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Is multiprocessing.Pool not allowed in Airflow task? - AssertionError: daemonic processes are not allowed to have children

Our airflow project has a task that queries from BigQuery and uses Pool to dump in parallel to local JSON files:
def dump_in_parallel(table_name):
base_query = f"select * from models.{table_name}"
all_conf_ids = range(1,10)
n_jobs = 4
with Pool(n_jobs) as p:
p.map(partial(dump_conf_id, base_query = base_query), all_conf_ids)
with open("/tmp/final_output.json", "wb") as f:
filenames = [f'/tmp/output_file_{i}.json' for i in all_conf_ids]
This task was working fine for us in airflow v1.10, but is no longer working in v2.1+. Section 2.1 here - https://blog.mbedded.ninja/programming/languages/python/python-multiprocessing/ - mentions "If you try and create a Pool from within a child worker that was already created with a Pool, you will run into the error: daemonic processes are not allowed to have children"
Here is the full Airflow error:
[2021-08-22 02:11:53,064] {taskinstance.py:1462} ERROR - Task failed with exception
Traceback (most recent call last):
File "/usr/local/lib/python3.7/site-packages/airflow/models/taskinstance.py", line 1164, in _run_raw_task
self._prepare_and_execute_task_with_callbacks(context, task)
File "/usr/local/lib/python3.7/site-packages/airflow/models/taskinstance.py", line 1282, in _prepare_and_execute_task_with_callbacks
result = self._execute_task(context, task_copy)
File "/usr/local/lib/python3.7/site-packages/airflow/models/taskinstance.py", line 1312, in _execute_task
result = task_copy.execute(context=context)
File "/usr/local/lib/python3.7/site-packages/airflow/operators/python.py", line 150, in execute
return_value = self.execute_callable()
File "/usr/local/lib/python3.7/site-packages/airflow/operators/python.py", line 161, in execute_callable
return self.python_callable(*self.op_args, **self.op_kwargs)
File "/usr/local/airflow/plugins/tasks/bigquery.py", line 249, in dump_in_parallel
with Pool(n_jobs) as p:
File "/usr/local/lib/python3.7/multiprocessing/context.py", line 119, in Pool
context=self.get_context())
File "/usr/local/lib/python3.7/multiprocessing/pool.py", line 176, in __init__
self._repopulate_pool()
File "/usr/local/lib/python3.7/multiprocessing/pool.py", line 241, in _repopulate_pool
w.start()
File "/usr/local/lib/python3.7/multiprocessing/process.py", line 110, in start
'daemonic processes are not allowed to have children'
AssertionError: daemonic processes are not allowed to have children
If it matters, we run airflow using the LocalExecutor. Any idea why this task that uses Pool would have been working in airflow v1.10 but no longer in airflow 2.1?

Airflow 2 uses different processing model under the hood to speed up processing, yet to maintain process-based isolation between running tasks.
That's why it uses forking and multiprocessing under the hook to run Tasks, but this also means that if you are using multiprocessing, you will hit the limits of Python multiprocessing that does not allow to chain multi-processing.
I am not 100% sure if it will work but you might try to set execute_tasks_new_python_interpreter configuration to True. https://airflow.apache.org/docs/apache-airflow/stable/configurations-ref.html#execute-tasks-new-python-interpreter . This setting will cause airflow to start a new Python interpreter when running task instead of forking/using multiprocessing (though I am not 100% sure of the latter). It will work quite a bit slower (up to a few seconds of overhead) though to run your task as the new Python interpreter will have to reinitialize and import all the airflow code before running your task.
If that does not work, then you can lunch your multiprocessing job using PythonVirtualenvOperator - that one will launch a new Python interpreter to run your python code and you should be able to use multiprocessing.

Replacing the multiprocessing library with billiard library works, per https://github.com/celery/celery/issues/4525. We have no idea why subbing one library in for the other resolves this issue though...

You can switch to joblib python library with lock backend and kill daemonic processes after execution with following instructions.

"Dictionary size changed during iteration" from Pebble ProcessPool

We've some parallel processing code which is built around Pebble, it's been working robustly for quite some time but we seem to have run into some odd edge-case.
Based on the exception trace (and the rock-simple code feeding it) I suspect that it's actually a bug in Pebble but who knows.
The code feeding the process pool is pretty trivial:
pool = ProcessPool(max_workers=10, max_tasks=10)
for path in filepaths:
try:
future = pool.schedule(function=self.analyse_file, args(path), timeout=30)
future.add_done_callback(self.process_result)
exception Exception as e:
print("Exception fired:" + e) # NOT where the exception is firing
pool.close()
pool.join()
So in essence, we schedule a bunch of stuff to run, close out the pool then wait for the pool to complete the scheduled tasks. NOTE: the exception is not being thrown in the schedule loop, it gets fired AFTER we call join().
This is the exception stack trace:
Traceback (most recent call last):
File "/home/user/.pyenv/versions/3.6.0/lib/python3.6/threading.py", line 916, in _bootstrap_inner
self.run()
File "/home/user/.pyenv/versions/3.6.0/lib/python3.6/threading.py", line 864, in run
self._target(*self._args, **self._kwargs)
File "/home/user/.pyenv/versions/scrapeapp/lib/python3.6/site-packages/pebble/pool/process.py", line 150, in task_scheduler_loop
pool_manager.schedule(task)
File "/home/user/.pyenv/versions/scrapeapp/lib/python3.6/site-packages/pebble/pool/process.py", line 198, in schedule
self.worker_manager.dispatch(task)
File "/home/user/.pyenv/versions/scrapeapp/lib/python3.6/site-packages/pebble/pool/process.py", line 327, in dispatch
self.pool_channel.send(WorkerTask(task.id, task.payload))
File "/home/user/.pyenv/versions/scrapeapp/lib/python3.6/site-packages/pebble/pool/channel.py", line 66, in send
return self.writer.send(obj)
File "/home/user/.pyenv/versions/3.6.0/lib/python3.6/multiprocessing/connection.py", line 206, in send
self._send_bytes(_ForkingPickler.dumps(obj))
File "/home/user/.pyenv/versions/3.6.0/lib/python3.6/multiprocessing/reduction.py", line 51, in dumps
cls(buf, protocol).dump(obj)
RuntimeError: dictionary changed size during iteration
I think it's got to be some weird race condition, as the code will work flawlessly on some data sets but fail at what appears to be a random point on another dataset.
We were using pebble 4.3.1 when we first ran into the issue (same version we'd had since the beginning), tried upgrading to 4.5.0, no change.
Has anybody run into similar issues with Pebble in the past? If so what was your fix?

Running threaded module with celery; daemonic processes are not allowed to have children

I have implemented a queue with celery in my flask app. Everything works good.
But I need to use this module called sublist3r and when I use it in a celery task I recieve this error:
[2019-02-16 21:32:52,658: INFO/ForkPoolWorker-6] Task tasks.task.addd[57793628-de25-4c89-a265-5fee69a8b2bf] succeeded in 0.0236732449848s: None
[2019-02-16 21:32:52,660: WARNING/ForkPoolWorker-6] Exception in thread Thread-1:
Traceback (most recent call last):
File "/usr/lib/python2.7/threading.py", line 801, in __bootstrap_inner
self.run()
File "/usr/lib/python2.7/threading.py", line 754, in run
self.__target(*self.__args, **self.__kwargs)
File "/home/me/code/proj/tasks/task.py", line 15, in getd
sub = sublist3r.main(url, 40, None, ports=None, silent=True,verbose=False, enable_bruteforce=False, engines=None)
File "/home/me/code/proj/sublist3r/sublist3r.py", line 871, in main
subdomains_queue = multiprocessing.Manager().list()
File "/usr/lib/python2.7/multiprocessing/__init__.py", line 99, in Manager
m.start()
File "/usr/lib/python2.7/multiprocessing/managers.py", line 524, in start
self._process.start()
File "/usr/lib/python2.7/multiprocessing/process.py", line 124, in start
'daemonic processes are not allowed to have children'
**AssertionError: daemonic processes are not allowed to have children**
Does this happen because I'm trying to use a module that uses threads?
How could I achieve using this module either in a queue or asynchronously?
Thank you

It appears that sublist3r uses multiprocessing and tries to kick off its own processes. You can't really do that within celery, because in production, celery will already kick off a worker in its own child process, and as you can tell from the error message, celery will not allow you to kick of the multiprocessing processes that sublist3r uses. If you want to use it, your best bet is to rewrite those classes in sublist3r yourself to derive from celery.Task instead of multiprocessing.Process.

Could not terminate process when use paramiko for command execution

I have a python script which does remote command execution, and I want to use multiprocessing in it.
I managed to get the program itself working, but when trying to interrupt it using CTRL+C, I get the following exception, and the program doesn't terminate correctly:
Traceback (most recent call last):
File "/usr/lib/python2.7/multiprocessing/process.py", line 258, in _bootstrap
self.run()
File "/usr/lib/python2.7/multiprocessing/process.py", line 114, in run
self._target(*self._args, **self._kwargs)
File "/usr/lib/python2.7/multiprocessing/pool.py", line 102, in worker
task = get()
File "/usr/lib/python2.7/multiprocessing/queues.py", line 376, in get
return recv()
KeyboardInterrupt
Caught KeyboardInterrupt, terminating workers
What am I doing wrong?

CTRL+C sends a SIGINT to the python process, which triggers a KeyboardInterrupt exception. Here, your parent process hits this exception, and since you are not catching it, it exits brutally, as well as its children.
If you want to handle CTRL+C, you need to catch this KeyboardInterrupt exception in your parent process. An example is given in this answer for instance. If you share your code we may find something more specific to your case.

Error in Python multiprocessing process

I am trying a write a python code having multiple processes whose structure and flow is something like this:
import multiprocessing
import ctypes
import time
import errno
m=multiprocessing.Manager()
mylist=m.list()
var1=m.Value('i',0)
var2=m.Value('i',1)
var3=m.Value('i',2)
var4=m.Value(ctypes.c_char_p,"a")
var5=m.Value(ctypes.c_char_p,"b")
var6=3
var7=4
var8=5
var9=6
var10=7
def func(var1,var2,var4,var5,mylist):
i=0
try:
if var1.value==0:
print var2.value,var4.value,var5.value
mylist.append(time.time())
elif var1.value==1:
i=i+2
print var2.value+2,var4.value,var5.value
mylist.append(time.time())
except IOError as e:
if e.errno==errno.EPIPE:
var3.value=var3.value+1
print "Error"
def work():
for i in range(var3.value):
print i,var6,var7,va8,var9,var10
p=multiprocessing.Process(target=func,args=(var1,var2,var4,var5,mylist))
p.start()
work()
When I run this code, sometimes it works perfectly, sometimes it does not run for exact amount of loop counts and sometimes I get following error:
0
1
Process Process-2:
Traceback (most recent call last):
File "/usr/lib64/python2.6/multiprocessing/process.py", line 232, in _bootstrap
self.run()
File "/usr/lib64/python2.6/multiprocessing/process.py", line 88, in run
self._target(*self._args, **self._kwargs)
File "dummy.py", line 19, in func
if var1.value==0:
File "/usr/lib64/python2.6/multiprocessing/managers.py", line 1005, in get
return self._callmethod('get')
File "/usr/lib64/python2.6/multiprocessing/managers.py", line 722, in _callmethod
self._connect()
File "/usr/lib64/python2.6/multiprocessing/managers.py", line 709, in _connect
conn = self._Client(self._token.address, authkey=self._authkey)
File "/usr/lib64/python2.6/multiprocessing/connection.py", line 149, in Client
answer_challenge(c, authkey)
File "/usr/lib64/python2.6/multiprocessing/connection.py", line 383, in answer_challenge
message = connection.recv_bytes(256) # reject large message
EOFError
What does this error mean? What wrong am I doing here? What this error indicates? Kindly guide me to the correct path. I am using CentOS 6.5

Working with shared variables in multiprocessing is tricky. Because of the python Global Interpreter Lock (GIL), multiprocessing is not directly possible in Python. When you use the multiprocessing module, you can launch several task on different process, BUT you can't share the memory.
In you case, you need this so you try to use shared memory. But what happens here is that you have several processes trying to read the same memory at the same time. To avoid memory corruption, a process lock the memory address it is currently reading, forbidding other processes to access it until it finishes reading.
Here you have 3 processes trying to evaluate var1.value in the first if loop of your func : the first process read the value, and the other are blocked, raising an error.
To avoid this mechanism, you should always manage the Lock of your shared variables yourself.
You can try with syntax:
var1=multiprocessing.Value('i',0) # create shared variable
var1.acquire() # get the lock : it will wait until lock is available
var1.value # read the value
var1.release() # release the lock
External documentation :
Locks : https://docs.python.org/2/librar/multiprocessing.html#synchronization-between-processes
GIL : https://docs.python.org/2/glossary.html#term-global-interpreter-lock