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Start by clarifying functional and non-functional requirements, then design a high-level architecture that separates concerns: listing service, bidding service, real-time notification service, and auction closing service. Dive into critical components like concurrency control for bids, time synchronization, and anti-sniping mechanisms, discussing trade-offs between consistency, latency, and scalability.
Pro tip: Emphasize idempotency and exactly-once processing for bids to handle retries and network issues, and discuss how to use distributed locks or optimistic concurrency to prevent race conditions. Also, mention the importance of clock synchronization across servers for accurate auction endings.
Ask clarifying questions to scope the system: expected scale (users, listings, bids per second), consistency vs. availability trade-offs, and specific features like anti-sniping rules (e.g., extend auction if bid in last minute).
Outline core services: listing service (CRUD for items), bidding service (handles bids), real-time notification service (WebSocket/SSE for price updates), watchlist service, and auction closing service (scheduled jobs). Use a message queue for asynchronous processing.
Design schemas for listings, bids, watchlists, and users. Choose databases: relational for transactions (bids) with strong consistency, NoSQL for high-throughput reads (listings), and in-memory stores (Redis) for real-time price and leaderboards.
Address bid concurrency: use optimistic locking (versioning) or distributed locks (e.g., Redis Redlock) to ensure only valid bids are accepted. Discuss idempotency keys to handle duplicate bid submissions.
Implement real-time price updates via WebSockets or pub/sub (e.g., Redis Pub/Sub, Kafka). For anti-sniping, use a scheduled service that checks for last-minute bids and extends auction end time, with clock synchronization (NTP) to avoid discrepancies.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went with read replicas plus an aggressive caching layer in front of listing data.
Start by clarifying the scale and read/write ratio, then propose a multi-layer caching strategy with CDN, application-level cache, and database read replicas. Discuss trade-offs between consistency and latency, and how to handle cache invalidation for auction updates.
Pro tip: Emphasize that auctions have time-sensitive data, so use short TTLs and event-driven invalidation to balance freshness and load. Mention monitoring cache hit rates and latency percentiles to validate the design.
Ask about expected QPS, read/write ratio, data size, and latency SLA to understand the problem scope.
Propose CDN for static assets, edge caching for listing pages, and application-level cache (e.g., Redis) for dynamic data.
Discuss cache invalidation strategies (TTL, write-through, event-driven) and trade-offs between consistency and latency.
Use read replicas, sharding, and denormalization to handle high read fan-out and reduce database load.
Define metrics (cache hit rate, p99 latency) and iterate on cache policies and infrastructure based on monitoring.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I went with WebSockets for persistent connections on active listings and a pub/sub backend to fan out bid events.
Start by clarifying requirements: scale, latency, consistency, and client types. Then propose a pub/sub architecture with WebSocket connections and a scalable message broker, discussing trade-offs and fallback mechanisms.
Pro tip: Emphasize the importance of idempotency and ordering in bid updates to prevent race conditions and ensure all viewers see the same sequence of bids.
Ask about expected scale (concurrent viewers per listing, total listings), latency requirements, consistency needs, and client platforms.
Propose a pub/sub model where bid events are published to a message broker (e.g., Kafka) and consumed by a service that pushes updates to clients via WebSockets.
Detail the WebSocket connection management, including authentication, subscription to specific listing channels, and handling reconnections.
Discuss partitioning by listing ID, using a distributed cache for latest bid state, and ensuring message ordering and idempotency.
Compare WebSockets vs. SSE vs. polling, discuss consistency vs. latency, and outline fallback to polling if WebSocket fails.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Covered the basics: auction close triggers a job, winner gets notified, payment is initiated, seller is paid out after some hold period.
Start by clarifying the auction type (e.g., first-price, second-price) and scale requirements, then walk through the end-to-end flow from auction close to settlement, highlighting key components like payment processing, ledger updates, and notifications. Emphasize idempotency, consistency, and failure handling to demonstrate production readiness.
Pro tip: Proactively discuss how you would handle edge cases like payment failures, concurrent auctions, and reconciliation, showing you think beyond the happy path. Also, mention monitoring and alerting for settlement anomalies to ensure system reliability.
Ask about auction type, scale, payment methods, and consistency requirements to scope the problem. State your assumptions clearly before diving into the design.
Outline the main stages: auction close detection, winner determination, payment initiation, settlement, and notification. Mention the services involved (e.g., auction service, payment service, ledger).
Describe how payment is processed (e.g., charging the winner's payment method), how funds are held/transferred, and how the ledger records debits/credits. Include idempotency keys and retry mechanisms.
Explain the data entities (e.g., auction, bid, payment, ledger entry) and how you ensure consistency (e.g., transactions, saga pattern, eventual consistency). Discuss how to handle partial failures.
Cover scenarios like payment failure, insufficient funds, auction cancellation, and reconciliation. Describe compensating actions, retries, and dead-letter queues.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the system's scale and requirements, then propose a multi-layered fraud detection system combining real-time rules, machine learning models, and graph analysis. Emphasize prevention through design (e.g., identity verification, bidding restrictions) and continuous monitoring with feedback loops.
Pro tip: Highlight the importance of balancing fraud prevention with user experience—overly aggressive measures can deter legitimate users. Also, mention that fraud patterns evolve, so the system must adapt via online learning and manual review.
Ask about scale (users, transactions per second), types of fraud (self-bidding, fake accounts, payment fraud), and business impact. This shows you understand the problem context before diving into solutions.
Propose a combination of rule-based checks (e.g., same IP/device for bidder and seller), anomaly detection (e.g., sudden price spikes), and machine learning models (e.g., supervised classification of historical fraud). Include graph analysis to uncover collusion rings.
Suggest preventive measures such as identity verification (KYC), bidding limits for new accounts, deposit requirements, and real-time blocking of suspicious activities. Emphasize that prevention is more cost-effective than post-hoc detection.
Describe how to continuously improve the system: collect labeled data from manual reviews, retrain models, and monitor key metrics (false positives/negatives). Also, set up alerts for emerging fraud patterns.
Discuss trade-offs between detection accuracy and latency, and between strictness and user experience. Explain how to scale the system using distributed processing (e.g., stream processing with Flink) and caching.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.