Work with Spark Data in Apache Spark Using SQL

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Apache Spark JDBC Driver

Rapidly create and deploy powerful Java applications that integrate with Apache Spark.

Access and process Spark Data in Apache Spark using the CData JDBC Driver.

Apache Spark is a fast and general engine for large-scale data processing. When paired with the CData JDBC Driver for Spark, Spark can work with live Spark data. This article describes how to connect to and query Spark data from a Spark shell.

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

Install the CData JDBC Driver for Spark

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

Start a Spark Shell and Connect to Spark Data

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

    Set the Server, Database, User, and Password connection properties to connect to SparkSQL.

    Built-in Connection String Designer

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

    java -jar cdata.jdbc.sparksql.jar

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

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

    scala> val sparksql_df ="jdbc").option("url", "jdbc:sparksql:Server=;").option("dbtable","Customers").option("driver","cdata.jdbc.sparksql.SparkSQLDriver").load()
  3. Once you connect and the data is loaded you will see the table schema displayed.
  4. Register the Spark data as a temporary table:

    scala> sparksql_df.registerTable("customers")
  5. Perform custom SQL queries against the Data using commands like the one below:

    scala> sparksql_df.sqlContext.sql("SELECT City, Balance 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 Spark in Apache Spark, you are able to perform fast and complex analytics on Spark 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.