Observability & Monitoring: Scenario-Based Questions

In microservices, observability helps you understand system behavior, diagnose issues, and improve reliability. With many independent services, standard patterns ensure consistent insights across the stack.

🔭 Three Pillars of Observability

• Metrics: Quantitative time-series data (e.g., latency, error rate, request rate).
• Logs: Structured application logs for contextual debugging.
• Traces: End-to-end request flows across services, captured with spans and tags.

📐 Core Patterns

• Correlation IDs: Propagate unique IDs across logs and traces to link a user journey.
• Standardized Logging: Consistent fields like timestamp, service, request_id, tenant_id.
• Service-Level Dashboards: One per service, showing SLOs, key metrics, and alerts.
• Alerting on SLOs: Prioritize actionable alerts tied to availability and latency thresholds.

🧰 Tools & Frameworks

• OpenTelemetry: Vendor-neutral standard for logs, metrics, and tracing.
• Prometheus & Grafana: Metrics collection and visualization.
• Jaeger / Tempo: Distributed tracing backends.
• Fluent Bit / Logstash: Log aggregation and routing.

✅ Best Practices

• Instrument every service with tracing and metrics from the start.
• Use RED (Rate, Errors, Duration) or USE (Utilization, Saturation, Errors) frameworks.
• Define golden signals and track them per environment (dev, staging, prod).
• Make dashboards accessible to both engineering and ops teams.

🚫 Common Pitfalls

• Too much noise — alert fatigue from unactionable signals.
• Missing trace context — no way to follow request paths across services.
• Isolated dashboards without unified views or drill-down paths.

📌 Real-World Insight

Observability isn't just about uptime — it's about confidence. High-performing teams build shared tools, enforce instrumentation standards, and review observability as part of postmortems and SRE practices.