python-resilience
Automatic retries, exponential backoff, timeouts, and fault-tolerant decorators for Python services.
- Covers transient vs. permanent failure classification, exponential backoff with jitter, bounded retries, and timeout patterns using the
tenacitylibrary - Includes nine production patterns: basic retry, selective error handling, HTTP status code retries, combined exception and status retries, retry logging, timeout decorators, stacked decorators, dependency injection for testing, and fail-safe defaults
- Provides best practices for retry strategy, including when to retry, duration caps, logging requirements, and graceful degradation for non-critical operations
- All patterns use decorators to separate infrastructure concerns from business logic, enabling reusable, testable, and maintainable fault-tolerant code
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.
When to Use This Skill
- Adding retry logic to external service calls
- Implementing timeouts for network operations
- Building fault-tolerant microservices
- Handling rate limiting and backpressure
- Creating infrastructure decorators
- Designing circuit breakers
Core Concepts
1. Transient vs Permanent Failures
Retry transient errors (network timeouts, temporary service issues). Don't retry permanent errors (invalid credentials, bad requests).
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