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I started with the happy path (customer places order, barista sees it, status updates) and that was fine, but I underestimated how much they wanted to dig into the payment side.
Start by clarifying requirements and scale, then design a high-level architecture that separates concerns: customer-facing apps, barista dashboard, and backend services. Focus on data modeling for orders and menu items, API design for ordering and payment, and real-time updates using WebSockets or pub/sub. Discuss trade-offs and potential bottlenecks.
Pro tip: Emphasize idempotency and consistency in payment and order processing, as these are critical in a real-world system. Also, consider how to handle peak loads and offline scenarios for baristas.
Ask questions to understand functional and non-functional requirements: number of stores, users, peak load, payment methods, customization options, and real-time expectations. Define scope and assumptions.
Sketch the main components: client apps (mobile/web), API gateway, microservices (menu, order, payment, notification), databases, and real-time communication layer. Explain data flow from browsing to order fulfillment.
Design key entities: MenuItem, Customization, Order, OrderItem, Payment, User. Define RESTful or GraphQL APIs for browsing, ordering, and payment. Discuss idempotency keys for payment and order submission.
Choose a mechanism for real-time status updates (e.g., WebSockets, Server-Sent Events, or pub/sub with message queues). Explain how customers and baristas receive updates on order status changes.
Discuss scaling strategies (horizontal scaling, caching, CDN for menu), handling failures (retries, dead-letter queues), and trade-offs between consistency and availability. Mention monitoring and analytics.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This felt like a natural extension but I think I jumped to 'shard by store ID' a bit too fast without justifying it.
Start by clarifying requirements and constraints, then propose a scalable architecture that separates concerns (e.g., order ingestion, processing, storage) and leverages horizontal scaling, partitioning, and asynchronous processing. Discuss trade-offs between consistency, availability, and latency, and how to handle peak loads with techniques like sharding, caching, and queue-based load leveling.
Pro tip: Emphasize multi-tenancy and data isolation from the start, as Salesforce values secure, scalable SaaS architectures. Also, quantify your reasoning with rough numbers (e.g., orders per second, storage per store) to show practical estimation skills.
Ask about expected number of stores, order volume growth, consistency needs, and latency SLAs. State assumptions like 10,000 orders/min peak, 1000 stores, and 99.9% availability.
Analyze the current single-store system to find bottlenecks (e.g., database, app server). Determine scaling dimensions: read vs. write heavy, data volume, and geographic distribution.
Propose a distributed system: load balancers, stateless app servers, sharded databases (by store or region), message queues for async order processing, and caching layers. Consider microservices for order, inventory, and payment.
Discuss partitioning strategies (e.g., sharding by store_id), replication for read scaling, and consistency models (e.g., eventual consistency for inventory, strong for payments). Mention distributed transactions or sagas if needed.
Explain load leveling with queues, auto-scaling, rate limiting, and backpressure. Discuss trade-offs: consistency vs. availability (CAP), cost vs. performance, and complexity vs. maintainability.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by acknowledging that consistency and failure handling are critical in distributed payment systems, then walk through a layered strategy: idempotency, retries with backoff, and reconciliation. Use a concrete example like duplicate order submissions to illustrate how you'd design for exactly-once semantics and eventual consistency.
Pro tip: Emphasize that idempotency keys must be generated client-side and stored server-side with a unique constraint, and that you'd monitor for duplicate attempts to detect issues early. This shows you think about both prevention and detection.
Enumerate scenarios: duplicate submissions due to user double-click, network retries, timeout retries, and partial failures in downstream services. This shows you understand the problem space.
Explain how to use idempotency keys (client-generated UUIDs) to uniquely identify each payment request, and ensure the server processes each key only once, storing the result for future retries.
Describe retry policies with exponential backoff and jitter, and how to handle non-idempotent operations by checking the state before retrying. Mention dead-letter queues for persistent failures.
Discuss patterns like saga, two-phase commit, or eventual consistency with compensating transactions. Highlight trade-offs between strong and eventual consistency in payment flows.
Explain how to log and monitor duplicate attempts, set up alerts, and run periodic reconciliation jobs to detect and resolve inconsistencies between systems.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.