Monitoring Distributed Systems Without Going Crazy

Monitoring distributed systems used to mean checking if servers were up. Now we track metrics, logs, and traces across hundreds of ephemeral containers. The game changed.

The Three Pillars

Metrics tell you what’s happening in aggregate. Request rate, error rate, latency percentiles. These numbers surface problems quickly but don’t explain causes.

Logs tell you what happened in detail. The request that failed, the error message, the stack trace. Essential for debugging but overwhelming at scale without good search.

Traces follow individual requests across services. When a user action touches six microservices, traces show where time went. Distributed tracing is complex to implement but invaluable for latency analysis.

Prometheus and Grafana

This combination dominates Kubernetes monitoring. Prometheus scrapes metrics from your applications and stores time-series data. Grafana visualizes it with customizable dashboards.

Both are open source with active communities. The learning curve is manageable. Start with provided dashboards for Kubernetes components, then build custom ones as you understand your applications.

Log Aggregation

Container logs disappear when containers die. You need centralized log storage that persists beyond container lifecycle.

The ELK stack (Elasticsearch, Logstash, Kibana) was standard for years. Loki, from Grafana Labs, offers a simpler alternative that integrates naturally with your existing Grafana dashboards.

Alerting That Works

Alert fatigue is real. Too many alerts and people ignore them all. Too few and problems slip through.

Focus alerts on symptoms, not causes. Users don’t care if CPU is high – they care if requests are slow. Alert on error rates and latency, not resource utilization.

SLOs and Error Budgets

Define service level objectives – “99.9% of requests complete within 500ms.” Track your error budget – the acceptable failure allocation within that target.

When error budget runs low, prioritize reliability over features. When error budget is healthy, ship faster. This framework makes reliability discussions concrete rather than emotional.