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Users can specify chunks to break up the Xarray Dataset that may differ from the Dataset's own chunking mechanism. This is useful, for example, when working with Zarr. Here, you can set the Zarr chunks to be big and the unravel chunks to be small (more partitions in the resulting DataFrame). This can help limit the overall memory copying during unraveling.
alxmrs
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…ojection. - (#21) Define `ProjectableStream` trait so `PrunableStreamingTable` no longer stores `Arc<PyArrowStreamPartition>` directly; uses `Arc<dyn ProjectableStream>` instead, decoupling the pruning logic from the Python-specific stream type. - (#22) Forward `filters` to `StreamingTable::scan()` in both the push-projection and fallback branches (was silently passing `&[]`). - (#23) Use `Arc<[String]>` for projected column names so N partition clones share one allocation via atomic refcount increment rather than N Vec copies. - (#24) When projection contains only dimension columns (empty `data_vars_needed`), use `ds[[]]` to avoid loading data variables unnecessarily. - (#25) Add `test_count_star_passes_none_projection` to verify COUNT(*) does not push a projection to the factory (factory must receive None). Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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Users can specify chunks to break up the Xarray Dataset that may differ from the Dataset's own chunking mechanism. This is useful, for example, when working with Zarr. Here, you can set the Zarr chunks to be big and the unravel chunks to be small (more partitions in the resulting DataFrame). This can help limit the overall memory copying during unraveling.