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feature_view.py
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681 lines (603 loc) · 27.4 KB
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# Copyright 2019 The Feast Authors
#
# 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.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.protos.feast.core.Transformation_pb2 import (
FeatureTransformationV2 as FeatureTransformationProto,
)
from feast.transformation.mode import TransformationMode
from feast.types import from_value_type
from feast.value_type import ValueType
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:
name: The unique name of the feature view.
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: The batch source of data where this group of features
is stored. This is optional ONLY if a push source is specified as the
stream_source, since push sources contain their own batch sources.
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.
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).
"""
name: str
entities: List[str]
ttl: Optional[timedelta]
batch_source: 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
materialization_intervals: List[Tuple[datetime, datetime]]
mode: Optional[Union["TransformationMode", str]]
def __init__(
self,
*,
name: str,
source: Union[DataSource, "FeatureView", List["FeatureView"]],
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 = "",
mode: Optional[Union["TransformationMode", str]] = None,
):
"""
Creates a FeatureView object.
Args:
name: The unique name of the feature view.
source: The source of data for this group of features. May be a stream source, or a batch source.
If a stream source, the source should contain a batch_source for backfills & batch materialization.
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.
mode (optional): The transformation mode for feature transformations. Only meaningful
when transformations are applied. Choose from TransformationMode enum values.
Raises:
ValueError: A field mapping conflicts with an Entity or a Feature.
"""
self.name = name
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 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
else:
# Derived view source definition
if not sink_source:
raise ValueError("Derived FeatureView must specify `sink_source`.")
self.batch_source = sink_source
# 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.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,
)
# 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 __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
):
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 = None
if self.batch_source:
batch_source_proto = self.batch_source.to_proto()
batch_source_proto.data_source_class_type = f"{self.batch_source.__class__.__module__}.{self.batch_source.__class__.__name__}"
stream_source_proto = None
if self.stream_source:
stream_source_proto = self.stream_source.to_proto()
stream_source_proto.data_source_class_type = f"{self.stream_source.__class__.__module__}.{self.stream_source.__class__.__name__}"
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:
from feast.protos.feast.core.Transformation_pb2 import (
SubstraitTransformationV2 as SubstraitTransformationProto,
)
from feast.protos.feast.core.Transformation_pb2 import (
UserDefinedFunctionV2 as UserDefinedFunctionProto,
)
transformation_proto = self.feature_transformation.to_proto()
if isinstance(transformation_proto, UserDefinedFunctionProto):
feature_transformation_proto = FeatureTransformationProto(
user_defined_function=transformation_proto,
)
elif isinstance(transformation_proto, SubstraitTransformationProto):
feature_transformation_proto = FeatureTransformationProto(
substrait_transformation=transformation_proto,
)
mode_str = ""
if self.mode:
mode_str = (
self.mode.value
if isinstance(self.mode, TransformationMode)
else self.mode
)
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,
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_str,
)
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)
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) -> "FeatureView":
return cls._from_proto_internal(feature_view_proto, seen={})
@classmethod
def _from_proto_internal(
cls,
feature_view_proto: FeatureViewProto,
seen: Dict[str, Union[None, "FeatureView"]],
) -> "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
)
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 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,
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,
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}"
)
# 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])