Python多线程多进程实例对比解析
作者：我太难了008
这篇文章主要介绍了Python多线程多进程实例对比解析,文中通过示例代码介绍的非常详细，对大家的学习或者工作具有一定的参考学习价值,需要的朋友可以参考下
多线程适合于多io操作
多进程适合于耗cpu(计算)的操作
# 多进程编程
# 耗cpu的操作,用多进程编程, 对于io操作来说,使用多线程编程
import time
from concurrent.futures import ThreadPoolExecutor, as_completed
from concurrent.futures import ProcessPoolExecutor
def fib(n):
if n <= 2:
return 1
return fib(n - 2) + fib(n - 1)
if __name__ == '__main__':
# 1. 对于耗cpu操作,多进程优于多线程
# 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(data)
# print("last time :{}".format(time.time() - start_time)) # 3.905290126800537
# 多进程 ,在window环境 下必须放在main方法中执行,否则抛异常
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(data)
print("last time :{}".format(time.time() - start_time)) # 2.6130592823028564
可以看到在耗cpu的应用中,多进程明显优于多线程 2.6130592823028564 < 3.905290126800537
下面模拟一个io操作
# 多进程编程
# 耗cpu的操作,用多进程编程, 对于io操作来说,使用多线程编程
import time
from concurrent.futures import ThreadPoolExecutor, as_completed
from concurrent.futures import ProcessPoolExecutor
def io_operation(n):
time.sleep(2)
return n
if __name__ == '__main__':
# 1. 对于耗cpu操作,多进程优于多线程
# with ThreadPoolExecutor(3) as executor:
# all_task = [executor.submit(io_operation, num) for num in range(25, 35)]
# start_time = time.time()
# for future in as_completed(all_task):
# data = future.result()
# print(data)
# print("last time :{}".format(time.time() - start_time)) # 8.00358772277832
# 多进程 ,在window环境 下必须放在main方法中执行,否则抛异常
with ProcessPoolExecutor(3) as executor:
all_task = [executor.submit(io_operation, num) for num in range(25, 35)]
start_time = time.time()
for future in as_completed(all_task):
data = future.result()
print(data)
print("last time :{}".format(time.time() - start_time)) # 8.12435245513916
可以看到 8.00358772277832 < 8.12435245513916, 即是多线程比多进程更牛逼!
以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持脚本之家。