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 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

How to Mock Fault Injection Percentage in Python

Simulate a service with a 30% failure rate using random.random to test error handling and retries.

fault-injection random testing

Python

import random

class Service:
    def call(self):
        if random.random() < 0.3:  # 30% failure rate
            raise ConnectionError("Simulated network fault")
        return "ok"

def main():
    svc = Service()
    random.seed(42)  # deterministic for demonstration
    results = []
    for _ in range(10):
     …

14 0 Open

Reliability & rate limiting medium

Retry with Exponential Backoff and Jitter in Python

A decorator-style retry wrapper that retries a flaky function with exponential backoff plus random jitter, then raises after the last attempt fails.

retry backoff jitter

Python

import random
import time

def retry_with_backoff(func, max_retries=3, base_delay=0.5, max_jitter=0.1):
    for attempt in range(max_retries + 1):
        try:
            return func()
        except Exception as e:
            if attempt == max_retries:
                raise
            delay = base_delay * (2 ** at…

14 0 Open

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