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 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 easy

How to Mock a Try Confirm Cancel Pattern in Python

Define a simple class with confirm and cancel methods, execute a try confirm with error handling, and print the final state.

try-except mock class

Python

class TCC:
    def __init__(self):
        self.confirmed = False
        self.cancelled = False

    def confirm(self):
        self.confirmed = True
        return "confirmed"

    def cancel(self):
        self.cancelled = True
        return "cancelled"

    def try_confirm(self):
        try:
            result =…

12 0 Open

Browse by section

Each section groups closely related Python snippets.