issue_comments: 773820054

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https://github.com/pydata/xarray/issues/2912#issuecomment-773820054 https://api.github.com/repos/pydata/xarray/issues/2912 773820054 MDEyOklzc3VlQ29tbWVudDc3MzgyMDA1NA== 60338532 2021-02-05T06:20:40Z 2021-02-05T06:56:05Z NONE

I am trying to perform a fairly simplistic operation on a dataset involving editing of variable and global attributes on individual netcdf files of 3.5GB each. The files load instantly using xr.open_dataset but dataset.to_netcdf() is too slow to export after the modifications. I have tried : 1. Without rechunking and dask invocations. 2. Varying chunk sizes followed by : 3. Usingload()before to_netcdf 4. Using persist() or compute () before to_netcdf I am working on a HPC with 10 distributed workers . In all cases, the time taken is more than 15 minutes per file. Is it expected? What else can I try to speed up this process apart from further parallelizing the single file operations using dask delayed?

 
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