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

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

Install the CData JDBC Driver for Phoenix

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

Start a Spark Shell and Connect to Phoenix Data

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

   $ spark-shell --jars /CData/CData JDBC Driver for Phoenix/lib/cdata.jdbc.apachephoenix.jar
   

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

   Connect to Apache Phoenix via the Phoenix Query Server. Set the Server and Port (if different from the default port) properties to connect to Apache Phoenix. The Server property will typically be the host name or IP address of the server hosting Apache Phoenix.

   Authenticating to Apache Phoenix

   By default, no authentication will be used (plain). If authentication is configured for your server, set AuthScheme to NEGOTIATE and set the User and Password properties (if necessary) to authenticate through Kerberos.

   Built-in Connection String Designer

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

   java -jar cdata.jdbc.apachephoenix.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 Phoenix, using the connection string generated above.

   scala> val apachephoenix_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:apachephoenix:Server=localhost;Port=8765;").option("dbtable","MyTable").option("driver","cdata.jdbc.apachephoenix.ApachePhoenixDriver").load()
   

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

4. Register the Phoenix data as a temporary table:

   scala> apachephoenix_df.registerTable("mytable")

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

   scala> apachephoenix_df.sqlContext.sql("SELECT Id, Column1 FROM MyTable WHERE Id = 123456").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 Phoenix in Apache Spark, you are able to perform fast and complex analytics on Phoenix 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.