Start by clarifying functional and non-functional requirements, focusing on low-latency bid updates, consistency, and scale. Then propose a high-level architecture with real-time communication (e.g., WebSockets) and a scalable data layer, diving into trade-offs for consistency and latency. Finally, discuss data modeling for auctions and bids, and how to handle edge cases like bid sniping and failures.
Pro tip: Emphasize the trade-off between strong consistency and low latency: using a centralized sequencer for bids can ensure fairness but may add latency, while eventual consistency with conflict resolution can reduce latency but risks disputes. Show awareness of Meta's scale and existing infrastructure (e.g., TAO, WebSockets) to ground your design.
Ask about scale (DAU, concurrent auctions), latency targets, consistency needs (e.g., must all viewers see the same highest bid instantly?), and auction rules (duration, bid increments, sniping prevention).
Outline components: client apps, API gateway, auction service, bid service, real-time notification service (WebSockets), and data stores (e.g., in-memory cache, distributed DB). Explain how a bid flows from client to all viewers.
Design schemas for auctions and bids, and choose a consistency model. Discuss using a centralized bid sequencer or a distributed consensus protocol (e.g., Raft) to order bids, and how to propagate the highest bid to viewers with low latency.
Address scaling: sharding by auction ID, using pub/sub (e.g., Kafka) for bid events, and WebSockets for push updates. Discuss caching the highest bid and using edge servers to reduce latency.
Analyze trade-offs: consistency vs. latency, cost vs. performance. Cover edge cases: network partitions, bid sniping, failed bids, and how to handle auction closure and payment integration.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Spent probably too long on the optimistic vs pessimistic locking debate before landing somewhere useful.
Start by clarifying requirements: bid ordering semantics (e.g., highest bid wins, time priority), consistency guarantees (linearizability vs. eventual consistency), and scale (QPS, number of concurrent bidders). Then propose a layered solution: use optimistic concurrency control (e.g., version numbers or CAS) for low contention, and pessimistic locking (e.g., row-level locks or distributed locks) for high contention, with a fallback to a serialized queue or single-writer per item. Finally, discuss trade-offs between latency, throughput, and correctness, and how to handle failures (e.g., retries, idempotency).
Pro tip: Emphasize that correctness under concurrency often requires a single source of truth per item (e.g., sharding by item ID) and that you should measure contention before choosing a strategy—over-engineering with distributed locks can hurt latency. Also, mention that Meta often values practical solutions that scale, so propose a design that can be implemented with existing infrastructure (e.g., using database transactions or a sequencer service).
Ask about bid ordering rules (e.g., highest bid, earliest bid), consistency level (strong vs. eventual), expected load (concurrent bidders per item), and latency requirements. This ensures you design for the right problem.
Decide between optimistic (e.g., version numbers, CAS) and pessimistic (e.g., locks, serialized queue) approaches based on contention level. For high contention, consider a single-writer per item or a distributed lock with a timeout.
Store bids with a monotonic sequence number or timestamp to enforce ordering. Use a database with ACID transactions or a distributed log (e.g., Kafka) to serialize bids per item.
Ensure idempotency for retries, handle lock timeouts, and define behavior for out-of-order bids (e.g., reject stale bids). Discuss how to recover from partial failures.
Compare latency, throughput, and complexity of each approach. Propose a hybrid solution (e.g., optimistic for low contention, pessimistic for high) and explain how to scale horizontally (e.g., sharding by item ID).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went with WebSockets for persistent connections and a pub/sub layer to fan out updates.
Start by clarifying requirements: scale (number of concurrent viewers, auction rate), latency target (e.g., sub-second), and consistency needs. Then propose a publish-subscribe architecture using WebSockets for persistent connections, with a fan-out service that receives bid updates from the auction service and pushes them to all connected clients. Discuss trade-offs between push vs. pull, and how to handle scaling with a distributed message broker like Kafka or Redis Pub/Sub.
Pro tip: Emphasize the importance of connection management and backpressure handling—real-time systems often fail under load due to slow consumers. Mention using a CDN or edge network for static assets and possibly for WebSocket termination to reduce latency globally.
Ask about scale (e.g., millions of concurrent viewers), latency expectations (e.g., <1 second), and consistency (e.g., eventual vs. strong). Also consider auction dynamics like bid frequency and update size.
Select WebSockets for bidirectional, low-latency communication. Discuss alternatives like Server-Sent Events (SSE) or long polling, and justify why WebSockets are suitable for high-frequency updates.
Use a message broker (e.g., Kafka, Redis Pub/Sub) to decouple bid producers from consumers. A fan-out service subscribes to bid updates and pushes them to all connected WebSocket servers, which then broadcast to clients.
Scale horizontally by adding more WebSocket servers and partitioning topics. Implement connection management (heartbeats, reconnection), backpressure handling, and load balancing. Consider geo-distribution for lower latency.
Compare push vs. pull models, and mention optimizations like batching updates, delta compression, and using edge servers. Also consider fallback mechanisms for clients that can't use WebSockets.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is where I felt most out of my depth.
Start by clarifying the scale and traffic patterns of celebrity auctions, then propose a multi-layered architecture that handles extreme spikes through horizontal scaling, caching, and asynchronous processing. Focus on trade-offs between consistency, latency, and cost, and explain how you would ensure fairness and prevent fraud during high-volume events.
Pro tip: Emphasize the importance of load testing and gradual rollout with canary deployments to validate scaling strategies before high-profile auctions, and mention how you would use real-time monitoring to dynamically adjust resources.
Ask questions to understand expected bid volume, latency requirements, consistency needs, and budget constraints. Define what 'dramatic spike' means in terms of QPS and concurrent users.
Propose a stateless, horizontally scalable service layer with load balancers and auto-scaling groups. Use sharding or partitioning for bid data to distribute load across multiple databases.
Use in-memory caches (e.g., Redis) for hot data like current highest bid and auction status. Queue bid writes asynchronously to smooth spikes and ensure durability, while updating cache for real-time reads.
Discuss mechanisms like optimistic concurrency control or distributed locks to prevent race conditions. Consider using a sequencer or timestamp service to order bids fairly and detect out-of-order events.
Outline a plan for load testing, real-time monitoring (e.g., metrics, logs, traces), and auto-scaling policies. Mention canary deployments and feature flags to safely roll out changes during low-traffic periods.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Pretty standard REST endpoints, nothing too surprising.
Start by clarifying requirements and constraints, then outline a RESTful API design with key endpoints for placing bids and retrieving auction state. Emphasize idempotency, real-time updates, and scalability considerations, and discuss trade-offs and potential optimizations.
Pro tip: Demonstrate awareness of concurrency and consistency challenges in auction systems, and propose mechanisms like optimistic locking or versioning to handle simultaneous bids. Also, mention how you would monitor and test the API for reliability and performance.
Ask questions to understand scale, latency requirements, consistency needs, and client types (e.g., mobile, web). This ensures your design addresses the right problems.
Identify main resources: auctions and bids. Design endpoints like POST /auctions/{id}/bids to place a bid and GET /auctions/{id} to retrieve auction state.
Specify request/response formats, authentication, idempotency keys, and error handling. Discuss how to handle concurrent bids and prevent race conditions.
Describe how clients fetch current state, including current highest bid, bid history, and time remaining. Consider pagination for bid history and caching strategies.
Explain how clients get live updates (e.g., WebSockets, polling, server-sent events) and how the system scales (e.g., sharding, read replicas, message queues).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked through at-least-once delivery with deduplication on the consumer side, and checkpointing auction state so a restart doesn't lose the current high bid.
Start by acknowledging that failures are inevitable in distributed systems, then systematically walk through detection, mitigation, and recovery for each scenario. Emphasize designing for idempotency, consistency, and graceful degradation to maintain user trust and system integrity.
Pro tip: Show that you think beyond just fixing the immediate issue—consider the user experience and business impact, and propose proactive measures like chaos testing and circuit breakers to prevent similar failures.
Enumerate potential failure scenarios (e.g., bid lost in transit, auction service down) and their root causes such as network partitions, server crashes, or message loss.
Describe how to detect failures quickly using health checks, logging, metrics, and alerting. Mention tools like Prometheus, Grafana, or distributed tracing.
Explain strategies to handle failures: for lost bids, use message queues with acknowledgments and retries; for service downtime, implement failover, replication, and graceful degradation.
Discuss ensuring data consistency and idempotent operations to avoid duplicate bids or lost updates, using techniques like unique bid IDs and transactional writes.
Propose preventive measures like chaos engineering, circuit breakers, and post-mortems to improve system resilience over time.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.