1

api/api_v1/endpoints/query.py

@@ -72,10 +72,19 @@ async def event_model(
         date_range = item['date_range']
         q['date_range'] = date_range
         df = await ckdb.query_dataframe(sql)
-        df.fillna(0,inplace=True)
+        df.fillna(0, inplace=True)
         if df.shape[0] == 0:
             continue
             # return schemas.Msg(code=0, msg='ok', data=[q])
+        if item['time_particle'] == 'total':
+            # for group, df_group in df.groupby(groupby):
+            #     df_group.reset_index(drop=True, inplace=True)
+            q['groups'].append(groupby)
+            q['values'].append(df['values'].to_list())
+            q['sum'].append(int(df['values'].sum()))
+            q['date_range']=[f'{i[0]}-{i[1]}' for i in df.set_index(['svrindex','name']).index]
+            res.append(q)
+            continue
 
         if groupby:
             # 有分组
@@ -103,7 +112,12 @@ async def event_model(
             df.sort_values('date', inplace=True)
             q['values'].append(df['values'].to_list())
             q['sum'].append(int(df['values'].sum()))
-        q['date_range'] = [d.strftime('%Y-%m-%d %H:%M:%S') for d in q['date_range']]
+        if item['time_particle'] in ('P1D', 'P1W'):
+            q['date_range'] = [d.strftime('%Y-%m-%d') for d in q['date_range']]
+        elif item['time_particle'] in ('P1M',):
+            q['date_range'] = [d.strftime('%Y-%m') for d in q['date_range']]
+        else:
+            q['date_range'] = [d.strftime('%Y-%m-%d %H:%M:%S') for d in q['date_range']]
         # q['eventNameDisplay']=item['event_name_display']
         res.append(q)
     return schemas.Msg(code=0, msg='ok', data=res)

core/config.py

@@ -262,6 +262,7 @@ class Settings(BaseSettings):
         "P1D": "D",
         "P1W": "W",
         "P1M": "MS",
+        "total": "D",
     }
 
     TIME_GRAIN_EXPRESSIONS = {
@@ -271,7 +272,8 @@ class Settings(BaseSettings):
         'PT10M': lambda col, zone: func.toStartOfTenMinutes(func.addHours(col, zone)).label('date'),
         'PT15M': lambda col, zone: func.toStartOfFifteenMinutes(func.addHours(col, zone)).label('date'),
         'PT1H': lambda col, zone: func.toStartOfHour(func.addHours(col, zone)).label('date'),
-        'P1D': lambda col, zone: func.toStartOfDay(func.addHours(col, zone)).label('date'),
+        'P1D': lambda col, zone: func.toDate(func.addHours(col, zone)).label('date'),
+        'total': lambda col, zone: func.toStartOfDay(func.addHours(col, zone)).label('date'),
         'P1W': lambda col, zone: func.toStartOfWeek(func.addHours(col, zone)).label('date'),
         'P1M': lambda col, zone: func.toStartOfMonth(func.addHours(col, zone)).label('date'),
     }

models/behavior_analysis.py

@@ -147,9 +147,10 @@ class BehaviorAnalysis:
         end_date: str = self.event_view.get('endTime')
         date_range = pd.date_range(start_date, end_date, freq=settings.PROPHET_TIME_GRAIN_MAP[self.time_particle],
                                    tz='UTC').tolist()
-        if self.time_particle in ('P1M','P1W'):
+        if self.time_particle in ('P1D', 'P1W', 'P1M'):
             date_range = [item.date() for item in date_range]
-
+            # start_date = date_range[0].strftime('%Y-%m-%d')
+            # end_date = date_range[-1].strftime('%Y-%m-%d')
 
         return start_date, end_date, date_range
 
@@ -284,8 +285,10 @@ class BehaviorAnalysis:
 
         for event in self.events:
             event_name_display = event.get('eventNameDisplay')
-            select_exprs = [
+            select_exprs = []
-                settings.TIME_GRAIN_EXPRESSIONS[self.time_particle](event_time_col, self.zone_time)]
+            if self.time_particle != 'total':
+                select_exprs.append(
+                    settings.TIME_GRAIN_EXPRESSIONS[self.time_particle](event_time_col, self.zone_time))
 
             base_where = [
                 func.addHours(event_time_col, self.zone_time) >= self.start_date,
@@ -342,7 +345,10 @@ class BehaviorAnalysis:
                     qry = sa.select(selectd).where(and_(*event_filter, *base_where))
 
             qry = qry.group_by(*select_exprs)
-            qry = qry.order_by(sa.Column('date'))
+            if self.time_particle != 'total':
+                qry = qry.order_by(sa.Column('date'))
+            else:
+                qry = qry.order_by(sa.Column('values').desc())
             qry = qry.limit(1000)
 
             sql = str(qry.compile(compile_kwargs={"literal_binds": True}))
@@ -351,7 +357,8 @@ class BehaviorAnalysis:
                          'groupby': [i.key for i in self.groupby],
                          'date_range': self.date_range,
                          'event_name': event_name_display or event_name,
-                         'format': format
+                         'format': format,
+                         'time_particle': self.time_particle
                          })
 
         return sqls
@@ -440,7 +447,7 @@ ORDER BY level
 
             qry = sa.select(event_date_col, *self.groupby, values_col) \
                 .where(and_(*where)) \
-                .group_by(event_date_col, *self.groupby, e_account_id_col)
+                .group_by(event_date_col, *self.groupby)
 
             sql = str(qry.compile(compile_kwargs={"literal_binds": True}))
             print(sql)