Metadata-Version: 1.1
Name: backoff
Version: 1.1.0
Summary: Function decoration for backoff and retry
Home-page: https://github.com/litl/backoff
Author: Bob Green
Author-email: bgreen@litl.com
License: MIT
Download-URL: https://github.com/litl/backoff/tarball/v1.1.0
Description: 
        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.
        
        ## Examples
        
        *Since Kenneth Reitz's [requests](http://python-requests.org) module
        has become a defacto standard for 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:
        
            @backoff.on_exception(backoff.expo,
                                  requests.exceptions.RequestException,
                                  max_tries=8)
            def get_url(url):
                return requests.get(url)
        
        The decorator will also accept a tuple of exceptions for cases where
        you want the same backoff behavior for more than one exception type:
        
            @backoff.on_exception(backoff.expo,
                                  (requests.exceptions.Timeout,
                                   requests.exceptions.ConnectionError),
                                  max_tries=8)
            def get_url(url):
                return requests.get(url)
        
        ### @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:
        
            @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:
        
            @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:
        
            @backoff.on_predicate(backoff.constant, interval=1)
            def poll_for_message(queue)
                return queue.get()
        
        ### Using multiple decorators
        
        The backoff decorators may also be combined to specify different
        backoff behavior for different cases:
        
            @backoff.on_predicate(backoff.fibo, max_value=13)
            @backoff.on_exception(backoff.expo,
                                  requests.exceptions.HTTPError,
                                  max_tries=4)
            @backoff.on_exception(backoff.expo,
                                  requests.exceptions.TimeoutError,
                                  max_tries=8)
            def poll_for_message(queue):
                return queue.get()
        
        ### 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
          * '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:
        
            def backoff_hdlr(details):
                print ("Backing off {wait:0.1f} seconds afters {tries} tries "
                       "calling function {func} 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.
        
        #### 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.
        
        ### Logging configuration
        
        Errors and 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:
        
            logging.getLogger('backoff').addHandler(logging.StreamHandler())
        
        The default logging level is ERROR, which corresponds to logging anytime
        `max_tries` is exceeded as well as any time a retryable exception is
        raised. If you would instead like to log any type of retry, you can
        set the logger level to INFO:
        
            logging.getLogger('backoff').setLevel(logging.INFO)
        
Keywords: backoff function decorator
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Operating System :: MacOS
Classifier: Operating System :: POSIX
Classifier: Operating System :: POSIX :: Linux
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2.6
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: Implementation
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Topic :: Internet :: WWW/HTTP
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Utilities
