Deep dive
Expanding on the short answer — what usually matters in practice:
- Context (tags): rate-limiting, token-bucket, api-gateway
- Scaling: what scales horizontally vs vertically, where bottlenecks appear.
- Reliability: retries/circuit breakers/idempotency, observability (logs/metrics/traces).
- Evolution: keep changes cheap (boundaries, contracts, tests).
- 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 "rate-limiting-—-name-two-strategies-and-where-yo"
function explain() {
// Start from the core idea:
// Common strategies are token bucket and leaky bucket (or fixed/sliding window). You can enf
}
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?