Python Library for Backoff/Retry Strategies

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Backoff-Utils is a Python library that provides Python functions and decorators that apply various backoff / retry strategies to your Python function and method calls.

The library has a consistent syntax for easy use, and has been tested on Python 2.7, 3.4, 3.5, 3.6, 3.7, and 3.8.


To install Backoff-Utils, just execute:

$ pip install backoff-utils


Once installed, to import Backoff-Utils into your project you can use:

#: Import the backoff() function.
from backoff_utils import backoff

#: Import the @apply_backoff() decorator.
from backoff_utils import apply_backoff

#: Import backoff strategies.
from backoff_utils import strategies


By design, Backoff-Utils are designed to rely on minimal dependencies. The only dependency they have outside of the Python standard library is:

  • validator-collection which provides for robust validation functionality.

    This library in turn has two external dependencies: * jsonschema, and * (when installed under Python 2.7) regex

    which is a drop-in replacement for Python 2.7’s (buggy) standard re module.

Hello, World Example

from backoff_utils import strategies

# Using a Function Call
from backoff_utils import backoff

def some_function(arg1, arg2, kwarg1 = None):
    # your code goes here

result = backoff(some_function,
                 args = ['value1', 'value2'],
                 kwargs = { 'kwarg1': 'value3' },
                 max_tries = 3,
                 max_delay = 3600,
                 strategy = strategies.Exponential)

# Using a Decorator
from backoff_utils import backoff

@apply_backoff(strategy = strategies.Exponential, max_tries = 3, max_delay = 3600)
def some_decorated_function(arg1, arg2, kwarg1 = None):
    # your code goes here

result = some_decorated_function('value1', 'value2', kwarg1 = 'value3')

Why Backoff-Utils?

Because now and again, stuff breaks.

Often, when making external API calls to third-party systems, something goes wrong. The internet might glitch. The API we’re calling might timeout. Gremlins might eat our packets. Any number of things can go wrong, and Murphy’s law tells us that they will.

Which is why we need backoff strategies. Basically, these are techniques that we can use to retry function calls after a given delay - and keep retrying them until either the function call works, or until we’ve tried so many times that we just give up and handle the error.

This library is meant to be an incredibly simple utility that provides a number of easy-to-use backoff strategies. Its core API is to expose:

  • the backoff() function, which lets you apply a given backoff strategy to any Python function call, and;
  • the @apply_backoff() decorator, which lets you decorate any function or method call so that a given backoff strategy is always applied when the decorated function/method is called.

See also

For more information about how to use the library, please see Using the Library

Library Features

Supported Strategies

The library supports five of the most-common backoff strategies that we’ve come across:

  • Exponential
  • Fibonacci
  • Fixed
  • Linear
  • Polynomial

In addtion, you can also create your own custom strategies as well.

See also

For more information about the backoff strategies supported, please see: Strategies Explained

Feedback, Support, and Contributing

We’re happy to maintain this library going forward, and would always love to hear users’ feedback - especially if you’re running into issues.

Please report issues or questions on the project’s Github page

We also welcome community contributions - for more information, please see the Contributor Guide .