LourTV commented on issue #55768:
URL: https://github.com/apache/airflow/issues/55768#issuecomment-3360876034

   @wjddn279  In our case, the significant growth in the dag_version and 
serialized_dag tables was caused by the use of a dynamic start_date in the DAG 
definition (start_date = datetime.now() - timedelta(days=1)). This resulted in 
Airflow generating a different hash on each execution, thereby creating a new 
DAG version every time.
   
   After identifying this issue, we updated the DAG to use a fixed start_date. 
Since making this change, the number of DAG versions has stabilized and is no 
longer increasing.
   
   Following this adjustment, we restarted Airflow and monitored the system for 
several hours to assess whether the memory usage issue persisted. 
Unfortunately, the results remain the same, although Airflow is no longer 
generating excessive DAG versions, we continue to observe a gradual increase in 
RAM and swap usage over time, eventually leading to an out-of-memory condition.


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