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

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

Install the CData JDBC Driver for ActiveTrail

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

Start a Spark Shell and Connect to ActiveTrail Data

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

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

2. With the shell running, you can connect to ActiveTrail 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 ActiveTrail Profile on disk (e.g. C:\profiles\ActiveTrail.apip). Next, set the ProfileSettings connection property to the connection string for ActiveTrail (see below).

   ActiveTrail API Profile Settings

   ActiveTrail uses API key authentication. To obtain an API key:

   1. Log in to your ActiveTrail account at https://app.activetrail.com
   2. Navigate to your account settings and locate the API integration section
   3. Generate or copy your existing API key

   After obtaining your API key, set the following connection properties:

   • AuthScheme: Set this to APIKey.
   • APIKey: Set this to your ActiveTrail API key.

   Built-in Connection String Designer

   For assistance in constructing the JDBC URL, use the connection string designer built into the ActiveTrail 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 ActiveTrail, using the connection string generated above.

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

   scala> api_df.registerTable("groupmembers")

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

   scala> api_df.sqlContext.sql("SELECT Id, Email FROM GroupMembers WHERE GroupId = 356069").collect.foreach(println)

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

   

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