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

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

Install the CData JDBC Driver for Reckon

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

Start a Spark Shell and Connect to Reckon Data

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

   $ spark-shell --jars /CData/CData JDBC Driver for Reckon/lib/cdata.jdbc.reckon.jar
   

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

   When you are connecting to a local Reckon instance, you do not need to set any connection properties.

   Requests to Reckon are made through the Remote Connector. The Remote Connector runs on the same machine as Reckon and accepts connections through a lightweight, embedded Web server. The server supports SSL/TLS, enabling users to connect securely from remote machines.

   The first time you connect to your company file, authorize the Remote Connector with Reckon. See the "Getting Started" chapter of the help documentation for a guide.

   Built-in Connection String Designer

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

   java -jar cdata.jdbc.reckon.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 Reckon, using the connection string generated above.

   scala> val reckon_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:reckon:User=RCUser;Password=RCUserPassword;URL=http://remotehost:8166;").option("dbtable","Customers").option("driver","cdata.jdbc.reckon.ReckonDriver").load()
   

3. Once you connect and the data is loaded you will see the table schema displayed.

4. Register the Reckon data as a temporary table:

   scala> reckon_df.registerTable("customers")

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

   scala> reckon_df.sqlContext.sql("SELECT Name, CustomerBalance FROM Customers WHERE Type = Commercial").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 Reckon in Apache Spark, you are able to perform fast and complex analytics on Reckon 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.