To truly understand the value the Circuit Breaker pattern brings to the table, we first need to get a clear grasp of the problems it seeks to solve.

Cascading Failures

In a distributed environment, a failure in one service can ripple through the system. Imagine Service A depends on Service B. If B is down or extremely slow, calls from A to B will hang until a timeout occurs. During that wait, A’s resources (threads, memory, etc.) are tied up. If this happens frequently or to many threads, Service A itself may become overwhelmed – it might run out of threads or memory, causing failures in A even though A is not broken itself. This scenario is called a cascading failure, where a single point of failure cascades into a system-wide issue.

Unresponsive Services and Wasted Work

Often, when a service is unresponsive, applications will retry or wait for the service to respond. Simply waiting on a slow service (with long timeouts) means users experience delays and threads stay blocked. Likewise, blindly retrying a known failing operation wastes CPU cycles and can overload the failing service even more. For example, if dozens of components keep retrying requests to a database that's already struggling, it just makes things worse. In cases where a fault is unlikely to resolve quickly (e.g. a network outage or a service crash), it’s pointless for an application to continuously attempt the operation. This not only fails to solve the problem but also prevents the application from handling the failure gracefully. Clearly, what’s needed is a way to detect failure quickly and stop sending traffic to the failing component before it drags down the entire system.