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

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

Install the CData JDBC Driver for Streak

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

Start a Spark Shell and Connect to Streak Data

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

   Use the following steps to generate a new API key for authenticating to Streak.

   1. Navigate to Gmail
   2. Click on the Streak dropdown to the right of the search bar
   3. Select the Integrations button. This will open a window where you can view existing integrations and create new API keys.
   4. Under the Streak API section of integrations, click the button to Create New Key.

   Built-in Connection String Designer

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

   java -jar cdata.jdbc.streak.jar

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

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

   scala> val streak_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:streak:ApiKey=8c84j9b4j54762ce809ej6a782d776j3;").option("dbtable","Users").option("driver","cdata.jdbc.streak.StreakDriver").load()
3. Once you connect and the data is loaded you will see the table schema displayed.

4. Register the Streak data as a temporary table:

   scala> streak_df.registerTable("users")

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

   scala> streak_df.sqlContext.sql("SELECT UserKey, Email FROM Users WHERE Email = user@domain.com").collect.foreach(println)

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

Using the CData JDBC Driver for Streak in Apache Spark, you are able to perform fast and complex analytics on Streak 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.