一般来说,使用线程有两种模式, 一种是创建线程要执行的函数, 把这个函数传递进Thread对象里,让它来执行. 另一种是直接从Thread继承,创建一个新的class,把线程执行的代码放到这个新的class里。
实现多线程网页爬虫,采用了多线程和锁机制,实现了广度优先算法的网页爬虫。
先给大家简单介绍下我的实现思路:
对于一个网络爬虫,如果要按广度遍历的方式下载,它是这样的:
1.从给定的入口网址把第一个网页下载下来
2.从第一个网页中提取出所有新的网页地址,放入下载列表中
3.按下载列表中的地址,下载所有新的网页
4.从所有新的网页中找出没有下载过的网页地址,更新下载列表
5.重复3、4两步,直到更新后的下载列表为空表时停止
python代码如下:
#!/usr/bin/env python #coding=utf-8 import threading import urllib import re import time g_mutex=threading.Condition() g_pages=[] #从中解析所有url链接 g_queueURL=[] #等待爬取的url链接列表 g_existURL=[] #已经爬取过的url链接列表 g_failedURL=[] #下载失败的url链接列表 g_totalcount=0 #下载过的页面数 class Crawler: def __init__(self,crawlername,url,threadnum): self.crawlername=crawlername self.url=url self.threadnum=threadnum self.threadpool=[] self.logfile=file("log.txt",'w') def craw(self): global g_queueURL g_queueURL.append(url) depth=0 print self.crawlername+" 启动..." while(len(g_queueURL)!=0): depth+=1 print 'Searching depth ',depth,'...\n\n' self.logfile.write("URL:"+g_queueURL[0]+"........") self.downloadAll() self.updateQueueURL() content='\n>>>Depth '+str(depth)+':\n' self.logfile.write(content) i=0 while i<len(g_queueURL): content=str(g_totalcount+i)+'->'+g_queueURL[i]+'\n' self.logfile.write(content) i+=1 def downloadAll(self): global g_queueURL global g_totalcount i=0 while i<len(g_queueURL): j=0 while j<self.threadnum and i+j < len(g_queueURL): g_totalcount+=1 threadresult=self.download(g_queueURL[i+j],str(g_totalcount)+'.html',j) if threadresult!=None: print 'Thread started:',i+j,'--File number =',g_totalcount j+=1 i+=j for thread in self.threadpool: thread.join(30) threadpool=[] g_queueURL=[] def download(self,url,filename,tid): crawthread=CrawlerThread(url,filename,tid) self.threadpool.append(crawthread) crawthread.start() def updateQueueURL(self): global g_queueURL global g_existURL newUrlList=[] for content in g_pages: newUrlList+=self.getUrl(content) g_queueURL=list(set(newUrlList)-set(g_existURL)) def getUrl(self,content): reg=r'"(http://.+?)"' regob=re.compile(reg,re.DOTALL) urllist=regob.findall(content) return urllist class CrawlerThread(threading.Thread): def __init__(self,url,filename,tid): threading.Thread.__init__(self) self.url=url self.filename=filename self.tid=tid def run(self): global g_mutex global g_failedURL global g_queueURL try: page=urllib.urlopen(self.url) html=page.read() fout=file(self.filename,'w') fout.write(html) fout.close() except Exception,e: g_mutex.acquire() g_existURL.append(self.url) g_failedURL.append(self.url) g_mutex.release() print 'Failed downloading and saving',self.url print e return None g_mutex.acquire() g_pages.append(html) g_existURL.append(self.url) g_mutex.release() if __name__=="__main__": url=raw_input("请输入url入口:\n") threadnum=int(raw_input("设置线程数:")) crawlername="小小爬虫" crawler=Crawler(crawlername,url,threadnum) crawler.craw()
以上代码就是给大家分享的基python实现多线程网页爬虫,希望大家喜欢。

Python and C each have their own advantages, and the choice should be based on project requirements. 1) Python is suitable for rapid development and data processing due to its concise syntax and dynamic typing. 2)C is suitable for high performance and system programming due to its static typing and manual memory management.

Choosing Python or C depends on project requirements: 1) If you need rapid development, data processing and prototype design, choose Python; 2) If you need high performance, low latency and close hardware control, choose C.

By investing 2 hours of Python learning every day, you can effectively improve your programming skills. 1. Learn new knowledge: read documents or watch tutorials. 2. Practice: Write code and complete exercises. 3. Review: Consolidate the content you have learned. 4. Project practice: Apply what you have learned in actual projects. Such a structured learning plan can help you systematically master Python and achieve career goals.

Methods to learn Python efficiently within two hours include: 1. Review the basic knowledge and ensure that you are familiar with Python installation and basic syntax; 2. Understand the core concepts of Python, such as variables, lists, functions, etc.; 3. Master basic and advanced usage by using examples; 4. Learn common errors and debugging techniques; 5. Apply performance optimization and best practices, such as using list comprehensions and following the PEP8 style guide.

Python is suitable for beginners and data science, and C is suitable for system programming and game development. 1. Python is simple and easy to use, suitable for data science and web development. 2.C provides high performance and control, suitable for game development and system programming. The choice should be based on project needs and personal interests.

Python is more suitable for data science and rapid development, while C is more suitable for high performance and system programming. 1. Python syntax is concise and easy to learn, suitable for data processing and scientific computing. 2.C has complex syntax but excellent performance and is often used in game development and system programming.

It is feasible to invest two hours a day to learn Python. 1. Learn new knowledge: Learn new concepts in one hour, such as lists and dictionaries. 2. Practice and exercises: Use one hour to perform programming exercises, such as writing small programs. Through reasonable planning and perseverance, you can master the core concepts of Python in a short time.

Python is easier to learn and use, while C is more powerful but complex. 1. Python syntax is concise and suitable for beginners. Dynamic typing and automatic memory management make it easy to use, but may cause runtime errors. 2.C provides low-level control and advanced features, suitable for high-performance applications, but has a high learning threshold and requires manual memory and type safety management.


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