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Work with Jira Data in Apache Spark Using SQL

Access and process Jira Data in Apache Spark using the CData JDBC Driver.

Apache Spark is a fast and general engine for large-scale data processing. When paired with the CData JDBC Driver for Jira, Spark can work with live Jira data. This article describes how to connect to and query Jira data from a Spark shell.

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

Install the CData JDBC Driver for Jira

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

Start a Spark Shell and Connect to Jira Data

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

    To connect to JIRA, provide the User and Password. Additionally, provide the Url; for example,

    Built-in Connection String Designer

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

    java -jar cdata.jdbc.jira.jar

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

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

    scala> val jira_df ="jdbc").option("url", "jdbc:jira:User=admin;Password=123abc;Url=;").option("dbtable","Issues").option("driver","cdata.jdbc.jira.JIRADriver").load()
  3. Once you connect and the data is loaded you will see the table schema displayed.
  4. Register the Jira data as a temporary table:

    scala> jira_df.registerTable("issues")
  5. Perform custom SQL queries against the Data using commands like the one below:

    scala> jira_df.sqlContext.sql("SELECT Summary, TimeSpent FROM Issues WHERE ReporterDisplayName = Bob").collect.foreach(println)

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

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