ENGINEERING

Pinpoint the deploy that slowed a query and open a Linear issue

When a Honeycomb trigger fires on rising query latency, this correlates the regression window against recent GitHub deploys, identifies the most likely offending PR.

CategoryEngineering
Enginesim
Difficultyintermediate
Triggerwebhook
Steps5
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerHoneycomb latency trigger firesHoneycomb
  • ActionFetch slow trace and regression window from HoneycombHoneycomb
  • ActionList GitHub deploys and merged PRs in the windowGitHubGitHub
  • LogicRank suspect PRs by deploy proximity and touched code path
  • OutputOpen Linear issue with trace and top suspect PRLinearLinear

What it does

Turns a Honeycomb latency alert into a named suspect. It pulls the slow trace, lines up the regression start against recent merges, and hands engineering a Linear issue that already points at the probable culprit PR — no manual archaeology.

When to use it

Run this whenever a service has a Honeycomb trigger watching p95/p99 on a query or endpoint and you want the on-call engineer to get a ranked suspect instead of a bare "latency is up" page.

How it works

  1. 1A Honeycomb trigger fires when query latency crosses its threshold and posts the trace and the regression window.
  2. 2The flow queries Honeycomb for the exemplar slow trace and the timestamp latency first stepped up.
  3. 3It pulls GitHub deploys and merged PRs that landed inside that window.
  4. 4A decision step ranks candidates by deploy time proximity and whether the PR touched the query's code path or migrations.
  5. 5It creates a Linear issue with the trace link, the regression timestamp, and the top suspect PR linked.
  6. 6The issue is assigned to the PR author's team for fast triage.

Set it up

What you configure once, before turning it on.

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

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