사용 방법
- 설치 명령을 복사하고 자신의 에이전트 환경에 설치하기 전에 소스 패키지를 확인하세요.
- 필요한 도구, 인증 정보, 지원 환경은 원본 문서에서 확인하세요.
- 입력과 원하는 산출물을 설명하고 출처와 결과를 대조하세요.
npx skills add https://github.com/wshobson/agents --skill python-resilience시작 전 확인
- 설치에는 모델 구독, API 한도, 파일 접근 권한이 포함되지 않습니다.
- 별점 수와 설치 수는 활동 지표이며 독립적인 품질·보안 검증이 아닙니다.
원본 Skill 문서
원본 저장소의 제목, 예시, 코드, 표, 링크, 이미지를 유지해 표시합니다.
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 issuesAuthenticationError- Invalid credentials won't become valid- HTTP 4xx errors (except 429) - Client errors are permanent
python
from tenacity import retry, retry_if_exception_type
import httpx
# Define what's retryable
RETRYABLE_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_attempt
import 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 logging
import 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
- Retry only transient errors - Don't retry bugs or authentication failures
- Use exponential backoff - Give services time to recover
- Add jitter - Prevent thundering herd from synchronized retries
- Cap total duration -
stop_after_attempt(5) | stop_after_delay(60) - Log every retry - Silent retries hide systemic problems
- Use decorators - Keep retry logic separate from business logic
- Inject dependencies - Make infrastructure testable
- Set timeouts everywhere - Every network call needs a timeout
- Fail gracefully - Return cached/default values for non-critical paths
- Monitor retry rates - High retry rates indicate underlying issues
