回归爬虫，拥抱scrapy&splash。抓facebook public post like、comment、share

上一个项目完成的时间是7月14日，今天是8月30日，已经过去了一个半月，这段时间做了第二个，学了一个半月的爬虫。
代码写了400多行，目前为止单个项目写的最多的代码。。

上班之前就有预感会做关于爬虫的东西，果不其然（捂脸）….
其实上学的时候学爬虫，特别是学scrapy，我的内心是拒绝的。因为scrapy看起来是个很臃肿的框架，上学那个阶段，并没有什么项目需要用到它，用用Request和BS4已经完全够用了。之前那个项目在维基百科上抓取subcategory时，就是用的Request和BS4，抓了1000多个页面，爬虫依然可以跑，这已经完全够了。

（部分内容可以略过）

关于facebook

现在公司要给我的任务是抓facebook上，搜索一个keyword，关于这个keyword的所有publick post，包括内容，时间，评论的人，点赞的人，分享的人，具体结构如下：

{   
     "task": task name,
     "type": "facebook_post",
     "url": default None，
     "keyword": search keyword,
     "timestamp": spider crawls timestamp,
     "post_data":[{
             "post_information":{
                 "post_from_user": post from user name,
                 "post_from_user_id": post from user id,
                 "post_id": post's id,
                 "post_type": "posts", "photos", "videos"
                 "post_content": post's content,
                 "post_link": post's article link url,
                 "post_time": post created time,
                 "post_likes_number": the number of post who likes it,
                 "post_shares_number": the number of post who share it,
                 "post_comments_number": the number of post who comment it
             },

             "post_likes":
             [
                 {
                     "id": user's account id,
                     "name": user's name
                 },
             ...],

             "post_shares":
             [
                 {
                     "id": user's account id,
                     "name": user's name
                 },
             ...],

             "post_comments":
             [
                 {
                     "id": user's account id,
                     "name": user's name
                     "comment_content": comment's content
                 },
             ...]
             },
     ...]    
  }

关于这个结构我说一下，facebook提供了一个官方的API，叫Graph API explorer，是一个很强大的API，在网页上你可以看到的所有公开信息，在这个API里都可以请求到。但是有两个限制：
1. rate limit，好像是一天最多只有4500次请求，具体数字我忘记了。
2. access token，这个API有个token，是短期的，2个小时就会过期，需要重新请求。也可以按照官方的方法换成长期的access token，有效期是2个月。但是这怎么能满足我呢，所以我申请了一个永久的access token，具体方法见：
申请facebook永久access token的方法。
Graph API具体用法参照facebook 官方文档。

关于这次爬虫用到的技术

关于scrapy 框架

熟悉scrapy框架就用了两三天，因为这个以前还真没接触过，那段时间是最蛋疼的一段时间了。但是，学一个框架最好的学习方法就是看官方文档。
用爬虫做大型的项目真的应该全面拥抱scrapy。这里说心得体会，就两点觉得很有必要说一下：
1. 熟悉到底是要return还是yield，return和yield不对的错误会很头疼的；
2. 理解你的response.status，这里对各种状态码的理解对debug有大用。

代码流程

登录部分，resquest登录页面，用scrapy.formresquest登录，如果登录失败退出爬虫，需要手动检查原因（facebook账号登录异常时有手机验证机制）；
构造search keyword url，直接进入public post页面，利用splash进行页面的JS渲染，返回第一个页面首先出现的几个post，F12打开开发者工具，研究下拉时的页面数据请求，观察源代码，构造出页面数据请求，这个就相当于不断下拉，刷新出新的post的动作。（这个等下要详细讲）
抓取每个post的ID，利用Graph API请求出每个post的likes，comments，shares的user‘s name and ID，返回item。

关于IP代理的处理

这次代码直接挂在了亚马逊的服务器上，所以没有怎么管这个，但是在本地测试的时候，不要以为换IP就不会被block掉，naive，这么简单的策略骗过不需要登录的站点还可以，你用一个账号不断的换IP登录，你的账号很快就被封掉了，特别是这种国外的社交网站，涉及到个人隐私的。
正确的办法是，将一个IP对应一个账号，并发抓取（我这里由于项目的任务调度问题，没有并发抓取），间隔时间设置大一些。当然API对resquest速度没有要求，使劲造。如果是纯网页的抓取，建议30s左右抓取一个页面，如果碰到等厉害的，就要用模拟JS动作了（等会讲）。

关于登录facebook

对于登录来说，facebook登录页面也没什么好讲的，看scary官方文档，就一个请求就可以自动填表了，代码如下：

return FormRequest.from_response(
         response,
         url = self.login_url,
         formid = "login_form",
         formdata = {
             "email": self.login_user,
             "pass": self.login_pass
         },
         callback = self.after_login
 )

formdata里的参数就是登录的username和password。
整个爬虫中，登录最重要一点，是需要在服务器端保持登录状态。这个就需要在request中保证cookie传递是正常进行的，当时遇到一个坑，splash request中cookie会自动的丢失一部分，这个时候就没办法保持登录的状态，现在很多网站登录和非登录看到的内容是不一样的，这个很重要，所以就要在splash request补上漏掉的cookie。

splash在scrapy中的应用

splash就不做介绍了，具体参看官方文档，也特别详细。
这里首先说明一下，为什么不用selenium+phantomjs，这里我说明一下为什么花了这么久写这个爬虫。
首先刚拿到这个项目，我也不知道从何入手，第一个想到的办法就是找有没有官方的API可以获取数据，也确实找到了Graph API，但是之前没有看文档，简单的试了一下，发现部分用户的数据是获取不到的（对方设置了隐私），而且当时老板的要求就是抓取用户的post，这没办法抓啊，API一开始就PASS掉了。转而去攻向页面的抓取。
页面抓取涉及到JS的渲染，目前常用的两种方法，一个是selenium+phantomjs，另一个是用splash写lua代码，解析整个爬取过程。
selenium+phantomjs我也试过，但是抓取的数据量一旦大了，phantomjs处理速度会很慢，而且selenium相比于splash来说，我更倾向于splash，因为它的功能更完善（主要是官方文档写的好），所以放弃了selenium+phantomjs。
在使用splash的过程中，遇到几个坑：
1. 在登录过程中cookie掉丢，导致页面之前是登录状态，后来就没有登陆了。甚至有时候，代码不变跑两次，一次是登录的状态，一次不是登录的状态（这个问题我现在都不知道为什么，有时候cookie可以完整的传递，有时候不行）。这个时候就要看cookie是什么样的了，lua代码如下：

local cookies = splash.args.headers['Cookie']

这个就是当前cookie的代码，在最好把它打印出来，看cookie有没有问题。如果有缺失，这里要进行一个请求请求，重置headers[‘Cookie’]，lua代码如下：

    splash:on_request(
        function(request)
            request:set_header('Cookie', cookies)
        end
    )

这个on_request可以当做一个前调函数去理解。

2. 在实际解析JS页面过程中，丢失了很多resquest请求，我们在抓包过程中，发现加载一个新的post会有很多个请求（大概有30个），最后实际上只有一半左右。看请求数量需要在lua代码的返回结果中加上 HAR包的返回结果，这是一个DEBUG的小技巧，还可以将解析出来的图片打印出来，看和浏览器上显示的有什么不同，具体lua代码如下：

    png2 = splash:png{render_all=true}
            return {
                url = splash.args.url,
                html = splash:html(),
                http_status = last_response.status,
                headers = last_response.headers,
                cookies = splash:get_cookies(),
                png2 = png2,
                data = json.encode(updates),
                har = json.encode(splash:har()),
            }

这里我返回了请求的url，网络状态，页面截图，还有HAR。
HAR的结果可以在http://www.softwareishard.com/har/viewer/中看到，如下图所示：
HAR包的显示结果
按照道理来说是不会丢失request的，但是出现这种情况我也很绝望啊！！
于是要完成这个项目我需要使用抓包，请求网页内部的数据接口了。这里是一个巨坑。。

3. 关于抓包网上有教程，我就不多说了，直接来到AJAX异步请求。
让我们来按下邪恶的F12。请求就是这些了：
这里写图片描述
然后在看看这些请求的结构，

红色箭头里包含的就是post的数据，一个请求里有3个post的数据。再看看这些请求的URL：
https://www.facebook.com/ajax/pagelet/generic.php/BrowseScrollingSetPagelet?dpr=1&data=%7B%22view%22%3A%22list%22%2C%22encoded_query%22%3A%22%7B%5C%22bqf%5C%22%3A%5C%22stories-public(stories-keyword(3d%2Bprinting))%5C%22%2C%5C%22browse_sid%5C%22%3Anull%2C%5C%22vertical%5C%22%3A%5C%22content%5C%22%2C%5C%22post_search_vertical%5C%22%3Anull%2C%5C%22intent_data%5C%22%3A%5C%22%7B%5C%5C%5C%22intent%5C%5C%5C%22%3A%5C%5C%5C%22posts%5C%5C%5C%22%2C%5C%5C%5C%22entity_id%5C%5C%5C%22%3Anull%2C%5C%5C%5C%22sub_intents%5C%5C%5C%22%3A%7B%5C%5C%5C%22newsy_by_nms%5C%5C%5C%22%3Atrue%2C%5C%5C%5C%22media%5C%5C%5C%22%3Atrue%2C%5C%5C%5C%22media_video%5C%5C%5C%22%3Atrue%2C%5C%5C%5C%22live%5C%5C%5C%22%3Atrue%2C%5C%5C%5C%22whitelisted_explicit_live_video_intent%5C%5C%5C%22%3Afalse%2C%5C%5C%5C%22commerce%5C%5C%5C%22%3Atrue%2C%5C%5C%5C%22hard_news%5C%5C%5C%22%3Atrue%2C%5C%5C%5C%22c_physical_place%5C%5C%5C%22%3Atrue%2C%5C%5C%5C%22c_entertainment_intent%5C%5C%5C%22%3Atrue%2C%5C%5C%5C%22sports%5C%5C%5C%22%3Atrue%7D%2C%5C%5C%5C%22user_confidence%5C%5C%5C%22%3A0.00094727595255506%2C%5C%5C%5C%22quel_topics%5C%5C%5C%22%3A[%7B%5C%5C%5C%22fbid%5C%5C%5C%22%3A112456495470085%2C%5C%5C%5C%22confidence%5C%5C%5C%22%3A0.97018970189702%2C%5C%5C%5C%22position%5C%5C%5C%22%3A0%2C%5C%5C%5C%22length%5C%5C%5C%22%3A11%7D]%2C%5C%5C%5C%22multi_label_intents%5C%5C%5C%22%3A[%7B%5C%5C%5C%22value%5C%5C%5C%22%3Atrue%2C%5C%5C%5C%22confidence%5C%5C%5C%22%3A2.3013026293484e-5%2C%5C%5C%5C%22source%5C%5C%5C%22%3Anull%7D%2C%7B%5C%5C%5C%22value%5C%5C%5C%22%3Atrue%2C%5C%5C%5C%22confidence%5C%5C%5C%22%3A3.8938988922155e-8%2C%5C%5C%5C%22source%5C%5C%5C%22%3Anull%7D%2C%7B%5C%5C%5C%22value%5C%5C%5C%22%3Atrue%2C%5C%5C%5C%22confidence%5C%5C%5C%22%3A0.00058737583458424%2C%5C%5C%5C%22source%5C%5C%5C%22%3Anull%7D%2C%7B%5C%5C%5C%22value%5C%5C%5C%22%3Atrue%2C%5C%5C%5C%22confidence%5C%5C%5C%22%3A0.20616441965103%2C%5C%5C%5C%22source%5C%5C%5C%22%3Anull%7D%2C%7B%5C%5C%5C%22value%5C%5C%5C%22%3Atrue%2C%5C%5C%5C%22confidence%5C%5C%5C%22%3A0.011218670755625%2C%5C%5C%5C%22source%5C%5C%5C%22%3Anull%7D%2C%7B%5C%5C%5C%22value%5C%5C%5C%22%3Atrue%2C%5C%5C%5C%22confidence%5C%5C%5C%22%3A8.2435115473345e-5%2C%5C%5C%5C%22source%5C%5C%5C%22%3Anull%7D%2C%7B%5C%5C%5C%22value%5C%5C%5C%22%3Atrue%2C%5C%5C%5C%22confidence%5C%5C%5C%22%3A0.024955861270428%2C%5C%5C%5C%22source%5C%5C%5C%22%3Anull%7D%2C%7B%5C%5C%5C%22value%5C%5C%5C%22%3Atrue%2C%5C%5C%5C%22confidence%5C%5C%5C%22%3A0.00013062660582364%2C%5C%5C%5C%22source%5C%5C%5C%22%3Anull%7D%2C%7B%5C%5C%5C%22value%5C%5C%5C%22%3Atrue%2C%5C%5C%5C%22confidence%5C%5C%5C%22%3A8.7018961494323e-5%2C%5C%5C%5C%22source%5C%5C%5C%22%3Anull%7D%2C%7B%5C%5C%5C%22value%5C%5C%5C%22%3Atrue%2C%5C%5C%5C%22confidence%5C%5C%5C%22%3A0.55965006351471%2C%5C%5C%5C%22source%5C%5C%5C%22%3Anull%7D%2C%7B%5C%5C%5C%22value%5C%5C%5C%22%3Atrue%2C%5C%5C%5C%22confidence%5C%5C%5C%22%3A0.012116322293878%2C%5C%5C%5C%22source%5C%5C%5C%22%3Anull%7D%2C%7B%5C%5C%5C%22value%5C%5C%5C%22%3Atrue%2C%5C%5C%5C%22confidence%5C%5C%5C%22%3A0.0013190114405006%2C%5C%5C%5C%22source%5C%5C%5C%22%3Anull%7D%2C%7B%5C%5C%5C%22value%5C%5C%5C%22%3Atrue%2C%5C%5C%5C%22confidence%5C%5C%5C%22%3A0.59801119565964%2C%5C%5C%5C%22source%5C%5C%5C%22%3Anull%7D%2C%7B%5C%5C%5C%22value%5C%5C%5C%22%3Atrue%2C%5C%5C%5C%22confidence%5C%5C%5C%22%3A0.25427937507629%2C%5C%5C%5C%22source%5C%5C%5C%22%3Anull%7D%2C%7B%5C%5C%5C%22value%5C%5C%5C%22%3Atrue%2C%5C%5C%5C%22confidence%5C%5C%5C%22%3A0.0019076929893345%2C%5C%5C%5C%22source%5C%5C%5C%22%3Anull%7D%2C%7B%5C%5C%5C%22value%5C%5C%5C%22%3Atrue%2C%5C%5C%5C%22confidence%5C%5C%5C%22%3A0.00043614517198876%2C%5C%5C%5C%22source%5C%5C%5C%22%3Anull%7D%2C%7B%5C%5C%5C%22value%5C%5C%5C%22%3Atrue%2C%5C%5C%5C%22confidence%5C%5C%5C%22%3A0.023136112838984%2C%5C%5C%5C%22source%5C%5C%5C%22%3Anull%7D%2C%7B%5C%5C%5C%22value%5C%5C%5C%22%3Atrue%2C%5C%5C%5C%22confidence%5C%5C%5C%22%3A0.014905215241015%2C%5C%5C%5C%22source%5C%5C%5C%22%3Anull%7D%2C%7B%5C%5C%5C%22value%5C%5C%5C%22%3Atrue%2C%5C%5C%5C%22confidence%5C%5C%5C%22%3A0.011931948363781%2C%5C%5C%5C%22source%5C%5C%5C%22%3Anull%7D%2C%7B%5C%5C%5C%22value%5C%5C%5C%22%3Atrue%2C%5C%5C%5C%22confidence%5C%5C%5C%22%3A0.11729157716036%2C%5C%5C%5C%22source%5C%5C%5C%22%3Anull%7D%2C%7B%5C%5C%5C%22value%5C%5C%5C%22%3Atrue%2C%5C%5C%5C%22confidence%5C%5C%5C%22%3A0.0028816317208111%2C%5C%5C%5C%22source%5C%5C%5C%22%3Anull%7D]%2C%5C%5C%5C%22annotated_string%5C%5C%5C%22%3A%5C%5C%5C%22%7B%5C%5C%5C%5C%5C%5C%5C%22tokens%5C%5C%5C%5C%5C%5C%5C%22%3A[%5C%5C%5C%5C%5C%5C%5C%223d%5C%5C%5C%5C%5C%5C%5C%22%2C%5C%5C%5C%5C%5C%5C%5C%22printing%5C%5C%5C%5C%5C%5C%5C%22]%2C%5C%5C%5C%5C%5C%5C%5C%22entities%5C%5C%5C%5C%5C%5C%5C%22%3A%7B%5C%5C%5C%5C%5C%5C%5C%220_1%5C%5C%5C%5C%5C%5C%5C%22%3A[%7B%5C%5C%5C%5C%5C%5C%5C%22mention%5C%5C%5C%5C%5C%5C%5C%22%3A%5C%5C%5C%5C%5C%5C%5C%223d%20printing%5C%5C%5C%5C%5C%5C%5C%22%2C%5C%5C%5C%5C%5C%5C%5C%22fbid%5C%5C%5C%5C%5C%5C%5C%22%3A150438908406955%2C%5C%5C%5C%5C%5C%5C%5C%22name%5C%5C%5C%5C%5C%5C%5C%22%3A%5C%5C%5C%5C%5C%5C%5C%223d%20printing%5C%5C%5C%5C%5C%5C%5C%22%2C%5C%5C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有15000个字符。。。。
这他妈要怎么构造！看到这个URL我内心是奔溃的，问组长这要怎么办，组长说一般页面内就有，如果没有就看网页内JS代码里的一些参数，这些URL就是这些参数构造出来的。。WTF，还要看JS代码！二营长，把。。所以那两天内心压力特别大。
然而现在还年轻，老板交待的任务不敢不完成，就硬磕这15000个字符。回头看其实还是自己内心太浮躁，碰到问题没有冷静的分析。
再看这个URL，一般爬虫拿到一个这么长的url，基本上是不可能让你去JS里找参数的，这样网站的成本也比较大啊。所以一般在页面内都有url的线索。仔细研究发现，这个url是由3个部分构成：

request_url = request_header + request_data + request_tail
request_header = "https://www.facebook.com/ajax/pagelet/generic.php/BrowseScrollingSetPagelet?dpr=1&data="
request_tail = "&__user=%s&__a=1&__dyn=5V4cjEzUGByK5A9UoHaEWC5EWq2WiWF3oyeqrWo8ovyUWdwIhE98n                       yUdUat0Hx24UJi28rxuF8WUOuVWxeUPxKcxaFQ3uaVVojxCVFEKjGqu58nUnAz8lUlwkEG9J7BwBx66EK2m5K5FLKE                           gDQ6EvG7ki4e2i8xqawDDhomx22yq3ui9Dx6WK6pESUK8Gm8CBz8swgE-6UCbx-8xnyESbwFxCQEx38y-fXgO&__af                           =jw&__req=c&__be=-1&__pc=EXP3%%3Aholdout_pkg&__rev=3207279" % str(facebook_account_id)

request_data就需要自己在页面中找啦，是一个类似json数据格式的东西，但是它的key都是变量，并不是字符串，所以先要吧这些变量用正则替换成字符串形式，然后在用json解析出来。
另外解析出来还不够，还要将request_url进行url encode，这个可以参照url encode转换表进行转换。

4.后来发现了Graph API，抓到post的ID就可以把我想要的内容获取到了，美滋滋，省去了很多的工作量。

说了这么多，代码附上：
chinwu’s facebook spider

最后打一个小广告，有需要抓数据的请联系我，有ML/NLP/Data Engineer 相关工作请联系我，坐标武汉。