Explore and add static checks for DAGs for early detection of common issues

[omkar-foss]

Description

As per users' feedback in the Airflow Debugging Survey 2024, 48.3% of respondents chose early issue detection during execution as one of their top 2 choices.

Use case/motivation

Goal of this issue:

• Enhance early detection of DAG issues to minimize dev time
• Some sort of static analysis similar to Ruff checks for DAGs
• Runtime analysis of DAGs if feasible can also be done to save dev time

Related issues

Parent Issue: #40975

Are you willing to submit a PR?

• Yes I am willing to submit a PR!

Code of Conduct

• I agree to follow this project's Code of Conduct

[omkar-foss]

In the context of this issue, DAG linting as mentioned by this article (shared by @potiuk on slack) can also be explored:

https://medium.com/@snir.isl/mastering-airflow-dag-standardization-with-pythons-ast-a-deep-dive-into-linting-at-scale-1396771a9b90

Inspired by above article, may be we can have commands like airflow dag lint <dagname>.py for checking common DAGs issues and airflow dag grade <dagname>.py for grading DAGs based on their code quality.

[potiuk]

Love that idea. We could even had some way of checking for "best practices" - like not using DB while parsing etc. This might also be then used as part of the upgrade-check mechanism that we are planning for Airflow 2-> 3 migration - see #41641 cc: @Lee-W

[Lee-W]

I feel it's a bit different. 🤔 But for not using DB while parsing, that's something we should check in the upgrade check. But the "best practices" thing would probably be something else. Probably integrating with ruff or building our own linter would be better.