Timeout control decorator and context managers, raise any exception in another thread
Raise asynchronous exceptions in other threads, control the timeout of blocks or callables with two context managers and two decorators.
Users of 1.0.0 should upgrade their source code:
Explications follow below…
This module provides:
Developed and tested with CPython 2.6, 2.7, 3.3 and 3.4 on MacOSX. Should work on any OS (xBSD, Linux, Windows) except when explicitly mentioned.
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.
Both work identically:
easy_install stopit pip install stopit
# You should prefer forking if you have a Github account git clone https://github.com/glenfant/stopit.git cd stopit python setup.py develop # Does it work for you ? python setup.py test
A stopit.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 stopit.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 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.
A function that raises an arbitrary exception in another thread
A context manager that “kills” its inner block execution that exceeds the provided time.
Methods and attributes
of a stopit.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 stopit.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 stopit # ... with stopit.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_mrg.state == to_ctx_mrg.EXECUTED: # All's fine, everything was executed within 10 seconds elif to_ctx_mrg.state == to_ctx_mrg.EXECUTING: # Hmm, that's not possible outside the block elif to_ctx_mrg.state == to_ctx_mrg.TIMED_OUT: # Eeek the 10 seconds timeout occurred while executing the block elif to_ctx_mrg.state == to_ctx_mrg.INTERRUPTED: # Oh you raised specifically the TimeoutException in the block elif to_ctx_mrg.state == to_ctx_mrg.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
A decorator that kills the function or method it decorates, if it does not return within a given time frame.
stopit.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.
@stopit.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.
@stopit.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)
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.
Using signaling based resources will not work under Windows or any OS that’s not based on Unix.
The stopit named logger emits a warning each time a block of code execution exceeds the associated timeout. To turn logging off, just:
import logging stopit_logger = logging.getLogger('stopit') stopit_logger.seLevel(logging.ERROR)
|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|
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:
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:
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.
>>> import threading >>> from stopit import async_raise, TimeoutException
In a real application, you should either use threading based timeout resources:
>>> from stopit import ThreadingTimeout as Timeout, threading_timeoutable as timeoutable #doctest: +SKIP
Or the POSIX signal based resources:
>>> from stopit import SignalingTimeout as Timeout, signaling_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()
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'
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.
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'
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
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'