from multiprocessing.dummy import Pool as ThreadPool  # 線程池

from multiprocessing.pool import ThreadPool   # 線程池，用法無區(qū)別，唯一區(qū)別這個(gè)是線程池

from concurrent.futures import ThreadPoolExecutor  # python原生線程池，這個(gè)更主流

import threadpool  # 線程池，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，子線程有返回值
    # pool.apply(func,(msg,))       # 阻塞,apply()源自內(nèi)建函數(shù)，用于間接的調(diào)用函數(shù)，并且按位置把元祖或字典作為參數(shù)傳入。子線程無返回值
    # pool.imap(func,[msg,])        # 非阻塞, 注意與apply傳的參數(shù)的區(qū)別 無返回值
    # pool.map(func, [msg, ])       # 阻塞 子線程無返回值

print('Mark~~~~~~~~~~~~~~~')
pool.close()
pool.join()  # 調(diào)用join之前，先調(diào)用close函數(shù)，否則會(huì)出錯(cuò)。執(zhí)行完close后不會(huì)有新的進(jìn)程加入到pool,join函數(shù)等待所有子進(jìn)程結(jié)束
print('sub-process done')

from multiprocessing.pool import ThreadPool   # 線程池，用法無區(qū)別，唯一區(qū)別這個(gè)是線程池
from multiprocessing.dummy import Pool as ThreadPool  # 線程池
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

# 定義一個(gè)準(zhǔn)備作為線程任務(wù)的函數(shù)
def action(max):
    my_sum = 0
    for i in range(max):
        print(threading.current_thread().name + '  ' + str(i))
        my_sum += i
    return my_sum
# 創(chuàng)建一個(gè)包含2條線程的線程池
pool = ThreadPoolExecutor(max_workers=2)
# 向線程池提交一個(gè)task, 20會(huì)作為action()函數(shù)的參數(shù)
future1 = pool.submit(action, 20)
# 向線程池再提交一個(gè)task, 30會(huì)作為action()函數(shù)的參數(shù)
future2 = pool.submit(action, 30)
# 判斷future1代表的任務(wù)是否結(jié)束
print(future1.done())
time.sleep(3)
# 判斷future2代表的任務(wù)是否結(jié)束
print(future2.done())
# 查看future1代表的任務(wù)返回的結(jié)果
print(future1.result())
# 查看future2代表的任務(wù)返回的結(jié)果
print(future2.result())
# 關(guān)閉線程池
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)]
    # 定義了一個(gè)線程池，表示最多可以創(chuàng)建poolsize這么多線程
    pool = threadpool.ThreadPool(2)
    # 調(diào)用makeRequests創(chuàng)建了要開啟多線程的函數(shù)，以及函數(shù)相關(guān)參數(shù)和回調(diào)函數(shù)，其中回調(diào)函數(shù)可以不寫
    requests = threadpool.makeRequests(hello, func_var)
    [pool.putRequest(req) for req in requests]   # 將所有要運(yùn)行多線程的請(qǐng)求扔進(jìn)線程池
    pool.wait()   # 等待所有線程完成工作后退出

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