import time
from concurrent.futures import ThreadPoolExecutor,as_completed
from concurrent.futures import ProcessPoolExecutor
#多进程编程
#耗CPU的操作，用多进程编程；对于IO操作，使用多线程编程；进程切换的代价要高于线程

#1. 对于耗CPU的操作，多进程优于多线程，比如计算和图形操作  机器学习
def fib(n):
    if n<=2:
        return 1;
    return fib(n-1) + fib(n-2)
"""
with ThreadPoolExecutor(3) as executor:
    all_task = [executor.submit(fib,(num)) for num in range(25,35)]
    start_time = time.time()
    for future in as_completed(all_task):
        data = future.result()
        print("exe result:{}".format(data))

    print("last time is:{}".format(time.time() - start_time))
"""
#window下编程  ProcessPoolExecutor要在main下面，linux下无此问题
#线程花费的时间 明显比进程要多
"""
if __name__ == "__main__":
    with ProcessPoolExecutor(3) as executor:
       all_task = [executor.submit(fib,(num)) for num in range(25,35)]
       start_time = time.time()
       for future in as_completed(all_task):
           data = future.result()
           print("exe result:{}".format(data))

    print("last time is:{}".format(time.time() - start_time))
"""
#2. 对于IO操作，多线程优于多进程
def random_sleep(n):
    time.sleep(n)
    return n

if __name__ == "__main__":
    #with ThreadPoolExecutor(3) as executor:
    with ProcessPoolExecutor(3) as executor:
       all_task = [executor.submit(random_sleep,(num)) for num in [2]*30]
       start_time = time.time()
       for future in as_completed(all_task):
           data = future.result()
           print("exe result:{}".format(data))

    print("last time is:{}".format(time.time() - start_time))