爬虫性能相关

Posted 黄土地上的黑石头

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性能相关

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。

import requests

def fetch_async(url):
    response = requests.get(url)
    return response


url_list = [\'http://www.github.com\', \'http://www.bing.com\']

for url in url_list:
    fetch_async(url)
1.同步执行
from concurrent.futures import ThreadPoolExecutor
import requests


def fetch_async(url):
    response = requests.get(url)
    return response


url_list = [\'http://www.github.com\', \'http://www.bing.com\']
pool = ThreadPoolExecutor(5)
for url in url_list:
    pool.submit(fetch_async, url)
pool.shutdown(wait=True)
2.多线程执行
from concurrent.futures import ThreadPoolExecutor
import requests

def fetch_async(url):
    response = requests.get(url)
    return response


def callback(future):
    print(future.result())


url_list = [\'http://www.github.com\', \'http://www.bing.com\']
pool = ThreadPoolExecutor(5)
for url in url_list:
    v = pool.submit(fetch_async, url)
    v.add_done_callback(callback)
pool.shutdown(wait=True)
2.多线程+回调函数执行
from concurrent.futures import ProcessPoolExecutor
import requests

def fetch_async(url):
    response = requests.get(url)
    return response


url_list = [\'http://www.github.com\', \'http://www.bing.com\']
pool = ProcessPoolExecutor(5)
for url in url_list:
    pool.submit(fetch_async, url)
pool.shutdown(wait=True)
3.多进程执行
from concurrent.futures import ProcessPoolExecutor
import requests


def fetch_async(url):
    response = requests.get(url)
    return response


def callback(future):
    print(future.result())


url_list = [\'http://www.github.com\', \'http://www.bing.com\']
pool = ProcessPoolExecutor(5)
for url in url_list:
    v = pool.submit(fetch_async, url)
    v.add_done_callback(callback)
pool.shutdown(wait=True)
3.多进程+回调函数执行

通过上述代码均可以完成对请求性能的提高，对于多线程和多进行的缺点是在IO阻塞时会造成了线程和进程的浪费，所以异步IO回事首选：

import asyncio


@asyncio.coroutine
def func1():
    print(\'before...func1......\')
    yield from asyncio.sleep(5)
    print(\'end...func1......\')


tasks = [func1(), func1()]

loop = asyncio.get_event_loop()
loop.run_until_complete(asyncio.gather(*tasks))
loop.close()
1.asyncio示例1
import asyncio


@asyncio.coroutine
def fetch_async(host, url=\'/\'):
    print(host, url)
    reader, writer = yield from asyncio.open_connection(host, 80)

    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\')

    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\', \'/wupeiqi/\'),
    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()
1.asyncio示例2
import aiohttp
import asyncio

@asyncio.coroutine
def fetch_async(url):
    print(url)
    response = yield from aiohttp.request(\'GET\', url)
    # data = yield from response.read()
    # print(url, data)
    print(url, response)
    response.close()

tasks = [fetch_async(\'http://www.google.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()
2.asyncio + aiohttp
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()
3.asyncio + requests
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={}),
# ])
4.gevent + requests
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)
5.grequests
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)

dlist = defer.DeferredList(deferred_list)
dlist.addBoth(all_done)

reactor.run()
6.Twisted示例
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()
7.Tornado
from twisted.internet import reactor
from twisted.web.client import getPage
import urllib.parse


def one_done(arg):
    print(arg)
    reactor.stop()

post_data = urllib.parse.urlencode({\'check_data\': \'adf\'})
post_data = bytes(post_data, encoding=\'utf8\')
headers = {b\'Content-Type\': b\'application/x-www-form-urlencoded\'}
response = getPage(bytes(\'http://dig.chouti.com/login\', encoding=\'utf8\'),
                   method=bytes(\'POST\', encoding=\'utf8\'),
                   postdata=post_data,
                   cookies={},
                   headers=headers)
response.addBoth(one_done)

reactor.run()
Twisted更多

以上均是Python内置以及第三方模块提供异步IO请求模块，使用简便大大提高效率，而对于异步IO请求的本质则是【非阻塞Socket】+【IO多路复用】：

import select
import socket
import time


class AsyncTimeoutException(TimeoutError):
    """
    请求超时异常类
    """

    def __init__(self, msg):
        self.msg = msg
        super(AsyncTimeoutException, self).__init__(msg)


class HttpContext(object):
    """封装请求和相应的基本数据"""

    def __init__(self, sock, host, port, method, url, data, callback, timeout=5):
        """
        sock: 请求的客户端socket对象
        host: 请求的主机名
        port: 请求的端口
        port: 请求的端口
        method: 请求方式
        url: 请求的URL
        data: 请求时请求体中的数据
        callback: 请求完成后的回调函数
        timeout: 请求的超时时间
        """
        self.sock = sock
        self.callback = callback
        self.host = host
        self.port = port
        self.method = method
        self.url = url
        self.data = data

        self.timeout = timeout

        self.__start_time = time.time()
        self.__buffer = []

    def is_timeout(self):
        """当前请求是否已经超时"""
        current_time = time.time()
        if (self.__start_time + self.timeout) < current_time:
            return True

    def fileno(self):
        """请求sockect对象的文件描述符，用于select监听"""
        return self.sock.fileno()

    def write(self, data):
        """在buffer中写入响应内容"""
        self.__buffer.append(data)

    def finish(self, exc=None):
        """在buffer中写入响应内容完成，执行请求的回调函数"""
        if not exc:
            response = b\'\'.join(self.__buffer)
            self.callback(self, response, exc)
        else:
            self.callback(self, None, exc)

    def send_request_data(self):
        content = """%s %s HTTP/1.0\\r\\nHost: %s\\r\\n\\r\\n%s""" % (
            self.method.upper(), self.url, self.host, self.data,)

        return content.encode(encoding=\'utf8\')


class AsyncRequest(object):
    def __init__(self):
        self.fds = []
        self.connections = []

    def add_request(self, host, port, method, url, data, callback, timeout):
        """创建一个要请求"""
        client = socket.socket()
        client.setblocking(False)
        try:
            client.connect((host, port))
        except BlockingIOError as e:
            pass
            # print(\'已经向远程发送连接的请求\')
        req = HttpContext(client, host, port, method, url, data, callback, timeout)
        self.connections.append(req)
        self.fds.append(req)

    def check_conn_timeout(self):
        """检查所有的请求，是否有已经连接超时，如果有则终止"""
        timeout_list = []
        for context in self.connections:
            if context.is_timeout():
                timeout_list.append(context)
        for context in timeout_list:
            context.finish(AsyncTimeoutException(\'请求超时\'))
            self.fds.remove(context)
            self.connections.remove(context)

    def running(self):
        """事件循环，用于检测请求的socket是否已经就绪，从而执行相关操作"""
        while True:
            r, w, e = select.select(self.fds, self.connections, self.fds, 0.05)

            if not self.fds:
                return

            for context in r:
                sock = context.sock
                while True:
                    try:
                        data = sock.recv(8096)
                        if not data:
                            self.fds.remove(context)
                            context.finish()
                            break
                        else:
                            context.write(data)
                    except BlockingIOError as e:
                        break
                    except TimeoutError as e:
                        self.fds.remove(context)
                        self.connections.remove(context)
                        context.finish(e)
                        break

            for context in w:
                # 已经连接成功远程服务器，开始向远程发送请求数据
                if context in self.fds:
                    data = context.send_request_data()
                    context.sock.sendall(data)
                    self.connections.remove(context)

            self.check_conn_timeout()


if __name__ == \'__main__\':
    def callback_func(context, response, ex):
        """
        :param context: HttpContext对象，内部封装了请求相关信息
        :param response: 请求响应内容
        :param ex: 是否出现异常（如果有异常则值为异常对象；否则值为None）
        :return:
        """
        print(context, response, ex)

    obj = AsyncRequest()
    url_list = [
        {\'host\': \'www.google.com\', \'port\': 80, \'method\': \'GET\', \'url\': \'/\', \'data\': \'\', \'timeout\': 5,
         \'callback\': callback_func},
        {\'host\': \'www.baidu.com\', \'port\': 80, \'method\': \'GET\', \'url\': \'/\', \'data\': \'\', \'timeout\': 5,
         \'callback\': callback_func},
        {\'host\': \'www.bing.com\', \'port\': 80, \'method\': \'GET\', \'url\': \'/\', \'data\': \'\', \'timeout\': 5,
         \'callback\': callback_func},
    ]
    for item in url_list:
        print(item)
        obj.add_request(**item)

    obj.running()
史上最牛逼的异步IO模块

Scrapy

Scrapy是一个为了爬取网站数据，提取结构性数据而编写的应用框架。 其可以应用在数据挖掘，信息处理或存储历史数据等一系列的程序中。
其最初是为了页面抓取 (更确切来说, 网络抓取 )所设计的， 也可以应用在获取API所返回的数据(例如 Amazon Associates Web Services ) 或者通用的网络爬虫。Scrapy用途广泛，可以用于数据挖掘、监测和自动化测试。

Scrapy 使用了 Twisted异步网络库来处理网络通讯。整体架构大致如下

Scrapy主要包括了以下组件：

  • 引擎(Scrapy)
    用来处理整个系统的数据流处理, 触发事务(框架核心)
  • 调度器(Scheduler)
    用来接受引擎发过来的请求, 压入队列中, 并在引擎再次请求的时候返回. 可以想像成一个URL（抓取网页的网址或者说是链接）的优先队列, 由它来决定下一个要抓取的网址是什么, 同时去除重复的网址
  • 下载器(Downloader)
    用于下载网页内容, 并将网页内容返回给蜘蛛(Scrapy下载器是建立在twisted这个高效的异步模型上的)
  • 爬虫(Spiders)
    爬虫是主要干活的, 用于从特定的网页中提取自己需要的信息, 即所谓的实体(Item)。用户也可以从中提取出链接,让Scrapy继续抓取下一个页面
  • 项目管道(Pipeline)
    负责处理爬虫从网页中抽取的实体，主要的功能是持久化实体、验证实体的有效性、清除不需要的信息。当页面被爬虫解析后，将被发送到项目管道，并经过几个特定的次序处理数据。
  • 下载器中间件(Downloader Middlewares)
    位于Scrapy引擎和下载器之间的框架，主要是处理Scrapy引擎与下载器之间的请求及响应。
  • 爬虫中间件(Spider Middlewares)
    介于Scrapy引擎和爬虫之间的框架，主要工作是处理蜘蛛的响应输入和请求输出。
  • 调度中间件(Scheduler Middewares)
    介于Scrapy引擎和调度之间的中间件，从Scrapy引擎发送到调度的请求和响应。

Scrapy运行流程大概如下：

    1. 引擎从调度器中取出一个链接(URL)用于接下来的抓取
    2. 引擎把URL封装成一个请求(Request)传给下载器
    3. 下载器把资源下载下来，并封装成应答包(Response)
    4. 爬虫解析Response
    5. 解析出实体（Item）,则交给实体管道进行进一步的处理
    6. 解析出的是链接（URL）,则把URL交给调度器等待抓取

 一、安装

Linux
      pip3 install scrapy
 
 
Windows
      a. pip3 install wheel
      b. 下载twisted http://www.lfd.uci.edu/~gohlke/pythonlibs/#twisted
      c. 进入下载目录，执行 pip3 install Twisted‑17.1.0‑cp35‑cp35m‑win_amd64.whl
      d. pip3 install scrapy
      e. 下载并安装pywin32：https://sourceforge.net/projects/pywin32/files/

二、基本使用

1. 基本命令

1. scrapy startproject 项目名称
   - 在当前目录中创建中创建一个项目文件（类似于Django）
 
2. scrapy genspider [-t template] <name> <domain>
   - 创建爬虫应用
   如：
      scrapy gensipider -t basic oldboy oldboy.com
      scrapy gensipider -t xmlfeed autohome autohome.com.cn
   PS:
      查看所有命令：scrapy gensipider -l
      查看模板命令：scrapy gensipider -d 模板名称
 
3. scrapy list
   - 展示爬虫应用列表
 
4. scrapy crawl 爬虫应用名称
   - 运行单独爬虫应用

2.项目结构以及爬虫应用简介

project_name/
   scrapy.cfg
   project_name/
       __init__.py
       items.py
       pipelines.py
       settings.py
       spiders/
           __init__.py
           爬虫1.py
           爬虫2.py
           爬虫3.py

文件说明：

  • scrapy.cfg  项目的主配置信息。（真正爬虫相关的配置信息在settings.py文件中）
  • items.py    设置数据存储模板，用于结构化数据，如：Django的Model
  • pipelines    数据处理行为，如：一般结构化的数据持久化
  • settings.py 配置文件，如：递归的层数、并发数，延迟下载等
  • spiders      爬虫目录，如：创建文件，编写爬虫规则

 注意：一般创建爬虫文件时，以网站域名命名

import scrapy
 
class XiaoHuarSpider(scrapy.spiders.Spider):
    name = "xiaohuar"                            # 爬虫名称 *****
    allowed_domains = ["xiaohuar.com"]  # 允许的域名
    start_urls = [
        "http://www.xiaohuar.com/hua/",   # 其实URL
    ]
 
    def parse(self, response):
        # 访问起始URL并获取结果后的回调函数
爬虫1.py
import sys,os
sys.stdout=io.TextIOWrapper(sys.stdout.buffer,encoding=\'gb18030\')
关于windows编码 io

 

3. 小试牛刀

import scrapy
from scrapy.selector import htmlXPathSelector
from scrapy.http.request import Request
 
 
class DigSpider(scrapy.Spider):
    # 爬虫应用的名称，通过此名称启动爬虫命令
    name = "dig"
 
    # 允许的域名
    allowed_domains = ["chouti.com"]
 
    # 起始URL
    start_urls = [
        \'http://dig.chouti.com/\',
    ]
 
    has_request_set = {}
 
    def parse(self, response):
        print(response.url)
 
        hxs = HtmlXPathSelector(response)
        page_list = hxs.select(\'//div[@id="dig_lcpage"]//a[re:test(@href, "/all/hot/recent/\\d+")]/@href\').extract()
        for page in page_list:
            page_url = \'http://dig.chouti.com%s\' % page
            key = self.md5(page_url)
            if key in self.has_request_set:
                pass
            else:
                self.has_request_set[key] = page_url
                obj = Request(url=page_url, method=\'GET\', callback=self.parse)
                yield obj
 
    @staticmethod
    def md5(val):
        import hashlib
        ha = hashlib.md5()
        ha.update(bytes(val, encoding=\'utf-8\'))
        key = ha.hexdigest()
        return key

执行此爬虫文件，则在终端进入项目目录执行如下命令：

scrapy crawl dig --nolog

对于上述代码重要之处在于：

  • Request是一个封装用户请求的类，在回调函数中yield该对象表示继续访问
  • HtmlXpathSelector用于结构化HTML代码并提供选择器功能

4. 选择器

#!/usr/bin/env python
# -*- coding:utf-8 -*-
from scrapy.selector import Selector, HtmlXPathSelector
from scrapy.http import HtmlResponse
html = """<!DOCTYPE html>
<html>
    <head lang="en">
        <meta charset="UTF-8">
        <title></title>
    </head>
    <body>
        <ul>
            <li class="item-"><a 
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