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

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

Install the CData JDBC Driver for ChargeOver

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

Start a Spark Shell and Connect to ChargeOver Data

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

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

2. With the shell running, you can connect to ChargeOver 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 ChargeOver Profile on disk (e.g. C:\profiles\Chargeover.apip). Next, set the ProfileSettings connection property to the connection string for ChargeOver (see below).

   ChargeOver API Profile Settings

   Log into ChargeOver, navigate to Settings > Developer > REST API, enable the API, and copy your Public Key (APIKey) and Private Key (APISecret).

   Built-in Connection String Designer

   For assistance in constructing the JDBC URL, use the connection string designer built into the ChargeOver 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 ChargeOver, using the connection string generated above.

   scala> val api_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:api:Profile=C:\profiles\Chargeover.apip;ProfileSettings='APIKey=your_public_key;APISecret=your_private_key;Domain=your_subdomain';").option("dbtable","AdminWorkers").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 ChargeOver data as a temporary table:

   scala> api_df.registerTable("adminworkers")

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

   scala> api_df.sqlContext.sql("SELECT Id, Email FROM AdminWorkers WHERE Username = john_doe").collect.foreach(println)

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

   

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