python爬虫实现POST request payload形式的请求

1. 背景

最近在爬取某个站点时，发现在POST数据时，使用的数据格式是request payload，有别于之前常见的 POST数据格式（Form data）。而使用Form data数据的提交方式时，无法提交成功。

1.1. Http请求中Form Data 和 Request Payload的区别

AJAX Post请求中常用的两种传参数的形式：form data 和 request payload

1.1.1. Form data

get请求的时候，我们的参数直接反映在url里面，形式为key1=value1&key2=value2形式，比如：

http://news.baidu.com/ns?word=NBA&tn=news&from=news&cl=2&rn=20&ct=1

而如果是post请求，那么表单参数是在请求体中，也是以key1=value1&key2=value2的形式在请求体中。通过chrome的开发者工具可以看到，如下：

RequestURL:http://127.0.0.1:8080/test/test.do
Request Method:POST
Status Code:200 OK

Request Headers
Accept:text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8
Accept-Encoding:gzip,deflate,sdch
Accept-Language:zh-CN,zh;q=0.8,en;q=0.6
AlexaToolbar-ALX_NS_PH:AlexaToolbar/alxg-3.2
Cache-Control:max-age=0
Connection:keep-alive
Content-Length:25
Content-Type:application/x-www-form-urlencoded
Cookie:JSESSIONID=74AC93F9F572980B6FC10474CD8EDD8D
Host:127.0.0.1:8080
Origin:http://127.0.0.1:8080
Referer:http://127.0.0.1:8080/test/index.jsp
User-Agent:Mozilla/5.0 (Windows NT 6.1)AppleWebKit/537.36 (KHTML, like Gecko) Chrome/33.0.1750.149 Safari/537.36

Form Data
name:mikan
address:street

Response Headers
Content-Length:2
Date:Sun, 11 May 2014 11:05:33 GMT
Server:Apache-Coyote/1.1

这里要注意post请求的Content-Type为application/x-www-form-urlencoded（默认的），参数是在请求体中，即上面请求中的Form Data。

前端代码：提交数据

xhr.setRequestHeader("Content-type","application/x-www-form-urlencoded");

xhr.send("name=foo&value=bar");

后端代码：接收提交的数据。在servlet中，可以通过request.getParameter(name)的形式来获取表单参数。

/**
 * 获取httpRequest的参数
 * 
 * @param request
 * @param name
 * @return
 */
protected String getParameterValue(HttpServletRequest request, String name) {
 return StringUtils.trimToEmpty(request.getParameter(name));
}

1.1.2. Request payload

如果使用原生AJAX POST请求的话，那么请求在chrome的开发者工具的表现如下，主要是参数在

Remote Address:192.168.234.240:80
Request URL:http://tuanbeta3.XXX.com/qimage/upload.htm
Request Method:POST
Status Code:200 OK

Request Headers
Accept:application/json, text/javascript, */*; q=0.01
Accept-Encoding:gzip,deflate,sdch
Accept-Language:zh-CN,zh;q=0.8,en;q=0.6
Connection:keep-alive
Content-Length:151
Content-Type:application/json;charset=UTF-8
Cookie:JSESSIONID=E08388788943A651924CA0A10C7ACAD0
Host:tuanbeta3.XXX.com
Origin:http://tuanbeta3.XXX.com
Referer:http://tuanbeta3.XXX.com/qimage/customerlist.htm?menu=19
User-Agent:Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/35.0.1916.114 Safari/537.36
X-Requested-With:XMLHttpRequest

Request Payload
[{widthEncode:NNNcaXN, heightEncode:NNNN5NN, displayUrl:201409/03/66I5P266rtT86oKq6,…}]

Response Headers
Connection:keep-alive
Content-Encoding:gzip
Content-Type:application/json;charset=UTF-8
Date:Thu, 04 Sep 2014 06:49:44 GMT
Server:nginx/1.4.7
Transfer-Encoding:chunked
Vary:Accept-Encoding

注意请求的Content-Type是application/json;charset=UTF-8，而请求表单的参数在Request Payload中。

后端代码：获取数据（这里使用org.apache.commons.io.）：

/**
 * 从 request 获取 payload 数据
 *
 * @param request
 * @return
 * @throws IOException
 */
private String getRequestPayload(HttpServletRequest request) throws IOException {
 return IOUtils.toString(request.getReader());
}

1.1.3. 二者区别

如果一个请求的Content-Type设置为application/x-www-form-urlencoded，那么这个Post请求会被认为是Http Post表单请求，那么请求主体将以一个标准的键值对和&的querystring形式出现。这种方式是HTML表单的默认设置，所以在过去这种方式更加常见。

其他形式的POST请求，是放到 Request payload 中（现在是为了方便阅读，使用了Json这样的数据格式），请求的Content-Type设置为application/json;charset=UTF-8或者不指定。

2. 环境

python 3.6.1

系统：win7

IDE：pycharm

requests 2.14.2

scrapy 1.4.0

3. 使用requests模块post payload请求

import json
import requests
import datetime

postUrl = 'https://sellercentral.amazon.com/fba/profitabilitycalculator/getafnfee?profitcalcToken=en2kXFaY81m513NydhTZ9sdb6hoj3D'
# payloadData数据
payloadData = {
 'afnPriceStr': 10,
 'currency':'USD',
 'productInfoMapping': {
  'asin': 'B072JW3Z6L',
  'dimensionUnit': 'inches',
 }
}
# 请求头设置
payloadHeader = {
 'Host': 'sellercentral.amazon.com',
 'Content-Type': 'application/json',
}
# 下载超时
timeOut = 25
# 代理
proxy = "183.12.50.118:8080"
proxies = {
 "http": proxy,
 "https": proxy,
}
r = requests.post(postUrl, data=json.dumps(payloadData), headers=payloadHeader)
dumpJsonData = json.dumps(payloadData)
print(f"dumpJsonData = {dumpJsonData}")
res = requests.post(postUrl, data=dumpJsonData, headers=payloadHeader, timeout=timeOut, proxies=proxies, allow_redirects=True)
# 下面这种直接填充json参数的方式也OK
# res = requests.post(postUrl, json=payloadData, headers=header)
print(f"responseTime = {datetime.datetime.now()}, statusCode = {res.status_code}, res text = {res.text}")

4. 在scrapy中post payload请求

这儿有个坏消息，那就是scrapy目前还不支持payload这种request请求。而且scrapy对formdata的请求也有很严格的要求，具体可以参考这篇文章：https://www.jb51.net/article/185824.htm

4.1. 分析scrapy源码

参考注解

# 文件：E:\Miniconda\Lib\site-packages\scrapy\http\request\form.py
class FormRequest(Request):

 def __init__(self, *args, **kwargs):
  formdata = kwargs.pop('formdata', None)
  if formdata and kwargs.get('method') is None:
   kwargs['method'] = 'POST'

  super(FormRequest, self).__init__(*args, **kwargs)

  if formdata:
   items = formdata.items() if isinstance(formdata, dict) else formdata
   querystr = _urlencode(items, self.encoding)
   # 这儿写死了，当提交数据时，设置好Content-Type，也就是form data类型
   # 就算改写这儿，后面也没有对 json数据解析的处理
   if self.method == 'POST':
    self.headers.setdefault(b'Content-Type', b'application/x-www-form-urlencoded')
    self._set_body(querystr)
   else:
    self._set_url(self.url + ('&' if '?' in self.url else '?') + querystr)

4.2. 思路：在scrapy中嵌入requests模块

分析请求

返回的查询结果

第一步：在爬虫中构造请求，把所有的参数以及必要信息带进去。

返回的查询结果

第一步：在爬虫中构造请求，把所有的参数以及必要信息带进去。

# 文件 mySpider.py中

payloadData = {}
payloadData['afnPriceStr'] = 0
payloadData['currency'] = asinInfo['currencyCodeHidden']
payloadData['futureFeeDate'] = asinInfo['futureFeeDateHidden']
payloadData['hasFutureFee'] = False
payloadData['hasTaxPage'] = True
payloadData['marketPlaceId'] = asinInfo['marketplaceIdHidden']
payloadData['mfnPriceStr'] = 0
payloadData['mfnShippingPriceStr'] = 0
payloadData['productInfoMapping'] = {}
payloadData['productInfoMapping']['asin'] = dataFieldJson['asin']
payloadData['productInfoMapping']['binding'] = dataFieldJson['binding']
payloadData['productInfoMapping']['dimensionUnit'] = dataFieldJson['dimensionUnit']
payloadData['productInfoMapping']['dimensionUnitString'] = dataFieldJson['dimensionUnitString']
payloadData['productInfoMapping']['encryptedMarketplaceId'] = dataFieldJson['encryptedMarketplaceId']
payloadData['productInfoMapping']['gl'] = dataFieldJson['gl']
payloadData['productInfoMapping']['height'] = dataFieldJson['height']
payloadData['productInfoMapping']['imageUrl'] = dataFieldJson['imageUrl']
payloadData['productInfoMapping']['isAsinLimits'] = dataFieldJson['isAsinLimits']
payloadData['productInfoMapping']['isWhiteGloveRequired'] = dataFieldJson['isWhiteGloveRequired']
payloadData['productInfoMapping']['length'] = dataFieldJson['length']
payloadData['productInfoMapping']['link'] = dataFieldJson['link']
payloadData['productInfoMapping']['originalUrl'] = dataFieldJson['originalUrl']
payloadData['productInfoMapping']['productGroup'] = dataFieldJson['productGroup']
payloadData['productInfoMapping']['subCategory'] = dataFieldJson['subCategory']
payloadData['productInfoMapping']['thumbStringUrl'] = dataFieldJson['thumbStringUrl']
payloadData['productInfoMapping']['title'] = dataFieldJson['title']
payloadData['productInfoMapping']['weight'] = dataFieldJson['weight']
payloadData['productInfoMapping']['weightUnit'] = dataFieldJson['weightUnit']
payloadData['productInfoMapping']['weightUnitString'] = dataFieldJson['weightUnitString']
payloadData['productInfoMapping']['width'] = dataFieldJson['width']
# https://sellercentral.amazon.com/fba/profitabilitycalculator/getafnfee?profitcalcToken=en2kXFaY81m513NydhTZ9sdb6hoj3D
postUrl = f"https://sellercentral.amazon.com/fba/profitabilitycalculator/getafnfee?profitcalcToken={asinInfo['tokenValue']}"
payloadHeader = {
 'Host': 'sellercentral.amazon.com',
 'Content-Type': 'application/json',
}
# scrapy源码：self.headers.setdefault(b'Content-Type', b'application/x-www-form-urlencoded')
print(f"payloadData = {payloadData}")
# 这个request并不真正用来调度，去发出请求，因为这种方式构造方式，是无法提交成功的，会返回404错误
# 这样构造主要是把查询参数提交出去，在下载中间件部分用request模块下载，用 “payloadFlag” 标记这种request
yield Request(url = postUrl,
    headers = payloadHeader,
    meta = {'payloadFlag': True, 'payloadData': payloadData, 'headers': payloadHeader, 'asinInfo': asinInfo},
    callback = self.parseAsinSearchFinallyRes,
    errback = self.error,
    dont_filter = True
    )

第二步：在中间件中，用requests模块处理这个请求

# 文件：middlewares.py

class PayLoadRequestMiddleware:
 def process_request(self, request, spider):
  # 如果有的请求是带有payload请求的，在这个里面处理掉
  if request.meta.get('payloadFlag', False):
   print(f"PayLoadRequestMiddleware enter")
   postUrl = request.url
   headers = request.meta.get('headers', {})
   payloadData = request.meta.get('payloadData', {})
   proxy = request.meta['proxy']
   proxies = {
    "http": proxy,
    "https": proxy,
   }
   timeOut = request.meta.get('download_timeout', 25)
   allow_redirects = request.meta.get('dont_redirect', False)
   dumpJsonData = json.dumps(payloadData)
   print(f"dumpJsonData = {dumpJsonData}")
   # 发现这个居然是个同步 阻塞的过程，太过影响速度了
   res = requests.post(postUrl, data=dumpJsonData, headers=headers, timeout=timeOut, proxies=proxies, allow_redirects=allow_redirects)
   # res = requests.post(postUrl, json=payloadData, headers=header)
   print(f"responseTime = {datetime.datetime.now()}, res text = {res.text}, statusCode = {res.status_code}")
   if res.status_code > 199 and res.status_code < 300:
    # 返回Response，就进入callback函数处理，不会再去下载这个请求
    return HtmlResponse(url=request.url,
         body=res.content,
         request=request,
         # 最好根据网页的具体编码而定
         encoding='utf-8',
         status=200)
   else:
    print(f"request mode getting page error, Exception = {e}")
    return HtmlResponse(url=request.url, status=500, request=request)

4.3. 遗留下的问题

scrapy之所以强大，就是因为并发度高。大家都知道，由于Python GIL的原因，导致python无法通过多线程来提高性能。但是至少可以做到下载与解析同步的过程，在下载空档的时候，进行数据的解析，调度等等，这都归功于scrapy采用的异步结构。

但是，我们在中间件中使用requests模块进行网页下载，因为这是个同步过程，所以会阻塞在这个地方，拉低了整个爬虫的效率。

所以，需要根据项目具体的情况，来决定合适的方案。当然这里又涉及到一个新的话题，就是scrapy提供的两种爬取模式：深度优先模式和广度优先模式。如何尽可能最大限度的利用scrapy的并发？在环境不稳定的情形下如何保证尽可能稳定的拿到数据？

深度优先模式和广度优先模式是在settings中设置的。

# 文件： settings.py

# DEPTH_PRIORITY（默认值为0）设置为一个正值后，Scrapy的调度器的队列就会从LIFO变成FIFO，因此抓取规则就由DFO（深度优先）变成了BFO（广度优先）
DEPTH_PRIORITY = 1, # 广度优先（肯呢个会累积大量的request，累计占有大量的内存，最终数据也在最后一批爬取）

深度优先：DEPTH_PRIORITY = 0

广度优先：DEPTH_PRIORITY = 1

想将这个过程做成异步的，一直没有思路，欢迎大神提出好的想法

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