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

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

Install the CData JDBC Driver for Greenhouse

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

Start a Spark Shell and Connect to Greenhouse Data

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

   $ spark-shell --jars /CData/CData JDBC Driver for Greenhouse/lib/cdata.jdbc.greenhouse.jar
   

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

   You need an API key to connect to Greenhouse. To create an API key, follow the steps below:

   1. Click the Configure icon in the navigation bar and locate Dev Center on the left.
   2. Select API Credential Management.
   3. Click Create New API Key.
   • Set "API Type" to Harvest.
   • Set "Partner" to custom.
   • Optionally, provide a description.
   4. Proceed to Manage permissions and select the appropriate permissions based on the resources you want to access through the driver.
   5. Copy the created key and set APIKey to that value.

   Built-in Connection String Designer

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

   java -jar cdata.jdbc.greenhouse.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 Greenhouse, using the connection string generated above.

   scala> val greenhouse_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:greenhouse:APIKey=YourAPIKey;").option("dbtable","Applications").option("driver","cdata.jdbc.greenhouse.GreenhouseDriver").load()
   

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

4. Register the Greenhouse data as a temporary table:

   scala> greenhouse_df.registerTable("applications")

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

   scala> greenhouse_df.sqlContext.sql("SELECT Id, CandidateId FROM Applications WHERE Status = Active").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 Greenhouse in Apache Spark, you are able to perform fast and complex analytics on Greenhouse 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.