ENGINEERING
Post-deploy p95 latency diff with change attribution
After each GitLab deploy, compares query latency percentiles before vs. after the release window, flags any query whose p95 regressed past threshold.
How it runs
The automated pipeline, trigger to output.
- TriggerGitLab pipeline-success webhook (prod deploy)GitLab
- ActionPull pre/post p50/p95/p99 per query from HoneycombHoneycomb
- LogicDiff windows, filter to p95 regressions past threshold
- ActionRead pipeline changeset to attribute suspect commitsGitLab
- OutputFile GitLab issue with delta and suspect commit rangeGitLab
What it does
When a GitLab pipeline marks a production deploy as successful, this workflow pulls per-query p50/p95/p99 latency from Honeycomb for the window just before and just after the release. It diffs the two windows, isolates queries whose p95 jumped past a relative and absolute threshold, then attributes each regression to the commits shipped in that deploy by reading the pipeline's changeset. Confirmed regressions become a GitLab issue with the offending query, the latency delta, and the suspect commit range.
When to use it
Run this when you ship database-backed services frequently and want every deploy automatically audited for query regressions instead of waiting for an on-call page. Ideal for teams where one deploy bundles several merge requests and you need the regression tied back to a specific change.
How it works
- 1GitLab pipeline-success webhook fires on a production deploy.
- 2Query Honeycomb for p50/p95/p99 grouped by normalized query, for the pre-deploy and post-deploy windows.
- 3Diff the windows and filter to queries breaching the p95 regression threshold.
- 4If none breach, exit quietly; otherwise read the pipeline changeset to attribute the suspect commits.
- 5File a GitLab issue with the regression table and suspect commit range.
Set it up
What you configure once, before turning it on.
- 1Connect GitLabRepos, MRs, pipelines, registry.
- 2Connect HoneycombDistributed traces and queries.
- 3Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 4Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 5Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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