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

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

Install the CData JDBC Driver for Bugzilla

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

Start a Spark Shell and Connect to Bugzilla Data

1. Open a terminal and start the Spark shell with the CData JDBC Driver for Bugzilla JAR file as the jars parameter: $ spark-shell --jars /CData/CData JDBC Driver for Bugzilla/lib/cdata.jdbc.bugzilla.jar
2. With the shell running, you can connect to Bugzilla with a JDBC URL and use the SQL Context load() function to read a table.

   You can authenticate to your Bugzilla account using two parameters:

   • URL: The URL of your Bugzilla developer's page (the Home page).
   • ApiKey: API Keys can be generated from the Preferences -> API Keys section of your Bugzilla developer's page.

   Built-in Connection String Designer

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

   java -jar cdata.jdbc.bugzilla.jar

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

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

   scala> val bugzilla_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:bugzilla:Url=http://yourdomain/Bugzilla;APIKey=abc123;").option("dbtable","Bugs").option("driver","cdata.jdbc.bugzilla.BugzillaDriver").load()
3. Once you connect and the data is loaded you will see the table schema displayed.

4. Register the Bugzilla data as a temporary table:

   scala> bugzilla_df.registerTable("bugs")

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

   scala> bugzilla_df.sqlContext.sql("SELECT Id, Summary FROM Bugs WHERE Creator = user@domain.com").collect.foreach(println)

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

Using the CData JDBC Driver for Bugzilla in Apache Spark, you are able to perform fast and complex analytics on Bugzilla data, combining the power and utility of Spark with your data. Download a free, 30 day trial of any of the 200+ CData JDBC Drivers and get started today.