time_task_ai_historical_missing_material.py 1.9 KB

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  1. from concurrent.futures import ThreadPoolExecutor
  2. import requests
  3. import datetime
  4. import json
  5. import pandas as pd
  6. from sqlalchemy import create_engine
  7. import uuid
  8. db_con_str = "mysql+pymysql://%s:%s@%s:%d/%s" % ("data", "hcst@2021", "139.186.27.96", 4000, "jeecg-boot")
  9. engine = create_engine(db_con_str, connect_args={'charset': 'utf8'})
  10. def send_request(account_id):
  11. url = 'http://139.186.27.96:31012/ai_historical_missing_material'
  12. request_data = json.dumps({"account_id": account_id})
  13. request = requests.post(url, request_data)
  14. return json.loads(request.text)
  15. # 0、 打印该定时任务被调用的时间
  16. unique_uuid = str(uuid.uuid4())
  17. print("***********", "补充历史遗漏素材-Start, unique_uuid = ", unique_uuid, datetime.datetime.now(), "**************")
  18. # 1、获取已有的账号
  19. sql = """
  20. select account_id from ctop_ai_kuaishou_advertiser_strategy where status = 1
  21. """
  22. acc_df = pd.read_sql(sql, engine)
  23. acc_list = [int(account_id) for account_id in acc_df['account_id'].unique()]
  24. print(acc_list)
  25. # 2、 5个账户一组进行发送请求
  26. batch_num = 5
  27. for i in range(0, len(acc_list), batch_num):
  28. if i + batch_num < len(acc_list):
  29. print(datetime.datetime.now(), tuple(acc_list[i:i + batch_num]))
  30. with ThreadPoolExecutor(max_workers=batch_num) as pool:
  31. results = pool.map(send_request, tuple(acc_list[i: i+batch_num]))
  32. for r in results:
  33. print('res = %s' % r)
  34. else:
  35. with ThreadPoolExecutor(max_workers=batch_num) as pool:
  36. print(datetime.datetime.now(), acc_list[i: len(acc_list)])
  37. results = pool.map(send_request, tuple(acc_list[i: len(acc_list)]))
  38. for r in results:
  39. print('res = %s' % r)
  40. # 3、 打印该定时任务结束的时间
  41. print("***********", "补充历史遗漏素材-End, unique_uuid = ", unique_uuid, datetime.datetime.now(), "**************")