Design a weather aggregation system evaluates requirements, scale assumptions, API/data design, architecture, trade-offs, failure modes, and rollout in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Design a weather data aggregation system that calls a third-party weather API and stores updates in a database every 10 minutes. Describe the architecture, scheduler design, rate-limiting and quota management, idempotency and deduplication, retries and backoff, schema design (hot vs. cold storage), caching strategy, data validation, multi-provider support, handling provider outages and delayed data, monitoring/alerting, and scalability across cities and regions.
Overview: Design a weather aggregation system evaluates requirements, scale assumptions, API/data design, architecture, trade-offs, failure modes, and rollout in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
System Design: Weather Data Aggregation (10-minute Updates)
Context
Build a service that periodically fetches weather data from third-party providers for many cities/regions, stores and serves the data reliably, and scales with growth. Assume near–real-time use cases (dashboards, alerts, APIs) and historical analytics.
Requirements
Design and describe the following aspects:
Architecture and data flow end-to-end.
Scheduler that triggers updates every 10 minutes across many cities and providers.
Rate-limiting and quota management for third-party APIs.
Idempotency and deduplication across retries and providers.
Retry strategy, backoff, circuit breaking, and dead-letter handling.
Schema design for hot vs. cold storage and partitioning.
Caching strategy for read performance and cost control.
Data validation, normalization, and quality checks.
Multi-provider support, abstraction, and result blending/failover.
Handling provider outages and delayed/late-arriving data.
Monitoring, alerting, and SLOs.
Scalability across cities and regions, including capacity planning.
State any assumptions you need and justify key trade-offs.
Clarifying Questions to Ask Guidance
Clarify users, core use cases, read/write patterns, scale, latency, availability, and data retention.
State explicit assumptions before making sizing or architecture decisions.
Prioritize the functional path first, then address reliability, security, observability, and rollout.
What a Strong Answer Covers Guidance
A scoped requirements summary with concrete non-goals and success metrics.
API, data model, architecture, consistency, capacity, and operations.
Reasoned trade-offs among simple and scalable designs, including bottlenecks and failure modes.
A validation, monitoring, migration, and launch plan appropriate for the risk level.
Follow-up Questions Guidance
What breaks first at 10x traffic or data volume?
How would you degrade gracefully during dependency failures?
What metrics and alerts would prove the design is healthy after launch?