Start by clarifying functional and non-functional requirements, such as scale, consistency needs, and multi-device sync. Then design the data model for carts per user per merchant, and outline the API and storage layers. Finally, discuss trade-offs around consistency, caching, and partition tolerance.
Pro tip: Emphasize idempotency and conflict resolution for cart updates across devices, as this is a common real-world challenge. Also, mention how you would handle cart merging when a user logs in from a guest session.
Ask about scale (users, merchants, items), consistency requirements (strong vs eventual), and features like guest carts, promotions, and inventory checks.
Define entities: User, Merchant, Cart, CartItem, and their relationships. Choose a storage solution (e.g., NoSQL for scalability, SQL for transactions) and discuss sharding by user or merchant.
Outline RESTful or gRPC endpoints for cart operations (add, update, remove, get). Describe how carts persist across devices using a central store and how to handle concurrent updates (e.g., versioning, timestamps).
Discuss caching (e.g., Redis), read/write patterns, and how to ensure high availability and partition tolerance. Mention monitoring and failure recovery.
Compare consistency models (strong vs eventual), storage choices (SQL vs NoSQL), and sync strategies (last-write-wins vs merge). Explain your recommendations based on requirements.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Blanked for a second on the recovery piece.
Start by clarifying the scope: are we designing a new checkout system or debugging an existing one? Then outline a high-level architecture that decouples inventory and payment services, using asynchronous messaging and idempotency to handle partial failures. Finally, dive into the specific failure scenario, explaining how you would detect, recover, and ensure consistency.
Pro tip: Emphasize idempotency and compensating transactions (sagas) rather than distributed transactions, as they are more scalable and align with Uber's microservices architecture. Also, mention the importance of monitoring and alerting for partial failures to enable quick remediation.
Ask about scale, consistency requirements, and existing systems. Determine if the checkout is for a single merchant or multiple, and what SLAs are expected.
Describe the sequence: cart validation, inventory reservation, payment authorization, and order confirmation. Highlight where synchronous vs asynchronous communication is used.
Explain how to handle a write failure to one merchant's cart: use idempotent operations, retries with exponential backoff, and compensating actions (e.g., release inventory, void payment) if needed.
Discuss patterns like Saga, outbox pattern, or event sourcing to maintain consistency across services without distributed transactions.
Outline how to detect failures (logging, metrics, tracing) and recover (dead-letter queues, manual intervention, reconciliation jobs).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements and scale, then propose a real-time detection system using event-driven architecture and caching. Explain how to surface changes to users via WebSocket or polling, and discuss trade-offs between consistency, latency, and cost.
Pro tip: Emphasize idempotency and graceful degradation: ensure that even if real-time updates fail, the checkout process validates stock and price to prevent user frustration and financial discrepancies.
Ask about scale (e.g., number of users, items), consistency needs (real-time vs. eventual), and user experience goals (e.g., immediate notification vs. on checkout).
Propose a system where inventory and pricing services publish events on changes. Use a message queue (e.g., Kafka) to propagate updates to a cart service that maintains a real-time view.
Describe how to push updates to clients via WebSockets or server-sent events, with fallback to polling. Include UI indicators (e.g., banners, badges) to highlight changes.
Discuss strategies like versioning, optimistic UI updates, and reconciliation at checkout. Ensure idempotent operations and handle race conditions.
Compare latency, cost, and complexity of different approaches. Suggest optimizations like batching updates or using in-memory caches for hot items.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Short discussion, felt more like a sanity check than a deep dive.
Start by clarifying requirements and constraints, then outline a high-level design covering data models, merge logic, and edge cases. Walk through the flow step-by-step, emphasizing consistency, idempotency, and scalability, and conclude with trade-offs and potential optimizations.
Pro tip: Discuss how to handle merge conflicts when the same item exists in both carts with different quantities or prices, and propose a deterministic resolution strategy (e.g., sum quantities, take the latest price). Also mention the importance of idempotency to handle retries safely.
Ask about expected cart size, concurrency, consistency requirements, and whether merging should be synchronous or asynchronous. Confirm if guest cart should be deleted after merge.
Describe how carts are stored (e.g., key-value store, relational DB) and the schema for cart items, including user ID, guest session ID, item details, and timestamps.
Outline the algorithm: fetch both carts, combine items, resolve conflicts (e.g., sum quantities, apply promotions), and persist the merged cart. Ensure atomicity via transactions or idempotent operations.
Address scenarios like empty carts, duplicate items, price changes, concurrent logins, and partial failures. Propose retry mechanisms and idempotency keys.
Explain how the design scales with millions of users, considering sharding, caching, and eventual consistency. Compare synchronous vs. asynchronous merge and their impact on user experience.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.