-
-
Notifications
You must be signed in to change notification settings - Fork 1.1k
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Add lazy backend ASV test #7426
Merged
Merged
Conversation
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
github-actions
bot
added
run-benchmark
Run the ASV benchmark workflow
topic-performance
labels
Jan 6, 2023
Illviljan
added
run-benchmark
Run the ASV benchmark workflow
and removed
run-benchmark
Run the ASV benchmark workflow
labels
Jan 6, 2023
Timings for the new ASV-tests:
|
Illviljan
removed
topic-performance
run-benchmark
Run the ASV benchmark workflow
labels
Jan 10, 2023
dcherian
reviewed
Jan 12, 2023
xr.open_dataset(self.filepaths[engine], engine=engine, chunks=chunks) | ||
|
||
|
||
class IOReadCustomEngine: |
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
Thanks this is a great benchmark.
Just a minor question: Shall we stick this in xarray.tests
instead? I'm not sure if we have something similar for our tests already.
dcherian
added a commit
to dcherian/xarray
that referenced
this pull request
Jan 18, 2023
* main: (41 commits) v2023.01.0 whats-new (pydata#7440) explain keep_attrs in docstring of apply_ufunc (pydata#7445) Add sentence to open_dataset docstring (pydata#7438) pin scipy version in doc environment (pydata#7436) Improve performance for backend datetime handling (pydata#7374) fix typo (pydata#7433) Add lazy backend ASV test (pydata#7426) Pull Request Labeler - Workaround sync-labels bug (pydata#7431) see also : groupby in resample doc and vice-versa (pydata#7425) Some alignment optimizations (pydata#7382) Make `broadcast` and `concat` work with the Array API (pydata#7387) remove `numbagg` and `numba` from the upstream-dev CI (pydata#7416) [pre-commit.ci] pre-commit autoupdate (pydata#7402) Preserve original dtype when accessing MultiIndex levels (pydata#7393) [pre-commit.ci] pre-commit autoupdate (pydata#7389) [pre-commit.ci] pre-commit autoupdate (pydata#7360) COMPAT: Adjust CFTimeIndex.get_loc for pandas 2.0 deprecation enforcement (pydata#7361) Avoid loading entire dataset by getting the nbytes in an array (pydata#7356) `keep_attrs` for pad (pydata#7267) Bump pypa/gh-action-pypi-publish from 1.5.1 to 1.6.4 (pydata#7375) ...
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Labels
plan to merge
Final call for comments
run-benchmark
Run the ASV benchmark workflow
topic-backends
topic-performance
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
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:
From the IOReadCustomEngine test we can see that chunking datasets with many variables (2000+) is considerably slower.