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

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

Getting Started

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Install the CData JDBC Driver for BigQuery

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

Start a Spark Shell and Connect to BigQuery Data

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

   $ spark-shell --jars /CData/CData JDBC Driver for BigQuery/lib/cdata.jdbc.googlebigquery.jar
   

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

   Google uses the OAuth authentication standard. To access Google APIs on behalf of individual users, you can use the embedded credentials or you can register your own OAuth app.

   OAuth also enables you to use a service account to connect on behalf of users in a Google Apps domain. To authenticate with a service account, register an application to obtain the OAuth JWT values.

   In addition to the OAuth values, specify the DatasetId and ProjectId. 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 BigQuery JDBC Driver. Either double-click the JAR file or execute the jar file from the command-line.

   java -jar cdata.jdbc.googlebigquery.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 BigQuery, using the connection string generated above.

   scala> val googlebigquery_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:googlebigquery:DataSetId=MyDataSetId;ProjectId=MyProjectId;InitiateOAuth=GETANDREFRESH;").option("dbtable","Orders").option("driver","cdata.jdbc.googlebigquery.GoogleBigQueryDriver").load()
   

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

4. Register the BigQuery data as a temporary table:

   scala> googlebigquery_df.registerTable("orders")

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

   scala> googlebigquery_df.sqlContext.sql("SELECT OrderName, Freight FROM Orders WHERE ShipCity = New York").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 BigQuery in Apache Spark, you are able to perform fast and complex analytics on BigQuery 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.