Analyze Elasticsearch Data in R

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Analyze Elasticsearch Data in R

Use standard R functions and the development environment of your choice to analyze Elasticsearch data with the CData JDBC Driver for Elasticsearch.

· Big Data Zone ·
Free Resource

You can access Elasticsearch data with pure R script and standard SQL on any machine where R and Java can be installed. You can use the CData JDBC Driver for Elasticsearch and the RJDBC package to work with remote Elasticsearch data in R. By using the CData Driver, you are leveraging a driver written for industry-proven standards to access your data in the popular, open-source R language. This article shows how to use the driver to execute SQL queries to Elasticsearch and visualize Elasticsearch data by calling standard R functions.

Install R

You can match the driver's performance gains from multithreading and managed code by running the multithreaded Microsoft R Open or by running open R linked with the BLAS/LAPACK libraries. This article uses Microsoft R Open 3.2.3, which is preconfigured to install packages from the Jan. 1, 2016 snapshot of the CRAN repository. This snapshot ensures reproducibility.

Load the RJDBC Package

To use the driver, download the RJDBC package. After installing the RJDBC package, the following line loads the package:


Connect to Elasticsearch as a JDBC Data Source

You will need the following information to connect to Elasticsearch as a JDBC data source:

  • Driver Class: Set this to cdata.jdbc.elasticsearch.ElasticsearchDriver.
  • Classpath: Set this to the location of the driver JAR. By default, this is the lib subfolder of the installation folder.

The DBI functions, such as dbConnect anddbSendQuery , provide a unified interface for writing data access code in R. Use the following line to initialize a DBI driver that can make JDBC requests to the CData JDBC Driver for Elasticsearch:

driver <- JDBC(driverClass = "cdata.jdbc.elasticsearch.ElasticsearchDriver", classPath = "MyInstallationDir\lib\cdata.jdbc.elasticsearch.jar", identifier.quote = "'")

You can now use DBI functions to connect to Elasticsearch and execute SQL queries. Initialize the JDBC connection with the dbConnect function. Below is a typical JDBC connection string:

conn <- dbConnect(driver,"Server=;Port=9200;User=admin;Password=123456;")

Set the Server and Port connection properties to connect. To authenticate, set the User and Password properties, PKI (public key infrastructure) properties, or both. To use PKI, set the SSLClientCert, SSLClientCertType, SSLClientCertSubject, and SSLClientCertPassword properties.

The data provider uses X-Pack Security for TLS/SSL and authentication. To connect over TLS/SSL, prefix the Server value with 'https://'. Note: TLS/SSL and client authentication must be enabled on X-Pack to use PKI.

Once the data provider is connected, X-Pack will then perform user authentication and grant role permissions based on the realms you have configured.

Schema Discovery

The driver models Elasticsearch APIs as relational tables, views, and stored procedures. Use the following line to retrieve the list of tables:


Execute SQL Queries

You can use the dbGetQuery function to execute any SQL query supported by the Elasticsearch API:

orders <- dbGetQuery(conn,"SELECT Orders.Freight, Customers.ContactName FROM Customers INNER JOIN Orders ON Customers.CustomerId=Orders.CustomerId")

You can view the results in a data viewer window with the following command:


Plot Elasticsearch Data

You can now analyze Elasticsearch data with any of the data visualization packages available in the CRAN repository. You can create simple bar plots with the built-in bar plot function:

barplot(orders$Freight, main="Elasticsearch Orders", names.arg = orders$OrderName, horiz=TRUE)

big data, data analysis, elasticsearch, r, tutorial

Published at DZone with permission of Jerod Johnson , DZone MVB. See the original article here.

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