Performance: Eager load dag_model in fetch_task_instances and optimize DagUnpausedDep

[rajat315315]

Description

Motivation

Addresses issue #70050 .

Eliminates the N+1 database queries storm when checking if DAGs are paused (DagUnpausedDep) in the scheduling loop. By utilizing SQLAlchemy joinedload for TI.dag_model when fetching task instances for a DAG run, the pause check can be resolved entirely in memory.

Proposed Changes

1. Query Optimization: Modified DagRun.fetch_task_instances in airflow/models/dagrun.py to add joinedload(TI.dag_model) to the select(TI) statement.
2. Dependency Check Update: Modified DagUnpausedDep._get_dep_statuses in airflow/ti_deps/deps/dag_unpaused_dep.py to use ti.dag_model.is_paused instead of running _is_dag_paused() database selects.

Verification

Ran benchmark tests on SQLite simulating a scheduler batch evaluation of 1,000 task instances:

• Before: 101 queries executed, taking 610.05 ms.
• After: 1 query executed, taking 320.95 ms (99% query count reduction, 1.90x speedup).
• Unit Tests: Passed successfully on airflow-core/tests/unit/models/test_dagrun.py.

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[rajat315315]

Benchmarking file attached.

benchmark_eager_loading.py

[SameerMesiah97]

The CI failures might have revealed a flaw in this implementation. If you look at this error:

AttributeError: 'NoneType' object has no attribute 'is_paused'

It looks like this change assumes ti.dag_model is always present, whereas previously _is_dag_paused() only relied on dag_id. Is there a reason we can guarantee the relationship is never None here, or does the dependency need to handle the missing relationship gracefully? Is it possible there are execution paths where ti.dag_model hasn't been eagerly loaded (or isn't present), making this optimization unsafe?

Theoretically, this change should improve performance. But I think the implemenation needs to be refined.

[rajat315315]

If ti.dag_model is not present or eagerly loaded (evaluates to None), it falls back to the database query in _is_dag_paused that was previously used. This preserves the performance optimization when ti.dag_model is present, while remaining perfectly safe in all other execution paths.