feat: measure job wall-clock via jobs API; bump django pin to v6.0.6#4
Merged
Conversation
Switch case_study_collect.py from run-level timing (run_started_at..updated_at) to the job's started_at..completed_at (the Actions jobs API). Run-level also counts run orchestration (run record -> job assignment) and finalization — platform overhead that differs between hosted and self-hosted runners and biased the comparison (~6s of a ~17s job on a sampled run). Queue time stays excluded. - bump the django case study pin v5.2.9 -> v6.0.6 (still Python 3.14) - methodology + results docs updated to the jobs-API timing source
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
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
1 participant
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Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be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.
Switch the collector from run-level timing (
run_started_at..updated_at) to the job'sstarted_at..completed_at(Actions jobs API). Run-level also counts run orchestration and finalization — platform overhead that differs between hosted and self-hosted runners and biased the comparison (~6s of a ~17s job on a sampled run); queue time stays excluded.case_study_collect.py: job-level wall-clock via the jobs API (two API hops: runs list → per-run job timing);ghfailures surface cleanly.methodology.md/results/README.mdupdated to the jobs-API timing source.