update

task/search_answer_ctr.py

@@ -73,7 +73,7 @@ spark.sql("CREATE TEMPORARY FUNCTION is_json AS 'com.gmei.hive.common.udf.UDFJso
 spark.sql("CREATE TEMPORARY FUNCTION arrayMerge AS 'com.gmei.hive.common.udf.UDFArryMerge'")
 
 task_list = []
-task_days = 3
+task_days = 30
 for t in range(1, task_days):
     day_num = 0 - t
     now = (datetime.datetime.now() + datetime.timedelta(days=day_num))
@@ -112,7 +112,7 @@ for t in range(1, task_days):
                        and page_code in ('search_result_diary','search_result_doctor','search_result_hospital','search_result_more'
                                ,'search_result_more_infomation','search_result_more_user','search_result_post','search_result_welfare'
                                ,'search_result_wiki','search_result_question_answer')
-                       AND card_content_type IN ('answer','qa')
+                       AND (card_content_type IN ('answer','qa','question') or card_type in ('answer','qa','question'))
                      )a
                      group by partition_day,card_content_type,device_id
                    )t1

task/search_diary_ctr.py

@@ -73,7 +73,7 @@ spark.sql("CREATE TEMPORARY FUNCTION is_json AS 'com.gmei.hive.common.udf.UDFJso
 spark.sql("CREATE TEMPORARY FUNCTION arrayMerge AS 'com.gmei.hive.common.udf.UDFArryMerge'")
 
 task_list = []
-task_days = 3
+task_days = 30
 for t in range(1, task_days):
     day_num = 0 - t
     now = (datetime.datetime.now() + datetime.timedelta(days=day_num))
@@ -112,7 +112,7 @@ for t in range(1, task_days):
                     and page_code in ('search_result_diary','search_result_doctor','search_result_hospital','search_result_more'
                             ,'search_result_more_infomation','search_result_more_user','search_result_post','search_result_welfare'
                             ,'search_result_wiki','search_result_question_answer')
-                    AND card_content_type IN ('diary')
+                    AND (card_content_type IN ('diary') or card_type = 'diary')
                   )a
                   group by partition_day,card_content_type,device_id
                 )t1

task/search_meigou_ctr.py

@@ -73,7 +73,7 @@ spark.sql("CREATE TEMPORARY FUNCTION is_json AS 'com.gmei.hive.common.udf.UDFJso
 spark.sql("CREATE TEMPORARY FUNCTION arrayMerge AS 'com.gmei.hive.common.udf.UDFArryMerge'")
 
 task_list = []
-task_days = 3
+task_days = 30
 for t in range(1, task_days):
     day_num = 0 - t
     now = (datetime.datetime.now() + datetime.timedelta(days=day_num))
@@ -112,7 +112,7 @@ for t in range(1, task_days):
                         and page_code in ('search_result_diary','search_result_doctor','search_result_hospital','search_result_more'
                                 ,'search_result_more_infomation','search_result_more_user','search_result_post','search_result_welfare'
                                 ,'search_result_wiki','search_result_question_answer')
-                        AND card_content_type IN ('service')
+                        AND (card_content_type IN ('service') or  card_type = 'service')
                       )a
                       group by partition_day,card_content_type,device_id
                     )t1

task/search_tractate_ctr.py

@@ -73,7 +73,7 @@ spark.sql("CREATE TEMPORARY FUNCTION is_json AS 'com.gmei.hive.common.udf.UDFJso
 spark.sql("CREATE TEMPORARY FUNCTION arrayMerge AS 'com.gmei.hive.common.udf.UDFArryMerge'")
 
 task_list = []
-task_days = 3
+task_days = 30
 for t in range(1, task_days):
     day_num = 0 - t
     now = (datetime.datetime.now() + datetime.timedelta(days=day_num))
@@ -112,7 +112,7 @@ for t in range(1, task_days):
                 and page_code in ('search_result_diary','search_result_doctor','search_result_hospital','search_result_more'
                         ,'search_result_more_infomation','search_result_more_user','search_result_post','search_result_welfare'
                         ,'search_result_wiki','search_result_question_answer')
-                AND card_content_type IN ('user_post')
+                AND (card_content_type IN ('user_post')  or card_type = 'user_post')
               )a
               group by partition_day,card_content_type,device_id
             )t1

task/spark_test.py

@@ -29,7 +29,17 @@ from pyspark.sql import SparkSession, DataFrame
 
 # from pyspark.sql.functions import lit
 # import pytispark.pytispark as pti
-
+from elasticsearch import Elasticsearch
+
+exists_es_dic = {}
+es = Elasticsearch([
+    {
+        'host': '172.16.31.17',
+        'port': 9200,
+    }, {
+        'host': '172.16.31.11',
+        'port': 9200,
+    }])
 
 def con_sql(sql):
     # 从数据库的表里获取数据
@@ -78,81 +88,10 @@ 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'")
 
-huidu_device_id_sql = r"""
-select t2.device_id from 
---(select distinct(first_device) as device_id from online.ml_user_history_detail where partition_date = {today_str} and last_active_date >= {last_30_day_str}) t2
-(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 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
-
-""".format(today_str='20200926', last_30_day_str='20200926')
 
-print(huidu_device_id_sql)
-huidu_device_id_df = spark.sql(huidu_device_id_sql)
-huidu_device_id_df.createOrReplaceTempView("dev_view")
+# print(huidu_device_id_sql)
+# huidu_device_id_df = spark.sql(huidu_device_id_sql)
+# huidu_device_id_df.createOrReplaceTempView("dev_view")
 sql_search_ctr = r"""
 SELECT query,search_pv,search_uv
 	FROM
@@ -267,4 +206,10 @@ sql_res = search_ctr_df.collect()
 print("-------------------------------")
 for res in sql_res:
     print(res.query,res.search_pv)
+    results = es.search(
+        index='gm-dbmw-diary-read',
+        doc_type='diary',
+        timeout='10s',
+        body=body
+    )