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

SeniorPrefer not to say
May 2026

Summary

System design round at Bytedance for a software engineer role. The whole session was basically one big auction system question with a lot of back-and-forth on architecture choices, which was actually kind of fun given I work with this stack daily.

Questions Asked (1)

Q1

Design a real-time auction system that can handle high read and write volumes, especially during peak bidding periods.

System DesignTechnical Trade-offsAPI & Integrations
Author's notes

I initially said WebSocket for everything and had to walk it back mid-discussion.

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AI HintsAI Generated

Suggested Approach

Start by clarifying functional and non-functional requirements, then design a scalable architecture that separates read and write paths, using in-memory data stores for real-time bidding and persistent storage for durability. Focus on handling peak load through partitioning, caching, and asynchronous processing, and discuss trade-offs between consistency and availability.

Pro tip: Emphasize idempotency and race condition handling in bid processing, as auctions require strict ordering and fairness. Also, mention how you would monitor and auto-scale during peak periods to maintain low latency.

1. Clarify Requirements

Ask about expected scale (e.g., concurrent users, bids per second), latency requirements, consistency needs, and auction rules (e.g., bid validity, closing time).

2. High-Level Architecture

Outline components: API gateway, bid service, auction service, real-time notification service, and data stores. Separate read and write paths for scalability.

3. Data Storage and Consistency

Choose appropriate databases: in-memory (e.g., Redis) for current bid state and leaderboard, and a durable store (e.g., Cassandra) for bid history. Discuss consistency models (e.g., eventual vs strong) and how to handle conflicts.

4. Scaling and Peak Handling

Describe partitioning (e.g., by auction ID), caching, load balancing, and asynchronous processing (e.g., message queues) to handle spikes. Mention auto-scaling and rate limiting.

5. Real-Time Updates and Trade-offs

Explain how to push updates to clients (e.g., WebSockets, SSE) and discuss trade-offs between latency, consistency, and cost. Address failure scenarios and idempotency.

Key Points to Mention

  • Use of in-memory data stores like Redis for low-latency bid processing and leaderboard.
  • Partitioning by auction ID to distribute load and enable horizontal scaling.
  • Idempotent bid processing and optimistic locking to prevent race conditions.
  • Asynchronous processing with message queues (e.g., Kafka) for bid validation and persistence.
  • Real-time communication via WebSockets or server-sent events for bid updates.
  • Monitoring, auto-scaling, and rate limiting to handle peak bidding periods.

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