I started with the happy path and it went fine, but the interviewer kept pushing on what happens when Stripe returns a 500 mid-charge.
Start by clarifying requirements and scale (e.g., expected traffic, donation volume, payment provider specifics). Then design a high-level architecture covering core components: campaign service, payment integration, receipt generation, and real-time progress updates. Finally, dive into key trade-offs like consistency, idempotency, and scalability.
Pro tip: Emphasize idempotency and reconciliation with the third-party payment provider to handle duplicate donations and ensure financial accuracy—this shows maturity in handling payments. Also, discuss how you'd handle real-time updates efficiently without overloading the system.
Ask about expected user scale, donation volume, payment provider (e.g., Stripe), and whether real-time updates need to be push-based. Clarify non-functional requirements like latency, consistency, and availability.
Outline main components: API gateway, campaign service, donation service, payment service (integrating third-party), receipt service, and real-time progress service. Sketch data flow from browsing to donation to receipt and progress update.
Detail the donation flow: idempotent donation creation, payment intent with third-party, webhook handling for payment confirmation, receipt generation (e.g., PDF via email), and updating campaign progress. Discuss data models for campaigns, donations, and receipts.
Explain how to update progress in real-time: use WebSockets or Server-Sent Events (SSE) for push updates, with a pub/sub system (e.g., Redis Pub/Sub, Kafka) to broadcast donation events. Consider caching and throttling to handle high read volume.
Discuss scaling (horizontal scaling, database sharding), consistency (eventual vs strong), idempotency, retries, and reconciliation with payment provider. Mention monitoring, alerting, and handling failures (e.g., payment failures, webhook retries).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked through queueing payments and async confirmation, then horizontal scaling of the API layer.
Start by clarifying the scale and traffic patterns (e.g., peak QPS, data volume) and the 3-day spike scenario. Then propose a scalable architecture that decouples donation processing from the core system, using asynchronous queues, auto-scaling, and caching to handle bursts. Finally, discuss trade-offs between consistency, availability, and cost, and how to ensure reliability during spikes.
Pro tip: Emphasize the importance of load testing and chaos engineering to validate scalability, and mention how you'd monitor and auto-scale in real-time to handle unexpected viral spikes.
Ask questions to understand expected peak load, data consistency needs, and budget constraints. This shows you don't assume and can tailor the solution.
Propose a decoupled, event-driven architecture with load balancers, API gateways, and microservices for donations. Use queues (e.g., Kafka) to buffer spikes and process asynchronously.
Detail auto-scaling groups for stateless services, database sharding or read replicas, and caching (Redis) for hot data. Consider serverless for spiky workloads.
Discuss trade-offs: e.g., eventual consistency vs. strong consistency, cost of over-provisioning vs. risk of downtime. Mention circuit breakers, retries, and idempotency.
Explain how you'd monitor metrics (QPS, latency, error rates) and set up alerts. Describe load testing and chaos experiments to validate the system under spike conditions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements: the fundraising total must be accurate in real-time, with no double-counting or lost donations. Then propose a system design that uses idempotent processing, a single source of truth, and reconciliation mechanisms to ensure correctness.
Pro tip: Emphasize that accuracy is achieved through a combination of idempotency, transactional integrity, and continuous reconciliation—not just one technique. Also, mention the importance of monitoring and alerting on discrepancies to catch issues early.
Ask questions to understand the scale, expected donation volume, latency requirements, and whether the total needs to be real-time or can be eventually consistent.
Ensure that each donation is processed exactly once, even with retries or duplicate messages, by using unique donation IDs and idempotent operations.
Store donations in a durable, transactional database and compute the total from that source, avoiding separate counters that can drift.
Periodically reconcile the displayed total with the source of truth, and log all changes for auditing and debugging.
Set up monitoring for discrepancies, processing errors, and latency, with alerts to quickly address any issues.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.