[jira] [Created] (AIRFLOW-6648) Timeout Feature - Provided statistical solution to long running/stuck jobs and take appropriate actions

["Golokesh Patra (Jira)" <ji...@apache.org>]

Golokesh Patra created AIRFLOW-6648:
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             Summary: Timeout Feature - Provided statistical solution to long running/stuck jobs and take appropriate actions
                 Key: AIRFLOW-6648
                 URL: https://issues.apache.org/jira/browse/AIRFLOW-6648
             Project: Apache Airflow
          Issue Type: Improvement
          Components: aws, DAG, database, operators
    Affects Versions: 1.10.0
         Environment: AWS Linux AMI -  Ubuntu 18.04.1 LTS (GNU/Linux 4.15.0-1027-aws x86_64)
            Reporter: Golokesh Patra
            Assignee: Golokesh Patra
         Attachments: image-2020-01-27-17-07-51-822.png, image-2020-01-27-17-08-09-867.png, image-2020-01-27-17-08-33-088.png, image-2020-01-27-17-22-07-433.png, image2019-3-25_12-33-57.png

Sometimes, across different type of tasks/jobs ,
 one might encounter issues where airflow jobs/tasks get stuck while they are in running state.
 Such issues will cause - Pipeline being stuck for no reason stalling other jobs/tasks which will be a disaster when such issues happen on Production.

This particular improvement aims to not only improve upon the TIMEOUT logic already in airflow, but to make it more functional and automated.

*Diagrammatically Explanation of the solution -* 
!image-2020-01-27-17-22-07-433.png!

*Detailed Theoretical Explanation -* 

With increasing Data & Complexity of tasks/job , besides the increasing load, the chances of memory leaks/stuck jobs/some infrastructural issues etc may occur thereby creating some unwanted results.
 Maybe on some day there was more data which resulted in a steep jump in the duration of the job; otherwise, the growth is expected to be gradual.
 And sometimes, the Jobs get stuck because of various issues and often requires termination followed by a restart.
 So, we are trying to make a logic which will automatically decide whether to
 * _terminate the Job_
 * _Terminate and Restart_
 * _Terminate and Mark as a failure so that downstream jobs don't get triggered._
 * _Take no action and inform DevOps regarding the issue ( Manual Action )_
 So, I just want to know, statistically, what will be the effective way to achieve the above outcomes.

Lets Consider 2 Jobs X & Y.

Jobs related Info -
 !image-2020-01-27-17-07-51-822.png!

!image-2020-01-27-17-08-09-867.png!

Then I was thinking of having a New Table which would be structured as -

+Derived table-+ 
 !image-2020-01-27-17-08-33-088.png!

( The above Example is theoretical and actual implementation might differ )

*LIMITATION -* 
 # For now , we have only tested the above on EMR ( Personal Usecase )
 # Testing Pending for Databricks. ( Personal Usecase )

Please do suggest any other services where this needs/can be used.



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