Timed-Threads 2.0.0

Absolute time deadlines and thread cancelling for Python asynchronous threads

Adds the ability to set relative elapsed time deadlines on asynchronous threads, and allows one thread to stop another by means of raising an exception.

Note that due to the GIL lock in Python 3.14, this does not give us any more concurrency.

It is hoped that in the future, in conjunction with an implementation of Python that does not have a global GIL lock there can be and implementation that improves concurrency of hardware threads.

The main motivation of this module is to support TimedConstraint in the open-source implementation of Mathematica, called Mathics3.

Overview

This module provides:

• a function that allows an exception to be raised in another thread, including the main thread.

• context managers that may stop its inner block activity on timeout.

• decorators that may stop its decorated callables on timeout.

Developed and tested with CPython 3.10+ using Python’s threading model.

Note

Signal based timeout controls, namely SignalTimeout context manager and signal_timeoutable decorator won’t work in Windows that has no support for signal.SIGALRM. Any help to work around this is welcome.

Installation

pip install Timed-Threads

To install from source:

pip install -e .

Public API

Exception

timed_threads.TimeoutException

A timed_threads.TimeoutException may be raised in a timeout context manager controlled block.

This exception may be propagated in your application at the end of execution of the context manager controlled block, see the swallow_ex parameter of the context managers.

Note that the timed_threads.TimeoutException is always swallowed after the execution of functions decorated with xxx_timeoutable(...). Anyway, you may catch this exception within the decorated function.

Threading based resources

Warning

Threading based resources will only work with CPython implementations since we use CPython specific low level API. This excludes Iron Python, Jython, Pypy, …

Will not stop the execution of blocking Python atomic instructions that acquire the GIL. In example, if the destination thread is actually executing a time.sleep(20), the asynchronous exception is effective after its execution.

timed_threads.async_raise

A function that raises an arbitrary exception in another thread

async_raise(tid, exception)

• tid is the thread identifier as provided by the ident attribute of a thread object. See the documentation of the threading module for further information.

• exception is the exception class or object to raise in the thread.

timed_threads.ThreadingTimeout

A context manager that “kills” its inner block execution that exceeds the provided time.

ThreadingTimeout(seconds, swallow_exc=True)

• seconds is the number of seconds allowed to the execution of the context managed block.

• swallow_exc : if False, the possible timed_threads.TimeoutException will be re-raised when quitting the context managed block. Attention: a True value does not swallow other potential exceptions.

Methods and attributes

of a timed_threads.ThreadingTimeout context manager.

Method / Attribute	Description
.cancel()	Cancels the timeout control. This method is intended for use within the block that’s under timeout control, specifically to cancel the timeout control. Means that all code executed after this call may be executed till the end.
.state	This attribute indicated the actual status of the timeout control. It may take the value of the EXECUTED, EXECUTING, TIMED_OUT, INTERRUPTED or CANCELED attributes. See below.
.EXECUTING	The timeout control is under execution. We are typically executing within the code under control of the context manager.
.EXECUTED	Good news: the code under timeout control completed normally within the assigned time frame.
.TIMED_OUT	Bad news: the code under timeout control has been sleeping too long. The objects supposed to be created or changed within the timeout controlled block should be considered as non existing or corrupted. Don’t play with them otherwise informed.
.INTERRUPTED	The code under timeout control may itself raise explicit timed_threads.TimeoutException for any application logic reason that may occur. This intentional exit can be spotted from outside the timeout controlled block with this state value.
.CANCELED	The timeout control has been intentionally canceled and the code running under timeout control did complete normally. But perhaps after the assigned time frame.

A typical usage:

import timed_threads
# ...
with timed_threads.ThreadingTimeout(10) as to_ctx_mgr:
    assert to_ctx_mgr.state == to_ctx_mgr.EXECUTING
    # Something potentially very long but which
    # ...

# OK, let's check what happened
if to_ctx_mgr.state == to_ctx_mgr.EXECUTED:
    # All's fine, everything was executed within 10 seconds
elif to_ctx_mgr.state == to_ctx_mgr.EXECUTING:
    # Hmm, that's not possible outside the block
elif to_ctx_mgr.state == to_ctx_mgr.TIMED_OUT:
    # Eeek the 10 seconds timeout occurred while executing the block
elif to_ctx_mgr.state == to_ctx_mgr.INTERRUPTED:
    # Oh you raised specifically the TimeoutException in the block
elif to_ctx_mgr.state == to_ctx_mgr.CANCELED:
    # Oh you called to_ctx_mgr.cancel() method within the block but it
    # executed till the end
else:
    # That's not possible

Notice that the context manager object may be considered as a boolean indicating (if True) that the block executed normally:

if to_ctx_mgr:
    # Yes, the code under timeout control completed
    # Objects it created or changed may be considered consistent

timed_threads.threading_timeoutable

A decorator that kills the function or method it decorates, if it does not return within a given time frame.

timed_threads.threading_timeoutable([default [, timeout_param]])

• default is the value to be returned by the decorated function or method of when its execution timed out, to notify the caller code that the function did not complete within the assigned time frame.

  If this parameter is not provided, the decorated function or method will return a None value when its execution times out.

  @timed_threads.threading_timeoutable(default='not finished')
  def infinite_loop():
      # As its name says...
  
  result = infinite_loop(timeout=5)
  assert result == 'not finished'

• timeout_param: The function or method you have decorated may require a timeout named parameter for whatever reason. This empowers you to change the name of the timeout parameter in the decorated function signature to whatever suits, and prevent a potential naming conflict.

  @timed_threads.threading_timeoutable(timeout_param='my_timeout')
  def some_slow_function(a, b, timeout='whatever'):
      # As its name says...
  
  result = some_slow_function(1, 2, timeout="something", my_timeout=2)

About the decorated function

or method…

As you noticed above, you just need to add the timeout parameter when calling the function or method. Or whatever other name for this you chose with the timeout_param of the decorator. When calling the real inner function or method, this parameter is removed.

Signaling based resources

Warning

Using signaling based resources will not work under Windows or any OS that’s not based on Unix.

timed_threads.SignalTimeout and timed_threads.signal_timeoutable have exactly the same API as their respective threading based resources, namely timed_threads.ThreadingTimeout and timed_threads.threading_timeoutable.

See the comparison chart that warns on the more or less subtle differences between the Threading based resources and the Signaling based resources.

Logging

The timed_threads named logger emits a warning each time a block of code execution exceeds the associated timeout. To turn logging off, just:

import logging
timed_threads_logger = logging.getLogger('timed_threads')
timed_threads_logger.setLevel(logging.ERROR)

Comparing thread based and signal based timeout control

Feature	Threading based resources	Signaling based resources
GIL	Can’t interrupt a long Python atomic instruction. e.g. if time.sleep(20.0) is actually executing, the timeout will take effect at the end of the execution of this line.	Don’t care of it
Thread safety	Yes : Thread safe as long as each thread uses its own ThreadingTimeout context manager or threading_timeoutable decorator.	Not thread safe. Could yield unpredictable results in a multithreads application.
Nestable context managers	Yes : you can nest threading based context managers	No : never nest a signaling based context manager in another one. The innermost context manager will automatically cancel the timeout control of outer ones.
Accuracy	Any positive floating value is accepted as timeout value. The accuracy depends on the GIL interval checking of your platform. See the doc on sys.getcheckinterval and sys.setcheckinterval for your Python version.	Due to the use of signal.SIGALRM, we need provide an integer number of seconds. So a timeout of 0.6 seconds will ve automatically converted into a timeout of zero second!
Supported platforms	Any CPython 2.6, 2.7 or 3.3 on any OS with threading support.	Any Python 2.6, 2.7 or 3.3 with signal.SIGALRM support. This excludes Windows boxes

Known issues

Timeout accuracy

Important: the way CPython supports threading and asynchronous features has impacts on the accuracy of the timeout. In other words, if you assign a 2.0 seconds timeout to a context managed block or a decorated callable, the effective code block / callable execution interruption may occur some fractions of seconds after this assigned timeout.

For more background about this issue - that cannot be fixed - please read Python gurus thoughts about Python threading, the GIL and context switching like these ones:

• http://pymotw.com/2/threading/

• https://wiki.python.org/moin/GlobalInterpreterLock

This is the reason why I am more “tolerant” on timeout accuracy in the tests you can read thereafter than I should be for a critical real-time application (that’s not in the scope of Python).

It is anyway possible to improve this accuracy at the expense of the global performances decreasing the check interval which defaults to 100. See:

• https://docs.python.org/2.7/library/sys.html#sys.getcheckinterval

• https://docs.python.org/2.7/library/sys.html#sys.getcheckinterval

If this is a real issue for users (want a precise timeout and not an approximative one), a future release will add the optional check_interval parameter to the context managers and decorators. This parameter will enable to lower temporarily the threads switching check interval, having a more accurate timeout at the expense of the overall performances while the context managed block or decorated functions are executing.

gevent support

Threading timeout control as mentioned in Threading based resources does not work as expected when used in the context of a gevent worker.

See the discussion in Issue 13 for more details.

Tests and demos

>>> import threading
>>> from timed_threads import async_raise, TimeoutException

In a real application, you should either use threading based timeout resources:

>>> from timed_threads import ThreadingTimeout as Timeout, threading_timeoutable as timeoutable  #doctest: +SKIP

Or the POSIX signal based resources:

>>> from timed_threads import SignalTimeout as Timeout, signal_timeoutable as timeoutable  #doctest: +SKIP

Let’s define some utilities:

>>> import time
>>> def fast_func():
...     return 0
>>> def variable_duration_func(duration):
...     t0 = time.time()
...     while True:
...         dummy = 0
...         if time.time() - t0 > duration:
...             break
>>> exc_traces = []
>>> def variable_duration_func_handling_exc(duration, exc_traces):
...     try:
...         t0 = time.time()
...         while True:
...             dummy = 0
...             if time.time() - t0 > duration:
...                 break
...     except Exception as exc:
...         exc_traces.append(exc)
>>> def func_with_exception():
...     raise LookupError()

async_raise function raises an exception in another thread

Testing async_raise() with a thread of 5 seconds:

>>> five_seconds_threads = threading.Thread(
...     target=variable_duration_func_handling_exc, args=(5.0, exc_traces))
>>> start_time = time.time()
>>> five_seconds_threads.start()
>>> thread_ident = five_seconds_threads.ident
>>> five_seconds_threads.is_alive()
True

We raise a LookupError in that thread:

>>> async_raise(thread_ident, LookupError)

Okay but we must wait few milliseconds the thread death since the exception is asynchronous:

>>> while five_seconds_threads.is_alive():
...     pass

And we can notice that we stopped the thread before it stopped by itself:

>>> time.time() - start_time < 0.5
True
>>> len(exc_traces)
1
>>> exc_traces[-1].__class__.__name__
'LookupError'

Timeout context manager

The context manager stops the execution of its inner block after a given time. You may manage the way the timeout occurs using a try: ... except: ... construct or by inspecting the context manager state attribute after the block.

Swallowing Timeout exceptions

We check that the fast functions return as outside our context manager:

>>> with Timeout(5.0) as timeout_ctx:
...     result = fast_func()
>>> result
0
>>> timeout_ctx.state == timeout_ctx.EXECUTED
True

And the context manager is considered as True (the block executed its last line):

>>> bool(timeout_ctx)
True

We check that slow functions are interrupted:

>>> start_time = time.time()
>>> with Timeout(2.0) as timeout_ctx:
...     variable_duration_func(5.0)
>>> time.time() - start_time < 2.2
True
>>> timeout_ctx.state == timeout_ctx.TIMED_OUT
True

And the context manager is considered as False since the block did timeout.

>>> bool(timeout_ctx)
False

Other exceptions are propagated and must be treated as usual:

>>> try:
...     with Timeout(5.0) as timeout_ctx:
...         result = func_with_exception()
... except LookupError:
...     result = 'exception_seen'
>>> timeout_ctx.state == timeout_ctx.EXECUTING
True
>>> result
'exception_seen'

Propagating TimeoutException

We can choose to propagate the TimeoutException too. Potential exceptions have to be handled:

>>> result = None
>>> start_time = time.time()
>>> try:
...     with Timeout(2.0, swallow_exc=False) as timeout_ctx:
...         variable_duration_func(5.0)
... except TimeoutException:
...     result = 'exception_seen'
>>> time.time() - start_time < 2.2
True
>>> result
'exception_seen'
>>> timeout_ctx.state == timeout_ctx.TIMED_OUT
True

Other exceptions must be handled too:

>>> result = None
>>> start_time = time.time()
>>> try:
...     with Timeout(2.0, swallow_exc=False) as timeout_ctx:
...         func_with_exception()
... except Exception:
...     result = 'exception_seen'
>>> time.time() - start_time < 0.1
True
>>> result
'exception_seen'
>>> timeout_ctx.state == timeout_ctx.EXECUTING
True

timeoutable callable decorator

This decorator stops the execution of any callable that should not last a certain amount of time.

You may use a decorated callable without timeout control if you don’t provide the timeout optional argument:

>>> @timeoutable()
... def fast_double(value):
...     return value * 2
>>> fast_double(3)
6

You may specify that timeout with the timeout optional argument. Interrupted callables return None:

>>> @timeoutable()
... def infinite():
...     while True:
...         pass
...     return 'whatever'
>>> infinite(timeout=1) is None
True

Or any other value provided to the timeoutable decorator parameter:

>>> @timeoutable('unexpected')
... def infinite():
...     while True:
...         pass
...     return 'whatever'
>>> infinite(timeout=1)
'unexpected'

If the timeout parameter name may clash with your callable signature, you may change it using timeout_param:

>>> @timeoutable('unexpected', timeout_param='my_timeout')
... def infinite():
...     while True:
...         pass
...     return 'whatever'
>>> infinite(my_timeout=1)
'unexpected'

It works on instance methods too:

>>> class Anything(object):
...     @timeoutable('unexpected')
...     def infinite(self, value):
...         assert type(value) is int
...         while True:
...             pass
>>> obj = Anything()
>>> obj.infinite(2, timeout=1)
'unexpected'

Links

Source code (clone, fork, …)

https://github.com/Mathics3/python-timed-threads/timed_threads

Issues tracker

https://github.com/Mathics3/python-timed-threads/timed_threads/issues

PyPI

https://pypi.python.org/pypi/timed_threads

Credits

• This is a modernization for newer Python of Gilles Lenfant stopit with some slight changes.

• This is a NIH package which is mainly a theft of Gabriel Ahtune’s recipe with tests, minor improvements and refactorings, documentation and setuptools awareness I made since I’m somehow tired to copy/paste this recipe among projects that need timeout control.

• Gilles Lenfant: package creator and maintainer.

License

This software is open source delivered under the terms of the MIT license. See the LICENSE file of this repository.