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DataFrame fusion when some layers are materialized#8451

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gjoseph92:dataframe-low-level-fuse-default
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DataFrame fusion when some layers are materialized#8451
gjoseph92 wants to merge 1 commit intodask:mainfrom
gjoseph92:dataframe-low-level-fuse-default

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Change the DataFrame optimization default so that DataFrames that may need low-level fusion still get it.

cc @rjzamora @ian-r-rose @jrbourbeau

Change the DataFrame optimization default so that DataFrames that may need low-level fusion still get it.

Closes dask#8447
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rjzamora commented Dec 3, 2021

At the moment, I am liking this approach. It will cause some pain for some mostly-HLG workflows, but I am struggling to think of a better solution and the obvious motivation to convert custom graphs to HLG Layers may be a good thing :)

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@rjzamora the one test that's failing is test_shuffle_priority, which you added in #7846. This is of course happening because of #7036: the priority annotations you've set in SimpleShuffleLayer get lost during optimization. This is very much a real failure.

If we just changed the test to use dask.datasets.timeseries instead of dd.from_pandas, it would work, because there would no longer be any materialized layers 😁 . Which shows just how brittle this all is!

@github-actions github-actions bot added the needs attention It's been a while since this was pushed on. Needs attention from the owner or a maintainer. label Jan 3, 2022
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Consider reactivating low-level DataFrame optimization when not all layers are Blockwise

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