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/4475#issuecomment-702307334,https://api.github.com/repos/pydata/xarray/issues/4475,702307334,MDEyOklzc3VlQ29tbWVudDcwMjMwNzMzNA==,2560426,2020-10-01T18:07:55Z,2020-10-01T18:07:55Z,NONE,"Sounds good, I'll do this in the meantime. Still quite interested in `save_mfdataset` dealing with these lower level details, if possible. The ideal case would be loading with `load_mfdataset`, defining some ops lazily, then piping that directly to `save_mfdataset`.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,712189206
https://github.com/pydata/xarray/issues/4475#issuecomment-702265883,https://api.github.com/repos/pydata/xarray/issues/4475,702265883,MDEyOklzc3VlQ29tbWVudDcwMjI2NTg4Mw==,2560426,2020-10-01T16:52:59Z,2020-10-01T16:52:59Z,NONE,"Multiple threads (the default), because it's recommended ""for numeric code that releases the GIL (like NumPy, Pandas, Scikit-Learn, Numba, …)"" according to the dask docs.

I guess I could do multi-threaded for the compute part (everything up to the definition of `ds`), then multi-process for the write part, but doesn't that then require me to load everything into memory before writing?","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,712189206
https://github.com/pydata/xarray/issues/4475#issuecomment-702178407,https://api.github.com/repos/pydata/xarray/issues/4475,702178407,MDEyOklzc3VlQ29tbWVudDcwMjE3ODQwNw==,2560426,2020-10-01T14:34:28Z,2020-10-01T14:34:28Z,NONE,"Thank you, this works for me. However, it's quite slow and seems to scale faster than linearly as the length of `datasets` increases (the number of groups in the `groupby`).

Could it be connected to https://github.com/pydata/xarray/issues/2912#issuecomment-485497398 where they suggest to use `save_mfdataset` instead of `to_netcdf`? If so, there's a stronger case for supporting delayed objects in `save_mfdataset` as you said.

Appreciate the help!","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,712189206
https://github.com/pydata/xarray/issues/4475#issuecomment-701676076,https://api.github.com/repos/pydata/xarray/issues/4475,701676076,MDEyOklzc3VlQ29tbWVudDcwMTY3NjA3Ng==,2560426,2020-09-30T22:17:24Z,2020-09-30T22:17:24Z,NONE,"Unfortunately that doesn't work:

`TypeError: save_mfdataset only supports writing Dataset objects, received type <class 'dask.delayed.Delayed'>`","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,712189206