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

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

Install the CData JDBC Driver for Impala

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

Start a Spark Shell and Connect to Impala Data

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

   In order to connect to Apache Impala, set the Server, Port, and ProtocolVersion. You may optionally specify a default Database. To connect using alternative methods, such as NOSASL, LDAP, or Kerberos, refer to the online Help documentation.

   Built-in Connection String Designer

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

   java -jar cdata.jdbc.apacheimpala.jar

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

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

   scala> val apacheimpala_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:apacheimpala:Server=127.0.0.1;Port=21050;").option("dbtable","Customers").option("driver","cdata.jdbc.apacheimpala.ApacheImpalaDriver").load()
3. Once you connect and the data is loaded you will see the table schema displayed.

4. Register the Impala data as a temporary table:

   scala> apacheimpala_df.registerTable("customers")

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

   scala> apacheimpala_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:

Using the CData JDBC Driver for Impala in Apache Spark, you are able to perform fast and complex analytics on Impala 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.