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easy to use retry decorator in python
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README.rst

retry

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Easy to use retry decorator.

Features

  • No external dependency (stdlib only).
  • (Optionally) Preserve function signatures (pip install decorator).
  • Original traceback, easy to debug.

Installation

$ pip install retry

API

retry decorator

def retry(exceptions=Exception, tries=-1, delay=0, max_delay=None, backoff=1, jitter=0, logger=logging_logger):
    """Return a retry decorator.

    :param exceptions: an exception or a tuple of exceptions to catch. default: Exception.
    :param tries: the maximum number of attempts. default: -1 (infinite).
    :param delay: initial delay between attempts. default: 0.
    :param max_delay: the maximum value of delay. default: None (no limit).
    :param backoff: multiplier applied to delay between attempts. default: 1 (no backoff).
    :param jitter: extra seconds added to delay between attempts. default: 0.
                   fixed if a number, random if a range tuple (min, max)
    :param logger: logger.warning(fmt, error, delay) will be called on failed attempts.
                   default: retry.logging_logger. if None, logging is disabled.
    """

Various retrying logic can be achieved by combination of arguments.

Examples

from retry import retry
@retry()
def make_trouble():
    '''Retry until succeed'''
@retry(ZeroDivisionError, tries=3, delay=2)
def make_trouble():
    '''Retry on ZeroDivisionError, raise error after 3 attempts, sleep 2 seconds between attempts.'''
@retry((ValueError, TypeError), delay=1, backoff=2)
def make_trouble():
    '''Retry on ValueError or TypeError, sleep 1, 2, 4, 8, ... seconds between attempts.'''
@retry((ValueError, TypeError), delay=1, backoff=2, max_delay=4)
def make_trouble():
    '''Retry on ValueError or TypeError, sleep 1, 2, 4, 4, ... seconds between attempts.'''
@retry(ValueError, delay=1, jitter=1)
def make_trouble():
    '''Retry on ValueError, sleep 1, 2, 3, 4, ... seconds between attempts.'''
# If you enable logging, you can get warnings like 'ValueError, retrying in
# 1 seconds'
if __name__ == '__main__':
    import logging
    logging.basicConfig()
    make_trouble()

retry_call

def retry_call(f, fargs=None, fkwargs=None, exceptions=Exception, tries=-1, delay=0, max_delay=None, backoff=1,
               jitter=0,
               logger=logging_logger):
    """
    Calls a function and re-executes it if it failed.

    :param f: the function to execute.
    :param fargs: the positional arguments of the function to execute.
    :param fkwargs: the named arguments of the function to execute.
    :param exceptions: an exception or a tuple of exceptions to catch. default: Exception.
    :param tries: the maximum number of attempts. default: -1 (infinite).
    :param delay: initial delay between attempts. default: 0.
    :param max_delay: the maximum value of delay. default: None (no limit).
    :param backoff: multiplier applied to delay between attempts. default: 1 (no backoff).
    :param jitter: extra seconds added to delay between attempts. default: 0.
                   fixed if a number, random if a range tuple (min, max)
    :param logger: logger.warning(fmt, error, delay) will be called on failed attempts.
                   default: retry.logging_logger. if None, logging is disabled.
    :returns: the result of the f function.
    """

This is very similar to the decorator, except that it takes a function and its arguments as parameters. The use case behind it is to be able to dynamically adjust the retry arguments.

import requests

from retry.api import retry_call


def make_trouble(service, info=None):
    if not info:
        info = ''
    r = requests.get(service + info)
    return r.text


def what_is_my_ip(approach=None):
    if approach == "optimistic":
        tries = 1
    elif approach == "conservative":
        tries = 3
    else:
        # skeptical
        tries = -1
    result = retry_call(make_trouble, fargs=["http://ipinfo.io/"], fkwargs={"info": "ip"}, tries=tries)
    print(result)

what_is_my_ip("conservative")
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