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Bump modules minimal Go version to 1.22 #28452
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👍 for container-platform files
Regression DetectorRegression Detector ResultsRun ID: 25d186eb-6096-4afa-bf22-c935f0918b10 Metrics dashboard Target profiles Baseline: fe13dea Performance changes are noted in the perf column of each table:
No significant changes in experiment optimization goalsConfidence level: 90.00% There were no significant changes in experiment optimization goals at this confidence level and effect size tolerance.
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perf | experiment | goal | Δ mean % | Δ mean % CI | links |
---|---|---|---|---|---|
➖ | otel_to_otel_logs | ingress throughput | +1.08 | [+0.27, +1.90] | Logs |
➖ | tcp_syslog_to_blackhole | ingress throughput | +1.04 | [-11.87, +13.96] | Logs |
➖ | pycheck_1000_100byte_tags | % cpu utilization | +0.77 | [-4.11, +5.66] | Logs |
➖ | idle | memory utilization | +0.33 | [+0.30, +0.36] | Logs |
➖ | basic_py_check | % cpu utilization | +0.20 | [-2.48, +2.88] | Logs |
➖ | tcp_dd_logs_filter_exclude | ingress throughput | +0.00 | [-0.01, +0.01] | Logs |
➖ | uds_dogstatsd_to_api | ingress throughput | -0.00 | [-0.00, +0.00] | Logs |
➖ | file_tree | memory utilization | -0.42 | [-0.49, -0.35] | Logs |
➖ | uds_dogstatsd_to_api_cpu | % cpu utilization | -0.62 | [-1.52, +0.27] | Logs |
Explanation
A regression test is an A/B test of target performance in a repeatable rig, where "performance" is measured as "comparison variant minus baseline variant" for an optimization goal (e.g., ingress throughput). Due to intrinsic variability in measuring that goal, we can only estimate its mean value for each experiment; we report uncertainty in that value as a 90.00% confidence interval denoted "Δ mean % CI".
For each experiment, we decide whether a change in performance is a "regression" -- a change worth investigating further -- if all of the following criteria are true:
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Its estimated |Δ mean %| ≥ 5.00%, indicating the change is big enough to merit a closer look.
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Its 90.00% confidence interval "Δ mean % CI" does not contain zero, indicating that if our statistical model is accurate, there is at least a 90.00% chance there is a difference in performance between baseline and comparison variants.
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Its configuration does not mark it "erratic".
Force-merging the PR to fix main. |
The backport to
To backport manually, run these commands in your terminal: # Fetch latest updates from GitHub
git fetch
# Create a new working tree
git worktree add .worktrees/backport-7.56.x 7.56.x
# Navigate to the new working tree
cd .worktrees/backport-7.56.x
# Create a new branch
git switch --create backport-28452-to-7.56.x
# Cherry-pick the merged commit of this pull request and resolve the conflicts
git cherry-pick -x --mainline 1 782ccb5f4e427cbbfb152e9c082c6d33d6e14b88
# Push it to GitHub
git push --set-upstream origin backport-28452-to-7.56.x
# Go back to the original working tree
cd ../..
# Delete the working tree
git worktree remove .worktrees/backport-7.56.x Then, create a pull request where the |
Now that the OpenTelemetry project has bumped their minimal version to 1.22, do the same. open-telemetry/opentelemetry-collector-contrib#34658 Stay up to date, and fix failing version compatibility test #incident-29728. (cherry picked from commit 782ccb5)
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👍 from processes
What does this PR do?
Now that the OpenTelemetry project has bumped their minimal version to 1.22, do the same.
open-telemetry/opentelemetry-collector-contrib#34658
Motivation
Stay up to date, and fix failing version compatibility test #incident-29728.
Additional Notes
Possible Drawbacks / Trade-offs
Describe how to test/QA your changes