python使用tornado实现简单爬虫
本文实例为大家分享了python使用tornado实现简单爬虫的具体代码,供大家参考,具体内容如下
代码在官方文档的示例代码中有,但是作为一个tornado新手来说阅读起来还是有点困难的,于是我在代码中添加了注释,方便理解,代码如下:
# coding=utf-8 #!/usr/bin/env python import time from datetime import timedelta try: from HTMLParser import HTMLParser from urlparse import urljoin, urldefrag except ImportError: from html.parser import HTMLParser from urllib.parse import urljoin, urldefrag from tornado import httpclient, gen, ioloop, queues # 设置要爬取的网址 base_url = 'http://www.baidu.com' # 设置worker数量 concurrency = 10 # 此代码会获取base_url下的所有其他url @gen.coroutine def get_links_from_url(url): try: # 通过异步向url发起请求 response = yield httpclient.AsyncHTTPClient().fetch(url) print('fetched %s' % url) # 响应如果是字节类型 进行解码 html = response.body if isinstance(response.body, str) \ else response.body.decode(errors='ignore') # 构建url列表 urls = [urljoin(url, remove_fragment(new_url)) for new_url in get_links(html)] except Exception as e: print('Exception: %s %s' % (e, url)) # 报错返回空列表 raise gen.Return([]) # 返回url列表 raise gen.Return(urls) def remove_fragment(url): #去除锚点 pure_url, frag = urldefrag(url) return pure_url def get_links(html): #从html页面里提取url class URLSeeker(HTMLParser): def __init__(self): HTMLParser.__init__(self) self.urls = [] def handle_starttag(self, tag, attrs): href = dict(attrs).get('href') if href and tag == 'a': self.urls.append(href) url_seeker = URLSeeker() url_seeker.feed(html) return url_seeker.urls @gen.coroutine def main(): # 创建队列 q = queues.Queue() # 记录开始时间戳 start = time.time() # 构建两个集合 fetching, fetched = set(), set() @gen.coroutine def fetch_url(): # 从队列中取出数据 current_url = yield q.get() try: # 如果取出的数据在队列中已经存在 返回 if current_url in fetching: return print('fetching %s' % current_url) # 如果不存在添加到集合当中 fetching.add(current_url) # 从新放入的链接中继续获取链接 urls = yield get_links_from_url(current_url) # 将已经请求玩的url放入第二个集合 fetched.add(current_url) for new_url in urls: # Only follow links beneath the base URL # 如果链接是以传入的url开始则放入队列 if new_url.startswith(base_url): yield q.put(new_url) finally: # 队列内数据减一 q.task_done() @gen.coroutine def worker(): while True: # 保证程序持续运行 yield fetch_url() # 将第一个url放入队列 q.put(base_url) # Start workers, then wait for the work queue to be empty. for _ in range(concurrency): # 启动对应数量的worker worker() # 等待队列数据处理完成 yield q.join(timeout=timedelta(seconds=300)) # 如果两个集合不相等抛出异常 assert fetching == fetched # 打印执行时间 print('Done in %d seconds, fetched %s URLs.' % ( time.time() - start, len(fetched))) if __name__ == '__main__': io_loop = ioloop.IOLoop.current() io_loop.run_sync(main)
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