I started with the distributed locking angle pretty quickly, maybe too quickly.
Start by clarifying functional and non-functional requirements, then design a high-level architecture that separates read-heavy seat browsing from write-heavy booking operations. Focus on concurrency control for seat holds and purchases, using techniques like optimistic locking, distributed locks, or queue-based serialization to handle flash sales at scale.
Pro tip: Emphasize the trade-offs between consistency and availability, and propose a hybrid approach: use a fast in-memory store for seat holds with TTL, and a durable database for final bookings. This shows you understand real-world constraints and can balance performance with reliability.
Ask about scale (e.g., users, events, seats), consistency needs, latency targets, and flash-sale patterns. Define core entities: events, seats, users, holds, and bookings.
Sketch components: API gateway, seat inventory service, hold service, booking service, and databases. Separate read and write paths, and consider caching for seat maps.
Detail how to prevent double-booking: use optimistic locking (versioning) for low contention, or pessimistic locking/distributed locks for high contention. For flash sales, consider a queue to serialize requests per seat or event.
Discuss partitioning by event or seat, using in-memory stores (e.g., Redis) for holds with TTL, and ensuring idempotency for booking operations. Plan for failover and data consistency.
Address trade-offs: strong vs. eventual consistency, latency vs. correctness, and cost. Handle edge cases like hold expiration, payment failures, and retries.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the scale and requirements (number of students, events, concurrency, reliability needs) to right-size the design. Then propose a simple, single-machine architecture using a lightweight web server, a relational database, and basic concurrency control, explicitly justifying why distributed components are unnecessary. Finally, discuss trade-offs and potential bottlenecks, showing awareness of how this differs from a large-scale distributed system.
Pro tip: Emphasize that simplicity and maintainability are features, not compromises—Meta values engineers who can avoid over-engineering and clearly articulate when a monolith is the right choice.
Ask about expected load (e.g., number of students, peak concurrent users, events per term) and non-functional needs like uptime and data durability. This ensures the design is appropriately sized and avoids unnecessary complexity.
Outline a single-server setup: a web application (e.g., Python/Flask or Node.js) serving a lightweight frontend, backed by a relational database (e.g., SQLite or PostgreSQL) on the same machine. Mention using a reverse proxy like Nginx for static files and TLS termination.
Explain how to handle simultaneous ticket purchases using database transactions with row-level locking or optimistic concurrency control to prevent overselling. Highlight that a single database instance simplifies consistency.
Cover basic backup strategies (e.g., nightly database dumps), monitoring, and graceful degradation. Acknowledge single point of failure and propose simple mitigations like automated restarts or a standby server if needed.
Contrast this design with a distributed system, noting that while it lacks horizontal scalability and high availability, it meets the school's needs with lower cost and complexity. Be ready to discuss when scaling out might become necessary.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Structure your answer around a layered architecture: start with client-side optimizations, then edge caching and CDN, then backend scaling and data layer. Emphasize trade-offs between consistency, latency, and cost, and tie each decision to the high-traffic on-sale scenario.
Pro tip: Quantify the impact of each optimization (e.g., 'caching reduces origin load by 90%') and mention how you'd measure success with metrics like p99 latency and cache hit ratio. Also, show awareness of failure modes and graceful degradation.
Ask about expected traffic volume, read/write ratio, consistency requirements, and budget. This shows you tailor solutions to the problem.
Discuss client-side caching, lazy loading, and CDN edge caching for static and dynamic content. Mention techniques like stale-while-revalidate and edge computing.
Cover horizontal scaling, read replicas, caching layers (Redis/Memcached), and database sharding. Explain how to handle spikes with autoscaling and queueing.
Explain how you balance consistency with availability (e.g., eventual consistency for search indexes) and the trade-offs between latency, cost, and complexity.
Describe load testing, real-time monitoring, and fallback strategies (e.g., serving stale data, disabling non-critical features) to maintain performance under extreme load.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I'd drilled this beforehand so the states came out clean: held, paying, confirmed, released.
Start by clarifying the scope and requirements of the seat reservation system, then define the states and transitions with clear invariants. Walk through the lifecycle from hold to confirmed or released, highlighting concurrency control, idempotency, and failure handling. Finally, discuss trade-offs and how you would scale the solution.
Pro tip: Emphasize idempotency and atomicity in state transitions; interviewers at Meta look for candidates who can design systems that handle retries and race conditions gracefully.
Ask questions to understand the expected scale, consistency needs, and edge cases (e.g., concurrent holds, expiration, payment failures). This ensures your design aligns with the interviewer's expectations.
Enumerate the states (e.g., Available, Held, Confirmed, Released, Expired) and the events that trigger transitions (e.g., hold request, payment success, timeout). Specify invariants like 'a seat can have at most one active hold'.
Describe the sequence: user requests hold -> seat transitions to Held with a TTL -> user completes payment -> seat transitions to Confirmed. Mention how the hold is released if payment fails or times out.
Explain how you prevent double-booking (e.g., optimistic locking, distributed locks, or conditional writes). Discuss idempotency for retries and how to handle partial failures (e.g., payment succeeded but confirmation failed).
Compare approaches (e.g., database transactions vs. event sourcing) and their impact on latency, consistency, and scalability. Mention how you would monitor and expire holds efficiently.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.