issue_comments: 732486436

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https://github.com/pydata/xarray/issues/4496#issuecomment-732486436 https://api.github.com/repos/pydata/xarray/issues/4496 732486436 MDEyOklzc3VlQ29tbWVudDczMjQ4NjQzNg== 1419010 2020-11-23T23:31:25Z 2020-11-23T23:31:39Z NONE

Hi. I'm trying to find an issue that is closest to the problem that I have, and this seems to be the best one, and most related.

Say, I have a zarr dataset with multiple variables Foo, Bar and Baz (and potentially, many more), there are 2 dimensions: x, y (potentially more). Say both Foo and Bar are large 2d arrays dims: x, y, Baz is relatively small 1d array dim: y. Say I would like to read that dataset with xarray but increase chunk from the native zarr chunk size for x and y but only for Foo and Bar, I would like to keep native chunking for Baz. afaiu currently I would do that with chunks parameter to open_dataset/open_zarr, but if I do do that via say dict(x=N, y=M) that will change chunking for all variables that use those dimensions, which isn't exactly what I need, I need those changed only for Foo and Bar. Is there a way to do that? Should that be part of the "harmonisation"? One could imagine that xarray could accept a dict of dict akin to {var: {dim: chunk_spec}} to specify chunking for specific variables.

Note that rechunk after reading is not what I want, I would like to specify chunking at read op.

Let me know if you would prefer me to open a completely new issue for this.

 
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