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  • WIP: Zarr backend · 2 ✖

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id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
364812486 https://github.com/pydata/xarray/pull/1528#issuecomment-364812486 https://api.github.com/repos/pydata/xarray/issues/1528 MDEyOklzc3VlQ29tbWVudDM2NDgxMjQ4Ng== jakirkham 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).

 
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   WIP: Zarr backend 253136694
350504017 https://github.com/pydata/xarray/pull/1528#issuecomment-350504017 https://api.github.com/repos/pydata/xarray/issues/1528 MDEyOklzc3VlQ29tbWVudDM1MDUwNDAxNw== jakirkham 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.

 
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   WIP: Zarr backend 253136694

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