yquaziii opened a new pull request, #72389:
URL: https://github.com/apache/airflow/pull/72389
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### Description
Currently, when a DAG reaches its `max_active_runs` limit, the scheduler
logs a generic message:
`DAG <dag_id> is at (or above) max_active_runs (<X> of <Y>), not creating
any more runs`
While this indicates that the limit has been reached, it does not provide
any context about *what* is occupying those active slots. To figure out why the
DAG is stuck, users currently have to manually navigate through the Airflow UI
or query the metadata database to find the hanging runs and tasks.
**This PR:**
Enhances the log message in `scheduler_job_runner.py`. When the
`max_active_runs` limit is hit, the scheduler will perform targeted queries to
fetch the specific `run_id`s and `task_id`s (along with their current states)
that are holding the active slots.
**New Log Output Example:**
`DAG test_dag is at (or above) max_active_runs (1 of 1), not creating any
more runs. Active Runs: scheduled__2023-10-18T09:55:00+00:00 | Active Tasks:
[sleep_task in scheduled__2023-10-18T09:55:00+00:00 (running)]`
**Implementation Note:**
To avoid unnecessary database load, the queries to fetch the active runs and
tasks are placed strictly *inside* the `if total_active_runs >=
dag.max_active_runs:` condition. This ensures there is zero performance
overhead during normal, healthy scheduling loops.
* Added unit tests in `test_scheduler_job_runner.py` to ensure the logging
fires with the correct format and data.
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