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I started with the happy path which felt right, browse events, pick seats, pay, done.
Start by clarifying requirements and scale, then design the core user flow from browsing to booking confirmation, focusing on data models, APIs, and concurrency control. Emphasize trade-offs like consistency vs. availability and how to handle high contention during peak booking times.
Pro tip: Proactively discuss how to prevent double-booking and handle payment failures, as these are critical in real systems and often overlooked. Show awareness of Microsoft's emphasis on scalability and reliability by mentioning Azure services like Cosmos DB and Service Bus.
Ask questions to understand functional and non-functional requirements, such as expected user load, peak concurrency, and consistency needs. Define scope: events, seats, payments, and notifications.
Outline the main components: event catalog, seat inventory, booking service, payment service, and notification service. Walk through the user flow: browse events -> select seats -> reserve -> pay -> confirm.
Design schemas for events, venues, seats, bookings, and users. Choose appropriate databases: e.g., SQL for transactions, NoSQL for catalog, and caching for seat maps.
Explain how to handle concurrent seat selection: optimistic vs. pessimistic locking, distributed locks, or queue-based reservation. Discuss trade-offs between consistency and availability.
Address scaling reads/writes, handling peak load, and ensuring fault tolerance. Discuss trade-offs like strong vs. eventual consistency, and how to handle 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 requirements and constraints, such as expected concurrency, consistency needs, and whether the system is distributed. Then propose a layered solution combining database transactions with appropriate isolation levels, optimistic or pessimistic locking, and idempotent booking requests to handle race conditions.
Pro tip: Acknowledge that seat booking is a classic concurrency problem and that the best solution depends on trade-offs between consistency, latency, and scalability; mentioning real-world examples like airline or ticketing systems shows practical insight.
Ask about expected load, consistency requirements (strong vs. eventual), and whether the system is single-node or distributed. This determines the appropriate concurrency control mechanism.
Discuss options like pessimistic locking (SELECT FOR UPDATE), optimistic locking (version numbers), or database constraints (unique index on seat+show). Explain trade-offs in terms of contention and throughput.
Ensure that retries or duplicate requests don't result in double bookings by using idempotency keys or unique request IDs. This is crucial for handling network failures and user retries.
If the system is distributed, consider using distributed locks (e.g., Redis Redlock) or a centralized coordination service (e.g., ZooKeeper). Discuss the CAP theorem trade-offs and potential for split-brain.
Mention the importance of stress testing under high concurrency and monitoring for deadlocks or lock contention. Suggest using tools like JMeter or Gatling to simulate concurrent bookings.
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 scale (e.g., expected concurrent users, event size, consistency needs) to frame the problem. Then propose a high-level architecture that handles high concurrency, focusing on scalability, consistency, and fault tolerance. Finally, dive into specific components like load balancing, caching, and database strategies, discussing trade-offs and potential bottlenecks.
Pro tip: Emphasize idempotency and fairness—show you understand that preventing double-booking and ensuring equitable access are as critical as raw throughput. Mention monitoring and graceful degradation to demonstrate production maturity.
Ask about scale (e.g., number of users, seats), consistency requirements (strong vs. eventual), and latency expectations. This shows you avoid assumptions and design for the actual problem.
Outline a scalable, distributed system: load balancers, stateless services, caching layers, and a database that can handle high write throughput. Mention horizontal scaling and partitioning.
Explain how to prevent overselling using techniques like optimistic locking, distributed locks, or queue-based serialization. Discuss trade-offs between consistency and availability.
Describe caching strategies (e.g., seat availability in Redis), CDN for static assets, and asynchronous processing for non-critical tasks. Highlight the importance of reducing database load.
Cover fault tolerance (redundancy, retries, circuit breakers), rate limiting, and monitoring/alerting. Discuss graceful degradation and post-mortem analysis.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by outlining the high-level integration flow with the payment provider, covering authentication, API calls, and webhook handling. Then dive into idempotency, explaining how to use idempotency keys, store request states, and handle retries safely. Emphasize reliability, consistency, and failure handling.
Pro tip: Mention that idempotency keys should be generated on the client side and stored server-side with a unique constraint, and that you should return the same response for duplicate requests. Also, highlight the importance of handling race conditions and timeouts gracefully.
Clarify payment flows (one-time, recurring), provider capabilities (idempotency support, webhooks), and compliance needs. Review provider API docs for idempotency key usage and error codes.
Define how your system will call the provider (e.g., via a service layer), handle authentication, and process asynchronous events like webhooks. Consider using a message queue for retries and decoupling.
Generate a unique idempotency key per payment attempt (e.g., UUID) and include it in the request header. Store the key and request state in a database with a unique constraint to detect duplicates.
On retry, check if the idempotency key exists; if so, return the stored response instead of reprocessing. Ensure atomic operations to avoid race conditions, and use exponential backoff for retries.
Implement logging, monitoring, and reconciliation jobs to detect inconsistencies between your system and the provider. Use webhooks to update payment status asynchronously.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Partial group cancellations are genuinely annoying to model.
Start by clarifying the requirements and constraints of group bookings, such as whether cancellations are per person or for the entire group, and how refunds should be calculated. Then, propose a data model that captures individual and group-level booking details, and design a system that handles refunds and cancellations atomically to avoid inconsistencies. Finally, discuss trade-offs and how you would ensure scalability and fault tolerance.
Pro tip: Demonstrate awareness of real-world complexities like partial cancellations affecting group discounts or shared resources, and propose idempotent operations to handle retries safely.
Ask questions to understand the business rules: Can individuals cancel independently? How are refunds calculated (pro-rata, fees)? Are there group-level constraints (minimum size, shared costs)?
Design entities like GroupBooking, IndividualBooking, Cancellation, and Refund. Capture relationships and states (e.g., confirmed, partially cancelled, fully cancelled).
Outline algorithms for processing cancellations and refunds, ensuring atomicity (e.g., using transactions) and handling partial cancellations by recalculating group totals and individual shares.
Discuss how to handle concurrent cancellations (e.g., optimistic locking) and ensure idempotency to avoid double refunds.
Talk about scaling to many groups, handling failures, and edge cases like last-minute cancellations or no-shows.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Read replicas and caching for the browse layer, stricter consistency on the reservation path.
Start by distinguishing read-heavy operations (search, seat map) from write-critical operations (reservation) and propose a CQRS-style separation with read replicas or caches for reads, while keeping writes on a strongly consistent primary. Then discuss how to handle consistency between the read and write paths, such as using optimistic concurrency or versioning to prevent stale reads from causing double-booking.
Pro tip: Emphasize that seat map reads can be eventually consistent for browsing, but the final reservation must be validated against the primary with a conditional write (e.g., 'UPDATE ... WHERE version = X') to ensure correctness. This shows you understand the trade-off between user experience and data integrity.
Ask about read/write ratios, latency requirements, and consistency needs. For example, event search can tolerate eventual consistency, but seat reservation requires strong consistency to avoid double-booking.
Propose a CQRS architecture: use read replicas, caching (e.g., Redis), or a search index (e.g., Elasticsearch) for read-heavy operations, while writes go to a primary database with ACID guarantees.
For event search, use a distributed search engine with sharding and replication. For seat maps, cache the seat layout and availability, updating it asynchronously via change data capture or event streams.
Use optimistic concurrency control (e.g., version numbers) or pessimistic locking on the primary for reservations. Validate seat availability at write time and handle conflicts gracefully.
For a user who just reserved a seat, ensure their subsequent reads reflect the change. This can be done by routing their reads to the primary for a short period or using session stickiness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.