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

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

Install the CData JDBC Driver for Redshift

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

Start a Spark Shell and Connect to Redshift Data

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

   $ spark-shell --jars /CData/CData JDBC Driver for Redshift/lib/cdata.jdbc.redshift.jar
   

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

   To connect to Redshift, set the following:

   • : Set this to the host name or IP address of the cluster hosting the Database you want to connect to.
   • : Set this to the port of the cluster.
   • : Set this to the name of the database. Or, leave this blank to use the default database of the authenticated user.
   • : Set this to the username you want to use to authenticate to the Server.
   • : Set this to the password you want to use to authenticate to the Server.

   You can obtain the and values in the AWS Management Console:

   1. Open the Amazon Redshift console (http://console.aws.amazon.com/redshift).
   2. On the Clusters page, click the name of the cluster.
   3. On the Configuration tab for the cluster, copy the cluster URL from the connection strings displayed.

   Built-in Connection String Designer

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

   java -jar cdata.jdbc.redshift.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 Redshift, using the connection string generated above.

   scala> val redshift_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:redshift:User=admin;Password=admin;Database=dev;Server=examplecluster.my.us-west-2.redshift.amazonaws.com;Port=5439;").option("dbtable","Orders").option("driver","cdata.jdbc.redshift.RedshiftDriver").load()
   

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

4. Register the Redshift data as a temporary table:

   scala> redshift_df.registerTable("orders")

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

   scala> redshift_df.sqlContext.sql("SELECT ShipName, ShipCity FROM Orders WHERE ShipCountry = USA").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 Redshift in Apache Spark, you are able to perform fast and complex analytics on Redshift 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.