Design Airbnb

Interview-Ready Approach

1) Clarify Scope and SLOs

• •Problem statement: Design Airbnb's marketplace - listing search, booking, payments, reviews, pricing, and trust at 150 M+ users.
• •Design for a peak load target around 10,417 RPS (including burst headroom).
• •Total users: 150,000,000+
• •Active listings: 7,000,000
• •Peak bookings/night: ~2,000,000
• •Search QPS (peak): ~20,000
• •Search latency: < 500 ms

2) Capacity Planning Method

• •Convert traffic and growth constraints into request rate, storage growth, and concurrency budgets.
• •Keep at least 2-3x safety margin per tier (ingress, compute, storage, async workers).
• •Reserve explicit latency budgets per hop so p95 can be defended in review.

3) Architecture Decisions

• •Databases: Define a clear system-of-record and design read/write paths separately before adding optimizations.
• •Search: Use primary store for writes and async index updates for search relevance + scale.
• •Caching: Put cache on hot read paths first and pick cache-aside or write-through explicitly.
• •Geo Distribution: Route users to nearest region/edge while keeping write-consistency boundaries explicit.
• •API Design: Standardize API boundaries, idempotency keys, pagination, and error contracts first.
• •Message Queues: Move non-blocking and retry-heavy work to async consumers with explicit retry and DLQ policies.

4) Reliability and Failure Strategy

• •Use strong write constraints (transactions or conditional writes) and explicit backup/restore strategy.
• •Track indexing lag and support reindex from source of truth.
• •Bound staleness with TTL + invalidation hooks for critical entities.
• •Design region failover and data residency controls as first-class requirements.
• •Apply strict input validation and backward-compatible versioning.

5) Validation Plan

• •Run one peak-load test, one dependency-degradation test, and one failover test.
• •Verify idempotency for all retried writes and async consumers.
• •Track user-facing SLOs first: p95 latency, error rate, and successful throughput.

6) Trade-offs to Call Out in Interviews

• •Databases: SQL gives stronger transactional guarantees; NoSQL often gives better write scaling and flexibility.
• •Search: Search index gives rich querying but introduces eventual consistency and index ops overhead.
• •Caching: Higher hit rate cuts latency/cost, but stale data and invalidation bugs become primary risks.
• •Geo Distribution: Global latency improves, but cross-region consistency and operations become harder.
• •API Design: Rich APIs improve developer speed but can create long-term compatibility burden.

Practical Notes

• •Search: Elasticsearch with geospatial queries (geo_bounding_box for map view). Denormalize listing + host + review data into a search document.
• •Availability: store as date ranges per listing in a relational DB. Use SELECT FOR UPDATE for atomic booking to prevent double-booking.
• •Map-based search: use geohash-based clustering to group nearby listings into markers when zoomed out. Switch to individual pins when zoomed in.

Why This Solution Works

Request path: The solution keeps ingress, service logic, and stateful dependencies separated so each layer can scale independently.

Reference flow: Web Clients -> DNS -> Load Balancer -> API Gateway -> Core Service -> Primary SQL DB -> Read Model DB -> Redis Cache

Design strengths

• •Cache sits on the read path to absorb repeated queries and keep DB pressure stable.
• •Async queue/event bus isolates bursty workloads and supports retries without blocking synchronous requests.

Interview defense

• •This design makes bottlenecks explicit (ingress, core compute, persistence, async workers).
• •It supports progressive scaling without re-architecting the core request path.
• •It keeps correctness-sensitive state changes in durable systems while offloading background work asynchronously.