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

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

Install the CData JDBC Driver for REST

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

Start a Spark Shell and Connect to REST Data

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

   $ spark-shell --jars /CData/CData JDBC Driver for REST/lib/cdata.jdbc.rest.jar
   

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

   See the Getting Started chapter in the data provider documentation to authenticate to your data source: The data provider models REST APIs as bidirectional database tables and XML/JSON files as read-only views (local files, files stored on popular cloud services, and FTP servers). The major authentication schemes are supported, including HTTP Basic, Digest, NTLM, OAuth, and FTP. See the Getting Started chapter in the data provider documentation for authentication guides.

   After setting the and providing any authentication values, set to "XML" or "JSON" and set to more closely match the data representation to the structure of your data.

   The property is the controlling property over how your data is represented into tables and toggles the following basic configurations.

   • Document (default): Model a top-level, document view of your REST data. The data provider returns nested elements as aggregates of data.
   • FlattenedDocuments: Implicitly join nested documents and their parents into a single table.
   • Relational: Return individual, related tables from hierarchical data. The tables contain a primary key and a foreign key that links to the parent document.

   See the Modeling REST Data chapter for more information on configuring the relational representation. You will also find the sample data used in the following examples. The data includes entries for people, the cars they own, and various maintenance services performed on those cars.

   Built-in Connection String Designer

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

   java -jar cdata.jdbc.rest.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 REST, using the connection string generated above.

   scala> val rest_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:rest:DataModel=Relational;URI=C:/people.xml;Format=XML;").option("dbtable","people").option("driver","cdata.jdbc.rest.RESTDriver").load()
   

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

4. Register the REST data as a temporary table:

   scala> rest_df.registerTable("people")

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

   scala> rest_df.sqlContext.sql("SELECT [ personal.name.first ], [ personal.name.last ] FROM people WHERE [ personal.name.last ] = Roberts").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 REST in Apache Spark, you are able to perform fast and complex analytics on REST 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.