I use several threads with child-threads. Now i want to stop a parent-thread or wait until a parent thread has done its work without checking and stopping all the child threads.
My thought is to wrap the parent thread with a process and then just to terminate the process, which seems to terminate the corresponding parent-thread with all its childs.
def worker(conn):
#this is the class including the parent thread
xi = Test_Risk_Calc('99082')
#start working
xi.test()
#finished
print ('EVERYTHING IS DONE, BUT CHILD THREADS ARE STILL ALIVE')
conn.send('EXIT SEND')
return
def main():
parent_conn, child_conn = Pipe()
p = multiprocessing.Process(target=worker, args=(child_conn,))
p.start()
#wait for finished parent-thread
print(parent_conn.recv())
p.terminate()
p.join()
print('JOINED, PROCESS AND ALL ITS THREADS ARE TERMINATED')
return
I am not sure if this is a proper way to solve the problem
I think it's okay like you've done it. However, you can avoid the "terminate" which always is a bit "hard".
The simple solution to that would be to start the child threads with the argument "daemon = True". This actually terminates the child threads automatically if their parent terminates.
Like I said before, this might seem a bit cleaner, but in the end, it would be the same, I think.
EDIT:
Maybe consider using asyncio (async/await) for concurrent programming. You could create tasks with
task = asyncio.create_task(my_task())
and cancel these tasks later
task.cancel()
Nice thing here is, that this "cancel" throws an exception into the task. So within the task you can do stuff like this:
async def my_task():
try:
... stuff ...
except asyncio.CancelledError:
... you can handle the cancellation, or ignore it
In python, tasks are not very useful. For concurrent execution you can better use asyncio and if you have cpu-bound tasks you can use multiprocessing (maybe in the form of process pools).
Related
I've got the following code which uses a concurrent.futures.ThreadPoolExecutor to launch processes of another program in a metered way (no more than 30 at a time). I additionally want the ability to stop all work if I ctrl-C the python process. This code works with one caveat: I have to ctrl-C twice. The first time I send the SIGINT, nothing happens; the second time, I see the "sending SIGKILL to processes", the processes die, and it works. What is happening to my first SIGINT?
execution_list = [['prog', 'arg1'], ['prog', 'arg2']] ... etc
processes = []
def launch_instance(args):
process = subprocess.Popen(args)
processes.append(process)
process.wait()
try:
with concurrent.futures.ThreadPoolExecutor(max_workers=30) as executor:
results = list(executor.map(launch_instance, execution_list))
except KeyboardInterrupt:
print('sending SIGKILL to processes')
for p in processes:
if p.poll() is None: #If process is still alive
p.send_signal(signal.SIGKILL)
I stumbled upon your question while trying to solve something similar. Not 100% sure that it will solve your use case (I'm not using subprocesses), but I think it will.
Your code will stay within the context manager of the executor as long as the jobs are still running. My educated guess is that the first KeyboardInterrupt will be caught by the ThreadPoolExecutor, whose default behaviour would be to not start any new jobs, wait until the current ones are finished, and then clean up (and probably reraise the KeyboardInterrupt). But the processes are probably long running, so you wouldn't notice. The second KeyboardInterrupt then interrupts this error handling.
How I solved my problem (inifinite background processes in separate threads) is with the following code:
from concurrent.futures import ThreadPoolExecutor
import signal
import threading
from time import sleep
def loop_worker(exiting):
while not exiting.is_set():
try:
print("started work")
sleep(10)
print("finished work")
except KeyboardInterrupt:
print("caught keyboardinterrupt") # never caught here. just for demonstration purposes
def loop_in_worker():
exiting = threading.Event()
def signal_handler(signum, frame):
print("Setting exiting event")
exiting.set()
signal.signal(signal.SIGTERM, signal_handler)
with ThreadPoolExecutor(max_workers=1) as executor:
executor.submit(loop_worker, exiting)
try:
while not exiting.is_set():
sleep(1)
print('waiting')
except KeyboardInterrupt:
print('Caught keyboardinterrupt')
exiting.set()
print("Main thread finished (and thus all others)")
if __name__ == '__main__':
loop_in_worker()
It uses an Event to signal to the threads that they should stop what they are doing. In the main loop, there is a loop just to keep busy and check for any exceptions. Note that this loop is within the context of the ThreadPoolExecutor.
As a bonus it also handles the SIGTERM signal by using the same exiting Event.
If you add a loop in between processes.append(process) and process.wait() that checks for a signal, then it will probably solve your use case as well. It depends on what you want to do with the running processes what actions you should take there.
If you run my script from the command line and press ctrl-C you should see something like:
started work
waiting
waiting
^CCaught keyboardinterrupt
# some time passes here
finished work
Main thread finished (and thus all others)
Inspiration for my solution came from this blog post
I'm using a concurrent.futures.ThreadPoolExecutor with a Queue, the code is something like this:
from concurrent.futures import ThreadPoolExecutor
from queue import Queue
def func(parent):
return parent//2, parent//2, parent<=2
def worker(parent, q):
child1, child2, end = func(parent)
print(parent)
if not end:
q.put(child1)
q.put(child2)
if __name__ == "__main__":
q = Queue()
q.put(100)
executor = ThreadPoolExecutor(max_workers=6)
while True:
parent = q.get()
future = executor.submit(worker, parent, q)
if q.empty() and future.done():
break
The problem with this code is that the future.done() is never True, and I cannot get out of this infinite while loop.
My expected outcome is to wait until there's nothing to process. i.e. the queue is empty, and all worker have done their job, nothing further shall be put into this queue. Then I can stop this loop and do sth else.
P.S. the actual func I'm using is more complex than the above example, but the problem is the same.
Your problem is that you're not patient enough: The call to executor.submit returns immediately, even if the implied call to worker hasn't happened -- that is exactly the point of asynchronous constructs like futures. So when you check future.done() directly afterwards, there is a good chance that this check is performed before the executor had time to execute worker, meaning that your future won't be done, yet.
You can verify this by inserting the following code between the call to submit and your if statement:
import time
time.sleep(0.1)
This does achieve what you're looking for, but it doesn't really solve your problem in an elegant way.
Looking deeper, you're problem is that your scheduled tasks may generate new tasks, and that you only know whether they did so when they have completed. This means you have to wait until the task you just scheduled has executed before you can decide whether to stop scheduling new tasks:
if __name__ == "__main__":
q = Queue()
q.put(100)
with ThreadPoolExecutor(max_workers=6) as executor:
while not q.empty():
parent = q.get()
future = executor.submit(worker, parent, q)
future.result() # Wait for task
Also make sure to call Executor.shutdown, or better to use the executor in a context manager (as shown above) so that all resources are freed correctly once you're done.
The thing I cannot figure out is that although ThreadPoolExecutor uses daemon workers, they will still run even if main thread exit.
I can provide a minimal example in python3.6.4:
import concurrent.futures
import time
def fn():
while True:
time.sleep(5)
print("Hello")
thread_pool = concurrent.futures.ThreadPoolExecutor()
thread_pool.submit(fn)
while True:
time.sleep(1)
print("Wow")
Both main thread and the worker thread are infinite loops. So if I use KeyboardInterrupt to terminate main thread, I expect that the whole program will terminate too. But actually the worker thread is still running even though it is a daemon thread.
The source code of ThreadPoolExecutor confirms that worker threads are daemon thread:
t = threading.Thread(target=_worker,
args=(weakref.ref(self, weakref_cb),
self._work_queue))
t.daemon = True
t.start()
self._threads.add(t)
Further, if I manually create a daemon thread, it works like a charm:
from threading import Thread
import time
def fn():
while True:
time.sleep(5)
print("Hello")
thread = Thread(target=fn)
thread.daemon = True
thread.start()
while True:
time.sleep(1)
print("Wow")
So I really cannot figure out this strange behavior.
Suddenly... I found why. According to much more source code of ThreadPoolExecutor:
# Workers are created as daemon threads. This is done to allow the interpreter
# to exit when there are still idle threads in a ThreadPoolExecutor's thread
# pool (i.e. shutdown() was not called). However, allowing workers to die with
# the interpreter has two undesirable properties:
# - The workers would still be running during interpreter shutdown,
# meaning that they would fail in unpredictable ways.
# - The workers could be killed while evaluating a work item, which could
# be bad if the callable being evaluated has external side-effects e.g.
# writing to a file.
#
# To work around this problem, an exit handler is installed which tells the
# workers to exit when their work queues are empty and then waits until the
# threads finish.
_threads_queues = weakref.WeakKeyDictionary()
_shutdown = False
def _python_exit():
global _shutdown
_shutdown = True
items = list(_threads_queues.items())
for t, q in items:
q.put(None)
for t, q in items:
t.join()
atexit.register(_python_exit)
There is an exit handler which will join all unfinished worker...
Here's the way to avoid this problem. Bad design can be beaten by another bad design. People write daemon=True only if they really know that the worker won't damage any objects or files.
In my case, I created TreadPoolExecutor with a single worker and after a single submit I just deleted the newly created thread from the queue so the interpreter won't wait till this thread stops on its own. Notice that worker threads are created after submit, not after the initialization of TreadPoolExecutor.
import concurrent.futures.thread
from concurrent.futures import ThreadPoolExecutor
...
executor = ThreadPoolExecutor(max_workers=1)
future = executor.submit(lambda: self._exec_file(args))
del concurrent.futures.thread._threads_queues[list(executor._threads)[0]]
It works in Python 3.8 but may not work in 3.9+ since this code is accessing private variables.
See the working piece of code on github
I have some code that needs to run against several other systems that may hang or have problems not under my control. I would like to use python's multiprocessing to spawn child processes to run independent of the main program and then when they hang or have problems terminate them, but I am not sure of the best way to go about this.
When terminate is called it does kill the child process, but then it becomes a defunct zombie that is not released until the process object is gone. The example code below where the loop never ends works to kill it and allow a respawn when called again, but does not seem like a good way of going about this (ie multiprocessing.Process() would be better in the __init__()).
Anyone have a suggestion?
class Process(object):
def __init__(self):
self.thing = Thing()
self.running_flag = multiprocessing.Value("i", 1)
def run(self):
self.process = multiprocessing.Process(target=self.thing.worker, args=(self.running_flag,))
self.process.start()
print self.process.pid
def pause_resume(self):
self.running_flag.value = not self.running_flag.value
def terminate(self):
self.process.terminate()
class Thing(object):
def __init__(self):
self.count = 1
def worker(self,running_flag):
while True:
if running_flag.value:
self.do_work()
def do_work(self):
print "working {0} ...".format(self.count)
self.count += 1
time.sleep(1)
You might run the child processes as daemons in the background.
process.daemon = True
Any errors and hangs (or an infinite loop) in a daemon process will not affect the main process, and it will only be terminated once the main process exits.
This will work for simple problems until you run into a lot of child daemon processes which will keep reaping memories from the parent process without any explicit control.
Best way is to set up a Queue to have all the child processes communicate to the parent process so that we can join them and clean up nicely. Here is some simple code that will check if a child processing is hanging (aka time.sleep(1000)), and send a message to the queue for the main process to take action on it:
import multiprocessing as mp
import time
import queue
running_flag = mp.Value("i", 1)
def worker(running_flag, q):
count = 1
while True:
if running_flag.value:
print(f"working {count} ...")
count += 1
q.put(count)
time.sleep(1)
if count > 3:
# Simulate hanging with sleep
print("hanging...")
time.sleep(1000)
def watchdog(q):
"""
This check the queue for updates and send a signal to it
when the child process isn't sending anything for too long
"""
while True:
try:
msg = q.get(timeout=10.0)
except queue.Empty as e:
print("[WATCHDOG]: Maybe WORKER is slacking")
q.put("KILL WORKER")
def main():
"""The main process"""
q = mp.Queue()
workr = mp.Process(target=worker, args=(running_flag, q))
wdog = mp.Process(target=watchdog, args=(q,))
# run the watchdog as daemon so it terminates with the main process
wdog.daemon = True
workr.start()
print("[MAIN]: starting process P1")
wdog.start()
# Poll the queue
while True:
msg = q.get()
if msg == "KILL WORKER":
print("[MAIN]: Terminating slacking WORKER")
workr.terminate()
time.sleep(0.1)
if not workr.is_alive():
print("[MAIN]: WORKER is a goner")
workr.join(timeout=1.0)
print("[MAIN]: Joined WORKER successfully!")
q.close()
break # watchdog process daemon gets terminated
if __name__ == '__main__':
main()
Without terminating worker, attempt to join() it to the main process would have blocked forever since worker has never finished.
The way Python multiprocessing handles processes is a bit confusing.
From the multiprocessing guidelines:
Joining zombie processes
On Unix when a process finishes but has not been joined it becomes a zombie. There should never be very many because each time a new process starts (or active_children() is called) all completed processes which have not yet been joined will be joined. Also calling a finished process’s Process.is_alive will join the process. Even so it is probably good practice to explicitly join all the processes that you start.
In order to avoid a process to become a zombie, you need to call it's join() method once you kill it.
If you want a simpler way to deal with the hanging calls in your system you can take a look at pebble.
I am a newbie in python programming, what I understand is that a process can be a daemon, but a thread in a daemon mode, I couldn't understand the usecase of this, I would request the python gurus to help me in understanding this.
Here is some basic code using threading:
import Queue
import threading
def basic_worker(queue):
while True:
item = queue.get()
# do_work(item)
print(item)
queue.task_done()
def basic():
# http://docs.python.org/library/queue.html
queue = Queue.Queue()
for i in range(3):
t = threading.Thread(target=basic_worker,args=(queue,))
t.daemon = True
t.start()
for item in range(4):
queue.put(item)
queue.join() # block until all tasks are done
print('got here')
basic()
When you run it, you get
% test.py
0
1
2
3
got here
Now comment out the line:
t.daemon = True
Run it again, and you'll see that the script prints the same result, but hangs.
The main thread ends (note that got here was printed), but the second thread never finishes.
In contrast, when t.daemon is set to True, the thread t is terminated when the main thread ends.
Note that "daemon threads" has little to do with daemon processes.
It looks like people intend to use Queue to explain threading, but I think there should be a much simpler way, by using time.sleep(), to demo a daemon thread.
Create daemon thread by setting the daemon parameter (default as None):
from threading import Thread
import time
def worker():
time.sleep(3)
print('daemon done')
thread = Thread(target=worker, daemon=True)
thread.start()
print('main done')
Output:
main done
Process finished with exit code 0
Remove the daemon argument, like:
thread = Thread(target=worker)
Re-run and see the output:
main done
daemon done
Process finished with exit code 0
Here we already see the difference of a daemon thread:
The entire Python program can exit if only daemon thread is left.
isDaemon() and setDaemon() are old getter/setter API. Using constructor argument, as above, or daemon property is recommended.
Module Queue has been renamed queue starting with Python3 to better reflect the fact that there are several queue classes (lifo, fifo, priority) in the module.
so please make the changes while using this example
In simple words...
What is a Daemon thread?
daemon threads can shut down any time in between their flow whereas non-daemon (i.e. user threads) execute completely.
daemon threads run intermittently in the background as long as other non-daemon threads are running.
When all of the non-daemon threads are complete, daemon threads terminate automatically (no matter whether they got fully executed or not).
daemon threads are service providers for user threads running in the same process.
python does not care about daemon threads to complete when in running state, NOT EVEN the finally block but python does give preference to non-daemon threads that are created by us.
daemon threads act as services in operating systems.
python stops the daemon threads when all user threads (in contrast to the daemon threads) are terminated. Hence daemon threads can be used to implement, for example, a monitoring functionality as the thread is stopped by the python as soon as all user threads have stopped.
In a nutshell
If you do something like this
thread = Thread(target=worker_method, daemon=True)
there is NO guarantee that worker_method will get executed completely.
Where does this behaviour be useful?
Consider two threads t1 (parent thread) and t2 (child thread). Let t2 be daemon. Now, you want to analyze the working of t1 while it is in running state; you can write the code to do this in t2.
Reference:
StackOverflow - What is a daemon thread in Java?
GeeksForGeeks - Python daemon threads
TutotrialsPoint - Concurrency in Python - Threads
Official Python Documentation
I've adapted #unutbu's answer for python 3. Make sure that you run this script from the command line and not some interactive environment like jupyter notebook.
import queue
import threading
def basic_worker(q):
while True:
item = q.get()
# do_work(item)
print(item)
q.task_done()
def basic():
q = queue.Queue()
for item in range(4):
q.put(item)
for i in range(3):
t = threading.Thread(target=basic_worker,args=(q,))
t.daemon = True
t.start()
q.join() # block until all tasks are done
print('got here')
basic()
So when you comment out the daemon line, you'll notice that the program does not finish, you'll have to interrupt it manually.
Setting the threads to daemon threads makes sure that they are killed once they have finished.
Note: you could achieve the same thing here without daemon threads, if you would replace the infinite while loop with another condition:
def basic_worker(q):
while not q.empty():
item = q.get()
# do_work(item)
print(item)
q.task_done()