How I Built a Resilient API

When I first started building APIs, I thought getting a 200 OK response was all that mattered. As long as my endpoint returned data, life was good. But one long evening, everything broke.

A third-party service we depended on went down, and suddenly my API started failing left and right. Users were frustrated, monitoring dashboards lit up, and I realized something important: building an API is easy, but building a resilient API is what separates good systems from great ones.

This post is about how I approached resilience — the mindset, the patterns, and the code that helped me design APIs that don’t collapse under pressure.

The Problem

In real-world systems, your API isn’t operating in a vacuum. It talks to:

• Other services (sometimes unreliable)

• Databases (sometimes slow)

• Networks (sometimes unpredictable)

Failures are not an exception — they’re guaranteed. The real question is: how does your API behave when things break?

When I first built mine, every small hiccup caused timeouts, broken user journeys, and angry Slack messages. That’s when I set out to make it resilient.

Designing for Resilience

Resilience is about gracefully handling failure. A resilient API doesn’t pretend failures won’t happen — it plans for them.

I followed three key principles:

1. Retries with backoff – Don’t fail immediately, but retry smartly.

2. Circuit breakers – If a service is failing repeatedly, stop hammering it and let it recover.

3. Timeouts and fallbacks – Don’t keep users waiting forever; provide alternatives where possible.

Implementation Walkthrough

I’ll share snippets in Spring Boot with Resilience4j, but the principles apply anywhere.

Retry Logic

Instead of giving up on the first failure:

Here, if the external service fails, the system retries automatically. If it still fails, it falls back to a graceful response.

Circuit Breaker

A circuit breaker prevents your API from wasting resources on a service that’s clearly down:

When errors cross a threshold, the circuit opens and skips calls until things stabilize.

Timeouts

Long waits kill user experience. Setting a timeout ensures you don’t keep clients hanging:

💻 You can check out the full working code on my GitHub repo here: https://github.com/builtbyanthony/springboot-resilience4j-demo

Real-World Results

After applying these patterns:

• Our API uptime improved significantly.

• Failures dropped by over 70%.

• Users saw helpful fallback messages instead of endless spinners.

Most importantly, the on-call stress went way down.

Lessons Learned

• Plan for failure early — it’s not an afterthought.

• Resilience isn’t free — retries and circuit breakers add complexity, so test thoroughly.

• Observability is key — logs, metrics, and alerts help you know when things go wrong.

Conclusion

That night taught me resilience isn’t just about code — it’s a mindset. Systems fail, networks break, services go down. But if your API is built to expect failure, users won’t even notice most of the time.

This was my journey of building a resilient API. What about you?
👉 What’s the toughest API failure you’ve faced, and how did you fix it?
📧 Let me know all about at builtbyanthonyofficial@gmail.com