Start by clarifying requirements and scale, then present a high-level architecture with clear separation of concerns. Dive into data modeling, real-time bidding, consistency, and scalability, discussing trade-offs and fault tolerance at each layer.
Pro tip: Emphasize the trade-off between consistency and latency in real-time bidding, and propose a pragmatic solution like using a sequencer to order bids. Show awareness of Meta's scale by discussing sharding and global distribution.
Ask questions to understand functional and non-functional requirements, such as number of users, items, bids per second, latency expectations, and consistency needs. Establish scale estimates to guide design decisions.
Sketch the main components: clients, API gateway, auction service, bid service, database, cache, message queue, and real-time communication layer (e.g., WebSockets). Explain how they interact.
Design schemas for users, items, auctions, and bids. Choose appropriate databases (e.g., SQL for transactions, NoSQL for scale) and discuss indexing, sharding, and replication strategies.
Detail how bids are processed in real-time, ensuring correct ordering and consistency. Discuss techniques like optimistic concurrency, distributed locks, or a sequencer service. Explain how updates are pushed to clients.
Describe how to scale each component horizontally, handle failures with redundancy, and ensure availability. Mention partitioning, load balancing, and disaster recovery.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went through the tradeoffs pretty mechanically.
Start by clarifying the requirements: scale, latency, and client constraints. Then compare polling, long polling, and SSE on dimensions like latency, server load, and complexity, and justify your choice based on the scenario. Conclude with a recommendation and mention fallback options.
Pro tip: Emphasize that SSE is ideal for unidirectional real-time updates from server to client, but if bidirectional communication is needed, WebSockets might be better. Also, discuss how to handle scalability with SSE using a pub/sub system like Redis.
Ask about expected number of concurrent clients, update frequency, latency requirements, and client types (web, mobile). This shows you don't jump to solutions.
Briefly explain polling, long polling, and SSE, highlighting their trade-offs in terms of latency, server load, and implementation complexity.
Map each option to the requirements. For example, polling may be too slow for real-time, long polling reduces latency but ties up server resources, SSE provides efficient one-way streaming.
Choose SSE for real-time bid updates due to its low latency, efficiency, and simplicity for unidirectional server-to-client communication. Mention fallback to long polling for older browsers.
Explain how to scale SSE with a pub/sub backend (e.g., Redis) and load balancers, and how to handle reconnections and missed updates.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying requirements: define bid consistency (exactly one winner, no lost valid bids) and the concurrency challenges near auction end. Then propose a design that uses a serialization point (e.g., a single-threaded auctioneer or a distributed lock) combined with idempotent bid processing and durable logging to guarantee correctness. Finally, discuss trade-offs between consistency, latency, and availability, and how to scale while maintaining guarantees.
Pro tip: Emphasize that you would use a monotonic sequence number or timestamp to order bids and detect ties, and that you would make bid acceptance idempotent to handle retries safely. This shows you understand both correctness and practical failure modes.
Define what 'bid consistency' means: exactly one winner, no valid bid lost, and fairness. Identify peak concurrency near auction end and the need for low latency.
Propose a single-threaded auctioneer per auction or a distributed lock (e.g., using ZooKeeper/etcd) to serialize bid processing. Discuss strong consistency vs. eventual consistency trade-offs.
Use a monotonic sequence number or timestamp to order bids. Make bid submission idempotent with a unique bid ID to handle retries. Persist bids to a durable log before acknowledging.
Describe how to recover from failures (e.g., replay log, leader election) and how to scale reads (e.g., separate read replicas) while writes go through the serialization point.
Compare with optimistic concurrency (e.g., compare-and-swap) and two-phase commit. Explain why your approach balances correctness and performance for Meta's scale.
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 propose a multi-layered solution that combines auction design (e.g., soft close, anti-sniping extensions) with robust client-server communication (idempotent bid submission, retries with backoff, and server-side deduplication). Emphasize trade-offs between fairness, latency, and complexity, and how you would validate the approach with metrics and testing.
Pro tip: Mention that you would use a server-side timestamp for bid ordering and implement a short anti-sniping extension window (e.g., 30 seconds) that resets on any bid, which is a common industry practice to prevent last-second sniping while keeping the auction fair.
Ask about the auction type (e.g., English, sealed-bid), expected scale, latency requirements, and business rules around sniping. This ensures your solution aligns with the actual needs.
Propose mechanisms like soft close (extending the auction if a bid arrives near the end) or a fixed anti-sniping window. Discuss how these rules affect fairness and user experience.
Address network delays and retries by using idempotent bid requests with unique client-generated IDs, server-side deduplication, and exponential backoff with jitter for retries. Consider using a message queue for asynchronous processing.
Use a centralized, authoritative server with a monotonic clock or logical timestamps to order bids. Ensure that bid acceptance is atomic and that the highest bid wins, even under concurrent submissions.
Define metrics (e.g., sniping rate, bid success rate, latency percentiles) and run load tests and chaos experiments to validate resilience. Be prepared to adjust rules based on data.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying requirements and scale, then walk through the end-to-end flow from auction close trigger to winner determination, covering data model, concurrency, and edge cases. Emphasize correctness under concurrent bids and system reliability.
Pro tip: Explicitly discuss how you handle ties and late bids, and mention idempotency and audit logging to show production maturity.
Ask about auction types (e.g., ascending, sealed-bid), expected QPS, consistency needs, and whether real-time updates are required. This sets the stage for design decisions.
Outline schemas for auctions, bids, and users, and choose a database (e.g., SQL for strong consistency or NoSQL for scale). Discuss indexing for efficient bid retrieval.
Explain how auctions are closed: scheduled job, event-driven, or lazy evaluation. Cover handling of clock skew and time zones.
Describe the algorithm to find the highest bid, including tie-breaking rules. Discuss locking, transactions, or optimistic concurrency to prevent race conditions.
Address late bids, no bids, ties, and system failures. Outline notifications, payment processing, and audit logging.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.