superset2/tests/unit_tests/dao/dataset_test.py

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import copy
from datetime import datetime
from typing import Any
from unittest.mock import MagicMock, patch

import pytest
from freezegun import freeze_time
from sqlalchemy.orm.session import Session

from superset.daos.base import BaseDAO
from superset.daos.dataset import DatasetDAO
from superset.sql.parse import Table


def test_validate_update_uniqueness(session: Session) -> None:
    """
    Test the `validate_update_uniqueness` static method.

    In particular, allow datasets with the same name in the same database as long as they
    are in different schemas
    """  # noqa: E501
    from superset import db
    from superset.connectors.sqla.models import SqlaTable
    from superset.models.core import Database

    SqlaTable.metadata.create_all(session.get_bind())

    database = Database(
        database_name="my_db",
        sqlalchemy_uri="sqlite://",
    )
    dataset1 = SqlaTable(
        table_name="my_dataset",
        schema="main",
        database=database,
    )
    dataset2 = SqlaTable(
        table_name="my_dataset",
        schema="dev",
        database=database,
    )
    db.session.add_all([database, dataset1, dataset2])
    db.session.flush()

    assert (
        DatasetDAO.validate_update_uniqueness(
            database=database,
            table=Table(dataset1.table_name, dataset1.schema),
            dataset_id=dataset1.id,
        )
        is True
    )

    assert (
        DatasetDAO.validate_update_uniqueness(
            database=database,
            table=Table(dataset1.table_name, dataset2.schema),
            dataset_id=dataset1.id,
        )
        is False
    )

    assert (
        DatasetDAO.validate_update_uniqueness(
            database=database,
            table=Table(dataset1.table_name),
            dataset_id=dataset1.id,
        )
        is True
    )


def test_logical_duplicate_catalog_predicate_is_null_aware(session: Session) -> None:
    """A row stored with ``catalog = NULL`` is the default catalog, so a probe
    normalized to the database default must still match it.

    Before the null-aware predicate the stored ``catalog`` was compared as-is,
    so a ``catalog = NULL`` row and a ``catalog = <default>`` probe were treated
    as different physical tables and a default-catalog twin slipped through.
    """
    from superset import db
    from superset.connectors.sqla.models import SqlaTable
    from superset.models.core import Database

    SqlaTable.metadata.create_all(session.get_bind())

    database = Database(database_name="cat_db", sqlalchemy_uri="sqlite://")
    stored_null = SqlaTable(
        table_name="t", schema="main", catalog=None, database=database
    )
    db.session.add_all([database, stored_null])
    db.session.flush()

    # Pretend the database exposes a non-NULL default catalog so the probe
    # normalizes to it; the NULL-stored row must still be recognized as a twin.
    with patch.object(Database, "get_default_catalog", return_value="default_cat"):
        probe = SqlaTable(
            table_name="t", schema="main", catalog="default_cat", database=database
        )
        db.session.add(probe)
        db.session.flush()
        assert DatasetDAO.has_active_logical_duplicate(probe) is True

        # A genuinely different (non-default) catalog is not a twin.
        other = SqlaTable(
            table_name="t", schema="main", catalog="other_cat", database=database
        )
        db.session.add(other)
        db.session.flush()
        assert DatasetDAO.has_active_logical_duplicate(other) is False


def test_find_soft_deleted_logical_duplicate(session: Session) -> None:
    """The importer create-path helper returns a soft-deleted twin (despite the
    visibility filter) and ignores active rows."""
    from superset import db
    from superset.connectors.sqla.models import SqlaTable
    from superset.models.core import Database

    SqlaTable.metadata.create_all(session.get_bind())

    database = Database(database_name="sd_db", sqlalchemy_uri="sqlite://")
    twin = SqlaTable(table_name="t", schema="main", database=database)
    db.session.add_all([database, twin])
    db.session.flush()

    table = Table("t", "main")

    # Active row: not a soft-deleted twin.
    assert DatasetDAO.find_soft_deleted_logical_duplicate(database, table) is None

    # Soft-delete it: now found despite the visibility filter.
    twin.deleted_at = datetime(2026, 1, 1, 12, 0, 0)
    db.session.flush()
    found = DatasetDAO.find_soft_deleted_logical_duplicate(database, table)
    assert found is not None
    assert found.id == twin.id

    # Catalog mismatch: a soft-deleted row in a *different* (non-default)
    # catalog is not the same physical table, so the null-aware predicate must
    # exclude it. (sqlite has no default catalog, so an explicit "other_cat"
    # row must only match an "other_cat" probe.)
    other_catalog_twin = SqlaTable(
        table_name="t2",
        schema="main",
        catalog="other_cat",
        database=database,
        deleted_at=datetime(2026, 1, 1, 12, 0, 0),
    )
    db.session.add(other_catalog_twin)
    db.session.flush()
    assert (
        DatasetDAO.find_soft_deleted_logical_duplicate(
            database, Table("t2", "main", "my_cat")
        )
        is None
    )
    # Same explicit catalog: found.
    same_catalog = DatasetDAO.find_soft_deleted_logical_duplicate(
        database, Table("t2", "main", "other_cat")
    )
    assert same_catalog is not None
    assert same_catalog.id == other_catalog_twin.id


@freeze_time("2025-01-01 00:00:00")
@patch.object(DatasetDAO, "update_columns")
@patch.object(DatasetDAO, "update_metrics")
@patch.object(BaseDAO, "update")
@pytest.mark.parametrize(
    "attributes,expected_attributes",
    [
        (
            {
                "columns": [{"id": 1, "name": "col1"}],
                "metrics": [{"id": 1, "name": "metric1"}],
            },
            {"changed_on": datetime(2025, 1, 1, 0, 0, 0)},
        ),
        (
            {
                "columns": [{"id": 1, "name": "col1"}],
                "metrics": [{"id": 1, "name": "metric1"}],
                "description": "test description",
            },
            {
                "description": "test description",
                "changed_on": datetime(2025, 1, 1, 0, 0, 0),
            },
        ),
        (
            {
                "columns": [{"id": 1, "name": "col1"}],
            },
            {"changed_on": datetime(2025, 1, 1, 0, 0, 0)},
        ),
        (
            {
                "columns": [{"id": 1, "name": "col1"}],
                "description": "test description",
            },
            {
                "description": "test description",
                "changed_on": datetime(2025, 1, 1, 0, 0, 0),
            },
        ),
        (
            {
                "metrics": [{"id": 1, "name": "metric1"}],
            },
            {"changed_on": datetime(2025, 1, 1, 0, 0, 0)},
        ),
        (
            {
                "metrics": [{"id": 1, "name": "metric1"}],
                "description": "test description",
            },
            {
                "description": "test description",
                "changed_on": datetime(2025, 1, 1, 0, 0, 0),
            },
        ),
        (
            {"description": "test description"},
            {"description": "test description"},
        ),
    ],
)
def test_update_dataset_related_metadata_updates_changed_on(
    base_update_mock: MagicMock,
    update_metrics_mock: MagicMock,
    update_columns_mock: MagicMock,
    attributes: dict[str, Any],
    expected_attributes: dict[str, Any],
) -> None:
    """
    Test that the changed_on property is updated when a metric or column is updated.
    """
    item = MagicMock()
    DatasetDAO.update(item, copy.deepcopy(attributes))

    if "columns" in attributes:
        update_columns_mock.assert_called_once_with(
            item, attributes["columns"], override_columns=False
        )
    else:
        update_columns_mock.assert_not_called()

    if "metrics" in attributes:
        update_metrics_mock.assert_called_once_with(item, attributes["metrics"])
    else:
        update_metrics_mock.assert_not_called()

    base_update_mock.assert_called_once_with(item, expected_attributes)


def _mock_dataset(catalog: str | None, default_catalog: str) -> MagicMock:
    """A SqlaTable-shaped mock with controllable catalog values."""
    model = MagicMock()
    model.id = 1
    model.database_id = 7
    model.schema = "public"
    model.table_name = "users"
    model.catalog = catalog
    model.database.get_default_catalog.return_value = default_catalog
    return model


def test_has_active_logical_duplicate_normalizes_unset_catalog(
    app_context: None,
) -> None:
    """A row stored with ``catalog = None`` is matched against the database
    default catalog, the same rule ``validate_uniqueness`` applies.

    This is the gap the soft-delete reviews flagged: restore/re-import
    compared the raw stored ``catalog`` while create/update normalized it, so
    a soft-deleted ``catalog = NULL`` row and an active default-catalog twin
    could be treated as different physical tables.
    """
    model = _mock_dataset(catalog=None, default_catalog="default_cat")

    with patch("superset.daos.dataset.db") as mock_db:
        mock_db.session.query.return_value.filter.return_value.first.return_value = None

        assert DatasetDAO.has_active_logical_duplicate(model) is False

    # Normalization must consult the database default when catalog is unset.
    model.database.get_default_catalog.assert_called_once()


def test_has_active_logical_duplicate_keeps_explicit_catalog(
    app_context: None,
) -> None:
    """An explicit catalog is matched exactly, but the database default is still
    consulted: the null-aware predicate needs it to decide whether the explicit
    catalog *is* the default (and should therefore also match ``catalog IS
    NULL`` rows)."""
    model = _mock_dataset(catalog="explicit_cat", default_catalog="default_cat")

    with patch("superset.daos.dataset.db") as mock_db:
        mock_db.session.query.return_value.filter.return_value.first.return_value = None

        assert DatasetDAO.has_active_logical_duplicate(model) is False

    model.database.get_default_catalog.assert_called_once()


def test_has_active_logical_duplicate_true_when_twin_found(
    app_context: None,
) -> None:
    """A matching active row makes the helper report a duplicate."""
    model = _mock_dataset(catalog="explicit_cat", default_catalog="default_cat")

    with patch("superset.daos.dataset.db") as mock_db:
        mock_db.session.query.return_value.filter.return_value.first.return_value = (
            MagicMock()
        )

        assert DatasetDAO.has_active_logical_duplicate(model) is True


def test_has_active_logical_duplicate_never_bypasses_visibility(
    app_context: None,
) -> None:
    """Contract tripwire for the docstring's do-not-add-the-bypass clause.

    The helper's active-rows-only semantics come from the SoftDeleteMixin
    listener; wrapping the query in ``skip_visibility_filter`` — the file's
    dominant pattern, so a plausible future "consistency" edit — would
    broaden the check to soft-deleted rows and silently refuse legitimate
    restores of legacy twin pairs. The suite stays green under that
    mutation without this pin.
    """
    model = _mock_dataset(catalog=None, default_catalog="default_cat")

    with (
        patch("superset.daos.dataset.db") as mock_db,
        patch("superset.models.helpers.skip_visibility_filter") as bypass_spy,
    ):
        mock_db.session.query.return_value.filter.return_value.first.return_value = None
        DatasetDAO.has_active_logical_duplicate(model)

    bypass_spy.assert_not_called()