#

# Simple example which uses a pool of workers to carry out some tasks.

#

# Notice that the results will probably not come out of the output

# queue in the same in the same order as the corresponding tasks were

# put on the input queue.  If it is important to get the results back

# in the original order then consider using `Pool.map()` or

# `Pool.imap()` (which will save on the amount of code needed anyway).

#

import time

import random

from multiprocessing import Process, Queue, current_process, freeze_support

#

# Function run by worker processes

#

def worker(input, output):

    for func, args in iter(input.get, 'STOP'):

        result = calculate(func, args)

        output.put(result)

#

# Function used to calculate result

#

def calculate(func, args):

    result = func(*args)

    return '%s says that %s%s = %s' % \

        (current_process().name, func.__name__, args, result)

#

# Functions referenced by tasks

#

def mul(a, b):

    time.sleep(0.5*random.random())

    return a * b

def plus(a, b):

    time.sleep(0.5*random.random())

    return a + b

#

#

#

def test():

    NUMBER_OF_PROCESSES = 4

    TASKS1 = [(mul, (i, 7)) for i in range(20)]

    TASKS2 = [(plus, (i, 8)) for i in range(10)]

    # Create queues

    task_queue = Queue()

    done_queue = Queue()

    # Submit tasks

    for task in TASKS1:

        task_queue.put(task)

    # Start worker processes

    for i in range(NUMBER_OF_PROCESSES):

        Process(target=worker, args=(task_queue, done_queue)).start()

    # Get and print results

    print 'Unordered results:'

    for i in range(len(TASKS1)):

        print '\t', done_queue.get()

    # Add more tasks using `put()`

    for task in TASKS2:

        task_queue.put(task)

    # Get and print some more results

    for i in range(len(TASKS2)):

        print '\t', done_queue.get()

    # Tell child processes to stop

    for i in range(NUMBER_OF_PROCESSES):

        task_queue.put('STOP')

if __name__ == '__main__':

    freeze_support()

    test()