Python中單線程、多線程和多進程的效率對比實驗

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title: Python中單線程、多線程與多進程的效率對比實驗
date: 2016-09-30 07:05:47
tags: [多線程,多進程,Python]
categories: [Python]

meta: Python中多線程和多進程的對比

Python是運行在解釋器中的語言，查找資料知道，python中有一個全局鎖（GIL），在使用多進程(Thread)的狀況下，不能發揮多核的優點。而使用多進程(Multiprocess)，則能夠發揮多核的優點真正地提升效率。

對比實驗

資料顯示，若是多線程的進程是CPU密集型的，那多線程並不能有多少效率上的提高，相反還可能會由於線程的頻繁切換，致使效率降低，推薦使用多進程；若是是IO密集型，多線程進程能夠利用IO阻塞等待時的空閒時間執行其餘線程，提高效率。因此咱們根據實驗對比不一樣場景的效率

操做系統	CPU	內存	硬盤
Windows 10	雙核	8GB	機械硬盤

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(1)引入所須要的模塊

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

(2)定義CPU密集的計算函數

def count(x, y):
    # 使程序完成50萬計算
    c = 0
    while c < 500000:
        c += 1
        x += x
        y += y

（3）定義IO密集的文件讀寫函數

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) 定義網絡請求函數

_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)測試線性執行IO密集操做、CPU密集操做所需時間、網絡請求密集型操做所需時間

# 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)

# 網絡請求密集型操做
t = time.time()
for x in range(10):
    http_request()
print("Line Http Request", time.time() - t)

輸出

CPU密集	95.6059999466	91.57099986076355	92.52800011634827	99.96799993515015
IO密集	24.25	21.76699995994568	21.769999980926514	22.060999870300293
網絡請求密集型	4.519999980926514	8.563999891281128	4.371000051498413	14.671000003814697

(6)測試多線程併發執行CPU密集操做所需時間

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)測試多線程併發執行IO密集操做所需時間

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)測試多線程併發執行網絡密集操做所需時間

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)測試多進程併發執行CPU密集操做所需時間

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)測試多進程併發執行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)測試多進程併發執行Http請求密集型操做

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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實驗結果

	CPU密集型操做	IO密集型操做	網絡請求密集型操做
線性操做	94.91824996469	22.46199995279	7.3296000004
多線程操做	101.1700000762	24.8605000973	0.5053332647
多進程操做	53.8899999857	12.7840000391	0.5045000315

經過上面的結果，咱們能夠看到：

• 多線程在IO密集型的操做下彷佛也沒有很大的優點（也許IO操做的任務再繁重一些就能體現出優點），在CPU密集型的操做下明顯地比單線程線性執行性能更差，可是對於網絡請求這種忙等阻塞線程的操做，多線程的優點便很是顯著了
• 多進程不管是在CPU密集型仍是IO密集型以及網絡請求密集型（常常發生線程阻塞的操做）中，都能體現出性能的優點。不過在相似網絡請求密集型的操做上，與多線程相差無幾，但卻更佔用CPU等資源，因此對於這種狀況下，咱們能夠選擇多線程來執行

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此文爲1年隨手寫的，多謝評論區指出謬誤，因數據是平均數，影響不大，故未作修改