After looking over Thread/Queue model, I would like to ask how to create something in a similar fashion:
I have, for example, 100 items that need processing, but I want to process them 20 at a time - so there are 20 slots, and once each slot is emptied ( item finished processing ) next one would be seated and started processing.
The limit doesn't need to be 20, it could be adjusted ( 30, 50 etc) ?
Thank you for your suggestions and answers!
Use multiprocessing:
import multiprocessing
import time
def process(x):
time.sleep(1)
return x
if __name__ == '__main__':
jobs = range(100)
pool = multiprocessing.Pool(20)
for result in pool.imap_unordered(process, jobs):
print(result)
pool.close()
pool.join()
Related
I'm working on an optimization problem, and you can see a simplified version of my code posted below (the origin code is too complicated for asking such a question, and I hope my simplified code has simulated the original one as much as possible).
My purpose:
use the function foo in the function optimization, but foo can take very long time due to some hard situations. So I use multiprocessing to set a time limit for execution of the function (proc.join(iter_time), the method is from an anwser from this question; How to limit execution time of a function call?).
My problem:
In the while loop, every time the generated values for extra are the same.
The list lst's length is always 1, which means in every iteration in the while loop it starts from an empty list.
My guess: possible reason can be each time I create a process the random seed is counting from the beginning, and each time the process is terminated, there could be some garbage collection mechanism to clean the memory the processused, so the list is cleared.
My question
Anyone know the real reason of such problems?
if not using multiprocessing, is there anyway else that I can realize my purpose while generate different random numbers? btw I have tried func_timeout but it has other problems that I cannot handle...
random.seed(123)
lst = [] # a global list for logging data
def foo(epoch):
...
extra = random.random()
lst.append(epoch + extra)
...
def optimization(loop_time, iter_time):
start = time.time()
epoch = 0
while time.time() <= start + loop_time:
proc = multiprocessing.Process(target=foo, args=(epoch,))
proc.start()
proc.join(iter_time)
if proc.is_alive(): # if the process is not terminated within time limit
print("Time out!")
proc.terminate()
if __name__ == '__main__':
optimization(300, 2)
You need to use shared memory if you want to share variables across processes. This is because child processes do not share their memory space with the parent. Simplest way to do this here would be to use managed lists and delete the line where you set a number seed. This is what is causing same number to be generated because all child processes will take the same seed to generate the random numbers. To get different random numbers either don't set a seed, or pass a different seed to each process:
import time, random
from multiprocessing import Manager, Process
def foo(epoch, lst):
extra = random.random()
lst.append(epoch + extra)
def optimization(loop_time, iter_time, lst):
start = time.time()
epoch = 0
while time.time() <= start + loop_time:
proc = Process(target=foo, args=(epoch, lst))
proc.start()
proc.join(iter_time)
if proc.is_alive(): # if the process is not terminated within time limit
print("Time out!")
proc.terminate()
print(lst)
if __name__ == '__main__':
manager = Manager()
lst = manager.list()
optimization(10, 2, lst)
Output
[0.2035898948744943, 0.07617925389396074, 0.6416754412198231, 0.6712193790613651, 0.419777147554235, 0.732982735576982, 0.7137712131028766, 0.22875414425414997, 0.3181113880578589, 0.5613367673646847, 0.8699685474084119, 0.9005359611195111, 0.23695341111251134, 0.05994288664062197, 0.2306562314450149, 0.15575356275408125, 0.07435292814989103, 0.8542361251850187, 0.13139055891993145, 0.5015152768477814, 0.19864873743952582, 0.2313646288041601, 0.28992667535697736, 0.6265055915510219, 0.7265797043535446, 0.9202923318284002, 0.6321511834038631, 0.6728367262605407, 0.6586979597202935, 0.1309226720786667, 0.563889613032526, 0.389358766191921, 0.37260564565714316, 0.24684684162272597, 0.5982042933298861, 0.896663326233504, 0.7884030244369596, 0.6202229004466849, 0.4417549843477827, 0.37304274232635715, 0.5442716244427301, 0.9915536257041505, 0.46278512685707873, 0.4868394190894778, 0.2133187095154937]
Keep in mind that using managers will affect performance of your code. Alternate to this, you could also use multiprocessing.Array, which is faster than managers but is less flexible in what data it can store, or Queues as well.
so I have a JSON user database, I want to check if user has a valid id or not, if not then remove it from database. I am using threads for this but each thread will start from the starting of database, I don't want that.
Example: if thread A starts from 10 then thread B will start from 20. Also when thread A ends, I want it to start from 30 instead of 20.
I am a beginner so detailed guide & explanation would be great!
Thanks for your help.
Here is an example :
import threading
import time
import typing
MAX_NUMBER = 57 # assumed to be inclusive
JOB_SIZE = 10
indexes = tuple(
tuple(range(0, MAX_NUMBER + 1, JOB_SIZE)) + (MAX_NUMBER + 1,)
)
jobs_spans = tuple(zip(indexes, indexes[1:])) # cf https://stackoverflow.com/a/21303286/11384184
print(jobs_spans)
# ((0, 10), (10, 20), (20, 30), (30, 40), (40, 50), (50, 58))
jobs_left = list(jobs_spans) # is thread-safe thanks to the GIL
def process_user(user_id: int) -> None:
sleep_duration = ((user_id // JOB_SIZE) % 3) * 0.4 + 0.1 # just to add some variance to each job
time.sleep(sleep_duration)
def process_users() -> typing.NoReturn:
while True:
try:
job = jobs_left.pop()
except IndexError:
break # no job left
else:
print(f"{threading.current_thread().name!r} processing users from {job[0]} to {job[1]} (exclusive) ...")
for user_id in range(job[0], job[1]):
process_user(user_id)
print(f"user {user_id} processed")
print(f"{threading.current_thread().name!r} finished")
if __name__ == "__main__":
thread1 = threading.Thread(target=process_users)
thread2 = threading.Thread(target=process_users)
thread1.start()
thread2.start()
thread1.join()
thread2.join()
I started by computing the spans that the jobs will cover, using only the number of users and the size of each job.
I use it to define a queue of jobs left. It is actually a list that the threads will pop onto.
I have two different functions :
one to process a user given its id, which has nothing to do with threading, i could use it the exact same way in a completely sequential program
one to handle the threading. It is the target of the threads, which means which code will get executed by each threads once it is starteded. It is an infinite loop, which try to get a new job until there is no more.
I join each thread to wait for its completion, before the script exits.
If you don't have time to understand Original Answer code, then you can use this. Its easy & small.
Original Source
from multiprocessing.dummy import Pool as ThreadPool
# Make the Pool of workers
pool = ThreadPool(4)
# Execute function in their own Threads
results = pool.map(func, arg)
# Close the pool and wait for the work to finish
pool.close()
pool.join()
func is your function that you want to execute.
arg is your function arg
Example:
names = ["John", "David", "Bob"]
def printNames(name):
print(name)
results = pool.map(printNames, names)
It will print all names from names list using printNames function.
function arg - names
Links
Multiprocessing (python.org, geeksforgeeks.org)
Functions (w3schools.com)
I wrote a script in Python 3.6 initially using a for loop which called an API, then putting all results into a pandas dataframe and writing them to a SQL database. (approximately 9,000 calls are made to that API every time the script runs).
Realising the calls inside the for loop were processed one-by-one, I decided to use the multiprocessing module to speed things up.
Therefore, I created a module level function called parallel_requests and now I call that instead of having the for loop:
list_of_lists = multiprocessing.Pool(processes=4).starmap(parallel_requests, zip(....))
Side note: I use starmap instead of map only because my parallel_requests function takes multiple arguments which I need to zip.
The good: this approach works and is much faster.
The bad: this approach works but is too fast. By using 4 processes (I tried that because I have 4 cores), parallel_requests is getting executed too fast. More than 15 calls per second are made to the API, and I'm getting blocked by the API itself.
In fact, it only works if I use 1 or 2 processes, otherwise it's too damn fast.
Essentially what I want is to keep using 4 processes, but also to limit the execution of my parallel_requests function to only 15 times per second overall.
Is there any parameter of multiprocessing.Pool that would help with this, or it's more complicated than that?
For this case I'd use a leaky bucket. You can have one process that fills a queue at the proscribed rate, with a maximum size that indicates how many requests you can "bank" if you don't make them at the maximum rate; the worker processes then just need to get from the queue before doing its work.
import time
def make_api_request(this, that, rate_queue):
rate_queue.get()
print("DEBUG: doing some work at {}".format(time.time()))
return this * that
def throttler(rate_queue, interval):
try:
while True:
if not rate_queue.full(): # avoid blocking
rate_queue.put(0)
time.sleep(interval)
except BrokenPipeError:
# main process is done
return
if __name__ == '__main__':
from multiprocessing import Pool, Manager, Process
from itertools import repeat
rq = Manager().Queue(maxsize=15) # conservative; no banking
pool = Pool(4)
Process(target=throttler, args=(rq, 1/15.)).start()
pool.starmap(make_api_request, zip(range(100), range(100, 200), repeat(rq)))
I'll look at the ideas posted here, but in the meantime I've just used a simple approach of opening and closing a Pool of 4 processes for every 15 requests and appending all the results in a list_of_lists.
Admittedly, not the best approach, since it takes time/resources to open/close a Pool, but it was the most handy solution for now.
# define a generator for use below
def chunks(l, n):
"""Yield successive n-sized chunks from l."""
for i in range(0, len(l), n):
yield l[i:i + n]
list_of_lists = []
for current_chunk in chunks(all_data, 15): # 15 is the API's limit of requests per second
pool = multiprocessing.Pool(processes=4)
res = pool.starmap(parallel_requests, zip(current_chunk, [to_symbol]*len(current_chunk), [query]*len(current_chunk), [start]*len(current_chunk), [stop]*len(current_chunk)) )
sleep(1) # Sleep for 1 second after every 15 API requests
list_of_lists.extend(res)
pool.close()
flatten_list = [item for sublist in list_of_lists for item in sublist] # use this to construct a `pandas` dataframe
PS: This solution is really not at all that fast due to the multiple opening/closing of pools. Thanks Nathan Vērzemnieks for suggesting to open just one pool, it's much faster, plus your processor won't look like it's running a stress test.
One way to do is to use Queue, which can share details about api-call timestamps with other processes.
Below is an example how this could work. It takes the oldest entry in queue, and if it is younger than one second, sleep functions is called for the duration of the difference.
from multiprocessing import Pool, Manager, queues
from random import randint
import time
MAX_CONNECTIONS = 10
PROCESS_COUNT = 4
def api_request(a, b):
time.sleep(randint(1, 9) * 0.03) # simulate request
return a, b, time.time()
def parallel_requests(a, b, the_queue):
try:
oldest = the_queue.get()
time_difference = time.time() - oldest
except queues.Empty:
time_difference = float("-inf")
if 0 < time_difference < 1:
time.sleep(1-time_difference)
else:
time_difference = 0
print("Current time: ", time.time(), "...after sleeping:", time_difference)
the_queue.put(time.time())
return api_request(a, b)
if __name__ == "__main__":
m = Manager()
q = m.Queue(maxsize=MAX_CONNECTIONS)
for _ in range(0, MAX_CONNECTIONS): # Fill the queue with zeroes
q.put(0)
p = Pool(PROCESS_COUNT)
# Create example data
data_length = 100
data1 = range(0, data_length) # Just some dummy-data
data2 = range(100, data_length+100) # Just some dummy-data
queue_iterable = [q] * (data_length+1) # required for starmap -function
list_of_lists = p.starmap(parallel_requests, zip(data1, data2, queue_iterable))
print(list_of_lists)
The use case I have in mind is as follows: I would like to start a range of jobs with ThreadPoolExecutor and then when a job completes, I would like to add a new job to the queue. I would also like to know when the next job finishes and repeat the procedure above. After I have had the opportunity to observe a predefined number of results, I would like to terminate everything properly. For instance, consider the following code, where the commented out bits in the run_con method show what I would like to achieve.
import numpy as np
import time
from concurrent import futures
MAX_WORKERS = 20
seed = 1234
np.random.seed(seed)
MSG = "Wall time: {:.2f}s"
def expensive_function(x):
time.sleep(x)
return x
def run_con(func, ts):
t0 = time.time()
workers = min(MAX_WORKERS, len(ts))
with futures.ThreadPoolExecutor(workers) as executor:
jobs = []
for t in ts:
jobs.append(executor.submit(func, t))
done = futures.as_completed(jobs)
for future in done:
print("Job complete: ", future.result())
# depending on some condition, add new job to jobs, e.g.
# jobs.append(func, np.random.random())
# update done generator
# if a threshold on total jobs is reached, close every thing down sensibly.
print(MSG.format(time.time()-t0))
ts = np.random.random(10)*5
print("Sleep times: ", ts)
run_con(expensive_function, ts)
Is this achievable with concurrent.futures? If not, what are the alternatives?
The following code starts three processes, they are in a pool to handle 20 worker calls:
import multiprocessing
def worker(nr):
print(nr)
numbers = [i for i in range(20)]
if __name__ == '__main__':
multiprocessing.freeze_support()
pool = multiprocessing.Pool(processes=3)
results = pool.map(worker, numbers)
pool.close()
pool.join()
Is there a way to start the processes in a sequence (as opposed to having them starting all at the same time), with a delay inserted between each process start?
If not using a Pool I would have used multiprocessing.Process(target=worker, args=(nr,)).start() in a loop, starting them one after the other and inserting the delay as needed. I find Pool to be extremely useful, though (together with the map call) so I would be glad to keep it if possible.
According to the documentation, no such control over pooled processes exists. You could however, simulate it with a lock:
import multiprocessing
import time
lock = multiprocessing.Lock()
def worker(nr):
lock.acquire()
time.sleep(0.100)
lock.release()
print(nr)
numbers = [i for i in range(20)]
if __name__ == '__main__':
multiprocessing.freeze_support()
pool = multiprocessing.Pool(processes=3)
results = pool.map(worker, numbers)
pool.close()
pool.join()
Your 3 processes will still start simultaneously. Well, what I mean is you don't have control over which process starts executing the callback first. But at least you get your delay. This effectively has each worker "starting" (but really, continuing) at designated intervals.
Ammendment resulting from discussion below:
Note that on Windows it's not possible to inherit a lock from a parent process. Instead, you can use multiprocessing.Manager().Lock() to communicate a global lock object between processes (with additional IPC overhead, of course). The global lock object needs to be initialized in each process, as well. This would look like:
from multiprocessing import Process, freeze_support
import multiprocessing
import time
from datetime import datetime as dt
def worker(nr):
glock.acquire()
print('started job: {} at {}'.format(nr, dt.now()))
time.sleep(1)
glock.release()
print('ended job: {} at {}'.format(nr, dt.now()))
numbers = [i for i in range(6)]
def init(lock):
global glock
glock = lock
if __name__ == '__main__':
multiprocessing.freeze_support()
lock = multiprocessing.Manager().Lock()
pool = multiprocessing.Pool(processes=3, initializer=init, initargs=(lock,))
results = pool.map(worker, numbers)
pool.close()
pool.join()
Couldn't you do something simple like this:
from multiprocessing import Process
from time import sleep
def f(n):
print 'started job: '+str(n)
sleep(3)
print 'ended job: '+str(n)
if __name__ == '__main__':
for i in range(0,100):
p = Process(target=f, args=(i,))
p.start()
sleep(1)
Result
started job: 0
started job: 1
started job: 2
ended job: 0
started job: 3
ended job: 1
started job: 4
ended job: 2
started job: 5
could you try defining a function that yields your values slowly?
def get_numbers_on_delay(numbers, delay):
for i in numbers:
yield i
time.sleep(delay)
and then:
results = pool.map(worker, get_numbers_on_delay(numbers, 5))
i haven't tested it, so i'm not sure, but give it a shot.
I couldn't get the locking answer to work for some reason so i implemented it this way.
I realize the question is old, but maybe someone else has the same problem.
It spawns all the processes similar to the locking solution, but sleeps before work based on their process name number.
from multiprocessing import current_process
from re import search
from time import sleep
def worker():
process_number = search('\d+', current_process().name).group()
time_between_workers = 5
sleep(time_between_workers * int(process_number))
#do your work here
Since the names given to the processes seem to be unique and incremental, this grabs the number of the process and sleeps based on that.
SpawnPoolWorker-1 sleeps 1 * 5 seconds, SpawnPoolWorker-2 sleeps 2 * 5 seconds etc.