Practice : Real-Time Logging

Purpose and Strategic Importance

Real-Time Logging provides immediate visibility into how systems behave during runtime. It enables rapid debugging, early detection of anomalies, and informed operational decisions - all of which are essential for building resilient, secure, and observable systems.

By surfacing structured, searchable logs in near real-time, teams can quickly trace events, investigate incidents, and respond proactively - reducing downtime, improving quality, and enabling safer deployments.

Description of the Practice

  • Applications emit logs as structured events to a centralised logging platform.
  • Logs are ingested, parsed, indexed, and made searchable in near real-time.
  • Common tools include ELK stack (Elasticsearch, Logstash, Kibana), Loki, Fluentd, and Datadog.
  • Logs should be meaningful, contextual, and correlated across systems (e.g. via request IDs).
  • Real-time log dashboards and alerts support proactive monitoring and incident response.

How to Practise It (Playbook)

1. Getting Started

  • Integrate structured logging into your application using standard libraries and formats (e.g. JSON, logfmt).
  • Emit logs for key lifecycle events (e.g. start-up, shutdown, errors, state changes).
  • Forward logs to a real-time log aggregator and visualise them in a dashboard.
  • Define basic filters (e.g. severity, service, environment) to enable quick exploration.

2. Scaling and Maturing

  • Enrich logs with contextual metadata: request IDs, user IDs, environment, service version.
  • Establish logging guidelines to avoid excessive noise or sensitive data exposure.
  • Set up anomaly detection or alerting based on log patterns (e.g. repeated errors, latency spikes).
  • Correlate logs with metrics and traces to form a complete observability stack.
  • Use logs to support incident reviews, service reliability analysis, and capacity planning.

3. Team Behaviours to Encourage

  • Log with empathy - write messages that future engineers (including you) will understand.
  • Treat logs as first-class observability tools - not just byproducts of debugging.
  • Use logs during swarm sessions and post-incident reviews to build shared understanding.
  • Continuously evolve what and how you log based on operational needs.

4. Watch Out For…

  • Log volume explosion - noisy logs can increase costs and bury signals.
  • Sensitive data exposure - always sanitise personal, security, and credential data.
  • Logs without structure - free-text messages are harder to parse and search.
  • Relying solely on logs without connecting them to metrics or traces.

5. Signals of Success

  • Teams use logs to detect and diagnose issues in real time.
  • Incident response time improves due to better visibility.
  • Log queries are shared, reused, and contribute to operational knowledge.
  • Logging practices are consistent, secure, and aligned with system evolution.
  • Logs are treated as strategic assets, not just engineering exhaust.