Separate "public" Dataset class from SQLA model#25727
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ashb merged 4 commits intoapache:mainfrom Aug 16, 2022
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Importing all of SQLA can be very heavy weight (slowing import times), and it can also make it harder if libraries want to build on top of the Dataset concept. Not to mention that the interal-API/db-isolation AIP should mean we don't want to import any SQLA models directly in user code.
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Some tests are failing:
==== API postgres: 3 failures ====
tests/api_connexion/endpoints/test_dag_run_endpoint.py::test__get_upstream_dataset_events_no_prior: sqlalchemy.orm.exc.UnmappedInstanceError: Class 'airflow.datasets.Dataset' is not mapped
tests/api_connexion/endpoints/test_dag_run_endpoint.py::test__get_upstream_dataset_events_with_prior: sqlalchemy.orm.exc.UnmappedInstanceError: Class 'airflow.datasets.Dataset' is not mapped
tests/api_connexion/schemas/test_dataset_schema.py::TestDatasetSchema::test_serialize: ValueError: Use of List object with `schedule` param is only supported for List[Dataset].
==== Core postgres: 3 failures ====
tests/models/test_taskinstance.py::TestTaskInstance::test_outlet_datasets: TypeError: unsupported operand type(s) for |: '_GenericAlias' and 'NoneType'
tests/models/test_taskinstance.py::TestTaskInstance::test_outlet_datasets_failed: TypeError: unsupported operand type(s) for |: '_GenericAlias' and 'NoneType'
tests/models/test_taskinstance.py::TestTaskInstance::test_outlet_datasets_skipped: TypeError: unsupported operand type(s) for |: '_GenericAlias' and 'NoneType'
==== Other postgres: 3 failures ====
tests/lineage/test_lineage.py::TestLineage::test_lineage: AttributeError: 'dict' object has no attribute '_context'
tests/lineage/test_lineage.py::TestLineage::test_lineage_render: AttributeError: 'dict' object has no attribute '_context'
tests/lineage/test_lineage.py::TestLineage::test_lineage_is_sent_to_backend: AttributeError: 'dict' object has no attribute '_context'
==== Providers postgres: 1 failure ====
tests/providers/papermill/operators/test_papermill.py::TestPapermillOperator::test_execute: AttributeError: 'dict' object has no attribute '_context'
==== WWW postgres: 1 failure ====
tests/www/views/test_views_grid.py::test_next_run_datasets: TypeError: __init__() got an unexpected keyword argument 'id'
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Overzealous refactoring ;) will fix those in the morning |
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mpeteuil
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Feb 21, 2024
Currently DAGs accept a [`Collection["Dataset"]`](https://github.com/apache/airflow/blob/0c02ead4d8a527cbf0a916b6344f255c520e637f/airflow/models/dag.py#L171) as an option for the `schedule`, but that collection cannot be a `set` because Datasets are not a hashable type. The interesting thing is that [the `DatasetModel` is actually already hashable](https://github.com/apache/airflow/blob/dec78ab3f140f35e507de825327652ec24d03522/airflow/models/dataset.py#L93-L100), so this introduces a bit of duplication since it's the same implementation. However, Airflow users are primarily interfacing with `Dataset`, not `DatasetModel` so I think it makes sense for `Dataset` to be hashable. I'm not sure how to square the duplication or what `__eq__` and `__hash__` provide for `DatasetModel` though. There was discussion on the original PR that created the `Dataset` (apache#24613) about whether to create two classes or one. In that discussion @kaxil mentioned: > I would slightly favour a separate `DatasetModel` and `Dataset` so `Dataset` becomes an extensible class, and `DatasetModel` just stores the info about the class. So users don't need to care about SQLAlchmey stuff when extending it. That first PR created the `Dataset` model as both SQLAlchemy and user space class though. It wasn't until later on (apache#25727) that the `DatasetModel` got broken out from `Dataset` and one became two. That provides a bit of background on why they both exist for anyone reading this who is curious.
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Feb 21, 2024
Currently DAGs accept a [`Collection["Dataset"]`](https://github.com/apache/airflow/blob/0c02ead4d8a527cbf0a916b6344f255c520e637f/airflow/models/dag.py#L171) as an option for the `schedule`, but that collection cannot be a `set` because Datasets are not a hashable type. The interesting thing is that [the `DatasetModel` is actually already hashable](https://github.com/apache/airflow/blob/dec78ab3f140f35e507de825327652ec24d03522/airflow/models/dataset.py#L93-L100), so this introduces a bit of duplication since it's the same implementation. However, Airflow users are primarily interfacing with `Dataset`, not `DatasetModel` so I think it makes sense for `Dataset` to be hashable. I'm not sure how to square the duplication or what `__eq__` and `__hash__` provide for `DatasetModel` though. There was discussion on the original PR that created the `Dataset` (#24613) about whether to create two classes or one. In that discussion @kaxil mentioned: > I would slightly favour a separate `DatasetModel` and `Dataset` so `Dataset` becomes an extensible class, and `DatasetModel` just stores the info about the class. So users don't need to care about SQLAlchmey stuff when extending it. That first PR created the `Dataset` model as both SQLAlchemy and user space class though. It wasn't until later on (#25727) that the `DatasetModel` got broken out from `Dataset` and one became two. That provides a bit of background on why they both exist for anyone reading this who is curious.
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Jul 19, 2024
Currently DAGs accept a [`Collection["Dataset"]`](https://github.com/apache/airflow/blob/0c02ead4d8a527cbf0a916b6344f255c520e637f/airflow/models/dag.py#L171) as an option for the `schedule`, but that collection cannot be a `set` because Datasets are not a hashable type. The interesting thing is that [the `DatasetModel` is actually already hashable](https://github.com/apache/airflow/blob/dec78ab3f140f35e507de825327652ec24d03522/airflow/models/dataset.py#L93-L100), so this introduces a bit of duplication since it's the same implementation. However, Airflow users are primarily interfacing with `Dataset`, not `DatasetModel` so I think it makes sense for `Dataset` to be hashable. I'm not sure how to square the duplication or what `__eq__` and `__hash__` provide for `DatasetModel` though. There was discussion on the original PR that created the `Dataset` (apache/airflow#24613) about whether to create two classes or one. In that discussion @kaxil mentioned: > I would slightly favour a separate `DatasetModel` and `Dataset` so `Dataset` becomes an extensible class, and `DatasetModel` just stores the info about the class. So users don't need to care about SQLAlchmey stuff when extending it. That first PR created the `Dataset` model as both SQLAlchemy and user space class though. It wasn't until later on (apache/airflow#25727) that the `DatasetModel` got broken out from `Dataset` and one became two. That provides a bit of background on why they both exist for anyone reading this who is curious. GitOrigin-RevId: d5d6f4b1885a99f5a5e3063dbd54e1f32700c1df
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Sep 20, 2024
Currently DAGs accept a [`Collection["Dataset"]`](https://github.com/apache/airflow/blob/0c02ead4d8a527cbf0a916b6344f255c520e637f/airflow/models/dag.py#L171) as an option for the `schedule`, but that collection cannot be a `set` because Datasets are not a hashable type. The interesting thing is that [the `DatasetModel` is actually already hashable](https://github.com/apache/airflow/blob/dec78ab3f140f35e507de825327652ec24d03522/airflow/models/dataset.py#L93-L100), so this introduces a bit of duplication since it's the same implementation. However, Airflow users are primarily interfacing with `Dataset`, not `DatasetModel` so I think it makes sense for `Dataset` to be hashable. I'm not sure how to square the duplication or what `__eq__` and `__hash__` provide for `DatasetModel` though. There was discussion on the original PR that created the `Dataset` (apache/airflow#24613) about whether to create two classes or one. In that discussion @kaxil mentioned: > I would slightly favour a separate `DatasetModel` and `Dataset` so `Dataset` becomes an extensible class, and `DatasetModel` just stores the info about the class. So users don't need to care about SQLAlchmey stuff when extending it. That first PR created the `Dataset` model as both SQLAlchemy and user space class though. It wasn't until later on (apache/airflow#25727) that the `DatasetModel` got broken out from `Dataset` and one became two. That provides a bit of background on why they both exist for anyone reading this who is curious. GitOrigin-RevId: d5d6f4b1885a99f5a5e3063dbd54e1f32700c1df
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Currently DAGs accept a [`Collection["Dataset"]`](https://github.com/apache/airflow/blob/0c02ead4d8a527cbf0a916b6344f255c520e637f/airflow/models/dag.py#L171) as an option for the `schedule`, but that collection cannot be a `set` because Datasets are not a hashable type. The interesting thing is that [the `DatasetModel` is actually already hashable](https://github.com/apache/airflow/blob/dec78ab3f140f35e507de825327652ec24d03522/airflow/models/dataset.py#L93-L100), so this introduces a bit of duplication since it's the same implementation. However, Airflow users are primarily interfacing with `Dataset`, not `DatasetModel` so I think it makes sense for `Dataset` to be hashable. I'm not sure how to square the duplication or what `__eq__` and `__hash__` provide for `DatasetModel` though. There was discussion on the original PR that created the `Dataset` (apache/airflow#24613) about whether to create two classes or one. In that discussion @kaxil mentioned: > I would slightly favour a separate `DatasetModel` and `Dataset` so `Dataset` becomes an extensible class, and `DatasetModel` just stores the info about the class. So users don't need to care about SQLAlchmey stuff when extending it. That first PR created the `Dataset` model as both SQLAlchemy and user space class though. It wasn't until later on (apache/airflow#25727) that the `DatasetModel` got broken out from `Dataset` and one became two. That provides a bit of background on why they both exist for anyone reading this who is curious. GitOrigin-RevId: d5d6f4b1885a99f5a5e3063dbd54e1f32700c1df
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Currently DAGs accept a [`Collection["Dataset"]`](https://github.com/apache/airflow/blob/0c02ead4d8a527cbf0a916b6344f255c520e637f/airflow/models/dag.py#L171) as an option for the `schedule`, but that collection cannot be a `set` because Datasets are not a hashable type. The interesting thing is that [the `DatasetModel` is actually already hashable](https://github.com/apache/airflow/blob/dec78ab3f140f35e507de825327652ec24d03522/airflow/models/dataset.py#L93-L100), so this introduces a bit of duplication since it's the same implementation. However, Airflow users are primarily interfacing with `Dataset`, not `DatasetModel` so I think it makes sense for `Dataset` to be hashable. I'm not sure how to square the duplication or what `__eq__` and `__hash__` provide for `DatasetModel` though. There was discussion on the original PR that created the `Dataset` (apache/airflow#24613) about whether to create two classes or one. In that discussion @kaxil mentioned: > I would slightly favour a separate `DatasetModel` and `Dataset` so `Dataset` becomes an extensible class, and `DatasetModel` just stores the info about the class. So users don't need to care about SQLAlchmey stuff when extending it. That first PR created the `Dataset` model as both SQLAlchemy and user space class though. It wasn't until later on (apache/airflow#25727) that the `DatasetModel` got broken out from `Dataset` and one became two. That provides a bit of background on why they both exist for anyone reading this who is curious. GitOrigin-RevId: d5d6f4b1885a99f5a5e3063dbd54e1f32700c1df
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Currently DAGs accept a [`Collection["Dataset"]`](https://github.com/apache/airflow/blob/0c02ead4d8a527cbf0a916b6344f255c520e637f/airflow/models/dag.py#L171) as an option for the `schedule`, but that collection cannot be a `set` because Datasets are not a hashable type. The interesting thing is that [the `DatasetModel` is actually already hashable](https://github.com/apache/airflow/blob/dec78ab3f140f35e507de825327652ec24d03522/airflow/models/dataset.py#L93-L100), so this introduces a bit of duplication since it's the same implementation. However, Airflow users are primarily interfacing with `Dataset`, not `DatasetModel` so I think it makes sense for `Dataset` to be hashable. I'm not sure how to square the duplication or what `__eq__` and `__hash__` provide for `DatasetModel` though. There was discussion on the original PR that created the `Dataset` (apache/airflow#24613) about whether to create two classes or one. In that discussion @kaxil mentioned: > I would slightly favour a separate `DatasetModel` and `Dataset` so `Dataset` becomes an extensible class, and `DatasetModel` just stores the info about the class. So users don't need to care about SQLAlchmey stuff when extending it. That first PR created the `Dataset` model as both SQLAlchemy and user space class though. It wasn't until later on (apache/airflow#25727) that the `DatasetModel` got broken out from `Dataset` and one became two. That provides a bit of background on why they both exist for anyone reading this who is curious. GitOrigin-RevId: d5d6f4b1885a99f5a5e3063dbd54e1f32700c1df
kosteev
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Sep 21, 2025
Currently DAGs accept a [`Collection["Dataset"]`](https://github.com/apache/airflow/blob/0c02ead4d8a527cbf0a916b6344f255c520e637f/airflow/models/dag.py#L171) as an option for the `schedule`, but that collection cannot be a `set` because Datasets are not a hashable type. The interesting thing is that [the `DatasetModel` is actually already hashable](https://github.com/apache/airflow/blob/dec78ab3f140f35e507de825327652ec24d03522/airflow/models/dataset.py#L93-L100), so this introduces a bit of duplication since it's the same implementation. However, Airflow users are primarily interfacing with `Dataset`, not `DatasetModel` so I think it makes sense for `Dataset` to be hashable. I'm not sure how to square the duplication or what `__eq__` and `__hash__` provide for `DatasetModel` though. There was discussion on the original PR that created the `Dataset` (apache/airflow#24613) about whether to create two classes or one. In that discussion @kaxil mentioned: > I would slightly favour a separate `DatasetModel` and `Dataset` so `Dataset` becomes an extensible class, and `DatasetModel` just stores the info about the class. So users don't need to care about SQLAlchmey stuff when extending it. That first PR created the `Dataset` model as both SQLAlchemy and user space class though. It wasn't until later on (apache/airflow#25727) that the `DatasetModel` got broken out from `Dataset` and one became two. That provides a bit of background on why they both exist for anyone reading this who is curious. GitOrigin-RevId: d5d6f4b1885a99f5a5e3063dbd54e1f32700c1df
kosteev
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Oct 19, 2025
Currently DAGs accept a [`Collection["Dataset"]`](https://github.com/apache/airflow/blob/0c02ead4d8a527cbf0a916b6344f255c520e637f/airflow/models/dag.py#L171) as an option for the `schedule`, but that collection cannot be a `set` because Datasets are not a hashable type. The interesting thing is that [the `DatasetModel` is actually already hashable](https://github.com/apache/airflow/blob/dec78ab3f140f35e507de825327652ec24d03522/airflow/models/dataset.py#L93-L100), so this introduces a bit of duplication since it's the same implementation. However, Airflow users are primarily interfacing with `Dataset`, not `DatasetModel` so I think it makes sense for `Dataset` to be hashable. I'm not sure how to square the duplication or what `__eq__` and `__hash__` provide for `DatasetModel` though. There was discussion on the original PR that created the `Dataset` (apache/airflow#24613) about whether to create two classes or one. In that discussion @kaxil mentioned: > I would slightly favour a separate `DatasetModel` and `Dataset` so `Dataset` becomes an extensible class, and `DatasetModel` just stores the info about the class. So users don't need to care about SQLAlchmey stuff when extending it. That first PR created the `Dataset` model as both SQLAlchemy and user space class though. It wasn't until later on (apache/airflow#25727) that the `DatasetModel` got broken out from `Dataset` and one became two. That provides a bit of background on why they both exist for anyone reading this who is curious. GitOrigin-RevId: d5d6f4b1885a99f5a5e3063dbd54e1f32700c1df
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Importing all of SQLA can be very heavy weight (slowing import times),
and it can also make it harder if libraries want to build on top of the
Dataset concept.
Not to mention that the interal-API/db-isolation AIP should mean we
don't want to import any SQLA models directly in user code.