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

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

Install the CData JDBC Driver for Postmark

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

Start a Spark Shell and Connect to Postmark Data

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

   $ spark-shell --jars /CData/CData JDBC Driver for Postmark/lib/cdata.jdbc.api.jar
   

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

   Using API Key Authentication

   Postmark uses server API tokens to authenticate requests. Each Postmark server has its own API token, which controls access to messages, bounces, templates, and statistics associated with that server.

   To obtain your Server API Token, log in to your Postmark account and navigate to the server you want to connect to. Go to API Tokens under the server settings and copy the token labeled Server API token.

   After setting the following connection properties, you are ready to connect:

   • AuthScheme: Set this to APIKey.
   • APIKey: Set this to your Postmark Server API Token. This value is sent as the X-Postmark-Server-Token header on every request.

   Example connection string:

   Profile=C:\profiles\Postmark.apip;AuthScheme=APIKey;ProfileSettings="APIKey=your-server-api-token"
   

   Connecting to Postmark

   Once the authentication is configured, you can connect to Postmark and query data from any of the available tables such as OutboundMessages, Bounces, and Templates.

   Built-in Connection String Designer

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

   java -jar cdata.jdbc.api.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 Postmark, using the connection string generated above.

   scala> val api_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:api:Profile=C:\profiles\Postmark.apip;AuthScheme=APIKey;ProfileSettings="APIKey=your-server-api-token"").option("dbtable","Bounces").option("driver","cdata.jdbc.api.APIDriver").load()
   

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

4. Register the Postmark data as a temporary table:

   scala> api_df.registerTable("bounces")

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

   scala> api_df.sqlContext.sql("SELECT , FROM Bounces WHERE = ").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 Postmark in Apache Spark, you are able to perform fast and complex analytics on Postmark 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.