feast/sdk/python/feast/batch_feature_view.py at v0.63.0 · feast-dev/feast · GitHub

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import functools

import warnings

from datetime import datetime, timedelta

from typing import Any, Callable, Dict, List, Optional, Tuple, Union

import dill

from feast import flags_helper

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.transformation.base import Transformation

from feast.transformation.mode import TransformationMode

warnings.simplefilter("once", RuntimeWarning)

SUPPORTED_BATCH_SOURCES = {

"BigQuerySource",

"FileSource",

"RedshiftSource",

"SnowflakeSource",

"SparkSource",

"TrinoSource",

"AthenaSource",

}

class BatchFeatureView(FeatureView):

"""

A batch feature view defines a logical group of features that has only a batch data source.

Attributes:

mode: The transformation mode to use for the batch 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 batch source of data where this group of features is stored.

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 batch feature view, typically the email of the primary maintainer.

udf: A user-defined function that applies transformations to the data in the batch feature view.

udf_string: A string representation of the user-defined function.

feature_transformation: A transformation object that defines how features are transformed.

Note, feature_transformation has precedence over udf and udf_string.

batch_engine: A dictionary containing configuration for the batch engine used to process the feature view.

Note, it will override the repo-level default batch engine config defined in the yaml file.

aggregations: A list of aggregations to be applied to the features in the batch feature view.

"""

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

timestamp_field: str

materialization_intervals: List[Tuple[datetime, datetime]]

udf: Optional[Callable[[Any], Any]]

udf_string: Optional[str]

feature_transformation: Optional[Transformation]

batch_engine: Optional[Dict[str, Any]]

aggregations: Optional[List[Aggregation]]

def __init__(

self,

mode: Union[TransformationMode, str] = TransformationMode.PYTHON,

source: Optional[

Union[DataSource, "BatchFeatureView", List["BatchFeatureView"]]

] = None,

sink_source: Optional[DataSource] = None,

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

ttl: Optional[timedelta] = None,

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

online: bool = False,

offline: bool = False,

description: str = "",

owner: str = "",

org: str = "",

schema: Optional[List[Field]] = None,

udf: Optional[Callable[[Any], Any]] = None,

udf_string: Optional[str] = "",

feature_transformation: Optional[Transformation] = None,

batch_engine: Optional[Dict[str, Any]] = None,

aggregations: Optional[List[Aggregation]] = None,

enable_validation: bool = False,

version: str = "latest",

if not flags_helper.is_test():

warnings.warn(

"Batch 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_BATCH_SOURCES

and source.to_proto().type != DataSourceProto.SourceType.CUSTOM_SOURCE

raise ValueError(

f"Batch feature views need a batch source, expected one of {SUPPORTED_BATCH_SOURCES} "

f"or CUSTOM_SOURCE, got {type(source).__name__}: {source.name} instead "

)

if source is None and aggregations:

raise ValueError(

"BatchFeatureView with aggregations requires a source to aggregate from."

)

if (

source is None

and not udf

and not feature_transformation

and not aggregations

raise ValueError(

"BatchFeatureView requires at least one of: source, udf, feature_transformation, or aggregations."

)

self.mode = mode

self.udf = udf

self.udf_string = udf_string

self.feature_transformation = (

feature_transformation or self.get_feature_transformation()

)

self.batch_engine = batch_engine

self.aggregations = aggregations or []

super().__init__(

name=name,

entities=entities,

ttl=ttl,

tags=tags,

online=online,

offline=offline,

description=description,

owner=owner,

org=org,

schema=schema,

source=source, # type: ignore[arg-type]

sink_source=sink_source,

mode=mode,

enable_validation=enable_validation,

version=version,

)

def get_feature_transformation(self) -> Optional[Transformation]:

if not self.udf:

return None

if self.mode in (

TransformationMode.PANDAS,

TransformationMode.PYTHON,

TransformationMode.SQL,

TransformationMode.RAY,

) or self.mode in ("pandas", "python", "sql", "ray"):

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 batch_feature_view(

mode: Union[TransformationMode, str] = TransformationMode.PYTHON,

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

ttl: Optional[timedelta] = None,

source: Optional[DataSource] = None,

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

online: bool = True,

offline: bool = True,

description: str = "",

owner: str = "",

org: str = "",

schema: Optional[List[Field]] = None,

enable_validation: bool = False,

version: str = "latest",

"""

Creates a BatchFeatureView object with the given user-defined function (UDF) as the transformation.

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)

batch_feature_view_obj = BatchFeatureView(

name=name or user_function.__name__,

mode=mode,

entities=entities,

ttl=ttl,

source=source,

tags=tags,

online=online,

offline=offline,

description=description,

owner=owner,

org=org,

schema=schema,

udf=user_function,

udf_string=udf_string,

enable_validation=enable_validation,

version=version,

)

functools.update_wrapper(wrapper=batch_feature_view_obj, wrapped=user_function)

return batch_feature_view_obj

return decorator

feast/sdk/python/feast/batch_feature_view.py at v0.63.0 · feast-dev/feast · GitHub

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