Start by clarifying requirements and scale (e.g., concurrent players, regions, latency targets), then propose a high-level architecture with a matchmaking service that uses a skill-based rating system and regional matchmakers. Dive into the match formation algorithm, data model, and trade-offs between latency, fairness, and wait time, and finish with scaling, consistency, failure handling, and monitoring.
Pro tip: Emphasize Roblox's unique constraints: massive scale, global player base, and the need to balance skill and latency while keeping wait times low. Mention using a rating system like Glicko-2 or TrueSkill and explain how you'd handle regional partitions and cross-region play as a fallback.
Ask about expected concurrent players, regional distribution, latency targets (e.g., <100ms), and acceptable wait times. Confirm whether matches are 1v1 or team-based, and if cross-region play is allowed.
Propose a matchmaking service with regional matchmakers, a player pool, and a match formation component. Include a rating service, game server allocation, and a message queue for match notifications.
Describe how to form matches using skill ratings and latency constraints. Outline the data model for players, match requests, and matches, and explain the algorithm (e.g., expanding skill range over time).
Explain how to scale matchmakers horizontally, partition by region, and handle consistency (e.g., eventual consistency for player ratings, strong consistency for match assignment). Discuss trade-offs.
Cover failure scenarios (e.g., matchmaker crashes, game server unavailability) and mitigation (retries, fallbacks). Describe monitoring metrics like match latency, wait time, skill disparity, and success rate.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.