How to work with Kafka Data in Apache Spark using SQL

Apache Spark is a fast and general engine for large-scale data processing. When paired with the CData JDBC Driver for Kafka, Spark can work with live Kafka data. This article describes how to connect to and query Kafka data from a Spark shell.

The CData JDBC Driver offers unmatched performance for interacting with live Kafka data due to optimized data processing built into the driver. When you issue complex SQL queries to Kafka, the driver pushes supported SQL operations, like filters and aggregations, directly to Kafka and utilizes the embedded SQL engine to process unsupported operations (often SQL functions and JOIN operations) client-side. With built-in dynamic metadata querying, you can work with and analyze Kafka data using native data types.

Install the CData JDBC Driver for Kafka

Download the CData JDBC Driver for Kafka installer, unzip the package, and run the JAR file to install the driver.

Start a Spark Shell and Connect to Kafka Data

1. Open a terminal and start the Spark shell with the CData JDBC Driver for Kafka JAR file as the jars parameter:

   $ spark-shell --jars /CData/CData JDBC Driver for Kafka/lib/cdata.jdbc.apachekafka.jar
   

2. With the shell running, you can connect to Kafka with a JDBC URL and use the SQL Context load() function to read a table.

   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.

   👁 Using the built-in connection string designer to generate a JDBC URL (Salesforce is shown.)
   

   Configure the connection to Kafka, using the connection string generated above.

   scala> val apachekafka_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:apachekafka:User=admin;Password=pass;BootStrapServers=https://localhost:9091;Topic=MyTopic;").option("dbtable","SampleTable_1").option("driver","cdata.jdbc.apachekafka.ApacheKafkaDriver").load()
   

3. Once you connect and the data is loaded you will see the table schema displayed.

4. Register the Kafka data as a temporary table:

   scala> apachekafka_df.registerTable("sampletable_1")

5. Perform custom SQL queries against the Data using commands like the one below:

   scala> apachekafka_df.sqlContext.sql("SELECT Id, Column1 FROM SampleTable_1 WHERE Column2 = 100").collect.foreach(println)

   You will see the results displayed in the console, similar to the following:

   👁 Data in Apache Spark (Salesforce is shown)
   

Using the CData JDBC Driver for Kafka in Apache Spark, you are able to perform fast and complex analytics on Kafka data, combining the power and utility of Spark with your data. Download a free, 30 day trial of any of the hundreds of CData JDBC Drivers and get started today.