In critical environments, systems don't just have to work: they have to stay standing when everything around them starts to fail. That's where resilience patterns come in: design decisions that protect the platform in real scenarios, not in perfect diagrams.
What is a resilience pattern?
It's a proven way to limit the impact of errors, prevent them from spreading, and allow the system to recover. It doesn't depend on a specific language or cloud; it's a way of thinking.
Key patterns I use and explain
🔌 Circuit Breaker
Prevents a failed service from dragging down the entire platform by cutting calls when repeated failures are detected.
🔄 Retry with Backoff
Retries failed calls in a controlled manner, without generating traffic storms or overloading services.
⏱️ Defined Timeouts
Avoids endless waits and frees resources when a response simply isn't going to arrive.
🚧 Bulkhead
Separates resources and capabilities so that a saturated service doesn't impact the rest of the system.
🔻 Fallbacks
Maintains a reduced but useful version of the service when the full version isn't possible.
📨 Queues & Async
Decouples critical processes to absorb peaks, avoid blocking and buy time during failures.
Resilience in production, not in presentations
A pattern is useless if it only exists in documents. It has to be:
- Implemented in the code.
- Reflected in the architecture.
- Visible in metrics and dashboards.
- Aligned with how the team operates incidents.
In production, resilience means an error doesn't become a major incident, and an incident doesn't become a crisis.
Designing for everything to work "when nothing fails" is easy. Designing to keep operating when things fail is what differentiates a serious platform.
