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  • dcherian · 3 ✖

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  • Means of zarr arrays cause a memory overload in dask workers · 3 ✖

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  • MEMBER · 3 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
1164672075 https://github.com/pydata/xarray/issues/6709#issuecomment-1164672075 https://api.github.com/repos/pydata/xarray/issues/6709 IC_kwDOAMm_X85Fa3xL dcherian 2448579 2022-06-23T17:18:05Z 2022-06-23T17:18:05Z MEMBER

Thanks @gjoseph92 I think there is a small but important increase in complexity here because we do ds.u.mean(), ds.v.mean(), (ds.u*ds.v).mean() all together so each chunk of ds.u and ds.v is used for two different outputs.

IIUC the example in https://github.com/dask/distributed/issues/6571 is basically (ds.u * ds.v).mean() purely.

 
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   Means of zarr arrays cause a memory overload in dask workers 1277437106
1164519484 https://github.com/pydata/xarray/issues/6709#issuecomment-1164519484 https://api.github.com/repos/pydata/xarray/issues/6709 IC_kwDOAMm_X85FaSg8 dcherian 2448579 2022-06-23T14:59:57Z 2022-06-23T15:07:11Z MEMBER

This looks like the classic distributed scheduling issue that might get addressed soon: https://github.com/dask/distributed/issues/6560

@gjoseph92 the MVCE in https://github.com/pydata/xarray/issues/6709#issuecomment-1164064469 might be a useful benchmark / regression test (ds.u.mean(), ds.v.mean(), (ds.u * ds.v).mean() all together). This kind of dependency -- variables and combinations of multiple variable being computed together -- turns up a lot in climate model workflows).

 
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   Means of zarr arrays cause a memory overload in dask workers 1277437106
1163255775 https://github.com/pydata/xarray/issues/6709#issuecomment-1163255775 https://api.github.com/repos/pydata/xarray/issues/6709 IC_kwDOAMm_X85FVd_f dcherian 2448579 2022-06-22T15:24:24Z 2022-06-22T15:24:24Z MEMBER

You can use dask.visualize(xarray_object)

You could try passing inline_array=True to open_zarr

 
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   Means of zarr arrays cause a memory overload in dask workers 1277437106

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