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  • martindurant · 1 ✖

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  • Threadlocking in DataArray calculations for zarr data depending on where it's loaded from (S3 vs local) · 1 ✖

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  • CONTRIBUTOR · 1 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
1020190813 https://github.com/pydata/xarray/issues/6033#issuecomment-1020190813 https://api.github.com/repos/pydata/xarray/issues/6033 IC_kwDOAMm_X848zuBd martindurant 6042212 2022-01-24T15:00:53Z 2022-01-24T15:00:53Z CONTRIBUTOR

It would be interesting to turn on s3fs logging to see the access pattern, if you are interested. python fsspec.utils.setup_logging(logger_name="s3fs") Particularly, I am interested in whether xarray is loading chunk-by chunk serially versus concurrently. It would be good to know your chunksize versus total array size.

The dask version is interesting: xr.open_zarr(lookup(f"{path_forecast}/surface"), chunks={}) # uses dask where the dask partition size will be the same as the underlying chunk size. If you find a lot of latency (small chunks), you can sometimes get an order of magnitude download performance increase by specifying the chunksize along some dimension(s) to be a multiple of the on-disk size. I wouldn't normally recommend Dask just for loading the data into memory, but feel free to experiment.

 
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   Threadlocking in DataArray calculations for zarr data depending on where it's loaded from (S3 vs local) 1064837571

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