Monitoring, Alerting and Observability Strategy
ELI5 Explanation
Monitoring tells you something is wrong. Observability helps you understand why it is wrong. Alerting tells the right person at the right time.
Technical Explanation
Build around golden signals: latency, traffic, errors, saturation. Use metrics for fast detection, logs for detail, traces for request path diagnosis. Alert only on user-impacting symptoms and SLO burn rates to reduce noise and pager fatigue.
Visual
Hands-on Commands
kubectl get --raw /metrics | head
kubectl get svc -n monitoring
kubectl describe prometheusrule -n monitoring
kubectl logs -n monitoring deploy/alertmanagerDebugging Scenario
Pager storms happen every night because CPU threshold alerts trigger on short spikes. Replace static threshold alert with burn-rate and sustained-window conditions. Add runbook links and ownership labels in alert annotations.
Beginner
- Difference between monitoring and observability?
- What are golden signals?
- Why are logs alone insufficient?
- What is alert fatigue?
- Why do we need runbooks?
Intermediate
- How do you design multi-window burn-rate alerts?
- When should you use RED vs USE metrics?
- How do labels impact cardinality cost?
- How do you avoid duplicate alerts?
- How do you monitor a black-box external API?
Scenario-based
- You have 200 alerts and miss major incidents. What do you cut first?
- Latency worsens but CPU is normal. Where do you look?
- Trace sampling misses rare errors. How do you adjust?
- On-call ignores alerts from one service. How do you regain trust?
- Metrics look healthy while users fail login. What telemetry gap exists?
Real-world Use Case
A SaaS platform replaced 180 threshold alerts with 24 symptom-focused alerts and SLO burn-rate policies. Pager volume dropped 62% and critical incident detection speed improved.
Summary
Strong observability strategy reduces noise and accelerates diagnosis. Next, you will apply it to incident handling and on-call operations.