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

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

Install the CData JDBC Driver for Oracle

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

Start a Spark Shell and Connect to Oracle Data

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

   To connect to Oracle, you'll first need to update your PATH variable and ensure it contains a folder location that includes the native DLLs. The native DLLs can be found in the lib folder inside the installation directory. Once you've done this, set the following to connect:

   • Port: The port used to connect to the server hosting the Oracle database.
   • User: The user Id provided for authentication with the Oracle database.
   • Password: The password provided for authentication with the Oracle database.
   • Service Name: The service name of the Oracle database.

   Built-in Connection String Designer

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

   java -jar cdata.jdbc.oracleoci.jar

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

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

   scala> val oracleoci_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:oracleoci:User=myuser;Password=mypassword;Server=localhost;Port=1521;").option("dbtable","Customers").option("driver","cdata.jdbc.oracleoci.OracleOCIDriver").load()
3. Once you connect and the data is loaded you will see the table schema displayed.

4. Register the Oracle data as a temporary table:

   scala> oracleoci_df.registerTable("customers")

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

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