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.

Easy Python 3.9+ Aug 9, 2026 Reliability & rate limiting 15 views 0 copies

chaos-engineering random resilience testing

Python code

29 lines

Python 3.9+

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)  # reproducible output

    for attempt in range(10):
        try:
            result = unreliable_function(success_rate=0.7)
            print(f"Attempt {attempt + 1}: {result}")
        except ConnectionError as e:
            print(f"Attempt {attempt + 1}: FAILED -> {e}")

    # Optionally count failures after many runs
    failures = 0
    for _ in range(1000):
        try:
            unreliable_function(success_rate=0.7)
        except ConnectionError:
            failures += 1

    print(f"\nFailure rate over 1000 runs: {failures / 1000:.2%}")

Output

stdout

Attempt 1: Operation completed successfully
Attempt 2: FAILED -> Simulated network failure
Attempt 3: Operation completed successfully
Attempt 4: FAILED -> Simulated network failure
Attempt 5: Operation completed successfully
Attempt 6: Operation completed successfully
Attempt 7: Operation completed successfully
Attempt 8: Operation completed successfully
Attempt 9: FAILED -> Simulated network failure
Attempt 10: Operation completed successfully

Failure rate over 1000 runs: 30.40%

How it works

The random.random() call returns a float between 0 and 1. When this value exceeds the success_rate, the function raises a ConnectionError, simulating a failure. By seeding the random number generator with random.seed(42), the sequence of failures becomes reproducible, which is useful for debugging. The loop catches the error and continues, demonstrating how your application can recover gracefully. This pattern is foundational for chaos engineering, where you deliberately inject failures to verify system robustness.

Common mistakes

Using `random.randint` instead of `random.random` which skews the probability distribution.
Forgetting to seed the random generator when you need deterministic test runs.
Catching the wrong exception type and missing the simulated failures.

Variations

Use a decorator to wrap any function with failure injection, making it reusable.
Use a parameter to randomly delay responses in addition to failing, simulating latency.

Real-world use cases

Validating retry logic in microservices by deliberately failing downstream calls during load tests.
Testing resilience of background jobs to transient infrastructure failures like database timeouts.
Simulating flaky network conditions in CI to ensure the app handles partial outages correctly.

Chaos Inject Random Failures in Python

Python code

Output

How it works

Common mistakes

Variations

Real-world use cases

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Python code

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Common mistakes

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Real-world use cases

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