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Exit all threads in case of Error in python

I'm working on a python project were I want the same behavior as in C for my threads. In C when the main thread exit, it kills all other threads.
The project contains a TCP error server that it used to get logs from other threads and other software .The TCP link is simplex.
Some errors must involve the end of the whole program.
For external software I can kill them using their PID.
For other threads I've tried sys._exit(), sometimes it works, and sometimes some threads remain.
If my other threads were looping I could use a semaphore or something like that, but it is only one iteration of a linear process.
I've thought about the design pattern Producer/Consumer or add a lot of lock.acquire()/lock.release() but I think it will add more complexity and it imply to break the linear thread.
I've had a look to other Stackoverflow question I've found those solutions:
Use sys._exit() but its success rate is not 100%.
Convert my threads into subprocess to kill them easily, but in my case I can't.
I'm looking for a solution, a design pattern or something else to solve it.
PS: I'm a C lover and each time I deal with Python I think to solutions as simple as to call exit() to kill all my threads.

If you make your worker threads daemon threads, they will die when all your non-daemon threads (e.g. the main thread) have exited.
http://docs.python.org/library/threading.html#threading.Thread.daemon
Thread daemon status isDaemon() is False, set it True by setDaemon(True)
Another solution :
To make the thread stop on Keyboard Interrupt signal (ctrl+c) you can catch the exception "KeyboardInterrup" and cleanup before exiting. Like this:
try:
start_thread() #And the rest of your main
except (KeyboardInterrupt, SystemExit):
cleanup_stop_thread();
sys.exit()

Python - Notifying another thread blocked on subprocess

I am creating a custom job scheduler with a web frontend in python 3.4 on linux. This program creates a daemon (consumer) thread that waits for jobs to come available in a PriorityQueue. These jobs can manually be added through the web interface which adds them to the queue. When the consumer thread finds a job, it executes a program using subprocess.run, and waits for it to finish.
The basic idea of the worker thread:
class Worker(threading.Thread):
def __init__(self, queue):
self.queue = queue
# more code here
def run(self):
while True:
try:
job = self.queue.get()
#do some work
proc = subprocess.run("myprogram", timeout=my_timeout)
#do some more things
except TimeoutExpired:
#do some administration
self.queue.add(job)
However:
This consumer should be able to receive some kind of signal from the frontend (main thread) that it should stop the current job and instead work on the next job in the queue (saving the state of the current job and adding it to the end of the queue again). This can (and will most likely) happen while blocked on subprocess.run().
The subprocesses can simply be killed (the program that is executed saves sme state in a file) but the worker thread needs to do some administration on the killed job to make sure it can be resumed later on.
There can be multiple such worker threads.
Signal handlers are not an option (since they are always handled by the main thread which is a webserver and should not be bothered with this).
Having an event loop in which the process actively polls for events (such as the child exiting, the timeout occurring or the interrupt event) is in this context not really a solution but an ugly hack. The jobs are performance-heavy and constant context switches are unwanted.
What synchronization primitives should I use to interrupt this thread or to make sure it waits for several events at the same time in a blocking fashion?

I think you've accidentally glossed over a simple solution: your second bullet point says that you have the ability to kill the programs that are running in subprocesses. Notice that subprocess.call returns the return code of the subprocess. This means that you can let the main thread kill the subprocess, and just check the return code to see if you need to do any cleanup. Even better, you could use subprocess.check_call instead, which will raise an exception for you if the returncode isn't 0. I don't know what platform you're working on, but on Linux, killed processes generally don't return a 0 if they're killed.
It could look something like this:
class Worker(threading.Thread):
def __init__(self, queue):
self.queue = queue
# more code here
def run(self):
while True:
try:
job = self.queue.get()
#do some work
subprocess.check_call("myprogram", timeout=my_timeout)
#do some more things
except (TimeoutExpired, subprocess.CalledProcessError):
#do some administration
self.queue.add(job)
Note that if you're using Python 3.5, you can use subprocess.run instead, and set the check argument to True.
If you have a strong need to handle the cases where the worker needs to be interrupted when it isn't running the subprocess, then I think you're going to have to use a polling loop, because I don't think the behavior you're looking for is supported for threads in Python. You can use a threading.Event object to pass the "stop working now" pseudo-signal from your main thread to the worker, and have the worker periodically check the state of that event object.
If you're willing to consider using multiple processing stead of threads, consider switching over to the multiprocessing module, which would allow you to handle signals. There is more overhead to spawning full-blown subprocesses instead of threads, but you're essentially looking for signal-like asynchronous behavior, and I don't think Python's threading library supports anything like that. One benefit though, would be that you would be freed from the Global Interpreter Lock(PDF link), so you may actually see some speed benefits if your worker processes (formerly threads) are doing anything CPU intensive.

Alternative python library for managing threads

I had some annoyances with spawning subprocesses, like getting correct output and so on. A wrapper library, envoy, solved all of my problems with an easy-to-use interface that gets rid of most problems.
Using thread, I sometimes struggle with hanging processes that does not end, external programs launched within threads that I can't reach anymore and so on.
Is there any "threading for dummies" python library out there? Thanks

Is there any "threading for dummies" python library out there?
No, there is not. threading is pretty simple to use in simple cases. You want to use it to introduce concurrency in your program. This means you want to use it whenever you want two or more actions to happen simultaneously, i.e. at the same time.
This is how you can let Peter build a house and let Igor drive to Moskow at the same time:
from threading import Thread
import time
def drive_bus():
time.sleep(1)
print "Igor: I'm Igor and I'm driving to... Moskow!"
time.sleep(9)
print "Igor: Yei, Moskow!"
def build_house():
print "Peter: Let's start building a large house..."
time.sleep(10.1)
print "Peter: Urks, we have no tools :-("
threads = [Thread(target=drive_bus), Thread(target=build_house)]
for t in threads:
t.start()
for t in threads:
t.join()
Isn't that simple? Define your function to be run in another thread. Create a threading.Thread instance with that function as target. Nothing happend so far, until you invoke start. It fires off the thread and immediately returns.
Before letting your main thread exit, you should wait for all the threads you have spawned to finish. This is what t.join() does: it blocks and waits for the thread t to finish. Only then it returns.

I would recommend reading more about the actual Python library - it is simple enough. Your problem with hanging threads, provided it prevents your application from exiting, may be solved by using daemon threads.
What kind of task are you trying to achieve? If you are trying to run a task in parallel without actual use of the custom threading, you may find the package multiprocessing useful. Furthermore, there is an interesting piece of information on the python wiki about parallel processing.
Could you elaborate a bit more on the task please?

is twisted incompatible with multiprocessing events and queues?

I am trying to simulate a network of applications that run using twisted. As part of my simulation I would like to synchronize certain events and be able to feed each process large amounts of data. I decided to use multiprocessing Events and Queues. However, my processes are getting hung.
I wrote the example code below to illustrate the problem. Specifically, (about 95% of the time on my sandy bridge machine), the 'run_in_thread' function finishes, however the 'print_done' callback is not called until after I press Ctrl-C.
Additionally, I can change several things in the example code to make this work more reliably such as: reducing the number of spawned processes, calling self.ready.set from reactor_ready, or changing the delay of deferLater.
I am guessing there is a race condition somewhere between the twisted reactor and blocking multiprocessing calls such as Queue.get() or Event.wait()?
What exactly is the problem I am running into? Is there a bug in my code that I am missing? Can I fix this or is twisted incompatible with multiprocessing events/queues?
Secondly, would something like spawnProcess or Ampoule be the recommended alternative? (as suggested in Mix Python Twisted with multiprocessing?)
Edits (as requested):
I've run into problems with all the reactors I've tried glib2reactor selectreactor, pollreactor, and epollreactor. The epollreactor seems to give the best results and seems to work fine for the example given below but still gives me the same (or a similar) problem in my application. I will continue investigating.
I'm running Gentoo Linux kernel 3.3 and 3.4, python 2.7, and I've tried Twisted 10.2.0, 11.0.0, 11.1.0, 12.0.0, and 12.1.0.
In addition to my sandy bridge machine, I see the same issue on my dual core amd machine.
#!/usr/bin/python
# -*- coding: utf-8 *-*
from twisted.internet import reactor
from twisted.internet import threads
from twisted.internet import task
from multiprocessing import Process
from multiprocessing import Event
class TestA(Process):
def __init__(self):
super(TestA, self).__init__()
self.ready = Event()
self.ready.clear()
self.start()
def run(self):
reactor.callWhenRunning(self.reactor_ready)
reactor.run()
def reactor_ready(self, *args):
task.deferLater(reactor, 1, self.node_ready)
return args
def node_ready(self, *args):
print 'node_ready'
self.ready.set()
return args
def reactor_running():
print 'reactor_running'
df = threads.deferToThread(run_in_thread)
df.addCallback(print_done)
def run_in_thread():
print 'run_in_thread'
for n in processes:
n.ready.wait()
def print_done(dfResult=None):
print 'print_done'
reactor.stop()
if __name__ == '__main__':
processes = [TestA() for i in range(8)]
reactor.callWhenRunning(reactor_running)
reactor.run()

The short answer is yes, Twisted and multiprocessing are not compatible with each other, and you cannot reliably use them as you are attempting to.
On all POSIX platforms, child process management is closely tied to SIGCHLD handling. POSIX signal handlers are process-global, and there can be only one per signal type.
Twisted and stdlib multiprocessing cannot both have a SIGCHLD handler installed. Only one of them can. That means only one of them can reliably manage child processes. Your example application doesn't control which of them will win that ability, so I would expect there to be some non-determinism in its behavior arising from that fact.
However, the more immediate problem with your example is that you load Twisted in the parent process and then use multiprocessing to fork and not exec all of the child processes. Twisted does not support being used like this. If you fork and then exec, there's no problem. However, the lack of an exec of a new process (perhaps a Python process using Twisted) leads to all kinds of extra shared state which Twisted does not account for. In your particular case, the shared state that causes this problem is the internal "waker fd" which is used to implement deferToThread. With the fd shared between the parent and all the children, when the parent tries to wake up the main thread to deliver the result of the deferToThread call, it most likely wakes up one of the child processes instead. The child process has nothing useful to do, so that's just a waste of time. Meanwhile the main thread in the parent never wakes up and never notices your threaded task is done.
It's possible you can avoid this issue by not loading any of Twisted until you've already created the child processes. This would turn your usage into a single-process use case as far as Twisted is concerned (in each process, it would be initially loaded, and then that process would not go on to fork at all, so there's no question of how fork and Twisted interact anymore). This means not even importing Twisted until after you've created the child processes.
Of course, this only helps you out as far as Twisted goes. Any other libraries you use could run into similar trouble (you mentioned glib2, that's a great example of another library that will totally choke if you try to use it like this).
I highly recommend not using the multiprocessing module at all. Instead, use any multi-process approach that involves fork and exec, not fork alone. Ampoule falls into that category.

What is a python thread

I have several questions regarding Python threads.
Is a Python thread a Python or OS implementation?
When I use htop a multi-threaded script has multiple entries - the same memory consumption, the same command but a different PID. Does this mean that a [Python] thread is actually a special kind of process? (I know there is a setting in htop to show these threads as one process - Hide userland threads)
Documentation says:
A thread can be flagged as a “daemon thread”. The significance of this
flag is that the entire Python program exits when only daemon threads
are left.
My interpretation/understanding was: main thread terminates when all non-daemon threads are terminated.
So python daemon threads are not part of Python program if "the entire Python program exits when only daemon threads are left"?

Python threads are implemented using OS threads in all implementations I know (C Python, PyPy and Jython). For each Python thread, there is an underlying OS thread.
Some operating systems (Linux being one of them) show all different threads launched by the same executable in the list of all running processes. This is an implementation detail of the OS, not of Python. On some other operating systems, you may not see those threads when listing all the processes.
The process will terminate when the last non-daemon thread finishes. At that point, all the daemon threads will be terminated. So, those threads are part of your process, but are not preventing it from terminating (while a regular thread will prevent it). That is implemented in pure Python. A process terminates when the system _exit function is called (it will kill all threads), and when the main thread terminates (or sys.exit is called), the Python interpreter checks if there is another non-daemon thread running. If there is none, then it calls _exit, otherwise it waits for the non-daemon threads to finish.
The daemon thread flag is implemented in pure Python by the threading module. When the module is loaded, a Thread object is created to represent the main thread, and it's _exitfunc method is registered as an atexit hook.
The code of this function is:
class _MainThread(Thread):
def _exitfunc(self):
self._Thread__stop()
t = _pickSomeNonDaemonThread()
if t:
if __debug__:
self._note("%s: waiting for other threads", self)
while t:
t.join()
t = _pickSomeNonDaemonThread()
if __debug__:
self._note("%s: exiting", self)
self._Thread__delete()
This function will be called by the Python interpreter when sys.exit is called, or when the main thread terminates. When the function returns, the interpreter will call the system _exit function. And the function will terminate, when there are only daemon threads running (if any).
When the _exit function is called, the OS will terminate all of the process threads, and then terminate the process. The Python runtime will not call the _exit function until all the non-daemon thread are done.
All threads are part of the process.
My interpretation/understanding was: main thread terminates when all
non-daemon threads are terminated.
So python daemon threads are not part of python program if "the entire
Python program exits when only daemon threads are left"?
Your understanding is incorrect. For the OS, a process is composed of many threads, all of which are equal (there is nothing special about the main thread for the OS, except that the C runtime add a call to _exit at the end of the main function). And the OS doesn't know about daemon threads. This is purely a Python concept.
The Python interpreter uses native thread to implement Python thread, but has to remember the list of threads created. And using its atexit hook, it ensures that the _exit function returns to the OS only when the last non-daemon thread terminates. When using "the entire Python program", the documentation refers to the whole process.
The following program can help understand the difference between daemon thread and regular thread:
import sys
import time
import threading
class WorkerThread(threading.Thread):
def run(self):
while True:
print 'Working hard'
time.sleep(0.5)
def main(args):
use_daemon = False
for arg in args:
if arg == '--use_daemon':
use_daemon = True
worker = WorkerThread()
worker.setDaemon(use_daemon)
worker.start()
time.sleep(1)
sys.exit(0)
if __name__ == '__main__':
main(sys.argv[1:])
If you execute this program with the '--use_daemon', you will see that the program will only print a small number of Working hard lines. Without this flag, the program will not terminate even when the main thread finishes, and the program will print Working hard lines until it is killed.

I'm not familiar with the implementation, so let's make an experiment:
import threading
import time
def target():
while True:
print 'Thread working...'
time.sleep(5)
NUM_THREADS = 5
for i in range(NUM_THREADS):
thread = threading.Thread(target=target)
thread.start()
The number of threads reported using ps -o cmd,nlwp <pid> is NUM_THREADS+1 (one more for the main thread), so as long as the OS tools detect the number of threads, they should be OS threads. I tried both with cpython and jython and, despite in jython there are some other threads running, for each extra thread that I add, ps increments the thread count by one.
I'm not sure about htop behaviour, but ps seems to be consistent.
I added the following line before starting the threads:
thread.daemon = True
When I executed the using cpython, the program terminated almost immediately and no process was found using ps, so my guess is that the program terminated together with the threads. In jython the program worked the same way (it didn't terminate), so maybe there are some other threads from the jvm that prevent the program from terminating or daemon threads aren't supported.
Note: I used Ubuntu 11.10 with python 2.7.2+ and jython 2.2.1 on java1.6.0_23

Python threads are practically an interpreter implementation, because the so called global interpreter lock (GIL), even if it's technically using the os-level threading mechanisms. On *nix it's utilizing the pthreads, but the GIL effectivly makes it a hybrid stucked to the application-level threading paradigm. So you will see it on *nix systems multiple times in a ps/top output, but it still behaves (performance-wise) like a software-implemented thread.
No, you are just seeing the kind of underlying thread implementation of your os. This kind of behaviur is exposed by *nix pthread-like threading or im told even windows does implement threads this way.
When your program closes, it waits for all threads to finish also. If you have threads, which could postpone the exit indefinitly, it may be wise to flag those threads as "daemons" and allow your program to finish even if those threads are still running.
Some reference material you might be interested:
Linux Gazette: Understanding Threading in Python.
Doug Hellman: Multi-processing techniques in Python
David Beazley: PyCon 2010:Understanding the Python GIL(Video-presentation)

There are great answers to the question, but I feel the daemon threads question is still not explained in a simple fashion. So this answer refers just to the third question
"main thread terminates when all non-daemon threads are terminated."
So python daemon threads are not part of Python program if "the entire Python program exits when only daemon threads are left"?
If you think about what a daemon is, it is usually a service. Some code that runs in an infinite loop, that serves request, fill queues, accepts connections, etc. Other threads use it. It has no purpose when running by itself (in a single process terms).
So the program can't wait for the daemon thread to terminate, because it might never happen. Python will end the program when all non daemon threads are done. It also stops the daemon threads.
To wait until a daemon thread has completed its work, use the join() method.
daemon_thread.join() will make Python to wait for the daemon thread as well before exiting. The join() also accepts a timeout argument.