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

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

Install the CData JDBC Driver for Jumpseller

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

Start a Spark Shell and Connect to Jumpseller Data

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

   $ spark-shell --jars /CData/CData JDBC Driver for Jumpseller/lib/cdata.jdbc.api.jar
   

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

   Start by setting the Profile connection property to the location of the Jumpseller Profile on disk (e.g. C:\profiles\Jumpseller.apip). Next, set the ProfileSettings connection property to the connection string for Jumpseller (see below).

   Jumpseller API Profile Settings

   In your Jumpseller store admin panel, navigate to Apps > Developers > Add Application to register an OAuth app and obtain a Client ID and Client Secret.

   Built-in Connection String Designer

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

   java -jar cdata.jdbc.api.jar
   

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

   

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

   scala> val api_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:api:Profile=C:\profiles\Jumpseller.apip;Authscheme=OAuth;OAuthClientId=your_client_id;OAuthClientSecret=your_client_secret;CallbackUrl=your_callback_url;").option("dbtable","Apps").option("driver","cdata.jdbc.api.APIDriver").load()
   

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

4. Register the Jumpseller data as a temporary table:

   scala> api_df.registerTable("apps")

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

   scala> api_df.sqlContext.sql("SELECT Code, Name FROM Apps WHERE Author = Jumpseller").collect.foreach(println)

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

   

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