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

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

Install the CData JDBC Driver for Redis

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

Start a Spark Shell and Connect to Redis Data

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

   $ spark-shell --jars /CData/CData JDBC Driver for Redis/lib/cdata.jdbc.redis.jar
   

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

   Set the following connection properties to connect to a Redis instance:

   • : Set this to the name or address of the server your Redis instance is running on. You can specify the port in .
   • : Set this to the password used to authenticate with a password-protected Redis instance , using the Redis AUTH command.

   Set to negotiate SSL/TLS encryption when you connect.

   Built-in Connection String Designer

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

   java -jar cdata.jdbc.redis.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 Redis, using the connection string generated above.

   scala> val redis_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:redis:Server=127.0.0.1;Port=6379;Password=myPassword;").option("dbtable","Customers").option("driver","cdata.jdbc.redis.RedisDriver").load()
   

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

4. Register the Redis data as a temporary table:

   scala> redis_df.registerTable("customers")

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

   scala> redis_df.sqlContext.sql("SELECT City, CompanyName FROM Customers WHERE Country = US").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 Redis in Apache Spark, you are able to perform fast and complex analytics on Redis 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.