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

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

Install the CData JDBC Driver for Google Spanner

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

Start a Spark Shell and Connect to Google Spanner Data

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

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

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

   Google Spanner uses the OAuth authentication standard. To authenticate using OAuth, you can use the embedded credentials or register an app with Google.

   See the Getting Started guide in the CData driver documentation for more information.

   Built-in Connection String Designer

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

   java -jar cdata.jdbc.googlespanner.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 Spanner, using the connection string generated above.

   scala> val googlespanner_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:googlespanner:ProjectId='project1';InstanceId='instance1';Database='db1';InitiateOAuth=GETANDREFRESH;").option("dbtable","Customer").option("driver","cdata.jdbc.googlespanner.GoogleSpannerDriver").load()
   

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

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

   scala> googlespanner_df.registerTable("customer")

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

   scala> googlespanner_df.sqlContext.sql("SELECT Name, TotalDue FROM Customer WHERE Id = 1").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 Spanner in Apache Spark, you are able to perform fast and complex analytics on Google Spanner 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.