Python是运行在解释器中的语言,查找资料知道,python中有一个全局锁(GIL),在使用多进程(Thread)的情况下,不能发挥多核的优势。而使用多进程(Multiprocess),则可以发挥多核的优势真正地提高效率。
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
操作系统
CPU
内存
硬盘
Windows 10
双核
8GB
机械硬盘
(1)引入所需要的模块
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import requests
import time
from threading
import Thread
from multiprocessing
import Process
(2)定义CPU密集的计算函数
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def count (x, y) :
c =
0
while c <
500000 :
c +=
1
x += x
y += y
(3)定义IO密集的文件读写函数
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def write () :
f = open(
"test.txt" ,
"w" )
for x
in range(
5000000 ):
f.write(
"testwrite\n" )
f.close()
def read () :
f = open(
"test.txt" ,
"r" )
lines = f.readlines()
f.close()
(4) 定义网络请求函数
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_head = {
'User-Agent' :
'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/48.0.2564.116 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
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t = time.time()
for x
in range(
10 ):
count(
1 ,
1 )
print(
"Line cpu" , time.time() - t)
t = time.time()
for x
in range(
10 ):
write()
read()
print(
"Line IO" , time.time() - t)
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、8.563999891281128、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
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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 、101.26400017738342、102.32200002670288
(7)测试多线程并发执行IO密集操作所需时间
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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
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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: 0.7419998645782471、0.3839998245239258、0.3900001049041748
(9)测试多进程并发执行CPU密集操作所需时间
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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
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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、13.059000015258789
(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、0.4760000705718994
实验结果
CPU密集型操作
IO密集型操作
网络请求密集型操作
线性操作
94.91824996469
22.46199995279
7.3296000004
多线程操作
101.1700000762
24.8605000973
0.5053332647
多进程操作
53.8899999857
12.7840000391
0.5045000315
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