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id node_id number title user state locked assignee milestone comments created_at updated_at ▲ closed_at author_association active_lock_reason draft pull_request body reactions performed_via_github_app state_reason repo type
735199603 MDExOlB1bGxSZXF1ZXN0NTE0NjMzODkx 4560 Optimize slice_slice for faster isel of huge datasets dionhaefner 11994217 closed 0     5 2020-11-03T10:26:38Z 2020-11-05T19:45:44Z 2020-11-05T19:07:24Z CONTRIBUTOR   0 pydata/xarray/pulls/4560

I noticed that reading small slices of huge datasets (>1e8 rows) was very slow, even if they were properly chunked. I traced the issue back to xarray.core.indexing.slice_slice, which essentially calls np.arange(ds_size) to compute a slice. This is obviously O(ds_size), even if the actual slice to be read is tiny.

You can see the issue in this gist:

https://gist.github.com/dionhaefner/a3e97bae0a4e28f0d39294074419a683

I took the liberty to optimize the function by computing the resulting slice arithmetically. With this in place, reading from disk is now the bottleneck as it should be. I saw performance increases by about a factor of 10, but this obviously varies with dimension size, slice size, and chunk size.

  • [x] Passes isort . && black . && mypy . && flake8
 
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     xarray 13221727 pull

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