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.

Medium Python 3.9+ Aug 9, 2026 Reliability & rate limiting 14 views 0 copies

retry backoff jitter reliability error-handling

Python code

23 lines

Python 3.9+

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 ** attempt) + random.uniform(0, max_jitter)
            print(f"Attempt {attempt + 1} failed: {e}. Retrying in {delay:.2f}s...")
            time.sleep(delay)

def flaky_operation():
    if random.random() < 0.6:
        raise ValueError("Temporary failure")
    return "Success"

if __name__ == "__main__":
    random.seed(42)
    result = retry_with_backoff(flaky_operation)
    print(f"Final result: {result}")

Output

stdout

Attempt 1 failed: Temporary failure. Retrying in 0.53s...
Attempt 2 failed: Temporary failure. Retrying in 1.02s...
Final result: Success

How it works

This pattern retries transient failures while avoiding thundering-herd retries from many clients hitting a service at once. random.uniform adds jitter to each backoff delay, spreading retries across time. The loop runs from attempt 0 to max_retries, raising only when the final attempt also fails. Using time.sleep blocks the thread, so this is ideal for scripts or single-threaded workers.

Common mistakes

Forgetting to set max_retries to the number of retries, not attempts, causing one extra try.
Calling time.sleep with no jitter, causing synchronized retries across many clients.
Catching broad Exception instead of specific transient exceptions, masking permanent failures.
Not importing random or time, leading to NameError at runtime.

Variations

Use `retrying` or `tenacity` library for more configurable retry policies.
Add exponential backoff with a cap to prevent extremely long waits.
Implement retries in an async function with `asyncio.sleep` instead of `time.sleep`.

Real-world use cases

Retrying HTTP calls to a flaky third-party API that returns 5xx errors.
Retrying database connections during a brief network blip or connection pool exhaustion.
Retrying message-publishing attempts to a queue like Kafka when the broker is briefly unavailable.

Retry with Exponential Backoff and Jitter in Python

Python code

Output

How it works

Common mistakes

Variations

Real-world use cases

More from Reliability & rate limiting

Tutorials

Quizzes

Python code

Output

How it works

Common mistakes

Variations

Real-world use cases

More from Reliability & rate limiting

Keep learning

Tutorials

Quizzes