Start by clarifying requirements and scale, then design a high-level architecture that separates vote ingestion, reward calculation, and reporting. Focus on idempotency, fraud prevention, and async processing to ensure correctness and scalability, and discuss trade-offs for each component.
Pro tip: Emphasize idempotency and fraud prevention early, as they are critical in financial systems; also, propose a phased rollout with monitoring to catch issues before full launch.
Ask questions to understand expected traffic, reward rules (e.g., cashback per upvote), eligibility criteria, and reporting needs. Establish non-functional requirements like latency, consistency, and fraud tolerance.
Propose tables for users, reviews, votes, rewards, and fraud signals. Include fields for idempotency keys, timestamps, and statuses to track processing.
Use unique constraints or idempotency keys on vote events to prevent double-counting. Implement fraud detection with rate limiting, anomaly detection, and manual review queues.
Design a pipeline where vote events are queued (e.g., Kafka), processed by workers that validate, deduplicate, and update reward balances. Use idempotent consumers and dead-letter queues for failures.
Create aggregated views or dashboards for real-time and historical reporting. Include alerts for fraud spikes, processing delays, and reward discrepancies.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.