This project tackles the issue of getting data out of Elasticsearch and into a tabular format in R.
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')
To use the development version of the package, which has the newest changes, you can install directly from GitHub
devtools::install_github("UptakeOpenSource/uptasticsearch", subdir = "r-pkg")
This package is not currently available on PyPi. To build the development version from source, clone this repo, then :
cd py-pkg
pip install .
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 ES 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 |
uptasticsearch does not currently support queries with authentication. This will be added in future versions.
When developing on this package, you may want to run Elasticsearch locally to speed up the testing cycle. We've provided some gross bash scripts at the root of this repo to help!
To run the code below, you will need Docker. Note that I've passed an argument to setup_local.sh indicating the major version of ES I want to run. If you don't do that, this script will just run the most recent major version of Elasticsearch. Look at the source code of setup_local.sh for a list of the valid arguments.
# Start up Elasticsearch on localhost:9200 and seed it with data
./setup_local.sh 5.5
# Run tests
make test_r
# Get test coverage and generate coverage report
make coverage_r
# Tear down the container and remove testing files
./cleanup_local.sh