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  • headtr1ck · 3 ✖

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  • Dataset.where performances regression. · 3 ✖

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  • COLLABORATOR · 3 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
1447037080 https://github.com/pydata/xarray/issues/7516#issuecomment-1447037080 https://api.github.com/repos/pydata/xarray/issues/7516 IC_kwDOAMm_X85WQAiY headtr1ck 43316012 2023-02-27T20:27:52Z 2023-02-27T20:27:52Z COLLABORATOR

I am a bit puzzled here... The dask graph looks identical, so it must be the way the indexers are constructed.

The major difference I can find is: The old version used np.unique while the new version uses xarrays cond.any(..)

Maybe someone with more experience in dask can help out?

 
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   Dataset.where performances regression. 1575938277
1445469752 https://github.com/pydata/xarray/issues/7516#issuecomment-1445469752 https://api.github.com/repos/pydata/xarray/issues/7516 IC_kwDOAMm_X85WKB44 headtr1ck 43316012 2023-02-26T21:16:35Z 2023-02-26T21:16:35Z COLLABORATOR

Git bisect pinpoints this to https://github.com/pydata/xarray/pull/6690 which funny enough, is my PR haha. I will look into it when I find time :)

 
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   Dataset.where performances regression. 1575938277
1445467918 https://github.com/pydata/xarray/issues/7516#issuecomment-1445467918 https://api.github.com/repos/pydata/xarray/issues/7516 IC_kwDOAMm_X85WKBcO headtr1ck 43316012 2023-02-26T21:07:56Z 2023-02-26T21:07:56Z COLLABORATOR

Can confirm, on my machine it went from 520ms to 5s

 
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   Dataset.where performances regression. 1575938277

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