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

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

Install the CData JDBC Driver for BugHerd

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

Start a Spark Shell and Connect to BugHerd Data

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

   
   $ spark-shell --jars /CData/CData JDBC Driver for BugHerd/lib/cdata.jdbc.api.jar
   

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

   Start by setting the Profile connection property to the location of the BugHerd Profile on disk (e.g. C:\profiles\BugHerd.apip). Next, set the ProfileSettings connection property to the connection string for BugHerd (see below).

   BugHerd API Profile Settings

   BugHerd uses HTTP Basic authentication with an API key as the username. To obtain an API key:

   1. Sign in to your BugHerd account at https://www.bugherd.com
   2. Navigate to Settings > General Settings
   3. Locate the API Key section and copy the generated key

   After obtaining your API key, set the following connection properties:

   • AuthScheme: Set this to APIKey.

   Set the following in the ProfileSettings connection property:

   • APIKey: Set this to your BugHerd API key. The driver uses the key as the Basic auth username and sets the password internally to the literal value 'x' as required by BugHerd.

   Built-in Connection String Designer

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

   
   java -jar cdata.jdbc.api.jar
   

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

   

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

   
   scala> val api_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:api:Profile=C:\profiles\BugHerd.apip;AuthScheme=APIKey;ProfileSettings='APIKey=your_api_key';").option("dbtable","Tasks").option("driver","cdata.jdbc.api.APIDriver").load()
   

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

4. Register the BugHerd data as a temporary table:

   scala> api_df.registerTable("tasks")

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

   scala> api_df.sqlContext.sql("SELECT Id, Title FROM Tasks WHERE ProjectId = 519666").collect.foreach(println)

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

   

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