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

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

Install the CData JDBC Driver for Clockify

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

Start a Spark Shell and Connect to Clockify Data

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

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

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

   Clockify API Profile Settings

   Log into your Clockify account, navigate to Profile Settings, and copy the API key from the API section.

   Built-in Connection String Designer

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

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

   scala> api_df.registerTable("approvalrequests")

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

   scala> api_df.sqlContext.sql("SELECT Id, WorkspaceId FROM ApprovalRequests WHERE StatusState = PENDING").collect.foreach(println)

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

   

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