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DATA OPS

Deploy-Correlated Axiom Log Noise Guard

When a GitHub deployment finishes, it waits, then checks whether the deployed service's Axiom ingest rate jumped.

CategoryData Ops
Enginesim
Difficultyintermediate
Triggerevent
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerGitHub deployment-status webhookGitHubGitHub
  • LogicWait for post-deploy measurement window
  • ActionQuery Axiom pre vs post ingest rateAxiom
  • LogicProceed only if rate jumped past factor
  • ActionQuery Axiom for new dominant log messageAxiom
  • OutputPost deploy regression alert to SlackSlack

What it does

Catches log-volume regressions at their source: a deploy. After a release ships, it measures the service's Axiom ingest rate before and after, and if the new build is flooding logs it names the dominant new log message and warns the team while the change is still fresh in mind.

When to use it

Use it when most of your ingest-cost surprises trace back to a single bad deploy adding a chatty debug line or a hot-path warning. It ties the spike to the exact commit and author so the fix is obvious.

How it works

  1. 1A GitHub deployment-status webhook triggers when a release reaches production.
  2. 2The flow waits for a measurement window so post-deploy traffic stabilizes.
  3. 3It queries Axiom for the service's ingest rate in the window after the deploy and a matching window before.
  4. 4A logic step compares the two and proceeds only if the post-deploy rate exceeds the pre-deploy rate by the configured factor.
  5. 5It runs an Axiom query to find which log message or level newly dominates the volume.
  6. 6It posts a Slack alert naming the commit, author, delta rate, and the offending log line for a fast revert-or-fix decision.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect GitHubRepos, issues, pull requests, actions.
  2. 2
    Connect AxiomLog streams, queries, dashboards.
  3. 3
    Connect SlackChannels, DMs, threads, mentions.
  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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