add

ai_test.py

 # -*- coding: utf-8 -*-
 import json
 import requests
+# url="http://127.0.0.1:4050/v1/once"
 url="http://doris.paas.test/v1/once"
+# 如果在生产环境doris 01上执行，把url改成"http://127.0.0.1:4050/v1/once"
 header_dict={'Content-Type': 'application/x-www-form-urlencoded'}
 param_dict={}
-param_dict["method"]="doris/search/skin_ai_total_content"
-a = [105, 2941, 780, 102, 873, 2060, 187, 2935, 2108, 912, 928, 2059, 4437, 822, 2866, 2778, 824, 821, 2938]
-b = [775, 2939, 789, 98, 872, 3042, 1890, 5074, 118, 811, 117, 92, 897, 152, 127, 126, 125, 123, 88, 115, 806]
-c = [796, 234, 233, 232, 797, 116, 236, 986, 1370, 87, 2923, 86, 85, 89, 253, 798, 810, 153, 864, 863, 151, 777, 2056]
-
-# [list(range(3000)),list(range(3000,6000)),list(range(6000,10000))]
-
-param_detail = {
-    "has_order": False,
-    "tag_list": [list(range(10000))],
-    "in_whitelist": False,
-    "user_city_tag_id": 328,
-    "device_id": "8"
-}
+# param_dict["method"]="doris/search/top_doctor"
+#
+# param_detail = {"size":10, "offset":0, "is_officer":True,"device_id":"hello",
+#                 "user_city_tag_id":328}
+
+# param_dict["method"]="doris/search/doctor_officer"
+# param_detail = {"device_id": 'xx',"size": 10,"offset":0, "query": "双眼皮","user_city_tag_id": 328,"is_officer":False}
+
+param_dict["method"]="doris/search/bangdan"
+param_detail = {"device_id": '2',"size": 10,"offset":0, "user_city_tag_id": 328,"is_officer":True}
+
+
+# a = [105, 2941, 780, 102, 873, 2060, 187, 2935, 2108, 912, 928, 2059, 4437, 822, 2866, 2778, 824, 821, 2938]
+# b = [775, 2939, 789, 98, 872, 3042, 1890, 5074, 118, 811, 117, 92, 897, 152, 127, 126, 125, 123, 88, 115, 806]
+# c = [796, 234, 233, 232, 797, 116, 236, 986, 1370, 87, 2923, 86, 85, 89, 253, 798, 810, 153, 864, 863, 151, 777, 2056]
+#
+# x = [list(range(3000)),list(range(3000,6000)),list(range(6000,10000))]
+
+a = [list(range(1000)),list(range(1000,2000)),list(range(2000,3000))]
+b = [list(range(3000,4000)),list(range(4000,5000)),list(range(5000,6000))]
+# b = [[],[],[]]
+c = [list(range(6000,7000)),list(range(7000,8000)),list(range(8000,10000))]
+x = [a,b,c]
+y = [list(range(0,7000)),list(range(7000,8000)),list(range(8000,10000))]
+
+# param_detail = {"has_order": True,"tag_list":y, "in_whitelist": True,"user_city_tag_id": 328,"device_id": '861916048000595'}
+
+# param_detail = {"device_id":"xx","user_city_tag_id":328,"offset":0,"is_officer":True}
 param_dict["params"]=json.dumps(param_detail)
 
 a = requests.post(url=url,data=param_dict,headers=header_dict)
 
 print(a.content)
-
-# 09FEDF48-82CF-4205-994C-9AAAA2D5DD08
\ No newline at end of file

make_data.py

-import redis
-import json
 import datetime
+import pymysql
+import pandas as pd
 
-def make_queue():
-    native = "58,59,60,61,62,63,64,65"
-    r.hset(key,"native_queue",native)
-    nearby = '88,89,90,91,92,93,94,95'
-    r.hset(key, "nearby_queue", nearby)
-    nation = '128,129,130,131,132,133,134,135,136,137,138,139'
-    r.hset(key, "nation_queue", nation)
-    megacity = '150,151,152,153,154,155,156,157,158,159'
-    r.hset(key, "megacity_queue", megacity)
+def doctor():
+    date_str = (datetime.datetime.now() - datetime.timedelta(days=1)).strftime("%Y%m%d")
+    date_tmp = (datetime.datetime.now() - datetime.timedelta(days=1)).strftime("%Y-%m-%d")
+    # sql = "select distinct doctor_id from statistic_doctor_rank_factor where partition_date = '{}'".format(date_str)
+    # doctor_list = pd.DataFrame(list(result))[0].values.tolist()
 
-
-
-def user_portr():
-    key = 'user_portrait_recommend_diary_queue:device_id:%s:%s' % \
-          (device_id, datetime.datetime.now().strftime('%Y-%m-%d'))
-    r.hset(key, "cursor", "0")
-    r.hset(key, "len_cursor", "0")
-    r.hset(key, "cpc_queue", json.dumps([58, 59, 60, 61, 62, 63, 64, 65]))
-    r.hset(key, "diary_queue", json.dumps([88, 89, 90, 91, 92, 93, 94, 95]))
-    print(r.hgetall(key))
+    sql = "select d.doctor_id,d.service_exposure_pv_30,d.service_ctr_30,d.expert_exposure_pv_30,d.expert_pv_30," \
+    "b.merchant_id," \
+    "m.doctor_ad_money_30_days,m.expand_rechange_amount_30,m.service_pv_30,m.expert_pv_30,m.organization_pv_30," \
+    "budan.budan_payment_30_days " \
+    "from statistic_doctor_rank_factor d " \
+    "left join hippo_merchantrelevance b on d.doctor_id = b.doctor_id " \
+    "left join statistic_merchant_rank_factor m on b.merchant_id = m.merchant_id " \
+    "left join al_meigou_service_smart_rank_budan_payment budan on b.merchant_id = budan.merchant_id " \
+    "where d.partition_date = '{}' and m.partition_date = '{}' " \
+    "and budan.stat_date = '{}'".format(date_str,date_str,date_tmp)
+    db = pymysql.connect(host='172.16.30.143', port=3306, user='work', passwd='BJQaT9VzDcuPBqkd', db='zhengxing')
+    cursor = db.cursor()
+    cursor.execute(sql)
+    result = cursor.fetchall()
+    df = pd.DataFrame(list(result))
+    print(df.head(6))
 
 
 if __name__ == "__main__":
-    device_id = "860445048868273"
-    user_portr()
+    doctor()