Clinton Fernandes

Cloud-native backend engineer sharing real-world Java, React, and AWS projects. Dive into serverless architectures, distributed systems, and production-ready code walkthroughs. Learn how to build scalable, cost-efficient apps on the modern web.

2 minute read

Resilience is one of those architectural buzzwords that doesn’t mean much—until production reminds you why you need it.

Most of us reach for libraries like Resilience4j or Hystrix when we want to wrap a method with a circuit breaker. And they work great… until your service starts scaling horizontally or going stateless.

That’s exactly the problem I ran into. We were running Spring Boot services on ECS with autoscaling and later migrated some to AWS Lambda. Everything was stateless, and the moment we needed to rely on a circuit breaker to prevent cascading failures, it fell apart.

🔥 The Problem: Circuit Breakers That Think Locally

Imagine you have 5 instances of a service, each with an in-memory circuit breaker protecting a flaky downstream call.

Here’s what happens:

  • Instance A detects 3 failures, trips its circuit.
  • Instance B is unaware, still making calls.
  • So are C, D, and E.
  • By the time all of them trip, the downstream is already on fire. 🚒

This is because traditional circuit breakers are memory-bound. Each instance maintains its own circuit state—there’s no coordination, no shared knowledge.

Which is fine… unless your infrastructure is distributed, which—let’s face it—is most of us now.

⚠️ Why This Matters in the Cloud

In systems built on AWS Lambda, ECS Fargate, or Kubernetes, your service might scale up to 20+ stateless containers. These environments are:

  • Ephemeral: Containers/lambdas come and go.
  • Stateless: No shared memory.
  • Disconnected: No awareness of sibling instance behavior.

This makes in-memory failure tracking completely useless when you’re trying to prevent a meltdown.

🧠 What I Needed Instead

I wanted a circuit breaker that was:

  • Distributed: All instances share the same circuit state.
  • Stateless-friendly: Works even if the instance gets replaced mid-request.
  • Pluggable: So I can swap storage backends (starting with DynamoDB).
  • Simple: Just drop an annotation and configure via YAML or env.

This led me to build cloud-circuitbreaker.

🛑 Quick Preview: What It Looks Like

@CloudCircuitBreaker(function = 'getUserData', fallback = 'fallbackFunction')
public Response getUserData(String userId) {
    return userService.fetchFromUpstream(userId);
}

public Response fallbackFunction(String userId) {
  return userService.fetchFromCache(userId)
}

This wraps your method. Behind the scenes, it tracks failures in a shared store (DynamoDB by default), and trips the breaker for all instances — not just the one executing the code.

✅ Coming Up Next

In Part 2, I’ll walk through how this works under the hood:

  • Why DynamoDB?
  • How failures are counted and TTL-managed
  • How configuration and annotations plug into your Java app

Want to see the source already? Check it out on GitHub:
🔗 clinton1719/cloud-circuitbreaker

If you’ve faced similar issues with Spring Boot or Resilience4j in cloud-native setups, I’d love to hear from you. Drop a comment or ping me on LinkedIn.

Until next time 👋

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