fix: bigquery snowflake source columns support

Author: eakmanrq

sqlmesh/core/engine_adapter/bigquery.py

@@ -169,17 +169,18 @@ def _df_to_source_queries(
         )
 
         def query_factory() -> Query:
-            if bigframes_pd and isinstance(df, bigframes_pd.DataFrame):
-                df.to_gbq(
+            ordered_df = df[list(source_columns_to_types)]
+            if bigframes_pd and isinstance(ordered_df, bigframes_pd.DataFrame):
+                ordered_df.to_gbq(
                     f"{temp_bq_table.project}.{temp_bq_table.dataset_id}.{temp_bq_table.table_id}",
                     if_exists="replace",
                 )
             elif not self.table_exists(temp_table):
                 # Make mypy happy
-                assert isinstance(df, pd.DataFrame)
+                assert isinstance(ordered_df, pd.DataFrame)
                 self._db_call(self.client.create_table, table=temp_bq_table, exists_ok=False)
                 result = self.__load_pandas_to_table(
-                    temp_bq_table, df, source_columns_to_types, replace=False
+                    temp_bq_table, ordered_df, source_columns_to_types, replace=False
                 )
                 if result.errors:
                     raise SQLMeshError(result.errors)

sqlmesh/core/engine_adapter/snowflake.py

@@ -378,6 +378,8 @@ def query_factory() -> Query:
             elif isinstance(df, pd.DataFrame):
                 from snowflake.connector.pandas_tools import write_pandas
 
+                ordered_df = df[list(source_columns_to_types)]
+
                 # Workaround for https://github.com/snowflakedb/snowflake-connector-python/issues/1034
                 # The above issue has already been fixed upstream, but we keep the following
                 # line anyway in order to support a wider range of Snowflake versions.
@@ -388,16 +390,16 @@ def query_factory() -> Query:
 
                 # See: https://stackoverflow.com/a/75627721
                 for column, kind in source_columns_to_types.items():
-                    if is_datetime64_any_dtype(df.dtypes[column]):
+                    if is_datetime64_any_dtype(ordered_df.dtypes[column]):
                         if kind.is_type("date"):  # type: ignore
-                            df[column] = pd.to_datetime(df[column]).dt.date  # type: ignore
-                        elif getattr(df.dtypes[column], "tz", None) is not None:  # type: ignore
-                            df[column] = pd.to_datetime(df[column]).dt.strftime(
+                            ordered_df[column] = pd.to_datetime(ordered_df[column]).dt.date  # type: ignore
+                        elif getattr(ordered_df.dtypes[column], "tz", None) is not None:  # type: ignore
+                            ordered_df[column] = pd.to_datetime(ordered_df[column]).dt.strftime(
                                 "%Y-%m-%d %H:%M:%S.%f%z"
                             )  # type: ignore
                         # https://github.com/snowflakedb/snowflake-connector-python/issues/1677
                         else:  # type: ignore
-                            df[column] = pd.to_datetime(df[column]).dt.strftime(
+                            ordered_df[column] = pd.to_datetime(ordered_df[column]).dt.strftime(
                                 "%Y-%m-%d %H:%M:%S.%f"
                             )  # type: ignore
 
@@ -407,7 +409,7 @@ def query_factory() -> Query:
 
                 write_pandas(
                     self._connection_pool.get(),
-                    df,
+                    ordered_df,
                     temp_table.name,
                     schema=temp_table.db or None,
                     database=database.sql(dialect=self.dialect) if database else None,

tests/core/engine_adapter/test_bigquery.py

@@ -487,7 +487,13 @@ def temp_table_exists(table: exp.Table) -> bool:
     retry_resp_call.errors = None
     retry_mock.return_value = retry_resp
     db_call_mock.return_value = AttributeDict({"errors": None})
-    df = pd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
+    df = pd.DataFrame(
+        {
+            "id": [1, 2, 3],
+            "ts": ["2025-01-01 00:00:00", "2025-01-01 00:00:00", "2025-01-01 00:00:00"],
+            "val": [7, 8, 9],
+        }
+    )
     adapter.merge(
         target_table="target",
         source_table=df,