Logging, Monitoring and Observability in Systems

Logging, monitoring and observability are the three pillars of reliable software systems. Logging records events as they happen, monitoring watches the health and capacity of services, and observability ties these signals together so you can explain what went wrong and why. Used together, they reduce downtime and speed up recovery for teams of any size.

Logging

Logging is your first source of truth. Do not log everything; log what matters in a structured format. Use fields that stay consistent across services: timestamp, level, service, trace_id, span_id, request_id, and a clear message. Example: ts=2025-09-22T14:30:00Z level=INFO svc=auth trace=abc123 span=def456 msg=‘user login’ user_id=987.

  • Use structured logs (JSON or key=value) to enable fast search and correlation.
  • Set standard log levels and avoid logging sensitive data.
  • Centralize logs and preserve context with trace_id and span_id to connect events across services.

Monitoring

Monitoring watches metrics and events that indicate health, capacity, or failure. Follow the golden signals: latency, errors, and saturation, plus throughput. Build dashboards that show trends over time, not just current values. Set alerts with clear on-call criteria and sensible thresholds, so teams respond promptly without fatigue.

  • Track per-service metrics like p95/p99 latency, error rate, and request volume.
  • Monitor resource usage (CPU, memory, disk, network) to spot strain before it becomes a problem.
  • Use dashboards that aggregate and correlate signals to spot patterns.

Observability

Observability is the ability to explain why a system behaved as it did, using the right data. It blends logs, metrics and traces to give full context. Distributed tracing helps you follow a request as it travels through services, while logs and metrics provide concrete details at each step.

  • Instrument critical paths and propagate context with trace identifiers.
  • Collect metadata such as environment, version, and user segments to enrich signals.
  • Aim for end-to-end visibility by linking logs, metrics and traces in a single view.

Practical steps to get started:

  • Define a service-level objective (SLO) and an error budget for a small, real service.
  • Implement structured logging and trace propagation from the first line of code.
  • Create a simple dashboard showing latency, errors and traffic, plus a runbook for common incidents.
  • Review incidents to refine logs, metrics and traces, then roll out improvements gradually.

A good telemetry culture balances detail with respect for privacy and performance. Automate where you can, keep data retention aligned with needs, and evolve instrumentation as the system grows.

Key Takeaways

  • Telemetry helps prevent outages by connecting logs, metrics and traces.
  • Start small with clear goals, then scale your observability practices.
  • A unified view across logs, metrics and traces speeds up diagnosis and recovery.