对比实验

资料显示，如果多线程的进程是CPU密集型的，那多线程并不能有多少效率上的提升，相反还可能会因为线程的频繁切换，导致效率下降，推荐使用多进程；如果是IO密集型，多线程进程可以利用IO阻塞等待时的空闲时间执行其他线程，提升效率。所以我们根据实验对比不同场景的效率

(1)引入所需要的模块

import requests
import time
from threading import Thread
from multiprocessing import Process

(2)定义CPU密集的计算函数

def count(x, y):
 # 使程序完成150万计算
 c = 0
 while c < 500000:
 c += 1
 x += x
 y += y

（3）定义IO密集的文件读写函数

def write():
 f = open("test.txt", "w")
 for x in range(5000000):
 f.write("testwrite
")
 f.close()

def read():
 f = open("test.txt", "r")
 lines = f.readlines()
 f.close()

(4) 定义网络请求函数

_head = {
 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; WOW) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/48.0.25.116 Safari/537.36'}
url = "http://www.tieba.com"
def http_request():
 try:
 webPage = requests.get(url, headers=_head)
 html = webPage.text
 return {"context": html}
 except Exception as e:
 return {"error": e}

(5)测试线性执行IO密集操作、CPU密集操作所需时间、网络请求密集型操作所需时间

# CPU密集操作
t = time.time()
for x in range(10):
 count(1, 1)
print("Line cpu", time.time() - t)

# IO密集操作
t = time.time()
for x in range(10):
 write()
 read()
print("Line IO", time.time() - t)

# 网络请求密集型操作
t = time.time()
for x in range(10):
 http_request()
print("Line Http Request", time.time() - t)

输出

CPU密集：95.6059999466、91.57099986076355 92.52800011634827、 99.96799993515015

IO密集：24.25、21.76699995994568、21.769999980926514、22.060999870300293

网络请求密集型: 4.519999980926514、8.5639991281128、4.371000051498413、4.522000074386597、14.671000003814697

(6)测试多线程并发执行CPU密集操作所需时间

counts = []
t = time.time()
for x in range(10):
 thread = Thread(target=count, args=(1,1))
 counts.append(thread)
 thread.start()

e = counts.__len__()
while True:
 for th in counts:
 if not th.is_alive():
 e -= 1
 if e <= 0:
 break
print(time.time() - t)

Output: 99.9240000248 、101.200017738342、102.32200002670288

(7)测试多线程并发执行IO密集操作所需时间

def io():
 write()
 read()

t = time.time()
ios = []
t = time.time()
for x in range(10):
 thread = Thread(target=count, args=(1,1))
 ios.append(thread)
 thread.start()

e = ios.__len__()
while True:
 for th in ios:
 if not th.is_alive():
 e -= 1
 if e <= 0:
 break
print(time.time() - t)

Output: 25.69700002670288、24.02400016784668

(8)测试多线程并发执行网络密集操作所需时间

t = time.time()
ios = []
t = time.time()
for x in range(10):
 thread = Thread(target=http_request)
 ios.append(thread)
 thread.start()

e = ios.__len__()
while True:
 for th in ios:
 if not th.is_alive():
 e -= 1
 if e <= 0:
 break
print("Thread Http Request", time.time() - t)

Output: 0.74199985782471、0.3839998245239258、0.3900001049041748

(9)测试多进程并发执行CPU密集操作所需时间

counts = []
t = time.time()
for x in range(10):
 process = Process(target=count, args=(1,1))
 counts.append(process)
 process.start()
e = counts.__len__()
while True:
 for th in counts:
 if not th.is_alive():
 e -= 1
 if e <= 0:
 break
print("Multiprocess cpu", time.time() - t)

Output: 54.342000007629395、53.437999963760376

(10)测试多进程并发执行IO密集型操作

t = time.time()
ios = []
t = time.time()
for x in range(10):
 process = Process(target=io)
 ios.append(process)
 process.start()

e = ios.__len__()
while True:
 for th in ios:
 if not th.is_alive():
 e -= 1
 if e <= 0:
 break
print("Multiprocess IO", time.time() - t)

Output: 12.509000062942505、13.0590000152587

(11)测试多进程并发执行Http请求密集型操作

t = time.time()
httprs = []
t = time.time()
for x in range(10):
 process = Process(target=http_request)
 ios.append(process)
 process.start()

e = httprs.__len__()
while True:
 for th in httprs:
 if not th.is_alive():
 e -= 1
 if e <= 0:
 break
print("Multiprocess Http Request", time.time() - t)

Output: 0.5329999923706055、0.47600007057194

实验结果

通过上面的结果，我们可以看到：

多线程在IO密集型的操作下似乎也没有很大的优势（也许IO操作的任务再繁重一些就能体现出优势），在CPU密集型的操作下明显地比单线程线性执行性能更差，但是对于网络请求这种忙等阻塞线程的操作，多线程的优势便非常显著了

多进程无论是在CPU密集型还是IO密集型以及网络请求密集型（经常发生线程阻塞的操作）中，都能体现出性能的优势。不过在类似网络请求密集型的操作上，与多线程相差无几，但却更占用CPU等资源，所以对于这种情况下，我们可以选择多线程来执行