There is a socket related function call in my code, that function is from another module thus out of my control, the problem is that it blocks for hours occasionally, which is totally unacceptable, How can I limit the function execution time from my code? I guess the solution must utilize another thread.
An improvement on #rik.the.vik's answer would be to use the with statement to give the timeout function some syntactic sugar:
import signal
from contextlib import contextmanager
class TimeoutException(Exception): pass
#contextmanager
def time_limit(seconds):
def signal_handler(signum, frame):
raise TimeoutException("Timed out!")
signal.signal(signal.SIGALRM, signal_handler)
signal.alarm(seconds)
try:
yield
finally:
signal.alarm(0)
try:
with time_limit(10):
long_function_call()
except TimeoutException as e:
print("Timed out!")
I'm not sure how cross-platform this might be, but using signals and alarm might be a good way of looking at this. With a little work you could make this completely generic as well and usable in any situation.
http://docs.python.org/library/signal.html
So your code is going to look something like this.
import signal
def signal_handler(signum, frame):
raise Exception("Timed out!")
signal.signal(signal.SIGALRM, signal_handler)
signal.alarm(10) # Ten seconds
try:
long_function_call()
except Exception, msg:
print "Timed out!"
Here's a Linux/OSX way to limit a function's running time. This is in case you don't want to use threads, and want your program to wait until the function ends, or the time limit expires.
from multiprocessing import Process
from time import sleep
def f(time):
sleep(time)
def run_with_limited_time(func, args, kwargs, time):
"""Runs a function with time limit
:param func: The function to run
:param args: The functions args, given as tuple
:param kwargs: The functions keywords, given as dict
:param time: The time limit in seconds
:return: True if the function ended successfully. False if it was terminated.
"""
p = Process(target=func, args=args, kwargs=kwargs)
p.start()
p.join(time)
if p.is_alive():
p.terminate()
return False
return True
if __name__ == '__main__':
print run_with_limited_time(f, (1.5, ), {}, 2.5) # True
print run_with_limited_time(f, (3.5, ), {}, 2.5) # False
I prefer a context manager approach because it allows the execution of multiple python statements within a with time_limit statement. Because windows system does not have SIGALARM, a more portable and perhaps more straightforward method could be using a Timer
from contextlib import contextmanager
import threading
import _thread
class TimeoutException(Exception):
def __init__(self, msg=''):
self.msg = msg
#contextmanager
def time_limit(seconds, msg=''):
timer = threading.Timer(seconds, lambda: _thread.interrupt_main())
timer.start()
try:
yield
except KeyboardInterrupt:
raise TimeoutException("Timed out for operation {}".format(msg))
finally:
# if the action ends in specified time, timer is canceled
timer.cancel()
import time
# ends after 5 seconds
with time_limit(5, 'sleep'):
for i in range(10):
time.sleep(1)
# this will actually end after 10 seconds
with time_limit(5, 'sleep'):
time.sleep(10)
The key technique here is the use of _thread.interrupt_main to interrupt the main thread from the timer thread. One caveat is that the main thread does not always respond to the KeyboardInterrupt raised by the Timer quickly. For example, time.sleep() calls a system function so a KeyboardInterrupt will be handled after the sleep call.
Here: a simple way of getting the desired effect:
https://pypi.org/project/func-timeout
This saved my life.
And now an example on how it works: lets say you have a huge list of items to be processed and you are iterating your function over those items. However, for some strange reason, your function get stuck on item n, without raising an exception. You need to other items to be processed, the more the better. In this case, you can set a timeout for processing each item:
import time
import func_timeout
def my_function(n):
"""Sleep for n seconds and return n squared."""
print(f'Processing {n}')
time.sleep(n)
return n**2
def main_controller(max_wait_time, all_data):
"""
Feed my_function with a list of itens to process (all_data).
However, if max_wait_time is exceeded, return the item and a fail info.
"""
res = []
for data in all_data:
try:
my_square = func_timeout.func_timeout(
max_wait_time, my_function, args=[data]
)
res.append((my_square, 'processed'))
except func_timeout.FunctionTimedOut:
print('error')
res.append((data, 'fail'))
continue
return res
timeout_time = 2.1 # my time limit
all_data = range(1, 10) # the data to be processed
res = main_controller(timeout_time, all_data)
print(res)
Doing this from within a signal handler is dangerous: you might be inside an exception handler at the time the exception is raised, and leave things in a broken state. For example,
def function_with_enforced_timeout():
f = open_temporary_file()
try:
...
finally:
here()
unlink(f.filename)
If your exception is raised here(), the temporary file will never be deleted.
The solution here is for asynchronous exceptions to be postponed until the code is not inside exception-handling code (an except or finally block), but Python doesn't do that.
Note that this won't interrupt anything while executing native code; it'll only interrupt it when the function returns, so this may not help this particular case. (SIGALRM itself might interrupt the call that's blocking--but socket code typically simply retries after an EINTR.)
Doing this with threads is a better idea, since it's more portable than signals. Since you're starting a worker thread and blocking until it finishes, there are none of the usual concurrency worries. Unfortunately, there's no way to deliver an exception asynchronously to another thread in Python (other thread APIs can do this). It'll also have the same issue with sending an exception during an exception handler, and require the same fix.
You don't have to use threads. You can use another process to do the blocking work, for instance, maybe using the subprocess module. If you want to share data structures between different parts of your program then Twisted is a great library for giving yourself control of this, and I'd recommend it if you care about blocking and expect to have this trouble a lot. The bad news with Twisted is you have to rewrite your code to avoid any blocking, and there is a fair learning curve.
You can use threads to avoid blocking, but I'd regard this as a last resort, since it exposes you to a whole world of pain. Read a good book on concurrency before even thinking about using threads in production, e.g. Jean Bacon's "Concurrent Systems". I work with a bunch of people who do really cool high performance stuff with threads, and we don't introduce threads into projects unless we really need them.
The only "safe" way to do this, in any language, is to use a secondary process to do that timeout-thing, otherwise you need to build your code in such a way that it will time out safely by itself, for instance by checking the time elapsed in a loop or similar. If changing the method isn't an option, a thread will not suffice.
Why? Because you're risking leaving things in a bad state when you do. If the thread is simply killed mid-method, locks being held, etc. will just be held, and cannot be released.
So look at the process way, do not look at the thread way.
I would usually prefer using a contextmanager as suggested by #josh-lee
But in case someone is interested in having this implemented as a decorator, here's an alternative.
Here's how it would look like:
import time
from timeout import timeout
class Test(object):
#timeout(2)
def test_a(self, foo, bar):
print foo
time.sleep(1)
print bar
return 'A Done'
#timeout(2)
def test_b(self, foo, bar):
print foo
time.sleep(3)
print bar
return 'B Done'
t = Test()
print t.test_a('python', 'rocks')
print t.test_b('timing', 'out')
And this is the timeout.py module:
import threading
class TimeoutError(Exception):
pass
class InterruptableThread(threading.Thread):
def __init__(self, func, *args, **kwargs):
threading.Thread.__init__(self)
self._func = func
self._args = args
self._kwargs = kwargs
self._result = None
def run(self):
self._result = self._func(*self._args, **self._kwargs)
#property
def result(self):
return self._result
class timeout(object):
def __init__(self, sec):
self._sec = sec
def __call__(self, f):
def wrapped_f(*args, **kwargs):
it = InterruptableThread(f, *args, **kwargs)
it.start()
it.join(self._sec)
if not it.is_alive():
return it.result
raise TimeoutError('execution expired')
return wrapped_f
The output:
python
rocks
A Done
timing
Traceback (most recent call last):
...
timeout.TimeoutError: execution expired
out
Notice that even if the TimeoutError is thrown, the decorated method will continue to run in a different thread. If you would also want this thread to be "stopped" see: Is there any way to kill a Thread in Python?
Using simple decorator
Here's the version I made after studying above answers. Pretty straight forward.
def function_timeout(seconds: int):
"""Wrapper of Decorator to pass arguments"""
def decorator(func):
#contextmanager
def time_limit(seconds_):
def signal_handler(signum, frame): # noqa
raise TimeoutException(f"Timed out in {seconds_} seconds!")
signal.signal(signal.SIGALRM, signal_handler)
signal.alarm(seconds_)
try:
yield
finally:
signal.alarm(0)
#wraps(func)
def wrapper(*args, **kwargs):
with time_limit(seconds):
return func(*args, **kwargs)
return wrapper
return decorator
How to use?
#function_timeout(seconds=5)
def my_naughty_function():
while True:
print("Try to stop me ;-p")
Well of course, don't forget to import the function if it is in a separate file.
Here's a timeout function I think I found via google and it works for me.
From:
http://code.activestate.com/recipes/473878/
def timeout(func, args=(), kwargs={}, timeout_duration=1, default=None):
'''This function will spwan a thread and run the given function using the args, kwargs and
return the given default value if the timeout_duration is exceeded
'''
import threading
class InterruptableThread(threading.Thread):
def __init__(self):
threading.Thread.__init__(self)
self.result = default
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 it.result
else:
return it.result
The method from #user2283347 is tested working, but we want to get rid of the traceback messages. Use pass trick from Remove traceback in Python on Ctrl-C, the modified code is:
from contextlib import contextmanager
import threading
import _thread
class TimeoutException(Exception): pass
#contextmanager
def time_limit(seconds):
timer = threading.Timer(seconds, lambda: _thread.interrupt_main())
timer.start()
try:
yield
except KeyboardInterrupt:
pass
finally:
# if the action ends in specified time, timer is canceled
timer.cancel()
def timeout_svm_score(i):
#from sklearn import svm
#import numpy as np
#from IPython.core.display import display
#%store -r names X Y
clf = svm.SVC(kernel='linear', C=1).fit(np.nan_to_num(X[[names[i]]]), Y)
score = clf.score(np.nan_to_num(X[[names[i]]]),Y)
#scoressvm.append((score, names[i]))
display((score, names[i]))
%%time
with time_limit(5):
i=0
timeout_svm_score(i)
#Wall time: 14.2 s
%%time
with time_limit(20):
i=0
timeout_svm_score(i)
#(0.04541284403669725, '计划飞行时间')
#Wall time: 16.1 s
%%time
with time_limit(5):
i=14
timeout_svm_score(i)
#Wall time: 5h 43min 41s
We can see that this method may need far long time to interrupt the calculation, we asked for 5 seconds, but it work out in 5 hours.
This code works for Windows Server Datacenter 2016 with python 3.7.3 and I didn't tested on Unix, after mixing some answers from Google and StackOverflow, it finally worked for me like this:
from multiprocessing import Process, Lock
import time
import os
def f(lock,id,sleepTime):
lock.acquire()
print("I'm P"+str(id)+" Process ID: "+str(os.getpid()))
lock.release()
time.sleep(sleepTime) #sleeps for some time
print("Process: "+str(id)+" took this much time:"+str(sleepTime))
time.sleep(sleepTime)
print("Process: "+str(id)+" took this much time:"+str(sleepTime*2))
if __name__ == '__main__':
timeout_function=float(9) # 9 seconds for max function time
print("Main Process ID: "+str(os.getpid()))
lock=Lock()
p1=Process(target=f, args=(lock,1,6,)) #Here you can change from 6 to 3 for instance, so you can watch the behavior
start=time.time()
print(type(start))
p1.start()
if p1.is_alive():
print("process running a")
else:
print("process not running a")
while p1.is_alive():
timeout=time.time()
if timeout-start > timeout_function:
p1.terminate()
print("process terminated")
print("watching, time passed: "+str(timeout-start) )
time.sleep(1)
if p1.is_alive():
print("process running b")
else:
print("process not running b")
p1.join()
if p1.is_alive():
print("process running c")
else:
print("process not running c")
end=time.time()
print("I am the main process, the two processes are done")
print("Time taken:- "+str(end-start)+" secs") #MainProcess terminates at approx ~ 5 secs.
time.sleep(5) # To see if on Task Manager the child process is really being terminated, and it is
print("finishing")
The main code is from this link:
Create two child process using python(windows)
Then I used .terminate() to kill the child process. You can see that the function f calls 2 prints, one after 5 seconds and another after 10 seconds. However, with a 7 seconds sleep and the terminate(), it does not show the last print.
It worked for me, hope it helps!
I have a single-line encrypted code in form of string and a function that decodes & execute the encrypted code (the code establishes backdoor connection to another computer in the network, but that isn't very relevent here) Once the encrypted code runs the program won't proceed to next line because the code does not stop (in order to keep the connection alive).
What I want to do is run the code for 10 seconds, then terminate it, and re-run it.
But I am not being able to terminate the code because it's encrypted and can't be changed. I thought I could run it on a separate thread then forcibly terminate it after 10 seconds, but the problem is all the solutions mentioned on the internet would require changing the encrypted code slightly.
Python does not seem to have a way to forcibly close a running thread after 'n' seconds, which would solve my problem very easily.
Following code demonstrates my problem:
import threading, time
encrypted_code = ")'cba'(tnirp :eurT elihw"
def decrypt_and_execute(encrypted_code):
exec(encrypted_code[::-1])
def run(encrypted_code, run_time):
my_thread = threading.Thread(target=decrypt_and_execute, args=(encrypted_code,))
my_thread.start()
time.sleep(run_time)
# CODE TO STOP 'my_thread' without changing 'encrypted_code' NEEDED HERE#
while True:
threading.Thread(target=run, args=(encrypted_code, 10)).start()
time.sleep(10)
Above was my attempted solution (encrypted code here is an infinite while loop that keeps printing abc).
Any code I tried to stop 'my_thread' without closing the whole application, did not work.
Maybe that technique of a thread with traces works for you, is a Thread subclass that you can stop. From https://www.geeksforgeeks.org/python-different-ways-to-kill-a-thread/
Or use subprocesses, they have terminate. That same post covers that too nicely.
Edit: for some reason, this failed to kill the thread in the case in question. The multiprocess module solved the issue: killing processes is a reliable way to stop things on all operating systems, and multiprocess module has a spawn method that gives a Thread like API for subprocesses, and can start new processes with Python code also on Windows without having Python installed.
Anyway for other cases this technique with threading should work too. Threads are rarely actually needed but sometimes they truly are.
# Python program using
# traces to kill threads
import sys
import trace
import threading
import time
class thread_with_trace(threading.Thread):
def __init__(self, *args, **keywords):
threading.Thread.__init__(self, *args, **keywords)
self.killed = False
def start(self):
self.__run_backup = self.run
self.run = self.__run
threading.Thread.start(self)
def __run(self):
sys.settrace(self.globaltrace)
self.__run_backup()
self.run = self.__run_backup
def globaltrace(self, frame, event, arg):
if event == 'call':
return self.localtrace
else:
return None
def localtrace(self, frame, event, arg):
if self.killed:
if event == 'line':
raise SystemExit()
return self.localtrace
def kill(self):
self.killed = True
def func():
while True:
print('thread running')
t1 = thread_with_trace(target = func)
t1.start()
time.sleep(2)
t1.kill()
t1.join()
if not t1.isAlive():
print('thread killed')
I have just moved on to python3 as a result of its concurrent futures module. I was wondering if I could get it to detect errors. I want to use concurrent futures to parallel program, if there are more efficient modules please let me know.
I do not like multiprocessing as it is too complicated and not much documentation is out. It would be great however if someone could write a Hello World without classes only functions using multiprocessing to parallel compute so that it is easy to understand.
Here is a simple script:
from concurrent.futures import ThreadPoolExecutor
def pri():
print("Hello World!!!")
def start():
try:
while True:
pri()
except KeyBoardInterrupt:
print("YOU PRESSED CTRL+C")
with ThreadPoolExecutor(max_workers=3) as exe:
exe.submit(start)
The above code was just a demo, of how CTRL+C will not work to print the statement.
What I want is to be able to call a function is an error is present. This error detection must be from the function itself.
Another example
import socket
from concurrent.futures import ThreadPoolExecutor
s = socket.socket(socket.AF_INET,socket.SOCK_STREAM)
def con():
try:
s.connect((x,y))
main()
except: socket.gaierror
err()
def err():
time.sleep(1)
con()
def main():
s.send("[+] Hello")
with ThreadPoolExecutor as exe:
exe.submit(con)
Way too late to the party, but maybe it'll help someone else...
I'm pretty sure the original question was not really answered. Folks got hung up on the fact that user5327424 was using a keyboard interrupt to raise an exception when the point was that the exception (however it was caused) was not raised. For example:
import concurrent.futures
def main():
numbers = range(10)
with concurrent.futures.ThreadPoolExecutor() as executor:
results = {executor.submit(raise_my_exception, number): number for number in numbers}
def raise_my_exception(number):
print('Proof that this function is getting called. %s' % number)
raise Exception('This never sees the light of day...')
main()
When the example code above is executed, you will see the text inside the print statement displayed on the screen, but you will never see the exception. This is because the results of each thread are held in the results object. You need to iterate that object to get to your exceptions. The following example shows how to access the results.
import concurrent.futures
def main():
numbers = range(10)
with concurrent.futures.ThreadPoolExecutor() as executor:
results = {executor.submit(raise_my_exception, number): number for number in numbers}
for result in results:
# This will cause the exception to be raised (but only the first one)
print(result.result())
def raise_my_exception(number):
print('Proof that this function is getting called. %s' % number)
raise Exception('This will be raised once the results are iterated.')
main()
I'm not sure I like this behavior or not, but it does allow the threads to fully execute, regardless of the exceptions encountered inside the individual threads.
Here's a solution. I'm not sure you like it, but I can't think of any other. I've modified your code to make it work.
from concurrent.futures import ThreadPoolExecutor
import time
quit = False
def pri():
print("Hello World!!!")
def start():
while quit is not True:
time.sleep(1)
pri()
try:
pool = ThreadPoolExecutor(max_workers=3)
pool.submit(start)
while quit is not True:
print("hei")
time.sleep(1)
except KeyboardInterrupt:
quit = True
Here are the points:
When you use with ThreadPoolExecutor(max_workers=3) as exe, it waits until all tasks have been done. Have a look at Doc
If wait is True then this method will not return until all the pending futures are done executing and the resources associated with the executor have been freed. If wait is False then this method will return immediately and the resources associated with the executor will be freed when all pending futures are done executing. Regardless of the value of wait, the entire Python program will not exit until all pending futures are done executing.
You can avoid having to call this method explicitly if you use the with statement, which will shutdown the Executor (waiting as if Executor.shutdown() were called with wait set to True)
It's like calling join() on a thread.
That's why I replaced it with:
pool = ThreadPoolExecutor(max_workers=3)
pool.submit(start)
Main thread must be doing "work" to be able to catch a Ctrl+C. So you can't just leave main thread there and exit, the simplest way is to run an infinite loop
Now that you have a loop running in main thread, when you hit CTRL+C, program will enter the except KeyboardInterrupt block and set quit=True. Then your worker thread can exit.
Strictly speaking, this is only a workaround. It seems to me it's impossible to have another way for this.
Edit
I'm not sure what's bothering you, but you can catch exception in another thread without problem:
import socket
import time
from concurrent.futures import ThreadPoolExecutor
s = socket.socket(socket.AF_INET,socket.SOCK_STREAM)
def con():
try:
raise socket.gaierror
main()
except socket.gaierror:
print("gaierror occurred")
err()
def err():
print("err invoked")
time.sleep(1)
con()
def main():
s.send("[+] Hello")
with ThreadPoolExecutor(3) as exe:
exe.submit(con)
Output
gaierror occurred
err invoked
gaierror occurred
err invoked
gaierror occurred
err invoked
gaierror occurred
...
I want to know how can I stop my program in console with CTRL+C or smth similar.
The problem is that there are two threads in my program. Thread one crawls the web and extracts some data and thread two displays this data in a readable format for the user. Both parts share same database. I run them like this :
from threading import Thread
import ResultsPresenter
def runSpider():
Thread(target=initSpider).start()
Thread(target=ResultsPresenter.runPresenter).start()
if __name__ == "__main__":
runSpider()
how can I do that?
Ok so I created my own thread class :
import threading
class MyThread(threading.Thread):
"""Thread class with a stop() method. The thread itself has to check
regularly for the stopped() condition."""
def __init__(self):
super(MyThread, self).__init__()
self._stop = threading.Event()
def stop(self):
self._stop.set()
def stopped(self):
return self._stop.isSet()
OK so I will post here snippets of resultPresenter and crawler.
Here is the code of resultPresenter :
# configuration
DEBUG = False
DATABASE = database.__path__[0] + '/database.db'
app = Flask(__name__)
app.config.from_object(__name__)
app.config.from_envvar('CRAWLER_SETTINGS', silent=True)
def runPresenter():
url = "http://127.0.0.1:5000"
webbrowser.open_new(url)
app.run()
There are also two more methods here that I omitted - one of them connects to the database and the second method loads html template to display result. I repeat this until conditions are met or user stops the program ( what I am trying to implement ). There are also two other methods too - one get's initial link from the command line and the second valitated arguments - if arguments are invalid I won't run crawl() method.
Here is short version of crawler :
def crawl(initialLink, maxDepth):
#here I am setting initial values, lists etc
while not(depth >= maxDepth or len(pagesToCrawl) <= 0):
#this is the main loop that stops when certain depth is
#reached or there is nothing to crawl
#Here I am popping urls from url queue, parse them and
#insert interesting data into the database
parser.close()
sock.close()
dataManager.closeConnection()
Here is the init file which starts those modules in threads:
import ResultsPresenter, MyThread, time, threading
def runSpider():
MyThread.MyThread(target=initSpider).start()
MyThread.MyThread(target=ResultsPresenter.runPresenter).start()
def initSpider():
import Crawler
import database.__init__
import schemas.__init__
import static.__init__
import templates.__init__
link, maxDepth = Crawler.getInitialLink()
if link:
Crawler.crawl(link, maxDepth)
killall = False
if __name__ == "__main__":
global killall
runSpider()
while True:
try:
time.sleep(1)
except:
for thread in threading.enumerate():
thread.stop()
killall = True
raise
Killing threads is not a good idea, since (as you already said) they may be performing some crucial operations on database. Thus you may define global flag, which will signal threads that they should finish what they are doing and quit.
killall = False
import time
if __name__ == "__main__":
global killall
runSpider()
while True:
try:
time.sleep(1)
except:
/* send a signal to threads, for example: */
killall = True
raise
and in each thread you check in a similar loop whether killall variable is set to True. If it is close all activity and quit the thread.
EDIT
First of all: the Exception is rather obvious. You are passing target argument to __init__, but you didn't declare it in __init__. Do it like this:
class MyThread(threading.Thread):
def __init__(self, *args, **kwargs):
super(MyThread, self).__init__(*args, **kwargs)
self._stop = threading.Event()
And secondly: you are not using my code. As I said: set the flag and check it in thread. When I say "thread" I actually mean the handler, i.e. ResultsPresenter.runPresenter or initSpide. Show us the code of one of these and I'll try to show you how to handle stopping.
EDIT 2
Assuming that the code of crawl function is in the same file (if it is not, then you have to import killall variable), you can do something like this
def crawl(initialLink, maxDepth):
global killall
# Initialization.
while not killall and not(depth >= maxDepth or len(pagesToCrawl) <= 0):
# note the killall variable in while loop!
# the other code
parser.close()
sock.close()
dataManager.closeConnection()
So basically you just say: "Hey, thread, quit the loop now!". Optionally you can literally break a loop:
while not(depth >= maxDepth or len(pagesToCrawl) <= 0):
# some code
if killall:
break
Of course it will still take some time before it quits (has to finish the loop and close parser, socket, etc.), but it should quit safely. That's the idea at least.
Try this:
ps aux | grep python
copy the id of the process you want to kill and:
kill -3 <process_id>
And in your code (adapted from here):
import signal
import sys
def signal_handler(signal, frame):
print 'You killed me!'
sys.exit(0)
signal.signal(signal.SIGQUIT, signal_handler)
print 'Kill me now'
signal.pause()
I'm writing some code for testing multithreaded programs (student homework--likely buggy), and want to be able to detect when they deadlock. When running properly, the programs regularly produce output to stdout, so that makes it fairly straightforward: if no output for X seconds, kill it and report deadlock. Here's the function prototype:
def run_with_watchdog(command, timeout):
"""Run shell command, watching for output. If the program doesn't
produce any output for <timeout> seconds, kill it and return 1.
If the program ends successfully, return 0."""
I can write it myself, but it's a bit tricky to get right, so I would prefer to use existing code if possible. Anyone written something similar?
Ok, see solution below. The subprocess module might also be relevant if you're doing something similar.
You can use expect (tcl) or pexpect (python) to do this.
import pexpect
c=pexpect.spawn('your_command')
c.expect("expected_output_regular_expression", timeout=10)
Here's a very slightly tested, but seemingly working, solution:
import sys
import time
import pexpect
# From http://pypi.python.org/pypi/pexpect/
DEADLOCK = 1
def run_with_watchdog(shell_command, timeout):
"""Run <shell_command>, watching for output, and echoing it to stdout.
If the program doesn't produce any output for <timeout> seconds,
kill it and return 1. If the program ends successfully, return 0.
Note: Assumes timeout is >> 1 second. """
child = pexpect.spawn('/bin/bash', ["-c", shell_command])
child.logfile_read = sys.stdout
while True:
try:
child.read_nonblocking(1000, timeout)
except pexpect.TIMEOUT:
# Child seems deadlocked. Kill it, return 1.
child.close(True)
return DEADLOCK
except pexpect.EOF:
# Reached EOF, means child finished properly.
return 0
# Don't spin continuously.
time.sleep(1)
if __name__ == "__main__":
print "Running with timer..."
ret = run_with_watchdog("./test-program < trace3.txt", 10)
if ret == DEADLOCK:
print "DEADLOCK!"
else:
print "Finished normally"
Another solution:
class Watchdog:
def __init__(self, timeout, userHandler=None): # timeout in seconds
self.timeout = timeout
if userHandler != None:
self.timer = Timer(self.timeout, userHandler)
else:
self.timer = Timer(self.timeout, self.handler)
def reset(self):
self.timer.cancel()
self.timer = Timer(self.timeout, self.handler)
def stop(self):
self.timer.cancel()
def handler(self):
raise self;
Usage if you want to make sure function finishes in less than x seconds:
watchdog = Watchdog(x)
try
... do something that might hang ...
except Watchdog:
... handle watchdog error ...
watchdog.stop()
Usage if you regularly execute something and want to make sure it is executed at least every y seconds:
def myHandler():
print "Watchdog expired"
watchdog = Watchdog(y, myHandler)
def doSomethingRegularly():
...
watchdog.reset()