Deep dive
Expanding on the short answer — what usually matters in practice:
- Context (tags): observability, logs, metrics, tracing
- Reliability: detect issues (monitoring) and limit blast radius (rollback, feature flags).
- Security: least privilege, secret rotation, supply chain.
- Automation: idempotency, repeatability, drift control.
- Explain the "why", not just the "what" (intuition + consequences).
- Trade-offs: what you gain/lose (time, memory, complexity, risk).
- Edge cases: empty inputs, large inputs, invalid inputs, concurrency.
Examples
A tiny example (an explanation template):
// Example: discuss trade-offs for "logs-vs-metrics-vs-traces-—-how-do-they-compleme"
function explain() {
// Start from the core idea:
// Metrics show trends and health, logs provide event details, and traces follow a request ac
}
Common pitfalls
- Too generic: no concrete trade-offs or examples.
- Mixing average-case and worst-case (e.g., complexity).
- Ignoring constraints: memory, concurrency, network/disk costs.
Interview follow-ups
- When would you choose an alternative and why?
- What production issues show up and how do you diagnose them?
- How would you test edge cases?