update

output/__init__.py

+# -*- coding:UTF-8 -*-
+# @Time  : 2020/9/14 14:52
+# @File  : __init__.py.py
+# @email : litao@igengmei.com
+# @author : litao
\ No newline at end of file

output/meigou_huidu_huisu.py

+# -*- coding:UTF-8 -*-
+# @Time  : 2020/9/14 14:53
+# @File  : meigou_huidu_huisu.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
+
+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 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')
+    cursor = db.cursor()
+    cursor.execute(sql)
+    result = cursor.fetchall()
+    db.close()
+    return result
+
+
+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("search_diary_ctr")
+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").appName(
+    "search_diary_ctr").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 = 50
+for t in range(1, 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")
+    today_str = now.strftime("%Y%m%d")
+    yesterday_str = (now + datetime.timedelta(days=-1)).strftime("%Y%m%d")
+    one_week_age_str = (now + datetime.timedelta(days=-7)).strftime("%Y%m%d")
+
+    sql_search_ctr = r"""
+    SELECT
+t1.partition_date as `日期`
+,active_type as `系统`
+,device_os_type as `活跃`
+,grey_type as `灰度类型`
+,round(NVL(sum(click_pv),0)/NVL(sum(exp_pv),0)*100,2) as `卡片点击pv/卡片精准曝光pv(%)`
+,round(NVL(sum(two_click_pv),0)/NVL(sum(exp_pv),0)*100,2) as `有效二跳pv/卡片精准曝光pv(%)`
+,round(NVL(sum(two_click_pv),0)/NVL(sum(click_pv),0)*100,2) as `有效二跳pv/卡片点击pv(%)`
+,round(NVL(sum(cpc_exp_pv),0)/NVL(sum(exp_pv),0)*100,2) as `cpc卡片曝光pv/卡片精准曝光pv(%)`
+,NVL(sum(click_pv),0) as `卡片点击pv`
+,NVL(sum(exp_pv),0) as `卡片曝光pv`
+,NVL(sum(two_click_pv),0) as `有效二跳pv`
+,NVL(sum(cpc_click_pv),0) as `cpc卡片点击pv`
+,NVL(sum(cpc_exp_pv),0) as `cpc卡片曝光pv`
+FROM
+(
+    SELECT partition_date
+         ,device_os_type
+         ,CASE WHEN active_type = '4'  THEN '老活'
+               WHEN active_type  IN ('1','2')  THEN '新增' END AS active_type
+         ,device_id
+         ,CASE WHEN substr(md5(device_id),-1) in ('0','1','2','3','4','5','6','7') THEN '灰度' ELSE '非灰' END AS grey_type 
+    FROM online.ml_device_day_active_status
+    WHERE partition_date>={start_day}
+    AND partition_date<= {partition_day}
+    AND active_type IN ('1','2','4')
+)t1
+JOIN
+(--精准曝光
+    SELECT cl_id,partition_date,card_id,count(1) as exp_pv,count(CASE WHEN get_json_object(exposure_card, '$.is_cpc')=1 THEN 1 END) as cpc_exp_pv
+    FROM online.ml_community_precise_exposure_detail
+    WHERE partition_date>={start_day}
+    AND partition_date<= {partition_day}
+    AND action in ('page_precise_exposure','home_choiceness_card_exposure') --7745版本action改为page_precise_exposure
+    AND page_name in('welfare_home')
+    AND tab_name in ('精选')
+    AND card_content_type ='service'
+    and (get_json_object(exposure_card,'$.in_page_pos')='' or get_json_object(exposure_card,'$.in_page_pos') is null)
+    group by partition_date,cl_id,card_id
+)t2
+on t1.device_id=t2.cl_id and t1.partition_date=t2.partition_date
+LEFT JOIN
+(--卡片点击
+    SELECT  cl_id,partition_date,params['card_id'] as card_id,count(1) as click_pv,count(CASE WHEN params['is_cpc']=1 THEN 1 ELSE 0 END) as cpc_click_pv
+    FROM online.bl_hdfs_maidian_updates
+    WHERE partition_date>={start_day}
+    AND partition_date<= {partition_day}
+    AND action='on_click_card'
+    AND params['tab_name']='精选'
+    AND params['page_name'] ='welfare_home'
+    AND params['card_content_type'] ='service'
+    GROUP BY cl_id,partition_date,params['card_id']
+)t3
+on t2.partition_date=t3.partition_date
+and t2.cl_id=t3.cl_id
+and t2.card_id=t3.card_id
+LEFT JOIN
+(--商祥二跳
+    SELECT cl_id,partition_date,params['service_id'] as service_id,count(1) as two_click_pv
+    FROM online.bl_hdfs_maidian_updates 
+    WHERE partition_date>={start_day}
+    AND partition_date<= {partition_day}
+    AND (referrer in ('welfare_home') 
+            or (params['referrer_link'] like '%[%' and json_split(params['referrer_link'])[size(json_split(params['referrer_link']))-1] in ('welfare_home')))
+    AND ((action in ('welfare_multiattribute_click_add','welfare_multiattribute_click_buy') AND page_name = 'welfare_detail') 
+            or action = 'welfare_detail_click_message') 
+    GROUP BY cl_id,partition_date,params['service_id']
+)t4
+on t3.partition_date=t4.partition_date
+and t3.cl_id=t4.cl_id
+and t3.card_id=t4.service_id
+LEFT JOIN
+(
+    SELECT distinct device_id
+    FROM dim.dim_device_user_staff   --去除内网用户
+    
+    UNION ALL
+    SELECT device_id
+    FROM ml.ml_d_ct_dv_devicespam_d   --剔除刷量设备
+    WHERE partition_day={partition_day}
+)a 
+    on t1.device_id=a.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>={start_day}
+          AND partition_date<= {partition_day}
+    )t1
+    JOIN
+    (  --医生账号
+        SELECT distinct user_id
+        FROM online.tl_hdfs_doctor_view
+        WHERE partition_date = {partition_day}
+
+        --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 = {partition_day}
+        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 = {partition_day}
+        )t1
+        JOIN
+        (
+            SELECT device_id
+            FROM online.ml_device_history_detail
+            WHERE partition_date = {partition_day}
+            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
+)b
+on t1.partition_date=b.partition_date and t1.device_id=b.device_id
+where (a.device_id is NULL or a.device_id ='')
+and (b.device_id is null or b.device_id ='')
+GROUP BY t1.partition_date 
+,grey_type,active_type,device_os_type
+order by 1
+    """.format(partition_day=today_str,start_day=yesterday_str)
+
+    print(sql_search_ctr)
+    search_ctr_df = spark.sql(sql_search_ctr)
+    # spam_pv_df.createOrReplaceTempView("dev_view")
+    search_ctr_df.show(1)
+    sql_res = search_ctr_df.collect()
+    for res in sql_res:
+        print(res)
+        task_list.append(res)
+print(task_list)
+# cursor.executemany()
+