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https://github.com/pydata/xarray/issues/4406#issuecomment-988359778 https://api.github.com/repos/pydata/xarray/issues/4406 988359778 IC_kwDOAMm_X8466Sxi 13684161 2021-12-08T00:05:24Z 2021-12-08T00:06:22Z NONE

I am having a similar issue as well. Using latest versions of dask, xarray, distributed, fsspec, and gcsfs. I use h5netcdf backend because it is the only one that works with fsspec's binary stream, reading from cloud.

My workflow consists of: 1. Start dask client with 1 process per CPU, and 2 threads each. This is because it doesn't scale up reading from the cloud with threads. 2. Opening 12x monthly climate data (hourly sampled) using xarray.open_mfdataset 3. Using reasonable dask chunks in the open function 4. Take monthly average across time axis, and write to local NetCDF. 5. Repeate 2-4 for different years.

It is a hit or miss. It hangs towards the middle or end of a year. Next time I run it, it doesn't.

Once it hangs, and I hit stop, in the traceback it is stuck at await of threading lock.

Any ideas how to avoid this?

Things I tried: 1. Use processes only, 1 thread per worker 2. lock=True, lock=False on open_mfdataset 3. Dask scheduler as: spawn and forkserver 4. Different (but recent) versions of all the libraries

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