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

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

Install the CData JDBC Driver for PivotalTracker

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

Start a Spark Shell and Connect to PivotalTracker Data

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

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

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

   PivotalTracker API Profile Settings

   Navigate to your Pivotal Tracker Profile settings and locate the API token section to copy your unique API token.

   Built-in Connection String Designer

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

   scala> val api_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:api:Profile=C:\profiles\PivotalTracker.apip;ProfileSettings='APIKey=your_api_token';").option("dbtable","AccountMemberships").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 PivotalTracker data as a temporary table:

   scala> api_df.registerTable("accountmemberships")

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

   scala> api_df.sqlContext.sql("SELECT AccountId, Id FROM AccountMemberships WHERE Admin = true").collect.foreach(println)

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

   

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