I went straight into the data model and probably should've asked more clarifying questions first.
Start by clarifying the requirements and scope of the rewarding system, such as what actions are rewarded and how rewards are distributed. Then outline a high-level design covering data models, services, and trade-offs, focusing on scalability and reliability. Conclude by discussing potential bottlenecks and how to address them.
Pro tip: Emphasize idempotency and exactly-once processing to prevent duplicate rewards, and discuss how to handle failures gracefully with retries and dead-letter queues. This shows you understand real-world distributed system challenges.
Ask questions to understand the system's goals, scale, and constraints. For example, what actions trigger rewards, how are rewards calculated, and what are the latency and consistency requirements?
Outline the main components: event ingestion, reward calculation, reward distribution, and data storage. Consider using a message queue for decoupling and a database for persistence.
Design schemas for users, events, rewards, and transactions. Consider using a ledger-based approach for auditability and to handle balance updates atomically.
Discuss partitioning, replication, and caching to handle scale. Address idempotency, exactly-once processing, and failure recovery to ensure reliability.
Talk about trade-offs between consistency and availability, and between latency and accuracy. Mention potential extensions like real-time analytics or fraud detection.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.