Metadata-Version: 1.2
Name: backoff
Version: 2.2.1
Summary: Function decoration for backoff and retry
Home-page: https://github.com/litl/backoff
Author: Bob Green
Author-email: rgreen@aquent.com
Maintainer: None
Maintainer-email: None
License: UNKNOWN
Description: backoff
        =======
        
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        **Function decoration for backoff and retry**
        
        This module provides function decorators which can be used to wrap a
        function such that it will be retried until some condition is met. It
        is meant to be of use when accessing unreliable resources with the
        potential for intermittent failures i.e. network resources and external
        APIs. Somewhat more generally, it may also be of use for dynamically
        polling resources for externally generated content.
        
        Decorators support both regular functions for synchronous code and
        `asyncio <https://docs.python.org/3/library/asyncio.html>`__'s coroutines
        for asynchronous code.
        
        Examples
        ========
        
        Since Kenneth Reitz's `requests <http://python-requests.org>`_ module
        has become a defacto standard for synchronous HTTP clients in Python,
        networking examples below are written using it, but it is in no way required
        by the backoff module.
        
        @backoff.on_exception
        ---------------------
        
        The ``on_exception`` decorator is used to retry when a specified exception
        is raised. Here's an example using exponential backoff when any
        ``requests`` exception is raised:
        
        .. code-block:: python
        
            @backoff.on_exception(backoff.expo,
                                  requests.exceptions.RequestException)
            def get_url(url):
                return requests.get(url)
        
        The decorator will also accept a tuple of exceptions for cases where
        the same backoff behavior is desired for more than one exception type:
        
        .. code-block:: python
        
            @backoff.on_exception(backoff.expo,
                                  (requests.exceptions.Timeout,
                                   requests.exceptions.ConnectionError))
            def get_url(url):
                return requests.get(url)
        
        **Give Up Conditions**
        
        Optional keyword arguments can specify conditions under which to give
        up.
        
        The keyword argument ``max_time`` specifies the maximum amount
        of total time in seconds that can elapse before giving up.
        
        .. code-block:: python
        
            @backoff.on_exception(backoff.expo,
                                  requests.exceptions.RequestException,
                                  max_time=60)
            def get_url(url):
                return requests.get(url)
        
        
        Keyword argument ``max_tries`` specifies the maximum number of calls
        to make to the target function before giving up.
        
        .. code-block:: python
        
            @backoff.on_exception(backoff.expo,
                                  requests.exceptions.RequestException,
                                  max_tries=8,
                                  jitter=None)
            def get_url(url):
                return requests.get(url)
        
        
        In some cases the raised exception instance itself may need to be
        inspected in order to determine if it is a retryable condition. The
        ``giveup`` keyword arg can be used to specify a function which accepts
        the exception and returns a truthy value if the exception should not
        be retried:
        
        .. code-block:: python
        
            def fatal_code(e):
                return 400 <= e.response.status_code < 500
        
            @backoff.on_exception(backoff.expo,
                                  requests.exceptions.RequestException,
                                  max_time=300,
                                  giveup=fatal_code)
            def get_url(url):
                return requests.get(url)
        
        By default, when a give up event occurs, the exception in question is reraised
        and so code calling an `on_exception`-decorated function may still
        need to do exception handling. This behavior can optionally be disabled
        using the `raise_on_giveup` keyword argument.
        
        In the code below, `requests.exceptions.RequestException` will not be raised
        when giveup occurs. Note that the decorated function will return `None` in this
        case, regardless of the logic in the `on_exception` handler.
        
        .. code-block:: python
        
            def fatal_code(e):
                return 400 <= e.response.status_code < 500
        
            @backoff.on_exception(backoff.expo,
                                  requests.exceptions.RequestException,
                                  max_time=300,
                                  raise_on_giveup=False,
                                  giveup=fatal_code)
            def get_url(url):
                return requests.get(url)
        
        This is useful for non-mission critical code where you still wish to retry
        the code inside of `backoff.on_exception` but wish to proceed with execution
        even if all retries fail.
        
        @backoff.on_predicate
        ---------------------
        
        The ``on_predicate`` decorator is used to retry when a particular
        condition is true of the return value of the target function.  This may
        be useful when polling a resource for externally generated content.
        
        Here's an example which uses a fibonacci sequence backoff when the
        return value of the target function is the empty list:
        
        .. code-block:: python
        
            @backoff.on_predicate(backoff.fibo, lambda x: x == [], max_value=13)
            def poll_for_messages(queue):
                return queue.get()
        
        Extra keyword arguments are passed when initializing the
        wait generator, so the ``max_value`` param above is passed as a keyword
        arg when initializing the fibo generator.
        
        When not specified, the predicate param defaults to the falsey test,
        so the above can more concisely be written:
        
        .. code-block:: python
        
            @backoff.on_predicate(backoff.fibo, max_value=13)
            def poll_for_message(queue):
                return queue.get()
        
        More simply, a function which continues polling every second until it
        gets a non-falsey result could be defined like like this:
        
        .. code-block:: python
        
            @backoff.on_predicate(backoff.constant, jitter=None, interval=1)
            def poll_for_message(queue):
                return queue.get()
        
        The jitter is disabled in order to keep the polling frequency fixed.  
        
        @backoff.runtime
        ----------------
        
        You can also use the ``backoff.runtime`` generator to make use of the
        return value or thrown exception of the decorated method.
        
        For example, to use the value in the ``Retry-After`` header of the response:
        
        .. code-block:: python
        
            @backoff.on_predicate(
                backoff.runtime,
                predicate=lambda r: r.status_code == 429,
                value=lambda r: int(r.headers.get("Retry-After")),
                jitter=None,
            )
            def get_url():
                return requests.get(url)
        
        Jitter
        ------
        
        A jitter algorithm can be supplied with the ``jitter`` keyword arg to
        either of the backoff decorators. This argument should be a function
        accepting the original unadulterated backoff value and returning it's
        jittered counterpart.
        
        As of version 1.2, the default jitter function ``backoff.full_jitter``
        implements the 'Full Jitter' algorithm as defined in the AWS
        Architecture Blog's `Exponential Backoff And Jitter
        <https://www.awsarchitectureblog.com/2015/03/backoff.html>`_ post.
        Note that with this algorithm, the time yielded by the wait generator
        is actually the *maximum* amount of time to wait.
        
        Previous versions of backoff defaulted to adding some random number of
        milliseconds (up to 1s) to the raw sleep value. If desired, this
        behavior is now available as ``backoff.random_jitter``.
        
        Using multiple decorators
        -------------------------
        
        The backoff decorators may also be combined to specify different
        backoff behavior for different cases:
        
        .. code-block:: python
        
            @backoff.on_predicate(backoff.fibo, max_value=13)
            @backoff.on_exception(backoff.expo,
                                  requests.exceptions.HTTPError,
                                  max_time=60)
            @backoff.on_exception(backoff.expo,
                                  requests.exceptions.Timeout,
                                  max_time=300)
            def poll_for_message(queue):
                return queue.get()
        
        
        Runtime Configuration
        ---------------------
        
        The decorator functions ``on_exception`` and ``on_predicate`` are
        generally evaluated at import time. This is fine when the keyword args
        are passed as constant values, but suppose we want to consult a
        dictionary with configuration options that only become available at
        runtime. The relevant values are not available at import time. Instead,
        decorator functions can be passed callables which are evaluated at
        runtime to obtain the value:
        
        .. code-block:: python
        
            def lookup_max_time():
                # pretend we have a global reference to 'app' here
                # and that it has a dictionary-like 'config' property
                return app.config["BACKOFF_MAX_TIME"]
        
            @backoff.on_exception(backoff.expo,
                                  ValueError,
                                  max_time=lookup_max_time)
        
        Event handlers
        --------------
        
        Both backoff decorators optionally accept event handler functions
        using the keyword arguments ``on_success``, ``on_backoff``, and ``on_giveup``.
        This may be useful in reporting statistics or performing other custom
        logging.
        
        Handlers must be callables with a unary signature accepting a dict
        argument. This dict contains the details of the invocation. Valid keys
        include:
        
        * *target*: reference to the function or method being invoked
        * *args*: positional arguments to func
        * *kwargs*: keyword arguments to func
        * *tries*: number of invocation tries so far
        * *elapsed*: elapsed time in seconds so far
        * *wait*: seconds to wait (``on_backoff`` handler only)
        * *value*: value triggering backoff (``on_predicate`` decorator only)
        
        A handler which prints the details of the backoff event could be
        implemented like so:
        
        .. code-block:: python
        
            def backoff_hdlr(details):
                print ("Backing off {wait:0.1f} seconds after {tries} tries "
                       "calling function {target} with args {args} and kwargs "
                       "{kwargs}".format(**details))
        
            @backoff.on_exception(backoff.expo,
                                  requests.exceptions.RequestException,
                                  on_backoff=backoff_hdlr)
            def get_url(url):
                return requests.get(url)
        
        **Multiple handlers per event type**
        
        In all cases, iterables of handler functions are also accepted, which
        are called in turn. For example, you might provide a simple list of
        handler functions as the value of the ``on_backoff`` keyword arg:
        
        .. code-block:: python
        
            @backoff.on_exception(backoff.expo,
                                  requests.exceptions.RequestException,
                                  on_backoff=[backoff_hdlr1, backoff_hdlr2])
            def get_url(url):
                return requests.get(url)
        
        **Getting exception info**
        
        In the case of the ``on_exception`` decorator, all ``on_backoff`` and
        ``on_giveup`` handlers are called from within the except block for the
        exception being handled. Therefore exception info is available to the
        handler functions via the python standard library, specifically
        ``sys.exc_info()`` or the ``traceback`` module. The exception is also
        available at the *exception* key in the `details` dict passed to the
        handlers.
        
        Asynchronous code
        -----------------
        
        Backoff supports asynchronous execution in Python 3.5 and above.
        
        To use backoff in asynchronous code based on
        `asyncio <https://docs.python.org/3/library/asyncio.html>`__
        you simply need to apply ``backoff.on_exception`` or ``backoff.on_predicate``
        to coroutines.
        You can also use coroutines for the ``on_success``, ``on_backoff``, and
        ``on_giveup`` event handlers, with the interface otherwise being identical.
        
        The following examples use `aiohttp <https://aiohttp.readthedocs.io/>`__
        asynchronous HTTP client/server library.
        
        .. code-block:: python
        
            @backoff.on_exception(backoff.expo, aiohttp.ClientError, max_time=60)
            async def get_url(url):
                async with aiohttp.ClientSession(raise_for_status=True) as session:
                    async with session.get(url) as response:
                        return await response.text()
        
        Logging configuration
        ---------------------
        
        By default, backoff and retry attempts are logged to the 'backoff'
        logger. By default, this logger is configured with a NullHandler, so
        there will be nothing output unless you configure a handler.
        Programmatically, this might be accomplished with something as simple
        as:
        
        .. code-block:: python
        
            logging.getLogger('backoff').addHandler(logging.StreamHandler())
        
        The default logging level is INFO, which corresponds to logging
        anytime a retry event occurs. If you would instead like to log
        only when a giveup event occurs, set the logger level to ERROR.
        
        .. code-block:: python
        
            logging.getLogger('backoff').setLevel(logging.ERROR)
        
        It is also possible to specify an alternate logger with the ``logger``
        keyword argument.  If a string value is specified the logger will be
        looked up by name.
        
        .. code-block:: python
        
           @backoff.on_exception(backoff.expo,
                                 requests.exceptions.RequestException,
        			 logger='my_logger')
           # ...
        
        It is also supported to specify a Logger (or LoggerAdapter) object
        directly.
        
        .. code-block:: python
        
            my_logger = logging.getLogger('my_logger')
            my_handler = logging.StreamHandler()
            my_logger.addHandler(my_handler)
            my_logger.setLevel(logging.ERROR)
        
            @backoff.on_exception(backoff.expo,
                                  requests.exceptions.RequestException,
        			  logger=my_logger)
            # ...
        
        Default logging can be disabled all together by specifying
        ``logger=None``. In this case, if desired alternative logging behavior
        could be defined by using custom event handlers.
        
Platform: UNKNOWN
Requires-Python: >=3.7,<4.0
