Fixing linter issues

Signed-off-by: Nick Quinn <nicholas_quinn@apple.com>
feast-dev · franciscojavierarceo · Mar 10, 2026 · Mar 7, 2026 · Mar 7, 2026 · Mar 7, 2026
commit fe5c0b07ec8be1922b97c412319c94cefbaab76e
@@ -82,7 +82,9 @@ def __init__(
         *,
         name: str,
         mode: Union[TransformationMode, str] = TransformationMode.PYTHON,
-        source: Union[DataSource, "BatchFeatureView", List["BatchFeatureView"]],
+        source: Optional[
+            Union[DataSource, "BatchFeatureView", List["BatchFeatureView"]]
+        ] = None,
         sink_source: Optional[DataSource] = None,
         entities: Optional[List[Entity]] = None,
         ttl: Optional[timedelta] = None,

@@ -702,11 +702,21 @@ def _make_inferences(
         )
 
         update_data_sources_with_inferred_event_timestamp_col(
-            [view.batch_source for view in views_to_update if view.batch_source is not None], self.config
+            [
+                view.batch_source
+                for view in views_to_update
+                if view.batch_source is not None
+            ],
+            self.config,
         )
 
         update_data_sources_with_inferred_event_timestamp_col(
-            [view.batch_source for view in sfvs_to_update if view.batch_source is not None], self.config
+            [
+                view.batch_source
+                for view in sfvs_to_update
+                if view.batch_source is not None
+            ],
+            self.config,
         )
 
         # New feature views may reference previously applied entities.
@@ -2417,9 +2427,7 @@ def write_to_offline_store(
         provider = self._get_provider()
         # Get columns of the batch source and the input dataframe.
         if feature_view.batch_source is None:
-            raise ValueError(
-                f"Feature view '{feature_view.name}' has no batch_source."
-            )
+            raise ValueError(f"Feature view '{feature_view.name}' has no batch_source.")
         column_names_and_types = (
             provider.get_table_column_names_and_types_from_data_source(
                 self.config, feature_view.batch_source

@@ -105,12 +105,9 @@ def from_feature_view_definition(feature_view: "FeatureView"):
                 name_alias=None,
                 features=feature_view.features,
                 desired_features=[],
-                timestamp_field=batch_source.created_timestamp_column
-                or None,
-                created_timestamp_column=batch_source.created_timestamp_column
-                or None,
-                date_partition_column=batch_source.date_partition_column
-                or None,
+                timestamp_field=batch_source.created_timestamp_column or None,
+                created_timestamp_column=batch_source.created_timestamp_column or None,
+                date_partition_column=batch_source.date_partition_column or None,
                 batch_source=batch_source or None,
             )
         else:

@@ -137,9 +137,7 @@ def resolve_feature_view_source(
     if not is_derived_view:
         # Regular feature view - use its batch_source directly
         if feature_view.batch_source is None:
-            raise ValueError(
-                f"Feature view '{feature_view.name}' has no batch_source."
-            )
+            raise ValueError(f"Feature view '{feature_view.name}' has no batch_source.")
         return FeatureViewSourceInfo(
             data_source=feature_view.batch_source,
             source_type="batch_source",

@@ -656,7 +656,10 @@ def _field_mapping(
     full_feature_names: bool,
 ) -> Tuple[dd.DataFrame, str]:
     # Rename columns by the field mapping dictionary if it exists
-    if feature_view.batch_source is not None and feature_view.batch_source.field_mapping:
+    if (
+        feature_view.batch_source is not None
+        and feature_view.batch_source.field_mapping
+    ):
         df_to_join = _run_dask_field_mapping(
             df_to_join, feature_view.batch_source.field_mapping
         )

@@ -73,9 +73,7 @@ def _get_offline_store_for_feature_view(
     ) -> OfflineStore:
         self._initialize_offline_stores(config)
         if feature_view.batch_source is None:
-            raise ValueError(
-                f"Feature view '{feature_view.name}' has no batch_source."
-            )
+            raise ValueError(f"Feature view '{feature_view.name}' has no batch_source.")
         source_type = feature_view.batch_source.source_type()
         store_key = self.get_source_key_from_type(source_type)
         if store_key is None:

@@ -340,9 +340,7 @@ def offline_write_batch_ibis(
     data_source_writer: Callable[[pyarrow.Table, DataSource, str], None],
 ):
     if feature_view.batch_source is None:
-        raise ValueError(
-            f"Feature view '{feature_view.name}' has no batch_source."
-        )
+        raise ValueError(f"Feature view '{feature_view.name}' has no batch_source.")
     pa_schema, column_names = get_pyarrow_schema_from_batch_source(
         config, feature_view.batch_source
     )

@@ -364,7 +364,10 @@ def _prep_rows_to_write_for_ingestion(
             # Note: A dictionary mapping of column names in this data
             #   source to feature names in a feature table or view. Only used for feature
             #   columns, not entity or timestamp columns.
-            if hasattr(feature_view, "batch_source") and feature_view.batch_source is not None:
+            if (
+                hasattr(feature_view, "batch_source")
+                and feature_view.batch_source is not None
+            ):
                 if feature_view.batch_source.field_mapping is not None:
                     table = _run_pyarrow_field_mapping(
                         table, feature_view.batch_source.field_mapping
@@ -410,7 +413,10 @@ async def ingest_df_async(
         )
 
     def ingest_df_to_offline_store(self, feature_view: FeatureView, table: pa.Table):
-        if feature_view.batch_source is not None and feature_view.batch_source.field_mapping is not None:
+        if (
+            feature_view.batch_source is not None
+            and feature_view.batch_source.field_mapping is not None
+        ):
             table = _run_pyarrow_field_mapping(
                 table, feature_view.batch_source.field_mapping
             )

@@ -164,7 +164,9 @@ def parse_repo(repo_root: Path) -> RepoContents:
 
                 # Handle batch sources defined with feature views.
                 batch_source = obj.batch_source
-                if batch_source is not None and not any((batch_source is ds) for ds in res.data_sources):
+                if batch_source is not None and not any(
+                    (batch_source is ds) for ds in res.data_sources
+                ):
                     res.data_sources.append(batch_source)
 
                 # Handle stream sources defined with feature views.
@@ -179,7 +181,9 @@ def parse_repo(repo_root: Path) -> RepoContents:
 
                 # Handle batch sources defined with feature views.
                 batch_source = obj.batch_source
-                if batch_source is not None and not any((batch_source is ds) for ds in res.data_sources):
+                if batch_source is not None and not any(
+                    (batch_source is ds) for ds in res.data_sources
+                ):
                     res.data_sources.append(batch_source)
                 assert obj.stream_source
                 stream_source = obj.stream_source
@@ -192,7 +196,9 @@ def parse_repo(repo_root: Path) -> RepoContents:
 
                 # Handle batch sources defined with feature views.
                 batch_source = obj.batch_source
-                if batch_source is not None and not any((batch_source is ds) for ds in res.data_sources):
+                if batch_source is not None and not any(
+                    (batch_source is ds) for ds in res.data_sources
+                ):
                     res.data_sources.append(batch_source)
             elif isinstance(obj, Entity) and not any(
                 (obj is entity) for entity in res.entities
@@ -231,7 +237,9 @@ def plan(
         # TODO: When we support multiple projects in a single repo, we should filter repo contents by project
         if not skip_source_validation:
             provider = store._get_provider()
-            data_sources = [t.batch_source for t in repo.feature_views if t.batch_source is not None]
+            data_sources = [
+                t.batch_source for t in repo.feature_views if t.batch_source is not None
+            ]
             # Make sure the data source used by this feature view is supported by Feast
             for data_source in data_sources:
                 provider.validate_data_source(store.config, data_source)
@@ -342,7 +350,9 @@ def apply_total_with_repo_instance(
 ):
     if not skip_source_validation:
         provider = store._get_provider()
-        data_sources = [t.batch_source for t in repo.feature_views if t.batch_source is not None]
+        data_sources = [
+            t.batch_source for t in repo.feature_views if t.batch_source is not None
+        ]
         # Make sure the data source used by this feature view is supported by Feast
         for data_source in data_sources:
             provider.validate_data_source(store.config, data_source)