I started with the data model and probably spent too long there.
Start by clarifying requirements and scale, then design the core matchmaking flow from queue to match assignment, and finally address global distribution and trade-offs. Focus on how skill and region are balanced, and how the system scales horizontally across regions.
Pro tip: Proactively discuss trade-offs between match quality and wait time, and how you'd measure and tune them—this shows product sense and operational maturity beyond pure system design.
Ask about player volume, peak concurrency, acceptable wait times, skill rating system, and regional constraints. Define functional and non-functional requirements.
Outline how players enter the queue (e.g., via API), how their attributes (skill, region, latency) are stored, and the matchmaking algorithm that groups compatible players.
Explain how to combine skill-based matchmaking (e.g., Elo/TrueSkill) with region/latency constraints, including fallback strategies when no ideal match exists.
Describe a multi-region deployment with regional matchmakers, data replication, and a global coordination layer for cross-region matches. Discuss partitioning and load balancing.
Cover trade-offs like match quality vs. wait time, consistency vs. availability, and how to handle failures (e.g., matchmaker crashes, region outages).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.