We Keep Coding

threading.get_ident() returns same ID between different threads when running pytest

I have created a small test case using pytest to demonstrate the issue:
from threading import Thread, get_ident
def test_threading():
threads = []
ids = []
def append_id():
# time.sleep(1)
ids.append(get_ident())
for _ in range(5):
thread = Thread(target=append_id)
threads.append(thread)
thread.start()
for thread in threads:
thread.join()
assert len(set(ids)) == 5
The test is failing because get_ident returns the same ID for different threads. But when I add time.sleep(1) in each thread, the test passes.
I'm not sure I understand why.
I'm using Python 3.9.0 and Pytest 7.1.2.

From the documentation of get_ident:
Return the ‘thread identifier’ of the current thread. This is a nonzero integer. Its value has no direct meaning; it is intended as a magic cookie to be used e.g. to index a dictionary of thread-specific data. Thread identifiers may be recycled when a thread exits and another thread is created.
Since your threads are running too quickly (without the time.sleep(1)), the ids are being recycled.
You can provide a name to each thread. This name does not have to be unique, but you can use it in your test (or in your application, if you need something that is unique in a context):
from threading import Thread, get_ident, current_thread
def test_threading():
threads = []
names = []
ids = []
def append_id():
# time.sleep(1)
ids.append(get_ident())
names.append(current_thread().name)
for i in range(5):
thread = Thread(target=append_id, name=f'Thread {i}')
threads.append(thread)
thread.start()
for thread in threads:
thread.join()
assert len(set(names)) == 5
print(f'Names: {names}')
print(f'Ids: {set(ids)}')

Get error flag/message from a queued process in Python multiprocessing

I am preparing a Python multiprocessing tool where I use Process and Queue commands. The queue is putting another script in a process to run in parallel. As a sanity check, in the queue, I want to check if there is any error happing in my other script and return a flag/message if there was an error (status = os.system() will run the process and status is a flag for error). But I can't output errors from the queue/child in the consumer process to the parent process. Following are the main parts of my code (shortened):
import os
import time
from multiprocessing import Process, Queue, Lock
command_queue = Queue()
lock = Lock()
p = Process(target=producer, args=(command_queue, lock, test_config_list_path))
for i in range(consumer_num):
c = Process(target=consumer, args=(command_queue, lock))
consumers.append(c)
p.daemon = True
p.start()
for c in consumers:
c.daemon = True
c.start()
p.join()
for c in consumers:
c.join()
if error_flag:
Stop_this_process_and_send_a_message!
def producer(queue, lock, ...):
for config_path in test_config_list_path:
queue.put((config_path, process_to_be_queued))
def consumer(queue, lock):
while True:
elem = queue.get()
if elem is None:
return
status = os.system(elem[1])
if status:
error_flag = 1
time.sleep(3)
Now I want to get that error_flag and use it in the main code to handle things. But seems I can't output error_flag from the consumer (child) part to the main part of the code. I'd appreciate it if someone can help with this.

Given your update, I also pass an multiprocessing.Event instance to your to_do process. This allows you to simply issue a call to wait on the event in the main process, which will block until a call to set is called on it. Naturally, when to_do or one of its threads detects a script error, it would call set on the event after setting error_flag.value to True. This will wake up the main process who can then call method terminate on the process, which will do what you want. On a normal completion of to_do, it still is necessary to call set on the event since the main process is blocking until the event has been set. But in this case the main process will just call join on the process.
Using a multiprocessing.Value instance alone would have required periodically checking its value in a loop, so I think waiting on a multiprocessing.Event is better. I have also made a couple of other updates to your code with comments, so please review them:
import multiprocessing
from ctypes import c_bool
...
def to_do(event, error_flag):
# Run the tests
wrapper_threads.main(event, error_flag)
# on error or normal process completion:
event.set()
def git_pull_change(path_to_repo):
repo = Repo(path)
current = repo.head.commit
repo.remotes.origin.pull()
if current == repo.head.commit:
print("Repo not changed. Sleep mode activated.")
# Call to time.sleep(some_number_of_seconds) should go here, right?
return False
else:
print("Repo changed. Start running the tests!")
return True
def main():
while True:
status = git_pull_change(git_path)
if status:
# The repo was just pulled, so no point in doing it again:
#repo = Repo(git_path)
#repo.remotes.origin.pull()
event = multiprocessing.Event()
error_flag = multiprocessing.Value(c_bool, False, lock=False)
process = multiprocessing.Process(target=to_do, args=(event, error_flag))
process.start()
# wait for an error or normal process completion:
event.wait()
if error_flag.value:
print('Error! breaking the process!!!!!!!!!!!!!!!!!!!!!!!')
process.terminate() # Kill the process
else:
process.join()
break

You should always tag multiprocessing questions with the platform you are running on. Since I do not see your process-creating code within a if __name__ == '__main__': block, I have to assume you are running on a platform that uses OS fork calls to create new processes, such as Linux.
That means your newly created processes inherit the value of error_flag when they are created but for all intents and purposes, if a process modifies this variable, it is modifying a local copy of this variable that exists in an address space that is unique to that process.
You need to create error_flag in shared memory and pass it as an argument to your process:
from multiprocessing import Value
from ctypes import c_bool
...
error_flag = Value(c_bool, False, lock=False)
for i in range(consumer_num):
c = Process(target=consumer, args=(command_queue, lock, error_flag))
consumers.append(c)
...
if error_flag.value:
...
#Stop_this_process_and_send_a_message!
def consumer(queue, lock, error_flag):
while True:
elem = queue.get()
if elem is None:
return
status = os.system(elem[1])
if status:
error_flag.value = True
time.sleep(3)
But I have a questions/comments for you. You have in your original code the following statement:
if error_flag:
Stop_this_process_and_send_a_message!
But this statement is located after you have already joined all the started processes. So what processes are there to stop and where are you sending a message to (you have potentially multiple consumers any of which might be setting the error_flag -- by the way, no need to have this done under a lock since setting the value True is an atomic action). And since you are joining all your processes, i.e. waiting for them to complete, I am not sure why you are making them daemon processes. You are also passing a Lock instance to your producer and consumers, but it is not being used at all.
Your consumers return when they get a None record from the queue. So if you have N consumers, the last N elements of test_config_path need to be None.
I also see no need for having the producer process. The main process could just as well write all the records to the queue either before or even after it starts the consumer processes.
The call to time.sleep(3) you have at the end of function consumer is unreachable.

So the above code summary is the inner process to run some tests in parallel. I removed the def function part from it, but just assume that is the wrapper_threads in the following code summary. Here I'll add the parent process which is checking a variable (let's assume a commit in my git repo). The following process is meant to run indefinitely and when there is a change it will trigger the multiprocess in the main question:
def to_do():
# Run the tests
wrapper_threads.main()
def git_pull_change(path_to_repo):
repo = Repo(path)
current = repo.head.commit
repo.remotes.origin.pull()
if current == repo.head.commit:
print("Repo not changed. Sleep mode activated.")
return False
else:
print("Repo changed. Start running the tests!")
return True
def main():
process = None
while True:
status = git_pull_change(git_path)
if status:
repo = Repo(git_path)
repo.remotes.origin.pull()
process = multiprocessing.Process(target=to_do)
process.start()
if error_flag.value:
print('Error! breaking the process!!!!!!!!!!!!!!!!!!!!!!!')
os.system('pkill -U user XXX')
break
Now I want to propagate that error_flag from the child process to this process and stop process XXX. The problem is that I don't know how to bring that error_flag to this (grand)parent process.

End a Process early in Python 3.6+

I've read that it's considered bad practice to kill a thread. (Is there any way to kill a Thread?) There are a LOT of answers there, and I'm wondering if even using a thread in the first place is the right answer for me.
I have a bunch multiprocessing.Processes. Essentially, each Process is doing this:
while some_condition:
result = self.function_to_execute(i, **kwargs_i)
# outQ is a multiprocessing.queue shared between all Processes
self.outQ.put(Result(i, result))
Problem is... I need a way to interrupt function_to_execute, but can't modify the function itself. Initially, I was thinking simply process.terminate(), but that appears to be unsafe with multiprocessing.queue.
Most likely (but not guaranteed), if I need to kill a thread, the 'main' program is going to be done soon. Is my safest option to do something like this? Or perhaps there is a more elegant solution than using a thread in the first place?
def thread_task():
while some_condition:
result = self.function_to_execute(i, **kwargs_i)
if (this_thread_is_not_daemonized):
self.outQ.put(Result(i, result))
t = Thread(target=thread_task)
t.start()
if end_early:
t.daemon = True
I believe the end result of this is that the Process that spawned the thread will continue to waste CPU cycles on a task I no longer care about the output for, but if the main program finishes, it'll clean up all my memory nicely.
The main problem with daemonizing a thread is that the main program could potentially continue for 30+ minutes even when I don't care about the output of that thread anymore.

From the threading docs:
If you want your threads to stop gracefully, make them non-daemonic
and use a suitable signalling mechanism such as an Event
Here is a contrived example of what I was thinking - no idea if it mimics what you are doing or can be adapted for your situation. Another caveat: I've never written any real concurrent code.
Create an Event object in the main process and pass it all the way to the thread.
Design the thread so that it loops until the Event object is set. Once you don't need the processing anymore SET the Event object in the main process. No need to modify the function being run in the thread.
from multiprocessing import Process, Queue, Event
from threading import Thread
import time, random, os
def f_to_run():
time.sleep(.2)
return random.randint(1,10)
class T(Thread):
def __init__(self, evt,q, func, parent):
self.evt = evt
self.q = q
self.func = func
self.parent = parent
super().__init__()
def run(self):
while not self.evt.is_set():
n = self.func()
self.q.put(f'PID {self.parent}-{self.name}: {n}')
def f(T,evt,q,func):
pid = os.getpid()
t = T(evt,q,func,pid)
t.start()
t.join()
q.put(f'PID {pid}-{t.name} is alive - {t.is_alive()}')
q.put(f'PID {pid}:DONE')
return 'foo done'
if __name__ == '__main__':
results = []
q = Queue()
evt = Event()
# two processes each with one thread
p= Process(target=f, args=(T, evt, q, f_to_run))
p1 = Process(target=f, args=(T, evt, q, f_to_run))
p.start()
p1.start()
while len(results) < 40:
results.append(q.get())
print('.',end='')
print('')
evt.set()
p.join()
p1.join()
while not q.empty():
results.append(q.get_nowait())
for thing in results:
print(thing)
I initially tried to use threading.Event but the multiprocessing module complained that it couldn't be pickled. I was actually surprised that the multiprocessing.Queue and multiprocessing.Event worked AND could be accessed by the thread.
Not sure why I started with a Thread subclass - I think I thought it would be easier to control/specify what happens in it's run method. But it can be done with a function also.
from multiprocessing import Process, Queue, Event
from threading import Thread
import time, random
def f_to_run():
time.sleep(.2)
return random.randint(1,10)
def t1(evt,q, func):
while not evt.is_set():
n = func()
q.put(n)
def g(t1,evt,q,func):
t = Thread(target=t1,args=(evt,q,func))
t.start()
t.join()
q.put(f'{t.name} is alive - {t.is_alive()}')
return 'foo'
if __name__ == '__main__':
q = Queue()
evt = Event()
p= Process(target=g, args=(t1, evt, q, f_to_run))
p.start()
time.sleep(5)
evt.set()
p.join()

Is it right to init multiprocess in class __init__?

from multiprocessing.dummy import Pool as ThreadPool
class TSNew:
def __init__(self):
self.redis_client = redis.StrictRedis(host="172.17.31.147", port=4401, db=0)
self.global_switch = 0
self.pool = ThreadPool(40) # init pool
self.dnn_model = None
self.nnf = None
self.md5sum_nnf = "initialize"
self.thread = threading.Thread(target=self.load_model_item)
self.ts_picked_ids = None
self.thread.start()
self.memory = deque(maxlen=3000)
self.process = threading.Thread(target=self.process_user_dict)
self.process.start()
def load_model_item(self):
'''
code
'''
def predict_memcache(self,user_dict):
'''
code
'''
def process_user_dict(self):
while True:
'''
code to generate user_dicts which is a list
'''
results = self.pool.map(self.predict_memcache, user_dicts)
'''
code
'''
TSNew_ = TSNew()
def get_user_result():
logging.info("----------------come in ------------------")
if request.method == 'POST':
user_dict_json = request.get_data()# userid
if user_dict_json == '' or user_dict_json is None:
logging.info("----------------user_dict_json is ''------------------")
return ''
try:
user_dict = json.loads(user_dict_json)
except:
logging.info("json load error, pass")
return ''
TSNew_.memory.append(user_dict)
logging.info('add to deque TSNew_.memory size: %d PID: %d', len(TSNew_.memory), os.getpid())
logging.info("add to deque userid: %s, nation: %s \n",user_dict['user_id'], user_dict['user_country'])
return 'SUCCESS\n'
#app.route('/', methods=['POST'])
def get_ts_gbdt_id():
return get_user_result()
from werkzeug.contrib.fixers import ProxyFix
app.wsgi_app = ProxyFix(app.wsgi_app)
if __name__ == '__main__':
app.run(host='0.0.0.0', port=4444)
I create a multi thread pool in class __init__ and I use the self.pool
to map the function of predict_memcache.
I have two doubts:
(a) Should I initialize the pool in __init__ or just init it right before
results = self.pool.map(self.predict_memcache, user_dicts)
(b) Since the pool is a multi thread operation and it is executed in the thread of process_user_dict, so is there any hidden error ?
Thanks.

Question (a):
It depends. If you need to run process_user_dict more than once, then it makes sense to start the pool in the constructor and keep it running. Creating a thread pool always comes with some overhead and by keeping the pool alive between calls to process_user_dict you would avoid that additional overhead.
If you just want to process one set of input, you can as well create your pool right inside process_user_dict. But probably not right before results = self.pool.map(self.predict_memcache, user_dicts) because that would create a pool for every iteration of your surrounding while loop.
In your specific case, it does not make any difference. You create your TSNew_ object on module-level, so that it remains alive (and with it the thread pool) while your app is running; the same thread pool from the same TSNew instance is used to process all the requests during the lifetime of app.run().
Since you seem to be using that construct with self.process = threading.Thread(target=self.process_user_dict) as some sort of listener on self.memory, creating the pool in the constructor is functionally equivalent to creating the pool inside of process_user_dict (but outside the loop).
Question (b):
Technically, there is no hidden error by default when creating a thread inside a thread. In the end, any additional thread's ultimate parent is always the MainThread, that is implicitly created for every instance of a Python interpreter. Basically, every time you create a thread inside a Python program, you create a thread in a thread.
Actually, your code does not even create a thread inside a thread. Your self.pool is created inside the MainThread. When the pool is instantiated via self.pool = ThreadPool(40) it creates the desired number (40) of worker threads, plus one worker handler thread, one task handler thread and one result handler thread. All of these are child threads of the MainThread. All you do with regards to your pool inside your thread under self.process is calling its map method to assign tasks to it.
However, I do not really see the point of what you are doing with that self.process here.
Making a guess, I would say that you want to start the loop in process_user_dict to act as kind of a listener on self.memory, so that the pool starts processing user_dict as soon as they start showing up in the deque in self.memory. From what I see you doing in get_user_result, you seem to get one user_dict per request. I understand that you might have concurrent user sessions passing in these dicts, but do you really see benfit from process_user_dict running in an infinite loop over simply calling TSNew_.process_user_dict() after TSNew_.memory.append(user_dict)? You could even omit self.memory completely and pass the dict directly to process_user_dict, unless I am missing something you did not show us.

how to kill zombie processes created by multiprocessing module?

I'm very new to multiprocessing module. And I just tried to create the following: I have one process that's job is to get message from RabbitMQ and pass it to internal queue (multiprocessing.Queue). Then what I want to do is : spawn a process when new message comes in. It works, but after the job is finished it leaves a zombie process not terminated by it's parent. Here is my code:
Main Process:
#!/usr/bin/env python
import multiprocessing
import logging
import consumer
import producer
import worker
import time
import base
conf = base.get_settings()
logger = base.logger(identity='launcher')
request_order_q = multiprocessing.Queue()
result_order_q = multiprocessing.Queue()
request_status_q = multiprocessing.Queue()
result_status_q = multiprocessing.Queue()
CONSUMER_KEYS = [{'queue':'product.order',
'routing_key':'product.order',
'internal_q':request_order_q}]
# {'queue':'product.status',
# 'routing_key':'product.status',
# 'internal_q':request_status_q}]
def main():
# Launch consumers
for key in CONSUMER_KEYS:
cons = consumer.RabbitConsumer(rabbit_q=key['queue'],
routing_key=key['routing_key'],
internal_q=key['internal_q'])
cons.start()
# Check reques_order_q if not empty spaw a process and process message
while True:
time.sleep(0.5)
if not request_order_q.empty():
handler = worker.Worker(request_order_q.get())
logger.info('Launching Worker')
handler.start()
if __name__ == "__main__":
main()
And here is my Worker:
import multiprocessing
import sys
import time
import base
conf = base.get_settings()
logger = base.logger(identity='worker')
class Worker(multiprocessing.Process):
def __init__(self, msg):
super(Worker, self).__init__()
self.msg = msg
self.daemon = True
def run(self):
logger.info('%s' % self.msg)
time.sleep(10)
sys.exit(1)
So after all the messages gets processed I can see processes with ps aux command. But I would really like them to be terminated once finished.
Thanks.

Using multiprocessing.active_children is better than Process.join. The function active_children cleans any zombies created since the last call to active_children. The method join awaits the selected process. During that time, other processes can terminate and become zombies, but the parent process will not notice, until the awaited method is joined. To see this in action:
import multiprocessing as mp
import time
def main():
n = 3
c = list()
for i in range(n):
d = dict(i=i)
p = mp.Process(target=count, kwargs=d)
p.start()
c.append(p)
for p in reversed(c):
p.join()
print('joined')
def count(i):
print(f'{i} going to sleep')
time.sleep(i * 10)
print(f'{i} woke up')
if __name__ == '__main__':
main()
The above will create 3 processes that terminate 10 seconds apart each. As the code is, the last process is joined first, so the other two, which terminated earlier, will be zombies for 20 seconds. You can see them with:
ps aux | grep Z
There will be no zombies if the processes are awaited in the sequence that they will terminate. Remove the call to the function reversed to see this case. However, in real applications we rarely know the sequence that children will terminate, so using the method multiprocessing.Process.join will result in some zombies.
The alternative active_children does not leave any zombies.
In the above example, replace the loop for p in reversed(c): with:
while True:
time.sleep(1)
if not mp.active_children():
break
and see what happens.

A couple of things:
Make sure the parent joins its children, to avoid zombies. See Python Multiprocessing Kill Processes
You can check whether a child is still running with the is_alive() member function. See http://docs.python.org/2/library/multiprocessing.html#multiprocessing.Process

Use active_children.
multiprocessing.active_children