Support for MariaDB database

.github/workflows/benchmarks.yml

@@ -46,6 +46,7 @@ jobs:
           - kdtree
           - kgn
           - luceneknn
+          - mariadb
           - milvus
           - mrpt
           - nndescent

ann_benchmarks/algorithms/mariadb/Dockerfile

@@ -0,0 +1,14 @@
+FROM ann-benchmarks
+
+RUN apt-get update
+
+# Install Python mariadb module
+RUN apt-get install -y libmariadb-dev
+RUN pip3 install mariadb
+
+# Install server
+RUN apt-get install -y curl sudo
+RUN curl -LsS https://r.mariadb.com/downloads/mariadb_repo_setup | sudo bash -s -- --mariadb-server-version="mariadb-11.8"
+RUN apt-get install -y mariadb-server
+
+WORKDIR /home/app

ann_benchmarks/algorithms/mariadb/config.yml

@@ -0,0 +1,17 @@
+float:
+  any:
+  - base_args: ['@metric']
+    constructor: MariaDB
+    disabled: false
+    docker_tag: ann-benchmarks-mariadb
+    module: ann_benchmarks.algorithms.mariadb
+    name: mariadb
+    run_groups:
+     myisam:
+       arg_groups: [{M: [6, 8, 12, 16, 32, 48], engine: 'MyISAM'}]
+       args: {}
+       query_args: [[10, 20, 30, 40]]
+     innodb:
+       arg_groups: [{M: [6, 8, 12, 16, 32, 48], engine: 'InnoDB'}]
+       args: {}
+       query_args: [[10, 20, 30, 40]]

ann_benchmarks/algorithms/mariadb/module.py

@@ -0,0 +1,148 @@
+import glob
+import os
+import subprocess
+import sys
+import time
+
+from itertools import chain
+from multiprocessing.pool import Pool
+
+import mariadb
+import numpy as np
+import psutil
+
+from ..base.module import BaseANN
+
+def vector_to_hex(v):
+    """Convert vector to bytes for database storage"""
+    return np.array(v, 'float32').tobytes()
+
+def many_queries(arg):
+    conn = mariadb.connect()
+    cur = conn.cursor()
+
+    res = []
+    for v in arg[2]:
+        cur.execute(arg[0], (vector_to_hex(v), arg[1]))
+        res.append([id for id, in cur.fetchall()])
+
+    return res
+
+class MariaDB(BaseANN):
+
+    def __init__(self, metric, method_param):
+        self._m = method_param['M']
+        self._engine = method_param['engine']
+        self._cur = None
+
+        self._metric_type = {"angular": "cosine", "euclidean": "euclidean"}.get(metric, None)
+        if self._metric_type is None:
+            raise Exception(f"[MariaDB] Not support metric type: {metric}!!!")
+
+        self._sql_create_table = f"CREATE TABLE ann.ann (id INT PRIMARY KEY, v VECTOR(%d) NOT NULL) ENGINE={self._engine}"
+        self._sql_insert = f"INSERT INTO ann.ann (id, v) VALUES (%s, %s)"
+        self._sql_create_index = f"ALTER TABLE ann.ann ADD VECTOR KEY v(v) DISTANCE={self._metric_type} M={self._m}"
+        self._sql_search = f"SELECT id FROM ann.ann ORDER by vec_distance_{self._metric_type}(v, %s) LIMIT %d"
+
+        self.start_db()
+
+        # Connect to MariaDB using Unix socket
+        conn = mariadb.connect()
+        self._cur = conn.cursor()
+
+    def start_db(self):
+        # Get free memory in MB
+        free_memory = psutil.virtual_memory().available
+
+        # Set buffer/cache size
+        innodb_buffer_size = int(free_memory * 0.4)
+        key_buffer_size = int(free_memory * 0.3)
+        mhnsw_cache_size = int(free_memory * 0.4)
+
+        subprocess.run(
+            f"service mariadb start --skip-networking "
+            f"--innodb-buffer-pool-size={innodb_buffer_size} "
+            f"--key-buffer-size={key_buffer_size} "
+            # f"--general_log=1 --general_log_file=/var/lib/mysql/general.log "
+            f"--mhnsw-max-cache-size={mhnsw_cache_size}",
+            shell=True,
+            check=True,
+            stdout=sys.stdout,
+            stderr=sys.stderr
+        )
+
+    def fit(self, X, batch_size=1000):
+        """
+        Fit the database with vectors
+
+        Parameters:
+            X: numpy array of vectors to insert
+            batch_size: number of records to insert in each batch
+        """
+        # Prepare database and table
+        self._cur.execute("SET GLOBAL max_allowed_packet = 1073741824")
+        self._cur.execute("DROP DATABASE IF EXISTS ann")
+        self._cur.execute("CREATE DATABASE ann")
+        self._cur.execute(self._sql_create_table, (len(X[0]),))
+
+        # Insert data in batches
+        print("Inserting data in batches...")
+        start_time = time.time()
+
+        batch = []
+        for i, embedding in enumerate(X):
+            batch.append((int(i), vector_to_hex(embedding)))
+
+            # Insert when batch is full
+            if len(batch) >= batch_size:
+                self._cur.executemany(self._sql_insert, batch)
+                batch.clear()
+
+        # Insert remaining records in final batch
+        if batch:
+            self._cur.executemany(self._sql_insert, batch)
+
+        insert_time = time.time() - start_time
+        print(f"Insert time for {len(X)} records: {insert_time:.2f}s")
+
+        self._cur.execute("COMMIT")
+        self._cur.execute("FLUSH TABLES")
+
+        # Create index
+        print("Creating index...")
+        start_time = time.time()
+        self._cur.execute(self._sql_create_index)
+
+        index_time = time.time() - start_time
+        print(f"Index creation time: {index_time:.2f}s")
+
+        self._cur.execute("COMMIT")
+        self._cur.execute("FLUSH TABLES")
+
+    def set_query_arguments(self, ef_search):
+        # Set ef_search
+        self._ef_search = ef_search
+        self._cur.execute(f"SET GLOBAL mhnsw_ef_search = {ef_search}")
+        self._cur.execute("COMMIT")
+
+    def query(self, v, n):
+        self._cur.execute(self._sql_search, (vector_to_hex(v), n))
+
+        return [id for id, in self._cur.fetchall()]
+
+    def batch_query(self, X, n):
+        XX=[]
+        for i in range(os.cpu_count()):
+            XX.append((self._sql_search, n, X[int(len(X)/os.cpu_count()*i):int(len(X)/os.cpu_count()*(i+1))]))
+        pool = Pool()
+        self._res = pool.map(many_queries, XX)
+
+    def get_batch_results(self):
+        return np.array(list(chain(*self._res)))
+
+    def get_memory_usage(self):
+        stem = '/var/lib/mysql/ann/ann#i#01.'
+        return sum(os.stat(f).st_size for f in glob.glob(stem + 'ibd') + glob.glob(stem + 'MY[ID]')) / 1024
+
+    def __str__(self):
+        return f"MariaDB(m={self._m}, ef_search={self._ef_search}, engine={self._engine})"