Observability & Monitoring: Scenario-Based Questions

Observability enables teams to understand the internal state of a system based on its outputs. In a microservices environment, observability must account for distributed services, asynchronous communication, and dynamic scaling.

📊 Core Observability Pillars

• Logs: Structured, queryable logs (JSON format) that capture application events and error traces.
• Metrics: Time-series data for CPU, memory, request rates, error counts, and latency.
• Traces: Distributed tracing for understanding cross-service requests and bottlenecks.

🏗️ High-Level Architecture

• Log Aggregation: Fluent Bit or Logstash → Elasticsearch or Loki.
• Metrics Collection: Prometheus (pull model), with Grafana for dashboards.
• Tracing: OpenTelemetry agents → Jaeger or Tempo for trace analysis.
• Alerting: Alertmanager or Grafana alerts with on-call rotation integration.

🧪 Key Monitoring Strategies

• Instrument code with OpenTelemetry SDKs (HTTP, DB, cache layers).
• Track golden signals: latency, traffic, errors, saturation.
• Use service-level dashboards (per microservice) and global heatmaps.
• Set SLOs/SLIs and measure error budgets over rolling windows.

✅ Best Practices

• Correlate logs, metrics, and traces with shared request IDs or trace IDs.
• Limit cardinality in metrics to prevent overload (e.g., labels with user IDs).
• Use anomaly detection for proactive alerting.
• Perform observability reviews as part of service onboarding.

🚫 Common Mistakes

• Relying only on logs without metrics or tracing.
• Alerting on low-priority issues or missing thresholds entirely.
• Using inconsistent instrumentation across services.

📌 Real-World Insight

Modern observability systems are not just about detecting failure — they enable debugging, capacity planning, and user impact analysis. At scale, success comes from standardization, self-service dashboards, and consistent telemetry pipelines.