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  • nbren12 · 6 ✖

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  • Performance: numpy indexes small amounts of data 1000 faster than xarray · 6 ✖

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  • CONTRIBUTOR · 6 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
786764651 https://github.com/pydata/xarray/issues/2799#issuecomment-786764651 https://api.github.com/repos/pydata/xarray/issues/2799 MDEyOklzc3VlQ29tbWVudDc4Njc2NDY1MQ== nbren12 1386642 2021-02-26T16:51:50Z 2021-02-26T16:51:50Z CONTRIBUTOR

@jhamman Weren't you talking about an xarray lite (TM) package?

 
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   Performance: numpy indexes small amounts of data 1000 faster than xarray 416962458
553294966 https://github.com/pydata/xarray/issues/2799#issuecomment-553294966 https://api.github.com/repos/pydata/xarray/issues/2799 MDEyOklzc3VlQ29tbWVudDU1MzI5NDk2Ng== nbren12 1386642 2019-11-13T08:32:05Z 2019-11-13T08:32:16Z CONTRIBUTOR

This variable workaround is awesome @max-sixty. Are there any guidelines on when to use Variable vs DataArray? Some calculations (e.g. fast difference and derivatives/stencil operations) seem cleaner without explicit coordinate labels.

 
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   Performance: numpy indexes small amounts of data 1000 faster than xarray 416962458
469451210 https://github.com/pydata/xarray/issues/2799#issuecomment-469451210 https://api.github.com/repos/pydata/xarray/issues/2799 MDEyOklzc3VlQ29tbWVudDQ2OTQ1MTIxMA== nbren12 1386642 2019-03-04T22:40:07Z 2019-03-04T22:40:07Z CONTRIBUTOR

Sure, I've been using that as a workaround as well. Unfortunately, that approach throws away all the nice info (e.g. metadata, coordinate) that xarray objects have and requires duplicating much of xarray's indexing logic.

 
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   Performance: numpy indexes small amounts of data 1000 faster than xarray 416962458
469447632 https://github.com/pydata/xarray/issues/2799#issuecomment-469447632 https://api.github.com/repos/pydata/xarray/issues/2799 MDEyOklzc3VlQ29tbWVudDQ2OTQ0NzYzMg== nbren12 1386642 2019-03-04T22:27:57Z 2019-03-04T22:27:57Z CONTRIBUTOR

@max-sixty I tend to agree this use case could be outside of the scope of xarray. It sounds like significant progress might require re-implementing core xarray objects in C/Cython. Without more than 10x improvement, I would probably just continue using numpy arrays.

 
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   Performance: numpy indexes small amounts of data 1000 faster than xarray 416962458
469443856 https://github.com/pydata/xarray/issues/2799#issuecomment-469443856 https://api.github.com/repos/pydata/xarray/issues/2799 MDEyOklzc3VlQ29tbWVudDQ2OTQ0Mzg1Ng== nbren12 1386642 2019-03-04T22:15:49Z 2019-03-04T22:15:49Z CONTRIBUTOR

Thanks so much @shoyer. I didn't realize there was that much overhead for a single function call. OTOH, 2x slower than numpy would be way better than 1000x.

After looking at the profiling info more, I tend to agree with your 10x maximum speed-up. A couple of particularly slow functions (e.g. Dataset._validate_indexers) account for about 75% of run time. However, the remaining 25% is split across several other pure python routines.

 
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   Performance: numpy indexes small amounts of data 1000 faster than xarray 416962458
469394020 https://github.com/pydata/xarray/issues/2799#issuecomment-469394020 https://api.github.com/repos/pydata/xarray/issues/2799 MDEyOklzc3VlQ29tbWVudDQ2OTM5NDAyMA== nbren12 1386642 2019-03-04T19:45:11Z 2019-03-04T19:45:11Z CONTRIBUTOR

cc @rabernat 

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   Performance: numpy indexes small amounts of data 1000 faster than xarray 416962458

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