python 多线程和多协程哪个好用

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IT狼人9号 2024-11-24 23:25:15

文章标签 python 多线程和多协程哪个好用 python中的多线程的优点 python 非阻塞服务器 文章分类 Python 后端开发

当我听说多线程编程时,我想到了加速我的程序的机会,但事实并非如此？

import eventlet
from eventlet.green import socket
from iptools import IpRangeList
class Scanner(object):
def __init__(self, ip_range, port_range, workers_num):
self.workers_num = workers_num or 1000
self.ip_range = self._get_ip_range(ip_range)
self.port_range = self._get_port_range(port_range)
self.scaned_range = self._get_scaned_range()
def _get_ip_range(self, ip_range):
return [ip for ip in IpRangeList(ip_range)]
def _get_port_range(self, port_range):
return [r for r in range(*port_range)]
def _get_scaned_range(self):
for ip in self.ip_range:
for port in self.port_range:
yield (ip, port)
def scan(self, address):
try:
return bool(socket.create_connection(address))
except:
return False
def run(self):
pool = eventlet.GreenPool(self.workers_num)
for status in pool.imap(self.scan, self.scaned_range):
if status:
yield True
def run_std(self):
for status in map(self.scan, self.scaned_range):
if status:
yield True
if __name__ == '__main__':
s = Scanner(('127.0.0.1'), (1, 65000), 100000)
import time
now = time.time()
open_ports = [i for i in s.run()]
print 'Eventlet time: %s (sec) open: %s' % (now - time.time(),
len(open_ports))
del s
s = Scanner(('127.0.0.1'), (1, 65000), 100000)
now = time.time()
open_ports = [i for i in s.run()]
print 'CPython time: %s (sec) open: %s' % (now - time.time(),
len(open_ports))

和结果：

Eventlet time: -4.40343403816 (sec) open: 2

CPython time: -4.48356699944 (sec) open: 2

而我的问题是,如果我运行此代码不是在我的笔记本电脑上,而是在服务器上并设置更多的工人价值,它将比CPython的版本运行得更快？

线程有什么优点？

加：

因此我使用原始cpython的线程重写app

import socket
from threading import Thread
from Queue import Queue
from iptools import IpRangeList
class Scanner(object):
def __init__(self, ip_range, port_range, workers_num):
self.workers_num = workers_num or 1000
self.ip_range = self._get_ip_range(ip_range)
self.port_range = self._get_port_range(port_range)
self.scaned_range = [i for i in self._get_scaned_range()]
def _get_ip_range(self, ip_range):
return [ip for ip in IpRangeList(ip_range)]
def _get_port_range(self, port_range):
return [r for r in range(*port_range)]
def _get_scaned_range(self):
for ip in self.ip_range:
for port in self.port_range:
yield (ip, port)
def scan(self, q):
while True:
try:
r = bool(socket.create_conection(q.get()))
except Exception:
r = False
q.task_done()
def run(self):
queue = Queue()
for address in self.scaned_range:
queue.put(address)
for i in range(self.workers_num):
worker = Thread(target=self.scan,args=(queue,))
worker.setDaemon(True)
worker.start()
queue.join()
if __name__ == '__main__':
s = Scanner(('127.0.0.1'), (1, 65000), 5)
import time
now = time.time()
s.run()
print time.time() - now

结果是

Cpython's thread: 1.4 sec

我认为这是一个非常好的结果.我将标准nmap扫描时间视为：

$nmap 127.0.0.1 -p1-65000
Starting Nmap 5.21 ( http://nmap.org ) at 2012-10-22 18:43 MSK
Nmap scan report for localhost (127.0.0.1)
Host is up (0.00021s latency).
Not shown: 64986 closed ports
PORT STATE SERVICE
53/tcp open domain
80/tcp open http
443/tcp open https
631/tcp open ipp
3306/tcp open mysql
6379/tcp open unknown
8000/tcp open http-alt
8020/tcp open unknown
8888/tcp open sun-answerbook
9980/tcp open unknown
27017/tcp open unknown
27634/tcp open unknown
28017/tcp open unknown
39900/tcp open unknown
Nmap done: 1 IP address (1 host up) scanned in 0.85 seconds

我现在的问题是：如何在Eventlet中实现线程,因为我可以理解这不是线程,而是针对Eventlet的特殊内容以及它们为什么不加速任务？

许多主要项目如OpenStack等使用Eventlet.

但为什么？只是以异步方式或其他方式对数据库进行繁重的查询？

解决方法:

Cpython线程：

>每个cpython线程映射到OS级别线程(用户空间中的轻量级进程/ pthread)

>如果有许多cpython线程同时执行python代码：由于全局解释器锁定,只有一个cpython线程可以一次解释python.当需要解释python指令时,剩余的线程将在GIL上被阻塞.当有许多python线程时,这会减慢很多东西.

>现在,如果你的python代码大部分时间都花在网络操作中(发送,连接等)：在这种情况下,将会有更少的线程争夺GIL来解释代码.所以GIL的效果并不是那么糟糕.

Eventlet /绿色线程：

>从上面我们知道cpython对线程有性能限制. Eventlets尝试通过使用在单个核心上运行的单个线程并使用非阻塞i / o来解决问题.

>绿色线程不是真正的OS级别线程.它们是并发的用户空间抽象.最重要的是,N个绿色线程将映射到1个OS线程.这避免了GIL问题.

>绿线互相合作,而不是先发制人.