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stream_feature_view.py
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import copy
import functools
import warnings
from datetime import datetime, timedelta
from types import FunctionType
from typing import Any, Dict, List, Optional, Tuple, Type, Union
import dill
from google.protobuf.duration_pb2 import Duration
from google.protobuf.message import Message
from typeguard import typechecked
from feast import flags_helper, utils
from feast.aggregation import Aggregation
from feast.data_source import DataSource
from feast.entity import Entity
from feast.feature_view import FeatureView
from feast.field import Field
from feast.protos.feast.core.DataSource_pb2 import DataSource as DataSourceProto
from feast.protos.feast.core.OnDemandFeatureView_pb2 import (
UserDefinedFunction as UserDefinedFunctionProto,
)
from feast.protos.feast.core.StreamFeatureView_pb2 import (
StreamFeatureView as StreamFeatureViewProto,
)
from feast.protos.feast.core.StreamFeatureView_pb2 import (
StreamFeatureViewSpec as StreamFeatureViewSpecProto,
)
from feast.protos.feast.core.Transformation_pb2 import (
FeatureTransformationV2 as FeatureTransformationProto,
)
from feast.protos.feast.core.Transformation_pb2 import (
UserDefinedFunctionV2 as UserDefinedFunctionProtoV2,
)
from feast.transformation.base import Transformation
from feast.transformation.mode import TransformationMode
warnings.simplefilter("once", RuntimeWarning)
SUPPORTED_STREAM_SOURCES = {"KafkaSource", "PushSource"}
@typechecked
class StreamFeatureView(FeatureView):
"""
A stream feature view defines a logical group of features that has both a stream data source and
a batch data source.
Attributes:
name: The unique name of the stream feature view.
mode: The transformation mode to use for the stream feature view. This can be one of TransformationMode.
entities: List of entities or entity join keys.
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.
schema: The schema of the feature view, including feature, timestamp, and entity
columns. If not specified, can be inferred from the underlying data source.
source: The stream source of data where this group of features is stored.
aggregations: List of aggregations registered with the stream feature view.
timestamp_field: Must be specified if aggregations are specified. Defines the timestamp column on which to aggregate windows.
enable_tiling: Enable tiling optimization for efficient windowed aggregations.
tiling_hop_size: Time interval between tiles (e.g., 5 minutes). Defaults to 5 minutes.
online: A boolean indicating whether online retrieval, and write to online store is enabled for this feature view.
offline: A boolean indicating whether offline retrieval, and write to offline store 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 stream feature view, typically the email of the primary maintainer.
udf: The user defined transformation function. This transformation function should have all of the corresponding imports imported within the function.
udf_string: The string representation of the user defined transformation function.
feature_transformation: The transformation to apply to the features.
Note, feature_transformation has precedence over udf and udf_string.
stream_engine: Optional dictionary containing stream engine specific configurations.
Note, it will override the repo-level default stream engine config defined in the yaml file.
"""
name: str
entities: List[str]
ttl: Optional[timedelta]
source: DataSource
sink_source: Optional[DataSource] = None
schema: List[Field]
entity_columns: List[Field]
features: List[Field]
online: bool
offline: bool
description: str
tags: Dict[str, str]
owner: str
mode: Union[TransformationMode, str]
materialization_intervals: List[Tuple[datetime, datetime]]
udf: Optional[FunctionType]
udf_string: Optional[str]
feature_transformation: Optional[Transformation]
stream_engine: Optional[Dict[str, Any]] = None
aggregations: List[Aggregation]
timestamp_field: str
enable_tiling: bool
tiling_hop_size: Optional[timedelta]
def __init__(
self,
*,
name: str,
source: Union[DataSource, "StreamFeatureView", List["StreamFeatureView"]],
sink_source: Optional[DataSource] = None,
entities: Optional[List[Entity]] = None,
ttl: timedelta = timedelta(days=0),
tags: Optional[Dict[str, str]] = None,
online: bool = True,
offline: bool = False,
description: str = "",
owner: str = "",
schema: Optional[List[Field]] = None,
aggregations: Optional[List[Aggregation]] = None,
mode: Union[str, TransformationMode] = TransformationMode.PYTHON,
timestamp_field: Optional[str] = "",
udf: Optional[FunctionType] = None,
udf_string: Optional[str] = "",
feature_transformation: Optional[Transformation] = None,
stream_engine: Optional[Dict[str, Any]] = None,
enable_tiling: bool = False,
tiling_hop_size: Optional[timedelta] = None,
):
if not flags_helper.is_test():
warnings.warn(
"Stream feature views are experimental features in alpha development. "
"Some functionality may still be unstable so functionality can change in the future.",
RuntimeWarning,
)
if isinstance(source, DataSource) and (
type(source).__name__ not in SUPPORTED_STREAM_SOURCES
and source.to_proto().type != DataSourceProto.SourceType.CUSTOM_SOURCE
):
raise ValueError(
f"Stream feature views need a stream source, expected one of {SUPPORTED_STREAM_SOURCES} "
f"or CUSTOM_SOURCE, got {type(source).__name__}: {source.name} instead "
)
if aggregations and not timestamp_field:
raise ValueError(
"aggregations must have a timestamp field associated with them to perform the aggregations"
)
self.mode = mode
self.udf = udf
self.udf_string = udf_string
self.aggregations = aggregations or []
self.timestamp_field = timestamp_field or ""
self.feature_transformation = (
feature_transformation or self.get_feature_transformation()
)
self.stream_engine = stream_engine
self.enable_tiling = enable_tiling
self.tiling_hop_size = tiling_hop_size
if enable_tiling and self.aggregations:
effective_hop_size = tiling_hop_size or timedelta(minutes=5)
time_windows = [
agg.time_window
for agg in self.aggregations
if agg.time_window is not None
]
if time_windows:
min_window_size = min(time_windows)
if effective_hop_size >= min_window_size:
raise ValueError(
f"tiling_hop_size ({effective_hop_size}) must be smaller than "
f"the minimum aggregation time_window ({min_window_size}). "
f"If hop_size >= window_size, the tiling algorithm will produce incorrect results."
)
super().__init__(
name=name,
entities=entities,
ttl=ttl,
tags=tags,
online=online,
offline=offline,
description=description,
owner=owner,
schema=schema,
source=source, # type: ignore[arg-type]
mode=mode,
sink_source=sink_source,
)
def get_feature_transformation(self) -> Optional[Transformation]:
if not self.udf:
# TODO: Currently StreamFeatureView allow no transformation, but this should be removed in the future
return None
if self.mode in (
TransformationMode.PANDAS,
TransformationMode.PYTHON,
TransformationMode.SPARK_SQL,
TransformationMode.SPARK,
) or self.mode in ("pandas", "python", "spark_sql", "spark"):
return Transformation(
mode=self.mode, udf=self.udf, udf_string=self.udf_string or ""
)
else:
raise ValueError(
f"Unsupported transformation mode: {self.mode} for StreamFeatureView"
)
def __eq__(self, other):
if not isinstance(other, StreamFeatureView):
raise TypeError("Comparisons should only involve StreamFeatureViews")
if not super().__eq__(other):
return False
if not self.udf:
return not other.udf
if not other.udf:
return False
if (
self.mode != other.mode
or self.timestamp_field != other.timestamp_field
or self.udf.__code__.co_code != other.udf.__code__.co_code
or self.udf_string != other.udf_string
or self.aggregations != other.aggregations
):
return False
return True
def __hash__(self) -> int:
return super().__hash__()
def to_proto(self):
meta = self.to_proto_meta()
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__}"
udf_proto, feature_transformation = None, None
if self.udf:
udf_proto = UserDefinedFunctionProto(
name=self.udf.__name__,
body=dill.dumps(self.udf, recurse=True),
body_text=self.udf_string,
)
udf_proto_v2 = UserDefinedFunctionProtoV2(
name=self.udf.__name__,
body=dill.dumps(self.udf, recurse=True),
body_text=self.udf_string,
)
feature_transformation = FeatureTransformationProto(
user_defined_function=udf_proto_v2,
)
mode = (
self.mode.value if isinstance(self.mode, TransformationMode) else self.mode
)
# Serialize tiling configuration
tiling_hop_size_duration = None
if self.tiling_hop_size is not None:
tiling_hop_size_duration = Duration()
tiling_hop_size_duration.FromTimedelta(self.tiling_hop_size)
spec = StreamFeatureViewSpecProto(
name=self.name,
entities=self.entities,
entity_columns=[field.to_proto() for field in self.entity_columns],
features=[field.to_proto() for field in self.schema],
user_defined_function=udf_proto,
feature_transformation=feature_transformation,
description=self.description,
tags=self.tags,
owner=self.owner,
ttl=ttl_duration,
online=self.online,
batch_source=batch_source_proto or None,
stream_source=stream_source_proto or None,
timestamp_field=self.timestamp_field,
aggregations=[agg.to_proto() for agg in self.aggregations],
mode=mode,
enable_tiling=self.enable_tiling,
tiling_hop_size=tiling_hop_size_duration,
)
return StreamFeatureViewProto(spec=spec, meta=meta)
@classmethod
def from_proto(cls, sfv_proto):
batch_source = (
DataSource.from_proto(sfv_proto.spec.batch_source)
if sfv_proto.spec.HasField("batch_source")
else None
)
stream_source = (
DataSource.from_proto(sfv_proto.spec.stream_source)
if sfv_proto.spec.HasField("stream_source")
else None
)
udf = (
dill.loads(sfv_proto.spec.user_defined_function.body)
if sfv_proto.spec.HasField("user_defined_function")
else None
)
udf_string = (
sfv_proto.spec.user_defined_function.body_text
if sfv_proto.spec.HasField("user_defined_function")
else None
)
# feature_transformation = (
# sfv_proto.spec.feature_transformation.user_defined_function.body_text
# if sfv_proto.spec.HasField("feature_transformation")
# else None
# )
stream_feature_view = cls(
name=sfv_proto.spec.name,
description=sfv_proto.spec.description,
tags=dict(sfv_proto.spec.tags),
owner=sfv_proto.spec.owner,
online=sfv_proto.spec.online,
schema=[
Field.from_proto(field_proto) for field_proto in sfv_proto.spec.features
],
ttl=(
timedelta(days=0)
if sfv_proto.spec.ttl.ToNanoseconds() == 0
else sfv_proto.spec.ttl.ToTimedelta()
),
source=stream_source,
mode=sfv_proto.spec.mode,
udf=udf,
udf_string=udf_string,
aggregations=[
Aggregation.from_proto(agg_proto)
for agg_proto in sfv_proto.spec.aggregations
],
timestamp_field=sfv_proto.spec.timestamp_field,
enable_tiling=sfv_proto.spec.enable_tiling,
tiling_hop_size=(
sfv_proto.spec.tiling_hop_size.ToTimedelta()
if sfv_proto.spec.HasField("tiling_hop_size")
and sfv_proto.spec.tiling_hop_size.ToNanoseconds() != 0
else None
),
)
if batch_source:
stream_feature_view.batch_source = batch_source
if stream_source:
stream_feature_view.stream_source = stream_source
stream_feature_view.entities = list(sfv_proto.spec.entities)
stream_feature_view.features = [
Field.from_proto(field_proto) for field_proto in sfv_proto.spec.features
]
stream_feature_view.entity_columns = [
Field.from_proto(field_proto)
for field_proto in sfv_proto.spec.entity_columns
]
if sfv_proto.meta.HasField("created_timestamp"):
stream_feature_view.created_timestamp = (
sfv_proto.meta.created_timestamp.ToDatetime()
)
if sfv_proto.meta.HasField("last_updated_timestamp"):
stream_feature_view.last_updated_timestamp = (
sfv_proto.meta.last_updated_timestamp.ToDatetime()
)
for interval in sfv_proto.meta.materialization_intervals:
stream_feature_view.materialization_intervals.append(
(
utils.make_tzaware(interval.start_time.ToDatetime()),
utils.make_tzaware(interval.end_time.ToDatetime()),
)
)
return stream_feature_view
def __copy__(self):
fv = StreamFeatureView(
name=self.name,
schema=self.schema,
ttl=self.ttl,
tags=self.tags,
online=self.online,
description=self.description,
owner=self.owner,
aggregations=self.aggregations,
mode=self.mode,
timestamp_field=self.timestamp_field,
source=self.stream_source if self.stream_source else self.batch_source,
udf=self.udf,
udf_string=self.udf_string,
feature_transformation=self.feature_transformation,
)
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
@property
def proto_class(self) -> Type[Message]:
return StreamFeatureViewProto
def stream_feature_view(
*,
entities: Optional[Union[List[Entity], List[str]]] = None,
ttl: Optional[timedelta] = None,
tags: Optional[Dict[str, str]] = None,
online: Optional[bool] = True,
description: Optional[str] = "",
owner: Optional[str] = "",
schema: Optional[List[Field]] = None,
source: Optional[DataSource] = None,
aggregations: Optional[List[Aggregation]] = None,
mode: Optional[str] = "spark",
timestamp_field: Optional[str] = "",
):
"""
Creates an StreamFeatureView object with the given user function as udf.
Please make sure that the udf contains all non-built in imports within the function to ensure that the execution
of a deserialized function does not miss imports.
"""
def mainify(obj):
# Needed to allow dill to properly serialize the udf. Otherwise, clients will need to have a file with the same
# name as the original file defining the sfv.
if obj.__module__ != "__main__":
obj.__module__ = "__main__"
def decorator(user_function):
udf_string = dill.source.getsource(user_function)
mainify(user_function)
stream_feature_view_obj = StreamFeatureView(
name=user_function.__name__,
entities=entities,
ttl=ttl,
source=source,
schema=schema,
udf=user_function,
udf_string=udf_string,
description=description,
tags=tags,
online=online,
owner=owner,
aggregations=aggregations,
mode=mode,
timestamp_field=timestamp_field,
)
functools.update_wrapper(wrapper=stream_feature_view_obj, wrapped=user_function)
return stream_feature_view_obj
return decorator