The right way to limit maximum number of threads running at once?

semaphore is a variable or abstract data type that is used to control access to a common resource by multiple processes in a concurrent system such as a multiprogramming operating system; this can help you here.

threadLimiter = threading.BoundedSemaphore(maximumNumberOfThreads) class MyThread(threading.Thread): def run(self): threadLimiter.acquire() try: self.Executemycode() finally: threadLimiter.release() def Executemycode(self): print(" Hello World!") # <your code here> 

This way you can easily limit the number of threads that will be executed concurrently during the program execution. Variable, 'maximumNumberOfThreads' can be used to define an upper limit on the maximum value of threads.

credits

I ran into this same problem and spent days (2 days to be precise) getting to the correct solution using a queue. I wasted a day going down the ThreadPoolExecutor path because there is no way to limit the number of threads that thing launches! I fed it a list of 5000 files to copy and the code went non-responsive once it got up to about 1500 concurrent file copies running all at once. The max_workers parameter on the ThreadPoolExecutor only controls how many workers are spinning up threads not how many threads get spun up.

Ok, anyway, here is a very simple example of using a Queue for this:

import threading, time, random from queue import Queue jobs = Queue() def do_stuff(q): while not q.empty(): value = q.get() time.sleep(random.randint(1, 10)) print(value) q.task_done() for i in range(10): jobs.put(i) for i in range(3): worker = threading.Thread(target=do_stuff, args=(jobs,)) worker.start() print("waiting for queue to complete", jobs.qsize(), "tasks") jobs.join() print("all done") 

It would be much easier to implement this as a thread pool or executor, using either multiprocessing.dummy.Pool, or concurrent.futures.ThreadPoolExecutor (or, if using Python 2.x, the backport futures). For example:

import concurrent def f(arg): print("Started a task. running=%s, arg=%s" % (running, arg)) for i in range(100000): pass print("Done") with concurrent.futures.ThreadPoolExecutor(8) as executor: while True: arg = get_task() executor.submit(f, arg) 

Of course if you can change the pull-model get_task to a push-model get_tasks that, e.g., yields tasks one at a time, this is even simpler:

with concurrent.futures.ThreadPoolExecutor(8) as executor: for arg in get_tasks(): executor.submit(f, arg) 

When you run out of tasks (e.g., get_task raises an exception, or get_tasks runs dry), this will automatically tell the executor to stop after it drains the queue, wait for it to stop, and clean up everything.

Simple and easiest way of limiting max threads using threading.activeCount() method

import threading, time maxthreads = 10 def do_stuff(i): print(i) print("Total Active threads are {0}".format(threading.activeCount())) time.sleep(20) count = 0 while True: if threading.activeCount() <= maxthreads: worker = threading.Thread(target=do_stuff, args=(count,)) worker.start() count += 1 

I've seen that most commonly written like:

threads = [threading.Thread(target=f) for _ in range(8)] for thread in threads: thread.start() ... for thread in threads: thread.join() 

If you want to maintain a fixed-size pool of running threads that process short-lived tasks than ask for new work, consider a solution built around Queues, like "How to wait until only the first thread is finished in Python".

concurrent.futures.ThreadPoolExecutor.map

concurrent.futures.ThreadPoolExecutor was mentioned at https://stackoverflow.com/a/19370282/895245 and here is an example of the map method which is often the most convenient method.

.map() is a parallel version of map(): it reads all the input immediately, then runs tasks in parallel, and returns in the same order as the input.

Usage:

./concurrent_map_exception.py [nproc [min [max]] 

concurrent_map_exception.py

import concurrent.futures import sys import time def my_func(i): time.sleep((abs(i) % 4) / 10.0) return 10.0 / i def my_get_work(min_, max_): for i in range(min_, max_): print('my_get_work: {}'.format(i)) yield i # CLI. argv_len = len(sys.argv) if argv_len > 1: nthreads = int(sys.argv[1]) if nthreads == 0: nthreads = None else: nthreads = None if argv_len > 2: min_ = int(sys.argv[2]) else: min_ = 1 if argv_len > 3: max_ = int(sys.argv[3]) else: max_ = 100 # Action. with concurrent.futures.ProcessPoolExecutor(max_workers=nthreads) as executor: for input, output in zip( my_get_work(min_, max_), executor.map(my_func, my_get_work(min_, max_)) ): print('result: {} {}'.format(input, output)) 

GitHub upstream.

So for example:

./concurrent_map_exception.py 1 1 5 

gives:

my_get_work: 1 my_get_work: 2 my_get_work: 3 my_get_work: 4 my_get_work: 1 result: 1 10.0 my_get_work: 2 result: 2 5.0 my_get_work: 3 result: 3 3.3333333333333335 my_get_work: 4 result: 4 2.5 

and:

./concurrent_map_exception.py 2 1 5 

gives the same output but runs faster because we now have 2 processes, and:

./concurrent_map_exception.py 1 -5 5 

gives:

my_get_work: -5 my_get_work: -4 my_get_work: -3 my_get_work: -2 my_get_work: -1 my_get_work: 0 my_get_work: 1 my_get_work: 2 my_get_work: 3 my_get_work: 4 my_get_work: -5 result: -5 -2.0 my_get_work: -4 result: -4 -2.5 my_get_work: -3 result: -3 -3.3333333333333335 my_get_work: -2 result: -2 -5.0 my_get_work: -1 result: -1 -10.0 my_get_work: 0 concurrent.futures.process._RemoteTraceback: """ Traceback (most recent call last): File "/usr/lib/python3.6/concurrent/futures/process.py", line 175, in _process_worker r = call_item.fn(*call_item.args, **call_item.kwargs) File "/usr/lib/python3.6/concurrent/futures/process.py", line 153, in _process_chunk return [fn(*args) for args in chunk] File "/usr/lib/python3.6/concurrent/futures/process.py", line 153, in <listcomp> return [fn(*args) for args in chunk] File "./concurrent_map_exception.py", line 24, in my_func return 10.0 / i ZeroDivisionError: float division by zero """ The above exception was the direct cause of the following exception: Traceback (most recent call last): File "./concurrent_map_exception.py", line 52, in <module> executor.map(my_func, my_get_work(min_, max_)) File "/usr/lib/python3.6/concurrent/futures/process.py", line 366, in _chain_from_iterable_of_lists for element in iterable: File "/usr/lib/python3.6/concurrent/futures/_base.py", line 586, in result_iterator yield fs.pop().result() File "/usr/lib/python3.6/concurrent/futures/_base.py", line 432, in result return self.__get_result() File "/usr/lib/python3.6/concurrent/futures/_base.py", line 384, in __get_result raise self._exception ZeroDivisionError: float division by zero 

So notice how it stops immediately on an exception.

Queue example with error handling

Queue was mentioned at https://stackoverflow.com/a/19369877/895245 but here is a full example.

Design goals:

• the input function does not need to be modified
• limits the number of threads
• queue sizes follow number of threads closely
• fetch input input only as needed, not everything upfront
• if an error happens, optionally stop soon afterwards
• is an exception is raised on the worker function, show the stack trace clearly

concurrent.futures.ThreadPoolExecutor is the nicest interface currently available in the stdlib that I've seen. However I could not find how to do all of the following:

• make it perfectly feed input little by little
• fail immediately on error
• accept functions with multiple arguments

because:

• .map(): reads all inputs at once and func can only take on argument
• .submit(): .shutdown() executes until all futures finish, and there is no blocking .submit() on max current work items. So how to avoid an ugly .cancel() loop over all futures after first failure?

Without further ado, here is my implementation. Test cases follow at the end of the script under __name__ == '__main__':

thread_pool.py

#!/usr/bin/env python3 ''' This file is MIT Licensed because I'm posting it on Stack Overflow: https://stackoverflow.com/questions/19369724/the-right-way-to-limit-maximum-number-of-threads-running-at-once/55263676#55263676 ''' from typing import Any, Callable, Dict, Iterable, Union import os import queue import sys import threading import time import traceback class ThreadPoolExitException(Exception): ''' An object of this class may be raised by output_handler_function to request early termination. It is also raised by submit() if submit_raise_exit=True. ''' pass class ThreadPool: ''' Start a pool of a limited number of threads to do some work. This is similar to the stdlib concurrent, but I could not find how to reach all my design goals with that implementation: * the input function does not need to be modified * limit the number of threads * queue sizes closely follow number of threads * if an exception happens, optionally stop soon afterwards This class form allows to use your own while loops with submit(). Exit soon after the first failure happens: .... python3 thread_pool.py 2 -10 20 handle_output_print .... Sample output: .... {'i': -9} -1.1111111111111112 None {'i': -8} -1.25 None {'i': -10} -1.0 None {'i': -6} -1.6666666666666667 None {'i': -7} -1.4285714285714286 None {'i': -4} -2.5 None {'i': -5} -2.0 None {'i': -2} -5.0 None {'i': -3} -3.3333333333333335 None {'i': 0} None ZeroDivisionError('float division by zero') {'i': -1} -10.0 None {'i': 1} 10.0 None {'i': 2} 5.0 None work_function or handle_output raised: Traceback (most recent call last): File "thread_pool.py", line 181, in _func_runner work_function_return = self.work_function(**work_function_input) File "thread_pool.py", line 281, in work_function_maybe_raise return 10.0 / i ZeroDivisionError: float division by zero work_function_input: {'i': 0} work_function_return: None .... Don't exit after first failure, run until end: .... python3 thread_pool.py 2 -10 20 handle_output_print_no_exit .... Store results in a queue for later inspection instead of printing immediately, then print everything at the end: .... python3 thread_pool.py 2 -10 20 handle_output_queue .... Exit soon after the handle_output raise. .... python3 thread_pool.py 2 -10 20 handle_output_raise .... Relying on this interface to abort execution is discouraged, this should usually only happen due to a programming error in the handler. Test that the argument called "thread_id" is passed to work_function and printed: .... python3 thread_pool.py 2 -10 20 handle_output_print thread_id .... Test with, ThreadPoolExitException and submit_raise_exit=True, same behaviour handle_output_print except for the different exit cause report: .... python3 thread_pool.py 2 -10 20 handle_output_raise_exit_exception .... ''' def __init__( self, work_function: Callable, handle_output: Union[Callable[[Any,Any,Exception],Any],None] = None, nthreads: Union[int,None] = None, thread_id_arg: Union[str,None] = None, submit_raise_exit: bool = False ): ''' Start in a thread pool immediately. join() must be called afterwards at some point. :param work_function: main work function to be evaluated. :param handle_output: called on work_function return values as they are returned. The function signature is: .... handle_output( work_function_input: Union[Dict,None], work_function_return, work_function_exception: Exception ) -> Union[Exception,None] .... where work_function_exception the exception that work_function raised, or None otherwise The first non-None return value of a call to this function is returned by submit(), get_handle_output_result() and join(). The intended semantic for this, is to return: * on success: ** None to continue execution ** ThreadPoolExitException() to request stop execution * if work_function_input or work_function_exception raise: ** the exception raised The ThreadPool user can then optionally terminate execution early on error or request with either: * an explicit submit() return value check + break if a submit loop is used * `with` + submit_raise_exit=True Default: a handler that just returns `exception`, which can normally be used by the submit loop to detect an error and exit immediately. :param nthreads: number of threads to use. Default: nproc. :param thread_id_arg: if not None, set the argument of work_function with this name to a 0-indexed thread ID. This allows function calls to coordinate usage of external resources such as files or ports. :param submit_raise_exit: if True, submit() raises ThreadPoolExitException() if get_handle_output_result() is not None. ''' self.work_function = work_function if handle_output is None: handle_output = lambda input, output, exception: exception self.handle_output = handle_output if nthreads is None: nthreads = len(os.sched_getaffinity(0)) self.thread_id_arg = thread_id_arg self.submit_raise_exit = submit_raise_exit self.nthreads = nthreads self.handle_output_result = None self.handle_output_result_lock = threading.Lock() self.in_queue = queue.Queue(maxsize=nthreads) self.threads = [] for i in range(self.nthreads): thread = threading.Thread( target=self._func_runner, args=(i,) ) self.threads.append(thread) thread.start() def __enter__(self): ''' __exit__ automatically calls join() for you. This is cool because it automatically ends the loop if an exception occurs. But don't forget that errors may happen after the last submit was called, so you likely want to check for that with get_handle_output_result() after the with. ''' return self def __exit__(self, exception_type, exception_value, exception_traceback): self.join() return exception_type is ThreadPoolExitException def _func_runner(self, thread_id): while True: work_function_input = self.in_queue.get(block=True) if work_function_input is None: break if self.thread_id_arg is not None: work_function_input[self.thread_id_arg] = thread_id try: work_function_exception = None work_function_return = self.work_function(**work_function_input) except Exception as e: work_function_exception = e work_function_return = None handle_output_exception = None try: handle_output_return = self.handle_output( work_function_input, work_function_return, work_function_exception ) except Exception as e: handle_output_exception = e handle_output_result = None if handle_output_exception is not None: handle_output_result = handle_output_exception elif handle_output_return is not None: handle_output_result = handle_output_return if handle_output_result is not None and self.handle_output_result is None: with self.handle_output_result_lock: self.handle_output_result = ( work_function_input, work_function_return, handle_output_result ) self.in_queue.task_done() @staticmethod def exception_traceback_string(exception): ''' Helper to get the traceback from an exception object. This is usually what you want to print if an error happens in a thread: https://stackoverflow.com/questions/3702675/how-to-print-the-full-traceback-without-halting-the-program/56199295#56199295 ''' return ''.join(traceback.format_exception( None, exception, exception.__traceback__) ) def get_handle_output_result(self): ''' :return: if a handle_output call has raised previously, return a tuple: .... (work_function_input, work_function_return, exception_raised) .... corresponding to the first such raise. Otherwise, if a handle_output returned non-None, a tuple: (work_function_input, work_function_return, handle_output_return) Otherwise, None. ''' return self.handle_output_result def join(self): ''' Request all threads to stop after they finish currently submitted work. :return: same as get_handle_output_result() ''' for thread in range(self.nthreads): self.in_queue.put(None) for thread in self.threads: thread.join() return self.get_handle_output_result() def submit( self, work_function_input: Union[Dict,None] =None ): ''' Submit work. Block if there is already enough work scheduled (~nthreads). :return: the same as get_handle_output_result ''' handle_output_result = self.get_handle_output_result() if handle_output_result is not None and self.submit_raise_exit: raise ThreadPoolExitException() if work_function_input is None: work_function_input = {} self.in_queue.put(work_function_input) return handle_output_result if __name__ == '__main__': def get_work(min_, max_): ''' Generate simple range work for work_function. ''' for i in range(min_, max_): yield {'i': i} def work_function_maybe_raise(i): ''' The main function that will be evaluated. It sleeps to simulate an IO operation. ''' time.sleep((abs(i) % 4) / 10.0) return 10.0 / i def work_function_get_thread(i, thread_id): time.sleep((abs(i) % 4) / 10.0) return thread_id def handle_output_print(input, output, exception): ''' Print outputs and exit immediately on failure. ''' print('{!r} {!r} {!r}'.format(input, output, exception)) return exception def handle_output_print_no_exit(input, output, exception): ''' Print outputs, don't exit on failure. ''' print('{!r} {!r} {!r}'.format(input, output, exception)) out_queue = queue.Queue() def handle_output_queue(input, output, exception): ''' Store outputs in a queue for later usage. ''' global out_queue out_queue.put((input, output, exception)) return exception def handle_output_raise(input, output, exception): ''' Raise if input == 0, to test that execution stops nicely if this raises. ''' print('{!r} {!r} {!r}'.format(input, output, exception)) if input['i'] == 0: raise Exception def handle_output_raise_exit_exception(input, output, exception): ''' Return a ThreadPoolExitException() if input == -5. Return the work_function exception if it raised. ''' print('{!r} {!r} {!r}'.format(input, output, exception)) if exception: return exception if output == 10.0 / -5: return ThreadPoolExitException() # CLI arguments. argv_len = len(sys.argv) if argv_len > 1: nthreads = int(sys.argv[1]) if nthreads == 0: nthreads = None else: nthreads = None if argv_len > 2: min_ = int(sys.argv[2]) else: min_ = 1 if argv_len > 3: max_ = int(sys.argv[3]) else: max_ = 100 if argv_len > 4: handle_output_funtion_string = sys.argv[4] else: handle_output_funtion_string = 'handle_output_print' handle_output = eval(handle_output_funtion_string) if argv_len > 5: work_function = work_function_get_thread thread_id_arg = sys.argv[5] else: work_function = work_function_maybe_raise thread_id_arg = None # Action. if handle_output is handle_output_raise_exit_exception: # `with` version with implicit join and submit raise # immediately when desired with ThreadPoolExitException. # # This is the more safe and convenient and DRY usage if # you can use `with`, so prefer it generally. with ThreadPool( work_function, handle_output, nthreads, thread_id_arg, submit_raise_exit=True ) as my_thread_pool: for work in get_work(min_, max_): my_thread_pool.submit(work) handle_output_result = my_thread_pool.get_handle_output_result() else: # Explicit error checking in submit loop to exit immediately # on error. my_thread_pool = ThreadPool( work_function, handle_output, nthreads, thread_id_arg, ) for work_function_input in get_work(min_, max_): handle_output_result = my_thread_pool.submit(work_function_input) if handle_output_result is not None: break handle_output_result = my_thread_pool.join() if handle_output_result is not None: work_function_input, work_function_return, exception = handle_output_result if type(exception) is ThreadPoolExitException: print('Early exit requested by handle_output with ThreadPoolExitException:') else: print('work_function or handle_output raised:') print(ThreadPool.exception_traceback_string(exception), end='') print('work_function_input: {!r}'.format(work_function_input)) print('work_function_return: {!r}'.format(work_function_return)) if handle_output == handle_output_queue: while not out_queue.empty(): print(out_queue.get()) 

GitHub upstream.

Tested in Python 3.7.3.

it can be achieved easily using ThreadPoolExecutor. Change the limit by using the max_workers argument.

from concurrent.futures import ThreadPoolExecutor import time pool = ThreadPoolExecutor(max_workers=10) def thread(num): print(num) time.sleep(3) for n in range(0, 1000): pool.submit(thread, n) pool.shutdown(wait=True) 

This could be done with a Semaphore Object. A semaphore manages an internal counter which is decremented by each acquire() call and incremented by each release() call. The counter can never go below zero; when acquire() finds that it is zero, it blocks, waiting until some other thread calls release().

A short example shows for a maximum of 5 threads in parallel, that one half of the threads are executed instantly and the others are blocked and wait:

import threading import time maxthreads = 5 pool_sema = threading.Semaphore(value=maxthreads) threads = list() def task(i): pool_sema.acquire() try: print("executed {}. thread".format(i)) time.sleep(2) except Exception as e: print("Error: problem with {0}. thread.\nMessage:{1}".format(i, e)) finally: pool_sema.release() def create_threads(number_of_threads): try: for i in range(number_of_threads): thread = threading.Thread(target=task,args=(str(i))) threads.append(thread) thread.start() except Exception as e: print("Error: unable to start thread {}".format(e)) if __name__ == '__main__': create_threads(10) 

output

executed 0. thread executed 1. thread executed 2. thread executed 3. thread executed 4. thread executed 5. thread executed 6. thread executed 7. thread executed 8. thread executed 9. thread 

For those who prefer to use list comprehension based on an input list:

import threading import time maxthreads = 5 pool_sema = threading.Semaphore(value=maxthreads) def task(i): pool_sema.acquire() try: print("executed {}. thread".format(i)) time.sleep(2) except Exception as e: print("Error: problem with {0}. thread.\nMessage:{1}".format(i, e)) finally: pool_sema.release() def create_threads(number_of_threads): try: threads = [threading.Thread(target=task, args=(str(i))) for i in range(number_of_threads)] [t.start() for t in threads] except Exception as e: print("Error: unable to start thread {}".format(e)) finally: [t.join() for t in threads] if __name__ == '__main__': create_threads(10) 

To apply limitation on threads creation, follow this example:

import threading import time def some_process(thread_num): count = 0 while count < 5: time.sleep(0.5) count += 1 print "%s: %s" % (thread_num, time.ctime(time.time())) print 'number of alive threads:{}'.format(threading.active_count()) def create_thread(): try: for i in range(1, 555): # trying to spawn 555 threads. thread = threading.Thread(target=some_process, args=(i,)) thread.start() if threading.active_count() == 100: # set maximum threads. thread.join() print threading.active_count() # number of alive threads. except Exception as e: print "Error: unable to start thread {}".format(e) if __name__ == '__main__': create_thread() 

Or:

Another way to set a thread number checker mutex/lock such as below example:

import threading import time def some_process(thread_num): count = 0 while count < 5: time.sleep(0.5) count += 1 # print "%s: %s" % (thread_num, time.ctime(time.time())) print 'number of alive threads:{}'.format(threading.active_count()) def create_thread2(number_of_desire_thread ): try: for i in range(1, 555): thread = threading.Thread(target=some_process, args=(i,)).start() while number_of_desire_thread <= threading.active_count(): '''mutex for avoiding to additional thread creation.''' pass print 'unlock' print threading.active_count() # number of alive threads. except Exception as e: print "Error: unable to start thread {}".format(e) if __name__ == '__main__': create_thread2(100) 

implement threading count

 if threading.active_count() > 10: sleep(1) continue 

example

if __name__ == "__main__": format = "%(asctime)s: %(message)s" logging.basicConfig(format=format, level=logging.INFO, datefmt="%Y-%m-%d %H:%M:%S") threads = [] logging.info("Start The Program!!!") for i in range(0, len(df)): #limit thread to 10 if threading.active_count() > 10: sleep(1) continue email = (df['Email'][i]) nama_lengkap = (df['Nama Lengkap'][i]) posisi = (df['Posisi'][i]) x = threading.Thread(target=main_program, args=(email, nama_lengkap, posisi,)) print(f"Thread {i} started") threads.append(x) x.start() # break for index, thread in enumerate(threads): thread.join() 

Use the following Python Code

import threading import time # Set this global variable to maximum number of threads you want to execute MAX_THREAD_COUNT = 30 #Sample program which will run in thread. This is place holder. Add your code to this program, to be executed in the thread def sampleThreadProgram(): time.sleep(1) #main program def main(): #Sample loop, replace it with your loop to execute threads. Currently it #will execute 1000 times the above placeholder function in multiple #threads, in parallel of MAX_THREAD_COUNT threads for x in range(1000): print('x is ' + str(x)) #Sleep and wait for 1 second and wait while some of the running threads #in MAX_THREAD_COUNT range are completed before raising new threads while len(threading.enumerate())>=MAX_THREAD_COUNT: print('running threads are' + str(len(threading.enumerate()))) time.sleep(1) # Start new thread since now number of threads are less than MAX_THREAD_COUNT t1 = threading.Thread(target=sampleThreadProgram, args=()) t1.start() main()