[GitHub] [airflow] FredericoCoelhoNunes edited a comment on issue #9975: max_active_runs = 1 can still create multiple active execution runs

[GitBox <gi...@apache.org>]

FredericoCoelhoNunes edited a comment on issue #9975:
URL: https://github.com/apache/airflow/issues/9975#issuecomment-790692853


   @zachliu Happy to hear you were able to create a stable API! Unfortunately I am not able to: when I use Python to select a failed DAG run, and clear all its task instances, for some reason it triggers some process in the scheduler and it gradually sets all other DAG runs to the "Running" state as well, over the course of a few minutes. Curiously, this doesn't happen if I clear the state of a single run in the UI.
   
   Here's the code I was trying to use:
   ```
   from airflow.models.dagrun import DagRun
   from airflow.models.taskinstance import clear_task_instances
   from airflow import settings
   
   session = settings.Session()
   
   # Around 30
   failed_dag_runs = DagRun.find(
       dag_id=DAG_ID,
       state='failed',
       session=session
   )
   
   for i, dag_run in enumerate(failed_dag_runs):
       run_id = dag_run.run_id
       task_instances = dag_run.get_task_instances(session=session)
       clear_task_instances(
           task_instances,
           session
       )
       session.commit()
       
       state = ""
       while state != 'success':
           <here I was checking the DAG run state, but even before it left this loop, it somehow clears the remaining DAG runs>
   ```
   
   I think Airflow really wasn't developed with this sort of "low level" usage in mind, as I feel that its behavior is hard to predict. I guess I will stick with manually clearing the runs one by one until this problem is fixed.


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