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/pull/1528#issuecomment-364812486,https://api.github.com/repos/pydata/xarray/issues/1528,364812486,MDEyOklzc3VlQ29tbWVudDM2NDgxMjQ4Ng==,3019665,2018-02-12T01:51:40Z,2018-02-12T01:51:40Z,NONE,"So Zarr supports storing structured arrays. Maybe that’s what you are looking for, @martindurant? Would suggest using the latest 2.2.0 RC though as it fixed a few issues in this regard (particularly with NumPy 1.14).","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,253136694
https://github.com/pydata/xarray/pull/1528#issuecomment-350504017,https://api.github.com/repos/pydata/xarray/issues/1528,350504017,MDEyOklzc3VlQ29tbWVudDM1MDUwNDAxNw==,3019665,2017-12-09T20:38:58Z,2017-12-09T20:38:58Z,NONE,"> Just to confirm, if writes are aligned with chunk boundaries in the destination array then no locking is required.

As a minor point to complement what Matthew and Alistair have already said, one can pretty easily `rechunk` beforehand so that the chunks will have a nice 1-to-1 non-overlapping mapping on disk. Not sure whether this strategy is good enough to make default. However have had no issues doing this myself. Also would expect it is better than holding one lock over the whole Zarr Array. Though there may be some strange edge cases that I have not encountered.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,253136694