to mddocs/docs/en/troubleshooting/index.md diff --git a/mddocs/docs/troubleshooting/spark.md b/mddocs/docs/en/troubleshooting/spark.md similarity index 100% rename from mddocs/docs/troubleshooting/spark.md rename to mddocs/docs/en/troubleshooting/spark.md diff --git a/mddocs/docs/index.md b/mddocs/docs/index.md deleted file mode 100644 index 46642bf4e..000000000 --- a/mddocs/docs/index.md +++ /dev/null @@ -1,87 +0,0 @@ -```{eval-rst} -.. include:: ../README.rst - :end-before: |Logo| -``` - -```{image} _static/logo_wide.svg -:alt: onETL logo -``` - -```{eval-rst} -.. include:: ../README.rst - :start-after: |Logo| - :end-before: documentation -``` - -```{toctree} -:caption: How to -:hidden: true -:maxdepth: 2 - -self -install/index -quickstart -concepts -logging -troubleshooting/index -``` - -```{toctree} -:caption: Connection -:hidden: true -:maxdepth: 3 - -connection/index -``` - -```{toctree} -:caption: DB classes -:hidden: true -:maxdepth: 3 - -db/index -``` - -```{toctree} -:caption: File classes -:hidden: true -:maxdepth: 3 - -file/index -``` - -```{toctree} -:caption: File DataFrame classes -:hidden: true -:maxdepth: 3 - -file_df/index -``` - -```{toctree} -:caption: Read strategies and HWM -:hidden: true -:maxdepth: 2 - -strategy/index -hwm_store/index -``` - -```{toctree} -:caption: Hooks & plugins -:hidden: true -:maxdepth: 2 - -hooks/index -plugins -``` - -```{toctree} -:caption: Development -:hidden: true -:maxdepth: 2 - -changelog -contributing -security -``` diff --git a/mddocs/docs/make.bat b/mddocs/docs/make.bat deleted file mode 100644 index 53ad1e82c..000000000 --- a/mddocs/docs/make.bat +++ /dev/null @@ -1,35 +0,0 @@ -@ECHO OFF - -pushd %~dp0 - -REM Command file for Sphinx documentation - -if "%SPHINXBUILD%" == "" ( - set SPHINXBUILD=sphinx-build -) -set SOURCEDIR=. -set BUILDDIR=_build - -if "%1" == "" goto help - -%SPHINXBUILD% >NUL 2>NUL -if errorlevel 9009 ( - echo. - echo.The 'sphinx-build' command was not found. Make sure you have Sphinx - echo.installed, then set the SPHINXBUILD environment variable to point - echo.to the full path of the 'sphinx-build' executable. Alternatively you - echo.may add the Sphinx directory to PATH. - echo. - echo.If you don't have Sphinx installed, grab it from - echo.https://www.sphinx-doc.org/ - exit /b 1 -) - -%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% -goto end - -:help -%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% - -:end -popd diff --git a/mddocs/docs/quickstart.md b/mddocs/docs/quickstart.md deleted file mode 100644 index d2367a3de..000000000 --- a/mddocs/docs/quickstart.md +++ /dev/null @@ -1,4 +0,0 @@ -```{eval-rst} -.. include:: ../README.rst - :start-after: quick-start -``` diff --git a/mddocs/ru/README.md b/mddocs/docs/ru/README.md similarity index 99% rename from mddocs/ru/README.md rename to mddocs/docs/ru/README.md index bed7994b8..9b1b76c68 100644 --- a/mddocs/ru/README.md +++ b/mddocs/docs/ru/README.md @@ -54,6 +54,8 @@ substitutions: (readme)= +[![Built with Material for MkDocs](https://img.shields.io/badge/Material_for_MkDocs-526CFE?style=for-the-badge&logo=MaterialForMkDocs&logoColor=white)](https://squidfunk.github.io/mkdocs-material/) + # onETL {{ Repo Status }} {{ PyPI Latest Release }} {{ PyPI License }} {{ PyPI Python Version }} {{ PyPI Downloads }} diff --git a/mddocs/ru/changelog/0.10.0.md b/mddocs/docs/ru/changelog/0.10.0.md similarity index 100% rename from mddocs/ru/changelog/0.10.0.md rename to mddocs/docs/ru/changelog/0.10.0.md diff --git a/mddocs/ru/changelog/0.10.1.md b/mddocs/docs/ru/changelog/0.10.1.md similarity index 100% rename from mddocs/ru/changelog/0.10.1.md rename to mddocs/docs/ru/changelog/0.10.1.md diff --git a/mddocs/ru/changelog/0.10.2.md b/mddocs/docs/ru/changelog/0.10.2.md similarity index 100% rename from mddocs/ru/changelog/0.10.2.md rename to mddocs/docs/ru/changelog/0.10.2.md diff --git a/mddocs/ru/changelog/0.11.0.md b/mddocs/docs/ru/changelog/0.11.0.md similarity index 100% rename from mddocs/ru/changelog/0.11.0.md rename to mddocs/docs/ru/changelog/0.11.0.md diff --git a/mddocs/ru/changelog/0.11.1.md b/mddocs/docs/ru/changelog/0.11.1.md similarity index 100% rename from mddocs/ru/changelog/0.11.1.md rename to mddocs/docs/ru/changelog/0.11.1.md diff --git a/mddocs/ru/changelog/0.11.2.md b/mddocs/docs/ru/changelog/0.11.2.md similarity index 100% rename from mddocs/ru/changelog/0.11.2.md rename to mddocs/docs/ru/changelog/0.11.2.md diff --git a/mddocs/ru/changelog/0.12.0.md b/mddocs/docs/ru/changelog/0.12.0.md similarity index 100% rename from mddocs/ru/changelog/0.12.0.md rename to mddocs/docs/ru/changelog/0.12.0.md diff --git a/mddocs/ru/changelog/0.12.1.md b/mddocs/docs/ru/changelog/0.12.1.md similarity index 100% rename from mddocs/ru/changelog/0.12.1.md rename to mddocs/docs/ru/changelog/0.12.1.md diff --git a/mddocs/ru/changelog/0.12.2.md b/mddocs/docs/ru/changelog/0.12.2.md similarity index 100% rename from mddocs/ru/changelog/0.12.2.md rename to mddocs/docs/ru/changelog/0.12.2.md diff --git a/mddocs/ru/changelog/0.12.3.md b/mddocs/docs/ru/changelog/0.12.3.md similarity index 100% rename from mddocs/ru/changelog/0.12.3.md rename to mddocs/docs/ru/changelog/0.12.3.md diff --git a/mddocs/ru/changelog/0.12.4.md b/mddocs/docs/ru/changelog/0.12.4.md similarity index 100% rename from mddocs/ru/changelog/0.12.4.md rename to mddocs/docs/ru/changelog/0.12.4.md diff --git a/mddocs/ru/changelog/0.12.5.md b/mddocs/docs/ru/changelog/0.12.5.md similarity index 100% rename from mddocs/ru/changelog/0.12.5.md rename to mddocs/docs/ru/changelog/0.12.5.md diff --git a/mddocs/ru/changelog/0.13.0.md b/mddocs/docs/ru/changelog/0.13.0.md similarity index 100% rename from mddocs/ru/changelog/0.13.0.md rename to mddocs/docs/ru/changelog/0.13.0.md diff --git a/mddocs/ru/changelog/0.13.1.md b/mddocs/docs/ru/changelog/0.13.1.md similarity index 100% rename from mddocs/ru/changelog/0.13.1.md rename to mddocs/docs/ru/changelog/0.13.1.md diff --git a/mddocs/ru/changelog/0.13.3.md b/mddocs/docs/ru/changelog/0.13.3.md similarity index 100% rename from mddocs/ru/changelog/0.13.3.md rename to mddocs/docs/ru/changelog/0.13.3.md diff --git a/mddocs/ru/changelog/0.13.4.md b/mddocs/docs/ru/changelog/0.13.4.md similarity index 100% rename from mddocs/ru/changelog/0.13.4.md rename to mddocs/docs/ru/changelog/0.13.4.md diff --git a/mddocs/ru/changelog/0.7.0.md b/mddocs/docs/ru/changelog/0.7.0.md similarity index 96% rename from mddocs/ru/changelog/0.7.0.md rename to mddocs/docs/ru/changelog/0.7.0.md index e1b285d79..43a6d0a8d 100644 --- a/mddocs/ru/changelog/0.7.0.md +++ b/mddocs/docs/ru/changelog/0.7.0.md @@ -19,14 +19,14 @@ Правильный способ - перечислить коннекторы, которые должны быть установлены: ```bash - pip install onetl[hdfs,ftp,kerberos] # кроме DB соединений + pip install onetl[hdfs,ftp,kerberos] # кроме подключений к БД ``` **Подробности** В onetl<0.7 установка пакета выглядит так: - ```bash title="before" + ```bash title="До" pip install onetl ``` @@ -36,7 +36,7 @@ Начиная с версии 0.7.0 процесс установки был изменен: - ``` bash title="after" + ``` bash title="После" linenums="1" pip install onetl # минимальная установка, только ядро onETL # нет extras для подключений к БД, потому что они используют Java пакеты, которые устанавливаются во время выполнения @@ -55,7 +55,7 @@ Кроме того, onETL проверяет, что некоторые требования отсутствуют, и выдает исключение с рекомендацией, как их установить: - ``` text title="exception while import Clickhouse connection" + ``` text title="исключение при импорте подключения Clickhouse" Cannot import module "pyspark". @@ -65,7 +65,7 @@ or inject PySpark to sys.path in some other way BEFORE creating MongoDB instance. ``` - ``` text title="exception while import FTP connection" + ``` text title="исключение при импорте подключения FTP" Cannot import module "ftputil". @@ -167,7 +167,7 @@ Ещё мы улучшили сообщение об исключении с рекомендацией, как отключить неработающий плагин: - ``` text title="exception message example" + ``` text title="пример сообщения об ошибке" Error while importing plugin 'mtspark' from package 'mtspark' v4.0.0. @@ -199,7 +199,7 @@ Теперь onETL показывает следующее сообщение об ошибке: - ``` text title="exception message example" + ``` text title="пример сообщения об ошибке" |Spark| Cannot import Java class 'com.mongodb.spark.sql.connector.MongoTableProvider'. diff --git a/mddocs/ru/changelog/0.7.1.md b/mddocs/docs/ru/changelog/0.7.1.md similarity index 100% rename from mddocs/ru/changelog/0.7.1.md rename to mddocs/docs/ru/changelog/0.7.1.md diff --git a/mddocs/ru/changelog/0.7.2.md b/mddocs/docs/ru/changelog/0.7.2.md similarity index 100% rename from mddocs/ru/changelog/0.7.2.md rename to mddocs/docs/ru/changelog/0.7.2.md diff --git a/mddocs/ru/changelog/0.8.0.md b/mddocs/docs/ru/changelog/0.8.0.md similarity index 100% rename from mddocs/ru/changelog/0.8.0.md rename to mddocs/docs/ru/changelog/0.8.0.md diff --git a/mddocs/ru/changelog/0.8.1.md b/mddocs/docs/ru/changelog/0.8.1.md similarity index 100% rename from mddocs/ru/changelog/0.8.1.md rename to mddocs/docs/ru/changelog/0.8.1.md diff --git a/mddocs/ru/changelog/0.9.0.md b/mddocs/docs/ru/changelog/0.9.0.md similarity index 100% rename from mddocs/ru/changelog/0.9.0.md rename to mddocs/docs/ru/changelog/0.9.0.md diff --git a/mddocs/ru/changelog/0.9.1.md b/mddocs/docs/ru/changelog/0.9.1.md similarity index 100% rename from mddocs/ru/changelog/0.9.1.md rename to mddocs/docs/ru/changelog/0.9.1.md diff --git a/mddocs/ru/changelog/0.9.2.md b/mddocs/docs/ru/changelog/0.9.2.md similarity index 100% rename from mddocs/ru/changelog/0.9.2.md rename to mddocs/docs/ru/changelog/0.9.2.md diff --git a/mddocs/ru/changelog/0.9.3.md b/mddocs/docs/ru/changelog/0.9.3.md similarity index 100% rename from mddocs/ru/changelog/0.9.3.md rename to mddocs/docs/ru/changelog/0.9.3.md diff --git a/mddocs/ru/changelog/0.9.4.md b/mddocs/docs/ru/changelog/0.9.4.md similarity index 100% rename from mddocs/ru/changelog/0.9.4.md rename to mddocs/docs/ru/changelog/0.9.4.md diff --git a/mddocs/ru/changelog/0.9.5.md b/mddocs/docs/ru/changelog/0.9.5.md similarity index 100% rename from mddocs/ru/changelog/0.9.5.md rename to mddocs/docs/ru/changelog/0.9.5.md diff --git a/mddocs/docs/ru/changelog/index.md b/mddocs/docs/ru/changelog/index.md new file mode 100644 index 000000000..04704744d --- /dev/null +++ b/mddocs/docs/ru/changelog/index.md @@ -0,0 +1,29 @@ +# Changelog + +- [0.13.4](../changelog/0.13.4) +- [0.13.3](../changelog/0.13.3) +- [0.13.1](../changelog/0.13.1) +- [0.13.0](../changelog/0.13.0) +- [0.12.5](../changelog/0.12.5) +- [0.12.4](../changelog/0.12.4) +- [0.12.3](../changelog/0.12.3) +- [0.12.2](../changelog/0.12.2) +- [0.12.1](../changelog/0.12.1) +- [0.12.0](../changelog/0.12.0) +- [0.11.2](../changelog/0.11.2) +- [0.11.1](../changelog/0.11.1) +- [0.11.0](../changelog/0.11.0) +- [0.10.2](../changelog/0.10.2) +- [0.10.1](../changelog/0.10.1) +- [0.10.0](../changelog/0.10.0) +- [0.9.5](../changelog/0.9.5) +- [0.9.4](../changelog/0.9.4) +- [0.9.3](../changelog/0.9.3) +- [0.9.2](../changelog/0.9.2) +- [0.9.1](../changelog/0.9.1) +- [0.9.0](../changelog/0.9.0) +- [0.8.1](../changelog/0.8.1) +- [0.8.0](../changelog/0.8.0) +- [0.7.2](../changelog/0.7.2) +- [0.7.1](../changelog/0.7.1) +- [0.7.0](../changelog/0.7.0) diff --git a/mddocs/docs/ru/concepts.md b/mddocs/docs/ru/concepts.md new file mode 100644 index 000000000..20de36e1c --- /dev/null +++ b/mddocs/docs/ru/concepts.md @@ -0,0 +1,484 @@ +# Концепции + +Здесь вы найдете подробную информацию о каждом из концептов onETL и о том, как их использовать. + +## Подключение (Connection) + +### Основы + +onETL предназначена для извлечения и загрузки данных в хранилища (БД и файловые), поэтому в ней есть концепт первого класса `Connection` для хранения учетных данных, используемых для взаимодействия с внешними системами. + +`Connection` - это, по сути, набор параметров, таких как имя пользователя, пароль, имя хоста. + +Чтобы создать подключение к определенному типу хранилища, необходимо использовать класс, соответствующий типу хранилища. Имя класса совпадает с именем типа хранилища (`Oracle`, `MSSQL`, `SFTP` и т. д.): + +```python +from onetl.connection import SFTP + +sftp = SFTP( + host="sftp.test.com", + user="onetl", + password="onetl", +) +``` + +Все типы подключений наследуются от родительского класса `BaseConnection`. + +### Диаграмма классов + +```plantuml + + @startuml + left to right direction + skinparam classFontSize 20 + skinparam class { + BackgroundColor<> LightGreen + BackgroundColor<> Khaki + BackgroundColor<> LightBlue + StereotypeFontColor<> Transparent + StereotypeFontColor<> Transparent + StereotypeFontColor<> Transparent + } + + class BaseConnection { + } + + class DBConnection <>{ + } + DBConnection --|> BaseConnection + + class Hive <>{ + } + Hive --|> DBConnection + + class Greenplum <>{ + } + Greenplum --|> DBConnection + + class MongoDB <>{ + } + MongoDB --|> DBConnection + + class Kafka <>{ + } + Kafka --|> DBConnection + + class JDBCConnection <>{ + } + JDBCConnection --|> DBConnection + + class Clickhouse <>{ + } + Clickhouse --|> JDBCConnection + + class MSSQL <>{ + } + MSSQL --|> JDBCConnection + + class MySQL <>{ + } + MySQL --|> JDBCConnection + + class Postgres <>{ + } + Postgres --|> JDBCConnection + + class Oracle <>{ + } + Oracle --|> JDBCConnection + + class Teradata <>{ + } + Teradata --|> JDBCConnection + + class FileConnection <>{ + } + FileConnection --|> BaseConnection + + class FTP <>{ + } + FTP --|> FileConnection + + class FTPS <>{ + } + FTPS --|> FileConnection + + class HDFS <>{ + } + HDFS --|> FileConnection + + class WebDAV <>{ + } + WebDAV --|> FileConnection + + class Samba <>{ + } + Samba --|> FileConnection + + class SFTP <>{ + } + SFTP --|> FileConnection + + class S3 <>{ + } + S3 --|> FileConnection + + class FileDFConnection <>{ + } + FileDFConnection --|> BaseConnection + + class SparkHDFS <>{ + } + SparkHDFS --|> FileDFConnection + + class SparkLocalFS <>{ + } + SparkLocalFS --|> FileDFConnection + + class SparkS3 <>{ + } + SparkS3 --|> FileDFConnection + + @enduml +``` + +```mermaid +classDiagram + BaseConnection <|-- DBConnection + DBConnection <|-- Hive + DBConnection <|-- Greenplum + DBConnection <|-- MongoDB + DBConnection <|-- Kafka + DBConnection <|-- JDBCConnection + JDBCConnection <|-- Clickhouse + JDBCConnection <|-- MSSQL + JDBCConnection <|-- MySQL + JDBCConnection <|-- Postgres + JDBCConnection <|-- Oracle + JDBCConnection <|-- Teradata + BaseConnection <|-- FileConnection + FileConnection <|-- FTP + FileConnection <|-- FTPS + FileConnection <|-- HDFS + FileConnection <|-- WebDAV + FileConnection <|-- Samba + FileConnection <|-- SFTP + FileConnection <|-- S3 + BaseConnection <|-- FileDFConnection + FileDFConnection <|-- SparkHDFS + FileDFConnection <|-- SparkLocalFS + FileDFConnection <|-- SparkS3 +``` + + +### Подключения к БД (DBConnection) + +Классы, унаследованные от `DBConnection`, можно использовать для доступа к базам данных. + +`DBConnection` можно создать следующим образом: + +```python +from onetl.connection import MSSQL + +mssql = MSSQL( + host="mssqldb.demo.com", + user="onetl", + password="onetl", + database="Telecom", + spark=spark, +) +``` + +где **spark** - это текущая сессия Apache Spark (SparkSession). `onETL` "под капотом" использует `Spark` и специальные Java-коннекторы для работы с базами данных. + +Описание других параметров см. в документации для {ref}`доступных DBConnections `. + +### Подключения к файловым хранилищам (FileConnection) + +Классы, унаследованные от `FileConnection`, можно использовать для доступа к файлам, хранящимся в различных файловых системах/файловых серверах. + +`FileConnection` можно создать следующим образом: + +```python +from onetl.connection import SFTP + +sftp = SFTP( + host="sftp.test.com", + user="onetl", + password="onetl", +) +``` + +Описание других параметров см. в документации для {ref}`доступных FileConnections `. + +### FileDFConnection + +Классы, унаследованные от `FileDFConnection`, можно использовать для доступа к файлам в виде Spark DataFrames. + +`FileDFConnection` можно создать следующим образом: + +```python +from onetl.connection import SparkHDFS + +spark_hdfs = SparkHDFS( + host="namenode1.domain.com", + cluster="mycluster", + spark=spark, +) +``` + +где **spark** - это текущая SparkSession. +`onETL` использует `Spark` и специальные Java-коннекторы под капотом для работы с DataFrames. + +Описание других параметров см. в документации для [доступных FileDFConnections](../connection/file_df_connection/). + +### Проверка доступности соединения + +После создания соединения вы можете проверить доступность базы данных/файловой системы с помощью метода `check()`: + +```python +mssql.check() +sftp.check() +spark_hdfs.check() +``` + +Он вызовет исключение, если база данных/файловая система недоступна. + +Этот метод возвращает само соединение, поэтому вы можете создать соединение и сразу же проверить его доступность: + +```Python +mssql = MSSQL( + host="mssqldb.demo.com", + user="onetl", + password="onetl", + database="Telecom", + spark=spark, +).check() # <-- +``` + +## Извлечение/Загрузка данных + +### Основы + +Как мы говорили выше, onETL используется для извлечения данных из удаленных систем и загрузки данных в них. + +onETL предоставляет несколько классов для этого: + +> * [DBReader](../db_/reader) +> * [DBWriter](../db_/writer) +> * [FileDFReader](../file_df/file_df_reader/file_df_reader) +> * [FileDFWriter](../file_df/file_df_writer/file_df_writer) +> * [FileDownloader](../file/file_downloader/file_downloader) +> * [FileUploader](../file/file_uploader/file_uploader) +> * [FileMover](../file/file_mover/file_mover) + +Все эти классы имеют метод `run()`, который запускает извлечение/загрузку данных: + +```python +from onetl.db import DBReader, DBWriter + +reader = DBReader( + connection=mssql, + source="dbo.demo_table", + columns=["column_1", "column_2"], +) + +# Read data as Spark DataFrame +df = reader.run() + +db_writer = DBWriter( + connection=hive, + target="dl_sb.demo_table", +) + +# Save Spark DataFrame to Hive table +writer.run(df) +``` + +### Извлечение данных + +Для извлечения данных используйте классы: + +| | Вариант использования | Connection | `run()` получает | `run()` возвращает | +| -- | - | - | - | --- | +| [`DBReader`](../db_/reader) | Чтение данных из базы данных | Любое [`DBConnection`](../connection/db_connection) | - | [Spark DataFrame](https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/dataframe.html#dataframe) | +| [`FileDFReader`](../file_df/file_df_reader/file_df_reader) | Чтение данных из файла или набора файлов | Любое [`FileDFConnection`](../connection/file_df_connection) | Нет входных данных или List[File path on FileSystem] | [Spark DataFrame](https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/dataframe.html#dataframe) | +| [`FileDownloader`](../file/file_downloader/file_downloader) | Загрузка файлов из удаленной ФС в локальную ФС | Любое [`FileConnection`](../connection/file_connection) | Нет входных данных или List[File path on remote FileSystem] | [`DownloadResult`](../file/file_downloader/result) | + +### Загрузка данных + +Для загрузки данных используйте классы: + +| | Вариант использования | Connection | `run()` получает | `run()` возвращает | +| - | -- | - | --- | -- | +| [`DBWriter`](../db_/writer) | Запись данных из DataFrame в базу данных | Любое [`DBConnection`](../connection/db_connection) | [Spark DataFrame](https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/dataframe.html#dataframe) | None | +| [`FileDFWriter`](../file_df/file_df_writer/file_df_writer) | Запись данных из DataFrame в папку | Любое [`FileDFConnection`](../connection/file_df_connection) | [Spark DataFrame](https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/dataframe.html#dataframe) | None | +| [`FileUploader`](../file/file_uploader/file_uploader) | Загрузка файлов из локальной ФС в удаленную ФС | Любое [`FileConnection`](../connection/file_connection) | List[File path on local FileSystem] | [`UploadResult`](../file/file_uploader/result) | + +### Манипулирование данными + +Для манипулирования данными используйте классы: + +| | Вариант использования | Connection | `run()` получает | `run()` возвращает | +| - | - | -- | -- | - | +| [`FileMover`](../file/file_mover/file_mover) | Перемещение файлов между каталогами в удаленной ФС | Любое [`FileConnection`](../connection/file_connection) | List[File path on remote FileSystem] | [`MoveResult`](../file/file_mover/result) | + +### Опции + +Классы извлечения и загрузки имеют параметр `options`, который имеет особое значение: + +> - все остальные параметры - *ЧТО* мы извлекаем / *КУДА* мы загружаем +> - параметр `options` - *КАК* мы извлекаем/загружаем данные + +```python +db_reader = DBReader( + # ЧТО мы читаем: + connection=mssql, + source="dbo.demo_table", # некоторая таблица из MSSQL + columns=["column_1", "column_2"], # но только определенный набор столбцов + where="column_2 > 1000", # только строки, соответствующие условию + # КАК мы читаем: + options=MSSQL.ReadOptions( + numPartitions=10, # чтение в 10 параллельных задачах + partitionColumn="id", # балансировка чтения данных путем назначения каждой задаче части данных с использованием выражения `hash(id) mod N` + partitioningMode="hash", + fetchsize=1000, # каждая задача будет получать блок из 1000 строк при каждой попытке чтения + ), +) + +db_writer = DBWriter( + # КУДА мы пишем - в некоторую таблицу в Hive + connection=hive, + target="dl_sb.demo_table", + # КАК мы пишем - перезаписываем все данные в существующей таблице + options=Hive.WriteOptions(if_exists="replace_entire_table"), +) + +file_downloader = FileDownloader( + # ЧТО мы загружаем - файлы из некоторого каталога в SFTP + connection=sftp, + source_path="/source", + filters=[Glob("*.csv")], # только CSV файлы + limits=[MaxFilesCount(1000)], # максимум 1000 файлов + # КУДА мы загружаем - в определенный каталог в локальной ФС + local_path="/some", + # КАК мы загружаем: + options=FileDownloader.Options( + delete_source=True, # после загрузки каждого файла удалите его из source_path + if_exists="replace_file", # заменить существующие файлы в local_path + ), +) + +file_uploader = FileUploader( + # ЧТО мы загружаем - файлы из некоторого локального каталога + local_path="/source", + # КУДА мы загружаем - определенный удаленный каталог в HDFS + connection=hdfs, + target_path="/some", + # КАК мы загружаем: + options=FileUploader.Options( + delete_local=True, # после загрузки каждого файла удалите его из local_path + if_exists="replace_file", # заменить существующие файлы в target_path + ), +) + +file_mover = FileMover( + # ЧТО мы перемещаем - файлы в некотором удаленном каталоге в HDFS + source_path="/source", + connection=hdfs, + # КУДА мы перемещаем файлы + target_path="/some", # определенный удаленный каталог в том же соединении HDFS + # КАК мы загружаем - заменить существующие файлы в target_path + options=FileMover.Options(if_exists="replace_file"), +) + +file_df_reader = FileDFReader( + # ЧТО мы читаем - *.csv файлы из некоторого каталога в S3 + connection=s3, + source_path="/source", + file_format=CSV(), + # КАК мы читаем - загружать файлы из /source/*.csv, а не из /source/nested/*.csv + options=FileDFReader.Options(recursive=False), +) + +file_df_writer = FileDFWriter( + # КУДА мы пишем - в виде .csv файлов в некотором каталоге в S3 + connection=s3, + target_path="/target", + file_format=CSV(), + # КАК мы пишем - заменить все существующие файлы в /target, если они есть + options=FileDFWriter.Options(if_exists="replace_entire_directory"), +) +``` + +Более подробную информацию об `options` можно найти в документации к основным классам: [`DBConnection`](../connection/db_connection) и [`FileDownloader`](../file/file_downloader/file_downloader) / [`FileUploader`](../file/file_uploader/file_uploader) / [`FileMover`](../file/file_mover/file_mover) / [`FileDFReader`](../file_df/file_df_reader/file_df_reader) / [`FileDFWriter`](../file_df/file_df_writer/file_df_writer) + +### Стратегии чтения + +onETL имеет несколько встроенных стратегий для чтения данных: + +1. [Стратегия моментального снимка](../strategy/snapshot_strategy) (стратегия по умолчанию) +2. [Инкрементная стратегия](../strategy/incremental_strategy) +3. [Пакетная стратегия моментального снимка](../strategy/snapshot_batch_strategy) +4. [Инкрементная пакетная стратегия](../strategy/incremental_batch_strategy) + +Например, инкрементная стратегия позволяет получать только новые данные из таблицы: + +```python +from onetl.strategy import IncrementalStrategy + +reader = DBReader( + connection=mssql, + source="dbo.demo_table", + hwm_column="id", # обнаруживать новые данные на основе значения столбца "id" +) + +# первый запуск +with IncrementalStrategy(): + df = reader.run() + +sleep(3600) + +# второй запуск +with IncrementalStrategy(): + # только строки, которые появились в источнике с момента предыдущего запуска + df = reader.run() +``` + +или получать только файлы, которые не были загружены ранее: + +```python +from onetl.strategy import IncrementalStrategy + +file_downloader = FileDownloader( + connection=sftp, + source_path="/remote", + local_path="/local", + hwm_type="file_list", # сохранить все загруженные файлы в список и исключить файлы, уже присутствующие в этом списке +) + +# первый запуск +with IncrementalStrategy(): + files = file_downloader.run() + +sleep(3600) + +# второй запуск +with IncrementalStrategy(): + # только файлы, которые появились в источнике с момента предыдущего запуска + files = file_downloader.run() +``` + +Большинство стратегий основаны на [`HWM`](../hwm_store/), пожалуйста, ознакомьтесь с документацией по каждой стратегии для получения более подробной информации. + +### Почему просто не использовать класс Connection для извлечения/загрузки? + +Соединения очень просты, у них есть только набор некоторых основных операций, например, `mkdir`, `remove_file`, `get_table_schema` и так далее. + +Высокоуровневые операции, такие как + + - Поддержка [`strategy`](../strategy/) + - Обработка отправки/получения метаданных + - Обработка различных опций, таких как `if_exists="replace_file"` в случае загрузки/выгрузки файлов + +перенесена в отдельный класс, который вызывает методы объекта соединения для выполнения некоторой сложной логики. diff --git a/mddocs/docs/ru/connection/db_connection/clickhouse/connection.md b/mddocs/docs/ru/connection/db_connection/clickhouse/connection.md new file mode 100644 index 000000000..06fa5a462 --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/clickhouse/connection.md @@ -0,0 +1,12 @@ +(clickhouse-connection)= + +# Clickhouse connection + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.clickhouse.connection +``` + +```{eval-rst} +.. autoclass:: Clickhouse + :members: get_packages, check +``` diff --git a/mddocs/docs/ru/connection/db_connection/clickhouse/execute.md b/mddocs/docs/ru/connection/db_connection/clickhouse/execute.md new file mode 100644 index 000000000..501fed530 --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/clickhouse/execute.md @@ -0,0 +1,125 @@ +(clickhouse-execute)= + +# Executing statements in Clickhouse + +```{eval-rst} +.. warning:: + + Methods below **read all the rows** returned from DB **to Spark driver memory**, and then convert them to DataFrame. + + Do **NOT** use them to read large amounts of data. Use :ref:`DBReader ` or :ref:`Clickhouse.sql ` instead. +``` + +## How to + +There are 2 ways to execute some statement in Clickhouse + +### Use `Clickhouse.fetch` + +Use this method to perform some `SELECT` query which returns **small number or rows**, like reading +Clickhouse config, or reading data from some reference table. Method returns Spark DataFrame. + +Method accepts {obj}`Clickhouse.FetchOptions `. + +Connection opened using this method should be then closed with `connection.close()` or `with connection:`. + +```{eval-rst} +.. warning:: + + Please take into account :ref:`clickhouse-types`. +``` + +#### Syntax support + +This method supports **any** query syntax supported by Clickhouse, like: + +- ✅︎ `SELECT ... FROM ...` +- ✅︎ `WITH alias AS (...) SELECT ...` +- ✅︎ `SELECT func(arg1, arg2)` - call function +- ✅︎ `SHOW ...` +- ❌ `SET ...; SELECT ...;` - multiple statements not supported + +#### Examples + +```python +from onetl.connection import Clickhouse + +clickhouse = Clickhouse(...) + +df = clickhouse.fetch( + "SELECT value FROM some.reference_table WHERE key = 'some_constant'", + options=Clickhouse.FetchOptions(queryTimeout=10), +) +clickhouse.close() +value = df.collect()[0][0] # get value from first row and first column +``` + +### Use `Clickhouse.execute` + +Use this method to execute DDL and DML operations. Each method call runs operation in a separated transaction, and then commits it. + +Method accepts {obj}`Clickhouse.ExecuteOptions `. + +Connection opened using this method should be then closed with `connection.close()` or `with connection:`. + +#### Syntax support + +This method supports **any** query syntax supported by Clickhouse, like: + +- ✅︎ `CREATE TABLE ...`, `CREATE VIEW ...`, and so on +- ✅︎ `ALTER ...` +- ✅︎ `INSERT INTO ... SELECT ...`, `UPDATE ...`, `DELETE ...`, and so on +- ✅︎ `DROP TABLE ...`, `DROP VIEW ...`, `TRUNCATE TABLE`, and so on +- ✅︎ other statements not mentioned here +- ❌ `SET ...; SELECT ...;` - multiple statements not supported + +#### Examples + +```python +from onetl.connection import Clickhouse + +clickhouse = Clickhouse(...) + +clickhouse.execute("DROP TABLE schema.table") +clickhouse.execute( + """ + CREATE TABLE schema.table ( + id UInt8, + key String, + value Float32 + ) + ENGINE = MergeTree() + ORDER BY id + """, + options=Clickhouse.ExecuteOptions(queryTimeout=10), +) +``` + +## Notes + +These methods **read all the rows** returned from DB **to Spark driver memory**, and then convert them to DataFrame. + +So it should **NOT** be used to read large amounts of data. Use {ref}`DBReader ` or {ref}`Clickhouse.sql ` instead. + +## Options + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.clickhouse.options +``` + +```{eval-rst} +.. autopydantic_model:: ClickhouseFetchOptions + :inherited-members: GenericOptions + :member-order: bysource + :model-show-field-summary: false + :field-show-constraints: false + +``` + +```{eval-rst} +.. autopydantic_model:: ClickhouseExecuteOptions + :inherited-members: GenericOptions + :member-order: bysource + :model-show-field-summary: false + :field-show-constraints: false +``` diff --git a/mddocs/docs/ru/connection/db_connection/clickhouse/index.md b/mddocs/docs/ru/connection/db_connection/clickhouse/index.md new file mode 100644 index 000000000..28c20dec1 --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/clickhouse/index.md @@ -0,0 +1,28 @@ +(clickhouse)= + +# Clickhouse + +```{toctree} +:caption: Connection +:maxdepth: 1 + +prerequisites +connection +``` + +```{toctree} +:caption: Operations +:maxdepth: 1 + +read +sql +write +execute +``` + +```{toctree} +:caption: Troubleshooting +:maxdepth: 1 + +types +``` diff --git a/mddocs/docs/ru/connection/db_connection/clickhouse/prerequisites.md b/mddocs/docs/ru/connection/db_connection/clickhouse/prerequisites.md new file mode 100644 index 000000000..563dbad3f --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/clickhouse/prerequisites.md @@ -0,0 +1,73 @@ +(clickhouse-prerequisites)= + +# Prerequisites + +## Version Compatibility + +- Clickhouse server versions: + : - Officially declared: 22.8 or higher + - Actually tested: 21.1, 25.1 +- Spark versions: 2.3.x - 3.5.x +- Java versions: 8 - 20 + +See [official documentation](https://clickhouse.com/docs/en/integrations/java#jdbc-driver). + +## Installing PySpark + +To use Clickhouse connector you should have PySpark installed (or injected to `sys.path`) +BEFORE creating the connector instance. + +See {ref}`install-spark` installation instruction for more details. + +## Connecting to Clickhouse + +### Connection port + +Connector can only use **HTTP** (usually `8123` port) or **HTTPS** (usually `8443` port) protocol. + +TCP and GRPC protocols are NOT supported. + +### Connecting to cluster + +It is possible to connect to Clickhouse cluster, and use it's load balancing capabilities to read or write data in parallel. +Each Spark executor can connect to random Clickhouse nodes, instead of sending all the data to a node specified in connection params. + +This requires all Clickhouse servers to run on different hosts, and **listen the same HTTP port**. +Set `auto_discovery=True` to enable this feature (disabled by default): + +```python +Clickhouse( + host="node1.of.cluster", + port=8123, + extra={ + "auto_discovery": True, + "load_balancing_policy": "roundRobin", + }, +) +``` + +See [official documentation](https://clickhouse.com/docs/en/integrations/java#configuring-node-discovery-load-balancing-and-failover). + +### Required grants + +Ask your Clickhouse cluster administrator to set following grants for a user, +used for creating a connection: + +```{eval-rst} +.. tabs:: + + .. code-tab:: sql Read + Write + + -- allow creating tables in the target schema + GRANT CREATE TABLE ON myschema.* TO username; + + -- allow read & write access to specific table + GRANT SELECT, INSERT ON myschema.mytable TO username; + + .. code-tab:: sql Read only + + -- allow read access to specific table + GRANT SELECT ON myschema.mytable TO username; +``` + +More details can be found in [official documentation](https://clickhouse.com/docs/en/sql-reference/statements/grant). diff --git a/mddocs/docs/ru/connection/db_connection/clickhouse/read.md b/mddocs/docs/ru/connection/db_connection/clickhouse/read.md new file mode 100644 index 000000000..2c62132ed --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/clickhouse/read.md @@ -0,0 +1,93 @@ +(clickhouse-read)= + +# Reading from Clickhouse using `DBReader` + +{obj}`DBReader ` supports {ref}`strategy` for incremental data reading, +but does not support custom queries, like `JOIN`. + +```{eval-rst} +.. warning:: + + Please take into account :ref:`clickhouse-types` +``` + +## Supported DBReader features + +- ✅︎ `columns` +- ✅︎ `where` +- ✅︎ `hwm`, supported strategies: +- - ✅︎ {ref}`snapshot-strategy` +- - ✅︎ {ref}`incremental-strategy` +- - ✅︎ {ref}`snapshot-batch-strategy` +- - ✅︎ {ref}`incremental-batch-strategy` +- ❌ `hint` (is not supported by Clickhouse) +- ❌ `df_schema` +- ✅︎ `options` (see {obj}`Clickhouse.ReadOptions `) + +## Examples + +Snapshot strategy: + +```python +from onetl.connection import Clickhouse +from onetl.db import DBReader + +clickhouse = Clickhouse(...) + +reader = DBReader( + connection=clickhouse, + source="schema.table", + columns=["id", "key", "CAST(value AS String) value", "updated_dt"], + where="key = 'something'", + options=Clickhouse.ReadOptions(partitionColumn="id", numPartitions=10), +) +df = reader.run() +``` + +Incremental strategy: + +```python +from onetl.connection import Clickhouse +from onetl.db import DBReader +from onetl.strategy import IncrementalStrategy + +clickhouse = Clickhouse(...) + +reader = DBReader( + connection=clickhouse, + source="schema.table", + columns=["id", "key", "CAST(value AS String) value", "updated_dt"], + where="key = 'something'", + hwm=DBReader.AutoDetectHWM(name="clickhouse_hwm", expression="updated_dt"), + options=Clickhouse.ReadOptions(partitionColumn="id", numPartitions=10), +) + +with IncrementalStrategy(): + df = reader.run() +``` + +## Recommendations + +### Select only required columns + +Instead of passing `"*"` in `DBReader(columns=[...])` prefer passing exact column names. This reduces the amount of data passed from Clickhouse to Spark. + +### Pay attention to `where` value + +Instead of filtering data on Spark side using `df.filter(df.column == 'value')` pass proper `DBReader(where="column = 'value'")` clause. +This both reduces the amount of data send from Clickhouse to Spark, and may also improve performance of the query. +Especially if there are indexes or partitions for columns used in `where` clause. + +## Options + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.clickhouse.options +``` + +```{eval-rst} +.. autopydantic_model:: ClickhouseReadOptions + :inherited-members: GenericOptions + :member-order: bysource + :model-show-field-summary: false + :field-show-constraints: false +``` diff --git a/mddocs/docs/ru/connection/db_connection/clickhouse/sql.md b/mddocs/docs/ru/connection/db_connection/clickhouse/sql.md new file mode 100644 index 000000000..bbe62adaa --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/clickhouse/sql.md @@ -0,0 +1,80 @@ +(clickhouse-sql)= + +# Reading from Clickhouse using `Clickhouse.sql` + +`Clickhouse.sql` allows passing custom SQL query, but does not support incremental strategies. + +```{eval-rst} +.. warning:: + + Please take into account :ref:`clickhouse-types` +``` + +```{eval-rst} +.. warning:: + + Statement is executed in **read-write** connection, so if you're calling some functions/procedures with DDL/DML statements inside, + they can change data in your database. +``` + +## Syntax support + +Only queries with the following syntax are supported: + +- ✅︎ `SELECT ... FROM ...` +- ✅︎ `WITH alias AS (...) SELECT ...` +- ❌ `SET ...; SELECT ...;` - multiple statements not supported + +## Examples + +```python +from onetl.connection import Clickhouse + +clickhouse = Clickhouse(...) +df = clickhouse.sql( + """ + SELECT + id, + key, + CAST(value AS String) value, + updated_at + FROM + some.mytable + WHERE + key = 'something' + """, + options=Clickhouse.SQLOptions( + partitionColumn="id", + numPartitions=10, + lowerBound=0, + upperBound=1000, + ), +) +``` + +## Recommendations + +### Select only required columns + +Instead of passing `SELECT * FROM ...` prefer passing exact column names `SELECT col1, col2, ...`. +This reduces the amount of data passed from Clickhouse to Spark. + +### Pay attention to `where` value + +Instead of filtering data on Spark side using `df.filter(df.column == 'value')` pass proper `WHERE column = 'value'` clause. +This both reduces the amount of data send from Clickhouse to Spark, and may also improve performance of the query. +Especially if there are indexes or partitions for columns used in `where` clause. + +## Options + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.clickhouse.options +``` + +```{eval-rst} +.. autopydantic_model:: ClickhouseSQLOptions + :inherited-members: GenericOptions + :member-order: bysource + :model-show-field-summary: false + :field-show-constraints: false +``` diff --git a/mddocs/docs/ru/connection/db_connection/clickhouse/types.md b/mddocs/docs/ru/connection/db_connection/clickhouse/types.md new file mode 100644 index 000000000..f288bfb21 --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/clickhouse/types.md @@ -0,0 +1,457 @@ +(clickhouse-types)= + +# Clickhouse \<-> Spark type mapping + +```{eval-rst} +.. note:: + + The results below are valid for Spark 3.5.5, and may differ on other Spark versions. +``` + +```{eval-rst} +.. note:: + + It is recommended to use `spark-dialect-extension `_ package, + which implements writing Arrays from Spark to Clickhouse, fixes dropping fractions of seconds in ``TimestampType``, + and fixes other type conversion issues. +``` + +## Type detection & casting + +Spark's DataFrames always have a `schema` which is a list of columns with corresponding Spark types. All operations on a column are performed using column type. + +### Reading from Clickhouse + +This is how Clickhouse connector performs this: + +- For each column in query result (`SELECT column1, column2, ... FROM table ...`) get column name and Clickhouse type. +- Find corresponding `Clickhouse type (read)` → `Spark type` combination (see below) for each DataFrame column. If no combination is found, raise exception. +- Create DataFrame from query with specific column names and Spark types. + +### Writing to some existing Clickhouse table + +This is how Clickhouse connector performs this: + +- Get names of columns in DataFrame. [^footnote-1] +- Perform `SELECT * FROM table LIMIT 0` query. +- Take only columns present in DataFrame (by name, case insensitive). For each found column get Clickhouse type. +- **Find corresponding** `Clickhouse type (read)` → `Spark type` **combination** (see below) for each DataFrame column. If no combination is found, raise exception. [^footnote-2] +- Find corresponding `Spark type` → `Clickhousetype (write)` combination (see below) for each DataFrame column. If no combination is found, raise exception. +- If `Clickhousetype (write)` match `Clickhouse type (read)`, no additional casts will be performed, DataFrame column will be written to Clickhouse as is. +- If `Clickhousetype (write)` does not match `Clickhouse type (read)`, DataFrame column will be casted to target column type **on Clickhouse side**. For example, you can write column with text data to `Int32` column, if column contains valid integer values within supported value range and precision. + +[^footnote-1]: This allows to write data to tables with `DEFAULT` columns - if DataFrame has no such column, + it will be populated by Clickhouse. + +[^footnote-2]: Yes, this is weird. + +### Create new table using Spark + +```{eval-rst} +.. warning:: + + ABSOLUTELY NOT RECOMMENDED! +``` + +This is how Clickhouse connector performs this: + +- Find corresponding `Spark type` → `Clickhouse type (create)` combination (see below) for each DataFrame column. If no combination is found, raise exception. +- Generate DDL for creating table in Clickhouse, like `CREATE TABLE (col1 ...)`, and run it. +- Write DataFrame to created table as is. + +But Spark does not have specific dialect for Clickhouse, so Generic JDBC dialect is used. +Generic dialect is using SQL ANSI type names while creating tables in target database, not database-specific types. + +If some cases this may lead to using wrong column type. For example, Spark creates column of type `TIMESTAMP` +which corresponds to Clickhouse type `DateTime32` (precision up to seconds) +instead of more precise `DateTime64` (precision up to nanoseconds). +This may lead to incidental precision loss, or sometimes data cannot be written to created table at all. + +So instead of relying on Spark to create tables: + +```{eval-rst} +.. dropdown:: See example + + .. code:: python + + writer = DBWriter( + connection=clickhouse, + target="default.target_tbl", + options=Clickhouse.WriteOptions( + if_exists="append", + # ENGINE is required by Clickhouse + createTableOptions="ENGINE = MergeTree() ORDER BY id", + ), + ) + writer.run(df) +``` + +Always prefer creating tables with specific types **BEFORE WRITING DATA**: + +```{eval-rst} +.. dropdown:: See example + + .. code:: python + + clickhouse.execute( + """ + CREATE TABLE default.target_tbl ( + id UInt8, + value DateTime64(6) -- specific type and precision + ) + ENGINE = MergeTree() + ORDER BY id + """, + ) + + writer = DBWriter( + connection=clickhouse, + target="default.target_tbl", + options=Clickhouse.WriteOptions(if_exists="append"), + ) + writer.run(df) +``` + +### References + +Here you can find source code with type conversions: + +- [Clickhouse -> JDBC](https://github.com/ClickHouse/clickhouse-java/blob/0.3.2/clickhouse-jdbc/src/main/java/com/clickhouse/jdbc/JdbcTypeMapping.java#L39-L176) +- [JDBC -> Spark](https://github.com/apache/spark/blob/v3.5.5/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JdbcUtils.scala#L307) +- [Spark -> JDBC](https://github.com/apache/spark/blob/v3.5.5/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JdbcUtils.scala#L141-L164) +- [JDBC -> Clickhouse](https://github.com/ClickHouse/clickhouse-java/blob/0.3.2/clickhouse-jdbc/src/main/java/com/clickhouse/jdbc/JdbcTypeMapping.java#L185-L311) + +## Supported types + +See [official documentation](https://clickhouse.com/docs/en/sql-reference/data-types) + +### Generic types + +- `LowCardinality(T)` is same as `T` +- `Nullable(T)` is same as `T`, but Spark column is inferred as `nullable=True` + +### Numeric types + +```{eval-rst} ++--------------------------------+-----------------------------------+-------------------------------+-------------------------------+ +| Clickhouse type (read) | Spark type | Clickhouse type (write) | Clickhouse type (create) | ++================================+===================================+===============================+===============================+ +| ``Bool`` | ``BooleanType()`` | ``Bool`` | ``UInt64`` | ++--------------------------------+-----------------------------------+-------------------------------+-------------------------------+ +| ``Decimal`` | ``DecimalType(P=10, S=0)`` | ``Decimal(P=10, S=0)`` | ``Decimal(P=10, S=0)`` | ++--------------------------------+-----------------------------------+-------------------------------+-------------------------------+ +| ``Decimal(P=0..38)`` | ``DecimalType(P=0..38, S=0)`` | ``Decimal(P=0..38, S=0)`` | ``Decimal(P=0..38, S=0)`` | ++--------------------------------+-----------------------------------+-------------------------------+-------------------------------+ +| ``Decimal(P=0..38, S=0..38)`` | ``DecimalType(P=0..38, S=0..38)`` | ``Decimal(P=0..38, S=0..38)`` | ``Decimal(P=0..38, S=0..38)`` | ++--------------------------------+-----------------------------------+-------------------------------+-------------------------------+ +| ``Decimal(P=39..76, S=0..76)`` | unsupported [3]_ | | | ++--------------------------------+-----------------------------------+-------------------------------+-------------------------------+ +| ``Decimal32(P=0..9)`` | ``DecimalType(P=9, S=0..9)`` | ``Decimal(P=9, S=0..9)`` | ``Decimal(P=9, S=0..9)`` | ++--------------------------------+-----------------------------------+-------------------------------+-------------------------------+ +| ``Decimal64(S=0..18)`` | ``DecimalType(P=18, S=0..18)`` | ``Decimal(P=18, S=0..18)`` | ``Decimal(P=18, S=0..18)`` | ++--------------------------------+-----------------------------------+-------------------------------+-------------------------------+ +| ``Decimal128(S=0..38)`` | ``DecimalType(P=38, S=0..38)`` | ``Decimal(P=38, S=0..38)`` | ``Decimal(P=38, S=0..38)`` | ++--------------------------------+-----------------------------------+-------------------------------+-------------------------------+ +| ``Decimal256(S=0..76)`` | unsupported [3]_ | | | ++--------------------------------+-----------------------------------+-------------------------------+-------------------------------+ +| ``Float32`` | ``FloatType()`` | ``Float32`` | ``Float32`` | ++--------------------------------+-----------------------------------+-------------------------------+-------------------------------+ +| ``Float64`` | ``DoubleType()`` | ``Float64`` | ``Float64`` | ++--------------------------------+-----------------------------------+-------------------------------+-------------------------------+ +| ``Int8`` | ``IntegerType()`` | ``Int32`` | ``Int32`` | ++--------------------------------+ | | | +| ``Int16`` | | | | ++--------------------------------+ | | | +| ``Int32`` | | | | ++--------------------------------+-----------------------------------+-------------------------------+-------------------------------+ +| ``Int64`` | ``LongType()`` | ``Int64`` | ``Int64`` | ++--------------------------------+-----------------------------------+-------------------------------+-------------------------------+ +| ``Int128`` | unsupported [3]_ | | | ++--------------------------------+ | | | +| ``Int256`` | | | | ++--------------------------------+-----------------------------------+-------------------------------+-------------------------------+ +| ``-`` | ``ByteType()`` | ``Int8`` | ``Int8`` | ++--------------------------------+-----------------------------------+-------------------------------+-------------------------------+ +| ``-`` | ``ShortType()`` | ``Int32`` | ``Int32`` | ++--------------------------------+-----------------------------------+-------------------------------+-------------------------------+ +| ``UInt8`` | ``IntegerType()`` | ``Int32`` | ``Int32`` | ++--------------------------------+-----------------------------------+-------------------------------+-------------------------------+ +| ``UInt16`` | ``LongType()`` | ``Int64`` | ``Int64`` | ++--------------------------------+-----------------------------------+-------------------------------+-------------------------------+ +| ``UInt32`` | ``DecimalType(20,0)`` | ``Decimal(20,0)`` | ``Decimal(20,0)`` | ++--------------------------------+ | | | +| ``UInt64`` | | | | ++--------------------------------+-----------------------------------+-------------------------------+-------------------------------+ +| ``UInt128`` | unsupported [3]_ | | | ++--------------------------------+ | | | +| ``UInt256`` | | | | ++--------------------------------+-----------------------------------+-------------------------------+-------------------------------+ +``` + +[^footnote-3]: Clickhouse support numeric types up to 256 bit - `Int256`, `UInt256`, `Decimal256(S)`, `Decimal(P=39..76, S=0..76)`. + + But Spark's `DecimalType(P, S)` supports maximum `P=38` (128 bit). It is impossible to read, write or operate with values of larger precision, + this leads to an exception. + +### Temporal types + +Notes: +: - Datetime with timezone has the same precision as without timezone + - `DateTime` is alias for `DateTime32` + - `TIMESTAMP` is alias for `DateTime32`, but `TIMESTAMP(N)` is alias for `DateTime64(N)` + +```{eval-rst} ++-----------------------------------+--------------------------------------+----------------------------------+-------------------------------+ +| Clickhouse type (read) | Spark type | Clickhouse type (write) | Clickhouse type (create) | ++===================================+======================================+==================================+===============================+ +| ``Date`` | ``DateType()`` | ``Date`` | ``Date`` | ++-----------------------------------+--------------------------------------+----------------------------------+-------------------------------+ +| ``Date32`` | ``DateType()`` | ``Date`` | ``Date``, | +| | | | **cannot insert data** [4]_ | ++-----------------------------------+--------------------------------------+----------------------------------+-------------------------------+ +| ``DateTime32``, seconds | ``TimestampType()``, microseconds | ``DateTime64(6)``, microseconds | ``DateTime32``, seconds | ++-----------------------------------+--------------------------------------+----------------------------------+-------------------------------+ +| ``DateTime64(3)``, milliseconds | ``TimestampType()``, microseconds | ``DateTime64(6)``, microseconds | ``DateTime32``, seconds, | +| | | | **precision loss** [5]_ | ++-----------------------------------+--------------------------------------+----------------------------------+-------------------------------+ +| ``DateTime64(6)``, microseconds | ``TimestampType()``, microseconds | | ``DateTime32``, seconds, | ++-----------------------------------+--------------------------------------+ | **precision loss** [7]_ | +| ``DateTime64(7..9)``, nanoseconds | ``TimestampType()``, microseconds, | | | +| | **precision loss** [6]_ | | | +| | | | | ++-----------------------------------+--------------------------------------+ | | +| ``-`` | ``TimestampNTZType()``, microseconds | | | ++-----------------------------------+--------------------------------------+----------------------------------+-------------------------------+ +| ``DateTime32(TZ)`` | unsupported [7]_ | | | ++-----------------------------------+ | | | +| ``DateTime64(P, TZ)`` | | | | ++-----------------------------------+--------------------------------------+----------------------------------+-------------------------------+ +| ``IntervalNanosecond`` | ``LongType()`` | ``Int64`` | ``Int64`` | ++-----------------------------------+ | | | +| ``IntervalMicrosecond`` | | | | ++-----------------------------------+ | | | +| ``IntervalMillisecond`` | | | | ++-----------------------------------+ | | | +| ``IntervalSecond`` | | | | ++-----------------------------------+ | | | +| ``IntervalMinute`` | | | | ++-----------------------------------+ | | | +| ``IntervalHour`` | | | | ++-----------------------------------+ | | | +| ``IntervalDay`` | | | | ++-----------------------------------+ | | | +| ``IntervalMonth`` | | | | ++-----------------------------------+ | | | +| ``IntervalQuarter`` | | | | ++-----------------------------------+ | | | +| ``IntervalWeek`` | | | | ++-----------------------------------+ | | | +| ``IntervalYear`` | | | | ++-----------------------------------+--------------------------------------+----------------------------------+-------------------------------+ +``` + +```{eval-rst} +.. warning:: + + Note that types in Clickhouse and Spark have different value ranges: + + +------------------------+-----------------------------------+-----------------------------------+---------------------+--------------------------------+--------------------------------+ + | Clickhouse type | Min value | Max value | Spark type | Min value | Max value | + +========================+===================================+===================================+=====================+================================+================================+ + | ``Date`` | ``1970-01-01`` | ``2149-06-06`` | ``DateType()`` | ``0001-01-01`` | ``9999-12-31`` | + +------------------------+-----------------------------------+-----------------------------------+---------------------+--------------------------------+--------------------------------+ + | ``DateTime32`` | ``1970-01-01 00:00:00`` | ``2106-02-07 06:28:15`` | ``TimestampType()`` | ``0001-01-01 00:00:00.000000`` | ``9999-12-31 23:59:59.999999`` | + +------------------------+-----------------------------------+-----------------------------------+ | | | + | ``DateTime64(P=0..8)`` | ``1900-01-01 00:00:00.00000000`` | ``2299-12-31 23:59:59.99999999`` | | | | + +------------------------+-----------------------------------+-----------------------------------+ | | | + | ``DateTime64(P=9)`` | ``1900-01-01 00:00:00.000000000`` | ``2262-04-11 23:47:16.999999999`` | | | | + +------------------------+-----------------------------------+-----------------------------------+---------------------+--------------------------------+--------------------------------+ + + So not all of values in Spark DataFrame can be written to Clickhouse. + + References: + * `Clickhouse Date documentation `_ + * `Clickhouse Datetime32 documentation `_ + * `Clickhouse Datetime64 documentation `_ + * `Spark DateType documentation `_ + * `Spark TimestampType documentation `_ +``` + +[^footnote-4]: `Date32` has different bytes representation than `Date`, and inserting value of type `Date32` to `Date` column + leads to errors on Clickhouse side, e.g. `Date(106617) should be between 0 and 65535 inclusive of both values`. + Although Spark does properly read the `Date32` column as `DateType()`, and there should be no difference at all. + Probably this is some bug in Clickhouse driver. + +[^footnote-5]: Generic JDBC dialect generates DDL with Clickhouse type `TIMESTAMP` which is alias for `DateTime32` with precision up to seconds (`23:59:59`). + Inserting data with milliseconds precision (`23:59:59.999`) will lead to **throwing away milliseconds**. + Solution: create table manually, with proper column type. + +[^footnote-6]: Clickhouse support datetime up to nanoseconds precision (`23:59:59.999999999`), + but Spark `TimestampType()` supports datetime up to microseconds precision (`23:59:59.999999`). + Nanoseconds will be lost during read or write operations. + Solution: create table manually, with proper column type. + +[^footnote-7]: Clickhouse will raise an exception that data in format `2001-01-01 23:59:59.999999` has data `.999999` which does not match format `YYYY-MM-DD hh:mm:ss` + of `DateTime32` column type (see [^footnote-5]). + So Spark can create Clickhouse table, but cannot write data to column of this type. + Solution: create table manually, with proper column type. + +### String types + +```{eval-rst} ++--------------------------------------+------------------+------------------------+--------------------------+ +| Clickhouse type (read) | Spark type | Clickhousetype (write) | Clickhouse type (create) | ++======================================+==================+========================+==========================+ +| ``FixedString(N)`` | ``StringType()`` | ``String`` | ``String`` | ++--------------------------------------+ | | | +| ``String`` | | | | ++--------------------------------------+ | | | +| ``Enum8`` | | | | ++--------------------------------------+ | | | +| ``Enum16`` | | | | ++--------------------------------------+ | | | +| ``IPv4`` | | | | ++--------------------------------------+ | | | +| ``IPv6`` | | | | ++--------------------------------------+ | | | +| ``UUID`` | | | | ++--------------------------------------+------------------+ | | +| ``-`` | ``BinaryType()`` | | | ++--------------------------------------+------------------+------------------------+--------------------------+ +``` + +## Unsupported types + +Columns of these Clickhouse types cannot be read by Spark: +: - `AggregateFunction(func, T)` + - `Array(T)` + - `JSON` + - `Map(K, V)` + - `MultiPolygon` + - `Nested(field1 T1, ...)` + - `Nothing` + - `Point` + - `Polygon` + - `Ring` + - `SimpleAggregateFunction(func, T)` + - `Tuple(T1, T2, ...)` + +Dataframe with these Spark types cannot be written to Clickhouse: +: - `ArrayType(T)` + - `BinaryType()` + - `CharType(N)` + - `DayTimeIntervalType(P, S)` + - `MapType(K, V)` + - `NullType()` + - `StructType([...])` + - `TimestampNTZType()` + - `VarcharType(N)` + +This is because Spark does not have dedicated Clickhouse dialect, and uses Generic JDBC dialect instead. +This dialect does not have type conversion between some types, like Clickhouse `Array` -> Spark `ArrayType()`, and vice versa. + +The is a way to avoid this - just cast everything to `String`. + +## Explicit type cast + +### `DBReader` + +Use `CAST` or `toJSONString` to get column data as string in JSON format, + +For parsing JSON columns in ClickHouse, {obj}`JSON.parse_column ` method. + +```python +from pyspark.sql.types import ArrayType, IntegerType + +from onetl.file.format import JSON +from onetl.connection import ClickHouse +from onetl.db import DBReader + +reader = DBReader( + connection=clickhouse, + target="default.source_tbl", + columns=[ + "id", + "toJSONString(array_column) array_column", + ], +) +df = reader.run() + +# Spark requires all columns to have some specific type, describe it +column_type = ArrayType(IntegerType()) + +json = JSON() +df = df.select( + df.id, + json.parse_column("array_column", column_type), +) +``` + +### `DBWriter` + +For writing JSON data to ClickHouse, use the {obj}`JSON.serialize_column ` method to convert a DataFrame column to JSON format efficiently and write it as a `String` column in Clickhouse. + +```python +from onetl.file.format import JSON +from onetl.connection import ClickHouse +from onetl.db import DBWriter + +clickhouse = ClickHouse(...) + +clickhouse.execute( + """ + CREATE TABLE default.target_tbl ( + id Int32, + array_column_json String, + ) + ENGINE = MergeTree() + ORDER BY id + """, +) + +json = JSON() +df = df.select( + df.id, + json.serialize_column(df.array_column).alias("array_column_json"), +) + +writer.run(df) +``` + +Then you can parse this column on Clickhouse side - for example, by creating a view: + +```sql +SELECT + id, + JSONExtract(json_column, 'Array(String)') AS array_column +FROM target_tbl +``` + +You can also use [ALIAS](https://clickhouse.com/docs/en/sql-reference/statements/create/table#alias) +or [MATERIALIZED](https://clickhouse.com/docs/en/sql-reference/statements/create/table#materialized) columns +to avoid writing such expression in every `SELECT` clause all the time: + +```sql +CREATE TABLE default.target_tbl ( + id Int32, + array_column_json String, + -- computed column + array_column Array(String) ALIAS JSONExtract(json_column, 'Array(String)') + -- or materialized column + -- array_column Array(String) MATERIALIZED JSONExtract(json_column, 'Array(String)') +) +ENGINE = MergeTree() +ORDER BY id +``` + +Downsides: + +- Using `SELECT JSONExtract(...)` or `ALIAS` column can be expensive, because value is calculated on every row access. This can be especially harmful if such column is used in `WHERE` clause. +- `ALIAS` and `MATERIALIZED` columns are not included in `SELECT *` clause, they should be added explicitly: `SELECT *, calculated_column FROM table`. + +```{eval-rst} +.. warning:: + + `EPHEMERAL `_ columns are not supported by Spark + because they cannot be selected to determine target column type. +``` diff --git a/mddocs/docs/ru/connection/db_connection/clickhouse/write.md b/mddocs/docs/ru/connection/db_connection/clickhouse/write.md new file mode 100644 index 000000000..fa39d81a2 --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/clickhouse/write.md @@ -0,0 +1,60 @@ +(clickhouse-write)= + +# Writing to Clickhouse using `DBWriter` + +For writing data to Clickhouse, use {obj}`DBWriter `. + +```{eval-rst} +.. warning:: + + Please take into account :ref:`clickhouse-types` +``` + +```{eval-rst} +.. warning:: + + It is always recommended to create table explicitly using :ref:`Clickhouse.execute ` + instead of relying on Spark's table DDL generation. + + This is because Spark's DDL generator can create columns with different precision and types than it is expected, + causing precision loss or other issues. +``` + +## Examples + +```python +from onetl.connection import Clickhouse +from onetl.db import DBWriter + +clickhouse = Clickhouse(...) + +df = ... # data is here + +writer = DBWriter( + connection=clickhouse, + target="schema.table", + options=Clickhouse.WriteOptions( + if_exists="append", + # ENGINE is required by Clickhouse + createTableOptions="ENGINE = MergeTree() ORDER BY id", + ), +) + +writer.run(df) +``` + +## Options + +Method above accepts {obj}`Clickhouse.WriteOptions ` + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.clickhouse.options +``` + +```{eval-rst} +.. autopydantic_model:: ClickhouseWriteOptions + :inherited-members: GenericOptions + :member-order: bysource + :model-show-field-summary: false + :field-show-constraints: false +``` diff --git a/mddocs/docs/ru/connection/db_connection/greenplum/connection.md b/mddocs/docs/ru/connection/db_connection/greenplum/connection.md new file mode 100644 index 000000000..e95a4e6a8 --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/greenplum/connection.md @@ -0,0 +1,12 @@ +(greenplum-connection)= + +# Greenplum connection + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.greenplum.connection +``` + +```{eval-rst} +.. autoclass:: Greenplum + :members: get_packages, check +``` diff --git a/mddocs/docs/ru/connection/db_connection/greenplum/execute.md b/mddocs/docs/ru/connection/db_connection/greenplum/execute.md new file mode 100644 index 000000000..0b10c0bc7 --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/greenplum/execute.md @@ -0,0 +1,159 @@ +(greenplum-execute)= + +# Executing statements in Greenplum + +```{eval-rst} +.. warning:: + + Methods below **read all the rows** returned from DB **to Spark driver memory**, and then convert them to DataFrame. + + Do **NOT** use them to read large amounts of data. Use :ref:`DBReader ` instead. +``` + +## How to + +There are 2 ways to execute some statement in Greenplum + +### Use `Greenplum.fetch` + +Use this method to perform some `SELECT` query which returns **small number or rows**, like reading +Greenplum config, or reading data from some reference table. Method returns Spark DataFrame. + +Method accepts {obj}`Greenplum.FetchOptions `. + +Connection opened using this method should be then closed with `connection.close()` or `with connection:`. + +```{eval-rst} +.. warning:: + + ``Greenplum.fetch`` is implemented using Postgres JDBC connection, + so types are handled a bit differently than in ``DBReader``. See :ref:`postgres-types`. +``` + +#### Syntax support + +This method supports **any** query syntax supported by Greenplum, like: + +- ✅︎ `SELECT ... FROM ...` +- ✅︎ `WITH alias AS (...) SELECT ...` +- ✅︎ `SELECT func(arg1, arg2)` or `{call func(arg1, arg2)}` - special syntax for calling functions +- ❌ `SET ...; SELECT ...;` - multiple statements not supported + +#### Examples + +```python +from onetl.connection import Greenplum + +greenplum = Greenplum(...) + +df = greenplum.fetch( + "SELECT value FROM some.reference_table WHERE key = 'some_constant'", + options=Greenplum.FetchOptions(queryTimeout=10), +) +greenplum.close() +value = df.collect()[0][0] # get value from first row and first column +``` + +### Use `Greenplum.execute` + +Use this method to execute DDL and DML operations. Each method call runs operation in a separated transaction, and then commits it. + +Method accepts {obj}`Greenplum.ExecuteOptions `. + +Connection opened using this method should be then closed with `connection.close()` or `with connection:`. + +#### Syntax support + +This method supports **any** query syntax supported by Greenplum, like: + +- ✅︎ `CREATE TABLE ...`, `CREATE VIEW ...`, and so on +- ✅︎ `ALTER ...` +- ✅︎ `INSERT INTO ... SELECT ...`, `UPDATE ...`, `DELETE ...`, and so on +- ✅︎ `DROP TABLE ...`, `DROP VIEW ...`, `TRUNCATE TABLE`, and so on +- ✅︎ `CALL procedure(arg1, arg2) ...` +- ✅︎ `SELECT func(arg1, arg2)` or `{call func(arg1, arg2)}` - special syntax for calling functions +- ✅︎ other statements not mentioned here +- ❌ `SET ...; SELECT ...;` - multiple statements not supported + +#### Examples + +```python +from onetl.connection import Greenplum + +greenplum = Greenplum(...) + +greenplum.execute("DROP TABLE schema.table") +greenplum.execute( + """ + CREATE TABLE schema.table ( + id int, + key text, + value real + ) + DISTRIBUTED BY id + """, + options=Greenplum.ExecuteOptions(queryTimeout=10), +) +``` + +## Interaction schema + +Unlike reading & writing, executing statements in Greenplum is done **only** through Greenplum master node, +without any interaction between Greenplum segments and Spark executors. More than that, Spark executors are not used in this case. + +The only port used while interacting with Greenplum in this case is `5432` (Greenplum master port). + +```{eval-rst} +.. dropdown:: Spark <-> Greenplum interaction during Greenplum.execute()/Greenplum.fetch() + + .. plantuml:: + + @startuml + title Greenplum master <-> Spark driver + box "Spark" + participant "Spark driver" + end box + + box "Greenplum" + participant "Greenplum master" + end box + + == Greenplum.check() == + + activate "Spark driver" + "Spark driver" -> "Greenplum master" ++ : CONNECT + + == Greenplum.execute(statement) == + "Spark driver" --> "Greenplum master" : EXECUTE statement + "Greenplum master" -> "Spark driver" : RETURN result + + == Greenplum.close() == + "Spark driver" --> "Greenplum master" : CLOSE CONNECTION + + deactivate "Greenplum master" + deactivate "Spark driver" + @enduml +``` + +## Options + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.greenplum.options +``` + +```{eval-rst} +.. autopydantic_model:: GreenplumFetchOptions + :inherited-members: GenericOptions + :member-order: bysource + :model-show-field-summary: false + :field-show-constraints: false + +``` + +```{eval-rst} +.. autopydantic_model:: GreenplumExecuteOptions + :inherited-members: GenericOptions + :member-order: bysource + :model-show-field-summary: false + :field-show-constraints: false +``` diff --git a/mddocs/docs/ru/connection/db_connection/greenplum/index.md b/mddocs/docs/ru/connection/db_connection/greenplum/index.md new file mode 100644 index 000000000..1707fc960 --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/greenplum/index.md @@ -0,0 +1,27 @@ +(greenplum)= + +# Greenplum + +```{toctree} +:caption: Connection +:maxdepth: 1 + +prerequisites +connection +``` + +```{toctree} +:caption: Operations +:maxdepth: 1 + +read +write +execute +``` + +```{toctree} +:caption: Troubleshooting +:maxdepth: 1 + +types +``` diff --git a/mddocs/docs/ru/connection/db_connection/greenplum/prerequisites.md b/mddocs/docs/ru/connection/db_connection/greenplum/prerequisites.md new file mode 100644 index 000000000..a2e55b950 --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/greenplum/prerequisites.md @@ -0,0 +1,382 @@ +(greenplum-prerequisites)= + +# Prerequisites + +## Version Compatibility + +- Greenplum server versions: + : - Officially declared: 5.x, 6.x, and 7.x (which requires `Greenplum.get_packages(package_version="2.3.0")` or higher) + - Actually tested: 6.23, 7.0 +- Spark versions: 2.3.x - 3.2.x (Spark 3.3+ is not supported yet) +- Java versions: 8 - 11 + +See [official documentation](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.2/greenplum-connector-spark/release_notes.html). + +## Installing PySpark + +To use Greenplum connector you should have PySpark installed (or injected to `sys.path`) +BEFORE creating the connector instance. + +See {ref}`install-spark` installation instruction for more details. + +## Downloading VMware package + +To use Greenplum connector you should download connector `.jar` file from +[VMware website](https://network.tanzu.vmware.com/products/vmware-greenplum#/releases/1413479/file_groups/16966) +and then pass it to Spark session. + +```{eval-rst} +.. warning:: + + Please pay attention to :ref:`Spark & Scala version compatibility `. +``` + +```{eval-rst} +.. warning:: + + There are issues with using package of version 2.3.0/2.3.1 with Greenplum 6.x - connector can + open transaction with ``SELECT * FROM table LIMIT 0`` query, but does not close it, which leads to deadlocks + during write. +``` + +There are several ways to do that. See {ref}`java-packages` for details. + +```{eval-rst} +.. note:: + + If you're uploading package to private package repo, use ``groupId=io.pivotal`` and ``artifactoryId=greenplum-spark_2.12`` + (``2.12`` is Scala version) to give uploaded package a proper name. +``` + +## Connecting to Greenplum + +### Interaction schema + +Spark executors open ports to listen incoming requests. +Greenplum segments are initiating connections to Spark executors using [EXTERNAL TABLE](https://docs.vmware.com/en/VMware-Greenplum/7/greenplum-database/ref_guide-sql_commands-CREATE_EXTERNAL_TABLE.html) +functionality, and send/read data using [gpfdist protocol](https://docs.vmware.com/en/VMware-Greenplum/7/greenplum-database/admin_guide-external-g-using-the-greenplum-parallel-file-server--gpfdist-.html#about-gpfdist-setup-and-performance-1). + +Data is **not** send through Greenplum master. +Greenplum master only receives commands to start reading/writing process, and manages all the metadata (external table location, schema and so on). + +More details can be found in [official documentation](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/overview.html). + +### Set number of connections + +```{eval-rst} +.. warning:: + + This is very important!!! + + If you don't limit number of connections, you can exceed the `max_connections `_ + limit set on the Greenplum side. It's usually not so high, e.g. 500-1000 connections max, + depending on your Greenplum instance settings and using connection balancers like ``pgbouncer``. + + Consuming all available connections means **nobody** (even admin users) can connect to Greenplum. +``` + +Each job on the Spark executor makes its own connection to Greenplum master node, +so you need to limit number of connections to avoid opening too many of them. + +- Reading about `5-10Gb` of data requires about `3-5` parallel connections. +- Reading about `20-30Gb` of data requires about `5-10` parallel connections. +- Reading about `50Gb` of data requires ~ `10-20` parallel connections. +- Reading about `100+Gb` of data requires `20-30` parallel connections. +- Opening more than `30-50` connections is not recommended. + +Number of connections can be limited by 2 ways: + +- By limiting number of Spark executors and number of cores per-executor. Max number of parallel jobs is `executors * cores`. + +```{eval-rst} +.. tabs:: + + .. code-tab:: py Spark with master=local + + spark = ( + SparkSession.builder + # Spark will run with 5 threads in local mode, allowing up to 5 parallel tasks + .config("spark.master", "local[5]") + .config("spark.executor.cores", 1) + ).getOrCreate() + + .. code-tab:: py Spark with master=yarn or master=k8s, dynamic allocation + + spark = ( + SparkSession.builder + .config("spark.master", "yarn") + # Spark will start MAX 10 executors with 1 core each (dynamically), so max number of parallel jobs is 10 + .config("spark.dynamicAllocation.maxExecutors", 10) + .config("spark.executor.cores", 1) + ).getOrCreate() + + .. code-tab:: py Spark with master=yarn or master=k8s, static allocation + + spark = ( + SparkSession.builder + .config("spark.master", "yarn") + # Spark will start EXACTLY 10 executors with 1 core each, so max number of parallel jobs is 10 + .config("spark.executor.instances", 10) + .config("spark.executor.cores", 1) + ).getOrCreate() +``` + +- By limiting connection pool size user by Spark (**only** for Spark with `master=local`): + +```python +spark = SparkSession.builder.config("spark.master", "local[*]").getOrCreate() + +# No matter how many executors are started and how many cores they have, +# number of connections cannot exceed pool size: +Greenplum( + ..., + extra={ + "pool.maxSize": 10, + }, +) +``` + +See [connection pooling](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/using_the_connector.html#jdbcconnpool) +documentation. + +- By setting {obj}`num_partitions ` + and {obj}`partition_column ` (not recommended). + +### Allowing connection to Greenplum master + +Ask your Greenplum cluster administrator to allow your user to connect to Greenplum master node, +e.g. by updating `pg_hba.conf` file. + +More details can be found in [official documentation](https://docs.vmware.com/en/VMware-Greenplum/7/greenplum-database/admin_guide-client_auth.html#limiting-concurrent-connections#allowing-connections-to-greenplum-database-0). + +### Set connection port + +#### Spark with `master=k8s` + +Please follow [the official documentation](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/configure.html#k8scfg) + +#### Spark with `master=yarn` or `master=local` + +To read data from Greenplum using Spark, following ports should be opened in firewall between Spark and Greenplum: + +- Spark driver and all Spark executors -> port `5432` on Greenplum master node. + + This port number should be set while connecting to Greenplum: + + ```python + greenplum = Greenplum(host="master.host", port=5432, ...) + ``` + +- Greenplum segments -> some port range (e.g. `41000-42000`) **listened by Spark executors**. + + This range should be set in `extra` option: + + ```python + greenplum = Greenplum( + ..., + extra={ + "server.port": "41000-42000", + }, + ) + ``` + + Number of ports in this range is `number of parallel running Spark sessions` * `number of parallel connections per session`. + + Number of connections per session (see below) is usually less than `30` (see above). + + Number of session depends on your environment: + : - For `master=local` only few ones-tens sessions can be started on the same host, depends on available RAM and CPU. + - For `master=yarn` hundreds or thousands of sessions can be started simultaneously, + but they are executing on different cluster nodes, so one port can be opened on different nodes at the same time. + +More details can be found in official documentation: +: - [port requirements](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/sys_reqs.html#network-port-requirements) + - [format of server.port value](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/options.html#server.port) + - [port troubleshooting](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/troubleshooting.html#port-errors) + +### Set connection host + +#### Spark with `master=k8s` + +Please follow [the official documentation](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/configure.html#k8scfg) + +#### Spark with `master=local` + +By default, Greenplum connector tries to resolve IP of current host, and then pass it as `gpfdist` URL to Greenplum segment. +This may fail in some cases. + +For example, IP can be resolved using `/etc/hosts` content like this: + +```text +127.0.0.1 localhost real-host-name +``` + +```bash +$ hostname -f +localhost + +$ hostname -i +127.0.0.1 +``` + +Reading/writing data to Greenplum will fail with following exception: + +```text +org.postgresql.util.PSQLException: ERROR: connection with gpfdist failed for +"gpfdist://127.0.0.1:49152/local-1709739764667/exec/driver", +effective url: "http://127.0.0.1:49152/local-1709739764667/exec/driver": +error code = 111 (Connection refused); (seg3 slice1 12.34.56.78:10003 pid=123456) +``` + +There are 2 ways to fix that: + +- Explicitly pass your host IP address to connector, like this + + ```python + import os + + # pass here real host IP (accessible from GP segments) + os.environ["HOST_IP"] = "192.168.1.1" + + greenplum = Greenplum( + ..., + extra={ + # connector will read IP from this environment variable + "server.hostEnv": "env.HOST_IP", + }, + spark=spark, + ) + ``` + + More details can be found in [official documentation](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/options.html#server.hostenv). + +- Update `/etc/hosts` file to include real host IP: + + ```text + 127.0.0.1 localhost + # this IP should be accessible from GP segments + 192.168.1.1 driver-host-name + ``` + + So Greenplum connector will properly resolve host IP. + +#### Spark with `master=yarn` + +The same issue with resolving IP address can occur on Hadoop cluster node, but it's tricky to fix, because each node has a different IP. + +There are 3 ways to fix that: + +- Pass node hostname to `gpfdist` URL. So IP will be resolved on segment side: + + ```python + greenplum = Greenplum( + ..., + extra={ + "server.useHostname": "true", + }, + ) + ``` + + But this may fail if Hadoop cluster node hostname cannot be resolved from Greenplum segment side. + + More details can be found in [official documentation](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/options.html#server.usehostname). + +- Set specific network interface to get IP address from: + + ```python + greenplum = Greenplum( + ..., + extra={ + "server.nic": "eth0", + }, + ) + ``` + + You can get list of network interfaces using this command. + + ```{eval-rst} + .. note:: + + This command should be executed on Hadoop cluster node, **not** Spark driver host! + ``` + + ```bash + $ ip address + 1: lo: mtu 65536 qdisc noqueue state UNKNOWN group default qlen 1000 + inet 127.0.0.1/8 scope host lo + valid_lft forever preferred_lft forever + 2: eth0: mtu 1500 qdisc fq_codel state UP group default qlen 1000 + inet 192.168.1.1/24 brd 192.168.1.255 scope global dynamic noprefixroute eth0 + valid_lft 83457sec preferred_lft 83457sec + ``` + + Note that in this case **each** Hadoop cluster node node should have network interface with name `eth0`. + + More details can be found in [official documentation](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/options.html#server.nic). + +- Update `/etc/hosts` on each Hadoop cluster node to include real node IP: + + ```text + 127.0.0.1 localhost + # this IP should be accessible from GP segments + 192.168.1.1 cluster-node-name + ``` + + So Greenplum connector will properly resolve node IP. + +### Set required grants + +Ask your Greenplum cluster administrator to set following grants for a user, +used for creating a connection: + +```{eval-rst} +.. tabs:: + + .. code-tab:: sql Read + Write + + -- get access to get tables metadata & cluster information + GRANT SELECT ON information_schema.tables TO username; + GRANT SELECT ON pg_attribute TO username; + GRANT SELECT ON pg_class TO username; + GRANT SELECT ON pg_namespace TO username; + GRANT SELECT ON pg_settings TO username; + GRANT SELECT ON pg_stats TO username; + GRANT SELECT ON gp_distributed_xacts TO username; + GRANT SELECT ON gp_segment_configuration TO username; + -- Greenplum 5.x only + GRANT SELECT ON gp_distribution_policy TO username; + + -- allow creating external tables in the same schema as source/target table + GRANT USAGE ON SCHEMA myschema TO username; + GRANT CREATE ON SCHEMA myschema TO username; + ALTER USER username CREATEEXTTABLE(type = 'readable', protocol = 'gpfdist') CREATEEXTTABLE(type = 'writable', protocol = 'gpfdist'); + + -- allow read access to specific table (to get column types) + -- allow write access to specific table + GRANT SELECT, INSERT ON myschema.mytable TO username; + + .. code-tab:: sql Read only + + -- get access to get tables metadata & cluster information + GRANT SELECT ON information_schema.tables TO username; + GRANT SELECT ON pg_attribute TO username; + GRANT SELECT ON pg_class TO username; + GRANT SELECT ON pg_namespace TO username; + GRANT SELECT ON pg_settings TO username; + GRANT SELECT ON pg_stats TO username; + GRANT SELECT ON gp_distributed_xacts TO username; + GRANT SELECT ON gp_segment_configuration TO username; + -- Greenplum 5.x only + GRANT SELECT ON gp_distribution_policy TO username; + + -- allow creating external tables in the same schema as source table + GRANT USAGE ON SCHEMA schema_to_read TO username; + GRANT CREATE ON SCHEMA schema_to_read TO username; + -- yes, ``writable`` for reading from GP, because data is written from Greenplum to Spark executor. + ALTER USER username CREATEEXTTABLE(type = 'writable', protocol = 'gpfdist'); + + -- allow read access to specific table + GRANT SELECT ON schema_to_read.table_to_read TO username; +``` + +More details can be found in [official documentation](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/install_cfg.html#role-privileges). diff --git a/mddocs/docs/ru/connection/db_connection/greenplum/read.md b/mddocs/docs/ru/connection/db_connection/greenplum/read.md new file mode 100644 index 000000000..502bbb915 --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/greenplum/read.md @@ -0,0 +1,386 @@ +(greenplum-read)= + +# Reading from Greenplum using `DBReader` + +Data can be read from Greenplum to Spark using {obj}`DBReader `. +It also supports {ref}`strategy` for incremental data reading. + +```{eval-rst} +.. warning:: + + Please take into account :ref:`greenplum-types`. +``` + +```{eval-rst} +.. note:: + + Unlike JDBC connectors, *Greenplum connector for Spark* does not support + executing **custom** SQL queries using ``.sql`` method. Connector can be used to only read data from a table or view. +``` + +## Supported DBReader features + +- ✅︎ `columns` (see note below) +- ✅︎ `where` (see note below) +- ✅︎ `hwm` (see note below), supported strategies: +- - ✅︎ {ref}`snapshot-strategy` +- - ✅︎ {ref}`incremental-strategy` +- - ✅︎ {ref}`snapshot-batch-strategy` +- - ✅︎ {ref}`incremental-batch-strategy` +- ❌ `hint` (is not supported by Greenplum) +- ❌ `df_schema` +- ✅︎ `options` (see {obj}`Greenplum.ReadOptions `) + +```{eval-rst} +.. warning:: + + In case of Greenplum connector, ``DBReader`` does not generate raw ``SELECT`` query. Instead it relies on Spark SQL syntax + which in some cases (using column projection and predicate pushdown) can be converted to Greenplum SQL. + + So ``columns``, ``where`` and ``hwm.expression`` should be specified in `Spark SQL `_ syntax, + not Greenplum SQL. + + This is OK: + + .. code-block:: python + + DBReader( + columns=[ + "some_column", + # this cast is executed on Spark side + "CAST(another_column AS STRING)", + ], + # this predicate is parsed by Spark, and can be pushed down to Greenplum + where="some_column LIKE 'val1%'", + ) + + This is will fail: + + .. code-block:: python + + DBReader( + columns=[ + "some_column", + # Spark does not have `text` type + "CAST(another_column AS text)", + ], + # Spark does not support ~ syntax for regexp matching + where="some_column ~ 'val1.*'", + ) +``` + +## Examples + +Snapshot strategy: + +```python +from onetl.connection import Greenplum +from onetl.db import DBReader + +greenplum = Greenplum(...) + +reader = DBReader( + connection=greenplum, + source="schema.table", + columns=["id", "key", "CAST(value AS string) value", "updated_dt"], + where="key = 'something'", +) +df = reader.run() +``` + +Incremental strategy: + +```python +from onetl.connection import Greenplum +from onetl.db import DBReader +from onetl.strategy import IncrementalStrategy + +greenplum = Greenplum(...) + +reader = DBReader( + connection=greenplum, + source="schema.table", + columns=["id", "key", "CAST(value AS string) value", "updated_dt"], + where="key = 'something'", + hwm=DBReader.AutoDetectHWM(name="greenplum_hwm", expression="updated_dt"), +) + +with IncrementalStrategy(): + df = reader.run() +``` + +## Interaction schema + +High-level schema is described in {ref}`greenplum-prerequisites`. You can find detailed interaction schema below. + +```{eval-rst} +.. dropdown:: Spark <-> Greenplum interaction during DBReader.run() + + .. plantuml:: + + @startuml + title Greenplum master <-> Spark driver + box "Spark" + participant "Spark driver" + participant "Spark executor1" + participant "Spark executor2" + participant "Spark executorN" + end box + + box "Greenplum" + participant "Greenplum master" + participant "Greenplum segment1" + participant "Greenplum segment2" + participant "Greenplum segmentN" + end box + + == Greenplum.check() == + + activate "Spark driver" + "Spark driver" -> "Greenplum master" ++ : CONNECT + + "Spark driver" --> "Greenplum master" : CHECK IF TABLE EXISTS gp_table + "Greenplum master" --> "Spark driver" : TABLE EXISTS + "Spark driver" -> "Greenplum master" : SHOW SCHEMA FOR gp_table + "Greenplum master" --> "Spark driver" : (id bigint, col1 int, col2 text, ...) + + == DBReader.run() == + + "Spark driver" -> "Spark executor1" ++ : START EXECUTOR FOR df(id bigint, col1 int, col2 text, ...) PARTITION 1 + "Spark driver" -> "Spark executor2" ++ : START EXECUTOR FOR df(id bigint, col1 int, col2 text, ...) PARTITION 2 + "Spark driver" -> "Spark executorN" ++ : START EXECUTOR FOR df(id bigint, col1 int, col2 text, ...) PARTITION N + + note right of "Spark driver" : This is done in parallel,\nexecutors are independent\n|\n|\n|\nV + "Spark executor1" -> "Greenplum master" ++ : CREATE WRITABLE EXTERNAL TABLE spark_executor1 (id bigint, col1 int, col2 text, ...) USING address=executor1_host:executor1_port;\nINSERT INTO EXTERNAL TABLE spark_executor1 FROM gp_table WHERE gp_segment_id = 1 + note right of "Greenplum master" : Each white vertical line here is a opened connection to master.\nUsually, **N+1** connections are created from Spark to Greenplum master + "Greenplum master" --> "Greenplum segment1" ++ : SELECT DATA FROM gp_table_data_on_segment1 TO spark_executor1 + note right of "Greenplum segment1" : No direct requests between Greenplum segments & Spark.\nData transfer is always initiated by Greenplum segments. + + "Spark executor2" -> "Greenplum master" ++ : CREATE WRITABLE EXTERNAL TABLE spark_executor2 (id bigint, col1 int, col2 text, ...) USING address=executor2_host:executor2_port;\nINSERT INTO EXTERNAL TABLE spark_executor2 FROM gp_table WHERE gp_segment_id = 2 + "Greenplum master" --> "Greenplum segment2" ++ : SELECT DATA FROM gp_table_data_on_segment2 TO spark_executor2 + + "Spark executorN" -> "Greenplum master" ++ : CREATE WRITABLE EXTERNAL TABLE spark_executorN (id bigint, col1 int, col2 text, ...) USING address=executorN_host:executorN_port;\nINSERT INTO EXTERNAL TABLE spark_executorN FROM gp_table WHERE gp_segment_id = N + "Greenplum master" --> "Greenplum segmentN" ++ : SELECT DATA FROM gp_table_data_on_segmentN TO spark_executorN + + "Greenplum segment1" ->o "Spark executor1" -- : INITIALIZE CONNECTION TO Spark executor1\nPUSH DATA TO Spark executor1 + note left of "Spark executor1" : Circle is an open GPFDIST port,\nlistened by executor + + "Greenplum segment2" ->o "Spark executor2" -- : INITIALIZE CONNECTION TO Spark executor2\nPUSH DATA TO Spark executor2 + "Greenplum segmentN" ->o "Spark executorN" -- : INITIALIZE CONNECTION TO Spark executorN\nPUSH DATA TO Spark executorN + + == Spark.stop() == + + "Spark executor1" --> "Greenplum master" : DROP TABLE spark_executor1 + deactivate "Greenplum master" + "Spark executor2" --> "Greenplum master" : DROP TABLE spark_executor2 + deactivate "Greenplum master" + "Spark executorN" --> "Greenplum master" : DROP TABLE spark_executorN + deactivate "Greenplum master" + + "Spark executor1" --> "Spark driver" -- : DONE + "Spark executor2" --> "Spark driver" -- : DONE + "Spark executorN" --> "Spark driver" -- : DONE + + "Spark driver" --> "Greenplum master" : CLOSE CONNECTION + deactivate "Greenplum master" + deactivate "Spark driver" + @enduml +``` + +## Recommendations + +### Select only required columns + +Instead of passing `"*"` in `DBReader(columns=[...])` prefer passing exact column names. This reduces the amount of data passed from Greenplum to Spark. + +### Pay attention to `where` value + +Instead of filtering data on Spark side using `df.filter(df.column == 'value')` pass proper `DBReader(where="column = 'value'")` clause. +This both reduces the amount of data send from Greenplum to Spark, and may also improve performance of the query. +Especially if there are indexes or partitions for columns used in `where` clause. + +### Read data in parallel + +`DBReader` in case of Greenplum connector requires view or table to have a column which is used by Spark +for parallel reads. + +Choosing proper column allows each Spark executor to read only part of data stored in the specified segment, +avoiding moving large amounts of data between segments, which improves reading performance. + +#### Using `gp_segment_id` + +By default, `DBReader` will use [gp_segment_id](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/troubleshooting.html#reading-from-a-view) +column for parallel data reading. Each DataFrame partition will contain data of a specific Greenplum segment. + +This allows each Spark executor read only data from specific Greenplum segment, avoiding moving large amounts of data between segments. + +If view is used, it is recommended to include `gp_segment_id` column to this view: + +```{eval-rst} +.. dropdown:: Reading from view with gp_segment_id column + + .. code-block:: python + + from onetl.connection import Greenplum + from onetl.db import DBReader + + greenplum = Greenplum(...) + + greenplum.execute( + """ + CREATE VIEW schema.view_with_gp_segment_id AS + SELECT + id, + some_column, + another_column, + gp_segment_id -- IMPORTANT + FROM schema.some_table + """, + ) + + reader = DBReader( + connection=greenplum, + source="schema.view_with_gp_segment_id", + ) + df = reader.run() +``` + +#### Using custom `partition_column` + +Sometimes table or view is lack of `gp_segment_id` column, but there is some column +with value range correlated with Greenplum segment distribution. + +In this case, custom column can be used instead: + +```{eval-rst} +.. dropdown:: Reading from view with custom partition_column + + .. code-block:: python + + from onetl.connection import Greenplum + from onetl.db import DBReader + + greenplum = Greenplum(...) + + greenplum.execute( + """ + CREATE VIEW schema.view_with_partition_column AS + SELECT + id, + some_column, + part_column -- correlated to greenplum segment ID + FROM schema.some_table + """, + ) + + reader = DBReader( + connection=greenplum, + source="schema.view_with_partition_column", + options=Greenplum.ReadOptions( + # parallelize data using specified column + partitionColumn="part_column", + # create 10 Spark tasks, each will read only part of table data + partitions=10, + ), + ) + df = reader.run() +``` + +#### Reading `DISTRIBUTED REPLICATED` tables + +Replicated tables do not have `gp_segment_id` column at all, so you need to set `partition_column` to some column name +of type integer/bigint/smallint. + +### Parallel `JOIN` execution + +In case of using views which require some data motion between Greenplum segments, like `JOIN` queries, another approach should be used. + +Each Spark executor N will run the same query, so each of N query will start its own JOIN process, leading to really heavy load on Greenplum segments. +**This should be avoided**. + +Instead is recommended to run `JOIN` query on Greenplum side, save the result to an intermediate table, +and then read this table using `DBReader`: + +```{eval-rst} +.. dropdown:: Reading from view using intermediate table + + .. code-block:: python + + from onetl.connection import Greenplum + from onetl.db import DBReader + + greenplum = Greenplum(...) + + greenplum.execute( + """ + CREATE UNLOGGED TABLE schema.intermediate_table AS + SELECT + id, + tbl1.col1, + tbl1.data, + tbl2.another_data + FROM + schema.table1 as tbl1 + JOIN + schema.table2 as tbl2 + ON + tbl1.col1 = tbl2.col2 + WHERE ... + """, + ) + + reader = DBReader( + connection=greenplum, + source="schema.intermediate_table", + ) + df = reader.run() + + # write dataframe somethere + + greenplum.execute( + """ + DROP TABLE schema.intermediate_table + """, + ) +``` + +```{eval-rst} +.. warning:: + + **NEVER** do that: + + .. code-block:: python + + df1 = DBReader(connection=greenplum, target="public.table1", ...).run() + df2 = DBReader(connection=greenplum, target="public.table2", ...).run() + + joined_df = df1.join(df2, on="col") + + This will lead to sending all the data from both ``table1`` and ``table2`` to Spark executor memory, and then ``JOIN`` + will be performed on Spark side, not inside Greenplum. This is **VERY** inefficient. +``` + +#### `TEMPORARY` tables notice + +Someone could think that writing data from view or result of `JOIN` to `TEMPORARY` table, +and then passing it to `DBReader`, is an efficient way to read data from Greenplum. This is because temp tables are not generating WAL files, +and are automatically deleted after finishing the transaction. + +That will **NOT** work. Each Spark executor establishes its own connection to Greenplum. +And each connection starts its own transaction which means that every executor will read empty temporary table. + +You should use [UNLOGGED](https://docs.vmware.com/en/VMware-Greenplum/7/greenplum-database/ref_guide-sql_commands-CREATE_TABLE.html) tables +to write data to intermediate table without generating WAL logs. + +## Options + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.greenplum.options +``` + +```{eval-rst} +.. autopydantic_model:: GreenplumReadOptions + :member-order: bysource + :model-show-field-summary: false + :field-show-constraints: false +``` diff --git a/mddocs/docs/ru/connection/db_connection/greenplum/types.md b/mddocs/docs/ru/connection/db_connection/greenplum/types.md new file mode 100644 index 000000000..1f9d83627 --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/greenplum/types.md @@ -0,0 +1,406 @@ +(greenplum-types)= + +# Greenplum \<-> Spark type mapping + +```{eval-rst} +.. note:: + + The results below are valid for Spark 3.2.4, and may differ on other Spark versions. +``` + +## Type detection & casting + +Spark's DataFrames always have a `schema` which is a list of columns with corresponding Spark types. All operations on a column are performed using column type. + +### Reading from Greenplum + +This is how Greenplum connector performs this: + +- Execute query `SELECT * FROM table LIMIT 0` [^footnote-1]. +- For each column in query result get column name and Greenplum type. +- Find corresponding `Greenplum type (read)` → `Spark type` combination (see below) for each DataFrame column. If no combination is found, raise exception. +- Use Spark column projection and predicate pushdown features to build a final query. +- Create DataFrame from generated query with inferred schema. + +[^footnote-1]: Yes, **all columns of a table**, not just selected ones. + This means that if source table **contains** columns with unsupported type, the entire table cannot be read. + +### Writing to some existing Greenplum table + +This is how Greenplum connector performs this: + +- Get names of columns in DataFrame. +- Perform `SELECT * FROM table LIMIT 0` query. +- For each column in query result get column name and Greenplum type. +- Match table columns with DataFrame columns (by name, case insensitive). + If some column is present only in target table, but not in DataFrame (like `DEFAULT` or `SERIAL` column), and vice versa, raise an exception. + See [Explicit type cast]. +- Find corresponding `Spark type` → `Greenplumtype (write)` combination (see below) for each DataFrame column. If no combination is found, raise exception. +- If `Greenplumtype (write)` match `Greenplum type (read)`, no additional casts will be performed, DataFrame column will be written to Greenplum as is. +- If `Greenplumtype (write)` does not match `Greenplum type (read)`, DataFrame column will be casted to target column type **on Greenplum side**. For example, you can write column with text data to `json` column which Greenplum connector currently does not support. + +### Create new table using Spark + +```{eval-rst} +.. warning:: + + ABSOLUTELY NOT RECOMMENDED! +``` + +This is how Greenplum connector performs this: + +- Find corresponding `Spark type` → `Greenplum type (create)` combination (see below) for each DataFrame column. If no combination is found, raise exception. +- Generate DDL for creating table in Greenplum, like `CREATE TABLE (col1 ...)`, and run it. +- Write DataFrame to created table as is. + +More details [can be found here](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/write_to_gpdb.html). + +But Greenplum connector support only limited number of types and almost no custom clauses (like `PARTITION BY`). +So instead of relying on Spark to create tables: + +```{eval-rst} +.. dropdown:: See example + + .. code:: python + + writer = DBWriter( + connection=greenplum, + target="public.table", + options=Greenplum.WriteOptions( + if_exists="append", + # by default distribution is random + distributedBy="id", + # partitionBy is not supported + ), + ) + writer.run(df) +``` + +Always prefer creating table with desired DDL **BEFORE WRITING DATA**: + +```{eval-rst} +.. dropdown:: See example + + .. code:: python + + greenplum.execute( + """ + CREATE TABLE public.table ( + id int32, + business_dt timestamp(6), + value json + ) + PARTITION BY RANGE (business_dt) + DISTRIBUTED BY id + """, + ) + + writer = DBWriter( + connection=greenplum, + target="public.table", + options=Greenplum.WriteOptions(if_exists="append"), + ) + writer.run(df) +``` + +See Greenplum [CREATE TABLE](https://docs.vmware.com/en/VMware-Greenplum/7/greenplum-database/ref_guide-sql_commands-CREATE_TABLE.html) documentation. + +## Supported types + +See: +: - [official connector documentation](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/2.3/greenplum-connector-spark/reference-datatype_mapping.html) + - [list of Greenplum types](https://docs.vmware.com/en/VMware-Greenplum/7/greenplum-database/ref_guide-data_types.html) + +### Numeric types + +```{eval-rst} ++----------------------------------+-----------------------------------+-------------------------------+-------------------------+ +| Greenplum type (read) | Spark type | Greenplumtype (write) | Greenplum type (create) | ++==================================+===================================+===============================+=========================+ +| ``decimal`` | ``DecimalType(P=38, S=18)`` | ``decimal(P=38, S=18)`` | ``decimal`` (unbounded) | ++----------------------------------+-----------------------------------+-------------------------------+ | +| ``decimal(P=0..38)`` | ``DecimalType(P=0..38, S=0)`` | ``decimal(P=0..38, S=0)`` | | ++----------------------------------+-----------------------------------+-------------------------------+ | +| ``decimal(P=0..38, S=0..38)`` | ``DecimalType(P=0..38, S=0..38)`` | ``decimal(P=0..38, S=0..38)`` | | ++----------------------------------+-----------------------------------+-------------------------------+-------------------------+ +| ``decimal(P=39.., S=0..)`` | unsupported [2]_ | | | ++----------------------------------+-----------------------------------+-------------------------------+-------------------------+ +| ``real`` | ``FloatType()`` | ``real`` | ``real`` | ++----------------------------------+-----------------------------------+-------------------------------+-------------------------+ +| ``double precision`` | ``DoubleType()`` | ``double precision`` | ``double precision`` | ++----------------------------------+-----------------------------------+-------------------------------+-------------------------+ +| ``-`` | ``ByteType()`` | unsupported | unsupported | ++----------------------------------+-----------------------------------+-------------------------------+-------------------------+ +| ``smallint`` | ``ShortType()`` | ``smallint`` | ``smallint`` | ++----------------------------------+-----------------------------------+-------------------------------+-------------------------+ +| ``integer`` | ``IntegerType()`` | ``integer`` | ``integer`` | ++----------------------------------+-----------------------------------+-------------------------------+-------------------------+ +| ``bigint`` | ``LongType()`` | ``bigint`` | ``bigint`` | ++----------------------------------+-----------------------------------+-------------------------------+-------------------------+ +| ``money`` | unsupported | | | ++----------------------------------+ | | | +| ``int4range`` | | | | ++----------------------------------+ | | | +| ``int8range`` | | | | ++----------------------------------+ | | | +| ``numrange`` | | | | ++----------------------------------+ | | | +| ``int2vector`` | | | | ++----------------------------------+-----------------------------------+-------------------------------+-------------------------+ +``` + +[^footnote-2]: Greenplum support decimal types with unlimited precision. + + But Spark's `DecimalType(P, S)` supports maximum `P=38` (128 bit). It is impossible to read, write or operate with values of larger precision, + this leads to an exception. + +### Temporal types + +```{eval-rst} ++------------------------------------+-------------------------+-----------------------+-------------------------+ +| Greenplum type (read) | Spark type | Greenplumtype (write) | Greenplum type (create) | ++====================================+=========================+=======================+=========================+ +| ``date`` | ``DateType()`` | ``date`` | ``date`` | ++------------------------------------+-------------------------+-----------------------+-------------------------+ +| ``time`` | ``TimestampType()``, | ``timestamp`` | ``timestamp`` | ++------------------------------------+ time format quirks [3]_ | | | +| ``time(0..6)`` | | | | ++------------------------------------+ | | | +| ``time with time zone`` | | | | ++------------------------------------+ | | | +| ``time(0..6) with time zone`` | | | | ++------------------------------------+-------------------------+-----------------------+-------------------------+ +| ``timestamp`` | ``TimestampType()`` | ``timestamp`` | ``timestamp`` | ++------------------------------------+ | | | +| ``timestamp(0..6)`` | | | | ++------------------------------------+ | | | +| ``timestamp with time zone`` | | | | ++------------------------------------+ | | | +| ``timestamp(0..6) with time zone`` | | | | ++------------------------------------+-------------------------+-----------------------+-------------------------+ +| ``interval`` or any precision | unsupported | | | ++------------------------------------+ | | | +| ``daterange`` | | | | ++------------------------------------+ | | | +| ``tsrange`` | | | | ++------------------------------------+ | | | +| ``tstzrange`` | | | | ++------------------------------------+-------------------------+-----------------------+-------------------------+ +``` + +```{eval-rst} +.. warning:: + + Note that types in Greenplum and Spark have different value ranges: + + +----------------+---------------------------------+----------------------------------+---------------------+--------------------------------+--------------------------------+ + | Greenplum type | Min value | Max value | Spark type | Min value | Max value | + +================+=================================+==================================+=====================+================================+================================+ + | ``date`` | ``-4713-01-01`` | ``5874897-01-01`` | ``DateType()`` | ``0001-01-01`` | ``9999-12-31`` | + +----------------+---------------------------------+----------------------------------+---------------------+--------------------------------+--------------------------------+ + | ``timestamp`` | ``-4713-01-01 00:00:00.000000`` | ``294276-12-31 23:59:59.999999`` | ``TimestampType()`` | ``0001-01-01 00:00:00.000000`` | ``9999-12-31 23:59:59.999999`` | + +----------------+---------------------------------+----------------------------------+ | | | + | ``time`` | ``00:00:00.000000`` | ``24:00:00.000000`` | | | | + +----------------+---------------------------------+----------------------------------+---------------------+--------------------------------+--------------------------------+ + + So not all of values can be read from Greenplum to Spark. + + References: + * `Greenplum types documentation `_ + * `Spark DateType documentation `_ + * `Spark TimestampType documentation `_ +``` + +[^footnote-3]: `time` type is the same as `timestamp` with date `1970-01-01`. So instead of reading data from Postgres like `23:59:59` + it is actually read `1970-01-01 23:59:59`, and vice versa. + +### String types + +```{eval-rst} ++-----------------------------+------------------+-----------------------+-------------------------+ +| Greenplum type (read) | Spark type | Greenplumtype (write) | Greenplum type (create) | ++=============================+==================+=======================+=========================+ +| ``character`` | ``StringType()`` | ``text`` | ``text`` | ++-----------------------------+ | | | +| ``character(N)`` | | | | ++-----------------------------+ | | | +| ``character varying`` | | | | ++-----------------------------+ | | | +| ``character varying(N)`` | | | | ++-----------------------------+ | | | +| ``text`` | | | | ++-----------------------------+ | | | +| ``xml`` | | | | ++-----------------------------+ | | | +| ``CREATE TYPE ... AS ENUM`` | | | | ++-----------------------------+------------------+-----------------------+-------------------------+ +| ``json`` | unsupported | | | ++-----------------------------+ | | | +| ``jsonb`` | | | | ++-----------------------------+------------------+-----------------------+-------------------------+ +``` + +### Binary types + +```{eval-rst} ++--------------------------+-------------------+-----------------------+-------------------------+ +| Greenplum type (read) | Spark type | Greenplumtype (write) | Greenplum type (create) | ++==========================+===================+=======================+=========================+ +| ``boolean`` | ``BooleanType()`` | ``boolean`` | ``boolean`` | ++--------------------------+-------------------+-----------------------+-------------------------+ +| ``bit`` | unsupported | | | ++--------------------------+ | | | +| ``bit(N)`` | | | | ++--------------------------+ | | | +| ``bit varying`` | | | | ++--------------------------+ | | | +| ``bit varying(N)`` | | | | ++--------------------------+-------------------+-----------------------+-------------------------+ +| ``bytea`` | unsupported [4]_ | | | ++--------------------------+-------------------+-----------------------+-------------------------+ +| ``-`` | ``BinaryType()`` | ``bytea`` | ``bytea`` | ++--------------------------+-------------------+-----------------------+-------------------------+ +``` + +[^footnote-4]: Yes, that's weird. + +### Struct types + +```{eval-rst} ++--------------------------------+------------------+-----------------------+-------------------------+ +| Greenplum type (read) | Spark type | Greenplumtype (write) | Greenplum type (create) | ++================================+==================+=======================+=========================+ +| ``T[]`` | unsupported | | | ++--------------------------------+------------------+-----------------------+-------------------------+ +| ``-`` | ``ArrayType()`` | unsupported | | ++--------------------------------+------------------+-----------------------+-------------------------+ +| ``CREATE TYPE sometype (...)`` | ``StringType()`` | ``text`` | ``text`` | ++--------------------------------+------------------+-----------------------+-------------------------+ +| ``-`` | ``StructType()`` | unsupported | | ++--------------------------------+------------------+ | | +| ``-`` | ``MapType()`` | | | ++--------------------------------+------------------+-----------------------+-------------------------+ +``` + +## Unsupported types + +Columns of these types cannot be read/written by Spark: +: - `cidr` + - `inet` + - `macaddr` + - `macaddr8` + - `circle` + - `box` + - `line` + - `lseg` + - `path` + - `point` + - `polygon` + - `tsvector` + - `tsquery` + - `uuid` + +The is a way to avoid this - just cast unsupported types to `text`. But the way this can be done is not a straightforward. + +## Explicit type cast + +### `DBReader` + +Direct casting of Greenplum types is not supported by DBReader due to the connector’s implementation specifics. + +```python +reader = DBReader( + connection=greenplum, + # will fail + columns=["CAST(unsupported_column AS text)"], +) +``` + +But there is a workaround - create a view with casting unsupported column to text (or any other supported type). +For example, you can use [to_json](https://www.postgresql.org/docs/current/functions-json.html) Postgres function to convert column of any type to string representation and then parse this column on Spark side using {obj}`JSON.parse_column ` method. + +```python +from pyspark.sql.types import ArrayType, IntegerType + +from onetl.connection import Greenplum +from onetl.db import DBReader +from onetl.file.format import JSON + +greenplum = Greenplum(...) + +greenplum.execute( + """ + CREATE VIEW schema.view_with_json_column AS + SELECT + id, + supported_column, + to_json(array_column) array_column_as_json, + gp_segment_id -- ! important ! + FROM + schema.table_with_unsupported_columns + """, +) + +# create dataframe using this view +reader = DBReader( + connection=greenplum, + source="schema.view_with_json_column", +) +df = reader.run() + +# Define the schema for the JSON data +json_scheme = ArrayType(IntegerType()) + +df = df.select( + df.id, + df.supported_column, + JSON().parse_column(df.array_column_as_json, json_scheme).alias("array_column"), +) +``` + +### `DBWriter` + +To write data to a `text` or `json` column in a Greenplum table, use {obj}`JSON.serialize_column ` method. + +```python +from onetl.connection import Greenplum +from onetl.db import DBWriter +from onetl.file.format import JSON + +greenplum = Greenplum(...) + +greenplum.execute( + """ + CREATE TABLE schema.target_table ( + id int, + supported_column timestamp, + array_column_as_json jsonb, -- or text + ) + DISTRIBUTED BY id + """, +) + +write_df = df.select( + df.id, + df.supported_column, + JSON().serialize_column(df.array_column).alias("array_column_json"), +) + +writer = DBWriter( + connection=greenplum, + target="schema.target_table", +) +writer.run(write_df) +``` + +Then you can parse this column on Greenplum side: + +```sql +SELECT + id, + supported_column, + -- access first item of an array + array_column_as_json->0 +FROM + schema.target_table +``` diff --git a/mddocs/docs/ru/connection/db_connection/greenplum/write.md b/mddocs/docs/ru/connection/db_connection/greenplum/write.md new file mode 100644 index 000000000..af367ad75 --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/greenplum/write.md @@ -0,0 +1,140 @@ +(greenplum-write)= + +# Writing to Greenplum using `DBWriter` + +For writing data to Greenplum, use {obj}`DBWriter ` +with {obj}`GreenplumWriteOptions `. + +```{eval-rst} +.. warning:: + + Please take into account :ref:`greenplum-types`. +``` + +```{eval-rst} +.. warning:: + + It is always recommended to create table explicitly using :ref:`Greenplum.execute ` + instead of relying on Spark's table DDL generation. + + This is because Spark's DDL generator can create columns with different types than it is expected. +``` + +## Examples + +```python +from onetl.connection import Greenplum +from onetl.db import DBWriter + +greenplum = Greenplum(...) + +df = ... # data is here + +writer = DBWriter( + connection=greenplum, + target="schema.table", + options=Greenplum.WriteOptions( + if_exists="append", + # by default distribution is random + distributedBy="id", + # partitionBy is not supported + ), +) + +writer.run(df) +``` + +## Interaction schema + +High-level schema is described in {ref}`greenplum-prerequisites`. You can find detailed interaction schema below. + +```{eval-rst} +.. dropdown:: Spark <-> Greenplum interaction during DBWriter.run() + + .. plantuml:: + + @startuml + title Greenplum master <-> Spark driver + box "Spark" + participant "Spark driver" + participant "Spark executor1" + participant "Spark executor2" + participant "Spark executorN" + end box + + box "Greenplum" + participant "Greenplum master" + participant "Greenplum segment1" + participant "Greenplum segment2" + participant "Greenplum segmentN" + end box + + == Greenplum.check() == + + activate "Spark driver" + "Spark driver" -> "Greenplum master" ++ : CONNECT + "Spark driver" --> "Greenplum master" ++ : CHECK IF TABLE EXISTS gp_table + "Greenplum master" --> "Spark driver" : TABLE NOT EXISTS + + == DBWriter.run(df) == + + "Spark driver" -> "Spark executor1" ++ : START EXECUTOR FOR df(id bigint, col1 int, col2 text, ...) PARTITION 1 + "Spark driver" -> "Spark executor2" ++ : START EXECUTOR FOR df(id bigint, col1 int, col2 text, ...) PARTITION 2 + "Spark driver" -> "Spark executorN" ++ : START EXECUTOR FOR df(id bigint, col1 int, col2 text, ...) PARTITION N + + note right of "Spark driver" : This is done in parallel,\nexecutors are independent\n|\n|\n|\nV + "Spark executor1" -> "Greenplum master" ++ : CREATE READABLE EXTERNAL TABLE spark_executor1 (id bigint, col1 int, col2 text, ...) USING address=executor1_host:executor1_port;\nINSERT INTO gp_table FROM spark_executor1 + note right of "Greenplum master" : Each white vertical line here is a opened connection to master.\nUsually, **N+1** connections are created from Spark to Greenplum master + "Greenplum master" --> "Greenplum segment1" ++ : SELECT DATA FROM spark_executor1 TO gp_table_data_on_segment1 + note right of "Greenplum segment1" : No direct requests between Greenplum segments & Spark.\nData transfer is always initiated by Greenplum segments. + + "Spark executor2" -> "Greenplum master" ++ : CREATE READABLE EXTERNAL TABLE spark_executor2 (id bigint, col1 int, col2 text, ...) USING address=executor2_host:executor2_port;\nINSERT INTO gp_table FROM spark_executor2 + "Greenplum master" --> "Greenplum segment2" ++ : SELECT DATA FROM spark_executor2 TO gp_table_data_on_segment2 + + "Spark executorN" -> "Greenplum master" ++ : CREATE READABLE EXTERNAL TABLE spark_executorN (id bigint, col1 int, col2 text, ...) USING address=executorN_host:executorN_port;\nINSERT INTO gp_table FROM spark_executorN + "Greenplum master" --> "Greenplum segmentN" ++ : SELECT DATA FROM spark_executorN TO gp_table_data_on_segmentN + + "Greenplum segment1" -->o "Spark executor1" : INITIALIZE CONNECTION TO Spark executor1 + "Spark executor1" -> "Greenplum segment1" : READ DATA FROM Spark executor1 + note left of "Spark executor1" : Circle is an open GPFDIST port,\nlistened by executor + deactivate "Greenplum segment1" + + "Greenplum segment2" -->o "Spark executor2" : INITIALIZE CONNECTION TO Spark executor2 + "Spark executor2" -> "Greenplum segment2" : READ DATA FROM Spark executor2 + deactivate "Greenplum segment2" + + "Greenplum segmentN" -->o "Spark executorN" : INITIALIZE CONNECTION TO Spark executorN + "Spark executorN" -> "Greenplum segmentN" : READ DATA FROM Spark executorN + deactivate "Greenplum segmentN" + + == Finished == + + "Spark executor1" --> "Greenplum master" : DROP TABLE spark_executor1 + deactivate "Greenplum master" + "Spark executor2" --> "Greenplum master" : DROP TABLE spark_executor2 + deactivate "Greenplum master" + "Spark executorN" --> "Greenplum master" : DROP TABLE spark_executorN + deactivate "Greenplum master" + + "Spark executor1" --> "Spark driver" -- : DONE + "Spark executor2" --> "Spark driver" -- : DONE + "Spark executorN" --> "Spark driver" -- : DONE + + "Spark driver" --> "Greenplum master" : CLOSE CONNECTION + deactivate "Greenplum master" + deactivate "Spark driver" + @enduml +``` + +## Options + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.greenplum.options +``` + +```{eval-rst} +.. autopydantic_model:: GreenplumWriteOptions + :member-order: bysource + :model-show-field-summary: false + :field-show-constraints: false +``` diff --git a/mddocs/docs/ru/connection/db_connection/hive/connection.md b/mddocs/docs/ru/connection/db_connection/hive/connection.md new file mode 100644 index 000000000..a9a0cfc55 --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/hive/connection.md @@ -0,0 +1,13 @@ +(hive-connection)= + +# Hive Connection + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.hive.connection +``` + +```{eval-rst} +.. autoclass:: Hive + :members: get_current, check + :member-order: bysource +``` diff --git a/mddocs/docs/ru/connection/db_connection/hive/execute.md b/mddocs/docs/ru/connection/db_connection/hive/execute.md new file mode 100644 index 000000000..d7b0694c4 --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/hive/execute.md @@ -0,0 +1,55 @@ +(hive-execute)= + +# Executing statements in Hive + +Use `Hive.execute(...)` to execute DDL and DML operations. + +## Syntax support + +This method supports **any** query syntax supported by Hive, like: + +- ✅︎ `CREATE TABLE ...`, `CREATE VIEW ...`, and so on +- ✅︎ `LOAD DATA ...`, and so on +- ✅︎ `ALTER ...` +- ✅︎ `INSERT INTO ... SELECT ...`, and so on +- ✅︎ `DROP TABLE ...`, `DROP VIEW ...`, and so on +- ✅︎ `MSCK REPAIR TABLE ...`, and so on +- ✅︎ other statements not mentioned here +- ❌ `SET ...; SELECT ...;` - multiple statements not supported + +```{eval-rst} +.. warning:: + + Actually, query should be written using `SparkSQL `_ syntax, not HiveQL. +``` + +## Examples + +```python +from onetl.connection import Hive + +hive = Hive(...) + +hive.execute("DROP TABLE schema.table") +hive.execute( + """ + CREATE TABLE schema.table ( + id NUMBER, + key VARCHAR, + value DOUBLE + ) + PARTITION BY (business_date DATE) + STORED AS orc + """ +) +``` + +### Details + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.hive.connection +``` + +```{eval-rst} +.. automethod:: Hive.execute +``` diff --git a/mddocs/docs/ru/connection/db_connection/hive/index.md b/mddocs/docs/ru/connection/db_connection/hive/index.md new file mode 100644 index 000000000..bb8e5ebcc --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/hive/index.md @@ -0,0 +1,28 @@ +(hive)= + +# Hive + +```{toctree} +:caption: Connection +:maxdepth: 1 + +prerequisites +connection +``` + +```{toctree} +:caption: Operations +:maxdepth: 1 + +read +sql +write +execute +``` + +```{toctree} +:caption: For developers +:maxdepth: 1 + +slots +``` diff --git a/mddocs/docs/ru/connection/db_connection/hive/prerequisites.md b/mddocs/docs/ru/connection/db_connection/hive/prerequisites.md new file mode 100644 index 000000000..79ff0e5dc --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/hive/prerequisites.md @@ -0,0 +1,134 @@ +(hive-prerequisites)= + +# Prerequisites + +```{eval-rst} +.. note:: + + onETL's Hive connection is actually SparkSession with access to `Hive Thrift Metastore `_ + and HDFS/S3. + All data motion is made using Spark. Hive Metastore is used only to store tables and partitions metadata. + + This connector does **NOT** require Hive server. It also does **NOT** use Hive JDBC connector. +``` + +## Version Compatibility + +- Hive Metastore version: + : - Officially declared: 0.12 - 3.1.3 (may require to add proper .jar file explicitly) + - Actually tested: 1.2.100, 2.3.10, 3.1.3 +- Spark versions: 2.3.x - 3.5.x +- Java versions: 8 - 20 + +See [official documentation](https://spark.apache.org/docs/latest/sql-data-sources-hive-tables.html). + +## Installing PySpark + +To use Hive connector you should have PySpark installed (or injected to `sys.path`) +BEFORE creating the connector instance. + +See {ref}`install-spark` installation instruction for more details. + +## Connecting to Hive Metastore + +```{eval-rst} +.. note:: + + If you're using managed Hadoop cluster, skip this step, as all Spark configs are should already present on the host. +``` + +Create `$SPARK_CONF_DIR/hive-site.xml` with Hive Metastore URL: + +```xml + + + + + hive.metastore.uris + thrift://metastore.host.name:9083 + + +``` + +Create `$SPARK_CONF_DIR/core-site.xml` with warehouse location ,e.g. HDFS IPC port of Hadoop namenode, or S3 bucket address & credentials: + +```{eval-rst} +.. tabs:: + + .. code-tab:: xml HDFS + + + + + + fs.defaultFS + hdfs://myhadoopcluster:9820 + + + + .. code-tab:: xml S3 + + + + + !-- See https://hadoop.apache.org/docs/current/hadoop-aws/tools/hadoop-aws/index.html#General_S3A_Client_configuration + + fs.defaultFS + s3a://mys3bucket/ + + + fs.s3a.bucket.mybucket.endpoint + http://s3.somain + + + fs.s3a.bucket.mybucket.connection.ssl.enabled + false + + + fs.s3a.bucket.mybucket.path.style.access + true + + + fs.s3a.bucket.mybucket.aws.credentials.provider + org.apache.hadoop.fs.s3a.SimpleAWSCredentialsProvider + + + fs.s3a.bucket.mybucket.access.key + some-user + + + fs.s3a.bucket.mybucket.secret.key + mysecrettoken + + +``` + +## Using Kerberos + +Some of Hadoop managed clusters use Kerberos authentication. In this case, you should call [kinit](https://web.mit.edu/kerberos/krb5-1.12/doc/user/user_commands/kinit.html) command +**BEFORE** starting Spark session to generate Kerberos ticket. See {ref}`install-kerberos`. + +Sometimes it is also required to pass keytab file to Spark config, allowing Spark executors to generate own Kerberos tickets: + +```{eval-rst} +.. tabs:: + + .. code-tab:: python Spark 3 + + SparkSession.builder + .option("spark.kerberos.access.hadoopFileSystems", "hdfs://namenode1.domain.com:9820,hdfs://namenode2.domain.com:9820") + .option("spark.kerberos.principal", "user") + .option("spark.kerberos.keytab", "/path/to/keytab") + .gerOrCreate() + + .. code-tab:: python Spark 2 + + SparkSession.builder + .option("spark.yarn.access.hadoopFileSystems", "hdfs://namenode1.domain.com:9820,hdfs://namenode2.domain.com:9820") + .option("spark.yarn.principal", "user") + .option("spark.yarn.keytab", "/path/to/keytab") + .gerOrCreate() +``` + +See [Spark security documentation](https://spark.apache.org/docs/latest/security.html#kerberos) +for more details. diff --git a/mddocs/docs/ru/connection/db_connection/hive/read.md b/mddocs/docs/ru/connection/db_connection/hive/read.md new file mode 100644 index 000000000..82433c988 --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/hive/read.md @@ -0,0 +1,95 @@ +(hive-read)= + +# Reading from Hive using `DBReader` + +{obj}`DBReader ` supports {ref}`strategy` for incremental data reading, +but does not support custom queries, like `JOIN`. + +## Supported DBReader features + +- ✅︎ `columns` +- ✅︎ `where` +- ✅︎ `hwm`, supported strategies: +- - ✅︎ {ref}`snapshot-strategy` +- - ✅︎ {ref}`incremental-strategy` +- - ✅︎ {ref}`snapshot-batch-strategy` +- - ✅︎ {ref}`incremental-batch-strategy` +- ❌ `hint` (is not supported by Hive) +- ❌ `df_schema` +- ❌ `options` (only Spark config params are used) + +```{eval-rst} +.. warning:: + + Actually, ``columns``, ``where`` and ``hwm.expression`` should be written using `SparkSQL `_ syntax, + not HiveQL. +``` + +## Examples + +Snapshot strategy: + +```python +from onetl.connection import Hive +from onetl.db import DBReader + +hive = Hive(...) + +reader = DBReader( + connection=hive, + source="schema.table", + columns=["id", "key", "CAST(value AS text) value", "updated_dt"], + where="key = 'something'", +) +df = reader.run() +``` + +Incremental strategy: + +```python +from onetl.connection import Hive +from onetl.db import DBReader +from onetl.strategy import IncrementalStrategy + +hive = Hive(...) + +reader = DBReader( + connection=hive, + source="schema.table", + columns=["id", "key", "CAST(value AS text) value", "updated_dt"], + where="key = 'something'", + hwm=DBReader.AutoDetectHWM(name="hive_hwm", expression="updated_dt"), +) + +with IncrementalStrategy(): + df = reader.run() +``` + +## Recommendations + +### Use column-based write formats + +Prefer these write formats: +: - [ORC](https://spark.apache.org/docs/latest/sql-data-sources-orc.html) + - [Parquet](https://spark.apache.org/docs/latest/sql-data-sources-parquet.html) + - [Iceberg](https://iceberg.apache.org/spark-quickstart/) + - [Hudi](https://hudi.apache.org/docs/quick-start-guide/) + - [Delta](https://docs.delta.io/latest/quick-start.html#set-up-apache-spark-with-delta-lake) + +For colum-based write formats, each file contains separated sections there column data is stored. The file footer contains +location of each column section/group. Spark can use this information to load only sections required by specific query, e.g. only selected columns, +to drastically speed up the query. + +Another advantage is high compression ratio, e.g. 10x-100x in comparison to JSON or CSV. + +### Select only required columns + +Instead of passing `"*"` in `DBReader(columns=[...])` prefer passing exact column names. +This drastically reduces the amount of data read by Spark, **if column-based file formats are used**. + +### Use partition columns in `where` clause + +Queries should include `WHERE` clause with filters on Hive partitioning columns. +This allows Spark to read only small set of files (*partition pruning*) instead of scanning the entire table, so this drastically increases performance. + +Supported operators are: `=`, `>`, `<` and `BETWEEN`, and only against some **static** value. diff --git a/mddocs/docs/ru/connection/db_connection/hive/slots.md b/mddocs/docs/ru/connection/db_connection/hive/slots.md new file mode 100644 index 000000000..8db1f7610 --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/hive/slots.md @@ -0,0 +1,13 @@ +(hive-slots)= + +# Hive Slots + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.hive.slots +``` + +```{eval-rst} +.. autoclass:: HiveSlots + :members: normalize_cluster_name, get_known_clusters, get_current_cluster + :member-order: bysource +``` diff --git a/mddocs/docs/ru/connection/db_connection/hive/sql.md b/mddocs/docs/ru/connection/db_connection/hive/sql.md new file mode 100644 index 000000000..9b035f8ff --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/hive/sql.md @@ -0,0 +1,79 @@ +(hive-sql)= + +# Reading from Hive using `Hive.sql` + +`Hive.sql` allows passing custom SQL query, but does not support incremental strategies. + +## Syntax support + +Only queries with the following syntax are supported: + +- ✅︎ `SELECT ... FROM ...` +- ✅︎ `WITH alias AS (...) SELECT ...` +- ❌ `SET ...; SELECT ...;` - multiple statements not supported + +```{eval-rst} +.. warning:: + + Actually, query should be written using `SparkSQL `_ syntax, not HiveQL. +``` + +## Examples + +```python +from onetl.connection import Hive + +hive = Hive(...) +df = hive.sql( + """ + SELECT + id, + key, + CAST(value AS text) value, + updated_at + FROM + some.mytable + WHERE + key = 'something' + """ +) +``` + +## Recommendations + +### Use column-based write formats + +Prefer these write formats: +: - [ORC](https://spark.apache.org/docs/latest/sql-data-sources-orc.html) + - [Parquet](https://spark.apache.org/docs/latest/sql-data-sources-parquet.html) + - [Iceberg](https://iceberg.apache.org/spark-quickstart/) + - [Hudi](https://hudi.apache.org/docs/quick-start-guide/) + - [Delta](https://docs.delta.io/latest/quick-start.html#set-up-apache-spark-with-delta-lake) + +For colum-based write formats, each file contains separated sections there column data is stored. The file footer contains +location of each column section/group. Spark can use this information to load only sections required by specific query, e.g. only selected columns, +to drastically speed up the query. + +Another advantage is high compression ratio, e.g. 10x-100x in comparison to JSON or CSV. + +### Select only required columns + +Instead of passing `SELECT * FROM ...` prefer passing exact column names `SELECT col1, col2, ...`. +This drastically reduces the amount of data read by Spark, **if column-based file formats are used**. + +### Use partition columns in `where` clause + +Queries should include `WHERE` clause with filters on Hive partitioning columns. +This allows Spark to read only small set of files (*partition pruning*) instead of scanning the entire table, so this drastically increases performance. + +Supported operators are: `=`, `>`, `<` and `BETWEEN`, and only against some **static** value. + +## Details + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.hive.connection +``` + +```{eval-rst} +.. automethod:: Hive.sql +``` diff --git a/mddocs/docs/ru/connection/db_connection/hive/write.md b/mddocs/docs/ru/connection/db_connection/hive/write.md new file mode 100644 index 000000000..8e6761146 --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/hive/write.md @@ -0,0 +1,180 @@ +(hive-write)= + +# Writing to Hive using `DBWriter` + +For writing data to Hive, use {obj}`DBWriter `. + +## Examples + +```python +from onetl.connection import Hive +from onetl.db import DBWriter + +hive = Hive(...) + +df = ... # data is here + +# Create dataframe with specific number of Spark partitions. +# Use the Hive partitioning columns to group the data. Create max 20 files per Hive partition. +# Also sort the data by column which most data is correlated with (e.g. user_id), reducing files size. + +num_files_per_partition = 20 +partition_columns = ["country", "business_date"] +sort_columns = ["user_id"] +write_df = df.repartition( + num_files_per_partition, + *partition_columns, + *sort_columns, +).sortWithinPartitions(*partition_columns, *sort_columns) + +writer = DBWriter( + connection=hive, + target="schema.table", + options=Hive.WriteOptions( + if_exists="append", + # Hive partitioning columns. + partitionBy=partition_columns, + ), +) + +writer.run(write_df) +``` + +## Recommendations + +### Use column-based write formats + +Prefer these write formats: +: - [ORC](https://spark.apache.org/docs/latest/sql-data-sources-orc.html) (**default**) + - [Parquet](https://spark.apache.org/docs/latest/sql-data-sources-parquet.html) + - [Iceberg](https://iceberg.apache.org/spark-quickstart/) + - [Hudi](https://hudi.apache.org/docs/quick-start-guide/) + - [Delta](https://docs.delta.io/latest/quick-start.html#set-up-apache-spark-with-delta-lake) + +```{eval-rst} +.. warning:: + When using ``DBWriter``, the default spark data format configured in ``spark.sql.sources.default`` is ignored, as ``Hive.WriteOptions(format=...)`` default value is explicitly set to ``orc``. +``` + +For column-based write formats, each file contains separated sections where column data is stored. The file footer contains +location of each column section/group. Spark can use this information to load only sections required by specific query, e.g. only selected columns, +to drastically speed up the query. + +Another advantage is high compression ratio, e.g. 10x-100x in comparison to JSON or CSV. + +### Use partitioning + +#### How does it work + +Hive support splitting data to partitions, which are different directories in filesystem with names like `some_col=value1/another_col=value2`. + +For example, dataframe with content like this: + +| country: string | business_date: date | user_id: int | bytes: long | +| --------------- | ------------------- | ------------ | ----------- | +| RU | 2024-01-01 | 1234 | 25325253525 | +| RU | 2024-01-01 | 2345 | 23234535243 | +| RU | 2024-01-02 | 1234 | 62346634564 | +| US | 2024-01-01 | 5678 | 4252345354 | +| US | 2024-01-02 | 5678 | 5474575745 | +| US | 2024-01-03 | 5678 | 3464574567 | + +With `partitionBy=["country", "business_dt"]` data will be stored as files in the following subfolders: +: - `/country=RU/business_date=2024-01-01/` + - `/country=RU/business_date=2024-01-02/` + - `/country=US/business_date=2024-01-01/` + - `/country=US/business_date=2024-01-02/` + - `/country=US/business_date=2024-01-03/` + +A separated subdirectory is created for each distinct combination of column values in the dataframe. + +Please do not confuse Spark dataframe partitions (a.k.a batches of data handled by Spark executors, usually in parallel) +and Hive partitioning (store data in different subdirectories). +Number of Spark dataframe partitions is correlated the number of files created in **each** Hive partition. +For example, Spark dataframe with 10 partitions and 5 distinct values of Hive partition columns will be saved as 5 subfolders with 10 files each = 50 files in total. +Without Hive partitioning, all the files are placed into one flat directory. + +#### But why? + +Queries which has `WHERE` clause with filters on Hive partitioning columns, like `WHERE country = 'RU' AND business_date='2024-01-01'`, will +read only files from this exact partitions, like `/country=RU/business_date=2024-01-01/`, and skip files from other partitions. + +This drastically increases performance and reduces the amount of memory used by Spark. +Consider using Hive partitioning in all tables. + +#### Which columns should I use? + +Usually Hive partitioning columns are based on event date or location, like `country: string`, `business_date: date`, `run_date: date` and so on. + +**Partition columns should contain data with low cardinality.** +Dates, small integers, strings with low number of possible values are OK. +But timestamp, float, decimals, longs (like user id), strings with lots oj unique values (like user name or email) should **NOT** be used as Hive partitioning columns. +Unlike some other databases, range and hash-based partitions are not supported. + +Partition column should be a part of a dataframe. If you want to partition values by date component of `business_dt: timestamp` column, +add a new column to dataframe like this: `df.withColumn("business_date", date(df.business_dt))`. + +### Use compression + +Using compression algorithms like `snappy`, `lz4` or `zstd` can reduce the size of files (up to 10x). + +### Prefer creating large files + +Storing millions of small files is not that HDFS and S3 are designed for. Minimal file size should be at least 10Mb, but usually it is like 128Mb+ or 256Mb+ (HDFS block size). +**NEVER** create files with few Kbytes in size. + +Number of files can be different in different cases. +On one hand, Spark Adaptive Query Execution (AQE) can merge small Spark dataframe partitions into one larger. +On the other hand, dataframes with skewed data can produce a larger number of files than expected. + +To create small amount of large files, you can reduce number of Spark dataframe partitions. +Use [df.repartition(N, columns...)](https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.DataFrame.repartition.html) function, +like this: `df.repartition(20, "col1", "col2")`. +This creates new Spark dataframe with partitions using `hash(df.col1 + df.col2) mod 20` expression, avoiding data skew. + +Note: larger dataframe partitions requires more resources (CPU, RAM) on Spark executor. The exact number of partitions +should be determined empirically, as it depends on the amount of data and available resources. + +### Sort data before writing + +Dataframe with sorted content: + +| country: string | business_date: date | user_id: int | business_dt: timestamp | bytes: long | +| --------------- | ------------------- | ------------ | ----------------------- | ----------- | +| RU | 2024-01-01 | 1234 | 2024-01-01T11:22:33.456 | 25325253525 | +| RU | 2024-01-01 | 1234 | 2024-01-01T12:23:44.567 | 25325253525 | +| RU | 2024-01-02 | 1234 | 2024-01-01T13:25:56.789 | 34335645635 | +| US | 2024-01-01 | 2345 | 2024-01-01T10:00:00.000 | 12341 | +| US | 2024-01-02 | 2345 | 2024-01-01T15:11:22.345 | 13435 | +| US | 2024-01-03 | 2345 | 2024-01-01T20:22:33.567 | 14564 | + +Has a much better compression rate than unsorted one, e.g. 2x or even higher: + +| country: string | business_date: date | user_id: int | business_dt: timestamp | bytes: long | +| --------------- | ------------------- | ------------ | ----------------------- | ----------- | +| RU | 2024-01-01 | 1234 | 2024-01-01T11:22:33.456 | 25325253525 | +| RU | 2024-01-01 | 6345 | 2024-12-01T23:03:44.567 | 25365 | +| RU | 2024-01-02 | 5234 | 2024-07-01T06:10:56.789 | 45643456747 | +| US | 2024-01-01 | 4582 | 2024-04-01T17:59:00.000 | 362546475 | +| US | 2024-01-02 | 2345 | 2024-09-01T04:24:22.345 | 3235 | +| US | 2024-01-03 | 3575 | 2024-03-01T21:37:33.567 | 346345764 | + +Choosing columns to sort data by is really depends on the data. If data is correlated with some specific +column, like in example above the amount of traffic is correlated with both `user_id` and `timestamp`, +use `df.sortWithinPartitions("user_id", "timestamp")` before writing the data. + +If `df.repartition(N, repartition_columns...)` is used in combination with `df.sortWithinPartitions(sort_columns...)`, +then `sort_columns` should start with `repartition_columns` or be equal to it. + +## Options + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.hive.options +``` + +```{eval-rst} +.. autopydantic_model:: HiveWriteOptions + :member-order: bysource + :model-show-field-summary: false + :field-show-constraints: false +``` diff --git a/mddocs/docs/ru/connection/db_connection/index.md b/mddocs/docs/ru/connection/db_connection/index.md new file mode 100644 index 000000000..4977cf5d5 --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/index.md @@ -0,0 +1,19 @@ +(db-connections)= + +# DB Connections + +```{toctree} +:caption: DB Connections +:maxdepth: 1 + +Clickhouse +Greenplum +Kafka +Hive +MongoDB +MSSQL +MySQL +Oracle +Postgres +Teradata +``` diff --git a/mddocs/docs/ru/connection/db_connection/kafka/auth.md b/mddocs/docs/ru/connection/db_connection/kafka/auth.md new file mode 100644 index 000000000..7d112d59a --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/kafka/auth.md @@ -0,0 +1,13 @@ +(kafka-auth)= + +# Kafka Auth + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.kafka.kafka_auth +``` + +```{eval-rst} +.. autoclass:: KafkaAuth + :members: get_options, cleanup + :member-order: bysource +``` diff --git a/mddocs/docs/ru/connection/db_connection/kafka/basic_auth.md b/mddocs/docs/ru/connection/db_connection/kafka/basic_auth.md new file mode 100644 index 000000000..cb3cded81 --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/kafka/basic_auth.md @@ -0,0 +1,14 @@ +(kafka-basic-auth)= + +# Kafka BasicAuth + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.kafka.kafka_basic_auth +``` + +```{eval-rst} +.. autopydantic_model:: KafkaBasicAuth + :member-order: bysource + :model-show-field-summary: false + :field-show-constraints: false +``` diff --git a/mddocs/docs/ru/connection/db_connection/kafka/connection.md b/mddocs/docs/ru/connection/db_connection/kafka/connection.md new file mode 100644 index 000000000..169b5b701 --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/kafka/connection.md @@ -0,0 +1,12 @@ +(kafka-connection)= + +# Kafka Connection + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.kafka.connection +``` + +```{eval-rst} +.. autoclass:: Kafka + :members: get_packages, get_exclude_packages, check, close +``` diff --git a/mddocs/docs/ru/connection/db_connection/kafka/index.md b/mddocs/docs/ru/connection/db_connection/kafka/index.md new file mode 100644 index 000000000..903feea96 --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/kafka/index.md @@ -0,0 +1,46 @@ +(kafka)= + +# Kafka + +```{toctree} +:caption: Connection +:maxdepth: 1 + +prerequisites +connection +troubleshooting +``` + +```{toctree} +:caption: Protocols +:maxdepth: 1 + +plaintext_protocol +ssl_protocol +``` + +```{toctree} +:caption: Auth methods +:maxdepth: 1 + +basic_auth +kerberos_auth +scram_auth +``` + +```{toctree} +:caption: Operations +:maxdepth: 1 + +read +write +``` + +```{toctree} +:caption: For developers +:maxdepth: 1 + +auth +protocol +slots +``` diff --git a/mddocs/docs/ru/connection/db_connection/kafka/kerberos_auth.md b/mddocs/docs/ru/connection/db_connection/kafka/kerberos_auth.md new file mode 100644 index 000000000..637c9e16e --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/kafka/kerberos_auth.md @@ -0,0 +1,14 @@ +(kafka-kerberos-auth)= + +# Kafka KerberosAuth + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.kafka.kafka_kerberos_auth +``` + +```{eval-rst} +.. autopydantic_model:: KafkaKerberosAuth + :member-order: bysource + :model-show-field-summary: false + :field-show-constraints: false +``` diff --git a/mddocs/docs/ru/connection/db_connection/kafka/plaintext_protocol.md b/mddocs/docs/ru/connection/db_connection/kafka/plaintext_protocol.md new file mode 100644 index 000000000..5bc9ae035 --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/kafka/plaintext_protocol.md @@ -0,0 +1,14 @@ +(kafka-plaintext-protocol)= + +# Kafka PlaintextProtocol + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.kafka.kafka_plaintext_protocol +``` + +```{eval-rst} +.. autopydantic_model:: KafkaPlaintextProtocol + :member-order: bysource + :model-show-field-summary: false + :field-show-constraints: false +``` diff --git a/mddocs/docs/ru/connection/db_connection/kafka/prerequisites.md b/mddocs/docs/ru/connection/db_connection/kafka/prerequisites.md new file mode 100644 index 000000000..3020648e4 --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/kafka/prerequisites.md @@ -0,0 +1,65 @@ +(kafka-prerequisites)= + +# Prerequisites + +## Version Compatibility + +- Kafka server versions: + : - Officially declared: 0.10 or higher + - Actually tested: 3.2.3, 3.9.0 (only Kafka 3.x supports message headers) +- Spark versions: 2.4.x - 3.5.x +- Java versions: 8 - 17 + +See [official documentation](https://spark.apache.org/docs/latest/structured-streaming-kafka-integration.html). + +## Installing PySpark + +To use Kafka connector you should have PySpark installed (or injected to `sys.path`) +BEFORE creating the connector instance. + +See {ref}`install-spark` installation instruction for more details. + +## Connecting to Kafka + +### Connection address + +Kafka is a distributed service, and usually has a list of brokers you can connect to (unlike other connectors, there only one host+port can be set). +Please contact your Kafka administrator to get addresses of these brokers, as there are no defaults. + +Also Kafka has a feature called *advertised listeners* - client connects to one broker, and received list of other brokers in the clusters. +So you don't have to pass all brokers to `addresses`, it can be some subset. Other broker addresses will be fetched directly from the cluster. + +### Connection protocol + +Kafka can support different connection protocols. List of currently supported protocols: +: - {obj}`PLAINTEXT ` (not secure) + - {obj}`SSL ` (secure, recommended) + +Note that specific port can listen for only one of these protocols, so it is important to set +proper port number + protocol combination. + +### Authentication mechanism + +Kafka can support different authentication mechanism (also known as [SASL](https://en.wikipedia.org/wiki/Simple_Authentication_and_Security_Layer)). + +List of currently supported mechanisms: +: - {obj}`PLAIN `. To no confuse this with `PLAINTEXT` connection protocol, onETL uses name `BasicAuth`. + - {obj}`GSSAPI `. To simplify naming, onETL uses name `KerberosAuth`. + - {obj}`SCRAM-SHA-256 or SCRAM-SHA-512 ` (recommended). + +Different mechanisms use different types of credentials (login + password, keytab file, and so on). + +Note that connection protocol and auth mechanism are set in pairs: +: - If you see `SASL_PLAINTEXT` this means `PLAINTEXT` connection protocol + some auth mechanism. + - If you see `SASL_SSL` this means `SSL` connection protocol + some auth mechanism. + - If you see just `PLAINTEXT` or `SSL` (**no** `SASL`), this means that authentication is disabled (anonymous access). + +Please contact your Kafka administrator to get details about enabled auth mechanism in a specific Kafka instance. + +### Required grants + +Ask your Kafka administrator to set following grants for a user, *if Kafka instance uses ACL*: +: - `Describe` + `Read` for reading data from Kafka (Consumer). + - `Describe` + `Write` for writing data from Kafka (Producer). + +More details can be found in [documentation](https://kafka.apache.org/documentation/#operations_in_kafka). diff --git a/mddocs/docs/ru/connection/db_connection/kafka/protocol.md b/mddocs/docs/ru/connection/db_connection/kafka/protocol.md new file mode 100644 index 000000000..b9278d009 --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/kafka/protocol.md @@ -0,0 +1,13 @@ +(kafka-protocol)= + +# Kafka Protocol + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.kafka.kafka_protocol +``` + +```{eval-rst} +.. autoclass:: KafkaProtocol + :members: get_options, cleanup + :member-order: bysource +``` diff --git a/mddocs/docs/ru/connection/db_connection/kafka/read.md b/mddocs/docs/ru/connection/db_connection/kafka/read.md new file mode 100644 index 000000000..be707f260 --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/kafka/read.md @@ -0,0 +1,137 @@ +(kafka-read)= + +# Reading from Kafka + +Data can be read from Kafka to Spark using {obj}`DBReader `. +It also supports {ref}`strategy` for incremental data reading. + +## Supported DBReader features + +- ❌ `columns` (is not supported by Kafka) +- ❌ `where` (is not supported by Kafka) +- ✅︎ `hwm`, supported strategies: +- - ✅︎ {ref}`snapshot-strategy` +- - ✅︎ {ref}`incremental-strategy` +- - ❌ {ref}`snapshot-batch-strategy` +- - ❌ {ref}`incremental-batch-strategy` +- ❌ `hint` (is not supported by Kafka) +- ❌ `df_schema` (see note below) +- ✅︎ `options` (see {obj}`Kafka.ReadOptions `) + +## Dataframe schema + +Unlike other DB connections, Kafka does not have concept of columns. +All the topics messages have the same set of fields, see structure below: + +```text +root +|-- key: binary (nullable = true) +|-- value: binary (nullable = true) +|-- topic: string (nullable = false) +|-- partition: integer (nullable = false) +|-- offset: integer (nullable = false) +|-- timestamp: timestamp (nullable = false) +|-- timestampType: integer (nullable = false) +|-- headers: struct (nullable = true) + |-- key: string (nullable = false) + |-- value: binary (nullable = true) +``` + +`headers` field is present in the dataframe only if `Kafka.ReadOptions(include_headers=True)` is passed (compatibility with Kafka 1.x). + +## Value deserialization + +To read `value` or `key` of other type than bytes (e.g. struct or integer), users have to deserialize values manually. + +This could be done using following methods: +: - {obj}`Avro.parse_column ` + - {obj}`JSON.parse_column ` + - {obj}`CSV.parse_column ` + - {obj}`XML.parse_column ` + +## Examples + +Snapshot strategy, `value` is Avro binary data: + +```python +from onetl.connection import Kafka +from onetl.db import DBReader, DBWriter +from onetl.file.format import Avro +from pyspark.sql.functions import decode + +# read all topic data from Kafka +kafka = Kafka(...) +reader = DBReader(connection=kafka, source="avro_topic") +read_df = reader.run() + +# parse Avro format to Spark struct +avro = Avro( + schema_dict={ + "type": "record", + "name": "Person", + "fields": [ + {"name": "name", "type": "string"}, + {"name": "age", "type": "int"}, + ], + } +) +deserialized_df = read_df.select( + # cast binary key to string + decode("key", "UTF-8").alias("key"), + avro.parse_column("value"), +) +``` + +Incremental strategy, `value` is JSON string: + +```{eval-rst} +.. note:: + + Currently Kafka connector does support only HWMs based on ``offset`` field. Other fields, like ``timestamp``, are not yet supported. +``` + +```python +from onetl.connection import Kafka +from onetl.db import DBReader, DBWriter +from onetl.file.format import JSON +from pyspark.sql.functions import decode + +kafka = Kafka(...) + +# read only new data from Kafka topic +reader = DBReader( + connection=kafka, + source="topic_name", + hwm=DBReader.AutoDetectHWM(name="kafka_hwm", expression="offset"), +) + +with IncrementalStrategy(): + read_df = reader.run() + +# parse JSON format to Spark struct +json = JSON() +schema = StructType( + [ + StructField("name", StringType(), nullable=True), + StructField("age", IntegerType(), nullable=True), + ], +) +deserialized_df = read_df.select( + # cast binary key to string + decode("key", "UTF-8").alias("key"), + json.parse_column("value", json), +) +``` + +## Options + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.kafka.options +``` + +```{eval-rst} +.. autopydantic_model:: KafkaReadOptions + :member-order: bysource + :model-show-field-summary: false + :field-show-constraints: false +``` diff --git a/mddocs/docs/ru/connection/db_connection/kafka/scram_auth.md b/mddocs/docs/ru/connection/db_connection/kafka/scram_auth.md new file mode 100644 index 000000000..5a27a46ea --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/kafka/scram_auth.md @@ -0,0 +1,14 @@ +(kafka-scram-auth)= + +# Kafka ScramAuth + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.kafka.kafka_scram_auth +``` + +```{eval-rst} +.. autopydantic_model:: KafkaScramAuth + :member-order: bysource + :model-show-field-summary: false + :field-show-constraints: false +``` diff --git a/mddocs/docs/ru/connection/db_connection/kafka/slots.md b/mddocs/docs/ru/connection/db_connection/kafka/slots.md new file mode 100644 index 000000000..0c58413fb --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/kafka/slots.md @@ -0,0 +1,13 @@ +(kafka-slots)= + +# Kafka Slots + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.kafka.slots +``` + +```{eval-rst} +.. autoclass:: KafkaSlots + :members: normalize_cluster_name, get_known_clusters, normalize_address, get_cluster_addresses + :member-order: bysource +``` diff --git a/mddocs/docs/ru/connection/db_connection/kafka/ssl_protocol.md b/mddocs/docs/ru/connection/db_connection/kafka/ssl_protocol.md new file mode 100644 index 000000000..6e1a7641d --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/kafka/ssl_protocol.md @@ -0,0 +1,14 @@ +(kafka-ssl-protocol)= + +# Kafka SSLProtocol + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.kafka.kafka_ssl_protocol +``` + +```{eval-rst} +.. autopydantic_model:: KafkaSSLProtocol + :member-order: bysource + :model-show-field-summary: false + :field-show-constraints: false +``` diff --git a/mddocs/docs/ru/connection/db_connection/kafka/troubleshooting.md b/mddocs/docs/ru/connection/db_connection/kafka/troubleshooting.md new file mode 100644 index 000000000..7a3536fbf --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/kafka/troubleshooting.md @@ -0,0 +1,13 @@ +(kafka-troubleshooting)= + +# Kafka Troubleshooting + +```{eval-rst} +.. note:: + + General guide: :ref:`troubleshooting`. +``` + +## Cannot connect using `SSL` protocol + +Please check that certificate files are not Base-64 encoded. diff --git a/mddocs/docs/ru/connection/db_connection/kafka/write.md b/mddocs/docs/ru/connection/db_connection/kafka/write.md new file mode 100644 index 000000000..cb3e3019a --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/kafka/write.md @@ -0,0 +1,75 @@ +(kafka-write)= + +# Writing to Kafka + +For writing data to Kafka, use {obj}`DBWriter ` with specific options (see below). + +## Dataframe schema + +Unlike other DB connections, Kafka does not have concept of columns. +All the topics messages have the same set of fields. Only some of them can be written: + +```text +root +|-- key: binary (nullable = true) +|-- value: binary (nullable = true) +|-- headers: struct (nullable = true) + |-- key: string (nullable = false) + |-- value: binary (nullable = true) +``` + +`headers` can be passed only with `Kafka.WriteOptions(include_headers=True)` (compatibility with Kafka 1.x). + +Field `topic` should not be present in the dataframe, as it is passed to `DBWriter(target=...)`. + +Other fields, like `partition`, `offset`, `timestamp` are set by Kafka, and cannot be passed explicitly. + +## Value serialization + +To write `value` or `key` of other type than bytes (e.g. struct or integer), users have to serialize values manually. + +This could be done using following methods: +: - {obj}`Avro.serialize_column ` + - {obj}`JSON.serialize_column ` + - {obj}`CSV.serialize_column ` + +## Examples + +Convert `value` to JSON string, and write to Kafka: + +```python +from onetl.connection import Kafka +from onetl.db import DBWriter +from onetl.file.format import JSON + +df = ... # original data is here + +# serialize struct data as JSON +json = JSON() +write_df = df.select( + df.key, + json.serialize_column(df.value), +) + +# write data to Kafka +kafka = Kafka(...) + +writer = DBWriter( + connection=kafka, + target="topic_name", +) +writer.run(write_df) +``` + +## Options + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.kafka.options +``` + +```{eval-rst} +.. autopydantic_model:: KafkaWriteOptions + :member-order: bysource + :model-show-field-summary: false + :field-show-constraints: false +``` diff --git a/mddocs/docs/ru/connection/db_connection/mongodb/connection.md b/mddocs/docs/ru/connection/db_connection/mongodb/connection.md new file mode 100644 index 000000000..b81c56ffc --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/mongodb/connection.md @@ -0,0 +1,13 @@ +(mongodb-connection)= + +# MongoDB Connection + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.mongodb.connection +``` + +```{eval-rst} +.. autoclass:: MongoDB + :members: get_packages, check + :member-order: bysource +``` diff --git a/mddocs/docs/ru/connection/db_connection/mongodb/index.md b/mddocs/docs/ru/connection/db_connection/mongodb/index.md new file mode 100644 index 000000000..fb9656117 --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/mongodb/index.md @@ -0,0 +1,27 @@ +(mongodb)= + +# MongoDB + +```{toctree} +:caption: Connection +:maxdepth: 1 + +prerequisites +connection +``` + +```{toctree} +:caption: Operations +:maxdepth: 1 + +read +pipeline +write +``` + +```{toctree} +:caption: Troubleshooting +:maxdepth: 1 + +types +``` diff --git a/mddocs/docs/ru/connection/db_connection/mongodb/pipeline.md b/mddocs/docs/ru/connection/db_connection/mongodb/pipeline.md new file mode 100644 index 000000000..2cc30458c --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/mongodb/pipeline.md @@ -0,0 +1,41 @@ +(mongodb-sql)= + +# Reading from MongoDB using `MongoDB.pipeline` + +{obj}`MongoDB.sql ` allows passing custom pipeline, +but does not support incremental strategies. + +```{eval-rst} +.. warning:: + + Please take into account :ref:`mongodb-types` +``` + +## Recommendations + +### Pay attention to `pipeline` value + +Instead of filtering data on Spark side using `df.filter(df.column == 'value')` pass proper `mongodb.pipeline(..., pipeline={"$match": {"column": {"$eq": "value"}}})` value. +This both reduces the amount of data send from MongoDB to Spark, and may also improve performance of the query. +Especially if there are indexes for columns used in `pipeline` value. + +## References + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.mongodb.connection +``` + +```{eval-rst} +.. automethod:: MongoDB.pipeline +``` + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.mongodb.options +``` + +```{eval-rst} +.. autopydantic_model:: MongoDBPipelineOptions + :member-order: bysource + :model-show-field-summary: false + :field-show-constraints: false +``` diff --git a/mddocs/docs/ru/connection/db_connection/mongodb/prerequisites.md b/mddocs/docs/ru/connection/db_connection/mongodb/prerequisites.md new file mode 100644 index 000000000..f491ca10e --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/mongodb/prerequisites.md @@ -0,0 +1,72 @@ +(mongodb-prerequisites)= + +# Prerequisites + +## Version Compatibility + +- MongoDB server versions: + : - Officially declared: 4.0 or higher + - Actually tested: 4.0.0, 8.0.4 +- Spark versions: 3.2.x - 3.5.x +- Java versions: 8 - 20 + +See [official documentation](https://www.mongodb.com/docs/spark-connector/). + +## Installing PySpark + +To use MongoDB connector you should have PySpark installed (or injected to `sys.path`) +BEFORE creating the connector instance. + +See {ref}`install-spark` installation instruction for more details. + +## Connecting to MongoDB + +### Connection host + +It is possible to connect to MongoDB host by using either DNS name of host or it's IP address. + +It is also possible to connect to MongoDB shared cluster: + +```python +mongo = MongoDB( + host="master.host.or.ip", + user="user", + password="*****", + database="target_database", + spark=spark, + extra={ + # read data from secondary cluster node, switch to primary if not available + "readPreference": "secondaryPreferred", + }, +) +``` + +Supported `readPreference` values are described in [official documentation](https://www.mongodb.com/docs/manual/core/read-preference/). + +### Connection port + +Connection is usually performed to port `27017`. Port may differ for different MongoDB instances. +Please ask your MongoDB administrator to provide required information. + +### Required grants + +Ask your MongoDB cluster administrator to set following grants for a user, +used for creating a connection: + +```{eval-rst} +.. tabs:: + + .. code-tab:: js Read + Write + + // allow writing data to specific database + db.grantRolesToUser("username", [{db: "somedb", role: "readWrite"}]) + + .. code-tab:: js Read only + + // allow reading data from specific database + db.grantRolesToUser("username", [{db: "somedb", role: "read"}]) +``` + +See: +: - [db.grantRolesToUser documentation](https://www.mongodb.com/docs/manual/reference/method/db.grantRolesToUser) + - [MongoDB builtin roles](https://www.mongodb.com/docs/manual/reference/built-in-roles) diff --git a/mddocs/docs/ru/connection/db_connection/mongodb/read.md b/mddocs/docs/ru/connection/db_connection/mongodb/read.md new file mode 100644 index 000000000..15cf34bb1 --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/mongodb/read.md @@ -0,0 +1,141 @@ +(mongodb-read)= + +# Reading from MongoDB using `DBReader` + +{obj}`DBReader ` supports {ref}`strategy` for incremental data reading, +but does not support custom pipelines, e.g. aggregation. + +```{eval-rst} +.. warning:: + + Please take into account :ref:`mongodb-types` +``` + +## Supported DBReader features + +- ❌ `columns` (for now, all document fields are read) +- ✅︎ `where` (passed to `{"$match": ...}` aggregation pipeline) +- ✅︎ `hwm`, supported strategies: +- - ✅︎ {ref}`snapshot-strategy` +- - ✅︎ {ref}`incremental-strategy` +- - ✅︎ {ref}`snapshot-batch-strategy` +- - ✅︎ {ref}`incremental-batch-strategy` +- - Note that `expression` field of HWM can only be a field name, not a custom expression +- ✅︎ `hint` (see [official documentation](https://www.mongodb.com/docs/v5.0/reference/operator/meta/hint/)) +- ✅︎ `df_schema` (mandatory) +- ✅︎ `options` (see {obj}`MongoDB.ReadOptions `) + +## Examples + +Snapshot strategy: + +```python +from onetl.connection import MongoDB +from onetl.db import DBReader + +from pyspark.sql.types import ( + StructType, + StructField, + IntegerType, + StringType, + TimestampType, +) + +mongodb = MongoDB(...) + +# mandatory +df_schema = StructType( + [ + StructField("_id", StringType()), + StructField("some", StringType()), + StructField( + "field", + StructType( + [ + StructField("nested", IntegerType()), + ], + ), + ), + StructField("updated_dt", TimestampType()), + ] +) + +reader = DBReader( + connection=mongodb, + source="some_collection", + df_schema=df_schema, + where={"field": {"$eq": 123}}, + hint={"field": 1}, + options=MongoDBReadOptions(batchSize=10000), +) +df = reader.run() +``` + +Incremental strategy: + +```python +from onetl.connection import MongoDB +from onetl.db import DBReader +from onetl.strategy import IncrementalStrategy + +from pyspark.sql.types import ( + StructType, + StructField, + IntegerType, + StringType, + TimestampType, +) + +mongodb = MongoDB(...) + +# mandatory +df_schema = StructType( + [ + StructField("_id", StringType()), + StructField("some", StringType()), + StructField( + "field", + StructType( + [ + StructField("nested", IntegerType()), + ], + ), + ), + StructField("updated_dt", TimestampType()), + ] +) + +reader = DBReader( + connection=mongodb, + source="some_collection", + df_schema=df_schema, + where={"field": {"$eq": 123}}, + hint={"field": 1}, + hwm=DBReader.AutoDetectHWM(name="mongodb_hwm", expression="updated_dt"), + options=MongoDBReadOptions(batchSize=10000), +) + +with IncrementalStrategy(): + df = reader.run() +``` + +## Recommendations + +### Pay attention to `where` value + +Instead of filtering data on Spark side using `df.filter(df.column == 'value')` pass proper `DBReader(where={"column": {"$eq": "value"}})` clause. +This both reduces the amount of data send from MongoDB to Spark, and may also improve performance of the query. +Especially if there are indexes for columns used in `where` clause. + +## Read options + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.mongodb.options +``` + +```{eval-rst} +.. autopydantic_model:: MongoDBReadOptions + :member-order: bysource + :model-show-field-summary: false + :field-show-constraints: false +``` diff --git a/mddocs/docs/ru/connection/db_connection/mongodb/types.md b/mddocs/docs/ru/connection/db_connection/mongodb/types.md new file mode 100644 index 000000000..5c6c506d0 --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/mongodb/types.md @@ -0,0 +1,269 @@ +(mongodb-types)= + +# MongoDB \<-> Spark type mapping + +```{eval-rst} +.. note:: + + The results below are valid for Spark 3.5.5, and may differ on other Spark versions. +``` + +## Type detection & casting + +Spark's DataFrames always have a `schema` which is a list of fields with corresponding Spark types. All operations on a field are performed using field type. + +MongoDB is, by design, \_\_schemaless\_\_. So there are 2 ways how this can be handled: + +- User provides DataFrame schema explicitly: + + ```{eval-rst} + .. dropdown:: See example + + .. code-block:: python + + from onetl.connection import MongoDB + from onetl.db import DBReader + + from pyspark.sql.types import ( + StructType, + StructField, + IntegerType, + StringType, + TimestampType, + ) + + mongodb = MongoDB(...) + + df_schema = StructType( + [ + StructField("_id", StringType()), + StructField("some", StringType()), + StructField( + "field", + StructType( + [ + StructField("nested", IntegerType()), + ] + ), + ), + ] + ) + + reader = DBReader( + connection=mongodb, + source="some_collection", + df_schema=df_schema, + ) + df = reader.run() + + # or + + df = mongodb.pipeline( + collection="some_collection", + df_schema=df_schema, + ) + ``` + +- Rely on MongoDB connector schema infer: + + ```python + df = mongodb.pipeline(collection="some_collection") + ``` + + In this case MongoDB connector read a sample of collection documents, and build DataFrame schema based on document fields and values. + +It is highly recommended to pass `df_schema` explicitly, to avoid type conversion issues. + +### References + +Here you can find source code with type conversions: + +- [MongoDB -> Spark](https://github.com/mongodb/mongo-spark/blob/r10.4.1/src/main/java/com/mongodb/spark/sql/connector/schema/InferSchema.java#L214-L260) +- [Spark -> MongoDB](https://github.com/mongodb/mongo-spark/blob/r10.4.1/src/main/java/com/mongodb/spark/sql/connector/schema/RowToBsonDocumentConverter.java#L157-L260) + +## Supported types + +See [official documentation](https://www.mongodb.com/docs/manual/reference/bson-types/) + +### Numeric types + +```{eval-rst} ++---------------------+-----------------------------+----------------------+ +| MongoDB type (read) | Spark type | MongoDB type (write) | ++=====================+=============================+======================+ +| ``Decimal128`` | ``DecimalType(P=34, S=32)`` | ``Decimal128`` | ++---------------------+-----------------------------+----------------------+ +| ``-`` | ``FloatType()`` | ``Double`` | ++---------------------+-----------------------------+ | +| ``Double`` | ``DoubleType()`` | | ++---------------------+-----------------------------+----------------------+ +| ``-`` | ``ByteType()`` | ``Int32`` | ++---------------------+-----------------------------+ | +| ``-`` | ``ShortType()`` | | ++---------------------+-----------------------------+ | +| ``Int32`` | ``IntegerType()`` | | ++---------------------+-----------------------------+----------------------+ +| ``Int64`` | ``LongType()`` | ``Int64`` | ++---------------------+-----------------------------+----------------------+ +``` + +### Temporal types + +```{eval-rst} ++------------------------+-----------------------------------+-------------------------+ +| MongoDB type (read) | Spark type | MongoDB type (write) | ++========================+===================================+=========================+ +| ``-`` | ``DateType()``, days | ``Date``, milliseconds | ++------------------------+-----------------------------------+-------------------------+ +| ``Date``, milliseconds | ``TimestampType()``, microseconds | ``Date``, milliseconds, | +| | | **precision loss** [2]_ | ++------------------------+-----------------------------------+-------------------------+ +| ``Timestamp``, seconds | ``TimestampType()``, microseconds | ``Date``, milliseconds | ++------------------------+-----------------------------------+-------------------------+ +| ``-`` | ``TimestampNTZType()`` | unsupported | ++------------------------+-----------------------------------+ | +| ``-`` | ``DayTimeIntervalType()`` | | ++------------------------+-----------------------------------+-------------------------+ +``` + +```{eval-rst} +.. warning:: + + Note that types in MongoDB and Spark have different value ranges: + + +---------------+--------------------------------+--------------------------------+---------------------+--------------------------------+--------------------------------+ + | MongoDB type | Min value | Max value | Spark type | Min value | Max value | + +===============+================================+================================+=====================+================================+================================+ + | ``Date`` | -290 million years | 290 million years | ``TimestampType()`` | ``0001-01-01 00:00:00.000000`` | ``9999-12-31 23:59:59.999999`` | + +---------------+--------------------------------+--------------------------------+ | | | + | ``Timestamp`` | ``1970-01-01 00:00:00`` | ``2106-02-07 09:28:16`` | | | | + +---------------+--------------------------------+--------------------------------+---------------------+--------------------------------+--------------------------------+ + + So not all values can be read from MongoDB to Spark, and can written from Spark DataFrame to MongoDB. + + References: + * `MongoDB Date type documentation `_ + * `MongoDB Timestamp documentation `_ + * `Spark DateType documentation `_ + * `Spark TimestampType documentation `_ +``` + +[^footnote-1]: MongoDB `Date` type has precision up to milliseconds (`23:59:59.999`). + Inserting data with microsecond precision (`23:59:59.999999`) + will lead to **throwing away microseconds**. + +### String types + +Note: fields of deprecated MongoDB type `Symbol` are excluded during read. + +```{eval-rst} ++---------------------+------------------+----------------------+ +| MongoDB type (read) | Spark type | MongoDB type (write) | ++=====================+==================+======================+ +| ``String`` | ``StringType()`` | ``String`` | ++---------------------+ | | +| ``Code`` | | | ++---------------------+ | | +| ``RegExp`` | | | ++---------------------+------------------+----------------------+ +``` + +### Binary types + +| MongoDB type (read) | Spark type | MongoDB type (write) | +| ------------------- | --------------- | -------------------- | +| `Boolean` | `BooleanType()` | `Boolean` | +| `Binary` | `BinaryType()` | `Binary` | + +### Struct types + +```{eval-rst} ++---------------------+-----------------------+----------------------+ +| MongoDB type (read) | Spark type | MongoDB type (write) | ++=====================+=======================+======================+ +| ``Array[T]`` | ``ArrayType(T)`` | ``Array[T]`` | ++---------------------+-----------------------+----------------------+ +| ``Object[...]`` | ``StructType([...])`` | ``Object[...]`` | ++---------------------+-----------------------+ | +| ``-`` | ``MapType(...)`` | | ++---------------------+-----------------------+----------------------+ +``` + +### Special types + +```{eval-rst} ++---------------------+---------------------------------------------------------+---------------------------------------+ +| MongoDB type (read) | Spark type | MongoDB type (write) | ++=====================+=========================================================+=======================================+ +| ``ObjectId`` | ``StringType()`` | ``String`` | ++---------------------+ | | +| ``MaxKey`` | | | ++---------------------+ | | +| ``MinKey`` | | | ++---------------------+---------------------------------------------------------+---------------------------------------+ +| ``Null`` | ``NullType()`` | ``Null`` | ++---------------------+ | | +| ``Undefined`` | | | ++---------------------+---------------------------------------------------------+---------------------------------------+ +| ``DBRef`` | ``StructType([$ref: StringType(), $id: StringType()])`` | ``Object[$ref: String, $id: String]`` | ++---------------------+---------------------------------------------------------+---------------------------------------+ +``` + +## Explicit type cast + +### `DBReader` + +Currently it is not possible to cast field types using `DBReader`. But this can be done using `MongoDB.pipeline`. + +### `MongoDB.pipeline` + +You can use `$project` aggregation to cast field types: + +```python +from pyspark.sql.types import IntegerType, StructField, StructType + +from onetl.connection import MongoDB +from onetl.db import DBReader + +mongodb = MongoDB(...) + +df = mongodb.pipeline( + collection="my_collection", + pipeline=[ + { + "$project": { + # convert unsupported_field to string + "unsupported_field_str": { + "$convert": { + "input": "$unsupported_field", + "to": "string", + }, + }, + # skip unsupported_field from result + "unsupported_field": 0, + } + } + ], +) + +# cast field content to proper Spark type +df = df.select( + df.id, + df.supported_field, + # explicit cast + df.unsupported_field_str.cast("integer").alias("parsed_integer"), +) +``` + +### `DBWriter` + +Convert dataframe field to string on Spark side, and then write it to MongoDB: + +```python +df = df.select( + df.id, + df.unsupported_field.cast("string").alias("array_field_json"), +) + +writer.run(df) +``` diff --git a/mddocs/docs/ru/connection/db_connection/mongodb/write.md b/mddocs/docs/ru/connection/db_connection/mongodb/write.md new file mode 100644 index 000000000..5b44d8613 --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/mongodb/write.md @@ -0,0 +1,47 @@ +(mongodb-write)= + +# Writing to MongoDB using `DBWriter` + +For writing data to MongoDB, use {obj}`DBWriter `. + +```{eval-rst} +.. warning:: + + Please take into account :ref:`mongodb-types` +``` + +## Examples + +```python +from onetl.connection import MongoDB +from onetl.db import DBWriter + +mongodb = MongoDB(...) + +df = ... # data is here + +writer = DBWriter( + connection=mongodb, + target="schema.table", + options=MongoDB.WriteOptions( + if_exists="append", + ), +) + +writer.run(df) +``` + +## Write options + +Method above accepts {obj}`MongoDB.WriteOptions ` + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.mongodb.options +``` + +```{eval-rst} +.. autopydantic_model:: MongoDBWriteOptions + :member-order: bysource + :model-show-field-summary: false + :field-show-constraints: false +``` diff --git a/mddocs/docs/ru/connection/db_connection/mssql/connection.md b/mddocs/docs/ru/connection/db_connection/mssql/connection.md new file mode 100644 index 000000000..e35ef6ed9 --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/mssql/connection.md @@ -0,0 +1,12 @@ +(mssql-connection)= + +# MSSQL connection + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.mssql.connection +``` + +```{eval-rst} +.. autoclass:: MSSQL + :members: get_packages, check +``` diff --git a/mddocs/docs/ru/connection/db_connection/mssql/execute.md b/mddocs/docs/ru/connection/db_connection/mssql/execute.md new file mode 100644 index 000000000..278d84229 --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/mssql/execute.md @@ -0,0 +1,117 @@ +(mssql-execute)= + +# Executing statements in MSSQL + +```{eval-rst} +.. warning:: + + Methods below **read all the rows** returned from DB **to Spark driver memory**, and then convert them to DataFrame. + + Do **NOT** use them to read large amounts of data. Use :ref:`DBReader ` or :ref:`MSSQL.sql ` instead. +``` + +## How to + +There are 2 ways to execute some statement in MSSQL + +### Use `MSSQL.fetch` + +Use this method to perform some `SELECT` query which returns **small number or rows**, like reading +MSSQL config, or reading data from some reference table. Method returns Spark DataFrame. + +Method accepts {obj}`MSSQL.FetchOptions `. + +Connection opened using this method should be then closed with `connection.close()` or `with connection:`. + +```{eval-rst} +.. warning:: + + Please take into account :ref:`mssql-types`. +``` + +#### Syntax support + +This method supports **any** query syntax supported by MSSQL, like: + +- ✅︎ `SELECT ... FROM ...` +- ✅︎ `WITH alias AS (...) SELECT ...` +- ✅︎ `SELECT func(arg1, arg2) FROM DUAL` - call function +- ❌ `SET ...; SELECT ...;` - multiple statements not supported + +#### Examples + +```python +from onetl.connection import MSSQL + +mssql = MSSQL(...) + +df = mssql.fetch( + "SELECT value FROM some.reference_table WHERE key = 'some_constant'", + options=MSSQL.FetchOptions(queryTimeout=10), +) +mssql.close() +value = df.collect()[0][0] # get value from first row and first column +``` + +### Use `MSSQL.execute` + +Use this method to execute DDL and DML operations. Each method call runs operation in a separated transaction, and then commits it. + +Method accepts {obj}`MSSQL.ExecuteOptions `. + +Connection opened using this method should be then closed with `connection.close()` or `with connection:`. + +#### Syntax support + +This method supports **any** query syntax supported by MSSQL, like: + +- ✅︎ `CREATE TABLE ...`, `CREATE VIEW ...` +- ✅︎ `ALTER ...` +- ✅︎ `INSERT INTO ... AS SELECT ...` +- ✅︎ `DROP TABLE ...`, `DROP VIEW ...`, `TRUNCATE TABLE`, and so on +- ✅︎ `EXEC procedure(arg1, arg2) ...` or `{call procedure(arg1, arg2)}` - special syntax for calling procedure +- ✅︎ `DECLARE ... BEGIN ... END` - execute PL/SQL statement +- ✅︎ other statements not mentioned here +- ❌ `SET ...; SELECT ...;` - multiple statements not supported + +#### Examples + +```python +from onetl.connection import MSSQL + +mssql = MSSQL(...) + +mssql.execute("DROP TABLE schema.table") +mssql.execute( + """ + CREATE TABLE schema.table ( + id bigint GENERATED ALWAYS AS IDENTITY, + key VARCHAR2(4000), + value NUMBER + ) + """, + options=MSSQL.ExecuteOptions(queryTimeout=10), +) +``` + +## Options + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.mssql.options +``` + +```{eval-rst} +.. autopydantic_model:: MSSQLFetchOptions + :inherited-members: GenericOptions + :member-order: bysource + :model-show-field-summary: false + :field-show-constraints: false +``` + +```{eval-rst} +.. autopydantic_model:: MSSQLExecuteOptions + :inherited-members: GenericOptions + :member-order: bysource + :model-show-field-summary: false + :field-show-constraints: false +``` diff --git a/mddocs/docs/ru/connection/db_connection/mssql/index.md b/mddocs/docs/ru/connection/db_connection/mssql/index.md new file mode 100644 index 000000000..16448c6a1 --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/mssql/index.md @@ -0,0 +1,28 @@ +(mssql)= + +# MSSQL + +```{toctree} +:caption: Connection +:maxdepth: 1 + +prerequisites +connection +``` + +```{toctree} +:caption: Operations +:maxdepth: 1 + +read +sql +write +execute +``` + +```{toctree} +:caption: Troubleshooting +:maxdepth: 1 + +types +``` diff --git a/mddocs/docs/ru/connection/db_connection/mssql/prerequisites.md b/mddocs/docs/ru/connection/db_connection/mssql/prerequisites.md new file mode 100644 index 000000000..89e62f322 --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/mssql/prerequisites.md @@ -0,0 +1,76 @@ +(mssql-prerequisites)= + +# Prerequisites + +## Version Compatibility + +- SQL Server versions: + : - Officially declared: 2016 - 2022 + - Actually tested: 2017, 2022 +- Spark versions: 2.3.x - 3.5.x +- Java versions: 8 - 20 + +See [official documentation](https://learn.microsoft.com/en-us/sql/connect/jdbc/system-requirements-for-the-jdbc-driver) +and [official compatibility matrix](https://learn.microsoft.com/en-us/sql/connect/jdbc/microsoft-jdbc-driver-for-sql-server-support-matrix). + +## Installing PySpark + +To use MSSQL connector you should have PySpark installed (or injected to `sys.path`) +BEFORE creating the connector instance. + +See {ref}`install-spark` installation instruction for more details. + +## Connecting to MSSQL + +### Connection port + +Connection is usually performed to port 1433. Port may differ for different MSSQL instances. +Please ask your MSSQL administrator to provide required information. + +For named MSSQL instances (`instanceName` option), [port number is optional](https://learn.microsoft.com/en-us/sql/connect/jdbc/building-the-connection-url?view=sql-server-ver16#named-and-multiple-sql-server-instances), and could be omitted. + +### Connection host + +It is possible to connect to MSSQL by using either DNS name of host or it's IP address. + +If you're using MSSQL cluster, it is currently possible to connect only to **one specific node**. +Connecting to multiple nodes to perform load balancing, as well as automatic failover to new master/replica are not supported. + +### Required grants + +Ask your MSSQL cluster administrator to set following grants for a user, +used for creating a connection: + +```{eval-rst} +.. tabs:: + + .. code-tab:: sql Read + Write (schema is owned by user) + + -- allow creating tables for user + GRANT CREATE TABLE TO username; + + -- allow read & write access to specific table + GRANT SELECT, INSERT ON username.mytable TO username; + + -- only if if_exists="replace_entire_table" is used: + -- allow dropping/truncating tables in any schema + GRANT ALTER ON username.mytable TO username; + + .. code-tab:: sql Read + Write (schema is not owned by user) + + -- allow creating tables for user + GRANT CREATE TABLE TO username; + + -- allow managing tables in specific schema, and inserting data to tables + GRANT ALTER, SELECT, INSERT ON SCHEMA::someschema TO username; + + .. code-tab:: sql Read only + + -- allow read access to specific table + GRANT SELECT ON someschema.mytable TO username; +``` + +More details can be found in official documentation: +: - [GRANT ON DATABASE](https://learn.microsoft.com/en-us/sql/t-sql/statements/grant-database-permissions-transact-sql) + - [GRANT ON OBJECT](https://learn.microsoft.com/en-us/sql/t-sql/statements/grant-object-permissions-transact-sql) + - [GRANT ON SCHEMA](https://learn.microsoft.com/en-us/sql/t-sql/statements/grant-schema-permissions-transact-sql) diff --git a/mddocs/docs/ru/connection/db_connection/mssql/read.md b/mddocs/docs/ru/connection/db_connection/mssql/read.md new file mode 100644 index 000000000..924d10e2f --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/mssql/read.md @@ -0,0 +1,93 @@ +(mssql-read)= + +# Reading from MSSQL using `DBReader` + +{obj}`DBReader ` supports {ref}`strategy` for incremental data reading, +but does not support custom queries, like `JOIN`. + +```{eval-rst} +.. warning:: + + Please take into account :ref:`mssql-types` +``` + +## Supported DBReader features + +- ✅︎ `columns` +- ✅︎ `where` +- ✅︎ `hwm`, supported strategies: +- - ✅︎ {ref}`snapshot-strategy` +- - ✅︎ {ref}`incremental-strategy` +- - ✅︎ {ref}`snapshot-batch-strategy` +- - ✅︎ {ref}`incremental-batch-strategy` +- ❌ `hint` (MSSQL does support hints, but DBReader not, at least for now) +- ❌ `df_schema` +- ✅︎ `options` (see {obj}`MSSQL.ReadOptions `) + +## Examples + +Snapshot strategy: + +```python +from onetl.connection import MSSQL +from onetl.db import DBReader + +mssql = MSSQL(...) + +reader = DBReader( + connection=mssql, + source="schema.table", + columns=["id", "key", "CAST(value AS text) value", "updated_dt"], + where="key = 'something'", + options=MSSQL.ReadOptions(partitionColumn="id", numPartitions=10), +) +df = reader.run() +``` + +Incremental strategy: + +```python +from onetl.connection import MSSQL +from onetl.db import DBReader +from onetl.strategy import IncrementalStrategy + +mssql = MSSQL(...) + +reader = DBReader( + connection=mssql, + source="schema.table", + columns=["id", "key", "CAST(value AS text) value", "updated_dt"], + where="key = 'something'", + hwm=DBReader.AutoDetectHWM(name="mssql_hwm", expression="updated_dt"), + options=MSSQL.ReadOptions(partitionColumn="id", numPartitions=10), +) + +with IncrementalStrategy(): + df = reader.run() +``` + +## Recommendations + +### Select only required columns + +Instead of passing `"*"` in `DBReader(columns=[...])` prefer passing exact column names. This reduces the amount of data passed from MSSQL to Spark. + +### Pay attention to `where` value + +Instead of filtering data on Spark side using `df.filter(df.column == 'value')` pass proper `DBReader(where="column = 'value'")` clause. +This both reduces the amount of data send from MSSQL to Spark, and may also improve performance of the query. +Especially if there are indexes or partitions for columns used in `where` clause. + +## Options + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.mssql.options +``` + +```{eval-rst} +.. autopydantic_model:: MSSQLReadOptions + :inherited-members: GenericOptions + :member-order: bysource + :model-show-field-summary: false + :field-show-constraints: false +``` diff --git a/mddocs/docs/ru/connection/db_connection/mssql/sql.md b/mddocs/docs/ru/connection/db_connection/mssql/sql.md new file mode 100644 index 000000000..de932e2d3 --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/mssql/sql.md @@ -0,0 +1,80 @@ +(mssql-sql)= + +# Reading from MSSQL using `MSSQL.sql` + +`MSSQL.sql` allows passing custom SQL query, but does not support incremental strategies. + +```{eval-rst} +.. warning:: + + Please take into account :ref:`mssql-types` +``` + +```{eval-rst} +.. warning:: + + Statement is executed in **read-write** connection, so if you're calling some functions/procedures with DDL/DML statements inside, + they can change data in your database. +``` + +## Syntax support + +Only queries with the following syntax are supported: + +- ✅︎ `SELECT ... FROM ...` +- ❌ `WITH alias AS (...) SELECT ...` +- ❌ `SET ...; SELECT ...;` - multiple statements not supported + +## Examples + +```python +from onetl.connection import MSSQL + +mssql = MSSQL(...) +df = mssql.sql( + """ + SELECT + id, + key, + CAST(value AS text) value, + updated_at + FROM + some.mytable + WHERE + key = 'something' + """, + options=MSSQL.SQLOptions( + partitionColumn="id", + numPartitions=10, + lowerBound=0, + upperBound=1000, + ), +) +``` + +## Recommendations + +### Select only required columns + +Instead of passing `SELECT * FROM ...` prefer passing exact column names `SELECT col1, col2, ...`. +This reduces the amount of data passed from MSSQL to Spark. + +### Pay attention to `where` value + +Instead of filtering data on Spark side using `df.filter(df.column == 'value')` pass proper `WHERE column = 'value'` clause. +This both reduces the amount of data send from MSSQL to Spark, and may also improve performance of the query. +Especially if there are indexes or partitions for columns used in `where` clause. + +## Options + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.mssql.options +``` + +```{eval-rst} +.. autopydantic_model:: MSSQLSQLOptions + :inherited-members: GenericOptions + :member-order: bysource + :model-show-field-summary: false + :field-show-constraints: false +``` diff --git a/mddocs/docs/ru/connection/db_connection/mssql/types.md b/mddocs/docs/ru/connection/db_connection/mssql/types.md new file mode 100644 index 000000000..23e4972c9 --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/mssql/types.md @@ -0,0 +1,376 @@ +(mssql-types)= + +# MSSQL \<-> Spark type mapping + +```{eval-rst} +.. note:: + + The results below are valid for Spark 3.5.5, and may differ on other Spark versions. +``` + +## Type detection & casting + +Spark's DataFrames always have a `schema` which is a list of columns with corresponding Spark types. All operations on a column are performed using column type. + +### Reading from MSSQL + +This is how MSSQL connector performs this: + +- For each column in query result (`SELECT column1, column2, ... FROM table ...`) get column name and MSSQL type. +- Find corresponding `MSSQL type (read)` → `Spark type` combination (see below) for each DataFrame column. If no combination is found, raise exception. +- Create DataFrame from query with specific column names and Spark types. + +### Writing to some existing MSSQL table + +This is how MSSQL connector performs this: + +- Get names of columns in DataFrame. [^footnote-1] +- Perform `SELECT * FROM table LIMIT 0` query. +- Take only columns present in DataFrame (by name, case insensitive). For each found column get MSSQL type. +- Find corresponding `Spark type` → `MSSQL type (write)` combination (see below) for each DataFrame column. If no combination is found, raise exception. +- If `MSSQL type (write)` match `MSSQL type (read)`, no additional casts will be performed, DataFrame column will be written to MSSQL as is. +- If `MSSQL type (write)` does not match `MSSQL type (read)`, DataFrame column will be casted to target column type **on MSSQL side**. + For example, you can write column with text data to `int` column, if column contains valid integer values within supported value range and precision [^footnote-2]. + +[^footnote-1]: This allows to write data to tables with `DEFAULT` and `GENERATED` columns - if DataFrame has no such column, + it will be populated by MSSQL. + +[^footnote-2]: This is true only if DataFrame column is a `StringType()`, because text value is parsed automatically to target column type. + + But other types cannot be silently converted, like `int -> text`. This requires explicit casting, see [DBWriter]. + +### Create new table using Spark + +```{eval-rst} +.. warning:: + + ABSOLUTELY NOT RECOMMENDED! +``` + +This is how MSSQL connector performs this: + +- Find corresponding `Spark type` → `MSSQL type (create)` combination (see below) for each DataFrame column. If no combination is found, raise exception. +- Generate DDL for creating table in MSSQL, like `CREATE TABLE (col1 ...)`, and run it. +- Write DataFrame to created table as is. + +But some cases this may lead to using wrong column type. For example, Spark creates column of type `timestamp` +which corresponds to MSSQL's type `timestamp(0)` (precision up to seconds) +instead of more precise `timestamp(6)` (precision up to nanoseconds). +This may lead to incidental precision loss, or sometimes data cannot be written to created table at all. + +So instead of relying on Spark to create tables: + +```{eval-rst} +.. dropdown:: See example + + .. code:: python + + writer = DBWriter( + connection=mssql, + target="myschema.target_tbl", + options=MSSQL.WriteOptions( + if_exists="append", + ), + ) + writer.run(df) +``` + +Always prefer creating tables with specific types **BEFORE WRITING DATA**: + +```{eval-rst} +.. dropdown:: See example + + .. code:: python + + mssql.execute( + """ + CREATE TABLE schema.table ( + id bigint, + key text, + value datetime2(6) -- specific type and precision + ) + """, + ) + + writer = DBWriter( + connection=mssql, + target="myschema.target_tbl", + options=MSSQL.WriteOptions(if_exists="append"), + ) + writer.run(df) +``` + +### References + +Here you can find source code with type conversions: + +- [MSSQL -> JDBC](https://github.com/microsoft/mssql-jdbc/blob/v12.2.0/src/main/java/com/microsoft/sqlserver/jdbc/SQLServerResultSetMetaData.java#L117-L170) +- [JDBC -> Spark](https://github.com/apache/spark/blob/v3.5.5/sql/core/src/main/scala/org/apache/spark/sql/jdbc/MsSqlServerDialect.scala#L135-L152) +- [Spark -> JDBC](https://github.com/apache/spark/blob/v3.5.5/sql/core/src/main/scala/org/apache/spark/sql/jdbc/MsSqlServerDialect.scala#L154-L163) +- [JDBC -> MSSQL](https://github.com/microsoft/mssql-jdbc/blob/v12.2.0/src/main/java/com/microsoft/sqlserver/jdbc/DataTypes.java#L625-L676) + +## Supported types + +See [official documentation](https://learn.microsoft.com/en-us/sql/t-sql/data-types/data-types-transact-sql) + +### Numeric types + +```{eval-rst} ++-------------------------------+-----------------------------------+-------------------------------+-------------------------------+ +| MSSQL type (read) | Spark type | MSSQL type (write) | MSSQL type (create) | ++===============================+===================================+===============================+===============================+ +| ``decimal`` | ``DecimalType(P=18, S=0)`` | ``decimal(P=18, S=0)`` | ``decimal(P=18, S=0)`` | ++-------------------------------+-----------------------------------+-------------------------------+-------------------------------+ +| ``decimal(P=0..38)`` | ``DecimalType(P=0..38, S=0)`` | ``decimal(P=0..38, S=0)`` | ``decimal(P=0..38, S=0)`` | ++-------------------------------+-----------------------------------+-------------------------------+-------------------------------+ +| ``decimal(P=0..38, S=0..38)`` | ``DecimalType(P=0..38, S=0..38)`` | ``decimal(P=0..38, S=0..38)`` | ``decimal(P=0..38, S=0..38)`` | ++-------------------------------+-----------------------------------+-------------------------------+-------------------------------+ +| ``real`` | ``FloatType()`` | ``real`` | ``real`` | ++-------------------------------+-----------------------------------+-------------------------------+-------------------------------+ +| ``float`` | ``DoubleType()`` | ``float`` | ``float`` | ++-------------------------------+-----------------------------------+-------------------------------+-------------------------------+ +| ``smallint`` | ``ShortType()`` | ``smallint`` | ``smallint`` | ++-------------------------------+-----------------------------------+-------------------------------+-------------------------------+ +| ``tinyint`` | ``IntegerType()`` | ``int`` | ``int`` | ++-------------------------------+ | | | +| ``int`` | | | | ++-------------------------------+-----------------------------------+-------------------------------+-------------------------------+ +| ``bigint`` | ``LongType()`` | ``bigint`` | ``bigint`` | ++-------------------------------+-----------------------------------+-------------------------------+-------------------------------+ +``` + +### Temporal types + +```{eval-rst} +.. note:: + + MSSQL ``timestamp`` type is alias for ``rowversion`` (see `Special types`_). It is not a temporal type! +``` + +```{eval-rst} ++------------------------------------------+--------------------------------------+-----------------------------------+-------------------------------+ +| MSSQL type (read) | Spark type | MSSQL type (write) | MSSQL type (create) | ++==========================================+======================================+===================================+===============================+ +| ``date`` | ``DateType()`` | ``date`` | ``date`` | ++------------------------------------------+--------------------------------------+-----------------------------------+-------------------------------+ +| ``smalldatetime``, minutes | ``TimestampType()``, microseconds | ``datetime2(6)``, microseconds | ``datetime``, milliseconds | ++------------------------------------------+ | | | +| ``datetime``, milliseconds | | | | ++------------------------------------------+ | | | +| ``datetime2(0)``, seconds | | | | ++------------------------------------------+ | | | +| ``datetime2(3)``, milliseconds | | | | ++------------------------------------------+--------------------------------------+-----------------------------------+-------------------------------+ +| ``datetime2(6)``, microseconds | ``TimestampType()``, microseconds | ``datetime2(6)``, microseconds | ``datetime``, milliseconds, | ++------------------------------------------+--------------------------------------+-----------------------------------+ **precision loss** [3]_ | +| ``datetime2(7)``, 100s of nanoseconds | ``TimestampType()``, microseconds, | ``datetime2(6)``, microseconds, | | +| | **precision loss** [4]_ | **precision loss** [4]_ | | ++------------------------------------------+--------------------------------------+-----------------------------------+-------------------------------+ +| ``time(0)``, seconds | ``TimestampType()``, microseconds, | ``datetime2(6)``, microseconds | ``datetime``, milliseconds | ++------------------------------------------+ with time format quirks [5]_ | | | +| ``time(3)``, milliseconds | | | | ++------------------------------------------+--------------------------------------+-----------------------------------+-------------------------------+ +| ``time(6)``, microseconds | ``TimestampType()``, microseconds, | ``datetime2(6)``, microseconds | ``datetime``, milliseconds, | ++ | with time format quirks [5]_ | | **precision loss** [3]_ | ++------------------------------------------+--------------------------------------+-----------------------------------+ + +| ``time``, 100s of nanoseconds | ``TimestampType()``, microseconds, | ``datetime2(6)``, microseconds | | ++------------------------------------------+ **precision loss** [4]_, | **precision loss** [3]_ | | +| ``time(7)``, 100s of nanoseconds | with time format quirks [5]_ | | | ++------------------------------------------+--------------------------------------+-----------------------------------+-------------------------------+ +| ``datetimeoffset`` | ``StringType()`` | ``nvarchar`` | ``nvarchar`` | ++------------------------------------------+--------------------------------------+-----------------------------------+-------------------------------+ +``` + +```{eval-rst} +.. warning:: + + Note that types in MSSQL and Spark have different value ranges: + + +-------------------+--------------------------------+--------------------------------+---------------------+--------------------------------+--------------------------------+ + | MySQL type | Min value | Max value | Spark type | Min value | Max value | + +===================+================================+================================+=====================+================================+================================+ + | ``smalldatetime`` | ``1900-01-01 00:00:00`` | ``2079-06-06 23:59:00`` | ``TimestampType()`` | ``0001-01-01 00:00:00.000000`` | ``9999-12-31 23:59:59.999999`` | + +-------------------+--------------------------------+--------------------------------+ | | | + | ``datetime`` | ``1753-01-01 00:00:00.000`` | ``9999-12-31 23:59:59.997`` | | | | + +-------------------+--------------------------------+--------------------------------+ | | | + | ``datetime2`` | ``0001-01-01 00:00:00.000000`` | ``9999-12-31 23:59:59.999999`` | | | | + +-------------------+--------------------------------+--------------------------------+ | | | + | ``time`` | ``00:00:00.0000000`` | ``23:59:59.9999999`` | | | | + +-------------------+--------------------------------+--------------------------------+---------------------+--------------------------------+--------------------------------+ + + So not all of values in Spark DataFrame can be written to MSSQL. + + References: + * `MSSQL date & time types documentation `_ + * `Spark DateType documentation `_ + * `Spark TimestampType documentation `_ +``` + +[^footnote-3]: MSSQL dialect for Spark generates DDL with type `datetime` which has precision up to milliseconds (`23:59:59.999`, 10{superscript}`-3` seconds). + Inserting data with microsecond and higher precision (`23:59:59.999999` .. `23.59:59.9999999`, 10{superscript}`-6` .. 10{superscript}`-7` seconds) + will lead to **throwing away microseconds**. + +[^footnote-4]: MSSQL support timestamp up to 100s of nanoseconds precision (`23:59:59.9999999999`, 10{superscript}`-7` seconds), + but Spark `TimestampType()` supports datetime up to microseconds precision (`23:59:59.999999`, 10{superscript}`-6` seconds). + Last digit will be lost during read or write operations. + +[^footnote-5]: `time` type is the same as `datetime2` with date `1970-01-01`. So instead of reading data from MSSQL like `23:59:59.999999` + it is actually read `1970-01-01 23:59:59.999999`, and vice versa. + +### String types + +```{eval-rst} ++-------------------+------------------+--------------------+---------------------+ +| MSSQL type (read) | Spark type | MSSQL type (write) | MSSQL type (create) | ++===================+==================+====================+=====================+ +| ``char`` | ``StringType()`` | ``nvarchar`` | ``nvarchar`` | ++-------------------+ | | | +| ``char(N)`` | | | | ++-------------------+ | | | +| ``nchar`` | | | | ++-------------------+ | | | +| ``nchar(N)`` | | | | ++-------------------+ | | | +| ``varchar`` | | | | ++-------------------+ | | | +| ``varchar(N)`` | | | | ++-------------------+ | | | +| ``nvarchar`` | | | | ++-------------------+ | | | +| ``nvarchar(N)`` | | | | ++-------------------+ | | | +| ``mediumtext`` | | | | ++-------------------+ | | | +| ``text`` | | | | ++-------------------+ | | | +| ``ntext`` | | | | ++-------------------+ | | | +| ``xml`` | | | | ++-------------------+------------------+--------------------+---------------------+ +``` + +### Binary types + +```{eval-rst} ++--------------------+-------------------+--------------------+---------------------+ +| MSSQL type (read) | Spark type | MSSQL type (write) | MSSQL type (create) | ++====================+===================+====================+=====================+ +| ``bit`` | ``BooleanType()`` | ``bit`` | ``bit`` | ++--------------------+-------------------+--------------------+---------------------+ +| ``binary`` | ``BinaryType()`` | ``varbinary`` | ``varbinary`` | ++--------------------+ | | | +| ``binary(N)`` | | | | ++--------------------+ | | | +| ``varbinary`` | | | | ++--------------------+ | | | +| ``varbinary(N)`` | | | | ++--------------------+ | | | +| ``image`` | | | | ++--------------------+-------------------+--------------------+---------------------+ +``` + +### Special types + +```{eval-rst} ++---------------------------+------------------+--------------------+---------------------+ +| MSSQL type (read) | Spark type | MSSQL type (write) | MSSQL type (create) | ++===========================+==================+====================+=====================+ +| ``geography`` | ``BinaryType()`` | ``varbinary`` | ``varbinary`` | ++---------------------------+ | | | +| ``geometry`` | | | | ++---------------------------+ | | | +| ``hierarchyid`` | | | | ++---------------------------+ | | | +| ``rowversion`` | | | | ++---------------------------+------------------+--------------------+---------------------+ +| ``sql_variant`` | unsupported | | | ++---------------------------+------------------+--------------------+---------------------+ +| ``sysname`` | ``StringType()`` | ``nvarchar`` | ``nvarchar`` | ++---------------------------+ | | | +| ``uniqueidentifier`` | | | | ++---------------------------+------------------+--------------------+---------------------+ +``` + +## Explicit type cast + +### `DBReader` + +It is possible to explicitly cast column type using `DBReader(columns=...)` syntax. + +For example, you can use `CAST(column AS text)` to convert data to string representation on MSSQL side, and so it will be read as Spark's `StringType()`: + +```python +from onetl.connection import MSSQL +from onetl.db import DBReader + +mssql = MSSQL(...) + +DBReader( + connection=mssql, + columns=[ + "id", + "supported_column", + "CAST(unsupported_column AS text) unsupported_column_str", + ], +) +df = reader.run() + +# cast column content to proper Spark type +df = df.select( + df.id, + df.supported_column, + # explicit cast + df.unsupported_column_str.cast("integer").alias("parsed_integer"), +) +``` + +### `DBWriter` + +Convert dataframe column to JSON using [to_json](https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.functions.to_json.html), +and write it as `text` column in MSSQL: + +```python +mssql.execute( + """ + CREATE TABLE schema.target_tbl ( + id bigint, + struct_column_json text -- any string type, actually + ) + """, +) + +from pyspark.sql.functions import to_json + +df = df.select( + df.id, + to_json(df.struct_column).alias("struct_column_json"), +) + +writer.run(df) +``` + +Then you can parse this column on MSSQL side - for example, by creating a view: + +```sql +SELECT + id, + JSON_VALUE(struct_column_json, "$.nested.field") AS nested_field +FROM target_tbl +``` + +Or by using [computed column](https://learn.microsoft.com/en-us/sql/relational-databases/tables/specify-computed-columns-in-a-table): + +```sql +CREATE TABLE schema.target_table ( + id bigint, + supported_column datetime2(6), + struct_column_json text, -- any string type, actually + -- computed column + nested_field AS (JSON_VALUE(struct_column_json, "$.nested.field")) + -- or persisted column + -- nested_field AS (JSON_VALUE(struct_column_json, "$.nested.field")) PERSISTED +) +``` + +By default, column value is calculated on every table read. +Column marked as `PERSISTED` is calculated during insert, but this require additional space. diff --git a/mddocs/docs/ru/connection/db_connection/mssql/write.md b/mddocs/docs/ru/connection/db_connection/mssql/write.md new file mode 100644 index 000000000..0b6d8db19 --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/mssql/write.md @@ -0,0 +1,56 @@ +(mssql-write)= + +# Writing to MSSQL using `DBWriter` + +For writing data to MSSQL, use {obj}`DBWriter `. + +```{eval-rst} +.. warning:: + + Please take into account :ref:`mssql-types` +``` + +```{eval-rst} +.. warning:: + + It is always recommended to create table explicitly using :ref:`MSSQL.execute ` + instead of relying on Spark's table DDL generation. + + This is because Spark's DDL generator can create columns with different precision and types than it is expected, + causing precision loss or other issues. +``` + +## Examples + +```python +from onetl.connection import MSSQL +from onetl.db import DBWriter + +mssql = MSSQL(...) + +df = ... # data is here + +writer = DBWriter( + connection=mssql, + target="schema.table", + options=MSSQL.WriteOptions(if_exists="append"), +) + +writer.run(df) +``` + +## Options + +Method above accepts {obj}`MSSQL.WriteOptions ` + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.mssql.options +``` + +```{eval-rst} +.. autopydantic_model:: MSSQLWriteOptions + :inherited-members: GenericOptions + :member-order: bysource + :model-show-field-summary: false + :field-show-constraints: false +``` diff --git a/mddocs/docs/ru/connection/db_connection/mysql/connection.md b/mddocs/docs/ru/connection/db_connection/mysql/connection.md new file mode 100644 index 000000000..1b2ba94c3 --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/mysql/connection.md @@ -0,0 +1,12 @@ +(mysql-connection)= + +# MySQL connection + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.mysql.connection +``` + +```{eval-rst} +.. autoclass:: MySQL + :members: get_packages, check +``` diff --git a/mddocs/docs/ru/connection/db_connection/mysql/execute.md b/mddocs/docs/ru/connection/db_connection/mysql/execute.md new file mode 100644 index 000000000..99c86be5b --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/mysql/execute.md @@ -0,0 +1,115 @@ +(mysql-execute)= + +# Executing statements in MySQL + +```{eval-rst} +.. warning:: + + Methods below **read all the rows** returned from DB **to Spark driver memory**, and then convert them to DataFrame. + + Do **NOT** use them to read large amounts of data. Use :ref:`DBReader ` or :ref:`MySQL.sql ` instead. +``` + +## How to + +There are 2 ways to execute some statement in MySQL + +### Use `MySQL.fetch` + +Use this method to perform some `SELECT` query which returns **small number or rows**, like reading +MySQL config, or reading data from some reference table. Method returns Spark DataFrame. + +Method accepts {obj}`MySQL.FetchOptions `. + +Connection opened using this method should be then closed with `connection.close()` or `with connection:`. + +```{eval-rst} +.. warning:: + + Please take into account :ref:`mysql-types`. +``` + +#### Syntax support + +This method supports **any** query syntax supported by MySQL, like: + +- ✅︎ `SELECT ... FROM ...` +- ✅︎ `WITH alias AS (...) SELECT ...` +- ✅︎ `SELECT func(arg1, arg2)` or `{?= call func(arg1, arg2)}` - special syntax for calling function +- ✅︎ `SHOW ...` +- ❌ `SET ...; SELECT ...;` - multiple statements not supported + +#### Examples + +```python +from onetl.connection import MySQL + +mysql = MySQL(...) + +df = mysql.fetch( + "SELECT value FROM some.reference_table WHERE key = 'some_constant'", + options=MySQL.FetchOptions(queryTimeout=10), +) +mysql.close() +value = df.collect()[0][0] # get value from first row and first column +``` + +### Use `MySQL.execute` + +Use this method to execute DDL and DML operations. Each method call runs operation in a separated transaction, and then commits it. + +Method accepts {obj}`MySQL.ExecuteOptions `. + +Connection opened using this method should be then closed with `connection.close()` or `with connection:`. + +#### Syntax support + +This method supports **any** query syntax supported by MySQL, like: + +- ✅︎ `CREATE TABLE ...`, `CREATE VIEW ...`, and so on +- ✅︎ `ALTER ...` +- ✅︎ `INSERT INTO ... SELECT ...`, `UPDATE ...`, `DELETE ...`, and so on +- ✅︎ `DROP TABLE ...`, `DROP VIEW ...`, and so on +- ✅︎ `CALL procedure(arg1, arg2) ...` or `{call procedure(arg1, arg2)}` - special syntax for calling procedure +- ✅︎ other statements not mentioned here +- ❌ `SET ...; SELECT ...;` - multiple statements not supported + +#### Examples + +```python +from onetl.connection import MySQL + +mysql = MySQL(...) + +mysql.execute("DROP TABLE schema.table") +mysql.execute( + """ + CREATE TABLE schema.table ( + id bigint, + key text, + value float + ) + ENGINE = InnoDB + """, + options=MySQL.ExecuteOptions(queryTimeout=10), +) +``` + +## Options + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.mysql.options +``` + +```{eval-rst} +.. autopydantic_model:: MySQLFetchOptions + :inherited-members: GenericOptions + :member-order: bysource + +``` + +```{eval-rst} +.. autopydantic_model:: MySQLExecuteOptions + :inherited-members: GenericOptions + :member-order: bysource +``` diff --git a/mddocs/docs/ru/connection/db_connection/mysql/index.md b/mddocs/docs/ru/connection/db_connection/mysql/index.md new file mode 100644 index 000000000..6b69e1c1b --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/mysql/index.md @@ -0,0 +1,28 @@ +(mysql)= + +# MySQL + +```{toctree} +:caption: Connection +:maxdepth: 1 + +prerequisites +connection +``` + +```{toctree} +:caption: Operations +:maxdepth: 1 + +read +sql +write +execute +``` + +```{toctree} +:caption: Troubleshooting +:maxdepth: 1 + +types +``` diff --git a/mddocs/docs/ru/connection/db_connection/mysql/prerequisites.md b/mddocs/docs/ru/connection/db_connection/mysql/prerequisites.md new file mode 100644 index 000000000..6292e7761 --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/mysql/prerequisites.md @@ -0,0 +1,61 @@ +(mysql-prerequisites)= + +# Prerequisites + +## Version Compatibility + +- MySQL server versions: + : - Officially declared: 8.0 - 9.2 + - Actually tested: 5.7.13, 9.2.0 +- Spark versions: 2.3.x - 3.5.x +- Java versions: 8 - 20 + +See [official documentation](https://dev.mysql.com/doc/connector-j/en/connector-j-versions.html). + +## Installing PySpark + +To use MySQL connector you should have PySpark installed (or injected to `sys.path`) +BEFORE creating the connector instance. + +See {ref}`install-spark` installation instruction for more details. + +## Connecting to MySQL + +### Connection host + +It is possible to connect to MySQL by using either DNS name of host or it's IP address. + +If you're using MySQL cluster, it is currently possible to connect only to **one specific node**. +Connecting to multiple nodes to perform load balancing, as well as automatic failover to new master/replica are not supported. + +### Connection port + +Connection is usually performed to port 3306. Port may differ for different MySQL instances. +Please ask your MySQL administrator to provide required information. + +### Required grants + +Ask your MySQL cluster administrator to set following grants for a user, +used for creating a connection: + +```{eval-rst} +.. tabs:: + + .. code-tab:: sql Read + Write + + -- allow creating tables in the target schema + GRANT CREATE ON myschema.* TO username@'192.168.1.%'; + + -- allow read & write access to specific table + GRANT SELECT, INSERT ON myschema.mytable TO username@'192.168.1.%'; + + .. code-tab:: sql Read only + + -- allow read access to specific table + GRANT SELECT ON myschema.mytable TO username@'192.168.1.%'; +``` + +In example above `'192.168.1.%''` is a network subnet `192.168.1.0 - 192.168.1.255` +where Spark driver and executors are running. To allow connecting user from any IP, use `'%'` (not secure!). + +More details can be found in [official documentation](https://dev.mysql.com/doc/refman/en/grant.html). diff --git a/mddocs/docs/ru/connection/db_connection/mysql/read.md b/mddocs/docs/ru/connection/db_connection/mysql/read.md new file mode 100644 index 000000000..c3751dca2 --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/mysql/read.md @@ -0,0 +1,93 @@ +(mysql-read)= + +# Reading from MySQL using `DBReader` + +{obj}`DBReader ` supports {ref}`strategy` for incremental data reading, +but does not support custom queries, like `JOIN`. + +```{eval-rst} +.. warning:: + + Please take into account :ref:`mysql-types` +``` + +## Supported DBReader features + +- ✅︎ `columns` +- ✅︎ `where` +- ✅︎ `hwm`, supported strategies: +- - ✅︎ {ref}`snapshot-strategy` +- - ✅︎ {ref}`incremental-strategy` +- - ✅︎ {ref}`snapshot-batch-strategy` +- - ✅︎ {ref}`incremental-batch-strategy` +- ✅︎ `hint` (see [official documentation](https://dev.mysql.com/doc/refman/en/optimizer-hints.html)) +- ❌ `df_schema` +- ✅︎ `options` (see {obj}`MySQL.ReadOptions `) + +## Examples + +Snapshot strategy: + +```python +from onetl.connection import MySQL +from onetl.db import DBReader + +mysql = MySQL(...) + +reader = DBReader( + connection=mysql, + source="schema.table", + columns=["id", "key", "CAST(value AS text) value", "updated_dt"], + where="key = 'something'", + hint="SKIP_SCAN(schema.table key_index)", + options=MySQL.ReadOptions(partitionColumn="id", numPartitions=10), +) +df = reader.run() +``` + +Incremental strategy: + +```python +from onetl.connection import MySQL +from onetl.db import DBReader +from onetl.strategy import IncrementalStrategy + +mysql = MySQL(...) + +reader = DBReader( + connection=mysql, + source="schema.table", + columns=["id", "key", "CAST(value AS text) value", "updated_dt"], + where="key = 'something'", + hint="SKIP_SCAN(schema.table key_index)", + hwm=DBReader.AutoDetectHWM(name="mysql_hwm", expression="updated_dt"), + options=MySQL.ReadOptions(partitionColumn="id", numPartitions=10), +) + +with IncrementalStrategy(): + df = reader.run() +``` + +## Recommendations + +### Select only required columns + +Instead of passing `"*"` in `DBReader(columns=[...])` prefer passing exact column names. This reduces the amount of data passed from Oracle to Spark. + +### Pay attention to `where` value + +Instead of filtering data on Spark side using `df.filter(df.column == 'value')` pass proper `DBReader(where="column = 'value'")` clause. +This both reduces the amount of data send from Oracle to Spark, and may also improve performance of the query. +Especially if there are indexes for columns used in `where` clause. + +## Options + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.mysql.options +``` + +```{eval-rst} +.. autopydantic_model:: MySQLReadOptions + :inherited-members: GenericOptions + :member-order: bysource +``` diff --git a/mddocs/docs/ru/connection/db_connection/mysql/sql.md b/mddocs/docs/ru/connection/db_connection/mysql/sql.md new file mode 100644 index 000000000..7d2dd4e51 --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/mysql/sql.md @@ -0,0 +1,81 @@ +(mysql-sql)= + +# Reading from MySQL using `MySQL.sql` + +`MySQL.sql` allows passing custom SQL query, but does not support incremental strategies. + +```{eval-rst} +.. warning:: + + Please take into account :ref:`mysql-types` +``` + +```{eval-rst} +.. warning:: + + Statement is executed in **read-write** connection, so if you're calling some functions/procedures with DDL/DML statements inside, + they can change data in your database. +``` + +## Syntax support + +Only queries with the following syntax are supported: + +- ✅︎ `SELECT ... FROM ...` +- ✅︎ `WITH alias AS (...) SELECT ...` +- ❌ `SHOW ...` +- ❌ `SET ...; SELECT ...;` - multiple statements not supported + +## Examples + +```python +from onetl.connection import MySQL + +mysql = MySQL(...) +df = mysql.sql( + """ + SELECT + id, + key, + CAST(value AS text) value, + updated_at + FROM + some.mytable + WHERE + key = 'something' + """, + options=MySQL.SQLOptions( + partitionColumn="id", + numPartitions=10, + lowerBound=0, + upperBound=1000, + ), +) +``` + +## Recommendations + +### Select only required columns + +Instead of passing `SELECT * FROM ...` prefer passing exact column names `SELECT col1, col2, ...`. +This reduces the amount of data passed from MySQL to Spark. + +### Pay attention to `where` value + +Instead of filtering data on Spark side using `df.filter(df.column == 'value')` pass proper `WHERE column = 'value'` clause. +This both reduces the amount of data send from MySQL to Spark, and may also improve performance of the query. +Especially if there are indexes or partitions for columns used in `where` clause. + +## Options + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.mysql.options +``` + +```{eval-rst} +.. autopydantic_model:: MySQLSQLOptions + :inherited-members: GenericOptions + :member-order: bysource + :model-show-field-summary: false + :field-show-constraints: false +``` diff --git a/mddocs/docs/ru/connection/db_connection/mysql/types.md b/mddocs/docs/ru/connection/db_connection/mysql/types.md new file mode 100644 index 000000000..df3d6849b --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/mysql/types.md @@ -0,0 +1,382 @@ +(mysql-types)= + +# MySQL \<-> Spark type mapping + +```{eval-rst} +.. note:: + + The results below are valid for Spark 3.5.5, and may differ on other Spark versions. +``` + +## Type detection & casting + +Spark's DataFrames always have a `schema` which is a list of columns with corresponding Spark types. All operations on a column are performed using column type. + +### Reading from MySQL + +This is how MySQL connector performs this: + +- For each column in query result (`SELECT column1, column2, ... FROM table ...`) get column name and MySQL type. +- Find corresponding `MySQL type (read)` → `Spark type` combination (see below) for each DataFrame column. If no combination is found, raise exception. +- Create DataFrame from query with specific column names and Spark types. + +### Writing to some existing MySQL table + +This is how MySQL connector performs this: + +- Get names of columns in DataFrame. [^footnote-1] +- Perform `SELECT * FROM table LIMIT 0` query. +- Take only columns present in DataFrame (by name, case insensitive). For each found column get MySQL type. +- Find corresponding `Spark type` → `MySQL type (write)` combination (see below) for each DataFrame column. If no combination is found, raise exception. +- If `MySQL type (write)` match `MySQL type (read)`, no additional casts will be performed, DataFrame column will be written to MySQL as is. +- If `MySQL type (write)` does not match `MySQL type (read)`, DataFrame column will be casted to target column type **on MySQL side**. For example, you can write column with text data to `int` column, if column contains valid integer values within supported value range and precision. + +[^footnote-1]: This allows to write data to tables with `DEFAULT` and `GENERATED` columns - if DataFrame has no such column, + it will be populated by MySQL. + +### Create new table using Spark + +```{eval-rst} +.. warning:: + + ABSOLUTELY NOT RECOMMENDED! +``` + +This is how MySQL connector performs this: + +- Find corresponding `Spark type` → `MySQL type (create)` combination (see below) for each DataFrame column. If no combination is found, raise exception. +- Generate DDL for creating table in MySQL, like `CREATE TABLE (col1 ...)`, and run it. +- Write DataFrame to created table as is. + +But some cases this may lead to using wrong column type. For example, Spark creates column of type `timestamp` +which corresponds to MySQL type `timestamp(0)` (precision up to seconds) +instead of more precise `timestamp(6)` (precision up to nanoseconds). +This may lead to incidental precision loss, or sometimes data cannot be written to created table at all. + +So instead of relying on Spark to create tables: + +```{eval-rst} +.. dropdown:: See example + + .. code:: python + + writer = DBWriter( + connection=mysql, + target="myschema.target_tbl", + options=MySQL.WriteOptions( + if_exists="append", + createTableOptions="ENGINE = InnoDB", + ), + ) + writer.run(df) +``` + +Always prefer creating tables with specific types **BEFORE WRITING DATA**: + +```{eval-rst} +.. dropdown:: See example + + .. code:: python + + mysql.execute( + """ + CREATE TABLE schema.table ( + id bigint, + key text, + value timestamp(6) -- specific type and precision + ) + ENGINE = InnoDB + """, + ) + + writer = DBWriter( + connection=mysql, + target="myschema.target_tbl", + options=MySQL.WriteOptions(if_exists="append"), + ) + writer.run(df) +``` + +### References + +Here you can find source code with type conversions: + +- [MySQL -> JDBC](https://github.com/mysql/mysql-connector-j/blob/8.0.33/src/main/core-api/java/com/mysql/cj/MysqlType.java#L44-L623) +- [JDBC -> Spark](https://github.com/apache/spark/blob/v3.5.5/sql/core/src/main/scala/org/apache/spark/sql/jdbc/MySQLDialect.scala#L104-L132) +- [Spark -> JDBC](https://github.com/apache/spark/blob/v3.5.5/sql/core/src/main/scala/org/apache/spark/sql/jdbc/MySQLDialect.scala#L204-L211) +- [JDBC -> MySQL](https://github.com/mysql/mysql-connector-j/blob/8.0.33/src/main/core-api/java/com/mysql/cj/MysqlType.java#L625-L867) + +## Supported types + +See [official documentation](https://dev.mysql.com/doc/refman/en/data-types.html) + +### Numeric types + +```{eval-rst} ++-------------------------------+-----------------------------------+-------------------------------+-------------------------------+ +| MySQL type (read) | Spark type | MySQL type (write) | MySQL type (create) | ++===============================+===================================+===============================+===============================+ +| ``decimal`` | ``DecimalType(P=10, S=0)`` | ``decimal(P=10, S=0)`` | ``decimal(P=10, S=0)`` | ++-------------------------------+-----------------------------------+-------------------------------+-------------------------------+ +| ``decimal(P=0..38)`` | ``DecimalType(P=0..38, S=0)`` | ``decimal(P=0..38, S=0)`` | ``decimal(P=0..38, S=0)`` | ++-------------------------------+-----------------------------------+-------------------------------+-------------------------------+ +| ``decimal(P=0..38, S=0..30)`` | ``DecimalType(P=0..38, S=0..30)`` | ``decimal(P=0..38, S=0..30)`` | ``decimal(P=0..38, S=0..30)`` | ++-------------------------------+-----------------------------------+-------------------------------+-------------------------------+ +| ``decimal(P=39..65, S=...)`` | unsupported [2]_ | | | ++-------------------------------+-----------------------------------+-------------------------------+-------------------------------+ +| ``float`` | ``DoubleType()`` | ``double`` | ``double`` | ++-------------------------------+ | | | +| ``double`` | | | | ++-------------------------------+-----------------------------------+-------------------------------+-------------------------------+ +| ``tinyint`` | ``IntegerType()`` | ``int`` | ``int`` | ++-------------------------------+ | | | +| ``smallint`` | | | | ++-------------------------------+ | | | +| ``mediumint`` | | | | ++-------------------------------+ | | | +| ``int`` | | | | ++-------------------------------+-----------------------------------+-------------------------------+-------------------------------+ +| ``bigint`` | ``LongType()`` | ``bigint`` | ``bigint`` | ++-------------------------------+-----------------------------------+-------------------------------+-------------------------------+ +``` + +[^footnote-2]: MySQL support decimal types with precision `P` up to 65. + + But Spark's `DecimalType(P, S)` supports maximum `P=38`. It is impossible to read, write or operate with values of larger precision, + this leads to an exception. + +### Temporal types + +```{eval-rst} ++-----------------------------------+--------------------------------------+-----------------------------------+-------------------------------+ +| MySQL type (read) | Spark type | MySQL type (write) | MySQL type (create) | ++===================================+======================================+===================================+===============================+ +| ``year`` | ``DateType()`` | ``date`` | ``date`` | ++-----------------------------------+ | | | +| ``date`` | | | | ++-----------------------------------+--------------------------------------+-----------------------------------+-------------------------------+ +| ``datetime``, seconds | ``TimestampType()``, microseconds | ``timestamp(6)``, microseconds | ``timestamp(0)``, seconds | ++-----------------------------------+ | | | +| ``timestamp``, seconds | | | | ++-----------------------------------+ | | | +| ``datetime(0)``, seconds | | | | ++-----------------------------------+ | | | +| ``timestamp(0)``, seconds | | | | ++-----------------------------------+--------------------------------------+-----------------------------------+-------------------------------+ +| ``datetime(3)``, milliseconds | ``TimestampType()``, microseconds | ``timestamp(6)``, microseconds | ``timestamp(0)``, seconds, | ++-----------------------------------+ | | **precision loss** [4]_, | +| ``timestamp(3)``, milliseconds | | | | ++-----------------------------------+ | | | +| ``datetime(6)``, microseconds | | | | ++-----------------------------------+ | | | +| ``timestamp(6)``, microseconds | | | | ++-----------------------------------+--------------------------------------+-----------------------------------+-------------------------------+ +| ``time``, seconds | ``TimestampType()``, microseconds, | ``timestamp(6)``, microseconds | ``timestamp(0)``, seconds | ++-----------------------------------+ with time format quirks [5]_ | | | +| ``time(0)``, seconds | | | | ++-----------------------------------+--------------------------------------+-----------------------------------+-------------------------------+ +| ``time(3)``, milliseconds | ``TimestampType()``, microseconds | ``timestamp(6)``, microseconds | ``timestamp(0)``, seconds, | ++-----------------------------------+ with time format quirks [5]_ | | **precision loss** [4]_, | +| ``time(6)``, microseconds | | | | ++-----------------------------------+--------------------------------------+-----------------------------------+-------------------------------+ +``` + +```{eval-rst} +.. warning:: + + Note that types in MySQL and Spark have different value ranges: + + +---------------+--------------------------------+--------------------------------+---------------------+--------------------------------+--------------------------------+ + | MySQL type | Min value | Max value | Spark type | Min value | Max value | + +===============+================================+================================+=====================+================================+================================+ + | ``year`` | ``1901`` | ``2155`` | ``DateType()`` | ``0001-01-01`` | ``9999-12-31`` | + +---------------+--------------------------------+--------------------------------+ | | | + | ``date`` | ``1000-01-01`` | ``9999-12-31`` | | | | + +---------------+--------------------------------+--------------------------------+---------------------+--------------------------------+--------------------------------+ + | ``datetime`` | ``1000-01-01 00:00:00.000000`` | ``9999-12-31 23:59:59.499999`` | ``TimestampType()`` | ``0001-01-01 00:00:00.000000`` | ``9999-12-31 23:59:59.999999`` | + +---------------+--------------------------------+--------------------------------+ | | | + | ``timestamp`` | ``1970-01-01 00:00:01.000000`` | ``9999-12-31 23:59:59.499999`` | | | | + +---------------+--------------------------------+--------------------------------+ | | | + | ``time`` | ``-838:59:59.000000`` | ``838:59:59.000000`` | | | | + +---------------+--------------------------------+--------------------------------+---------------------+--------------------------------+--------------------------------+ + + So Spark can read all the values from MySQL, but not all of values in Spark DataFrame can be written to MySQL. + + References: + * `MySQL year documentation `_ + * `MySQL date, datetime & timestamp documentation `_ + * `MySQL time documentation `_ + * `Spark DateType documentation `_ + * `Spark TimestampType documentation `_ +``` + +[^footnote-3]: MySQL dialect generates DDL with MySQL type `timestamp` which is alias for `timestamp(0)` with precision up to seconds (`23:59:59`). + Inserting data with microseconds precision (`23:59:59.999999`) will lead to **throwing away microseconds**. + +[^footnote-4]: `time` type is the same as `timestamp` with date `1970-01-01`. So instead of reading data from MySQL like `23:59:59` + it is actually read `1970-01-01 23:59:59`, and vice versa. + +### String types + +```{eval-rst} ++-------------------------------+------------------+--------------------+---------------------+ +| MySQL type (read) | Spark type | MySQL type (write) | MySQL type (create) | ++===============================+==================+====================+=====================+ +| ``char`` | ``StringType()`` | ``longtext`` | ``longtext`` | ++-------------------------------+ | | | +| ``char(N)`` | | | | ++-------------------------------+ | | | +| ``varchar(N)`` | | | | ++-------------------------------+ | | | +| ``mediumtext`` | | | | ++-------------------------------+ | | | +| ``text`` | | | | ++-------------------------------+ | | | +| ``longtext`` | | | | ++-------------------------------+ | | | +| ``json`` | | | | ++-------------------------------+ | | | +| ``enum("val1", "val2", ...)`` | | | | ++-------------------------------+ | | | +| ``set("val1", "val2", ...)`` | | | | ++-------------------------------+------------------+--------------------+---------------------+ +``` + +### Binary types + +```{eval-rst} ++-------------------+------------------+--------------------+---------------------+ +| MySQL type (read) | Spark type | MySQL type (write) | MySQL type (create) | ++===================+==================+====================+=====================+ +| ``binary`` | ``BinaryType()`` | ``blob`` | ``blob`` | ++-------------------+ | | | +| ``binary(N)`` | | | | ++-------------------+ | | | +| ``varbinary(N)`` | | | | ++-------------------+ | | | +| ``mediumblob`` | | | | ++-------------------+ | | | +| ``blob`` | | | | ++-------------------+ | | | +| ``longblob`` | | | | ++-------------------+------------------+--------------------+---------------------+ +``` + +### Geometry types + +```{eval-rst} ++------------------------+------------------+--------------------+---------------------+ +| MySQL type (read) | Spark type | MySQL type (write) | MySQL type (create) | ++========================+==================+====================+=====================+ +| ``point`` | ``BinaryType()`` | ``blob`` | ``blob`` | ++------------------------+ | | | +| ``linestring`` | | | | ++------------------------+ | | | +| ``polygon`` | | | | ++------------------------+ | | | +| ``geometry`` | | | | ++------------------------+ | | | +| ``multipoint`` | | | | ++------------------------+ | | | +| ``multilinestring`` | | | | ++------------------------+ | | | +| ``multipolygon`` | | | | ++------------------------+ | | | +| ``geometrycollection`` | | | | ++------------------------+------------------+--------------------+---------------------+ +``` + +## Explicit type cast + +### `DBReader` + +It is possible to explicitly cast column type using `DBReader(columns=...)` syntax. + +For example, you can use `CAST(column AS text)` to convert data to string representation on MySQL side, and so it will be read as Spark's `StringType()`. + +It is also possible to use [JSON_OBJECT](https://dev.mysql.com/doc/refman/en/json.html) MySQL function and parse JSON columns in MySQL with the {obj}`JSON.parse_column ` method. + +```python +from pyspark.sql.types import IntegerType, StructType, StructField + +from onetl.connection import MySQL +from onetl.db import DBReader +from onetl.file.format import JSON + +mysql = MySQL(...) + +DBReader( + connection=mysql, + columns=[ + "id", + "supported_column", + "CAST(unsupported_column AS text) unsupported_column_str", + # or + "JSON_OBJECT('key', value_column) json_column", + ], +) +df = reader.run() + +json_scheme = StructType([StructField("key", IntegerType())]) + +df = df.select( + df.id, + df.supported_column, + # explicit cast + df.unsupported_column_str.cast("integer").alias("parsed_integer"), + JSON().parse_column("json_column", json_scheme).alias("struct_column"), +) +``` + +### `DBWriter` + +To write JSON data to a `json` or `text` column in a MySQL table, use the {obj}`JSON.serialize_column ` method. + +```python +from onetl.connection import MySQL +from onetl.db import DBWriter +from onetl.file.format import JSON + +mysql.execute( + """ + CREATE TABLE schema.target_tbl ( + id bigint, + array_column_json json -- any string type, actually + ) + ENGINE = InnoDB + """, +) + +df = df.select( + df.id, + JSON().serialize_column(df.array_column).alias("array_column_json"), +) + +writer.run(df) +``` + +Then you can parse this column on MySQL side - for example, by creating a view: + +```sql +SELECT + id, + array_column_json->"$[0]" AS array_item +FROM target_tbl +``` + +Or by using [GENERATED column](https://dev.mysql.com/doc/refman/en/create-table-generated-columns.html): + +```sql +CREATE TABLE schema.target_table ( + id bigint, + supported_column timestamp, + array_column_json json, -- any string type, actually + -- virtual column + array_item_0 GENERATED ALWAYS AS (array_column_json->"$[0]")) VIRTUAL + -- or stired column + -- array_item_0 GENERATED ALWAYS AS (array_column_json->"$[0]")) STORED +) +``` + +`VIRTUAL` column value is calculated on every table read. +`STORED` column value is calculated during insert, but this require additional space. diff --git a/mddocs/docs/ru/connection/db_connection/mysql/write.md b/mddocs/docs/ru/connection/db_connection/mysql/write.md new file mode 100644 index 000000000..7f7f2a5d2 --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/mysql/write.md @@ -0,0 +1,60 @@ +(mysql-write)= + +# Writing to MySQL using `DBWriter` + +For writing data to MySQL, use {obj}`DBWriter `. + +```{eval-rst} +.. warning:: + + Please take into account :ref:`mysql-types` +``` + +```{eval-rst} +.. warning:: + + It is always recommended to create table explicitly using :ref:`MySQL.execute ` + instead of relying on Spark's table DDL generation. + + This is because Spark's DDL generator can create columns with different precision and types than it is expected, + causing precision loss or other issues. +``` + +## Examples + +```python +from onetl.connection import MySQL +from onetl.db import DBWriter + +mysql = MySQL(...) + +df = ... # data is here + +writer = DBWriter( + connection=mysql, + target="schema.table", + options=MySQL.WriteOptions( + if_exists="append", + # ENGINE is required by MySQL + createTableOptions="ENGINE = MergeTree() ORDER BY id", + ), +) + +writer.run(df) +``` + +## Options + +Method above accepts {obj}`MySQL.WriteOptions ` + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.mysql.options +``` + +```{eval-rst} +.. autopydantic_model:: MySQLWriteOptions + :inherited-members: GenericOptions + :member-order: bysource + :model-show-field-summary: false + :field-show-constraints: false +``` diff --git a/mddocs/docs/ru/connection/db_connection/oracle/connection.md b/mddocs/docs/ru/connection/db_connection/oracle/connection.md new file mode 100644 index 000000000..26fc8a01e --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/oracle/connection.md @@ -0,0 +1,12 @@ +(oracle-connection)= + +# Oracle connection + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.oracle.connection +``` + +```{eval-rst} +.. autoclass:: Oracle + :members: get_packages, check +``` diff --git a/mddocs/docs/ru/connection/db_connection/oracle/execute.md b/mddocs/docs/ru/connection/db_connection/oracle/execute.md new file mode 100644 index 000000000..28b5baa39 --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/oracle/execute.md @@ -0,0 +1,115 @@ +(oracle-execute)= + +# Executing statements in Oracle + +```{eval-rst} +.. warning:: + + Methods below **read all the rows** returned from DB **to Spark driver memory**, and then convert them to DataFrame. + + Do **NOT** use them to read large amounts of data. Use :ref:`DBReader ` or :ref:`Oracle.sql ` instead. +``` + +## How to + +There are 2 ways to execute some statement in Oracle + +### Use `Oracle.fetch` + +Use this method to execute some `SELECT` query which returns **small number or rows**, like reading +Oracle config, or reading data from some reference table. Method returns Spark DataFrame. + +Method accepts {obj}`Oracle.FetchOptions `. + +Connection opened using this method should be then closed with `connection.close()` or `with connection:`. + +```{eval-rst} +.. warning:: + + Please take into account :ref:`oracle-types`. +``` + +#### Syntax support + +This method supports **any** query syntax supported by Oracle, like: + +- ✅︎ `SELECT ... FROM ...` +- ✅︎ `WITH alias AS (...) SELECT ...` +- ✅︎ `SELECT func(arg1, arg2) FROM DUAL` - call function +- ✅︎ `SHOW ...` +- ❌ `SET ...; SELECT ...;` - multiple statements not supported + +#### Examples + +```python +from onetl.connection import Oracle + +oracle = Oracle(...) + +df = oracle.fetch( + "SELECT value FROM some.reference_table WHERE key = 'some_constant'", + options=Oracle.FetchOptions(queryTimeout=10), +) +oracle.close() +value = df.collect()[0][0] # get value from first row and first column +``` + +### Use `Oracle.execute` + +Use this method to execute DDL and DML operations. Each method call runs operation in a separated transaction, and then commits it. + +Method accepts {obj}`Oracle.ExecuteOptions `. + +Connection opened using this method should be then closed with `connection.close()` or `with connection:`. + +#### Syntax support + +This method supports **any** query syntax supported by Oracle, like: + +- ✅︎ `CREATE TABLE ...`, `CREATE VIEW ...` +- ✅︎ `ALTER ...` +- ✅︎ `INSERT INTO ... SELECT ...`, `UPDATE ...`, `DELETE ...`, and so on +- ✅︎ `DROP TABLE ...`, `DROP VIEW ...`, `TRUNCATE TABLE`, and so on +- ✅︎ `CALL procedure(arg1, arg2) ...` or `{call procedure(arg1, arg2)}` - special syntax for calling procedure +- ✅︎ `DECLARE ... BEGIN ... END` - execute PL/SQL statement +- ✅︎ other statements not mentioned here +- ❌ `SET ...; SELECT ...;` - multiple statements not supported + +#### Examples + +```python +from onetl.connection import Oracle + +oracle = Oracle(...) + +oracle.execute("DROP TABLE schema.table") +oracle.execute( + """ + CREATE TABLE schema.table ( + id bigint GENERATED ALWAYS AS IDENTITY, + key VARCHAR2(4000), + value NUMBER + ) + """, + options=Oracle.ExecuteOptions(queryTimeout=10), +) +``` + +## Options + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.oracle.options +``` + +```{eval-rst} +.. autopydantic_model:: OracleFetchOptions + :inherited-members: GenericOptions + :member-order: bysource + +``` + +```{eval-rst} +.. autopydantic_model:: OracleExecuteOptions + :inherited-members: GenericOptions + :member-order: bysource +``` diff --git a/mddocs/docs/ru/connection/db_connection/oracle/index.md b/mddocs/docs/ru/connection/db_connection/oracle/index.md new file mode 100644 index 000000000..5218cc287 --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/oracle/index.md @@ -0,0 +1,28 @@ +(oracle)= + +# Oracle + +```{toctree} +:caption: Connection +:maxdepth: 1 + +prerequisites +connection +``` + +```{toctree} +:caption: Operations +:maxdepth: 1 + +read +sql +write +execute +``` + +```{toctree} +:caption: Troubleshooting +:maxdepth: 1 + +types +``` diff --git a/mddocs/docs/ru/connection/db_connection/oracle/prerequisites.md b/mddocs/docs/ru/connection/db_connection/oracle/prerequisites.md new file mode 100644 index 000000000..5a350937b --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/oracle/prerequisites.md @@ -0,0 +1,108 @@ +(oracle-prerequisites)= + +# Prerequisites + +## Version Compatibility + +- Oracle Server versions: + : - Officially declared: 19c, 21c, 23ai + - Actually tested: 11.2, 23.5 +- Spark versions: 2.3.x - 3.5.x +- Java versions: 8 - 20 + +See [official documentation](https://www.oracle.com/cis/database/technologies/appdev/jdbc-downloads.html). + +## Installing PySpark + +To use Oracle connector you should have PySpark installed (or injected to `sys.path`) +BEFORE creating the connector instance. + +See {ref}`install-spark` installation instruction for more details. + +## Connecting to Oracle + +### Connection port + +Connection is usually performed to port 1521. Port may differ for different Oracle instances. +Please ask your Oracle administrator to provide required information. + +### Connection host + +It is possible to connect to Oracle by using either DNS name of host or it's IP address. + +If you're using Oracle cluster, it is currently possible to connect only to **one specific node**. +Connecting to multiple nodes to perform load balancing, as well as automatic failover to new master/replica are not supported. + +### Connect as proxy user + +It is possible to connect to database as another user without knowing this user password. + +This can be enabled by granting user a special `CONNECT THROUGH` permission: + +```sql +ALTER USER schema_owner GRANT CONNECT THROUGH proxy_user; +``` + +Then you can connect to Oracle using credentials of `proxy_user` but specify that you need permissions of `schema_owner`: + +```python +oracle = Oracle( + ..., + user="proxy_user[schema_owner]", + password="proxy_user password", +) +``` + +See [official documentation](https://oracle-base.com/articles/misc/proxy-users-and-connect-through). + +### Required grants + +Ask your Oracle cluster administrator to set following grants for a user, +used for creating a connection: + +```{eval-rst} +.. tabs:: + + .. code-tab:: sql Read + Write (schema is owned by user) + + -- allow user to log in + GRANT CREATE SESSION TO username; + + -- allow creating tables in user schema + GRANT CREATE TABLE TO username; + + -- allow read & write access to specific table + GRANT SELECT, INSERT ON username.mytable TO username; + + .. code-tab:: sql Read + Write (schema is not owned by user) + + -- allow user to log in + GRANT CREATE SESSION TO username; + + -- allow creating tables in any schema, + -- as Oracle does not support specifying exact schema name + GRANT CREATE ANY TABLE TO username; + + -- allow read & write access to specific table + GRANT SELECT, INSERT ON someschema.mytable TO username; + + -- only if if_exists="replace_entire_table" is used: + -- allow dropping/truncating tables in any schema, + -- as Oracle does not support specifying exact schema name + GRANT DROP ANY TABLE TO username; + + .. code-tab:: sql Read only + + -- allow user to log in + GRANT CREATE SESSION TO username; + + -- allow read access to specific table + GRANT SELECT ON someschema.mytable TO username; +``` + +More details can be found in official documentation: +: - [GRANT](https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/GRANT.html) + - [SELECT](https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/SELECT.html) + - [CREATE TABLE](https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/SELECT.html) + - [INSERT](https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/INSERT.html) + - [TRUNCATE TABLE](https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/TRUNCATE-TABLE.html) diff --git a/mddocs/docs/ru/connection/db_connection/oracle/read.md b/mddocs/docs/ru/connection/db_connection/oracle/read.md new file mode 100644 index 000000000..4f11e86e7 --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/oracle/read.md @@ -0,0 +1,93 @@ +(oracle-read)= + +# Reading from Oracle using `DBReader` + +{obj}`DBReader ` supports {ref}`strategy` for incremental data reading, +but does not support custom queries, like `JOIN`. + +```{eval-rst} +.. warning:: + + Please take into account :ref:`oracle-types` +``` + +## Supported DBReader features + +- ✅︎ `columns` +- ✅︎ `where` +- ✅︎ `hwm`, supported strategies: +- - ✅︎ {ref}`snapshot-strategy` +- - ✅︎ {ref}`incremental-strategy` +- - ✅︎ {ref}`snapshot-batch-strategy` +- - ✅︎ {ref}`incremental-batch-strategy` +- ✅︎ `hint` (see [official documentation](https://docs.oracle.com/cd/B10500_01/server.920/a96533/hintsref.htm)) +- ❌ `df_schema` +- ✅︎ `options` (see {obj}`Oracle.ReadOptions `) + +## Examples + +Snapshot strategy: + +```python +from onetl.connection import Oracle +from onetl.db import DBReader + +oracle = Oracle(...) + +reader = DBReader( + connection=oracle, + source="schema.table", + columns=["id", "key", "CAST(value AS VARCHAR2(4000)) value", "updated_dt"], + where="key = 'something'", + hint="INDEX(schema.table key_index)", + options=Oracle.ReadOptions(partitionColumn="id", numPartitions=10), +) +df = reader.run() +``` + +Incremental strategy: + +```python +from onetl.connection import Oracle +from onetl.db import DBReader +from onetl.strategy import IncrementalStrategy + +oracle = Oracle(...) + +reader = DBReader( + connection=oracle, + source="schema.table", + columns=["id", "key", "CAST(value AS VARCHAR2(4000)) value", "updated_dt"], + where="key = 'something'", + hint="INDEX(schema.table key_index)", + hwm=DBReader.AutoDetectHWM(name="oracle_hwm", expression="updated_dt"), + options=Oracle.ReadOptions(partitionColumn="id", numPartitions=10), +) + +with IncrementalStrategy(): + df = reader.run() +``` + +## Recommendations + +### Select only required columns + +Instead of passing `"*"` in `DBReader(columns=[...])` prefer passing exact column names. This reduces the amount of data passed from Oracle to Spark. + +### Pay attention to `where` value + +Instead of filtering data on Spark side using `df.filter(df.column == 'value')` pass proper `DBReader(where="column = 'value'")` clause. +This both reduces the amount of data send from Oracle to Spark, and may also improve performance of the query. +Especially if there are indexes or partitions for columns used in `where` clause. + +## Options + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.oracle.options +``` + +```{eval-rst} +.. autopydantic_model:: OracleReadOptions + :inherited-members: GenericOptions + :member-order: bysource +``` diff --git a/mddocs/docs/ru/connection/db_connection/oracle/sql.md b/mddocs/docs/ru/connection/db_connection/oracle/sql.md new file mode 100644 index 000000000..73d82e739 --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/oracle/sql.md @@ -0,0 +1,81 @@ +(oracle-sql)= + +# Reading from Oracle using `Oracle.sql` + +`Oracle.sql` allows passing custom SQL query, but does not support incremental strategies. + +```{eval-rst} +.. warning:: + + Please take into account :ref:`oracle-types` +``` + +```{eval-rst} +.. warning:: + + Statement is executed in **read-write** connection, so if you're calling some functions/procedures with DDL/DML statements inside, + they can change data in your database. +``` + +## Syntax support + +Only queries with the following syntax are supported: + +- ✅︎ `SELECT ... FROM ...` +- ✅︎ `WITH alias AS (...) SELECT ...` +- ❌ `SHOW ...` +- ❌ `SET ...; SELECT ...;` - multiple statements not supported + +## Examples + +```python +from onetl.connection import Oracle + +oracle = Oracle(...) +df = oracle.sql( + """ + SELECT + id, + key, + CAST(value AS VARCHAR2(4000)) value, + updated_at + FROM + some.mytable + WHERE + key = 'something' + """, + options=Oracle.SQLOptions( + partitionColumn="id", + numPartitions=10, + lowerBound=0, + upperBound=1000, + ), +) +``` + +## Recommendations + +### Select only required columns + +Instead of passing `SELECT * FROM ...` prefer passing exact column names `SELECT col1, col2, ...`. +This reduces the amount of data passed from Oracle to Spark. + +### Pay attention to `where` value + +Instead of filtering data on Spark side using `df.filter(df.column == 'value')` pass proper `WHERE column = 'value'` clause. +This both reduces the amount of data send from Oracle to Spark, and may also improve performance of the query. +Especially if there are indexes or partitions for columns used in `where` clause. + +## Options + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.oracle.options +``` + +```{eval-rst} +.. autopydantic_model:: OracleSQLOptions + :inherited-members: GenericOptions + :member-order: bysource + :model-show-field-summary: false + :field-show-constraints: false +``` diff --git a/mddocs/docs/ru/connection/db_connection/oracle/types.md b/mddocs/docs/ru/connection/db_connection/oracle/types.md new file mode 100644 index 000000000..7097bd439 --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/oracle/types.md @@ -0,0 +1,400 @@ +(oracle-types)= + +# Oracle \<-> Spark type mapping + +```{eval-rst} +.. note:: + + The results below are valid for Spark 3.5.5, and may differ on other Spark versions. +``` + +## Type detection & casting + +Spark's DataFrames always have a `schema` which is a list of columns with corresponding Spark types. All operations on a column are performed using column type. + +### Reading from Oracle + +This is how Oracle connector performs this: + +- For each column in query result (`SELECT column1, column2, ... FROM table ...`) get column name and Oracle type. +- Find corresponding `Oracle type (read)` → `Spark type` combination (see below) for each DataFrame column. If no combination is found, raise exception. +- Create DataFrame from query with specific column names and Spark types. + +### Writing to some existing Oracle table + +This is how Oracle connector performs this: + +- Get names of columns in DataFrame. [^footnote-1] +- Perform `SELECT * FROM table LIMIT 0` query. +- Take only columns present in DataFrame (by name, case insensitive). For each found column get Clickhouse type. +- **Find corresponding** `Oracle type (read)` → `Spark type` **combination** (see below) for each DataFrame column. If no combination is found, raise exception. [^footnote-2] +- Find corresponding `Spark type` → `Oracle type (write)` combination (see below) for each DataFrame column. If no combination is found, raise exception. +- If `Oracle type (write)` match `Oracle type (read)`, no additional casts will be performed, DataFrame column will be written to Oracle as is. +- If `Oracle type (write)` does not match `Oracle type (read)`, DataFrame column will be casted to target column type **on Oracle side**. + For example, you can write column with text data to `int` column, if column contains valid integer values within supported value range and precision. + +[^footnote-1]: This allows to write data to tables with `DEFAULT` and `GENERATED` columns - if DataFrame has no such column, + it will be populated by Oracle. + +[^footnote-2]: Yes, this is weird. + +### Create new table using Spark + +```{eval-rst} +.. warning:: + + ABSOLUTELY NOT RECOMMENDED! +``` + +This is how Oracle connector performs this: + +- Find corresponding `Spark type` → `Oracle type (create)` combination (see below) for each DataFrame column. If no combination is found, raise exception. +- Generate DDL for creating table in Oracle, like `CREATE TABLE (col1 ...)`, and run it. +- Write DataFrame to created table as is. + +But Oracle connector support only limited number of types and almost no custom clauses (like `PARTITION BY`, `INDEX`, etc). +So instead of relying on Spark to create tables: + +```{eval-rst} +.. dropdown:: See example + + .. code:: python + + writer = DBWriter( + connection=oracle, + target="public.table", + options=Oracle.WriteOptions(if_exists="append"), + ) + writer.run(df) +``` + +Always prefer creating table with desired DDL **BEFORE WRITING DATA**: + +```{eval-rst} +.. dropdown:: See example + + .. code:: python + + oracle.execute( + """ + CREATE TABLE username.table ( + id NUMBER, + business_dt TIMESTAMP(6), + value VARCHAR2(2000) + ) + """, + ) + + writer = DBWriter( + connection=oracle, + target="public.table", + options=Oracle.WriteOptions(if_exists="append"), + ) + writer.run(df) +``` + +See Oracle [CREATE TABLE](https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/SELECT.html) documentation. + +## Supported types + +### References + +See [List of Oracle types](https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/Data-Types.html). + +Here you can find source code with type conversions: + +- [JDBC -> Spark](https://github.com/apache/spark/blob/v3.5.5/sql/core/src/main/scala/org/apache/spark/sql/jdbc/OracleDialect.scala#L83-L109) +- [Spark -> JDBC](https://github.com/apache/spark/blob/v3.5.5/sql/core/src/main/scala/org/apache/spark/sql/jdbc/OracleDialect.scala#L111-L123) + +### Numeric types + +```{eval-rst} ++----------------------------------+-----------------------------------+-------------------------------+---------------------------+ +| Oracle type (read) | Spark type | Oracle type (write) | Oracle type (create) | ++==================================+===================================+===============================+===========================+ +| ``NUMBER`` | ``DecimalType(P=38, S=10)`` | ``NUMBER(P=38, S=10)`` | ``NUMBER(P=38, S=10)`` | ++----------------------------------+-----------------------------------+-------------------------------+---------------------------+ +| ``NUMBER(P=0..38)`` | ``DecimalType(P=0..38, S=0)`` | ``NUMBER(P=0..38, S=0)`` | ``NUMBER(P=38, S=0)`` | ++----------------------------------+-----------------------------------+-------------------------------+---------------------------+ +| ``NUMBER(P=0..38, S=0..38)`` | ``DecimalType(P=0..38, S=0..38)`` | ``NUMBER(P=0..38, S=0..38)`` | ``NUMBER(P=38, S=0..38)`` | ++----------------------------------+-----------------------------------+-------------------------------+---------------------------+ +| ``NUMBER(P=..., S=-127..-1)`` | unsupported [3]_ | | | ++----------------------------------+-----------------------------------+-------------------------------+---------------------------+ +| ``FLOAT`` | ``DecimalType(P=38, S=10)`` | ``NUMBER(P=38, S=10)`` | ``NUMBER(P=38, S=10)`` | ++----------------------------------+ | | | +| ``FLOAT(N)`` | | | | ++----------------------------------+ | | | +| ``REAL`` | | | | ++----------------------------------+ | | | +| ``DOUBLE PRECISION`` | | | | ++----------------------------------+-----------------------------------+-------------------------------+---------------------------+ +| ``BINARY_FLOAT`` | ``FloatType()`` | ``NUMBER(P=19, S=4)`` | ``NUMBER(P=19, S=4)`` | ++----------------------------------+-----------------------------------+ | | +| ``BINARY_DOUBLE`` | ``DoubleType()`` | | | ++----------------------------------+-----------------------------------+-------------------------------+---------------------------+ +| ``SMALLINT`` | ``DecimalType(P=38, S=0)`` | ``NUMBER(P=38, S=0)`` | ``NUMBER(P=38, S=0)`` | ++----------------------------------+ | | | +| ``INTEGER`` | | | | ++----------------------------------+-----------------------------------+-------------------------------+---------------------------+ +| ``LONG`` | ``StringType()`` | ``CLOB`` | ``CLOB`` | ++----------------------------------+-----------------------------------+-------------------------------+---------------------------+ +``` + +[^footnote-3]: Oracle support decimal types with negative scale, like `NUMBER(38, -10)`. Spark doesn't. + +### Temporal types + +```{eval-rst} ++--------------------------------------------+------------------------------------+---------------------------------+---------------------------------+ +| Oracle type (read) | Spark type | Oracle type (write) | Oracle type (create) | ++============================================+====================================+=================================+=================================+ +| ``DATE``, days | ``TimestampType()``, microseconds | ``TIMESTAMP(6)``, microseconds | ``TIMESTAMP(6)``, microseconds | ++--------------------------------------------+------------------------------------+---------------------------------+---------------------------------+ +| ``TIMESTAMP``, microseconds | ``TimestampType()``, microseconds | ``TIMESTAMP(6)``, microseconds | ``TIMESTAMP(6)``, microseconds | ++--------------------------------------------+ | | | +| ``TIMESTAMP(0)``, seconds | | | | ++--------------------------------------------+ | | | +| ``TIMESTAMP(3)``, milliseconds | | | | ++--------------------------------------------+ | | | +| ``TIMESTAMP(6)``, microseconds | | | | ++--------------------------------------------+------------------------------------+---------------------------------+---------------------------------+ +| ``TIMESTAMP(9)``, nanoseconds | ``TimestampType()``, microseconds, | ``TIMESTAMP(6)``, microseconds, | ``TIMESTAMP(6)``, microseconds, | +| | **precision loss** [4]_ | **precision loss** | **precision loss** | ++--------------------------------------------+------------------------------------+---------------------------------+---------------------------------+ +| ``TIMESTAMP WITH TIME ZONE`` | unsupported | | | ++--------------------------------------------+ | | | +| ``TIMESTAMP(N) WITH TIME ZONE`` | | | | ++--------------------------------------------+ | | | +| ``TIMESTAMP WITH LOCAL TIME ZONE`` | | | | ++--------------------------------------------+ | | | +| ``TIMESTAMP(N) WITH LOCAL TIME ZONE`` | | | | ++--------------------------------------------+ | | | +| ``INTERVAL YEAR TO MONTH`` | | | | ++--------------------------------------------+ | | | +| ``INTERVAL DAY TO SECOND`` | | | | ++--------------------------------------------+------------------------------------+---------------------------------+---------------------------------+ +``` + +```{eval-rst} +.. warning:: + + Note that types in Oracle and Spark have different value ranges: + + +---------------+------------------------------------+-----------------------------------+---------------------+--------------------------------+--------------------------------+ + | Oracle type | Min value | Max value | Spark type | Min value | Max value | + +===============+====================================+===================================+=====================+================================+================================+ + | ``date`` | ``-4712-01-01`` | ``9999-01-01`` | ``DateType()`` | ``0001-01-01`` | ``9999-12-31`` | + +---------------+------------------------------------+-----------------------------------+---------------------+--------------------------------+--------------------------------+ + | ``timestamp`` | ``-4712-01-01 00:00:00.000000000`` | ``9999-12-31 23:59:59.999999999`` | ``TimestampType()`` | ``0001-01-01 00:00:00.000000`` | ``9999-12-31 23:59:59.999999`` | + +---------------+------------------------------------+-----------------------------------+---------------------+--------------------------------+--------------------------------+ + + So not all of values can be read from Oracle to Spark. + + References: + * `Oracle date, timestamp and intervals documentation `_ + * `Spark DateType documentation `_ + * `Spark TimestampType documentation `_ +``` + +[^footnote-4]: Oracle support timestamp up to nanoseconds precision (`23:59:59.999999999`), + but Spark `TimestampType()` supports datetime up to microseconds precision (`23:59:59.999999`). + Nanoseconds will be lost during read or write operations. + +### String types + +```{eval-rst} ++-----------------------------+------------------+---------------------+----------------------+ +| Oracle type (read) | Spark type | Oracle type (write) | Oracle type (create) | ++=============================+==================+=====================+======================+ +| ``CHAR`` | ``StringType()`` | ``CLOB`` | ``CLOB`` | ++-----------------------------+ | | | +| ``CHAR(N CHAR)`` | | | | ++-----------------------------+ | | | +| ``CHAR(N BYTE)`` | | | | ++-----------------------------+ | | | +| ``NCHAR`` | | | | ++-----------------------------+ | | | +| ``NCHAR(N)`` | | | | ++-----------------------------+ | | | +| ``VARCHAR(N)`` | | | | ++-----------------------------+ | | | +| ``LONG VARCHAR`` | | | | ++-----------------------------+ | | | +| ``VARCHAR2(N CHAR)`` | | | | ++-----------------------------+ | | | +| ``VARCHAR2(N BYTE)`` | | | | ++-----------------------------+ | | | +| ``NVARCHAR2(N)`` | | | | ++-----------------------------+ | | | +| ``CLOB`` | | | | ++-----------------------------+ | | | +| ``NCLOB`` | | | | ++-----------------------------+------------------+---------------------+----------------------+ +``` + +### Binary types + +```{eval-rst} ++--------------------------+------------------+---------------------+----------------------+ +| Oracle type (read) | Spark type | Oracle type (write) | Oracle type (create) | ++==========================+==================+=====================+======================+ +| ``RAW(N)`` | ``BinaryType()`` | ``BLOB`` | ``BLOB`` | ++--------------------------+ | | | +| ``LONG RAW`` | | | | ++--------------------------+ | | | +| ``BLOB`` | | | | ++--------------------------+------------------+---------------------+----------------------+ +| ``BFILE`` | unsupported | | | ++--------------------------+------------------+---------------------+----------------------+ +``` + +### Struct types + +```{eval-rst} ++-------------------------------------+------------------+---------------------+----------------------+ +| Oracle type (read) | Spark type | Oracle type (write) | Oracle type (create) | ++=====================================+==================+=====================+======================+ +| ``XMLType`` | ``StringType()`` | ``CLOB`` | ``CLOB`` | ++-------------------------------------+ | | | +| ``URIType`` | | | | ++-------------------------------------+ | | | +| ``DBURIType`` | | | | ++-------------------------------------+ | | | +| ``XDBURIType`` | | | | ++-------------------------------------+ | | | +| ``HTTPURIType`` | | | | ++-------------------------------------+ | | | +| ``CREATE TYPE ... AS OBJECT (...)`` | | | | ++-------------------------------------+------------------+---------------------+----------------------+ +| ``JSON`` | unsupported | | | ++-------------------------------------+ | | | +| ``CREATE TYPE ... AS VARRAY ...`` | | | | ++-------------------------------------+ | | | +| ``CREATE TYPE ... AS TABLE OF ...`` | | | | ++-------------------------------------+------------------+---------------------+----------------------+ +``` + +### Special types + +```{eval-rst} ++--------------------+-------------------+---------------------+----------------------+ +| Oracle type (read) | Spark type | Oracle type (write) | Oracle type (create) | ++====================+===================+=====================+======================+ +| ``BOOLEAN`` | ``BooleanType()`` | ``BOOLEAN`` | ``NUMBER(P=1, S=0)`` | ++--------------------+-------------------+---------------------+----------------------+ +| ``ROWID`` | ``StringType()`` | ``CLOB`` | ``CLOB`` | ++--------------------+ | | | +| ``UROWID`` | | | | ++--------------------+ | | | +| ``UROWID(N)`` | | | | ++--------------------+-------------------+---------------------+----------------------+ +| ``ANYTYPE`` | unsupported | | | ++--------------------+ | | | +| ``ANYDATA`` | | | | ++--------------------+ | | | +| ``ANYDATASET`` | | | | ++--------------------+-------------------+---------------------+----------------------+ +``` + +## Explicit type cast + +### `DBReader` + +It is possible to explicitly cast column of unsupported type using `DBReader(columns=...)` syntax. + +For example, you can use `CAST(column AS CLOB)` to convert data to string representation on Oracle side, and so it will be read as Spark's `StringType()`. + +It is also possible to use [JSON_ARRAY](https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/JSON_ARRAY.html) +or [JSON_OBJECT](https://docs.oracle.com/en/database/oracle/oracle-database/23/sqlrf/JSON_OBJECT.html) Oracle functions +to convert column of any type to string representation. Then this JSON string can then be effectively parsed using the {obj}`JSON.parse_column ` method. + +```python +from onetl.file.format import JSON +from pyspark.sql.types import IntegerType, StructType, StructField + +from onetl.connection import Oracle +from onetl.db import DBReader + +oracle = Oracle(...) + +DBReader( + connection=oracle, + columns=[ + "id", + "supported_column", + "CAST(unsupported_column AS VARCHAR2(4000)) unsupported_column_str", + # or + "JSON_ARRAY(array_column) array_column_json", + ], +) +df = reader.run() + +json_scheme = StructType([StructField("key", IntegerType())]) + +df = df.select( + df.id, + df.supported_column, + df.unsupported_column_str.cast("integer").alias("parsed_integer"), + JSON().parse_column("array_column_json", json_scheme).alias("array_column"), +) +``` + +### `DBWriter` + +It is always possible to convert data on Spark side to string, and then write it to text column in Oracle table. + +To serialize and write JSON data to a `text` or `json` column in an Oracle table use the {obj}`JSON.serialize_column ` method. + +```python +from onetl.connection import Oracle +from onetl.db import DBWriter +from onetl.file.format import JSON + +oracle = Oracle(...) + +oracle.execute( + """ + CREATE TABLE schema.target_table ( + id INTEGER, + supported_column TIMESTAMP, + array_column_json VARCHAR2(4000) -- any string type, actually + ) + """, +) + +write_df = df.select( + df.id, + df.supported_column, + JSON().serialize_column(df.unsupported_column).alias("array_column_json"), +) + +writer = DBWriter( + connection=oracle, + target="schema.target_table", +) +writer.run(write_df) +``` + +Then you can parse this column on Oracle side - for example, by creating a view: + +```sql +SELECT + id, + supported_column, + JSON_VALUE(array_column_json, '$[0]' RETURNING NUMBER) AS array_item_0 +FROM + schema.target_table +``` + +Or by using [VIRTUAL column](https://oracle-base.com/articles/11g/virtual-columns-11gr1): + +```sql +CREATE TABLE schema.target_table ( + id INTEGER, + supported_column TIMESTAMP, + array_column_json VARCHAR2(4000), -- any string type, actually + array_item_0 GENERATED ALWAYS AS (JSON_VALUE(array_column_json, '$[0]' RETURNING NUMBER)) VIRTUAL +) +``` + +But data will be parsed on each table read in any case, as Oracle does no support `GENERATED ALWAYS AS (...) STORED` columns. diff --git a/mddocs/docs/ru/connection/db_connection/oracle/write.md b/mddocs/docs/ru/connection/db_connection/oracle/write.md new file mode 100644 index 000000000..09e021c79 --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/oracle/write.md @@ -0,0 +1,56 @@ +(oracle-write)= + +# Writing to Oracle using `DBWriter` + +For writing data to Oracle, use {obj}`DBWriter `. + +```{eval-rst} +.. warning:: + + Please take into account :ref:`oracle-types` +``` + +```{eval-rst} +.. warning:: + + It is always recommended to create table explicitly using :ref:`Oracle.execute ` + instead of relying on Spark's table DDL generation. + + This is because Spark's DDL generator can create columns with different precision and types than it is expected, + causing precision loss or other issues. +``` + +## Examples + +```python +from onetl.connection import Oracle +from onetl.db import DBWriter + +oracle = Oracle(...) + +df = ... # data is here + +writer = DBWriter( + connection=oracle, + target="schema.table", + options=Oracle.WriteOptions(if_exists="append"), +) + +writer.run(df) +``` + +## Options + +Method above accepts {obj}`OracleWriteOptions ` + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.oracle.options +``` + +```{eval-rst} +.. autopydantic_model:: OracleWriteOptions + :inherited-members: GenericOptions + :member-order: bysource + :model-show-field-summary: false + :field-show-constraints: false +``` diff --git a/mddocs/docs/ru/connection/db_connection/postgres/connection.md b/mddocs/docs/ru/connection/db_connection/postgres/connection.md new file mode 100644 index 000000000..80c59021c --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/postgres/connection.md @@ -0,0 +1,12 @@ +(postgres-connection)= + +# Postgres connection + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.postgres.connection +``` + +```{eval-rst} +.. autoclass:: Postgres + :members: get_packages, check +``` diff --git a/mddocs/docs/ru/connection/db_connection/postgres/execute.md b/mddocs/docs/ru/connection/db_connection/postgres/execute.md new file mode 100644 index 000000000..6964b43e9 --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/postgres/execute.md @@ -0,0 +1,113 @@ +(postgres-execute)= + +# Executing statements in Postgres + +```{eval-rst} +.. warning:: + + Methods below **read all the rows** returned from DB **to Spark driver memory**, and then convert them to DataFrame. + + Do **NOT** use them to read large amounts of data. Use :ref:`DBReader ` or :ref:`Postgres.sql ` instead. +``` + +## How to + +There are 2 ways to execute some statement in Postgres + +### Use `Postgres.fetch` + +Use this method to execute some `SELECT` query which returns **small number or rows**, like reading +Postgres config, or reading data from some reference table. Method returns Spark DataFrame. + +Method accepts {obj}`Postgres.FetchOptions `. + +Connection opened using this method should be then closed with `connection.close()` or `with connection:`. + +```{eval-rst} +.. warning:: + + Please take into account :ref:`postgres-types`. +``` + +#### Syntax support + +This method supports **any** query syntax supported by Postgres, like: + +- ✅︎ `SELECT ... FROM ...` +- ✅︎ `WITH alias AS (...) SELECT ...` +- ❌ `SET ...; SELECT ...;` - multiple statements not supported + +#### Examples + +```python +from onetl.connection import Postgres + +postgres = Postgres(...) + +df = postgres.fetch( + "SELECT value FROM some.reference_table WHERE key = 'some_constant'", + options=Postgres.FetchOptions(queryTimeout=10), +) +postgres.close() +value = df.collect()[0][0] # get value from first row and first column +``` + +### Use `Postgres.execute` + +Use this method to execute DDL and DML operations. Each method call runs operation in a separated transaction, and then commits it. + +Method accepts {obj}`Postgres.ExecuteOptions `. + +Connection opened using this method should be then closed with `connection.close()` or `with connection:`. + +#### Syntax support + +This method supports **any** query syntax supported by Postgres, like: + +- ✅︎ `CREATE TABLE ...`, `CREATE VIEW ...`, and so on +- ✅︎ `ALTER ...` +- ✅︎ `INSERT INTO ... SELECT ...`, `UPDATE ...`, `DELETE ...`, and so on +- ✅︎ `DROP TABLE ...`, `DROP VIEW ...`, `TRUNCATE TABLE`, and so on +- ✅︎ `CALL procedure(arg1, arg2) ...` +- ✅︎ `SELECT func(arg1, arg2)` or `{call func(arg1, arg2)}` - special syntax for calling functions +- ✅︎ other statements not mentioned here +- ❌ `SET ...; SELECT ...;` - multiple statements not supported + +#### Examples + +```python +from onetl.connection import Postgres + +postgres = Postgres(...) + +postgres.execute("DROP TABLE schema.table") +postgres.execute( + """ + CREATE TABLE schema.table ( + id bigint GENERATED ALWAYS AS IDENTITY, + key text, + value real + ) + """, + options=Postgres.ExecuteOptions(queryTimeout=10), +) +``` + +## Options + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.postgres.options +``` + +```{eval-rst} +.. autopydantic_model:: PostgresFetchOptions + :inherited-members: GenericOptions + :member-order: bysource + +``` + +```{eval-rst} +.. autopydantic_model:: PostgresExecuteOptions + :inherited-members: GenericOptions + :member-order: bysource +``` diff --git a/mddocs/docs/ru/connection/db_connection/postgres/index.md b/mddocs/docs/ru/connection/db_connection/postgres/index.md new file mode 100644 index 000000000..56442bfb0 --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/postgres/index.md @@ -0,0 +1,28 @@ +(postgres)= + +# Postgres + +```{toctree} +:caption: Connection +:maxdepth: 1 + +prerequisites +connection +``` + +```{toctree} +:caption: Operations +:maxdepth: 1 + +read +sql +write +execute +``` + +```{toctree} +:caption: Troubleshooting +:maxdepth: 1 + +types +``` diff --git a/mddocs/docs/ru/connection/db_connection/postgres/prerequisites.md b/mddocs/docs/ru/connection/db_connection/postgres/prerequisites.md new file mode 100644 index 000000000..d2221552d --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/postgres/prerequisites.md @@ -0,0 +1,71 @@ +(postgres-prerequisites)= + +# Prerequisites + +## Version Compatibility + +- PostgreSQL server versions: + : - Officially declared: 8.2 - 17 + - Actually tested: 9.4.26, 17.3 +- Spark versions: 2.3.x - 3.5.x +- Java versions: 8 - 20 + +See [official documentation](https://jdbc.postgresql.org/). + +## Installing PySpark + +To use Postgres connector you should have PySpark installed (or injected to `sys.path`) +BEFORE creating the connector instance. + +See {ref}`install-spark` installation instruction for more details. + +## Connecting to Postgres + +### Allowing connection to Postgres instance + +Ask your Postgres administrator to allow your user (and probably IP) to connect to instance, +e.g. by updating `pg_hba.conf` file. + +See [official documentation](https://www.postgresql.org/docs/current/auth-pg-hba-conf.html). + +### Connection port + +Connection is usually performed to port 5432. Port may differ for different Postgres instances. +Please ask your Postgres administrator to provide required information. + +### Connection host + +It is possible to connect to Postgres by using either DNS name of host or it's IP address. + +If you're using Postgres cluster, it is currently possible to connect only to **one specific node**. +Connecting to multiple nodes to perform load balancing, as well as automatic failover to new master/replica are not supported. + +### Required grants + +Ask your Postgres cluster administrator to set following grants for a user, +used for creating a connection: + +```{eval-rst} +.. tabs:: + + .. code-tab:: sql Read + Write + + -- allow creating tables in specific schema + GRANT USAGE, CREATE ON SCHEMA myschema TO username; + + -- allow read & write access to specific table + GRANT SELECT, INSERT ON myschema.mytable TO username; + + -- only if if_exists="replace_entire_table" is used: + GRANT TRUNCATE ON myschema.mytable TO username; + + .. code-tab:: sql Read only + + -- allow creating tables in specific schema + GRANT USAGE ON SCHEMA myschema TO username; + + -- allow read access to specific table + GRANT SELECT ON myschema.mytable TO username; +``` + +More details can be found in [official documentation](https://www.postgresql.org/docs/current/sql-grant.html). diff --git a/mddocs/docs/ru/connection/db_connection/postgres/read.md b/mddocs/docs/ru/connection/db_connection/postgres/read.md new file mode 100644 index 000000000..71db1908c --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/postgres/read.md @@ -0,0 +1,91 @@ +(postgres-read)= + +# Reading from Postgres using `DBReader` + +{obj}`DBReader ` supports {ref}`strategy` for incremental data reading, +but does not support custom queries, like `JOIN`. + +```{eval-rst} +.. warning:: + + Please take into account :ref:`postgres-types` +``` + +## Supported DBReader features + +- ✅︎ `columns` +- ✅︎ `where` +- ✅︎ `hwm`, supported strategies: +- - ✅︎ {ref}`snapshot-strategy` +- - ✅︎ {ref}`incremental-strategy` +- - ✅︎ {ref}`snapshot-batch-strategy` +- - ✅︎ {ref}`incremental-batch-strategy` +- ❌ `hint` (is not supported by Postgres) +- ❌ `df_schema` +- ✅︎ `options` (see {obj}`Postgres.ReadOptions `) + +## Examples + +Snapshot strategy: + +```python +from onetl.connection import Postgres +from onetl.db import DBReader + +postgres = Postgres(...) + +reader = DBReader( + connection=postgres, + source="schema.table", + columns=["id", "key", "CAST(value AS text) value", "updated_dt"], + where="key = 'something'", + options=Postgres.ReadOptions(partitionColumn="id", numPartitions=10), +) +df = reader.run() +``` + +Incremental strategy: + +```python +from onetl.connection import Postgres +from onetl.db import DBReader +from onetl.strategy import IncrementalStrategy + +postgres = Postgres(...) + +reader = DBReader( + connection=postgres, + source="schema.table", + columns=["id", "key", "CAST(value AS text) value", "updated_dt"], + where="key = 'something'", + hwm=DBReader.AutoDetectHWM(name="postgres_hwm", expression="updated_dt"), + options=Postgres.ReadOptions(partitionColumn="id", numPartitions=10), +) + +with IncrementalStrategy(): + df = reader.run() +``` + +## Recommendations + +### Select only required columns + +Instead of passing `"*"` in `DBReader(columns=[...])` prefer passing exact column names. This reduces the amount of data passed from Postgres to Spark. + +### Pay attention to `where` value + +Instead of filtering data on Spark side using `df.filter(df.column == 'value')` pass proper `DBReader(where="column = 'value'")` clause. +This both reduces the amount of data send from Postgres to Spark, and may also improve performance of the query. +Especially if there are indexes or partitions for columns used in `where` clause. + +## Options + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.postgres.options +``` + +```{eval-rst} +.. autopydantic_model:: PostgresReadOptions + :inherited-members: GenericOptions + :member-order: bysource +``` diff --git a/mddocs/docs/ru/connection/db_connection/postgres/sql.md b/mddocs/docs/ru/connection/db_connection/postgres/sql.md new file mode 100644 index 000000000..adceab0dc --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/postgres/sql.md @@ -0,0 +1,80 @@ +(postgres-sql)= + +# Reading from Postgres using `Postgres.sql` + +`Postgres.sql` allows passing custom SQL query, but does not support incremental strategies. + +```{eval-rst} +.. warning:: + + Please take into account :ref:`postgres-types` +``` + +```{eval-rst} +.. warning:: + + Statement is executed in **read-write** connection, so if you're calling some functions/procedures with DDL/DML statements inside, + they can change data in your database. +``` + +## Syntax support + +Only queries with the following syntax are supported: + +- ✅︎ `SELECT ... FROM ...` +- ✅︎ `WITH alias AS (...) SELECT ...` +- ❌ `SET ...; SELECT ...;` - multiple statements not supported + +## Examples + +```python +from onetl.connection import Postgres + +postgres = Postgres(...) +df = postgres.sql( + """ + SELECT + id, + key, + CAST(value AS text) value, + updated_at + FROM + some.mytable + WHERE + key = 'something' + """, + options=Postgres.SQLOptions( + partitionColumn="id", + numPartitions=10, + lowerBound=0, + upperBound=1000, + ), +) +``` + +## Recommendations + +### Select only required columns + +Instead of passing `SELECT * FROM ...` prefer passing exact column names `SELECT col1, col2, ...`. +This reduces the amount of data passed from Postgres to Spark. + +### Pay attention to `where` value + +Instead of filtering data on Spark side using `df.filter(df.column == 'value')` pass proper `WHERE column = 'value'` clause. +This both reduces the amount of data send from Postgres to Spark, and may also improve performance of the query. +Especially if there are indexes or partitions for columns used in `where` clause. + +## Options + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.postgres.options +``` + +```{eval-rst} +.. autopydantic_model:: PostgresSQLOptions + :inherited-members: GenericOptions + :member-order: bysource + :model-show-field-summary: false + :field-show-constraints: false +``` diff --git a/mddocs/docs/ru/connection/db_connection/postgres/types.md b/mddocs/docs/ru/connection/db_connection/postgres/types.md new file mode 100644 index 000000000..3b3540383 --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/postgres/types.md @@ -0,0 +1,490 @@ +(postgres-types)= + +# Postgres \<-> Spark type mapping + +```{eval-rst} +.. note:: + + The results below are valid for Spark 3.5.5, and may differ on other Spark versions. +``` + +## Type detection & casting + +Spark's DataFrames always have a `schema` which is a list of columns with corresponding Spark types. All operations on a column are performed using column type. + +### Reading from Postgres + +This is how Postgres connector performs this: + +- For each column in query result (`SELECT column1, column2, ... FROM table ...`) get column name and Postgres type. +- Find corresponding `Postgres type (read)` → `Spark type` combination (see below) for each DataFrame column [^footnote-1]. If no combination is found, raise exception. +- Create DataFrame from query with specific column names and Spark types. + +[^footnote-1]: All Postgres types that doesn't have corresponding Java type are converted to `String`. + +### Writing to some existing Postgres table + +This is how Postgres connector performs this: + +- Get names of columns in DataFrame. [^footnote-1] +- Perform `SELECT * FROM table LIMIT 0` query. +- Take only columns present in DataFrame (by name, case insensitive) [^footnote-2]. For each found column get Postgres type. +- Find corresponding `Spark type` → `Postgres type (write)` combination (see below) for each DataFrame column. If no combination is found, raise exception. +- If `Postgres type (write)` match `Postgres type (read)`, no additional casts will be performed, DataFrame column will be written to Postgres as is. +- If `Postgres type (write)` does not match `Postgres type (read)`, DataFrame column will be casted to target column type **on Postgres side**. + For example, you can write column with text data to `int` column, if column contains valid integer values within supported value range and precision [^footnote-3]. + +[^footnote-2]: This allows to write data to tables with `DEFAULT` and `GENERATED` columns - if DataFrame has no such column, + it will be populated by Postgres. + +[^footnote-3]: This is true only if either DataFrame column is a `StringType()`, or target column is `text` type. + + But other types cannot be silently converted, like `bytea -> bit(N)`. This requires explicit casting, see [Manual conversion to string]. + +### Create new table using Spark + +```{eval-rst} +.. warning:: + + ABSOLUTELY NOT RECOMMENDED! +``` + +This is how Postgres connector performs this: + +- Find corresponding `Spark type` → `Postgres type (create)` combination (see below) for each DataFrame column. If no combination is found, raise exception. +- Generate DDL for creating table in Postgres, like `CREATE TABLE (col1 ...)`, and run it. +- Write DataFrame to created table as is. + +But Postgres connector support only limited number of types and almost no custom clauses (like `PARTITION BY`, `INDEX`, etc). +So instead of relying on Spark to create tables: + +```{eval-rst} +.. dropdown:: See example + + .. code:: python + + writer = DBWriter( + connection=postgres, + target="public.table", + options=Postgres.WriteOptions( + if_exists="append", + createTableOptions="PARTITION BY RANGE (id)", + ), + ) + writer.run(df) +``` + +Always prefer creating table with desired DDL **BEFORE WRITING DATA**: + +```{eval-rst} +.. dropdown:: See example + + .. code:: python + + postgres.execute( + """ + CREATE TABLE public.table ( + id bigint, + business_dt timestamp(6), + value json + ) + PARTITION BY RANGE (Id) + """, + ) + + writer = DBWriter( + connection=postgres, + target="public.table", + options=Postgres.WriteOptions(if_exists="append"), + ) + writer.run(df) +``` + +See Postgres [CREATE TABLE](https://www.postgresql.org/docs/current/sql-createtable.html) documentation. + +## Supported types + +### References + +See [List of Postgres types](https://www.postgresql.org/docs/current/datatype.html). + +Here you can find source code with type conversions: + +- [Postgres \<-> JDBC](https://github.com/pgjdbc/pgjdbc/blob/REL42.6.0/pgjdbc/src/main/java/org/postgresql/jdbc/TypeInfoCache.java#L78-L112) +- [JDBC -> Spark](https://github.com/apache/spark/blob/v3.5.5/sql/core/src/main/scala/org/apache/spark/sql/jdbc/PostgresDialect.scala#L52-L108) +- [Spark -> JDBC](https://github.com/apache/spark/blob/v3.5.5/sql/core/src/main/scala/org/apache/spark/sql/jdbc/PostgresDialect.scala#L118-L132) + +### Numeric types + +```{eval-rst} ++----------------------------------+-----------------------------------+-------------------------------+-------------------------+ +| Postgres type (read) | Spark type | Postgres type (write) | Postgres type (create) | ++==================================+===================================+===============================+=========================+ +| ``decimal`` | ``DecimalType(P=38, S=18)`` | ``decimal(P=38, S=18)`` | ``decimal`` (unbounded) | ++----------------------------------+-----------------------------------+-------------------------------+ | +| ``decimal(P=0..38)`` | ``DecimalType(P=0..38, S=0)`` | ``decimal(P=0..38, S=0)`` | | ++----------------------------------+-----------------------------------+-------------------------------+ | +| ``decimal(P=0..38, S=0..38)`` | ``DecimalType(P=0..38, S=0..38)`` | ``decimal(P=0..38, S=0..38)`` | | ++----------------------------------+-----------------------------------+-------------------------------+-------------------------+ +| ``decimal(P=39.., S=0..)`` | unsupported [4]_ | | | ++----------------------------------+-----------------------------------+-------------------------------+-------------------------+ +| ``decimal(P=.., S=..-1)`` | unsupported [5]_ | | | ++----------------------------------+-----------------------------------+-------------------------------+-------------------------+ +| ``real`` | ``FloatType()`` | ``real`` | ``real`` | ++----------------------------------+-----------------------------------+-------------------------------+-------------------------+ +| ``double precision`` | ``DoubleType()`` | ``double precision`` | ``double precision`` | ++----------------------------------+-----------------------------------+-------------------------------+-------------------------+ +| ``smallint`` | ``ShortType()`` | ``smallint`` | ``smallint`` | ++----------------------------------+-----------------------------------+ | | +| ``-`` | ``ByteType()`` | | | ++----------------------------------+-----------------------------------+-------------------------------+-------------------------+ +| ``integer`` | ``IntegerType()`` | ``integer`` | ``integer`` | ++----------------------------------+-----------------------------------+-------------------------------+-------------------------+ +| ``bigint`` | ``LongType()`` | ``bigint`` | ``bigint`` | ++----------------------------------+-----------------------------------+-------------------------------+-------------------------+ +| ``money`` | ``StringType()`` [1]_ | ``text`` | ``text`` | ++----------------------------------+ | | | +| ``int4range`` | | | | ++----------------------------------+ | | | +| ``int8range`` | | | | ++----------------------------------+ | | | +| ``numrange`` | | | | ++----------------------------------+ | | | +| ``int2vector`` | | | | ++----------------------------------+-----------------------------------+-------------------------------+-------------------------+ +``` + +[^footnote-4]: Postgres support decimal types with unlimited precision. + + But Spark's `DecimalType(P, S)` supports maximum `P=38` (128 bit). It is impossible to read, write or operate with values of larger precision, + this leads to an exception. + +[^footnote-5]: Postgres support decimal types with negative scale, like `decimal(38, -10)`. Spark doesn't. + +### Temporal types + +```{eval-rst} ++------------------------------------+------------------------------+-----------------------+-------------------------+ +| Postgres type (read) | Spark type | Postgres type (write) | Postgres type (create) | ++====================================+==============================+=======================+=========================+ +| ``date`` | ``DateType()`` | ``date`` | ``date`` | ++------------------------------------+------------------------------+-----------------------+-------------------------+ +| ``time`` | ``TimestampType()``, | ``timestamp(6)`` | ``timestamp(6)`` | ++------------------------------------+ with time format quirks [6]_ | | | +| ``time(0..6)`` | | | | ++------------------------------------+ | | | +| ``time with time zone`` | | | | ++------------------------------------+ | | | +| ``time(0..6) with time zone`` | | | | ++------------------------------------+------------------------------+-----------------------+-------------------------+ +| ``timestamp`` | ``TimestampType()`` | ``timestamp(6)`` | ``timestamp(6)`` | ++------------------------------------+ | | | +| ``timestamp(0..6)`` | | | | ++------------------------------------+ | | | +| ``timestamp with time zone`` | | | | ++------------------------------------+ | | | +| ``timestamp(0..6) with time zone`` | | | | ++------------------------------------+------------------------------+-----------------------+-------------------------+ +| ``-`` | ``TimestampNTZType()`` | ``timestamp(6)`` | ``timestamp(6)`` | ++------------------------------------+------------------------------+-----------------------+-------------------------+ +| ``interval`` of any precision | ``StringType()`` [1]_ | ``text`` | ``text`` | ++------------------------------------+------------------------------+-----------------------+-------------------------+ +| ``-`` | ``DayTimeIntervalType()`` | unsupported | unsupported | ++------------------------------------+------------------------------+-----------------------+-------------------------+ +| ``-`` | ``YearMonthIntervalType()`` | unsupported | unsupported | ++------------------------------------+------------------------------+-----------------------+-------------------------+ +| ``daterange`` | ``StringType()`` [1]_ | ``text`` | ``text`` | ++------------------------------------+ | | | +| ``tsrange`` | | | | ++------------------------------------+ | | | +| ``tstzrange`` | | | | ++------------------------------------+------------------------------+-----------------------+-------------------------+ +``` + +```{eval-rst} +.. warning:: + + Note that types in Postgres and Spark have different value ranges: + + +---------------+---------------------------------+----------------------------------+---------------------+--------------------------------+--------------------------------+ + | Postgres type | Min value | Max value | Spark type | Min value | Max value | + +===============+=================================+==================================+=====================+================================+================================+ + | ``date`` | ``-4713-01-01`` | ``5874897-01-01`` | ``DateType()`` | ``0001-01-01`` | ``9999-12-31`` | + +---------------+---------------------------------+----------------------------------+---------------------+--------------------------------+--------------------------------+ + | ``timestamp`` | ``-4713-01-01 00:00:00.000000`` | ``294276-12-31 23:59:59.999999`` | ``TimestampType()`` | ``0001-01-01 00:00:00.000000`` | ``9999-12-31 23:59:59.999999`` | + +---------------+---------------------------------+----------------------------------+ | | | + | ``time`` | ``00:00:00.000000`` | ``24:00:00.000000`` | | | | + +---------------+---------------------------------+----------------------------------+---------------------+--------------------------------+--------------------------------+ + + So not all of values can be read from Postgres to Spark. + + References: + * `Postgres date/time types documentation `_ + * `Spark DateType documentation `_ + * `Spark TimestampType documentation `_ +``` + +[^footnote-6]: `time` type is the same as `timestamp` with date `1970-01-01`. So instead of reading data from Postgres like `23:59:59` + it is actually read `1970-01-01 23:59:59`, and vice versa. + +### String types + +```{eval-rst} ++-----------------------------+-----------------------+-----------------------+-------------------------+ +| Postgres type (read) | Spark type | Postgres type (write) | Postgres type (create) | ++=============================+=======================+=======================+=========================+ +| ``character`` | ``StringType()`` | ``text`` | ``text`` | ++-----------------------------+ | | | +| ``character(N)`` | | | | ++-----------------------------+ | | | +| ``character varying`` | | | | ++-----------------------------+ | | | +| ``character varying(N)`` | | | | ++-----------------------------+ | | | +| ``text`` | | | | ++-----------------------------+ | | | +| ``json`` | | | | ++-----------------------------+ | | | +| ``jsonb`` | | | | ++-----------------------------+ | | | +| ``xml`` | | | | ++-----------------------------+-----------------------+ | | +| ``CREATE TYPE ... AS ENUM`` | ``StringType()`` [1]_ | | | ++-----------------------------+ | | | +| ``tsvector`` | | | | ++-----------------------------+ | | | +| ``tsquery`` | | | | ++-----------------------------+-----------------------+-----------------------+-------------------------+ +| ``-`` | ``CharType()`` | ``unsupported`` | ``unsupported`` | ++-----------------------------+-----------------------+-----------------------+-------------------------+ +| ``-`` | ``VarcharType()`` | ``unsupported`` | ``unsupported`` | ++-----------------------------+-----------------------+-----------------------+-------------------------+ +``` + +### Binary types + +```{eval-rst} ++--------------------------+-----------------------+-----------------------------+-------------------------+ +| Postgres type (read) | Spark type | Postgres type (write) | Postgres type (create) | ++==========================+=======================+=============================+=========================+ +| ``boolean`` | ``BooleanType()`` | ``boolean`` | ``boolean`` | ++--------------------------+-----------------------+-----------------------------+-------------------------+ +| ``bit`` | ``BooleanType()`` | ``bool``, | ``bool`` | ++--------------------------+ | **cannot insert data** [3]_ | | +| ``bit(N=1)`` | | | | ++--------------------------+-----------------------+-----------------------------+-------------------------+ +| ``bit(N=2..)`` | ``ByteType()`` | ``bytea``, | ``bytea`` | +| | | **cannot insert data** [3]_ | | ++--------------------------+-----------------------+-----------------------------+-------------------------+ +| ``bit varying`` | ``StringType()`` [1]_ | ``text`` | ``text`` | ++--------------------------+ | | | +| ``bit varying(N)`` | | | | ++--------------------------+-----------------------+-----------------------------+-------------------------+ +| ``bytea`` | ``BinaryType()`` | ``bytea`` | ``bytea`` | ++--------------------------+-----------------------+-----------------------------+-------------------------+ +``` + +### Struct types + +```{eval-rst} ++--------------------------------+-----------------------+-----------------------+-------------------------+ +| Postgres type (read) | Spark type | Postgres type (write) | Postgres type (create) | ++================================+=======================+=======================+=========================+ +| ``T[]`` | ``ArrayType(T)`` | ``T[]`` | ``T[]`` | ++--------------------------------+-----------------------+-----------------------+-------------------------+ +| ``T[][]`` | unsupported | | | ++--------------------------------+-----------------------+-----------------------+-------------------------+ +| ``CREATE TYPE sometype (...)`` | ``StringType()`` [1]_ | ``text`` | ``text`` | ++--------------------------------+-----------------------+-----------------------+-------------------------+ +| ``-`` | ``StructType()`` | unsupported | | ++--------------------------------+-----------------------+ | | +| ``-`` | ``MapType()`` | | | ++--------------------------------+-----------------------+-----------------------+-------------------------+ +``` + +### Network types + +```{eval-rst} ++----------------------+-----------------------+-----------------------+-------------------------+ +| Postgres type (read) | Spark type | Postgres type (write) | Postgres type (create) | ++======================+=======================+=======================+=========================+ +| ``cidr`` | ``StringType()`` [1]_ | ``text`` | ``text`` | ++----------------------+ | | | +| ``inet`` | | | | ++----------------------+ | | | +| ``macaddr`` | | | | ++----------------------+ | | | +| ``macaddr8`` | | | | ++----------------------+-----------------------+-----------------------+-------------------------+ +``` + +### Geo types + +```{eval-rst} ++----------------------+-----------------------+-----------------------+-------------------------+ +| Postgres type (read) | Spark type | Postgres type (write) | Postgres type (create) | ++======================+=======================+=======================+=========================+ +| ``circle`` | ``StringType()`` [1]_ | ``text`` | ``text`` | ++----------------------+ | | | +| ``box`` | | | | ++----------------------+ | | | +| ``line`` | | | | ++----------------------+ | | | +| ``lseg`` | | | | ++----------------------+ | | | +| ``path`` | | | | ++----------------------+ | | | +| ``point`` | | | | ++----------------------+ | | | +| ``polygon`` | | | | ++----------------------+ | | | +| ``polygon`` | | | | ++----------------------+-----------------------+-----------------------+-------------------------+ +``` + +## Explicit type cast + +### `DBReader` + +It is possible to explicitly cast column of unsupported type using `DBReader(columns=...)` syntax. + +For example, you can use `CAST(column AS text)` to convert data to string representation on Postgres side, and so it will be read as Spark's `StringType()`. + +It is also possible to use [to_json](https://www.postgresql.org/docs/current/functions-json.html) Postgres function to convert column of any type to string representation, and then parse this column on Spark side you can use the {obj}`JSON.parse_column ` method: + +```python +from pyspark.sql.types import IntegerType + +from onetl.connection import Postgres +from onetl.db import DBReader +from onetl.file.format import JSON + +postgres = Postgres(...) + +DBReader( + connection=postgres, + columns=[ + "id", + "supported_column", + "CAST(unsupported_column AS text) unsupported_column_str", + # or + "to_json(unsupported_column) array_column_json", + ], +) +df = reader.run() + +json_schema = StructType( + [ + StructField("id", IntegerType(), nullable=True), + StructField("name", StringType(), nullable=True), + ..., + ] +) +df = df.select( + df.id, + df.supported_column, + # explicit cast + df.unsupported_column_str.cast("integer").alias("parsed_integer"), + JSON().parse_column("array_column_json", json_schema).alias("json_string"), +) +``` + +### `DBWriter` + +It is always possible to convert data on the Spark side to a string, and then write it to a text column in a Postgres table. + +#### Using JSON.serialize_column + +You can use the {obj}`JSON.serialize_column ` method for data serialization: + +```python +from onetl.file.format import JSON +from pyspark.sql.functions import col + +from onetl.connection import Postgres +from onetl.db import DBWriter + +postgres = Postgres(...) + +postgres.execute( + """ + CREATE TABLE schema.target_table ( + id int, + supported_column timestamp, + array_column_json jsonb -- any column type, actually + ) + """, +) + +write_df = df.select( + df.id, + df.supported_column, + JSON().serialize_column(df.unsupported_column).alias("array_column_json"), +) + +writer = DBWriter( + connection=postgres, + target="schema.target_table", +) +writer.run(write_df) +``` + +Then you can parse this column on the Postgres side (for example, by creating a view): + +```sql +SELECT + id, + supported_column, + array_column_json->'0' AS array_item_0 +FROM + schema.target_table +``` + +To avoid casting the value on every table read you can use [GENERATED ALWAYS STORED](https://www.postgresql.org/docs/current/ddl-generated-columns.html) column, but this requires 2x space (for original and parsed value). + +#### Manual conversion to string + +Postgres connector also supports conversion text value directly to target column type, if this value has a proper format. + +For example, you can write data like `[123, 345)` to `int8range` type because Postgres allows cast `'[123, 345)'::int8range'`: + +```python +from pyspark.sql.ftypes import StringType +from pyspark.sql.functions import udf + +from onetl.connection import Postgres +from onetl.db import DBReader + +postgres = Postgres(...) + +postgres.execute( + """ + CREATE TABLE schema.target_table ( + id int, + range_column int8range -- any column type, actually + ) + """, +) + + +@udf(returnType=StringType()) +def array_to_range(value: tuple): + """This UDF allows to convert tuple[start, end] to Postgres' range format""" + start, end = value + return f"[{start},{end})" + + +write_df = df.select( + df.id, + array_to_range(df.range_column).alias("range_column"), +) + +writer = DBWriter( + connection=postgres, + target="schema.target_table", +) +writer.run(write_df) +``` + +This can be tricky to implement and may lead to longer write process. +But this does not require extra space on Postgres side, and allows to avoid explicit value cast on every table read. diff --git a/mddocs/docs/ru/connection/db_connection/postgres/write.md b/mddocs/docs/ru/connection/db_connection/postgres/write.md new file mode 100644 index 000000000..8f01de26a --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/postgres/write.md @@ -0,0 +1,56 @@ +(postgres-write)= + +# Writing to Postgres using `DBWriter` + +For writing data to Postgres, use {obj}`DBWriter `. + +```{eval-rst} +.. warning:: + + Please take into account :ref:`postgres-types` +``` + +```{eval-rst} +.. warning:: + + It is always recommended to create table explicitly using :ref:`Postgres.execute ` + instead of relying on Spark's table DDL generation. + + This is because Spark's DDL generator can create columns with different precision and types than it is expected, + causing precision loss or other issues. +``` + +## Examples + +```python +from onetl.connection import Postgres +from onetl.db import DBWriter + +postgres = Postgres(...) + +df = ... # data is here + +writer = DBWriter( + connection=postgres, + target="schema.table", + options=Postgres.WriteOptions(if_exists="append"), +) + +writer.run(df) +``` + +## Options + +Method above accepts {obj}`Postgres.WriteOptions ` + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.postgres.options +``` + +```{eval-rst} +.. autopydantic_model:: PostgresWriteOptions + :inherited-members: GenericOptions + :member-order: bysource + :model-show-field-summary: false + :field-show-constraints: false +``` diff --git a/mddocs/docs/ru/connection/db_connection/teradata/connection.md b/mddocs/docs/ru/connection/db_connection/teradata/connection.md new file mode 100644 index 000000000..0804437cf --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/teradata/connection.md @@ -0,0 +1,12 @@ +(teradata-connection)= + +# Teradata connection + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.teradata.connection +``` + +```{eval-rst} +.. autoclass:: Teradata + :members: get_packages, check +``` diff --git a/mddocs/docs/ru/connection/db_connection/teradata/execute.md b/mddocs/docs/ru/connection/db_connection/teradata/execute.md new file mode 100644 index 000000000..de1b018fe --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/teradata/execute.md @@ -0,0 +1,110 @@ +(teradata-execute)= + +# Executing statements in Teradata + +```{eval-rst} +.. warning:: + + Methods below **read all the rows** returned from DB **to Spark driver memory**, and then convert them to DataFrame. + + Do **NOT** use them to read large amounts of data. Use :ref:`DBReader ` or :ref:`Teradata.sql ` instead. +``` + +## How to + +There are 2 ways to execute some statement in Teradata + +### Use `Teradata.fetch` + +Use this method to execute some `SELECT` query which returns **small number or rows**, like reading +Teradata config, or reading data from some reference table. Method returns Spark DataFrame. + +Method accepts {obj}`Teradata.FetchOptions `. + +Connection opened using this method should be then closed with `connection.close()` or `with connection:`. + +#### Syntax support + +This method supports **any** query syntax supported by Teradata, like: + +- ✅︎ `SELECT ... FROM ...` +- ✅︎ `WITH alias AS (...) SELECT ...` +- ✅︎ `SHOW ...` +- ❌ `SET ...; SELECT ...;` - multiple statements not supported + +#### Examples + +```python +from onetl.connection import Teradata + +teradata = Teradata(...) + +df = teradata.fetch( + "SELECT value FROM some.reference_table WHERE key = 'some_constant'", + options=Teradata.FetchOptions(queryTimeout=10), +) +teradata.close() +value = df.collect()[0][0] # get value from first row and first column +``` + +### Use `Teradata.execute` + +Use this method to execute DDL and DML operations. Each method call runs operation in a separated transaction, and then commits it. + +Method accepts {obj}`Teradata.ExecuteOptions `. + +Connection opened using this method should be then closed with `connection.close()` or `with connection:`. + +#### Syntax support + +This method supports **any** query syntax supported by Teradata, like: + +- ✅︎ `CREATE TABLE ...`, `CREATE VIEW ...`, and so on +- ✅︎ `ALTER ...` +- ✅︎ `INSERT INTO ... SELECT ...`, `UPDATE ...`, `DELETE ...`, and so on +- ✅︎ `DROP TABLE ...`, `DROP VIEW ...`, `TRUNCATE TABLE`, and so on +- ✅︎ `CALL procedure(arg1, arg2) ...` or `{call procedure(arg1, arg2)}` - special syntax for calling procedure +- ✅︎ `EXECUTE macro(arg1, arg2)` +- ✅︎ `EXECUTE FUNCTION ...` +- ✅︎ other statements not mentioned here +- ❌ `SET ...; SELECT ...;` - multiple statements not supported + +#### Examples + +```python +from onetl.connection import Teradata + +teradata = Teradata(...) + +teradata.execute("DROP TABLE database.table") +teradata.execute( + """ + CREATE MULTISET TABLE database.table AS ( + id BIGINT, + key VARCHAR, + value REAL + ) + NO PRIMARY INDEX + """, + options=Teradata.ExecuteOptions(queryTimeout=10), +) +``` + +## Options + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.teradata.options +``` + +```{eval-rst} +.. autopydantic_model:: TeradataFetchOptions + :inherited-members: GenericOptions + :member-order: bysource + +``` + +```{eval-rst} +.. autopydantic_model:: TeradataExecuteOptions + :inherited-members: GenericOptions + :member-order: bysource +``` diff --git a/mddocs/docs/ru/connection/db_connection/teradata/index.md b/mddocs/docs/ru/connection/db_connection/teradata/index.md new file mode 100644 index 000000000..f86e25726 --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/teradata/index.md @@ -0,0 +1,21 @@ +(teradata)= + +# Teradata + +```{toctree} +:caption: Connection +:maxdepth: 1 + +prerequisites +connection +``` + +```{toctree} +:caption: Operations +:maxdepth: 1 + +read +sql +write +execute +``` diff --git a/mddocs/docs/ru/connection/db_connection/teradata/prerequisites.md b/mddocs/docs/ru/connection/db_connection/teradata/prerequisites.md new file mode 100644 index 000000000..d180ffdeb --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/teradata/prerequisites.md @@ -0,0 +1,57 @@ +(teradata-prerequisites)= + +# Prerequisites + +## Version Compatibility + +- Teradata server versions: + : - Officially declared: 16.10 - 20.0 + - Actually tested: 16.10 +- Spark versions: 2.3.x - 3.5.x +- Java versions: 8 - 20 + +See [official documentation](https://teradata-docs.s3.amazonaws.com/doc/connectivity/jdbc/reference/current/platformMatrix.html). + +## Installing PySpark + +To use Teradata connector you should have PySpark installed (or injected to `sys.path`) +BEFORE creating the connector instance. + +See {ref}`install-spark` installation instruction for more details. + +## Connecting to Teradata + +### Connection host + +It is possible to connect to Teradata by using either DNS name Parsing Engine (PE) host, or it's IP address. + +### Connection port + +Connection is usually performed to port `1025`. Port may differ for different Teradata instances. +Please ask your Teradata administrator to provide required information. + +### Required grants + +Ask your Teradata cluster administrator to set following grants for a user, +used for creating a connection: + +```{eval-rst} +.. tabs:: + + .. code-tab:: sql Read + Write + + -- allow creating tables in the target schema + GRANT CREATE TABLE ON database TO username; + + -- allow read & write access to specific table + GRANT SELECT, INSERT ON database.mytable TO username; + + .. code-tab:: sql Read only + + -- allow read access to specific table + GRANT SELECT ON database.mytable TO username; +``` + +See: +: - [Teradata access rights](https://www.dwhpro.com/teradata-access-rights/) + - [GRANT documentation](https://teradata.github.io/presto/docs/0.167-t/sql/grant.html) diff --git a/mddocs/docs/ru/connection/db_connection/teradata/read.md b/mddocs/docs/ru/connection/db_connection/teradata/read.md new file mode 100644 index 000000000..c57b09119 --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/teradata/read.md @@ -0,0 +1,117 @@ +(teradata-read)= + +# Reading from Teradata using `DBReader` + +{obj}`DBReader ` supports {ref}`strategy` for incremental data reading, +but does not support custom queries, like `JOIN`. + +## Supported DBReader features + +- ✅︎ `columns` +- ✅︎ `where` +- ✅︎ `hwm`, supported strategies: +- - ✅︎ {ref}`snapshot-strategy` +- - ✅︎ {ref}`incremental-strategy` +- - ✅︎ {ref}`snapshot-batch-strategy` +- - ✅︎ {ref}`incremental-batch-strategy` +- ❌ `hint` (is not supported by Teradata) +- ❌ `df_schema` +- ✅︎ `options` (see {obj}`Teradata.ReadOptions `) + +## Examples + +Snapshot strategy: + +```python +from onetl.connection import Teradata +from onetl.db import DBReader + +teradata = Teradata(...) + +reader = DBReader( + connection=teradata, + source="database.table", + columns=["id", "key", "CAST(value AS VARCHAR) value", "updated_dt"], + where="key = 'something'", + options=Teradata.ReadOptions( + partitioning_mode="hash", + partitionColumn="id", + numPartitions=10, + ), +) +df = reader.run() +``` + +Incremental strategy: + +```python +from onetl.connection import Teradata +from onetl.db import DBReader +from onetl.strategy import IncrementalStrategy + +teradata = Teradata(...) + +reader = DBReader( + connection=teradata, + source="database.table", + columns=["id", "key", "CAST(value AS VARCHAR) value", "updated_dt"], + where="key = 'something'", + hwm=DBReader.AutoDetectHWM(name="teradata_hwm", expression="updated_dt"), + options=Teradata.ReadOptions( + partitioning_mode="hash", + partitionColumn="id", + numPartitions=10, + ), +) + +with IncrementalStrategy(): + df = reader.run() +``` + +## Recommendations + +### Select only required columns + +Instead of passing `"*"` in `DBReader(columns=[...])` prefer passing exact column names. This reduces the amount of data passed from Teradata to Spark. + +### Pay attention to `where` value + +Instead of filtering data on Spark side using `df.filter(df.column == 'value')` pass proper `DBReader(where="column = 'value'")` clause. +This both reduces the amount of data send from Teradata to Spark, and may also improve performance of the query. +Especially if there are indexes or partitions for columns used in `where` clause. + +### Read data in parallel + +`DBReader` can read data in multiple parallel connections by passing `Teradata.ReadOptions(numPartitions=..., partitionColumn=...)`. + +In the example above, Spark opens 10 parallel connections, and data is evenly distributed between all these connections using expression +`HASHAMP(HASHBUCKET(HASHROW({partition_column}))) MOD {num_partitions}`. +This allows sending each Spark worker only some piece of data, reducing resource consumption. +`partition_column` here can be table column of any type. + +It is also possible to use `partitioning_mode="mod"` or `partitioning_mode="range"`, but in this case +`partition_column` have to be an integer, should not contain `NULL`, and values to be uniformly distributed. +It is also less performant than `partitioning_mode="hash"` due to Teradata `HASHAMP` implementation. + +### Do **NOT** use `TYPE=FASTEXPORT` + +Teradata supports several [different connection types](https://teradata-docs.s3.amazonaws.com/doc/connectivity/jdbc/reference/current/jdbcug_chapter_2.html#BABFGFAF): +: - `TYPE=DEFAULT` - perform plain `SELECT` queries + - `TYPE=FASTEXPORT` - uses special FastExport protocol for select queries + +But `TYPE=FASTEXPORT` uses exclusive lock on the source table, so it is impossible to use multiple Spark workers parallel data read. +This leads to sending all the data to just one Spark worker, which is slow and takes a lot of RAM. + +Prefer using `partitioning_mode="hash"` from example above. + +## Options + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.teradata.options +``` + +```{eval-rst} +.. autopydantic_model:: TeradataReadOptions + :inherited-members: GenericOptions + :member-order: bysource +``` diff --git a/mddocs/docs/ru/connection/db_connection/teradata/sql.md b/mddocs/docs/ru/connection/db_connection/teradata/sql.md new file mode 100644 index 000000000..ad0919dce --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/teradata/sql.md @@ -0,0 +1,76 @@ +(teradata-sql)= + +# Reading from Teradata using `Teradata.sql` + +`Teradata.sql` allows passing custom SQL query, but does not support incremental strategies. + +```{eval-rst} +.. warning:: + + Statement is executed in **read-write** connection, so if you're calling some functions/procedures with DDL/DML statements inside, + they can change data in your database. +``` + +## Syntax support + +Only queries with the following syntax are supported: + +- ✅︎ `SELECT ... FROM ...` +- ✅︎ `WITH alias AS (...) SELECT ...` +- ❌ `SHOW ...` +- ❌ `SET ...; SELECT ...;` - multiple statements not supported + +## Examples + +```python +from onetl.connection import Teradata + +teradata = Teradata(...) +df = teradata.sql( + """ + SELECT + id, + key, + CAST(value AS VARCHAR) AS value, + updated_at, + HASHAMP(HASHBUCKET(HASHROW(id))) MOD 10 AS part_column + FROM + database.mytable + WHERE + key = 'something' + """, + options=Teradata.SQLOptions( + partitionColumn="id", + numPartitions=10, + lowerBound=0, + upperBound=1000, + ), +) +``` + +## Recommendations + +### Select only required columns + +Instead of passing `SELECT * FROM ...` prefer passing exact column names `SELECT col1, col2, ...`. +This reduces the amount of data passed from Teradata to Spark. + +### Pay attention to `where` value + +Instead of filtering data on Spark side using `df.filter(df.column == 'value')` pass proper `WHERE column = 'value'` clause. +This both reduces the amount of data send from Teradata to Spark, and may also improve performance of the query. +Especially if there are indexes or partitions for columns used in `where` clause. + +## Options + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.teradata.options +``` + +```{eval-rst} +.. autopydantic_model:: TeradataSQLOptions + :inherited-members: GenericOptions + :member-order: bysource + :model-show-field-summary: false + :field-show-constraints: false +``` diff --git a/mddocs/docs/ru/connection/db_connection/teradata/write.md b/mddocs/docs/ru/connection/db_connection/teradata/write.md new file mode 100644 index 000000000..6d1f6644f --- /dev/null +++ b/mddocs/docs/ru/connection/db_connection/teradata/write.md @@ -0,0 +1,120 @@ +(teradata-write)= + +# Writing to Teradata using `DBWriter` + +For writing data to Teradata, use {obj}`DBWriter `. + +```{eval-rst} +.. warning:: + + It is always recommended to create table explicitly using :ref:`Teradata.execute ` + instead of relying on Spark's table DDL generation. + + This is because Spark's DDL generator can create columns with different precision and types than it is expected, + causing precision loss or other issues. +``` + +## Examples + +```python +from onetl.connection import Teradata +from onetl.db import DBWriter + +teradata = Teradata( + ..., + extra={"TYPE": "FASTLOAD", "TMODE": "TERA"}, +) + +df = ... # data is here + +writer = DBWriter( + connection=teradata, + target="database.table", + options=Teradata.WriteOptions( + if_exists="append", + # avoid creating SET table, use MULTISET + createTableOptions="NO PRIMARY INDEX", + ), +) + +writer.run(df.repartition(1)) +``` + +## Recommendations + +### Number of connections + +Teradata is not MVCC based, so write operations take exclusive lock on the entire table. +So **it is impossible to write data to Teradata table in multiple parallel connections**, no exceptions. + +The only way to write to Teradata without making deadlocks is write dataframe with exactly 1 partition. + +It can be implemented using `df.repartition(1)`: + +```python +# do NOT use df.coalesce(1) as it can freeze +writer.run(df.repartition(1)) +``` + +This moves all the data to just one Spark worker, so it may consume a lot of RAM. It is usually require to increase `spark.executor.memory` to handle this. + +Another way is to write all dataframe partitions one-by-one: + +```python +from pyspark.sql.functions import spark_partition_id + +# get list of all partitions in the dataframe +partitions = sorted(df.select(spark_partition_id()).distinct().collect()) + +for partition in partitions: + # get only part of data within this exact partition + part_df = df.where(**partition.asDict()).coalesce(1) + + writer.run(part_df) +``` + +This require even data distribution for all partitions to avoid data skew and spikes of RAM consuming. + +### Choosing connection type + +Teradata supports several [different connection types](https://teradata-docs.s3.amazonaws.com/doc/connectivity/jdbc/reference/current/jdbcug_chapter_2.html#BABFGFAF): +: - `TYPE=DEFAULT` - perform plain `INSERT` queries + - `TYPE=FASTLOAD` - uses special FastLoad protocol for insert queries + +It is always recommended to use `TYPE=FASTLOAD` because: +: - It provides higher performance + - It properly handles inserting `NULL` values (`TYPE=DEFAULT` raises an exception) + +But it can be used only during write, not read. + +### Choosing transaction mode + +Teradata supports [2 different transaction modes](https://teradata-docs.s3.amazonaws.com/doc/connectivity/jdbc/reference/current/jdbcug_chapter_2.html#TMODESEC): +: - `TMODE=ANSI` + - `TMODE=TERA` + +Choosing one of the modes can alter connector behavior. For example: +: - Inserting data which exceeds table column length, like insert `CHAR(25)` to column with type `CHAR(24)`: + - - `TMODE=ANSI` - raises exception + - - `TMODE=TERA` - truncates input string to 24 symbols + - Creating table using Spark: + - - `TMODE=ANSI` - creates `MULTISET` table + - - `TMODE=TERA` - creates `SET` table with `PRIMARY KEY` is a first column in dataframe. + This can lead to slower insert time, because each row will be checked against a unique index. + Fortunately, this can be disabled by passing custom `createTableOptions`. + +## Options + +Method above accepts {obj}`Teradata.WriteOptions ` + +```{eval-rst} +.. currentmodule:: onetl.connection.db_connection.teradata.options +``` + +```{eval-rst} +.. autopydantic_model:: TeradataWriteOptions + :inherited-members: GenericOptions + :member-order: bysource + :model-show-field-summary: false + :field-show-constraints: false +``` diff --git a/mddocs/docs/ru/connection/file_connection/ftp.md b/mddocs/docs/ru/connection/file_connection/ftp.md new file mode 100644 index 000000000..8877f479e --- /dev/null +++ b/mddocs/docs/ru/connection/file_connection/ftp.md @@ -0,0 +1,12 @@ +(ftp)= + +# FTP connection + +```{eval-rst} +.. currentmodule:: onetl.connection.file_connection.ftp +``` + +```{eval-rst} +.. autoclass:: FTP + :members: __init__, check, path_exists, is_file, is_dir, get_stat, resolve_dir, resolve_file, create_dir, remove_file, remove_dir, rename_dir, rename_file, list_dir, walk, download_file, upload_file +``` diff --git a/mddocs/docs/ru/connection/file_connection/ftps.md b/mddocs/docs/ru/connection/file_connection/ftps.md new file mode 100644 index 000000000..668be45b5 --- /dev/null +++ b/mddocs/docs/ru/connection/file_connection/ftps.md @@ -0,0 +1,12 @@ +(ftps)= + +# FTPS connection + +```{eval-rst} +.. currentmodule:: onetl.connection.file_connection.ftps +``` + +```{eval-rst} +.. autoclass:: FTPS + :members: __init__, check, path_exists, is_file, is_dir, get_stat, resolve_dir, resolve_file, create_dir, remove_file, remove_dir, rename_dir, rename_file, list_dir, walk, download_file, upload_file +``` diff --git a/mddocs/docs/ru/connection/file_connection/hdfs/connection.md b/mddocs/docs/ru/connection/file_connection/hdfs/connection.md new file mode 100644 index 000000000..b1cffa679 --- /dev/null +++ b/mddocs/docs/ru/connection/file_connection/hdfs/connection.md @@ -0,0 +1,12 @@ +(hdfs-connection)= + +# HDFS connection + +```{eval-rst} +.. currentmodule:: onetl.connection.file_connection.hdfs.connection +``` + +```{eval-rst} +.. autoclass:: HDFS + :members: get_current, check, path_exists, is_file, is_dir, get_stat, resolve_dir, resolve_file, create_dir, remove_file, remove_dir, rename_dir, rename_file, list_dir, walk, download_file, upload_file +``` diff --git a/mddocs/docs/ru/connection/file_connection/hdfs/index.md b/mddocs/docs/ru/connection/file_connection/hdfs/index.md new file mode 100644 index 000000000..a4c9fa148 --- /dev/null +++ b/mddocs/docs/ru/connection/file_connection/hdfs/index.md @@ -0,0 +1,17 @@ +(hdfs)= + +# HDFS + +```{toctree} +:caption: Connection +:maxdepth: 1 + +connection +``` + +```{toctree} +:caption: For developers +:maxdepth: 1 + +slots +``` diff --git a/mddocs/docs/ru/connection/file_connection/hdfs/slots.md b/mddocs/docs/ru/connection/file_connection/hdfs/slots.md new file mode 100644 index 000000000..88f7e2943 --- /dev/null +++ b/mddocs/docs/ru/connection/file_connection/hdfs/slots.md @@ -0,0 +1,13 @@ +(hdfs-slots)= + +# HDFS Slots + +```{eval-rst} +.. currentmodule:: onetl.connection.file_connection.hdfs.slots +``` + +```{eval-rst} +.. autoclass:: HDFSSlots + :members: normalize_cluster_name, normalize_namenode_host, get_known_clusters, get_cluster_namenodes, get_current_cluster, get_webhdfs_port, is_namenode_active + :member-order: bysource +``` diff --git a/mddocs/docs/ru/connection/file_connection/index.md b/mddocs/docs/ru/connection/file_connection/index.md new file mode 100644 index 000000000..4b581e0fd --- /dev/null +++ b/mddocs/docs/ru/connection/file_connection/index.md @@ -0,0 +1,16 @@ +(file-connections)= + +# File Connections + +```{toctree} +:caption: File Connections +:maxdepth: 1 + +FTP +FTPS +HDFS +Samba +SFTP +S3 +Webdav +``` diff --git a/mddocs/docs/ru/connection/file_connection/s3.md b/mddocs/docs/ru/connection/file_connection/s3.md new file mode 100644 index 000000000..c69a3ed8b --- /dev/null +++ b/mddocs/docs/ru/connection/file_connection/s3.md @@ -0,0 +1,12 @@ +(s3)= + +# S3 connection + +```{eval-rst} +.. currentmodule:: onetl.connection.file_connection.s3 +``` + +```{eval-rst} +.. autoclass:: S3 + :members: __init__, check, path_exists, is_file, is_dir, get_stat, resolve_dir, resolve_file, create_dir, remove_file, remove_dir, rename_file, list_dir, walk, download_file, upload_file +``` diff --git a/mddocs/docs/ru/connection/file_connection/samba.md b/mddocs/docs/ru/connection/file_connection/samba.md new file mode 100644 index 000000000..29f9f3f81 --- /dev/null +++ b/mddocs/docs/ru/connection/file_connection/samba.md @@ -0,0 +1,12 @@ +(samba)= + +# Samba connection + +```{eval-rst} +.. currentmodule:: onetl.connection.file_connection.samba +``` + +```{eval-rst} +.. autoclass:: Samba + :members: __init__, check, path_exists, is_file, is_dir, get_stat, resolve_dir, resolve_file, create_dir, remove_file, remove_dir, rename_file, list_dir, download_file, upload_file +``` diff --git a/mddocs/docs/ru/connection/file_connection/sftp.md b/mddocs/docs/ru/connection/file_connection/sftp.md new file mode 100644 index 000000000..45c7affa6 --- /dev/null +++ b/mddocs/docs/ru/connection/file_connection/sftp.md @@ -0,0 +1,12 @@ +(sftp)= + +# SFTP connection + +```{eval-rst} +.. currentmodule:: onetl.connection.file_connection.sftp +``` + +```{eval-rst} +.. autoclass:: SFTP + :members: __init__, check, path_exists, is_file, is_dir, get_stat, resolve_dir, resolve_file, create_dir, remove_file, remove_dir, rename_dir, rename_file, list_dir, walk, download_file, upload_file +``` diff --git a/mddocs/docs/ru/connection/file_connection/webdav.md b/mddocs/docs/ru/connection/file_connection/webdav.md new file mode 100644 index 000000000..5fd5ba006 --- /dev/null +++ b/mddocs/docs/ru/connection/file_connection/webdav.md @@ -0,0 +1,12 @@ +(webdav)= + +# WebDAV connection + +```{eval-rst} +.. currentmodule:: onetl.connection.file_connection.webdav +``` + +```{eval-rst} +.. autoclass:: WebDAV + :members: __init__, check, path_exists, is_file, is_dir, get_stat, resolve_dir, resolve_file, create_dir, remove_file, remove_dir, rename_file, list_dir, walk, download_file, upload_file +``` diff --git a/mddocs/docs/ru/connection/file_df_connection/base.md b/mddocs/docs/ru/connection/file_df_connection/base.md new file mode 100644 index 000000000..4b3935bf7 --- /dev/null +++ b/mddocs/docs/ru/connection/file_df_connection/base.md @@ -0,0 +1,12 @@ +(base-file-df-connection)= + +# Base interface + +```{eval-rst} +.. currentmodule:: onetl.base.base_file_df_connection +``` + +```{eval-rst} +.. autoclass:: BaseFileDFConnection + :members: check, check_if_format_supported, read_files_as_df, write_df_as_files +``` diff --git a/mddocs/docs/ru/connection/file_df_connection/index.md b/mddocs/docs/ru/connection/file_df_connection/index.md new file mode 100644 index 000000000..c6caecc4a --- /dev/null +++ b/mddocs/docs/ru/connection/file_df_connection/index.md @@ -0,0 +1,15 @@ +# Подключения к файловым системам с использованием DataFrame + +* [Spark LocalFS](spark_local_fs.md) +* [Spark HDFS](spark_hdfs/index.md) + * [Prerequisites](spark_hdfs/prerequisites.md) + * [Connection](spark_hdfs/connection.md) + * [Slots](spark_hdfs/slots.md) +* [Spark S3](spark_s3/index.md) + * [Prerequisites](spark_s3/prerequisites.md) + * [Connection](spark_s3/connection.md) + * [Troubleshooting](spark_s3/troubleshooting.md) + +# Для разработчиков + +* [Base interface](base.md) \ No newline at end of file diff --git a/mddocs/docs/ru/connection/file_df_connection/spark_hdfs/connection.md b/mddocs/docs/ru/connection/file_df_connection/spark_hdfs/connection.md new file mode 100644 index 000000000..08b8e4c3b --- /dev/null +++ b/mddocs/docs/ru/connection/file_df_connection/spark_hdfs/connection.md @@ -0,0 +1,12 @@ +(spark-hdfs-connection)= + +# Spark HDFS Connection + +```{eval-rst} +.. currentmodule:: onetl.connection.file_df_connection.spark_hdfs.connection +``` + +```{eval-rst} +.. autoclass:: SparkHDFS + :members: check, get_current +``` diff --git a/mddocs/docs/ru/connection/file_df_connection/spark_hdfs/index.md b/mddocs/docs/ru/connection/file_df_connection/spark_hdfs/index.md new file mode 100644 index 000000000..77d347d7a --- /dev/null +++ b/mddocs/docs/ru/connection/file_df_connection/spark_hdfs/index.md @@ -0,0 +1,18 @@ +(spark-hdfs)= + +# Spark HDFS + +```{toctree} +:caption: Connection +:maxdepth: 1 + +prerequisites +Connection +``` + +```{toctree} +:caption: For developers +:maxdepth: 1 + +Slots +``` diff --git a/mddocs/docs/ru/connection/file_df_connection/spark_hdfs/prerequisites.md b/mddocs/docs/ru/connection/file_df_connection/spark_hdfs/prerequisites.md new file mode 100644 index 000000000..f946b3a70 --- /dev/null +++ b/mddocs/docs/ru/connection/file_df_connection/spark_hdfs/prerequisites.md @@ -0,0 +1,46 @@ +(spark-hdfs-prerequisites)= + +# Prerequisites + +## Version Compatibility + +- Hadoop versions: 2.x, 3.x (only with Hadoop 3.x libraries) +- Spark versions: 2.3.x - 3.5.x +- Java versions: 8 - 20 + +## Installing PySpark + +To use SparkHDFS connector you should have PySpark installed (or injected to `sys.path`) +BEFORE creating the connector instance. + +See {ref}`install-spark` installation instruction for more details. + +## Using Kerberos + +Some of Hadoop managed clusters use Kerberos authentication. In this case, you should call [kinit](https://web.mit.edu/kerberos/krb5-1.12/doc/user/user_commands/kinit.html) command +**BEFORE** starting Spark session to generate Kerberos ticket. See {ref}`install-kerberos`. + +Sometimes it is also required to pass keytab file to Spark config, allowing Spark executors to generate own Kerberos tickets: + +```{eval-rst} +.. tabs:: + + .. code-tab:: python Spark 3 + + SparkSession.builder + .option("spark.kerberos.access.hadoopFileSystems", "hdfs://namenode1.domain.com:9820,hdfs://namenode2.domain.com:9820") + .option("spark.kerberos.principal", "user") + .option("spark.kerberos.keytab", "/path/to/keytab") + .gerOrCreate() + + .. code-tab:: python Spark 2 + + SparkSession.builder + .option("spark.yarn.access.hadoopFileSystems", "hdfs://namenode1.domain.com:9820,hdfs://namenode2.domain.com:9820") + .option("spark.yarn.principal", "user") + .option("spark.yarn.keytab", "/path/to/keytab") + .gerOrCreate() +``` + +See [Spark security documentation](https://spark.apache.org/docs/latest/security.html#kerberos) +for more details. diff --git a/mddocs/docs/ru/connection/file_df_connection/spark_hdfs/slots.md b/mddocs/docs/ru/connection/file_df_connection/spark_hdfs/slots.md new file mode 100644 index 000000000..715578579 --- /dev/null +++ b/mddocs/docs/ru/connection/file_df_connection/spark_hdfs/slots.md @@ -0,0 +1,13 @@ +(spark-hdfs-slots)= + +# Spark HDFS Slots + +```{eval-rst} +.. currentmodule:: onetl.connection.file_df_connection.spark_hdfs.slots +``` + +```{eval-rst} +.. autoclass:: SparkHDFSSlots + :members: normalize_cluster_name, normalize_namenode_host, get_known_clusters, get_cluster_namenodes, get_current_cluster, get_ipc_port, is_namenode_active + :member-order: bysource +``` diff --git a/mddocs/docs/ru/connection/file_df_connection/spark_local_fs.md b/mddocs/docs/ru/connection/file_df_connection/spark_local_fs.md new file mode 100644 index 000000000..524d1c676 --- /dev/null +++ b/mddocs/docs/ru/connection/file_df_connection/spark_local_fs.md @@ -0,0 +1,12 @@ +(spark-local-fs)= + +# Spark LocalFS + +```{eval-rst} +.. currentmodule:: onetl.connection.file_df_connection.spark_local_fs +``` + +```{eval-rst} +.. autoclass:: SparkLocalFS + :members: check +``` diff --git a/mddocs/docs/ru/connection/file_df_connection/spark_s3/connection.md b/mddocs/docs/ru/connection/file_df_connection/spark_s3/connection.md new file mode 100644 index 000000000..82de3999f --- /dev/null +++ b/mddocs/docs/ru/connection/file_df_connection/spark_s3/connection.md @@ -0,0 +1,12 @@ +(spark-s3-connection)= + +# Spark S3 Connection + +```{eval-rst} +.. currentmodule:: onetl.connection.file_df_connection.spark_s3.connection +``` + +```{eval-rst} +.. autoclass:: SparkS3 + :members: check, close, get_packages, get_exclude_packages +``` diff --git a/mddocs/docs/ru/connection/file_df_connection/spark_s3/index.md b/mddocs/docs/ru/connection/file_df_connection/spark_s3/index.md new file mode 100644 index 000000000..1ecf94737 --- /dev/null +++ b/mddocs/docs/ru/connection/file_df_connection/spark_s3/index.md @@ -0,0 +1,12 @@ +(spark-s3)= + +# Spark S3 + +```{toctree} +:caption: Connection +:maxdepth: 1 + +prerequisites +Connection +Troubleshooting +``` diff --git a/mddocs/docs/ru/connection/file_df_connection/spark_s3/prerequisites.md b/mddocs/docs/ru/connection/file_df_connection/spark_s3/prerequisites.md new file mode 100644 index 000000000..9135275f3 --- /dev/null +++ b/mddocs/docs/ru/connection/file_df_connection/spark_s3/prerequisites.md @@ -0,0 +1,61 @@ +(spark-s3-prerequisites)= + +# Prerequisites + +## Version Compatibility + +- Spark versions: 3.2.x - 3.5.x (only with Hadoop 3.x libraries) +- Java versions: 8 - 20 + +## Installing PySpark + +To use SparkS3 connector you should have PySpark installed (or injected to `sys.path`) +BEFORE creating the connector instance. + +See {ref}`install-spark` installation instruction for more details. + +## Connecting to S3 + +### Bucket access style + +AWS and some other S3 cloud providers allows bucket access using domain style only, e.g. `https://mybucket.s3provider.com`. + +Other implementations, like Minio, by default allows path style access only, e.g. `https://s3provider.com/mybucket` +(see [MINIO_DOMAIN](https://min.io/docs/minio/linux/reference/minio-server/minio-server.html#envvar.MINIO_DOMAIN)). + +You should set `path.style.access` to `True` or `False`, to choose the preferred style. + +### Authentication + +Different S3 instances can use different authentication methods, like: +: - `access_key + secret_key` (or username + password) + - `access_key + secret_key + session_token` + +Usually these are just passed to SparkS3 constructor: + +```python +SparkS3( + access_key=..., + secret_key=..., + session_token=..., +) +``` + +But some S3 cloud providers, like AWS, may require custom credential providers. You can pass them like: + +```python +SparkS3( + extra={ + # provider class + "aws.credentials.provider": "org.apache.hadoop.fs.s3a.auth.AssumedRoleCredentialProvider", + # other options, if needed + "assumed.role.arn": "arn:aws:iam::90066806600238:role/s3-restricted", + }, +) +``` + +See [Hadoop-AWS](https://hadoop.apache.org/docs/stable/hadoop-aws/tools/hadoop-aws/index.html#Changing_Authentication_Providers) documentation. + +## Troubleshooting + +See {ref}`spark-s3-troubleshooting`. diff --git a/mddocs/docs/ru/connection/file_df_connection/spark_s3/troubleshooting.md b/mddocs/docs/ru/connection/file_df_connection/spark_s3/troubleshooting.md new file mode 100644 index 000000000..5f998779d --- /dev/null +++ b/mddocs/docs/ru/connection/file_df_connection/spark_s3/troubleshooting.md @@ -0,0 +1,377 @@ +(spark-s3-troubleshooting)= + +# Spark S3 Troubleshooting + +```{eval-rst} +.. note:: + + General guide: :ref:`troubleshooting`. +``` + +More details: + +- [Hadoop AWS Troubleshooting Guide](https://hadoop.apache.org/docs/stable/hadoop-aws/tools/hadoop-aws/troubleshooting_s3a.html) +- [Hadoop AWS Performance Guide](https://hadoop.apache.org/docs/stable/hadoop-aws/tools/hadoop-aws/performance.html) +- [Spark integration with Cloud Infrastructures](https://spark.apache.org/docs/latest/cloud-integration.html) + +## `SparkS3.check()` and other methods hang + +### Details + +S3 may not respond for connection attempts for a long time if it's under heavy load. +To handle this, Hadoop AWS library has retry mechanism. By default it retries 7 times with 500ms interval. + +Hadoop AWS is based on AWS SDK library, which also has retry mechanism. This mechanism is not disabled because it handles different +errors than Hadoop AWS, so they complement each other. Default number of attempts in AWS SDK is 20 with minimal 5s interval, +which is exponentially increasing with each failed attempt. + +It is not a problem if S3 source is not accessible at all, like hostname cannot be resolved, or port is not opened. +These errors are not recoverable, and retry mechanism is not activated. + +But errors like SSL issues, are considered recoverable, and this causing retry of retry over increasing interval. +So user is waiting for [almost 15 minutes](https://issues.apache.org/jira/browse/HADOOP-18839) just to get exception message. + +### How to determine reason + +#### Make logging more verbose + +Change Spark session log level to {ref}`DEBUG ` to print result of each attempt. +Resulting logs will look like this + +```{eval-rst} +.. dropdown:: See log + + .. code:: text + + 23/08/03 11:25:10 DEBUG S3AFileSystem: Using S3ABlockOutputStream with buffer = disk; block=67108864; queue limit=4 + 23/08/03 11:25:10 DEBUG S3Guard: Metastore option source [core-default.xml] + 23/08/03 11:25:10 DEBUG S3Guard: Using NullMetadataStore metadata store for s3a filesystem + 23/08/03 11:25:10 DEBUG S3AFileSystem: S3Guard is disabled on this bucket: test-bucket + 23/08/03 11:25:10 DEBUG DirectoryPolicyImpl: Directory markers will be deleted + 23/08/03 11:25:10 DEBUG S3AFileSystem: Directory marker retention policy is DirectoryMarkerRetention{policy='delete'} + 23/08/03 11:25:10 DEBUG S3AUtils: Value of fs.s3a.multipart.purge.age is 86400 + 23/08/03 11:25:10 DEBUG S3AUtils: Value of fs.s3a.bulk.delete.page.size is 250 + 23/08/03 11:25:10 DEBUG FileSystem: Creating FS s3a://test-bucket/fake: duration 0:01.029s + 23/08/03 11:25:10 DEBUG IOStatisticsStoreImpl: Incrementing counter op_is_directory by 1 with final value 1 + 23/08/03 11:25:10 DEBUG S3AFileSystem: Getting path status for s3a://test-bucket/fake (fake); needEmptyDirectory=false + 23/08/03 11:25:10 DEBUG S3AFileSystem: S3GetFileStatus s3a://test-bucket/fake + 23/08/03 11:25:10 DEBUG S3AFileSystem: LIST List test-bucket:/fake/ delimiter=/ keys=2 requester pays=false + 23/08/03 11:25:10 DEBUG S3AFileSystem: Starting: LIST + 23/08/03 11:25:10 DEBUG IOStatisticsStoreImpl: Incrementing counter object_list_request by 1 with final value 1 + 23/08/03 11:25:10 DEBUG AWSCredentialProviderList: Using credentials from SimpleAWSCredentialsProvider + 23/08/03 11:25:10 DEBUG request: Sending Request: GET https://test-bucket.localhost:9000 / Parameters: ({"list-type":["2"],"delimiter":["/"],"max-keys":["2"],"prefix":["fake/"],"fetch-owner":["false"]}Headers: (amz-sdk-invocation-id: e6d62603-96e4-a80f-10a1-816e0822bc71, Content-Type: application/octet-stream, User-Agent: Hadoop 3.3.4, aws-sdk-java/1.12.262 Linux/6.4.7-1-MANJARO OpenJDK_64-Bit_Server_VM/25.292-b10 java/1.8.0_292 scala/2.12.17 vendor/AdoptOpenJDK cfg/retry-mode/legacy, ) + 23/08/03 11:25:10 DEBUG AWS4Signer: AWS4 Canonical Request: '"GET + / + delimiter=%2F&fetch-owner=false&list-type=2&max-keys=2&prefix=fake%2F + amz-sdk-invocation-id:e6d62603-96e4-a80f-10a1-816e0822bc71 + amz-sdk-request:attempt=1;max=21 + amz-sdk-retry:0/0/500 + content-type:application/octet-stream + host:test-bucket.localhost:9000 + user-agent:Hadoop 3.3.4, aws-sdk-java/1.12.262 Linux/6.4.7-1-MANJARO OpenJDK_64-Bit_Server_VM/25.292-b10 java/1.8.0_292 scala/2.12.17 vendor/AdoptOpenJDK cfg/retry-mode/legacy + x-amz-content-sha256:UNSIGNED-PAYLOAD + x-amz-date:20230803T112510Z + + amz-sdk-invocation-id;amz-sdk-request;amz-sdk-retry;content-type;host;user-agent;x-amz-content-sha256;x-amz-date + UNSIGNED-PAYLOAD" + 23/08/03 11:25:10 DEBUG AWS4Signer: AWS4 String to Sign: '"AWS4-HMAC-SHA256 + 20230803T112510Z + 20230803/us-east-1/s3/aws4_request + 31a317bb7f6d97248dd0cf03429d701cbb3e29ce889cfbb98ba7a34c57a3bfba" + 23/08/03 11:25:10 DEBUG AWS4Signer: Generating a new signing key as the signing key not available in the cache for the date 1691020800000 + 23/08/03 11:25:10 DEBUG RequestAddCookies: CookieSpec selected: default + 23/08/03 11:25:10 DEBUG RequestAuthCache: Auth cache not set in the context + 23/08/03 11:25:10 DEBUG PoolingHttpClientConnectionManager: Connection request: [route: {s}->https://test-bucket.localhost:9000][total available: 0; route allocated: 0 of 96; total allocated: 0 of 96] + 23/08/03 11:25:10 DEBUG PoolingHttpClientConnectionManager: Connection leased: [id: 0][route: {s}->https://test-bucket.localhost:9000][total available: 0; route allocated: 1 of 96; total allocated: 1 of 96] + 23/08/03 11:25:10 DEBUG MainClientExec: Opening connection {s}->https://test-bucket.localhost:9000 + 23/08/03 11:25:10 DEBUG DefaultHttpClientConnectionOperator: Connecting to test-bucket.localhost/127.0.0.1:9000 + 23/08/03 11:25:10 DEBUG SSLConnectionSocketFactory: Connecting socket to test-bucket.localhost/127.0.0.1:9000 with timeout 5000 + 23/08/03 11:25:10 DEBUG SSLConnectionSocketFactory: Enabled protocols: [TLSv1.2] + 23/08/03 11:25:10 DEBUG SSLConnectionSocketFactory: Enabled cipher suites:[TLS_ECDHE_ECDSA_WITH_AES_256_CBC_SHA384, TLS_ECDHE_RSA_WITH_AES_256_CBC_SHA384, TLS_RSA_WITH_AES_256_CBC_SHA256, TLS_ECDH_ECDSA_WITH_AES_256_CBC_SHA384, TLS_ECDH_RSA_WITH_AES_256_CBC_SHA384, TLS_DHE_RSA_WITH_AES_256_CBC_SHA256, TLS_DHE_DSS_WITH_AES_256_CBC_SHA256, TLS_ECDHE_ECDSA_WITH_AES_256_CBC_SHA, TLS_ECDHE_RSA_WITH_AES_256_CBC_SHA, TLS_RSA_WITH_AES_256_CBC_SHA, TLS_ECDH_ECDSA_WITH_AES_256_CBC_SHA, TLS_ECDH_RSA_WITH_AES_256_CBC_SHA, TLS_DHE_RSA_WITH_AES_256_CBC_SHA, TLS_DHE_DSS_WITH_AES_256_CBC_SHA, TLS_ECDHE_ECDSA_WITH_AES_128_CBC_SHA256, TLS_ECDHE_RSA_WITH_AES_128_CBC_SHA256, TLS_RSA_WITH_AES_128_CBC_SHA256, TLS_ECDH_ECDSA_WITH_AES_128_CBC_SHA256, TLS_ECDH_RSA_WITH_AES_128_CBC_SHA256, TLS_DHE_RSA_WITH_AES_128_CBC_SHA256, TLS_DHE_DSS_WITH_AES_128_CBC_SHA256, TLS_ECDHE_ECDSA_WITH_AES_128_CBC_SHA, TLS_ECDHE_RSA_WITH_AES_128_CBC_SHA, TLS_RSA_WITH_AES_128_CBC_SHA, TLS_ECDH_ECDSA_WITH_AES_128_CBC_SHA, TLS_ECDH_RSA_WITH_AES_128_CBC_SHA, TLS_DHE_RSA_WITH_AES_128_CBC_SHA, TLS_DHE_DSS_WITH_AES_128_CBC_SHA, TLS_EMPTY_RENEGOTIATION_INFO_SCSV] + 23/08/03 11:25:10 DEBUG SSLConnectionSocketFactory: Starting handshake + 23/08/03 11:25:10 DEBUG ClientConnectionManagerFactory: + java.lang.reflect.InvocationTargetException + at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) + at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62) + at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) + at java.lang.reflect.Method.invoke(Method.java:498) + at com.amazonaws.http.conn.ClientConnectionManagerFactory$Handler.invoke(ClientConnectionManagerFactory.java:76) + at com.amazonaws.http.conn.$Proxy32.connect(Unknown Source) + at com.amazonaws.thirdparty.apache.http.impl.execchain.MainClientExec.establishRoute(MainClientExec.java:393) + at com.amazonaws.thirdparty.apache.http.impl.execchain.MainClientExec.execute(MainClientExec.java:236) + at com.amazonaws.thirdparty.apache.http.impl.execchain.ProtocolExec.execute(ProtocolExec.java:186) + at com.amazonaws.thirdparty.apache.http.impl.client.InternalHttpClient.doExecute(InternalHttpClient.java:185) + at com.amazonaws.thirdparty.apache.http.impl.client.CloseableHttpClient.execute(CloseableHttpClient.java:83) + at com.amazonaws.thirdparty.apache.http.impl.client.CloseableHttpClient.execute(CloseableHttpClient.java:56) + at com.amazonaws.http.apache.client.impl.SdkHttpClient.execute(SdkHttpClient.java:72) + at com.amazonaws.http.AmazonHttpClient$RequestExecutor.executeOneRequest(AmazonHttpClient.java:1346) + at com.amazonaws.http.AmazonHttpClient$RequestExecutor.executeHelper(AmazonHttpClient.java:1157) + at com.amazonaws.http.AmazonHttpClient$RequestExecutor.doExecute(AmazonHttpClient.java:814) + at com.amazonaws.http.AmazonHttpClient$RequestExecutor.executeWithTimer(AmazonHttpClient.java:781) + at com.amazonaws.http.AmazonHttpClient$RequestExecutor.execute(AmazonHttpClient.java:755) + at com.amazonaws.http.AmazonHttpClient$RequestExecutor.access$500(AmazonHttpClient.java:715) + at com.amazonaws.http.AmazonHttpClient$RequestExecutionBuilderImpl.execute(AmazonHttpClient.java:697) + at com.amazonaws.http.AmazonHttpClient.execute(AmazonHttpClient.java:561) + at com.amazonaws.http.AmazonHttpClient.execute(AmazonHttpClient.java:541) + at com.amazonaws.services.s3.AmazonS3Client.invoke(AmazonS3Client.java:5456) + at com.amazonaws.services.s3.AmazonS3Client.invoke(AmazonS3Client.java:5403) + at com.amazonaws.services.s3.AmazonS3Client.invoke(AmazonS3Client.java:5397) + at com.amazonaws.services.s3.AmazonS3Client.listObjectsV2(AmazonS3Client.java:971) + at org.apache.hadoop.fs.s3a.S3AFileSystem.lambda$listObjects$11(S3AFileSystem.java:2595) + at org.apache.hadoop.fs.statistics.impl.IOStatisticsBinding.lambda$trackDurationOfOperation$5(IOStatisticsBinding.java:499) + at org.apache.hadoop.fs.s3a.Invoker.retryUntranslated(Invoker.java:414) + at org.apache.hadoop.fs.s3a.Invoker.retryUntranslated(Invoker.java:377) + at org.apache.hadoop.fs.s3a.S3AFileSystem.listObjects(S3AFileSystem.java:2586) + at org.apache.hadoop.fs.s3a.S3AFileSystem.s3GetFileStatus(S3AFileSystem.java:3832) + at org.apache.hadoop.fs.s3a.S3AFileSystem.innerGetFileStatus(S3AFileSystem.java:3688) + at org.apache.hadoop.fs.s3a.S3AFileSystem.lambda$isDirectory$35(S3AFileSystem.java:4724) + at org.apache.hadoop.fs.statistics.impl.IOStatisticsBinding.lambda$trackDurationOfOperation$5(IOStatisticsBinding.java:499) + at org.apache.hadoop.fs.statistics.impl.IOStatisticsBinding.trackDuration(IOStatisticsBinding.java:444) + at org.apache.hadoop.fs.s3a.S3AFileSystem.trackDurationAndSpan(S3AFileSystem.java:2337) + at org.apache.hadoop.fs.s3a.S3AFileSystem.trackDurationAndSpan(S3AFileSystem.java:2356) + at org.apache.hadoop.fs.s3a.S3AFileSystem.isDirectory(S3AFileSystem.java:4722) + at org.apache.spark.sql.execution.streaming.FileStreamSink$.hasMetadata(FileStreamSink.scala:54) + at org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:366) + at org.apache.spark.sql.DataFrameReader.loadV1Source(DataFrameReader.scala:229) + at org.apache.spark.sql.DataFrameReader.$anonfun$load$2(DataFrameReader.scala:211) + at scala.Option.getOrElse(Option.scala:189) + at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:211) + at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:186) + at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) + at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62) + at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) + at java.lang.reflect.Method.invoke(Method.java:498) + at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244) + at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:374) + at py4j.Gateway.invoke(Gateway.java:282) + at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132) + at py4j.commands.CallCommand.execute(CallCommand.java:79) + at py4j.ClientServerConnection.waitForCommands(ClientServerConnection.java:182) + at py4j.ClientServerConnection.run(ClientServerConnection.java:106) + at java.lang.Thread.run(Thread.java:748) + Caused by: javax.net.ssl.SSLException: Unsupported or unrecognized SSL message + at sun.security.ssl.SSLSocketInputRecord.handleUnknownRecord(SSLSocketInputRecord.java:448) + at sun.security.ssl.SSLSocketInputRecord.decode(SSLSocketInputRecord.java:184) + at sun.security.ssl.SSLTransport.decode(SSLTransport.java:109) + at sun.security.ssl.SSLSocketImpl.decode(SSLSocketImpl.java:1383) + at sun.security.ssl.SSLSocketImpl.readHandshakeRecord(SSLSocketImpl.java:1291) + at sun.security.ssl.SSLSocketImpl.startHandshake(SSLSocketImpl.java:435) + at com.amazonaws.thirdparty.apache.http.conn.ssl.SSLConnectionSocketFactory.createLayeredSocket(SSLConnectionSocketFactory.java:436) + at com.amazonaws.thirdparty.apache.http.conn.ssl.SSLConnectionSocketFactory.connectSocket(SSLConnectionSocketFactory.java:384) + at com.amazonaws.thirdparty.apache.http.impl.conn.DefaultHttpClientConnectionOperator.connect(DefaultHttpClientConnectionOperator.java:142) + at com.amazonaws.thirdparty.apache.http.impl.conn.PoolingHttpClientConnectionManager.connect(PoolingHttpClientConnectionManager.java:376) + ... 58 more + 23/08/03 11:25:10 DEBUG DefaultManagedHttpClientConnection: http-outgoing-0: Shutdown connection + 23/08/03 11:25:10 DEBUG MainClientExec: Connection discarded + 23/08/03 11:25:10 DEBUG PoolingHttpClientConnectionManager: Connection released: [id: 0][route: {s}->https://test-bucket.localhost:9000][total available: 0; route allocated: 0 of 96; total allocated: 0 of 96] + 23/08/03 11:25:10 DEBUG AmazonHttpClient: Unable to execute HTTP request: Unsupported or unrecognized SSL message Request will be retried. + 23/08/03 11:25:10 DEBUG request: Retrying Request: GET https://test-bucket.localhost:9000 / Parameters: ({"list-type":["2"],"delimiter":["/"],"max-keys":["2"],"prefix":["fake/"],"fetch-owner":["false"]}Headers: (amz-sdk-invocation-id: e6d62603-96e4-a80f-10a1-816e0822bc71, Content-Type: application/octet-stream, User-Agent: Hadoop 3.3.4, aws-sdk-java/1.12.262 Linux/6.4.7-1-MANJARO OpenJDK_64-Bit_Server_VM/25.292-b10 java/1.8.0_292 scala/2.12.17 vendor/AdoptOpenJDK cfg/retry-mode/legacy, ) + 23/08/03 11:25:10 DEBUG AmazonHttpClient: Retriable error detected, will retry in 49ms, attempt number: 0 +``` + +#### Change number of retries + +You can also change number of retries performed by both libraries using `extra` parameter: + +```python +spark_s3 = SparkS3( + ..., + extra={ + "attempts.maximum": 1, + "retry.limit": 1, + }, +) +``` + +So accessing S3 will fail almost immediately if there is any error. + +### Most common mistakes + +#### No network access + +```text +Caused by: java.net.ConnectException: Connection refused +``` + +Mostly caused by: + +- Trying to access port number which S3 server does not listen +- You're trying to access host which is unreachable from your network (e.g. running behind some proxy or VPN) +- There are some firewall restrictions for accessing specific host or port + +#### Using HTTPS protocol for HTTP port + +```text +Caused by: javax.net.ssl.SSLException: Unsupported or unrecognized SSL message +``` + +By default, SparkS3 uses HTTPS protocol for connection. +If you change port number, this does not lead to changing protocol: + +```python +spark_s3 = SparkS3(host="s3provider.com", port=8080, ...) +``` + +You should pass protocol explicitly: + +```python +spark_s3 = SparkS3(host="s3provider.com", port=8080, protocol="http", ...) +``` + +#### SSL certificate is self-signed + +```text +sun.security.provider.certpath.SunCertPathBuilderException: unable to find valid certification path to requested target +``` + +To connect to HTTPS port with self-signed certificate, you should +[add certificate chain to Java TrustedStore](https://stackoverflow.com/questions/373295/digital-certificate-how-to-import-cer-file-in-to-truststore-file-using). + +Another option is to disable SSL check: + +```python +spark_s3 = SparkS3( + ..., + extra={ + "connection.ssl.enabled": False, + }, +) +``` + +But is is **NOT** recommended. + +#### Accessing S3 without domain-style access style support + +```text +Caused by: java.net.UnknownHostException: my-bucket.s3provider.com +``` + +To use path-style access, use option below: + +```python +spark_s3 = SparkS3( + host="s3provider.com", + bucket="my-bucket", + ..., + extra={ + "path.style.access": True, + }, +) +``` + +## Slow or unstable writing to S3 + +Hadoop AWS allows to use different writing strategies for different S3 implementations, depending +on list of supported features by server. + +These strategies are called [committers](https://hadoop.apache.org/docs/stable/hadoop-aws/tools/hadoop-aws/committers.html). +There are [different types of committers](https://hadoop.apache.org/docs/stable/hadoop-aws/tools/hadoop-aws/committers.html#Switching_to_an_S3A_Committer): + +- `file` (default) +- `directory` +- `partitioned` +- `magic` + +### `file` committer + +This committer is quite slow and unstable, so it is not recommended to use: + +```text +WARN AbstractS3ACommitterFactory: Using standard FileOutputCommitter to commit work. This is slow and potentially unsafe. +``` + +This is caused by the fact it creates files in the temp directory on remote filesystem, and after all of them are written successfully, +they are moved to target directory on same remote filesystem. + +This is not an issue for HDFS which does support file move operations and also support renaming directory +as atomic operation with `O(1)` time complexity. + +But S3 does support only file copying, so moving is performed via copy + delete. +Also it does not support atomic directory rename operation. Instead, renaming files with the same prefix has time complexity `O(n)`. + +### `directory` and `partitioned` committers + +These are [staging committers](https://hadoop.apache.org/docs/stable/hadoop-aws/tools/hadoop-aws/committer_architecture.html), +meaning that they create temp directories on local filesystem, and after all files are written successfully, +they will be uploaded to S3. Local filesystems do support file moving and directory renaming, +so these committers does not have issues that `file` committer has. + +But they both require free space on local filesystem, and this may be an issue if user need to write large amount of data. +Also this can be an issue for container environment, like Kubernetes, there resources should be allocated before starting a container. + +### `magic` committer + +This committer uses multipart upload feature of S3 API, allowing to create multiple files +and after all of them were written successfully finish the transaction. Before transaction is finished, +files will not be accessible by other clients. + +Because it does not require neither file moving operations, nor directory atomic rename, +upload process is done in most efficient way S3 support. +This [drastically increases writing performance](https://spot.io/blog/improve-apache-spark-performance-with-the-s3-magic-committer/). + +To use this committer, set [following properties](https://github.com/apache/spark/pull/32518) while creating Spark session. + +```{eval-rst} +.. tabs:: + + .. code-tab:: py S3 your main distributed filesystem (Spark on Kubernetes) + + # https://issues.apache.org/jira/browse/SPARK-23977 + # https://spark.apache.org/docs/latest/cloud-integration.html#committing-work-into-cloud-storage-safely-and-fast + spark = ( + SparkSession.builder.appName("spark-app-name") + .config("spark.hadoop.fs.s3a.committer.magic.enabled", "true") + .config("spark.hadoop.fs.s3a.committer.name", "magic") + .config("spark.hadoop.mapreduce.outputcommitter.factory.scheme.s3a", "org.apache.hadoop.fs.s3a.commit.S3ACommitterFactory") + .config("spark.sql.parquet.output.committer.class", "org.apache.spark.internal.io.cloud.BindingParquetOutputCommitter") + .config("spark.sql.sources.commitProtocolClass", "org.apache.spark.internal.io.cloud.PathOutputCommitProtocol") + .getOrCreate() + ) + + .. code-tab:: py HDFS is your main distributed filesystem (Spark on Hadoop) + + # https://community.cloudera.com/t5/Support-Questions/spark-sql-sources-partitionOverwriteMode-dynamic-quot-not/m-p/343483/highlight/true + spark = ( + SparkSession.builder.appName("spark-app-name") + .config("spark.hadoop.fs.s3a.committer.magic.enabled", "true") + .config("spark.hadoop.fs.s3a.committer.name", "magic") + .getOrCreate() + ) +``` + +```{eval-rst} +.. warning:: + + ``magic`` committer requires S3 implementation to have strong consistency - file upload API return response only + if it was written on enough number of cluster nodes, and any cluster node error does not lead to missing or corrupting files. + + Some S3 implementations does have strong consistency + (like `AWS S3 `_ and + `MinIO `_), some not. Please contact your S3 provider + to get information about S3 implementation consistency. +``` + +```{eval-rst} +.. warning:: + + ``magic`` committer does not support ``if_exists="replace_overlapping_partitions"``. + Either use another ``if_exists`` value, or use ``partitioned`` committer. +``` + +### See also + +- [directory.marker.retention="keep"](https://hadoop.apache.org/docs/stable/hadoop-aws/tools/hadoop-aws/directory_markers.html) + +## Slow reading from S3 + +Please read following documentation: + +- [prefetch.enabled](https://hadoop.apache.org/docs/stable/hadoop-aws/tools/hadoop-aws/prefetching.html) +- [experimental.input.fadvise](https://hadoop.apache.org/docs/stable/hadoop-aws/tools/hadoop-aws/performance.html#Improving_data_input_performance_through_fadvise) +- [Parquet and ORC I/O settings](https://spark.apache.org/docs/latest/cloud-integration.html#parquet-io-settings) + +If you're reading data from row-based formats, like {ref}`csv-file-format`, prefer +[experimental.input.fadvise="sequential" with increased readahead.range](https://issues.apache.org/jira/browse/HADOOP-17789?focusedCommentId=17383559#comment-17383559). + +But for other file formats, especially using compression, prefer +[experimental.input.fadvise="normal"](https://issues.apache.org/jira/browse/HADOOP-17789?focusedCommentId=17383743#comment-17383743) diff --git a/mddocs/docs/ru/connection/index.md b/mddocs/docs/ru/connection/index.md new file mode 100644 index 000000000..41ab3aafd --- /dev/null +++ b/mddocs/docs/ru/connection/index.md @@ -0,0 +1,34 @@ +# Подключение + + ## Подключения к БД + +* [DB Connections](db_connection/index.md) + * [Clickhouse](db_connection/clickhouse/index.md) + * [Greenplum](db_connection/greenplum/index.md) + * [Kafka](db_connection/kafka/index.md) + * [Hive](db_connection/hive/index.md) + * [MongoDB](db_connection/mongodb/index.md) + * [MSSQL](db_connection/mssql/index.md) + * [MySQL](db_connection/mysql/index.md) + * [Oracle](db_connection/oracle/index.md) + * [Postgres](db_connection/postgres/index.md) + * [Teradata](db_connection/teradata/index.md) + + ## Подключения к файловым системам\серверам + +* [File Connections](file_connection/index.md) + * [FTP](file_connection/ftp.md) + * [FTPS](file_connection/ftps.md) + * [HDFS](file_connection/hdfs/index.md) + * [Samba](file_connection/samba.md) + * [SFTP](file_connection/sftp.md) + * [S3](file_connection/s3.md) + * [Webdav](file_connection/webdav.md) + + ## Подключения к файловым системам с использованием DataFrame + +* [File DataFrame Connections](file_df_connection/index.md) + * [Spark LocalFS](file_df_connection/spark_local_fs.md) + * [Spark HDFS](file_df_connection/spark_hdfs/index.md) + * [Spark S3](file_df_connection/spark_s3/index.md) + * [Base interface](file_df_connection/base.md) \ No newline at end of file diff --git a/mddocs/ru/CONTRIBUTING.md b/mddocs/docs/ru/contributing.md similarity index 61% rename from mddocs/ru/CONTRIBUTING.md rename to mddocs/docs/ru/contributing.md index cb3d79377..de2732222 100644 --- a/mddocs/ru/CONTRIBUTING.md +++ b/mddocs/docs/ru/contributing.md @@ -1,14 +1,14 @@ # Руководство по участию -Добро пожаловать! Существует множество способов внести свой вклад, включая отправку отчетов об ошибках, улучшение документации, отправку запросов на добавление функций, рецензирование новых материалов или внесение кода, который можно включить в проект. +Добро пожаловать! Существует множество способов внести свой вклад, включая отправку отчетов об ошибках, улучшение документации, отправку запросов на добавление функций, рецензирование новых материалов или предоставление кода, который можно включить в проект. ## Ограничения При разработке следует придерживаться следующих пунктов: -- Некоторые компании до сих пор используют старые версии Spark, например 2.3.1. Поэтому, по возможности, необходимо поддерживать такую совместимость, например, добавлять ветки для разных версий Spark. -- Разные пользователи используют onETL по-разному - некоторые используют только коннекторы к БД, другие только файловые подключения. Зависимости, специфичные для коннекторов, должны быть необязательными. -- Вместо создания классов с большим количеством различных опций, лучше разделить их на более мелкие классы, например, класс опций, контекстный менеджер и т.д., и использовать композицию. +* Некоторые компании до сих пор используют старые версии Spark, например 2.3.1. Поэтому, по возможности, необходимо сохранять совместимость, например, добавлять ветки для разных версий Spark. +* Разные пользователи используют onETL по-разному - некоторые используют только DB коннекторы, некоторые только файлы. Зависимости, специфичные для коннекторов, должны быть необязательными. +* Вместо создания классов с большим количеством различных опций, лучше разделить их на более мелкие классы, например, класс опций, контекстный менеджер и т.д., и использовать композицию. ## Начальная настройка для локальной разработки @@ -80,7 +80,7 @@ pre-commit install --install-hooks pre-commit run ``` -## Как +## Как тестировать ### Запустите тесты локально @@ -110,7 +110,7 @@ docker-compose --profile mongodb up -d docker-compose run --rm onetl ./run_tests.sh ``` -Вы можете передать дополнительные аргументы и они будут переданы в pytest: +Вы можете передать дополнительные аргументы, они будут переданы в pytest: ```bash docker-compose run --rm onetl ./run_tests.sh -m mongodb -lsx -vvvv --log-cli-level=INFO @@ -140,30 +140,28 @@ docker-compose --profile all down -v !!! warning - Для локального запуска тестов HDFS необходимо добавить следующую строку в ``/etc/hosts`` (путь к файлу зависит от ОС): + Для локального запуска тестов HDFS необходимо добавить следующую строку в файл `/etc/hosts` (путь к файлу зависит от ОС): - ```text - # HDFS server returns container hostname as connection address, causing error in DNS resolution - 127.0.0.1 hdfs + ```default + # HDFS server returns container hostname as connection address, causing error in DNS resolution + 127.0.0.1 hdfs ``` !!! note - Для запуска тестов Oracle необходимо установить `Oracle instantclient `__, - и передать его путь в переменные окружения ``ONETL_ORA_CLIENT_PATH`` и ``LD_LIBRARY_PATH``, - например, ``ONETL_ORA_CLIENT_PATH=/path/to/client64/lib``. - - Также может потребоваться добавить этот же путь в переменную окружения ``LD_LIBRARY_PATH`` + Для запуска тестов Oracle необходимо установить [Oracle instantclient](https://www.oracle.com/database/technologies/instant-client.html) + и передать его путь в переменные окружения `ONETL_ORA_CLIENT_PATH` и `LD_LIBRARY_PATH`, + например, `ONETL_ORA_CLIENT_PATH=/path/to/client64/lib`. + Также может потребоваться добавить тот же путь в переменную окружения `LD_LIBRARY_PATH`. !!! note Для запуска тестов Greenplum необходимо: - * Скачать `VMware Greenplum connector for Spark `_ - * Либо переместить его в ``~/.ivy2/jars/``, либо передать путь к файлу в ``CLASSPATH`` - * Установить переменную окружения ``ONETL_GP_PACKAGE_VERSION=local``. - + * Скачать [VMware Greenplum connector for Spark](https://onetl.readthedocs.io/en/latest/connection/db_connection/greenplum/prerequisites.html) + * Либо переместить его в `~/.ivy2/jars/`, либо передать путь к файлу в `CLASSPATH` + * Установить переменную окружения `ONETL_GP_PACKAGE_VERSION=local`. Запустите все контейнеры с зависимостями: @@ -214,11 +212,11 @@ make html ## Процесс рецензирования -Пожалуйста, создайте новую задачу GitHub для любых значительных изменений и улучшений, которые вы хотите внести. Опишите функцию, которую вы хотели бы видеть, зачем она вам нужна и как она будет работать. Обсудите свои идеи прозрачно и получите отзывы сообщества, прежде чем продолжить. +Пожалуйста, создайте новую задачу GitHub для любых значительных изменений и улучшений, которые вы хотите внести. Укажите функцию, которую вы хотели бы видеть, зачем она вам нужна и как она будет работать. Обсуждайте свои идеи открыто и получайте отзывы сообщества, прежде чем продолжить. -Значительные изменения, которые вы хотите внести в проект, должны быть предварительно обсуждены в задаче GitHub, в которой четко изложены изменения и преимущества этой функции. +Значительные изменения, которые вы хотите внести в проект, следует сначала обсудить в задаче GitHub, в которой четко изложены изменения и преимущества этой функции. -Небольшие изменения можно сразу же создать и отправить в репозиторий GitHub в виде запроса на включение изменений (Pull Request). +Небольшие изменения можно сразу же создать и отправить в репозиторий GitHub в виде запроса на включение (Pull Request). ### Создайте pull request @@ -229,23 +227,45 @@ git commit -m "Commit message" git push ``` -Затем откройте интерфейс Github и [создайте pull request](https://docs.github.com/en/get-started/quickstart/contributing-to-projects#making-a-pull-request). Пожалуйста, следуйте руководству из шаблона тела PR. +Затем откройте интерфейс Github и [создайте pull request](https://docs.github.com/en/get-started/quickstart/contributing-to-projects#making-a-pull-request). +Пожалуйста, следуйте руководству из шаблона тела PR. После создания pull request он получает соответствующий номер, например, 123 (`pr_number`). ### Напишите release notes -`onETL` использует [towncrier](https://pypi.org/project/towncrier/) для управления журналом изменений. - -Чтобы отправить заметку об изменении для вашего PR, добавьте текстовый файл в папку [docs/changelog/next_release](./next_release). Он должен содержать объяснение того, как применение этого PR изменит способ взаимодействия конечных пользователей с проектом. Одного предложения обычно достаточно, но не стесняйтесь добавлять столько деталей, сколько считаете необходимым для того, чтобы пользователи поняли, что это значит. - -**Используйте прошедшее время** для текста в вашем фрагменте, потому что, в сочетании с другими, он станет частью "сводки новостей", сообщающей читателям **что изменилось** в конкретной версии библиотеки *с момента предыдущей версии*. - -Вам также следует использовать синтаксис reStructuredText для выделения кода (встроенного или блочного), связывания частей документации или внешних сайтов. Если вы хотите подписать свое изменение, не стесняйтесь добавить `` -- by\:user:`github-username` `` в конце (замените `github-username` на свое собственное!). - -Наконец, назовите свой файл в соответствии с соглашением, которое понимает Towncrier: он должен начинаться с номера задачи или PR, за которым следует точка, затем добавьте тип патча, например, `feature`, `doc`, `misc` и т.д., и добавьте `.rst` в качестве суффикса. Если вам нужно добавить более одного фрагмента, вы можете добавить необязательный порядковый номер (разделенный другой точкой) между типом и суффиксом. - -В общем, имя будет соответствовать шаблону `..rst`, где категории: +`onETL` использует [towncrier](https://pypi.org/project/towncrier/) +для управления журналом изменений. + +Чтобы отправить заметку об изменении для вашего PR, добавьте текстовый файл в +папку [docs/changelog/next_release](./next_release). Он должен содержать +объяснение того, как применение этого PR изменит способ +взаимодействия конечных пользователей с проектом. Одного предложения обычно +достаточно, но не стесняйтесь добавлять столько деталей, сколько считаете необходимым +для того, чтобы пользователи поняли, что это значит. + +**Используйте прошедшее время** для текста в вашем фрагменте, потому что, +в сочетании с другими, он будет частью "сводки новостей", +рассказывающей читателям **что изменилось** в конкретной версии +библиотеки *с момента предыдущей версии*. + +Вам также следует использовать +синтаксис reStructuredText для выделения кода (встроенного или блочного), +связывания частей документации или внешних сайтов. +Если вы хотите подписать свое изменение, не стесняйтесь добавить `-- by +:user:`github-username`` в конце (замените `github-username` +на свой собственный!). + +Наконец, назовите свой файл в соответствии с соглашением, которое понимает Towncrier: +он должен начинаться с номера задачи или PR, за которым следует точка, +затем добавьте тип патча, например, `feature`, +`doc`, `misc` и т.д., и добавьте `.rst` в качестве суффикса. Если вам +нужно добавить более одного фрагмента, вы можете добавить необязательный +порядковый номер (разделенный другой точкой) между типом +и суффиксом. + +В общем, имя будет соответствовать шаблону `..rst`, +где категории: - `feature`: Любая новая функция - `bugfix`: Исправление ошибки @@ -254,39 +274,36 @@ git push - `dependency`: Изменения, связанные с зависимостями - `misc`: Внутренние изменения в репозитории, такие как CI, тесты и изменения сборки -Pull request может иметь более одного из этих компонентов, например, изменение кода может представить новую функцию, которая устаревает старую функцию, в этом случае следует добавить два фрагмента. Нет необходимо делать отдельный фрагмент документации для изменений документации, сопровождающих соответствующие изменения кода. +Pull request может иметь более одного из этих компонентов, например, +изменение кода может ввести новую функцию, которая устаревает старую +функцию, в этом случае следует добавить два фрагмента. Нет +необходимо делать отдельный фрагмент документации для изменений документации, +сопровождающих соответствующие изменения кода. #### Примеры добавления записей в журнал изменений в ваши Pull Requests -```rst title="docs/changelog/next_release/1234.doc.1.rst" - +```rst Added a ``:github:user:`` role to Sphinx config -- by :github:user:`someuser` ``` -```rst title="docs/changelog/next_release/2345.bugfix.rst" - +```rst Fixed behavior of ``WebDAV`` connector -- by :github:user:`someuser` ``` -```rst title="docs/changelog/next_release/3456.feature.rst" - +```rst Added support of ``timeout`` in ``S3`` connector -- by :github:user:`someuser`, :github:user:`anotheruser` and :github:user:`otheruser` ``` -!!! tip - - См. `pyproject.toml `_ для всех доступных категорий (``tool.towncrier.type``). - -#### Как пропустить проверку change notes? +#### Как пропустить проверку заметок об изменениях? Просто добавьте метку `ci:skip-changelog` к pull request. #### Процесс релиза -Перед тем, как делать релиз из ветки `develop`, выполните следующие шаги: +Перед тем, как сделать релиз из ветки `develop`, выполните следующие шаги: -0. Перейдите в ветку `develop` и обновите ее до актуального состояния +1. Перейдите в ветку `develop` и обновите ее до актуального состояния ```bash git checkout develop @@ -299,38 +316,38 @@ git pull -p cp "docs/changelog/NEXT_RELEASE.rst" "docs/changelog/temp_NEXT_RELEASE.rst" ``` -2. Соберите Release notes с помощью Towncrier +1. Соберите Release notes с помощью Towncrier ```bash VERSION=$(cat onetl/VERSION) towncrier build "--version=${VERSION}" --yes ``` -3. Измените файл с журналом изменений на номер версии релиза +1. Измените файл с журналом изменений на номер версии релиза ```bash mv docs/changelog/NEXT_RELEASE.rst "docs/changelog/${VERSION}.rst" ``` -4. Удалите содержимое над заголовком номера версии в файле `${VERSION}.rst` +1. Удалите содержимое над заголовком номера версии в файле `${VERSION}.rst` ```bash awk '!/^.*towncrier release notes start/' "docs/changelog/${VERSION}.rst" > temp && mv temp "docs/changelog/${VERSION}.rst" ``` -5. Обновите индекс журнала изменений +1. Обновите индекс журнала изменений ```bash awk -v version=${VERSION} '/DRAFT/{print;print " " version;next}1' docs/changelog/index.rst > temp && mv temp docs/changelog/index.rst ``` -6. Восстановите файл `NEXT_RELEASE.rst` из резервной копии +1. Восстановите файл `NEXT_RELEASE.rst` из резервной копии ```bash mv "docs/changelog/temp_NEXT_RELEASE.rst" "docs/changelog/NEXT_RELEASE.rst" ``` -7. Зафиксируйте и отправьте изменения в ветку `develop` +1. Зафиксируйте и отправьте изменения в ветку `develop` ```bash git add . @@ -338,7 +355,7 @@ git commit -m "Prepare for release ${VERSION}" git push ``` -8. Объедините ветку `develop` с `master`, **БЕЗ** squashing +1. Слейте ветку `develop` в `master`, **БЕЗ** squashing ```bash git checkout master @@ -347,14 +364,14 @@ git merge develop git push ``` -9. Добавьте git tag к последнему коммиту в ветке `master` +1. Добавьте git tag к последнему коммиту в ветке `master` ```bash git tag "$VERSION" git push origin "$VERSION" ``` -10. Обновите версию в ветке `develop` **после релиза**: +1. Обновите версию в ветке `develop` **после релиза**: ```bash git checkout develop @@ -366,5 +383,3 @@ git add . git commit -m "Bump version" git push ``` - -[towncrier philosophy](https://towncrier.readthedocs.io/en/stable/#philosophy) diff --git a/mddocs/docs/ru/db_/index.md b/mddocs/docs/ru/db_/index.md new file mode 100644 index 000000000..494a026b1 --- /dev/null +++ b/mddocs/docs/ru/db_/index.md @@ -0,0 +1,11 @@ +# DB + +lslsv + +```{toctree} +:caption: DB classes +:maxdepth: 1 + +db_reader +db_writer +``` diff --git a/mddocs/docs/ru/db_/reader.md b/mddocs/docs/ru/db_/reader.md new file mode 100644 index 000000000..912e3d127 --- /dev/null +++ b/mddocs/docs/ru/db_/reader.md @@ -0,0 +1,22 @@ +# DB Reader + + +ыыыыы + +```{eval-rst} +.. currentmodule:: onetl.db.db_reader.db_reader +``` + +```{eval-rst} +.. autosummary:: + + DBReader + DBReader.run + DBReader.has_data + DBReader.raise_if_no_data +``` + +```{eval-rst} +.. autoclass:: DBReader + :members: run, has_data, raise_if_no_data +``` diff --git a/mddocs/docs/ru/db_/writer.md b/mddocs/docs/ru/db_/writer.md new file mode 100644 index 000000000..ea66d2834 --- /dev/null +++ b/mddocs/docs/ru/db_/writer.md @@ -0,0 +1,19 @@ +# DB Writer + + +бубуб +```{eval-rst} +.. currentmodule:: onetl.db.db_writer.db_writer +``` + +```{eval-rst} +.. autosummary:: + + DBWriter + DBWriter.run +``` + +```{eval-rst} +.. autoclass:: DBWriter + :members: run +``` diff --git a/mddocs/docs/ru/index.md b/mddocs/docs/ru/index.md new file mode 100644 index 000000000..367dcce11 --- /dev/null +++ b/mddocs/docs/ru/index.md @@ -0,0 +1,18 @@ +# onETL + +{{ repo_status_badge }} +{{ pypi_release_bage }} +{{ pypi_license_bage }} +{{ pypi_pyversion_bage }} +{{ pypi_downloads_bage }} + +{{ docs_status_badge }} +{{ ci_status_badge }} +{{ precommit_badge }} + + +{{ onetl_logo_wide }} + +----8<---- +../mddocs/docs/ru/snippet_0.md +----8<---- diff --git a/mddocs/ru/logging.md b/mddocs/docs/ru/logging.md similarity index 87% rename from mddocs/ru/logging.md rename to mddocs/docs/ru/logging.md index dae7a9d73..feb9a7d8c 100644 --- a/mddocs/ru/logging.md +++ b/mddocs/docs/ru/logging.md @@ -1,14 +1,10 @@ -(logging)= +# Логирование -# Logging +Логирование очень важно для понимания того, что происходит под капотом onETL. -Logging is quite important to understand what's going on under the hood of onETL. +Уровень логирования по умолчанию для интерпретаторов Python - `WARNING`, но большинство логов onETL находятся на уровне `INFO`, поэтому пользователи обычно мало что видят. -Default logging level for Python interpreters is `WARNING`, -but most of onETL logs are in `INFO` level, so users usually don't see much. - -To change logging level, there is a function {obj}`setup_logging ` -which should be called at the top of the script: +Чтобы изменить уровень логирования, есть функция [setup_logging](../logging/#onetl.log.setup_logging), которую следует вызывать в начале скрипта: ```python from onetl.log import setup_logging @@ -16,16 +12,15 @@ from other.lib import some, more, imports setup_logging() -# rest of code +# остальной код ... ``` -This changes both log level and log formatting to something like this: +Это изменяет как уровень логирования, так и формат логирования на что-то вроде этого: -```{eval-rst} -.. dropdown:: See logs +??? "Посмотреть логи" - .. code:: text + ```text 2024-04-12 10:12:10,834 [INFO ] MainThread: |onETL| Using IncrementalStrategy as a strategy 2024-04-12 10:12:10,835 [INFO ] MainThread: =================================== DBReader.run() starts =================================== @@ -135,37 +130,27 @@ This changes both log level and log formatting to something like this: 2024-04-12 11:06:07,397 [INFO ] MainThread: entity = 'source_source_schema.table', 2024-04-12 11:06:07,397 [INFO ] MainThread: expression = 'updated_at', 2024-04-12 11:06:07,397 [INFO ] MainThread: value = datetime.datetime(2024, 4, 12, 13, 12, 2, 123000), - 2024-04-12 11:06:07,397 [INFO ] MainThread: ) 2024-04-12 11:06:07,495 [INFO ] MainThread: |IncrementalStrategy| HWM has been saved -``` + ``` -Each step performed by onETL is extensively logged, which should help with debugging. +Каждый шаг, выполняемый onETL, тщательно логируется, что должно помочь с отладкой. -You can make logs even more verbose by changing level to `DEBUG`: +Вы можете сделать логи еще более подробными, изменив уровень на `DEBUG`: ```python from onetl.log import setup_logging setup_logging(level="DEBUG", enable_clients=True) -# rest of code +# остальной код ... ``` -This also changes log level for all underlying Python libraries, e.g. showing each HTTP request being made, and so on. +Это также изменяет уровень логирования для всех базовых библиотек Python, например, показывая каждый выполняемый HTTP-запрос и т. д. -```{eval-rst} -.. currentmodule:: onetl.log -``` - -```{eval-rst} -.. autofunction:: setup_logging -``` - -```{eval-rst} -.. autofunction:: setup_clients_logging -``` - -```{eval-rst} -.. autofunction:: set_default_logging_format -``` +::: onetl.log + options: + members: + - setup_logging + - setup_clients_logging + - set_default_logging_format diff --git a/mddocs/docs/ru/plugins.md b/mddocs/docs/ru/plugins.md new file mode 100644 index 000000000..afbd7a634 --- /dev/null +++ b/mddocs/docs/ru/plugins.md @@ -0,0 +1,140 @@ +# Плагины + +:octicons-versions-16: **добавлено в версии 0.6.0** + + +## Что такое плагины? + +### Термины + +- `Plugin` - Python пакет, который реализует дополнительную функциональность для onETL, например [hooks](../hooks/) +- `Plugin autoimport` - поведение onETL, которое позволяет автоматически импортировать этот пакет, если он содержит правильные метаданные (`entry_points`) + +### Функциональность + +Механизм плагинов позволяет: + +- Автоматически регистрировать [hooks](../hooks/), которые могут изменять поведение onETL +- Автоматически регистрировать новые классы, такие как тип HWM, хранилища HWM и т.д. + +### Ограничения + +В отличие от других проектов (таких как *Airflow 1.x*), плагины не внедряют импортированные классы или функции в пространство имен `onetl.*`. +Пользователи должны импортировать классы из пакета плагина **явно**, чтобы избежать конфликтов имен. + +## Как реализовать плагин? + +Создайте Python пакет `some-plugin` с файлом `some_plugin/setup.py`: + +```python +# some_plugin/setup.py +from setuptools import setup + +setup( + # если вы хотите импортировать что-то из onETL, добавьте это в список requirements + install_requires=["onetl"], + entry_points={ + # этот ключ включает функциональность автоимпорта плагинов + "onetl.plugins": [ + "some-plugin-name=some_plugin.module", # автоматически импортировать все содержимое модуля + "some-plugin-class=some_plugin.module.internals:MyClass", # импортировать определенный класс + "some-plugin-function=some_plugin.module.internals:my_function", # импортировать определенную функцию + ], + }, +) +``` + +См. [документацию setuptools для entry_points](https://setuptools.pypa.io/en/latest/userguide/entry_point.html) + +## Как импортируются плагины? + +- Пользователь должен установить пакет, реализующий плагин: + +```bash +pip install some-package +``` + +- Затем пользователь должен импортировать что-то из модуля `onetl` или его подмодулей: + +```python +import onetl +from onetl.connection import SomeConnection + +# и так далее +``` + +- Этот импорт автоматически выполняет что-то вроде: + +```python +import some_plugin.module +from some_plugin.module.internals import MyClass +from some_plugin.module.internals import my_function +``` + +Если конкретный модуль/класс/функция использует некоторые возможности регистрации onETL, например [`hook-decorator`](../hooks/hook.html#hook-decorator), он будет выполнен во время этого импорта. + +## Как включить/отключить плагины? + +:octicons-versions-16: **добавлено в версии 0.7.0** + +### Отключить/включить все плагины + +По умолчанию плагины включены. + +Чтобы отключить их, вы можете установить переменную окружения `ONETL_PLUGINS_ENABLED` в `false` ДО импорта onETL. Это отключит весь автоимпорт плагинов. + +Но пользователь по-прежнему сможет явно импортировать `some_plugin.module`, выполняя все декораторы и возможности регистрации onETL. + +### Отключить конкретный плагин (черный список) + +Если какой-то плагин не работает во время импорта, вы можете отключить его, установив переменную окружения `ONETL_PLUGINS_BLACKLIST=some-failing-plugin`. Несколько имен плагинов можно передать используя `,` в качестве разделителя. + +Опять же, эту переменную окружения следует установить ДО импорта onETL. + +### Отключить все плагины, кроме конкретного (белый список) + +Вы также можете отключить все плагины, кроме конкретного, установив переменную окружения +`ONETL_PLUGINS_WHITELIST=some-not-failing-plugin`. Несколько имен плагинов можно передать через `,` в качестве разделителя. + +Опять же, эту переменную окружения следует установить ДО импорта onETL. + +Если установлены обе переменные окружения, белый и черный списки, черный список имеет более высокий приоритет. + +## Как увидеть логи механизма плагинов? + +Регистрация плагинов выдает логи с уровнем `INFO`: + +```python +import logging + +logging.basicConfig(level=logging.INFO) +``` + +```text +INFO |onETL| Found 2 plugins +INFO |onETL| Loading plugin 'my-plugin' +INFO |onETL| Skipping plugin 'failing' because it is in a blacklist +``` + +Более подробные логи выдаются с уровнем `DEBUG`, чтобы сделать вывод менее многословным: + +```python +import logging + +logging.basicConfig(level=logging.DEBUG) +``` + +```text +DEBUG |onETL| Searching for plugins with group 'onetl.plugins' +DEBUG |Plugins| Plugins whitelist: [] +DEBUG |Plugins| Plugins blacklist: ['failing-plugin'] +INFO |Plugins| Found 2 plugins +INFO |onETL| Loading plugin (1/2): +DEBUG name: 'my-plugin' +DEBUG package: 'my-package' +DEBUG version: '0.1.0' +DEBUG importing: 'my_package.my_module:MyClass' +DEBUG |onETL| Successfully loaded plugin 'my-plugin' +DEBUG source: '/usr/lib/python3.11/site-packages/my_package/my_module/my_class.py' +INFO |onETL| Skipping plugin 'failing' because it is in a blacklist +``` diff --git a/mddocs/docs/ru/quickstart.md b/mddocs/docs/ru/quickstart.md new file mode 100644 index 000000000..06a7cb19c --- /dev/null +++ b/mddocs/docs/ru/quickstart.md @@ -0,0 +1,540 @@ +# onETL + +{{ repo_status_badge }} +{{ pypi_release_bage }} +{{ pypi_license_bage }} +{{ pypi_pyversion_bage }} +{{ pypi_downloads_bage }} + +{{ docs_status_badge }} +{{ ci_status_badge }} +{{ precommit_badge }} + + +{{ onetl_logo_wide }} + +----8<---- +../mddocs/docs/ru/snippet_0.md +----8<---- + + + +## Документация + +Смотрите на [ReadTheDocs](https://onetl.readthedocs.io/ru/latest/) + +## Как установить + + + +### Минимальная установка + + + +Базовый пакет `onetl` содержит: + +- `DBReader`, `DBWriter` и связанные с ними классы +- `FileDownloader`, `FileUploader`, `FileMover` и вспомогательные классы, такие как файловые фильтры и лимиты +- `FileDFReader`, `FileDFWriter` и дополняющие их классы, такие как, например, форматы файлов +- Стратегии чтения и классы HWM +- Поддержку плагинов + +Базовый пакет можно установить выполнив: + +```bash +pip install onetl +``` + +!!! warning + + Этот способ установки пакета НЕ включает в него какие-либо подключения. + Этот метод рекомендуется использовать в сторонних библиотеках, которые требуют установки ``onetl``, но не используют ее классы подключений. + + +### С подключениями к БД и возможностью чтения файловых данных в Spark DataFrame + + + +Все классы подключений к БД (`Clickhouse`, `Greenplum`, `Hive` и другие) также как и все классы представления файловых данных как DataFrame (`SparkHDFS`, `SparkLocalFS`, `SparkS3`) требуют установки Spark. + + + +Во-первых, необходимо установить JDK. Точная инструкция по установке зависит от вашей ОС, вот несколько примеров: + +```shell +yum install java-1.8.0-openjdk-devel # CentOS 7 | Spark 2 +dnf install java-11-openjdk-devel # CentOS 8 | Spark 3 +apt-get install openjdk-11-jdk # Debian-based | Spark 3 +``` + + + +#### Матрица совместимости + +| Spark | Python | Java | Scala | +| --------------------------------------------------------- | ---------- | ---------- | ----- | +| [2.3.x](https://spark.apache.org/docs/2.3.1/#downloading) | 3.7 only | 8 only | 2.11 | +| [2.4.x](https://spark.apache.org/docs/2.4.8/#downloading) | 3.7 only | 8 only | 2.11 | +| [3.2.x](https://spark.apache.org/docs/3.2.4/#downloading) | 3.7 - 3.10 | 8u201 - 11 | 2.12 | +| [3.3.x](https://spark.apache.org/docs/3.3.4/#downloading) | 3.7 - 3.12 | 8u201 - 17 | 2.12 | +| [3.4.x](https://spark.apache.org/docs/3.4.4/#downloading) | 3.7 - 3.12 | 8u362 - 20 | 2.12 | +| [3.5.x](https://spark.apache.org/docs/3.5.5/#downloading) | 3.8 - 3.13 | 8u371 - 20 | 2.12 | + + + +Затем необходимо установить PySpark, передав `spark` в `extras`: + +```bash +pip install onetl[spark] # установить последнюю версию PySpark +``` + +или установить PySpark явно: + +```bash +pip install onetl pyspark==3.5.5 # установить определенную версию PySpark +``` + +или внедрите PySpark в `sys.path` каким-либо другим способом ДО создания экземпляра класса. **В противном случае объект подключения не может быть создан.** + +### С файловыми подключениями + + + +Все классы файловых (но не *FileDF*) подключений (`FTP`, `SFTP`, `HDFS` и т.д.) требуют установки определенных Python клиентов. + +Каждый клиент можно установить явно, передав имя коннектора (в нижнем регистре) в `extras`: + +```bash +pip install onetl[ftp] # конкретный коннектор +pip install onetl[ftp,ftps,sftp,hdfs,s3,webdav,samba] # несколько коннекторов +``` + +Чтобы установить все файловые коннекторы сразу, вы можете передать `files` в `extras`: + +```bash +pip install onetl[files] +``` + +**В противном случае импорт класса завершится неудачей.** + +### С поддержкой Kerberos + + + +Многие экземпляры Hadoop развернуты с поддержкой Kerberos, поэтому для правильной работы некоторых соединений требуется дополнительная настройка. + +- `HDFS`
+ Использует [requests-kerberos](https://pypi.org/project/requests-kerberos/) и [GSSApi](https://pypi.org/project/gssapi/) для аутентификации. Он также выполняет `kinit` для создания тикета Kerberos. +- `Hive` и `SparkHDFS`
+ требуют наличия тикета Kerberos перед созданием сеанса Spark. + +Таким образом, необходимо установить пакеты ОС содержащие: + +- Библиотеки `krb5` +- Заголовочные файлы для `krb5` +- `gcc` или другой компилятор для C + +Точная инструкция по установке зависит от вашей ОС, вот несколько примеров: + +```bash +apt install libkrb5-dev krb5-user gcc # Debian-based +dnf install krb5-devel krb5-libs krb5-workstation gcc # CentOS, OracleLinux +``` + +Также вы должны передать `kerberos` в `extras` для установки необходимых пакетов Python: + +```bash +pip install onetl[kerberos] +``` + +### Полный пакет + + + +Чтобы установить все коннекторы и зависимости, вы можете передать `all` в `extras`: + +```bash +pip install onetl[all] + +# это то же самое, что и +pip install onetl[spark,files,kerberos] +``` + +!!! warning + + Этот метод потребляет много дискового пространства и требует установки библиотек Java и Kerberos в вашей ОС. + + + + +## Быстрый старт + +### MSSQL → Hive + +Чтение данных из MSSQL, преобразование и запись в Hive. + +```bash +# установить onETL и PySpark +pip install onetl[spark] +``` + +```python +# Импорт pyspark для инициализации SparkSession +from pyspark.sql import SparkSession + +# Импорт функции для настройки логирования onETL +from onetl.log import setup_logging + +# Импорт необходимых подключений +from onetl.connection import MSSQL, Hive + +# Импорт классов onETL для чтения и записи данных +from onetl.db import DBReader, DBWriter + +# Изменение уровня логирования на INFO, а также настройка формата логирования и обработчика +setup_logging() + +# Инициализация SparkSession с загрузкой драйвера MSSQL +maven_packages = MSSQL.get_packages() +spark = ( + SparkSession.builder.appName("spark_app_onetl_demo") + .config("spark.jars.packages", ",".join(maven_packages)) + .enableHiveSupport() # for Hive + .getOrCreate() +) + +# Инициализация подключения к MSSQL и проверка его доступности +mssql = MSSQL( + host="mssqldb.demo.com", + user="onetl", + password="onetl", + database="Telecom", + spark=spark, + # These options are passed to MSSQL JDBC Driver: + extra={"applicationIntent": "ReadOnly"}, +).check() + +# >>> INFO:|MSSQL| Connection is available + +# Инициализация DBReader +reader = DBReader( + connection=mssql, + source="dbo.demo_table", + columns=["on", "etl"], + # Установка опций чтения MSSQL: + options=MSSQL.ReadOptions(fetchsize=10000), +) + +# Проверка, что таблица содержит данные +reader.raise_if_no_data() + +# Чтение данных в DataFrame +df = reader.run() +df.printSchema() +# root +# |-- id: integer (nullable = true) +# |-- phone_number: string (nullable = true) +# |-- region: string (nullable = true) +# |-- birth_date: date (nullable = true) +# |-- registered_at: timestamp (nullable = true) +# |-- account_balance: double (nullable = true) + +# Применение любых трансформаций PySpark +from pyspark.sql.functions import lit + +df_to_write = df.withColumn("engine", lit("onetl")) +df_to_write.printSchema() +# root +# |-- id: integer (nullable = true) +# |-- phone_number: string (nullable = true) +# |-- region: string (nullable = true) +# |-- birth_date: date (nullable = true) +# |-- registered_at: timestamp (nullable = true) +# |-- account_balance: double (nullable = true) +# |-- engine: string (nullable = false) + +# Инициализация подключения Hive (не требует загрузки драйвера Hive в сессию Spark) +hive = Hive(cluster="rnd-dwh", spark=spark) + +# Инициализация DBWriter +db_writer = DBWriter( + connection=hive, + target="dl_sb.demo_table", + # Установка опций записи Hive: + options=Hive.WriteOptions(if_exists="replace_entire_table"), +) + +# Запись данных из DataFrame в Hive +db_writer.run(df_to_write) + +# Успех! +``` + +### SFTP → HDFS + +Получение файлов из SFTP и загрузка их в HDFS. + +```bash +# установить onETL с клиентами SFTP и HDFS, и поддержкой Kerberos +pip install onetl[hdfs,sftp,kerberos] +``` + +```python +# Импорт функции для настройки логирования onETL +from onetl.log import setup_logging + +# Импорт необходимых подключений +from onetl.connection import SFTP, HDFS + +# Импорт классов onETL для скачивания и загрузки файлов +from onetl.file import FileDownloader, FileUploader + +# Импорт вспомогательных классов filter & limit +from onetl.file.filter import Glob, ExcludeDir +from onetl.file.limit import MaxFilesCount + +# Изменение уровня логирования на INFO, а также настройка формата логирования и обработчика +setup_logging() + +# Инициализация подключения к SFTP и его проверка +sftp = SFTP( + host="sftp.test.com", + user="someuser", + password="somepassword", +).check() + +# >>> INFO:|SFTP| Connection is available + +# Инициализация объекта скачивания файлов +file_downloader = FileDownloader( + connection=sftp, + source_path="/remote/tests/Report", # path on SFTP + local_path="/local/onetl/Report", # local fs path + filters=[ + # скачивать только если имена файлов совпадают с glob + Glob("*.csv"), + # исключить файлы из директории + ExcludeDir("/remote/tests/Report/exclude_dir/"), + ], + limits=[ + # скачивать не более 1000 за запуск + MaxFilesCount(1000), + ], + options=FileDownloader.Options( + # Удалить файлы из SFTP после успешной загрузки + delete_source=True, + # пометить файлы ошибкой, если в локальном пути существует файл с таким же именем + if_exists="error", + ), +) + +# Скачать файлы на локальную файловую систему +download_result = downloader.run() + +# Метод run возвращает объект DownloadResult, +# который содержит коллекцию полученных файлов разделенную на 4 категории: +# successful - успешно скачанные файлы +# failed - файлы, которые не удалось скачать +# ignored - файлы, которые были игнорированы по фильтрам +# missing - файлы, которые отсутствовали на SFTP +download_result + +# DownloadResult( +# successful=[ +# LocalPath('/local/onetl/Report/file_1.json'), +# LocalPath('/local/onetl/Report/file_2.json'), +# ], +# failed=[FailedRemoteFile('/remote/onetl/Report/file_3.json')], +# ignored=[RemoteFile('/remote/onetl/Report/file_4.json')], +# missing=[], +# ) + +# Выбросить исключение если есть файлы с ошибкой или на удаленном сервере не было файлов +download_result.raise_if_failed() or download_result.raise_if_empty() + +# Выполняйте любые необходимые вам трансформации с файлами: переименуйте, удалите строку заголовков в csv... +renamed_files = my_rename_function(download_result.success) + +# function removed "_" from file names +# [ +# LocalPath('/home/onetl/Report/file1.json'), +# LocalPath('/home/onetl/Report/file2.json'), +# ] + +# Инициализировать подключение к HDFS +hdfs = HDFS( + host="my.name.node", + user="someuser", + password="somepassword", # or keytab +) + +# Инициализировать объект загрузки файлов +file_uploader = FileUploader( + connection=hdfs, + target_path="/user/onetl/Report/", # hdfs path +) + +# Загрузить файлы из локальной ФС в HDFS +upload_result = file_uploader.run(renamed_files) + +# Метод run вернет объект UploadResult, +# который содержит коллекцию полученных файлов разделенную на 4 категории: +# successful - успешно скачанные файлы +# failed - файлы, которые не удалось скачать +# ignored - файлы, которые были игнорированы по фильтрам +# missing - файлы, которые отсутствовали на SFTP +upload_result + +# UploadResult( +# successful=[RemoteFile('/user/onetl/Report/file1.json')], +# failed=[FailedLocalFile('/local/onetl/Report/file2.json')], +# ignored=[], +# missing=[], +# ) + +# Выбросить исключение если есть файлы с ошибкой или на локальной ФС не было файлов +upload_result.raise_if_failed() or upload_result.raise_if_empty() or upload_result.raise_if_missing() + +# Успех! +``` + +### S3 → Postgres + +Чтение файлов непосредственно из пути S3, преобразование их в DataFrame, преобразование его и затем запись в базу данных. + +```bash +# установить onETL и PySpark +pip install onetl[spark] +``` + +```python +# Импорт pyspark для инициализации SparkSession +from pyspark.sql import SparkSession + +# Импорт функции для настройки логирования onETL +from onetl.log import setup_logging + +# Импорт необходимых подключений +from onetl.connection import Postgres, SparkS3 + +# Импорт классов onETL необходимых для чтения файлов +from onetl.file import FileDFReader +from onetl.file.format import CSV + +# импорт классов onETL для записи данных +from onetl.db import DBWriter + +# Изменение уровня логирования на INFO, а также настройка формата логирования и обработчика +setup_logging() + +# Инициализация SparkSession с включение библиотеки Hadoop AWS и JDBC драйвера Postgres +maven_packages = SparkS3.get_packages(spark_version="3.5.5") | Postgres.get_packages() +exclude_packages = SparkS3.get_exclude_packages() +spark = ( + SparkSession.builder.appName("spark_app_onetl_demo") + .config("spark.jars.packages", ",".join(maven_packages)) + .config("spark.jars.excludes", ",".join(exclude_packages)) + .getOrCreate() +) + +# Инициализация подключения к S3 и его проверка +spark_s3 = SparkS3( + host="s3.test.com", + protocol="https", + bucket="my-bucket", + access_key="somekey", + secret_key="somesecret", + # Access bucket as s3.test.com/my-bucket + extra={"path.style.access": True}, + spark=spark, +).check() + +# >>> INFO:|SparkS3| Connection is available + +# Опишите формат файла и опции его парсинга +csv = CSV( + delimiter=";", + header=True, + encoding="utf-8", +) + +# Опишите структуру данных в файле для загрузки их в DataFrame +from pyspark.sql.types import ( + DateType, + DoubleType, + IntegerType, + StringType, + StructField, + StructType, + TimestampType, +) + +df_schema = StructType( + [ + StructField("id", IntegerType()), + StructField("phone_number", StringType()), + StructField("region", StringType()), + StructField("birth_date", DateType()), + StructField("registered_at", TimestampType()), + StructField("account_balance", DoubleType()), + ], +) + +# Инициализация объекта чтения из файлов в DataFrame +reader = FileDFReader( + connection=spark_s3, + source_path="/remote/tests/Report", # путь на S3, по которому расположены файлы *.csv + format=csv, # формат файлов со специфическими опциями разбора + df_schema=df_schema, # колонки и их типы +) + +# Прочитать данные из файла на S3 в Spark DataFrame +df = reader.run() + +# Проверить что схема DataFrame такая как ожидается +df.printSchema() +# root +# |-- id: integer (nullable = true) +# |-- phone_number: string (nullable = true) +# |-- region: string (nullable = true) +# |-- birth_date: date (nullable = true) +# |-- registered_at: timestamp (nullable = true) +# |-- account_balance: double (nullable = true) + +# Применить любые трансформации PySpark +from pyspark.sql.functions import lit + +df_to_write = df.withColumn("engine", lit("onetl")) +df_to_write.printSchema() +# root +# |-- id: integer (nullable = true) +# |-- phone_number: string (nullable = true) +# |-- region: string (nullable = true) +# |-- birth_date: date (nullable = true) +# |-- registered_at: timestamp (nullable = true) +# |-- account_balance: double (nullable = true) +# |-- engine: string (nullable = false) + +# Инициализировать подключение к Postgres +postgres = Postgres( + host="192.169.11.23", + user="onetl", + password="somepassword", + database="mydb", + spark=spark, +) + +# Инициализировать объект DBWriter +db_writer = DBWriter( + connection=postgres, + # write to specific table + target="public.my_table", + # with some writing options + options=Postgres.WriteOptions(if_exists="append"), +) + +# Записать DataFrame в таблицу Postgres +db_writer.run(df_to_write) + +# Успех! +``` diff --git a/mddocs/docs/ru/security.md b/mddocs/docs/ru/security.md new file mode 100644 index 000000000..ddd24f99f --- /dev/null +++ b/mddocs/docs/ru/security.md @@ -0,0 +1,25 @@ +# Безопасность + +## Поддерживаемые версии Python + +3.7 или выше + +## Рекомендации по безопасности при разработке продукта + +1. Обновите зависимости до последней стабильной версии +2. Создайте SBOM для проекта +3. Выполните SAST (Static Application Security Testing), где это возможно + +## Требования к безопасности при разработке продукта + +1. Отсутствие бинарных файлов в репозитории +2. Отсутствие паролей, ключей, токенов доступа в исходном коде +3. Отсутствие уязвимостей уровня "Critical" и/или "High" в предоставленном исходном коде + +## Отчеты об уязвимостях + +Пожалуйста, используйте электронную почту [mailto:onetools@mts.ru](mailto:onetools@mts.ru) для сообщения о проблемах безопасности или о чем-либо, что может повлечь за собой какие-либо последствия для безопасности. + +Пожалуйста, избегайте любого публичного раскрытия (включая регистрацию проблем) по крайней мере до тех пор, пока она не будет исправлена. + +Заранее благодарим за понимание. diff --git a/mddocs/docs/ru/snippet_0.md b/mddocs/docs/ru/snippet_0.md new file mode 100644 index 000000000..addf2379c --- /dev/null +++ b/mddocs/docs/ru/snippet_0.md @@ -0,0 +1,44 @@ +## Что такое onETL? + +Python ETL/ELT библиотека, основанная на [Apache Spark](https://spark.apache.org/) и других инструментах с открытым исходным кодом. + +## Цели + +- Предложить унифицированные классы для извлечения (**E**) и загрузки данных (**L**) при работе с различными хранилищами. +- Обеспечить возможность использования [Spark DataFrame API](https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.DataFrame.html) для выполнения преобразований (**T**) (в терминах *ETL*). +- Предоставить прямой доступ к базе данных, позволяющий выполнять SQL-запросы, а также DDL, DML и вызывать функции/процедуры. Эта функциональность предназначена для построения *ELT* конвейеров. +- Обеспечить поддержку различных [стратегий чтения](../strategy/) для инкрементной и пакетной выборки данных. +- Предоставить [хуки](../hooks/) и [плагины](../plugins) для изменения поведения внутренних классов. + +## Не цели + +- onETL не является заменой Spark. Она просто предоставляет дополнительные функциональные возможности, которых нет в Spark, и улучшает UX для конечных пользователей. +- onETL не является фреймворком, поскольку не имеет требований к структуре проекта, именованию, способу запуска ETL/ELT процессов, конфигурации и т.д. Все это должно быть реализовано в каком-то другом инструменте. +- onETL намеренно разрабатывается без какой-либо интеграции с программным обеспечением для планирования, таким как, например, Apache Airflow. Все интеграции должны быть реализованы как отдельные инструменты. +- Только пакетные процессы обработки данных, без их потоковой передачи. Для потоковой передачи используйте [Apache Flink](https://flink.apache.org/). + +## Требования + +- **Python** 3.7 - 3.13 +- PySpark 2.3.x - 3.5.x (зависит от используемого коннектора) +- Java 8+ (требуется Spark, см. ниже) +- Kerberos libs & GCC (требуется коннекторами `Hive`, `HDFS` и `SparkHDFS`) + +## Поддерживаемые хранилища + + +| Тип | Хранилище | На базе | +|--------------------|--------------|-------------------------------------------------------------------------------------------------------------------------| +| База данных {: rowspan=5} | Clickhouse
MSSQL
MySQL
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Teradata |

Apache Spark [JDBC Data Source](https://spark.apache.org/docs/latest/sql-data-sources-jdbc.html) | +| Hive | Apache Spark [Hive integration](https://spark.apache.org/docs/latest/sql-data-sources-hive-tables.html) | +| Kafka | Apache Spark [Kafka integration](https://spark.apache.org/docs/latest/structured-streaming-kafka-integration.html) | +| Greenplum | VMware [Greenplum Spark connector](https://docs.vmware.com/en/VMware-Greenplum-Connector-for-Apache-Spark/index.html) | +| MongoDB | [MongoDB Spark connector](https://www.mongodb.com/docs/spark-connector/current) | +| Файл {: rowspan=6} | HDFS | [HDFS Python client](https://pypi.org/project/hdfs/) | +| S3 | [minio-py client](https://pypi.org/project/minio/) | +| SFTP | [Paramiko library](https://pypi.org/project/paramiko/) | +| FTP
FTPS | [FTPUtil library](https://pypi.org/project/ftputil/) | +| WebDAV | [WebdavClient3 library](https://pypi.org/project/webdavclient3/) | +| Samba | [pysmb library](https://pypi.org/project/pysmb/) | +| Файлы как DataFrame {: rowspan=2} | SparkLocalFS
SparkHDFS | Apache Spark [File Data Source](https://spark.apache.org/docs/latest/sql-data-sources-generic-options.html) | +| SparkS3 | Библиотека [Hadoop AWS](https://hadoop.apache.org/docs/current3/hadoop-aws/tools/hadoop-aws/index.html) | diff --git a/mddocs/docs/ru/test.md b/mddocs/docs/ru/test.md new file mode 100644 index 000000000..9407d373b --- /dev/null +++ b/mddocs/docs/ru/test.md @@ -0,0 +1,5 @@ +# Check includes + +----8<---- +../mddocs/docs/ru/quickstart.md +----8<---- \ No newline at end of file diff --git a/mddocs/docs/security.md b/mddocs/docs/security.md deleted file mode 100644 index 05a7bef5a..000000000 --- a/mddocs/docs/security.md +++ /dev/null @@ -1,3 +0,0 @@ -```{eval-rst} -.. include:: ../SECURITY.rst -``` diff --git a/mddocs/generated/en/404.html b/mddocs/generated/en/404.html new file mode 100644 index 000000000..d346b84e9 --- /dev/null +++ b/mddocs/generated/en/404.html @@ -0,0 +1,1244 @@ + + + + + + + + + + + + + + + + + + + onETL Docs + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

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p{fill:var(--md-mermaid-label-bg-color);color:var(--md-mermaid-edge-color)}.edgePath .path,.flowchart-link{stroke:var(--md-mermaid-edge-color);stroke-width:.05rem}.edgePath .arrowheadPath{fill:var(--md-mermaid-edge-color);stroke:none}.cluster rect{fill:var(--md-default-fg-color--lightest);stroke:var(--md-default-fg-color--lighter)}.cluster span{color:var(--md-mermaid-label-fg-color);font-family:var(--md-mermaid-font-family)}g #flowchart-circleEnd,g #flowchart-circleStart,g #flowchart-crossEnd,g #flowchart-crossStart,g #flowchart-pointEnd,g #flowchart-pointStart{stroke:none}.classDiagramTitleText{fill:var(--md-mermaid-label-fg-color)}g.classGroup line,g.classGroup rect{fill:var(--md-mermaid-node-bg-color);stroke:var(--md-mermaid-node-fg-color)}g.classGroup text{fill:var(--md-mermaid-label-fg-color);font-family:var(--md-mermaid-font-family)}.classLabel .box{fill:var(--md-mermaid-label-bg-color);background-color:var(--md-mermaid-label-bg-color);opacity:1}.classLabel .label{fill:var(--md-mermaid-label-fg-color);font-family:var(--md-mermaid-font-family)}.node .divider{stroke:var(--md-mermaid-node-fg-color)}.relation{stroke:var(--md-mermaid-edge-color)}.cardinality{fill:var(--md-mermaid-label-fg-color);font-family:var(--md-mermaid-font-family)}.cardinality text{fill:inherit!important}defs marker.marker.composition.class path,defs marker.marker.dependency.class path,defs marker.marker.extension.class path{fill:var(--md-mermaid-edge-color)!important;stroke:var(--md-mermaid-edge-color)!important}defs marker.marker.aggregation.class path{fill:var(--md-mermaid-label-bg-color)!important;stroke:var(--md-mermaid-edge-color)!important}.statediagramTitleText{fill:var(--md-mermaid-label-fg-color)}g.stateGroup rect{fill:var(--md-mermaid-node-bg-color);stroke:var(--md-mermaid-node-fg-color)}g.stateGroup .state-title{fill:var(--md-mermaid-label-fg-color)!important;font-family:var(--md-mermaid-font-family)}g.stateGroup .composit{fill:var(--md-mermaid-label-bg-color)}.nodeLabel,.nodeLabel p{color:var(--md-mermaid-label-fg-color);font-family:var(--md-mermaid-font-family)}a .nodeLabel{text-decoration:underline}.node circle.state-end,.node circle.state-start,.start-state{fill:var(--md-mermaid-edge-color);stroke:none}.end-state-inner,.end-state-outer{fill:var(--md-mermaid-edge-color)}.end-state-inner,.node circle.state-end{stroke:var(--md-mermaid-label-bg-color)}.transition{stroke:var(--md-mermaid-edge-color)}[id^=state-fork] rect,[id^=state-join] rect{fill:var(--md-mermaid-edge-color)!important;stroke:none!important}.statediagram-cluster.statediagram-cluster .inner{fill:var(--md-default-bg-color)}.statediagram-cluster rect{fill:var(--md-mermaid-node-bg-color);stroke:var(--md-mermaid-node-fg-color)}.statediagram-state rect.divider{fill:var(--md-default-fg-color--lightest);stroke:var(--md-default-fg-color--lighter)}defs #statediagram-barbEnd{stroke:var(--md-mermaid-edge-color)}[id^=entity] path,[id^=entity] rect{fill:var(--md-default-bg-color)}.relationshipLine{stroke:var(--md-mermaid-edge-color)}defs .marker.oneOrMore.er *,defs .marker.onlyOne.er *,defs .marker.zeroOrMore.er *,defs .marker.zeroOrOne.er *{stroke:var(--md-mermaid-edge-color)!important}text:not([class]):last-child{fill:var(--md-mermaid-label-fg-color)}.actor{fill:var(--md-mermaid-sequence-actor-bg-color);stroke:var(--md-mermaid-sequence-actor-border-color)}text.actor>tspan{fill:var(--md-mermaid-sequence-actor-fg-color);font-family:var(--md-mermaid-font-family)}line{stroke:var(--md-mermaid-sequence-actor-line-color)}.actor-man circle,.actor-man line{fill:var(--md-mermaid-sequence-actorman-bg-color);stroke:var(--md-mermaid-sequence-actorman-line-color)}.messageLine0,.messageLine1{stroke:var(--md-mermaid-sequence-message-line-color)}.note{fill:var(--md-mermaid-sequence-note-bg-color);stroke:var(--md-mermaid-sequence-note-border-color)}.loopText,.loopText>tspan,.messageText,.noteText>tspan{stroke:none;font-family:var(--md-mermaid-font-family)!important}.messageText{fill:var(--md-mermaid-sequence-message-fg-color)}.loopText,.loopText>tspan{fill:var(--md-mermaid-sequence-loop-fg-color)}.noteText>tspan{fill:var(--md-mermaid-sequence-note-fg-color)}#arrowhead path{fill:var(--md-mermaid-sequence-message-line-color);stroke:none}.loopLine{fill:var(--md-mermaid-sequence-loop-bg-color);stroke:var(--md-mermaid-sequence-loop-border-color)}.labelBox{fill:var(--md-mermaid-sequence-label-bg-color);stroke:none}.labelText,.labelText>span{fill:var(--md-mermaid-sequence-label-fg-color);font-family:var(--md-mermaid-font-family)}.sequenceNumber{fill:var(--md-mermaid-sequence-number-fg-color)}rect.rect{fill:var(--md-mermaid-sequence-box-bg-color);stroke:none}rect.rect+text.text{fill:var(--md-mermaid-sequence-box-fg-color)}defs #sequencenumber{fill:var(--md-mermaid-sequence-number-bg-color)!important}";var Gr,Qa=0;function Ka(){return typeof mermaid=="undefined"||mermaid instanceof Element?wt("https://unpkg.com/mermaid@11/dist/mermaid.min.js"):I(void 0)}function Wn(e){return e.classList.remove("mermaid"),Gr||(Gr=Ka().pipe(w(()=>mermaid.initialize({startOnLoad:!1,themeCSS:Un,sequence:{actorFontSize:"16px",messageFontSize:"16px",noteFontSize:"16px"}})),m(()=>{}),G(1))),Gr.subscribe(()=>co(null,null,function*(){e.classList.add("mermaid");let t=`__mermaid_${Qa++}`,r=x("div",{class:"mermaid"}),o=e.textContent,{svg:n,fn:i}=yield mermaid.render(t,o),a=r.attachShadow({mode:"closed"});a.innerHTML=n,e.replaceWith(r),i==null||i(a)})),Gr.pipe(m(()=>({ref:e})))}var Vn=x("table");function Dn(e){return e.replaceWith(Vn),Vn.replaceWith(An(e)),I({ref:e})}function Ya(e){let t=e.find(r=>r.checked)||e[0];return O(...e.map(r=>h(r,"change").pipe(m(()=>R(`label[for="${r.id}"]`))))).pipe(Q(R(`label[for="${t.id}"]`)),m(r=>({active:r})))}function zn(e,{viewport$:t,target$:r}){let o=R(".tabbed-labels",e),n=P(":scope > input",e),i=Kr("prev");e.append(i);let a=Kr("next");return e.append(a),C(()=>{let s=new g,p=s.pipe(Z(),ie(!0));N([s,ge(e),tt(e)]).pipe(W(p),Me(1,me)).subscribe({next([{active:c},l]){let f=De(c),{width:u}=ce(c);e.style.setProperty("--md-indicator-x",`${f.x}px`),e.style.setProperty("--md-indicator-width",`${u}px`);let d=pr(o);(f.xd.x+l.width)&&o.scrollTo({left:Math.max(0,f.x-16),behavior:"smooth"})},complete(){e.style.removeProperty("--md-indicator-x"),e.style.removeProperty("--md-indicator-width")}}),N([ze(o),ge(o)]).pipe(W(p)).subscribe(([c,l])=>{let f=Tt(o);i.hidden=c.x<16,a.hidden=c.x>f.width-l.width-16}),O(h(i,"click").pipe(m(()=>-1)),h(a,"click").pipe(m(()=>1))).pipe(W(p)).subscribe(c=>{let{width:l}=ce(o);o.scrollBy({left:l*c,behavior:"smooth"})}),r.pipe(W(p),b(c=>n.includes(c))).subscribe(c=>c.click()),o.classList.add("tabbed-labels--linked");for(let c of n){let l=R(`label[for="${c.id}"]`);l.replaceChildren(x("a",{href:`#${l.htmlFor}`,tabIndex:-1},...Array.from(l.childNodes))),h(l.firstElementChild,"click").pipe(W(p),b(f=>!(f.metaKey||f.ctrlKey)),w(f=>{f.preventDefault(),f.stopPropagation()})).subscribe(()=>{history.replaceState({},"",`#${l.htmlFor}`),l.click()})}return B("content.tabs.link")&&s.pipe(Ce(1),re(t)).subscribe(([{active:c},{offset:l}])=>{let f=c.innerText.trim();if(c.hasAttribute("data-md-switching"))c.removeAttribute("data-md-switching");else{let u=e.offsetTop-l.y;for(let y of P("[data-tabs]"))for(let L of P(":scope > input",y)){let X=R(`label[for="${L.id}"]`);if(X!==c&&X.innerText.trim()===f){X.setAttribute("data-md-switching",""),L.click();break}}window.scrollTo({top:e.offsetTop-u});let d=__md_get("__tabs")||[];__md_set("__tabs",[...new Set([f,...d])])}}),s.pipe(W(p)).subscribe(()=>{for(let c of P("audio, video",e))c.pause()}),Ya(n).pipe(w(c=>s.next(c)),_(()=>s.complete()),m(c=>$({ref:e},c)))}).pipe(Ke(se))}function Nn(e,{viewport$:t,target$:r,print$:o}){return O(...P(".annotate:not(.highlight)",e).map(n=>Pn(n,{target$:r,print$:o})),...P("pre:not(.mermaid) > code",e).map(n=>jn(n,{target$:r,print$:o})),...P("pre.mermaid",e).map(n=>Wn(n)),...P("table:not([class])",e).map(n=>Dn(n)),...P("details",e).map(n=>Fn(n,{target$:r,print$:o})),...P("[data-tabs]",e).map(n=>zn(n,{viewport$:t,target$:r})),...P("[title]",e).filter(()=>B("content.tooltips")).map(n=>mt(n,{viewport$:t})))}function Ba(e,{alert$:t}){return t.pipe(v(r=>O(I(!0),I(!1).pipe(Ge(2e3))).pipe(m(o=>({message:r,active:o})))))}function qn(e,t){let r=R(".md-typeset",e);return C(()=>{let o=new g;return o.subscribe(({message:n,active:i})=>{e.classList.toggle("md-dialog--active",i),r.textContent=n}),Ba(e,t).pipe(w(n=>o.next(n)),_(()=>o.complete()),m(n=>$({ref:e},n)))})}var Ga=0;function Ja(e,t){document.body.append(e);let{width:r}=ce(e);e.style.setProperty("--md-tooltip-width",`${r}px`),e.remove();let o=cr(t),n=typeof o!="undefined"?ze(o):I({x:0,y:0}),i=O(et(t),Ht(t)).pipe(K());return N([i,n]).pipe(m(([a,s])=>{let{x:p,y:c}=De(t),l=ce(t),f=t.closest("table");return f&&t.parentElement&&(p+=f.offsetLeft+t.parentElement.offsetLeft,c+=f.offsetTop+t.parentElement.offsetTop),{active:a,offset:{x:p-s.x+l.width/2-r/2,y:c-s.y+l.height+8}}}))}function Qn(e){let t=e.title;if(!t.length)return S;let r=`__tooltip_${Ga++}`,o=Pt(r,"inline"),n=R(".md-typeset",o);return n.innerHTML=t,C(()=>{let i=new g;return i.subscribe({next({offset:a}){o.style.setProperty("--md-tooltip-x",`${a.x}px`),o.style.setProperty("--md-tooltip-y",`${a.y}px`)},complete(){o.style.removeProperty("--md-tooltip-x"),o.style.removeProperty("--md-tooltip-y")}}),O(i.pipe(b(({active:a})=>a)),i.pipe(_e(250),b(({active:a})=>!a))).subscribe({next({active:a}){a?(e.insertAdjacentElement("afterend",o),e.setAttribute("aria-describedby",r),e.removeAttribute("title")):(o.remove(),e.removeAttribute("aria-describedby"),e.setAttribute("title",t))},complete(){o.remove(),e.removeAttribute("aria-describedby"),e.setAttribute("title",t)}}),i.pipe(Me(16,me)).subscribe(({active:a})=>{o.classList.toggle("md-tooltip--active",a)}),i.pipe(pt(125,me),b(()=>!!e.offsetParent),m(()=>e.offsetParent.getBoundingClientRect()),m(({x:a})=>a)).subscribe({next(a){a?o.style.setProperty("--md-tooltip-0",`${-a}px`):o.style.removeProperty("--md-tooltip-0")},complete(){o.style.removeProperty("--md-tooltip-0")}}),Ja(o,e).pipe(w(a=>i.next(a)),_(()=>i.complete()),m(a=>$({ref:e},a)))}).pipe(Ke(se))}function Xa({viewport$:e}){if(!B("header.autohide"))return I(!1);let t=e.pipe(m(({offset:{y:n}})=>n),Be(2,1),m(([n,i])=>[nMath.abs(i-n.y)>100),m(([,[n]])=>n),K()),o=Ne("search");return N([e,o]).pipe(m(([{offset:n},i])=>n.y>400&&!i),K(),v(n=>n?r:I(!1)),Q(!1))}function Kn(e,t){return C(()=>N([ge(e),Xa(t)])).pipe(m(([{height:r},o])=>({height:r,hidden:o})),K((r,o)=>r.height===o.height&&r.hidden===o.hidden),G(1))}function Yn(e,{header$:t,main$:r}){return C(()=>{let o=new g,n=o.pipe(Z(),ie(!0));o.pipe(te("active"),He(t)).subscribe(([{active:a},{hidden:s}])=>{e.classList.toggle("md-header--shadow",a&&!s),e.hidden=s});let i=ue(P("[title]",e)).pipe(b(()=>B("content.tooltips")),ne(a=>Qn(a)));return r.subscribe(o),t.pipe(W(n),m(a=>$({ref:e},a)),Re(i.pipe(W(n))))})}function Za(e,{viewport$:t,header$:r}){return mr(e,{viewport$:t,header$:r}).pipe(m(({offset:{y:o}})=>{let{height:n}=ce(e);return{active:o>=n}}),te("active"))}function Bn(e,t){return C(()=>{let r=new g;r.subscribe({next({active:n}){e.classList.toggle("md-header__title--active",n)},complete(){e.classList.remove("md-header__title--active")}});let o=fe(".md-content h1");return typeof o=="undefined"?S:Za(o,t).pipe(w(n=>r.next(n)),_(()=>r.complete()),m(n=>$({ref:e},n)))})}function Gn(e,{viewport$:t,header$:r}){let o=r.pipe(m(({height:i})=>i),K()),n=o.pipe(v(()=>ge(e).pipe(m(({height:i})=>({top:e.offsetTop,bottom:e.offsetTop+i})),te("bottom"))));return N([o,n,t]).pipe(m(([i,{top:a,bottom:s},{offset:{y:p},size:{height:c}}])=>(c=Math.max(0,c-Math.max(0,a-p,i)-Math.max(0,c+p-s)),{offset:a-i,height:c,active:a-i<=p})),K((i,a)=>i.offset===a.offset&&i.height===a.height&&i.active===a.active))}function es(e){let t=__md_get("__palette")||{index:e.findIndex(o=>matchMedia(o.getAttribute("data-md-color-media")).matches)},r=Math.max(0,Math.min(t.index,e.length-1));return I(...e).pipe(ne(o=>h(o,"change").pipe(m(()=>o))),Q(e[r]),m(o=>({index:e.indexOf(o),color:{media:o.getAttribute("data-md-color-media"),scheme:o.getAttribute("data-md-color-scheme"),primary:o.getAttribute("data-md-color-primary"),accent:o.getAttribute("data-md-color-accent")}})),G(1))}function Jn(e){let t=P("input",e),r=x("meta",{name:"theme-color"});document.head.appendChild(r);let o=x("meta",{name:"color-scheme"});document.head.appendChild(o);let n=$t("(prefers-color-scheme: light)");return C(()=>{let i=new g;return i.subscribe(a=>{if(document.body.setAttribute("data-md-color-switching",""),a.color.media==="(prefers-color-scheme)"){let s=matchMedia("(prefers-color-scheme: light)"),p=document.querySelector(s.matches?"[data-md-color-media='(prefers-color-scheme: light)']":"[data-md-color-media='(prefers-color-scheme: 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Jr=Lt(Br());function ts(e){e.setAttribute("data-md-copying","");let t=e.closest("[data-copy]"),r=t?t.getAttribute("data-copy"):e.innerText;return e.removeAttribute("data-md-copying"),r.trimEnd()}function Zn({alert$:e}){Jr.default.isSupported()&&new j(t=>{new Jr.default("[data-clipboard-target], [data-clipboard-text]",{text:r=>r.getAttribute("data-clipboard-text")||ts(R(r.getAttribute("data-clipboard-target")))}).on("success",r=>t.next(r))}).pipe(w(t=>{t.trigger.focus()}),m(()=>Ee("clipboard.copied"))).subscribe(e)}function ei(e,t){return e.protocol=t.protocol,e.hostname=t.hostname,e}function rs(e,t){let r=new Map;for(let o of P("url",e)){let n=R("loc",o),i=[ei(new URL(n.textContent),t)];r.set(`${i[0]}`,i);for(let a of P("[rel=alternate]",o)){let s=a.getAttribute("href");s!=null&&i.push(ei(new URL(s),t))}}return r}function ur(e){return un(new URL("sitemap.xml",e)).pipe(m(t=>rs(t,new URL(e))),de(()=>I(new Map)))}function os(e,t){if(!(e.target instanceof Element))return S;let r=e.target.closest("a");if(r===null)return S;if(r.target||e.metaKey||e.ctrlKey)return S;let o=new URL(r.href);return o.search=o.hash="",t.has(`${o}`)?(e.preventDefault(),I(new URL(r.href))):S}function ti(e){let t=new Map;for(let r of P(":scope > *",e.head))t.set(r.outerHTML,r);return t}function ri(e){for(let t of P("[href], [src]",e))for(let r of["href","src"]){let o=t.getAttribute(r);if(o&&!/^(?:[a-z]+:)?\/\//i.test(o)){t[r]=t[r];break}}return I(e)}function ns(e){for(let o of["[data-md-component=announce]","[data-md-component=container]","[data-md-component=header-topic]","[data-md-component=outdated]","[data-md-component=logo]","[data-md-component=skip]",...B("navigation.tabs.sticky")?["[data-md-component=tabs]"]:[]]){let n=fe(o),i=fe(o,e);typeof n!="undefined"&&typeof i!="undefined"&&n.replaceWith(i)}let t=ti(document);for(let[o,n]of ti(e))t.has(o)?t.delete(o):document.head.appendChild(n);for(let o of t.values()){let 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O(s.pipe(re(e,(p,c)=>c)),s.pipe(v(()=>e),te("hash")),e.pipe(K((p,c)=>p.pathname===c.pathname&&p.hash===c.hash),v(()=>i),w(()=>history.back()))).subscribe(p=>{var c,l;history.state!==null||!p.hash?window.scrollTo(0,(l=(c=history.state)==null?void 0:c.y)!=null?l:0):(history.scrollRestoration="auto",pn(p.hash),history.scrollRestoration="manual")}),e.subscribe(()=>{history.scrollRestoration="manual"}),h(window,"beforeunload").subscribe(()=>{history.scrollRestoration="auto"}),t.pipe(te("offset"),_e(100)).subscribe(({offset:p})=>{history.replaceState(p,"")}),s}var ni=Lt(qr());function ii(e){let t=e.separator.split("|").map(n=>n.replace(/(\(\?[!=<][^)]+\))/g,"").length===0?"\uFFFD":n).join("|"),r=new RegExp(t,"img"),o=(n,i,a)=>`${i}${a}`;return n=>{n=n.replace(/[\s*+\-:~^]+/g," ").trim();let i=new RegExp(`(^|${e.separator}|)(${n.replace(/[|\\{}()[\]^$+*?.-]/g,"\\$&").replace(r,"|")})`,"img");return a=>(0,ni.default)(a).replace(i,o).replace(/<\/mark>(\s+)]*>/img,"$1")}}function It(e){return 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rt=Kn(Se("header"),{viewport$:Oe}),jt=ot.pipe(m(()=>Se("main")),v(e=>Gn(e,{viewport$:Oe,header$:rt})),G(1)),xs=O(...ae("consent").map(e=>En(e,{target$:Ot})),...ae("dialog").map(e=>qn(e,{alert$:ro})),...ae("palette").map(e=>Jn(e)),...ae("progress").map(e=>Xn(e,{progress$:oo})),...ae("search").map(e=>ui(e,{index$:Ai,keyboard$:to})),...ae("source").map(e=>gi(e))),Es=C(()=>O(...ae("announce").map(e=>xn(e)),...ae("content").map(e=>Nn(e,{viewport$:Oe,target$:Ot,print$:_i})),...ae("content").map(e=>B("search.highlight")?di(e,{index$:Ai,location$:Ft}):S),...ae("header").map(e=>Yn(e,{viewport$:Oe,header$:rt,main$:jt})),...ae("header-title").map(e=>Bn(e,{viewport$:Oe,header$:rt})),...ae("sidebar").map(e=>e.getAttribute("data-md-type")==="navigation"?zr(Mi,()=>Zr(e,{viewport$:Oe,header$:rt,main$:jt})):zr(hr,()=>Zr(e,{viewport$:Oe,header$:rt,main$:jt}))),...ae("tabs").map(e=>yi(e,{viewport$:Oe,header$:rt})),...ae("toc").map(e=>xi(e,{viewport$:Oe,header$:rt,main$:jt,target$:Ot})),...ae("top").map(e=>Ei(e,{viewport$:Oe,header$:rt,main$:jt,target$:Ot})))),Ci=ot.pipe(v(()=>Es),Re(xs),G(1));Ci.subscribe();window.document$=ot;window.location$=Ft;window.target$=Ot;window.keyboard$=to;window.viewport$=Oe;window.tablet$=hr;window.screen$=Mi;window.print$=_i;window.alert$=ro;window.progress$=oo;window.component$=Ci;})(); +//# sourceMappingURL=bundle.13a4f30d.min.js.map + diff --git a/mddocs/generated/en/assets/javascripts/bundle.13a4f30d.min.js.map b/mddocs/generated/en/assets/javascripts/bundle.13a4f30d.min.js.map new file mode 100644 index 000000000..8941cb895 --- /dev/null +++ b/mddocs/generated/en/assets/javascripts/bundle.13a4f30d.min.js.map @@ -0,0 +1,7 @@ +{ + "version": 3, + "sources": ["node_modules/focus-visible/dist/focus-visible.js", "node_modules/escape-html/index.js", "node_modules/clipboard/dist/clipboard.js", "src/templates/assets/javascripts/bundle.ts", "node_modules/tslib/tslib.es6.mjs", "node_modules/rxjs/src/internal/util/isFunction.ts", "node_modules/rxjs/src/internal/util/createErrorClass.ts", "node_modules/rxjs/src/internal/util/UnsubscriptionError.ts", "node_modules/rxjs/src/internal/util/arrRemove.ts", "node_modules/rxjs/src/internal/Subscription.ts", "node_modules/rxjs/src/internal/config.ts", "node_modules/rxjs/src/internal/scheduler/timeoutProvider.ts", "node_modules/rxjs/src/internal/util/reportUnhandledError.ts", "node_modules/rxjs/src/internal/util/noop.ts", "node_modules/rxjs/src/internal/NotificationFactories.ts", "node_modules/rxjs/src/internal/util/errorContext.ts", "node_modules/rxjs/src/internal/Subscriber.ts", "node_modules/rxjs/src/internal/symbol/observable.ts", "node_modules/rxjs/src/internal/util/identity.ts", "node_modules/rxjs/src/internal/util/pipe.ts", "node_modules/rxjs/src/internal/Observable.ts", "node_modules/rxjs/src/internal/util/lift.ts", "node_modules/rxjs/src/internal/operators/OperatorSubscriber.ts", "node_modules/rxjs/src/internal/scheduler/animationFrameProvider.ts", "node_modules/rxjs/src/internal/util/ObjectUnsubscribedError.ts", "node_modules/rxjs/src/internal/Subject.ts", "node_modules/rxjs/src/internal/BehaviorSubject.ts", "node_modules/rxjs/src/internal/scheduler/dateTimestampProvider.ts", "node_modules/rxjs/src/internal/ReplaySubject.ts", "node_modules/rxjs/src/internal/scheduler/Action.ts", "node_modules/rxjs/src/internal/scheduler/intervalProvider.ts", "node_modules/rxjs/src/internal/scheduler/AsyncAction.ts", "node_modules/rxjs/src/internal/Scheduler.ts", "node_modules/rxjs/src/internal/scheduler/AsyncScheduler.ts", "node_modules/rxjs/src/internal/scheduler/async.ts", "node_modules/rxjs/src/internal/scheduler/QueueAction.ts", "node_modules/rxjs/src/internal/scheduler/QueueScheduler.ts", "node_modules/rxjs/src/internal/scheduler/queue.ts", "node_modules/rxjs/src/internal/scheduler/AnimationFrameAction.ts", "node_modules/rxjs/src/internal/scheduler/AnimationFrameScheduler.ts", "node_modules/rxjs/src/internal/scheduler/animationFrame.ts", "node_modules/rxjs/src/internal/observable/empty.ts", "node_modules/rxjs/src/internal/util/isScheduler.ts", "node_modules/rxjs/src/internal/util/args.ts", "node_modules/rxjs/src/internal/util/isArrayLike.ts", "node_modules/rxjs/src/internal/util/isPromise.ts", "node_modules/rxjs/src/internal/util/isInteropObservable.ts", "node_modules/rxjs/src/internal/util/isAsyncIterable.ts", "node_modules/rxjs/src/internal/util/throwUnobservableError.ts", "node_modules/rxjs/src/internal/symbol/iterator.ts", "node_modules/rxjs/src/internal/util/isIterable.ts", "node_modules/rxjs/src/internal/util/isReadableStreamLike.ts", "node_modules/rxjs/src/internal/observable/innerFrom.ts", "node_modules/rxjs/src/internal/util/executeSchedule.ts", "node_modules/rxjs/src/internal/operators/observeOn.ts", "node_modules/rxjs/src/internal/operators/subscribeOn.ts", "node_modules/rxjs/src/internal/scheduled/scheduleObservable.ts", "node_modules/rxjs/src/internal/scheduled/schedulePromise.ts", "node_modules/rxjs/src/internal/scheduled/scheduleArray.ts", "node_modules/rxjs/src/internal/scheduled/scheduleIterable.ts", "node_modules/rxjs/src/internal/scheduled/scheduleAsyncIterable.ts", "node_modules/rxjs/src/internal/scheduled/scheduleReadableStreamLike.ts", "node_modules/rxjs/src/internal/scheduled/scheduled.ts", "node_modules/rxjs/src/internal/observable/from.ts", "node_modules/rxjs/src/internal/observable/of.ts", "node_modules/rxjs/src/internal/observable/throwError.ts", "node_modules/rxjs/src/internal/util/EmptyError.ts", "node_modules/rxjs/src/internal/util/isDate.ts", "node_modules/rxjs/src/internal/operators/map.ts", "node_modules/rxjs/src/internal/util/mapOneOrManyArgs.ts", "node_modules/rxjs/src/internal/util/argsArgArrayOrObject.ts", "node_modules/rxjs/src/internal/util/createObject.ts", "node_modules/rxjs/src/internal/observable/combineLatest.ts", "node_modules/rxjs/src/internal/operators/mergeInternals.ts", "node_modules/rxjs/src/internal/operators/mergeMap.ts", "node_modules/rxjs/src/internal/operators/mergeAll.ts", "node_modules/rxjs/src/internal/operators/concatAll.ts", "node_modules/rxjs/src/internal/observable/concat.ts", "node_modules/rxjs/src/internal/observable/defer.ts", "node_modules/rxjs/src/internal/observable/fromEvent.ts", "node_modules/rxjs/src/internal/observable/fromEventPattern.ts", "node_modules/rxjs/src/internal/observable/timer.ts", "node_modules/rxjs/src/internal/observable/merge.ts", "node_modules/rxjs/src/internal/observable/never.ts", "node_modules/rxjs/src/internal/util/argsOrArgArray.ts", "node_modules/rxjs/src/internal/operators/filter.ts", "node_modules/rxjs/src/internal/observable/zip.ts", "node_modules/rxjs/src/internal/operators/audit.ts", "node_modules/rxjs/src/internal/operators/auditTime.ts", "node_modules/rxjs/src/internal/operators/bufferCount.ts", "node_modules/rxjs/src/internal/operators/catchError.ts", "node_modules/rxjs/src/internal/operators/scanInternals.ts", "node_modules/rxjs/src/internal/operators/combineLatest.ts", "node_modules/rxjs/src/internal/operators/combineLatestWith.ts", "node_modules/rxjs/src/internal/operators/debounce.ts", "node_modules/rxjs/src/internal/operators/debounceTime.ts", "node_modules/rxjs/src/internal/operators/defaultIfEmpty.ts", "node_modules/rxjs/src/internal/operators/take.ts", "node_modules/rxjs/src/internal/operators/ignoreElements.ts", "node_modules/rxjs/src/internal/operators/mapTo.ts", "node_modules/rxjs/src/internal/operators/delayWhen.ts", "node_modules/rxjs/src/internal/operators/delay.ts", "node_modules/rxjs/src/internal/operators/distinctUntilChanged.ts", "node_modules/rxjs/src/internal/operators/distinctUntilKeyChanged.ts", "node_modules/rxjs/src/internal/operators/throwIfEmpty.ts", "node_modules/rxjs/src/internal/operators/endWith.ts", "node_modules/rxjs/src/internal/operators/finalize.ts", "node_modules/rxjs/src/internal/operators/first.ts", "node_modules/rxjs/src/internal/operators/takeLast.ts", "node_modules/rxjs/src/internal/operators/merge.ts", "node_modules/rxjs/src/internal/operators/mergeWith.ts", "node_modules/rxjs/src/internal/operators/repeat.ts", "node_modules/rxjs/src/internal/operators/scan.ts", "node_modules/rxjs/src/internal/operators/share.ts", "node_modules/rxjs/src/internal/operators/shareReplay.ts", "node_modules/rxjs/src/internal/operators/skip.ts", "node_modules/rxjs/src/internal/operators/skipUntil.ts", "node_modules/rxjs/src/internal/operators/startWith.ts", "node_modules/rxjs/src/internal/operators/switchMap.ts", "node_modules/rxjs/src/internal/operators/takeUntil.ts", "node_modules/rxjs/src/internal/operators/takeWhile.ts", "node_modules/rxjs/src/internal/operators/tap.ts", "node_modules/rxjs/src/internal/operators/throttle.ts", "node_modules/rxjs/src/internal/operators/throttleTime.ts", "node_modules/rxjs/src/internal/operators/withLatestFrom.ts", "node_modules/rxjs/src/internal/operators/zip.ts", "node_modules/rxjs/src/internal/operators/zipWith.ts", "src/templates/assets/javascripts/browser/document/index.ts", "src/templates/assets/javascripts/browser/element/_/index.ts", "src/templates/assets/javascripts/browser/element/focus/index.ts", "src/templates/assets/javascripts/browser/element/hover/index.ts", "src/templates/assets/javascripts/utilities/h/index.ts", "src/templates/assets/javascripts/utilities/round/index.ts", "src/templates/assets/javascripts/browser/script/index.ts", "src/templates/assets/javascripts/browser/element/size/_/index.ts", "src/templates/assets/javascripts/browser/element/size/content/index.ts", "src/templates/assets/javascripts/browser/element/offset/_/index.ts", "src/templates/assets/javascripts/browser/element/offset/content/index.ts", "src/templates/assets/javascripts/browser/element/visibility/index.ts", "src/templates/assets/javascripts/browser/toggle/index.ts", "src/templates/assets/javascripts/browser/keyboard/index.ts", "src/templates/assets/javascripts/browser/location/_/index.ts", "src/templates/assets/javascripts/browser/location/hash/index.ts", "src/templates/assets/javascripts/browser/media/index.ts", "src/templates/assets/javascripts/browser/request/index.ts", "src/templates/assets/javascripts/browser/viewport/offset/index.ts", "src/templates/assets/javascripts/browser/viewport/size/index.ts", "src/templates/assets/javascripts/browser/viewport/_/index.ts", "src/templates/assets/javascripts/browser/viewport/at/index.ts", "src/templates/assets/javascripts/browser/worker/index.ts", "src/templates/assets/javascripts/_/index.ts", "src/templates/assets/javascripts/components/_/index.ts", "src/templates/assets/javascripts/components/announce/index.ts", "src/templates/assets/javascripts/components/consent/index.ts", "src/templates/assets/javascripts/templates/tooltip/index.tsx", "src/templates/assets/javascripts/templates/annotation/index.tsx", "src/templates/assets/javascripts/templates/clipboard/index.tsx", "src/templates/assets/javascripts/templates/search/index.tsx", "src/templates/assets/javascripts/templates/source/index.tsx", "src/templates/assets/javascripts/templates/tabbed/index.tsx", "src/templates/assets/javascripts/templates/table/index.tsx", "src/templates/assets/javascripts/templates/version/index.tsx", "src/templates/assets/javascripts/components/tooltip2/index.ts", "src/templates/assets/javascripts/components/content/annotation/_/index.ts", "src/templates/assets/javascripts/components/content/annotation/list/index.ts", "src/templates/assets/javascripts/components/content/annotation/block/index.ts", "src/templates/assets/javascripts/components/content/code/_/index.ts", "src/templates/assets/javascripts/components/content/details/index.ts", "src/templates/assets/javascripts/components/content/mermaid/index.css", "src/templates/assets/javascripts/components/content/mermaid/index.ts", "src/templates/assets/javascripts/components/content/table/index.ts", "src/templates/assets/javascripts/components/content/tabs/index.ts", "src/templates/assets/javascripts/components/content/_/index.ts", "src/templates/assets/javascripts/components/dialog/index.ts", "src/templates/assets/javascripts/components/tooltip/index.ts", "src/templates/assets/javascripts/components/header/_/index.ts", "src/templates/assets/javascripts/components/header/title/index.ts", "src/templates/assets/javascripts/components/main/index.ts", "src/templates/assets/javascripts/components/palette/index.ts", "src/templates/assets/javascripts/components/progress/index.ts", "src/templates/assets/javascripts/integrations/clipboard/index.ts", "src/templates/assets/javascripts/integrations/sitemap/index.ts", "src/templates/assets/javascripts/integrations/instant/index.ts", "src/templates/assets/javascripts/integrations/search/highlighter/index.ts", "src/templates/assets/javascripts/integrations/search/worker/message/index.ts", "src/templates/assets/javascripts/integrations/search/worker/_/index.ts", "src/templates/assets/javascripts/integrations/version/findurl/index.ts", "src/templates/assets/javascripts/integrations/version/index.ts", "src/templates/assets/javascripts/components/search/query/index.ts", "src/templates/assets/javascripts/components/search/result/index.ts", "src/templates/assets/javascripts/components/search/share/index.ts", "src/templates/assets/javascripts/components/search/suggest/index.ts", "src/templates/assets/javascripts/components/search/_/index.ts", "src/templates/assets/javascripts/components/search/highlight/index.ts", "src/templates/assets/javascripts/components/sidebar/index.ts", "src/templates/assets/javascripts/components/source/facts/github/index.ts", "src/templates/assets/javascripts/components/source/facts/gitlab/index.ts", "src/templates/assets/javascripts/components/source/facts/_/index.ts", "src/templates/assets/javascripts/components/source/_/index.ts", "src/templates/assets/javascripts/components/tabs/index.ts", "src/templates/assets/javascripts/components/toc/index.ts", "src/templates/assets/javascripts/components/top/index.ts", "src/templates/assets/javascripts/patches/ellipsis/index.ts", "src/templates/assets/javascripts/patches/indeterminate/index.ts", "src/templates/assets/javascripts/patches/scrollfix/index.ts", "src/templates/assets/javascripts/patches/scrolllock/index.ts", "src/templates/assets/javascripts/polyfills/index.ts"], + "sourcesContent": ["(function (global, factory) {\n typeof exports === 'object' && typeof module !== 'undefined' ? factory() :\n typeof define === 'function' && define.amd ? define(factory) :\n (factory());\n}(this, (function () { 'use strict';\n\n /**\n * Applies the :focus-visible polyfill at the given scope.\n * A scope in this case is either the top-level Document or a Shadow Root.\n *\n * @param {(Document|ShadowRoot)} scope\n * @see https://github.com/WICG/focus-visible\n */\n function applyFocusVisiblePolyfill(scope) {\n var hadKeyboardEvent = true;\n var hadFocusVisibleRecently = false;\n var hadFocusVisibleRecentlyTimeout = null;\n\n var inputTypesAllowlist = {\n text: true,\n search: true,\n url: true,\n tel: true,\n email: true,\n password: true,\n number: true,\n date: true,\n month: true,\n week: true,\n time: true,\n datetime: true,\n 'datetime-local': true\n };\n\n /**\n * Helper function for legacy browsers and iframes which sometimes focus\n * elements like document, body, and non-interactive SVG.\n * @param {Element} el\n */\n function isValidFocusTarget(el) {\n if (\n el &&\n el !== document &&\n el.nodeName !== 'HTML' &&\n el.nodeName !== 'BODY' &&\n 'classList' in el &&\n 'contains' in el.classList\n ) {\n return true;\n }\n return false;\n }\n\n /**\n * Computes whether the given element should automatically trigger the\n * `focus-visible` class being added, i.e. whether it should always match\n * `:focus-visible` when focused.\n * @param {Element} el\n * @return {boolean}\n */\n function focusTriggersKeyboardModality(el) {\n var type = el.type;\n var tagName = el.tagName;\n\n if (tagName === 'INPUT' && inputTypesAllowlist[type] && !el.readOnly) {\n return true;\n }\n\n if (tagName === 'TEXTAREA' && !el.readOnly) {\n return true;\n }\n\n if (el.isContentEditable) {\n return true;\n }\n\n return false;\n }\n\n /**\n * Add the `focus-visible` class to the given element if it was not added by\n * the author.\n * @param {Element} el\n */\n function addFocusVisibleClass(el) {\n if (el.classList.contains('focus-visible')) {\n return;\n }\n el.classList.add('focus-visible');\n el.setAttribute('data-focus-visible-added', '');\n }\n\n /**\n * Remove the `focus-visible` class from the given element if it was not\n * originally added by the author.\n * @param {Element} el\n */\n function removeFocusVisibleClass(el) {\n if (!el.hasAttribute('data-focus-visible-added')) {\n return;\n }\n el.classList.remove('focus-visible');\n el.removeAttribute('data-focus-visible-added');\n }\n\n /**\n * If the most recent user interaction was via the keyboard;\n * and the key press did not include a meta, alt/option, or control key;\n * then the modality is keyboard. Otherwise, the modality is not keyboard.\n * Apply `focus-visible` to any current active element and keep track\n * of our keyboard modality state with `hadKeyboardEvent`.\n * @param {KeyboardEvent} e\n */\n function onKeyDown(e) {\n if (e.metaKey || e.altKey || e.ctrlKey) {\n return;\n }\n\n if (isValidFocusTarget(scope.activeElement)) {\n addFocusVisibleClass(scope.activeElement);\n }\n\n hadKeyboardEvent = true;\n }\n\n /**\n * If at any point a user clicks with a pointing device, ensure that we change\n * the modality away from keyboard.\n * This avoids the situation where a user presses a key on an already focused\n * element, and then clicks on a different element, focusing it with a\n * pointing device, while we still think we're in keyboard modality.\n * @param {Event} e\n */\n function onPointerDown(e) {\n hadKeyboardEvent = false;\n }\n\n /**\n * On `focus`, add the `focus-visible` class to the target if:\n * - the target received focus as a result of keyboard navigation, or\n * - the event target is an element that will likely require interaction\n * via the keyboard (e.g. a text box)\n * @param {Event} e\n */\n function onFocus(e) {\n // Prevent IE from focusing the document or HTML element.\n if (!isValidFocusTarget(e.target)) {\n return;\n }\n\n if (hadKeyboardEvent || focusTriggersKeyboardModality(e.target)) {\n addFocusVisibleClass(e.target);\n }\n }\n\n /**\n * On `blur`, remove the `focus-visible` class from the target.\n * @param {Event} e\n */\n function onBlur(e) {\n if (!isValidFocusTarget(e.target)) {\n return;\n }\n\n if (\n e.target.classList.contains('focus-visible') ||\n e.target.hasAttribute('data-focus-visible-added')\n ) {\n // To detect a tab/window switch, we look for a blur event followed\n // rapidly by a visibility change.\n // If we don't see a visibility change within 100ms, it's probably a\n // regular focus change.\n hadFocusVisibleRecently = true;\n window.clearTimeout(hadFocusVisibleRecentlyTimeout);\n hadFocusVisibleRecentlyTimeout = window.setTimeout(function() {\n hadFocusVisibleRecently = false;\n }, 100);\n removeFocusVisibleClass(e.target);\n }\n }\n\n /**\n * If the user changes tabs, keep track of whether or not the previously\n * focused element had .focus-visible.\n * @param {Event} e\n */\n function onVisibilityChange(e) {\n if (document.visibilityState === 'hidden') {\n // If the tab becomes active again, the browser will handle calling focus\n // on the element (Safari actually calls it twice).\n // If this tab change caused a blur on an element with focus-visible,\n // re-apply the class when the user switches back to the tab.\n if (hadFocusVisibleRecently) {\n hadKeyboardEvent = true;\n }\n addInitialPointerMoveListeners();\n }\n }\n\n /**\n * Add a group of listeners to detect usage of any pointing devices.\n * These listeners will be added when the polyfill first loads, and anytime\n * the window is blurred, so that they are active when the window regains\n * focus.\n */\n function addInitialPointerMoveListeners() {\n document.addEventListener('mousemove', onInitialPointerMove);\n document.addEventListener('mousedown', onInitialPointerMove);\n document.addEventListener('mouseup', onInitialPointerMove);\n document.addEventListener('pointermove', onInitialPointerMove);\n document.addEventListener('pointerdown', onInitialPointerMove);\n document.addEventListener('pointerup', onInitialPointerMove);\n document.addEventListener('touchmove', onInitialPointerMove);\n document.addEventListener('touchstart', onInitialPointerMove);\n document.addEventListener('touchend', onInitialPointerMove);\n }\n\n function removeInitialPointerMoveListeners() {\n document.removeEventListener('mousemove', onInitialPointerMove);\n document.removeEventListener('mousedown', onInitialPointerMove);\n document.removeEventListener('mouseup', onInitialPointerMove);\n document.removeEventListener('pointermove', onInitialPointerMove);\n document.removeEventListener('pointerdown', onInitialPointerMove);\n document.removeEventListener('pointerup', onInitialPointerMove);\n document.removeEventListener('touchmove', onInitialPointerMove);\n document.removeEventListener('touchstart', onInitialPointerMove);\n document.removeEventListener('touchend', onInitialPointerMove);\n }\n\n /**\n * When the polfyill first loads, assume the user is in keyboard modality.\n * If any event is received from a pointing device (e.g. mouse, pointer,\n * touch), turn off keyboard modality.\n * This accounts for situations where focus enters the page from the URL bar.\n * @param {Event} e\n */\n function onInitialPointerMove(e) {\n // Work around a Safari quirk that fires a mousemove on whenever the\n // window blurs, even if you're tabbing out of the page. \u00AF\\_(\u30C4)_/\u00AF\n if (e.target.nodeName && e.target.nodeName.toLowerCase() === 'html') {\n return;\n }\n\n hadKeyboardEvent = false;\n removeInitialPointerMoveListeners();\n }\n\n // For some kinds of state, we are interested in changes at the global scope\n // only. For example, global pointer input, global key presses and global\n // visibility change should affect the state at every scope:\n document.addEventListener('keydown', onKeyDown, true);\n document.addEventListener('mousedown', onPointerDown, true);\n document.addEventListener('pointerdown', onPointerDown, true);\n document.addEventListener('touchstart', onPointerDown, true);\n document.addEventListener('visibilitychange', onVisibilityChange, true);\n\n addInitialPointerMoveListeners();\n\n // For focus and blur, we specifically care about state changes in the local\n // scope. This is because focus / blur events that originate from within a\n // shadow root are not re-dispatched from the host element if it was already\n // the active element in its own scope:\n scope.addEventListener('focus', onFocus, true);\n scope.addEventListener('blur', onBlur, true);\n\n // We detect that a node is a ShadowRoot by ensuring that it is a\n // DocumentFragment and also has a host property. This check covers native\n // implementation and polyfill implementation transparently. If we only cared\n // about the native implementation, we could just check if the scope was\n // an instance of a ShadowRoot.\n if (scope.nodeType === Node.DOCUMENT_FRAGMENT_NODE && scope.host) {\n // Since a ShadowRoot is a special kind of DocumentFragment, it does not\n // have a root element to add a class to. So, we add this attribute to the\n // host element instead:\n scope.host.setAttribute('data-js-focus-visible', '');\n } else if (scope.nodeType === Node.DOCUMENT_NODE) {\n document.documentElement.classList.add('js-focus-visible');\n document.documentElement.setAttribute('data-js-focus-visible', '');\n }\n }\n\n // It is important to wrap all references to global window and document in\n // these checks to support server-side rendering use cases\n // @see https://github.com/WICG/focus-visible/issues/199\n if (typeof window !== 'undefined' && typeof document !== 'undefined') {\n // Make the polyfill helper globally available. This can be used as a signal\n // to interested libraries that wish to coordinate with the polyfill for e.g.,\n // applying the polyfill to a shadow root:\n window.applyFocusVisiblePolyfill = applyFocusVisiblePolyfill;\n\n // Notify interested libraries of the polyfill's presence, in case the\n // polyfill was loaded lazily:\n var event;\n\n try {\n event = new CustomEvent('focus-visible-polyfill-ready');\n } catch (error) {\n // IE11 does not support using CustomEvent as a constructor directly:\n event = document.createEvent('CustomEvent');\n event.initCustomEvent('focus-visible-polyfill-ready', false, false, {});\n }\n\n window.dispatchEvent(event);\n }\n\n if (typeof document !== 'undefined') {\n // Apply the polyfill to the global document, so that no JavaScript\n // coordination is required to use the polyfill in the top-level document:\n applyFocusVisiblePolyfill(document);\n }\n\n})));\n", "/*!\n * escape-html\n * Copyright(c) 2012-2013 TJ Holowaychuk\n * Copyright(c) 2015 Andreas Lubbe\n * Copyright(c) 2015 Tiancheng \"Timothy\" Gu\n * MIT Licensed\n */\n\n'use strict';\n\n/**\n * Module variables.\n * @private\n */\n\nvar matchHtmlRegExp = /[\"'&<>]/;\n\n/**\n * Module exports.\n * @public\n */\n\nmodule.exports = escapeHtml;\n\n/**\n * Escape special characters in the given string of html.\n *\n * @param {string} string The string to escape for inserting into HTML\n * @return {string}\n * @public\n */\n\nfunction escapeHtml(string) {\n var str = '' + string;\n var match = matchHtmlRegExp.exec(str);\n\n if (!match) {\n return str;\n }\n\n var escape;\n var html = '';\n var index = 0;\n var lastIndex = 0;\n\n for (index = match.index; index < str.length; index++) {\n switch (str.charCodeAt(index)) {\n case 34: // \"\n escape = '"';\n break;\n case 38: // &\n escape = '&';\n break;\n case 39: // '\n escape = ''';\n break;\n case 60: // <\n escape = '<';\n break;\n case 62: // >\n escape = '>';\n break;\n default:\n continue;\n }\n\n if (lastIndex !== index) {\n html += str.substring(lastIndex, index);\n }\n\n lastIndex = index + 1;\n html += escape;\n }\n\n return lastIndex !== index\n ? html + str.substring(lastIndex, index)\n : html;\n}\n", "/*!\n * clipboard.js v2.0.11\n * https://clipboardjs.com/\n *\n * Licensed MIT \u00A9 Zeno Rocha\n */\n(function webpackUniversalModuleDefinition(root, factory) {\n\tif(typeof exports === 'object' && typeof module === 'object')\n\t\tmodule.exports = factory();\n\telse if(typeof define === 'function' && define.amd)\n\t\tdefine([], factory);\n\telse if(typeof exports === 'object')\n\t\texports[\"ClipboardJS\"] = factory();\n\telse\n\t\troot[\"ClipboardJS\"] = factory();\n})(this, function() {\nreturn /******/ (function() { // webpackBootstrap\n/******/ \tvar __webpack_modules__ = ({\n\n/***/ 686:\n/***/ (function(__unused_webpack_module, __webpack_exports__, __webpack_require__) {\n\n\"use strict\";\n\n// EXPORTS\n__webpack_require__.d(__webpack_exports__, {\n \"default\": function() { return /* binding */ clipboard; }\n});\n\n// EXTERNAL MODULE: ./node_modules/tiny-emitter/index.js\nvar tiny_emitter = __webpack_require__(279);\nvar tiny_emitter_default = /*#__PURE__*/__webpack_require__.n(tiny_emitter);\n// EXTERNAL MODULE: ./node_modules/good-listener/src/listen.js\nvar listen = __webpack_require__(370);\nvar listen_default = /*#__PURE__*/__webpack_require__.n(listen);\n// EXTERNAL MODULE: ./node_modules/select/src/select.js\nvar src_select = __webpack_require__(817);\nvar select_default = /*#__PURE__*/__webpack_require__.n(src_select);\n;// CONCATENATED MODULE: ./src/common/command.js\n/**\n * Executes a given operation type.\n * @param {String} type\n * @return {Boolean}\n */\nfunction command(type) {\n try {\n return document.execCommand(type);\n } catch (err) {\n return false;\n }\n}\n;// CONCATENATED MODULE: ./src/actions/cut.js\n\n\n/**\n * Cut action wrapper.\n * @param {String|HTMLElement} target\n * @return {String}\n */\n\nvar ClipboardActionCut = function ClipboardActionCut(target) {\n var selectedText = select_default()(target);\n command('cut');\n return selectedText;\n};\n\n/* harmony default export */ var actions_cut = (ClipboardActionCut);\n;// CONCATENATED MODULE: ./src/common/create-fake-element.js\n/**\n * Creates a fake textarea element with a value.\n * @param {String} value\n * @return {HTMLElement}\n */\nfunction createFakeElement(value) {\n var isRTL = document.documentElement.getAttribute('dir') === 'rtl';\n var fakeElement = document.createElement('textarea'); // Prevent zooming on iOS\n\n fakeElement.style.fontSize = '12pt'; // Reset box model\n\n fakeElement.style.border = '0';\n fakeElement.style.padding = '0';\n fakeElement.style.margin = '0'; // Move element out of screen horizontally\n\n fakeElement.style.position = 'absolute';\n fakeElement.style[isRTL ? 'right' : 'left'] = '-9999px'; // Move element to the same position vertically\n\n var yPosition = window.pageYOffset || document.documentElement.scrollTop;\n fakeElement.style.top = \"\".concat(yPosition, \"px\");\n fakeElement.setAttribute('readonly', '');\n fakeElement.value = value;\n return fakeElement;\n}\n;// CONCATENATED MODULE: ./src/actions/copy.js\n\n\n\n/**\n * Create fake copy action wrapper using a fake element.\n * @param {String} target\n * @param {Object} options\n * @return {String}\n */\n\nvar fakeCopyAction = function fakeCopyAction(value, options) {\n var fakeElement = createFakeElement(value);\n options.container.appendChild(fakeElement);\n var selectedText = select_default()(fakeElement);\n command('copy');\n fakeElement.remove();\n return selectedText;\n};\n/**\n * Copy action wrapper.\n * @param {String|HTMLElement} target\n * @param {Object} options\n * @return {String}\n */\n\n\nvar ClipboardActionCopy = function ClipboardActionCopy(target) {\n var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {\n container: document.body\n };\n var selectedText = '';\n\n if (typeof target === 'string') {\n selectedText = fakeCopyAction(target, options);\n } else if (target instanceof HTMLInputElement && !['text', 'search', 'url', 'tel', 'password'].includes(target === null || target === void 0 ? void 0 : target.type)) {\n // If input type doesn't support `setSelectionRange`. Simulate it. https://developer.mozilla.org/en-US/docs/Web/API/HTMLInputElement/setSelectionRange\n selectedText = fakeCopyAction(target.value, options);\n } else {\n selectedText = select_default()(target);\n command('copy');\n }\n\n return selectedText;\n};\n\n/* harmony default export */ var actions_copy = (ClipboardActionCopy);\n;// CONCATENATED MODULE: ./src/actions/default.js\nfunction _typeof(obj) { \"@babel/helpers - typeof\"; if (typeof Symbol === \"function\" && typeof Symbol.iterator === \"symbol\") { _typeof = function _typeof(obj) { return typeof obj; }; } else { _typeof = function _typeof(obj) { return obj && typeof Symbol === \"function\" && obj.constructor === Symbol && obj !== Symbol.prototype ? \"symbol\" : typeof obj; }; } return _typeof(obj); }\n\n\n\n/**\n * Inner function which performs selection from either `text` or `target`\n * properties and then executes copy or cut operations.\n * @param {Object} options\n */\n\nvar ClipboardActionDefault = function ClipboardActionDefault() {\n var options = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : {};\n // Defines base properties passed from constructor.\n var _options$action = options.action,\n action = _options$action === void 0 ? 'copy' : _options$action,\n container = options.container,\n target = options.target,\n text = options.text; // Sets the `action` to be performed which can be either 'copy' or 'cut'.\n\n if (action !== 'copy' && action !== 'cut') {\n throw new Error('Invalid \"action\" value, use either \"copy\" or \"cut\"');\n } // Sets the `target` property using an element that will be have its content copied.\n\n\n if (target !== undefined) {\n if (target && _typeof(target) === 'object' && target.nodeType === 1) {\n if (action === 'copy' && target.hasAttribute('disabled')) {\n throw new Error('Invalid \"target\" attribute. Please use \"readonly\" instead of \"disabled\" attribute');\n }\n\n if (action === 'cut' && (target.hasAttribute('readonly') || target.hasAttribute('disabled'))) {\n throw new Error('Invalid \"target\" attribute. You can\\'t cut text from elements with \"readonly\" or \"disabled\" attributes');\n }\n } else {\n throw new Error('Invalid \"target\" value, use a valid Element');\n }\n } // Define selection strategy based on `text` property.\n\n\n if (text) {\n return actions_copy(text, {\n container: container\n });\n } // Defines which selection strategy based on `target` property.\n\n\n if (target) {\n return action === 'cut' ? actions_cut(target) : actions_copy(target, {\n container: container\n });\n }\n};\n\n/* harmony default export */ var actions_default = (ClipboardActionDefault);\n;// CONCATENATED MODULE: ./src/clipboard.js\nfunction clipboard_typeof(obj) { \"@babel/helpers - typeof\"; if (typeof Symbol === \"function\" && typeof Symbol.iterator === \"symbol\") { clipboard_typeof = function _typeof(obj) { return typeof obj; }; } else { clipboard_typeof = function _typeof(obj) { return obj && typeof Symbol === \"function\" && obj.constructor === Symbol && obj !== Symbol.prototype ? \"symbol\" : typeof obj; }; } return clipboard_typeof(obj); }\n\nfunction _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError(\"Cannot call a class as a function\"); } }\n\nfunction _defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if (\"value\" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } }\n\nfunction _createClass(Constructor, protoProps, staticProps) { if (protoProps) _defineProperties(Constructor.prototype, protoProps); if (staticProps) _defineProperties(Constructor, staticProps); return Constructor; }\n\nfunction _inherits(subClass, superClass) { if (typeof superClass !== \"function\" && superClass !== null) { throw new TypeError(\"Super expression must either be null or a function\"); } subClass.prototype = Object.create(superClass && superClass.prototype, { constructor: { value: subClass, writable: true, configurable: true } }); if (superClass) _setPrototypeOf(subClass, superClass); }\n\nfunction _setPrototypeOf(o, p) { _setPrototypeOf = Object.setPrototypeOf || function _setPrototypeOf(o, p) { o.__proto__ = p; return o; }; return _setPrototypeOf(o, p); }\n\nfunction _createSuper(Derived) { var hasNativeReflectConstruct = _isNativeReflectConstruct(); return function _createSuperInternal() { var Super = _getPrototypeOf(Derived), result; if (hasNativeReflectConstruct) { var NewTarget = _getPrototypeOf(this).constructor; result = Reflect.construct(Super, arguments, NewTarget); } else { result = Super.apply(this, arguments); } return _possibleConstructorReturn(this, result); }; }\n\nfunction _possibleConstructorReturn(self, call) { if (call && (clipboard_typeof(call) === \"object\" || typeof call === \"function\")) { return call; } return _assertThisInitialized(self); }\n\nfunction _assertThisInitialized(self) { if (self === void 0) { throw new ReferenceError(\"this hasn't been initialised - super() hasn't been called\"); } return self; }\n\nfunction _isNativeReflectConstruct() { if (typeof Reflect === \"undefined\" || !Reflect.construct) return false; if (Reflect.construct.sham) return false; if (typeof Proxy === \"function\") return true; try { Date.prototype.toString.call(Reflect.construct(Date, [], function () {})); return true; } catch (e) { return false; } }\n\nfunction _getPrototypeOf(o) { _getPrototypeOf = Object.setPrototypeOf ? Object.getPrototypeOf : function _getPrototypeOf(o) { return o.__proto__ || Object.getPrototypeOf(o); }; return _getPrototypeOf(o); }\n\n\n\n\n\n\n/**\n * Helper function to retrieve attribute value.\n * @param {String} suffix\n * @param {Element} element\n */\n\nfunction getAttributeValue(suffix, element) {\n var attribute = \"data-clipboard-\".concat(suffix);\n\n if (!element.hasAttribute(attribute)) {\n return;\n }\n\n return element.getAttribute(attribute);\n}\n/**\n * Base class which takes one or more elements, adds event listeners to them,\n * and instantiates a new `ClipboardAction` on each click.\n */\n\n\nvar Clipboard = /*#__PURE__*/function (_Emitter) {\n _inherits(Clipboard, _Emitter);\n\n var _super = _createSuper(Clipboard);\n\n /**\n * @param {String|HTMLElement|HTMLCollection|NodeList} trigger\n * @param {Object} options\n */\n function Clipboard(trigger, options) {\n var _this;\n\n _classCallCheck(this, Clipboard);\n\n _this = _super.call(this);\n\n _this.resolveOptions(options);\n\n _this.listenClick(trigger);\n\n return _this;\n }\n /**\n * Defines if attributes would be resolved using internal setter functions\n * or custom functions that were passed in the constructor.\n * @param {Object} options\n */\n\n\n _createClass(Clipboard, [{\n key: \"resolveOptions\",\n value: function resolveOptions() {\n var options = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : {};\n this.action = typeof options.action === 'function' ? options.action : this.defaultAction;\n this.target = typeof options.target === 'function' ? options.target : this.defaultTarget;\n this.text = typeof options.text === 'function' ? options.text : this.defaultText;\n this.container = clipboard_typeof(options.container) === 'object' ? options.container : document.body;\n }\n /**\n * Adds a click event listener to the passed trigger.\n * @param {String|HTMLElement|HTMLCollection|NodeList} trigger\n */\n\n }, {\n key: \"listenClick\",\n value: function listenClick(trigger) {\n var _this2 = this;\n\n this.listener = listen_default()(trigger, 'click', function (e) {\n return _this2.onClick(e);\n });\n }\n /**\n * Defines a new `ClipboardAction` on each click event.\n * @param {Event} e\n */\n\n }, {\n key: \"onClick\",\n value: function onClick(e) {\n var trigger = e.delegateTarget || e.currentTarget;\n var action = this.action(trigger) || 'copy';\n var text = actions_default({\n action: action,\n container: this.container,\n target: this.target(trigger),\n text: this.text(trigger)\n }); // Fires an event based on the copy operation result.\n\n this.emit(text ? 'success' : 'error', {\n action: action,\n text: text,\n trigger: trigger,\n clearSelection: function clearSelection() {\n if (trigger) {\n trigger.focus();\n }\n\n window.getSelection().removeAllRanges();\n }\n });\n }\n /**\n * Default `action` lookup function.\n * @param {Element} trigger\n */\n\n }, {\n key: \"defaultAction\",\n value: function defaultAction(trigger) {\n return getAttributeValue('action', trigger);\n }\n /**\n * Default `target` lookup function.\n * @param {Element} trigger\n */\n\n }, {\n key: \"defaultTarget\",\n value: function defaultTarget(trigger) {\n var selector = getAttributeValue('target', trigger);\n\n if (selector) {\n return document.querySelector(selector);\n }\n }\n /**\n * Allow fire programmatically a copy action\n * @param {String|HTMLElement} target\n * @param {Object} options\n * @returns Text copied.\n */\n\n }, {\n key: \"defaultText\",\n\n /**\n * Default `text` lookup function.\n * @param {Element} trigger\n */\n value: function defaultText(trigger) {\n return getAttributeValue('text', trigger);\n }\n /**\n * Destroy lifecycle.\n */\n\n }, {\n key: \"destroy\",\n value: function destroy() {\n this.listener.destroy();\n }\n }], [{\n key: \"copy\",\n value: function copy(target) {\n var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {\n container: document.body\n };\n return actions_copy(target, options);\n }\n /**\n * Allow fire programmatically a cut action\n * @param {String|HTMLElement} target\n * @returns Text cutted.\n */\n\n }, {\n key: \"cut\",\n value: function cut(target) {\n return actions_cut(target);\n }\n /**\n * Returns the support of the given action, or all actions if no action is\n * given.\n * @param {String} [action]\n */\n\n }, {\n key: \"isSupported\",\n value: function isSupported() {\n var action = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : ['copy', 'cut'];\n var actions = typeof action === 'string' ? [action] : action;\n var support = !!document.queryCommandSupported;\n actions.forEach(function (action) {\n support = support && !!document.queryCommandSupported(action);\n });\n return support;\n }\n }]);\n\n return Clipboard;\n}((tiny_emitter_default()));\n\n/* harmony default export */ var clipboard = (Clipboard);\n\n/***/ }),\n\n/***/ 828:\n/***/ (function(module) {\n\nvar DOCUMENT_NODE_TYPE = 9;\n\n/**\n * A polyfill for Element.matches()\n */\nif (typeof Element !== 'undefined' && !Element.prototype.matches) {\n var proto = Element.prototype;\n\n proto.matches = proto.matchesSelector ||\n proto.mozMatchesSelector ||\n proto.msMatchesSelector ||\n proto.oMatchesSelector ||\n proto.webkitMatchesSelector;\n}\n\n/**\n * Finds the closest parent that matches a selector.\n *\n * @param {Element} element\n * @param {String} selector\n * @return {Function}\n */\nfunction closest (element, selector) {\n while (element && element.nodeType !== DOCUMENT_NODE_TYPE) {\n if (typeof element.matches === 'function' &&\n element.matches(selector)) {\n return element;\n }\n element = element.parentNode;\n }\n}\n\nmodule.exports = closest;\n\n\n/***/ }),\n\n/***/ 438:\n/***/ (function(module, __unused_webpack_exports, __webpack_require__) {\n\nvar closest = __webpack_require__(828);\n\n/**\n * Delegates event to a selector.\n *\n * @param {Element} element\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @param {Boolean} useCapture\n * @return {Object}\n */\nfunction _delegate(element, selector, type, callback, useCapture) {\n var listenerFn = listener.apply(this, arguments);\n\n element.addEventListener(type, listenerFn, useCapture);\n\n return {\n destroy: function() {\n element.removeEventListener(type, listenerFn, useCapture);\n }\n }\n}\n\n/**\n * Delegates event to a selector.\n *\n * @param {Element|String|Array} [elements]\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @param {Boolean} useCapture\n * @return {Object}\n */\nfunction delegate(elements, selector, type, callback, useCapture) {\n // Handle the regular Element usage\n if (typeof elements.addEventListener === 'function') {\n return _delegate.apply(null, arguments);\n }\n\n // Handle Element-less usage, it defaults to global delegation\n if (typeof type === 'function') {\n // Use `document` as the first parameter, then apply arguments\n // This is a short way to .unshift `arguments` without running into deoptimizations\n return _delegate.bind(null, document).apply(null, arguments);\n }\n\n // Handle Selector-based usage\n if (typeof elements === 'string') {\n elements = document.querySelectorAll(elements);\n }\n\n // Handle Array-like based usage\n return Array.prototype.map.call(elements, function (element) {\n return _delegate(element, selector, type, callback, useCapture);\n });\n}\n\n/**\n * Finds closest match and invokes callback.\n *\n * @param {Element} element\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @return {Function}\n */\nfunction listener(element, selector, type, callback) {\n return function(e) {\n e.delegateTarget = closest(e.target, selector);\n\n if (e.delegateTarget) {\n callback.call(element, e);\n }\n }\n}\n\nmodule.exports = delegate;\n\n\n/***/ }),\n\n/***/ 879:\n/***/ (function(__unused_webpack_module, exports) {\n\n/**\n * Check if argument is a HTML element.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.node = function(value) {\n return value !== undefined\n && value instanceof HTMLElement\n && value.nodeType === 1;\n};\n\n/**\n * Check if argument is a list of HTML elements.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.nodeList = function(value) {\n var type = Object.prototype.toString.call(value);\n\n return value !== undefined\n && (type === '[object NodeList]' || type === '[object HTMLCollection]')\n && ('length' in value)\n && (value.length === 0 || exports.node(value[0]));\n};\n\n/**\n * Check if argument is a string.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.string = function(value) {\n return typeof value === 'string'\n || value instanceof String;\n};\n\n/**\n * Check if argument is a function.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.fn = function(value) {\n var type = Object.prototype.toString.call(value);\n\n return type === '[object Function]';\n};\n\n\n/***/ }),\n\n/***/ 370:\n/***/ (function(module, __unused_webpack_exports, __webpack_require__) {\n\nvar is = __webpack_require__(879);\nvar delegate = __webpack_require__(438);\n\n/**\n * Validates all params and calls the right\n * listener function based on its target type.\n *\n * @param {String|HTMLElement|HTMLCollection|NodeList} target\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listen(target, type, callback) {\n if (!target && !type && !callback) {\n throw new Error('Missing required arguments');\n }\n\n if (!is.string(type)) {\n throw new TypeError('Second argument must be a String');\n }\n\n if (!is.fn(callback)) {\n throw new TypeError('Third argument must be a Function');\n }\n\n if (is.node(target)) {\n return listenNode(target, type, callback);\n }\n else if (is.nodeList(target)) {\n return listenNodeList(target, type, callback);\n }\n else if (is.string(target)) {\n return listenSelector(target, type, callback);\n }\n else {\n throw new TypeError('First argument must be a String, HTMLElement, HTMLCollection, or NodeList');\n }\n}\n\n/**\n * Adds an event listener to a HTML element\n * and returns a remove listener function.\n *\n * @param {HTMLElement} node\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listenNode(node, type, callback) {\n node.addEventListener(type, callback);\n\n return {\n destroy: function() {\n node.removeEventListener(type, callback);\n }\n }\n}\n\n/**\n * Add an event listener to a list of HTML elements\n * and returns a remove listener function.\n *\n * @param {NodeList|HTMLCollection} nodeList\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listenNodeList(nodeList, type, callback) {\n Array.prototype.forEach.call(nodeList, function(node) {\n node.addEventListener(type, callback);\n });\n\n return {\n destroy: function() {\n Array.prototype.forEach.call(nodeList, function(node) {\n node.removeEventListener(type, callback);\n });\n }\n }\n}\n\n/**\n * Add an event listener to a selector\n * and returns a remove listener function.\n *\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listenSelector(selector, type, callback) {\n return delegate(document.body, selector, type, callback);\n}\n\nmodule.exports = listen;\n\n\n/***/ }),\n\n/***/ 817:\n/***/ (function(module) {\n\nfunction select(element) {\n var selectedText;\n\n if (element.nodeName === 'SELECT') {\n element.focus();\n\n selectedText = element.value;\n }\n else if (element.nodeName === 'INPUT' || element.nodeName === 'TEXTAREA') {\n var isReadOnly = element.hasAttribute('readonly');\n\n if (!isReadOnly) {\n element.setAttribute('readonly', '');\n }\n\n element.select();\n element.setSelectionRange(0, element.value.length);\n\n if (!isReadOnly) {\n element.removeAttribute('readonly');\n }\n\n selectedText = element.value;\n }\n else {\n if (element.hasAttribute('contenteditable')) {\n element.focus();\n }\n\n var selection = window.getSelection();\n var range = document.createRange();\n\n range.selectNodeContents(element);\n selection.removeAllRanges();\n selection.addRange(range);\n\n selectedText = selection.toString();\n }\n\n return selectedText;\n}\n\nmodule.exports = select;\n\n\n/***/ }),\n\n/***/ 279:\n/***/ (function(module) {\n\nfunction E () {\n // Keep this empty so it's easier to inherit from\n // (via https://github.com/lipsmack from https://github.com/scottcorgan/tiny-emitter/issues/3)\n}\n\nE.prototype = {\n on: function (name, callback, ctx) {\n var e = this.e || (this.e = {});\n\n (e[name] || (e[name] = [])).push({\n fn: callback,\n ctx: ctx\n });\n\n return this;\n },\n\n once: function (name, callback, ctx) {\n var self = this;\n function listener () {\n self.off(name, listener);\n callback.apply(ctx, arguments);\n };\n\n listener._ = callback\n return this.on(name, listener, ctx);\n },\n\n emit: function (name) {\n var data = [].slice.call(arguments, 1);\n var evtArr = ((this.e || (this.e = {}))[name] || []).slice();\n var i = 0;\n var len = evtArr.length;\n\n for (i; i < len; i++) {\n evtArr[i].fn.apply(evtArr[i].ctx, data);\n }\n\n return this;\n },\n\n off: function (name, callback) {\n var e = this.e || (this.e = {});\n var evts = e[name];\n var liveEvents = [];\n\n if (evts && callback) {\n for (var i = 0, len = evts.length; i < len; i++) {\n if (evts[i].fn !== callback && evts[i].fn._ !== callback)\n liveEvents.push(evts[i]);\n }\n }\n\n // Remove event from queue to prevent memory leak\n // Suggested by https://github.com/lazd\n // Ref: https://github.com/scottcorgan/tiny-emitter/commit/c6ebfaa9bc973b33d110a84a307742b7cf94c953#commitcomment-5024910\n\n (liveEvents.length)\n ? e[name] = liveEvents\n : delete e[name];\n\n return this;\n }\n};\n\nmodule.exports = E;\nmodule.exports.TinyEmitter = E;\n\n\n/***/ })\n\n/******/ \t});\n/************************************************************************/\n/******/ \t// The module cache\n/******/ \tvar __webpack_module_cache__ = {};\n/******/ \t\n/******/ \t// The require function\n/******/ \tfunction __webpack_require__(moduleId) {\n/******/ \t\t// Check if module is in cache\n/******/ \t\tif(__webpack_module_cache__[moduleId]) {\n/******/ \t\t\treturn __webpack_module_cache__[moduleId].exports;\n/******/ \t\t}\n/******/ \t\t// Create a new module (and put it into the cache)\n/******/ \t\tvar module = __webpack_module_cache__[moduleId] = {\n/******/ \t\t\t// no module.id needed\n/******/ \t\t\t// no module.loaded needed\n/******/ \t\t\texports: {}\n/******/ \t\t};\n/******/ \t\n/******/ \t\t// Execute the module function\n/******/ \t\t__webpack_modules__[moduleId](module, module.exports, __webpack_require__);\n/******/ \t\n/******/ \t\t// Return the exports of the module\n/******/ \t\treturn module.exports;\n/******/ \t}\n/******/ \t\n/************************************************************************/\n/******/ \t/* webpack/runtime/compat get default export */\n/******/ \t!function() {\n/******/ \t\t// getDefaultExport function for compatibility with non-harmony modules\n/******/ \t\t__webpack_require__.n = function(module) {\n/******/ \t\t\tvar getter = module && module.__esModule ?\n/******/ \t\t\t\tfunction() { return module['default']; } :\n/******/ \t\t\t\tfunction() { return module; };\n/******/ \t\t\t__webpack_require__.d(getter, { a: getter });\n/******/ \t\t\treturn getter;\n/******/ \t\t};\n/******/ \t}();\n/******/ \t\n/******/ \t/* webpack/runtime/define property getters */\n/******/ \t!function() {\n/******/ \t\t// define getter functions for harmony exports\n/******/ \t\t__webpack_require__.d = function(exports, definition) {\n/******/ \t\t\tfor(var key in definition) {\n/******/ \t\t\t\tif(__webpack_require__.o(definition, key) && !__webpack_require__.o(exports, key)) {\n/******/ \t\t\t\t\tObject.defineProperty(exports, key, { enumerable: true, get: definition[key] });\n/******/ \t\t\t\t}\n/******/ \t\t\t}\n/******/ \t\t};\n/******/ \t}();\n/******/ \t\n/******/ \t/* webpack/runtime/hasOwnProperty shorthand */\n/******/ \t!function() {\n/******/ \t\t__webpack_require__.o = function(obj, prop) { return Object.prototype.hasOwnProperty.call(obj, prop); }\n/******/ \t}();\n/******/ \t\n/************************************************************************/\n/******/ \t// module exports must be returned from runtime so entry inlining is disabled\n/******/ \t// startup\n/******/ \t// Load entry module and return exports\n/******/ \treturn __webpack_require__(686);\n/******/ })()\n.default;\n});", "/*\n * Copyright (c) 2016-2025 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport \"focus-visible\"\n\nimport {\n EMPTY,\n NEVER,\n Observable,\n Subject,\n defer,\n delay,\n filter,\n map,\n merge,\n mergeWith,\n shareReplay,\n switchMap\n} from \"rxjs\"\n\nimport { configuration, feature } from \"./_\"\nimport {\n at,\n getActiveElement,\n getOptionalElement,\n requestJSON,\n setLocation,\n setToggle,\n watchDocument,\n watchKeyboard,\n watchLocation,\n watchLocationTarget,\n watchMedia,\n watchPrint,\n watchScript,\n watchViewport\n} from \"./browser\"\nimport {\n getComponentElement,\n getComponentElements,\n mountAnnounce,\n mountBackToTop,\n mountConsent,\n mountContent,\n mountDialog,\n mountHeader,\n mountHeaderTitle,\n mountPalette,\n mountProgress,\n mountSearch,\n mountSearchHiglight,\n mountSidebar,\n mountSource,\n mountTableOfContents,\n mountTabs,\n watchHeader,\n watchMain\n} from \"./components\"\nimport {\n SearchIndex,\n setupClipboardJS,\n setupInstantNavigation,\n setupVersionSelector\n} from \"./integrations\"\nimport {\n patchEllipsis,\n patchIndeterminate,\n patchScrollfix,\n patchScrolllock\n} from \"./patches\"\nimport \"./polyfills\"\n\n/* ----------------------------------------------------------------------------\n * Functions - @todo refactor\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch search index\n *\n * @returns Search index observable\n */\nfunction fetchSearchIndex(): Observable {\n if (location.protocol === \"file:\") {\n return watchScript(\n `${new URL(\"search/search_index.js\", config.base)}`\n )\n .pipe(\n // @ts-ignore - @todo fix typings\n map(() => __index),\n shareReplay(1)\n )\n } else {\n return requestJSON(\n new URL(\"search/search_index.json\", config.base)\n )\n }\n}\n\n/* ----------------------------------------------------------------------------\n * Application\n * ------------------------------------------------------------------------- */\n\n/* Yay, JavaScript is available */\ndocument.documentElement.classList.remove(\"no-js\")\ndocument.documentElement.classList.add(\"js\")\n\n/* Set up navigation observables and subjects */\nconst document$ = watchDocument()\nconst location$ = watchLocation()\nconst target$ = watchLocationTarget(location$)\nconst keyboard$ = watchKeyboard()\n\n/* Set up media observables */\nconst viewport$ = watchViewport()\nconst tablet$ = watchMedia(\"(min-width: 960px)\")\nconst screen$ = watchMedia(\"(min-width: 1220px)\")\nconst print$ = watchPrint()\n\n/* Retrieve search index, if search is enabled */\nconst config = configuration()\nconst index$ = document.forms.namedItem(\"search\")\n ? fetchSearchIndex()\n : NEVER\n\n/* Set up Clipboard.js integration */\nconst alert$ = new Subject()\nsetupClipboardJS({ alert$ })\n\n/* Set up progress indicator */\nconst progress$ = new Subject()\n\n/* Set up instant navigation, if enabled */\nif (feature(\"navigation.instant\"))\n setupInstantNavigation({ location$, viewport$, progress$ })\n .subscribe(document$)\n\n/* Set up version selector */\nif (config.version?.provider === \"mike\")\n setupVersionSelector({ document$ })\n\n/* Always close drawer and search on navigation */\nmerge(location$, target$)\n .pipe(\n delay(125)\n )\n .subscribe(() => {\n setToggle(\"drawer\", false)\n setToggle(\"search\", false)\n })\n\n/* Set up global keyboard handlers */\nkeyboard$\n .pipe(\n filter(({ mode }) => mode === \"global\")\n )\n .subscribe(key => {\n switch (key.type) {\n\n /* Go to previous page */\n case \"p\":\n case \",\":\n const prev = getOptionalElement(\"link[rel=prev]\")\n if (typeof prev !== \"undefined\")\n setLocation(prev)\n break\n\n /* Go to next page */\n case \"n\":\n case \".\":\n const next = getOptionalElement(\"link[rel=next]\")\n if (typeof next !== \"undefined\")\n setLocation(next)\n break\n\n /* Expand navigation, see https://bit.ly/3ZjG5io */\n case \"Enter\":\n const active = getActiveElement()\n if (active instanceof HTMLLabelElement)\n active.click()\n }\n })\n\n/* Set up patches */\npatchEllipsis({ viewport$, document$ })\npatchIndeterminate({ document$, tablet$ })\npatchScrollfix({ document$ })\npatchScrolllock({ viewport$, tablet$ })\n\n/* Set up header and main area observable */\nconst header$ = watchHeader(getComponentElement(\"header\"), { viewport$ })\nconst main$ = document$\n .pipe(\n map(() => getComponentElement(\"main\")),\n switchMap(el => watchMain(el, { viewport$, header$ })),\n shareReplay(1)\n )\n\n/* Set up control component observables */\nconst control$ = merge(\n\n /* Consent */\n ...getComponentElements(\"consent\")\n .map(el => mountConsent(el, { target$ })),\n\n /* Dialog */\n ...getComponentElements(\"dialog\")\n .map(el => mountDialog(el, { alert$ })),\n\n /* Color palette */\n ...getComponentElements(\"palette\")\n .map(el => mountPalette(el)),\n\n /* Progress bar */\n ...getComponentElements(\"progress\")\n .map(el => mountProgress(el, { progress$ })),\n\n /* Search */\n ...getComponentElements(\"search\")\n .map(el => mountSearch(el, { index$, keyboard$ })),\n\n /* Repository information */\n ...getComponentElements(\"source\")\n .map(el => mountSource(el))\n)\n\n/* Set up content component observables */\nconst content$ = defer(() => merge(\n\n /* Announcement bar */\n ...getComponentElements(\"announce\")\n .map(el => mountAnnounce(el)),\n\n /* Content */\n ...getComponentElements(\"content\")\n .map(el => mountContent(el, { viewport$, target$, print$ })),\n\n /* Search highlighting */\n ...getComponentElements(\"content\")\n .map(el => feature(\"search.highlight\")\n ? mountSearchHiglight(el, { index$, location$ })\n : EMPTY\n ),\n\n /* Header */\n ...getComponentElements(\"header\")\n .map(el => mountHeader(el, { viewport$, header$, main$ })),\n\n /* Header title */\n ...getComponentElements(\"header-title\")\n .map(el => mountHeaderTitle(el, { viewport$, header$ })),\n\n /* Sidebar */\n ...getComponentElements(\"sidebar\")\n .map(el => el.getAttribute(\"data-md-type\") === \"navigation\"\n ? at(screen$, () => mountSidebar(el, { viewport$, header$, main$ }))\n : at(tablet$, () => mountSidebar(el, { viewport$, header$, main$ }))\n ),\n\n /* Navigation tabs */\n ...getComponentElements(\"tabs\")\n .map(el => mountTabs(el, { viewport$, header$ })),\n\n /* Table of contents */\n ...getComponentElements(\"toc\")\n .map(el => mountTableOfContents(el, {\n viewport$, header$, main$, target$\n })),\n\n /* Back-to-top button */\n ...getComponentElements(\"top\")\n .map(el => mountBackToTop(el, { viewport$, header$, main$, target$ }))\n))\n\n/* Set up component observables */\nconst component$ = document$\n .pipe(\n switchMap(() => content$),\n mergeWith(control$),\n shareReplay(1)\n )\n\n/* Subscribe to all components */\ncomponent$.subscribe()\n\n/* ----------------------------------------------------------------------------\n * Exports\n * ------------------------------------------------------------------------- */\n\nwindow.document$ = document$ /* Document observable */\nwindow.location$ = location$ /* Location subject */\nwindow.target$ = target$ /* Location target observable */\nwindow.keyboard$ = keyboard$ /* Keyboard observable */\nwindow.viewport$ = viewport$ /* Viewport observable */\nwindow.tablet$ = tablet$ /* Media tablet observable */\nwindow.screen$ = screen$ /* Media screen observable */\nwindow.print$ = print$ /* Media print observable */\nwindow.alert$ = alert$ /* Alert subject */\nwindow.progress$ = progress$ /* Progress indicator subject */\nwindow.component$ = component$ /* Component observable */\n", "/******************************************************************************\nCopyright (c) Microsoft Corporation.\n\nPermission to use, copy, modify, and/or distribute this software for any\npurpose with or without fee is hereby granted.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH\nREGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY\nAND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT,\nINDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM\nLOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR\nOTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR\nPERFORMANCE OF THIS SOFTWARE.\n***************************************************************************** */\n/* global Reflect, Promise, SuppressedError, Symbol, Iterator */\n\nvar extendStatics = function(d, b) {\n extendStatics = Object.setPrototypeOf ||\n ({ __proto__: [] } instanceof Array && function (d, b) { d.__proto__ = b; }) ||\n function (d, b) { for (var p in b) if (Object.prototype.hasOwnProperty.call(b, p)) d[p] = b[p]; };\n return extendStatics(d, b);\n};\n\nexport function __extends(d, b) {\n if (typeof b !== \"function\" && b !== null)\n throw new TypeError(\"Class extends value \" + String(b) + \" is not a constructor or null\");\n extendStatics(d, b);\n function __() { this.constructor = d; }\n d.prototype = b === null ? Object.create(b) : (__.prototype = b.prototype, new __());\n}\n\nexport var __assign = function() {\n __assign = Object.assign || function __assign(t) {\n for (var s, i = 1, n = arguments.length; i < n; i++) {\n s = arguments[i];\n for (var p in s) if (Object.prototype.hasOwnProperty.call(s, p)) t[p] = s[p];\n }\n return t;\n }\n return __assign.apply(this, arguments);\n}\n\nexport function __rest(s, e) {\n var t = {};\n for (var p in s) if (Object.prototype.hasOwnProperty.call(s, p) && e.indexOf(p) < 0)\n t[p] = s[p];\n if (s != null && typeof Object.getOwnPropertySymbols === \"function\")\n for (var i = 0, p = Object.getOwnPropertySymbols(s); i < p.length; i++) {\n if (e.indexOf(p[i]) < 0 && Object.prototype.propertyIsEnumerable.call(s, p[i]))\n t[p[i]] = s[p[i]];\n }\n return t;\n}\n\nexport function __decorate(decorators, target, key, desc) {\n var c = arguments.length, r = c < 3 ? target : desc === null ? desc = Object.getOwnPropertyDescriptor(target, key) : desc, d;\n if (typeof Reflect === \"object\" && typeof Reflect.decorate === \"function\") r = Reflect.decorate(decorators, target, key, desc);\n else for (var i = decorators.length - 1; i >= 0; i--) if (d = decorators[i]) r = (c < 3 ? d(r) : c > 3 ? d(target, key, r) : d(target, key)) || r;\n return c > 3 && r && Object.defineProperty(target, key, r), r;\n}\n\nexport function __param(paramIndex, decorator) {\n return function (target, key) { decorator(target, key, paramIndex); }\n}\n\nexport function __esDecorate(ctor, descriptorIn, decorators, contextIn, initializers, extraInitializers) {\n function accept(f) { if (f !== void 0 && typeof f !== \"function\") throw new TypeError(\"Function expected\"); return f; }\n var kind = contextIn.kind, key = kind === \"getter\" ? \"get\" : kind === \"setter\" ? \"set\" : \"value\";\n var target = !descriptorIn && ctor ? contextIn[\"static\"] ? ctor : ctor.prototype : null;\n var descriptor = descriptorIn || (target ? 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(function(o, m, k, k2) {\n if (k2 === undefined) k2 = k;\n var desc = Object.getOwnPropertyDescriptor(m, k);\n if (!desc || (\"get\" in desc ? !m.__esModule : desc.writable || desc.configurable)) {\n desc = { enumerable: true, get: function() { return m[k]; } };\n }\n Object.defineProperty(o, k2, desc);\n}) : (function(o, m, k, k2) {\n if (k2 === undefined) k2 = k;\n o[k2] = m[k];\n});\n\nexport function __exportStar(m, o) {\n for (var p in m) if (p !== \"default\" && !Object.prototype.hasOwnProperty.call(o, p)) __createBinding(o, m, p);\n}\n\nexport function __values(o) {\n var s = typeof Symbol === \"function\" && Symbol.iterator, m = s && o[s], i = 0;\n if (m) return m.call(o);\n if (o && typeof o.length === \"number\") return {\n next: function () {\n if (o && i >= o.length) o = void 0;\n return { value: o && o[i++], done: !o };\n }\n };\n throw new TypeError(s ? \"Object is not iterable.\" : \"Symbol.iterator is not defined.\");\n}\n\nexport function __read(o, n) {\n var m = typeof Symbol === \"function\" && o[Symbol.iterator];\n if (!m) return o;\n var i = m.call(o), r, ar = [], e;\n try {\n while ((n === void 0 || n-- > 0) && !(r = i.next()).done) ar.push(r.value);\n }\n catch (error) { e = { error: error }; }\n finally {\n try {\n if (r && !r.done && (m = i[\"return\"])) m.call(i);\n }\n finally { if (e) throw e.error; }\n }\n return ar;\n}\n\n/** @deprecated */\nexport function __spread() {\n for (var ar = [], i = 0; i < arguments.length; i++)\n ar = ar.concat(__read(arguments[i]));\n return ar;\n}\n\n/** @deprecated */\nexport function __spreadArrays() {\n for (var s = 0, i = 0, il = arguments.length; i < il; i++) s += arguments[i].length;\n for (var r = Array(s), k = 0, i = 0; i < il; i++)\n for (var a = arguments[i], j = 0, jl = a.length; j < jl; j++, k++)\n r[k] = a[j];\n return r;\n}\n\nexport function __spreadArray(to, from, pack) {\n if (pack || arguments.length === 2) for (var i = 0, l = from.length, ar; i < l; i++) {\n if (ar || !(i in from)) {\n if (!ar) ar = Array.prototype.slice.call(from, 0, i);\n ar[i] = from[i];\n }\n }\n return to.concat(ar || Array.prototype.slice.call(from));\n}\n\nexport function __await(v) {\n return this instanceof __await ? (this.v = v, this) : new __await(v);\n}\n\nexport function __asyncGenerator(thisArg, _arguments, generator) {\n if (!Symbol.asyncIterator) throw new TypeError(\"Symbol.asyncIterator is not defined.\");\n var g = generator.apply(thisArg, _arguments || []), i, q = [];\n return i = Object.create((typeof AsyncIterator === \"function\" ? AsyncIterator : Object).prototype), verb(\"next\"), verb(\"throw\"), verb(\"return\", awaitReturn), i[Symbol.asyncIterator] = function () { return this; }, i;\n function awaitReturn(f) { return function (v) { return Promise.resolve(v).then(f, reject); }; }\n function verb(n, f) { if (g[n]) { i[n] = function (v) { return new Promise(function (a, b) { q.push([n, v, a, b]) > 1 || resume(n, v); }); }; if (f) i[n] = f(i[n]); } }\n function resume(n, v) { try { step(g[n](v)); } catch (e) { settle(q[0][3], e); } }\n function step(r) { r.value instanceof __await ? Promise.resolve(r.value.v).then(fulfill, reject) : settle(q[0][2], r); }\n function fulfill(value) { resume(\"next\", value); }\n function reject(value) { resume(\"throw\", value); }\n function settle(f, v) { if (f(v), q.shift(), q.length) resume(q[0][0], q[0][1]); }\n}\n\nexport function __asyncDelegator(o) {\n var i, p;\n return i = {}, verb(\"next\"), verb(\"throw\", function (e) { throw e; }), verb(\"return\"), i[Symbol.iterator] = function () { return this; }, i;\n function verb(n, f) { i[n] = o[n] ? function (v) { return (p = !p) ? { value: __await(o[n](v)), done: false } : f ? f(v) : v; } : f; }\n}\n\nexport function __asyncValues(o) {\n if (!Symbol.asyncIterator) throw new TypeError(\"Symbol.asyncIterator is not defined.\");\n var m = o[Symbol.asyncIterator], i;\n return m ? m.call(o) : (o = typeof __values === \"function\" ? __values(o) : o[Symbol.iterator](), i = {}, verb(\"next\"), verb(\"throw\"), verb(\"return\"), i[Symbol.asyncIterator] = function () { return this; }, i);\n function verb(n) { i[n] = o[n] && function (v) { return new Promise(function (resolve, reject) { v = o[n](v), settle(resolve, reject, v.done, v.value); }); }; }\n function settle(resolve, reject, d, v) { Promise.resolve(v).then(function(v) { resolve({ value: v, done: d }); }, reject); }\n}\n\nexport function __makeTemplateObject(cooked, raw) {\n if (Object.defineProperty) { Object.defineProperty(cooked, \"raw\", { value: raw }); } else { cooked.raw = raw; }\n return cooked;\n};\n\nvar __setModuleDefault = Object.create ? (function(o, v) {\n Object.defineProperty(o, \"default\", { enumerable: true, value: v });\n}) : function(o, v) {\n o[\"default\"] = v;\n};\n\nexport function __importStar(mod) {\n if (mod && mod.__esModule) return mod;\n var result = {};\n if (mod != null) for (var k in mod) if (k !== \"default\" && Object.prototype.hasOwnProperty.call(mod, k)) __createBinding(result, mod, k);\n __setModuleDefault(result, mod);\n return result;\n}\n\nexport function __importDefault(mod) {\n return (mod && mod.__esModule) ? mod : { default: mod };\n}\n\nexport function __classPrivateFieldGet(receiver, state, kind, f) {\n if (kind === \"a\" && !f) throw new TypeError(\"Private accessor was defined without a getter\");\n if (typeof state === \"function\" ? receiver !== state || !f : !state.has(receiver)) throw new TypeError(\"Cannot read private member from an object whose class did not declare it\");\n return kind === \"m\" ? f : kind === \"a\" ? f.call(receiver) : f ? f.value : state.get(receiver);\n}\n\nexport function __classPrivateFieldSet(receiver, state, value, kind, f) {\n if (kind === \"m\") throw new TypeError(\"Private method is not writable\");\n if (kind === \"a\" && !f) throw new TypeError(\"Private accessor was defined without a setter\");\n if (typeof state === \"function\" ? receiver !== state || !f : !state.has(receiver)) throw new TypeError(\"Cannot write private member to an object whose class did not declare it\");\n return (kind === \"a\" ? f.call(receiver, value) : f ? f.value = value : state.set(receiver, value)), value;\n}\n\nexport function __classPrivateFieldIn(state, receiver) {\n if (receiver === null || (typeof receiver !== \"object\" && typeof receiver !== \"function\")) throw new TypeError(\"Cannot use 'in' operator on non-object\");\n return typeof state === \"function\" ? receiver === state : state.has(receiver);\n}\n\nexport function __addDisposableResource(env, value, async) {\n if (value !== null && value !== void 0) {\n if (typeof value !== \"object\" && typeof value !== \"function\") throw new TypeError(\"Object expected.\");\n var dispose, inner;\n if (async) {\n if (!Symbol.asyncDispose) throw new TypeError(\"Symbol.asyncDispose is not defined.\");\n dispose = value[Symbol.asyncDispose];\n }\n if (dispose === void 0) {\n if (!Symbol.dispose) throw new TypeError(\"Symbol.dispose is not defined.\");\n dispose = value[Symbol.dispose];\n if (async) inner = dispose;\n }\n if (typeof dispose !== \"function\") throw new TypeError(\"Object not disposable.\");\n if (inner) dispose = function() { try { inner.call(this); } catch (e) { return Promise.reject(e); } };\n env.stack.push({ value: value, dispose: dispose, async: async });\n }\n else if (async) {\n env.stack.push({ async: true });\n }\n return value;\n}\n\nvar _SuppressedError = typeof SuppressedError === \"function\" ? SuppressedError : function (error, suppressed, message) {\n var e = new Error(message);\n return e.name = \"SuppressedError\", e.error = error, e.suppressed = suppressed, e;\n};\n\nexport function __disposeResources(env) {\n function fail(e) {\n env.error = env.hasError ? new _SuppressedError(e, env.error, \"An error was suppressed during disposal.\") : e;\n env.hasError = true;\n }\n var r, s = 0;\n function next() {\n while (r = env.stack.pop()) {\n try {\n if (!r.async && s === 1) return s = 0, env.stack.push(r), Promise.resolve().then(next);\n if (r.dispose) {\n var result = r.dispose.call(r.value);\n if (r.async) return s |= 2, Promise.resolve(result).then(next, function(e) { fail(e); return next(); });\n }\n else s |= 1;\n }\n catch (e) {\n fail(e);\n }\n }\n if (s === 1) return env.hasError ? Promise.reject(env.error) : Promise.resolve();\n if (env.hasError) throw env.error;\n }\n return next();\n}\n\nexport default {\n __extends,\n __assign,\n __rest,\n __decorate,\n __param,\n __metadata,\n __awaiter,\n __generator,\n __createBinding,\n __exportStar,\n __values,\n __read,\n __spread,\n __spreadArrays,\n __spreadArray,\n __await,\n __asyncGenerator,\n __asyncDelegator,\n __asyncValues,\n __makeTemplateObject,\n __importStar,\n __importDefault,\n __classPrivateFieldGet,\n __classPrivateFieldSet,\n __classPrivateFieldIn,\n __addDisposableResource,\n __disposeResources,\n};\n", "/**\n * Returns true if the object is a function.\n * @param value The value to check\n */\nexport function isFunction(value: any): value is (...args: any[]) => any {\n return typeof value === 'function';\n}\n", "/**\n * Used to create Error subclasses until the community moves away from ES5.\n *\n * This is because compiling from TypeScript down to ES5 has issues with subclassing Errors\n * as well as other built-in types: https://github.com/Microsoft/TypeScript/issues/12123\n *\n * @param createImpl A factory function to create the actual constructor implementation. The returned\n * function should be a named function that calls `_super` internally.\n */\nexport function createErrorClass(createImpl: (_super: any) => any): T {\n const _super = (instance: any) => {\n Error.call(instance);\n instance.stack = new Error().stack;\n };\n\n const ctorFunc = createImpl(_super);\n ctorFunc.prototype = Object.create(Error.prototype);\n ctorFunc.prototype.constructor = ctorFunc;\n return ctorFunc;\n}\n", "import { createErrorClass } from './createErrorClass';\n\nexport interface UnsubscriptionError extends Error {\n readonly errors: any[];\n}\n\nexport interface UnsubscriptionErrorCtor {\n /**\n * @deprecated Internal implementation detail. Do not construct error instances.\n * Cannot be tagged as internal: https://github.com/ReactiveX/rxjs/issues/6269\n */\n new (errors: any[]): UnsubscriptionError;\n}\n\n/**\n * An error thrown when one or more errors have occurred during the\n * `unsubscribe` of a {@link Subscription}.\n */\nexport const UnsubscriptionError: UnsubscriptionErrorCtor = createErrorClass(\n (_super) =>\n function UnsubscriptionErrorImpl(this: any, errors: (Error | string)[]) {\n _super(this);\n this.message = errors\n ? `${errors.length} errors occurred during unsubscription:\n${errors.map((err, i) => `${i + 1}) ${err.toString()}`).join('\\n ')}`\n : '';\n this.name = 'UnsubscriptionError';\n this.errors = errors;\n }\n);\n", "/**\n * Removes an item from an array, mutating it.\n * @param arr The array to remove the item from\n * @param item The item to remove\n */\nexport function arrRemove(arr: T[] | undefined | null, item: T) {\n if (arr) {\n const index = arr.indexOf(item);\n 0 <= index && arr.splice(index, 1);\n }\n}\n", "import { isFunction } from './util/isFunction';\nimport { UnsubscriptionError } from './util/UnsubscriptionError';\nimport { SubscriptionLike, TeardownLogic, Unsubscribable } from './types';\nimport { arrRemove } from './util/arrRemove';\n\n/**\n * Represents a disposable resource, such as the execution of an Observable. A\n * Subscription has one important method, `unsubscribe`, that takes no argument\n * and just disposes the resource held by the subscription.\n *\n * Additionally, subscriptions may be grouped together through the `add()`\n * method, which will attach a child Subscription to the current Subscription.\n * When a Subscription is unsubscribed, all its children (and its grandchildren)\n * will be unsubscribed as well.\n */\nexport class Subscription implements SubscriptionLike {\n public static EMPTY = (() => {\n const empty = new Subscription();\n empty.closed = true;\n return empty;\n })();\n\n /**\n * A flag to indicate whether this Subscription has already been unsubscribed.\n */\n public closed = false;\n\n private _parentage: Subscription[] | Subscription | null = null;\n\n /**\n * The list of registered finalizers to execute upon unsubscription. Adding and removing from this\n * list occurs in the {@link #add} and {@link #remove} methods.\n */\n private _finalizers: Exclude[] | null = null;\n\n /**\n * @param initialTeardown A function executed first as part of the finalization\n * process that is kicked off when {@link #unsubscribe} is called.\n */\n constructor(private initialTeardown?: () => void) {}\n\n /**\n * Disposes the resources held by the subscription. May, for instance, cancel\n * an ongoing Observable execution or cancel any other type of work that\n * started when the Subscription was created.\n */\n unsubscribe(): void {\n let errors: any[] | undefined;\n\n if (!this.closed) {\n this.closed = true;\n\n // Remove this from it's parents.\n const { _parentage } = this;\n if (_parentage) {\n this._parentage = null;\n if (Array.isArray(_parentage)) {\n for (const parent of _parentage) {\n parent.remove(this);\n }\n } else {\n _parentage.remove(this);\n }\n }\n\n const { initialTeardown: initialFinalizer } = this;\n if (isFunction(initialFinalizer)) {\n try {\n initialFinalizer();\n } catch (e) {\n errors = e instanceof UnsubscriptionError ? e.errors : [e];\n }\n }\n\n const { _finalizers } = this;\n if (_finalizers) {\n this._finalizers = null;\n for (const finalizer of _finalizers) {\n try {\n execFinalizer(finalizer);\n } catch (err) {\n errors = errors ?? [];\n if (err instanceof UnsubscriptionError) {\n errors = [...errors, ...err.errors];\n } else {\n errors.push(err);\n }\n }\n }\n }\n\n if (errors) {\n throw new UnsubscriptionError(errors);\n }\n }\n }\n\n /**\n * Adds a finalizer to this subscription, so that finalization will be unsubscribed/called\n * when this subscription is unsubscribed. If this subscription is already {@link #closed},\n * because it has already been unsubscribed, then whatever finalizer is passed to it\n * will automatically be executed (unless the finalizer itself is also a closed subscription).\n *\n * Closed Subscriptions cannot be added as finalizers to any subscription. Adding a closed\n * subscription to a any subscription will result in no operation. (A noop).\n *\n * Adding a subscription to itself, or adding `null` or `undefined` will not perform any\n * operation at all. (A noop).\n *\n * `Subscription` instances that are added to this instance will automatically remove themselves\n * if they are unsubscribed. Functions and {@link Unsubscribable} objects that you wish to remove\n * will need to be removed manually with {@link #remove}\n *\n * @param teardown The finalization logic to add to this subscription.\n */\n add(teardown: TeardownLogic): void {\n // Only add the finalizer if it's not undefined\n // and don't add a subscription to itself.\n if (teardown && teardown !== this) {\n if (this.closed) {\n // If this subscription is already closed,\n // execute whatever finalizer is handed to it automatically.\n execFinalizer(teardown);\n } else {\n if (teardown instanceof Subscription) {\n // We don't add closed subscriptions, and we don't add the same subscription\n // twice. Subscription unsubscribe is idempotent.\n if (teardown.closed || teardown._hasParent(this)) {\n return;\n }\n teardown._addParent(this);\n }\n (this._finalizers = this._finalizers ?? []).push(teardown);\n }\n }\n }\n\n /**\n * Checks to see if a this subscription already has a particular parent.\n * This will signal that this subscription has already been added to the parent in question.\n * @param parent the parent to check for\n */\n private _hasParent(parent: Subscription) {\n const { _parentage } = this;\n return _parentage === parent || (Array.isArray(_parentage) && _parentage.includes(parent));\n }\n\n /**\n * Adds a parent to this subscription so it can be removed from the parent if it\n * unsubscribes on it's own.\n *\n * NOTE: THIS ASSUMES THAT {@link _hasParent} HAS ALREADY BEEN CHECKED.\n * @param parent The parent subscription to add\n */\n private _addParent(parent: Subscription) {\n const { _parentage } = this;\n this._parentage = Array.isArray(_parentage) ? (_parentage.push(parent), _parentage) : _parentage ? [_parentage, parent] : parent;\n }\n\n /**\n * Called on a child when it is removed via {@link #remove}.\n * @param parent The parent to remove\n */\n private _removeParent(parent: Subscription) {\n const { _parentage } = this;\n if (_parentage === parent) {\n this._parentage = null;\n } else if (Array.isArray(_parentage)) {\n arrRemove(_parentage, parent);\n }\n }\n\n /**\n * Removes a finalizer from this subscription that was previously added with the {@link #add} method.\n *\n * Note that `Subscription` instances, when unsubscribed, will automatically remove themselves\n * from every other `Subscription` they have been added to. This means that using the `remove` method\n * is not a common thing and should be used thoughtfully.\n *\n * If you add the same finalizer instance of a function or an unsubscribable object to a `Subscription` instance\n * more than once, you will need to call `remove` the same number of times to remove all instances.\n *\n * All finalizer instances are removed to free up memory upon unsubscription.\n *\n * @param teardown The finalizer to remove from this subscription\n */\n remove(teardown: Exclude): void {\n const { _finalizers } = this;\n _finalizers && arrRemove(_finalizers, teardown);\n\n if (teardown instanceof Subscription) {\n teardown._removeParent(this);\n }\n }\n}\n\nexport const EMPTY_SUBSCRIPTION = Subscription.EMPTY;\n\nexport function isSubscription(value: any): value is Subscription {\n return (\n value instanceof Subscription ||\n (value && 'closed' in value && isFunction(value.remove) && isFunction(value.add) && isFunction(value.unsubscribe))\n );\n}\n\nfunction execFinalizer(finalizer: Unsubscribable | (() => void)) {\n if (isFunction(finalizer)) {\n finalizer();\n } else {\n finalizer.unsubscribe();\n }\n}\n", "import { Subscriber } from './Subscriber';\nimport { ObservableNotification } from './types';\n\n/**\n * The {@link GlobalConfig} object for RxJS. It is used to configure things\n * like how to react on unhandled errors.\n */\nexport const config: GlobalConfig = {\n onUnhandledError: null,\n onStoppedNotification: null,\n Promise: undefined,\n useDeprecatedSynchronousErrorHandling: false,\n useDeprecatedNextContext: false,\n};\n\n/**\n * The global configuration object for RxJS, used to configure things\n * like how to react on unhandled errors. Accessible via {@link config}\n * object.\n */\nexport interface GlobalConfig {\n /**\n * A registration point for unhandled errors from RxJS. These are errors that\n * cannot were not handled by consuming code in the usual subscription path. For\n * example, if you have this configured, and you subscribe to an observable without\n * providing an error handler, errors from that subscription will end up here. This\n * will _always_ be called asynchronously on another job in the runtime. This is because\n * we do not want errors thrown in this user-configured handler to interfere with the\n * behavior of the library.\n */\n onUnhandledError: ((err: any) => void) | null;\n\n /**\n * A registration point for notifications that cannot be sent to subscribers because they\n * have completed, errored or have been explicitly unsubscribed. By default, next, complete\n * and error notifications sent to stopped subscribers are noops. However, sometimes callers\n * might want a different behavior. For example, with sources that attempt to report errors\n * to stopped subscribers, a caller can configure RxJS to throw an unhandled error instead.\n * This will _always_ be called asynchronously on another job in the runtime. This is because\n * we do not want errors thrown in this user-configured handler to interfere with the\n * behavior of the library.\n */\n onStoppedNotification: ((notification: ObservableNotification, subscriber: Subscriber) => void) | null;\n\n /**\n * The promise constructor used by default for {@link Observable#toPromise toPromise} and {@link Observable#forEach forEach}\n * methods.\n *\n * @deprecated As of version 8, RxJS will no longer support this sort of injection of a\n * Promise constructor. If you need a Promise implementation other than native promises,\n * please polyfill/patch Promise as you see appropriate. Will be removed in v8.\n */\n Promise?: PromiseConstructorLike;\n\n /**\n * If true, turns on synchronous error rethrowing, which is a deprecated behavior\n * in v6 and higher. This behavior enables bad patterns like wrapping a subscribe\n * call in a try/catch block. It also enables producer interference, a nasty bug\n * where a multicast can be broken for all observers by a downstream consumer with\n * an unhandled error. DO NOT USE THIS FLAG UNLESS IT'S NEEDED TO BUY TIME\n * FOR MIGRATION REASONS.\n *\n * @deprecated As of version 8, RxJS will no longer support synchronous throwing\n * of unhandled errors. All errors will be thrown on a separate call stack to prevent bad\n * behaviors described above. Will be removed in v8.\n */\n useDeprecatedSynchronousErrorHandling: boolean;\n\n /**\n * If true, enables an as-of-yet undocumented feature from v5: The ability to access\n * `unsubscribe()` via `this` context in `next` functions created in observers passed\n * to `subscribe`.\n *\n * This is being removed because the performance was severely problematic, and it could also cause\n * issues when types other than POJOs are passed to subscribe as subscribers, as they will likely have\n * their `this` context overwritten.\n *\n * @deprecated As of version 8, RxJS will no longer support altering the\n * context of next functions provided as part of an observer to Subscribe. Instead,\n * you will have access to a subscription or a signal or token that will allow you to do things like\n * unsubscribe and test closed status. Will be removed in v8.\n */\n useDeprecatedNextContext: boolean;\n}\n", "import type { TimerHandle } from './timerHandle';\ntype SetTimeoutFunction = (handler: () => void, timeout?: number, ...args: any[]) => TimerHandle;\ntype ClearTimeoutFunction = (handle: TimerHandle) => void;\n\ninterface TimeoutProvider {\n setTimeout: SetTimeoutFunction;\n clearTimeout: ClearTimeoutFunction;\n delegate:\n | {\n setTimeout: SetTimeoutFunction;\n clearTimeout: ClearTimeoutFunction;\n }\n | undefined;\n}\n\nexport const timeoutProvider: TimeoutProvider = {\n // When accessing the delegate, use the variable rather than `this` so that\n // the functions can be called without being bound to the provider.\n setTimeout(handler: () => void, timeout?: number, ...args) {\n const { delegate } = timeoutProvider;\n if (delegate?.setTimeout) {\n return delegate.setTimeout(handler, timeout, ...args);\n }\n return setTimeout(handler, timeout, ...args);\n },\n clearTimeout(handle) {\n const { delegate } = timeoutProvider;\n return (delegate?.clearTimeout || clearTimeout)(handle as any);\n },\n delegate: undefined,\n};\n", "import { config } from '../config';\nimport { timeoutProvider } from '../scheduler/timeoutProvider';\n\n/**\n * Handles an error on another job either with the user-configured {@link onUnhandledError},\n * or by throwing it on that new job so it can be picked up by `window.onerror`, `process.on('error')`, etc.\n *\n * This should be called whenever there is an error that is out-of-band with the subscription\n * or when an error hits a terminal boundary of the subscription and no error handler was provided.\n *\n * @param err the error to report\n */\nexport function reportUnhandledError(err: any) {\n timeoutProvider.setTimeout(() => {\n const { onUnhandledError } = config;\n if (onUnhandledError) {\n // Execute the user-configured error handler.\n onUnhandledError(err);\n } else {\n // Throw so it is picked up by the runtime's uncaught error mechanism.\n throw err;\n }\n });\n}\n", "/* tslint:disable:no-empty */\nexport function noop() { }\n", "import { CompleteNotification, NextNotification, ErrorNotification } from './types';\n\n/**\n * A completion object optimized for memory use and created to be the\n * same \"shape\" as other notifications in v8.\n * @internal\n */\nexport const COMPLETE_NOTIFICATION = (() => createNotification('C', undefined, undefined) as CompleteNotification)();\n\n/**\n * Internal use only. Creates an optimized error notification that is the same \"shape\"\n * as other notifications.\n * @internal\n */\nexport function errorNotification(error: any): ErrorNotification {\n return createNotification('E', undefined, error) as any;\n}\n\n/**\n * Internal use only. Creates an optimized next notification that is the same \"shape\"\n * as other notifications.\n * @internal\n */\nexport function nextNotification(value: T) {\n return createNotification('N', value, undefined) as NextNotification;\n}\n\n/**\n * Ensures that all notifications created internally have the same \"shape\" in v8.\n *\n * TODO: This is only exported to support a crazy legacy test in `groupBy`.\n * @internal\n */\nexport function createNotification(kind: 'N' | 'E' | 'C', value: any, error: any) {\n return {\n kind,\n value,\n error,\n };\n}\n", "import { config } from '../config';\n\nlet context: { errorThrown: boolean; error: any } | null = null;\n\n/**\n * Handles dealing with errors for super-gross mode. Creates a context, in which\n * any synchronously thrown errors will be passed to {@link captureError}. Which\n * will record the error such that it will be rethrown after the call back is complete.\n * TODO: Remove in v8\n * @param cb An immediately executed function.\n */\nexport function errorContext(cb: () => void) {\n if (config.useDeprecatedSynchronousErrorHandling) {\n const isRoot = !context;\n if (isRoot) {\n context = { errorThrown: false, error: null };\n }\n cb();\n if (isRoot) {\n const { errorThrown, error } = context!;\n context = null;\n if (errorThrown) {\n throw error;\n }\n }\n } else {\n // This is the general non-deprecated path for everyone that\n // isn't crazy enough to use super-gross mode (useDeprecatedSynchronousErrorHandling)\n cb();\n }\n}\n\n/**\n * Captures errors only in super-gross mode.\n * @param err the error to capture\n */\nexport function captureError(err: any) {\n if (config.useDeprecatedSynchronousErrorHandling && context) {\n context.errorThrown = true;\n context.error = err;\n }\n}\n", "import { isFunction } from './util/isFunction';\nimport { Observer, ObservableNotification } from './types';\nimport { isSubscription, Subscription } from './Subscription';\nimport { config } from './config';\nimport { reportUnhandledError } from './util/reportUnhandledError';\nimport { noop } from './util/noop';\nimport { nextNotification, errorNotification, COMPLETE_NOTIFICATION } from './NotificationFactories';\nimport { timeoutProvider } from './scheduler/timeoutProvider';\nimport { captureError } from './util/errorContext';\n\n/**\n * Implements the {@link Observer} interface and extends the\n * {@link Subscription} class. While the {@link Observer} is the public API for\n * consuming the values of an {@link Observable}, all Observers get converted to\n * a Subscriber, in order to provide Subscription-like capabilities such as\n * `unsubscribe`. Subscriber is a common type in RxJS, and crucial for\n * implementing operators, but it is rarely used as a public API.\n */\nexport class Subscriber extends Subscription implements Observer {\n /**\n * A static factory for a Subscriber, given a (potentially partial) definition\n * of an Observer.\n * @param next The `next` callback of an Observer.\n * @param error The `error` callback of an\n * Observer.\n * @param complete The `complete` callback of an\n * Observer.\n * @return A Subscriber wrapping the (partially defined)\n * Observer represented by the given arguments.\n * @deprecated Do not use. Will be removed in v8. There is no replacement for this\n * method, and there is no reason to be creating instances of `Subscriber` directly.\n * If you have a specific use case, please file an issue.\n */\n static create(next?: (x?: T) => void, error?: (e?: any) => void, complete?: () => void): Subscriber {\n return new SafeSubscriber(next, error, complete);\n }\n\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n protected isStopped: boolean = false;\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n protected destination: Subscriber | Observer; // this `any` is the escape hatch to erase extra type param (e.g. R)\n\n /**\n * @deprecated Internal implementation detail, do not use directly. Will be made internal in v8.\n * There is no reason to directly create an instance of Subscriber. This type is exported for typings reasons.\n */\n constructor(destination?: Subscriber | Observer) {\n super();\n if (destination) {\n this.destination = destination;\n // Automatically chain subscriptions together here.\n // if destination is a Subscription, then it is a Subscriber.\n if (isSubscription(destination)) {\n destination.add(this);\n }\n } else {\n this.destination = EMPTY_OBSERVER;\n }\n }\n\n /**\n * The {@link Observer} callback to receive notifications of type `next` from\n * the Observable, with a value. The Observable may call this method 0 or more\n * times.\n * @param value The `next` value.\n */\n next(value: T): void {\n if (this.isStopped) {\n handleStoppedNotification(nextNotification(value), this);\n } else {\n this._next(value!);\n }\n }\n\n /**\n * The {@link Observer} callback to receive notifications of type `error` from\n * the Observable, with an attached `Error`. Notifies the Observer that\n * the Observable has experienced an error condition.\n * @param err The `error` exception.\n */\n error(err?: any): void {\n if (this.isStopped) {\n handleStoppedNotification(errorNotification(err), this);\n } else {\n this.isStopped = true;\n this._error(err);\n }\n }\n\n /**\n * The {@link Observer} callback to receive a valueless notification of type\n * `complete` from the Observable. Notifies the Observer that the Observable\n * has finished sending push-based notifications.\n */\n complete(): void {\n if (this.isStopped) {\n handleStoppedNotification(COMPLETE_NOTIFICATION, this);\n } else {\n this.isStopped = true;\n this._complete();\n }\n }\n\n unsubscribe(): void {\n if (!this.closed) {\n this.isStopped = true;\n super.unsubscribe();\n this.destination = null!;\n }\n }\n\n protected _next(value: T): void {\n this.destination.next(value);\n }\n\n protected _error(err: any): void {\n try {\n this.destination.error(err);\n } finally {\n this.unsubscribe();\n }\n }\n\n protected _complete(): void {\n try {\n this.destination.complete();\n } finally {\n this.unsubscribe();\n }\n }\n}\n\n/**\n * This bind is captured here because we want to be able to have\n * compatibility with monoid libraries that tend to use a method named\n * `bind`. In particular, a library called Monio requires this.\n */\nconst _bind = Function.prototype.bind;\n\nfunction bind any>(fn: Fn, thisArg: any): Fn {\n return _bind.call(fn, thisArg);\n}\n\n/**\n * Internal optimization only, DO NOT EXPOSE.\n * @internal\n */\nclass ConsumerObserver implements Observer {\n constructor(private partialObserver: Partial>) {}\n\n next(value: T): void {\n const { partialObserver } = this;\n if (partialObserver.next) {\n try {\n partialObserver.next(value);\n } catch (error) {\n handleUnhandledError(error);\n }\n }\n }\n\n error(err: any): void {\n const { partialObserver } = this;\n if (partialObserver.error) {\n try {\n partialObserver.error(err);\n } catch (error) {\n handleUnhandledError(error);\n }\n } else {\n handleUnhandledError(err);\n }\n }\n\n complete(): void {\n const { partialObserver } = this;\n if (partialObserver.complete) {\n try {\n partialObserver.complete();\n } catch (error) {\n handleUnhandledError(error);\n }\n }\n }\n}\n\nexport class SafeSubscriber extends Subscriber {\n constructor(\n observerOrNext?: Partial> | ((value: T) => void) | null,\n error?: ((e?: any) => void) | null,\n complete?: (() => void) | null\n ) {\n super();\n\n let partialObserver: Partial>;\n if (isFunction(observerOrNext) || !observerOrNext) {\n // The first argument is a function, not an observer. The next\n // two arguments *could* be observers, or they could be empty.\n partialObserver = {\n next: (observerOrNext ?? undefined) as ((value: T) => void) | undefined,\n error: error ?? undefined,\n complete: complete ?? undefined,\n };\n } else {\n // The first argument is a partial observer.\n let context: any;\n if (this && config.useDeprecatedNextContext) {\n // This is a deprecated path that made `this.unsubscribe()` available in\n // next handler functions passed to subscribe. This only exists behind a flag\n // now, as it is *very* slow.\n context = Object.create(observerOrNext);\n context.unsubscribe = () => this.unsubscribe();\n partialObserver = {\n next: observerOrNext.next && bind(observerOrNext.next, context),\n error: observerOrNext.error && bind(observerOrNext.error, context),\n complete: observerOrNext.complete && bind(observerOrNext.complete, context),\n };\n } else {\n // The \"normal\" path. Just use the partial observer directly.\n partialObserver = observerOrNext;\n }\n }\n\n // Wrap the partial observer to ensure it's a full observer, and\n // make sure proper error handling is accounted for.\n this.destination = new ConsumerObserver(partialObserver);\n }\n}\n\nfunction handleUnhandledError(error: any) {\n if (config.useDeprecatedSynchronousErrorHandling) {\n captureError(error);\n } else {\n // Ideal path, we report this as an unhandled error,\n // which is thrown on a new call stack.\n reportUnhandledError(error);\n }\n}\n\n/**\n * An error handler used when no error handler was supplied\n * to the SafeSubscriber -- meaning no error handler was supplied\n * do the `subscribe` call on our observable.\n * @param err The error to handle\n */\nfunction defaultErrorHandler(err: any) {\n throw err;\n}\n\n/**\n * A handler for notifications that cannot be sent to a stopped subscriber.\n * @param notification The notification being sent.\n * @param subscriber The stopped subscriber.\n */\nfunction handleStoppedNotification(notification: ObservableNotification, subscriber: Subscriber) {\n const { onStoppedNotification } = config;\n onStoppedNotification && timeoutProvider.setTimeout(() => onStoppedNotification(notification, subscriber));\n}\n\n/**\n * The observer used as a stub for subscriptions where the user did not\n * pass any arguments to `subscribe`. Comes with the default error handling\n * behavior.\n */\nexport const EMPTY_OBSERVER: Readonly> & { closed: true } = {\n closed: true,\n next: noop,\n error: defaultErrorHandler,\n complete: noop,\n};\n", "/**\n * Symbol.observable or a string \"@@observable\". Used for interop\n *\n * @deprecated We will no longer be exporting this symbol in upcoming versions of RxJS.\n * Instead polyfill and use Symbol.observable directly *or* use https://www.npmjs.com/package/symbol-observable\n */\nexport const observable: string | symbol = (() => (typeof Symbol === 'function' && Symbol.observable) || '@@observable')();\n", "/**\n * This function takes one parameter and just returns it. Simply put,\n * this is like `(x: T): T => x`.\n *\n * ## Examples\n *\n * This is useful in some cases when using things like `mergeMap`\n *\n * ```ts\n * import { interval, take, map, range, mergeMap, identity } from 'rxjs';\n *\n * const source$ = interval(1000).pipe(take(5));\n *\n * const result$ = source$.pipe(\n * map(i => range(i)),\n * mergeMap(identity) // same as mergeMap(x => x)\n * );\n *\n * result$.subscribe({\n * next: console.log\n * });\n * ```\n *\n * Or when you want to selectively apply an operator\n *\n * ```ts\n * import { interval, take, identity } from 'rxjs';\n *\n * const shouldLimit = () => Math.random() < 0.5;\n *\n * const source$ = interval(1000);\n *\n * const result$ = source$.pipe(shouldLimit() ? take(5) : identity);\n *\n * result$.subscribe({\n * next: console.log\n * });\n * ```\n *\n * @param x Any value that is returned by this function\n * @returns The value passed as the first parameter to this function\n */\nexport function identity(x: T): T {\n return x;\n}\n", "import { identity } from './identity';\nimport { UnaryFunction } from '../types';\n\nexport function pipe(): typeof identity;\nexport function pipe(fn1: UnaryFunction): UnaryFunction;\nexport function pipe(fn1: UnaryFunction, fn2: UnaryFunction): UnaryFunction;\nexport function pipe(fn1: UnaryFunction, fn2: UnaryFunction, fn3: UnaryFunction): UnaryFunction;\nexport function pipe(\n fn1: UnaryFunction,\n fn2: UnaryFunction,\n fn3: UnaryFunction,\n fn4: UnaryFunction\n): UnaryFunction;\nexport function pipe(\n fn1: UnaryFunction,\n fn2: UnaryFunction,\n fn3: UnaryFunction,\n fn4: UnaryFunction,\n fn5: UnaryFunction\n): UnaryFunction;\nexport function pipe(\n fn1: UnaryFunction,\n fn2: UnaryFunction,\n fn3: UnaryFunction,\n fn4: UnaryFunction,\n fn5: UnaryFunction,\n fn6: UnaryFunction\n): UnaryFunction;\nexport function pipe(\n fn1: UnaryFunction,\n fn2: UnaryFunction,\n fn3: UnaryFunction,\n fn4: UnaryFunction,\n fn5: UnaryFunction,\n fn6: UnaryFunction,\n fn7: UnaryFunction\n): UnaryFunction;\nexport function pipe(\n fn1: UnaryFunction,\n fn2: UnaryFunction,\n fn3: UnaryFunction,\n fn4: UnaryFunction,\n fn5: UnaryFunction,\n fn6: UnaryFunction,\n fn7: UnaryFunction,\n fn8: UnaryFunction\n): UnaryFunction;\nexport function pipe(\n fn1: UnaryFunction,\n fn2: UnaryFunction,\n fn3: UnaryFunction,\n fn4: UnaryFunction,\n fn5: UnaryFunction,\n fn6: UnaryFunction,\n fn7: UnaryFunction,\n fn8: UnaryFunction,\n fn9: UnaryFunction\n): UnaryFunction;\nexport function pipe(\n fn1: UnaryFunction,\n fn2: UnaryFunction,\n fn3: UnaryFunction,\n fn4: UnaryFunction,\n fn5: UnaryFunction,\n fn6: UnaryFunction,\n fn7: UnaryFunction,\n fn8: UnaryFunction,\n fn9: UnaryFunction,\n ...fns: UnaryFunction[]\n): UnaryFunction;\n\n/**\n * pipe() can be called on one or more functions, each of which can take one argument (\"UnaryFunction\")\n * and uses it to return a value.\n * It returns a function that takes one argument, passes it to the first UnaryFunction, and then\n * passes the result to the next one, passes that result to the next one, and so on. \n */\nexport function pipe(...fns: Array>): UnaryFunction {\n return pipeFromArray(fns);\n}\n\n/** @internal */\nexport function pipeFromArray(fns: Array>): UnaryFunction {\n if (fns.length === 0) {\n return identity as UnaryFunction;\n }\n\n if (fns.length === 1) {\n return fns[0];\n }\n\n return function piped(input: T): R {\n return fns.reduce((prev: any, fn: UnaryFunction) => fn(prev), input as any);\n };\n}\n", "import { Operator } from './Operator';\nimport { SafeSubscriber, Subscriber } from './Subscriber';\nimport { isSubscription, Subscription } from './Subscription';\nimport { TeardownLogic, OperatorFunction, Subscribable, Observer } from './types';\nimport { observable as Symbol_observable } from './symbol/observable';\nimport { pipeFromArray } from './util/pipe';\nimport { config } from './config';\nimport { isFunction } from './util/isFunction';\nimport { errorContext } from './util/errorContext';\n\n/**\n * A representation of any set of values over any amount of time. This is the most basic building block\n * of RxJS.\n */\nexport class Observable implements Subscribable {\n /**\n * @deprecated Internal implementation detail, do not use directly. Will be made internal in v8.\n */\n source: Observable | undefined;\n\n /**\n * @deprecated Internal implementation detail, do not use directly. Will be made internal in v8.\n */\n operator: Operator | undefined;\n\n /**\n * @param subscribe The function that is called when the Observable is\n * initially subscribed to. This function is given a Subscriber, to which new values\n * can be `next`ed, or an `error` method can be called to raise an error, or\n * `complete` can be called to notify of a successful completion.\n */\n constructor(subscribe?: (this: Observable, subscriber: Subscriber) => TeardownLogic) {\n if (subscribe) {\n this._subscribe = subscribe;\n }\n }\n\n // HACK: Since TypeScript inherits static properties too, we have to\n // fight against TypeScript here so Subject can have a different static create signature\n /**\n * Creates a new Observable by calling the Observable constructor\n * @param subscribe the subscriber function to be passed to the Observable constructor\n * @return A new observable.\n * @deprecated Use `new Observable()` instead. Will be removed in v8.\n */\n static create: (...args: any[]) => any = (subscribe?: (subscriber: Subscriber) => TeardownLogic) => {\n return new Observable(subscribe);\n };\n\n /**\n * Creates a new Observable, with this Observable instance as the source, and the passed\n * operator defined as the new observable's operator.\n * @param operator the operator defining the operation to take on the observable\n * @return A new observable with the Operator applied.\n * @deprecated Internal implementation detail, do not use directly. Will be made internal in v8.\n * If you have implemented an operator using `lift`, it is recommended that you create an\n * operator by simply returning `new Observable()` directly. See \"Creating new operators from\n * scratch\" section here: https://rxjs.dev/guide/operators\n */\n lift(operator?: Operator): Observable {\n const observable = new Observable();\n observable.source = this;\n observable.operator = operator;\n return observable;\n }\n\n subscribe(observerOrNext?: Partial> | ((value: T) => void)): Subscription;\n /** @deprecated Instead of passing separate callback arguments, use an observer argument. Signatures taking separate callback arguments will be removed in v8. Details: https://rxjs.dev/deprecations/subscribe-arguments */\n subscribe(next?: ((value: T) => void) | null, error?: ((error: any) => void) | null, complete?: (() => void) | null): Subscription;\n /**\n * Invokes an execution of an Observable and registers Observer handlers for notifications it will emit.\n *\n * Use it when you have all these Observables, but still nothing is happening.\n *\n * `subscribe` is not a regular operator, but a method that calls Observable's internal `subscribe` function. It\n * might be for example a function that you passed to Observable's constructor, but most of the time it is\n * a library implementation, which defines what will be emitted by an Observable, and when it be will emitted. This means\n * that calling `subscribe` is actually the moment when Observable starts its work, not when it is created, as it is often\n * the thought.\n *\n * Apart from starting the execution of an Observable, this method allows you to listen for values\n * that an Observable emits, as well as for when it completes or errors. You can achieve this in two\n * of the following ways.\n *\n * The first way is creating an object that implements {@link Observer} interface. It should have methods\n * defined by that interface, but note that it should be just a regular JavaScript object, which you can create\n * yourself in any way you want (ES6 class, classic function constructor, object literal etc.). In particular, do\n * not attempt to use any RxJS implementation details to create Observers - you don't need them. Remember also\n * that your object does not have to implement all methods. If you find yourself creating a method that doesn't\n * do anything, you can simply omit it. Note however, if the `error` method is not provided and an error happens,\n * it will be thrown asynchronously. Errors thrown asynchronously cannot be caught using `try`/`catch`. Instead,\n * use the {@link onUnhandledError} configuration option or use a runtime handler (like `window.onerror` or\n * `process.on('error)`) to be notified of unhandled errors. Because of this, it's recommended that you provide\n * an `error` method to avoid missing thrown errors.\n *\n * The second way is to give up on Observer object altogether and simply provide callback functions in place of its methods.\n * This means you can provide three functions as arguments to `subscribe`, where the first function is equivalent\n * of a `next` method, the second of an `error` method and the third of a `complete` method. Just as in case of an Observer,\n * if you do not need to listen for something, you can omit a function by passing `undefined` or `null`,\n * since `subscribe` recognizes these functions by where they were placed in function call. When it comes\n * to the `error` function, as with an Observer, if not provided, errors emitted by an Observable will be thrown asynchronously.\n *\n * You can, however, subscribe with no parameters at all. This may be the case where you're not interested in terminal events\n * and you also handled emissions internally by using operators (e.g. using `tap`).\n *\n * Whichever style of calling `subscribe` you use, in both cases it returns a Subscription object.\n * This object allows you to call `unsubscribe` on it, which in turn will stop the work that an Observable does and will clean\n * up all resources that an Observable used. Note that cancelling a subscription will not call `complete` callback\n * provided to `subscribe` function, which is reserved for a regular completion signal that comes from an Observable.\n *\n * Remember that callbacks provided to `subscribe` are not guaranteed to be called asynchronously.\n * It is an Observable itself that decides when these functions will be called. For example {@link of}\n * by default emits all its values synchronously. Always check documentation for how given Observable\n * will behave when subscribed and if its default behavior can be modified with a `scheduler`.\n *\n * #### Examples\n *\n * Subscribe with an {@link guide/observer Observer}\n *\n * ```ts\n * import { of } from 'rxjs';\n *\n * const sumObserver = {\n * sum: 0,\n * next(value) {\n * console.log('Adding: ' + value);\n * this.sum = this.sum + value;\n * },\n * error() {\n * // We actually could just remove this method,\n * // since we do not really care about errors right now.\n * },\n * complete() {\n * console.log('Sum equals: ' + this.sum);\n * }\n * };\n *\n * of(1, 2, 3) // Synchronously emits 1, 2, 3 and then completes.\n * .subscribe(sumObserver);\n *\n * // Logs:\n * // 'Adding: 1'\n * // 'Adding: 2'\n * // 'Adding: 3'\n * // 'Sum equals: 6'\n * ```\n *\n * Subscribe with functions ({@link deprecations/subscribe-arguments deprecated})\n *\n * ```ts\n * import { of } from 'rxjs'\n *\n * let sum = 0;\n *\n * of(1, 2, 3).subscribe(\n * value => {\n * console.log('Adding: ' + value);\n * sum = sum + value;\n * },\n * undefined,\n * () => console.log('Sum equals: ' + sum)\n * );\n *\n * // Logs:\n * // 'Adding: 1'\n * // 'Adding: 2'\n * // 'Adding: 3'\n * // 'Sum equals: 6'\n * ```\n *\n * Cancel a subscription\n *\n * ```ts\n * import { interval } from 'rxjs';\n *\n * const subscription = interval(1000).subscribe({\n * next(num) {\n * console.log(num)\n * },\n * complete() {\n * // Will not be called, even when cancelling subscription.\n * console.log('completed!');\n * }\n * });\n *\n * setTimeout(() => {\n * subscription.unsubscribe();\n * console.log('unsubscribed!');\n * }, 2500);\n *\n * // Logs:\n * // 0 after 1s\n * // 1 after 2s\n * // 'unsubscribed!' after 2.5s\n * ```\n *\n * @param observerOrNext Either an {@link Observer} with some or all callback methods,\n * or the `next` handler that is called for each value emitted from the subscribed Observable.\n * @param error A handler for a terminal event resulting from an error. If no error handler is provided,\n * the error will be thrown asynchronously as unhandled.\n * @param complete A handler for a terminal event resulting from successful completion.\n * @return A subscription reference to the registered handlers.\n */\n subscribe(\n observerOrNext?: Partial> | ((value: T) => void) | null,\n error?: ((error: any) => void) | null,\n complete?: (() => void) | null\n ): Subscription {\n const subscriber = isSubscriber(observerOrNext) ? observerOrNext : new SafeSubscriber(observerOrNext, error, complete);\n\n errorContext(() => {\n const { operator, source } = this;\n subscriber.add(\n operator\n ? // We're dealing with a subscription in the\n // operator chain to one of our lifted operators.\n operator.call(subscriber, source)\n : source\n ? // If `source` has a value, but `operator` does not, something that\n // had intimate knowledge of our API, like our `Subject`, must have\n // set it. We're going to just call `_subscribe` directly.\n this._subscribe(subscriber)\n : // In all other cases, we're likely wrapping a user-provided initializer\n // function, so we need to catch errors and handle them appropriately.\n this._trySubscribe(subscriber)\n );\n });\n\n return subscriber;\n }\n\n /** @internal */\n protected _trySubscribe(sink: Subscriber): TeardownLogic {\n try {\n return this._subscribe(sink);\n } catch (err) {\n // We don't need to return anything in this case,\n // because it's just going to try to `add()` to a subscription\n // above.\n sink.error(err);\n }\n }\n\n /**\n * Used as a NON-CANCELLABLE means of subscribing to an observable, for use with\n * APIs that expect promises, like `async/await`. You cannot unsubscribe from this.\n *\n * **WARNING**: Only use this with observables you *know* will complete. If the source\n * observable does not complete, you will end up with a promise that is hung up, and\n * potentially all of the state of an async function hanging out in memory. To avoid\n * this situation, look into adding something like {@link timeout}, {@link take},\n * {@link takeWhile}, or {@link takeUntil} amongst others.\n *\n * #### Example\n *\n * ```ts\n * import { interval, take } from 'rxjs';\n *\n * const source$ = interval(1000).pipe(take(4));\n *\n * async function getTotal() {\n * let total = 0;\n *\n * await source$.forEach(value => {\n * total += value;\n * console.log('observable -> ' + value);\n * });\n *\n * return total;\n * }\n *\n * getTotal().then(\n * total => console.log('Total: ' + total)\n * );\n *\n * // Expected:\n * // 'observable -> 0'\n * // 'observable -> 1'\n * // 'observable -> 2'\n * // 'observable -> 3'\n * // 'Total: 6'\n * ```\n *\n * @param next A handler for each value emitted by the observable.\n * @return A promise that either resolves on observable completion or\n * rejects with the handled error.\n */\n forEach(next: (value: T) => void): Promise;\n\n /**\n * @param next a handler for each value emitted by the observable\n * @param promiseCtor a constructor function used to instantiate the Promise\n * @return a promise that either resolves on observable completion or\n * rejects with the handled error\n * @deprecated Passing a Promise constructor will no longer be available\n * in upcoming versions of RxJS. This is because it adds weight to the library, for very\n * little benefit. If you need this functionality, it is recommended that you either\n * polyfill Promise, or you create an adapter to convert the returned native promise\n * to whatever promise implementation you wanted. Will be removed in v8.\n */\n forEach(next: (value: T) => void, promiseCtor: PromiseConstructorLike): Promise;\n\n forEach(next: (value: T) => void, promiseCtor?: PromiseConstructorLike): Promise {\n promiseCtor = getPromiseCtor(promiseCtor);\n\n return new promiseCtor((resolve, reject) => {\n const subscriber = new SafeSubscriber({\n next: (value) => {\n try {\n next(value);\n } catch (err) {\n reject(err);\n subscriber.unsubscribe();\n }\n },\n error: reject,\n complete: resolve,\n });\n this.subscribe(subscriber);\n }) as Promise;\n }\n\n /** @internal */\n protected _subscribe(subscriber: Subscriber): TeardownLogic {\n return this.source?.subscribe(subscriber);\n }\n\n /**\n * An interop point defined by the es7-observable spec https://github.com/zenparsing/es-observable\n * @return This instance of the observable.\n */\n [Symbol_observable]() {\n return this;\n }\n\n /* tslint:disable:max-line-length */\n pipe(): Observable;\n pipe(op1: OperatorFunction): Observable;\n pipe(op1: OperatorFunction, op2: OperatorFunction): Observable;\n pipe(op1: OperatorFunction, op2: OperatorFunction, op3: OperatorFunction): Observable;\n pipe(\n op1: OperatorFunction,\n op2: OperatorFunction,\n op3: OperatorFunction,\n op4: OperatorFunction\n ): Observable;\n pipe(\n op1: OperatorFunction,\n op2: OperatorFunction,\n op3: OperatorFunction,\n op4: OperatorFunction,\n op5: OperatorFunction\n ): Observable;\n pipe(\n op1: OperatorFunction,\n op2: OperatorFunction,\n op3: OperatorFunction,\n op4: OperatorFunction,\n op5: OperatorFunction,\n op6: OperatorFunction\n ): Observable;\n pipe(\n op1: OperatorFunction,\n op2: OperatorFunction,\n op3: OperatorFunction,\n op4: OperatorFunction,\n op5: OperatorFunction,\n op6: OperatorFunction,\n op7: OperatorFunction\n ): Observable;\n pipe(\n op1: OperatorFunction,\n op2: OperatorFunction,\n op3: OperatorFunction,\n op4: OperatorFunction,\n op5: OperatorFunction,\n op6: OperatorFunction,\n op7: OperatorFunction,\n op8: OperatorFunction\n ): Observable;\n pipe(\n op1: OperatorFunction,\n op2: OperatorFunction,\n op3: OperatorFunction,\n op4: OperatorFunction,\n op5: OperatorFunction,\n op6: OperatorFunction,\n op7: OperatorFunction,\n op8: OperatorFunction,\n op9: OperatorFunction\n ): Observable;\n pipe(\n op1: OperatorFunction,\n op2: OperatorFunction,\n op3: OperatorFunction,\n op4: OperatorFunction,\n op5: OperatorFunction,\n op6: OperatorFunction,\n op7: OperatorFunction,\n op8: OperatorFunction,\n op9: OperatorFunction,\n ...operations: OperatorFunction[]\n ): Observable;\n /* tslint:enable:max-line-length */\n\n /**\n * Used to stitch together functional operators into a chain.\n *\n * ## Example\n *\n * ```ts\n * import { interval, filter, map, scan } from 'rxjs';\n *\n * interval(1000)\n * .pipe(\n * filter(x => x % 2 === 0),\n * map(x => x + x),\n * scan((acc, x) => acc + x)\n * )\n * .subscribe(x => console.log(x));\n * ```\n *\n * @return The Observable result of all the operators having been called\n * in the order they were passed in.\n */\n pipe(...operations: OperatorFunction[]): Observable {\n return pipeFromArray(operations)(this);\n }\n\n /* tslint:disable:max-line-length */\n /** @deprecated Replaced with {@link firstValueFrom} and {@link lastValueFrom}. Will be removed in v8. Details: https://rxjs.dev/deprecations/to-promise */\n toPromise(): Promise;\n /** @deprecated Replaced with {@link firstValueFrom} and {@link lastValueFrom}. Will be removed in v8. Details: https://rxjs.dev/deprecations/to-promise */\n toPromise(PromiseCtor: typeof Promise): Promise;\n /** @deprecated Replaced with {@link firstValueFrom} and {@link lastValueFrom}. Will be removed in v8. Details: https://rxjs.dev/deprecations/to-promise */\n toPromise(PromiseCtor: PromiseConstructorLike): Promise;\n /* tslint:enable:max-line-length */\n\n /**\n * Subscribe to this Observable and get a Promise resolving on\n * `complete` with the last emission (if any).\n *\n * **WARNING**: Only use this with observables you *know* will complete. If the source\n * observable does not complete, you will end up with a promise that is hung up, and\n * potentially all of the state of an async function hanging out in memory. To avoid\n * this situation, look into adding something like {@link timeout}, {@link take},\n * {@link takeWhile}, or {@link takeUntil} amongst others.\n *\n * @param [promiseCtor] a constructor function used to instantiate\n * the Promise\n * @return A Promise that resolves with the last value emit, or\n * rejects on an error. If there were no emissions, Promise\n * resolves with undefined.\n * @deprecated Replaced with {@link firstValueFrom} and {@link lastValueFrom}. Will be removed in v8. Details: https://rxjs.dev/deprecations/to-promise\n */\n toPromise(promiseCtor?: PromiseConstructorLike): Promise {\n promiseCtor = getPromiseCtor(promiseCtor);\n\n return new promiseCtor((resolve, reject) => {\n let value: T | undefined;\n this.subscribe(\n (x: T) => (value = x),\n (err: any) => reject(err),\n () => resolve(value)\n );\n }) as Promise;\n }\n}\n\n/**\n * Decides between a passed promise constructor from consuming code,\n * A default configured promise constructor, and the native promise\n * constructor and returns it. If nothing can be found, it will throw\n * an error.\n * @param promiseCtor The optional promise constructor to passed by consuming code\n */\nfunction getPromiseCtor(promiseCtor: PromiseConstructorLike | undefined) {\n return promiseCtor ?? config.Promise ?? Promise;\n}\n\nfunction isObserver(value: any): value is Observer {\n return value && isFunction(value.next) && isFunction(value.error) && isFunction(value.complete);\n}\n\nfunction isSubscriber(value: any): value is Subscriber {\n return (value && value instanceof Subscriber) || (isObserver(value) && isSubscription(value));\n}\n", "import { Observable } from '../Observable';\nimport { Subscriber } from '../Subscriber';\nimport { OperatorFunction } from '../types';\nimport { isFunction } from './isFunction';\n\n/**\n * Used to determine if an object is an Observable with a lift function.\n */\nexport function hasLift(source: any): source is { lift: InstanceType['lift'] } {\n return isFunction(source?.lift);\n}\n\n/**\n * Creates an `OperatorFunction`. Used to define operators throughout the library in a concise way.\n * @param init The logic to connect the liftedSource to the subscriber at the moment of subscription.\n */\nexport function operate(\n init: (liftedSource: Observable, subscriber: Subscriber) => (() => void) | void\n): OperatorFunction {\n return (source: Observable) => {\n if (hasLift(source)) {\n return source.lift(function (this: Subscriber, liftedSource: Observable) {\n try {\n return init(liftedSource, this);\n } catch (err) {\n this.error(err);\n }\n });\n }\n throw new TypeError('Unable to lift unknown Observable type');\n };\n}\n", "import { Subscriber } from '../Subscriber';\n\n/**\n * Creates an instance of an `OperatorSubscriber`.\n * @param destination The downstream subscriber.\n * @param onNext Handles next values, only called if this subscriber is not stopped or closed. Any\n * error that occurs in this function is caught and sent to the `error` method of this subscriber.\n * @param onError Handles errors from the subscription, any errors that occur in this handler are caught\n * and send to the `destination` error handler.\n * @param onComplete Handles completion notification from the subscription. Any errors that occur in\n * this handler are sent to the `destination` error handler.\n * @param onFinalize Additional teardown logic here. This will only be called on teardown if the\n * subscriber itself is not already closed. This is called after all other teardown logic is executed.\n */\nexport function createOperatorSubscriber(\n destination: Subscriber,\n onNext?: (value: T) => void,\n onComplete?: () => void,\n onError?: (err: any) => void,\n onFinalize?: () => void\n): Subscriber {\n return new OperatorSubscriber(destination, onNext, onComplete, onError, onFinalize);\n}\n\n/**\n * A generic helper for allowing operators to be created with a Subscriber and\n * use closures to capture necessary state from the operator function itself.\n */\nexport class OperatorSubscriber extends Subscriber {\n /**\n * Creates an instance of an `OperatorSubscriber`.\n * @param destination The downstream subscriber.\n * @param onNext Handles next values, only called if this subscriber is not stopped or closed. Any\n * error that occurs in this function is caught and sent to the `error` method of this subscriber.\n * @param onError Handles errors from the subscription, any errors that occur in this handler are caught\n * and send to the `destination` error handler.\n * @param onComplete Handles completion notification from the subscription. Any errors that occur in\n * this handler are sent to the `destination` error handler.\n * @param onFinalize Additional finalization logic here. This will only be called on finalization if the\n * subscriber itself is not already closed. This is called after all other finalization logic is executed.\n * @param shouldUnsubscribe An optional check to see if an unsubscribe call should truly unsubscribe.\n * NOTE: This currently **ONLY** exists to support the strange behavior of {@link groupBy}, where unsubscription\n * to the resulting observable does not actually disconnect from the source if there are active subscriptions\n * to any grouped observable. (DO NOT EXPOSE OR USE EXTERNALLY!!!)\n */\n constructor(\n destination: Subscriber,\n onNext?: (value: T) => void,\n onComplete?: () => void,\n onError?: (err: any) => void,\n private onFinalize?: () => void,\n private shouldUnsubscribe?: () => boolean\n ) {\n // It's important - for performance reasons - that all of this class's\n // members are initialized and that they are always initialized in the same\n // order. This will ensure that all OperatorSubscriber instances have the\n // same hidden class in V8. This, in turn, will help keep the number of\n // hidden classes involved in property accesses within the base class as\n // low as possible. If the number of hidden classes involved exceeds four,\n // the property accesses will become megamorphic and performance penalties\n // will be incurred - i.e. inline caches won't be used.\n //\n // The reasons for ensuring all instances have the same hidden class are\n // further discussed in this blog post from Benedikt Meurer:\n // https://benediktmeurer.de/2018/03/23/impact-of-polymorphism-on-component-based-frameworks-like-react/\n super(destination);\n this._next = onNext\n ? function (this: OperatorSubscriber, value: T) {\n try {\n onNext(value);\n } catch (err) {\n destination.error(err);\n }\n }\n : super._next;\n this._error = onError\n ? function (this: OperatorSubscriber, err: any) {\n try {\n onError(err);\n } catch (err) {\n // Send any errors that occur down stream.\n destination.error(err);\n } finally {\n // Ensure finalization.\n this.unsubscribe();\n }\n }\n : super._error;\n this._complete = onComplete\n ? function (this: OperatorSubscriber) {\n try {\n onComplete();\n } catch (err) {\n // Send any errors that occur down stream.\n destination.error(err);\n } finally {\n // Ensure finalization.\n this.unsubscribe();\n }\n }\n : super._complete;\n }\n\n unsubscribe() {\n if (!this.shouldUnsubscribe || this.shouldUnsubscribe()) {\n const { closed } = this;\n super.unsubscribe();\n // Execute additional teardown if we have any and we didn't already do so.\n !closed && this.onFinalize?.();\n }\n }\n}\n", "import { Subscription } from '../Subscription';\n\ninterface AnimationFrameProvider {\n schedule(callback: FrameRequestCallback): Subscription;\n requestAnimationFrame: typeof requestAnimationFrame;\n cancelAnimationFrame: typeof cancelAnimationFrame;\n delegate:\n | {\n requestAnimationFrame: typeof requestAnimationFrame;\n cancelAnimationFrame: typeof cancelAnimationFrame;\n }\n | undefined;\n}\n\nexport const animationFrameProvider: AnimationFrameProvider = {\n // When accessing the delegate, use the variable rather than `this` so that\n // the functions can be called without being bound to the provider.\n schedule(callback) {\n let request = requestAnimationFrame;\n let cancel: typeof cancelAnimationFrame | undefined = cancelAnimationFrame;\n const { delegate } = animationFrameProvider;\n if (delegate) {\n request = delegate.requestAnimationFrame;\n cancel = delegate.cancelAnimationFrame;\n }\n const handle = request((timestamp) => {\n // Clear the cancel function. The request has been fulfilled, so\n // attempting to cancel the request upon unsubscription would be\n // pointless.\n cancel = undefined;\n callback(timestamp);\n });\n return new Subscription(() => cancel?.(handle));\n },\n requestAnimationFrame(...args) {\n const { delegate } = animationFrameProvider;\n return (delegate?.requestAnimationFrame || requestAnimationFrame)(...args);\n },\n cancelAnimationFrame(...args) {\n const { delegate } = animationFrameProvider;\n return (delegate?.cancelAnimationFrame || cancelAnimationFrame)(...args);\n },\n delegate: undefined,\n};\n", "import { createErrorClass } from './createErrorClass';\n\nexport interface ObjectUnsubscribedError extends Error {}\n\nexport interface ObjectUnsubscribedErrorCtor {\n /**\n * @deprecated Internal implementation detail. Do not construct error instances.\n * Cannot be tagged as internal: https://github.com/ReactiveX/rxjs/issues/6269\n */\n new (): ObjectUnsubscribedError;\n}\n\n/**\n * An error thrown when an action is invalid because the object has been\n * unsubscribed.\n *\n * @see {@link Subject}\n * @see {@link BehaviorSubject}\n *\n * @class ObjectUnsubscribedError\n */\nexport const ObjectUnsubscribedError: ObjectUnsubscribedErrorCtor = createErrorClass(\n (_super) =>\n function ObjectUnsubscribedErrorImpl(this: any) {\n _super(this);\n this.name = 'ObjectUnsubscribedError';\n this.message = 'object unsubscribed';\n }\n);\n", "import { Operator } from './Operator';\nimport { Observable } from './Observable';\nimport { Subscriber } from './Subscriber';\nimport { Subscription, EMPTY_SUBSCRIPTION } from './Subscription';\nimport { Observer, SubscriptionLike, TeardownLogic } from './types';\nimport { ObjectUnsubscribedError } from './util/ObjectUnsubscribedError';\nimport { arrRemove } from './util/arrRemove';\nimport { errorContext } from './util/errorContext';\n\n/**\n * A Subject is a special type of Observable that allows values to be\n * multicasted to many Observers. Subjects are like EventEmitters.\n *\n * Every Subject is an Observable and an Observer. You can subscribe to a\n * Subject, and you can call next to feed values as well as error and complete.\n */\nexport class Subject extends Observable implements SubscriptionLike {\n closed = false;\n\n private currentObservers: Observer[] | null = null;\n\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n observers: Observer[] = [];\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n isStopped = false;\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n hasError = false;\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n thrownError: any = null;\n\n /**\n * Creates a \"subject\" by basically gluing an observer to an observable.\n *\n * @deprecated Recommended you do not use. Will be removed at some point in the future. Plans for replacement still under discussion.\n */\n static create: (...args: any[]) => any = (destination: Observer, source: Observable): AnonymousSubject => {\n return new AnonymousSubject(destination, source);\n };\n\n constructor() {\n // NOTE: This must be here to obscure Observable's constructor.\n super();\n }\n\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n lift(operator: Operator): Observable {\n const subject = new AnonymousSubject(this, this);\n subject.operator = operator as any;\n return subject as any;\n }\n\n /** @internal */\n protected _throwIfClosed() {\n if (this.closed) {\n throw new ObjectUnsubscribedError();\n }\n }\n\n next(value: T) {\n errorContext(() => {\n this._throwIfClosed();\n if (!this.isStopped) {\n if (!this.currentObservers) {\n this.currentObservers = Array.from(this.observers);\n }\n for (const observer of this.currentObservers) {\n observer.next(value);\n }\n }\n });\n }\n\n error(err: any) {\n errorContext(() => {\n this._throwIfClosed();\n if (!this.isStopped) {\n this.hasError = this.isStopped = true;\n this.thrownError = err;\n const { observers } = this;\n while (observers.length) {\n observers.shift()!.error(err);\n }\n }\n });\n }\n\n complete() {\n errorContext(() => {\n this._throwIfClosed();\n if (!this.isStopped) {\n this.isStopped = true;\n const { observers } = this;\n while (observers.length) {\n observers.shift()!.complete();\n }\n }\n });\n }\n\n unsubscribe() {\n this.isStopped = this.closed = true;\n this.observers = this.currentObservers = null!;\n }\n\n get observed() {\n return this.observers?.length > 0;\n }\n\n /** @internal */\n protected _trySubscribe(subscriber: Subscriber): TeardownLogic {\n this._throwIfClosed();\n return super._trySubscribe(subscriber);\n }\n\n /** @internal */\n protected _subscribe(subscriber: Subscriber): Subscription {\n this._throwIfClosed();\n this._checkFinalizedStatuses(subscriber);\n return this._innerSubscribe(subscriber);\n }\n\n /** @internal */\n protected _innerSubscribe(subscriber: Subscriber) {\n const { hasError, isStopped, observers } = this;\n if (hasError || isStopped) {\n return EMPTY_SUBSCRIPTION;\n }\n this.currentObservers = null;\n observers.push(subscriber);\n return new Subscription(() => {\n this.currentObservers = null;\n arrRemove(observers, subscriber);\n });\n }\n\n /** @internal */\n protected _checkFinalizedStatuses(subscriber: Subscriber) {\n const { hasError, thrownError, isStopped } = this;\n if (hasError) {\n subscriber.error(thrownError);\n } else if (isStopped) {\n subscriber.complete();\n }\n }\n\n /**\n * Creates a new Observable with this Subject as the source. You can do this\n * to create custom Observer-side logic of the Subject and conceal it from\n * code that uses the Observable.\n * @return Observable that this Subject casts to.\n */\n asObservable(): Observable {\n const observable: any = new Observable();\n observable.source = this;\n return observable;\n }\n}\n\nexport class AnonymousSubject extends Subject {\n constructor(\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n public destination?: Observer,\n source?: Observable\n ) {\n super();\n this.source = source;\n }\n\n next(value: T) {\n this.destination?.next?.(value);\n }\n\n error(err: any) {\n this.destination?.error?.(err);\n }\n\n complete() {\n this.destination?.complete?.();\n }\n\n /** @internal */\n protected _subscribe(subscriber: Subscriber): Subscription {\n return this.source?.subscribe(subscriber) ?? EMPTY_SUBSCRIPTION;\n }\n}\n", "import { Subject } from './Subject';\nimport { Subscriber } from './Subscriber';\nimport { Subscription } from './Subscription';\n\n/**\n * A variant of Subject that requires an initial value and emits its current\n * value whenever it is subscribed to.\n */\nexport class BehaviorSubject extends Subject {\n constructor(private _value: T) {\n super();\n }\n\n get value(): T {\n return this.getValue();\n }\n\n /** @internal */\n protected _subscribe(subscriber: Subscriber): Subscription {\n const subscription = super._subscribe(subscriber);\n !subscription.closed && subscriber.next(this._value);\n return subscription;\n }\n\n getValue(): T {\n const { hasError, thrownError, _value } = this;\n if (hasError) {\n throw thrownError;\n }\n this._throwIfClosed();\n return _value;\n }\n\n next(value: T): void {\n super.next((this._value = value));\n }\n}\n", "import { TimestampProvider } from '../types';\n\ninterface DateTimestampProvider extends TimestampProvider {\n delegate: TimestampProvider | undefined;\n}\n\nexport const dateTimestampProvider: DateTimestampProvider = {\n now() {\n // Use the variable rather than `this` so that the function can be called\n // without being bound to the provider.\n return (dateTimestampProvider.delegate || Date).now();\n },\n delegate: undefined,\n};\n", "import { Subject } from './Subject';\nimport { TimestampProvider } from './types';\nimport { Subscriber } from './Subscriber';\nimport { Subscription } from './Subscription';\nimport { dateTimestampProvider } from './scheduler/dateTimestampProvider';\n\n/**\n * A variant of {@link Subject} that \"replays\" old values to new subscribers by emitting them when they first subscribe.\n *\n * `ReplaySubject` has an internal buffer that will store a specified number of values that it has observed. Like `Subject`,\n * `ReplaySubject` \"observes\" values by having them passed to its `next` method. When it observes a value, it will store that\n * value for a time determined by the configuration of the `ReplaySubject`, as passed to its constructor.\n *\n * When a new subscriber subscribes to the `ReplaySubject` instance, it will synchronously emit all values in its buffer in\n * a First-In-First-Out (FIFO) manner. The `ReplaySubject` will also complete, if it has observed completion; and it will\n * error if it has observed an error.\n *\n * There are two main configuration items to be concerned with:\n *\n * 1. `bufferSize` - This will determine how many items are stored in the buffer, defaults to infinite.\n * 2. `windowTime` - The amount of time to hold a value in the buffer before removing it from the buffer.\n *\n * Both configurations may exist simultaneously. So if you would like to buffer a maximum of 3 values, as long as the values\n * are less than 2 seconds old, you could do so with a `new ReplaySubject(3, 2000)`.\n *\n * ### Differences with BehaviorSubject\n *\n * `BehaviorSubject` is similar to `new ReplaySubject(1)`, with a couple of exceptions:\n *\n * 1. `BehaviorSubject` comes \"primed\" with a single value upon construction.\n * 2. `ReplaySubject` will replay values, even after observing an error, where `BehaviorSubject` will not.\n *\n * @see {@link Subject}\n * @see {@link BehaviorSubject}\n * @see {@link shareReplay}\n */\nexport class ReplaySubject extends Subject {\n private _buffer: (T | number)[] = [];\n private _infiniteTimeWindow = true;\n\n /**\n * @param _bufferSize The size of the buffer to replay on subscription\n * @param _windowTime The amount of time the buffered items will stay buffered\n * @param _timestampProvider An object with a `now()` method that provides the current timestamp. This is used to\n * calculate the amount of time something has been buffered.\n */\n constructor(\n private _bufferSize = Infinity,\n private _windowTime = Infinity,\n private _timestampProvider: TimestampProvider = dateTimestampProvider\n ) {\n super();\n this._infiniteTimeWindow = _windowTime === Infinity;\n this._bufferSize = Math.max(1, _bufferSize);\n this._windowTime = Math.max(1, _windowTime);\n }\n\n next(value: T): void {\n const { isStopped, _buffer, _infiniteTimeWindow, _timestampProvider, _windowTime } = this;\n if (!isStopped) {\n _buffer.push(value);\n !_infiniteTimeWindow && _buffer.push(_timestampProvider.now() + _windowTime);\n }\n this._trimBuffer();\n super.next(value);\n }\n\n /** @internal */\n protected _subscribe(subscriber: Subscriber): Subscription {\n this._throwIfClosed();\n this._trimBuffer();\n\n const subscription = this._innerSubscribe(subscriber);\n\n const { _infiniteTimeWindow, _buffer } = this;\n // We use a copy here, so reentrant code does not mutate our array while we're\n // emitting it to a new subscriber.\n const copy = _buffer.slice();\n for (let i = 0; i < copy.length && !subscriber.closed; i += _infiniteTimeWindow ? 1 : 2) {\n subscriber.next(copy[i] as T);\n }\n\n this._checkFinalizedStatuses(subscriber);\n\n return subscription;\n }\n\n private _trimBuffer() {\n const { _bufferSize, _timestampProvider, _buffer, _infiniteTimeWindow } = this;\n // If we don't have an infinite buffer size, and we're over the length,\n // use splice to truncate the old buffer values off. Note that we have to\n // double the size for instances where we're not using an infinite time window\n // because we're storing the values and the timestamps in the same array.\n const adjustedBufferSize = (_infiniteTimeWindow ? 1 : 2) * _bufferSize;\n _bufferSize < Infinity && adjustedBufferSize < _buffer.length && _buffer.splice(0, _buffer.length - adjustedBufferSize);\n\n // Now, if we're not in an infinite time window, remove all values where the time is\n // older than what is allowed.\n if (!_infiniteTimeWindow) {\n const now = _timestampProvider.now();\n let last = 0;\n // Search the array for the first timestamp that isn't expired and\n // truncate the buffer up to that point.\n for (let i = 1; i < _buffer.length && (_buffer[i] as number) <= now; i += 2) {\n last = i;\n }\n last && _buffer.splice(0, last + 1);\n }\n }\n}\n", "import { Scheduler } from '../Scheduler';\nimport { Subscription } from '../Subscription';\nimport { SchedulerAction } from '../types';\n\n/**\n * A unit of work to be executed in a `scheduler`. An action is typically\n * created from within a {@link SchedulerLike} and an RxJS user does not need to concern\n * themselves about creating and manipulating an Action.\n *\n * ```ts\n * class Action extends Subscription {\n * new (scheduler: Scheduler, work: (state?: T) => void);\n * schedule(state?: T, delay: number = 0): Subscription;\n * }\n * ```\n */\nexport class Action extends Subscription {\n constructor(scheduler: Scheduler, work: (this: SchedulerAction, state?: T) => void) {\n super();\n }\n /**\n * Schedules this action on its parent {@link SchedulerLike} for execution. May be passed\n * some context object, `state`. May happen at some point in the future,\n * according to the `delay` parameter, if specified.\n * @param state Some contextual data that the `work` function uses when called by the\n * Scheduler.\n * @param delay Time to wait before executing the work, where the time unit is implicit\n * and defined by the Scheduler.\n * @return A subscription in order to be able to unsubscribe the scheduled work.\n */\n public schedule(state?: T, delay: number = 0): Subscription {\n return this;\n }\n}\n", "import type { TimerHandle } from './timerHandle';\ntype SetIntervalFunction = (handler: () => void, timeout?: number, ...args: any[]) => TimerHandle;\ntype ClearIntervalFunction = (handle: TimerHandle) => void;\n\ninterface IntervalProvider {\n setInterval: SetIntervalFunction;\n clearInterval: ClearIntervalFunction;\n delegate:\n | {\n setInterval: SetIntervalFunction;\n clearInterval: ClearIntervalFunction;\n }\n | undefined;\n}\n\nexport const intervalProvider: IntervalProvider = {\n // When accessing the delegate, use the variable rather than `this` so that\n // the functions can be called without being bound to the provider.\n setInterval(handler: () => void, timeout?: number, ...args) {\n const { delegate } = intervalProvider;\n if (delegate?.setInterval) {\n return delegate.setInterval(handler, timeout, ...args);\n }\n return setInterval(handler, timeout, ...args);\n },\n clearInterval(handle) {\n const { delegate } = intervalProvider;\n return (delegate?.clearInterval || clearInterval)(handle as any);\n },\n delegate: undefined,\n};\n", "import { Action } from './Action';\nimport { SchedulerAction } from '../types';\nimport { Subscription } from '../Subscription';\nimport { AsyncScheduler } from './AsyncScheduler';\nimport { intervalProvider } from './intervalProvider';\nimport { arrRemove } from '../util/arrRemove';\nimport { TimerHandle } from './timerHandle';\n\nexport class AsyncAction extends Action {\n public id: TimerHandle | undefined;\n public state?: T;\n // @ts-ignore: Property has no initializer and is not definitely assigned\n public delay: number;\n protected pending: boolean = false;\n\n constructor(protected scheduler: AsyncScheduler, protected work: (this: SchedulerAction, state?: T) => void) {\n super(scheduler, work);\n }\n\n public schedule(state?: T, delay: number = 0): Subscription {\n if (this.closed) {\n return this;\n }\n\n // Always replace the current state with the new state.\n this.state = state;\n\n const id = this.id;\n const scheduler = this.scheduler;\n\n //\n // Important implementation note:\n //\n // Actions only execute once by default, unless rescheduled from within the\n // scheduled callback. This allows us to implement single and repeat\n // actions via the same code path, without adding API surface area, as well\n // as mimic traditional recursion but across asynchronous boundaries.\n //\n // However, JS runtimes and timers distinguish between intervals achieved by\n // serial `setTimeout` calls vs. a single `setInterval` call. An interval of\n // serial `setTimeout` calls can be individually delayed, which delays\n // scheduling the next `setTimeout`, and so on. `setInterval` attempts to\n // guarantee the interval callback will be invoked more precisely to the\n // interval period, regardless of load.\n //\n // Therefore, we use `setInterval` to schedule single and repeat actions.\n // If the action reschedules itself with the same delay, the interval is not\n // canceled. If the action doesn't reschedule, or reschedules with a\n // different delay, the interval will be canceled after scheduled callback\n // execution.\n //\n if (id != null) {\n this.id = this.recycleAsyncId(scheduler, id, delay);\n }\n\n // Set the pending flag indicating that this action has been scheduled, or\n // has recursively rescheduled itself.\n this.pending = true;\n\n this.delay = delay;\n // If this action has already an async Id, don't request a new one.\n this.id = this.id ?? this.requestAsyncId(scheduler, this.id, delay);\n\n return this;\n }\n\n protected requestAsyncId(scheduler: AsyncScheduler, _id?: TimerHandle, delay: number = 0): TimerHandle {\n return intervalProvider.setInterval(scheduler.flush.bind(scheduler, this), delay);\n }\n\n protected recycleAsyncId(_scheduler: AsyncScheduler, id?: TimerHandle, delay: number | null = 0): TimerHandle | undefined {\n // If this action is rescheduled with the same delay time, don't clear the interval id.\n if (delay != null && this.delay === delay && this.pending === false) {\n return id;\n }\n // Otherwise, if the action's delay time is different from the current delay,\n // or the action has been rescheduled before it's executed, clear the interval id\n if (id != null) {\n intervalProvider.clearInterval(id);\n }\n\n return undefined;\n }\n\n /**\n * Immediately executes this action and the `work` it contains.\n */\n public execute(state: T, delay: number): any {\n if (this.closed) {\n return new Error('executing a cancelled action');\n }\n\n this.pending = false;\n const error = this._execute(state, delay);\n if (error) {\n return error;\n } else if (this.pending === false && this.id != null) {\n // Dequeue if the action didn't reschedule itself. Don't call\n // unsubscribe(), because the action could reschedule later.\n // For example:\n // ```\n // scheduler.schedule(function doWork(counter) {\n // /* ... I'm a busy worker bee ... */\n // var originalAction = this;\n // /* wait 100ms before rescheduling the action */\n // setTimeout(function () {\n // originalAction.schedule(counter + 1);\n // }, 100);\n // }, 1000);\n // ```\n this.id = this.recycleAsyncId(this.scheduler, this.id, null);\n }\n }\n\n protected _execute(state: T, _delay: number): any {\n let errored: boolean = false;\n let errorValue: any;\n try {\n this.work(state);\n } catch (e) {\n errored = true;\n // HACK: Since code elsewhere is relying on the \"truthiness\" of the\n // return here, we can't have it return \"\" or 0 or false.\n // TODO: Clean this up when we refactor schedulers mid-version-8 or so.\n errorValue = e ? e : new Error('Scheduled action threw falsy error');\n }\n if (errored) {\n this.unsubscribe();\n return errorValue;\n }\n }\n\n unsubscribe() {\n if (!this.closed) {\n const { id, scheduler } = this;\n const { actions } = scheduler;\n\n this.work = this.state = this.scheduler = null!;\n this.pending = false;\n\n arrRemove(actions, this);\n if (id != null) {\n this.id = this.recycleAsyncId(scheduler, id, null);\n }\n\n this.delay = null!;\n super.unsubscribe();\n }\n }\n}\n", "import { Action } from './scheduler/Action';\nimport { Subscription } from './Subscription';\nimport { SchedulerLike, SchedulerAction } from './types';\nimport { dateTimestampProvider } from './scheduler/dateTimestampProvider';\n\n/**\n * An execution context and a data structure to order tasks and schedule their\n * execution. Provides a notion of (potentially virtual) time, through the\n * `now()` getter method.\n *\n * Each unit of work in a Scheduler is called an `Action`.\n *\n * ```ts\n * class Scheduler {\n * now(): number;\n * schedule(work, delay?, state?): Subscription;\n * }\n * ```\n *\n * @deprecated Scheduler is an internal implementation detail of RxJS, and\n * should not be used directly. Rather, create your own class and implement\n * {@link SchedulerLike}. Will be made internal in v8.\n */\nexport class Scheduler implements SchedulerLike {\n public static now: () => number = dateTimestampProvider.now;\n\n constructor(private schedulerActionCtor: typeof Action, now: () => number = Scheduler.now) {\n this.now = now;\n }\n\n /**\n * A getter method that returns a number representing the current time\n * (at the time this function was called) according to the scheduler's own\n * internal clock.\n * @return A number that represents the current time. May or may not\n * have a relation to wall-clock time. May or may not refer to a time unit\n * (e.g. milliseconds).\n */\n public now: () => number;\n\n /**\n * Schedules a function, `work`, for execution. May happen at some point in\n * the future, according to the `delay` parameter, if specified. May be passed\n * some context object, `state`, which will be passed to the `work` function.\n *\n * The given arguments will be processed an stored as an Action object in a\n * queue of actions.\n *\n * @param work A function representing a task, or some unit of work to be\n * executed by the Scheduler.\n * @param delay Time to wait before executing the work, where the time unit is\n * implicit and defined by the Scheduler itself.\n * @param state Some contextual data that the `work` function uses when called\n * by the Scheduler.\n * @return A subscription in order to be able to unsubscribe the scheduled work.\n */\n public schedule(work: (this: SchedulerAction, state?: T) => void, delay: number = 0, state?: T): Subscription {\n return new this.schedulerActionCtor(this, work).schedule(state, delay);\n }\n}\n", "import { Scheduler } from '../Scheduler';\nimport { Action } from './Action';\nimport { AsyncAction } from './AsyncAction';\nimport { TimerHandle } from './timerHandle';\n\nexport class AsyncScheduler extends Scheduler {\n public actions: Array> = [];\n /**\n * A flag to indicate whether the Scheduler is currently executing a batch of\n * queued actions.\n * @internal\n */\n public _active: boolean = false;\n /**\n * An internal ID used to track the latest asynchronous task such as those\n * coming from `setTimeout`, `setInterval`, `requestAnimationFrame`, and\n * others.\n * @internal\n */\n public _scheduled: TimerHandle | undefined;\n\n constructor(SchedulerAction: typeof Action, now: () => number = Scheduler.now) {\n super(SchedulerAction, now);\n }\n\n public flush(action: AsyncAction): void {\n const { actions } = this;\n\n if (this._active) {\n actions.push(action);\n return;\n }\n\n let error: any;\n this._active = true;\n\n do {\n if ((error = action.execute(action.state, action.delay))) {\n break;\n }\n } while ((action = actions.shift()!)); // exhaust the scheduler queue\n\n this._active = false;\n\n if (error) {\n while ((action = actions.shift()!)) {\n action.unsubscribe();\n }\n throw error;\n }\n }\n}\n", "import { AsyncAction } from './AsyncAction';\nimport { AsyncScheduler } from './AsyncScheduler';\n\n/**\n *\n * Async Scheduler\n *\n * Schedule task as if you used setTimeout(task, duration)\n *\n * `async` scheduler schedules tasks asynchronously, by putting them on the JavaScript\n * event loop queue. It is best used to delay tasks in time or to schedule tasks repeating\n * in intervals.\n *\n * If you just want to \"defer\" task, that is to perform it right after currently\n * executing synchronous code ends (commonly achieved by `setTimeout(deferredTask, 0)`),\n * better choice will be the {@link asapScheduler} scheduler.\n *\n * ## Examples\n * Use async scheduler to delay task\n * ```ts\n * import { asyncScheduler } from 'rxjs';\n *\n * const task = () => console.log('it works!');\n *\n * asyncScheduler.schedule(task, 2000);\n *\n * // After 2 seconds logs:\n * // \"it works!\"\n * ```\n *\n * Use async scheduler to repeat task in intervals\n * ```ts\n * import { asyncScheduler } from 'rxjs';\n *\n * function task(state) {\n * console.log(state);\n * this.schedule(state + 1, 1000); // `this` references currently executing Action,\n * // which we reschedule with new state and delay\n * }\n *\n * asyncScheduler.schedule(task, 3000, 0);\n *\n * // Logs:\n * // 0 after 3s\n * // 1 after 4s\n * // 2 after 5s\n * // 3 after 6s\n * ```\n */\n\nexport const asyncScheduler = new AsyncScheduler(AsyncAction);\n\n/**\n * @deprecated Renamed to {@link asyncScheduler}. Will be removed in v8.\n */\nexport const async = asyncScheduler;\n", "import { AsyncAction } from './AsyncAction';\nimport { Subscription } from '../Subscription';\nimport { QueueScheduler } from './QueueScheduler';\nimport { SchedulerAction } from '../types';\nimport { TimerHandle } from './timerHandle';\n\nexport class QueueAction extends AsyncAction {\n constructor(protected scheduler: QueueScheduler, protected work: (this: SchedulerAction, state?: T) => void) {\n super(scheduler, work);\n }\n\n public schedule(state?: T, delay: number = 0): Subscription {\n if (delay > 0) {\n return super.schedule(state, delay);\n }\n this.delay = delay;\n this.state = state;\n this.scheduler.flush(this);\n return this;\n }\n\n public execute(state: T, delay: number): any {\n return delay > 0 || this.closed ? super.execute(state, delay) : this._execute(state, delay);\n }\n\n protected requestAsyncId(scheduler: QueueScheduler, id?: TimerHandle, delay: number = 0): TimerHandle {\n // If delay exists and is greater than 0, or if the delay is null (the\n // action wasn't rescheduled) but was originally scheduled as an async\n // action, then recycle as an async action.\n\n if ((delay != null && delay > 0) || (delay == null && this.delay > 0)) {\n return super.requestAsyncId(scheduler, id, delay);\n }\n\n // Otherwise flush the scheduler starting with this action.\n scheduler.flush(this);\n\n // HACK: In the past, this was returning `void`. However, `void` isn't a valid\n // `TimerHandle`, and generally the return value here isn't really used. So the\n // compromise is to return `0` which is both \"falsy\" and a valid `TimerHandle`,\n // as opposed to refactoring every other instanceo of `requestAsyncId`.\n return 0;\n }\n}\n", "import { AsyncScheduler } from './AsyncScheduler';\n\nexport class QueueScheduler extends AsyncScheduler {\n}\n", "import { QueueAction } from './QueueAction';\nimport { QueueScheduler } from './QueueScheduler';\n\n/**\n *\n * Queue Scheduler\n *\n * Put every next task on a queue, instead of executing it immediately\n *\n * `queue` scheduler, when used with delay, behaves the same as {@link asyncScheduler} scheduler.\n *\n * When used without delay, it schedules given task synchronously - executes it right when\n * it is scheduled. However when called recursively, that is when inside the scheduled task,\n * another task is scheduled with queue scheduler, instead of executing immediately as well,\n * that task will be put on a queue and wait for current one to finish.\n *\n * This means that when you execute task with `queue` scheduler, you are sure it will end\n * before any other task scheduled with that scheduler will start.\n *\n * ## Examples\n * Schedule recursively first, then do something\n * ```ts\n * import { queueScheduler } from 'rxjs';\n *\n * queueScheduler.schedule(() => {\n * queueScheduler.schedule(() => console.log('second')); // will not happen now, but will be put on a queue\n *\n * console.log('first');\n * });\n *\n * // Logs:\n * // \"first\"\n * // \"second\"\n * ```\n *\n * Reschedule itself recursively\n * ```ts\n * import { queueScheduler } from 'rxjs';\n *\n * queueScheduler.schedule(function(state) {\n * if (state !== 0) {\n * console.log('before', state);\n * this.schedule(state - 1); // `this` references currently executing Action,\n * // which we reschedule with new state\n * console.log('after', state);\n * }\n * }, 0, 3);\n *\n * // In scheduler that runs recursively, you would expect:\n * // \"before\", 3\n * // \"before\", 2\n * // \"before\", 1\n * // \"after\", 1\n * // \"after\", 2\n * // \"after\", 3\n *\n * // But with queue it logs:\n * // \"before\", 3\n * // \"after\", 3\n * // \"before\", 2\n * // \"after\", 2\n * // \"before\", 1\n * // \"after\", 1\n * ```\n */\n\nexport const queueScheduler = new QueueScheduler(QueueAction);\n\n/**\n * @deprecated Renamed to {@link queueScheduler}. Will be removed in v8.\n */\nexport const queue = queueScheduler;\n", "import { AsyncAction } from './AsyncAction';\nimport { AnimationFrameScheduler } from './AnimationFrameScheduler';\nimport { SchedulerAction } from '../types';\nimport { animationFrameProvider } from './animationFrameProvider';\nimport { TimerHandle } from './timerHandle';\n\nexport class AnimationFrameAction extends AsyncAction {\n constructor(protected scheduler: AnimationFrameScheduler, protected work: (this: SchedulerAction, state?: T) => void) {\n super(scheduler, work);\n }\n\n protected requestAsyncId(scheduler: AnimationFrameScheduler, id?: TimerHandle, delay: number = 0): TimerHandle {\n // If delay is greater than 0, request as an async action.\n if (delay !== null && delay > 0) {\n return super.requestAsyncId(scheduler, id, delay);\n }\n // Push the action to the end of the scheduler queue.\n scheduler.actions.push(this);\n // If an animation frame has already been requested, don't request another\n // one. If an animation frame hasn't been requested yet, request one. Return\n // the current animation frame request id.\n return scheduler._scheduled || (scheduler._scheduled = animationFrameProvider.requestAnimationFrame(() => scheduler.flush(undefined)));\n }\n\n protected recycleAsyncId(scheduler: AnimationFrameScheduler, id?: TimerHandle, delay: number = 0): TimerHandle | undefined {\n // If delay exists and is greater than 0, or if the delay is null (the\n // action wasn't rescheduled) but was originally scheduled as an async\n // action, then recycle as an async action.\n if (delay != null ? delay > 0 : this.delay > 0) {\n return super.recycleAsyncId(scheduler, id, delay);\n }\n // If the scheduler queue has no remaining actions with the same async id,\n // cancel the requested animation frame and set the scheduled flag to\n // undefined so the next AnimationFrameAction will request its own.\n const { actions } = scheduler;\n if (id != null && id === scheduler._scheduled && actions[actions.length - 1]?.id !== id) {\n animationFrameProvider.cancelAnimationFrame(id as number);\n scheduler._scheduled = undefined;\n }\n // Return undefined so the action knows to request a new async id if it's rescheduled.\n return undefined;\n }\n}\n", "import { AsyncAction } from './AsyncAction';\nimport { AsyncScheduler } from './AsyncScheduler';\n\nexport class AnimationFrameScheduler extends AsyncScheduler {\n public flush(action?: AsyncAction): void {\n this._active = true;\n // The async id that effects a call to flush is stored in _scheduled.\n // Before executing an action, it's necessary to check the action's async\n // id to determine whether it's supposed to be executed in the current\n // flush.\n // Previous implementations of this method used a count to determine this,\n // but that was unsound, as actions that are unsubscribed - i.e. cancelled -\n // are removed from the actions array and that can shift actions that are\n // scheduled to be executed in a subsequent flush into positions at which\n // they are executed within the current flush.\n let flushId;\n if (action) {\n flushId = action.id;\n } else {\n flushId = this._scheduled;\n this._scheduled = undefined;\n }\n\n const { actions } = this;\n let error: any;\n action = action || actions.shift()!;\n\n do {\n if ((error = action.execute(action.state, action.delay))) {\n break;\n }\n } while ((action = actions[0]) && action.id === flushId && actions.shift());\n\n this._active = false;\n\n if (error) {\n while ((action = actions[0]) && action.id === flushId && actions.shift()) {\n action.unsubscribe();\n }\n throw error;\n }\n }\n}\n", "import { AnimationFrameAction } from './AnimationFrameAction';\nimport { AnimationFrameScheduler } from './AnimationFrameScheduler';\n\n/**\n *\n * Animation Frame Scheduler\n *\n * Perform task when `window.requestAnimationFrame` would fire\n *\n * When `animationFrame` scheduler is used with delay, it will fall back to {@link asyncScheduler} scheduler\n * behaviour.\n *\n * Without delay, `animationFrame` scheduler can be used to create smooth browser animations.\n * It makes sure scheduled task will happen just before next browser content repaint,\n * thus performing animations as efficiently as possible.\n *\n * ## Example\n * Schedule div height animation\n * ```ts\n * // html:
\n * import { animationFrameScheduler } from 'rxjs';\n *\n * const div = document.querySelector('div');\n *\n * animationFrameScheduler.schedule(function(height) {\n * div.style.height = height + \"px\";\n *\n * this.schedule(height + 1); // `this` references currently executing Action,\n * // which we reschedule with new state\n * }, 0, 0);\n *\n * // You will see a div element growing in height\n * ```\n */\n\nexport const animationFrameScheduler = new AnimationFrameScheduler(AnimationFrameAction);\n\n/**\n * @deprecated Renamed to {@link animationFrameScheduler}. Will be removed in v8.\n */\nexport const animationFrame = animationFrameScheduler;\n", "import { Observable } from '../Observable';\nimport { SchedulerLike } from '../types';\n\n/**\n * A simple Observable that emits no items to the Observer and immediately\n * emits a complete notification.\n *\n * Just emits 'complete', and nothing else.\n *\n * ![](empty.png)\n *\n * A simple Observable that only emits the complete notification. It can be used\n * for composing with other Observables, such as in a {@link mergeMap}.\n *\n * ## Examples\n *\n * Log complete notification\n *\n * ```ts\n * import { EMPTY } from 'rxjs';\n *\n * EMPTY.subscribe({\n * next: () => console.log('Next'),\n * complete: () => console.log('Complete!')\n * });\n *\n * // Outputs\n * // Complete!\n * ```\n *\n * Emit the number 7, then complete\n *\n * ```ts\n * import { EMPTY, startWith } from 'rxjs';\n *\n * const result = EMPTY.pipe(startWith(7));\n * result.subscribe(x => console.log(x));\n *\n * // Outputs\n * // 7\n * ```\n *\n * Map and flatten only odd numbers to the sequence `'a'`, `'b'`, `'c'`\n *\n * ```ts\n * import { interval, mergeMap, of, EMPTY } from 'rxjs';\n *\n * const interval$ = interval(1000);\n * const result = interval$.pipe(\n * mergeMap(x => x % 2 === 1 ? of('a', 'b', 'c') : EMPTY),\n * );\n * result.subscribe(x => console.log(x));\n *\n * // Results in the following to the console:\n * // x is equal to the count on the interval, e.g. (0, 1, 2, 3, ...)\n * // x will occur every 1000ms\n * // if x % 2 is equal to 1, print a, b, c (each on its own)\n * // if x % 2 is not equal to 1, nothing will be output\n * ```\n *\n * @see {@link Observable}\n * @see {@link NEVER}\n * @see {@link of}\n * @see {@link throwError}\n */\nexport const EMPTY = new Observable((subscriber) => subscriber.complete());\n\n/**\n * @param scheduler A {@link SchedulerLike} to use for scheduling\n * the emission of the complete notification.\n * @deprecated Replaced with the {@link EMPTY} constant or {@link scheduled} (e.g. `scheduled([], scheduler)`). Will be removed in v8.\n */\nexport function empty(scheduler?: SchedulerLike) {\n return scheduler ? emptyScheduled(scheduler) : EMPTY;\n}\n\nfunction emptyScheduled(scheduler: SchedulerLike) {\n return new Observable((subscriber) => scheduler.schedule(() => subscriber.complete()));\n}\n", "import { SchedulerLike } from '../types';\nimport { isFunction } from './isFunction';\n\nexport function isScheduler(value: any): value is SchedulerLike {\n return value && isFunction(value.schedule);\n}\n", "import { SchedulerLike } from '../types';\nimport { isFunction } from './isFunction';\nimport { isScheduler } from './isScheduler';\n\nfunction last(arr: T[]): T | undefined {\n return arr[arr.length - 1];\n}\n\nexport function popResultSelector(args: any[]): ((...args: unknown[]) => unknown) | undefined {\n return isFunction(last(args)) ? args.pop() : undefined;\n}\n\nexport function popScheduler(args: any[]): SchedulerLike | undefined {\n return isScheduler(last(args)) ? args.pop() : undefined;\n}\n\nexport function popNumber(args: any[], defaultValue: number): number {\n return typeof last(args) === 'number' ? args.pop()! : defaultValue;\n}\n", "export const isArrayLike = ((x: any): x is ArrayLike => x && typeof x.length === 'number' && typeof x !== 'function');", "import { isFunction } from \"./isFunction\";\n\n/**\n * Tests to see if the object is \"thennable\".\n * @param value the object to test\n */\nexport function isPromise(value: any): value is PromiseLike {\n return isFunction(value?.then);\n}\n", "import { InteropObservable } from '../types';\nimport { observable as Symbol_observable } from '../symbol/observable';\nimport { isFunction } from './isFunction';\n\n/** Identifies an input as being Observable (but not necessary an Rx Observable) */\nexport function isInteropObservable(input: any): input is InteropObservable {\n return isFunction(input[Symbol_observable]);\n}\n", "import { isFunction } from './isFunction';\n\nexport function isAsyncIterable(obj: any): obj is AsyncIterable {\n return Symbol.asyncIterator && isFunction(obj?.[Symbol.asyncIterator]);\n}\n", "/**\n * Creates the TypeError to throw if an invalid object is passed to `from` or `scheduled`.\n * @param input The object that was passed.\n */\nexport function createInvalidObservableTypeError(input: any) {\n // TODO: We should create error codes that can be looked up, so this can be less verbose.\n return new TypeError(\n `You provided ${\n input !== null && typeof input === 'object' ? 'an invalid object' : `'${input}'`\n } where a stream was expected. You can provide an Observable, Promise, ReadableStream, Array, AsyncIterable, or Iterable.`\n );\n}\n", "export function getSymbolIterator(): symbol {\n if (typeof Symbol !== 'function' || !Symbol.iterator) {\n return '@@iterator' as any;\n }\n\n return Symbol.iterator;\n}\n\nexport const iterator = getSymbolIterator();\n", "import { iterator as Symbol_iterator } from '../symbol/iterator';\nimport { isFunction } from './isFunction';\n\n/** Identifies an input as being an Iterable */\nexport function isIterable(input: any): input is Iterable {\n return isFunction(input?.[Symbol_iterator]);\n}\n", "import { ReadableStreamLike } from '../types';\nimport { isFunction } from './isFunction';\n\nexport async function* readableStreamLikeToAsyncGenerator(readableStream: ReadableStreamLike): AsyncGenerator {\n const reader = readableStream.getReader();\n try {\n while (true) {\n const { value, done } = await reader.read();\n if (done) {\n return;\n }\n yield value!;\n }\n } finally {\n reader.releaseLock();\n }\n}\n\nexport function isReadableStreamLike(obj: any): obj is ReadableStreamLike {\n // We don't want to use instanceof checks because they would return\n // false for instances from another Realm, like an

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