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GeezerVison is showing github.com — fix: Keep the BatchFeatureView type and settings when copying it by LuisFigueroaG · Pull Request #6989 · feast-dev/feast

fix: Keep the BatchFeatureView type and settings when copying it #6989

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ntkathole merged 2 commits into feast-dev:master from LuisFigueroaG:fix/batch-feature-view-copy

Oct 13, 2026

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fix: Keep the BatchFeatureView type and settings when copying it #6989

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cc1b588

fix: Keep the BatchFeatureView type and settings when copying it

LuisFigueroaG Oct 9, 2026

822bdc5

fix: Add type hints to BatchFeatureView.__copy__ and its test

LuisFigueroaG Oct 9, 2026

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36 changes: 35 additions & 1 deletion 36 sdk/python/feast/batch_feature_view.py

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Original file line number	Diff line number	Diff line change
		@@ -1,7 +1,8 @@
		import copy
		import functools
		import warnings
		from datetime import datetime, timedelta
		from typing import Any, Callable, Dict, List, Optional, Tuple, Union
		from typing import Any, Callable, Dict, List, Optional, Tuple, Union, cast

		import dill

Expand Down Expand Up		@@ -161,6 +162,39 @@ def __init__(
		version=version,
		)

		def __copy__(self) -> "BatchFeatureView":
		fv = BatchFeatureView(
		name=self.name,
		mode=cast(Union[TransformationMode, str], self.mode),
		source=cast(List["BatchFeatureView"], self.source_views)
		if self.source_views
		else self.batch_source,
		sink_source=self.batch_source if self.source_views else None,
		schema=self.schema,
		ttl=self.ttl,
		tags=self.tags,
		online=self.online,
		offline=self.offline,
		description=self.description,
		owner=self.owner,
		org=self.org,
		udf=self.udf,
		udf_string=self.udf_string,
		feature_transformation=self.feature_transformation,
		batch_engine=self.batch_engine,
		aggregations=self.aggregations,
		enable_validation=self.enable_validation,
		version=self.version,
		)
		fv.enabled = self.enabled
		fv.state = self.state
		fv.entities = self.entities
		fv.features = copy.copy(self.features)
		fv.entity_columns = copy.copy(self.entity_columns)
		fv.projection = copy.copy(self.projection)
		fv._raw_feature_transformation_proto = self._raw_feature_transformation_proto
		return fv

		def get_feature_transformation(self) -> Optional[Transformation]:
		if not self.udf:
		return None
Expand Down

42 changes: 42 additions & 0 deletions 42 sdk/python/tests/unit/test_feature_views.py

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Original file line number	Diff line number	Diff line change
		@@ -1,9 +1,11 @@
		import copy
		from datetime import timedelta

		import pandas as pd
		import pytest
		from typeguard import TypeCheckError

		from feast.aggregation import Aggregation
		from feast.batch_feature_view import BatchFeatureView
		from feast.data_format import AvroFormat
		from feast.data_source import KafkaSource
Expand Down Expand Up		@@ -404,6 +406,46 @@ def transform_udf(df: pd.DataFrame) -> pd.DataFrame:
		assert second_deserialized.feature_transformation is not None


		def test_batch_feature_view_copy() -> None:
		def transform_udf(df: pd.DataFrame) -> pd.DataFrame:
		return df

		bfv = BatchFeatureView(
		name="test_batch_feature_view",
		entities=[Entity(name="test_entity", join_keys=["entity_id"])],
		schema=[
		Field(name="entity_id", dtype=String),
		Field(name="input_feature", dtype=Int64),
		],
		source=FileSource(
		name="test-source",
		path="test_data.parquet",
		timestamp_field="event_timestamp",
		),
		udf=transform_udf,
		mode="pandas",
		batch_engine={"spark.sql.shuffle.partitions": "4"},
		aggregations=[
		Aggregation(
		column="input_feature", function="sum", time_window=timedelta(days=1)
		)
		],
		)

		bfv_copy = copy.copy(bfv)
		assert isinstance(bfv_copy, BatchFeatureView)
		assert bfv_copy == bfv
		assert bfv_copy.mode == "pandas"
		assert bfv_copy.udf is transform_udf
		assert bfv_copy.feature_transformation is bfv.feature_transformation
		assert bfv_copy.batch_engine == {"spark.sql.shuffle.partitions": "4"}
		assert bfv_copy.aggregations == bfv.aggregations

		projected = bfv[["input_feature"]]
		assert isinstance(projected, BatchFeatureView)
		assert projected.to_proto().spec.HasField("feature_transformation")


		def test_transformation_mode_serialization():
		"""
		Test that transformation mode is properly serialized to proto and deserialized back.
Expand Down

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