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I started with the usual API and data model stuff but they pushed me pretty fast toward the hard parts.
Start by clarifying requirements and scale, then design the system in layers: event browsing with caching, seat map rendering with real-time updates, seat holding with distributed locking, and ticket delivery with idempotency. Focus on trade-offs between consistency, availability, and latency, and explain how you'd handle high concurrency during popular onsales.
Pro tip: Emphasize the importance of idempotency and exactly-once semantics for payment and ticket issuance, and discuss how you'd handle seat holds expiring gracefully to avoid inventory leaks.
Ask about expected traffic (e.g., millions of concurrent users during onsales), consistency needs (strong for seat inventory, eventual for browsing), and latency targets. Define functional and non-functional requirements.
Outline core services: event catalog, seat map service, inventory/hold service, payment, and ticket delivery. Choose a microservices architecture with appropriate data stores (e.g., Redis for caching, SQL for transactions, NoSQL for events).
Detail browsing (CDN, caching, read replicas), seat selection (real-time updates via WebSockets, optimistic UI), holding seats (distributed locks, TTL, reservation service), and purchase (saga pattern, idempotent payment).
Discuss partitioning (by event, venue), load balancing, auto-scaling, and fault tolerance. Explain how to handle spikes with queueing, rate limiting, and graceful degradation.
Summarize key trade-offs (e.g., strong vs. eventual consistency, latency vs. accuracy) and mention monitoring, alerting, and metrics for system health.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is where the conversation got interesting.
Start by clarifying requirements and scale, then propose a layered architecture that combines distributed locking, optimistic concurrency control, and queueing to serialize seat allocation. Emphasize trade-offs between consistency, latency, and availability, and discuss how to handle failures and retries.
Pro tip: Mention that you would use a two-phase approach: first, reserve the seat with a short-lived lock (e.g., Redis with TTL), then confirm the booking asynchronously, to reduce contention and improve user experience. Also, highlight the importance of idempotency keys to prevent duplicate bookings from retries.
Ask about expected traffic (e.g., millions of concurrent users), consistency requirements (strong vs eventual), and latency tolerance. This shows you understand the problem context before jumping to solutions.
Propose using a distributed lock service like Redis or ZooKeeper to ensure only one user can hold a seat at a time. Discuss lock granularity (per seat vs per row) and TTL to avoid deadlocks.
Use version numbers or timestamps on seat records to detect conflicts. If a conflict occurs, retry or return an error, ensuring no double-booking without heavy locking.
Place users in a virtual queue (e.g., using Kafka or SQS) to process seat requests sequentially, preventing thundering herd. Apply rate limiting to protect backend services.
Design for retries with idempotency keys so that duplicate requests don't result in double bookings. Discuss fallback strategies if locks expire or services fail.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Genuinely had not thought about this one deeply before.
Start by clarifying requirements and constraints, such as expected traffic, fairness definition, and integration with existing systems. Then propose a high-level architecture that includes a queueing mechanism, token issuance, and rate limiting, while discussing trade-offs between fairness, latency, and scalability. Finally, dive into key components like distributed queue management, session validation, and monitoring.
Pro tip: Demonstrate awareness of real-world constraints by mentioning that perfect fairness is impossible; instead, aim for 'perceived fairness' and handle edge cases like users with multiple tabs or bots. Also, tie your design to Microsoft's ecosystem (e.g., Azure services) to show cultural fit.
Ask questions to understand the scale (e.g., millions of users), fairness goals (e.g., first-come-first-served, lottery), and non-functional requirements like latency and availability.
Outline the main components: a waiting room service that issues queue tokens, a distributed queue (e.g., Redis or Azure Service Bus), and a gatekeeper that admits users at a controlled rate.
Explain how to ensure fairness: use a FIFO queue with token expiration, randomize admission for lottery-style fairness, and prevent abuse via CAPTCHAs and rate limiting per IP/user.
Discuss scaling the queue horizontally, using consistent hashing for partitioning, and ensuring high availability with redundant components and graceful degradation.
Articulate trade-offs between strict fairness and system throughput, and describe monitoring metrics (queue length, admission rate) and alerting for anomalies.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Ran a bit short on time here so this felt rushed.
Start by clarifying the system's requirements and constraints, then explain how you would design idempotent payment processing using idempotency keys and state machines. Finally, walk through the cancellation and refund flow, emphasizing consistency, error handling, and trade-offs between consistency and availability.
Pro tip: Demonstrate awareness of real-world payment system challenges like network failures, duplicate requests, and partial failures, and mention how you would use idempotency keys and transactional outbox patterns to ensure exactly-once semantics.
Ask questions to understand the scale, consistency requirements, and existing infrastructure. This shows you don't jump to solutions without context.
Explain how to use idempotency keys to deduplicate requests and ensure that retries don't result in double charges. Discuss storing keys with a unique constraint and returning the same response for repeated requests.
Describe how cancellations are handled idempotently, possibly by updating the payment state to 'cancelled' and ensuring that only valid state transitions occur. Mention handling race conditions with optimistic locking or conditional updates.
Outline the refund process, including idempotent refund requests, partial refunds, and integration with payment providers. Emphasize the need for a refund state machine and reconciliation with external systems.
Talk about trade-offs between consistency and availability, and how to handle failures (e.g., retries, dead-letter queues, compensating transactions). Mention monitoring and alerting for anomalies.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.