How to work with OData Services 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 OData, Spark can work with live OData services. This article describes how to connect to and query OData services from a Spark shell.

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

Getting Started

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Install the CData JDBC Driver for OData

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

Start a Spark Shell and Connect to OData Services

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

   $ spark-shell --jars /CData/CData JDBC Driver for OData/lib/cdata.jdbc.odata.jar
   

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

   The User and Password properties, under the Authentication section, must be set to valid OData user credentials. In addition, specify a URL to a valid OData server organization root or OData services file.

   Built-in Connection String Designer

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

   java -jar cdata.jdbc.odata.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 OData, using the connection string generated above.

   scala> val odata_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:odata:URL=http://services.odata.org/V4/Northwind/Northwind.svc;UseIdUrl=True;OData Version=4.0;Data Format=ATOM;").option("dbtable","Orders").option("driver","cdata.jdbc.odata.ODataDriver").load()
   

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

4. Register the OData services as a temporary table:

   scala> odata_df.registerTable("orders")

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

   scala> odata_df.sqlContext.sql("SELECT OrderName, Freight FROM Orders WHERE ShipCity = New York").collect.foreach(println)

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

   👁 Services in Apache Spark (Salesforce is shown)
   

Using the CData JDBC Driver for OData in Apache Spark, you are able to perform fast and complex analytics on OData services, 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.