I recently built a weather app with a caching proxy layer, and it turned into a practical lesson in full-stack system design. The frontend is a straightforward React app, but adding a proxy between the client and the external weather API made me think harder about performance, cost, and data freshness.

Why a Proxy?

The problem was simple: external API calls are expensive and slow. If multiple users request weather for the same city within a short window, there's no reason to hit the API repeatedly. A caching proxy solves this by storing responses temporarily and serving cached data when possible. Implementing it yourself forces you to understand the trade-offs involved.

What Worked Well

Designing the proxy as an intelligent middleman was the most interesting part. Instead of just forwarding requests, it makes decisions about when to use cached data versus fetching fresh information. This shifted my thinking from "call the API" to "manage network efficiency and cost".

The cache hit path was satisfying to implement. Response times dropped to milliseconds for cached requests, and the backend only called the external API when necessary.

I also appreciated how the proxy naturally functions as a rate limiter. If ten users request "London" simultaneously, only one external API call happens within the TTL window. That kind of implicit optimization emerged from the design.

The Challenges

• Cache invalidation and TTL selection. I settled on a 5-minute TTL for weather data, as temperature changes aren't urgent enough to justify constant refreshing. This forced me to think through the trade-offs: shorter TTLs mean fresher data but higher API costs.
• Sequencing cache operations correctly. The logic is: check cache, fetch from API if stale, update cache, respond. Small mistakes in ordering could block the frontend or return inconsistent data. I had to think carefully about error handling, too—each layer needs to degrade gracefully.
• Handling the cold start problem. On the first request for any city, the cache is empty, and there's unavoidable latency. The lesson was recognizing where complexity (like prefetching) adds value versus where it's premature optimization.

What I Learned

• System design is about constraints. I had to balance frontend expectations, backend efficiency, and external API limitations.
• Simplicity usually wins. A fixed TTL was simpler, reliable, and appropriate for the problem. Knowing when not to add complexity is as important as knowing when to add it.
• Debugging requires understanding layers. When something broke, I had to trace the path through the frontend, proxy logic, cache read/write, and external API call.
• Instrumentation matters. I added logging for cache hits, misses, and API calls, which was essential for understanding actual versus intended behaviour.

Next Steps

The proxy works well, but there are a few improvements I want to explore:

• Configurable TTL per city: Some locations might benefit from more frequent updates.
• Cache prefetching: Proactively fetch popular cities to reduce first-request latency.
• Better monitoring: Track cache hit rates and API costs over time to validate the design assumptions.

Building this project reinforced that even small applications benefit from thinking about concurrency, caching, and cost.