Start by clarifying functional and non-functional requirements, then sketch a high-level architecture with core services and data flows. Dive into key components like real-time tracking, matching, and notifications, discussing trade-offs and scaling strategies. Conclude by addressing bottlenecks and potential improvements.
Pro tip: Emphasize idempotency and exactly-once processing for order and delivery state transitions, as duplicates or lost updates can cause real-world issues like double charges or missed deliveries.
Ask questions to understand scope: expected scale (users, orders per day), latency requirements, consistency needs, and whether payments are in scope. Define core entities: customers, merchants, couriers, orders, deliveries.
Outline main services: API gateway, user/merchant/courier services, order service, matching service, tracking service, notification service. Choose a database (e.g., SQL for transactions, NoSQL for location data) and message queue for async communication.
Detail order placement (inventory check, payment), courier matching (geo-based, load balancing), real-time tracking (WebSocket, pub/sub), and notifications (push, SMS). Discuss state machines for order and delivery statuses.
Explain how to scale each component (e.g., sharding, caching, CDN for static assets). Discuss fault tolerance: retries, idempotency, dead-letter queues, and graceful degradation.
Compare design choices: SQL vs NoSQL, polling vs WebSockets, push vs pull for notifications. Highlight trade-offs between consistency, availability, and latency.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is where I spent the most time and honestly still feel shaky on the anonymous cart merging piece.
Start by clarifying requirements and scale, then propose a layered persistence architecture with local storage for offline resilience and server-side storage for cross-device sync. Address anonymous vs. logged-in user flows and conflict resolution strategies, emphasizing trade-offs and eventual consistency.
Pro tip: Demonstrate awareness of real-world constraints like storage limits, sync frequency, and user experience during conflicts; propose a merge strategy that prioritizes user intent and minimizes data loss.
Ask about expected user base, cart size, item types, and consistency requirements. Determine if real-time sync is needed or if eventual consistency suffices.
Define a cart schema with item IDs, quantities, timestamps, and versioning. Propose local storage (e.g., IndexedDB, SQLite) for offline access and a server-side store (e.g., DynamoDB, Cassandra) for durability and cross-device sync.
For anonymous users, generate a unique device ID and store cart locally; optionally sync to server with a temporary session. On login, merge the anonymous cart with the user's server-side cart using a conflict resolution policy.
Use a sync protocol (e.g., last-write-wins, operational transformation, or CRDTs) to reconcile changes. For carts, a merge strategy that sums quantities or takes the latest timestamp per item often works, but consider user prompts for ambiguous conflicts.
Design for network outages with retry queues and exponential backoff. Optimize for low latency by caching and batching updates. Discuss trade-offs between consistency, availability, and partition tolerance (CAP theorem).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I went straight to a stream-based ingestion pipeline and talked about writing latest position to a low-latency store while keeping a time-series log separately.
Start by clarifying requirements and scale (e.g., number of couriers, update frequency, latency needs), then design a high-level architecture covering ingestion, storage, fan-out, rate limiting, and privacy. Walk through each component, discussing trade-offs and justifying your choices with respect to scalability, reliability, and cost.
Pro tip: Emphasize decoupling and backpressure: use a message queue (like Kafka) to absorb bursts and allow independent scaling of consumers. Also, proactively mention privacy-by-design principles (e.g., data minimization, encryption) to show you consider non-functional requirements early.
Ask questions to understand the expected number of couriers, update frequency, latency requirements, and privacy regulations. This will guide your design decisions.
Propose a scalable ingestion layer (e.g., API gateway + Kafka) that can handle high throughput and provide backpressure. Discuss partitioning by courier ID for ordered processing.
Choose a storage solution (e.g., time-series DB or wide-column store) for efficient writes and queries. Consider retention policies and archival to cold storage.
Design a pub/sub system to push updates to customers and dispatch. Use WebSockets or push notifications for real-time, and consider caching for recent locations.
Implement rate limiting per courier and per consumer to prevent abuse. Apply privacy measures: anonymize data, encrypt in transit and at rest, and enforce access controls.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Drew out the states on the whiteboard and that helped.
Start by defining the order states and the events that trigger transitions, then explain how services communicate (e.g., via events or synchronous calls) to enact those transitions. Emphasize how you enforce valid state changes using techniques like optimistic locking, idempotency, and state machine validation. Conclude with trade-offs and how you handle failures and out-of-order events.
Pro tip: Show you understand that state machines are not just about states but about the invariants and side effects; mention how you'd audit and monitor transitions to catch anomalies in production.
List the core order states (e.g., Created, Paid, Shipped, Delivered, Cancelled) and the allowed transitions between them, including triggers (user actions, system events).
Explain how services (e.g., Order, Payment, Inventory) communicate to enact transitions—using synchronous calls for immediate consistency or asynchronous events for scalability and decoupling.
Describe mechanisms to prevent invalid or out-of-order changes: state validation, optimistic concurrency control, idempotent operations, and event ordering (e.g., via sequence numbers or versioning).
Discuss how to handle failures (e.g., payment failure after order creation) using sagas, compensating transactions, or retries with idempotency.
Compare approaches (e.g., orchestration vs. choreography, strong vs. eventual consistency) and mention monitoring/auditing of state transitions for observability.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying requirements and scale (e.g., peak QPS, latency SLOs, consistency needs) to frame the design. Then walk through the system layer by layer: API design, caching, queuing, failure handling, and observability, explaining how each handles spikes. Emphasize trade-offs and justify choices based on the specific constraints of meal-time traffic.
Pro tip: Proactively discuss how you would validate the design under load (e.g., load testing, chaos engineering) and how you would iterate based on observability data—this shows you think beyond the whiteboard and consider real-world operation.
Ask about expected peak traffic (e.g., 10x normal), latency SLOs, data consistency requirements, and budget constraints. This ensures your design targets the right problems.
Propose stateless APIs, rate limiting, and idempotent endpoints. Consider using GraphQL or gRPC for efficiency, and discuss how to handle read vs. write heavy operations.
Describe multi-layer caching (CDN, Redis) with appropriate TTLs and invalidation strategies. For writes, use message queues (e.g., Kafka) to decouple services and smooth spikes.
Outline strategies like circuit breakers, retries with exponential backoff, graceful degradation, and fallbacks. Discuss how to prioritize critical functionality during overload.
Specify metrics (latency, error rates, queue depths), logging, and tracing. Explain how you would use dashboards and alerts to detect and diagnose issues during spikes.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.