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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
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()以上就是关于总结Python爬虫性能的内容，如果你们有学习到知识或者技能，可以把它分享出去让更多的人看到。
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