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Work with IBM Cloud SQL Query Data in Apache Spark Using SQL

Access and process IBM Cloud SQL Query 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 IBM Cloud SQL Query, Spark can work with live IBM Cloud SQL Query data. This article describes how to connect to and query IBM Cloud SQL Query data from a Spark shell.

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

Install the CData JDBC Driver for IBM Cloud SQL Query

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

Start a Spark Shell and Connect to IBM Cloud SQL Query Data

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

    IBM Cloud SQL 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 SQL Query JDBC Driver. Either double-click the JAR file or execute the jar file from the command-line.

    java -jar cdata.jdbc.ibmcloudsqlquery.jar

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

    Configure the connection to IBM Cloud SQL Query, using the connection string generated above.

    scala> val ibmcloudsqlquery_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:ibmcloudsqlquery: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.ibmcloudsqlquery.IBMCloudSQLQueryDriver").load()
  3. Once you connect and the data is loaded you will see the table schema displayed.
  4. Register the IBM Cloud SQL Query data as a temporary table:

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

    scala> ibmcloudsqlquery_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 SQL Query in Apache Spark, you are able to perform fast and complex analytics on IBM Cloud SQL Query 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.