爬虫必知必会（5）_scrapy框架_基础

一、移动端数据的爬取

• 基于某一款抓包工具,fiddler,青花瓷,miteproxy
• fillder进行一个基本的配置:tools->options->connection->allow remote ...
• http://fillder所在pc机的ip:58083/:访问到一张提供了证书下载功能的页面
• fiddler所在的机器和手机在同一网段下:在手机浏览器中访问http://fillder所在pc机的ip:58083/
• 获取子页面进行证书的下载和安装(证书信任的操作)
• 配置你的手机的代理:将手机的代理配置成fiddler所对应pc机的ip和fillder自己的端口
• 就可以让fiddler捕获手机发起的http和https的请求

二、scrapy,pyspider

• 什么是框架?如何学习框架?
• 就是一个集成了各种功能且具有很强通用性(可以被应用在各种不同的需求中)的一个项目模板.
• 我们只需要学习框架中封装好的相关功能的使用即可.

1.scrapy集成了哪些功能:

• 高性能的数据解析操作,持久化存储操作,高性能的数据下载的操作.....

2.环境的安装:

1. pip3 install wheel
2. 下载twisted http://www.lfd.uci.edu/~gohlke/pythonlibs/#twisted
3. 进入下载目录，执行 pip3 install Twisted‑17.1.0‑cp35‑cp35m‑win_amd64.whl
4. pip3 install pywin32
5. pip3 install scrapy

3.scrapy的基本使用

1. 创建一个工程:scrapy startproject firstBlood
2. 必须在spiders这个目录下创建一个爬虫文件
3. cd proName
4. scrapy genspider spiderName www.xxx.com
5. 执行工程:scrapy crawl spiderName
6. settings.py:
7. 不遵从robots协议
8. 进行UA伪装
9. 进行日志等级设定:LOG_LEVEL = 'ERROR'

4.持久化存储:

• 基于终端指令:
  • 特性:只可以将parse方法的返回值存储到本地的磁盘文件中
  • 指令:scrapy crawl spiderName -o filePath
• 基于管道:实现流程

　　　　1.数据解析
　　　　2.在item类中定义相关的属性
　　　　3.将解析的数据存储或者封装到一个item类型的对象(items文件中对应类的对象)
　　　　4.向管道提交item
　　　　5.在管道文件的process_item方法中接收item进行持久化存储
　　　　6.在配置文件中开启管道

4.1将同一份数据持久化到不同的平台中?

• 分析:

　　　　1.管道文件中的一个管道类负责数据的一种形式的持久化存储
　　　　2.爬虫文件向管道提交的item只会提交给优先级最高的那一个管道类
　　　　3.在管道类的process_item中的return item表示的是将当前管道接收的item返回/提交给下一个即将被执行的管道类

5.在scrapy中如何进行手动请求发送(GET)

• 使用场景:爬取多个页码对应的页面源码数据
• yield scrapy.Request(url,callback)
• 在scrapy中如何进行手动请求发送(POST)

• data = { #post请求的请求参数
  'kw':'aaa'
  }
  yield scrapy.FormRequest(url,formdata=data,callback)

6.scrapy五大核心组件的工作流程:

• 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。

 1 # -*- coding: utf-8 -*-
 2 import scrapy
 3 from qiubaiPro.items import QiubaiproItem
 4
 5 class QiubaiSpider(scrapy.Spider):
 6     name = 'qiubai'
 7     # allowed_domains = ['www.xxx.com']
 8     start_urls = ['https://www.qiushibaike.com/text/']
 9     def start_requests(self):
10         for url in self.start_urls:
11             yield scrapy.Request(url,callback=self.parse)
12     #基于终端指令的持久化存储操作
13     # def parse(self, response):
14     #     div_list = response.xpath('//*[@id="content-left"]/div')
15     #     all_data = []
16     #     for div in div_list:
17     #         #scrapy中的xpath返回的列表的列表元素一定是Selector对象,我们最终想要的解析的
18     #         #数据一定是存储在该对象中
19     #         #extract()将Selector对象中data参数的值取出
20     #         # author = div.xpath('./div[1]/a[2]/h2/text()')[0].extract()
21     #         author = div.xpath('./div[1]/a[2]/h2/text()').extract_first()
22     #         #列表直接调用extract表示的是将extract作用到每一个列表元素中
23     #         content = div.xpath('./a[1]/div/span//text()').extract()
24     #         content = ''.join(content)
25     #         dic = {
26     #             'author':author,
27     #             'content':content
28     #         }
29     #         all_data.append(dic)
30     #     return all_data
31     #基于管道的持久化存储
32     # def parse(self, response):
33     #     div_list = response.xpath('//*[@id="content-left"]/div')
34     #     all_data = []
35     #     for div in div_list:
36     #         #scrapy中的xpath返回的列表的列表元素一定是Selector对象,我们最终想要的解析的
37     #         #数据一定是存储在该对象中
38     #         #extract()将Selector对象中data参数的值取出
39     #         # author = div.xpath('./div[1]/a[2]/h2/text()')[0].extract()
40     #         author = div.xpath('./div[1]/a[2]/h2/text()').extract_first()
41     #         #列表直接调用extract表示的是将extract作用到每一个列表元素中
42     #         content = div.xpath('./a[1]/div/span//text()').extract()
43     #         content = ''.join(content)
44     #
45     #         #将解析的数据存储到item对象
46     #         item = QiubaiproItem()
47     #         item['author'] = author
48     #         item['content'] = content
49     #
50     #         #将item提交给管道
51     #         yield item #item一定是提交给了优先级最高的管道类
52
53     #将多个页码对应的页面数据进行爬取和解析的操作
54     url = 'https://www.qiushibaike.com/text/page/%d/'#通用的url模板
55     pageNum = 1
56     #parse第一次调用表示的是用来解析第一页对应页面中的段子内容和作者
57     def parse(self, response):
58         div_list = response.xpath('//*[@id="content-left"]/div')
59         all_data = []
60         for div in div_list:
61             # scrapy中的xpath返回的列表的列表元素一定是Selector对象,我们最终想要的解析的
62             # 数据一定是存储在该对象中
63             # extract()将Selector对象中data参数的值取出
64             # author = div.xpath('./div[1]/a[2]/h2/text()')[0].extract()
65             author = div.xpath('./div[1]/a[2]/h2/text()').extract_first()
66             # 列表直接调用extract表示的是将extract作用到每一个列表元素中
67             content = div.xpath('./a[1]/div/span//text()').extract()
68             content = ''.join(content)
69
70             # 将解析的数据存储到item对象
71             item = QiubaiproItem()
72             item['author'] = author
73             item['content'] = content
74
75             # 将item提交给管道
76             yield item  # item一定是提交给了优先级最高的管道类
77
78         if self.pageNum <= 5:
79             self.pageNum += 1
80             new_url = format(self.url%self.pageNum)
81             #手动请求(get)的发送
82             yield scrapy.Request(new_url,callback=self.parse)

qiubai.py

# -*- coding: utf-8 -*-

# Scrapy settings for qiubaiPro project
#
# For simplicity, this file contains only settings considered important or
# commonly used. You can find more settings consulting the documentation:
#
#     https://docs.scrapy.org/en/latest/topics/settings.html
#     https://docs.scrapy.org/en/latest/topics/downloader-middleware.html
#     https://docs.scrapy.org/en/latest/topics/spider-middleware.html

BOT_NAME = 'qiubaiPro'
USER_AGENT = 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/76.0.3809.132 Safari/537.36'

SPIDER_MODULES = ['qiubaiPro.spiders']
NEWSPIDER_MODULE = 'qiubaiPro.spiders'

# Crawl responsibly by identifying yourself (and your website) on the user-agent
#USER_AGENT = 'qiubaiPro (+http://www.yourdomain.com)'

# Obey robots.txt rules
ROBOTSTXT_OBEY = False
LOG_LEVEL = 'ERROR'

# Configure maximum concurrent requests performed by Scrapy (default: 16)
#CONCURRENT_REQUESTS = 32

# Configure a delay for requests for the same website (default: 0)
# See https://docs.scrapy.org/en/latest/topics/settings.html#download-delay
# See also autothrottle settings and docs
#DOWNLOAD_DELAY = 3
# The download delay setting will honor only one of:
#CONCURRENT_REQUESTS_PER_DOMAIN = 16
#CONCURRENT_REQUESTS_PER_IP = 16

# Disable cookies (enabled by default)
#COOKIES_ENABLED = False

# Disable Telnet Console (enabled by default)
#TELNETCONSOLE_ENABLED = False

# Override the default request headers:
#DEFAULT_REQUEST_HEADERS = {
#   'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',
#   'Accept-Language': 'en',
#}

# Enable or disable spider middlewares
# See https://docs.scrapy.org/en/latest/topics/spider-middleware.html
#SPIDER_MIDDLEWARES = {
#    'qiubaiPro.middlewares.QiubaiproSpiderMiddleware': 543,
#}

# Enable or disable downloader middlewares
# See https://docs.scrapy.org/en/latest/topics/downloader-middleware.html
#DOWNLOADER_MIDDLEWARES = {
#    'qiubaiPro.middlewares.QiubaiproDownloaderMiddleware': 543,
#}

# Enable or disable extensions
# See https://docs.scrapy.org/en/latest/topics/extensions.html
#EXTENSIONS = {
#    'scrapy.extensions.telnet.TelnetConsole': None,
#}

# Configure item pipelines
# See https://docs.scrapy.org/en/latest/topics/item-pipeline.html
ITEM_PIPELINES = {
   'qiubaiPro.pipelines.QiubaiproPipeline': 300, #300表示的是优先级
   #  'qiubaiPro.pipelines.MysqlPL': 301,
   #  'qiubaiPro.pipelines.RedisPL': 302,
}

# Enable and configure the AutoThrottle extension (disabled by default)
# See https://docs.scrapy.org/en/latest/topics/autothrottle.html
#AUTOTHROTTLE_ENABLED = True
# The initial download delay
#AUTOTHROTTLE_START_DELAY = 5
# The maximum download delay to be set in case of high latencies
#AUTOTHROTTLE_MAX_DELAY = 60
# The average number of requests Scrapy should be sending in parallel to
# each remote server
#AUTOTHROTTLE_TARGET_CONCURRENCY = 1.0
# Enable showing throttling stats for every response received:
#AUTOTHROTTLE_DEBUG = False

# Enable and configure HTTP caching (disabled by default)
# See https://docs.scrapy.org/en/latest/topics/downloader-middleware.html#httpcache-middleware-settings
#HTTPCACHE_ENABLED = True
#HTTPCACHE_EXPIRATION_SECS = 0
#HTTPCACHE_DIR = 'httpcache'
#HTTPCACHE_IGNORE_HTTP_CODES = []
#HTTPCACHE_STORAGE = 'scrapy.extensions.httpcache.FilesystemCacheStorage'

settings

# -*- coding: utf-8 -*-

# Define here the models for your scraped items
#
# See documentation in:
# https://docs.scrapy.org/en/latest/topics/items.html

import scrapy

class QiubaiproItem(scrapy.Item):
    # define the fields for your item here like:
    author = scrapy.Field() #Field可以将其理解成是一个万能的数据类型
    content = scrapy.Field()

items.py

# -*- coding: utf-8 -*-

# Define your item pipelines here
#
# Don't forget to add your pipeline to the ITEM_PIPELINES setting
# See: https://docs.scrapy.org/en/latest/topics/item-pipeline.html

import pymysql
from redis import Redis
class QiubaiproPipeline(object):
    fp = None
    def open_spider(self,spider):
        print('开始爬虫......')
        self.fp = open('qiushibaike.txt','w',encoding='utf-8')

    #使用来接收爬虫文件提交过来的item,然后将其进行任意形式的持久化存储
    #参数item:就是接收到的item对象
    #该方法每接收一个item就会调用一次
    def process_item(self, item, spider):
        author = item['author']
        content= item['content']

        self.fp.write(author+':'+content+'\n')
        return item #item是返回给了下一个即将被执行的管道类

    def close_spider(self,spider):
        print('结束爬虫!')
        self.fp.close()

#负责将数据存储到mysql
class MysqlPL(object):
    conn = None
    cursor = None
    def open_spider(self,spider):
        self.conn = pymysql.Connect(host='127.0.0.1',port=3306,user='root',password='123',db='spider',charset='utf8')
        print(self.conn)
    def process_item(self,item,spider):
        author = item['author']
        content = item['content']

        sql = 'insert into qiubai values ("%s","%s")'%(author,content)
        self.cursor = self.conn.cursor()
        try:
            self.cursor.execute(sql)
            self.conn.commit()
        except Exception as e:
            print(e)
            self.conn.rollback()
        return item
    def close_spider(self,spider):
        self.cursor.close()
        self.conn.close()

class RedisPL(object):
    conn = None
    def open_spider(self,spider):
        self.conn = Redis(host='127.0.0.1',port=6379)
        print(self.conn)
    def process_item(self,item,spider):
        self.conn.lpush('all_data',item)
        #注意:如果将字典写入redis报错:pip install -U redis==2.10.6

pipelines.py