I would simply dockerize the Airflow tasks and keep them in JS as-is.
Then you write the short DAG description in Python but make the task executor launch the Docker containers.
And then you're done.
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There are some jobs that contain rather simple JavaScript snippets, and I was trying to design a first prototype that simply takes the JS parts and runs them in a transpiler.
In this respect, I found a couple of packages that could be leveraged: - js2py: https://github.com/PiotrDabkowski/Js2Py - mini-racer: https://github.com/bpcreech/PyMiniRacer Yet, both seem to be abandoned packages that might not be suitable for usage in production.
Therefore, I was thinking about parsing and translating Javascript's abstract syntax trees to Python. Whereas a colleague suggested I bring up an LLM pipeline.
How much of an overkill that might be? Has anyone else ever dealt with a JavaScript-to-Python migration and could share heads-ups on strategies or pitfalls to avoid?
Separate the concerns: migrate the task orchestration to Airflow (or whatever) while keeping the actual Javascript task code largely unchanged.