The hold expiration part is where I spent too long.
Start by clarifying requirements and scale, then design a data model that prevents overselling using atomic operations and distributed locking. Propose a two-phase reservation flow with temporary holds and TTL-based expiration, and discuss trade-offs between consistency, latency, and availability under high contention.
Pro tip: Emphasize idempotency and graceful degradation: use idempotency keys for hold and checkout requests, and design for partial failures (e.g., if the hold service is down, fall back to a queue or retry with backoff) to avoid double-booking or lost sales.
Ask about expected traffic, contention levels, consistency needs, and whether holds are per-seat or per-room. Define functional and non-functional requirements (e.g., latency, availability, durability).
Choose a storage layer (e.g., relational DB with transactions or distributed KV store) and model inventory, holds, and reservations. Ensure atomic updates to prevent overselling, using techniques like optimistic concurrency or row-level locks.
Design a two-phase flow: first, place a temporary hold with a TTL; second, confirm and convert to a reservation. Use a distributed lock or atomic compare-and-swap to acquire holds, and a background job or TTL-based expiration to release expired holds.
Address contention with sharding by event/seat, queueing requests, or using a token-based system. Discuss trade-offs: strong consistency vs. availability, and how to scale reads (caching) and writes (partitioning).
Make hold and checkout operations idempotent using request IDs. Plan for failure scenarios: retries, timeouts, and compensation (e.g., releasing holds if payment fails). Monitor and alert on overselling attempts.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.