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

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

Install the CData JDBC Driver for Gumroad

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

Start a Spark Shell and Connect to Gumroad Data

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

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

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

   Using OAuth Authentication

   To authenticate to Gumroad and connect to your own data or to allow other users to connect to their data, you can use the OAuth 2.0 standard. This is the recommended authentication method.

   First you need to register an OAuth application with Gumroad. You can create an OAuth application by visiting your Gumroad account settings at https://app.gumroad.com/settings/advanced and navigating to the Applications section.

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

   • AuthScheme: Set this to OAuth.
   • InitiateOAuth: Set this to GETANDREFRESH. The CData API Profile for Gumroad will automatically walk through the OAuth process in order to obtain the access token.
   • OAuthClientID: Set this to the client_id that is specified in your app settings.
   • OAuthClientSecret: Set this to the client_secret that is specified in your app settings.
   • CallbackURL: Set this to the Redirect URI you specified in your app settings.

   Example connection string

   Profile=C:\profiles\Gumroad.apip;AuthScheme=OAuth;InitiateOAuth=GETANDREFRESH;OAuthClientId=your_client_id;OAuthClientSecret=your_client_secret;CallbackUrl=your_callback_url;
   

   Built-in Connection String Designer

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

   scala> val api_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:api:Profile=C:\profiles\Gumroad.apip;AuthScheme=OAuth;InitiateOAuth=GETANDREFRESH;OAuthClientId=your_client_id;OAuthClientSecret=your_client_secret;CallbackUrl=your_callback_url;").option("dbtable","CustomFields").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 Gumroad data as a temporary table:

   scala> api_df.registerTable("customfields")

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

   scala> api_df.sqlContext.sql("SELECT ,  FROM CustomFields WHERE ProductId = prod_abc123xyz").collect.foreach(println)

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

   

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