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

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

Install the CData JDBC Driver for Act-On

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

Start a Spark Shell and Connect to Act-On Data

1. Open a terminal and start the Spark shell with the CData JDBC Driver for Act-On JAR file as the jars parameter: $ spark-shell --jars /CData/CData JDBC Driver for Act-On/lib/cdata.jdbc.acton.jar
2. With the shell running, you can connect to Act-On with a JDBC URL and use the SQL Context load() function to read a table.

   ActOn uses the OAuth authentication standard. To authenticate using OAuth, you will need to create an app to obtain the OAuthClientId, OAuthClientSecret, and CallbackURL connection properties.

   See the Getting Started guide in the CData driver documentation for more information.

   Built-in Connection String Designer

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

   java -jar cdata.jdbc.acton.jar

   Fill in the connection properties and copy the connection string to the clipboard.

   Configure the connection to Act-On, using the connection string generated above.

   scala> val acton_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:acton:").option("dbtable","Images").option("driver","cdata.jdbc.acton.ActOnDriver").load()
3. Once you connect and the data is loaded you will see the table schema displayed.

4. Register the Act-On data as a temporary table:

   scala> acton_df.registerTable("images")

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

   scala> acton_df.sqlContext.sql("SELECT Id, Name FROM Images WHERE FolderName = New Folder").collect.foreach(println)

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

Using the CData JDBC Driver for Act-On in Apache Spark, you are able to perform fast and complex analytics on Act-On data, combining the power and utility of Spark with your data. Download a free, 30 day trial of any of the 200+ CData JDBC Drivers and get started today.