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

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

Install the CData JDBC Driver for Paddle

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

Start a Spark Shell and Connect to Paddle Data

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

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

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

   Using API Key Authentication

   Paddle uses API key authentication. To obtain an API key:

   1. Sign in to your Paddle account at https://vendors.paddle.com
   2. Navigate to Developer Tools > Authentication
   3. Click "Generate API Key"
   4. Assign the appropriate permissions for the data you wish to access
   5. Copy the generated key (sandbox keys begin with pdl_sdbx_apikey_; production keys begin with pdl_live_apikey_)

   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 Paddle API key.

   Example Connection String

   Profile=C:\profiles\Paddle.apip;AuthScheme=APIKey;ProfileSettings="APIKey=your_api_key";
   

   Connecting to Paddle

   Once the authentication is configured, you can connect to Paddle and query data from any of the available tables such as Products, Customers, Subscriptions, and Transactions.

   Built-in Connection String Designer

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

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

   scala> api_df.registerTable("products")

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

   scala> api_df.sqlContext.sql("SELECT , FROM Products 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 Paddle in Apache Spark, you are able to perform fast and complex analytics on Paddle 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.