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

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

Install the CData JDBC Driver for Jira Assets

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

Start a Spark Shell and Connect to Jira Assets Data

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

   Jira Assets supports connecting and authenticating via the APIToken.

   To generate an API token:

   1. Log in to your Atlassian account.
   2. Navigate to Security < Create and manage API Token < Create API Token.

   Atlassian generates and then displays the API token.

   After you have generated the API token, set these parameters:

   • AuthScheme: APIToken.
   • User: The login of the authenticating user.
   • APIToken: The API token you just generated.

   You are now ready to connect and authenticate to Jira Assets.

   Built-in Connection String Designer

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

   java -jar cdata.jdbc.jiraassets.jar

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

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

   scala> val jiraassets_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:jiraassets:User=MyUser;APIToken=myApiToken;Url=https://yoursitename.atlassian.net").option("dbtable","Objects").option("driver","cdata.jdbc.jiraassets.JiraAssetsDriver").load()
3. Once you connect and the data is loaded you will see the table schema displayed.

4. Register the Jira Assets data as a temporary table:

   scala> jiraassets_df.registerTable("objects")

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

   scala> jiraassets_df.sqlContext.sql("SELECT ID, Name FROM Objects WHERE Label = SYD-1").collect.foreach(println)

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

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