业务类型汇总添加缓存

app/api/account/crud.py

+import json
 import math
 import threading
 import time
 from concurrent.futures.thread import ThreadPoolExecutor
 import pandas as pd
 from sqlalchemy.orm import Session
-from core.config.env import env
+from core.config.env import env, red
 from libs.business import TYPE_NAME, GUILD_NAME
 from libs.db_link import LinkMysql
 from libs.functions import get_now_timestamp, time_str_to_timestamp, \
@@ -750,6 +751,8 @@ class HomePageDisplay(object):
 
     def get_month_data(self):
 
+        is_data = red.get('account_sum-' + self.unique_tag + '-' + str(self.date))
+        if not is_data:
             acc_sql = "select unique_tag,uuid from fi_account"
             guild_sql = "select uuid from guild"
             count_sql = f"select count(id) as num from {self.date}"
@@ -777,15 +780,19 @@ class HomePageDisplay(object):
                     Logger(20).logger.info('没找到系统账户')
                     return [], 0, 0
                 assets_cond.append(f" uuid='{acc_uuid[0]}'")
-        ths_count = math.ceil(count[0]['num']/10000000)
+            ths_count = math.ceil(count[0]['num']/5000000)
             ths = []
             for x in range(ths_count):
-            assets_sql = f"select reference_type,type,sum(amount) as amount from {self.date} where {' and '.join(assets_cond)} GROUP BY reference_type,type LIMIT {x * 10000000},{(x+1) * 10000000}"
+                assets_sql = f"select reference_type,type,sum(cast(amount as FLOAT(3))) as amount from {self.date} where {' and '.join(assets_cond)} GROUP BY reference_type,type LIMIT {x * 5000000},{(x+1) * 5000000}"
                 ths.append(threading.Thread(target=self.th_task, args=[assets_sql]))
             for y in range(ths_count):
                 ths[y].start()
             for z in range(ths_count):
                 ths[z].join()
+            redis_dict = {"my_data": self.bus_data}
+            red.set('account_sum-' + self.unique_tag + '-' + str(self.date), json.dumps(redis_dict), 3600)
+        else:
+            self.bus_data = json.loads(is_data)['my_data']
         # 数据分类
         income = []
         outcome = []