Observability and Monitoring for Reliable Systems

Observability and monitoring are two sides of the same coin. Monitoring collects signals from a system, while observability is the ability to understand why those signals change. In reliable systems, teams combine both to detect problems early and diagnose issues quickly.

To start, build a simple data plan. Identify critical services, choose a small, stable set of core signals, and decide how long to keep data. Prefer breadth over complexity: metrics, logs, and traces should work together. Add instrumentation in code and automate data collection with deployments, so gaps do not appear after changes.

Practical data sources:

• Metrics: latency, error rate, request volume, CPU and memory use.
• Logs: structured messages with context like user id, request id.
• Traces: end-to-end paths that reveal bottlenecks.
• Events: deployments, feature flags, outages.
• Configuration data: topology, feature states.

Alerting and runbooks:

• Define SLOs and an error budget to guide alerts.
• Write clear, actionable alert messages with links to dashboards and traces.
• Assign on-call owners and maintain runbooks that explain steps to take.
• Review alerts regularly to cut noise and adjust thresholds.

Keeping it usable:

• Start small and grow as needed.
• Use dashboards focused on SLO status and recent incidents.
• Tag data by service, environment, and version.
• Retain data wisely to keep performance healthy.

Example: a small web service A service emits latency, error rate, and a simple trace for slow requests. If latency climbs past the SLO limit, an alert pops up. The alert shows the latest trace and a quick checklist in the runbook. An on-call engineer can see the deployment history and the error pattern, speeding recovery.

Key Takeaways

• Combine metrics, logs, and traces to diagnose issues fast.
• Define SLOs and alerts that are clear and actionable.
• Build instrumentation into the code and review data regularly.