前言

标题有点唬人，以前了解过研究 gevent，twisted，scrapy（基于 twisted）。最近有个想法：这些东西比如做爬虫，谁的效率更好呢？ 我就写了以下程序（附件）测试然后用 timeit（跑 3 次，每次 10 遍，时间有限）看效果

原理：

  1. 为了防止远程网络的问题，从一个网站爬下网页代码（html），页面下载本地放在了我的本机（gentoo+apache）
  2. 然后爬虫去分析这些页面上面的链接（开始是主页），再挖掘其他页面，抓取页面关键字（我这里就是个‘py’） 程序打包 Crawler.tar.bz2

先看代码树：

dongwm@localhost ~ $ tree Crawler/
Crawler/
├── common_Crawler.py  #标准爬虫，里面只是多线程编程，抓取分析类在common.py
├── common.py  #共用函数，里面只是抓取页面分析页面关键字
├── common.pyc #你懂得
├── Crawler #scrapy和django框架差不多的用法
│   ├── __init__.py
│   ├── __init__.pyc
│   ├── items.py #不需要利用，默认
│   ├── pipelines.py
│   ├── settings.py
│   ├── settings.pyc
│   └── spiders #抓取脚本文件夹
│       ├── __init__.py
│       ├── __init__.pyc
│       ├── spiders.py #我做的分析页面，这个和多线程/gevent调用的抓取分析类不同，我使用了内置方法（大家可以修改共用函数改成scrapy的方式，这样三种效果就更准确了）
│       └── spiders.pyc
├── gevent_Crawler.py #gevent版本爬虫，效果和标准版一样，抓取分析类也是common.py 保证其他环节相同，只是一个多线程，一个用协程
├── scrapy.cfg
└── scrapy_Crawler.py #因为scrapy使用是命令行，我用subproess封装了命令，然后使用timeit计算效果

2 directories, 16 files

```#### 实验前准备：

停掉我本机使用的耗费资源的进程 firefox，vmware，compiz 等，直到负载保持一个相对拨波动平衡

#### 测试程序：

1. common.py

!/usr/bin/python

coding=utf-8

Version 1 by Dongwm 2013/01/10

脚本作用：多线程抓取

方式： lxml + xpath + requests

import requests from cStringIO import StringIO from lxml import etree

class Crawler(object):

def __init__(self, app):
    self.deep = 2  #指定网页的抓取深度        
    self.url = '' #指定网站地址
    self.key = 'by' #搜索这个词
    self.tp = app #连接池回调实例
    self.visitedUrl = [] #抓取的网页放入列表,防止重复抓取

def _hasCrawler(self, url): 
    '''判断是否已经抓取过这个页面'''
    return (True if url in self.visitedUrl else False)

def getPageSource(self, url, key, deep): 
    ''' 抓取页面,分析,入库.
    '''
    if self._hasCrawler(url): #发现重复直接return
        return 
    else:
        self.visitedUrl.append(url) #发现新地址假如到这个列
    r = requests.get('http://localhost/%s' % url)
    encoding = r.encoding #判断页面的编码
    result = r.text.encode('utf-8').decode(encoding)
    #f = StringIO(r.text.encode('utf-8'))
    try:  
        self._xpath(url, result, ['a'], unicode(key, 'utf8'), deep) #分析页面中的连接地址,以及它的内容
        self._xpath(url, result, ['title', 'p', 'li', 'div'], unicode(key, "utf8"), deep) #分析这几个标签的内容
    except TypeError: #对编码类型异常处理,有些深度页面和主页的编码不同
        self._xpath(url, result, ['a'], key, deep)
        self._xpath(url, result, ['title', 'p', 'li', 'div'], key, deep)
    return True

def _xpath(self, weburl, data, xpath, key, deep):
    page = etree.HTML(data)
    for i in xpath:
        hrefs = page.xpath(u"//%s" % i) #根据xpath标签
        if deep >1:
            for href in hrefs:
                url = href.attrib.get('href','')
                if not url.startswith('java') and not url.startswith('#') and not \
                    url.startswith('mailto') and url.endswith('html'):  #过滤javascript和发送邮件的链接
                        self.tp.add_job(self.getPageSource,url, key, deep-1) #递归调用,直到符合的深
        for href in hrefs:
            value = href.text  #抓取相应标签的内容
            if value:
                m = re.compile(r'.*%s.*' % key).match(value) #根据key匹配相应内容

def work(self):
    self.tp.add_job(self.getPageSource, self.url, self.key, self.deep)
    self.tp.wait_for_complete() #等待线程池完成
1. common_Crawler.py

!/usr/bin/python

coding=utf-8

Version 1 by Dongwm 2013/01/10

脚本作用：多线程

import time import threading import Queue from common import Crawler

lock = threading.Lock () #设置线程锁

class MyThread(threading.Thread):

def __init__(self, workQueue, timeout=1, **kwargs):
    threading.Thread.__init__(self, kwargs=kwargs)
    self.timeout = timeout #线程在结束前等待任务队列多长时间
    self.setDaemon(True)  #设置deamon,表示主线程死掉,子线程不跟随死掉
    self.workQueue = workQueue
    self.start() #初始化直接启动线程

def run(self):
    '''重载run方法'''
    while True:
        try:
            #lock.acquire() #线程安全上锁 PS:queue 实现就是线程安全的，没有必要上锁 ,否者可以put/get_nowait
            callable, args = self.workQueue.get(timeout=self.timeout) #从工作队列中获取一个任务
            res = callable(*args)  #执行的任务
            #lock.release()  #执行完,释放锁 
        except Queue.Empty: #任务队列空的时候结束此线程
            break
        except Exception, e:
            return -1

class ThreadPool(object):

def __init__(self, num_of_threads):
     self.workQueue = Queue.Queue()
     self.threads = []
     self.__createThreadPool(num_of_threads)

def __createThreadPool(self, num_of_threads):
    for i in range(num_of_threads):
         thread = MyThread(self.workQueue)
         self.threads.append(thread)

def wait_for_complete(self):
    '''等待所有线程完成'''
    while len(self.threads):
        thread = self.threads.pop()
        if thread.isAlive():  #判断线程是否还存活来决定是否调用join
            thread.join()

def add_job( self, callable, *args):
    '''增加任务,放到队列里面'''
    self.workQueue.put((callable, args))

def main():

tp = ThreadPool(10) 
crawler = Crawler(tp)
crawler.work()

if name == 'main':

import timeit
t = timeit.Timer("main()") 
t.repeat(3, 10)
1. gevent_Crawler.py

!/usr/bin/python

coding=utf-8

Version 1 by Dongwm 2013/01/10

脚本作用：gevent

import gevent.monkey gevent.monkey.patch_all() from gevent.queue import Empty, Queue import gevent from common import Crawler

class GeventLine(object):

def __init__(self, workQueue, timeout=1, **kwargs):
    self.timeout = timeout #线程在结束前等待任务队列多长时间
    self.workQueue = workQueue

def run(self):
    '''重载run方法'''
    while True:
        try:
            callable, args = self.workQueue.get(timeout=self.timeout) #从工作队列中获取一个任务
            res = callable(*args)  #执行的任务
            print res
        except Empty:
            break
        except Exception, e:
            print e
            return -1

class GeventPool(object):

def __init__(self, num_of_threads):
         self.workQueue = Queue()
         self.threads = []
         self.__createThreadPool(num_of_threads)

def __createThreadPool(self, num_of_threads):
    for i in range(num_of_threads):
         thread = GeventLine(self.workQueue)
         self.threads.append(gevent.spawn(thread.run))


def wait_for_complete(self):
    '''等待所有线程完成'''

    while len(self.threads):
        thread = self.threads.pop()
        thread.join()
    gevent.shutdown()

def add_job( self, callable, *args):
    '''增加任务,放到队列里面'''
    self.workQueue.put((callable, args))

def main(): tp = GeventPool(10) crawler = Crawler(tp) crawler.work()

if name == 'main':

import timeit
t = timeit.Timer("main()") 
t.repeat(3, 10)
1. Crawler/spiders/spiders.py

from scrapy.contrib.spiders import CrawlSpider, Rule from scrapy.selector import HtmlXPathSelector from scrapy.contrib.linkextractors.sgml import SgmlLinkExtractor from scrapy.item import Item

class MySpider(CrawlSpider): name = 'localhost' allowed_domains = ['localhost'] start_urls = ['http://localhost'] rules = ( Rule(SgmlLinkExtractor(allow=(r'http://localhost/.*')), callback="parse_item"),
)
def parse_item(self, response): hxs = HtmlXPathSelector(response) hxs.select('//[@]/text ()').re (r'py') #实现了 common.py 里面的抓取和分析，但是 common.py 是抓取五种标签，分 2 次抓取，这里是抓取所有标签，不够严禁

1. scrapy_Crawler.py #时间有限，没有研究模块调用，也不够严禁

!/usr/bin/python

coding=utf-8

Version 1 by Dongwm 2013/01/10

脚本作用：scrapy

from subprocess import call

def main(): call('scrapy crawl localhost --nolog', shell=True)

if name == 'main':

import timeit
t = timeit.Timer("main()") 
t.repeat(3, 10)

```#### 实验过程

1. 同时启动三个终端，一起跑（手点回车，肯定有点延迟）
dongwm@localhost ~/Crawler $ python scrapy_Crawler.py
10000000 loops, best of 3: 0.024 usec per loop
10000000 loops, best of 3: 0.0222 usec per loop
10000000 loops, best of 3: 0.0222 usec per loop
10000000 loops, best of 3: 0.0222 usec per loop
10000000 loops, best of 3: 0.0222 usec per loop
10000000 loops, best of 3: 0.0223 usec per loop
10000000 loops, best of 3: 0.0222 usec per loop
10000000 loops, best of 3: 0.0222 usec per loop
10000000 loops, best of 3: 0.0222 usec per loop
10000000 loops, best of 3: 0.0222 usec per loop
10000000 loops, best of 3: 0.0222 usec per loop
10000000 loops, best of 3: 0.0222 usec per loop
10000000 loops, best of 3: 0.0222 usec per loop
10000000 loops, best of 3: 0.0222 usec per loop
10000000 loops, best of 3: 0.0222 usec per loop
10000000 loops, best of 3: 0.0222 usec per loop
10000000 loops, best of 3: 0.0222 usec per loop
10000000 loops, best of 3: 0.0222 usec per loop
10000000 loops, best of 3: 0.0222 usec per loop
10000000 loops, best of 3: 0.0222 usec per loop
10000000 loops, best of 3: 0.0222 usec per loop
10000000 loops, best of 3: 0.0223 usec per loop
10000000 loops, best of 3: 0.0222 usec per loop
10000000 loops, best of 3: 0.0222 usec per loop
10000000 loops, best of 3: 0.0222 usec per loop
10000000 loops, best of 3: 0.0222 usec per loop
10000000 loops, best of 3: 0.0222 usec per loop
10000000 loops, best of 3: 0.0223 usec per loop
10000000 loops, best of 3: 0.0222 usec per loop
10000000 loops, best of 3: 0.0223 usec per loop #他是最快跑完的，非常快～～  数据很稳定

dongwm@localhost ~/Crawler $ python gevent_Crawler.py
100000000 loops, best of 3: 0.0134 usec per loop
100000000 loops, best of 3: 0.0131 usec per loop
100000000 loops, best of 3: 0.0132 usec per loop
100000000 loops, best of 3: 0.0132 usec per loop
100000000 loops, best of 3: 0.0132 usec per loop
100000000 loops, best of 3: 0.0132 usec per loop
100000000 loops, best of 3: 0.0132 usec per loop
100000000 loops, best of 3: 0.0132 usec per loop
100000000 loops, best of 3: 0.0132 usec per loop
100000000 loops, best of 3: 0.0132 usec per loop
100000000 loops, best of 3: 0.0134 usec per loop
100000000 loops, best of 3: 0.0132 usec per loop
100000000 loops, best of 3: 0.0133 usec per loop
100000000 loops, best of 3: 0.0133 usec per loop
100000000 loops, best of 3: 0.0133 usec per loop
100000000 loops, best of 3: 0.0132 usec per loop
100000000 loops, best of 3: 0.0133 usec per loop
100000000 loops, best of 3: 0.0132 usec per loop
100000000 loops, best of 3: 0.0126 usec per loop
100000000 loops, best of 3: 0.0124 usec per loop
100000000 loops, best of 3: 0.0125 usec per loop
100000000 loops, best of 3: 0.0124 usec per loop
100000000 loops, best of 3: 0.0125 usec per loop
100000000 loops, best of 3: 0.0123 usec per loop
100000000 loops, best of 3: 0.0125 usec per loop
100000000 loops, best of 3: 0.0124 usec per loop
100000000 loops, best of 3: 0.0125 usec per loop
100000000 loops, best of 3: 0.0124 usec per loop
100000000 loops, best of 3: 0.0125 usec per loop
100000000 loops, best of 3: 0.0123 usec per loop  #跑得很慢，不知道是不是timeit的原因(或者调用的优先级太低，抢资源能力不行)，很奇怪，但是它的数据最快，数据稳定在0.0123-0.0133


dongwm@localhost ~/Crawler $ python common_Crawler.py
100000000 loops, best of 3: 0.0274 usec per loop
10000000 loops, best of 3: 0.0245 usec per loop
10000000 loops, best of 3: 0.0252 usec per loop
10000000 loops, best of 3: 0.0239 usec per loop
10000000 loops, best of 3: 0.025 usec per loop
10000000 loops, best of 3: 0.0273 usec per loop
10000000 loops, best of 3: 0.0255 usec per loop
10000000 loops, best of 3: 0.0261 usec per loop
10000000 loops, best of 3: 0.0275 usec per loop
10000000 loops, best of 3: 0.0261 usec per loop
10000000 loops, best of 3: 0.0257 usec per loop
10000000 loops, best of 3: 0.0273 usec per loop
10000000 loops, best of 3: 0.0241 usec per loop
10000000 loops, best of 3: 0.0257 usec per loop
10000000 loops, best of 3: 0.0275 usec per loop
10000000 loops, best of 3: 0.0241 usec per loop
10000000 loops, best of 3: 0.0259 usec per loop
10000000 loops, best of 3: 0.0251 usec per loop
10000000 loops, best of 3: 0.0193 usec per loop
10000000 loops, best of 3: 0.0176 usec per loop
100000000 loops, best of 3: 0.0199 usec per loop
100000000 loops, best of 3: 0.0167 usec per loop
100000000 loops, best of 3: 0.018 usec per loop
10000000 loops, best of 3: 0.0179 usec per loop
100000000 loops, best of 3: 0.0173 usec per loop
100000000 loops, best of 3: 0.0172 usec per loop
100000000 loops, best of 3: 0.018 usec per loop
100000000 loops, best of 3: 0.0162 usec per loop
100000000 loops, best of 3: 0.0179 usec per loop
100000000 loops, best of 3: 0.0171 usec per loop  #第二跑得快，但是还是数据不稳定，时间在0.017-0.026之间
2. 挨个启动，待负载保持一个相对拨波动平衡 在换另一个
dongwm@localhost ~/Crawler $ python scrapy_Crawler.py
100000000 loops, best of 3: 0.0124 usec per loop
100000000 loops, best of 3: 0.0125 usec per loop
100000000 loops, best of 3: 0.0122 usec per loop
100000000 loops, best of 3: 0.0124 usec per loop
100000000 loops, best of 3: 0.0123 usec per loop
100000000 loops, best of 3: 0.0124 usec per loop
100000000 loops, best of 3: 0.0123 usec per loop
100000000 loops, best of 3: 0.0124 usec per loop
100000000 loops, best of 3: 0.0122 usec per loop
100000000 loops, best of 3: 0.0125 usec per loop
100000000 loops, best of 3: 0.0123 usec per loop
100000000 loops, best of 3: 0.0125 usec per loop
100000000 loops, best of 3: 0.0122 usec per loop
100000000 loops, best of 3: 0.0125 usec per loop
100000000 loops, best of 3: 0.0122 usec per loop
100000000 loops, best of 3: 0.0125 usec per loop
100000000 loops, best of 3: 0.0124 usec per loop
100000000 loops, best of 3: 0.0126 usec per loop
100000000 loops, best of 3: 0.0124 usec per loop
100000000 loops, best of 3: 0.0125 usec per loop
100000000 loops, best of 3: 0.0124 usec per loop
100000000 loops, best of 3: 0.0125 usec per loop
100000000 loops, best of 3: 0.0124 usec per loop
100000000 loops, best of 3: 0.0125 usec per loop
100000000 loops, best of 3: 0.0124 usec per loop
100000000 loops, best of 3: 0.0125 usec per loop
100000000 loops, best of 3: 0.0125 usec per loop
100000000 loops, best of 3: 0.0125 usec per loop
100000000 loops, best of 3: 0.0124 usec per loop
100000000 loops, best of 3: 0.0125 usec per loop   #数据很稳定，在0.0122-0.0126之间 机器负载在1.3左右,最高超过了1.4（闲暇0.6左右）

```

dongwm@localhost ~/Crawler $ python gevent_Crawler.py 100000000 loops, best of 3: 0.0125 usec per loop 100000000 loops, best of 3: 0.0126 usec per loop 100000000 loops, best of 3: 0.0125 usec per loop 100000000 loops, best of 3: 0.0126 usec per loop 100000000 loops, best of 3: 0.0125 usec per loop 100000000 loops, best of 3: 0.0126 usec per loop 100000000 loops, best of 3: 0.0125 usec per loop 100000000 loops, best of 3: 0.0126 usec per loop 100000000 loops, best of 3: 0.0125 usec per loop 100000000 loops, best of 3: 0.0126 usec per loop 100000000 loops, best of 3: 0.0125 usec per loop 100000000 loops, best of 3: 0.0126 usec per loop 100000000 loops, best of 3: 0.0125 usec per loop 100000000 loops, best of 3: 0.0126 usec per loop 100000000 loops, best of 3: 0.0125 usec per loop 100000000 loops, best of 3: 0.0124 usec per loop 100000000 loops, best of 3: 0.0125 usec per loop 100000000 loops, best of 3: 0.0124 usec per loop 100000000 loops, best of 3: 0.0125 usec per loop 100000000 loops, best of 3: 0.0124 usec per loop 100000000 loops, best of 3: 0.0125 usec per loop 100000000 loops, best of 3: 0.0124 usec per loop 100000000 loops, best of 3: 0.0125 usec per loop 100000000 loops, best of 3: 0.0124 usec per loop 100000000 loops, best of 3: 0.0125 usec per loop 100000000 loops, best of 3: 0.0124 usec per loop 100000000 loops, best of 3: 0.0125 usec per loop 100000000 loops, best of 3: 0.0124 usec per loop 100000000 loops, best of 3: 0.0125 usec per loop 100000000 loops, best of 3: 0.0124 usec per loop #数据很稳定，在 0.0124-0.0126 之间 机器负载在 1.2 左右（闲暇 0.6 左右）

``````

dongwm@localhost ~/Crawler $ python common_Crawler.py 10000000 loops, best of 3: 0.0135 usec per loop 100000000 loops, best of 3: 0.0185 usec per loop 10000000 loops, best of 3: 0.0174 usec per loop 100000000 loops, best of 3: 0.019 usec per loop 10000000 loops, best of 3: 0.016 usec per loop 10000000 loops, best of 3: 0.0181 usec per loop 10000000 loops, best of 3: 0.0146 usec per loop 100000000 loops, best of 3: 0.0192 usec per loop 10000000 loops, best of 3: 0.0165 usec per loop 10000000 loops, best of 3: 0.0176 usec per loop 10000000 loops, best of 3: 0.0177 usec per loop 10000000 loops, best of 3: 0.0182 usec per loop 100000000 loops, best of 3: 0.0195 usec per loop 10000000 loops, best of 3: 0.0163 usec per loop 10000000 loops, best of 3: 0.0161 usec per loop 100000000 loops, best of 3: 0.0191 usec per loop 100000000 loops, best of 3: 0.0193 usec per loop 10000000 loops, best of 3: 0.0147 usec per loop 100000000 loops, best of 3: 0.0197 usec per loop 10000000 loops, best of 3: 0.0178 usec per loop 10000000 loops, best of 3: 0.0172 usec per loop 100000000 loops, best of 3: 0.022 usec per loop 100000000 loops, best of 3: 0.0191 usec per loop 10000000 loops, best of 3: 0.0208 usec per loop 10000000 loops, best of 3: 0.0144 usec per loop 10000000 loops, best of 3: 0.0201 usec per loop 100000000 loops, best of 3: 0.0195 usec per loop 100000000 loops, best of 3: 0.0231 usec per loop 10000000 loops, best of 3: 0.0149 usec per loop 100000000 loops, best of 3: 0.0211 usec per loop #数据有点不稳定，浮动较大，但是最要在 0.016-0.019 机器负载曾经长时间在 1.01, 最高未超过 1.1 （闲暇 0.6 左右）

```#### 一些我的看法

虽然我的实验有不够严禁的地方，我的代码能力也有限（希望有朋友看见代码能提供修改意见或更 NB 的版本），但是效果还是比较明显的，我总结下

  1. gevent 确实性能很好，并且很稳定，占用 io 一般 (据说长时间使用有内存泄露的问题？我不理解)
  2. scrapy 这个框架把爬虫封装的很好，只需要最少的代码就能实现，性能也不差 gevent
  3. 多线程编程确实有瓶颈，并且不稳定