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I started with functional requirements which felt natural: browse events, check seat availability, hold a seat, pay, get a ticket, cancel.
Start by clarifying functional and non-functional requirements, then propose a high-level architecture that separates concerns (e.g., event catalog, booking, payment). Dive into data modeling and API design, highlighting trade-offs and scalability challenges like concurrency and consistency.
Pro tip: Emphasize idempotency and distributed locking to handle concurrent bookings, and discuss how you'd monitor and mitigate hot partitions in a high-traffic event like a concert ticket sale.
Ask questions to define scope: functional (search, book, pay, cancel) and non-functional (scalability, consistency, latency, availability). Identify key entities and user flows.
Outline core entities (Event, Venue, Seat, Booking, User, Payment) and relationships. Define RESTful API endpoints for searching events, reserving seats, and processing payments.
Sketch components: API gateway, microservices (event, booking, payment, notification), databases (SQL for transactions, NoSQL for catalog), caching, and message queues for async processing.
Discuss concurrency control (optimistic vs pessimistic locking), seat reservation timeouts, payment integration, and scaling for peak loads. Mention trade-offs between consistency and availability.
Recap the design, highlight how it meets requirements, and invite feedback. Be prepared to dive deeper into any component based on interviewer interest.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements and constraints, then propose a layered solution combining database transactions with appropriate isolation levels, optimistic or pessimistic locking, and possibly a distributed lock or queue for high concurrency. Discuss trade-offs between consistency, latency, and scalability, and mention how to handle failures and retries.
Pro tip: Emphasize that the database is the source of truth and that application-level checks alone are insufficient; also mention that you would measure and monitor contention to decide when to introduce more complex mechanisms like distributed locks.
Ask about expected concurrency, consistency requirements (strong vs eventual), latency tolerance, and whether the system is single-node or distributed. This shows you don't jump to solutions without understanding the problem.
Explain that you would rely on ACID transactions with an isolation level like Serializable or Repeatable Read, and use SELECT ... FOR UPDATE to lock the seat row before checking availability and updating.
Discuss trade-offs: optimistic locking (version numbers) works well for low contention but causes retries under high contention; pessimistic locking (row locks) prevents conflicts but can reduce throughput. Choose based on expected load.
For distributed systems, mention using a distributed lock (e.g., Redis Redlock, ZooKeeper) or a message queue to serialize bookings per seat, but note the added complexity and potential for bottlenecks.
Describe how to handle retries, timeouts, and partial failures: use idempotent operations, unique constraints, and compensating actions. Also mention monitoring and alerting for overselling attempts.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by framing the problem as a distributed transaction challenge, then propose an idempotency-based solution using unique keys and state machines. Walk through the payment and booking flow, highlighting where idempotency and reconciliation prevent duplicates.
Pro tip: Emphasize that idempotency keys must be generated client-side and stored server-side with a TTL, and that you should always design for at-least-once delivery with idempotent consumers.
Explain that payment and booking are separate systems, so network failures or timeouts can cause ambiguity about whether the operation succeeded. This leads to risks of double charging or duplicate bookings.
Propose that the client generates a unique idempotency key for each payment/booking attempt. The server stores this key and associates it with the operation's result, ensuring repeated requests with the same key return the same response without re-executing.
Model the payment and booking as a state machine (e.g., PENDING, PAYMENT_PROCESSING, BOOKING_CONFIRMED, FAILED). Use this to track progress and handle retries safely by checking the current state before acting.
For transient failures, retry with exponential backoff and jitter, but only if the operation is idempotent. Ensure that retries use the same idempotency key to avoid duplicates.
Implement a reconciliation process that periodically checks for inconsistencies between payment and booking systems. Use logging and alerts to detect and resolve duplicate attempts or stuck transactions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Caching was the obvious answer and I went there immediately.
Start by clarifying the system's current architecture and read patterns, then propose a multi-layered caching strategy combined with database read replicas and possibly a CDN for static assets. Emphasize trade-offs between consistency, latency, and cost, and discuss how to handle cache invalidation during high-demand events.
Pro tip: Mention the importance of pre-warming caches and using a write-through or write-behind strategy for inventory updates to avoid overselling, showing you understand real-world flash sale challenges.
Ask about expected traffic volume, read/write ratio, consistency requirements, and budget constraints to tailor your solution.
Analyze the current read path: database queries, API calls, and static content delivery to pinpoint where scaling is needed.
Suggest in-memory caches (e.g., Redis), CDN for static assets, and application-level caching with appropriate TTLs and invalidation strategies.
Introduce read replicas, sharding, or NoSQL solutions for horizontal scaling, and discuss consistency trade-offs.
Discuss consistency vs. availability, cost implications, and how to monitor and auto-scale during spikes.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.