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

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

Install the CData JDBC Driver for Jira Service Desk

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

Start a Spark Shell and Connect to Jira Service Desk Data

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

   You can establish a connection to any Jira Service Desk Cloud account or Server instance.

   Connecting with a Cloud Account

   To connect to a Cloud account, you'll first need to retrieve an APIToken. To generate one, log in to your Atlassian account and navigate to API tokens > Create API token. The generated token will be displayed.

   Supply the following to connect to data:

   • User: Set this to the username of the authenticating user.
   • APIToken: Set this to the API token found previously.

   Connecting with a Service Account

   To authenticate with a service account, you will need to supply the following connection properties:

   • User: Set this to the username of the authenticating user.
   • Password: Set this to the password of the authenticating user.
   • URL: Set this to the URL associated with your JIRA Service Desk endpoint. For example, https://yoursitename.atlassian.net.

   Note: Password has been deprecated for connecting to a Cloud Account and is now used only to connect to a Server Instance.

   Accessing Custom Fields

   By default, the connector only surfaces system fields. To access the custom fields for Issues, set IncludeCustomFields.

   Built-in Connection String Designer

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

   java -jar cdata.jdbc.jiraservicedesk.jar

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

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

   scala> val jiraservicedesk_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:jiraservicedesk:ApiKey=myApiKey;User=MyUser;").option("dbtable","Requests").option("driver","cdata.jdbc.jiraservicedesk.JiraServiceDeskDriver").load()
3. Once you connect and the data is loaded you will see the table schema displayed.

4. Register the Jira Service Desk data as a temporary table:

   scala> jiraservicedesk_df.registerTable("requests")

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

   scala> jiraservicedesk_df.sqlContext.sql("SELECT RequestId, ReporterName FROM Requests WHERE CurrentStatus = Open").collect.foreach(println)

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

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