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.
Since Kenneth Reitz's requests 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.
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)
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:
def fatal_code(e):
return 400 <= e.response.status_code < 500
@backoff.on_exception(backoff.expo,
requests.exceptions.RequestException,
max_tries=8,
giveup=fatal_code)
def get_url(url):
return requests.get(url)
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()
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 post.
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
.
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()
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:
def lookup_max_tries():
# pretend we have a global reference to 'app' here
# and that it has a dictionary-like 'config' property
return app.config["BACKOFF_MAX_TRIES"]
@backoff.on_exception(backoff.expo,
ValueError,
max_tries=lookup_max_tries)
More cleverly, you might define a function which returns a lookup function for a specified variable:
def config(app, name):
return functools.partial(app.config.get, name)
@backoff.on_exception(backoff.expo,
ValueError,
max_value=config(app, "BACKOFF_MAX_VALUE")
max_tries=config(app, "BACKOFF_MAX_TRIES"))
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. For example, you might provide a simple list of handler functions as the value of the on_backoff
keyword arg:
@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.
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)