Unit Testing DLT Pipelines

[dm7]

Now we are moving our DLT Pipelines into production, we would like to start looking at unit testing the transformation logic inside DLT notebooks.

We want to know how we can unit test the PySpark logic/transformations independently without having to spin up a DLT pipeline. Mainly because you can run a DLT notebook and it will output saying it's fine and to create a pipeline, but when you run the pipeline it will then throw the actual errors associated with things like incorrect schema locations etc. It's also hard to debug transformations within DLT as you can't readily inspect inputs/outputs or add debug logic.

Does anyone have any guidance on suitable approaches towards unit testing DLT pipeline notebooks? Thanks

[Kaniz_Fatma · Accepted solution]

Hi @dm7, 

• Instead of embedding all your transformation logic directly in the DLT notebook, create separate Python modules (files) for your transformations.
• This allows you to interactively test transformations from notebooks and write unit tests specifical...¹.
• You can then validate the results of your transformations using unit tests.
• Consider using specialized unit testing frameworks designed for Spark, such as:
  • Chispa: A library that provides additional testing functionality for Spark DataFrames. It includes methods for asserting DataFrame equality, schema validation, and more.
  • spark-testing-base: Another library that simplifies unit testing for Spark applications.
• These frameworks make it easier to write and execute tests for your PySpark transformations
• Create mock DataFrames with sample data that represent the expected input and output of your transformations.
• Write test cases that apply your transformations to the mock input and verify that the resulting output matches the expected DataFrame.
• You can use assertions or specialized testing libraries to validate the correctness of your transformations.
• While debugging transformations within DLT notebooks can be challenging, consider the following techniques:
  • Logging: Add logging statements to your transformations to capture intermediate results or debug information.
  • Sample Data: Use small sample data to test your transformations interactively.
  • Visual Inspection: Inspect DataFrames visually by displaying a few rows using .show() or .limit() methods.
• Remember that unit testing is essential for catching issues early and ensuring the robustness of your PySpark transformations. By following these practices, you can improve the reliability of your DLT pipelines. 
• If you have any further questions or need additional guidance, feel free to ask! 

[dm7]

Hi Kaniz - what if we have some CDC change data capture stages in a DLT pipeline?
E.g. we have a CDC stage which uses SCD type 1 to take the latest record based on datetime. - How would we go about unit testing this code functions correctly? As it is a native DLT function so couldn't lift and shift this to a separate Python notebook