對比實驗
資料顯示，如果多線程的進程是CPU密集型的，那多線程並不能有多少效率上的提升，相反還可能會因為線程的頻繁切換，導致效率下降，推薦使用多進程；如果是IO密集型，多線程進程可以利用IO阻塞等待時的空閒時間執行其他線程，提升效率。所以我們根據實驗對比不同場景的效率
| 作業系統 | CPU | 內存 | 硬碟 |
|-----------|-------|------|--------|
| Windows 10 | 雙核 |8GB|機械硬碟|
(1)引入所需要的模塊
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150萬計算
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密集操作、CPU密集操作所需時間、網絡請求密集型操作所需時間
# 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)
# 網絡請求密集型操作
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、4.522000074386597、14.671000003814697
(6)測試多線程並發執行CPU密集操作所需時間
counts = []
t = time.time()
for x in range(10):
thread = Thread(target=count, args=(1,1))
counts.append(thread)
thread.start()
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)測試多線程並發執行IO密集操作所需時間
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()
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)測試多線程並發執行網絡密集操作所需時間
t = time.time()
ios = []
t = time.time()
for x in range(10):
thread = Thread(target=http_request)
ios.append(thread)
thread.start()
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)測試多進程並發執行CPU密集操作所需時間
counts = []
t = time.time()
for x in range(10):
process = Process(target=count, args=(1,1))
counts.append(process)
process.start()
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)測試多進程並發執行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、13.059000015258789
(11)測試多進程並發執行Http請求密集型操作
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
實驗結果
通過上面的結果，我們可以看到：
多線程在IO密集型的操作下似乎也沒有很大的優勢（也許IO操作的任務再繁重一些就能體現出優勢），在CPU密集型的操作下明顯地比單線程線性執行性能更差，但是對於網絡請求這種忙等阻塞線程的操作，多線程的優勢便非常顯著了
多進程無論是在CPU密集型還是IO密集型以及網絡請求密集型（經常發生線程阻塞的操作）中，都能體現出性能的優勢。不過在類似網絡請求密集型的操作上，與多線程相差無幾，但卻更占用CPU等資源，所以對於這種情況下，我們可以選擇多線程來執行
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