2026 NOTE by rfhb

On 2026-01-17, this repository was forked from https://github.com/ropensci-archive/elastic to explore efforts for maintaining R package elastic until such time that a current R package for a Lucene-using database could be found or developed. The orignal package elastic by https://ropensci.org/ was archived on CRAN on 2026-01-14 at the original maintainer’s request.

Since then, changes in this repository include updates to code and tests, see NEWS.md.

Package elastic is again available from CRAN since 2026-01-25 and can be installed with

install.packages("elastic")

This README file remains to be reviewed and updated.

To install the development version of package ‘elastic’ from this repository, run in R

devtools::install_github("rfhb/elastic")

2025-12-02 NOTE by rOpenSci

This package has been archived on CRAN. The former README is now in README-not.md.

2025-12-01 README.md

elastic

A general purpose R interface to Elasticsearch

Elasticsearch info

Compatibility

This client is developed following the latest stable releases, currently v7.10.0. It is generally compatible with older versions of Elasticsearch. Unlike the Python client, we try to keep as much compatibility as possible within a single version of this client, as that’s an easier setup in R world.

Security

You’re fine running ES locally on your machine, but be careful just throwing up ES on a server with a public IP address - make sure to think about security.

Elastic has paid products - but probably only applicable to enterprise users
DIY security - there are a variety of techniques for securing your Elasticsearch installation. A number of resources are collected in a blog post - tools include putting your ES behind something like Nginx, putting basic auth on top of it, using https, etc.

Installation

Stable version from CRAN

install.packages("elastic")

Development version from GitHub

remotes::install_github("ropensci/elastic")

library('elastic')

Install Elasticsearch

Elasticsearch installation help

w/ Docker

Pull the official elasticsearch image

# elasticsearch needs to have a version tag. We're pulling 7.10.1 here
docker pull elasticsearch:7.10.1

Then start up a container

docker run -d -p 9200:9200 elasticsearch:7.10.1

Then elasticsearch should be available on port 9200, try curl localhost:9200 and you should get the familiar message indicating ES is on.

If you’re using boot2docker, you’ll need to use the IP address in place of localhost. Get it by doing boot2docker ip.

on OSX

Download zip or tar file from Elasticsearch see here for download, e.g., curl -L -O https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.10.0-darwin-x86_64.tar.gz
Extract: tar -zxvf elasticsearch-7.10.0-darwin-x86_64.tar.gz
Move it: sudo mv elasticsearch-7.10.0 /usr/local
Navigate to /usr/local: cd /usr/local
Delete symlinked elasticsearch directory: rm -rf elasticsearch
Add shortcut: sudo ln -s elasticsearch-7.10.0 elasticsearch (replace version with your version)

You can also install via Homebrew: brew install elasticsearch

Note: for the 1.6 and greater upgrades of Elasticsearch, they want you to have java 8 or greater. I downloaded Java 8 from here https://www.oracle.com/java/ and it seemed to work great.

Upgrading Elasticsearch

I am not totally clear on best practice here, but from what I understand, when you upgrade to a new version of Elasticsearch, place old elasticsearch/data and elasticsearch/config directories into the new installation (elasticsearch/ dir). The new elasticsearch instance with replaced data and config directories should automatically update data to the new version and start working. Maybe if you use homebrew on a Mac to upgrade it takes care of this for you - not sure.

Obviously, upgrading Elasticsearch while keeping it running is a different thing (some help here from Elastic).

Start Elasticsearch

Navigate to elasticsearch: cd /usr/local/elasticsearch
Start elasticsearch: bin/elasticsearch

I create a little bash shortcut called es that does both of the above commands in one step (cd /usr/local/elasticsearch && bin/elasticsearch).

Initialization

The function connect() is used before doing anything else to set the connection details to your remote or local elasticsearch store. The details created by connect() are written to your options for the current session, and are used by elastic functions.

x <- connect(port = 9200)

If you’re following along here with a local instance of Elasticsearch, you’ll use x below to do more stuff.

For AWS hosted elasticsearch, make sure to specify path = “” and the correct port - transport schema pair.

connect(host = <aws_es_endpoint>, path = "", port = 80, transport_schema  = "http")
  # or
connect(host = <aws_es_endpoint>, path = "", port = 443, transport_schema  = "https")

If you are using Elastic Cloud or an installation with authentication (X-pack), make sure to specify path = ““, user =”“, pwd =”” and the correct port - transport schema pair.

connect(host = <ec_endpoint>, path = "", user="test", pwd = "1234", port = 9243, transport_schema  = "https")

Get some data

Elasticsearch has a bulk load API to load data in fast. The format is pretty weird though. It’s sort of JSON, but would pass no JSON linter. I include a few data sets in elastic so it’s easy to get up and running, and so when you run examples in this package they’ll actually run the same way (hopefully).

I have prepare a non-exported function useful for preparing the weird format that Elasticsearch wants for bulk data loads, that is somewhat specific to PLOS data (See below), but you could modify for your purposes. See make_bulk_plos() and make_bulk_gbif() here.

Shakespeare data

Elasticsearch provides some data on Shakespeare plays. I’ve provided a subset of this data in this package. Get the path for the file specific to your machine:

shakespeare <- system.file("examples", "shakespeare_data.json", package = "elastic")
# If you're on Elastic v6 or greater, use this one
shakespeare <- system.file("examples", "shakespeare_data_.json", package = "elastic")
shakespeare <- type_remover(shakespeare)

Then load the data into Elasticsearch:

make sure to create your connection object with connect()

# x <- connect()  # do this now if you didn't do this above
invisible(docs_bulk(x, shakespeare))

If you need some big data to play with, the shakespeare dataset is a good one to start with. You can get the whole thing and pop it into Elasticsearch (beware, may take up to 10 minutes or so.):

curl -XGET https://download.elastic.co/demos/kibana/gettingstarted/shakespeare_6.0.json > shakespeare.json
curl -XPUT localhost:9200/_bulk --data-binary @shakespeare.json

Public Library of Science (PLOS) data

A dataset inluded in the elastic package is metadata for PLOS scholarly articles. Get the file path, then load:

if (index_exists(x, "plos")) index_delete(x, "plos")
plosdat <- system.file("examples", "plos_data.json", package = "elastic")
plosdat <- type_remover(plosdat)
invisible(docs_bulk(x, plosdat))

Global Biodiversity Information Facility (GBIF) data

A dataset inluded in the elastic package is data for GBIF species occurrence records. Get the file path, then load:

if (index_exists(x, "gbif")) index_delete(x, "gbif")
gbifdat <- system.file("examples", "gbif_data.json", package = "elastic")
gbifdat <- type_remover(gbifdat)
invisible(docs_bulk(x, gbifdat))

GBIF geo data with a coordinates element to allow geo_shape queries

if (index_exists(x, "gbifgeo")) index_delete(x, "gbifgeo")
gbifgeo <- system.file("examples", "gbif_geo.json", package = "elastic")
gbifgeo <- type_remover(gbifgeo)
invisible(docs_bulk(x, gbifgeo))

More data sets

There are more datasets formatted for bulk loading in the sckott/elastic_data GitHub repository. Find it at https://github.com/sckott/elastic_data

Search

Search the plos index and only return 1 result

Search(x, index = "plos", size = 1)$hits$hits

Search the plos index, and query for antibody, limit to 1 result

Search(x, index = "plos", q = "antibody", size = 1)$hits$hits

Get documents

Get document with id=4

docs_get(x, index = 'plos', id = 4)

Get certain fields

docs_get(x, index = 'plos', id = 4, fields = 'id')

Get multiple documents via the multiget API

Same index and different document ids

docs_mget(x, index = "plos", id = 1:2)

Parsing

You can optionally get back raw json from Search(), docs_get(), and docs_mget() setting parameter raw=TRUE.

For example:

(out <- docs_mget(x, index = "plos", id = 1:2, raw = TRUE))

Then parse

jsonlite::fromJSON(out)

Known pain points

On secure Elasticsearch servers:
- HEAD requests don’t seem to work, not sure why
- If you allow only GET requests, a number of functions that require POST requests obviously then won’t work. A big one is Search(), but you can use Search_uri() to get around this, which uses GET instead of POST, but you can’t pass a more complicated query via the body

Screencast

A screencast introducing the package: vimeo.com/124659179