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

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

Install the CData JDBC Driver for Rootly

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

Start a Spark Shell and Connect to Rootly Data

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

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

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

   Using API Key Authentication

   To authenticate using an API key, you will need to obtain your API key from your Rootly account settings.

   To get your API key:

   1. Log in to your Rootly account
   2. Navigate to Settings > API & Integrations
   3. Click on "API Tokens"
   4. Copy the generated token

   After setting the following connection properties, you are ready to connect:

   • AuthScheme: Set this to APIKey.
   • APIKey: Set this to your Rootly API token.

   Example Connection String

   Profile=Rootly.apip;Authscheme=APIKey;ProfileSettings="APIKey=your_apikey";
   

   Built-in Connection String Designer

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

   scala> val api_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:api:Profile=Rootly.apip;Authscheme=APIKey;ProfileSettings="APIKey=your_apikey";").option("dbtable","Incidents").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 Rootly data as a temporary table:

   scala> api_df.registerTable("incidents")

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

   scala> api_df.sqlContext.sql("SELECT ,  FROM Incidents WHERE Status = started").collect.foreach(println)

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

   

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