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
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密集操作、CPU密集操作所需时间、网络请求密集型操作所需时间
# 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)
(6)测试多线程并发执行CPU密集操作所需时间
counts = [] t = time.time() for x in range(10): thread = Thread(target=count, args=(1,1)) counts.append(thread) thread.start() e = counts.__len__() while True: for th in counts: if not th.is_alive(): e -= 1 if e <= 0: break print(time.time() - t)
(7)测试多线程并发执行IO密集操作所需时间
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() e = ios.__len__() while True: for th in ios: if not th.is_alive(): e -= 1 if e <= 0: break print(time.time() - t)
(8)测试多线程并发执行网络密集操作所需时间
t = time.time() ios = [] t = time.time() for x in range(10): thread = Thread(target=http_request) ios.append(thread) thread.start() 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)
(9)测试多进程并发执行CPU密集操作所需时间
counts = [] t = time.time() for x in range(10): process = Process(target=count, args=(1,1)) counts.append(process) process.start() 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)
(10)测试多进程并发执行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)
(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)
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