Source code for feast.stream_feature_view

import copy
import functools
import warnings
from datetime import datetime, timedelta
from types import FunctionType
from typing import Dict, List, Optional, Tuple, Union

import dill
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,

warnings.simplefilter("once", RuntimeWarning)

SUPPORTED_STREAM_SOURCES = {"KafkaSource", "PushSource"}

[docs]@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. 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. mode: The mode of execution. timestamp_field: Must be specified if aggregations are specified. Defines the timestamp column on which to aggregate windows. 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 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. """ name: str entities: List[str] ttl: Optional[timedelta] source: DataSource schema: List[Field] entity_columns: List[Field] features: List[Field] online: bool description: str tags: Dict[str, str] owner: str aggregations: List[Aggregation] mode: str timestamp_field: str materialization_intervals: List[Tuple[datetime, datetime]] udf: Optional[FunctionType] udf_string: Optional[str] def __init__( self, *, name: str, source: DataSource, entities: Optional[Union[List[Entity], List[str]]] = None, ttl: timedelta = timedelta(days=0), tags: Optional[Dict[str, str]] = None, online: Optional[bool] = True, description: Optional[str] = "", owner: Optional[str] = "", schema: Optional[List[Field]] = None, aggregations: Optional[List[Aggregation]] = None, mode: Optional[str] = "spark", timestamp_field: Optional[str] = "", udf: Optional[FunctionType] = None, udf_string: Optional[str] = "", ): 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 ( 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__}: {} instead " ) if aggregations and not timestamp_field: raise ValueError( "aggregations must have a timestamp field associated with them to perform the aggregations" ) self.aggregations = aggregations or [] self.mode = mode or "" self.timestamp_field = timestamp_field or "" self.udf = udf self.udf_string = udf_string super().__init__( name=name, entities=entities, ttl=ttl, tags=tags, online=online, description=description, owner=owner, schema=schema, source=source, ) 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__()
[docs] 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 = None if self.udf: udf_proto = UserDefinedFunctionProto( name=self.udf.__name__, body=dill.dumps(self.udf, recurse=True), body_text=self.udf_string, ) spec = StreamFeatureViewSpecProto(, 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, description=self.description, tags=self.tags, owner=self.owner, ttl=ttl_duration,, 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=self.mode, ) return StreamFeatureViewProto(spec=spec, meta=meta)
[docs] @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 ) stream_feature_view = cls(, description=sfv_proto.spec.description, tags=dict(sfv_proto.spec.tags), owner=sfv_proto.spec.owner,, 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, ) 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(, schema=self.schema, entities=self.entities, ttl=self.ttl, tags=self.tags,, description=self.description, owner=self.owner, aggregations=self.aggregations, mode=self.mode, timestamp_field=self.timestamp_field, source=self.source, udf=self.udf, ) fv.projection = copy.copy(self.projection) return fv
[docs]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