🔭Key Takeaways
- 1Three pillars: Logs (what happened), Metrics (what is the state), Traces (how requests flow across services)
- 2SLI (indicator) → SLO (objective) → SLA (agreement) → Error Budget (allowance for failure)
- 3Structured logging (JSON) + correlation IDs enable tracking requests across services
- 4Distributed tracing (Jaeger, OpenTelemetry) is essential for debugging microservice latency
Understanding System Behavior in Production
Observability is the ability to understand what's happening inside your system from its external outputs. In microservices with dozens of services, you can't SSH into a server and grep logs — you need structured, correlated, searchable telemetry.
The Three Pillars
| Pillar | What | Tools | Question Answered |
|---|---|---|---|
| Logs | Timestamped events (structured JSON) | ELK Stack, Datadog Logs, CloudWatch | What happened? |
| Metrics | Numerical measurements over time | Prometheus + Grafana, Datadog, CloudWatch | What is the current state? |
| Traces | Request flow across services | Jaeger, Zipkin, AWS X-Ray, OpenTelemetry | Where is the bottleneck? |
SLI (Service Level Indicator)
A measurable metric: request latency, error rate, availability. Example: 'Proportion of requests completed in < 200ms.'
Advantages
- •Structured observability drastically reduces MTTR
- •Error budgets balance reliability with velocity
- •OpenTelemetry provides vendor-neutral instrumentation
Disadvantages
- •Observability infrastructure is expensive at scale
- •Too many alerts cause alert fatigue
- •Instrumentation requires upfront investment in every service
🧪 Test Your Understanding
Knowledge Check1/1
What tool category helps debug latency across microservices?