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

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

Install the CData JDBC Driver for Reply.io

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

Start a Spark Shell and Connect to Reply.io Data

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

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

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

   The Reply.io API uses API Key authentication via the x-api-key request header.

   Using API Key Authentication

   Your Reply.io API key is required to create a connection. To obtain your API key:

   1. Log into your Reply.io account.
   2. Click your profile icon and select Settings.
   3. Navigate to the API section.
   4. Copy your API Key.

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

   • AuthScheme: Set this to APIKey.
   • APIKey: Set this to your Reply.io API key.
   • UserEmail (optional): Set this to the email address of the Reply.io user on whose behalf requests are made.

   Example connection string:

   Profile=C:\profiles\ReplyIO.apip;AuthScheme=APIKey;ProfileSettings='APIKey=your_api_key';
   

   Built-in Connection String Designer

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

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

   Configure the connection to Reply.io, using the connection string generated above.

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

   scala> api_df.registerTable("billinginfo")

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

   scala> api_df.sqlContext.sql("SELECT , FROM BillingInfo WHERE = ").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 Reply.io in Apache Spark, you are able to perform fast and complex analytics on Reply.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.