Pool可以提供指定数量的进程供用户调用，当有新的请求提交到pool中时，如果池还没有满，那么就会创建一个新的进程用来执行该请求；但如果池中的进程数已经达到规定最大值，那么该请求就会等待，直到池中有进程结束，才会创建新的进程来执行它。

1. 使用进程池

import multiprocessing

import time

def func(msg):

print("msg:", msg)

time.sleep(3)

print("end")

if __name__ == "__main__":

pool = multiprocessing.Pool(processes = 3)

for i in range(4):

msg = "hello%d" %(i)

pool.apply_async(func, (msg, )) #维持执行的进程总数为processes，当一个进程执行完毕后会添加新的进程进去

print("Mark~ Mark~ Mark~~~~~~~~~~~~~~~~~~~~~~")

pool.close()

pool.join() #调用join之前，先调用close函数，否则会出错。执行完close后不会有新的进程加入到pool,join函数等待所有子进程结束

print("Sub-process(es) done.")

输出:

Mark~ Mark~ Mark~~~~~~~~~~~~~~~~~~~~~~

msg: hello 0

msg: hello 1

msg: hello 2

end

msg: hello 3

end

end

end

Sub-process(es) done.apply_async(func[, args[, kwds[, callback]]]) 它是非阻塞，apply(func[, args[, kwds]])是阻塞的(理解区别，看1和2结果区别)

close() 关闭pool，使其不在接受新的任务。

terminate() 结束工作进程，不在处理未完成的任务。

join() 主进程阻塞，等待子进程的退出， join方法要在close或terminate之后使用.

执行说明：创建一个进程池pool，并设定进程的数量为3，range(4)会相继产生四个对象[0, 1, 2, 3]，四个对象被提交到pool中，因pool指定进程数为3，所以0、1、2会直接送到进程中执行，当其中一个执行完事后才空出一个进程处理对象3，所以会出现输出“msg: hello 3”出现在"end"后。因为为非阻塞，主函数会自己执行自个的，不搭理进程的执行，运行完for循环后直接输出“Mark~ Mark~ Mark~~~~~~~~~~~~~~~~~~~~~~”(可能为其他结果. 如: mMsg: hark~ Mark~ Mark~~~~~~~~~~~~~~~~~~~~~~ello 0)，主程序在pool.join() 处等待各个进程的结束。

2. 使用进程池(阻塞)

import multiprocessing

import time

def func(msg):

print("msg:", msg)

time.sleep(3)

print("end")

if __name__ == "__main__":

pool = multiprocessing.Pool(processes = 3)

for i in range(4):

msg = "hello%d" %(i)

pool.apply(func, (msg, )) #维持执行的进程总数为processes，当一个进程执行完毕后会添加新的进程进去

print("Mark~ Mark~ Mark~~~~~~~~~~~~~~~~~~~~~~")

pool.close()

pool.join() #调用join之前，先调用close函数，否则会出错。执行完close后不会有新的进程加入到pool,join函数等待所有子进程结束

print("Sub-process(es) done.")

输出:

msg: hello 0

end

msg: hello 1

end

msg: hello 2

end

msg: hello 3

end

Mark~ Mark~ Mark~~~~~~~~~~~~~~~~~~~~~~

Sub-process(es) done.

3. 使用进程池，并关注结果

import multiprocessing

import time

def func(msg):

print("msg:", msg)

time.sleep(3)

print("end")

return "done " + msg

if __name__ == "__main__":

pool = multiprocessing.Pool(processes=4)

result = []

for i in range(3):

msg = "hello%d" %(i)

result.append(pool.apply_async(func, (msg, )))

pool.close()

pool.join()

for res in result:

print(":::", res.get())

print("Sub-process(es) done.")

输出:

msg: hello 0

msg: hello 1

msg: hello 2

end

end

end

::: done hello 0

::: done hello 1

::: done hello 2

Sub-process(es) done.get()函数得出每个返回结果的值

4. 使用多个目标函数

import multiprocessing

import os, time, random

def Lee():

print("\nRun task Lee-%s" % (os.getpid())) # os.getpid()获取当前的进程的ID

start = time.time()

time.sleep(random.random() * 10) # random.random()随机生成0-1之间的小数

end = time.time()

print('Task Lee, runs%0.2fseconds.' % (end - start))

def Marlon():

print("\nRun task Marlon-%s" % (os.getpid()))

start = time.time()

time.sleep(random.random() * 40)

end = time.time()

print('Task Marlon runs%0.2fseconds.' % (end - start))

def Allen():

print("\nRun task Allen-%s" % (os.getpid()))

start = time.time()

time.sleep(random.random() * 30)

end = time.time()

print('Task Allen runs%0.2fseconds.' % (end - start))

def Frank():

print("\nRun task Frank-%s" % (os.getpid()))

start = time.time()

time.sleep(random.random() * 20)

end = time.time()

print('Task Frank runs%0.2fseconds.' % (end - start))

if __name__ == '__main__':

function_list = [Lee, Marlon, Allen, Frank]

print("parent process%s" % (os.getpid()))

pool = multiprocessing.Pool(4)

for func in function_list:

pool.apply_async(func) # Pool执行函数，apply执行函数,当有一个进程执行完毕后，会添加一个新的进程到pool中

print('Waiting for all subprocesses done...')

pool.close()

pool.join() # 调用join之前，一定要先调用close() 函数，否则会出错, close()执行后不会有新的进程加入到pool,join函数等待素有子进程结束

print('All subprocesses done.')

输出;

parent process 89128

Waiting for all subprocesses done...

Run task Lee-42544

Run task Marlon-90952

Run task Allen-86188

Run task Frank-92504

Task Lee, runs 3.49 seconds.

Task Marlon runs 10.92 seconds.

Task Frank runs 12.82 seconds.

Task Allen runs 18.44 seconds.

All subprocesses done.

5. multiprocessing pool map

import multiprocessing

def m1(x):

print(x * x)

if __name__ == '__main__':

pool = multiprocessing.Pool(multiprocessing.cpu_count())

i_list = range(8)

pool.map(m1, i_list)

输出:

0

1

4

9

16

25

36

49