FrankYang0529 opened a new pull request, #72048:
URL: https://github.com/apache/airflow/pull/72048
## Why
* Other distributed executors follow the `BaseExecutor` workload pipeline
and record each workload in `self.running`. `KubernetesExecutor` does it in
`_process_workloads()`, so `slots_available` reflects what they have in flight.
* `EdgeExecutor` writes queued tasks straight into the `edge_job` table and
never records them in `self.running`. Nothing has written to that set since
#51009 removed the Airflow 2 dispatch path. `slots_available` is therefore
always `parallelism` and `[core] parallelism` has no effect on `EdgeExecutor`.
* The empty set also makes the `if job.key in self.running:` block in
`_purge_jobs()` unreachable, so the executor never reports task state to the
scheduler.
## How
* `queue_workload()` records the key of an `ExecuteTask` workload in
`self.running`.
* `_purge_jobs()` reconciles `self.running` against every job row of the
team, queued ones included. The previous reconciliation covered only the six
non-queued states, so a key added at queue time was dropped again on the next
`sync()`, before a worker could pick the job up. The `_get_tracked_job_keys`
read takes no row locks: an edge worker fetches its next job with `FOR UPDATE
SKIP LOCKED`, so locking queued rows here would make it come back empty.
* `EdgeJobModel.key` returns the `airflow.models` `TaskInstanceKey` instead
of the `airflow.sdk` one. `state_class_for_key()` dispatches with `isinstance`
and the two are unrelated classes, so `running_state()` would raise `TypeError`
as soon as `running` became non-empty.
## Verification
* `uv run --project providers/edge3 pytest providers/edge3/tests/unit`
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