184 lines
5.9 KiB
Python
184 lines
5.9 KiB
Python
# -*- encoding: utf-8 -*-
|
||
#
|
||
# Copyright 2016–2021 Julien Danjou
|
||
# Copyright 2016 Joshua Harlow
|
||
# Copyright 2013-2014 Ray Holder
|
||
#
|
||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||
# you may not use this file except in compliance with the License.
|
||
# You may obtain a copy of the License at
|
||
#
|
||
# http://www.apache.org/licenses/LICENSE-2.0
|
||
#
|
||
# Unless required by applicable law or agreed to in writing, software
|
||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||
# See the License for the specific language governing permissions and
|
||
# limitations under the License.
|
||
|
||
import abc
|
||
import random
|
||
|
||
from pip._vendor import six
|
||
|
||
from pip._vendor.tenacity import _utils
|
||
|
||
|
||
@six.add_metaclass(abc.ABCMeta)
|
||
class wait_base(object):
|
||
"""Abstract base class for wait strategies."""
|
||
|
||
@abc.abstractmethod
|
||
def __call__(self, retry_state):
|
||
pass
|
||
|
||
def __add__(self, other):
|
||
return wait_combine(self, other)
|
||
|
||
def __radd__(self, other):
|
||
# make it possible to use multiple waits with the built-in sum function
|
||
if other == 0:
|
||
return self
|
||
return self.__add__(other)
|
||
|
||
|
||
class wait_fixed(wait_base):
|
||
"""Wait strategy that waits a fixed amount of time between each retry."""
|
||
|
||
def __init__(self, wait):
|
||
self.wait_fixed = wait
|
||
|
||
def __call__(self, retry_state):
|
||
return self.wait_fixed
|
||
|
||
|
||
class wait_none(wait_fixed):
|
||
"""Wait strategy that doesn't wait at all before retrying."""
|
||
|
||
def __init__(self):
|
||
super(wait_none, self).__init__(0)
|
||
|
||
|
||
class wait_random(wait_base):
|
||
"""Wait strategy that waits a random amount of time between min/max."""
|
||
|
||
def __init__(self, min=0, max=1): # noqa
|
||
self.wait_random_min = min
|
||
self.wait_random_max = max
|
||
|
||
def __call__(self, retry_state):
|
||
return self.wait_random_min + (
|
||
random.random() * (self.wait_random_max - self.wait_random_min)
|
||
)
|
||
|
||
|
||
class wait_combine(wait_base):
|
||
"""Combine several waiting strategies."""
|
||
|
||
def __init__(self, *strategies):
|
||
self.wait_funcs = strategies
|
||
|
||
def __call__(self, retry_state):
|
||
return sum(x(retry_state=retry_state) for x in self.wait_funcs)
|
||
|
||
|
||
class wait_chain(wait_base):
|
||
"""Chain two or more waiting strategies.
|
||
|
||
If all strategies are exhausted, the very last strategy is used
|
||
thereafter.
|
||
|
||
For example::
|
||
|
||
@retry(wait=wait_chain(*[wait_fixed(1) for i in range(3)] +
|
||
[wait_fixed(2) for j in range(5)] +
|
||
[wait_fixed(5) for k in range(4)))
|
||
def wait_chained():
|
||
print("Wait 1s for 3 attempts, 2s for 5 attempts and 5s
|
||
thereafter.")
|
||
"""
|
||
|
||
def __init__(self, *strategies):
|
||
self.strategies = strategies
|
||
|
||
def __call__(self, retry_state):
|
||
wait_func_no = min(max(retry_state.attempt_number, 1), len(self.strategies))
|
||
wait_func = self.strategies[wait_func_no - 1]
|
||
return wait_func(retry_state=retry_state)
|
||
|
||
|
||
class wait_incrementing(wait_base):
|
||
"""Wait an incremental amount of time after each attempt.
|
||
|
||
Starting at a starting value and incrementing by a value for each attempt
|
||
(and restricting the upper limit to some maximum value).
|
||
"""
|
||
|
||
def __init__(self, start=0, increment=100, max=_utils.MAX_WAIT): # noqa
|
||
self.start = start
|
||
self.increment = increment
|
||
self.max = max
|
||
|
||
def __call__(self, retry_state):
|
||
result = self.start + (self.increment * (retry_state.attempt_number - 1))
|
||
return max(0, min(result, self.max))
|
||
|
||
|
||
class wait_exponential(wait_base):
|
||
"""Wait strategy that applies exponential backoff.
|
||
|
||
It allows for a customized multiplier and an ability to restrict the
|
||
upper and lower limits to some maximum and minimum value.
|
||
|
||
The intervals are fixed (i.e. there is no jitter), so this strategy is
|
||
suitable for balancing retries against latency when a required resource is
|
||
unavailable for an unknown duration, but *not* suitable for resolving
|
||
contention between multiple processes for a shared resource. Use
|
||
wait_random_exponential for the latter case.
|
||
"""
|
||
|
||
def __init__(self, multiplier=1, max=_utils.MAX_WAIT, exp_base=2, min=0): # noqa
|
||
self.multiplier = multiplier
|
||
self.min = min
|
||
self.max = max
|
||
self.exp_base = exp_base
|
||
|
||
def __call__(self, retry_state):
|
||
try:
|
||
exp = self.exp_base ** (retry_state.attempt_number - 1)
|
||
result = self.multiplier * exp
|
||
except OverflowError:
|
||
return self.max
|
||
return max(max(0, self.min), min(result, self.max))
|
||
|
||
|
||
class wait_random_exponential(wait_exponential):
|
||
"""Random wait with exponentially widening window.
|
||
|
||
An exponential backoff strategy used to mediate contention between multiple
|
||
uncoordinated processes for a shared resource in distributed systems. This
|
||
is the sense in which "exponential backoff" is meant in e.g. Ethernet
|
||
networking, and corresponds to the "Full Jitter" algorithm described in
|
||
this blog post:
|
||
|
||
https://aws.amazon.com/blogs/architecture/exponential-backoff-and-jitter/
|
||
|
||
Each retry occurs at a random time in a geometrically expanding interval.
|
||
It allows for a custom multiplier and an ability to restrict the upper
|
||
limit of the random interval to some maximum value.
|
||
|
||
Example::
|
||
|
||
wait_random_exponential(multiplier=0.5, # initial window 0.5s
|
||
max=60) # max 60s timeout
|
||
|
||
When waiting for an unavailable resource to become available again, as
|
||
opposed to trying to resolve contention for a shared resource, the
|
||
wait_exponential strategy (which uses a fixed interval) may be preferable.
|
||
|
||
"""
|
||
|
||
def __call__(self, retry_state):
|
||
high = super(wait_random_exponential, self).__call__(retry_state=retry_state)
|
||
return random.uniform(0, high)
|