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

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

Install the CData JDBC Driver for IBM Cloud Data Engine

Download the CData JDBC Driver for IBM Cloud Data Engine installer, unzip the package, and run the JAR file to install the driver.

Start a Spark Shell and Connect to IBM Cloud Data Engine Data

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

   IBM Cloud Data Engine uses the OAuth and HMAC authentication standards. See the "Getting Started" chapter of the help documentation for a guide to using OAuth.

   Built-in Connection String Designer

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

   java -jar cdata.jdbc.ibmclouddataengine.jar

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

   Configure the connection to IBM Cloud Data Engine, using the connection string generated above.

   scala> val ibmclouddataengine_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:ibmclouddataengine:Api Key=MyAPIKey;Instance CRN=myInstanceCRN;Region=myRegion;Schema=mySchema;OAuth Client Id=myOAuthClientId;OAuth Client Secret=myOAuthClientSecret;").option("dbtable","Jobs").option("driver","cdata.jdbc.ibmclouddataengine.IBMCloudDataEngineDriver").load()
3. Once you connect and the data is loaded you will see the table schema displayed.

4. Register the IBM Cloud Data Engine data as a temporary table:

   scala> ibmclouddataengine_df.registerTable("jobs")

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

   scala> ibmclouddataengine_df.sqlContext.sql("SELECT Id, Status FROM Jobs WHERE UserId = user@domain.com").collect.foreach(println)

   You will see the results displayed in the console, similar to the following:

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