I jumped straight into the data model before clarifying scale and that was a mistake.
Start by clarifying functional and non-functional requirements, then sketch a high-level architecture that separates read-heavy browsing from transactional seat holds and checkout. Dive deep into the seat hold mechanism, ensuring atomicity and concurrency control, and discuss trade-offs between consistency, latency, and cost.
Pro tip: Emphasize idempotency and distributed locking for seat holds to prevent double-booking, and discuss how to handle expired holds gracefully with a background sweeper or TTL-based eviction.
Ask about scale (events, users, concurrent holds), consistency needs (strong vs eventual), and refund policies. Define core entities: events, venues, seats, holds, orders, payments, refunds.
Propose a microservices or modular monolith design with separate services for event catalog, seat inventory, booking, payment, and refunds. Use a CDN and cache for event browsing, and a relational or NewSQL database for transactional integrity.
Design a seat hold system using distributed locks (e.g., Redis Redlock) or optimistic concurrency with versioning. Ensure holds are atomic, have a TTL, and are released on expiry or checkout completion.
Outline a saga or two-phase commit pattern to coordinate payment and seat reservation. Use idempotent APIs and handle failures with compensating transactions (e.g., release hold if payment fails).
Describe refund processing: validate eligibility, reverse payment, release seats back to inventory, and update order status. Discuss asynchronous processing and reconciliation.
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 scale, latency, and consistency needs. Then, propose a solution that uses a combination of database transactions, locking mechanisms, and possibly distributed coordination to ensure atomic seat reservation. Discuss trade-offs between consistency, availability, and performance, and consider optimizations like optimistic concurrency control or seat holds.
Pro tip: Demonstrate awareness of real-world constraints by mentioning how you would handle failures and retries, and how you would monitor and alert on double-booking attempts. Also, relate your solution to Meta's scale and existing infrastructure, such as using TAO or Zookeeper for coordination.
Ask questions to understand the scale (e.g., number of seats, concurrent users), consistency requirements (strong vs. eventual), and latency expectations. This shows you can tailor the solution to the specific context.
Decide between strong consistency (e.g., using transactions with serializable isolation) and optimistic concurrency control (e.g., versioning). Explain why strong consistency is typically needed to prevent double-booking.
Propose a concrete approach: e.g., use a database with ACID transactions, row-level locks, or a distributed lock service. Describe how a user's request would atomically check and update seat status.
Discuss how to scale the solution (e.g., sharding by event or seat section) and handle failures (e.g., retries, idempotency, timeouts). Mention monitoring and alerting for anomalies.
Compare alternatives like pessimistic vs. optimistic locking, and discuss their impact on throughput, latency, and user experience. Conclude with a recommended approach.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Virtual waiting room was the first thing I said and they seemed fine with that.
Start by clarifying the requirements and scale, then propose a multi-layered architecture that isolates flash sale traffic from normal traffic. Focus on techniques like caching, queueing, rate limiting, and graceful degradation to protect the overall system.
Pro tip: Emphasize the importance of load shedding and backpressure to prevent cascading failures, and discuss how to prioritize critical user flows during spikes.
Ask questions to understand expected traffic volume, flash sale duration, and business impact. Define what 'degrading experience' means for different user segments.
Propose separating flash sale traffic from normal traffic using dedicated services or queues. Prioritize critical paths like browsing and checkout for all users.
Use caching (CDN, Redis), asynchronous processing (message queues), and auto-scaling to handle load. Apply rate limiting and circuit breakers to prevent overload.
Define fallback mechanisms such as static content, simplified UI, or waiting rooms. Ensure non-essential features are disabled under high load.
Set up real-time monitoring and alerts for key metrics. Be prepared to adjust strategies based on live traffic patterns.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying requirements and constraints, then describe a robust payment flow with idempotency and retry mechanisms. Emphasize how you prevent double charges through idempotency keys, state management, and reconciliation.
Pro tip: Mention that you would use idempotency keys on every payment request and store them with a unique constraint to ensure exactly-once processing. Also, discuss the importance of handling edge cases like network timeouts and partial failures.
Ask about expected scale, payment providers, and failure scenarios. Confirm that the goal is to avoid double charges while ensuring payments eventually succeed.
Describe how each payment request includes a unique idempotency key generated by the client. The server stores this key and ensures that repeated requests with the same key return the same result without recharging.
Explain that retries should be safe and only occur for transient errors. Use exponential backoff with jitter, and ensure retries are idempotent by reusing the same idempotency key.
Discuss maintaining a payment state machine (e.g., pending, succeeded, failed) and reconciling with the payment provider's records to detect and resolve discrepancies.
Mention setting up monitoring for retry rates, duplicate charge attempts, and reconciliation failures. Alert on anomalies to quickly address issues.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Partitioning by event ID was the obvious answer and I said it immediately.
Start by clarifying the requirements: data volume, event rate, query patterns, and consistency needs. Then propose a partitioning strategy that balances write scalability and read efficiency, such as partitioning by a high-cardinality key like user ID or event time, and discuss how to handle hotspots and rebalancing. Finally, explain trade-offs between different partitioning schemes and how they affect latency, throughput, and operational complexity.
Pro tip: Demonstrate awareness of real-world constraints by mentioning that partitioning alone isn't enough—you also need to consider replication, indexing, and query routing to avoid bottlenecks. Also, proactively discuss how you would monitor and rebalance partitions as data grows.
Ask about data volume, event rate, read/write ratio, latency requirements, and consistency needs to tailor your partitioning strategy.
Select a key that distributes load evenly and aligns with common query patterns, such as user ID, event type, or time bucket, and explain why.
Decide between hash, range, or composite partitioning, and describe how data will be distributed across nodes or shards.
Explain how to handle uneven load, rebalance partitions, and scale out by adding nodes without downtime.
Compare your approach with alternatives, highlighting impacts on latency, throughput, consistency, and operational overhead.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.