我基本上是试图将给定的一组数据时间与预定义的阈值进行比较。最终目标是在列超过阈值时获取列中的行。
以下是我迄今为止尝试的代码:
#!/usr/bin/python
from datetime import datetime
import sys
import logging
import operator
import pymysql
import pandas as pd
db_endpoint = "awsendpoint"
db_username="user"
db_password="password"
db_name="database_name"
port = 3306
logger = logging.getLogger()
logger.setLevel(logging.INFO)
try:
conn = pymysql.connect(db_endpoint, user=db_username,
passwd=db_password, db=db_name, connect_timeout=5)
except:
logger.error("ERROR: Unexpected error: Could not connect to MySql instance.")
sys.exit()
logger.info("SUCCESS: Connection to RDS mysql instance succeeded")
cur=conn.cursor()
cur.execute("select talendjobname, taskstartdate from taskexecutionhistory where basicstatus = 'RUNNING'")
#OUTPUT is :
[('Prod_Adobe_Master_Process_v2', datetime.datetime(2018, 12, 17, 3, 30)), ('Prod_Sales_n_DG_Master_Process_v2', datetime.datetime(2018, 12, 17, 4, 0)), ('SDG_download_mail_attachments', datetime.datetime(2018, 12, 23, 3, 0, 1))]
aws = []
for row in cur:
aws.append(row)
# All working upto this.
aws = pd.DataFrame(aws)
aws_time = aws.iloc[:,1]
## I am getting the longer running jobs with respect to current time.
def days_between(d1):
# d1 = datetime.strptime(d1, "%Y-%m-%d")
return abs((datetime.now() - d1))
#Here is the problem
OUTPUT is a list of : 3Days 11 hours 30 mins,
2Days 10 hours 12 mins,
so on and so forth
我的阈值是8小时,我无法与此结果进行比较。我想得到一份只跨过这个门槛的工作清单。
我还尝试了其他一些方法:
time_passed = []
for i in range(0,len(aws_time.index)):
x = days_between(aws_time[i])
time_passed.append(x)
让我知道我遗漏了什么,或者是否有任何不同的方法。TimeDelta是我正在努力学习的主要课程。我试图处理字符串操作,但也无法将输出转换为字符串。