Недавно обновленный ответ
Если вы ищете способ поиска тайм-аута без использования сигналов, вот один способ. Во-первых, поскольку вы используете многопоточность, давайте сделаем это явным и воспользуемся модулем concurrent.futures, который обладает большой гибкостью.
Когда «задание» отправляется исполнителю пула, a Future instance возвращается немедленно без блокировки до тех пор, пока для этого экземпляра не будет выполнен вызов result. Вы можете указать значение timeout, чтобы, если результат недоступен в течение периода ожидания, будет выдано исключение. Идея состоит в том, чтобы передать рабочему потоку экземпляр ThreadPoolExecutor и запустить критический фрагмент кода, который должен быть завершен в течение определенного периода времени в его собственном рабочем потоке. Экземпляр Future будет создан для этого временного кода, но на этот раз вызов result будет указывать значение timeout:
from concurrent.futures import ThreadPoolExecutor, TimeoutError
import time
def main():
listOfLinks = ['a', 'b', 'c', 'd', 'e']
futures = []
"""
To prevent timeout errors due to lack of threads, you need at least one extra thread
in addition to the ones being created here so that at least one time_critical thread
can start. Of course, ideally you would like all the time_critical threads to be able to
start without waiting. So, whereas the minimum number of max_workers would be 6 in this
case, the ideal number would be 5 * 2 = 10.
"""
with ThreadPoolExecutor(max_workers=10) as executor:
# pass executor to our worker
futures = [executor.submit(processRunSeveralTimesInParallel, tuple, executor) for tuple in enumerate(listOfLinks)]
for future in futures:
result = future.result()
print('result is', result)
def processRunSeveralTimesInParallel(tuple, executor):
link_number = tuple[0]
link = tuple[1]
# long running sequence of instructions up until this point and then
# allow 2 seconds for this part:
for i in range(10):
future = executor.submit(time_critical, link, i)
try:
future.result(timeout=2) # time_critical does not return a result other than None
except TimeoutError:
handle_exception(link, i)
return link * link_number
def time_critical(link, trial_number):
if link == 'd' and trial_number == 7:
time.sleep(3) # generate a TimeoutError
def handle_exception(link, trial_number):
print(f'There was a timeout for link {link}, trial number {trial_number}.')
if __name__ == '__main__':
main()
Выводит:
result is
result is b
result is cc
There was a timeout for link d, trial number 7.
result is ddd
result is eeee
Использование потоков и многопроцессорность
from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor, TimeoutError
import os
import time
def main():
listOfLinks = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']
futures = []
cpu_count = os.cpu_count()
with ThreadPoolExecutor(max_workers=cpu_count) as thread_executor, ProcessPoolExecutor(max_workers=cpu_count) as process_executor:
# pass executor to our worker
futures = [thread_executor.submit(processRunSeveralTimesInParallel, tuple, process_executor) for tuple in enumerate(listOfLinks)]
for future in futures:
result = future.result()
print('result is', result)
def processRunSeveralTimesInParallel(tuple, executor):
link_number = tuple[0]
link = tuple[1]
# long running sequence of instructions up until this point and then
# allow 2 seconds for this part:
for i in range(10):
future = executor.submit(time_critical, link, i)
try:
future.result(timeout=2) # time_critical does not return a result other than None
except TimeoutError:
handle_exception(link, i)
return link * link_number
def time_critical(link, trial_number):
if link == 'd' and trial_number == 7:
time.sleep(3) # generate a TimeoutError
def handle_exception(link, trial_number):
print(f'There was a timeout for link {link}, trial number {trial_number}.')
if __name__ == '__main__':
main()
Печать:
result is
result is b
result is cc
There was a timeout for link d, trial number 7.
result is ddd
result is eeee
result is fffff
result is gggggg
result is hhhhhhh
result is iiiiiiii
result is jjjjjjjjj
Исключительно многопроцессорная обработка
from concurrent.futures import ProcessPoolExecutor
from multiprocessing import Process
import os
import time
def main():
listOfLinks = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']
futures = []
workers = os.cpu_count() // 2
with ProcessPoolExecutor(max_workers=workers) as process_executor:
# pass executor to our worker
futures = [process_executor.submit(processRunSeveralTimesInParallel, tuple) for tuple in enumerate(listOfLinks)]
for future in futures:
result = future.result()
print('result is', result)
def processRunSeveralTimesInParallel(tuple):
link_number = tuple[0]
link = tuple[1]
# long running sequence of instructions up until this point and then
# allow 2 seconds for this part:
for i in range(10):
p = Process(target=time_critical, args=(link, i))
p.start()
p.join(timeout=2) # don't block for more than 2 seconds
if p.exitcode is None: # subprocess did not terminate
p.terminate() # we will terminate it
handle_exception(link, i)
return link * link_number
def time_critical(link, trial_number):
if link == 'd' and trial_number == 7:
time.sleep(3) # generate a TimeoutError
def handle_exception(link, trial_number):
print(f'There was a timeout for link {link}, trial number {trial_number}.')
if __name__ == '__main__':
main()
Печать:
result is
result is b
result is cc
There was a timeout for link d, trial number 7.
result is ddd
result is eeee
result is fffff
result is gggggg
result is hhhhhhh
result is iiiiiiii
result is jjjjjjjjj