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

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

Install the CData JDBC Driver for Oracle Service Cloud

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

Start a Spark Shell and Connect to Oracle Service Cloud Data

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

   $ spark-shell --jars /CData/CData JDBC Driver for Oracle Service Cloud/lib/cdata.jdbc.oracleservicecloud.jar
   

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

   Using Basic Authentication

   You must set the following to authenticate to Oracle Service Cloud:

   • Url: The Url of the account to connect to.
   • User: The username of the authenticating account.
   • Password: The password of the authenticating account.

   Built-in Connection String Designer

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

   java -jar cdata.jdbc.oracleservicecloud.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 Oracle Service Cloud, using the connection string generated above.

   scala> val oracleservicecloud_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:oracleservicecloud:Url=https://abc.rightnowdemo.com;User=user;Password=password;").option("dbtable","Accounts").option("driver","cdata.jdbc.oracleservicecloud.OracleServiceCloudDriver").load()
   

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

4. Register the Oracle Service Cloud data as a temporary table:

   scala> oracleservicecloud_df.registerTable("accounts")

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

   scala> oracleservicecloud_df.sqlContext.sql("SELECT Id, LookupName FROM Accounts WHERE DisplayOrder = 12").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 Oracle Service Cloud in Apache Spark, you are able to perform fast and complex analytics on Oracle Service Cloud 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.