python爬虫如何POST request payload形式的请求

Posted 2021-01-14 sthu

python爬虫如何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。

 

 

于是上网查了下二者的区别：http://xiaobaoqiu.github.io/blog/2014/09/04/form-data-vs-request-payload/，下面做了搬运工（侵权立删…）
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://blog.csdn.net/zwq912318834/article/details/78292536
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。
# 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
---------------------
作者：Kosmoo
来源：CSDN
原文：https://blog.csdn.net/zwq912318834/article/details/79930423
版权声明：本文为博主原创文章，转载请附上博文链接！