Add Real-World DAG Debugging Strategies to Airflow Docs
- Dominant language
- Python
- Stars
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- Forks
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- Avg merge
- 2d 10h
- Merged PRs (30d)
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Description
### Description
Right now, most of the documentation around debugging Airflow DAGs references `dag.test()` and the command line. While this is a viable way to debug DAGs, it's not typically the way that it is done at the enterprise-level.
Update this docs page to include more "practical" or E2E testing techniques for DAGs. This might include running a Task and inspecting its XComs. Or re-running a single Task via the UI to validate that it ran successfully. There are tons of ways that this is done!
Existing Docs: https://airflow.apache.org/docs/apache-airflow/stable/core-concepts/debug.html
### Are you willing to submit a PR?
- [ ] Yes I am willing to submit a PR!
### Code of Conduct
- [x] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)
Contributor guide
Research direction
Start with the existing debug documentation page at https://airflow.apache.org/docs/apache-airflow/stable/core-concepts/debug.html and review its dag.test() and command-line guidance. Research practical enterprise DAG debugging approaches such as inspecting XComs and re-running a task from the UI, then update the page with useful E2E techniques and verify the documentation builds correctly.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- data-engineering, documentation
- Issue type
- Documentation
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Active
- Clarity
- Mostly clear
- Newbie friendliness
- 68/100