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

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

Install the CData JDBC Driver for Drip

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

Start a Spark Shell and Connect to Drip Data

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

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

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

   Drip API Profile Settings

   To use Token Authentication, specify your APIKey within the ProfileSettings connection property. The APIKey should be set to your Drip personal API Token.

   Built-in Connection String Designer

   For assistance in constructing the JDBC URL, use the connection string designer built into the Drip 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.

   👁 Using the built-in connection string designer to generate a JDBC URL (Salesforce is shown.)
   

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

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

   scala> api_df.registerTable("broadcasts")

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

   scala> api_df.sqlContext.sql("SELECT Id, Name FROM Broadcasts WHERE Status = scheduled").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 Drip in Apache Spark, you are able to perform fast and complex analytics on Drip 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.