How to add a timeout to a function in Python

Question

The principal problem with your code is the overuse of the double underscore namespace conflict prevention in a class that isn't intended to be subclassed at all.

In general, self.__foo is a code smell that should be accompanied by a comment along the lines of # This is a mixin and we don't want arbitrary subclasses to have a namespace conflict.

Further the client API of this method would look like this:

def mymethod(): pass

mymethod = add_timeout(mymethod, 15)

# start the processing    
timeout_obj = mymethod()
try:
    # access the property, which is really a function call
    ret = timeout_obj.value
except TimeoutError:
    # handle a timeout here
    ret = None

This is not very pythonic at all and a better client api would be:

@timeout(15)
def mymethod(): pass

try:
    my_method()
except TimeoutError:
    pass

You are using @property in your class for something that is a state mutating accessor, this is not a good idea. For instance, what would happen when .value is accessed twice? It looks like it would fail because queue.get() would return trash because the queue is already empty.

Remove @property entirely. Don't use it in this context, it's not suitable for your use-case. Make call block when called and return the value or raise the exception itself. If you really must have value accessed later, make it a method like .get() or .value().

This code for the _target should be rewritten a little:

def _target(queue, function, *args, **kwargs):
    try:
        queue.put((True, function(*args, **kwargs)))
    except:
        queue.put((False, exc_info())) # get *all* the exec info, don't do exc_info[1]

# then later:
    raise exc_info[0], exc_info[1], exc_info[2]

That way the stack trace will be preserved correctly and visible to the programmer.

I think you've made a reasonable first crack at writing a useful library, I like the usage of the processing module to achieve the goals.

This is how to get the decorator syntax Jerub mentioned

def timeout(limit=None):
    if limit is None:
        limit = DEFAULT_TIMEOUT
    if limit <= 0:
        raise TimeoutError() # why not ValueError here?
    def wrap(function):
        return _Timeout(function,limit)
    return wrap

@timeout(15)
def mymethod(): pass

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

The Pebble library was designed to offer cross-platform implementation capable of dealing with problematic logic which could crash, segfault or run indefinitely.

from pebble import concurrent

@concurrent.process(timeout=10)
def function(foo, bar=0):
    return foo + bar

future = function(1, bar=2)

try:
    result = future.result()  # blocks until results are ready
except Exception as error:
    print("Function raised %s" % error)
    print(error.traceback)  # traceback of the function
except TimeoutError as error:
    print("Function took longer than %d seconds" % error.args[1])

The decorator works as well with static and class methods. I would not recommend to decorate methods nevertheless, as it is a quite error prone practice.