I jumped straight into the leaderboard storage layer and kind of forgot to ask clarifying questions first.
Start by clarifying the requirements and scale, then propose a high-level architecture that separates real-time ranking updates from batch processing. Focus on data modeling for user metrics and leaderboards, and discuss trade-offs between consistency, latency, and cost.
Pro tip: Emphasize how you would handle peak concurrent usage during popular live classes, and propose a hybrid approach using in-memory stores for real-time leaderboards and a durable database for historical data.
Ask questions to understand the scope: what metrics are ranked (e.g., output, distance), how often rankings update, required latency, scale (users per class, concurrent classes), and whether historical rankings are needed.
Outline components: data ingestion from bikes, a real-time processing pipeline (e.g., stream processing), a ranking service, and storage layers (in-memory for live leaderboards, persistent for history).
Define how to model user performance data and compute rankings. Consider using sorted sets (e.g., Redis ZSET) for efficient rank queries and updates, and discuss tie-breaking rules.
Discuss partitioning by class or user, handling hot keys, and trade-offs between strong consistency (e.g., using a database) and eventual consistency (e.g., using caches) for performance.
Explain how to handle failures (e.g., data loss, service outages) with retries, dead-letter queues, and fallback mechanisms. Mention monitoring for latency and accuracy.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.