Github user Stibbons commented on the issue:
https://github.com/apache/spark/pull/13599
Maybe we can try to split this work in several parts to ease merge. I
clearly think python job should be deployed a bit like jar dependencies are
specified for scala (with `--packages`), and this proposal provides such
feature to Pyspark.
For the moment, Python deployment described in this PR works on standalone
environment, and according to Jeff on YARN as well, and I soon I have the Mesos
cluster configured (We are close to have it working here at work), we then can
cover the main 3 deployment method.
Could it be possible to have iterative merges, starting with a limited
support (maybe behind a feature gate/"experimental feature"?) and iteratively
increasing the extend of this feature, instead of a big bang change that is
unfortunately not yet merged :( ?
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