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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