html_url,issue_url,id,node_id,user,created_at,updated_at,author_association,body,reactions,performed_via_github_app,issue
https://github.com/pydata/xarray/issues/1836#issuecomment-361466652,https://api.github.com/repos/pydata/xarray/issues/1836,361466652,MDEyOklzc3VlQ29tbWVudDM2MTQ2NjY1Mg==,2443309,2018-01-30T03:35:07Z,2018-01-30T03:35:07Z,MEMBER,"I tried the above example with the multiprocessing and distributed schedulers. With the multiprocessing scheduler, I can reproduce the error described above. With the distributed scheduler, no error is encountered. 


```Python
In [4]: import xarray as xr
   ...: import numpy as np
   ...: import dask.multiprocessing
   ...:
   ...: from dask.distributed import Client
   ...:
   ...: client = Client()
   ...: print(client)
   ...:
   ...: # Generate dummy data and build xarray dataset
   ...: mat = np.random.rand(10, 90, 90)
   ...: ds = xr.Dataset(data_vars={'foo': (('time', 'x', 'y'), mat)})
   ...:
   ...: # Write dataset to netcdf without compression
   ...: ds.to_netcdf('dummy_data_3d.nc')
   ...: # Write with zlib compersison
   ...: ds.to_netcdf('dummy_data_3d_with_compression.nc',
   ...:              encoding={'foo': {'zlib': True}})
   ...: # Write data as int16 with scale factor applied
   ...: ds.to_netcdf('dummy_data_3d_with_scale_factor.nc',
   ...:              encoding={'foo': {'dtype': 'int16',
   ...:                                'scale_factor': 0.01,
   ...:                                '_FillValue': -9999}})
   ...:
   ...: # Load data from netCDF files
   ...: ds_vanilla = xr.open_dataset('dummy_data_3d.nc', chunks={'time': 1})
   ...: ds_scaled = xr.open_dataset('dummy_data_3d_with_scale_factor.nc', chunks={'time': 1})
   ...: ds_compressed = xr.open_dataset('dummy_data_3d_with_compression.nc', chunks={'time': 1})
   ...:
   ...: # Do computation using dask's multiprocessing scheduler
   ...: foo = ds_vanilla.foo.mean(dim=['x', 'y']).compute()
   ...: foo = ds_scaled.foo.mean(dim=['x', 'y']).compute()
   ...: foo = ds_compressed.foo.mean(dim=['x', 'y']).compute()
```
----

I personally don't have any use cases that would prefer the multiprocessing scheduler over the distributed scheduler but I have been working on improving the I/O performance and stability with xarray and dask lately. If anyone would like to work on this, I'd gladly help this get cleaned up or put a more definitive no on whether or not this can/should work.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,289342234