ENGINEERING

Post-deploy canary: catch query slowdowns and gate a rollback

After each GitHub deploy this watches Honeycomb for new query latency regressions, and if the just-shipped change is the clear cause it pages on-call and proposes a rollback…

CategoryEngineering
Enginesim
Difficultyadvanced
Triggerevent
Steps5
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerGitHub deployment event firesGitHubGitHub
  • ActionQuery Honeycomb latency on touched paths after soakHoneycomb
  • LogicCompare post-deploy latency to baseline
  • ActionPage on-call if regression attributed to deployPagerDutyPagerDuty
  • OutputPost rollback recommendation to SlackSlack

What it does

Acts as an automatic canary tied to the deploy itself. Right after a release it watches the queries that release could touch, and if latency degrades it attributes the regression to the new deploy and escalates with a rollback recommendation.

When to use it

Use it on services where a bad query plan can ship silently and you want the deploy that caused it caught within minutes, not at the next morning's digest.

How it works

  1. 1A GitHub deployment event triggers the flow with the released commit range.
  2. 2After a soak window it queries Honeycomb for query latency on paths the commits touched.
  3. 3A decision step compares post-deploy latency to the pre-deploy baseline.
  4. 4If a regression clears the threshold and aligns with the deploy, it pages on-call via PagerDuty.
  5. 5It posts a Slack message naming the suspect commits with a rollback recommendation.
  6. 6If latency is clean, the run closes silently with a logged all-clear.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect GitHubRepos, issues, pull requests, actions.
  2. 2
    Connect HoneycombDistributed traces and queries.
  3. 3
    Connect PagerDutyIncidents, on-call, escalations.
  4. 4
    Connect SlackChannels, DMs, threads, mentions.
  5. 5
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  6. 6
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  7. 7
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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