/usr/lib/python3/dist-packages/tenacity/wait.py is in python3-tenacity 4.8.0-0ubuntu1.
This file is owned by root:root, with mode 0o644.
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 | # Copyright 2016 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
import six
from tenacity import _utils
@six.add_metaclass(abc.ABCMeta)
class wait_base(object):
"""Abstract base class for wait strategies."""
@abc.abstractmethod
def __call__(self, previous_attempt_number, delay_since_first_attempt,
last_result=None):
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, previous_attempt_number, delay_since_first_attempt,
last_result=None):
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, previous_attempt_number, delay_since_first_attempt,
last_result=None):
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, previous_attempt_number, delay_since_first_attempt,
last_result=None):
return sum(map(
lambda x: x(previous_attempt_number, delay_since_first_attempt),
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 = list(strategies)
def __call__(self, previous_attempt_number, delay_since_first_attempt,
last_result=None):
wait_func = self.strategies[0]
if len(self.strategies) > 1:
self.strategies.pop(0)
return wait_func(previous_attempt_number, delay_since_first_attempt)
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, previous_attempt_number, delay_since_first_attempt,
last_result=None):
result = self.start + (
self.increment * (previous_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 limit to some maximum 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): # noqa
self.multiplier = multiplier
self.max = max
self.exp_base = exp_base
def __call__(self, previous_attempt_number, delay_since_first_attempt,
last_result=None):
try:
exp = self.exp_base ** previous_attempt_number
result = self.multiplier * exp
except OverflowError:
return self.max
return max(0, 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
unco-ordinated 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://www.awsarchitectureblog.com/2015/03/backoff.html
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, previous_attempt_number, delay_since_first_attempt,
last_result=None):
high = super(wait_random_exponential, self).__call__(
previous_attempt_number, delay_since_first_attempt)
return random.uniform(0, high)
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