Observability has gotten complicated with all the tools, standards, and architectural patterns to choose from. As someone who has implemented monitoring for systems processing millions of requests, I learned everything there is to know about what actually helps when things break. Here’s what works.
The Three Pillars
Probably should have led with this section, honestly, because understanding these fundamentals changes how you think about observability. 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. That’s what makes symptom-based alerting endearing to us on-call engineers – you get woken up for things users actually notice. 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.
