这里我们通过请求网页例子来一步步理解爬虫性能

当我们有一个列表存放了一些url需要我们获取相关数据，我们首先想到的是循环

简单的循环串行

这一种方法相对来说是最慢的，因为一个一个循环，耗时是最长的，是所有的时间总和

代码如下：

import requests

url_list = [

‘http://www.baidu.com’,

‘http://www.pythonsite.com’,

‘http://www.cnblogs.com/’

]

for url in url_list:

result = requests.get(url)

print(result.text)

通过线程池

通过线程池的方式访问，这样整体的耗时是所有连接里耗时最久的那个，相对循环来说快了很多

import requests

from concurrent.futures import ThreadPoolExecutor

def fetch_request(url):

result = requests.get(url)

print(result.text)

url_list = [

‘http://www.baidu.com’,

‘http://www.bing.com’,

‘http://www.cnblogs.com/’

]

pool = ThreadPoolExecutor(10)

for url in url_list:

#去线程池中获取一个线程，线程去执行fetch_request方法

pool.submit(fetch_request,url)

pool.shutdown(True)

线程池+回调函数

这里定义了一个回调函数callback

from concurrent.futures import ThreadPoolExecutor

import requests

def fetch_async(url):

response = requests.get(url)

return response

def callback(future):

print(future.result().text)

url_list = [

‘http://www.baidu.com’,

‘http://www.bing.com’,

‘http://www.cnblogs.com/’

]

pool = ThreadPoolExecutor(5)

for url in url_list:

v = pool.submit(fetch_async,url)

#这里调用回调函数

v.add_done_callback(callback)

pool.shutdown()

通过进程池

通过进程池的方式访问，同样的也是取决于耗时最长的，但是相对于线程来说，进程需要耗费更多的资源，同时这里是访问url时IO操作，所以这里线程池比进程池更好

import requests

from concurrent.futures import ProcessPoolExecutor

def fetch_request(url):

result = requests.get(url)

print(result.text)

url_list = [

‘http://www.baidu.com’,

‘http://www.bing.com’,

‘http://www.cnblogs.com/’

]

pool = ProcessPoolExecutor(10)

for url in url_list:

#去进程池中获取一个线程，子进程程去执行fetch_request方法

pool.submit(fetch_request,url)

pool.shutdown(True)

进程池+回调函数

这种方式和线程+回调函数的效果是一样的，相对来说开进程比开线程浪费资源

from concurrent.futures import ProcessPoolExecutor

import requests

def fetch_async(url):

response = requests.get(url)

return response

def callback(future):

print(future.result().text)

url_list = [

‘http://www.baidu.com’,

‘http://www.bing.com’,

‘http://www.cnblogs.com/’

]

pool = ProcessPoolExecutor(5)

for url in url_list:

v = pool.submit(fetch_async, url)

# 这里调用回调函数

v.add_done_callback(callback)

pool.shutdown()

主流的单线程实现并发的几种方式

asyncio

gevent

Twisted

Tornado

下面分别是这四种代码的实现例子：

asyncio例子1：

import asyncio

@asyncio.coroutine #通过这个装饰器装饰

def func1():

print(‘before…func1……’)

# 这里必须用yield from，并且这里必须是asyncio.sleep不能是time.sleep

yield from asyncio.sleep(2)

print(‘end…func1……’)

tasks = [func1(), func1()]

loop = asyncio.get_event_loop()

loop.run_until_complete(asyncio.gather(*tasks))

loop.close()

上述的效果是同时会打印两个before的内容，然后等待2秒打印end内容

这里asyncio并没有提供我们发送http请求的方法，但是我们可以在yield from这里构造http请求的方法。

asyncio例子2：

import asyncio

@asyncio.coroutine

def fetch_async(host, url=’/’):

print(“—-“,host, url)

reader, writer = yield from asyncio.open_connection(host, 80)

#构造请求头内容

request_header_content = “””GET %s HTTP/1.0\r\nHost: %s\r\n\r\n””” % (url, host,)

request_header_content = bytes(request_header_content, encoding=’utf-8′)

#发送请求

writer.write(request_header_content)

yield from writer.drain()

text = yield from reader.read()

print(host, url, text)

writer.close()

tasks = [

fetch_async(‘www.cnblogs.com’, ‘/zhaof/’),

fetch_async(‘dig.chouti.com’, ‘/pic/show?nid=4073644713430508&lid=10273091’)

]

loop = asyncio.get_event_loop()

results = loop.run_until_complete(asyncio.gather(*tasks))

loop.close()

asyncio + aiohttp 代码例子：

import aiohttp

import asyncio

@asyncio.coroutine

def fetch_async(url):

print(url)

response = yield from aiohttp.request(‘GET’, url)

print(url, response)

response.close()

tasks = [fetch_async(‘http://baidu.com/’), fetch_async(‘http://www.chouti.com/’)]

event_loop = asyncio.get_event_loop()

results = event_loop.run_until_complete(asyncio.gather(*tasks))

event_loop.close()

asyncio+requests代码例子

import asyncio

import requests

@asyncio.coroutine

def fetch_async(func, *args):

loop = asyncio.get_event_loop()

future = loop.run_in_executor(None, func, *args)

response = yield from future

print(response.url, response.content)

tasks = [

fetch_async(requests.get, ‘http://www.cnblogs.com/wupeiqi/’),

fetch_async(requests.get, ‘http://dig.chouti.com/pic/show?nid=4073644713430508&lid=10273091’)

]

loop = asyncio.get_event_loop()

results = loop.run_until_complete(asyncio.gather(*tasks))

loop.close()

gevent+requests代码例子

import gevent

import requests

from gevent import monkey

monkey.patch_all()

def fetch_async(method, url, req_kwargs):

print(method, url, req_kwargs)

response = requests.request(method=method, url=url, **req_kwargs)

print(response.url, response.content)

# ##### 发送请求 #####

gevent.joinall([

gevent.spawn(fetch_async, method=’get’, url=’https://www.python.org/’, req_kwargs={}),

gevent.spawn(fetch_async, method=’get’, url=’https://www.yahoo.com/’, req_kwargs={}),

gevent.spawn(fetch_async, method=’get’, url=’https://github.com/’, req_kwargs={}),

])

# ##### 发送请求（协程池控制最大协程数量） #####

# from gevent.pool import Pool

# pool = Pool(None)

# gevent.joinall([

# pool.spawn(fetch_async, method=’get’, url=’https://www.python.org/’, req_kwargs={}),

# pool.spawn(fetch_async, method=’get’, url=’https://www.yahoo.com/’, req_kwargs={}),

# pool.spawn(fetch_async, method=’get’, url=’https://www.github.com/’, req_kwargs={}),

# ])

grequests代码例子

这个是讲requests+gevent进行了封装

import grequests

request_list = [

grequests.get(‘http://httpbin.org/delay/1’, timeout=0.001),

grequests.get(‘http://fakedomain/’),

grequests.get(‘http://httpbin.org/status/500’)

]

# ##### 执行并获取响应列表 #####

# response_list = grequests.map(request_list)

# print(response_list)

# ##### 执行并获取响应列表（处理异常） #####

# def exception_handler(request, exception):

# print(request,exception)

# print(“Request failed”)

# response_list = grequests.map(request_list, exception_handler=exception_handler)

# print(response_list)

twisted代码例子

#getPage相当于requets模块，defer特殊的返回值，rector是做事件循环

from twisted.web.client import getPage, defer

from twisted.internet import reactor

def all_done(arg):

reactor.stop()

def callback(contents):

print(contents)

deferred_list = []

url_list = [‘http://www.bing.com’, ‘http://www.baidu.com’, ]

for url in url_list:

deferred = getPage(bytes(url, encoding=’utf8′))

deferred.addCallback(callback)

deferred_list.append(deferred)

#这里就是进就行一种检测，判断所有的请求知否执行完毕

dlist = defer.DeferredList(deferred_list)

dlist.addBoth(all_done)

reactor.run()

tornado代码例子

from tornado.httpclient import AsyncHTTPClient

from tornado.httpclient import HTTPRequest

from tornado import ioloop

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.body)

def func():

url_list = [

‘http://www.baidu.com’,

‘http://www.bing.com’,

]

for url in url_list:

print(url)

http_client = AsyncHTTPClient()

http_client.fetch(HTTPRequest(url), handle_response)

ioloop.IOLoop.current().add_callback(func)

ioloop.IOLoop.current().start()

以上就是Python 爬虫性能相关总结的详细内容，更多关于Python 爬虫性能的资料请关注脚本之家其它相关文章！