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

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

Install the CData JDBC Driver for Delighted

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

Start a Spark Shell and Connect to Delighted Data

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

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

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

   Delighted API Profile Settings

   Your private API Key is linked to your Delighted account and can be obtained from your account administrator or the Delighted account settings.

   Built-in Connection String Designer

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

   scala> val api_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:api:Profile=C:\profiles\Delighted.apip;ProfileSettings='APIKey=your_api_key';").option("dbtable","BouncedPeople").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 Delighted data as a temporary table:

   scala> api_df.registerTable("bouncedpeople")

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

   scala> api_df.sqlContext.sql("SELECT PersonId, Name FROM BouncedPeople WHERE BouncedAt = 2026-01-15T10:30:00Z").collect.foreach(println)

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

   

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