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

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

Install the CData JDBC Driver for SendGrid

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

Start a Spark Shell and Connect to SendGrid Data

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

   To make use of all the available features, provide the User and Password connection properties.

   To connect with limited features, you can set the APIKey connection property instead. See the "Getting Started" chapter of the help documentation for a guide to obtaining the API key.

   Built-in Connection String Designer

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

   java -jar cdata.jdbc.sendgrid.jar

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

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

   scala> val sendgrid_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:sendgrid:User=admin;Password=abc123;").option("dbtable","AdvancedStats").option("driver","cdata.jdbc.sendgrid.SendGridDriver").load()
3. Once you connect and the data is loaded you will see the table schema displayed.

4. Register the SendGrid data as a temporary table:

   scala> sendgrid_df.registerTable("advancedstats")

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

   scala> sendgrid_df.sqlContext.sql("SELECT Name, Clicks FROM AdvancedStats WHERE Type = Device").collect.foreach(println)

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

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