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Tenacity is an Apache 2.0 licensed general-purpose retrying library, written in Python, to simplify the task of adding retry behavior to just about anything.


pip install tenacity


Tenacity isn't api compatible with retrying but adds significant new functionality and fixes a number of longstanding bugs.

The simplest use case is retrying a flaky function whenever an Exception occurs until a value is returned.

import random
from tenacity import retry

def do_something_unreliable():
    if random.randint(0, 10) > 1:
        raise IOError("Broken sauce, everything is hosed!!!111one")
        return "Awesome sauce!"


Basic Retry

As you saw above, the default behavior is to retry forever without waiting when an exception is raised.

def never_gonna_give_you_up():
    print("Retry forever ignoring Exceptions, don't wait between retries")
    raise Exception


Let’s be a little less persistent and set some boundaries, such as the number of attempts before giving up.

def stop_after_7_attempts():
    print("Stopping after 7 attempts")
    raise Exception

We don’t have all day, so let’s set a boundary for how long we should be retrying stuff.

def stop_after_10_s():
    print("Stopping after 10 seconds")
    raise Exception

You can combine several stop conditions by using the | operator:

@retry(stop=(stop_after_delay(10) | stop_after_attempt(5)))
def stop_after_10_s_or_5_retries():
    print("Stopping after 10 seconds or 5 retries")
    raise Exception

Waiting before retrying

Most things don’t like to be polled as fast as possible, so let’s just wait 2 seconds between retries.

def wait_2_s():
    print("Wait 2 second between retries")
    raise Exception

Some things perform best with a bit of randomness injected.

@retry(wait=wait_random(min=1, max=2))
def wait_random_1_to_2_s():
    print("Randomly wait 1 to 2 seconds between retries")
    raise Exception

Then again, it’s hard to beat exponential backoff when retrying distributed services and other remote endpoints.

@retry(wait=wait_exponential(multiplier=1, min=4, max=10))
def wait_exponential_1():
    print("Wait 2^x * 1 second between each retry starting with 4 seconds, then up to 10 seconds, then 10 seconds afterwards")
    raise Exception

Then again, it’s also hard to beat combining fixed waits and jitter (to help avoid thundering herds) when retrying distributed services and other remote endpoints.

@retry(wait=wait_fixed(3) + wait_random(0, 2))
def wait_fixed_jitter():
    print("Wait at least 3 seconds, and add up to 2 seconds of random delay")
    raise Exception

When multiple processes are in contention for a shared resource, exponentially increasing jitter helps minimise collisions.

@retry(wait=wait_random_exponential(multiplier=1, max=60))
def wait_exponential_jitter():
    print("Randomly wait up to 2^x * 1 seconds between each retry until the range reaches 60 seconds, then randomly up to 60 seconds afterwards")
    raise Exception

Whether to retry

We have a few options for dealing with retries that raise specific or general exceptions, as in the cases here.

def might_io_error():
    print("Retry forever with no wait if an IOError occurs, raise any other errors")
    raise Exception

We can also use the result of the function to alter the behavior of retrying.

def is_none_p(value):
    """Return True if value is None"""
    return value is None

def might_return_none():
    print("Retry with no wait if return value is None")

We can also combine several conditions:

def is_none_p(value):
    """Return True if value is None"""
    return value is None

@retry(retry=(retry_if_result(is_none_p) | retry_if_exception_type()))
def might_return_none():
    print("Retry forever ignoring Exceptions with no wait if return value is None")

Any combination of stop, wait, etc. is also supported to give you the freedom to mix and match.

It’s also possible to retry explicitly at any time by raising the TryAgain exception:

def do_something():
    result = something_else()
    if result == 23:
       raise TryAgain


Last update: 2021-11-19