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

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

Install the CData JDBC Driver for Respond.io

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

Start a Spark Shell and Connect to Respond.io Data

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

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

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

   Respondio API Profile Settings

   Respond.io uses API key authentication. To obtain an API key:

   1. Log in to your Respond.io account.
   2. Navigate to Settings in your workspace.
   3. Select Developer under the Integrations section.
   4. Copy the API key displayed on the page.

   After obtaining your API key, set the following connection properties:

   • AuthScheme: Set this to APIKey.

   Set the following in the ProfileSettings connection property:

   • APIKey: Set this to your Respond.io API key.

   Built-in Connection String Designer

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

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

   scala> api_df.registerTable("contacts")

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

   scala> api_df.sqlContext.sql("SELECT Id, Email FROM Contacts WHERE Identifier = id:438180634").collect.foreach(println)

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

   

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