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

Staff
Jun 2026

Summary

Meta system design round for an EM role. One question, big scope, lots of moving parts. The chess angle felt like a curveball but it's really just a real-time multiplayer problem dressed up.

Questions Asked (1)

Q1

Design an online Chess platform that is fast, reliable, and can handle 100 million monthly active users each playing roughly one game per week.

System DesignTechnical Trade-offsAPI & Integrations
Author's notes

The chess framing threw me a bit at first because I started thinking about game logic before I thought about scale.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and scale, then focus on the core real-time game service and matchmaking, and finally address data storage, scalability, and reliability. Emphasize trade-offs and justify your choices based on the constraints.

Pro tip: Discuss how you would handle the 'thundering herd' problem when millions of users try to matchmake simultaneously, and propose a solution like consistent hashing with virtual nodes or a queue-based system.

1. Clarify Requirements and Scale

Ask questions to understand functional and non-functional requirements, such as game rules, latency, consistency, and availability. Estimate the scale: 100M MAU, ~25M weekly active users, ~3.6M daily games, and peak concurrency.

2. High-Level Architecture

Outline the main components: client, API gateway, matchmaking service, game service, database, cache, and message queue. Sketch a diagram and explain data flow.

3. Deep Dive into Core Components

Detail the matchmaking algorithm (e.g., Elo-based), game state management (in-memory with persistence), and real-time communication (WebSockets). Discuss how to ensure low latency and high availability.

4. Data Storage and Consistency

Choose databases for different needs: a relational DB for user profiles and game history, a cache for active games, and possibly a time-series DB for analytics. Discuss consistency models and replication.

5. Scalability and Reliability

Explain how to scale horizontally (sharding, partitioning), handle failures (redundancy, failover), and monitor the system. Address trade-offs like CAP theorem and cost.

Key Points to Mention

  • Use WebSockets for real-time communication between players and servers.
  • Implement matchmaking using a rating system (e.g., Elo or Glicko) and a queue with efficient pairing.
  • Store game state in memory during play and persist moves to a database for durability and replay.
  • Shard game servers by game ID or user ID to distribute load and enable horizontal scaling.
  • Use a distributed cache like Redis for session and game state to reduce database load.
  • Ensure idempotency and exactly-once processing for moves using sequence numbers or versioning.

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