I started with the schema and felt okay there.
Start by clarifying requirements (e.g., scale, read/write ratio, consistency needs) and then walk through the design in a structured way: data model, API, concurrency, and scaling. Emphasize trade-offs and justify your choices, especially around handling double-booking and scaling reads/writes.
Pro tip: Proactively discuss how you'd handle edge cases like time zone differences, cancellations, and waitlists, and mention monitoring and metrics for booking success rates. This shows you think beyond the happy path and consider operational concerns.
Ask questions to understand expected scale (users, courses, bookings per second), read/write patterns, consistency requirements, and any constraints like time zones or recurring slots.
Propose a schema with tables for users, courses, time slots, and bookings. Discuss indexes, constraints (e.g., unique on slot_id and student_id), and how to handle cancellations and waitlists.
Outline RESTful endpoints for listing available slots, booking, canceling, and viewing bookings. Mention idempotency keys for booking requests and pagination for listing slots.
Explain how to prevent double-booking using transactions, optimistic locking (versioning), or pessimistic locking. Discuss trade-offs between consistency and performance.
Describe scaling strategies: read replicas for slot searches, sharding by course or time, caching popular slots, and using a queue for booking requests to smooth spikes.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This came as a follow-up and honestly it's where the interview got interesting.
Start by clarifying the booking scenario (e.g., tutor time slots) and the contention level, then compare optimistic locking, pessimistic locking, and distributed locks in terms of correctness, performance, and complexity. Recommend a hybrid approach: use optimistic locking for low contention, pessimistic locking for high contention within a single database, and distributed locks only when coordinating across services or databases.
Pro tip: Emphasize that the best solution is often to avoid contention altogether by designing the system to serialize bookings per resource (e.g., using a queue or partitioning by tutor ID), and mention that distributed locks add latency and failure modes, so they should be a last resort.
Ask about the expected contention level, consistency requirements, and whether the system is single-node or distributed. This sets the context for comparing locking strategies.
Describe how it works with version numbers or timestamps, and that it's efficient for low contention but can cause retries and poor user experience under high contention.
Describe how it locks rows or tables upfront, ensuring serialized access. It's simple and effective for high contention within a single database but can lead to deadlocks and reduced throughput.
Describe using Redis, ZooKeeper, or etcd to coordinate across services. It's necessary for distributed systems but adds complexity, latency, and potential for lock failures.
Propose a hybrid or alternative approach (e.g., database constraints, queues, or partitioning) and justify based on the scenario. Highlight that the choice depends on contention level and system architecture.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.