Start by clarifying functional and non-functional requirements, then design the data model and API for cart operations, ensuring consistency and scalability. Discuss trade-offs between consistency models, storage choices, and how to handle concurrent updates and pricing changes.
Pro tip: Emphasize the need for idempotent operations and optimistic concurrency control to handle duplicate requests and concurrent cart modifications, which is critical in a high-traffic food delivery system.
Ask questions to understand scope: single restaurant vs multi-restaurant carts, guest vs authenticated users, real-time menu updates, and expected scale. Define functional (add/remove items, update quantity, checkout) and non-functional (latency, consistency, availability) requirements.
Propose a schema for carts, cart items, and menu items, considering relationships and denormalization for performance. Define RESTful or GraphQL APIs for cart operations, including idempotency keys for mutations.
Select a database (e.g., DynamoDB, Cassandra, or PostgreSQL) based on access patterns and consistency needs. Discuss trade-offs between strong consistency (for accurate pricing) and eventual consistency (for availability), and how to handle menu price changes.
Explain how to partition data (e.g., by user ID) to scale horizontally. Implement optimistic concurrency control (versioning) or distributed locks to handle concurrent cart updates, and use caching for read-heavy operations.
Cover scenarios like item availability changes, price updates, cart expiration, and merging guest carts upon login. Discuss integration with order service, payment, and inventory systems, ensuring idempotency and fault tolerance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Said optimistic first, then kind of walked it back when they asked what happens on conflict.
Start by clarifying the requirements and constraints of the cart editing scenario, then compare optimistic and pessimistic concurrency control in terms of user experience, system complexity, and consistency. Finally, propose a hybrid or context-specific solution, such as optimistic locking with conflict resolution for most cases, and explain the tradeoffs.
Pro tip: Demonstrate awareness of real-world constraints like network latency and user experience by suggesting a strategy that minimizes user friction, such as optimistic concurrency with automatic merge or user-assisted conflict resolution.
Ask questions to understand the expected frequency of concurrent edits, tolerance for conflicts, and user experience goals. This shows you don't jump to solutions without context.
Describe how it works (e.g., version numbers, timestamps) and its benefits: high concurrency, no locking overhead. Mention drawbacks: potential conflicts requiring resolution, possible lost updates if not handled.
Describe locking mechanisms (e.g., database locks, distributed locks) and its benefits: strong consistency, no conflicts. Mention drawbacks: reduced concurrency, potential deadlocks, and poor user experience if locks are held long.
Discuss how each approach affects user experience, system scalability, and complexity. For example, optimistic is better for low-contention scenarios, while pessimistic may be needed for high-contention or critical sections.
Recommend a specific approach or hybrid (e.g., optimistic with conflict resolution UI, or pessimistic for specific operations) and explain why it fits the cart use case, considering Uber's scale and user expectations.
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 constraints, such as whether the price change is allowed to affect in-progress checkouts and what the business rules are. Then, propose a design that handles the race condition, such as versioning or locking, and discuss trade-offs between consistency and user experience. Finally, outline how you would implement and test the solution.
Pro tip: Demonstrate awareness of the business impact: sometimes it's better to honor the original price to avoid user frustration, even if it means a small loss. Mentioning this shows you think beyond pure technical correctness.
Ask questions to understand the expected behavior: Should the user see the new price? Is the old price honored? What are the consistency requirements? This shows you don't jump to solutions.
Explain that the price change can happen concurrently with checkout, leading to inconsistency. Discuss scenarios like user seeing old price but being charged new price, or vice versa.
Suggest approaches like optimistic concurrency control (versioning), pessimistic locking, or snapshot isolation. Explain how you would implement it, e.g., storing the price at checkout initiation and validating at payment.
Compare consistency vs. availability, user experience vs. business rules, and complexity vs. correctness. Mention that sometimes a business decision (e.g., honor original price) simplifies the technical solution.
Describe how you would test the solution (e.g., race condition tests, load tests) and monitor for price mismatches in production.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the cart service's access patterns and consistency requirements, then propose a hybrid storage architecture that separates the write-heavy cart updates from low-latency reads. Justify each choice with trade-offs around scalability, latency, and cost, and mention how you would handle data consistency and failure scenarios.
Pro tip: Emphasize that you would use an in-memory data store like Redis for the active cart with write-behind persistence to a durable store like Cassandra or DynamoDB, and highlight how this aligns with Uber's need for real-time, high-throughput cart operations.
Ask about expected write throughput, read latency SLA, data size, and consistency needs (e.g., eventual vs. strong). This shows you don't jump to solutions without understanding the problem.
Choose a write-optimized store such as Cassandra or DynamoDB for durable persistence, explaining how its LSM-tree or SSD-based design handles high write volumes and provides tunable consistency.
Introduce an in-memory cache like Redis or Memcached to serve reads with sub-millisecond latency, and describe cache invalidation strategies (e.g., TTL, write-through) to keep data fresh.
Explain how writes propagate from cache to durable store (e.g., write-behind, change data capture) and how you handle conflicts, idempotency, and eventual consistency for cart operations.
Cover partitioning/sharding by user ID, replication for availability, and trade-offs like cost, operational complexity, and potential data loss in the cache layer.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went with REST, scoped cart operations to a cart ID with item-level endpoints.
Start by clarifying requirements (e.g., scale, consistency needs, client types) and then outline a RESTful API with core cart operations (add, update, remove, get). For idempotency, propose using idempotency keys for write operations, ensuring that retries with the same key produce the same result without side effects.
Pro tip: Mention that idempotency keys should be stored with a TTL and that the server should return the original response for duplicate requests, not just a success code. Also, highlight the importance of handling concurrent requests with the same key using locking or atomic operations.
Ask about expected traffic, consistency requirements, and client behavior to tailor the design. This shows you consider context before diving into solutions.
Define resource-oriented endpoints for cart operations (e.g., POST /carts/{cartId}/items, PATCH /carts/{cartId}/items/{itemId}, DELETE /carts/{cartId}/items/{itemId}, GET /carts/{cartId}). Specify request/response schemas and status codes.
Explain that clients generate a unique idempotency key (e.g., UUID) for each write request and include it in a header (e.g., Idempotency-Key). The server stores the key and the response, and on duplicate requests, returns the stored response.
Discuss how to handle concurrent requests with the same key (e.g., using distributed locks or atomic writes) and where to store keys (e.g., Redis with TTL, database). Mention trade-offs between consistency and latency.
Wrap up by summarizing the design and highlighting trade-offs (e.g., storage overhead, TTL choices, failure scenarios). This demonstrates holistic thinking.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I put this in a separate pricing service called at checkout time rather than baking it into the cart.
Start by clarifying the scope and requirements, then propose a modular architecture where pricing logic is centralized in a dedicated service. Walk through the checkout flow, explaining how each component (promotions, coupons, taxes, delivery fees) is applied in a deterministic order, and discuss trade-offs like consistency, latency, and scalability.
Pro tip: Emphasize idempotency and auditability: ensure that applying these adjustments is idempotent to handle retries, and log every calculation for debugging and compliance. Also, mention that taxes and fees may vary by region, so the system should be configurable and extensible.
Ask about expected scale, consistency needs, regulatory requirements, and whether real-time or batch processing is acceptable. Confirm the order of applying promotions, coupons, taxes, and fees.
Propose a dedicated Pricing Service that encapsulates all logic for promotions, coupons, taxes, and delivery fees. It should expose APIs for calculating the final price given a cart and user context.
Outline a deterministic sequence: apply promotions, then coupons, then taxes, then delivery fees. Explain how each step may depend on previous ones and how to handle stacking rules and exclusions.
Discuss how the service retrieves promotion rules, coupon validity, tax rates, and delivery fee schedules from databases or external services. Mention caching and fallback strategies for performance and resilience.
Talk about idempotency, versioning of pricing rules, auditing, and horizontal scaling. Consider using event-driven updates for rule changes and handling high concurrency during peak times.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.