update

new_user_analysis/new_user_has_protratit_rate.py

@@ -4,24 +4,6 @@
 # @email : litao@igengmei.com
 # @author : litao
 
-# -*- coding:UTF-8 -*-
-# @Time  : 2020/9/15 16:17
-# @File  : user_behavior_path.py
-# @email : litao@igengmei.com
-# @author : litao
-
-
-# -*- coding:UTF-8 -*-
-# @Time  : 2020/9/8 13:39
-# @File  : spark_test.py
-# @email : litao@igengmei.com
-# @author : litao
-
-# -*- coding:UTF-8 -*-
-# @Time  : 2020/9/4 17:07
-# @File  : search_meigou_ctr.py
-# @email : litao@igengmei.com
-# @author : litao
 
 import hashlib
 import json

new_user_analysis/new_user_project_protratit.py

+# -*- coding:UTF-8 -*-
+# @Time  : 2020/11/24 9:48
+# @File  : new_user_project_protratit.py
+# @email : litao@igengmei.com
+# @author : litao
+
+
+import hashlib
+import json
+
+import pymysql
+import xlwt, datetime
+import redis
+# from pyhive import hive
+from maintenance.func_send_email_with_file import send_file_email
+from typing import Dict, List
+from elasticsearch_7 import Elasticsearch
+from elasticsearch_7.helpers import scan
+import sys
+import time
+from pyspark import SparkConf
+from pyspark.sql import SparkSession, DataFrame
+from meta_base_code.utils.func_from_redis_get_portrait import *
+
+
+# from pyspark.sql.functions import lit
+# import pytispark.pytispark as pti
+
+
+def con_sql(sql):
+    # 从数据库的表里获取数据
+
+    # db = pymysql.connect(host='172.16.40.158', port=4000, user='st_user', passwd='aqpuBLYzEV7tML5RPsN1pntUzFy',
+    #                      db='jerry_prod')
+    db = pymysql.connect(host='172.16.30.136', port=3306, user='doris', passwd='o5gbA27hXHHm',
+                         db='doris_prod')
+    cursor = db.cursor()
+    cursor.execute(sql)
+    result = cursor.fetchall()
+    db.close()
+    return result
+
+
+exists_es_dic = {}
+es = Elasticsearch([
+    {
+        'host': '172.16.31.17',
+        'port': 9200,
+    }, {
+        'host': '172.16.31.11',
+        'port': 9200,
+    }])
+
+startTime = time.time()
+sparkConf = SparkConf()
+sparkConf.set("spark.sql.crossJoin.enabled", True)
+sparkConf.set("spark.debug.maxToStringFields", "100")
+sparkConf.set("spark.tispark.plan.allow_index_double_read", False)
+sparkConf.set("spark.tispark.plan.allow_index_read", True)
+sparkConf.set("spark.hive.mapred.supports.subdirectories", True)
+sparkConf.set("spark.hadoop.mapreduce.input.fileinputformat.input.dir.recursive", True)
+sparkConf.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
+sparkConf.set("mapreduce.output.fileoutputformat.compress", False)
+sparkConf.set("mapreduce.map.output.compress", False)
+sparkConf.set("prod.gold.jdbcuri",
+              "jdbc:mysql://172.16.30.136/doris_prod?user=doris&password=o5gbA27hXHHm&rewriteBatchedStatements=true")
+sparkConf.set("prod.mimas.jdbcuri",
+              "jdbc:mysql://172.16.30.138/mimas_prod?user=mimas&password=GJL3UJe1Ck9ggL6aKnZCq4cRvM&rewriteBatchedStatements=true")
+sparkConf.set("prod.gaia.jdbcuri",
+              "jdbc:mysql://172.16.30.143/zhengxing?user=work&password=BJQaT9VzDcuPBqkd&rewriteBatchedStatements=true")
+sparkConf.set("prod.tidb.jdbcuri",
+              "jdbc:mysql://172.16.40.158:4000/eagle?user=st_user&password=aqpuBLYzEV7tML5RPsN1pntUzFy&rewriteBatchedStatements=true")
+sparkConf.set("prod.jerry.jdbcuri",
+              "jdbc:mysql://172.16.40.158:4000/jerry_prod?user=st_user&password=aqpuBLYzEV7tML5RPsN1pntUzFy&rewriteBatchedStatements=true")
+sparkConf.set("prod.tispark.pd.addresses", "172.16.40.158:2379")
+sparkConf.set("prod.tispark.pd.addresses", "172.16.40.170:4000")
+sparkConf.set("prod.tidb.database", "jerry_prod")
+sparkConf.setAppName("new_user_project_protratit")
+
+spark = (SparkSession.builder.config(conf=sparkConf).config("spark.sql.extensions", "org.apache.spark.sql.TiExtensions")
+         .config("spark.tispark.pd.addresses", "172.16.40.170:2379").enableHiveSupport().getOrCreate())
+
+spark.sql("ADD JAR hdfs:///user/hive/share/lib/udf/brickhouse-0.7.1-SNAPSHOT.jar")
+spark.sql("ADD JAR hdfs:///user/hive/share/lib/udf/hive-udf-1.0-SNAPSHOT.jar")
+spark.sql("CREATE TEMPORARY FUNCTION json_map AS 'brickhouse.udf.json.JsonMapUDF'")
+spark.sql("CREATE TEMPORARY FUNCTION is_json AS 'com.gmei.hive.common.udf.UDFJsonFormatCheck'")
+spark.sql("CREATE TEMPORARY FUNCTION arrayMerge AS 'com.gmei.hive.common.udf.UDFArryMerge'")
+
+task_list = []
+task_days = 3
+
+es_query_tractate = {"query": {
+            "bool": {
+                "must": [
+                    {"term": {"is_online": True}},
+                    {"range": {"content_level": {"gte": 3}}},
+                    {"terms": {"operators_add_tags": [3315]}}
+
+                ],
+                "must_not": [{"term": {"status": 4}},
+                {"term": {"show_by_index": 2}}
+                             ]
+            }
+        }
+        }
+tractate_res_scan = scan(es,es_query_tractate,index="gm-dbmw-tractate-read")
+tractate_dict = {}
+for tractate_json in tractate_res_scan:
+    _id = tractate_json["_id"]
+    content_keyword_list = tractate_json["source"]["content_keyword"]
+    for content_keyword in content_keyword_list:
+        if content_keyword in tractate_dict:
+            tractate_dict[content_keyword] += 1
+        else:
+            tractate_dict[content_keyword] = 1
+
+es_query_answer = {"query": {
+            "bool": {
+                "must": [
+                    {"term": {"is_online": True}},
+                    {"range": {"content_level": {"gte": 3}}},
+                    {"terms": {"operators_add_tags": [3315]}},
+                    {"range": {"content_length": {"gte": 30}}}
+                ]
+            }
+        }
+        }
+answer_res_scan = scan(es,es_query_tractate,index="gm-dbmw-answer-read")
+answer_dict = {}
+for answer_json in answer_res_scan:
+    _id = answer_json["_id"]
+    content_keyword_list = answer_json["source"]["content_keyword"]
+    for content_keyword in content_keyword_list:
+        if content_keyword in tractate_dict:
+            answer_dict[content_keyword] += 1
+        else:
+            answer_dict[content_keyword] = 1
+
+
+for t in range(2, task_days):
+    day_num = 0 - t
+    now = (datetime.datetime.now() + datetime.timedelta(days=day_num))
+    last_30_day_str = (now + datetime.timedelta(days=-30)).strftime("%Y%m%d")
+    tomorrow_str = (datetime.datetime.now() + datetime.timedelta(days=day_num + 1)).strftime("%Y%m%d")
+    today_str = now.strftime("%Y%m%d")
+    today_str_format = now.strftime("%Y-%m-%d")
+    yesterday_str = (now + datetime.timedelta(days=-1)).strftime("%Y%m%d")
+    yesterday_str_format = (now + datetime.timedelta(days=-1)).strftime("%Y-%m-%d")
+    one_week_age_str = (now + datetime.timedelta(days=-7)).strftime("%Y%m%d")
+    new_urser_device_id_sql = r"""
+    select t2.device_id as device_id from 
+    (select device_id from online.ml_device_day_active_status where partition_date = '{today_str}' and active_type in (1,2)) t2
+    LEFT join (
+    select first_device from online.ml_user_history_detail where partition_date = '{tomorrow_str}' and last_active_date = '{today_str}'
+    ) on first_device = t2.device_id
+    LEFT JOIN
+    (
+        select distinct device_id
+        from ml.ml_d_ct_dv_devicespam_d  --去除机构刷单设备，即作弊设备（浏览和曝光事件去除）
+        WHERE partition_day='{today_str}'
+
+        union all
+
+        select distinct device_id
+        from dim.dim_device_user_staff   --去除内网用户
+    )spam_pv
+    on spam_pv.device_id=t2.device_id
+        LEFT JOIN
+    (
+        SELECT partition_date,device_id
+        FROM
+            (--找出user_id当天活跃的第一个设备id
+          SELECT user_id,partition_date,
+                  if(size(device_list) > 0, device_list [ 0 ], '') AS device_id
+            FROM online.ml_user_updates
+            WHERE partition_date='{today_str}'
+        )t1
+                JOIN
+            (  --医生账号
+          SELECT distinct user_id
+          FROM online.tl_hdfs_doctor_view
+          WHERE partition_date = '{today_str}'
+
+          --马甲账号/模特用户
+          UNION ALL
+          SELECT user_id
+          FROM ml.ml_c_ct_ui_user_dimen_d
+          WHERE partition_day = '{today_str}'
+          AND (is_puppet = 'true' or is_classifyuser = 'true')
+
+          UNION ALL
+          --公司内网覆盖用户
+          select distinct user_id
+          from dim.dim_device_user_staff
+
+          UNION ALL
+          --登陆过医生设备
+          SELECT distinct t1.user_id
+            FROM
+            (
+                SELECT user_id, v.device_id as device_id
+                FROM online.ml_user_history_detail
+                    LATERAL VIEW EXPLODE(device_history_list) v AS device_id
+                WHERE partition_date = '{today_str}'
+            ) t1
+            JOIN
+            (
+                SELECT device_id
+                FROM online.ml_device_history_detail
+                WHERE partition_date = '{today_str}'
+                AND is_login_doctor = '1'
+            ) t2
+                ON t1.device_id = t2.device_id
+        )t2
+            on t1.user_id=t2.user_id
+        group by partition_date,device_id
+    )dev
+    on t2.device_id=dev.device_id
+WHERE spam_pv.device_id IS NULL
+  and dev.device_id is null and first_device is not null
+    """.format(today_str=today_str, yesterday_str_format=yesterday_str_format, today_str_format=today_str_format,
+               tomorrow_str=tomorrow_str)
+
+    print(new_urser_device_id_sql)
+    new_urser_device_id_df = spark.sql(new_urser_device_id_sql)
+    new_urser_device_id_df.createOrReplaceTempView("device_id_view")
+    new_urser_device_id_df.show(1)
+    sql_res = new_urser_device_id_df.collect()
+    res_dict = {}
+    portrait_dict = {
+        "first_demands": {},
+        "second_demands": {},
+        "first_solutions": {},
+        "second_solutions": {},
+        "first_positions": {},
+        "second_positions": {},
+        "projects": {},
+        'anecdote_tags': {}
+    }
+    no_portrait_device_id_list = []
+    print("-------------------------------")
+    count_not_has_portratit = 0
+
+    for count_user_count, res in enumerate(sql_res):
+        # print(count, res)
+        portratit_res = get_user_portrait_tag3_from_redis(res.device_id)
+        sql = """select cl_id, projects from kafka_tag3_log
+  where cl_id = '%s' and event_cn = 'kyc' """ % res.device_id
+        # print(count_user_count, res, portratit_res)
+        sql_res_list = con_sql(sql)
+        kyc_str_list = []
+        if sql_res_list:
+            print(sql_res_list, type(sql_res_list))
+            kyc_str_list = sql_res_list[0][1].split(",")
+
+        temp_count = 0
+        for demand in portratit_res:
+            if portratit_res[demand]:
+                try:
+                    for tag in portratit_res[demand][0:3]:
+                        if tag in portrait_dict[demand]:
+                            portrait_dict[demand][tag] += 1
+                        else:
+                            portrait_dict[demand][tag] = 1
+                        if tag in kyc_str_list and demand == "projects":
+                            if portrait_dict["projects"].get(tag):
+                                portrait_dict["projects"][tag] -= 1
+                except Exception as e:
+                    print("error ", e)
+
+                temp_count += 1
+        if not temp_count:
+            count_not_has_portratit += 1
+            no_portrait_device_id_list.append(res.device_id)
+
+    print(portrait_dict)
+    print(count_user_count + 1, count_not_has_portratit)
+    print("-------------------------------")
+
+    for protratit_type in portrait_dict["projects"]:
+        partition_date = today_str
+        pid = hashlib.md5((partition_date + protratit_type).encode("utf8")).hexdigest()
+        action_count = portrait_dict["projects"][protratit_type]
+        answer_count = answer_dict.get(protratit_type,0)
+        tractate_count = tractate_dict.get(protratit_type,0)
+        total_count = answer_count + tractate_count
+
+        instert_sql = """replace into new_user_project_count(
+                    partition_day,pid,protratit_count,protratit_type,answer_count,tractate_count,total_count) VALUES('{partition_day}','{pid}',{protratit_count},'{protratit_type}',{answer_count},{tractate_count},{total_count});""".format(
+            partition_day=today_str, pid=pid, protratit_count=action_count
+            , protratit_type=protratit_type, answer_count=answer_count, tractate_count=tractate_count, total_count=total_count
+        )
+        print(instert_sql)
+        # cursor.execute("set names 'UTF8'")
+        db = pymysql.connect(host='172.16.40.158', port=4000, user='st_user', passwd='aqpuBLYzEV7tML5RPsN1pntUzFy',
+                             db='jerry_prod')
+        cursor = db.cursor()
+        res = cursor.execute(instert_sql)
+        db.commit()
+        print(res)

new_user_analysis/user_behavior_path.py

@@ -5,18 +5,6 @@
 # @author : litao
 
 
-# -*- coding:UTF-8 -*-
-# @Time  : 2020/9/8 13:39
-# @File  : spark_test.py
-# @email : litao@igengmei.com
-# @author : litao
-
-# -*- coding:UTF-8 -*-
-# @Time  : 2020/9/4 17:07
-# @File  : search_meigou_ctr.py
-# @email : litao@igengmei.com
-# @author : litao
-
 import hashlib
 import json