How to work with Cassandra 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 Cassandra, Spark can work with live Cassandra data. This article describes how to connect to and query Cassandra data from a Spark shell.

The CData JDBC Driver offers unmatched performance for interacting with live Cassandra data due to optimized data processing built into the driver. When you issue complex SQL queries to Cassandra, the driver pushes supported SQL operations, like filters and aggregations, directly to Cassandra 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 Cassandra data using native data types.

Install the CData JDBC Driver for Cassandra

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

Start a Spark Shell and Connect to Cassandra Data

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

   $ spark-shell --jars /CData/CData JDBC Driver for Cassandra/lib/cdata.jdbc.cassandra.jar
   

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

   Set the Server, Port, and Database connection properties to connect to Cassandra. Additionally, to use internal authentication set the User and Password connection properties.

   Built-in Connection String Designer

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

   java -jar cdata.jdbc.cassandra.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 Cassandra, using the connection string generated above.

   scala> val cassandra_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:cassandra:Database=MyCassandraDB;Port=7000;Server=127.0.0.1;").option("dbtable","Customer").option("driver","cdata.jdbc.cassandra.CassandraDriver").load()
   

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

4. Register the Cassandra data as a temporary table:

   scala> cassandra_df.registerTable("customer")

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

   scala> cassandra_df.sqlContext.sql("SELECT City, TotalDue FROM Customer WHERE FirstName = Bob").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 Cassandra in Apache Spark, you are able to perform fast and complex analytics on Cassandra 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.