The receipt object is what actually gets graded, not whether your strategy code is pretty.
Start by clarifying requirements and constraints, then model the core domain objects (Cart, Item, Customization) with clear interfaces. Next, design a flexible pricing strategy using composition, and finally show how to generate an itemized receipt that aggregates all charges. Emphasize extensibility and trade-offs in your design choices.
Pro tip: Demonstrate awareness of real-world complexities like concurrent cart modifications and pricing rule conflicts, and propose how to handle them (e.g., versioning, rule prioritization). This shows you think beyond basic OOP.
Ask questions to understand expected scale, customization types, pricing rules, and receipt format. Confirm whether the system is for a single restaurant or multi-restaurant, and if real-time updates are needed.
Identify main entities: Cart, CartItem, MenuItem, CustomizationOption, and their relationships. Sketch class diagrams with key attributes and methods, focusing on interfaces for extensibility.
Use the Strategy pattern to encapsulate pricing rules (e.g., base price, customization surcharges, discounts, taxes). Explain how strategies can be composed and applied in a defined order.
Design a Receipt class that aggregates line items, subtotals, taxes, discounts, and total. Ensure it can be generated from the cart and pricing strategies, with clear breakdowns.
Highlight design decisions like immutability vs. mutability, interface segregation, and how to add new customizations or pricing rules without modifying existing code.
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, then propose a modular pipeline architecture where each pricing component (discounts, surge, membership, promotions) is a pluggable rule applied in a configurable order. Emphasize extensibility, correctness, and performance, and discuss trade-offs like synchronous vs. asynchronous processing and conflict resolution.
Pro tip: Show awareness of real-world complexities like idempotency, auditability, and the need for a rules engine or DSL to allow business users to define order and conditions without code changes. Mention how you'd handle edge cases like overlapping promotions and surge pricing caps.
Ask questions to understand the scale, latency requirements, consistency needs, and who defines the pricing rules. Identify key entities like rides, users, and promotions.
Propose a pipeline of pricing rules where each rule modifies the price sequentially. Use a configuration-driven approach to define the order and conditions for each rule.
Specify a common interface for rules (e.g., apply(context) -> price) and how rules are composed (e.g., chain of responsibility, decorator pattern). Discuss how to handle rule conflicts and precedence.
Discuss caching, precomputation, and asynchronous processing for non-critical rules. Consider how to shard or parallelize rule evaluation if needed.
Include logging, metrics, and tracing for each rule application. Design for easy addition of new rule types and dynamic updates without redeployment.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went with optimistic concurrency on a version column.
Start by clarifying the requirements and constraints, such as consistency needs and latency tolerance. Then discuss concurrency control mechanisms like optimistic locking, pessimistic locking, or CRDTs, and explain how you would choose based on trade-offs. Finally, outline a concrete solution with conflict resolution and failure handling.
Pro tip: Mention that you would first check if the cart can be modeled as a CRDT or use per-item operations to avoid conflicts altogether, showing you think about avoiding locks when possible. Also, discuss how you would measure and monitor conflict rates to validate the chosen approach.
Ask about consistency requirements (strong vs eventual), expected concurrency levels, and user experience goals (e.g., should updates never be lost or is merging acceptable?).
List possible approaches: optimistic locking (version numbers), pessimistic locking, last-write-wins, CRDTs, or operational transforms. Briefly explain each.
Compare options based on latency, complexity, scalability, and user experience. For example, optimistic locking is simple but may cause retries; CRDTs avoid conflicts but are complex.
Choose an approach and detail the implementation: e.g., use version numbers per cart, detect conflicts on write, and either reject with a merge prompt or auto-merge using item-level operations.
Discuss handling network partitions, retries, and how to monitor conflict rates. Mention idempotency and ensuring operations are commutative where possible.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Frame the problem as a consistency and concurrency challenge in distributed systems, focusing on how to handle stale data between cart view and checkout. Discuss strategies like price locking, versioning, and user notification, and weigh trade-offs between consistency, availability, and user experience.
Pro tip: Emphasize that the solution should align with business goals—such as minimizing cart abandonment while preventing revenue loss—and propose a configurable policy (e.g., honor price for X minutes) rather than a one-size-fits-all rule.
Ask about business rules: should the user be notified of changes? Is there a grace period? What are the consistency requirements (strong vs. eventual)?
Explain that the cart view and checkout are separate requests, so data can become stale due to concurrent updates, requiring a mechanism to detect and handle changes.
Suggest storing a snapshot of price and promotions with a version or timestamp when the cart is viewed, and validating against the latest at checkout.
At checkout, re-validate prices and promos; if changed, either block the checkout, apply the new price with user confirmation, or honor the old price based on policy.
Cover trade-offs like user experience vs. revenue, and edge cases such as concurrent modifications, expired promos, and partial cart changes.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Used a cart_id plus version as a combined idempotency token.
Start by defining idempotency in the context of payments: a retry of the same logical request should not create a second charge. Then propose a concrete mechanism, such as an idempotency key generated by the client and enforced by the server with a unique constraint and stored response, and explain how it handles retries, concurrent requests, and failures.
Pro tip: Emphasize that idempotency must be enforced at the server side with a persistent store, not just in the client, and discuss how to handle the race condition where two identical requests arrive simultaneously.
Define what 'idempotent' means for checkout: same logical operation, same result, no duplicate side effects. Identify the retry scenarios (client timeout, network failure, server crash) and the need for exactly-once semantics.
Have the client generate a unique idempotency key per checkout attempt and send it in a header or request body. The server uses this key to deduplicate requests.
Store the idempotency key with the request state and response in a persistent store (e.g., database) with a unique constraint. On a new request, check if the key exists; if so, return the stored response without re-executing the charge.
Use atomic operations (e.g., INSERT ... ON CONFLICT) to handle concurrent requests with the same key. If the first request is still processing, return a 409 Conflict or instruct the client to retry later. Ensure the key and response are stored transactionally with the charge.
Define key expiration (e.g., 24 hours) to avoid unbounded storage. Discuss how to handle partial failures, such as when the charge succeeds but storing the response fails, and how to recover.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This came in the last few minutes as a follow-up and felt like a gut check more than a deep dive.
Start by clarifying the scale and requirements, then outline a multi-region architecture that partitions orders by region and uses asynchronous replication for global consistency. Focus on trade-offs between consistency, availability, and latency, and explain how components like sharding, load balancing, and caching enable horizontal scaling.
Pro tip: Emphasize that at Uber's scale, you must design for failure and eventual consistency; mention specific techniques like cell-based architecture and idempotency to show practical experience.
Ask questions to understand the expected read/write ratio, latency requirements, consistency needs, and regional distribution of orders. This ensures your design targets the right constraints.
Propose a multi-region active-active or active-passive setup with regional clusters, each handling local orders. Use a global load balancer to route users to the nearest region.
Explain how to shard orders by region or user ID to distribute load. Discuss sharding strategies (e.g., consistent hashing) and how to handle hotspots.
Detail the replication strategy across regions (e.g., asynchronous multi-master or synchronous with quorum). Discuss trade-offs between consistency and latency, and how to handle conflicts.
Describe how to scale each component horizontally (e.g., stateless services, distributed databases) and ensure fault tolerance with redundancy, failover, and monitoring.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.