讲师的博客:https://www.cnblogs.com/wupeiqi/p/6229292.html
在编写爬虫时,性能的消耗主要在IO请求中,当单进程单线程模式下请求URL时必然会引起等待,从而使得请求整体变慢。
比如找10个国外的资源爬取,慢的效果就很明显。
直接用一个for循环,把所有的请求串起来一次执行。这样的效率会很低:
import requests
from bs4 import BeautifulSoup
url_list = [
'https://github.com/explore',
'https://www.djangoproject.com/',
'http://www.python-requests.org/en/master/',
'https://jquery.com/',
'https://getbootstrap.com/',
'https://www.solarwinds.com/',
'https://www.zabbix.com/',
'http://open-falcon.org/',
'https://www.python.org/',
'http://www.jetbrains.com/',
]
if __name__ == '__main__':
for url in url_list:
r = requests.get(url)
r.encoding = 'utf-8'
soup = BeautifulSoup(r.text, features='html.parser')
title = soup.find('title')
print(title)
下面是使用线程池(进程池)实现的方式。这里多进程和多线程的效果一样,但是线程更省资源。
import requests
from bs4 import BeautifulSoup
from concurrent.futures import ThreadPoolExecutor
# from concurrent.futures import ProcessPoolExecutor # 进程池
url_list = [
'https://github.com/explore',
# 省略多个url
'http://www.jetbrains.com/',
]
def fetch_request(url):
r = requests.get(url)
r.encoding = 'utf-8'
soup = BeautifulSoup(r.text, features='html.parser')
title = soup.find('title')
print(title)
if __name__ == '__main__':
pool = ThreadPoolExecutor(10)
# pool = ProcessPoolExecutor(10) # 进程池
for url in url_list:
pool.submit(fetch_request, url)
pool.shutdown(True)
上面的例子用到的模块,还支持使用回调函数,把代码稍稍改一下:
import requests
from bs4 import BeautifulSoup
from concurrent.futures import ProcessPoolExecutor
url_list = [
'https://github.com/explore',
# 省略多个url
'http://www.jetbrains.com/',
]
def fetch_request(url):
response = requests.get(url)
response.encoding = 'utf-8'
soup = BeautifulSoup(response.text, features='html.parser')
title = soup.find('title')
return str(title) # 这里返回的,就是下面回调函数的入参。不转str会报错
def callback(result):
print(result.result())
if __name__ == '__main__':
pool = ProcessPoolExecutor(10)
for url in url_list:
v = pool.submit(fetch_request, url)
v.add_done_callback(callback)
pool.shutdown(True)
多进程和多线程的回调函数用法也是一样的。
这里简单的需求,是不需要用到回调函数。不过作为线程池的一个用法,多一个示例。
多线程和多进程的缺点是在IO阻塞时会造成了线程和进程的浪费,所以异步IO是更好的方式。
异步IO请求的本质则是非阻塞Socket + IO多路复用。这里只需要一个线程,而每一个请求则是一个协程。
下面就是各种Python内置以及第三方提供的异步IO请求模块。这些模块,使用简便,大大提高效率。
这个是内置模块
先看下模块是怎么调用的。这里是python3.4版本的用法,到3.5版本有新的 async/await 关键字可以用。不过向下兼容,旧的装饰器的 asyncio/yield from 的用法还是可以使用的。
用法示例:
import asyncio
@asyncio.coroutine
def func(n):
print('before func %s...' % n)
yield from asyncio.sleep(3)
print('end func %s...' % n)
if __name__ == '__main__':
tasks = []
for i in range(5):
tasks.append(func(i))
loop = asyncio.get_event_loop()
loop.run_until_complete(asyncio.gather(*tasks))
loop.close()
这里注意一下装饰器和被它装饰的函数。在tasks.append()里,添加的是函数的调用,添加的是func()而不是func,带括号的。所以一般情况下是要执行这个函数。当然这里给函数加了装饰器,就不会直接执行了,而是等到下面在执行的。
通过TCP发送HTTP请求
asyncio模块只提供了发送tcp的功能,无法直接发送http请求。不过在理解了Web服务的本质的基础上,http本质上还是tcp。http请求还是通过tcp发送字符串,只是字符串有特定的格式。字符串分为请求头和请求体,请求头和请求体之间使用 "/r/n/r/n" 分隔,而请求头和请求头之间使用 "/r/n" 分隔。下面就是一个基本的GET请求的格式:
"""
GET /index HTTP/1.0\r\n
HOST: 1.1.1.1
\r\n\r\n
"""
所以只要按上面的方式对字符串进行封装,然后通过tcp发送,这就是http了。下面这个就是用 asyncio 手动封装http报头的示例:
import asyncio
from bs4 import BeautifulSoup
url_list = [
('www.python-requests.org', '/en/master/'),
('open-falcon.org', '/'),
('www.jetbrains.com', '/'),
('www.nga.cn', '/'),
('edu.51cto.com', '/'),
]
@asyncio.coroutine
def fetch_async(host, url):
reader, writer = yield from asyncio.open_connection(host, 80) # 建立tcp连接
request_header_content = "GET %s HTTP/1.0\r\nHost: %s\r\n\r\n" % (url, host) # 这个是GET请求
request_header_content = request_header_content.encode('utf-8') # 最终发送的是bytes类型
writer.write(request_header_content) # 发出请求
yield from writer.drain()
text = yield from reader.read() # 接收到的当然也是bytes类型
text = text.decode('utf-8')
soup = BeautifulSoup(text, features='html.parser')
title = soup.find('title')
print(title)
writer.close()
if __name__ == '__main__':
tasks = []
for host, url in url_list:
tasks.append(fetch_async(host, url))
loop = asyncio.get_event_loop()
loop.run_until_complete(asyncio.gather(*tasks))
loop.close()
通过TCP发送HTTPS
上面这样只能发http请求。https主要是2个差别,默认的端口号是443,还有就是需要ssl。好在 asyncio.open_connection
是提供支持ssl的,只需要加上ssl=True的参数(这个参数的默认是False,所以上面不用指定)。下面是支持https的版本:
import asyncio
from bs4 import BeautifulSoup
url_list = [
'https://github.com/explore',
# 省略多个url
'http://www.jetbrains.com/',
]
@asyncio.coroutine
def fetch_async(host, url='/', port=80, ssl=False):
reader, writer = yield from asyncio.open_connection(host, port, ssl=ssl) # 建立tcp连接
request_header_content = "GET %s HTTP/1.0\r\nHost: %s\r\n\r\n" % (url, host) # 这个是GET请求
request_header_content = request_header_content.encode('utf-8') # 最终发送的是bytes类型
writer.write(request_header_content) # 发出请求
yield from writer.drain()
text = yield from reader.read() # 接收到的当然也是bytes类型
text = text.decode('utf-8')
soup = BeautifulSoup(text, features='html.parser')
title = soup.find('title')
print(title)
writer.close()
if __name__ == '__main__':
from urllib.parse import urlparse
tasks = []
for url in url_list:
url_parse = urlparse(url)
if url_parse.scheme == "https":
tasks.append(fetch_async(url_parse.netloc, url_parse.path, 443, True))
else:
tasks.append(fetch_async(url_parse.netloc, url_parse.path))
loop = asyncio.get_event_loop()
loop.run_until_complete(asyncio.gather(*tasks))
loop.close()
讲师博客里的代码是版本的问题,运行不了会报错。因为从 python3.5 开始,引入了 async/await 。大概记录一下原因:
在Python3.5以后,原生协程不能用于迭代,未被装饰的生成器不能yield from一个原生协程
什么是原生协程?用async关键字定义的就是原生线程。asyncio是Python 3.4版本引入的标准库,是用装饰器的方式来定义协程的(上面的例子就是)。到了python3.5版本,引入了async关键字来定义协程,并且向下兼容,之前的装饰器的方法也能用。
再来看一下aiohttp模块。粗略的看一下源码,旧版本(2.x及之前),用的是 asyncio/yield from 。3.x版本开始,都改用 async/await 了。旧版的 yield from 是不能调用新版的用async关键字定义的原生协程的,所以会报错。
之前的例子用的是 asyncio/yield from ,但是这里的 aishttp 用的是 async/await ,所以无法再用 yield from 了。下面是用 async/await 的例子:
import aiohttp
import asyncio
from bs4 import BeautifulSoup
url_list = [
'https://github.com/explore',
# 省略多个url
'http://www.jetbrains.com/',
]
async def fetch_async(url):
async with aiohttp.request('GET', url) as r:
text = await r.text('utf-8')
soup = BeautifulSoup(text, features='html.parser')
title = soup.find('title')
print(title)
if __name__ == '__main__':
tasks = []
for url in url_list:
tasks.append(fetch_async(url))
loop = asyncio.get_event_loop()
loop.run_until_complete(asyncio.gather(*tasks))
loop.close()
后面的例子还会继续用到 asyncio/yield from ,而且这个例子也不好找。
不过 async/await 才是推荐的用法,好在改一下也不难,而且网上例子也多。
import asyncio
import requests
from bs4 import BeautifulSoup
url_list = [
'https://github.com/explore',
# 省略多个url
'http://www.jetbrains.com/',
]
@asyncio.coroutine
def fetch_async(func, *args):
loop = asyncio.get_event_loop()
future = loop.run_in_executor(None, func, *args)
response = yield from future
response.encoding = 'utf-8'
soup = BeautifulSoup(response.text, features='html.parser')
title = soup.find('title')
print(title)
if __name__ == '__main__':
tasks = []
for url in url_list:
tasks.append(fetch_async(requests.get, url))
loop = asyncio.get_event_loop()
loop.run_until_complete(asyncio.gather(*tasks))
loop.close()
from bs4 import BeautifulSoup
import gevent
from gevent import monkey
monkey.patch_all() # 必须放在requests模块导入前
import requests
url_list = [
'https://github.com/explore',
# 省略多个url
'http://www.jetbrains.com/',
]
def fetch_request(url):
r = requests.get(url)
r.encoding = 'utf-8'
soup = BeautifulSoup(r.text, features='html.parser')
title = soup.find('title')
print(title)
if __name__ == '__main__':
g_list = []
for url in url_list:
g_list.append(gevent.spawn(fetch_request, url=url))
gevent.joinall(g_list)
grequests 模块,就是 gevent + requests 。有人用代码又把这两个模块再封装了一层。就写个例子:
import grequests
from bs4 import BeautifulSoup
url_list = [
'https://github.com/explore',
# 省略多个url
'http://www.jetbrains.com/',
]
def exception_handler(request, exception):
print(request, exception)
print("Request failed")
def callback(r, *args, **kwargs):
r.encoding = 'utf-8'
soup = BeautifulSoup(r.text, features='html.parser')
title = soup.find('title')
print(title)
if __name__ == '__main__':
request_list = [grequests.get(url, timeout=10, callback=callback) for url in url_list]
response_list = grequests.map(request_list, exception_handler=exception_handler, gtimeout=10)
print(response_list)
之前用for循环写列表太Low了,这里用列表生成式的写法。grequests.get里的timeout是单个任务的超时时间,grequests.map里的gtimeout则是整体任务的超时时间。
exception_handler方法是请求有异常时的处理方法。如果单个任务超时,就会抛出异常,如果任务整体超时,则还没有结束的任务返回None,没有异常。
直接安装模块会报错,去官网翻了一下 http://twistedmatrix.com 。找到了pip的安装方法
The recommended way is to run pip install Twisted, preferably inside a virtualenv.
On Linux, and BSDs, you will need a C compiler (such as GCC).
On macOS you will need to run xcode-select --install.
If you are installing on Windows, pip install Twisted[windows_platform] will install the Windows-specific requirements.
所以应该用下面的命令,安装windwos用的版本:
pip install -i https://mirrors.163.com/pypi/simple Twisted[windows_platform]
但是还是不行,错误信息如下:
error: Microsoft Visual C++ 14.0 is required. Get it with "Microsoft Visual C++ Build Tools": http://landinghub.visualstudio.com/visual-cpp-build-tools
Twisted 模块安装
最终在网上找到解决方法,就是本地安装。先去下载这个模块对应版本的whl文件:
https://www.lfd.uci.edu/~gohlke/pythonlibs/#twisted
然后用 pip 执行本地安装:
pip install E:/Downloads/Twisted-18.9.0-cp36-cp36m-win_amd64.whl
发GET请求
from bs4 import BeautifulSoup
from twisted.web.client import getPage, defer
from twisted.internet import reactor
url_list = [
'https://github.com/explore',
# 略多个url
'http://www.jetbrains.com/',
]
def all_done(arg):
reactor.stop()
def callback(contents):
soup = BeautifulSoup(contents, features='html.parser')
title = soup.find('title')
print(title)
if __name__ == '__main__':
deferred_list = []
for url in url_list:
deferred = getPage(url.encode('utf-8')) # 发请求
deferred.addCallback(callback) # 请求返回后的回调函数
deferred_list.append(deferred) # 把所有的请求加到列表里,后面要检测
dlist = defer.DeferredList(deferred_list) # 检测所有的请求
dlist.addBoth(all_done) # 检测到所有请求都执行完,执行的方法
reactor.run() # 开启一个死循环,不停的执行,all_done函数里的stop()方法会停止这个循环
发POST请求
from twisted.internet import reactor
from twisted.web.client import getPage
import urllib.parse
def one_done(arg):
print(arg)
print(arg.decode())
reactor.stop()
post_data = urllib.parse.urlencode({'check_data': 'TEST'})
post_data = post_data.encode('utf-8')
headers = {b'Content-Type': b'application/x-www-form-urlencoded'}
response = getPage(b'http://dig.chouti.com/login',
method=b'POST',
postdata=post_data,
cookies={},
headers=headers)
response.addBoth(one_done)
reactor.run()
这里只有个例子,之后可能还要再学一下:
from bs4 import BeautifulSoup
from tornado.httpclient import AsyncHTTPClient
from tornado.httpclient import HTTPRequest
from tornado import ioloop
url_list = [
'https://github.com/explore',
'https://www.djangoproject.com/',
'http://www.python-requests.org/en/master/',
'https://jquery.com/',
'https://getbootstrap.com/',
'https://www.solarwinds.com/',
'https://www.zabbix.com/',
'http://open-falcon.org/',
'https://www.python.org/',
'http://www.jetbrains.com/',
]
def asynchronous_fetch():
http_client = AsyncHTTPClient()
# 创建一个函数内的函数,来处理返回的结果
def handle_response(response):
"""
处理返回值内容(需要维护计数器,来停止IO循环),调用 ioloop.IOLoop.current().stop()
:param response:
:return:
"""
if response.error:
print("Error:", response.error)
else:
# print(response.headers)
# print(response.body)
soup = BeautifulSoup(response.body, features='html.parser')
title = soup.find('title')
print(title)
# 自己加的停止的方法,实现方法可能不是很正规
# print(response.effective_url)
curr_url = response.effective_url
if curr_url in url_list:
url_list.remove(curr_url)
if not url_list:
ioloop.IOLoop.current().stop()
for url in url_list:
# 异步处理结束后会调用指定的callback的函数
http_client.fetch(HTTPRequest(url), callback=handle_response)
# 下面这句和上面效果一样,模块内部会判断参数的isinstance是否是HTTPRequest
# 如果不是则,HTTPRequest(url, **kwargs)
# 这里的**kwargs,就是如果要给请求加任何参数,就用关键参数传参
# http_client.fetch(url, callback=handle_response)
if __name__ == '__main__':
ioloop.IOLoop.current().add_callback(asynchronous_fetch)
ioloop.IOLoop.current().start()
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