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

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

Install the CData JDBC Driver for Bitbucket

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

Start a Spark Shell and Connect to Bitbucket Data

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

   $ spark-shell --jars /CData/CData JDBC Driver for Bitbucket/lib/cdata.jdbc.bitbucket.jar
   

2. With the shell running, you can connect to Bitbucket with a JDBC URL and use the SQL Context load() function to read a table.

   For most queries, you must set the Workspace. The only exception to this is the Workspaces table, which does not require this property to be set, as querying it provides a list of workspace slugs that can be used to set Workspace. To query this table, you must set Schema to 'Information' and execute the query SELECT * FROM Workspaces>.

   Setting Schema to 'Information' displays general information. To connect to Bitbucket, set these parameters:

   • Schema: To show general information about a workspace, such as its users, repositories, and projects, set this to Information. Otherwise, set this to the schema of the repository or project you are querying. To get a full set of available schemas, query the sys_schemas table.
   • Workspace: Required if you are not querying the Workspaces table. This property is not required for querying the Workspaces table, as that query only returns a list of workspace slugs that can be used to set Workspace.

   Authenticating to Bitbucket

   Bitbucket supports OAuth authentication only. To enable this authentication from all OAuth flows, you must create a custom OAuth application, and set AuthScheme to OAuth.

   Be sure to review the Help documentation for the required connection properties for you specific authentication needs (desktop applications, web applications, and headless machines).

   Creating a custom OAuth application

   From your Bitbucket account:

   1. Go to Settings (the gear icon) and select Workspace Settings.
   2. In the Apps and Features section, select OAuth Consumers.
   3. Click Add Consumer.
   4. Enter a name and description for your custom application.
   5. Set the callback URL:
      • For desktop applications and headless machines, use http://localhost:33333 or another port number of your choice. The URI you set here becomes the CallbackURL property.
      • For web applications, set the callback URL to a trusted redirect URL. This URL is the web location the user returns to with the token that verifies that your application has been granted access.
   6. If you plan to use client credentials to authenticate, you must select This is a private consumer. In the driver, you must set AuthScheme to client.
   7. Select which permissions to give your OAuth application. These determine what data you can read and write with it.
   8. To save the new custom application, click Save.
   9. After the application has been saved, you can select it to view its settings. The application's Key and Secret are displayed. Record these for future use. You will use the Key to set the OAuthClientId and the Secret to set the OAuthClientSecret.

   Built-in Connection String Designer

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

   java -jar cdata.jdbc.bitbucket.jar
   

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

   👁 Using the built-in connection string designer to generate a JDBC URL (Salesforce is shown.)
   

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

   scala> val bitbucket_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:bitbucket:Workspace=myworkspaceslug;Schema=Information;InitiateOAuth=GETANDREFRESH;").option("dbtable","Issues").option("driver","cdata.jdbc.bitbucket.BitbucketDriver").load()
   

3. Once you connect and the data is loaded you will see the table schema displayed.

4. Register the Bitbucket data as a temporary table:

   scala> bitbucket_df.registerTable("issues")

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

   scala> bitbucket_df.sqlContext.sql("SELECT Title, ContentRaw FROM Issues WHERE Id = 1").collect.foreach(println)

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

   👁 Data in Apache Spark (Salesforce is shown)
   

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