Meta·Software Engineer·Onsite - System Design / Architecture·Senior

Senior

Jun 2026

Summary

Meta SWE system design round, got asked to design a weather app. Not the most complex prompt but there's more to it than you'd think once you start pulling on the threads.

Questions Asked (1)

Design a weather application.

System DesignTechnical Trade-offsAPI & Integrations

Author's notes

Started with clarifying questions about scale and which features actually matter, which helped.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the scope and requirements of the weather application, such as target users, platforms, and key features. Then, outline a high-level architecture covering data ingestion, processing, storage, and delivery, and dive into critical components like API design, caching, and scalability. Finally, discuss trade-offs and potential challenges, showing a balanced view of performance, cost, and reliability.

Pro tip: Demonstrate awareness of real-world constraints by discussing how to handle third-party API rate limits and failures, and propose a fallback strategy to ensure the app remains functional.

1. Clarify Requirements

Ask questions to understand the expected scale, platforms (mobile/web), key features (current conditions, forecasts, alerts), and non-functional requirements like latency and availability.

2. High-Level Architecture

Sketch the main components: data sources (weather APIs, sensors), backend services (ingestion, processing, storage), and client-facing APIs. Explain how data flows from source to user.

3. Deep Dive into Components

Elaborate on critical parts: API design (REST/GraphQL), caching strategy (CDN, Redis), database choice (time-series vs. relational), and how to handle real-time updates (WebSockets, push notifications).

4. Scalability and Reliability

Discuss how to scale horizontally, handle failures (circuit breakers, retries), and ensure high availability. Mention monitoring, logging, and alerting.

5. Trade-offs and Optimizations

Compare alternatives (e.g., polling vs. push, SQL vs. NoSQL) and justify choices based on requirements. Highlight potential bottlenecks and mitigation strategies.

Key Points to Mention

Integration with third-party weather APIs (e.g., OpenWeatherMap) and handling rate limits, failures, and data consistency.
Caching strategies at multiple levels (client, CDN, server) to reduce latency and API costs.
Choice of database: time-series databases (e.g., InfluxDB) for historical data vs. relational for user data.
API design considerations: REST vs. GraphQL, versioning, and authentication.
Real-time data delivery: push notifications, WebSockets, or server-sent events for severe weather alerts.
Scalability patterns: load balancing, auto-scaling, and microservices architecture.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.