Reference library

Reliability & rate limiting

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

3 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 Inject Random Latency for Chaos Testing in Python

Mock unreliable services by wrapping functions with a decorator that adds random network-like delays before execution.

chaos-engineering decorators latency

Python

import random
import time
from functools import wraps

def inject_latency(func):
    @wraps(func)
    def wrapper(*args, **kwargs):
        latency = random.uniform(0.1, 0.5)
        print(f"Injecting {latency:.3f}s latency...")
        time.sleep(latency)
        return func(*args, **kwargs)
    return wrapper

@inje…

12 0 Open

Reliability & rate limiting easy

Rate Limiting with a Simple Python RateLimiter Class

A beginner-friendly Python rate limiter that tracks call timestamps and enforces a maximum number of calls within a rolling time window, with a helper to validate positive integers.

rate-limiting time api

Python

import time

class RateLimiter:
    def __init__(self, max_calls, period_seconds):
        self.max_calls = max_calls
        self.period_seconds = period_seconds
        self.calls = []

    def is_allowed(self):
        now = time.time()
        while self.calls and now - self.calls[0] >= self.period_seconds:
      …

13 0 Open

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