python-resiliencelisted

# Python Resilience Patterns Build fault-tolerant Python applications that gracefully handle transient failures, network issues, and service outages. Resilience patterns keep systems running when dependencies are unreliable. ## Core Concepts ### 1. Transient vs Permanent Failures Retry transient errors (network timeouts, temporary service issues). Don't retry permanent errors (invalid credentials, bad requests). ### 2. Exponential Backoff Increase wait time between retries to avoid overwhelming recovering services. ### 3. Jitter Add randomness to backoff to prevent thundering herd when many clients retry simultaneously. ### 4. Bounded Retries Cap both attempt count and total duration to prevent infinite retry loops. ## Quick Start ```python from tenacity import retry, stop_after_attempt, wait_exponential_jitter @retry( stop=stop_after_attempt(3), wait=wait_exponential_jitter(initial=1, max=10), ) def call_external_service(request: dict) -> dict: return httpx.post("https://api.example.com", json=request).json() ``` ## Fundamental Patterns ### Pattern 1: Basic Retry with Tenacity Use the `tenacity` library for production-grade retry logic. For simpler cases, consider built-in retry functionality or a lightweight custom implementation. ```python from tenacity import ( retry, stop_after_attempt, stop_after_delay, wait_exponential_jitter, retry_if_exception_type, ) TRANSIENT_ERRORS = (ConnectionError, TimeoutError, OSError) @retry