关于python线程池的四种实现方式

from multiprocessing.dummy import Pool as ThreadPool  # 线程池

from multiprocessing.pool import ThreadPool   # 线程池，用法无区别，唯一区别这个是线程池

from concurrent.futures import ThreadPoolExecutor  # python原生线程池，这个更主流

import threadpool  # 线程池，需要 pip install threadpool，很早之前的

from multiprocessing.dummy import Pool as Pool
import time

def func(msg):
    print('msg:', msg)
    time.sleep(2)
    print('end:')
    
pool = Pool(processes=3)
for i in range(1, 5):
    msg = 'hello %d' % (i)
    pool.apply_async(func, (msg,))  # 非阻塞，子线程有返回值
    # pool.apply(func,(msg,))       # 阻塞,apply()源自内建函数，用于间接的调用函数，并且按位置把元祖或字典作为参数传入。子线程无返回值
    # pool.imap(func,[msg,])        # 非阻塞, 注意与apply传的参数的区别 无返回值
    # pool.map(func, [msg, ])       # 阻塞 子线程无返回值

print('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 done')

from multiprocessing.pool import ThreadPool   # 线程池，用法无区别，唯一区别这个是线程池
from multiprocessing.dummy import Pool as ThreadPool  # 线程池
import os
import time

print("hi outside of main()")


def hello(x):
    print("inside hello()")
    print("Proccess id: %s" %(os.getpid()))
    time.sleep(3)
    return x*x


if __name__ == "__main__":
    p = ThreadPool(5)
    pool_output = p.map(hello, range(3))
    print(pool_output)

from concurrent.futures import ThreadPoolExecutor
import threading
import time

# 定义一个准备作为线程任务的函数
def action(max):
    my_sum = 0
    for i in range(max):
        print(threading.current_thread().name + '  ' + str(i))
        my_sum += i
    return my_sum
# 创建一个包含2条线程的线程池
pool = ThreadPoolExecutor(max_workers=2)
# 向线程池提交一个task, 20会作为action()函数的参数
future1 = pool.submit(action, 20)
# 向线程池再提交一个task, 30会作为action()函数的参数
future2 = pool.submit(action, 30)
# 判断future1代表的任务是否结束
print(future1.done())
time.sleep(3)
# 判断future2代表的任务是否结束
print(future2.done())
# 查看future1代表的任务返回的结果
print(future1.result())
# 查看future2代表的任务返回的结果
print(future2.result())
# 关闭线程池
pool.shutdown()

import threadpool


def hello(m, n, o):
    """"""
    print("m = %s, n = %s, o = %s" % (m, n, o))


if __name__ == '__main__':
    # 方法1
    # lst_vars_1 = ['1', '2', '3']
    # lst_vars_2 = ['4', '5', '6']
    # func_var = [(lst_vars_1, None), (lst_vars_2, None)]
    # 方法2
    dict_vars_1 = {'m': '1', 'n': '2', 'o': '3'}
    dict_vars_2 = {'m': '4', 'n': '5', 'o': '6'}
    func_var = [(None, dict_vars_1), (None, dict_vars_2)]
    # 定义了一个线程池，表示最多可以创建poolsize这么多线程
    pool = threadpool.ThreadPool(2)
    # 调用makeRequests创建了要开启多线程的函数，以及函数相关参数和回调函数，其中回调函数可以不写
    requests = threadpool.makeRequests(hello, func_var)
    [pool.putRequest(req) for req in requests]   # 将所有要运行多线程的请求扔进线程池
    pool.wait()   # 等待所有线程完成工作后退出

"""
[pool.putRequest(req) for req in requests]等同于
　　for req in requests:  
　　　　 pool.putRequest(req) 
"""