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.

Logging: Examples use structlog to match python-observability. Where tenacity hooks need a stdlib-style logger (e.g. before_sleep_log), define a small callback that forwards to structlog instead of wiring two logging systems in parallel.

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.

Fail-safe scope: Patterns like fail_safe (later in this skill) fit non-critical reads or enrichments where a logged default is acceptable. For payments, auth, and data integrity, prefer errors to surface�coordinate with python-error-handling fail-fast boundaries.

Quick Start

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.

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

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.

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.

import httpx
import structlog
from tenacity import (
    retry,
    retry_if_exception_type,
    retry_if_result,
    stop_after_attempt,
    wait_exponential_jitter,
)

logger = structlog.get_logger()

def _log_before_sleep(retry_state) -> None:
    exc = retry_state.outcome.exception()
    logger.warning(
        "Retrying HTTP call",
        attempt=retry_state.attempt_number,
        exception_type=type(exc).__name__ if exc else None,
    )

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=_log_before_sleep,
)
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)

Advanced Patterns

Pattern 5: Logging Retry Attempts

Track retry behavior for debugging and alerting.

from tenacity import retry, stop_after_attempt, wait_exponential
import structlog

logger = structlog.get_logger()

def log_retry_attempt(retry_state):
    """Log detailed retry information."""
    exception = retry_state.outcome.exception()
    logger.warning(
        "Retrying operation",
        attempt=retry_state.attempt_number,
        exception_type=type(exception).__name__,
        exception_message=str(exception),
        next_wait_seconds=retry_state.next_action.sleep if retry_state.next_action else None,
    )

@retry(
    stop=stop_after_attempt(3),
    wait=wait_exponential(multiplier=1, max=10),
    before_sleep=log_retry_attempt,
)
def call_with_logging(request: dict) -> dict:
    """External call with retry logging."""
    ...

Pattern 6: Timeout Decorator

Create reusable timeout decorators for consistent timeout handling.

import asyncio
from functools import wraps
from typing import TypeVar, Callable

T = TypeVar("T")

def with_timeout(seconds: float):
    """Decorator to add timeout to async functions."""
    def decorator(func: Callable[..., T]) -> Callable[..., T]:
        @wraps(func)
        async def wrapper(*args, **kwargs) -> T:
            return await asyncio.wait_for(
                func(*args, **kwargs),
                timeout=seconds,
            )
        return wrapper
    return decorator

@with_timeout(30)
async def fetch_with_timeout(url: str) -> dict:
    """Fetch URL with 30 second timeout."""
    async with httpx.AsyncClient() as client:
        response = await client.get(url)
        return response.json()

Pattern 7: Cross-Cutting Concerns via Decorators

Stack decorators to separate infrastructure from business logic.

from functools import wraps
from typing import TypeVar, Callable
import structlog

logger = structlog.get_logger()
T = TypeVar("T")

def traced(name: str | None = None):
    """Add tracing to function calls."""
    def decorator(func: Callable[..., T]) -> Callable[..., T]:
        span_name = name or func.__name__

        @wraps(func)
        async def wrapper(*args, **kwargs) -> T:
            logger.info("Operation started", operation=span_name)
            try:
                result = await func(*args, **kwargs)
                logger.info("Operation completed", operation=span_name)
                return result
            except Exception as e:
                logger.error("Operation failed", operation=span_name, error=str(e))
                raise
        return wrapper
    return decorator

# Stack multiple concerns
@traced("fetch_user_data")
@with_timeout(30)
@retry(stop=stop_after_attempt(3), wait=wait_exponential_jitter())
async def fetch_user_data(user_id: str) -> dict:
    """Fetch user with tracing, timeout, and retry."""
    ...

Pattern 8: Dependency Injection for Testability

Pass infrastructure components through constructors for easy testing.

Note: Protocol methods below use ... as stubs�that is normal for typing.Protocol and is not the FastAPI/Pydantic �no ... defaults� rule; see the Official FastAPI skill section Do not use Ellipsis for path operations or Pydantic models for scope.

from dataclasses import dataclass
from typing import Protocol

class Logger(Protocol):
    def info(self, msg: str, **kwargs) -> None: ...
    def error(self, msg: str, **kwargs) -> None: ...

class MetricsClient(Protocol):
    def increment(self, metric: str, tags: dict | None = None) -> None: ...
    def timing(self, metric: str, value: float) -> None: ...

@dataclass
class UserService:
    """Service with injected infrastructure."""

    repository: UserRepository
    logger: Logger
    metrics: MetricsClient

    async def get_user(self, user_id: str) -> User:
        self.logger.info("Fetching user", user_id=user_id)
        start = time.perf_counter()

        try:
            user = await self.repository.get(user_id)
            self.metrics.increment("user.fetch.success")
            return user
        except Exception as e:
            self.metrics.increment("user.fetch.error")
            self.logger.error("Failed to fetch user", user_id=user_id, error=str(e))
            raise
        finally:
            elapsed = time.perf_counter() - start
            self.metrics.timing("user.fetch.duration", elapsed)

# Easy to test with fakes
service = UserService(
    repository=FakeRepository(),
    logger=FakeLogger(),
    metrics=FakeMetrics(),
)

Pattern 9: Fail-Safe Defaults

Degrade gracefully when non-critical operations fail. Do not wrap authoritative or compliance-sensitive flows this way; see Fail-safe scope under Core Concepts and python-error-handling.

Relationship to python-anti-patterns � Bare Exception Handling: fail_safe deliberately catches Exception and returns a default. That is not the except Exception: pass anti-pattern as long as you (a) log the failure, (b) restrict usage to non-critical read paths (recommendations, enrichment, cache warm-up), and (c) never silence errors on authoritative writes, payments, auth, or audit-relevant code. Apply it at one clearly-named decorator boundary so reviewers can see the swallow on sight.

from typing import TypeVar
from collections.abc import Callable

T = TypeVar("T")

def fail_safe(default: T, log_failure: bool = True):
    """Return default value on failure instead of raising."""
    def decorator(func: Callable[..., T]) -> Callable[..., T]:
        @wraps(func)
        async def wrapper(*args, **kwargs) -> T:
            try:
                return await func(*args, **kwargs)
            except Exception as e:
                if log_failure:
                    logger.warning(
                        "Operation failed, using default",
                        function=func.__name__,
                        error=str(e),
                    )
                return default
        return wrapper
    return decorator

@fail_safe(default=[])
async def get_recommendations(user_id: str) -> list[str]:
    """Get recommendations, return empty list on failure."""
    ...

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