# 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
import typing
from tenacity import _utils
if typing.TYPE_CHECKING:
from tenacity import RetryCallState
class wait_base(abc.ABC):
"""Abstract base class for wait strategies."""
@abc.abstractmethod
def __call__(self, retry_state: "RetryCallState") -> float:
pass
def __add__(self, other: "wait_base") -> "wait_combine":
return wait_combine(self, other)
def __radd__(self, other: "wait_base") -> typing.Union["wait_combine", "wait_base"]:
# make it possible to use multiple waits with the built-in sum function
if other == 0: # type: ignore[comparison-overlap]
return self
return self.__add__(other)
WaitBaseT = typing.Union[wait_base, typing.Callable[["RetryCallState"], typing.Union[float, int]]]
class wait_fixed(wait_base):
"""Wait strategy that waits a fixed amount of time between each retry."""
def __init__(self, wait: _utils.time_unit_type) -> None:
self.wait_fixed = _utils.to_seconds(wait)
def __call__(self, retry_state: "RetryCallState") -> float:
return self.wait_fixed
class wait_none(wait_fixed):
"""Wait strategy that doesn't wait at all before retrying."""
def __init__(self) -> None:
super().__init__(0)
class wait_random(wait_base):
"""Wait strategy that waits a random amount of time between min/max."""
def __init__(self, min: _utils.time_unit_type = 0, max: _utils.time_unit_type = 1) -> None: # noqa
self.wait_random_min = _utils.to_seconds(min)
self.wait_random_max = _utils.to_seconds(max)
def __call__(self, retry_state: "RetryCallState") -> float:
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: wait_base) -> None:
self.wait_funcs = strategies
def __call__(self, retry_state: "RetryCallState") -> float:
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: wait_base) -> None:
self.strategies = strategies
def __call__(self, retry_state: "RetryCallState") -> float:
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: _utils.time_unit_type = 0,
increment: _utils.time_unit_type = 100,
max: _utils.time_unit_type = _utils.MAX_WAIT, # noqa
) -> None:
self.start = _utils.to_seconds(start)
self.increment = _utils.to_seconds(increment)
self.max = _utils.to_seconds(max)
def __call__(self, retry_state: "RetryCallState") -> float:
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: typing.Union[int, float] = 1,
max: _utils.time_unit_type = _utils.MAX_WAIT, # noqa
exp_base: typing.Union[int, float] = 2,
min: _utils.time_unit_type = 0, # noqa
) -> None:
self.multiplier = multiplier
self.min = _utils.to_seconds(min)
self.max = _utils.to_seconds(max)
self.exp_base = exp_base
def __call__(self, retry_state: "RetryCallState") -> float:
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: "RetryCallState") -> float:
high = super().__call__(retry_state=retry_state)
return random.uniform(0, high)
class wait_exponential_jitter(wait_base):
"""Wait strategy that applies exponential backoff and jitter.
It allows for a customized initial wait, maximum wait and jitter.
This implements the strategy described here:
https://cloud.google.com/storage/docs/retry-strategy
The wait time is min(initial * 2**n + random.uniform(0, jitter), maximum)
where n is the retry count.
"""
def __init__(
self,
initial: float = 1,
max: float = _utils.MAX_WAIT, # noqa
exp_base: float = 2,
jitter: float = 1,
) -> None:
self.initial = initial
self.max = max
self.exp_base = exp_base
self.jitter = jitter
def __call__(self, retry_state: "RetryCallState") -> float:
jitter = random.uniform(0, self.jitter)
try:
exp = self.exp_base ** (retry_state.attempt_number - 1)
result = self.initial * exp + jitter
except OverflowError:
result = self.max
return max(0, min(result, self.max))
