Explain Metrics vs Logs vs Traces.
In observability, metrics are numeric time-series that summarize system health, logs are timestamped event records with detailed context, and traces map a request’s path across services to reveal latency and dependencies.
When to Use
- Use metrics to track SLIs like latency, error rates, and throughput.
- Use logs for debugging, auditing, and investigating specific failures.
- Use traces to analyze distributed systems, bottlenecks, or cross-service delays.
Example
If checkout latency spikes, metrics raise an alert, a trace shows the cart → payment bottleneck, and logs reveal a gateway timeout.
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Why Is It Important
Picking the right signal reduces MTTR, avoids data overload, and improves reliability by turning raw data into actionable insights.
Interview Tips
Start with the one-line definition, then explain the “alert → trace → log” workflow. Mention tools like Prometheus, ELK, and OpenTelemetry. Highlight trade-offs like metric cardinality and trace sampling.
Trade-offs
- Metrics: cheap, fast, trendable; limited detail.
- Logs: rich context; costly at scale.
- Traces: show causality; may miss rare events if sampled.
Pitfalls
- Over-alerting on logs.
- Not controlling metric label cardinality.
- Missing trace headers.
- Assuming one signal replaces the others.
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