uptasticsearch tackles the issue of getting data out of Elasticsearch and into a tabular format in R. It should work for all versions of Elasticsearch from 1.0.0 onwards, but is not regularly tested against all of them. If you run into a problem, please open an issue.
The core functionality of this package is the es_search() function. This returns a data.table containing the parsed result of any given query. Note that this includes aggs queries.
Releases of this package can be installed from CRAN:
install.packages(
'uptasticsearch'
, repos = "http://cran.rstudio.com"
)or from conda-forge
conda install -c conda-forge r-uptasticsearchTo use the development version of the package, which has the newest changes, you can install directly from GitHub
remotes::install_github(
"uptake/uptasticsearch"
, subdir = "r-pkg"
)The examples presented here pertain to a fictional Elasticsearch index holding some information on a movie theater business.
The most common use case for this package will be the case where you have an Elasticsearch query and want to get a data frame representation of many resulting documents.
In the example below, we use uptasticsearch to look for all survey results in which customers said their satisfaction was "low" or "very low" and mentioned food in their comments.
library(uptasticsearch)
# Build your query in an R string
qbody <- '{
"query": {
"filtered": {
"filter": {
"bool": {
"must": [
{
"exists": {
"field": "customer_comments"
}
},
{
"terms": {
"overall_satisfaction": ["very low", "low"]
}
}
]
}
}
},
"query": {
"match_phrase": {
"customer_comments": "food"
}
}
}
}'
# Execute the query, parse into a data.table
commentDT <- es_search(
es_host = 'http://mydb.mycompany.com:9200'
, es_index = "survey_results"
, query_body = qbody
, scroll = "1m"
, n_cores = 4
)Elasticsearch ships with a rich set of aggregations for creating summarized views of your data. uptasticsearch has built-in support for these aggregations.
In the example below, we use uptasticsearch to create daily timeseries of summary statistics like total revenue and average payment amount.
library(uptasticsearch)
# Build your query in an R string
qbody <- '{
"query": {
"filtered": {
"filter": {
"bool": {
"must": [
{
"exists": {
"field": "pmt_amount"
}
}
]
}
}
}
},
"aggs": {
"timestamp": {
"date_histogram": {
"field": "timestamp",
"interval": "day"
},
"aggs": {
"revenue": {
"extended_stats": {
"field": "pmt_amount"
}
}
}
}
},
"size": 0
}'
# Execute the query, parse result into a data.table
revenueDT <- es_search(
es_host = 'http://mydb.mycompany.com:9200'
, es_index = "transactions"
, size = 1000
, query_body = qbody
, n_cores = 1
)In the example above, we used the date_histogram and extended_stats aggregations.
es_search() has built-in support for many other aggregations and combinations of aggregations, with more on the way.
Please see the table below for the current status of the package.
Note that names of the form "agg1 - agg2" refer to the ability to handled aggregations nested inside other aggregations.
| Agg type | R support? |
|---|---|
| "cardinality" | YES |
| "date_histogram" | YES |
| date_histogram - cardinality | YES |
| date_histogram - extended_stats | YES |
| date_histogram - histogram | YES |
| date_histogram - percentiles | YES |
| date_histogram - significant_terms | YES |
| date_histogram - stats | YES |
| date_histogram - terms | YES |
| "extended_stats" | YES |
| "histogram" | YES |
| "percentiles" | YES |
| "significant terms" | YES |
| "stats" | YES |
| "terms" | YES |
| terms - cardinality | YES |
| terms - date_histogram | YES |
| terms - date_histogram - cardinality | YES |
| terms - date_histogram - extended_stats | YES |
| terms - date_histogram - histogram | YES |
| terms - date_histogram - percentiles | YES |
| terms - date_histogram - significant_terms | YES |
| terms - date_histogram - stats | YES |
| terms - date_histogram - terms | YES |
| terms - extended_stats | YES |
| terms - histogram | YES |
| terms - percentiles | YES |
| terms - significant_terms | YES |
| terms - stats | YES |
| terms - terms | YES |