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  • TomNicholas · 2 ✖

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  • We need a fast path for open_mfdataset · 2 ✖

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id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
531905844 https://github.com/pydata/xarray/issues/1823#issuecomment-531905844 https://api.github.com/repos/pydata/xarray/issues/1823 MDEyOklzc3VlQ29tbWVudDUzMTkwNTg0NA== TomNicholas 35968931 2019-09-16T18:43:52Z 2019-09-16T18:43:52Z MEMBER

This is big if true!

But surely to close an issue raised by complaints about speed, we should really have some new asv speed tests?

 
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   We need a fast path for open_mfdataset 288184220
489027263 https://github.com/pydata/xarray/issues/1823#issuecomment-489027263 https://api.github.com/repos/pydata/xarray/issues/1823 MDEyOklzc3VlQ29tbWVudDQ4OTAyNzI2Mw== TomNicholas 35968931 2019-05-03T09:25:00Z 2019-05-03T09:25:00Z MEMBER

@dcherian I'm sorry, I'm very interested in this but after reading the issues I'm still not clear on what's being proposed:

What exactly is the bottleneck? Is it reading the coords from all the files? Is it loading the coord values into memory? Is it performing the alignment checks on those coords once they're in memory? Is it performing alignment checks on the dimensions? Is this suggestion relevant to datasets that don't have any coords?

Which of these steps would a join='exact' option omit?

A related optimization would be to allow the user to pass coords='minimal' (or other concat coords options) via open_mfdataset.

But this is already an option to open_mfdataset?

 
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   We need a fast path for open_mfdataset 288184220

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