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/1379#issuecomment-475573383,https://api.github.com/repos/pydata/xarray/issues/1379,475573383,MDEyOklzc3VlQ29tbWVudDQ3NTU3MzM4Mw==,26384082,2019-03-22T10:45:41Z,2019-03-22T10:45:41Z,NONE,"In order to maintain a list of currently relevant issues, we mark issues as stale after a period of inactivity

If this issue remains relevant, please comment here or remove the `stale` label; otherwise it will be marked as closed automatically
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,223231729
https://github.com/pydata/xarray/issues/1379#issuecomment-295993132,https://api.github.com/repos/pydata/xarray/issues/1379,295993132,MDEyOklzc3VlQ29tbWVudDI5NTk5MzEzMg==,7799184,2017-04-21T00:54:28Z,2017-04-21T10:05:27Z,CONTRIBUTOR,"I realised that some of the Datasets I was trying to concatenate had different coordinate values (for coordinates that I was assuming to be the same) so I guess xr.concat was trying to align these coordinates before concatenating and the resultant Dataset ended up being much larger than it should have been. When I ensure I only concatenate Datasets with consistent coordinates, I can do it.

However still resource consumption is quite high compared to when I so the same thing with numpy arrays. The memory increased by 42% using xr.concat (against 6% using np.concatenate) and the whole processing took about 4 times longer. ","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,223231729
https://github.com/pydata/xarray/issues/1379#issuecomment-296111630,https://api.github.com/repos/pydata/xarray/issues/1379,296111630,MDEyOklzc3VlQ29tbWVudDI5NjExMTYzMA==,1217238,2017-04-21T07:42:45Z,2017-04-21T07:42:45Z,MEMBER,"Alignment and broadcasting means that `xarray.concat` is inherently going to be slower than `np.concatenate`. But little effort has gone into optimizing it, so it is quite likely that performance could be improved with some effort.

My guess is that some combination of automatic alignment and/or broadcasting in `concat` is causing the issue with exploding memory usage here. See https://github.com/pydata/xarray/issues/1354 for related discussion -- contributions would certainly be welcome here.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,223231729
https://github.com/pydata/xarray/issues/1379#issuecomment-295970641,https://api.github.com/repos/pydata/xarray/issues/1379,295970641,MDEyOklzc3VlQ29tbWVudDI5NTk3MDY0MQ==,7799184,2017-04-20T23:41:38Z,2017-04-20T23:41:38Z,CONTRIBUTOR,"Also, reading all Datasets into a list and then trying to concatenate this list of Datasets at once also blows memory up.","{""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,223231729