python3 进程池

进程池无IO堵塞的情况

# coding:utf-8
import time
from multiprocessing import Process, Pool

def func(n):
    pass

if __name__ == ‘__main__‘:
    num = 10
    start_pool_time = time.time()
    pool = Pool(5)
    pool.map(func, range(num))  # map是异步执行的，并且自带close和join
    print("通过进程池执行的时间:", time.time() - start_pool_time)

    std_start_time = time.time()
    for i in range(num):
        pass
    print("正常执行的执行时间:", time.time() - std_start_time)

    pro_start_time = time.time()
    p_lst = []
    for i in range(num):
        p = Process(target=func, args=(i,))
        p.start()
        p_lst.append(p)

    [pp.join() for pp in p_lst]
    print("多进程的执行时间:", time.time() - pro_start_time)

# 通过进程池执行的时间: 0.46875
# 正常执行的执行时间: 0.0
# 多进程的执行时间: 0.828125

#一般约定俗成的是进程池中的进程数量为CPU的数量，工作中要看具体情况来考量。

有IO阻塞的情况

# coding:utf-8
import time
from multiprocessing import Process, Pool

def func(n):
    time.sleep(1)

if __name__ == ‘__main__‘:
    num = 10
    start_pool_time = time.time()
    pool = Pool(5)
    pool.map(func, range(num))
    print("通过进程池执行的时间:", time.time() - start_pool_time)

    std_start_time = time.time()
    for i in range(num):
        time.sleep(1)
    print("正常执行的执行时间:", time.time() - std_start_time)

    pro_start_time = time.time()
    p_lst = []
    for i in range(num):
        p = Process(target=func, args=(i,))
        p.start()
        p_lst.append(p)

    [pp.join() for pp in p_lst]
    print("多进程的执行时间:", time.time() - pro_start_time)

# 通过进程池执行的时间: 2.578125
# 正常执行的执行时间: 10.0
# 多进程的执行时间: 1.75

原文地址：https://www.cnblogs.com/lilyxiaoyy/p/10986358.html