标签：agent Oz 并发 process 情况下 多线程并发 user -o close
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。
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 |
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import requests
import time
from threading import Thread
from multiprocessing import Process
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def count(x, y):
# 使程序完成150万计算
c = 0
while c < 500000:
c += 1
x += x
y += y
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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()
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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}
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# 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)
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输出
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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)
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Output: 99.9240000248 、101.26400017738342、102.32200002670288
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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)
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Output: 25.69700002670288、24.02400016784668
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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)
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Output: 0.7419998645782471、0.3839998245239258、0.3900001049041748
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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)
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Output: 54.342000007629395、53.437999963760376
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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)
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Output: 12.509000062942505、13.059000015258789
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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)
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Output: 0.5329999923706055、0.4760000705718994
| 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：
标签：agent Oz 并发 process 情况下 多线程并发 user -o close
原文地址：https://www.cnblogs.com/arun-python/p/9165051.html
