issue_comments: 1137572812
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| 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/6633#issuecomment-1137572812 | https://api.github.com/repos/pydata/xarray/issues/6633 | 1137572812 | IC_kwDOAMm_X85DzfvM | 1217238 | 2022-05-25T17:10:04Z | 2022-05-25T17:10:04Z | MEMBER | Early versions of Xarray used to have lazy loading of data for indexes, but we removed this for the sake of simplicity. In principle we could restore lazy indexes, but another option (post explicit index refactor) might be an option for opening a dataset without creating indexes for 1D coordinates along dimensions. Another way to solve this sort of challenges might be to load index data in parallel when using Dask. Right now I believe the data corresponding to indexes is always loaded eagerly, without using Dask. All that said -- Do you have a specific example where this has been problematic? In my experience it has been pretty reasonable to use xarray.Dataset objects for schema-like templates, even with index data needing to be loaded eagerly. Possibly another Zarr chunking scheme for your index data could be more efficient? |
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