Weather Dashboard API

Interview-Ready Approach

1) Clarify Scope and SLOs

• •Problem statement: Design a weather API that aggregates data from multiple providers, caches results, and serves forecasts.
• •Design for a peak load target around 100 RPS (including burst headroom).
• •API calls/day: ~500,000
• •Active API keys: ~10,000
• •Cache TTL: 10 minutes
• •Response time: < 200 ms (cached) / < 2 s (uncached)
• •Locations tracked: ~50,000

2) Capacity Planning Method

• •Convert traffic and growth constraints into request rate, storage growth, and concurrency budgets.
• •Keep at least 2-3x safety margin per tier (ingress, compute, storage, async workers).
• •Reserve explicit latency budgets per hop so p95 can be defended in review.

3) Architecture Decisions

• •Caching: Put cache on hot read paths first and pick cache-aside or write-through explicitly.
• •API Design: Standardize API boundaries, idempotency keys, pagination, and error contracts first.
• •Databases: Define a clear system-of-record and design read/write paths separately before adding optimizations.

4) Reliability and Failure Strategy

• •Bound staleness with TTL + invalidation hooks for critical entities.
• •Apply strict input validation and backward-compatible versioning.
• •Use strong write constraints (transactions or conditional writes) and explicit backup/restore strategy.

5) Validation Plan

• •Run one peak-load test, one dependency-degradation test, and one failover test.
• •Verify idempotency for all retried writes and async consumers.
• •Track user-facing SLOs first: p95 latency, error rate, and successful throughput.

6) Trade-offs to Call Out in Interviews

• •Caching: Higher hit rate cuts latency/cost, but stale data and invalidation bugs become primary risks.
• •API Design: Rich APIs improve developer speed but can create long-term compatibility burden.
• •Databases: SQL gives stronger transactional guarantees; NoSQL often gives better write scaling and flexibility.

Practical Notes

• •Cache weather data in Redis keyed by a normalized location identifier (e.g., geohash at precision 5).
• •Use a circuit breaker for upstream provider calls - if one is down, fall back to the others.
• •Rate limiting: use a sliding window counter in Redis per API key.

Why This Solution Works

Request path: The solution keeps ingress, service logic, and stateful dependencies separated so each layer can scale independently.

Reference flow: Web Clients -> API Gateway -> API Service -> Primary SQL DB -> Redis Cache

Design strengths

• •Cache sits on the read path to absorb repeated queries and keep DB pressure stable.

Interview defense

• •This design makes bottlenecks explicit (ingress, core compute, persistence, async workers).
• •It supports progressive scaling without re-architecting the core request path.
• •It keeps correctness-sensitive state changes in durable systems while offloading background work asynchronously.