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.
The package is not yet on CRAN, but we'll be submitting soon. For now, you can install using devtools.
devtools::install_github("UptakeOpenSource/uptasticsearch", subdir = 'r-pkg/uptasticsearch')
We plan to release a Python implementation of this functionality, but that is not available at this time. Check back often!
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.
# 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 theses aggregations.
In the example below, we use uptasticsearch to create daily timeseries of summary statistics like total revenue and average payment amount.
# 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 |
| "stats" | YES |
This is a fairly new project and, as the version number indicates, should be regarded as a work in progress.
uptasticsearch does not currently support queries with authentication. This will be added in future versions.