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

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

Install the CData JDBC Driver for Everhour

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

Start a Spark Shell and Connect to Everhour Data

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

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

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

   Everhour API Profile Settings

   Obtain your API Key from your Everhour profile page (scroll to the bottom of the page).

   Built-in Connection String Designer

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

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

   scala> api_df.registerTable("assignments")

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

   scala> api_df.sqlContext.sql("SELECT Id, ProjectId FROM Assignments WHERE Type = assignment").collect.foreach(println)

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

   

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