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

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

Install the CData JDBC Driver for ClickUp

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

Start a Spark Shell and Connect to ClickUp Data

1. Open a terminal and start the Spark shell with the CData JDBC Driver for ClickUp JAR file as the jars parameter: $ spark-shell --jars /CData/CData JDBC Driver for ClickUp/lib/cdata.jdbc.api.jar
2. With the shell running, you can connect to ClickUp 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 ClickUp Profile on disk (e.g. C:\profiles\ClickUp.apip). Next, set the ProfileSettings connection property to the connection string for ClickUp (see below).

   ClickUp API Profile Settings

   In order to authenticate to ClickUp, you'll need to provide your API Key. You can find this token in your user settings, under the Apps section. At the top of the page you have the option to generate a personal token. Set the API Key to your personal token in the ProfileSettings property to connect.

   Built-in Connection String Designer

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

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

   scala> api_df.registerTable("tasks")

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

   scala> api_df.sqlContext.sql("SELECT Id, Name FROM Tasks WHERE Priority = High").collect.foreach(println)

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

Using the CData JDBC Driver for ClickUp in Apache Spark, you are able to perform fast and complex analytics on ClickUp data, combining the power and utility of Spark with your data. Download a free, 30 day trial of any of the 200+ CData JDBC Drivers and get started today.