Tenacity

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. It originates from a fork of Retrying

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

import random
from tenacity import retry

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

print(do_something_unreliable())

Features

• Generic Decorator API
• Specify stop condition (i.e. limit by number of attempts)
• Specify wait condition (i.e. exponential backoff sleeping between attempts)
• Customize retrying on Exceptions
• Customize retrying on expected returned result
• Retry on coroutines

Installation

To install tenacity, simply:

$ pip install tenacity

Examples

As you saw above, the default behavior is to retry forever without waiting.

@retry
def never_give_up_never_surrender():
    print("Retry forever ignoring Exceptions, don't wait between retries")

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

@retry(stop=stop_after_attempt(7))
def stop_after_7_attempts():
    print("Stopping after 7 attempts")

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

@retry(stop=stop_after_delay(10))
def stop_after_10_s():
    print("Stopping after 10 seconds")

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

@retry(wait=wait_fixed(2))
def wait_2_s():
    print("Wait 2 second between retries")

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")

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

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

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")

Sometimes it's necessary to build a chain of backoffs.

@retry(wait=wait_chain(*[wait_fixed(3) for i in range(3)] +
                       [wait_fixed(7) for i in range(2)] +
                       [wait_fixed(9)]))
def wait_fixed_chained():
    print("Wait 3s for 3 attempts, 7s for the next 2 attempts and 9s for all attempts thereafter")

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

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

@retry(retry=retry_if_exception(retry_if_io_error))
def only_raise_retry_error_when_not_io_error():
    print("Retry forever with no wait if an IOError occurs, raise any other errors wrapped in RetryError")

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

@retry(retry=retry_if_result(is_none_p))
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:

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

While callables that "timeout" retrying raise a RetryError by default, we can reraise the last attempt's exception if needed:

@retry(reraise=True, stop=stop_after_attempt(3))
def raise_my_exception():
    raise MyException("Fail")

try:
    raise_my_exception()
except MyException:
    # timed out retrying

It's possible to execute an action before any attempt of calling the function by using the before callback function:

logger = logging.getLogger(__name__)

@retry(stop=stop_after_attempt(3), before=before_log(logger, logging.DEBUG))
def raise_my_exception():
    raise MyException("Fail")

In the same spirit, It's possible to execute after a call that failed:

logger = logging.getLogger(__name__)

@retry(stop=stop_after_attempt(3), after=after_log(logger, logging.DEBUG))
def raise_my_exception():
    raise MyException("Fail")

You can access the statistics about the retry made over a function by using the retry attribute attached to the function and its statistics attribute:

@retry(stop=stop_after_attempt(3)
def raise_my_exception():
    raise MyException("Fail")

try:
    raise_my_exception()
except Exception:
    pass

print(raise_my_exception.retry.statistics)

Finally, retry works also on asyncio coroutines. Sleeps are done asynchronously too.

@retry
async def my_async_function(loop):
    await loop.getaddrinfo('8.8.8.8', 53)

Contribute

1. Check for open issues or open a fresh issue to start a discussion around a feature idea or a bug.
2. Fork the repository on GitHub to start making your changes to the master branch (or branch off of it).
3. Write a test which shows that the bug was fixed or that the feature works as expected.
4. Make the docs better (or more detailed, or more easier to read, or ...)