Analyze Kafka Data in R

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Apache Kafka JDBC Driver

Rapidly create and deploy powerful Java applications that integrate with Apache Kafka.



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

Access Kafka 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 Kafka and the RJDBC package to work with remote Kafka 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 Kafka and visualize Kafka data by calling standard R functions.

Install R

You can match the driver's performance gains from multi-threading 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:

library(RJDBC)

Connect to Kafka as a JDBC Data Source

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

  • Driver Class: Set this to cdata.jdbc.apachekafka.ApacheKafkaDriver
  • 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 and dbSendQuery, 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 Kafka:

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

You can now use DBI functions to connect to Kafka and execute SQL queries. Initialize the JDBC connection with the dbConnect function.

Set BootstrapServers and the Topic properties to specify the address of your Apache Kafka server, as well as the topic you would like to interact with.

Authorization Mechanisms

  • SASL Plain: The User and Password properties should be specified. AuthScheme should be set to 'Plain'.
  • SASL SSL: The User and Password properties should be specified. AuthScheme should be set to 'Scram'. UseSSL should be set to true.
  • SSL: The SSLCert and SSLCertPassword properties should be specified. UseSSL should be set to true.
  • Kerberos: The User and Password properties should be specified. AuthScheme should be set to 'Kerberos'.

You may be required to trust the server certificate. In such cases, specify the TrustStorePath and the TrustStorePassword if necessary.

Built-in Connection String Designer

For assistance in constructing the JDBC URL, use the connection string designer built into the Kafka JDBC Driver. Either double-click the JAR file or execute the jar file from the command-line.

java -jar cdata.jdbc.apachekafka.jar

Fill in the connection properties and copy the connection string to the clipboard.

Below is a sample dbConnect call, including a typical JDBC connection string:

conn <- dbConnect(driver,"jdbc:apachekafka:User=admin;Password=pass;BootStrapServers=https://localhost:9091;Topic=MyTopic;")

Schema Discovery

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

dbListTables(conn)

Execute SQL Queries

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

sampletable_1 <- dbGetQuery(conn,"SELECT Id, Column1 FROM SampleTable_1 WHERE Column2 = '100'")

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

View(sampletable_1)

Plot Kafka Data

You can now analyze Kafka 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:

par(las=2,ps=10,mar=c(5,15,4,2)) barplot(sampletable_1$Column1, main="Kafka SampleTable_1", names.arg = sampletable_1$Id, horiz=TRUE)