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

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

Install the CData JDBC Driver for Discourse

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

Start a Spark Shell and Connect to Discourse Data

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

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

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

   The Discourse API uses API Key authentication.

   Using API Key Authentication

   Discourse requires API Key and Username for authentication. API Keys are generated in the Discourse Admin panel under the API section. You can create user-specific API keys or all-users API keys. Once you have obtained the API Key, set it along with the Domain and Username in the ProfileSettings connection property.

   Example Connection string

   Profile=C:\profiles\Discourse.apip;ProfileSettings='Domain=forum.example.com;APIKey=your_api_key;Username=your_username;'AuthScheme=APIKey;
   

   Built-in Connection String Designer

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

   scala> val api_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:api:Profile=C:\profiles\Discourse.apip;ProfileSettings='Domain=forum.example.com;APIKey=your_api_key;Username=your_username;'AuthScheme=APIKey;").option("dbtable","Backups").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 Discourse data as a temporary table:

   scala> api_df.registerTable("backups")

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

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

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

   

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