7. 进程池：

1.进程池和线程池差不多

import time
from concurrent.futures import ProcessPoolExecutor
def task(num):
    print("执行", num)
    time.sleep(2)
if __name__ == '__main__':
    pool = ProcessPoolExecutor(4)
    for i in range(10):
        pool.submit(task, i)
    # 等待进程池中的任务都执行完毕后，再继续往后执行。
    pool.shutdown(True)
    print(1)

2.进程池的回调函数使用主进程实现

import time
from concurrent.futures import ProcessPoolExecutor
import multiprocessing
def task(num):
    print("执行", num)
    time.sleep(2)
    return num
def done(res):
    print(multiprocessing.current_process())
    time.sleep(1)
    print(res.result())
    time.sleep(1)
if __name__ == '__main__':
    pool = ProcessPoolExecutor(4)
    for i in range(50):
        fur = pool.submit(task, i)
        fur.add_done_callback(done) # done的调用由主进程处理（与线程池不同）
    print(multiprocessing.current_process())
    pool.shutdown(True)

3.如果要对进程池使用锁，则需要manage的锁才可以完成

import time
import multiprocessing
from concurrent.futures.process import ProcessPoolExecutor
def task(lock):
    print("开始")
    # lock.acquire()
    # lock.relase()
    with lock:
        # 假设文件中保存的内容就是一个值：10
        with open('f1.txt', mode='r', encoding='utf-8') as f:
            current_num = int(f.read())
        print("排队抢票了")
        time.sleep(1)
        current_num -= 1
        with open('f1.txt', mode='w', encoding='utf-8') as f:
            f.write(str(current_num))
if __name__ == '__main__':
    pool = ProcessPoolExecutor()
    # lock_object = multiprocessing.RLock() # 不能使用
    manager = multiprocessing.Manager()
    lock_object = manager.RLock() # Lock
    for i in range(10):
        pool.submit(task, lock_object)