Start by clarifying requirements and scale, then design a distributed, event-driven pipeline that decouples rule evaluation from ingestion. Focus on partitioning, caching, and asynchronous processing to handle millions of expenses per day, and discuss trade-offs between consistency, latency, and cost.
Pro tip: Emphasize idempotency and exactly-once processing for violation tracking and notifications, as duplicate alerts or missed violations can erode trust in the system. Also, consider a tiered rule evaluation strategy: simple rules first, complex ones later, to optimize performance.
Ask about expected read/write patterns, latency requirements, rule complexity, and notification channels. Confirm the need for real-time vs. batch evaluation and fault tolerance guarantees.
Propose a scalable storage solution for expenses and rules, such as a distributed database (e.g., Cassandra, DynamoDB) for expenses and a versioned rule store (e.g., PostgreSQL with caching). Consider indexing and partitioning strategies.
Outline an event-driven pipeline using a message queue (e.g., Kafka) to ingest expenses, a stream processing layer (e.g., Flink, Spark Streaming) for rule evaluation, and a results store. Discuss horizontal scaling and partitioning by user or card.
Design a violation tracking system with idempotent writes and a notification service that batches or throttles alerts. Use a separate queue for notifications to decouple from evaluation and ensure retries.
Describe replication, checkpointing, and dead-letter queues for fault tolerance. Include monitoring for rule evaluation latency, queue depths, and error rates, with alerting and auto-scaling.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.