I have found a code creating a timeout function here, which does not seem to work. The complete test code is below:
def timeout(func, args=(), kwargs={}, timeout_duration=1, default=None):
import threading
class InterruptableThread(threading.Thread):
def __init__(self):
threading.Thread.__init__(self)
self.result = None
def run(self):
try:
self.result = func(*args, **kwargs)
except:
self.result = default
it = InterruptableThread()
it.start()
it.join(timeout_duration)
if it.isAlive():
return default
else:
return it.result
def foo():
while True:
pass
timeout(foo,timeout_duration=3)
Expected behavior: code ends within 3 seconds. Where is the problem?
A thread can not gracefully kill another thread, so with your current code,
foo
never terminates. (Withthread.daemon = True
the Python program will exit when only daemon threads are left, but that does not allow you to terminatefoo
without also terminating the main thread.)Some people have tried to use signals to halt execution, but this may be unsafe in some cases.
If you can modify
foo
, there are many solutions possible. For instance, you could check for athreading.Event
to break out of the while-loop.But if you can not modify
foo
, you could run it in a subprocess using themultiprocessing
module since unlike threads, subprocesses can be terminated. Here is an example of how that might look:yields
Note that this has some limitations too.
subprocesses receive a copy of the parent processes' variables. If you modify a variable in a subprocess, it will NOT affect the parent process. If your function
func
needs to modify variables, you will need to use a shared variable.arguments (passed through
args
) and keywords (kwds
) must be picklable.timeout
function creates aPool
every time you call it. This was necessary since we neededpool.terminate()
to terminatefoo
. There might be a better way, but I haven't thought of it.You need to turn
it
into a daemon thread:Otherwise it's created as a user thread, and the process won't get stopped until all the user threads have finished.
Note that with your implementation the thread will continue to run and consume resources even after you've timed out waiting for it. CPython's Global Interpreter Lock could exacerbate the issue further.