from datetime import datetime

from typing import TYPE_CHECKING, Dict, List, Optional, Union

from google.protobuf.json_format import MessageToJson

from typeguard import typechecked

from feast.base_feature_view import BaseFeatureView

from feast.errors import (

FeastObjectNotFoundException,

FeatureViewMissingDuringFeatureServiceInference,

)

from feast.feature_logging import LoggingConfig

from feast.feature_view import FeatureView

from feast.feature_view_projection import FeatureViewProjection

from feast.field import Field

from feast.labeling.label_view import LabelView

from feast.on_demand_feature_view import OnDemandFeatureView

from feast.protos.feast.core.FeatureService_pb2 import (

FeatureService as FeatureServiceProto,

)

from feast.protos.feast.core.FeatureService_pb2 import (

FeatureServiceMeta as FeatureServiceMetaProto,

)

from feast.protos.feast.core.FeatureService_pb2 import (

FeatureServiceSpec as FeatureServiceSpecProto,

)

if TYPE_CHECKING:

from feast.infra.registry.base_registry import BaseRegistry

@typechecked

class FeatureService:

"""

A feature service defines a logical group of features from one or more feature views.

This group of features can be retrieved together during training or serving.

Attributes:

name: The unique name of the feature service.

feature_view_projections: A list containing feature views and feature view

projections, representing the features in the feature service.

description: A human-readable description.

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

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

maintainer.

created_timestamp: The time when the feature service was created.

last_updated_timestamp: The time when the feature service was last updated.

"""

name: str

_features: List[Union[FeatureView, OnDemandFeatureView, LabelView]]

feature_view_projections: List[FeatureViewProjection]

description: str

tags: Dict[str, str]

owner: str

created_timestamp: Optional[datetime] = None

last_updated_timestamp: Optional[datetime] = None

logging_config: Optional[LoggingConfig] = None

precompute_online: bool = False

def __init__(

self,

*,

name: str,

features: List[Union[FeatureView, OnDemandFeatureView, LabelView]],

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

description: str = "",

owner: str = "",

logging_config: Optional[LoggingConfig] = None,

precompute_online: bool = False,

):

"""

Creates a FeatureService object.

Args:

name: The unique name of the feature service.

features: A list containing feature views and feature view

projections, representing the features in the feature service.

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.

precompute_online (optional): When True, a pre-computed feature vector is

maintained per entity for single-read online retrieval.

"""

self.name = name

self._features = features

self.feature_view_projections = []

self.description = description

self.tags = tags or {}

self.owner = owner

self.created_timestamp = None

self.last_updated_timestamp = None

self.logging_config = logging_config

self.precompute_online = precompute_online

for feature_grouping in self._features:

if isinstance(feature_grouping, BaseFeatureView):

self.feature_view_projections.append(feature_grouping.projection)

def infer_features(

self, fvs_to_update: Dict[str, Union[FeatureView, BaseFeatureView]]

):

"""

Infers the features for the projections of this feature service, and updates this feature

service in place.

This method is necessary since feature services may rely on feature views which require

feature inference.

Args:

fvs_to_update: A mapping of feature view names to corresponding feature views that

contains all the feature views necessary to run inference.

"""

for feature_grouping in self._features:

if isinstance(feature_grouping, BaseFeatureView):

projection = feature_grouping.projection

if projection.desired_features:

# The projection wants to select a specific set of inferred features.

# Example: FeatureService(features=[fv[["inferred_feature"]]]), where

# 'fv' is a feature view that was defined without a schema.

if feature_grouping.name in fvs_to_update:

# First we validate that the selected features have actually been inferred.

desired_features = set(projection.desired_features)

actual_features = set(

[

f.name

for f in fvs_to_update[feature_grouping.name].features

]

)

assert desired_features.issubset(actual_features)

# Then we extract the selected features and add them to the projection.

projection.features = []

for f in fvs_to_update[feature_grouping.name].features:

if f.name in desired_features:

projection.features.append(f)

else:

raise FeatureViewMissingDuringFeatureServiceInference(

feature_view_name=feature_grouping.name,

feature_service_name=self.name,

)

continue

if projection.features:

# The projection has already selected features from a feature view with a

# known schema, so no action needs to be taken.

# Example: FeatureService(features=[fv[["existing_feature"]]]), where

# 'existing_feature' was defined as part of the schema of 'fv'.

# Example: FeatureService(features=[fv]), where 'fv' was defined with a schema.

continue

# The projection wants to select all possible inferred features.

# Example: FeatureService(features=[fv]), where 'fv' is a feature view that

# was defined without a schema.

if feature_grouping.name in fvs_to_update:

projection.features = fvs_to_update[feature_grouping.name].features

else:

raise FeatureViewMissingDuringFeatureServiceInference(

feature_view_name=feature_grouping.name,

feature_service_name=self.name,

)

else:

raise ValueError(

f"The feature service {self.name} has been provided with an invalid type "

f'{type(feature_grouping)} as part of the "features" argument.)'

)

def prepare_for_apply(

self,

registry: "BaseRegistry",

project: str,

allow_cache: bool = False,

) -> "FeatureService":

"""

Materialize feature view projections before registry apply.

Uses the same FeatureService construction and ``infer_features`` path as

``FeatureStore.apply`` for SDK-defined services.

When the service is already fully resolved (SDK path where _features is

set and projections already have features populated via infer_features,

OR the proto deserialization path where projections carry full dtype

info), this is a no-op.

"""

from feast.types import Invalid

if self._features and all(p.features for p in self.feature_view_projections):

return self

if (

not self._features

and self.feature_view_projections

and all(

p.features and all(f.dtype != Invalid for f in p.features)

for p in self.feature_view_projections

)

):

return self

fvs_to_update: Dict[str, Union[FeatureView, BaseFeatureView]] = {}

if self._features:

for feature_grouping in self._features:

if isinstance(feature_grouping, BaseFeatureView):

fvs_to_update[feature_grouping.name] = (

registry.get_any_feature_view(

feature_grouping.name, project, allow_cache=allow_cache

)

)

self.infer_features(fvs_to_update=fvs_to_update)

return self

resolved_features: List[Union[FeatureView, OnDemandFeatureView, LabelView]] = []

for projection in self.feature_view_projections:

try:

feature_view = registry.get_any_feature_view(

projection.name, project, allow_cache=allow_cache

)

except FeastObjectNotFoundException as exc:

raise FeastObjectNotFoundException(

f"Feature view '{projection.name}' not found in project '{project}'"

) from exc

if not isinstance(

feature_view, (FeatureView, OnDemandFeatureView, LabelView)

):

raise ValueError(

f"Cannot resolve projection for feature view '{projection.name}'"

)

fvs_to_update[feature_view.name] = feature_view

features_by_name = {

feature.name: feature for feature in feature_view.features

}

if self._projection_matches_registry_features(projection, features_by_name):

resolved_features.append(feature_view.with_projection(projection))

elif projection.desired_features:

resolved_features.append(

feature_view[list(projection.desired_features)]

)

elif not projection.features:

resolved_features.append(feature_view)

else:

resolved_features.append(

feature_view[[feature.name for feature in projection.features]]

)

prepared = FeatureService(

name=self.name,

features=resolved_features,

tags=self.tags,

description=self.description,

owner=self.owner,

logging_config=self.logging_config,

precompute_online=self.precompute_online,

)

prepared.created_timestamp = self.created_timestamp

prepared.last_updated_timestamp = self.last_updated_timestamp

prepared.infer_features(fvs_to_update=fvs_to_update)

self._features = prepared._features

self.feature_view_projections = prepared.feature_view_projections

return self

@staticmethod

def _projection_matches_registry_features(

projection: FeatureViewProjection,

features_by_name: Dict[str, Field],

) -> bool:

if not projection.features:

return False

for feature in projection.features:

if feature.name not in features_by_name:

return False

if feature != features_by_name[feature.name]:

return False

return True

def __repr__(self):

items = (f"{k} = {v}" for k, v in self.__dict__.items())

return f"<{self.__class__.__name__}({', '.join(items)})>"

def __str__(self):

return str(MessageToJson(self.to_proto()))

def __hash__(self):

return hash(self.name)

def __eq__(self, other):

if not isinstance(other, FeatureService):

return False

if (

self.name != other.name

or self.description != other.description

or self.tags != other.tags

or self.owner != other.owner

or self.precompute_online != other.precompute_online

):

return False

if sorted(self.feature_view_projections) != sorted(

other.feature_view_projections

):

return False

return True

@classmethod

def from_proto(cls, feature_service_proto: FeatureServiceProto):

"""

Converts a FeatureServiceProto to a FeatureService object.

Args:

feature_service_proto: A protobuf representation of a FeatureService.

"""

fs = cls(

name=feature_service_proto.spec.name,

features=[],

tags=dict(feature_service_proto.spec.tags),

description=feature_service_proto.spec.description,

owner=feature_service_proto.spec.owner,

logging_config=LoggingConfig.from_proto(

feature_service_proto.spec.logging_config

),

precompute_online=feature_service_proto.spec.precompute_online,

)

fs.feature_view_projections.extend(

[

FeatureViewProjection.from_proto(projection)

for projection in feature_service_proto.spec.features

]

)

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

fs.created_timestamp = (

feature_service_proto.meta.created_timestamp.ToDatetime()

)

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

fs.last_updated_timestamp = (

feature_service_proto.meta.last_updated_timestamp.ToDatetime()

)

return fs

def to_proto(self) -> FeatureServiceProto:

"""

Converts a feature service to its protobuf representation.

Returns:

A FeatureServiceProto protobuf.

"""

meta = FeatureServiceMetaProto()

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)

spec = FeatureServiceSpecProto(

name=self.name,

features=[

projection.to_proto() for projection in self.feature_view_projections

],

tags=self.tags,

description=self.description,

owner=self.owner,

logging_config=self.logging_config.to_proto()

if self.logging_config

else None,

precompute_online=self.precompute_online,

)

return FeatureServiceProto(spec=spec, meta=meta)

@classmethod

def build_apply_request(

cls,

*,

name: str,

project: str,

feature_view_refs: List[tuple[str, Optional[List[str]]]],

description: str = "",

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

owner: str = "",

commit: bool = True,

):

"""Build an unresolved ApplyFeatureServiceRequest from feature view refs."""

from feast.protos.feast.core.Feature_pb2 import FeatureSpecV2

from feast.protos.feast.core.FeatureViewProjection_pb2 import (

FeatureViewProjection as FeatureViewProjectionProto,

)

from feast.protos.feast.registry import RegistryServer_pb2

projections = []

for feature_view_name, feature_names in feature_view_refs:

projection = FeatureViewProjectionProto(

feature_view_name=feature_view_name,

)

if feature_names:

for feature_name in feature_names:

projection.feature_columns.append(FeatureSpecV2(name=feature_name))

projections.append(projection)

spec = FeatureServiceSpecProto(

name=name,

features=projections,

tags=tags or {},

description=description,

owner=owner,

)

return RegistryServer_pb2.ApplyFeatureServiceRequest(

feature_service=FeatureServiceProto(spec=spec),

project=project,

commit=commit,

)

def validate(self):

if not self.precompute_online:

return

for fv in self._features:

if isinstance(fv, OnDemandFeatureView) and not fv.write_to_online_store:

raise ValueError(

f"FeatureService '{self.name}' has precompute_online=True but "

f"contains OnDemandFeatureView '{fv.name}' with "

f"write_to_online_store=False. On-demand transforms computed at "

f"serve time cannot be pre-computed."

)