107 lines
3.5 KiB
Python
107 lines
3.5 KiB
Python
import json
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import pandas as pd
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from fastapi import APIRouter, Depends, Request
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import crud, schemas
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from api import deps
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from db.ckdb import get_ck_db, CKDrive
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from db.redisdb import get_redis_pool, RedisDrive
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from models import ToSql
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router = APIRouter()
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@router.post("/sql")
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async def query_sql(
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request: Request,
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data_in: schemas.Sql,
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ckdb: CKDrive = Depends(get_ck_db),
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current_user: schemas.UserDB = Depends(deps.get_current_user)
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) -> schemas.Msg:
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"""原 sql 查询 """
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data = await ckdb.execute(data_in.sql)
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return schemas.Msg(code=0, msg='ok', data=data)
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@router.post("/event_model_sql")
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async def event_model_sql(
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request: Request,
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game: str,
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data_in: schemas.CkQuery,
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ckdb: CKDrive = Depends(get_ck_db),
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rdb: RedisDrive = Depends(get_redis_pool),
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current_user: schemas.UserDB = Depends(deps.get_current_user)
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) -> schemas.Msg:
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""" 事件分析模型 sql"""
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columns_json = await rdb.get(f'{game}_event')
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event_columns = json.loads(columns_json)
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columns_json = await rdb.get(f'{game}_event')
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user_columns = json.loads(columns_json)
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to_sql = ToSql(data_in.dict(), game, event_columns.keys(), user_columns.keys())
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res = to_sql.get_sql_query_event_model()
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return schemas.Msg(code=0, msg='ok', data=res)
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@router.post("/event_model")
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async def event_model(
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request: Request,
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game: str,
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data_in: schemas.CkQuery,
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ckdb: CKDrive = Depends(get_ck_db),
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rdb: RedisDrive = Depends(get_redis_pool),
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current_user: schemas.UserDB = Depends(deps.get_current_user)
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) -> schemas.Msg:
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""" 事件分析"""
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columns_json = await rdb.get(f'{game}_event')
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event_columns = json.loads(columns_json)
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columns_json = await rdb.get(f'{game}_event')
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user_columns = json.loads(columns_json)
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to_sql = ToSql(data_in.dict(), game, event_columns.keys(), user_columns.keys())
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sqls = to_sql.get_sql_query_event_model()
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res = []
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for item in sqls:
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q = {
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'groups': [],
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'values': [],
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'event_name': item['event_name']
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}
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sql = item['sql']
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groupby = item['groupby'][1:]
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date_range = item['date_range']
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q['date_range'] = date_range
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df = await ckdb.query_dataframe(sql)
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if df.shape[0] == 0:
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return schemas.Msg(code=0, msg='ok', data=q)
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if groupby:
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# 有分组
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for group, df_group in df.groupby(groupby):
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df_group.reset_index(drop=True, inplace=True)
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q['groups'].append(group)
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concat_data = []
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for i in set(date_range) - set(df_group['date']):
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if len(groupby) > 1:
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concat_data.append((i, *group, 0))
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else:
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concat_data.append((i, group, 0))
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df_group = pd.concat([df_group, pd.DataFrame(concat_data, columns=df_group.columns)])
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df_group.sort_values('date', inplace=True)
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q['values'].append(df_group['values'].to_list())
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else:
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# 无分组
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concat_data = []
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for i in set(date_range) - set(df['date']):
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concat_data.append((i, 0))
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df = pd.concat([df, pd.DataFrame(concat_data, columns=df.columns)])
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q['values'].append(df['values'].to_list())
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q['date_range'] = [d.strftime('%Y-%m/%d %H:%M:%S') for d in q['date_range']]
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res.append(q)
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return schemas.Msg(code=0, msg='ok', data=res)
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