Python 高级编程和异步IO并发编程 --11_9 多线程与多进程对比

'''
多进程编程
# Pythong中由于有GIL锁的存在，服务发挥CPU多核的优势，多线程无法并发，充分利用多核，但多进程可以。
# 对于IO操作时，瓶颈不在CPU，使用多线程编程
# 进程切换的代价高于线程，因此能用多线程进来使用多线程。
#1. 对于耗费CPU的操作，多进程优于多线程
'''''
from concurrent.futures import ThreadPoolExecutor,as_completed
import time
def fib(n):
    if n <= 2:
        return 1
    return fib(n-1) + fib(n-2)

'''多线程操作'''
with ThreadPoolExecutor(3) as executer:
    all_task = [executer.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))

exe result: 75025
exe result: 121393
exe result: 196418
exe result: 317811
exe result: 514229
exe result: 832040
exe result: 1346269
exe result: 2178309
exe result: 3524578
exe result: 5702887
last time is: 5.594969987869263

from concurrent.futures import ThreadPoolExecutor,as_completed
from concurrent.futures import ProcessPoolExecutor
import time
def fib(n):
    if n <= 2:
        return 1
    return fib(n-1) + fib(n-2)
 # '''多进程编程，后面的执行语句必须放到”if __name__ == "__main__": 后面'''
if __name__ == "__main__":
    with ProcessPoolExecutor(3) as executer:
        all_task = [executer.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))

exe result: 75025
exe result: 121393
exe result: 196418
exe result: 317811
exe result: 514229
exe result: 832040
exe result: 1346269
exe result: 2178309
exe result: 3524578
exe result: 5702887
last time is: 4.888859987258911

通过对比可以发现，多余耗费大量计算操作的操作，多进程操作比多线程操作更快。

性能没有翻倍？ 因为多进程的进程切换，比多线程的线程切换更耗时。

import time
from concurrent.futures import ThreadPoolExecutor,as_completed
from concurrent.futures import ProcessPoolExecutor

'''
# 对于io操作来说，多线程优于多进程。
'''
def random_sleep(n):
    time.sleep(n)
    return n

if __name__ == "__main__":
    with ProcessPoolExecutor(3) as executer:
        all_task = [executer.submit(random_sleep,(num)) for num in [2]*30]  # 执行30次等待2秒
        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))

exe result: 2
exe result: 2
exe result: 2
exe result: 2
exe result: 2
exe result: 2
exe result: 2
exe result: 2
exe result: 2
exe result: 2
exe result: 2
exe result: 2
exe result: 2
exe result: 2
exe result: 2
exe result: 2
exe result: 2
exe result: 2
exe result: 2
exe result: 2
exe result: 2
exe result: 2
exe result: 2
exe result: 2
exe result: 2
exe result: 2
exe result: 2
exe result: 2
exe result: 2
exe result: 2
last time is: 20.831419944763184

import time
from concurrent.futures import ThreadPoolExecutor,as_completed
from concurrent.futures import ProcessPoolExecutor

# 对于io操作来说，多线程优于多进程。
def random_sleep(n):
    time.sleep(n)
    return n

if __name__ == "__main__":
    with ThreadPoolExecutor(3) as executer:
        all_task = [executer.submit(random_sleep,(num)) for num in [2]*30]  # 执行30次等待2秒
        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))

exe result: 2
exe result: 2
exe result: 2
exe result: 2
exe result: 2
exe result: 2
exe result: 2
exe result: 2
exe result: 2
exe result: 2
exe result: 2
exe result: 2
exe result: 2
exe result: 2
exe result: 2
exe result: 2
exe result: 2
exe result: 2
exe result: 2
exe result: 2
exe result: 2
exe result: 2
exe result: 2
exe result: 2
exe result: 2
exe result: 2
exe result: 2
exe result: 2
exe result: 2
exe result: 2
last time is: 20.146894216537476

通过上面示例，虽然领先不多，但也可说明多线程比多进程稍微快一点。

对于操作系统，开放的线程数高于进程数，尽量使用多线程。