Start by clarifying requirements and constraints (game modes, party sizes, skill metrics, regions, latency targets, scale). Then present a high-level architecture with a matchmaker service, queue manager, and game server allocator, and dive into skill bucketing, region sharding, and trade-offs between match quality and wait time. Finally, address edge cases like stale players, dropouts, fairness, anti-smurf, and scaling to millions of concurrent players.
Pro tip: Emphasize that matchmaking is a multi-objective optimization problem: you're balancing wait time, match quality, and fairness, and you should explicitly state how you'd measure and tune these trade-offs (e.g., via A/B testing and dynamic thresholds). Also, mention that you'd design for graceful degradation under load spikes.
Ask about game modes (e.g., 1v1, 5v5, battle royale), party sizes, skill metrics (MMR, rank), regions, latency targets, and scale (millions of concurrent players). Confirm non-functional requirements like wait time SLAs and fairness goals.
Outline components: matchmaker service, queue manager, game server allocator, player skill/region database, and telemetry. Describe the flow from player entering queue to match formation and server assignment.
Explain how to bucket players by skill (e.g., dynamic buckets based on MMR distribution) and shard by region to minimize latency. Discuss how to handle parties with mixed skill/region.
Describe strategies like expanding skill/region search over time, using a cost function that combines wait time and skill difference, and dynamically adjusting thresholds based on queue depth.
Cover stale players (timeouts, requeue), dropouts (backfill), anti-smurf (detection via stats, placement matches), and scaling (horizontal scaling, sharding by region/game mode, caching, async processing).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.