The Dark Stack at Peak Traffic: Dynamic SLIs That Change On-Call Behavior
When your observability goes dark in a surge, you don’t need more dashboards—you need signals that steer triage and gating.
On-call is not a battle of dashboards; it’s a discipline of predictive signals that steer safe, fast deployments.Back to all posts
Your on-call rotation is a ritual of denial: you chase dashboards that look healthy but can’t predict the next outage. I’ve watched teams bake in more dashboards only to have the data become noise during a surge. The real shift is defining SLIs that predict trouble and changing how on-call engineers respond when those警
Leading indicators must tie to the actual customer journey, not to internal abstractions. For checkout, for example, measure drift in P95 latency, rising error rate, queue depth, and saturation of downstream services; those signals tell you where to shine a flashlight rather than just ring the alarm bell.
Instrument once, gate everywhere: OpenTelemetry captures traces, metrics, and logs, Prometheus stores the metrics, and Alertmanager routes them with burn-rate thresholds. An example rule might look like: alert: SLI_Drift; expr: rate(http_request_duration_seconds_bucket{le="0.5"}[5m]) > 0.2; for: 10m; labels: {severity:
When you pair SLI health with rollout automation, you turn on-call from firefighting to controlled experimentation. If SLI drift crosses the threshold, trigger a canary rollback; if the drift persists, pause deployments until triage proves the system returns to healthy SLOs. This requires a policy layer—OPA or Kyverno—
In practice, you’ll run drills that simulate SLI breaches in staging and production with immediate rollback hooks. Track MTTR, time-to-dix, and the percentage of releases that meet SLOs in the first 24 hours; over time, you’ll see a material drop in true incidents and a steadier pace of safe changes.
Key takeaways
- Lead indicators beat vanity metrics every time; drift and burn-rate tell you where the fault will land.
- SLIs must map to customer journeys and to on-call playbooks, not teams or dashboards.
- Automate triage and deployment gates with GitOps and policy-as-code to reduce MTTR and burnout.
- Run regular drills that test SLI health in staging and production to keep on-call readiness sharp.
Implementation checklist
- Define critical customer journeys and map 3–5 leading indicators per journey.
- Instrument services with OpenTelemetry and export metrics to Prometheus; establish a single source of truth.
- Create SLOs and burn-rate budgets; deploy alert rules that escalate only when drift persists.
- Implement canary deployments gated by SLI health via ArgoCD and OPA/Kyverno policy checks.
- Automate runbooks and run quarterly on-call drills to validate triage playbooks.
- Integrate incident backlog with a modernization backlog to close the reliability gap
Questions we hear from teams
- Why are leading indicators better than vanity metrics for on-call readiness?
- Leading indicators forecast risk and trigger structured triage, whereas vanity metrics mask hidden faults until customers notice.
- How do you avoid alert fatigue when rolling out dynamic SLIs?
- Start with a small, journey-based scope, use burn-rate budgets, automate rollbacks, and gate deployments with policy checks so alerts remain meaningful.
- What’s the first concrete step to start dynamic SLIs in a legacy stack?
- Define 2–3 critical customer journeys, instrument with OpenTelemetry, and publish a baseline SLI drift threshold to trigger your first automated action.
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