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619 lines (567 loc) · 21.6 KB
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#!/usr/bin/python
# coding=utf-8
import cookielib
import copy
import json
import urllib
import urllib2
import datetime
import sys
reload(sys)
sys.setdefaultencoding("utf-8")
def sendRequest(posturl, postData, headers):
postData = urllib.urlencode(postData)
request = urllib2.Request(posturl, postData, headers)
response = urllib2.urlopen(request)
text = response.read()
return text
def sendQuery(url, postData):
headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; WOW64; rv:14.0) Gecko/20100101 Firefox/14.0.1'}
text = sendRequest(url, postData, headers)
return text
def login(hosturl, username, password):
posturl = hosturl + '/login'
headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; WOW64; rv:14.0) Gecko/20100101 Firefox/14.0.1',
'Referer': posturl}
postData = {
'userName': username,
'password': password
}
text = sendRequest(posturl, postData, headers)
return text
def init():
cj = cookielib.LWPCookieJar()
cookie_support = urllib2.HTTPCookieProcessor(cj)
opener = urllib2.build_opener(cookie_support, urllib2.HTTPHandler)
urllib2.install_opener(opener)
h = urllib2.urlopen(hosturl)
if __name__ == "__main__":
# hosturl = 'http://10.73.37.63:8080'
hosturl = 'http://localhost:8080'
init()
login(hosturl, "zhongsf", "123456")
# 封装所有请求url和参数,
# 请求参数定义
# 用户整体分析参数
overview_param = {
"orgName": "",
"segment1Name": "",
"segment2Name": "",
"productModel": '',
"manualCache": True
}
# 用户购买分析
buy_analytics_param = {
"orgName": '',
"segments": '',
"segment": '',
"productModel": '',
"province": '',
"cityLevel": '',
"city": '',
"county": '',
"startTime": '',
"endTime": '',
"buyNumLow": '',
"buyNumHight": '',
"buyMoneyLow": '',
"buyMoneyHigh": '',
"platform": '',
"manualCache": True
}
# 用户安装分析
install_analytics_param = {
"orgName": '',
"segments": '',
"segment": '',
"productModel": '',
"province": '',
"cityLevel": '',
"city": '',
"county": '',
"startTime": '',
"endTime": '',
"buyNumLow": '',
"buyNumHight": '',
"buyMoneyLow": '',
"buyMoneyHigh": '',
"platform": '',
"manualCache": True
}
# 用户保养分析
upkeep_analytics_param = {
"orgName": '',
"segments": "",
"segment": "",
"productModel": '',
"province": '',
"cityLevel": "",
"city": '',
"county": '',
"startTime": '',
"endTime": '',
"num_low": "",
"num_high": "",
"manualCache": True
}
# 用户维修分析
service_analytics_param = upkeep_analytics_param
# 用户咨询分析
consult_analytics_param = upkeep_analytics_param
# 用户投诉分析
complaints_analytics_param = upkeep_analytics_param
# 标签示意
tag_cloud_param = {
"orgName": '',
"segments": "",
"segment": "",
"model": '',
"startTime": "",
"endTime": '',
"manualCache": True
}
# 标签分析
label_analysis_param = {
"orgName": '',
"segments": '',
"segment": '',
# "models": '',
# "labels": '',
"relation": "or",
"manualCache": True
}
# 用户价值与忠诚度
loyalty_param = {
"orgName": "",
"segments": "",
"interval": "200",
"percentile": "97",
"manualCache": True
}
# 用户价值详情检索
value_search_param = {
"orgName": "",
"segments": "",
"sumBuyAmountLow": "",
"sumBuyAmountHigh": "",
"sumBuyCountLow": "",
"sumBuyCountHigh": "",
"manualCache": True
}
# 用户搜索引擎
search_user_param = {
"orgName": "",
"segments": "",
"segment": "",
# "model": '',
# "province": '',
# "city": '',
# "county": '',
"fetchBuyInfo": False,
# "buyStartTime": '',
# "buyEndTime": '',
# "platform": '',
"businessType": "",
# "businessStartTime": '',
# "businessEndTime": '',
"exceptbusType": "",
# "exceptbusStartTime": '',
# "exceptbusEndTime": '',
"cityLevel": "",
"userArea": "",
"jobcharacte": "",
"motorhomes": "",
"familys": "",
"likefavors": "",
"usertypes": "",
"userValueName": "",
"complains": "",
"repairs": "",
"activityjoins": "",
# "userLabel": '',
# "exceptUserLabel": '',
# "name": '',
# "mobile": '',
"labelIds": "",
"exceptLabelIds": "",
# "relation": '',
"labelRelation": "or",
# "inputAddress": '',
"range": 0.0,
# "buyPlatform": '',
"manualCache": True
}
# 产品推荐引擎
potential_users_param = {
"orgName": "",
"segment1Name": "",
"segment2Name": "",
"productLevel": "",
"productFunc": "",
# "productNeed": 'null',
# "province": 'null',
# "city": 'null',
# "county": 'null',
"cityLevel": "",
"userArea": "",
"productComplain": "",
"productRepair": "",
"activityjoins": "",
"jobcharacte": "",
"motorhomes": "",
"familys": "",
"likefavors": "",
"usertypes": "",
"manualCache": True
}
# 各个请求的url和参数定义, 1: 代表启用,0: 代表不启用
# 首页地域分布
home_area_data = [
"/home/area-data",
{"manualCache": True}
]
testurl = [
"/marketing-tools/user-search/value-search?manualCache=True&orgName=%E9%9B%86%E5%9B%A2%E6%80%BB%E9%83%A8&sumBuyAmountLow=&sumBuyCountLow=&segments=%E7%B2%BE%E5%AF%86%E7%A9%BA%E8%B0%83&sumBuyCountHigh=&sumBuyAmountHigh="
]
# 用户特征 - 用户整体分析
overview_module = ["base", "segment", "base_userArea", "base_lastYear", "base_userSleep", "business"]
overview = []
for module in overview_module:
overview_param["module"] = module
tmp = [
"/user-overview/overview/" + module,
overview_param
]
overview.append(tmp)
# 用户特征 - 用户购买分析
buy_analytics_module = ["buy_amount", "buy_lastYearTrend", "buy_lastYearTrend_new", "buy_last15Trend",
"buy_time", "price", "platform", "area", "buy_product_segment", "buy_product_model",
"buy_freq", "buy_money", "buy_value"]
buy_analytics = []
for module in buy_analytics_module:
buy_analytics_param["module"] = module
tmp = [
"/user-overview/buy-analytics/" + module,
buy_analytics_param
]
buy_analytics.append(tmp)
# 用户特征 - 用户安装分析
install_analytics_module = ["install_amount", "install_lastYear", "install_lastYearNew", "area",
"install_product_segment", "install_product_model", "install_frequents"]
install_analytics = []
for module in install_analytics_module:
install_analytics_param["module"] = module
tmp = [
"/user-overview/install-analytics/" + module,
install_analytics_param
]
install_analytics.append(tmp)
# 用户特征 - 用户保养分析
upkeep_analytics_module = ["userKeep_num", "userKeep_lastYear", "userKeep_lastYearTrend", "area",
"userKeep_product_segment", "userKeep_product_model", "userKeep_frequents"]
upkeep_analytics = []
for module in upkeep_analytics_module:
upkeep_analytics_param["module"] = module
tmp = [
"/user-overview/upkeep-analytics/" + module,
upkeep_analytics_param
]
upkeep_analytics.append(tmp)
# 用户特征 - 用户维修分析
service_analytics_module = ["userService_num", "userService_lastYear", "userService_lastYearTrend", "area",
"duration", "userService_product_segment", "userService_product_model",
"userService_frequents"]
service_analytics = []
for module in service_analytics_module:
service_analytics_param["module"] = module
tmp = [
"/user-overview/service-analytics/" + module,
service_analytics_param
]
service_analytics.append(tmp)
# 用户特征 - 用户咨询分析
consult_analytics_module = ["consult_num", "consult_lastYear", "consult_lastYearTrend", "area",
"consult_product_segment", "consult_product_model", "consult_frequents"]
consult_analytics = []
for module in consult_analytics_module:
consult_analytics_param["module"] = module
tmp = [
"/user-overview/consult-analytics/" + module,
consult_analytics_param
]
consult_analytics.append(tmp)
# 用户特征 - 用户投诉分析
complaints_analytics_module = ["complaint_num", "complaint_lastYear", "complaint_lastYearTrend", "area",
"emergency", "complaint_product_segment", "complaint_product_model",
"complaint_frequents"]
complaints_analytics = []
for module in complaints_analytics_module:
complaints_analytics_param["module"] = module
tmp = [
"/user-overview/complaints-analytics/" + module,
complaints_analytics_param
]
complaints_analytics.append(tmp)
# 用户标签 - 标签介绍 - 标签示意(云图)
label_Analysis_tag_cloud = [
"/user-tags/tag-introduction/tag-cloud",
tag_cloud_param
]
# 用户标签 - 标签分析 - 标签规模分析
label_Analysis_scale = [
"/user-labels/label-Analysis/scale/labelAll",
label_analysis_param
]
# 用户标签 - 标签分析 - 购物特征分析
label_Analysis_buyFeature_module = ["buyCount", "onlineBuyCount", "buyCategory", "buyPrice",
"buyType", "buyPsychology", "buyPromotionPrefer"]
label_Analysis_buyFeature = []
for module in label_Analysis_buyFeature_module:
label_analysis_param["module"] = module
tmp = [
"/user-labels/label-Analysis/buyFeature/" + module,
label_analysis_param
]
label_Analysis_buyFeature.append(tmp)
# 用户标签 - 标签分析 - 产品偏好分析
label_Analysis_productLevel_module = ["productLevel", "productFunction"]
label_Analysis_productLevel = []
for module in label_Analysis_productLevel_module:
label_analysis_param["module"] = module
tmp = [
"/user-labels/label-Analysis/ProductPrefer/" + module,
label_analysis_param
]
label_Analysis_productLevel.append(tmp)
# 用户标签 - 标签分析 - 渠道偏好分析
label_Analysis_ChannelPrefer_module = ["buyChannelPrefer", "buyShopCount", "buyInternetCount",
"buyPlatformCount"]
label_Analysis_ChannelPrefer = []
for module in label_Analysis_ChannelPrefer_module:
label_analysis_param["module"] = module
tmp = [
"/user-labels/label-Analysis/ChannelPrefer/" + module,
label_analysis_param
]
label_Analysis_ChannelPrefer.append(tmp)
# 用户标签 - 标签分析 - 时间偏好分析
label_Analysis_TimePrefer_module = ["buyPromotionPerfer", "buyPromotionDayPrefer", "buyTimePrefer"]
label_Analysis_TimePrefer = []
for module in label_Analysis_TimePrefer_module:
label_analysis_param["module"] = module
tmp = [
"/user-labels/label-Analysis/TimePrefer/" + module,
label_analysis_param
]
label_Analysis_TimePrefer.append(tmp)
# 用户标签 - 标签分析 - 群体结构分析
label_Analysis_Groups_module = ["buyGroupsCity", "buyGroupsCounty", "buyGroupsAge", "buyGroupsStablity",
"buyGroupsIncome", "buyGroupsUserTypes", "buyGroupsJob", "buyGroupsCars",
"buyGroupsFamily", "buyGroupsLifePrefer", "buyGroupsInterest", "buyGroupsHabits"]
label_Analysis_Groups = []
for module in label_Analysis_Groups_module:
label_analysis_param["module"] = module
tmp = [
"/user-labels/label-Analysis/Groups/" + module,
label_analysis_param
]
label_Analysis_Groups.append(tmp)
# 用户标签 - 标签分析 - 用户价值分析
label_Analysis_userValues_module = ["userValue", "userValueConsumeAblity", "userValueLoyal", "userValueActiveness"]
label_Analysis_userValues = []
for module in label_Analysis_userValues_module:
label_analysis_param["module"] = module
tmp = [
"/user-labels/label-Analysis/userValues/" + module,
label_analysis_param
]
label_Analysis_userValues.append(tmp)
# 用户标签 - 标签分析 - 网络特征分析
label_Analysis_NetFeature_module = ["netActivity", "netFlow", "netMobile", "netWorkDay", "newWeekDay"]
label_Analysis_NetFeature = []
for module in label_Analysis_NetFeature_module:
label_analysis_param["module"] = module
tmp = [
"/user-labels/label-Analysis/NetFeature/" + module,
label_analysis_param
]
label_Analysis_NetFeature.append(tmp)
# 用户标签 - 标签分析 - 用户需求分析
label_Analysis_userNeed_module = ["userNeedAirCondition", "userNeedKitchen", "userNeedWashMachine",
"userNeedFridge", "userNeedLifeElectric", "userNeedWaterHeaters",
"userNeedEnvEquipment"]
label_Analysis_userNeed = []
for module in label_Analysis_userNeed_module:
label_analysis_param["module"] = module
tmp = [
"/user-labels/label-Analysis/userNeed/" + module,
label_analysis_param
]
label_Analysis_userNeed.append(tmp)
# 用户挖掘 - 用户价值与忠诚度
user_values_loyalty = [
"/user-values/loyalty/data",
loyalty_param
]
# 用户挖掘 - 用户价值详情检索
user_values_value_search = [
"/marketing-tools/user-search/value-search",
value_search_param
]
# 画像工具 - 用户搜索引擎
user_search_searchUser = [
"/marketing-tools/user-search/searchUser",
search_user_param
]
# # 画像工具 - 用户搜索引擎 - 目标用户分析
label_Analysis_targetUser_module = ["targetUserLabel", "buyFreqAttribute", "userValueAttribte",
"buyCategoryAttribute", "buyTypeAttribute", "productLevelPrefer",
"buyChannelPrefer", "homeAndCarAttribute", "userLifePreferAttribute",
"userContact", "userValueActiveness"]
label_Analysis_targetUser = []
for module in label_Analysis_targetUser_module:
search_user_param["module"] = module
tmp = [
"/marketing-tools/user-search/targetUser-Analysis/" + module,
search_user_param
]
label_Analysis_targetUser.append(tmp)
# 画像工具 - 产品推荐引擎
potential_users_search = [
"/marketing-tools/potential-users/search",
potential_users_param
]
# 获取事业部-产品大类-产品小类
def getSeg():
orgUserSegUrl = hosturl + '/util/org-user-seg'
orgUserUrl = hosturl + '/util/org-user'
orgUserSeg = json.loads(sendQuery(orgUserSegUrl, ''))
orgUser = json.loads(sendQuery(orgUserUrl, ''))
orgUserTemp = orgUser['data']
orgUserSegTemp = orgUserSeg['data']
result = {}
for value in orgUserTemp:
for key, second in value.iteritems():
secondList = []
for th in second:
for value1 in orgUserSegTemp:
for x in value1:
if x == th:
secondList.append(value1)
result[key] = secondList
return result
# 根据事业部-产品大类-产品小类组合查询条件 TODO:此处需要优化
labels = getSeg()
label_analysis_param_list = []
for key, value in labels.iteritems():
label_analysis_param_tmp = copy.copy(label_analysis_param)
label_analysis_param_tmp["orgName"] = key
label_analysis_param_list.append(label_analysis_param_tmp)
for key, value in labels.iteritems():
label_analysis_param_tmp = copy.copy(label_analysis_param)
label_analysis_param_tmp["orgName"] = key
for value1 in value:
for key1, value2 in value1.iteritems():
label_analysis_param_tmp2 = copy.copy(label_analysis_param_tmp)
label_analysis_param_tmp2["segments"] = key1
label_analysis_param_list.append(label_analysis_param_tmp2)
for key, value in labels.iteritems():
label_analysis_param_tmp = copy.copy(label_analysis_param)
label_analysis_param_tmp["orgName"] = key
for value1 in value:
for key1, value2 in value1.iteritems():
label_analysis_param_tmp2 = copy.copy(label_analysis_param_tmp)
label_analysis_param_tmp2["segments"] = key1
for value3 in value2:
label_analysis_param_tmp3 = copy.copy(label_analysis_param_tmp2)
label_analysis_param_tmp3["segment"] = value3
label_analysis_param_list.append(label_analysis_param_tmp3)
# print "label_analysis_param_list.len:", len(label_analysis_param_list)
# print label_analysis_param_list
# 根据事业部-产品大类-产品小类获取标签分析查询url
def get_label_Analysis_cond(param_list, label_Analysis):
label_Analysis_cond = []
for param in param_list:
if isinstance(label_Analysis[0], list):
for label in label_Analysis:
label_Analysis_tmp2 = [label[0], param]
label_Analysis_cond.append(label_Analysis_tmp2)
else:
label_Analysis_tmp = [label_Analysis[0], param]
label_Analysis_cond.append(label_Analysis_tmp)
return label_Analysis_cond
label_Analysis_scale_cond = get_label_Analysis_cond(label_analysis_param_list, label_Analysis_scale)
# print label_Analysis_scale_cond
# label_Analysis_buyFeature_cond = get_label_Analysis_cond(label_analysis_param_list, label_Analysis_buyFeature)
# label_Analysis_productLevel_cond = get_label_Analysis_cond(label_analysis_param_list, label_Analysis_productLevel)
# label_Analysis_ChannelPrefer_cond = get_label_Analysis_cond(label_analysis_param_list, label_Analysis_ChannelPrefer)
# label_Analysis_TimePrefer_cond = get_label_Analysis_cond(label_analysis_param_list, label_Analysis_TimePrefer)
# label_Analysis_Groups_cond = get_label_Analysis_cond(label_analysis_param_list, label_Analysis_Groups)
# label_Analysis_userValues_cond = get_label_Analysis_cond(label_analysis_param_list, label_Analysis_userValues)
# label_Analysis_NetFeature_cond = get_label_Analysis_cond(label_analysis_param_list, label_Analysis_NetFeature)
# label_Analysis_userNeed_cond = get_label_Analysis_cond(label_analysis_param_list, label_Analysis_userNeed)
# 装箱所有请求url和参数
request_url_all = [
testurl
# home_area_data,
# overview,
# buy_analytics,
# install_analytics,
# upkeep_analytics,
# service_analytics,
# consult_analytics,
# complaints_analytics,
# label_Analysis_tag_cloud,
# label_Analysis_scale,
# label_Analysis_buyFeature,
# label_Analysis_productLevel,
# label_Analysis_ChannelPrefer,
# label_Analysis_TimePrefer,
# label_Analysis_Groups,
# label_Analysis_userValues,
# label_Analysis_NetFeature,
# label_Analysis_userNeed,
# user_values_loyalty,
# user_values_value_search,
# user_search_searchUser,
# label_Analysis_targetUser,
# potential_users_search,
#
# label_Analysis_scale_cond,
# 禁用以下cache
# label_Analysis_buyFeature_cond,
# label_Analysis_productLevel_cond,
# label_Analysis_ChannelPrefer_cond,
# label_Analysis_TimePrefer_cond,
# label_Analysis_Groups_cond,
# label_Analysis_userValues_cond,
# label_Analysis_NetFeature_cond,
# label_Analysis_userNeed_cond
]
# 发生请求方法
def sendReq(sendReqs):
start_time = datetime.datetime.now()
action = sendReqs[0]
postData = sendReqs[1]
url = hosturl + action
text = sendQuery(url, postData)
print "请求url:", url, "耗时:", (datetime.datetime.now() - start_time).seconds, "秒, 返回数据长度:", \
len(text), "请求参数:", postData
# print "请求url:", url, "请求参数:", postData
# 开始发生请求
print "开始发起cache请求..."
start_time = datetime.datetime.now()
reqCont = 0
for requests in request_url_all:
if isinstance(requests[0], list):
for reqs in requests:
req = reqs[0]
if isinstance(req, list):
pass
else:
sendReq(reqs)
reqCont += 1
else:
sendReq(requests)
reqCont += 1
print "cache请求结束. 共发送[", reqCont, "]个请求. 耗时:", (datetime.datetime.now() - start_time).seconds