A couple of years ago, I wrote about the Python retrying library. This library was designed to retry the execution of a task when a failure occurred.
I started to spread usage of this library in various projects, such as Gnocchi, these last years. Unfortunately, it started to get very hard to contribute and send patches to the upstream retrying project. I spent several months trying to work with the original author. But after a while, I had to come to the conclusion that I would be unable to fix bugs and enhance it at the pace I would like to. Therefore, I had to take a difficult decision and decided to fork the library.
Here comes tenacity
I picked a new name and rewrote parts of the API of retrying that were not working correctly or were too complicated. I also fixed bugs with the help of Joshua, and named this new library tenacity. It works in the same manner as retrying does, except that it is written in a more functional way and offers some nifty new features.
The basic usage is to use it as a decorator:
This will make the function
do_something_and_retry_on_any_exception be called
over and over again until it stops raising an exception. It would have been
hard to design anything simpler. Obviously, this is a pretty rare case, as one
usually wants to e.g. wait some time between retries. For that, tenacity
offers a large panel of waiting methods:
Or a simple exponential back-off method can be used instead:
What is especially interesting with tenacity, is that you can easily combine
several methods. For example, you can combine
tenacity.wait.wait_fixed to wait a number of seconds defined in an interval:
@tenacity.retry(wait=tenacity.wait_fixed(10) + wait.wait_random(0, 3))
This will make the function being retried wait randomly between 10 and 13 seconds before trying again.
tenacity offers more customization, such as retrying on some exceptions only.
You can retry every second to execute the function only if the exception raised
do_something is an instance of
IOError, e.g. a network communication
You can combine several condition easily by using the
operators. They are used to make the code retry if an
IOError exception is
raised, or if no result is returned. Also, a stop condition is added with the
stop keyword arguments. It allows to specify a condition unrelated to the
function result of exception to stop, such as a number of attemps or a delay.
tenacity.retry_if_result(lambda result: result == None))
The functional approach of tenacity makes it easy and clean to combine a lot of condition for various use cases with simple binary operators.
tenacity can also be used without decorator by using the object
that implements its main behaviour, and usig its
call method. This allows to
call any function with different retry conditions, or to retry any piece of
code that do not use the decorator at all – like code from an external library.
r = tenacity.Retrying(
This also allows you to re-use that object without creating one new each time, saving some memory!
I hope you'll like it and will find it some use. Feel free to fork it, report bug or ask for new features on its GitHub!