feast/sdk/python/feast/feature_view.py at v0.63.0 · feast-dev/feast · GitHub

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# Licensed under the Apache License, Version 2.0 (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at

# https://www.apache.org/licenses/LICENSE-2.0

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

# See the License for the specific language governing permissions and

# limitations under the License.

import copy

import warnings

from datetime import datetime, timedelta

from typing import Dict, List, Optional, Tuple, Type, Union

from google.protobuf.duration_pb2 import Duration

from google.protobuf.message import Message

from typeguard import typechecked

from feast import utils

from feast.base_feature_view import BaseFeatureView

from feast.data_source import DataSource, KafkaSource, KinesisSource, PushSource

from feast.entity import Entity

from feast.feature_view_projection import FeatureViewProjection

from feast.field import Field

from feast.proto_utils import (

mode_to_string,

serialize_data_source,

transformation_to_proto,

)

from feast.protos.feast.core.FeatureView_pb2 import FeatureView as FeatureViewProto

from feast.protos.feast.core.FeatureView_pb2 import (

FeatureViewMeta as FeatureViewMetaProto,

)

from feast.protos.feast.core.FeatureView_pb2 import (

FeatureViewSpec as FeatureViewSpecProto,

)

from feast.protos.feast.core.FeatureView_pb2 import (

MaterializationInterval as MaterializationIntervalProto,

)

from feast.transformation.mode import TransformationMode

from feast.types import from_value_type

from feast.value_type import ValueType

from feast.version_utils import normalize_version_string

warnings.simplefilter("once", DeprecationWarning)

# DUMMY_ENTITY is a placeholder entity used in entityless FeatureViews

DUMMY_ENTITY_ID = "__dummy_id"

DUMMY_ENTITY_NAME = "__dummy"

DUMMY_ENTITY_VAL = ""

DUMMY_ENTITY = Entity(

name=DUMMY_ENTITY_NAME,

join_keys=[DUMMY_ENTITY_ID],

value_type=ValueType.UNKNOWN,

)

DUMMY_ENTITY_FIELD = Field(

name=DUMMY_ENTITY_ID,

dtype=from_value_type(ValueType.STRING),

)

@typechecked

class FeatureView(BaseFeatureView):

"""

A FeatureView defines a logical group of features.

Attributes:

entities: The list of names of entities that this feature view is associated with.

ttl: The amount of time this group of features lives. A ttl of 0 indicates that

this group of features lives forever. Note that large ttl's or a ttl of 0

can result in extremely computationally intensive queries.

batch_source: Optional batch source of data where this group of features

is stored. If no source is provided, this will be None.

stream_source: The stream source of data where this group of features is stored.

schema: The schema of the feature view, including feature, timestamp, and entity

columns. If not specified, can be inferred from the underlying data source.

entity_columns: The list of entity columns contained in the schema. If not specified,

can be inferred from the underlying data source.

features: The list of feature columns contained in the schema. If not specified,

can be inferred from the underlying data source.

online: A boolean indicating whether online retrieval is enabled for this feature

view.

description: A human-readable description.

tags: A dictionary of key-value pairs to store arbitrary metadata.

owner: The owner of the feature view, typically the email of the primary

maintainer.

org: The organizational unit that owns this feature view (e.g. "ads", "search").

Defaults to empty string.

mode: The transformation mode for feature transformations. Only meaningful when

transformations are applied. Choose from TransformationMode enum values

(e.g., PYTHON, PANDAS, RAY, SQL, SPARK, SUBSTRAIT).

"""

entities: List[str]

ttl: Optional[timedelta]

batch_source: Optional[DataSource]

stream_source: Optional[DataSource]

source_views: Optional[List["FeatureView"]]

entity_columns: List[Field]

features: List[Field]

online: bool

offline: bool

description: str

tags: Dict[str, str]

owner: str

org: str

materialization_intervals: List[Tuple[datetime, datetime]]

mode: Optional[Union["TransformationMode", str]]

enable_validation: bool

def __init__(

self,

source: Optional[Union[DataSource, "FeatureView", List["FeatureView"]]] = None,

sink_source: Optional[DataSource] = None,

schema: Optional[List[Field]] = None,

entities: Optional[List[Entity]] = None,

ttl: Optional[timedelta] = timedelta(days=0),

online: bool = True,

offline: bool = False,

description: str = "",

tags: Optional[Dict[str, str]] = None,

owner: str = "",

org: str = "",

mode: Optional[Union["TransformationMode", str]] = None,

enable_validation: bool = False,

version: str = "latest",

"""

Creates a FeatureView object.

Args:

source (optional): The source of data for this group of features. May be a stream source,

a batch source, a FeatureView, or a list of FeatureViews. If None, the feature view

has no associated data source.

schema (optional): The schema of the feature view, including feature, timestamp,

and entity columns.

# TODO: clarify that schema is only useful here...

entities (optional): The list of entities with which this group of features is associated.

ttl (optional): The amount of time this group of features lives. A ttl of 0 indicates that

this group of features lives forever. Note that large ttl's or a ttl of 0

can result in extremely computationally intensive queries.

online (optional): A boolean indicating whether online retrieval is enabled for

this feature view.

offline (optional): A boolean indicating whether write to offline store is enabled for

this feature view.

description (optional): A human-readable description.

tags (optional): A dictionary of key-value pairs to store arbitrary metadata.

owner (optional): The owner of the feature view, typically the email of the

primary maintainer.

org (optional): The organizational unit that owns this feature view

(e.g. "ads", "search").

mode (optional): The transformation mode for feature transformations. Only meaningful

when transformations are applied. Choose from TransformationMode enum values.

enable_validation (optional): If True, enables schema validation during materialization

to check that data conforms to the declared feature types. Default is False.

version (optional): Version string for definition management. Controls which historical

snapshot is active after ``feast apply``. Only one version can be active per feature

view name per project. For concurrent multi-version testing, use separate projects

or distinct feature view names. Default is "latest".

Raises:

ValueError: A field mapping conflicts with an Entity or a Feature.

"""

self.name = name

self.version = version

self.enable_validation = enable_validation

self.entities = [e.name for e in entities] if entities else [DUMMY_ENTITY_NAME]

self.ttl = ttl

schema = schema or []

self.mode = mode

# Normalize source

self.stream_source = None

self.data_source: Optional[DataSource] = None

self.source_views: List[FeatureView] = []

if source is None:

pass # data_source remains None, source_views remains []

elif isinstance(source, DataSource):

self.data_source = source

elif isinstance(source, FeatureView):

self.source_views = [source]

elif isinstance(source, list) and all(

isinstance(sv, FeatureView) for sv in source

self.source_views = source

else:

raise TypeError(

"source must be a DataSource, a FeatureView, or a list of FeatureView."

)

# Set up stream, batch and derived view sources

if (

isinstance(self.data_source, PushSource)

or isinstance(self.data_source, KafkaSource)

or isinstance(self.data_source, KinesisSource)

# Stream source definition

self.stream_source = self.data_source

if not self.data_source.batch_source:

raise ValueError(

f"A batch_source needs to be specified for stream source `{self.data_source.name}`"

)

self.batch_source = self.data_source.batch_source

elif self.data_source:

# Batch source definition

self.batch_source = self.data_source

elif self.source_views:

# Derived view source definition

if not sink_source:

raise ValueError("Derived FeatureView must specify `sink_source`.")

self.batch_source = sink_source

else:

# source=None - no batch source

self.batch_source = None

# Initialize features and entity columns.

features: List[Field] = []

self.entity_columns = []

join_keys: List[str] = []

if entities:

for entity in entities:

join_keys.append(entity.join_key)

# Ensure that entities have unique join keys.

if len(set(join_keys)) < len(join_keys):

raise ValueError(

"A feature view should not have entities that share a join key."

)

for field in schema:

if field.name in join_keys:

self.entity_columns.append(field)

# Confirm that the inferred type matches the specified entity type, if it exists.

matching_entities = (

[e for e in entities if e.join_key == field.name]

if entities

else []

)

assert len(matching_entities) == 1

entity = matching_entities[0]

if entity.value_type != ValueType.UNKNOWN:

if from_value_type(entity.value_type) != field.dtype:

raise ValueError(

f"Entity {entity.name} has type {entity.value_type}, which does not match the inferred type {field.dtype}."

)

else:

features.append(field)

assert len([f for f in features if f.vector_index]) < 2, (

f"Only one vector feature is allowed per feature view. Please update {self.name}."

)

# TODO(felixwang9817): Add more robust validation of features.

if self.batch_source is not None:

cols = [field.name for field in schema]

for col in cols:

if (

self.batch_source.field_mapping is not None

and col in self.batch_source.field_mapping.keys()

raise ValueError(

f"The field {col} is mapped to {self.batch_source.field_mapping[col]} for this data source. "

f"Please either remove this field mapping or use {self.batch_source.field_mapping[col]} as the "

f"Entity or Feature name."

)

super().__init__(

name=name,

features=features,

description=description,

tags=tags,

owner=owner,

source=self.batch_source,

)

self.org = org

self.online = online

self.offline = offline

self.mode = mode

self.materialization_intervals = []

def __hash__(self):

return super().__hash__()

def __copy__(self):

fv = FeatureView(

name=self.name,

ttl=self.ttl,

source=self.source_views

if self.source_views

else (self.stream_source if self.stream_source else self.batch_source),

schema=self.schema,

tags=self.tags,

online=self.online,

offline=self.offline,

sink_source=self.batch_source if self.source_views else None,

enable_validation=self.enable_validation,

version=self.version,

description=self.description,

owner=self.owner,

org=self.org,

)

# This is deliberately set outside of the FV initialization as we do not have the Entity objects.

fv.entities = self.entities

fv.features = copy.copy(self.features)

fv.entity_columns = copy.copy(self.entity_columns)

fv.projection = copy.copy(self.projection)

return fv

def _schema_or_udf_changed(self, other: "BaseFeatureView") -> bool:

"""Check for FeatureView schema/UDF changes."""

if super()._schema_or_udf_changed(other):

return True

if not isinstance(other, FeatureView):

return True

# Schema-related fields

if sorted(self.entities) != sorted(other.entities):

return True

if sorted(self.entity_columns) != sorted(other.entity_columns):

return True

if self.source_views != other.source_views:

return True

# Skip UDF-related data source fields: batch_source, stream_source

# (treat as deployment configuration, not schema changes)

# Skip configuration: ttl, online, offline, enable_validation

# Skip metadata: materialization_intervals (excluded in current equality)

return False

def __eq__(self, other):

if not isinstance(other, FeatureView):

raise TypeError(

"Comparisons should only involve FeatureView class objects."

)

if not super().__eq__(other):

return False

if (

sorted(self.entities) != sorted(other.entities)

or self.ttl != other.ttl

or self.online != other.online

or self.offline != other.offline

or self.batch_source != other.batch_source

or self.stream_source != other.stream_source

or sorted(self.entity_columns) != sorted(other.entity_columns)

or self.source_views != other.source_views

or self.materialization_intervals != other.materialization_intervals

or self.enable_validation != other.enable_validation

or normalize_version_string(self.version)

!= normalize_version_string(other.version)

or self.org != other.org

return False

return True

@property

def join_keys(self) -> List[str]:

"""Returns a list of all the join keys."""

return [entity.name for entity in self.entity_columns]

@property

def schema(self) -> List[Field]:

return list(set(self.entity_columns + self.features))

def ensure_valid(self):

"""

Validates the state of this feature view locally.

Raises:

ValueError: The feature view does not have a name or does not have entities.

"""

super().ensure_valid()

if not self.entities:

raise ValueError("Feature view has no entities.")

@property

def proto_class(self) -> Type[Message]:

return FeatureViewProto

def with_join_key_map(self, join_key_map: Dict[str, str]):

"""

Returns a copy of this feature view with the join key map set to the given map.

This join_key mapping operation is only used as part of query operations and will

not modify the underlying FeatureView.

Args:

join_key_map: A map of join keys in which the left is the join_key that

corresponds with the feature data and the right corresponds with the entity data.

Examples:

Join a location feature data table to both the origin column and destination

column of the entity data.

temperatures_feature_service = FeatureService(

name="temperatures",

features=[

location_stats_feature_view

.with_name("origin_stats")

.with_join_key_map(

{"location_id": "origin_id"}

location_stats_feature_view

.with_name("destination_stats")

.with_join_key_map(

{"location_id": "destination_id"}

)

"""

cp = self.__copy__()

cp.projection.join_key_map = join_key_map

return cp

def update_materialization_intervals(

self, existing_materialization_intervals: List[Tuple[datetime, datetime]]

if (

len(existing_materialization_intervals) > 0

and len(self.materialization_intervals) == 0

for interval in existing_materialization_intervals:

self.materialization_intervals.append((interval[0], interval[1]))

def to_proto(self) -> FeatureViewProto:

"""

Converts a feature view object to its protobuf representation.

Returns:

A FeatureViewProto protobuf.

"""

return self._to_proto_internal(seen={})

def _to_proto_internal(

self, seen: Dict[str, Union[None, FeatureViewProto]]

) -> FeatureViewProto:

if self.name in seen:

if seen[self.name] is None:

raise ValueError(

f"Cycle detected during serialization of FeatureView: {self.name}"

)

return seen[self.name] # type: ignore[return-value]

seen[self.name] = None

spec = self.to_proto_spec(seen)

meta = self.to_proto_meta()

proto = FeatureViewProto(spec=spec, meta=meta)

seen[self.name] = proto

return proto

def to_proto_spec(

self, seen: Dict[str, Union[None, FeatureViewProto]]

) -> FeatureViewSpecProto:

ttl_duration = self.get_ttl_duration()

batch_source_proto = serialize_data_source(self.batch_source)

stream_source_proto = serialize_data_source(self.stream_source)

source_view_protos = None

if self.source_views:

source_view_protos = [

view._to_proto_internal(seen).spec for view in self.source_views

]

feature_transformation_proto = None

if hasattr(self, "feature_transformation") and self.feature_transformation:

feature_transformation_proto = transformation_to_proto(

self.feature_transformation

)

return FeatureViewSpecProto(

name=self.name,

entities=self.entities,

entity_columns=[field.to_proto() for field in self.entity_columns],

features=[feature.to_proto() for feature in self.features],

description=self.description,

tags=self.tags,

owner=self.owner,

org=self.org,

ttl=(ttl_duration if ttl_duration is not None else None),

online=self.online,

offline=self.offline,

batch_source=batch_source_proto,

stream_source=stream_source_proto,

source_views=source_view_protos,

feature_transformation=feature_transformation_proto,

mode=mode_to_string(self.mode),

enable_validation=self.enable_validation,

version=self.version,

)

def to_proto_meta(self):

meta = FeatureViewMetaProto(materialization_intervals=[])

if self.created_timestamp:

meta.created_timestamp.FromDatetime(self.created_timestamp)

if self.last_updated_timestamp:

meta.last_updated_timestamp.FromDatetime(self.last_updated_timestamp)

for interval in self.materialization_intervals:

interval_proto = MaterializationIntervalProto()

interval_proto.start_time.FromDatetime(interval[0])

interval_proto.end_time.FromDatetime(interval[1])

meta.materialization_intervals.append(interval_proto)

if self.current_version_number is not None:

meta.current_version_number = self.current_version_number

return meta

def get_ttl_duration(self):

ttl_duration = None

if self.ttl is not None:

ttl_duration = Duration()

ttl_duration.FromTimedelta(self.ttl)

return ttl_duration

@classmethod

def from_proto(

cls, feature_view_proto: FeatureViewProto, skip_udf: bool = False

) -> "FeatureView":

return cls._from_proto_internal(feature_view_proto, seen={}, skip_udf=skip_udf)

@classmethod

def _from_proto_internal(

cls,

feature_view_proto: FeatureViewProto,

seen: Dict[str, Union[None, "FeatureView"]],

skip_udf: bool = False,

) -> "FeatureView":

"""

Creates a feature view from a protobuf representation of a feature view.

Args:

feature_view_proto: A protobuf representation of a feature view.

seen: A dictionary to keep track of already seen feature views to avoid recursion.

Returns:

A FeatureViewProto object based on the feature view protobuf.

"""

feature_view_name = feature_view_proto.spec.name

if feature_view_name in seen:

if seen[feature_view_name] is None:

raise ValueError(

f"Cycle detected while deserializing FeatureView: {feature_view_name}"

)

return seen[feature_view_name] # type: ignore[return-value]

seen[feature_view_name] = None

batch_source = (

DataSource.from_proto(feature_view_proto.spec.batch_source)

if feature_view_proto.spec.HasField("batch_source")

else None

)

stream_source = (

DataSource.from_proto(feature_view_proto.spec.stream_source)

if feature_view_proto.spec.HasField("stream_source")

else None

)

source_views = [

FeatureView._from_proto_internal(

FeatureViewProto(spec=view_spec, meta=None), seen, skip_udf=skip_udf

)

for view_spec in feature_view_proto.spec.source_views

]

has_transformation = feature_view_proto.spec.HasField("feature_transformation")

if has_transformation and cls == FeatureView:

from feast.batch_feature_view import BatchFeatureView

from feast.transformation.factory import get_transformation_class_from_type

from feast.transformation.python_transformation import PythonTransformation

from feast.transformation.substrait_transformation import (

SubstraitTransformation,

)

feature_transformation_proto = (

feature_view_proto.spec.feature_transformation

)

transformation = None

if not skip_udf:

if feature_transformation_proto.HasField("user_defined_function"):

udf_proto = feature_transformation_proto.user_defined_function

if udf_proto.mode:

try:

transformation_class = get_transformation_class_from_type(

udf_proto.mode

)

transformation = transformation_class.from_proto(udf_proto)

except (ValueError, KeyError):

transformation = PythonTransformation.from_proto(udf_proto)

else:

transformation = PythonTransformation.from_proto(udf_proto)

elif feature_transformation_proto.HasField("substrait_transformation"):

transformation = SubstraitTransformation.from_proto(

feature_transformation_proto.substrait_transformation

)

mode: Union[TransformationMode, str]

if feature_view_proto.spec.mode:

mode = feature_view_proto.spec.mode

elif transformation and hasattr(transformation, "mode"):

mode = transformation.mode

else:

mode = TransformationMode.PYTHON

feature_view: FeatureView = BatchFeatureView( # type: ignore[assignment]

name=feature_view_proto.spec.name,

description=feature_view_proto.spec.description,

tags=dict(feature_view_proto.spec.tags),

owner=feature_view_proto.spec.owner,

org=feature_view_proto.spec.org,

online=feature_view_proto.spec.online,

offline=feature_view_proto.spec.offline,

ttl=(

timedelta(days=0)

if feature_view_proto.spec.ttl.ToNanoseconds() == 0

else feature_view_proto.spec.ttl.ToTimedelta()

source=source_views if source_views else batch_source, # type: ignore[arg-type]

sink_source=batch_source if source_views else None,

mode=mode,

feature_transformation=transformation,

)

else:

mode_from_spec = (

feature_view_proto.spec.mode if feature_view_proto.spec.mode else None

)

feature_view = cls( # type: ignore[assignment]

name=feature_view_proto.spec.name,

description=feature_view_proto.spec.description,

tags=dict(feature_view_proto.spec.tags),

owner=feature_view_proto.spec.owner,

org=feature_view_proto.spec.org,

online=feature_view_proto.spec.online,

offline=feature_view_proto.spec.offline,

ttl=(

timedelta(days=0)

if feature_view_proto.spec.ttl.ToNanoseconds() == 0

else feature_view_proto.spec.ttl.ToTimedelta()

source=source_views if source_views else batch_source,

sink_source=batch_source if source_views else None,

mode=mode_from_spec,

)

if stream_source:

feature_view.stream_source = stream_source

# This avoids the deprecation warning.

feature_view.entities = list(feature_view_proto.spec.entities)

# Instead of passing in a schema, we set the features and entity columns.

feature_view.features = [

Field.from_proto(field_proto)

for field_proto in feature_view_proto.spec.features

]

feature_view.entity_columns = [

Field.from_proto(field_proto)

for field_proto in feature_view_proto.spec.entity_columns

]

if len(feature_view.entities) != len(feature_view.entity_columns):

warnings.warn(

f"There are some mismatches in your feature view: {feature_view.name} registered entities. Please check if you have applied your entities correctly."

f"Entities: {feature_view.entities} vs Entity Columns: {feature_view.entity_columns}"

)

# Restore enable_validation from proto field.

feature_view.enable_validation = feature_view_proto.spec.enable_validation

# Restore version fields.

spec_version = feature_view_proto.spec.version

feature_view.version = spec_version or "latest"

cvn = feature_view_proto.meta.current_version_number

if cvn > 0:

feature_view.current_version_number = cvn

elif cvn == 0 and spec_version and spec_version.lower() != "latest":

feature_view.current_version_number = 0

else:

feature_view.current_version_number = None

# FeatureViewProjections are not saved in the FeatureView proto.

# Create the default projection.

feature_view.projection = FeatureViewProjection.from_feature_view_definition(

feature_view

)

if feature_view_proto.meta.HasField("created_timestamp"):

feature_view.created_timestamp = (

feature_view_proto.meta.created_timestamp.ToDatetime()

)

if feature_view_proto.meta.HasField("last_updated_timestamp"):

feature_view.last_updated_timestamp = (

feature_view_proto.meta.last_updated_timestamp.ToDatetime()

)

for interval in feature_view_proto.meta.materialization_intervals:

feature_view.materialization_intervals.append(

(

utils.make_tzaware(interval.start_time.ToDatetime()),

utils.make_tzaware(interval.end_time.ToDatetime()),

)

seen[feature_view_name] = feature_view

return feature_view

@property

def most_recent_end_time(self) -> Optional[datetime]:

"""

Retrieves the latest time up to which the feature view has been materialized.

Returns:

The latest time, or None if the feature view has not been materialized.

"""

if len(self.materialization_intervals) == 0:

return None

return max([interval[1] for interval in self.materialization_intervals])

feast/sdk/python/feast/feature_view.py at v0.63.0 · feast-dev/feast · GitHub

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