Explain Sampling vs Tail-Based Sampling.
Sampling randomly or rate-limits telemetry traces upfront to reduce costs, while tail-based sampling makes the decision after evaluating the trace outcome, prioritizing slow or error-heavy “tail” requests.
When to Use
- Sampling: High-throughput services where cost control and trend visibility are key.
- Tail-based sampling: Debugging critical errors, latency spikes, or SLO violations where capturing bad experiences matters most.
Example
Collect 1% of traces normally but 100% of requests failing with status!=200 or latency >500ms.
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Why Is It Important
It helps teams balance observability cost with insight—keeping signal-rich traces around failures while avoiding data overload.
Interview Tips
Start by defining both approaches in one line. Then contrast decision timing (head vs. after evaluation). Use the example above and relate it to SLO-driven monitoring.
Trade-offs
- Gains: Better focus on real issues, improved MTTR, efficient storage.
- Losses: Less representative data, complexity in tuning thresholds, extra processing overhead.
Pitfalls
- Per-service random sampling can break full-trace visibility.
- Misconfigured thresholds may skip subtle issues.
- Over-reliance on tail events risks missing overall performance trends.
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