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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 re
import warnings
from datetime import datetime, timedelta
from typing import Dict, List, Optional, Tuple, Union
from google.protobuf.duration_pb2 import Duration
from google.protobuf.json_format import MessageToJson
from feast import utils
from feast.data_source import DataSource
from feast.errors import RegistryInferenceFailure
from feast.feature import Feature
from feast.feature_view_projection import FeatureViewProjection
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.repo_config import RepoConfig
from feast.usage import log_exceptions
from feast.value_type import ValueType
warnings.simplefilter("once", DeprecationWarning)
class FeatureView:
"""
A FeatureView defines a logical grouping of serveable features.
Args:
name: Name of the group of features.
entities: The entities to which this group of features is associated.
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.
input: The source of data where this group of features is stored.
batch_source (optional): The batch source of data where this group of features
is stored.
stream_source (optional): The stream source of data where this group of features
is stored.
features (optional): The set of features defined as part of this FeatureView.
tags (optional): A dictionary of key-value pairs used for organizing
FeatureViews.
"""
name: str
entities: List[str]
features: List[Feature]
tags: Optional[Dict[str, str]]
ttl: timedelta
online: bool
input: DataSource
batch_source: DataSource
stream_source: Optional[DataSource] = None
created_timestamp: Optional[datetime] = None
last_updated_timestamp: Optional[datetime] = None
materialization_intervals: List[Tuple[datetime, datetime]]
@log_exceptions
def __init__(
self,
name: str,
entities: List[str],
ttl: Union[Duration, timedelta],
input: Optional[DataSource] = None,
batch_source: Optional[DataSource] = None,
stream_source: Optional[DataSource] = None,
features: Optional[List[Feature]] = None,
tags: Optional[Dict[str, str]] = None,
online: bool = True,
):
"""
Creates a FeatureView object.
Raises:
ValueError: A field mapping conflicts with an Entity or a Feature.
"""
if input is not None:
warnings.warn(
(
"The argument 'input' is being deprecated. Please use 'batch_source' "
"instead. Feast 0.13 and onwards will not support the argument 'input'."
),
DeprecationWarning,
)
_input = input or batch_source
assert _input is not None
_features = features or []
cols = [entity for entity in entities] + [feat.name for feat in _features]
for col in cols:
if _input.field_mapping is not None and col in _input.field_mapping.keys():
raise ValueError(
f"The field {col} is mapped to {_input.field_mapping[col]} for this data source. "
f"Please either remove this field mapping or use {_input.field_mapping[col]} as the "
f"Entity or Feature name."
)
self.name = name
self.entities = entities
self.features = _features
self.tags = tags if tags is not None else {}
if isinstance(ttl, Duration):
self.ttl = timedelta(seconds=int(ttl.seconds))
else:
self.ttl = ttl
self.online = online
self.input = _input
self.batch_source = _input
self.stream_source = stream_source
self.materialization_intervals = []
self.created_timestamp: Optional[datetime] = None
self.last_updated_timestamp: Optional[datetime] = None
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 __getitem__(self, item) -> FeatureViewProjection:
assert isinstance(item, list)
referenced_features = []
for feature in self.features:
if feature.name in item:
referenced_features.append(feature)
return FeatureViewProjection(self.name, referenced_features)
def __eq__(self, other):
if not isinstance(other, FeatureView):
raise TypeError(
"Comparisons should only involve FeatureView class objects."
)
if (
self.tags != other.tags
or self.name != other.name
or self.ttl != other.ttl
or self.online != other.online
):
return False
if sorted(self.entities) != sorted(other.entities):
return False
if sorted(self.features) != sorted(other.features):
return False
if self.batch_source != other.batch_source:
return False
if self.stream_source != other.stream_source:
return False
return True
def is_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.
"""
if not self.name:
raise ValueError("Feature view needs a name.")
if not self.entities:
raise ValueError("Feature view has no entities.")
def to_proto(self) -> FeatureViewProto:
"""
Converts a feature view object to its protobuf representation.
Returns:
A FeatureViewProto protobuf.
"""
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)
ttl_duration = None
if self.ttl is not None:
ttl_duration = Duration()
ttl_duration.FromTimedelta(self.ttl)
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__}"
spec = FeatureViewSpecProto(
name=self.name,
entities=self.entities,
features=[feature.to_proto() for feature in self.features],
tags=self.tags,
ttl=(ttl_duration if ttl_duration is not None else None),
online=self.online,
batch_source=batch_source_proto,
stream_source=stream_source_proto,
)
return FeatureViewProto(spec=spec, meta=meta)
@classmethod
def from_proto(cls, feature_view_proto: FeatureViewProto):
"""
Creates a feature view from a protobuf representation of a feature view.
Args:
feature_view_proto: A protobuf representation of a feature view.
Returns:
A FeatureViewProto object based on the feature view protobuf.
"""
batch_source = DataSource.from_proto(feature_view_proto.spec.batch_source)
stream_source = (
DataSource.from_proto(feature_view_proto.spec.stream_source)
if feature_view_proto.spec.HasField("stream_source")
else None
)
feature_view = cls(
name=feature_view_proto.spec.name,
entities=[entity for entity in feature_view_proto.spec.entities],
features=[
Feature(
name=feature.name,
dtype=ValueType(feature.value_type),
labels=dict(feature.labels),
)
for feature in feature_view_proto.spec.features
],
tags=dict(feature_view_proto.spec.tags),
online=feature_view_proto.spec.online,
ttl=(
None
if feature_view_proto.spec.ttl.seconds == 0
and feature_view_proto.spec.ttl.nanos == 0
else feature_view_proto.spec.ttl
),
batch_source=batch_source,
stream_source=stream_source,
)
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()),
)
)
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])
def infer_features_from_batch_source(self, config: RepoConfig):
"""
Infers the set of features associated to this feature view from the input source.
Args:
config: Configuration object used to configure the feature store.
Raises:
RegistryInferenceFailure: The set of features could not be inferred.
"""
if not self.features:
columns_to_exclude = {
self.batch_source.event_timestamp_column,
self.batch_source.created_timestamp_column,
} | set(self.entities)
for (
col_name,
col_datatype,
) in self.batch_source.get_table_column_names_and_types(config):
if col_name not in columns_to_exclude and not re.match(
"^__|__$",
col_name, # double underscores often signal an internal-use column
):
feature_name = (
self.batch_source.field_mapping[col_name]
if col_name in self.batch_source.field_mapping.keys()
else col_name
)
self.features.append(
Feature(
feature_name,
self.batch_source.source_datatype_to_feast_value_type()(
col_datatype
),
)
)
if not self.features:
raise RegistryInferenceFailure(
"FeatureView",
f"Could not infer Features for the FeatureView named {self.name}.",
)