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2 rows where "created_at" is on date 2023-01-06, repo = 13221727 and user = 14371165 sorted by updated_at descending

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  • xarray · 2 ✖
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
1522810384 PR_kwDOAMm_X85G1P52 7424 array api - Add tests for aggregations Illviljan 14371165 open 0     3 2023-01-06T16:10:05Z 2023-04-20T13:37:13Z   MEMBER   1 pydata/xarray/pulls/7424
  • [ ] Closes #7243
  • [x] Tests added
  • [ ] User visible changes (including notable bug fixes) are documented in whats-new.rst
  • [ ] New functions/methods are listed in api.rst
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    xarray 13221727 pull
1523232313 PR_kwDOAMm_X85G2q5I 7426 Add lazy backend ASV test Illviljan 14371165 closed 0     1 2023-01-06T22:01:26Z 2023-01-12T16:00:05Z 2023-01-11T18:56:25Z MEMBER   0 pydata/xarray/pulls/7426

This tests xr.open_dataset without any slow file reading that can quickly become the majority of the performance time.

Related to #7374.

Timings for the new ASV-tests: ```

[ 50.85%] ··· dataset_io.IOReadCustomEngine.time_open_dataset ok [ 50.85%] ··· ======== ============ chunks
-------- ------------ None 265±4ms
{} 1.17±0.02s ======== ============ [ 54.69%] ··· dataset_io.IOReadSingleFile.time_read_dataset ok [ 54.69%] ··· ========= ============= ============= -- chunks
--------- --------------------------- engine None {}
========= ============= ============= scipy 4.81±0.1ms 6.65±0.01ms netcdf4 8.41±0.08ms 10.9±0.2ms ========= ============= ============= ``` From the IOReadCustomEngine test we can see that chunking datasets with many variables (2000+) is considerably slower.

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    xarray 13221727 pull

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