I spent way too long on the browse/search flow and then scrambled when they pushed on concurrency for seat holds.
Start by clarifying requirements and scale (e.g., millions of concurrent users, thousands of events, seat-level inventory). Then design a high-level architecture that separates read-heavy browsing from write-heavy purchasing, using caching, queues, and distributed locking to handle spikes. Finally, dive into data modeling for seats and transactions, and discuss trade-offs like consistency vs. availability.
Pro tip: Emphasize that seat inventory is a finite, highly contended resource, so you need a reservation system with short-lived locks and idempotent payment processing to prevent overselling. Also, mention that virtual waiting rooms are key to smoothing traffic spikes and improving user experience.
Ask questions to understand functional and non-functional requirements: number of events, seats per event, peak concurrent users, expected latency, consistency needs, and budget. Define scope (e.g., seat selection, payment, refunds).
Sketch a diagram with components: load balancers, web servers, caching layer (Redis), database (SQL for transactions, NoSQL for event metadata), message queue (Kafka), and payment service. Separate read path (browsing) from write path (purchasing).
Design schemas for events, venues, seats, and orders. Discuss how to represent seat availability (e.g., seat map with status) and ensure ACID transactions for bookings. Consider using optimistic concurrency or distributed locks.
Propose strategies: virtual waiting room (queue) to throttle users, rate limiting, auto-scaling, caching seat maps, and asynchronous order processing. Use queues to decouple booking from payment.
Discuss trade-offs: strong vs. eventual consistency, SQL vs. NoSQL, synchronous vs. asynchronous processing. Identify bottlenecks (database writes, lock contention) and mitigation (sharding, partitioning).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This one surprised me a bit because I expected more Ticketmaster follow-up.
Start by clarifying requirements and scale, then design a high-level architecture covering code submission, sandboxed execution, and result retrieval. Dive into the critical components: secure sandboxing, language runtime management, and global distribution, discussing trade-offs and scalability strategies.
Pro tip: Emphasize security and isolation early—interviewers expect you to treat untrusted code execution as the highest risk. Also, discuss how you'd handle language versioning and dependency management, as these are often overlooked but critical for a real platform.
Ask about expected user base, languages supported, execution time limits, and security requirements. Establish non-functional needs like latency, availability, and cost constraints.
Outline the main components: API gateway, submission service, execution workers, result storage, and user interface. Sketch data flow from code submission to result retrieval.
Detail how to isolate untrusted code using containers, VMs, or microVMs (e.g., Firecracker). Discuss resource limits, network isolation, and preventing escape attacks.
Explain how to scale execution workers horizontally, use queues for load leveling, and deploy in multiple regions for low latency. Address data consistency and failover.
Discuss trade-offs between cold start latency and cost, language runtime management, caching compiled artifacts, and monitoring/alerting for security and performance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.