多线程
多线程即是同时运行多个子程序,就像爬取10页网页,笨方法就是从第一页爬到第十页这样最少需要十几分钟
而用多线程创建10个线程同时爬十个网页,理论上速度可扩大10倍
例如:
- import threading,time
- class MyThread(threading.Thread):
- def __init__(self,threadname):
- threading.Thread.__init__(self,name=threadname)
- def run(self):
- for i in xrange(10):
- print self.getName(),i
- time.sleep(1)
- my = MyThread('test')
- my.start()
定义类继承threading.Thread,然后在__init__里首先调用threading.Thread的__init__方法即可创建线程
重写类的run()方法即可,把你要在线程执行时做的事情都放到里面,这个线程就可以工作了
本例中线程的工作就是每隔一秒输出一下,现在线程就处于“ready”状态或者也称为“runnable”状态。为什么不称为“running”状态呢?其实是有原因的。因为我们的计算机一般是不具有真正并行处理能力的。我们所谓的多线程只是把时间分成片段,然后隔一个时间段就让一个线程执行一下,然后进入“sleeping ”状态,然后唤醒另一个在“sleeping”的线程,如此循环runnable->sleeping->runnable... ,只是因为计算机执行速度很快,而时间片段间隔很小,我们感受不到,以为是同时进行的。所以说一个线程在start了之后只是处在了可以运行的状态,他什么时候运行还是由系统来进行调度的。那一个线程什么时候会“dead”呢?一般来说当线程对象的run方法执行结束或者在执行中抛出异常的话,那么这个线程就会结束了。系统会自动对“dead”状态线程进行清理。如果一个线程A在执行的过程中需要等待另一个线程tB执行结束后才能运行的话,那就可以在A在调用B的B.join()方法,另外还可以给join()传入等待的时间。
t1 = MyThread('t1')
print t1.getName(),t1.isDaemon()
t1.setDaemon(True)
print t1.getName(),t1.isDaemon()
t1.start()
print ('main thread exit')
# -*- coding:utf-8 -*- import requests from lxml import etree import queue import threading import time import json class thread_crawl(threading.Thread): ''''' 抓取线程类 ''' def __init__(self, threadID, q): threading.Thread.__init__(self) self.threadID = threadID self.q = q def run(self): print ("Starting " + self.threadID) self.qiushi_spider() print( "Exiting ", self.threadID) def qiushi_spider(self): # page = 1 while True: if self.q.empty(): break else: page = self.q.get() print ('qiushi_spider=', self.threadID, ',page=', str(page)) url = 'http://www.qiushibaike.com/hot/page/' + str(page) + '/' headers = { 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/52.0.2743.116 Safari/537.36', 'Accept-Language': 'zh-CN,zh;q=0.8'} # 多次尝试失败结束、防止死循环 timeout = 4 while timeout > 0: timeout -= 1 try: content = requests.get(url, headers=headers) data_queue.put(content.text) break except Exception: print ('qiushi_spider', e) if timeout < 0: print ('timeout', url) class Thread_Parser(threading.Thread): ''''' 页面解析类; ''' def __init__(self, threadID, queue, lock, f): threading.Thread.__init__(self) self.threadID = threadID self.queue = queue self.lock = lock self.f = f def run(self): print ('starting ', self.threadID) global total, exitFlag_Parser while not exitFlag_Parser: try: ''''' 调用队列对象的get()方法从队头删除并返回一个项目。可选参数为block,默认为True。 如果队列为空且block为True,get()就使调用线程暂停,直至有项目可用。 如果队列为空且block为False,队列将引发Empty异常。 ''' item = self.queue.get(False) if not item: pass self.parse_data(item) self.queue.task_done() print ('Thread_Parser=', self.threadID, ',total=', total) except: pass print ('Exiting ', self.threadID) def parse_data(self, item): ''''' 解析网页函数 :param item: 网页内容 :return: ''' global total try: html = etree.HTML(item) result = html.xpath('//div[contains(@id,"qiushi_tag")]') for site in result: try: imgUrl = site.xpath('.//img/@src')[0] title = site.xpath('.//h2')[0].text content = site.xpath('.//div[@class="content"]')[0].text.strip() vote = None comments = None try: vote = site.xpath('.//i')[0].text comments = site.xpath('.//i')[1].text except: pass result = { 'imgUrl': imgUrl, 'title': title, 'content': content, 'vote': vote, 'comments': comments, } with self.lock: # print 'write %s' % json.dumps(result) self.f.write(json.dumps(result, ensure_ascii=False).encode('utf-8') + "\n") except Exception: print ('site in result', e) except Exception: print ('parse_data', e) with self.lock: total += 1 data_queue = queue.Queue() exitFlag_Parser = False lock = threading.Lock() total = 0 def main(): output = open('qiushibaike.json', 'a') #初始化网页页码page从1-10个页面 pageQueue = queue.Queue(50) for page in range(1, 11): pageQueue.put(page) #初始化采集线程 crawlthreads = [] crawlList = ["crawl-1", "crawl-2", "crawl-3"] for threadID in crawlList: thread = thread_crawl(threadID, pageQueue) thread.start() crawlthreads.append(thread) #初始化解析线程parserList parserthreads = [] parserList = ["parser-1", "parser-2", "parser-3"] #分别启动parserList for threadID in parserList: thread = Thread_Parser(threadID, data_queue, lock, output) thread.start() parserthreads.append(thread) # 等待队列清空 while not pageQueue.empty(): pass # 等待所有线程完成 for t in crawlthreads: t.join() while not data_queue.empty(): pass # 通知线程是时候退出 global exitFlag_Parser exitFlag_Parser = True for t in parserthreads: t.join() print ("Exiting Main Thread") with lock: output.close() if __name__ == '__main__': main()