code refactoring

ads/feature_store/common/spark_session_singleton.py

@@ -62,6 +62,7 @@ def __init__(self, metastore_id: str = None):
         )
 
         if not developer_enabled() and metastore_id:
+            print("Not Developer Enabled")
             # Get the authentication credentials for the OCI data catalog service
             auth = copy.copy(ads.auth.default_signer())
 
@@ -79,6 +80,7 @@ def __init__(self, metastore_id: str = None):
                 .config("spark.driver.memory", "16G")
 
         if developer_enabled():
+            print("Developer Enabled")
             # Configure spark session with delta jars only in developer mode. In other cases,
             # jars should be part of the conda pack
             self.spark_session = configure_spark_with_delta_pip(

ads/feature_store/common/utils/utility.py

@@ -44,7 +44,7 @@
 
 
 def get_execution_engine_type(
-    data_frame: Union[DataFrame, pd.DataFrame]
+        data_frame: Union[DataFrame, pd.DataFrame]
 ) -> ExecutionEngine:
     """
     Determines the execution engine type for a given DataFrame.
@@ -84,7 +84,7 @@ def get_metastore_id(feature_store_id: str):
 
 
 def validate_delta_format_parameters(
-    timestamp: datetime = None, version_number: int = None, is_restore: bool = False
+        timestamp: datetime = None, version_number: int = None, is_restore: bool = False
 ):
     """
     Validate the user input provided as part of preview, restore APIs for ingested data, Ingested data is
@@ -118,9 +118,9 @@ def validate_delta_format_parameters(
 
 
 def get_features(
-    output_columns: List[dict],
-    parent_id: str,
-    entity_type: EntityType = EntityType.FEATURE_GROUP,
+        output_columns: List[dict],
+        parent_id: str,
+        entity_type: EntityType = EntityType.FEATURE_GROUP,
 ) -> List[Feature]:
     """
     Returns a list of features, given a list of output_columns and a feature_group_id.
@@ -154,8 +154,8 @@ def get_features(
     return features
 
 
-def get_schema_from_pandas_df(df: pd.DataFrame):
-    spark = SparkSessionSingleton().get_spark_session()
+def get_schema_from_pandas_df(df: pd.DataFrame, feature_store_id: str):
+    spark = SparkSessionSingleton(get_metastore_id(feature_store_id)).get_spark_session()
     converted_df = spark.createDataFrame(df)
     return get_schema_from_spark_df(converted_df)
 
@@ -174,27 +174,28 @@ def get_schema_from_spark_df(df: DataFrame):
     return schema_details
 
 
-def get_schema_from_df(data_frame: Union[DataFrame, pd.DataFrame]) -> List[dict]:
+def get_schema_from_df(data_frame: Union[DataFrame, pd.DataFrame], feature_store_id: str) -> List[dict]:
     """
     Given a DataFrame, returns a list of dictionaries that describe its schema.
     If the DataFrame is a pandas DataFrame, it uses pandas methods to get the schema.
     If it's a PySpark DataFrame, it uses PySpark methods to get the schema.
     """
     if isinstance(data_frame, pd.DataFrame):
-        return get_schema_from_pandas_df(data_frame)
+        return get_schema_from_pandas_df(data_frame, feature_store_id)
     else:
         return get_schema_from_spark_df(data_frame)
 
 
 def get_input_features_from_df(
-    data_frame: Union[DataFrame, pd.DataFrame]
+        data_frame: Union[DataFrame, pd.DataFrame],
+        feature_store_id: str
 ) -> List[FeatureDetail]:
     """
     Given a DataFrame, returns a list of FeatureDetail objects that represent its input features.
     Each FeatureDetail object contains information about a single input feature, such as its name, data type, and
     whether it's categorical or numerical.
     """
-    schema_details = get_schema_from_df(data_frame)
+    schema_details = get_schema_from_df(data_frame, feature_store_id)
     feature_details = []
 
     for schema_detail in schema_details:
@@ -204,7 +205,7 @@ def get_input_features_from_df(
 
 
 def convert_expectation_suite_to_expectation(
-    expectation_suite: ExpectationSuite, expectation_type: ExpectationType
+        expectation_suite: ExpectationSuite, expectation_type: ExpectationType
 ):
     """
     Convert an ExpectationSuite object to an Expectation object with detailed rule information.
@@ -282,7 +283,7 @@ def convert_pandas_datatype_with_schema(
         else:
             logger.warning("column" + column + "doesn't exist in the input feature details")
             columns_to_remove.append(column)
-    return input_df.drop(columns = columns_to_remove)
+    return input_df.drop(columns=columns_to_remove)
 
 
 def convert_spark_dataframe_with_schema(

ads/feature_store/entity.py

@@ -370,7 +370,7 @@ def create_feature_group(
         raw_feature_details = (
             input_feature_details
             if input_feature_details
-            else get_input_features_from_df(schema_details_dataframe)
+            else get_input_features_from_df(schema_details_dataframe, self.feature_store_id)
         )
 
         self.oci_feature_group = self._build_feature_group(

ads/feature_store/feature_group.py

@@ -476,7 +476,11 @@ def with_input_feature_details(
     def with_schema_details_from_dataframe(
         self, data_frame: Union[DataFrame, pd.DataFrame]
     ) -> "FeatureGroup":
-        schema_details = get_schema_from_df(data_frame)
+
+        if not self.feature_store_id:
+            raise ValueError("FeatureStore id must be set before calling `with_schema_details_from_dataframe`")
+
+        schema_details = get_schema_from_df(data_frame, self.feature_store_id)
         feature_details = []
 
         for schema_detail in schema_details:

ads/feature_store/feature_store.py

@@ -426,12 +426,11 @@ def list_entities_df(
 
     def _build_transformation(
         self,
-        transformation_mode: TransformationMode,
-        source_code_func=None,
+        source_code_func,
+        transformation_mode,
         display_name: str = None,
         description: str = None,
         compartment_id: str = None,
-        sql_query: str = None,
     ):
         transformation = (
             Transformation()
@@ -443,19 +442,17 @@ def _build_transformation(
                 compartment_id if compartment_id else self.compartment_id
             )
             .with_feature_store_id(self.id)
-            .with_transformation_query_input(sql_query)
         )
 
         return transformation
 
     def create_transformation(
         self,
+        source_code_func,
         transformation_mode: TransformationMode,
-        source_code_func=None,
         display_name: str = None,
         description: str = None,
         compartment_id: str = None,
-        sql_query: str = None,
     ) -> "Transformation":
         """Creates transformation resource from feature store.
 
@@ -471,9 +468,6 @@ def create_transformation(
              description for the entity.
         compartment_id: str
              compartment_id for the entity.
-        sql_query: str
-           inline sql query to be passed for transformation creation,
-            Please ensure to use DATA_SOURCE_INPUT as FROM table name
 
         Returns
         -------
@@ -486,12 +480,11 @@ def create_transformation(
             )
 
         self.oci_transformation = self._build_transformation(
-            transformation_mode,
             source_code_func,
+            transformation_mode,
             display_name,
             description,
             compartment_id,
-            sql_query,
         )
 
         return self.oci_transformation.create()