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

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

Install the CData JDBC Driver for Canny

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

Start a Spark Shell and Connect to Canny Data

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

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

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

   Canny API Profile Settings

   Canny uses a secret API key to control access to the API. The API key is sent as a JSON body parameter named 'apiKey' in every HTTP POST request to the Canny API endpoints.

   To authenticate with the Canny API, you need your company-level secret API key. You can find this key in your Canny account under Settings > API.

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

   • AuthScheme: Set this to APIKey.
   • APIKey: Set this to your Canny company-level secret API key from the Settings > API page in your Canny dashboard.

   Built-in Connection String Designer

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

   
   scala> val api_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:api:Profile=C:\profiles\Canny.apip;ProfileSettings='APIKey=your-canny-api-key';").option("dbtable","Boards").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 Canny data as a temporary table:

   scala> api_df.registerTable("boards")

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

   scala> api_df.sqlContext.sql("SELECT Id, Name FROM Boards WHERE IsPrivate = false").collect.foreach(println)

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

   

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