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

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

Install the CData JDBC Driver for Google Directory

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

Start a Spark Shell and Connect to Google Directory Data

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

   $ spark-shell --jars /CData/CData JDBC Driver for Google Directory/lib/cdata.jdbc.googledirectory.jar
   

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

   Google uses the OAuth authentication standard. You can authorize the data provider to access Google Spreadsheets as an individual user or with a Google Apps Domain service account. See the Getting Started section of the data provider help documentation for an authentication guide.

   Built-in Connection String Designer

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

   java -jar cdata.jdbc.googledirectory.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 Google Directory, using the connection string generated above.

   scala> val googledirectory_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:googledirectory:OAuthClientId=MyOAuthClientId;OAuthClientSecret=MyOAuthClientSecret;CallbackURL=http://localhost;InitiateOAuth=GETANDREFRESH;").option("dbtable","MyTable").option("driver","cdata.jdbc.googledirectory.GoogleDirectoryDriver").load()
   

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

4. Register the Google Directory data as a temporary table:

   scala> googledirectory_df.registerTable("mytable")

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

   scala> googledirectory_df.sqlContext.sql("SELECT Id, Description FROM MyTable WHERE Status = confirmed").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 Google Directory in Apache Spark, you are able to perform fast and complex analytics on Google Directory 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.