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  • rabernat · 6 ✖

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  • open_mfdataset usage and limitations. · 6 ✖

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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
510169853 https://github.com/pydata/xarray/issues/2501#issuecomment-510169853 https://api.github.com/repos/pydata/xarray/issues/2501 MDEyOklzc3VlQ29tbWVudDUxMDE2OTg1Mw== rabernat 1197350 2019-07-10T18:10:37Z 2019-07-10T18:10:37Z MEMBER

I believe that the memory issue is basically the same as https://github.com/dask/distributed/issues/2602.

The graphs look like: read --> rechunk --> write.

Reading and rechunking increase memory consumption. Writing relieves it. In Rich's case, the workers just load too much data before they write it. Eventually they run out of memory.

 
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   open_mfdataset usage and limitations. 372848074
506482057 https://github.com/pydata/xarray/issues/2501#issuecomment-506482057 https://api.github.com/repos/pydata/xarray/issues/2501 MDEyOklzc3VlQ29tbWVudDUwNjQ4MjA1Nw== rabernat 1197350 2019-06-27T19:36:51Z 2019-06-27T19:36:51Z MEMBER

@rsignell-usgs

Can you post the xarray repr of two sample files post pre-processing function?

 
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   open_mfdataset usage and limitations. 372848074
506481845 https://github.com/pydata/xarray/issues/2501#issuecomment-506481845 https://api.github.com/repos/pydata/xarray/issues/2501 MDEyOklzc3VlQ29tbWVudDUwNjQ4MTg0NQ== rabernat 1197350 2019-06-27T19:36:11Z 2019-06-27T19:36:11Z MEMBER

Are there any datasets on https://pangeo-data.github.io/pangeo-datastore/ that would exhibit this poor behavior?

The datasets in our cloud datastore are designed explicitly to avoid this problem!

 
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   open_mfdataset usage and limitations. 372848074
503641038 https://github.com/pydata/xarray/issues/2501#issuecomment-503641038 https://api.github.com/repos/pydata/xarray/issues/2501 MDEyOklzc3VlQ29tbWVudDUwMzY0MTAzOA== rabernat 1197350 2019-06-19T16:48:29Z 2019-06-19T16:48:29Z MEMBER

Try writing a preprocessor function that drops all coordinates python def drop_coords(ds): return ds.reset_coords(drop=True)

 
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   open_mfdataset usage and limitations. 372848074
432342306 https://github.com/pydata/xarray/issues/2501#issuecomment-432342306 https://api.github.com/repos/pydata/xarray/issues/2501 MDEyOklzc3VlQ29tbWVudDQzMjM0MjMwNg== rabernat 1197350 2018-10-23T17:27:50Z 2018-10-23T17:27:50Z MEMBER

^ I'm assuming you're in a notebook. If not, call print instead of display.

 
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   open_mfdataset usage and limitations. 372848074
432342180 https://github.com/pydata/xarray/issues/2501#issuecomment-432342180 https://api.github.com/repos/pydata/xarray/issues/2501 MDEyOklzc3VlQ29tbWVudDQzMjM0MjE4MA== rabernat 1197350 2018-10-23T17:27:30Z 2018-10-23T17:27:30Z MEMBER

In open_mfdataset, all of the dimensions and coordinates of the individual files have to be checked and verified to be compatible. That is often the source of slow performance with open_mfdataset.

To help us help you debug, please provide more information about the files your are opening. Specifically, please call open_dataset() directly on the first two files and copy and paste the output here. Specifically, do something like this python from glob import glob import xarray as xr all_files = glob('*1002*.nc') display(xr.open_dataset(all_files[0])) display(xr.open_dataset(all_files[1]))

 
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   open_mfdataset usage and limitations. 372848074

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