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/2494#issuecomment-431439592,https://api.github.com/repos/pydata/xarray/issues/2494,431439592,MDEyOklzc3VlQ29tbWVudDQzMTQzOTU5Mg==,2443309,2018-10-19T17:34:25Z,2018-10-19T17:34:25Z,MEMBER,"To clear a few things up, the parallel option in `netCDF4.Dataset` is not the same as the parallel option in `xarray.opne_mfdataset`. In xarray, that option is meant to help speed up the time it takes to open *many* files at once. If you are using dask distributed, this should be done using that scheduler.

If you are only seeing thread parallelism in the `open_mfdataset(..., parallel=True)` call, I would start by looking at your dask distributed setup.

Can you try this workflow with and without the parallel option and report back: 

```Python 
client = Client(...)
ds = xr.open_mfdataset(myfiles_path, concat_dim='t', engine='h5netcdf', paralel=...)
x = ds['x'].load().data
y = ds['y'].load().data
ds.close()
```

Provided that you are setting up distributed to use multiple processes, you should get parallelism from multiple processes in this case. 

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