Enhancement Request: Improve Documentation for the pyspark Decorator #45887
Description
What do you see as an issue?
The current documentation for the pyspark decorator is incomplete and lacks sufficient examples to demonstrate its full potential. Specifically:
It does not clearly explain how to handle errors when setting up the Spark context or connection (conn_id).
There is limited guidance on how to use the config_kwargs parameter to customize Spark configurations.
Advanced use cases, such as managing large datasets or integrating with existing Spark workflows, are missing.
Solving the problem
The problem can be solved by:
Adding Detailed Examples:
Provide examples that show how to handle errors during Spark session setup or misconfigured connection IDs.
Include use cases for leveraging config_kwargs to set specific Spark configurations like executor memory or core allocation.
Expanding Advanced Use Cases:
Document how the decorator can handle large datasets efficiently.
Include examples of integrating pyspark tasks with existing Spark jobs.
Highlighting Edge Cases:
Add scenarios where the Spark context might fail to initialize and how to debug these issues.
Improving Parameter Documentation:
Clearly describe each parameter (conn_id, config_kwargs, multiple_outputs) and their potential values with examples.
Anything else
This problem occurs every time a user refers to the documentation but cannot find sufficient information to address specific questions or scenarios. Enhancing the documentation will benefit all users and encourage wider adoption of the pyspark decorator.
Are you willing to submit PR?
- Yes I am willing to submit a PR!
Code of Conduct
- I agree to follow this project's Code of Conduct