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Meta·Software Engineer·Onsite - System Design / Architecture·Senior

Senior
May 2026

Summary

Meta system design round for a software engineering role. The question was a full-blown online auction system, which sounds fun until you realize how many moving parts they actually want you to cover in under an hour.

Questions Asked (1)

Q1

Design an online auction system that handles listing creation, real-time bidding, bid increments, reserve prices, buy-it-now, anti-sniping, and auction closeout. Cover APIs, data model, consistency for winning bids, real-time updates, concurrency control, fraud detection, payments and settlement, scalability, fault tolerance, and monitoring.

System DesignData ModelingTechnical Trade-offs
Author's notes

This is basically a distributed systems gauntlet dressed up as a product question.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and scale, then design the core data model and APIs for listing and bidding. Focus on the critical path: real-time bid updates, concurrency control for bid acceptance, and auction closeout with consistency. Finally, address scalability, fault tolerance, fraud detection, payments, and monitoring as cross-cutting concerns.

Pro tip: Emphasize idempotency and exactly-once processing for bid acceptance and payment settlement, as these are crucial for correctness in a distributed auction system. Also, discuss how anti-sniping can be implemented with a dynamic auction end time and how it affects system design.

1. Requirements and Scale

Clarify functional and non-functional requirements: number of users, auctions, bids per second, latency, consistency needs, and budget. Ask about read/write ratio and geographic distribution.

2. High-Level Design

Sketch the main components: API gateway, auction service, bid service, real-time notification service, database, cache, message queue, and payment service. Define the data flow for listing creation, bidding, and auction close.

3. Data Model and APIs

Design schemas for users, auctions, bids, and transactions. Define RESTful or gRPC APIs for creating auctions, placing bids, getting auction details, and subscribing to real-time updates.

4. Concurrency and Consistency

Explain how to handle concurrent bids using optimistic locking, distributed locks, or a serialized queue. Ensure the winning bid is consistent and auction closeout is atomic, possibly using a saga pattern or two-phase commit.

5. Scalability, Fault Tolerance, and Monitoring

Discuss partitioning auctions by ID, using read replicas, caching, and CDN for static assets. Implement retries, circuit breakers, and idempotency. Monitor with metrics, logging, and tracing.

Key Points to Mention

  • Real-time updates via WebSockets or Server-Sent Events, with pub/sub (e.g., Redis Pub/Sub, Kafka) for scalability.
  • Concurrency control: optimistic concurrency (version numbers) or pessimistic locking (e.g., Redis distributed locks) to prevent race conditions on bids.
  • Anti-sniping: extend auction end time if a bid is placed within a threshold (e.g., last 5 minutes), implemented with a scheduled job or delayed message.
  • Reserve price and buy-it-now: enforce reserve price before accepting bids; buy-it-now immediately ends auction and triggers payment.
  • Fraud detection: use rate limiting, anomaly detection (e.g., bidding patterns), and user reputation systems.
  • Payments and settlement: integrate with payment gateway, use idempotent transactions, handle failures with retries and compensation (saga pattern).

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.