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

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

Install the CData JDBC Driver for Freshdesk

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

Start a Spark Shell and Connect to Freshdesk Data

1. Open a terminal and start the Spark shell with the CData JDBC Driver for Freshdesk JAR file as the jars parameter: $ spark-shell --jars /CData/CData JDBC Driver for Freshdesk/lib/cdata.jdbc.freshdesk.jar
2. With the shell running, you can connect to Freshdesk with a JDBC URL and use the SQL Context load() function to read a table.

   FreshDesk makes use of basic authentication. To connect to data, set the following connection properties:

   • Domain: Set this to the domain associated with your FreshDesk account. For example, in your URL: https://my_domain.freshdesk.com.
   • APIKey: Set this to the API key associated with your FreshDesk account. To retrieve your API key, Log into your support Portal -> Click on profile picture in the top right corner -> profile settings page. The API key will be available below the change password section to the right.

   Built-in Connection String Designer

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

   java -jar cdata.jdbc.freshdesk.jar

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

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

   scala> val freshdesk_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:freshdesk:Domain=MyDomain;APIKey=myAPIKey;").option("dbtable","Tickets").option("driver","cdata.jdbc.freshdesk.FreshDeskDriver").load()
3. Once you connect and the data is loaded you will see the table schema displayed.

4. Register the Freshdesk data as a temporary table:

   scala> freshdesk_df.registerTable("tickets")

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

   scala> freshdesk_df.sqlContext.sql("SELECT Id, Name FROM Tickets WHERE Status = 2").collect.foreach(println)

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

Using the CData JDBC Driver for Freshdesk in Apache Spark, you are able to perform fast and complex analytics on Freshdesk data, combining the power and utility of Spark with your data. Download a free, 30 day trial of any of the 200+ CData JDBC Drivers and get started today.