Monitoring & Observability Essentials

Monitoring and observability keep systems reliable. In DevOps, you’re not just watching dashboards—you’re building feedback loops.

Theory first: reliability is a feedback system

Observability works when telemetry closes the loop between user impact, system behavior, and engineering action. Metrics, logs, and traces are only useful if they shorten detection and diagnosis time.

Use this theory to guide implementation: instrument what matters to reliability goals, define explicit service objectives, and build alerts that trigger action—not noise.

Learning outcomes

You’ll learn:

• signals: metrics, logs, traces
• how to design meaningful alerts
• why SLOs matter for reliability

1) Metrics, logs, traces (the three signals)

• Metrics: numeric time-series (e.g. latency p95, error rate)
• Logs: event records (e.g. request failed with reason)
• Traces: end-to-end request paths (distributed systems debugging)

2) Monitoring: “is it broken?”

Monitoring typically uses thresholds:

• error rate > 1%
• CPU > 90%
• disk free < 10%

3) Observability: “why is it broken?”

Observability needs correlation:

• every request has an ID (trace/request id)
• logs include structured fields
• metrics can be sliced by endpoint/tenant

4) Alerting that doesn’t wake you up at 3am

Good alert rules:

• alert on impact, not noise
• include how to respond
• use burn-rate or multi-window strategies for SLOs (conceptually)

Anti-patterns:

• alert on every threshold breach without context
• alert without runbooks/owners

5) Practical setup mindset

When building an app/service:

• emit metrics (latency, errors)
• emit structured logs (level, service, request id)
• propagate trace IDs
• define 2–5 key dashboards and 3–6 alerts

Next steps

Next tutorials:

• setting up alert rules (conceptual)
• instrumenting apps with traces/structured logs
• OpenTelemetry mindset