单机下进行分布式爬取数据(windows下单机模拟多机进行分布式爬虫)
URL管理器ControlNode/ URLManager.py
#coding:utf-8
import cPickle
import hashlib
class UrlManager(object):
def __init__(self):
self.new_urls = self.load_progress('new_urls.txt')#未爬取URL集合
self.old_urls = self.load_progress('old_urls.txt')#已爬取URL集合
def has_new_url(self):
'''
判断是否有未爬取的URL
:return:
'''
return self.new_url_size()!=0
def get_new_url(self):
'''
获取一个未爬取的URL
:return:
'''
new_url = self.new_urls.pop()
m = hashlib.md5()
m.update(new_url)
self.old_urls.add(m.hexdigest()[8:-8])
return new_url
def add_new_url(self,url):
'''
将新的URL添加到未爬取的URL集合中
:param url:单个URL
:return:
'''
if url is None:
return
m = hashlib.md5()
m.update(url)
url_md5 = m.hexdigest()[8:-8]
if url not in self.new_urls and url_md5 not in self.old_urls:
self.new_urls.add(url)
def add_new_urls(self,urls):
'''
将新的URLS添加到未爬取的URL集合中
:param urls:url集合
:return:
'''
if urls is None or len(urls)==0:
return
for url in urls:
self.add_new_url(url)
def new_url_size(self):
'''
获取未爬取URL集合的s大小
:return:
'''
return len(self.new_urls)
def old_url_size(self):
'''
获取已经爬取URL集合的大小
:return:
'''
return len(self.old_urls)
def save_progress(self,path,data):
'''
保存进度
:param path:文件路径
:param data:数据
:return:
'''
with open(path, 'wb') as f:
cPickle.dump(data, f)
def load_progress(self,path):
'''
从本地文件加载进度
:param path:文件路径
:return:返回set集合
'''
print '[+] 从文件加载进度: %s' % path
try:
with open(path, 'rb') as f:
tmp = cPickle.load(f)
return tmp
except:
print '[!] 无进度文件, 创建: %s' % path
return set()
数据存储端：ControlNode/DataOutput.py
#coding:utf-8
import codecs
import time
class DataOutput(object):
def __init__(self):
self.filepath='baike_%s.html'%(time.strftime("%Y_%m_%d_%H_%M_%S", time.localtime()) )
self.output_head(self.filepath)
self.datas=[]
def store_data(self,data):
if data is None:
return
self.datas.append(data)
if len(self.datas)>10:
self.output_html(self.filepath)
def output_head(self,path):
'''
将HTML头写进去
:return:
'''
fout=codecs.open(path,'w',encoding='utf-8')
fout.write("")
fout.write("")
fout.write("")
fout.close()
def output_html(self,path):
'''
将数据写入HTML文件中
:param path: 文件路径
:return:
'''
fout=codecs.open(path,'a',encoding='utf-8')
for data in self.datas:
fout.write("")
fout.write("%s "%data['url'])
fout.write("%s "%data['title'])
fout.write("%s "%data['summary'])
fout.write(" ")
self.datas=[]
fout.close()
def ouput_end(self,path):
'''
输出HTML结束
:param path: 文件存储路径
:return:
'''
fout=codecs.open(path,'a',encoding='utf-8')
fout.write("
")
fout.write("")
fout.write("")
fout.close()控制节点端ControlNode/NodeManager.py
#coding:utf-8
from multiprocessing.managers import BaseManager
import time
from multiprocessing import Process, Queue
from DataOutput import DataOutput
from UrlManager import UrlManager
class NodeManager(object):
def start_Manager(self,url_q,result_q):
'''
创建一个分布式管理器
:param url_q: url队列
:param result_q: 结果队列
:return:
'''
#把创建的两个队列注册在网络上，利用register方法，callable参数关联了Queue对象，
# 将Queue对象在网络中暴露
BaseManager.register('get_task_queue',callable=lambda:url_q)
BaseManager.register('get_result_queue',callable=lambda:result_q)
#绑定端口8001，设置验证口令‘baike’。这个相当于对象的初始化
manager=BaseManager(address=('',8001),authkey='baike')
#返回manager对象
return manager
def url_manager_proc(self,url_q,conn_q,root_url):
url_manager = UrlManager()
url_manager.add_new_url(root_url)
while True:
while(url_manager.has_new_url()):
#从URL管理器获取新的url
new_url = url_manager.get_new_url()
#将新的URL发给工作节点
url_q.put(new_url)
print 'old_url=',url_manager.old_url_size()
#加一个判断条件，当爬去2000个链接后就关闭,并保存进度
if(url_manager.old_url_size()>2000):
#通知爬行节点工作结束
url_q.put('end')
print '控制节点发起结束通知!'
#关闭管理节点，同时存储set状态
url_manager.save_progress('new_urls.txt',url_manager.new_urls)
url_manager.save_progress('old_urls.txt',url_manager.old_urls)
return
#将从result_solve_proc获取到的urls添加到URL管理器之间
try:
if not conn_q.empty():
urls = conn_q.get()
url_manager.add_new_urls(urls)
except BaseException,e:
time.sleep(0.1)#延时休息
def result_solve_proc(self,result_q,conn_q,store_q):
while(True):
try:
if not result_q.empty():
content = result_q.get(True)
if content['new_urls']=='end':
#结果分析进程接受通知然后结束
print '结果分析进程接受通知然后结束!'
store_q.put('end')
return
conn_q.put(content['new_urls'])#url为set类型
store_q.put(content['data'])#解析出来的数据为dict类型
else:
time.sleep(0.1)#延时休息
except BaseException,e:
time.sleep(0.1)#延时休息
def store_proc(self,store_q):
output = DataOutput()
while True:
if not store_q.empty():
data = store_q.get()
if data=='end':
print '存储进程接受通知然后结束!'
output.ouput_end(output.filepath)
return
output.store_data(data)
else:
time.sleep(0.1)
pass
if __name__=='__main__':
#初始化4个队列
url_q = Queue()
result_q = Queue()
store_q = Queue()
conn_q = Queue()
#创建分布式管理器
node = NodeManager()
manager = node.start_Manager(url_q,result_q)
#创建URL管理进程、 数据提取进程和数据存储进程
url_manager_proc = Process(target=node.url_manager_proc, args=(url_q,conn_q,'http://baike.baidu.com/view/284853.htm',))
result_solve_proc = Process(target=node.result_solve_proc, args=(result_q,conn_q,store_q,))
store_proc = Process(target=node.store_proc, args=(store_q,))
#启动3个进程和分布式管理器
url_manager_proc.start()
result_solve_proc.start()
store_proc.start()
manager.get_server().serve_forever()
以下部署在另一台机器上，为爬虫端
爬虫调度器SpiderNode/SpiderWork.py
#coding:utf-8
from multiprocessing.managers import BaseManager
from HtmlDownloader import HtmlDownloader
from HtmlParser import HtmlParser
class SpiderWork(object):
def __init__(self):
#初始化分布式进程中的工作节点的连接工作
# 实现第一步：使用BaseManager注册获取Queue的方法名称
BaseManager.register('get_task_queue')
BaseManager.register('get_result_queue')
# 实现第二步：连接到服务器:
server_addr = '127.0.0.1'
print('Connect to server %s...' % server_addr)
# 端口和验证口令注意保持与服务进程设置的完全一致:
self.m = BaseManager(address=(server_addr, 8001), authkey='baike')
# 从网络连接:
self.m.connect()
# 实现第三步：获取Queue的对象:
self.task = self.m.get_task_queue()
self.result = self.m.get_result_queue()
#初始化网页下载器和解析器
self.downloader = HtmlDownloader()
self.parser = HtmlParser()
print 'init finish'
def crawl(self):
while(True):
try:
if not self.task.empty():
url = self.task.get()
if url =='end':
print '控制节点通知爬虫节点停止工作...'
#接着通知其它节点停止工作
self.result.put({'new_urls':'end','data':'end'})
return
print '爬虫节点正在解析:%s'%url.encode('utf-8')
content = self.downloader.download(url)
new_urls,data = self.parser.parser(url,content)
self.result.put({"new_urls":new_urls,"data":data})
except EOFError,e:
print "连接工作节点失败"
return
except Exception,e:
print e
print 'Crawl fali '
if __name__=="__main__":
spider = SpiderWork()
spider.crawl()HTML解析器SpiderNode/HtmlParser.py
#coding:utf-8
import re
import urlparse
from bs4 import BeautifulSoup
class HtmlParser(object):
def parser(self,page_url,html_cont):
'''
用于解析网页内容抽取URL和数据
:param page_url: 下载页面的URL
:param html_cont: 下载的网页内容
:return:返回URL和数据
'''
if page_url is None or html_cont is None:
return
soup = BeautifulSoup(html_cont,'html.parser',from_encoding='utf-8')
new_urls = self._get_new_urls(page_url,soup)
new_data = self._get_new_data(page_url,soup)
return new_urls,new_data
def _get_new_urls(self,page_url,soup):
'''
抽取新的URL集合
:param page_url: 下载页面的URL
:param soup:soup
:return: 返回新的URL集合
'''
new_urls = set()
#抽取符合要求的a标签
# 原书代码
# links = soup.find_all('a', href=re.compile(r'/view/\d+\.htm'))
#2017-07-03 更新,原因百度词条的链接形式发生改变
links = soup.find_all('a',href=re.compile(r'/item/.*'))
for link in links:
#提取href属性
new_url = link['href']
#拼接成完整网址
new_full_url = urlparse.urljoin(page_url,new_url)
new_urls.add(new_full_url)
return new_urls
def _get_new_data(self,page_url,soup):
'''
抽取有效数据
:param page_url:下载页面的URL
:param soup:
:return:返回有效数据
'''
data={}
data['url']=page_url
title = soup.find('dd',class_='lemmaWgt-lemmaTitle-title').find('h1')
data['title']=title.get_text()
summary = soup.find('div',class_='lemma-summary')
#获取到tag中包含的所有文版内容包括子孙tag中的内容,并将结果作为Unicode字符串返回
data['summary']=summary.get_text()
return data
HTML下载器SpiderNode/HtmlDownloader.py
#coding:utf-8
import requests
class HtmlDownloader(object):
def download(self,url):
if url is None:
return None
user_agent = 'Mozilla/4.0 (compatible; MSIE 5.5; Windows NT)'
headers={'User-Agent':user_agent}
r = requests.get(url,headers=headers)
if r.status_code==200:
r.encoding='utf-8'
return r.text
return None