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

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

Install the CData JDBC Driver for PostgreSQL

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

Start a Spark Shell and Connect to PostgreSQL Data

1. Open a terminal and start the Spark shell with the CData JDBC Driver for PostgreSQL JAR file as the jars parameter: $ spark-shell --jars /CData/CData JDBC Driver for PostgreSQL/lib/cdata.jdbc.postgresql.jar
2. With the shell running, you can connect to PostgreSQL with a JDBC URL and use the SQL Context load() function to read a table.

   To connect to PostgreSQL, 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 data provider connects to the user's default database.

   Built-in Connection String Designer

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

   java -jar cdata.jdbc.postgresql.jar

   Fill in the connection properties and copy the connection string to the clipboard.

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

   scala> val postgresql_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:postgresql:User=postgres;Password=admin;Database=postgres;Server=127.0.0.1;Port=5432;").option("dbtable","Orders").option("driver","cdata.jdbc.postgresql.PostgreSQLDriver").load()
3. Once you connect and the data is loaded you will see the table schema displayed.

4. Register the PostgreSQL data as a temporary table:

   scala> postgresql_df.registerTable("orders")

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

   scala> postgresql_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:

Using the CData JDBC Driver for PostgreSQL in Apache Spark, you are able to perform fast and complex analytics on PostgreSQL data, combining the power and utility of Spark with your data. Download a free, 30 day trial of any of the 200+ CData JDBC Drivers and get started today.