removing unnecessary fix

Signed-off-by: Cortland Goffena <30168413+cmgoffena13@users.noreply.github.com>

Author: cmgoffena13

sqlmesh/core/engine_adapter/databricks.py

@@ -31,34 +31,6 @@
 logger = logging.getLogger(__name__)
 
 
-def _to_pandas(table: t.Any) -> pd.DataFrame:
-    """Convert a databricks-sql-connector fetchall_arrow() result to pandas.
-
-    Connector 4.x makes PyArrow optional. When PyArrow cannot be imported (PyArrow 25
-    requires NumPy >= 2), fetchall_arrow() returns a ColumnTable with ``column_names``
-    and ``column_table`` instead of a PyArrow table.
-    """
-    import pandas as pd
-
-    to_pandas = getattr(table, "to_pandas", None)
-    if callable(to_pandas):
-        return to_pandas()
-
-    column_names = getattr(table, "column_names", None)
-    column_table = getattr(table, "column_table", None)
-    if column_names is None or column_table is None:
-        raise SQLMeshError(
-            f"Unexpected Databricks fetchall_arrow() result {type(table).__name__!r} "
-            "with no to_pandas(). PyArrow may be missing or incompatible with the "
-            "installed NumPy (PyArrow 25+ requires NumPy >= 2)."
-        )
-
-    # Build from rows so duplicate names are kept. A dict would collapse them,
-    # which is not what PyArrow's to_pandas() does.
-    columns = [list(values) for values in column_table]
-    return pd.DataFrame(list(zip(*columns)), columns=list(column_names))
-
-
 def _query_tags(
     query_tags: t.Optional[t.Union[exp.Expr, str, int, float, bool]],
 ) -> t.Optional[t.Dict[str, t.Optional[str]]]:
@@ -305,7 +277,7 @@ def _fetch_native_df(
                 query, quote_identifiers=quote_identifiers
             )
         self.execute(query)
-        return _to_pandas(self.cursor.fetchall_arrow())
+        return self.cursor.fetchall_arrow().to_pandas()
 
     def fetchdf(
         self, query: t.Union[exp.Expr, str], quote_identifiers: bool = False

tests/core/engine_adapter/test_databricks.py

@@ -837,76 +837,3 @@ def test_databricks_connect_cluster_id_not_overridden_by_falsy_serverless(
         token="mytoken",
         cluster_id="0123-456789-mycluster",
     )
-
-
-def test_fetch_native_df_uses_arrow_to_pandas(make_mocked_engine_adapter: t.Callable):
-    adapter = make_mocked_engine_adapter(DatabricksEngineAdapter, default_catalog="test_catalog")
-    arrow_table = pd.DataFrame({"name": ["orders"]})
-    fetchall_arrow = adapter.cursor.fetchall_arrow
-    fetchall_arrow.return_value.to_pandas.return_value = arrow_table
-
-    df = adapter._fetch_native_df("SELECT 1")
-
-    assert df.to_dict(orient="records") == [{"name": "orders"}]
-    fetchall_arrow.return_value.to_pandas.assert_called_once_with()
-
-
-def test_fetch_native_df_column_table_without_to_pandas(make_mocked_engine_adapter: t.Callable):
-    """PyArrow 25 fails to import on NumPy < 2, so the connector returns a ColumnTable."""
-    adapter = make_mocked_engine_adapter(DatabricksEngineAdapter, default_catalog="test_catalog")
-
-    class _ColumnTable:
-        column_names = ["name", "schema", "catalog", "type"]
-        column_table = [("orders",), ("analytics",), ("main",), ("table",)]
-
-    adapter.cursor.fetchall_arrow.return_value = _ColumnTable()
-
-    df = adapter._fetch_native_df("SELECT 1")
-
-    assert df.to_dict(orient="records") == [
-        {"name": "orders", "schema": "analytics", "catalog": "main", "type": "table"}
-    ]
-
-
-def test_fetch_native_df_empty_column_table(make_mocked_engine_adapter: t.Callable):
-    adapter = make_mocked_engine_adapter(DatabricksEngineAdapter, default_catalog="test_catalog")
-
-    class _ColumnTable:
-        column_names = ["name", "schema", "catalog", "type"]
-        column_table = [(), (), (), ()]
-
-    adapter.cursor.fetchall_arrow.return_value = _ColumnTable()
-
-    df = adapter._fetch_native_df("SELECT 1")
-
-    assert list(df.columns) == ["name", "schema", "catalog", "type"]
-    assert df.empty
-
-
-def test_fetch_native_df_column_table_keeps_duplicate_columns(
-    make_mocked_engine_adapter: t.Callable,
-):
-    adapter = make_mocked_engine_adapter(DatabricksEngineAdapter, default_catalog="test_catalog")
-
-    class _ColumnTable:
-        column_names = ["a", "a"]
-        column_table = [("x",), ("y",)]
-
-    adapter.cursor.fetchall_arrow.return_value = _ColumnTable()
-
-    df = adapter._fetch_native_df("SELECT 1")
-
-    assert list(df.columns) == ["a", "a"]
-    assert df.iloc[0].tolist() == ["x", "y"]
-
-
-def test_fetch_native_df_rejects_unknown_arrow_result(make_mocked_engine_adapter: t.Callable):
-    adapter = make_mocked_engine_adapter(DatabricksEngineAdapter, default_catalog="test_catalog")
-
-    class _Weird:
-        pass
-
-    adapter.cursor.fetchall_arrow.return_value = _Weird()
-
-    with pytest.raises(SQLMeshError, match="Unexpected Databricks fetchall_arrow"):
-        adapter._fetch_native_df("SELECT 1")