python-resilience

# 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).

### 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(
    retry=retry_if_exception_type(TRANSIENT_ERRORS),
    stop=stop_after_attempt(5) | stop_after_delay(60),
    wait=wait_exponential_jitter(initial=1, max=30),
)
def fetch_data(url: str) -> dict:
    """Fetch data with automati