Start by clarifying requirements and scale (e.g., events, seats, peak load), then design a distributed system with strong consistency for inventory and reservations. Focus on preventing double-selling using atomic operations, distributed locks, or optimistic concurrency, and handle seat expiration with TTL and a cleanup service. Integrate payment asynchronously with idempotency and compensate on failure.
Pro tip: Emphasize that the hardest part is not the happy path but handling race conditions and failures—use idempotency keys, transactional outbox, and reconciliation to ensure correctness. Also, mention that you'd start with a simple design and iterate based on bottlenecks, showing pragmatism.
Ask about expected traffic (e.g., millions of concurrent users), seat types (general admission vs. reserved), and consistency needs. Define functional and non-functional requirements.
Outline components: API gateway, inventory service, reservation service, payment service, waitlist service, and databases. Consider using a distributed cache (Redis) for seat holds and a relational DB for durable inventory.
Design seat inventory with atomic decrement (e.g., using Redis Lua scripts or DB transactions). Implement seat reservation with TTL (e.g., Redis keys with expiry) and a background job to release expired holds.
Integrate payment via asynchronous processing with idempotency keys to avoid double charges. Implement waitlist as a queue (e.g., Kafka) that notifies users when seats become available.
Use optimistic concurrency (versioning) or distributed locks (e.g., Redlock) for seat allocation. Ensure idempotent operations and implement compensation (e.g., release seat if payment fails) and reconciliation.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.