update

task/search_strategy_d.py

@@ -718,35 +718,35 @@ for t in range(1, task_days):
     for res in sql_res:
         print(res)
     device_df.createOrReplaceTempView("data_table")
-
-    collects_sql = """
-    SELECT device_type,active_type,channel_type,ROUND(if(NVL(sum(uv),0) <> 0 ,NVL(sum(search_core_pv),0)/NVL(sum(uv),0) ,0),5) as core_pv_division_uv,
-    ROUND(if(NVL(sum(uv),0) <> 0 ,NVL(sum(search_pv),0)/NVL(sum(uv),0) , 0),5) as pv_division_uv
-    FROM data_table GROUP BY device_type,active_type,channel_type
-    """
-    finnal_df = spark.sql(collects_sql)
-
-    finnal_df.show(1, False)
-    sql_res = finnal_df.collect()
-    for res in sql_res:
-        # print(res)
-        device_type = res.device_type
-        active_type = res.active_type
-        channel_type = res.channel_type
-        core_pv_division_uv = res.core_pv_division_uv
-        pv_division_uv = res.pv_division_uv
-        pid = hashlib.md5(
-            (today_str + device_type + active_type + channel_type).encode("utf8")).hexdigest()
-        instert_sql = """replace into search_strategy_d(
-            day_id,device_type,active_type,channel_type,core_pv_division_uv,pv_division_uv,pid
-            ) VALUES('{day_id}','{device_type}','{active_type}','{channel_type}',{core_pv_division_uv},{pv_division_uv},'{pid}');""".format(
-            day_id=today_str, device_type=device_type,
-            active_type=active_type, channel_type=channel_type, core_pv_division_uv=core_pv_division_uv,pv_division_uv=pv_division_uv,pid=pid
-
-        )
-        print(instert_sql)
-        # cursor.execute("set names 'UTF8'")
-        res = cursor.execute(instert_sql)
-        db.commit()
-        print(res)
+    #
+    # collects_sql = """
+    # SELECT device_type,active_type,channel_type,ROUND(if(NVL(sum(uv),0) <> 0 ,NVL(sum(search_core_pv),0)/NVL(sum(uv),0) ,0),5) as core_pv_division_uv,
+    # ROUND(if(NVL(sum(uv),0) <> 0 ,NVL(sum(search_pv),0)/NVL(sum(uv),0) , 0),5) as pv_division_uv
+    # FROM data_table GROUP BY device_type,active_type,channel_type
+    # """
+    # finnal_df = spark.sql(collects_sql)
+    #
+    # finnal_df.show(1, False)
+    # sql_res = finnal_df.collect()
+    # for res in sql_res:
+    #     # print(res)
+    #     device_type = res.device_type
+    #     active_type = res.active_type
+    #     channel_type = res.channel_type
+    #     core_pv_division_uv = res.core_pv_division_uv
+    #     pv_division_uv = res.pv_division_uv
+    #     pid = hashlib.md5(
+    #         (today_str + device_type + active_type + channel_type).encode("utf8")).hexdigest()
+    #     instert_sql = """replace into search_strategy_d(
+    #         day_id,device_type,active_type,channel_type,core_pv_division_uv,pv_division_uv,pid
+    #         ) VALUES('{day_id}','{device_type}','{active_type}','{channel_type}',{core_pv_division_uv},{pv_division_uv},'{pid}');""".format(
+    #         day_id=today_str, device_type=device_type,
+    #         active_type=active_type, channel_type=channel_type, core_pv_division_uv=core_pv_division_uv,pv_division_uv=pv_division_uv,pid=pid
+    #
+    #     )
+    #     print(instert_sql)
+    #     # cursor.execute("set names 'UTF8'")
+    #     res = cursor.execute(instert_sql)
+    #     db.commit()
+    #     print(res)
 db.close()