feast/sdk/python/feast/stream_feature_view.py at update-sf-test · feast-dev/feast · GitHub

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import copy

import functools

import warnings

from datetime import datetime, timedelta

from types import FunctionType

from typing import Dict, List, Optional, Tuple, Type, Union

import dill

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.pandas_transformation import PandasTransformation

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:

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.

"""

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]

feature_transformation: Optional[PandasTransformation]

def __init__(

self,

source: DataSource,

entities: Optional[List[Entity]] = None,

ttl: timedelta = timedelta(days=0),

tags: Optional[Dict[str, str]] = None,

online: bool = True,

description: str = "",

owner: 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] = "",

feature_transformation: Optional[Union[PandasTransformation]] = 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 (

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.aggregations = aggregations or []

self.mode = mode or ""

self.timestamp_field = timestamp_field or ""

self.udf = udf

self.udf_string = udf_string

self.feature_transformation = feature_transformation

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__()

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,

)

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=self.mode,

)

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,

feature_transformation=PandasTransformation(udf, udf_string)

if udf

else None,

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(

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,

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,

feature_transformation=PandasTransformation(user_function, 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

feast/sdk/python/feast/stream_feature_view.py at update-sf-test · feast-dev/feast · GitHub

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