[FIXED] How to add a timeout to a function in Python

Issue

Many attempts have been made in the past to add timeout functionality in Python such that when a specified time limit expired, waiting code could move on. Unfortunately, previous recipes either allowed the running function to continue running and consuming resources or else killed the function using a platform-specific method of thread termination. The purpose of this wiki is to develop a cross-platform answer to this problem that many programmers have had to tackle for various programming projects.

#! /usr/bin/env python
"""Provide way to add timeout specifications to arbitrary functions.

There are many ways to add a timeout to a function, but no solution
is both cross-platform and capable of terminating the procedure. This
module use the multiprocessing module to solve both of those problems."""

################################################################################

__author__ = 'Stephen "Zero" Chappell <Noctis.Skytower@gmail.com>'
__date__ = '11 February 2010'
__version__ = '$Revision: 3 $'

################################################################################

import inspect
import sys
import time
import multiprocessing

################################################################################

def add_timeout(function, limit=60):
    """Add a timeout parameter to a function and return it.

    It is illegal to pass anything other than a function as the first
    parameter. If the limit is not given, it gets a default value equal
    to one minute. The function is wrapped and returned to the caller."""
    assert inspect.isfunction(function)
    if limit <= 0:
        raise ValueError()
    return _Timeout(function, limit)

class NotReadyError(Exception): pass

################################################################################

def _target(queue, function, *args, **kwargs):
    """Run a function with arguments and return output via a queue.

    This is a helper function for the Process created in _Timeout. It runs
    the function with positional arguments and keyword arguments and then
    returns the function's output by way of a queue. If an exception gets
    raised, it is returned to _Timeout to be raised by the value property."""
    try:
        queue.put((True, function(*args, **kwargs)))
    except:
        queue.put((False, sys.exc_info()[1]))

class _Timeout:

    """Wrap a function and add a timeout (limit) attribute to it.

    Instances of this class are automatically generated by the add_timeout
    function defined above. Wrapping a function allows asynchronous calls
    to be made and termination of execution after a timeout has passed."""

    def __init__(self, function, limit):
        """Initialize instance in preparation for being called."""
        self.__limit = limit
        self.__function = function
        self.__timeout = time.clock()
        self.__process = multiprocessing.Process()
        self.__queue = multiprocessing.Queue()

    def __call__(self, *args, **kwargs):
        """Execute the embedded function object asynchronously.

        The function given to the constructor is transparently called and
        requires that "ready" be intermittently polled. If and when it is
        True, the "value" property may then be checked for returned data."""
        self.cancel()
        self.__queue = multiprocessing.Queue(1)
        args = (self.__queue, self.__function) + args
        self.__process = multiprocessing.Process(target=_target,
                                                 args=args,
                                                 kwargs=kwargs)
        self.__process.daemon = True
        self.__process.start()
        self.__timeout = self.__limit + time.clock()

    def cancel(self):
        """Terminate any possible execution of the embedded function."""
        if self.__process.is_alive():
            self.__process.terminate()

    @property
    def ready(self):
        """Read-only property indicating status of "value" property."""
        if self.__queue.full():
            return True
        elif not self.__queue.empty():
            return True
        elif self.__timeout < time.clock():
            self.cancel()
        else:
            return False

    @property
    def value(self):
        """Read-only property containing data returned from function."""
        if self.ready is True:
            flag, load = self.__queue.get()
            if flag:
                return load
            raise load
        raise NotReadyError()

    def __get_limit(self):
        return self.__limit

    def __set_limit(self, value):
        if value <= 0:
            raise ValueError()
        self.__limit = value

    limit = property(__get_limit, __set_limit,
                     doc="Property for controlling the value of the timeout.")

---

Edit: This code was written for Python 3.x and was not designed for class methods as a decoration. The multiprocessing module was not designed to modify class instances across the process boundaries.

Solution

This question was asked over 9 years ago, and Python has changed a decent amount since then as has my repertoire of experience. After reviewing other APIs in the standard library and wanting to partially replicate one in particular, the follow module was written to serve a similar purpose as the one posted in the question.

asynchronous.py

#! /usr/bin/env python3
import _thread
import abc as _abc
import collections as _collections
import enum as _enum
import math as _math
import multiprocessing as _multiprocessing
import operator as _operator
import queue as _queue
import signal as _signal
import sys as _sys
import time as _time

__all__ = (
    'Executor',
    'get_timeout',
    'set_timeout',
    'submit',
    'map_',
    'shutdown'
)


class _Base(metaclass=_abc.ABCMeta):
    __slots__ = (
        '__timeout',
    )

    @_abc.abstractmethod
    def __init__(self, timeout):
        self.timeout = _math.inf if timeout is None else timeout

    def get_timeout(self):
        return self.__timeout

    def set_timeout(self, value):
        if not isinstance(value, (float, int)):
            raise TypeError('value must be of type float or int')
        if value <= 0:
            raise ValueError('value must be greater than zero')
        self.__timeout = value

    timeout = property(get_timeout, set_timeout)


def _run_and_catch(fn, args, kwargs):
    # noinspection PyPep8,PyBroadException
    try:
        return False, fn(*args, **kwargs)
    except:
        return True, _sys.exc_info()[1]


def _run(fn, args, kwargs, queue):
    queue.put_nowait(_run_and_catch(fn, args, kwargs))


class _State(_enum.IntEnum):
    PENDING = _enum.auto()
    RUNNING = _enum.auto()
    CANCELLED = _enum.auto()
    FINISHED = _enum.auto()
    ERROR = _enum.auto()


def _run_and_catch_loop(iterable, *args, **kwargs):
    exception = None
    for fn in iterable:
        error, value = _run_and_catch(fn, args, kwargs)
        if error:
            exception = value
    if exception:
        raise exception


class _Future(_Base):
    __slots__ = (
        '__queue',
        '__process',
        '__start_time',
        '__callbacks',
        '__result',
        '__mutex'
    )

    def __init__(self, timeout, fn, args, kwargs):
        super().__init__(timeout)
        self.__queue = _multiprocessing.Queue(1)
        self.__process = _multiprocessing.Process(
            target=_run,
            args=(fn, args, kwargs, self.__queue),
            daemon=True
        )
        self.__start_time = _math.inf
        self.__callbacks = _collections.deque()
        self.__result = True, TimeoutError()
        self.__mutex = _thread.allocate_lock()

    @property
    def __state(self):
        pid, exitcode = self.__process.pid, self.__process.exitcode
        return (_State.PENDING if pid is None else
                _State.RUNNING if exitcode is None else
                _State.CANCELLED if exitcode == -_signal.SIGTERM else
                _State.FINISHED if exitcode == 0 else
                _State.ERROR)

    def __repr__(self):
        root = f'{type(self).__name__} at {id(self)} state={self.__state.name}'
        if self.__state < _State.CANCELLED:
            return f'<{root}>'
        error, value = self.__result
        suffix = f'{"raised" if error else "returned"} {type(value).__name__}'
        return f'<{root} {suffix}>'

    def __consume_callbacks(self):
        while self.__callbacks:
            yield self.__callbacks.popleft()

    def __invoke_callbacks(self):
        self.__process.join()
        _run_and_catch_loop(self.__consume_callbacks(), self)

    def cancel(self):
        self.__process.terminate()
        self.__invoke_callbacks()

    def __auto_cancel(self):
        elapsed_time = _time.perf_counter() - self.__start_time
        if elapsed_time > self.timeout:
            self.cancel()
        return elapsed_time

    def cancelled(self):
        self.__auto_cancel()
        return self.__state is _State.CANCELLED

    def running(self):
        self.__auto_cancel()
        return self.__state is _State.RUNNING

    def done(self):
        self.__auto_cancel()
        return self.__state > _State.RUNNING

    def __handle_result(self, error, value):
        self.__result = error, value
        self.__invoke_callbacks()

    def __ensure_termination(self):
        with self.__mutex:
            elapsed_time = self.__auto_cancel()
            if not self.__queue.empty():
                self.__handle_result(*self.__queue.get_nowait())
            elif self.__state < _State.CANCELLED:
                remaining_time = self.timeout - elapsed_time
                if remaining_time == _math.inf:
                    remaining_time = None
                try:
                    result = self.__queue.get(True, remaining_time)
                except _queue.Empty:
                    self.cancel()
                else:
                    self.__handle_result(*result)

    def result(self):
        self.__ensure_termination()
        error, value = self.__result
        if error:
            raise value
        return value

    def exception(self):
        self.__ensure_termination()
        error, value = self.__result
        if error:
            return value

    def add_done_callback(self, fn):
        if self.done():
            fn(self)
        else:
            self.__callbacks.append(fn)

    def _set_running_or_notify_cancel(self):
        if self.__state is _State.PENDING:
            self.__process.start()
            self.__start_time = _time.perf_counter()
        else:
            self.cancel()


class Executor(_Base):
    __slots__ = (
        '__futures',
    )

    def __init__(self, timeout=None):
        super().__init__(timeout)
        self.__futures = set()

    def submit(self, fn, *args, **kwargs):
        future = _Future(self.timeout, fn, args, kwargs)
        self.__futures.add(future)
        future.add_done_callback(self.__futures.remove)
        # noinspection PyProtectedMember
        future._set_running_or_notify_cancel()
        return future

    @staticmethod
    def __cancel_futures(iterable):
        _run_and_catch_loop(map(_operator.attrgetter('cancel'), iterable))

    def map(self, fn, *iterables):
        futures = tuple(self.submit(fn, *args) for args in zip(*iterables))

        def result_iterator():
            future_iterator = iter(futures)
            try:
                for future in future_iterator:
                    yield future.result()
            finally:
                self.__cancel_futures(future_iterator)

        return result_iterator()

    def shutdown(self):
        self.__cancel_futures(frozenset(self.__futures))

    def __enter__(self):
        return self

    def __exit__(self, exc_type, exc_val, exc_tb):
        self.shutdown()
        return False


_executor = Executor()
get_timeout = _executor.get_timeout
set_timeout = _executor.set_timeout
submit = _executor.submit
map_ = _executor.map
shutdown = _executor.shutdown
del _executor

Answered By - Noctis Skytower

Answer Checked By - Mary Flores (PHPFixing Volunteer)