DEVOPS
Datadog Cache-Efficiency Alert to GitLab Rollback Triage
Receives a Datadog monitor alert when Cloudflare cache hit ratio breaches its SLO, then auto-triages the suspect config change and drafts a GitLab rollback MR for human approval.
How it runs
The automated pipeline, trigger to output.
- TriggerDatadog cache-SLO monitor webhookDatadog
- ActionConfirm ratio + list ruleset changes in window (Cloudflare)Cloudflare
- LogicConfig-driven breach (recent ruleset edit)?
- ActionMap ruleset change to GitLab commitGitLab
- ActionOpen draft revert MR linking the alertGitLab
- OutputNotify channel with alert + suspect commit + MRSlack
What it does
Instead of polling, this workflow reacts to a Datadog monitor you already own. When Datadog fires a cache-hit-ratio SLO breach via webhook, the workflow pulls the alert window, asks Cloudflare which cache rules changed in that window, matches the change to a GitLab commit, and drafts a rollback merge request in draft state for an engineer to review and merge.
When to use it
Use it when your cache SLOs already live in Datadog and you want the alert to do more than page someone — you want it to arrive with the likely root-cause commit and a pre-built rollback attached. Keeps Datadog as the single source of alerting truth.
How it works
- 1Datadog monitor webhook triggers the workflow on a cache-hit-ratio breach.
- 2Cloudflare confirms the current ratio and lists ruleset changes inside the alert window.
- 3A logic step verifies the breach is config-driven (a recent ruleset edit exists) rather than traffic-driven.
- 4GitLab maps the ruleset change to its originating commit.
- 5GitLab opens a draft revert MR linking the Datadog alert.
- 6Slack notifies the channel with the alert, suspect commit, and draft MR.
Set it up
What you configure once, before turning it on.
- 1Connect DatadogMetrics, traces, log search.
- 2Connect CloudflareWorkers, Pages, R2, KV — the edge stack.
- 3Connect GitLabRepos, MRs, pipelines, registry.
- 4Connect SlackChannels, DMs, threads, mentions.
- 5Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 6Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 7Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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