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

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

Install the CData JDBC Driver for Toggl

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

Start a Spark Shell and Connect to Toggl Data

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

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

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

   Toggl API Profile Settings

   Obtain your API key from your Toggl account Profile Settings page, where it is listed under the API section.

   Built-in Connection String Designer

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

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

   scala> api_df.registerTable("clients")

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

   scala> api_df.sqlContext.sql("SELECT Id, WorkspaceId FROM Clients WHERE IsArchived = false").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 Toggl in Apache Spark, you are able to perform fast and complex analytics on Toggl 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.