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

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

Install the CData JDBC Driver for Pinterest

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

Start a Spark Shell and Connect to Pinterest Data

1. Open a terminal and start the Spark shell with the CData JDBC Driver for Pinterest JAR file as the jars parameter: $ spark-shell --jars /CData/CData JDBC Driver for Pinterest/lib/cdata.jdbc.pinterest.jar
2. With the shell running, you can connect to Pinterest with a JDBC URL and use the SQL Context load() function to read a table.

   Pinterest authentication is based on the standard OAuth flow. To authenticate, you must initially create an app via the Pinterest developer platform where you can obtain an OAuthClientId, OAuthClientSecret, and CallbackURL.

   Set InitiateOAuth to GETANDREFRESH and set OAuthClientId, OAuthClientSecret, and CallbackURL based on the property values for the app you created.

   See the Help documentation for other OAuth authentication flows.

   Built-in Connection String Designer

   For assistance in constructing the JDBC URL, use the connection string designer built into the Pinterest JDBC Driver. Either double-click the JAR file or execute the jar file from the command-line.

   java -jar cdata.jdbc.pinterest.jar

   Fill in the connection properties and copy the connection string to the clipboard.

   Configure the connection to Pinterest, using the connection string generated above.

   scala> val pinterest_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:pinterest:OAuthClientId=YourClientId;OAuthClientSecret=YourClientSecret;CallbackURL='https://localhost:33333'").option("dbtable","Users").option("driver","cdata.jdbc.pinterest.PinterestDriver").load()
3. Once you connect and the data is loaded you will see the table schema displayed.

4. Register the Pinterest data as a temporary table:

   scala> pinterest_df.registerTable("users")

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

   scala> pinterest_df.sqlContext.sql("SELECT Id, Username FROM Users WHERE FirstName = Jane").collect.foreach(println)

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

Using the CData JDBC Driver for Pinterest in Apache Spark, you are able to perform fast and complex analytics on Pinterest data, combining the power and utility of Spark with your data. Download a free, 30 day trial of any of the 200+ CData JDBC Drivers and get started today.