Python Resilience

作者 wshobson46891e7e60da无许可证收录于 2026年10月8日更新于 2026年10月8日

Python resilience patterns including automatic retries, exponential backoff, timeouts, and fault-tolerant decorators. Use when adding retry logic, implementing timeouts, building fault-tolerant services, or handling transient failures.

仅含说明Software Development
AI 生成的概览

指导 Python 开发者为不可靠调用添加重试、指数退避、抖动、超时和容错装饰器。

功能
该技能提供用于处理瞬时故障、网络问题和服务中断的 Python 容错模式。它讲解瞬时错误与永久错误的区别、指数退避、抖动和有界重试，并给出使用 tenacity 库配合 httpx 的完整示例，包括按异常、按 HTTP 状态码或两者同时触发重试。它还列出最佳实践，例如限制总时长、记录每次重试以及为所有调用设置超时。
适用场景
适用于为外部服务调用添加重试逻辑、为网络操作实现超时，或构建容错服务和微服务。也适合处理限流、背压和熔断器设计。
运行要求
仅为说明性内容，不附带脚本。示例假定使用 Python 以及 tenacity 和 httpx 包，并需要网络访问以执行所示 HTTP 调用。随附的 references/details.md 文件包含进阶模式。

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 automatic retry on transient failures."""    response = httpx.get(url, timeout=30)    response.raise_for_status()    return response.json()

Pattern 2: Retry Only Appropriate Errors

Whitelist specific transient exceptions. Never retry:

  • ValueError, TypeError - These are bugs, not transient issues
  • AuthenticationError - Invalid credentials won't become valid
  • HTTP 4xx errors (except 429) - Client errors are permanent
python
from tenacity import retry, retry_if_exception_typeimport httpx
# Define what's retryableRETRYABLE_EXCEPTIONS = (    ConnectionError,    TimeoutError,    httpx.ConnectTimeout,    httpx.ReadTimeout,)
@retry(    retry=retry_if_exception_type(RETRYABLE_EXCEPTIONS),    stop=stop_after_attempt(3),    wait=wait_exponential_jitter(initial=1, max=10),)def resilient_api_call(endpoint: str) -> dict:    """Make API call with retry on network issues."""    return httpx.get(endpoint, timeout=10).json()

Pattern 3: HTTP Status Code Retries

Retry specific HTTP status codes that indicate transient issues.

python
from tenacity import retry, retry_if_result, stop_after_attemptimport httpx
RETRY_STATUS_CODES = {429, 502, 503, 504}
def should_retry_response(response: httpx.Response) -> bool:    """Check if response indicates a retryable error."""    return response.status_code in RETRY_STATUS_CODES
@retry(    retry=retry_if_result(should_retry_response),    stop=stop_after_attempt(3),    wait=wait_exponential_jitter(initial=1, max=10),)def http_request(method: str, url: str, **kwargs) -> httpx.Response:    """Make HTTP request with retry on transient status codes."""    return httpx.request(method, url, timeout=30, **kwargs)

Pattern 4: Combined Exception and Status Retry

Handle both network exceptions and HTTP status codes.

python
from tenacity import (    retry,    retry_if_exception_type,    retry_if_result,    stop_after_attempt,    wait_exponential_jitter,    before_sleep_log,)import loggingimport httpx
logger = logging.getLogger(__name__)
TRANSIENT_EXCEPTIONS = (    ConnectionError,    TimeoutError,    httpx.ConnectError,    httpx.ReadTimeout,)RETRY_STATUS_CODES = {429, 500, 502, 503, 504}
def is_retryable_response(response: httpx.Response) -> bool:    return response.status_code in RETRY_STATUS_CODES
@retry(    retry=(        retry_if_exception_type(TRANSIENT_EXCEPTIONS) |        retry_if_result(is_retryable_response)    ),    stop=stop_after_attempt(5),    wait=wait_exponential_jitter(initial=1, max=30),    before_sleep=before_sleep_log(logger, logging.WARNING),)def robust_http_call(    method: str,    url: str,    **kwargs,) -> httpx.Response:    """HTTP call with comprehensive retry handling."""    return httpx.request(method, url, timeout=30, **kwargs)

Detailed worked examples and patterns

Detailed sections (starting with ## Advanced Patterns) live in references/details.md. Read that file when the navigation summary above is insufficient.

Best Practices Summary

  1. Retry only transient errors - Don't retry bugs or authentication failures
  2. Use exponential backoff - Give services time to recover
  3. Add jitter - Prevent thundering herd from synchronized retries
  4. Cap total duration - stop_after_attempt(5) | stop_after_delay(60)
  5. Log every retry - Silent retries hide systemic problems
  6. Use decorators - Keep retry logic separate from business logic
  7. Inject dependencies - Make infrastructure testable
  8. Set timeouts everywhere - Every network call needs a timeout
  9. Fail gracefully - Return cached/default values for non-critical paths
  10. Monitor retry rates - High retry rates indicate underlying issues

来源与署名

来源：wshobson/agents位于plugins/python-development/skills/python-resilience提交46891e7

许可证： 无许可证

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