Dataset builder: add _to_athena_query method

Author: michaelneely

src/sagemaker/feature_store/dataset_builder.py

@@ -438,54 +438,13 @@ def to_csv_file(self) -> Tuple[str, str]:
             os.remove(local_file_name)
             temp_table_name = f'dataframe_{temp_id.replace("-", "_")}'
             self._create_temp_table(temp_table_name, desired_s3_folder)
-            base_features = list(self._base.columns)
-            event_time_identifier_feature_dtype = self._base[
-                self._event_time_identifier_feature_name
-            ].dtypes
-            self._event_time_identifier_feature_type = (
-                FeatureGroup.DTYPE_TO_FEATURE_DEFINITION_CLS_MAP.get(
-                    str(event_time_identifier_feature_dtype), None
-                )
-            )
-            query_string = self._construct_query_string(
-                FeatureGroupToBeMerged(
-                    base_features,
-                    self._included_feature_names if self._included_feature_names else base_features,
-                    self._included_feature_names if self._included_feature_names else base_features,
-                    _DEFAULT_CATALOG,
-                    _DEFAULT_DATABASE,
-                    temp_table_name,
-                    self._record_identifier_feature_name,
-                    FeatureDefinition(
-                        self._event_time_identifier_feature_name,
-                        self._event_time_identifier_feature_type,
-                    ),
-                    None,
-                    TableType.DATA_FRAME,
-                )
-            )
-            query_result = self._run_query(query_string, _DEFAULT_CATALOG, _DEFAULT_DATABASE)
+            query_result = self._run_query(*self._to_athena_query(temp_table_name=temp_table_name))
             # TODO: cleanup temp table, need more clarification, keep it for now
             return query_result.get("QueryExecution", {}).get("ResultConfiguration", {}).get(
                 "OutputLocation", None
             ), query_result.get("QueryExecution", {}).get("Query", None)
         if isinstance(self._base, FeatureGroup):
-            base_feature_group = construct_feature_group_to_be_merged(
-                self._base, self._included_feature_names
-            )
-            self._record_identifier_feature_name = base_feature_group.record_identifier_feature_name
-            self._event_time_identifier_feature_name = (
-                base_feature_group.event_time_identifier_feature.feature_name
-            )
-            self._event_time_identifier_feature_type = (
-                base_feature_group.event_time_identifier_feature.feature_type
-            )
-            query_string = self._construct_query_string(base_feature_group)
-            query_result = self._run_query(
-                query_string,
-                base_feature_group.catalog,
-                base_feature_group.database,
-            )
+            query_result = self._run_query(*self._to_athena_query())
             return query_result.get("QueryExecution", {}).get("ResultConfiguration", {}).get(
                 "OutputLocation", None
             ), query_result.get("QueryExecution", {}).get("Query", None)
@@ -1058,6 +1017,67 @@ def _construct_athena_table_column_string(self, column: str) -> str:
             raise RuntimeError(f"The dataframe type {dataframe_type} is not supported yet.")
         return f"{column} {self._DATAFRAME_TYPE_TO_COLUMN_TYPE_MAP.get(str(dataframe_type), None)}"
 
+    def _to_athena_query(self, temp_table_name: str = None) -> Tuple[str, str, str]:
+        """Internal method for constructing an Athena query.
+
+        Args:
+            temp_table_name (str): The temporary Athena table name of the base pandas.DataFrame. Defaults to None.
+
+        Returns:
+            The query string.
+            The name of the catalog to be used in the query execution.
+            The database to be used in the query execution.
+
+        Raises:
+            ValueError: temp_table_name must be provided if the base is a pandas.DataFrame.
+        """
+        if isinstance(self._base, pd.DataFrame):
+            if temp_table_name is None:
+                raise ValueError("temp_table_name must be provided for a pandas.DataFrame base.")
+            base_features = list(self._base.columns)
+            event_time_identifier_feature_dtype = self._base[
+                self._event_time_identifier_feature_name
+            ].dtypes
+            self._event_time_identifier_feature_type = (
+                FeatureGroup.DTYPE_TO_FEATURE_DEFINITION_CLS_MAP.get(
+                    str(event_time_identifier_feature_dtype), None
+                )
+            )
+            catalog = _DEFAULT_CATALOG
+            database = _DEFAULT_DATABASE
+            query_string = self._construct_query_string(
+                FeatureGroupToBeMerged(
+                    base_features,
+                    self._included_feature_names if self._included_feature_names else base_features,
+                    self._included_feature_names if self._included_feature_names else base_features,
+                    catalog,
+                    database,
+                    temp_table_name,
+                    self._record_identifier_feature_name,
+                    FeatureDefinition(
+                        self._event_time_identifier_feature_name,
+                        self._event_time_identifier_feature_type,
+                    ),
+                    None,
+                    TableType.DATA_FRAME,
+                )
+            )
+        if isinstance(self._base, FeatureGroup):
+            base_feature_group = construct_feature_group_to_be_merged(
+                self._base, self._included_feature_names
+            )
+            self._record_identifier_feature_name = base_feature_group.record_identifier_feature_name
+            self._event_time_identifier_feature_name = (
+                base_feature_group.event_time_identifier_feature.feature_name
+            )
+            self._event_time_identifier_feature_type = (
+                base_feature_group.event_time_identifier_feature.feature_type
+            )
+            catalog = base_feature_group.catalog
+            database = base_feature_group.database
+            query_string = self._construct_query_string(base_feature_group)
+        return query_string, catalog, database
+
     def _run_query(self, query_string: str, catalog: str, database: str) -> Dict[str, Any]:
         """Internal method for execute Athena query, wait for query finish and get query result.