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

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

Install the CData JDBC Driver for AfterShip

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

Start a Spark Shell and Connect to AfterShip Data

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

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

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

   Start by setting the Profile connection property to the location of the AfterShip Profile on disk (e.g. C:\profiles\Aftership.apip). Next, set the ProfileSettings connection property to the connection string for AfterShip (see below).

   AfterShip API Profile Settings

   Obtain your API key from the AfterShip account management panel by navigating to My Account > Settings > API Keys.

   Built-in Connection String Designer

   For assistance in constructing the JDBC URL, use the connection string designer built into the AfterShip 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.

   

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

   scala> val api_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:api:Profile=C:\profiles\Aftership.apip;ProfileSettings='APIKey=your_api_key';").option("dbtable","AllCouriers").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 AfterShip data as a temporary table:

   scala> api_df.registerTable("allcouriers")

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

   scala> api_df.sqlContext.sql("SELECT Slug, Name FROM AllCouriers WHERE Name = FedEx").collect.foreach(println)

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

   

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