This was basically the whole interview in one prompt.
Start by clarifying requirements (scale, latency, consistency) and then walk through the end-to-end flow: ingestion from third-party webhooks, async processing with a queue, idempotent updates, retry with backoff, and storage separation for current state vs. event history. Emphasize trade-offs and how each component scales independently, and finish with read scaling strategies like caching and read replicas.
Pro tip: Explicitly call out that third-party delivery services are unreliable and may send duplicate or out-of-order updates—design idempotency and event ordering from the start, not as an afterthought. Also, mention that you'd use a dead-letter queue for poison messages and monitor queue depth as a key scaling signal.
Ask about expected scale (deliveries per second, number of active shipments), latency requirements for status updates, and consistency needs (e.g., eventual vs. strong). Define what 'real-time' means and identify key entities (shipment, event, status).
Propose an API gateway or webhook endpoint that receives updates from third-party services, validates and enqueues them into a durable message queue (e.g., Kafka, SQS). Use a separate consumer service to process messages asynchronously, decoupling ingestion from processing.
Assign a unique idempotency key to each update (e.g., shipment ID + event timestamp + status) and use a deduplication store (e.g., Redis or database unique constraint) to ignore duplicates. Implement retries with exponential backoff and jitter, and route failed messages to a dead-letter queue after max attempts.
Store current shipment state in a low-latency database (e.g., DynamoDB, Cassandra) for fast reads, and append all events to an immutable event log (e.g., Kafka, S3) for audit and replay. Use change data capture or a stream processor to update the state store from the event log.
Introduce a caching layer (e.g., Redis) for frequently accessed shipment statuses, use read replicas or a distributed cache to handle high read volume, and consider materialized views or denormalized tables for common query patterns. Ensure cache invalidation on state updates.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.