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

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

Install the CData JDBC Driver for Greenplum

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

Start a Spark Shell and Connect to Greenplum Data

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

   $ spark-shell --jars /CData/CData JDBC Driver for Greenplum/lib/cdata.jdbc.greenplum.jar
   

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

   To connect to Greenplum, set the Server, Port (the default port is 5432), and Database connection properties and set the User and Password you wish to use to authenticate to the server. If the Database property is not specified, the default database for the authenticate user is used.

   Built-in Connection String Designer

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

   java -jar cdata.jdbc.greenplum.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 Greenplum, using the connection string generated above.

   scala> val greenplum_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:greenplum:User=user;Password=admin;Database=dbname;Server=127.0.0.1;Port=5432;").option("dbtable","Orders").option("driver","cdata.jdbc.greenplum.GreenplumDriver").load()
   

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

4. Register the Greenplum data as a temporary table:

   scala> greenplum_df.registerTable("orders")

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

   scala> greenplum_df.sqlContext.sql("SELECT Freight, ShipName 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 Greenplum in Apache Spark, you are able to perform fast and complex analytics on Greenplum 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.