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

SeniorPrefer not to say

Jul 2026

Summary

Meta system design round for a software engineer role. The question was a real-time bidding system and the interviewer pushed back hard on my locking strategy, which made things interesting.

Questions Asked (1)

Design a real-time auction system where users can place bids and always see the current highest bid.

System DesignTechnical Trade-offsData Modeling

Author's notes

I went with optimistic locking early on and felt pretty good about it.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements such as scale, latency, consistency, and auction rules. Then design a high-level architecture focusing on real-time bid propagation and consistency, and dive into data models and trade-offs for handling concurrent bids.

Pro tip: Emphasize the importance of idempotency and ordering in bid processing to prevent race conditions and ensure fairness, and discuss how to handle out-of-order messages in a distributed system.

1. Clarify Requirements

Ask questions to understand scale (e.g., number of concurrent auctions, bids per second), latency requirements (e.g., sub-second updates), consistency needs (e.g., strong consistency for highest bid), and auction rules (e.g., bid increments, auction duration).

2. High-Level Design

Outline components: clients, API gateway, bid service, auction service, real-time messaging (WebSocket/SSE), database, and cache. Describe data flow for placing a bid and broadcasting updates.

3. Deep Dive into Critical Components

Detail the bid processing logic: how to handle concurrent bids atomically (e.g., using optimistic locking or distributed locks), ensure the highest bid is consistently stored, and broadcast updates to all watchers in real-time.

4. Data Modeling and Storage

Design schemas for auctions, bids, and users. Choose appropriate databases (e.g., relational for transactions, NoSQL for scale) and caching strategies (e.g., Redis for current highest bid) to meet latency and consistency requirements.

5. Trade-offs and Scalability

Discuss trade-offs between consistency and availability (CAP theorem), latency vs. durability, and scaling strategies (sharding, replication, pub/sub). Address failure scenarios and how to handle them.

Key Points to Mention

Use WebSockets or Server-Sent Events for real-time updates to clients.
Implement optimistic concurrency control or distributed locks to handle concurrent bids.
Cache the current highest bid in Redis for fast reads and use a write-through strategy to persist to a database.
Ensure idempotency of bid submissions to handle retries and duplicate messages.
Consider using a message queue (e.g., Kafka) to decouple bid processing and broadcasting for scalability.
Design for fault tolerance and high availability with replication and failover mechanisms.

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