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Start by clarifying requirements (latency SLA, consistency, scale) and then propose a fan-out architecture using asynchronous parallel calls to seller services with per-seller timeouts and circuit breakers. Address partial failures by returning partial results with per-item status, and implement caching with short TTLs and stale-while-revalidate to balance freshness and latency.
Pro tip: Emphasize idempotency and observability: design the endpoint to be safely retryable and include detailed metrics/logs for each seller call to quickly diagnose issues in production.
Ask about expected QPS, number of sellers, latency SLA, consistency needs (e.g., can prices be slightly stale?), and failure tolerance. This shapes the design.
Define request/response schema: input list of book IDs and quantities; output includes per-item price, availability, seller, and status (success/timeout/error). Consider using a batch endpoint with a max batch size.
Use an async, non-blocking approach (e.g., thread pool, reactive streams, or async I/O) to call multiple seller services in parallel. Aggregate results as they arrive, with a global deadline to bound latency.
Set per-seller timeouts (e.g., 100-200ms) and use circuit breakers to avoid cascading failures. For timeouts, return partial results with a status indicating unavailability; optionally retry idempotent calls with backoff.
Cache seller responses with short TTLs (e.g., 10-30 seconds) and use stale-while-revalidate to serve stale data while refreshing. Consider per-seller cache and invalidation on price updates if possible.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Noisy neighbor problems are something I've thought about before but I blanked on the clean framing for a second.
Start by framing the problem as multi-tenant isolation with fairness and tail-latency goals, then propose a layered defense: per-tenant rate limiting at the edge, separate queues/thread pools for noisy tenants, and tail-latency protection via hedged requests or circuit breakers. Discuss trade-offs like resource efficiency vs. isolation and how to dynamically adjust limits based on tenant behavior.
Pro tip: Emphasize that isolation should be adaptive and observable—use per-tenant metrics to detect noisy neighbors and automatically throttle or shed load, rather than relying on static limits. Also, mention that you'd validate the design with load tests simulating a single tenant flooding the system.
Ask about the scale, tenant count, SLAs, and whether the system is multi-tenant by design. Clarify what 'flooding' means (e.g., request rate, payload size) and the impact on other tenants.
Propose a token bucket or sliding window rate limiter per tenant, enforced at the API gateway or service mesh. Discuss dynamic limits based on tenant tier or historical usage, and how to handle bursts.
Suggest separate queues or thread pools per tenant (or per tenant group) to prevent head-of-line blocking. For efficiency, consider a shared pool with priority scheduling and fair queuing, but ensure noisy tenants can't monopolize.
Implement mechanisms like hedged requests, circuit breakers, and load shedding to maintain tail latency for well-behaved tenants. Use timeouts and bulkheads to contain failures.
Instrument per-tenant metrics (latency, error rates, queue depths) and use them to auto-tune limits or trigger alerts. Plan for gradual rollout and A/B testing of isolation strategies.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.