seanmuth opened a new pull request, #68305:
URL: https://github.com/apache/airflow/pull/68305
`VaultBackend.get_connection()` constructed the returned `Connection` using
`airflow.models.connection.Connection` — the SQLAlchemy ORM model. In
Airflow 3,
instantiating this model triggers lazy mapper initialisation for the *entire*
Airflow model registry. In `PythonVirtualenvOperator` subprocesses (and other
isolated task contexts), `DagModel` has not been imported when the secrets
backend is consulted, so `DagScheduleAssetNameReference` cannot resolve its
`'DagModel'` relationship string, raising:
```
sqlalchemy.exc.InvalidRequestError: One or more mappers failed to initialize
…
Triggering mapper: 'mapped class
DagScheduleAssetNameReference->dag_schedule_asset_name_reference'.
Original exception was: … expression 'DagModel' failed to locate a name
('DagModel').
```
Because `context.py`'s secrets-backend loop catches all exceptions at `DEBUG`
level (without `exc_info`), this error is silently swallowed. The connection
appears undefined even when Vault returns a valid `200` response with the
correct data.
**Fix:** switch to `airflow.providers.common.compat.sdk.Connection`, which
resolves to the Pydantic SDK `Connection` in Airflow 3 (no SQLAlchemy mapper
involvement) and falls back to the SQLAlchemy model in Airflow 2. For the
URI path, use `Connection.from_uri()` guarded by `AIRFLOW_V_3_0_PLUS` to
handle both Airflow versions correctly.
Two new regression tests verify that `get_connection()` returns an instance
of
the SDK `Connection` type rather than the SQLAlchemy ORM model.
---
##### Was generative AI tooling used to co-author this PR?
- [X] Yes — Claude Code (Sonnet 4.6)
Generated-by: Claude Code (Sonnet 4.6) following [the
guidelines](https://github.com/apache/airflow/blob/main/contributing-docs/05_pull_requests.rst#gen-ai-assisted-contributions)
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