Reference library

Reliability & rate limiting

Retries, exponential backoff, circuit breakers, token buckets, and idempotent handlers.

4 matches

Chaos Inject Random Failures in Python

Simulate random failures in a Python function to test error handling and resilience, using random thresholds and controllable success rates.

chaos-engineering random resilience

Python

import random


def unreliable_function(success_rate: float = 0.7) -> str:
    """Simulate a function that sometimes fails."""
    if random.random() > success_rate:
        raise ConnectionError("Simulated network failure")
    return "Operation completed successfully"


if __name__ == "__main__":
    random.seed(42)…

15 0 Open

Reliability & rate limiting easy

How to Build a Rate Limiter in Python

A beginner-friendly token bucket rate limiter with retry logic for handling API rate limits in Python.

rate-limiting token-bucket retry

Python

import time
import random

class RateLimiter:
    """Simple token bucket rate limiter for beginners."""
    
    def __init__(self, max_tokens=5, refill_rate=1.0):
        self.max_tokens = max_tokens
        self.tokens = max_tokens
        self.refill_rate = refill_rate  # tokens per second
        self.last_refill …

15 0 Open

Reliability & rate limiting easy

How to Retry on Specific Exception Tuples in Python

A decorator-based retry pattern that retries a function only when it raises exceptions specified in a tuple, with configurable retries and delay.

retry decorator exceptions

Python

import time
import random
from unittest.mock import patch


def retry_on_exceptions(retries=3, exceptions=(ValueError,), delay=0.1):
    def decorator(func):
        def wrapper(*args, **kwargs):
            for attempt in range(retries):
                try:
                    return func(*args, **kwargs)
          …

15 0 Open

Reliability & rate limiting easy

Implementing Fallback with Cached Stale Data in Python

This code demonstrates a resilient data-fetching pattern that caches successful responses, falls back to cached data when the external API fails, and returns stale data as a last-resort fallback.

cache fallback resilience

Python

import random
import time

# Simulated cache dictionary: key -> (value, timestamp)
_cache = {}
_CACHE_TTL = 3  # seconds

# Mock data source (simulates an unreliable external API)
def fetch_mock_data(key):
    failure = random.random() < 0.4  # 40% chance of failure
    if failure:
        raise ConnectionError("Mock …

14 0 Open

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