from datetime import datetime

from typing import 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 FeatureViewMissingDuringFeatureServiceInference

from feast.feature_logging import LoggingConfig

from feast.feature_view import FeatureView

from feast.feature_view_projection import FeatureViewProjection

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,

)

from feast.usage import log_exceptions

@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]]

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

@log_exceptions

def __init__(

self,

*,

name: str,

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

tags: Dict[str, str] = None,

description: str = "",

owner: str = "",

logging_config: Optional[LoggingConfig] = None,

):

"""

Creates a FeatureService object.

Args:

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 (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.

"""

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

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, FeatureView]):

"""

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 __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):

raise TypeError(

"Comparisons should only involve FeatureService class objects."

)

if (

self.name != other.name

or self.description != other.description

or self.tags != other.tags

or self.owner != other.owner

):

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

),

)

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,

)

return FeatureServiceProto(spec=spec, meta=meta)

def validate(self):

pass