← Salesforce Interview Insights
I started with the happy path and it went fine, but the interviewer kept pushing on what happens at peak hours.
Start by clarifying functional and non-functional requirements, then sketch a high-level architecture covering core services like menu, order, payment, and tracking. Dive into data modeling and key trade-offs such as consistency, scalability, and real-time updates, while addressing edge cases like refunds and store-specific inventory.
Pro tip: Emphasize idempotency and state machines for order lifecycle and payment processing to handle retries and failures gracefully, and discuss how you'd leverage Salesforce's platform services (e.g., Commerce Cloud, Service Cloud) for a cohesive solution.
Ask questions to understand scale, user types (customers, baristas, admins), store-specific constraints, and non-functional needs like latency, availability, and consistency.
Outline main components: API gateway, menu service, order service, payment service, store service, tracking service, and notification service. Consider using microservices for scalability and separation of concerns.
Design schemas for menu items, stores, orders, payments, and refunds. Choose appropriate databases (e.g., SQL for transactions, NoSQL for menu, in-memory for real-time status) and discuss indexing and partitioning.
Walk through critical flows: browsing, ordering, payment, tracking, pickup, and refunds. Discuss trade-offs like consistency vs. availability, synchronous vs. asynchronous processing, and push vs. pull for real-time updates.
Address scaling strategies (horizontal scaling, caching, CDN), fault tolerance (retries, idempotency, circuit breakers), and edge cases (payment failures, store closures, refund policies).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying requirements: real-time delivery, offline store devices, and exactly-once state transitions. Then propose an event-driven architecture with a message broker (e.g., Kafka) and idempotent consumers, using store device heartbeats to detect offline status and a retry mechanism with exponential backoff. Finally, discuss trade-offs between consistency, latency, and complexity.
Pro tip: Emphasize idempotency keys and deduplication at the consumer level, and mention that you'd use a store device's last processed event ID to avoid reprocessing. This shows you understand the practical challenges of distributed systems.
Ask about expected latency, scale (number of stores/orders), and consistency requirements (e.g., exactly-once vs. at-least-once). Confirm that store devices may go offline and that duplicate state transitions are unacceptable.
Propose using a message broker (e.g., Kafka, Pub/Sub) to decouple order events from store devices. Customers publish order status updates to a topic; store devices subscribe. For real-time push, consider WebSockets or server-sent events to devices.
Implement heartbeats from store devices to detect liveness. If a device is offline, queue messages in a durable store (e.g., Kafka with retention) and retry with exponential backoff when it reconnects. Use a dead-letter queue for persistent failures.
Make consumers idempotent by including a unique event ID and having the store device track processed IDs (e.g., in a local database). Alternatively, use a distributed lock or transactional outbox pattern to deduplicate.
Compare at-least-once with idempotency vs. exactly-once semantics (which may require 2PC). Mention monitoring for lag, retries, and duplicate detection. Highlight that simplicity and reliability often trump strict real-time guarantees.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.