The whole interview was basically this one question with layers peeled off one by one.
Start by clarifying requirements and scale, then design a high-level architecture that separates concerns: real-time bid updates via WebSockets, concurrency control using optimistic locking or a queue, and end-of-auction semantics with a reliable timer service. Dive into trade-offs for consistency, latency, and fault tolerance, and discuss how to handle edge cases like bid sniping and network partitions.
Pro tip: Proactively discuss how you would handle the 'last-second bid' problem and ensure fairness—this shows you understand real-world auction dynamics and can design for edge cases that impact user trust.
Ask questions to understand functional and non-functional requirements: number of concurrent users, auction duration, bid update latency, consistency needs, and whether auctions can be extended. Estimate scale to inform design decisions.
Outline core components: API gateway, auction service, bid service, real-time notification service (WebSockets), database, and cache. Explain how they interact to support bidding and updates.
Detail how to handle concurrent bids: use optimistic locking with versioning, a distributed queue for serialization, or a consensus protocol. Discuss trade-offs between strong consistency and latency.
Describe the pub/sub mechanism for broadcasting bid updates to all watchers. Cover WebSocket connections, scaling with a message broker (e.g., Kafka, Redis Pub/Sub), and handling reconnections.
Explain how to reliably close auctions: use a distributed timer service (e.g., scheduled jobs with idempotency), handle last-second bids with anti-sniping extensions, and ensure atomic winner determination and payment processing.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Had a three-sentence comparison ready for this and it paid off.
Start by clarifying the requirements: scale, latency, client types, and reliability needs. Then compare SSE, WebSockets, and long polling across dimensions like directionality, overhead, and infrastructure support, and recommend a solution with trade-offs and a fallback plan.
Pro tip: At Meta's scale, the real challenge isn't the protocol but the fan-out and connection management. Mention that you'd use a pub/sub layer (e.g., Redis or Kafka) to decouple bid events from client connections, and consider connection draining during deploys.
Ask about scale (number of clients, bids per second), latency tolerance, client platforms (web, mobile), and reliability guarantees. This shapes the protocol choice.
Evaluate SSE, WebSockets, and long polling on key dimensions: communication direction, overhead, browser support, and complexity. Highlight that SSE is unidirectional server-to-client, WebSockets are full-duplex, and long polling is a hack with high overhead.
Based on requirements, propose a primary protocol (e.g., WebSockets for real-time bidding) and a fallback (e.g., long polling for legacy clients). Explain why it fits.
Discuss how to handle many concurrent connections: load balancing, pub/sub for fan-out, heartbeats, reconnection logic, and message ordering/deduplication.
Conclude with the main trade-offs: WebSockets offer low latency but require more infrastructure; SSE is simpler but unidirectional; long polling is universally supported but inefficient.
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 bid volume and consistency needs. Then propose a solution using atomic operations or distributed locks to serialize bid processing, and discuss trade-offs like latency and scalability. Finally, outline how to handle edge cases like clock skew and network delays.
Pro tip: Emphasize idempotency and monotonicity: ensure that even if bids are processed out of order, the final state is correct and no duplicate bids are accepted. This shows you think beyond just locking.
Ask about the expected load, consistency requirements (strong vs. eventual), and whether the system is distributed. This sets the stage for choosing the right approach.
Explain that the race occurs when multiple bids read the current highest bid simultaneously and both try to update it, leading to lost updates or inconsistent state.
Discuss options like database transactions with SELECT FOR UPDATE, optimistic concurrency control with versioning, distributed locks (e.g., Redis, ZooKeeper), or atomic operations (e.g., compare-and-swap).
Explain how the chosen solution scales (e.g., sharding by auction ID) and handles failures (e.g., lock timeouts, retries with backoff).
Discuss clock synchronization, network partitions, and idempotency. Mention that the final bid should be determined by a consistent rule (e.g., highest bid, earliest timestamp).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Anchored on auctions times peak bidders times bids per second and the interviewer seemed fine with that.
Start by clarifying the scope and assumptions of the auction system, such as user base, auction types, and peak patterns. Then break down QPS into read and write operations, using a top-down estimation based on daily active users and average actions per user, and adjust for peak traffic. Finally, validate with a bottom-up approach using known constraints like auction duration and bid frequency.
Pro tip: Always state your assumptions explicitly and round numbers to powers of 10 for easy mental math; interviewers care more about your structured thinking than exact figures.
Ask questions to understand the system: number of users, auction types (e.g., timed, live), typical auction duration, and expected user actions (browsing, bidding). Establish a time frame (e.g., daily active users) and peak-to-average ratio.
Calculate read operations: how many times users view auctions, search, or refresh pages. Use DAU and average reads per user per day, then convert to QPS by dividing by 86400 seconds and multiplying by peak factor.
Calculate write operations: bids placed, auction creations, and updates. Use DAU, percentage of users who bid, and average bids per bidder per day. Convert to QPS similarly, considering peak spikes during auction endings.
Sum read and write QPS to get total QPS. Sanity-check with a bottom-up approach: e.g., if an auction has X bids over Y minutes, what's the QPS per auction? Multiply by concurrent auctions.
Highlight that QPS is not uniform; peak QPS can be 2-5x average. Mention strategies like caching, sharding, and rate limiting to handle peaks.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.