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: / lib / python3.9 / site-packages / ansible / module_utils /
Filename : api.py
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# This code is part of Ansible, but is an independent component.
# This particular file snippet, and this file snippet only, is BSD licensed.
# Modules you write using this snippet, which is embedded dynamically by Ansible
# still belong to the author of the module, and may assign their own license
# to the complete work.
#
# Copyright: (c) 2015, Brian Coca, <bcoca@ansible.com>
#
# Simplified BSD License (see licenses/simplified_bsd.txt or https://opensource.org/licenses/BSD-2-Clause)
"""
This module adds shared support for generic api modules

In order to use this module, include it as part of a custom
module as shown below.

The 'api' module provides the following common argument specs:

    * rate limit spec
        - rate: number of requests per time unit (int)
        - rate_limit: time window in which the limit is applied in seconds

    * retry spec
        - retries: number of attempts
        - retry_pause: delay between attempts in seconds
"""
from __future__ import (absolute_import, division, print_function)
__metaclass__ = type

import copy
import functools
import itertools
import random
import sys
import time

import ansible.module_utils.compat.typing as t


def rate_limit_argument_spec(spec=None):
    """Creates an argument spec for working with rate limiting"""
    arg_spec = (dict(
        rate=dict(type='int'),
        rate_limit=dict(type='int'),
    ))
    if spec:
        arg_spec.update(spec)
    return arg_spec


def retry_argument_spec(spec=None):
    """Creates an argument spec for working with retrying"""
    arg_spec = (dict(
        retries=dict(type='int'),
        retry_pause=dict(type='float', default=1),
    ))
    if spec:
        arg_spec.update(spec)
    return arg_spec


def basic_auth_argument_spec(spec=None):
    arg_spec = (dict(
        api_username=dict(type='str'),
        api_password=dict(type='str', no_log=True),
        api_url=dict(type='str'),
        validate_certs=dict(type='bool', default=True)
    ))
    if spec:
        arg_spec.update(spec)
    return arg_spec


def rate_limit(rate=None, rate_limit=None):
    """rate limiting decorator"""
    minrate = None
    if rate is not None and rate_limit is not None:
        minrate = float(rate_limit) / float(rate)

    def wrapper(f):
        last = [0.0]

        def ratelimited(*args, **kwargs):
            if sys.version_info >= (3, 8):
                real_time = time.process_time
            else:
                real_time = time.clock
            if minrate is not None:
                elapsed = real_time() - last[0]
                left = minrate - elapsed
                if left > 0:
                    time.sleep(left)
                last[0] = real_time()
            ret = f(*args, **kwargs)
            return ret

        return ratelimited
    return wrapper


def retry(retries=None, retry_pause=1):
    """Retry decorator"""
    def wrapper(f):

        def retried(*args, **kwargs):
            retry_count = 0
            if retries is not None:
                ret = None
                while True:
                    retry_count += 1
                    if retry_count >= retries:
                        raise Exception("Retry limit exceeded: %d" % retries)
                    try:
                        ret = f(*args, **kwargs)
                    except Exception:
                        pass
                    if ret:
                        break
                    time.sleep(retry_pause)
                return ret

        return retried
    return wrapper


def generate_jittered_backoff(retries=10, delay_base=3, delay_threshold=60):
    """The "Full Jitter" backoff strategy.

    Ref: https://www.awsarchitectureblog.com/2015/03/backoff.html

    :param retries: The number of delays to generate.
    :param delay_base: The base time in seconds used to calculate the exponential backoff.
    :param delay_threshold: The maximum time in seconds for any delay.
    """
    for retry in range(0, retries):
        yield random.randint(0, min(delay_threshold, delay_base * 2 ** retry))


def retry_never(exception_or_result):
    return False


def retry_with_delays_and_condition(backoff_iterator, should_retry_error=None):
    """Generic retry decorator.

    :param backoff_iterator: An iterable of delays in seconds.
    :param should_retry_error: A callable that takes an exception of the decorated function and decides whether to retry or not (returns a bool).
    """
    def _emit_isolated_iterator_copies(original_iterator):  # type: (t.Iterable[t.Any]) -> t.Generator
        # Ref: https://stackoverflow.com/a/30232619/595220
        _copiable_iterator, _first_iterator_copy = itertools.tee(original_iterator)
        yield _first_iterator_copy
        while True:
            yield copy.copy(_copiable_iterator)
    backoff_iterator_generator = _emit_isolated_iterator_copies(backoff_iterator)
    del backoff_iterator  # prevent accidental use elsewhere

    if should_retry_error is None:
        should_retry_error = retry_never

    def function_wrapper(function):
        @functools.wraps(function)
        def run_function(*args, **kwargs):
            """This assumes the function has not already been called.
            If backoff_iterator is empty, we should still run the function a single time with no delay.
            """
            call_retryable_function = functools.partial(function, *args, **kwargs)

            for delay in next(backoff_iterator_generator):
                try:
                    return call_retryable_function()
                except Exception as e:
                    if not should_retry_error(e):
                        raise
                time.sleep(delay)

            # Only or final attempt
            return call_retryable_function()
        return run_function
    return function_wrapper