necmo opened a new issue, #65220:
URL: https://github.com/apache/airflow/issues/65220
### Apache Airflow version
3.2.0
### What happened
DAG versions increase every ~30 seconds (on every scheduler parse cycle)
even though the DAG file has not changed. The `dag_version` table accumulates
hundreds of entries during a single DAG run.
By diffing two consecutive serialized DAG versions, I found the only
difference is the memory address in the `ArgNotSet` repr:
```diff
- "logical_date": "<airflow.serialization.definitions.notset.ArgNotSet
object at 0x7fe833fa8f20>"
+ "logical_date": "<airflow.serialization.definitions.notset.ArgNotSet
object at 0x7f115effa180>"
```
### What you think should happen instead
DAG version should remain stable when the DAG file and its dependencies have
not changed.
### How to reproduce
1. Create a DAG using `TriggerDagRunOperator` without explicitly setting
`logical_date`
2. Register the DAG with `default_args`
3. Observe the `dag_version` table growing every parse cycle (~30 seconds)
```python
from airflow.providers.standard.operators.trigger_dagrun import
TriggerDagRunOperator
default_args = {
"email_on_failure": True,
"email_on_retry": False,
}
@dag(default_args=default_args, ...)
def my_dag():
trigger = TriggerDagRunOperator(
task_id="trigger_other_dag",
trigger_dag_id="other_dag",
wait_for_completion=True,
# logical_date is NOT set → defaults to ArgNotSet
)
```
Verify with this query:
```sql
SELECT dv.version_number, sd.data
FROM dag_version dv
JOIN serialized_dag sd ON sd.dag_id = dv.dag_id AND sd.dag_version_id = dv.id
WHERE dv.dag_id = '<dag_id>'
ORDER BY dv.version_number DESC
LIMIT 2;
```
Diff the two `data` JSON values — the only difference is the `ArgNotSet`
memory address.
### Root cause
`ArgNotSet` (`airflow/serialization/definitions/notset.py`) has no custom
`__repr__`:
```python
class ArgNotSet:
"""Sentinel type for annotations, useful when None is not viable."""
```
While the primary `serialize()` path handles `ArgNotSet` via `is_arg_set()`
check, there are code paths (such as `default_args` dict values or certain
operator parameters) where `ArgNotSet` bypasses proper encoding and falls
through to `str()`/`repr()`, producing a memory-address-dependent string.
Since the scheduler runs `DagFileProcessor` as separate subprocesses, each
process allocates `ArgNotSet` at a different address → different serialized
JSON → new `dag_version` on every parse.
### Suggested fix
Add a stable `__repr__` to `ArgNotSet`:
```python
class ArgNotSet:
"""Sentinel type for annotations, useful when None is not viable."""
def __repr__(self) -> str:
return "NOTSET"
```
Alternatively (or additionally), ensure all serialization code paths handle
`ArgNotSet` via `is_arg_set()` before falling through to `str()`.
### Workaround
Explicitly set `logical_date=None` in `default_args` or on the operator to
prevent the `ArgNotSet` sentinel from being serialized.
### Related issues
- #56471 - Non-deterministic DAG serialization for dynamically generated DAGs
- #60868 - Reserialize results in multiple entries in serialized_dag table
- #59465 - S3DagBundle causes continuous DAG version increments
### Are you willing to submit PR?
Yes
### Operating System
Linux (Kubernetes)
### Versions of Apache Airflow Providers
`apache-airflow-providers-standard`
### Deployment
Kubernetes
### Deployment details
Airflow 3.2.0 on Kubernetes with default scheduler configuration.
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