020 使用Tornado和协程爬取博客园文章

Python3.5后 Tornado官方建议使用async和await的方式实现异步程序,尝试了下使用Tornado和协程爬取博客园的文章并使用peewee_async异步写入MySQL数据库。

一. 博客园文章抓取测试:

这里我以我自己的一篇文章详情作为测试url,https://www.cnblogs.com/FG123/p/9934244.html ,主要是抓取文章标题、内容及作者信息:

 

文章标题、内容、作者用户名可通过上述的详情页url获取,但是作者信息需通过http://www.cnblogs.com/mvc/blog/news.aspx?blogApp=FG123获取,FG123是我这篇文章的作者用户名,下面看使用beautiful soup抓取测试的代码及结果:

detail_article_html = requests.get("https://www.cnblogs.com/FG123/p/9934244.html").content
author_profile_html = requests.get("http://www.cnblogs.com/mvc/blog/news.aspx?blogApp=FG123").content
detail_soup = BeautifulSoup(detail_article_html)
title = detail_soup.find(id="cb_post_title_url").get_text()
info = detail_soup.find(id="cnblogs_post_body")
author_soup = BeautifulSoup(author_profile_html)
author = author_soup.select('div > a')
author_name = author[0].get_text()
blog_age = author[1].get_text()
fans_num = author[2].get_text()
follow_num = author[3].get_text()

print("文章标题:{}".format(title))
print("博主昵称:{}".format(author_name))
print("博主园龄:{}".format(blog_age))
print("粉丝数:{}".format(fans_num))
print("关注数:{}".format(follow_num))
print("文章内容:{}".format(info))

结果:

 

二. 使用Tornado和协程异步抓取逻辑:

这里的抓取逻辑采用tornado官方文档爬虫例子的逻辑,使用Tornado的Queue实现异步生产者/消费者模式,当Queue满时会切换协程,首先定义协程通过解析url获取相关链接并去除无效的链接:

 

 1 async def get_links_from_url(url):
 2     """
 3     通过AsyncHTTPClient异步fetch url,
 4     通过BeautifulSoup提取解析内容中的所有url
 5     :param url:
 6     :return:
 7     """
 8     response = await httpclient.AsyncHTTPClient().fetch(url)
 9     print('fetched %s' % url)
10 
11     html = response.body.decode("utf8", errors='ignore')
12     soup = BeautifulSoup(html)
13     return set([urljoin(url, remove_fragment(a.get("href")))
14             for a in soup.find_all("a", href=True)])
15 
16 
17 def remove_fragment(url):
18     """
19     去除无效的链接
20     :param url: 
21     :return: 
22     """
23     pure_url, frag = urldefrag(url)
24     return pure_url
View Code

 

当前url通过调用协程获取它包含的有效url_list,并将非外链接的url放入tornado的queue中:

 1 async def fetch_url(current_url):
 2         """
 3         fetching是已爬取过的url集合,
 4         通过调用协程get_links_from_url获取current_url所有的url,
 5         并将 非外链接 放入到queue中
 6         :param current_url:
 7         :return:
 8         """
 9         if current_url in fetching:
10             return
11 
12         print('fetching %s' % current_url)
13         fetching.add(current_url)
14         urls = await get_links_from_url(current_url)
15         fetched.add(current_url)
16 
17         for new_url in urls:
18             # 非外链接
19             if new_url.startswith(base_url) and new_url.endswith(".html"):
20                 await q.put(new_url)
View Code

 

使用async for的方式取出queue中的url,并调用协程fetch_url获取它包含的urls,调用协程get_info_data获取url页面详情数据:

 1 async def worker():
 2         """
 3         使用async for的方式取出q中的url
 4         并调用协程fetch_url获取它包含的urls
 5         调用协程get_info_data获取url页面详情数据
 6         :return:
 7         """
 8         async for url in q:
 9             if url is None:
10                 return
11             try:
12                 await fetch_url(url)
13                 await get_info_data(url)
14             except Exception as e:
15                 print('Exception: %s %s' % (e, url))
16             finally:
17                 q.task_done()
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定义主协程,通过tornado的gen.multi同时初始化concurrency个协程,并将协程放入到事件循环中等待完成,等到队列全部为空或超时的时候放入与协程数量相同的None来结束协程的事件循环。

 1 async def main():
 2     """
 3     主协程,通过tornado的gen.multi同时初始化concurrency个协程,
 4     并将协程放入到事件循环中等待完成,等到队列全部为空或超时
 5     :return:
 6     """
 7     q = queues.Queue()
 8     start = time.time()
 9     fetching, fetched = set(), set()
10 
11     # 放入初始url到队列
12     await q.put(base_url)
13 
14     workers = gen.multi([worker() for _ in range(concurrency)])
15     await q.join(timeout=timedelta(seconds=300))
16     assert fetching == fetched
17     print('Done in %d seconds, fetched %s URLs.' % (
18         time.time() - start, len(fetched)))
19 
20     # 队列中放入concurrency数量的None 结束相应协程 在worker()中取到None会结束
21     for _ in range(concurrency):
22         await q.put(None)
23         
24     await workers
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三. 使用peewee_async和aiomysql将爬取的数据异步写入MySQL数据库

使用peewee创建并生成model:

 

 1 # coding:utf-8
 2 from peewee import *
 3 import peewee_async
 4 
 5 database = peewee_async.MySQLDatabase(
 6     'xxx', host="192.168.xx.xx",
 7     port=3306, user="root", password="xxxxxx"
 8 )
 9 
10 objects = peewee_async.Manager(database)
11 
12 database.set_allow_sync(True)
13 
14 
15 class Blogger(Model):
16     article_id = CharField(max_length=50, verbose_name="文章ID")
17     title = CharField(max_length=150, verbose_name="标题")
18     content = TextField(null=True, verbose_name="内容")
19     author_name = CharField(max_length=50, verbose_name="博主昵称")
20     blog_age = CharField(max_length=50, verbose_name="园龄")
21     fans_num = IntegerField(null=True, verbose_name="粉丝数")
22     follow_num = IntegerField(null=True, verbose_name="关注数")
23 
24     class Meta:
25         database = database
26         table_name = "blogger"
27 
28 
29 def init_table():
30     database.create_tables([Blogger])
31 
32 
33 if __name__ == "__main__":
34     init_table()
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获取博客文章的详情信息,并将信息异步写入MySQL数据库:

 1 async def get_info_data(url):
 2     """
 3     获取详情信息并异步写入MySQL数据库
 4     :param url:
 5     :return:
 6     """
 7     response = await httpclient.AsyncHTTPClient().fetch(url)
 8     html = response.body.decode("utf8")
 9     soup = BeautifulSoup(html)
10     title = soup.find(id="cb_post_title_url").get_text()
11     content = soup.find(id="cnblogs_post_body")
12     name = url.split("/")[3]
13     article_id = url.split("/")[-1].split(".")[0]
14     author_url = "http://www.cnblogs.com/mvc/blog/news.aspx?blogApp={}".format(name)
15     author_response = await httpclient.AsyncHTTPClient().fetch(author_url)
16     author_html = author_response.body.decode("utf8")
17     author_soup = BeautifulSoup(author_html)
18     author = author_soup.select('div > a')
19     author_name = author[0].get_text()
20     blog_age = author[1].get_text()
21     fans_num = author[2].get_text()
22     follow_num = author[3].get_text()
23     await objects.create(
24         Blogger, title=title,
25         article_id=article_id,
26         content=content,
27         author_name=author_name,
28         blog_age=blog_age,
29         fans_num=fans_num,
30         follow_num=follow_num
31     )
View Code

 

爬取结果:

 简单体验了下使用Tornado结合协程的方式爬取博客园,这里我开启了10个协程,已经感觉速度很快了,协程间的切换开销是非常小的,而且一个线程或进程可以拥有多个协程,经过实测相比多线程的爬虫确实要快些。

posted @ 2019-12-11 13:31  ABDM  阅读(165)  评论(0编辑  收藏  举报