Python中单线程、多线程和多进程的效率对比实验

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title: Python中单线程、多线程与多进程的效率对比实验
date: 2016-09-30 07:05:47
tags: [多线程,多进程,Python]
categories: [Python]

meta: Python中多线程和多进程的对比

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)的状况下，不能发挥多核的优点。而使用多进程(Multiprocess)，则能够发挥多核的优点真正地提升效率。

对比实验

资料显示，若是多线程的进程是CPU密集型的，那多线程并不能有多少效率上的提高，相反还可能会由于线程的频繁切换，致使效率降低，推荐使用多进程；若是是IO密集型，多线程进程能够利用IO阻塞等待时的空闲时间执行其余线程，提高效率。因此咱们根据实验对比不一样场景的效率

操做系统	CPU	内存	硬盘
Windows 10	双核	8GB	机械硬盘

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(1)引入所须要的模块

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50万计算
    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\n")
    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; 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密集操做、CPU密集操做所需时间、网络请求密集型操做所需时间

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

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

# 网络请求密集型操做
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	14.671000003814697

(6)测试多线程并发执行CPU密集操做所需时间

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

while True:
    e = len(counts)
    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密集操做所需时间

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()


while True:
    e = len(ios)
    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做所需时间

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

while True:
    e = len(ios)
    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密集操做所需时间

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

while True:
    e = len(counts)
    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密集型操做

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


while True:
    e = len(ios)
    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请求密集型操做

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

while True:
    e = len(httprs)
    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|

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实验结果

	CPU密集型操做	IO密集型操做	网络请求密集型操做
线性操做	94.91824996469	22.46199995279	7.3296000004
多线程操做	101.1700000762	24.8605000973	0.5053332647
多进程操做	53.8899999857	12.7840000391	0.5045000315

经过上面的结果，咱们能够看到：

• 多线程在IO密集型的操做下彷佛也没有很大的优点（也许IO操做的任务再繁重一些就能体现出优点），在CPU密集型的操做下明显地比单线程线性执行性能更差，可是对于网络请求这种忙等阻塞线程的操做，多线程的优点便很是显著了
• 多进程不管是在CPU密集型仍是IO密集型以及网络请求密集型（常常发生线程阻塞的操做）中，都能体现出性能的优点。不过在相似网络请求密集型的操做上，与多线程相差无几，但却更占用CPU等资源，因此对于这种状况下，咱们能够选择多线程来执行

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此文为1年随手写的，多谢评论区指出谬误，因数据是平均数，影响不大，故未作修改