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

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

Install the CData JDBC Driver for PagerDuty

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

Start a Spark Shell and Connect to PagerDuty Data

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

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

2. With the shell running, you can connect to PagerDuty 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 PagerDuty Profile on disk (e.g. C:\profiles\PagerDuty.apip). Next, set the ProfileSettings connection property to the connection string for PagerDuty (see below).

   PagerDuty API Profile Settings

   Register an OAuth application via PagerDuty's Developer Mode to obtain a Client ID and Client Secret. The callback URL must match the redirect URI configured in your app settings.

   Built-in Connection String Designer

   For assistance in constructing the JDBC URL, use the connection string designer built into the PagerDuty 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 PagerDuty, using the connection string generated above.

   scala> val api_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:api:Profile=C:\profiles\PagerDuty.apip;Authscheme=OAuth;OAuthClientId=your_client_id;OAuthClientSecret=your_client_secret;CallbackUrl=your_callback_url;").option("dbtable","Addons").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 PagerDuty data as a temporary table:

   scala> api_df.registerTable("addons")

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

   scala> api_df.sqlContext.sql("SELECT Id, Type FROM Addons WHERE Type = full_page_addon").collect.foreach(println)

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

   

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