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

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

Install the CData JDBC Driver for Oracle SCM

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

Start a Spark Shell and Connect to Oracle SCM Data

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

   $ spark-shell --jars /CData/CData JDBC Driver for Oracle SCM/lib/cdata.jdbc.oraclescm.jar
   

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

   The following connection properties are required to connect to Oracle SCM data.

   • Url: The URL of the account that you want to connect to. Typically, this will be the URL of your Oracle Cloud service. For example, https://servername.fa.us2.oraclecloud.com.
   • User: The username of your Oracle Cloud service account.
   • Password: The password of your Oracle Cloud service account.

   Built-in Connection String Designer

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

   java -jar cdata.jdbc.oraclescm.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 SCM, using the connection string generated above.

   scala> val oraclescm_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:oraclescm:Url=https://myinstance.oraclecloud.com;User=user;Password=password;").option("dbtable","Carriers").option("driver","cdata.jdbc.oraclescm.OracleSCMDriver").load()
   

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

4. Register the Oracle SCM data as a temporary table:

   scala> oraclescm_df.registerTable("carriers")

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

   scala> oraclescm_df.sqlContext.sql("SELECT CarrierId, CarrierName FROM Carriers WHERE ActiveFlag = false").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 SCM in Apache Spark, you are able to perform fast and complex analytics on Oracle SCM 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.