I started with requirement clarification which felt good, asked about the main actors (customer, restaurant, courier), payment flow, scale expectations.
Start by clarifying functional and non-functional requirements, then estimate scale (users, orders, restaurants). Design the high-level architecture covering key services like order management, restaurant search, delivery dispatch, and payment, then dive deep into critical components such as real-time tracking and matching algorithms, discussing trade-offs.
Pro tip: Emphasize the two-sided marketplace dynamics: balancing supply (drivers) and demand (orders) in real-time. Show how you'd handle peak loads and ensure low-latency updates for order tracking.
Ask questions to define scope: user types (customers, restaurants, drivers), core features (search, order, payment, tracking), and non-functional needs (latency, consistency, availability).
Estimate daily active users, orders per second, number of restaurants and drivers, and data storage needs. This informs capacity planning and technology choices.
Sketch the main components: API gateway, user service, restaurant service, order service, payment service, delivery dispatch, and notification service. Define how they interact.
Pick 2-3 areas to detail: e.g., real-time order tracking (WebSockets, geospatial indexing), driver matching algorithm (proximity, availability), and payment consistency (idempotency, transactions).
Discuss trade-offs: SQL vs NoSQL for orders, push vs pull for updates, consistency vs availability. Identify bottlenecks (e.g., database sharding, caching) and mitigation strategies.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the system's scope and scale, then propose a data model that separates order metadata from order items and uses an event-driven state machine for transitions. Walk through the state transitions from placed to delivered, highlighting idempotency, consistency, and failure handling.
Pro tip: Emphasize that state transitions should be immutable events stored in an append-only log, enabling auditability and replay—this shows you think beyond basic CRUD and consider real-world reliability at Meta's scale.
Ask about expected order volume, read/write patterns, and consistency needs to tailor the data model and state machine.
Define tables/collections for orders, order items, users, and payments, normalizing where appropriate and denormalizing for read performance.
Enumerate states (e.g., PLACED, CONFIRMED, PREPARING, SHIPPED, DELIVERED) and allowed transitions, ensuring each transition is triggered by a specific event.
Use an event log or message queue to publish state changes, and make transition handlers idempotent to handle retries and duplicates.
Discuss how to handle partial failures (e.g., payment success but order not placed), use compensating transactions, and monitor state distribution and latency.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the problem scope and requirements, then propose a scalable system design that includes matching algorithms and ETA estimation. Discuss trade-offs between different approaches and how you would validate and iterate on the solution.
Pro tip: Emphasize real-time constraints and how you would handle dynamic factors like courier availability, traffic, and order priority to demonstrate production-level thinking.
Ask questions to understand scale, latency requirements, and business objectives (e.g., minimize delivery time vs. cost).
Outline the system components: order service, courier service, matching engine, ETA service, and data stores.
Propose an algorithm (e.g., greedy, Hungarian, or auction-based) and discuss factors like distance, courier rating, and current load.
Describe methods for ETA: historical data, real-time traffic, machine learning models, and how to update dynamically.
Discuss how to scale (sharding, caching, async processing) and trade-offs (accuracy vs. speed, fairness vs. efficiency).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Pretty standard reliability question but I appreciated it because it forced me to actually audit what I'd drawn.
Start by systematically walking through the architecture layer by layer (client, load balancer, application servers, databases, caches, etc.) to identify components whose failure would bring down the entire system. For each single point of failure, propose concrete mitigation strategies such as redundancy, failover, replication, and graceful degradation, and discuss trade-offs.
Pro tip: Don't just list SPOFs—prioritize them by blast radius and likelihood, and explain how you'd monitor and test failover (e.g., chaos engineering). This shows you think about operational resilience, not just design.
Ask clarifying questions about the architecture's scale, criticality, and existing redundancy to focus your analysis. State any assumptions you make about components and their interactions.
Systematically go through each layer (DNS, load balancers, app servers, databases, caches, message queues, etc.) and identify components that, if they fail, would cause a complete outage or significant degradation.
Rank the identified SPOFs based on their blast radius (e.g., global vs. partial outage) and the probability of failure. This helps focus on the most critical ones first.
For each high-priority SPOF, suggest specific solutions such as redundancy (active-active or active-passive), replication, sharding, circuit breakers, graceful degradation, and automated failover.
Explain the trade-offs of each mitigation (cost, complexity, consistency) and how you would validate resilience through testing (e.g., chaos engineering, load testing, failover drills).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked about partitioning orders by region since delivery is inherently geographic.
Start by clarifying the system's requirements and current bottlenecks, then propose a layered scaling strategy that addresses both read and write paths. Discuss partitioning (sharding) strategies, replication, and caching, and explain how you would handle rebalancing and hot spots. Finally, tie your choices back to trade-offs like consistency, latency, and operational complexity.
Pro tip: Meta interviewers value pragmatic, data-driven decisions: quantify the expected load (e.g., QPS, data size) and justify each scaling step with metrics, while acknowledging the trade-offs you're accepting.
Ask about expected traffic patterns, data volume, read/write ratio, latency SLOs, and consistency requirements to ground your scaling decisions.
Analyze the current architecture to find single points of failure and resource limits (CPU, storage, network, DB connections) and determine whether to scale vertically or horizontally.
Choose a sharding key that distributes load evenly, select a partitioning scheme (range, hash, consistent hashing), and decide on replication for fault tolerance and read scaling.
Explain how you would detect and mitigate hot partitions, rebalance data when adding nodes, and ensure availability during failures (e.g., via quorum, failover).
Compare consistency vs. availability, latency vs. throughput, and complexity vs. scalability; mention monitoring, auto-scaling, and cost implications.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.