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

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

Install the CData JDBC Driver for Databox

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

Start a Spark Shell and Connect to Databox Data

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

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

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

   Databox API Profile Settings

   To use the Databox API, you need to generate a personal access key from your Databox account. Navigate to the Databox account settings under Data Manager and generate an API key (prefixed with pak_).

   After setting the following connection properties, you are ready to connect:

   • AuthScheme: Set this to APIKey.
   • APIKey: Set this to your Databox personal access key.

   Built-in Connection String Designer

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

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

   scala> api_df.registerTable("datasets")

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

   scala> api_df.sqlContext.sql("SELECT Id, Title FROM Datasets WHERE DataSourceId = 4976164").collect.foreach(println)

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

   

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