resilience-patterns

A circuit breaker in Python prevents repeated calls to a failing service by failing fast instead of waiting for timeouts. This pattern monitors service outages and trips the breaker to halt requests, preventing cascading errors across your application.

Retrying failed Python functions with exponential backoff automatically retries operations using increasing delays. This avoids overwhelming an external service during temporary outages, ensuring your application recovers gracefully without causing additional load on the dependency.

Enforcing a timeout on Python operations limits maximum execution times, preventing indefinite hangs when calling external services. By utilizing standard Python libraries for threading and signals, the timeout wrapper terminates operations that exceed the defined duration.

Standard Python libraries fully support fault tolerance without external dependencies. The patterns utilize native threading, signals, and context management to implement resilience mechanisms like circuit breakers and timeouts directly within your application stack.

Graceful degradation in Python allows applications to continue functioning with reduced capabilities when non-critical services fail. You should use this pattern when a dependency outage would otherwise crash your app, ensuring core features remain operational.

A Python application crashes when a third-party API is slow because it lacks resilience patterns for handling external service failures. Implementing timeouts, retries, and circuit breakers prevents these cascading errors and ensures graceful operation despite dependency downtime.