Start by clarifying requirements and scale, then design a high-level architecture with key components: matchmaking service, game server, persistence layer, and real-time communication. Dive into critical flows like move synchronization, validation, and disconnect handling, emphasizing trade-offs and scalability.
Pro tip: Demonstrate deep understanding by discussing how to handle race conditions and ensure consistency in move validation and turn timers, especially during reconnections. Also, mention using WebSockets for real-time communication and a message queue for reliability.
Ask about expected user scale, latency requirements, and features like rating systems or spectator limits. Define functional and non-functional requirements to guide design decisions.
Outline main components: matchmaking service, game servers, database, and real-time communication layer. Explain how they interact and scale horizontally.
Detail matchmaking algorithm, move synchronization via WebSockets, server-side validation using chess rules, and persistence of move history. Discuss turn timers and disconnect/reconnect handling.
Explain how spectators subscribe to game updates without affecting gameplay, and how post-game rating updates are computed and stored, possibly using Elo or Glicko systems.
Address scaling with load balancers, sharding, and caching. Discuss fault tolerance, data consistency, and monitoring for real-time systems.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Kept it server-side, which is the obvious answer, but the follow-up was about what you do when the client optimistically renders a move and the server rejects it.
Start by clarifying the game's requirements (real-time vs turn-based, competitive vs casual) to determine the appropriate authority model. Then propose a server-authoritative architecture where all move validation occurs on the server, explaining why this prevents cheating and ensures consistency. Finally, discuss trade-offs and potential optimizations like client-side prediction and rollback.
Pro tip: Acknowledge that while server authority is ideal for competitive games, it introduces latency; propose a hybrid approach with client-side prediction and server reconciliation to maintain responsiveness while preserving integrity.
Ask about game type, real-time constraints, and cheat sensitivity to tailor your answer. This shows you understand that authority models depend on context.
Propose a server-authoritative model for competitive games, explaining that the server is the single source of truth. Mention alternatives like peer-to-peer or client-authoritative and why they're less secure.
Describe where move validation lives: on the server, ideally in a dedicated game logic module. Explain that it checks rules, game state, and player permissions before applying moves.
Discuss techniques like client-side prediction, server reconciliation, and rollback to keep the game responsive despite server round-trips. Mention that these must be carefully implemented to avoid inconsistencies.
Compare server-authoritative vs client-authoritative in terms of security, latency, and cost. Mention scaling considerations like sharding game sessions and using efficient state synchronization.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I went with a games table and an append-only moves table keyed by game ID and move number.
Start by clarifying the scope—what kind of game (turn-based, real-time, single/multiplayer) and expected scale. Then outline the core entities (Game, Move, Player) and their relationships, and discuss consistency requirements such as atomicity of moves, ordering, and idempotency. Finally, tie the model to the guarantees needed for correctness and scalability.
Pro tip: Emphasize that the consistency model should be driven by the game's rules—e.g., turn-based games need linearizability per game, while real-time games might tolerate eventual consistency with conflict resolution. This shows you think about trade-offs, not just schema.
Ask about game type, scale, latency, and consistency needs. This ensures your design fits the problem context.
Identify Game, Move, Player, and any supporting entities like BoardState. Describe their attributes and relationships.
Propose a logical schema (e.g., tables or documents) with keys, indexes, and how moves are stored and linked to games.
Discuss required guarantees: atomicity of move application, ordering, idempotency, isolation levels, and how to handle concurrent moves.
Explain how the model scales (sharding by game ID, caching) and trade-offs between consistency and availability.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked about stateless game workers with game state cached in something like Redis, routing each game's WebSocket connections to the same node using consistent hashing.
Start by clarifying the requirements and constraints of the current system, such as expected number of concurrent matches, latency targets, and match duration. Then propose a scalable architecture that separates matchmaking, match execution, and state management, using horizontal scaling, partitioning, and asynchronous processing. Discuss trade-offs between consistency, availability, and cost, and how you would validate the design with load testing and monitoring.
Pro tip: Emphasize the importance of idempotency and graceful degradation: when scaling, ensure that match operations can be retried safely and that the system can shed load or queue matches during spikes without losing data or corrupting state.
Ask questions to understand the scale: how many concurrent matches, expected growth, latency SLAs, match duration, and consistency requirements. This ensures your solution addresses the actual problem.
Analyze the existing system to find single points of contention, such as a central matchmaking service, shared database, or synchronous communication. This helps prioritize what to scale first.
Outline a distributed design: partition matches by game or region, use a message queue for matchmaking, stateless match servers, and a distributed cache or database for state. Consider sharding and replication.
Discuss trade-offs like consistency vs. availability, cost vs. performance, and how to handle failures (e.g., match server crashes, network partitions). Mention techniques like retries, circuit breakers, and fallbacks.
Describe how you would test the scaled system with load testing, chaos engineering, and monitoring key metrics (latency, throughput, error rates). Explain how you would iterate based on findings.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.