python-resilience

Automatic retries in Python use fault-tolerant decorators to re-run failed external API calls. This skill provides code patterns using libraries like tenacity to handle transient failures by automatically retrying requests that return 5xx errors.

Exponential backoff and jitter prevent overwhelming services by increasing wait times between retries with added randomness. This skill provides Python patterns to implement these resilience techniques, ensuring network requests fail predictably without causing service outages.

Timeouts in Python set strict time limits for operations to prevent indefinite hangs. This skill provides patterns using asyncio to implement timeouts, ensuring your application gracefully handles network issues and service outages without blocking indefinitely.

Fault-tolerant decorators can be applied to async Python applications using asyncio. This skill provides resilience patterns including retry logic, exponential backoff, and timeouts that integrate with async code to build fault-tolerant services.

Python services fail on 5xx errors because they lack resilience patterns like automatic retries. This skill solves transient failures by providing decorator-based retry logic with exponential backoff, ensuring operations eventually succeed or fail predictably.

Structlog is used alongside tenacity, httpx, and asyncio in this skill to provide structured logging for resilience patterns. While the core retry and timeout mechanisms work independently, structlog helps monitor retry behavior and track transient failures.