Optimize dynamic DAG updates to avoid loading large serialized DAGs #57592
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When updating dynamic DAGs (those without task instances), Airflow was loading the entire `SerializedDagModel` object from the database, which could contain megabytes of JSON data, just to update a few fields which was completely unnecesary. This change replaces the object-loading approach with a direct SQL UPDATE statement, significantly improving performance for deployments with large or frequently-changing dynamic DAGs. The optimization uses SQLAlchemy's update() construct to modify only the necessary columns (_data, _data_compressed, dag_hash) without fetching the existing row, reducing both database load and network transfer. Additionally, removed an unnecessary session.merge() call on dag_version, as the object is already tracked by the session after being loaded.
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Backport failed to create: v3-1-test. View the failure log Run details
You can attempt to backport this manually by running: cherry_picker 27c9b94 v3-1-testThis should apply the commit to the v3-1-test branch and leave the commit in conflict state marking After you have resolved the conflicts, you can continue the backport process by running: cherry_picker --continue |
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Oct 31, 2025
…57592) When updating dynamic DAGs (those without task instances), Airflow was loading the entire `SerializedDagModel` object from the database, which could contain megabytes of JSON data, just to update a few fields which was completely unnecesary. This change replaces the object-loading approach with a direct SQL UPDATE statement, significantly improving performance for deployments with large or frequently-changing dynamic DAGs. The optimization uses SQLAlchemy's update() construct to modify only the necessary columns (_data, _data_compressed, dag_hash) without fetching the existing row, reducing both database load and network transfer. Additionally, removed an unnecessary session.merge() call on dag_version, as the object is already tracked by the session after being loaded. (cherry picked from commit 27c9b94)
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When updating dynamic DAGs (those without task instances), Airflow was loading the entire
SerializedDagModelobject from the database, which could contain megabytes of JSON data, just to update a few fields which was completely unnecesary.This change replaces the object-loading approach with a direct SQL UPDATE statement, significantly improving performance for deployments with large or frequently-changing dynamic DAGs.
The optimization uses SQLAlchemy's
update()construct to modify only the necessary columns (_data,_data_compressed,dag_hash) without fetching the existing row, reducing both database load and network transfer.^ Add meaningful description above
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