Threading pool similar to the multiprocessing Pool?

Question

Is there a Pool class for worker threads, similar to the multiprocessing module's Pool class?

I like for example the easy way to parallelize a map function

def long_running_func(p):
    c_func_no_gil(p)

p = multiprocessing.Pool(4)
xs = p.map(long_running_func, range(100))

however I would like to do it without the overhead of creating new processes.

I know about the GIL. However, in my usecase, the function will be an IO-bound C function for which the python wrapper will release the GIL before the actual function call.

Do I have to write my own threading pool?

Answer 1

If you don't mind executing other's code, here's mine:

Note: There is lot of extra code you may want to remove [added for better clarificaiton and demonstration how it works]

Note: Python naming conventions were used for method names and variable names instead of camelCase.

Working procedure:

1. MultiThread class will initiate with no of instances of threads by sharing lock, work queue, exit flag and results.
2. SingleThread will be started by MultiThread once it creates all instances.
3. We can add works using MultiThread (It will take care of locking).
4. SingleThreads will process work queue using a lock in middle.
5. Once your work is done, you can destroy all threads with shared boolean value.
6. Here, work can be anything. It can automatically import (uncomment import line) and process module using given arguments.
7. Results will be added to results and we can get using get_results

Code:

import threading
import queue


class SingleThread(threading.Thread):
    def __init__(self, name, work_queue, lock, exit_flag, results):
        threading.Thread.__init__(self)
        self.name = name
        self.work_queue = work_queue
        self.lock = lock
        self.exit_flag = exit_flag
        self.results = results

    def run(self):
        # print("Coming %s with parameters %s", self.name, self.exit_flag)
        while not self.exit_flag:
            # print(self.exit_flag)
            self.lock.acquire()
            if not self.work_queue.empty():
                work = self.work_queue.get()
                module, operation, args, kwargs = work.module, work.operation, work.args, work.kwargs
                self.lock.release()
                print("Processing : " + operation + " with parameters " + str(args) + " and " + str(kwargs) + " by " + self.name + "\n")
                # module = __import__(module_name)
                result = str(getattr(module, operation)(*args, **kwargs))
                print("Result : " + result + " for operation " + operation + " and input " + str(args) + " " + str(kwargs))
                self.results.append(result)
            else:
                self.lock.release()
        # process_work_queue(self.work_queue)

class MultiThread:
    def __init__(self, no_of_threads):
        self.exit_flag = bool_instance()
        self.queue_lock = threading.Lock()
        self.threads = []
        self.work_queue = queue.Queue()
        self.results = []
        for index in range(0, no_of_threads):
            thread = SingleThread("Thread" + str(index+1), self.work_queue, self.queue_lock, self.exit_flag, self.results)
            thread.start()
            self.threads.append(thread)

    def add_work(self, work):
        self.queue_lock.acquire()
        self.work_queue._put(work)
        self.queue_lock.release()

    def destroy(self):
        self.exit_flag.value = True
        for thread in self.threads:
            thread.join()

    def get_results(self):
        return self.results


class Work:
    def __init__(self, module, operation, args, kwargs={}):
        self.module = module
        self.operation = operation
        self.args = args
        self.kwargs = kwargs


class SimpleOperations:
    def sum(self, *args):
        return sum([int(arg) for arg in args])

    @staticmethod
    def mul(a, b, c=0):
        return int(a) * int(b) + int(c)


class bool_instance:
    def __init__(self, value=False):
        self.value = value

    def __setattr__(self, key, value):
        if key != "value":
            raise AttributeError("Only value can be set!")
        if not isinstance(value, bool):
            raise AttributeError("Only True/False can be set!")
        self.__dict__[key] = value
        # super.__setattr__(key, bool(value))

    def __bool__(self):
        return self.value

if __name__ == "__main__":
    multi_thread = MultiThread(5)
    multi_thread.add_work(Work(SimpleOperations(), "mul", [2, 3], {"c":4}))
    while True:
        data_input = input()
        if data_input == "":
            pass
        elif data_input == "break":
            break
        else:
            work = data_input.split()
            multi_thread.add_work(Work(SimpleOperations(), work[0], work[1:], {}))
    multi_thread.destroy()
    print(multi_thread.get_results())

Answer 2

Yes, there is a threading pool similar to the multiprocessing Pool, however, it is hidden somewhat and not properly documented. You can import it by following way:-

from multiprocessing.pool import ThreadPool

Just I show you simple example

def test_multithread_stringio_read_csv(self):
        # see gh-11786
        max_row_range = 10000
        num_files = 100

        bytes_to_df = [
            '\n'.join(
                ['%d,%d,%d' % (i, i, i) for i in range(max_row_range)]
            ).encode() for j in range(num_files)]
        files = [BytesIO(b) for b in bytes_to_df]

        # read all files in many threads
        pool = ThreadPool(8)
        results = pool.map(self.read_csv, files)
        first_result = results[0]

        for result in results:
            tm.assert_frame_equal(first_result, result) 

Answer 3

another way can be adding the process to thethread queue pool

import concurrent.futures
with concurrent.futures.ThreadPoolExecutor(max_workers=cpus) as executor:
    for i in range(10):
        a = executor.submit(arg1, arg2,....)

Answer 4

Here's the result I finally ended up using. It's a modified version of the classes by dgorissen above.

File: threadpool.py

from queue import Queue, Empty
import threading
from threading import Thread


class Worker(Thread):
    _TIMEOUT = 2
    """ Thread executing tasks from a given tasks queue. Thread is signalable, 
        to exit
    """
    def __init__(self, tasks, th_num):
        Thread.__init__(self)
        self.tasks = tasks
        self.daemon, self.th_num = True, th_num
        self.done = threading.Event()
        self.start()

    def run(self):       
        while not self.done.is_set():
            try:
                func, args, kwargs = self.tasks.get(block=True,
                                                   timeout=self._TIMEOUT)
                try:
                    func(*args, **kwargs)
                except Exception as e:
                    print(e)
                finally:
                    self.tasks.task_done()
            except Empty as e:
                pass
        return

    def signal_exit(self):
        """ Signal to thread to exit """
        self.done.set()


class ThreadPool:
    """Pool of threads consuming tasks from a queue"""
    def __init__(self, num_threads, tasks=[]):
        self.tasks = Queue(num_threads)
        self.workers = []
        self.done = False
        self._init_workers(num_threads)
        for task in tasks:
            self.tasks.put(task)

    def _init_workers(self, num_threads):
        for i in range(num_threads):
            self.workers.append(Worker(self.tasks, i))

    def add_task(self, func, *args, **kwargs):
        """Add a task to the queue"""
        self.tasks.put((func, args, kwargs))

    def _close_all_threads(self):
        """ Signal all threads to exit and lose the references to them """
        for workr in self.workers:
            workr.signal_exit()
        self.workers = []

    def wait_completion(self):
        """Wait for completion of all the tasks in the queue"""
        self.tasks.join()

    def __del__(self):
        self._close_all_threads()


def create_task(func, *args, **kwargs):
    return (func, args, kwargs)

To use the pool

from random import randrange
from time import sleep

delays = [randrange(1, 10) for i in range(30)]

def wait_delay(d):
    print('sleeping for (%d)sec' % d)
    sleep(d)

pool = ThreadPool(20)
for i, d in enumerate(delays):
    pool.add_task(wait_delay, d)
pool.wait_completion()

Answer 5

For something very simple and lightweight (slightly modified from here):

from Queue import Queue
from threading import Thread


class Worker(Thread):
    """Thread executing tasks from a given tasks queue"""
    def __init__(self, tasks):
        Thread.__init__(self)
        self.tasks = tasks
        self.daemon = True
        self.start()

    def run(self):
        while True:
            func, args, kargs = self.tasks.get()
            try:
                func(*args, **kargs)
            except Exception, e:
                print e
            finally:
                self.tasks.task_done()


class ThreadPool:
    """Pool of threads consuming tasks from a queue"""
    def __init__(self, num_threads):
        self.tasks = Queue(num_threads)
        for _ in range(num_threads):
            Worker(self.tasks)

    def add_task(self, func, *args, **kargs):
        """Add a task to the queue"""
        self.tasks.put((func, args, kargs))

    def wait_completion(self):
        """Wait for completion of all the tasks in the queue"""
        self.tasks.join()

if __name__ == '__main__':
    from random import randrange
    from time import sleep

    delays = [randrange(1, 10) for i in range(100)]

    def wait_delay(d):
        print 'sleeping for (%d)sec' % d
        sleep(d)

    pool = ThreadPool(20)

    for i, d in enumerate(delays):
        pool.add_task(wait_delay, d)

    pool.wait_completion()

To support callbacks on task completion you can just add the callback to the task tuple.

Answer 6

Yes, and it seems to have (more or less) the same API.

import multiprocessing

def worker(lnk):
    ....    
def start_process():
    .....
....

if(PROCESS):
    pool = multiprocessing.Pool(processes=POOL_SIZE, initializer=start_process)
else:
    pool = multiprocessing.pool.ThreadPool(processes=POOL_SIZE, 
                                           initializer=start_process)

pool.map(worker, inputs)
....

Answer 7

Hi to use the thread pool in Python you can use this library :

from multiprocessing.dummy import Pool as ThreadPool

and then for use, this library do like that :

pool = ThreadPool(threads)
results = pool.map(service, tasks)
pool.close()
pool.join()
return results

The threads are the number of threads that you want and tasks are a list of task that most map to the service.

Answer 8

There is no built in thread based pool. However, it can be very quick to implement a producer/consumer queue with the Queue class.

From: https://docs.python.org/2/library/queue.html

from threading import Thread
from Queue import Queue
def worker():
    while True:
        item = q.get()
        do_work(item)
        q.task_done()

q = Queue()
for i in range(num_worker_threads):
     t = Thread(target=worker)
     t.daemon = True
     t.start()

for item in source():
    q.put(item)

q.join()       # block until all tasks are done

Answer 9

In Python 3 you can use concurrent.futures.ThreadPoolExecutor, i.e.:

executor = ThreadPoolExecutor(max_workers=10)
a = executor.submit(my_function)

See the docs for more info and examples.

Answer 10

I just found out that there actually is a thread-based Pool interface in the multiprocessing module, however it is hidden somewhat and not properly documented.

It can be imported via

from multiprocessing.pool import ThreadPool

It is implemented using a dummy Process class wrapping a python thread. This thread-based Process class can be found in multiprocessing.dummy which is mentioned briefly in the docs. This dummy module supposedly provides the whole multiprocessing interface based on threads.

Answer 11

The overhead of creating the new processes is minimal, especially when it's just 4 of them. I doubt this is a performance hot spot of your application. Keep it simple, optimize where you have to and where profiling results point to.