Splash抓取jd

迈不过友情╰ 2022-12-06 12:37 111阅读 0赞

# 一、概述 #

在上一篇文章中，链接如下：[https://www.cnblogs.com/xiao987334176/p/13656055.html][https_www.cnblogs.com_xiao987334176_p_13656055.html]

已经介绍了如何使用Splash抓取javaScript动态渲染页面

这里做一下项目实战，以爬取京东商城商品冰淇淋为例吧

## 环境说明 ##

操作系统：centos 7.6

docker版本：19.03.12

ip地址：192.168.0.10

说明：使用docker安装Splash服务

操作系统：windows 10

python版本：3.7.9

ip地址：192.168.0.9

说明：使用Pycharm开发工具，用于本地开发。

关于Splash的使用，参考上一篇文章，这里就不做说明了。

# 二、分析页面 #

打开京东商城，输入关键字：冰淇淋，滑动滚动条，我们发现随着滚动条向下滑动，越来越多的商品信息被刷新了，这说明该页面部分是ajax加载

![format_png][]

注意：每一条商品信息，都是在<div class="gl-i-wrap"></div>里面的。

我们打开scrapy shell 爬取该页面，如下图：

scrapy shell "https://search.jd.com/Search?keyword=%E5%86%B0%E6%B7%87%E6%B7%8B&enc=utf-8"

输出：

...
    [s]   view(response)    View response in a browser
    >>>

**注意：url不要用单引号，否则会报错。**

接下来，输入以下命令，使用css选择器

>>> response.css('div.gl-i-wrap')
    [<Selector xpath="descendant-or-self::div[@class and contains(concat(' ', normalize-space(@class), ' '
    ), ' gl-i-wrap ')]"
    ...

返回了很多Selector 对象。

统计商品信息个数

>>> len(response.css('div.gl-i-wrap'))
    30

得到返回结果发现只有30个冰淇凌的信息，而我们再页面中明明看见了60个冰淇凌信息，这是为什么呢？

答：这也说明了刚开始页面只用30个冰淇淋信息，而我们滑动滑块时，执行了js代码，并向后台发送了ajax请求，浏览器拿到数据后再进一步渲染出另外了30个信息

我们可以点击network选项卡再次确认：

![format_png 1][]

鉴于此，我们就想出了一种解决方案：即用js代码模拟用户滑动滑块到底的行为再结合execute端点提供的js代码执行服务即可（小伙伴们让我们开始实践吧）

![format_png 2][]

** 注意：<div id="footer-2017"></div>就是页面底部了，因此，只需要滑动到底部即可。**

首先：模拟用户行为

在console，输入以下命令：

e = document.getElementById("footer-2017")
    e.scrollIntoView(true)

效果如下，就直接滑动到底部了。

![format_png 3][]

参数解释：

scrollIntoView是一个与页面（容器）滚动相关的API（官方解释），该API只有boolean类型的参数能得到良好的支持（firefox 36+都支持）

参数为true时调用该函数，页面（或容器）发生滚动，使element的顶部与视图（容器）顶部对齐；

## 使用scrapy.Request ##

上面我们使用Request发送请求，观察结果只有30条。为什么呢？因为页面时动态加载的所有我们只收到了30个冰淇淋的信息。

所以这里，使用scrapy.Request发送请求，并使用execute 端点解决这个问题。

打开上一篇文章中的爬虫项目dynamic\_page，使用Pycharm打开，并点开Terminal

输入dir，确保当前目录是dynamic\_page

(crawler) E:\python_script\爬虫\dynamic_page>dir
     驱动器 E 中的卷是 file
     卷的序列号是 1607-A400
    
     E:\python_script\爬虫\dynamic_page 的目录
    
    2020/09/12  10:37    <DIR>          .
    2020/09/12  10:37    <DIR>          ..
    2020/09/12  10:20               211 bin.py
    2020/09/12  14:30                 0 dynamicpage_pipline.json
    2020/09/12  10:36    <DIR>          dynamic_page
    2020/09/12  10:33                 0 result.csv
    2020/09/12  10:18               267 scrapy.cfg
                   4 个文件            478 字节
                   3 个目录 260,445,159,424 可用字节

接下来打开scrapy shell，输入命令：

scrapy shell

输出：

...
    [s]   view(response)    View response in a browser
    >>>

最后粘贴以下代码：

from scrapy_splash import SplashRequest  #使用scrapy.splash.Request发送请求
    
    url = "https://search.jd.com/Search?keyword=%E5%86%B0%E6%B7%87%E6%B7%8B&enc=utf-8"
    lua = '''
    function main(splash)
        splash:go(splash.args.url)
        splash:wait(3)
        splash:runjs("document.getElementById('footer-2017').scrollIntoView(true)")
        splash:wait(3)
        return splash:html()
    end
    '''
    fetch(SplashRequest(url,endpoint = 'execute',args= {
        'lua_source':lua})) #再次请求，我们可以看到现在已通过splash服务的8050端点渲染了js代码，并成果返回结果
    len(response.css('div.gl-i-wrap'))

效果如下：

[s]   view(response)    View response in a browser
    >>> from scrapy_splash import SplashRequest  #使用scrapy.splash.Request发送请求
    >>> url = 'https://search.jd.com/Search?keyword=%E5%86%B0%E6%B7%87%E6%B7%8B&enc=utf-8'
    >>> lua = '''
    ... function main(splash)
    ...     splash:go(splash.args.url)
    ...     splash:wait(3)
    ...     splash:runjs("document.getElementById('footer-2017').scrollIntoView(true)")
    ...     splash:wait(3)
    ...     return splash:html()
    ... end
    ... '''
    >>> fetch(SplashRequest(url,endpoint = 'execute',args= {'lua_source':lua})) #再次请求，我们可以看到现
    在已通过splash服务的8050端点渲染了js代码，并成果返回结果
    2020-09-12 14:30:54 [scrapy.core.engine] INFO: Spider opened
    2020-09-12 14:30:54 [scrapy.downloadermiddlewares.redirect] DEBUG: Redirecting (302) to <GET https://w
    ww.jd.com/error.aspx> from <GET https://search.jd.com/robots.txt>
    2020-09-12 14:30:55 [scrapy.core.engine] DEBUG: Crawled (200) <GET https://www.jd.com/error.aspx> (ref
    erer: None)
    2020-09-12 14:30:55 [protego] DEBUG: Rule at line 27 without any user agent to enforce it on.
    2020-09-12 14:30:55 [protego] DEBUG: Rule at line 83 without any user agent to enforce it on.
    2020-09-12 14:30:55 [protego] DEBUG: Rule at line 92 without any user agent to enforce it on.
    2020-09-12 14:30:55 [protego] DEBUG: Rule at line 202 without any user agent to enforce it on.
    2020-09-12 14:30:55 [protego] DEBUG: Rule at line 351 without any user agent to enforce it on.
    2020-09-12 14:30:55 [protego] DEBUG: Rule at line 375 without any user agent to enforce it on.
    2020-09-12 14:30:55 [protego] DEBUG: Rule at line 376 without any user agent to enforce it on.
    2020-09-12 14:30:55 [protego] DEBUG: Rule at line 385 without any user agent to enforce it on.
    2020-09-12 14:30:55 [protego] DEBUG: Rule at line 386 without any user agent to enforce it on.
    2020-09-12 14:30:55 [protego] DEBUG: Rule at line 387 without any user agent to enforce it on.
    2020-09-12 14:30:55 [protego] DEBUG: Rule at line 388 without any user agent to enforce it on.
    2020-09-12 14:30:55 [protego] DEBUG: Rule at line 389 without any user agent to enforce it on.
    2020-09-12 14:30:55 [protego] DEBUG: Rule at line 397 without any user agent to enforce it on.
    2020-09-12 14:30:55 [protego] DEBUG: Rule at line 400 without any user agent to enforce it on.
    2020-09-12 14:30:55 [protego] DEBUG: Rule at line 403 without any user agent to enforce it on.
    2020-09-12 14:30:55 [protego] DEBUG: Rule at line 404 without any user agent to enforce it on.
    2020-09-12 14:30:55 [protego] DEBUG: Rule at line 405 without any user agent to enforce it on.
    2020-09-12 14:30:55 [protego] DEBUG: Rule at line 406 without any user agent to enforce it on.
    2020-09-12 14:30:55 [protego] DEBUG: Rule at line 407 without any user agent to enforce it on.
    2020-09-12 14:30:55 [protego] DEBUG: Rule at line 408 without any user agent to enforce it on.
    2020-09-12 14:30:55 [scrapy.core.engine] DEBUG: Crawled (404) <GET http://192.168.0.10:8050/robots.txt
    > (referer: None)
    2020-09-12 14:31:10 [scrapy.core.engine] DEBUG: Crawled (200) <GET https://search.jd.com/Search?keywor
    d=%E5%86%B0%E6%B7%87%E6%B7%8B&enc=utf-8 via http://192.168.0.10:8050/execute> (referer: None)
    >>> len(response.css('div.gl-i-wrap'))
    60

**注意：由于fetch不能直接在python代码中执行，所以只能在scrapy shell 模式中执行。**

最后的任务就回归到了提取内容了阶段了，小伙伴让我们完成整个代码吧！---这里结合scrapy shell 进行测试

# 三、代码实现 #

## 新建项目 ##

这里对目录就没有什么要求了，找个空目录就行。

打开Pycharm，并打开Terminal，执行以下命令

scrapy startproject ice_cream
    cd ice_cream
    scrapy genspider jd search.jd.com

在scrapy.cfg同级目录，创建bin.py，用于启动Scrapy项目，内容如下：

#在项目根目录下新建：bin.py
    from scrapy.cmdline import execute
    # 第三个参数是：爬虫程序名
    execute(['scrapy', 'crawl', 'jd',"--nolog"])

创建好的项目树形目录如下：

./
    ├── bin.py
    ├── ice_cream
    │   ├── __init__.py
    │   ├── items.py
    │   ├── middlewares.py
    │   ├── pipelines.py
    │   ├── settings.py
    │   └── spiders
    │       ├── __init__.py
    │       └── jd.py
    └── scrapy.cfg

## 修改settIngs.py ##

改写settIngs.py文件这里小伙伴们可参考github（https://github.com/scrapy-plugins/scrapy-splash）---上面有详细的说明

在最后添加如下内容：

# Splash服务器地址
    SPLASH_URL = 'http://192.168.0.10:8050'
    # 开启两个下载中间件，并调整HttpCompressionMiddlewares的次序
    DOWNLOADER_MIDDLEWARES = {
        'scrapy_splash.SplashCookiesMiddleware': 723,
        'scrapy_splash.SplashMiddleware': 725,
        'scrapy.downloadermiddlewares.httpcompression.HttpCompressionMiddleware': 810,
    }
    # 设置去重过滤器
    DUPEFILTER_CLASS = 'scrapy_splash.SplashAwareDupeFilter'
    # 用来支持cache_args（可选）
    SPIDER_MIDDLEWARES = {
        'scrapy_splash.SplashDeduplicateArgsMiddleware': 100,
    }
    DUPEFILTER_CLASS = 'scrapy_splash.SplashAwareDupeFilter'
    HTTPCACHE_STORAGE = 'scrapy_splash.SplashAwareFSCacheStorage'
    ITEM_PIPELINES = {
       'ice_cream.pipelines.IceCreamPipeline': 100,
    }

**注意：请根据实际情况，修改Splash服务器地址，其他的不需要改动。**

修改文件jd.py

![aHR0cHM6Ly9pbWFnZXMuY25ibG9ncy5jb20vT3V0bGluaW5nSW5kaWNhdG9ycy9Db250cmFjdGVkQmxvY2suZ2lm][] ![aHR0cHM6Ly9pbWFnZXMuY25ibG9ncy5jb20vT3V0bGluaW5nSW5kaWNhdG9ycy9FeHBhbmRlZEJsb2NrU3RhcnQuZ2lm][]

# -*- coding: utf-8 -*-
    import scrapy
    from scrapy_splash import SplashRequest
    from ice_cream.items import IceCreamItem
    
    #自定义lua脚本
    lua = '''
    function main(splash)
        splash:go(splash.args.url)
        splash:wait(3)
        splash:runjs("document.getElementById('footer-2017').scrollIntoView(true)")
        splash:wait(3)
        return splash:html()
        end
    '''
    
    
    class JdSpider(scrapy.Spider):
        name = 'jd'
        allowed_domains = ['search.jd.com']
        start_urls = ['https://search.jd.com/Search?keyword=%E5%86%B0%E6%B7%87%E6%B7%8B&enc=utf-8']
        base_url = 'https://search.jd.com/Search?keyword=%E5%86%B0%E6%B7%87%E6%B7%8B&enc=utf-8'
        # 自定义配置，注意：变量名必须是custom_settings
        custom_settings = {
            'REQUEST_HEADERS': {
                'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/62.0.3202.75 Safari/537.36',
            }
        }
    
        def parse(self, response):
            # 这里能获取100页
            page_num = int(response.css('span.fp-text i::text').extract_first())
            # 但是100页太多了，这里固定为2页
            page_num = 2
            # print("page_num", page_num)
            for i in range(page_num):
                url = '%s?page=%s' % (self.base_url, 2 * i + 1)  # 通过观察我们发现url页面间有规律
                # print("url", url)
                yield SplashRequest(url, headers=self.settings.get('REQUEST_HEADERS'), endpoint='execute',
                                    args={
         'lua_source': lua}, callback=self.parse_item)
    
        def parse_item(self, response):  # 页面解析函数
            # 创建item字段对象，用来存储信息
            item = IceCreamItem()
    
            for sel in response.css('div.gl-i-wrap'):
                name = sel.css('div.p-name em').extract_first()
                price = sel.css('div.p-price i::text').extract_first()
                # print("name", name)
                # print("price", price)
    
                item['name'] = name
                item['price'] = price
                yield item
                # yield {
         
                #     'name': name,
                #     'price': price,
                # }

修改items.py

![aHR0cHM6Ly9pbWFnZXMuY25ibG9ncy5jb20vT3V0bGluaW5nSW5kaWNhdG9ycy9Db250cmFjdGVkQmxvY2suZ2lm][] ![aHR0cHM6Ly9pbWFnZXMuY25ibG9ncy5jb20vT3V0bGluaW5nSW5kaWNhdG9ycy9FeHBhbmRlZEJsb2NrU3RhcnQuZ2lm][]

# -*- 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 IceCreamItem(scrapy.Item):
        # define the fields for your item here like:
        # name = scrapy.Field()
        # 与itcast.py定义的一一对应
        name = scrapy.Field()
        price = scrapy.Field()

修改pipelines.py

![aHR0cHM6Ly9pbWFnZXMuY25ibG9ncy5jb20vT3V0bGluaW5nSW5kaWNhdG9ycy9Db250cmFjdGVkQmxvY2suZ2lm][] ![aHR0cHM6Ly9pbWFnZXMuY25ibG9ncy5jb20vT3V0bGluaW5nSW5kaWNhdG9ycy9FeHBhbmRlZEJsb2NrU3RhcnQuZ2lm][]

# -*- 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 json
    
    class IceCreamPipeline(object):
        def __init__(self):
    
            #python3保存文件 必须需要'wb' 保存为json格式
            self.f = open("ice_cream_pipline.json",'wb')
    
        def process_item(self, item, spider):
            # 读取item中的数据 并换行处理
            content = json.dumps(dict(item), ensure_ascii=False) + ',\n'
            self.f.write(content.encode('utf=8'))
    
            return item
    
        def close_spider(self,spider):
            #关闭文件
            self.f.close()

执行bin.py，等待1分钟，就会生成文件ice\_cream\_pipline.json

打开json文件，内容如下：

{
        "name": "<em><span class=\"p-tag\" style=\"background-color:#c81623\">京东超市</span>\t\n明治（meiji）草莓白巧克力雪糕 245g（6支）彩盒 <font class=\"skcolor_ljg\">冰淇淋</font></em>", "price": "46.80"},
    {
        "name": "<em><span class=\"p-tag\" style=\"background-color:#c81623\">京东超市</span>\t\n伊利 巧乐兹香草巧克力口味脆皮甜筒雪糕<font class=\"skcolor_ljg\">冰淇淋</font>冰激凌冷饮 73g*6/盒</em>", "price": "32.80"},
    ...

本文参考链接：

[https://www.cnblogs.com/518894-lu/p/9067208.html][https_www.cnblogs.com_518894-lu_p_9067208.html]

[https_www.cnblogs.com_xiao987334176_p_13656055.html]: https://www.cnblogs.com/xiao987334176/p/13656055.html
[format_png]: /images/20221123/b71eef68fc1142c3a5afe1e934e9e308.png
[format_png 1]: /images/20221123/f0a2ac766dc84670996bf123c36eb66c.png
[format_png 2]: /images/20221123/e4fbd45d2e7640f8b7613fc8c076d924.png
[format_png 3]: /images/20221123/9a804315e84943a0bff9a1cbf7e952af.png
[aHR0cHM6Ly9pbWFnZXMuY25ibG9ncy5jb20vT3V0bGluaW5nSW5kaWNhdG9ycy9Db250cmFjdGVkQmxvY2suZ2lm]: http://imgconvert.csdnimg.cn/aHR0cHM6Ly9pbWFnZXMuY25ibG9ncy5jb20vT3V0bGluaW5nSW5kaWNhdG9ycy9Db250cmFjdGVkQmxvY2suZ2lm
[aHR0cHM6Ly9pbWFnZXMuY25ibG9ncy5jb20vT3V0bGluaW5nSW5kaWNhdG9ycy9FeHBhbmRlZEJsb2NrU3RhcnQuZ2lm]: /images/20221123/dc807cdec0234a2cb4d730ef20ef1cd4.png
[https_www.cnblogs.com_518894-lu_p_9067208.html]: https://www.cnblogs.com/518894-lu/p/9067208.html