This is the kind of question where you can go in ten directions and none of them are wrong, which is almost worse than having a clear answer.
Start by clarifying functional and non-functional requirements, such as skill-based matching, latency targets, and scale. Then propose a high-level architecture with a matchmaking queue and a matching algorithm, and dive into data models, partitioning, and trade-offs. Finally, discuss how to handle dynamic player behavior and ensure fairness at scale.
Pro tip: Emphasize that matchmaking is a multi-objective optimization problem: balancing wait time, match quality, and fairness. Show awareness that perfect solutions are impossible, so you need to define acceptable trade-offs and monitor them with metrics.
Ask about game type, player attributes (skill, latency, party size), match quality metrics, and scale (concurrent players, peak QPS). Define what 'fair' means in this context.
Outline components: matchmaking service, player pool, matching algorithm, and game server allocation. Consider using a queue-based system with periodic matching batches.
Propose an algorithm that groups players by skill and latency, using techniques like bucketing, sliding windows, or greedy matching with constraints. Discuss how to handle parties and roles.
Address partitioning (e.g., by region, game mode), sharding the player pool, and using in-memory data stores for low latency. Discuss horizontal scaling and load balancing.
Discuss trade-offs between match quality and wait time, and how to tune parameters. Define metrics like average wait time, skill difference, and player retention to monitor success.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.