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

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

Install the CData JDBC Driver for Facebook

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

Start a Spark Shell and Connect to Facebook Data

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

   $ spark-shell --jars /CData/CData JDBC Driver for Facebook/lib/cdata.jdbc.facebook.jar
   

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

   Most tables require user authentication as well as application authentication. Facebook uses the OAuth authentication standard. To authenticate to Facebook, you can use the embedded OAuthClientId, OAuthClientSecret, and CallbackURL or you can obtain your own by registering an app with Facebook.

   See the Getting Started chapter of the help documentation for a guide to using OAuth.

   Built-in Connection String Designer

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

   java -jar cdata.jdbc.facebook.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 Facebook, using the connection string generated above.

   scala> val facebook_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:facebook:InitiateOAuth=GETANDREFRESH;").option("dbtable","Posts").option("driver","cdata.jdbc.facebook.FacebookDriver").load()
   

3. Once you connect and the data is loaded you will see the table schema displayed.

4. Register the Facebook data as a temporary table:

   scala> facebook_df.registerTable("posts")

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

   scala> facebook_df.sqlContext.sql("SELECT FromName, LikesCount FROM Posts WHERE Target = thesimpsons").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 Facebook in Apache Spark, you are able to perform fast and complex analytics on Facebook 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.