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/2852#issuecomment-478621867,https://api.github.com/repos/pydata/xarray/issues/2852,478621867,MDEyOklzc3VlQ29tbWVudDQ3ODYyMTg2Nw==,1217238,2019-04-01T15:16:30Z,2019-04-01T15:16:30Z,MEMBER,Roughly how many unique labels do you have?,"{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,425320466
https://github.com/pydata/xarray/issues/2852#issuecomment-478415169,https://api.github.com/repos/pydata/xarray/issues/2852,478415169,MDEyOklzc3VlQ29tbWVudDQ3ODQxNTE2OQ==,1217238,2019-04-01T02:31:58Z,2019-04-01T02:31:58Z,MEMBER,"The current design of `GroupBy.apply()` in xarray is entirely ignorant of dask: it simply uses a `for` loop over the grouped variable to built up a computation with high level array operations.

This makes operations that group over large keys stored in dask inefficient. This *could* be done efficiently (`dask.dataframe` does this, and might be worth trying in your case) but it's a more challenging distributed computing problem, and xarray's current data model would not know how large of a dimension to create for the returned ararys (doing this properly would require supporting arrays with unknown dimension sizes).","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,425320466