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  • Zac-HD · 2 ✖

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  • Tremendous slowdown when using dask integration · 2 ✖

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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
431718845 https://github.com/pydata/xarray/issues/2499#issuecomment-431718845 https://api.github.com/repos/pydata/xarray/issues/2499 MDEyOklzc3VlQ29tbWVudDQzMTcxODg0NQ== Zac-HD 12229877 2018-10-22T00:50:22Z 2018-10-22T00:50:22Z CONTRIBUTOR

I'd also try to find a way to use a groupby or apply_along_axis without stacking and unstacking the data, and to choose chunks that match the layout on disk - i.e. try lon=1 if the order is time, lat, lon. If the time observations are not contiguous in memory, it's probably worth reshaping the whole array and writing it back to disk up front.

 
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   Tremendous slowdown when using dask integration 372244156
431657200 https://github.com/pydata/xarray/issues/2499#issuecomment-431657200 https://api.github.com/repos/pydata/xarray/issues/2499 MDEyOklzc3VlQ29tbWVudDQzMTY1NzIwMA== Zac-HD 12229877 2018-10-21T10:30:23Z 2018-10-21T10:30:23Z CONTRIBUTOR

dataset = xr.open_dataset(netcdf_precip, chunks={'lat': 1})

This makes me really suspicious - lat=1 is a very very small chunk size, and completely unchunked in time and lon. Without knowing anything else, I'd try chunks=dict(lat=200, lon=200) or higher depending on the time dim - Dask is most efficient with chunks of around 10MB for most workloads.

This all also depends on the data layout on disk too - can you share repr(xr.open_dataset(netcdf_precip))? What does ncdump say?

 
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   Tremendous slowdown when using dask integration 372244156

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