Fix hypothesis tests that occasionally make the CI runners OOM [9796087821] #2624
Conversation
This file contains hidden or 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
vasil-pashov
requested review from
alexowens90,
IvoDD and
poodlewars
as code owners
vasil-pashov
added
patch
vasil-pashov
changed the title
vasil-pashov
force-pushed
the
vasil.pashov/fix-oom-flaky-resampling
branch
3 times, most recently
from
c27166a to
a081149
Compare
grusev
approved these changes
Aug 29, 2025
The issue is that we need to output a dense dataframe for Pandas. So if the date range is big and the frequency is small we can end up with too many rows in the output dataframe eating the memory of the runner. This also allows us to test all possible frequencies and extend the date range for generating dataframes.
vasil-pashov
force-pushed
the
vasil.pashov/fix-oom-flaky-resampling
branch
from
a081149 to
c28301d
Compare
alexowens90
approved these changes
Aug 29, 2025
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
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Outdated suggestions cannot be applied.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.
Reference Issues/PRs
Monday 9796087821
What does this implement or fix?
The issue is that we need to output a dense dataframe for Pandas. So if the date range is big and the frequency is small we can end up with too many rows in the output dataframe eating the memory of the runner.
This also allows us to test all possible frequencies and extend the date range for generating dataframes.
Any other comments?
Checklist
Checklist for code changes...