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  • agoodm · 3 ✖

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  • Update contains_cftime_datetimes to avoid loading entire variable array · 3 ✖

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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
1458453929 https://github.com/pydata/xarray/pull/7494#issuecomment-1458453929 https://api.github.com/repos/pydata/xarray/issues/7494 IC_kwDOAMm_X85W7j2p agoodm 5179430 2023-03-07T16:22:21Z 2023-03-07T16:22:21Z CONTRIBUTOR

Thanks @Illviljan and @dcherian for helping to see this through.

 
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   Update contains_cftime_datetimes to avoid loading entire variable array 1563270549
1411206291 https://github.com/pydata/xarray/pull/7494#issuecomment-1411206291 https://api.github.com/repos/pydata/xarray/issues/7494 IC_kwDOAMm_X85UHUyT agoodm 5179430 2023-01-31T23:17:38Z 2023-01-31T23:17:38Z CONTRIBUTOR

@Illviljan I gave your update a quick test, it seems to work well enough and still maintains the performance improvement. It looks fine to me though I guess it looks like you still need to fix this failing mypy stuff now?

 
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   Update contains_cftime_datetimes to avoid loading entire variable array 1563270549
1410253782 https://github.com/pydata/xarray/pull/7494#issuecomment-1410253782 https://api.github.com/repos/pydata/xarray/issues/7494 IC_kwDOAMm_X85UDsPW agoodm 5179430 2023-01-31T12:22:02Z 2023-01-31T12:26:37Z CONTRIBUTOR

Thanks for the PR. However, does that actually make a difference? To me it looks like _contains_cftime_datetimes also only considers one element of the array.

https://github.com/pydata/xarray/blob/b4515582ffc8b7f63632bfccd109d19889d00384/xarray/core/common.py#L1779-L1780

This isn't actually the line of code that's causing the performance bottleneck, it's the access to var.data in the function call that is actually problematic as I explained in the issue thread. You can verify this yourself running this simple example before and after applying the changes in this PR:

```python import numpy as np import xarray as xr

str_array = np.arange(100000000).astype(str) ds = xr.DataArray(dims=('x',), data=str_array).to_dataset(name='str_array') ds = ds.chunk(x=10000) ds['str_array'] = ds.str_array.astype('O') # Needs to actually be object dtype to show the problem ds.to_zarr('str_array.zarr')

%time xr.open_zarr('str_array.zarr') ```

 
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   Update contains_cftime_datetimes to avoid loading entire variable array 1563270549

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