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Return multiprocessing results, Queue or manager.list?

Note: this question is different from that question, notably in when the jobs are dispatched to the workers and when the results are gathered.
So I have this code:
mp_jobqueue = MP.Queue()
mp_mgr = MP.Manager()
mp_state = mp_mgr.dict()
mp_faileds = mp_mgr.list()
# the processing in process_data_worker is very CPU-intensive,
# thus totally not suitable for async.
workers: List[MP.Process] = []
for ident in range(0, WORKER_COUNT):
print(ident, end=" ", flush=True)
mp_state[ident] = None
w = MP.Process(
target=process_data_worker,
args=(mp_jobqueue, mp_state, mp_faileds),
)
w.start()
workers.append(w)
# fetch_data asynchronously fetches chunks of data,
# each chunk will be directly fed into the job queue to be processed
# by the workers
asyncio.run(fetch_data(mp_jobqueue))
# when we reach here, all data-fetching should have been finished
# and submitted to the workers' job queue
# wait until mp_jobqueue is empty AND all workers are IDLE
safed_workers = 0
while not mp_jobqueue.is_empty() or safed_workers < WORKER_COUNT:
time.sleep(1.0)
safed_workers = sum(1 for state in mp_state.values() if state == "IDLE")
# gather failed results
faileds = list(mp_faileds)
# close manager first to prevent GetOverlappedResult error
mp_mgr.shutdown()
mp_mgr.join()
# disband the workers
[mp_jobqueue.put("DIE") for _ in workers]
time.sleep(1.0)
mp_jobqueue.close()
[w.join() for w in workers]
So as you can see, I cannot use pool.map() to gather the "faileds".
This got me thinking, though:
Will it be better (performance-wise) to use another Queue for mp_faileds instead of a list like it is now? Because I only need an object that can handle "add into bag" and "take out from bag until bag is empty".
Edit: Just found out about multiprocessing.queues.SimpleQueue. The answers to this question, notably this particular answer, seems to hint that SimpleQueue might be even faster. Can someone confirm?

Python multiprocessing finish the work correctly, but the processes still alive (Linux)

I use python multiprocessing to compute some sort of scores on DNA sequences from a large file.
For that I write and use the script below.
I use a Linux machine with 48 cpu in python 3.8 environment.
Th code work fine, and terminate the work correctly and print the processing time at the end.
Problem: when I use the htop command, I find that all 48 processes are still alive.
I don't know why, and I don't know what to add to my script to avoid this.
import csv
import sys
import concurrent.futures
from itertools import combinations
import psutil
import time
nb_cpu = psutil.cpu_count(logical=False)
def fun_job(seq_1, seq_2): # seq_i : (id, string)
start = time.time()
score_dist = compute_score_dist(seq_1[1], seq_2[1])
end = time.time()
return seq_1[0], seq_2[0], score_dist, end - start # id seq1, id seq2, score, time
def help_fun_job(nested_pair):
return fun_job(nested_pair[0], nested_pair[1])
def compute_using_multi_processing(list_comb_ids, dict_ids_seqs):
start = time.perf_counter()
with concurrent.futures.ProcessPoolExecutor(max_workers=nb_cpu) as executor:
results = executor.map(help_fun_job,
[((pair_ids[0], dict_ids_seqs[pair_ids[0]]), (pair_ids[1], dict_ids_seqs[pair_ids[1]]))
for pair_ids in list_comb_ids])
save_results_to_csv(results)
finish = time.perf_counter()
proccessing_time = str(datetime.timedelta(seconds=round(finish - start, 2)))
print(f' Processing time Finished in {proccessing_time} hh:mm:ss')
def main():
print("nb_cpu in this machine : ", nb_cpu)
file_path = sys.argv[1]
dict_ids_seqs = get_dict_ids_seqs(file_path)
list_ids = list(dict_ids_seqs) # This will convert the dict_keys to a list
list_combined_ids = list(combinations(list_ids, 2))
compute_using_multi_processing(list_combined_ids, dict_ids_seqs)
if __name__ == '__main__':
main()
Thank you for your help.
Edit : add the complete code for fun_job (after #Booboo answer)
from Bio import Align
def fun_job(seq_1, seq_2): # seq_i : (id, string)
start = time.time()
aligner = Align.PairwiseAligner()
aligner.mode = 'global'
score_dist = aligner.score(seq_1[1],seq_2[1])
end = time.time()
return seq_1[0], seq_2[0], score_dist, end - start # id seq1, id seq2, score, time

When the with ... as executor: block exits, there is an implicit call to executor.shutdown(wait=True). This will wait for all pending futures to to be done executing "and the resources associated with the executor have been freed", which presumably includes terminating the processes in the pool (if possible?). Why your program terminates (or does it?) or at least you say all the futures have completed executing, while the processes have not terminated is a bit of a mystery. But you haven't provided the code for fun_job, so who can say why this is so?
One thing you might try is to switch to using the multiprocessing.pool.Pool class from the multiprocessing module. It supports a terminate method, which is implicitly called when its context manager with block exits, that explicitly attempts to terminate all processes in the pool:
#import concurrent.futures
import multiprocessing
... # etc.
def compute_using_multi_processing(list_comb_ids, dict_ids_seqs):
start = time.perf_counter()
with multiprocessing.Pool(processes=nb_cpu) as executor:
results = executor.map(help_fun_job,
[((pair_ids[0], dict_ids_seqs[pair_ids[0]]), (pair_ids[1], dict_ids_seqs[pair_ids[1]]))
for pair_ids in list_comb_ids])
save_results_to_csv(results)
finish = time.perf_counter()
proccessing_time = str(datetime.timedelta(seconds=round(finish - start, 2)))
print(f' Processing time Finished in {proccessing_time} hh:mm:ss')

Stop multiprocess pool when a condition is met and continue with program

I've been trying to wrap my head around multiprocessing using an old python bitcoin mining program. Although relatively useless for mining, I figured this would be a great way to explore multiprocessing. However, I've hit a wall when it comes to stopping the processes when one of them achieves the goal they are all working towards.
I want to kill all multiprocessing pools when one of them finds the solution. Then allow the program to continue. I have tried terminate() and join(). I've attempted to include an Event(). I've tried using Process instead of Pool with the direction of a similar issue here: Killing a multiprocessing process when condition is met. However, same problem. How can I stop all processes after a condition is met without exiting the program with something like sys.exit() that would kill the entire program?
I tried also apply_sync with the direction from this post: Python Multiprocess Pool. How to exit the script when one of the worker process determines no more work needs to be done? However, it did not solve the problem of needing to continue executing the final functions of the program. In fact, it actually slowed the program significantly.
For clarity, I've included the code I tried based on the above mentioned link here:
from multiprocessing import Pool
from hashlib import sha256
import time
def SHA256(text):
return sha256(text.encode("ascii")).hexdigest()
def solution_helper(args):
solution, nonce = do_job(args)
if solution:
print(f"\nNonce Found: {nonce}\n")
return True
else:
return False
class Mining():
def __init__(self, workers, initargs):
self.pool = Pool(processes=workers, initargs=initargs)
def callback(self, result):
if result:
print('Solution Found...Terminating Processes...')
self.pool.terminate()
def do_job(self):
for args in values:
start_nonce = args[0]
end_nonce = args[1]
prefix_str = '0'*difficulty
self.pool.apply_async(solution_helper, args=args, callback=self.callback)
start = time.time()
for nonce in range(start_nonce, end_nonce):
text = str(block_number) + transactions + previous_hash + str(nonce)
new_hash = SHA256(text)
if new_hash.startswith(prefix_str):
print(f"Hashing: {text}")
print(f"\nSuccessfully mined bitcoin with nonce value: {nonce}\n")
print(f"New hash: {new_hash}")
total_time = str((time.time()-start))
print(f"\nEnd mning... Mining took {total_time} seconds\n")
return new_hash, nonce
self.pool.close()
self.pool.join()
print('.Goodbye.')
block_number = 5
transactions = """
bill->steve->20,
jan->phillis->45
"""
previous_hash = '0000000b7c7723e4d3a8654c975fe4dd23d4d37f22d0ea7e5abde2225d1567dc6'
values = [(20000, 100000), (100000, 1000000), (1000000, 10000000), (10000000, 100000000)]
difficulty = 4
m = Mining(5, values)
m.do_job()
Here's the basic concept. It works great to start the processes, but I cannot figure out how to stop them:
from multiprocessing import Pool
from hashlib import sha256
import functools
MAX_NONCE = 1000000000
def SHA256(text):
return sha256(text.encode("ascii")).hexdigest()
def nonce(block_number, transactions, previous_hash, prefix_str):
import time
start = time.time()
for nonce in range(MAX_NONCE):
text = str(block_number) + transactions + previous_hash + str(nonce)
new_hash = SHA256(text)
if new_hash.startswith(prefix_str):
print(f"\nYay! Successfully mined bitcoins with nonce value:{nonce}")
total_time = str((time.time()-start))
print(f"\nend mining. Mining took: {total_time} seconds\n")
print(new_hash + "\n")
def mine(block_number, transactions, previous_hash, prefix_zeros):
from multiprocessing import Pool
with Pool(4) as p:
prefix_str = '0'*prefix_zeros
p.map(nonce(block_number, transactions, previous_hash, prefix_str), [20000, 40000, 60000, 80000, 100000])
if __name__=='__main__':
transactions="""
bill->steve->20,
jan->phillis->45
"""
difficulty=7
print("\nstart mining\n")
new_hash = mine(5, transactions, '0000000b7c7723e4d3a8654c975fe4dd23d4d37f22d0ea7e5abde2225d1567dc6', difficulty)
# Do some other things... Here is where I'd like to get to after the multiproccesses are killed
print(f"\nMission Complete...{new_hash}\n") <---This never gets a chance to happen

Python multi-threading method

I've heard that Python multi-threading is a bit tricky, and I am not sure what is the best way to go about implementing what I need. Let's say I have a function called IO_intensive_function that does some API call which may take a while to get a response.
Say the process of queuing jobs can look something like this:
import thread
for job_args in jobs:
thread.start_new_thread(IO_intense_function, (job_args))
Would the IO_intense_function now just execute its task in the background and allow me to queue in more jobs?
I also looked at this question, which seems like the approach is to just do the following:
from multiprocessing.dummy import Pool as ThreadPool
pool = ThreadPool(2)
results = pool.map(IO_intensive_function, jobs)
As I don't need those tasks to communicate with each other, the only goal is to send my API requests as fast as possible. Is this the most efficient way? Thanks.
Edit:
The way I am making the API request is through a Thrift service.

I had to create code to do something similar recently. I've tried to make it generic below. Note I'm a novice coder, so please forgive the inelegance. What you may find valuable, however, is some of the error processing I found it necessary to embed to capture disconnects, etc.
I also found it valuable to perform the json processing in a threaded manner. You have the threads working for you, so why go "serial" again for a processing step when you can extract the info in parallel.
It is possible I will have mis-coded in making it generic. Please don't hesitate to ask follow-ups and I will clarify.
import requests
from multiprocessing.dummy import Pool as ThreadPool
from src_code.config import Config
with open(Config.API_PATH + '/api_security_key.pem') as f:
my_key = f.read().rstrip("\n")
f.close()
base_url = "https://api.my_api_destination.com/v1"
headers = {"Authorization": "Bearer %s" % my_key}
itm = list()
itm.append(base_url)
itm.append(headers)
def call_API(call_var):
base_url = call_var[0]
headers = call_var[1]
call_specific_tag = call_var[2]
endpoint = f'/api_path/{call_specific_tag}'
connection_tries = 0
for i in range(3):
try:
dat = requests.get((base_url + endpoint), headers=headers).json()
except:
connection_tries += 1
print(f'Call for {api_specific_tag} failed after {i} attempt(s). Pausing for 240 seconds.')
time.sleep(240)
else:
break
tag = list()
vars_to_capture_01 = list()
vars_to_capture_02 = list()
connection_tries = 0
try:
if 'record_id' in dat:
vars_to_capture_01.append(dat['record_id'])
vars_to_capture_02.append(dat['second_item_of_interest'])
else:
vars_to_capture_01.append(call_specific_tag)
print(f'Call specific tag {call_specific_tag} is unavailable. Successful pull.')
vars_to_capture_02.append(-1)
except:
print(f'{call_specific_tag} is unavailable. Unsuccessful pull.')
vars_to_capture_01.append(call_specific_tag)
vars_to_capture_02.append(-1)
time.sleep(240)
pack = list()
pack.append(vars_to_capture_01)
pack.append(vars_to_capture_02)
return pack
vars_to_capture_01 = list()
vars_to_capture_02 = list()
i = 0
max_i = len(all_tags)
while i < max_i:
ind_rng = range(i, min((i + 10), (max_i)), 1)
itm_lst = (itm.copy())
call_var = [itm_lst + [all_tags[q]] for q in ind_rng]
#packed = call_API(call_var[0]) # for testing of function without pooling
pool = ThreadPool(len(call_var))
packed = pool.map(call_API, call_var)
pool.close()
pool.join()
for pack in packed:
try:
vars_to_capture_01.append(pack[0][0])
except:
print(f'Unpacking error for {all_tags[i]}.')
vars_to_capture_02.append(pack[1][0])

For network API request you can use asyncio. Have a look at this article https://realpython.com/python-concurrency/#asyncio-version for an example how to implement it.

How would I go about using concurrent.futures and queues for a real-time scenario?

It is fairly easy to do parallel work with Python 3's concurrent.futures module as shown below.
with concurrent.futures.ThreadPoolExecutor(max_workers=10) as executor:
future_to = {executor.submit(do_work, input, 60): input for input in dictionary}
for future in concurrent.futures.as_completed(future_to):
data = future.result()
It is also very handy to insert and retrieve items into a Queue.
q = queue.Queue()
for task in tasks:
q.put(task)
while not q.empty():
q.get()
I have a script running in background listening for updates. Now, in theory assume that, as those updates arrive, I would queue them and do work on them concurrently using the ThreadPoolExecutor.
Now, individually, all of these components work in isolation, and make sense, but how do I go about using them together? I am not aware if it is possible to feed the ThreadPoolExecutor work from the queue in real time unless the data to work from is predetermined?
In a nutshell, all I want to do is, receive updates of say 4 messages a second, shove them in a queue, and get my concurrent.futures to work on them. If I don't, then I am stuck with a sequential approach which is slow.
Let's take the canonical example in the Python documentation below:
with concurrent.futures.ThreadPoolExecutor(max_workers=5) as executor:
future_to_url = {executor.submit(load_url, url, 60): url for url in URLS}
for future in concurrent.futures.as_completed(future_to_url):
url = future_to_url[future]
try:
data = future.result()
except Exception as exc:
print('%r generated an exception: %s' % (url, exc))
else:
print('%r page is %d bytes' % (url, len(data)))
The list of URLS is fixed. Is it possible to feed this list in real-time and get the worker to process it as they come by, perhaps from a queue for management purposes? I am a bit confused on whether my approach is actually possible?

The example from the Python docs, expanded to take its work from a queue. A change to note, is that this code uses concurrent.futures.wait instead of concurrent.futures.as_completed to allow new work to be started while waiting for other work to complete.
import concurrent.futures
import urllib.request
import time
import queue
q = queue.Queue()
URLS = ['http://www.foxnews.com/',
'http://www.cnn.com/',
'http://europe.wsj.com/',
'http://www.bbc.co.uk/',
'http://some-made-up-domain.com/']
def feed_the_workers(spacing):
""" Simulate outside actors sending in work to do, request each url twice """
for url in URLS + URLS:
time.sleep(spacing)
q.put(url)
return "DONE FEEDING"
def load_url(url, timeout):
""" Retrieve a single page and report the URL and contents """
with urllib.request.urlopen(url, timeout=timeout) as conn:
return conn.read()
# We can use a with statement to ensure threads are cleaned up promptly
with concurrent.futures.ThreadPoolExecutor(max_workers=5) as executor:
# start a future for a thread which sends work in through the queue
future_to_url = {
executor.submit(feed_the_workers, 0.25): 'FEEDER DONE'}
while future_to_url:
# check for status of the futures which are currently working
done, not_done = concurrent.futures.wait(
future_to_url, timeout=0.25,
return_when=concurrent.futures.FIRST_COMPLETED)
# if there is incoming work, start a new future
while not q.empty():
# fetch a url from the queue
url = q.get()
# Start the load operation and mark the future with its URL
future_to_url[executor.submit(load_url, url, 60)] = url
# process any completed futures
for future in done:
url = future_to_url[future]
try:
data = future.result()
except Exception as exc:
print('%r generated an exception: %s' % (url, exc))
else:
if url == 'FEEDER DONE':
print(data)
else:
print('%r page is %d bytes' % (url, len(data)))
# remove the now completed future
del future_to_url[future]
Output from fetching each url twice:
'http://www.foxnews.com/' page is 67574 bytes
'http://www.cnn.com/' page is 136975 bytes
'http://www.bbc.co.uk/' page is 193780 bytes
'http://some-made-up-domain.com/' page is 896 bytes
'http://www.foxnews.com/' page is 67574 bytes
'http://www.cnn.com/' page is 136975 bytes
DONE FEEDING
'http://www.bbc.co.uk/' page is 193605 bytes
'http://some-made-up-domain.com/' page is 896 bytes
'http://europe.wsj.com/' page is 874649 bytes
'http://europe.wsj.com/' page is 874649 bytes

At work I found a situation where I wanted to do parallel work on an unbounded stream of data. I created a small library inspired by the excellent answer already provided by Stephen Rauch.
I originally approached this problem by thinking about two separate threads, one that submits work to a queue and one that monitors the queue for any completed tasks and makes more room for new work to come in. This is similar to what Stephen Rauch proposed, where he consumes the stream using a feed_the_workers function that runs in a separate thread.
Talking to one of my colleagues, he helped me realize that you can get away with doing everything in a single thread if you define a buffered iterator that allows you to control how many elements are let out of the input stream every time you are ready to submit more work to the thread pool.
So we introduce the BufferedIter class
class BufferedIter(object):
def __init__(self, iterator):
self.iter = iterator
def nextN(self, n):
vals = []
for _ in range(n):
vals.append(next(self.iter))
return vals
which allows us to define the stream processor in the following way
import logging
import queue
import signal
import sys
import time
from concurrent.futures import ThreadPoolExecutor, wait, ALL_COMPLETED
level = logging.DEBUG
log = logging.getLogger(__name__)
handler = logging.StreamHandler(sys.stdout)
handler.setFormatter(logging.Formatter('%(asctime)s %(message)s'))
handler.setLevel(level)
log.addHandler(handler)
log.setLevel(level)
WAIT_SLEEP = 1 # second, adjust this based on the timescale of your tasks
def stream_processor(input_stream, task, num_workers):
# Use a queue to signal shutdown.
shutting_down = queue.Queue()
def shutdown(signum, frame):
log.warning('Caught signal %d, shutting down gracefully ...' % signum)
# Put an item in the shutting down queue to signal shutdown.
shutting_down.put(None)
# Register the signal handler
signal.signal(signal.SIGTERM, shutdown)
signal.signal(signal.SIGINT, shutdown)
def is_shutting_down():
return not shutting_down.empty()
futures = dict()
buffer = BufferedIter(input_stream)
with ThreadPoolExecutor(num_workers) as executor:
num_success = 0
num_failure = 0
while True:
idle_workers = num_workers - len(futures)
if not is_shutting_down():
items = buffer.nextN(idle_workers)
for data in items:
futures[executor.submit(task, data)] = data
done, _ = wait(futures, timeout=WAIT_SLEEP, return_when=ALL_COMPLETED)
for f in done:
data = futures[f]
try:
f.result(timeout=0)
except Exception as exc:
log.error('future encountered an exception: %r, %s' % (data, exc))
num_failure += 1
else:
log.info('future finished successfully: %r' % data)
num_success += 1
del futures[f]
if is_shutting_down() and len(futures) == 0:
break
log.info("num_success=%d, num_failure=%d" % (num_success, num_failure))
Below we show an example for how to use the stream processor
import itertools
def integers():
"""Simulate an infinite stream of work."""
for i in itertools.count():
yield i
def task(x):
"""The task we would like to perform in parallel.
With some delay to simulate a time consuming job.
With a baked in exception to simulate errors.
"""
time.sleep(3)
if x == 4:
raise ValueError('bad luck')
return x * x
stream_processor(integers(), task, num_workers=3)
The output for this example is shown below
2019-01-15 22:34:40,193 future finished successfully: 1
2019-01-15 22:34:40,193 future finished successfully: 0
2019-01-15 22:34:40,193 future finished successfully: 2
2019-01-15 22:34:43,201 future finished successfully: 5
2019-01-15 22:34:43,201 future encountered an exception: 4, bad luck
2019-01-15 22:34:43,202 future finished successfully: 3
2019-01-15 22:34:46,208 future finished successfully: 6
2019-01-15 22:34:46,209 future finished successfully: 7
2019-01-15 22:34:46,209 future finished successfully: 8
2019-01-15 22:34:49,215 future finished successfully: 11
2019-01-15 22:34:49,215 future finished successfully: 10
2019-01-15 22:34:49,215 future finished successfully: 9
^C <=== THIS IS WHEN I HIT Ctrl-C
2019-01-15 22:34:50,648 Caught signal 2, shutting down gracefully ...
2019-01-15 22:34:52,221 future finished successfully: 13
2019-01-15 22:34:52,222 future finished successfully: 14
2019-01-15 22:34:52,222 future finished successfully: 12
2019-01-15 22:34:52,222 num_success=14, num_failure=1

I really liked the interesting approach by #pedro above. However, when processing thousands of files, I noticed that at the end a StopIteration would be thrown and some files would always be skipped. I had to make a little modification to as follows. Very useful answer again.
class BufferedIter(object):
def __init__(self, iterator):
self.iter = iterator
def nextN(self, n):
vals = []
try:
for _ in range(n):
vals.append(next(self.iter))
return vals, False
except StopIteration as e:
return vals, True
-- Call as follows
...
if not is_shutting_down():
items, is_finished = buffer.nextN(idle_workers)
if is_finished:
stop()
...
-- Where stop is a function that simply tells to shutdown
def stop():
shutting_down.put(None)

It is possible to gain the benefits of the executor without strictly having to use a Queue. New tasks are submitted from the main thread. The undone futures are tracked and waited on until all futures are done.
import concurrent.futures
import sys
import time
sys.setrecursionlimit(64) # This is only for demonstration purposes to trigger a RecursionError. Do not set in practice.
def slow_factorial(n: int) -> int:
time.sleep(0.01)
if n == 0:
return 1
else:
return n * slow_factorial(n-1)
initial_inputs = [0, 1, 5, 20, 200, 100, 50, 51, 55, 40, 44, 21, 222, 333, 202, 1000, 10, 9000, 9009, 99, 9999]
for executor_class in (concurrent.futures.ThreadPoolExecutor, concurrent.futures.ProcessPoolExecutor):
for max_workers in (4, 8, 16, 32):
start_time = time.monotonic()
with executor_class(max_workers=max_workers) as executor:
futures_to_n = {executor.submit(slow_factorial, n): n for n in initial_inputs}
while futures_to_n:
futures_done, futures_not_done = concurrent.futures.wait(futures_to_n, return_when=concurrent.futures.FIRST_COMPLETED)
# Note: Length of futures_done is often > 1.
for future in futures_done:
n = futures_to_n.pop(future)
try:
factorial_n = future.result()
except RecursionError:
n_smaller = int(n ** 0.9)
future = executor.submit(slow_factorial, n_smaller)
futures_to_n[future] = n_smaller
# print(f'Failed to compute factorial of {n}. Trying to compute factorial of a smaller number {n_smaller} instead.')
else:
# print(f'Factorial of {n} is {factorial_n}.')
pass
used_time = time.monotonic() - start_time
executor_type = executor_class.__name__.removesuffix('PoolExecutor').lower()
print(f'Workflow took {used_time:.1f}s with {max_workers} {executor_type} workers.')
print()
Output:
Workflow took 9.4s with 4 thread workers.
Workflow took 6.3s with 8 thread workers.
Workflow took 5.4s with 16 thread workers.
Workflow took 5.2s with 32 thread workers.
Workflow took 9.0s with 4 process workers.
Workflow took 5.9s with 8 process workers.
Workflow took 5.1s with 16 process workers.
Workflow took 4.9s with 32 process workers.
For more clarity, uncomment the two print statements. As per the output above, there is an asymptotic speed benefit with more workers.