ENGINEERING

EXPLAIN ANALYZE Evidence Comment for Risky Queries

When a PR adds a query flagged as over its latency envelope, run EXPLAIN ANALYZE against a Postgres replica and post the plan plus the Honeycomb baseline as a PR comment…

CategoryEngineering
Enginesim
Difficultyadvanced
Triggerevent
Steps5
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerPR review requestedGitHubGitHub
  • LogicIdentify flagged query and bind sample params
  • ActionRun EXPLAIN ANALYZE on replicaPostgreSQLPostgres
  • ActionFetch Honeycomb p95 baselineHoneycomb
  • OutputPost execution plan PR commentGitHubGitHub

What it does

When a pull request introduces a query that the budget gate flags as risky, this workflow gathers concrete evidence instead of just a red X. It runs EXPLAIN ANALYZE on a read replica and posts the execution plan alongside the Honeycomb p95 baseline as an inline PR comment, giving reviewers the data to approve or reject quickly.

When to use it

Use it to make slow-query reviews fact-based. Ideal when reviewers lack database expertise and need the planner output and production baseline served to them in the PR.

How it works

  1. 1A pull request review-requested event fires from GitHub.
  2. 2A logic step identifies the flagged query and substitutes representative bound parameters.
  3. 3A Postgres action runs EXPLAIN ANALYZE against a non-production replica and captures the plan, cost, and row estimates.
  4. 4A Honeycomb action fetches the current p95 baseline for the same query shape for comparison.
  5. 5A GitHub action posts a formatted PR comment with the plan, the baseline, and a flag on sequential scans or bad estimates.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect GitHubRepos, issues, pull requests, actions.
  2. 2
    Connect PostgresAny Postgres URL — query, write, migrate.
  3. 3
    Connect HoneycombDistributed traces and queries.
  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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