# Copyright 2020 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 enum

import warnings

from abc import ABC, abstractmethod

from datetime import datetime, timedelta, timezone

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

from google.protobuf.duration_pb2 import Duration

from google.protobuf.json_format import MessageToJson

from typeguard import typechecked

from feast import type_map

from feast.credentials import ConnectionRef

from feast.data_format import StreamFormat

from feast.field import Field

from feast.protos.feast.core.DataSource_pb2 import DataSource as DataSourceProto

from feast.repo_config import RepoConfig, get_data_source_class_from_type

from feast.types import from_value_type

from feast.utils import _utc_now

from feast.value_type import ValueType

class KafkaOptions:

"""

DataSource Kafka options used to source features from Kafka messages

"""

def __init__(

self,

kafka_bootstrap_servers: str,

message_format: StreamFormat,

topic: str,

watermark_delay_threshold: Optional[timedelta] = None,

):

self.kafka_bootstrap_servers = kafka_bootstrap_servers

self.message_format = message_format

self.topic = topic

self.watermark_delay_threshold = watermark_delay_threshold

@classmethod

def from_proto(cls, kafka_options_proto: DataSourceProto.KafkaOptions):

"""

Creates a KafkaOptions from a protobuf representation of a kafka option

Args:

kafka_options_proto: A protobuf representation of a DataSource

Returns:

Returns a KafkaOptions object based on the kafka_options protobuf

"""

watermark_delay_threshold = None

if kafka_options_proto.HasField("watermark_delay_threshold"):

watermark_delay_threshold = (

kafka_options_proto.watermark_delay_threshold.ToTimedelta()

)

kafka_options = cls(

kafka_bootstrap_servers=kafka_options_proto.kafka_bootstrap_servers,

message_format=StreamFormat.from_proto(kafka_options_proto.message_format),

topic=kafka_options_proto.topic,

watermark_delay_threshold=watermark_delay_threshold,

)

return kafka_options

def to_proto(self) -> DataSourceProto.KafkaOptions:

"""

Converts an KafkaOptionsProto object to its protobuf representation.

Returns:

KafkaOptionsProto protobuf

"""

watermark_delay_threshold = None

if self.watermark_delay_threshold is not None:

watermark_delay_threshold = Duration()

watermark_delay_threshold.FromTimedelta(self.watermark_delay_threshold)

kafka_options_proto = DataSourceProto.KafkaOptions(

kafka_bootstrap_servers=self.kafka_bootstrap_servers,

message_format=self.message_format.to_proto(),

topic=self.topic,

watermark_delay_threshold=watermark_delay_threshold,

)

return kafka_options_proto

class KinesisOptions:

"""

DataSource Kinesis options used to source features from Kinesis records

"""

def __init__(

self,

record_format: StreamFormat,

region: str,

stream_name: str,

):

self.record_format = record_format

self.region = region

self.stream_name = stream_name

@classmethod

def from_proto(cls, kinesis_options_proto: DataSourceProto.KinesisOptions):

"""

Creates a KinesisOptions from a protobuf representation of a kinesis option

Args:

kinesis_options_proto: A protobuf representation of a DataSource

Returns:

Returns a KinesisOptions object based on the kinesis_options protobuf

"""

kinesis_options = cls(

record_format=StreamFormat.from_proto(kinesis_options_proto.record_format),

region=kinesis_options_proto.region,

stream_name=kinesis_options_proto.stream_name,

)

return kinesis_options

def to_proto(self) -> DataSourceProto.KinesisOptions:

"""

Converts an KinesisOptionsProto object to its protobuf representation.

Returns:

KinesisOptionsProto protobuf

"""

kinesis_options_proto = DataSourceProto.KinesisOptions(

record_format=self.record_format.to_proto(),

region=self.region,

stream_name=self.stream_name,

)

return kinesis_options_proto

_DATA_SOURCE_OPTIONS = {

DataSourceProto.SourceType.BATCH_FILE: "feast.infra.offline_stores.file_source.FileSource",

DataSourceProto.SourceType.BATCH_BIGQUERY: "feast.infra.offline_stores.bigquery_source.BigQuerySource",

DataSourceProto.SourceType.BATCH_REDSHIFT: "feast.infra.offline_stores.redshift_source.RedshiftSource",

DataSourceProto.SourceType.BATCH_SNOWFLAKE: "feast.infra.offline_stores.snowflake_source.SnowflakeSource",

DataSourceProto.SourceType.BATCH_TRINO: "feast.infra.offline_stores.contrib.trino_offline_store.trino_source.TrinoSource",

DataSourceProto.SourceType.BATCH_SPARK: "feast.infra.offline_stores.contrib.spark_offline_store.spark_source.SparkSource",

DataSourceProto.SourceType.BATCH_ATHENA: "feast.infra.offline_stores.contrib.athena_offline_store.athena_source.AthenaSource",

DataSourceProto.SourceType.BATCH_ICEBERG: "feast.infra.data_sources.contrib.iceberg_catalog.iceberg_source.IcebergSource",

DataSourceProto.SourceType.STREAM_KAFKA: "feast.data_source.KafkaSource",

DataSourceProto.SourceType.STREAM_KINESIS: "feast.data_source.KinesisSource",

DataSourceProto.SourceType.REQUEST_SOURCE: "feast.data_source.RequestSource",

DataSourceProto.SourceType.PUSH_SOURCE: "feast.data_source.PushSource",

}

_DATA_SOURCE_FOR_OFFLINE_STORE = {

DataSourceProto.SourceType.BATCH_FILE: "feast.infra.offline_stores.dask.DaskOfflineStore",

DataSourceProto.SourceType.BATCH_BIGQUERY: "feast.infra.offline_stores.bigquery.BigQueryOfflineStore",

DataSourceProto.SourceType.BATCH_REDSHIFT: "feast.infra.offline_stores.redshift.RedshiftOfflineStore",

DataSourceProto.SourceType.BATCH_SNOWFLAKE: "feast.infra.offline_stores.snowflake.SnowflakeOfflineStore",

DataSourceProto.SourceType.BATCH_TRINO: "feast.infra.offline_stores.contrib.trino_offline_store.trino.TrinoOfflineStore",

DataSourceProto.SourceType.BATCH_SPARK: "feast.infra.offline_stores.contrib.spark_offline_store.spark.SparkOfflineStore",

DataSourceProto.SourceType.BATCH_ATHENA: "feast.infra.offline_stores.contrib.athena_offline_store.athena.AthenaOfflineStore",

}

@typechecked

class DataSource(ABC):

"""

DataSource that can be used to source features.

Args:

name: Name of data source, which should be unique within a project

timestamp_field (optional): Event timestamp field used for point-in-time joins of

feature values.

created_timestamp_column (optional): Timestamp column indicating when the row

was created, used for deduplicating rows.

field_mapping (optional): A dictionary mapping of column names in this data

source to feature names in a feature table or view. Only used for feature

columns and timestamp columns, not entity columns.

description (optional) A human-readable description.

tags (optional): A dictionary of key-value pairs to store arbitrary metadata.

owner (optional): The owner of the data source, typically the email of the primary

maintainer.

date_partition_column (optional): Timestamp column used for partitioning. Not supported by all offline stores.

connection_ref (optional): Connection reference for this data source.

When set, OfflineStores resolve connection type and credentials at runtime

via the registered CredentialProvider instead of using ambient env vars or

the global offline_store config. All fields except provider and name are optional.

created_timestamp: The time when the data source was created.

last_updated_timestamp: The time when the data source was last updated.

"""

name: str

timestamp_field: str

created_timestamp_column: str

field_mapping: Dict[str, str]

description: str

tags: Dict[str, str]

owner: str

date_partition_column: str

timestamp_field_type: str

connection_ref: Optional[ConnectionRef]

created_timestamp: Optional[datetime]

last_updated_timestamp: Optional[datetime]

def __init__(

self,

*,

name: str,

timestamp_field: Optional[str] = None,

created_timestamp_column: Optional[str] = None,

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

description: Optional[str] = "",

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

owner: Optional[str] = "",

date_partition_column: Optional[str] = None,

timestamp_field_type: Optional[str] = None,

connection_ref: Optional[ConnectionRef] = None,

):

"""

Creates a DataSource object.

Args:

name: Name of data source, which should be unique within a project.

timestamp_field (optional): Event timestamp field used for point-in-time joins of

feature values.

created_timestamp_column (optional): Timestamp column indicating when the row

was created, used for deduplicating rows.

field_mapping (optional): A dictionary mapping of column names in this data

source to feature names in a feature table or view. Only used for feature

columns, not entity or timestamp columns.

description (optional): A human-readable description.

tags (optional): A dictionary of key-value pairs to store arbitrary metadata.

owner (optional): The owner of the data source, typically the email of the primary

maintainer.

date_partition_column (optional): Timestamp column used for partitioning. Not supported by all stores

timestamp_field_type (optional): Type of the timestamp_field column.

Defaults to "TIMESTAMP". Set to "DATE" when the event timestamp column

is a DATE type, so SQL generation uses date-only comparisons.

connection_ref (optional): Connection reference for this data source.

See :class:`~feast.credentials.ConnectionRef`.

"""

self.name = name

self.timestamp_field = timestamp_field or ""

self.created_timestamp_column = (

created_timestamp_column if created_timestamp_column else ""

)

self.field_mapping = field_mapping if field_mapping else {}

if (

self.timestamp_field

and self.timestamp_field == self.created_timestamp_column

):

raise ValueError(

"Please do not use the same column for 'timestamp_field' and 'created_timestamp_column'."

)

self.description = description or ""

self.tags = tags or {}

self.owner = owner or ""

self.date_partition_column = (

date_partition_column if date_partition_column else ""

)

self.timestamp_field_type = timestamp_field_type if timestamp_field_type else ""

self.connection_ref = connection_ref

now = _utc_now()

self.created_timestamp = now

self.last_updated_timestamp = now

def resolve_credentials(self) -> Optional[Dict[str, str]]:

"""Resolve credentials from the ``connection_ref`` if set.

Returns ``None`` when no ``connection_ref`` is configured (ambient

credentials are used instead).

"""

if self.connection_ref is None:

return None

from feast.credentials import resolve_credentials

return resolve_credentials(self.connection_ref)

def __hash__(self):

return hash((self.name, self.timestamp_field))

def __str__(self):

return str(MessageToJson(self.to_proto()))

def __eq__(self, other):

if other is None:

return False

if not isinstance(other, DataSource):

return False

if (

self.name != other.name

or self.timestamp_field != other.timestamp_field

or self.created_timestamp_column != other.created_timestamp_column

or self.field_mapping != other.field_mapping

or self.date_partition_column != other.date_partition_column

or self.timestamp_field_type != other.timestamp_field_type

or self.description != other.description

or self.tags != other.tags

or self.owner != other.owner

or self.connection_ref != other.connection_ref

):

return False

return True

@staticmethod

@abstractmethod

def from_proto(data_source: DataSourceProto) -> Any:

"""

Converts data source config in protobuf spec to a DataSource class object.

Args:

data_source: A protobuf representation of a DataSource.

Returns:

A DataSource class object.

Raises:

ValueError: The type of DataSource could not be identified.

"""

data_source_type = data_source.type

if not data_source_type or (

data_source_type

not in list(_DATA_SOURCE_OPTIONS.keys())

+ [DataSourceProto.SourceType.CUSTOM_SOURCE]

):

raise ValueError("Could not identify the source type being added.")

if data_source_type == DataSourceProto.SourceType.CUSTOM_SOURCE:

cls = get_data_source_class_from_type(data_source.data_source_class_type)

data_source_instance = cls.from_proto(data_source)

else:

cls = get_data_source_class_from_type(

_DATA_SOURCE_OPTIONS[data_source_type]

)

data_source_instance = cls.from_proto(data_source)

data_source_instance._extract_timestamps_from_proto(data_source)

data_source_instance.connection_ref = ConnectionRef.from_tags(

dict(data_source.tags)

)

return data_source_instance

def to_proto(self) -> DataSourceProto:

"""

Converts a DataSourceProto object to its protobuf representation.

"""

proto = self._to_proto_impl()

self._set_timestamps_in_proto(proto)

if self.connection_ref is not None:

for key, value in self.connection_ref.to_tags().items():

proto.tags[key] = value

return proto

@abstractmethod

def _to_proto_impl(self) -> DataSourceProto:

"""

Subclass implementation of protobuf conversion.

This should be implemented by each DataSource subclass.

"""

raise NotImplementedError

def validate(self, config: RepoConfig):

"""

Validates the underlying data source.

Args:

config: Configuration object used to configure a feature store.

"""

raise NotImplementedError

@staticmethod

@abstractmethod

def source_datatype_to_feast_value_type() -> Callable[[str], ValueType]:

"""

Returns the callable method that returns Feast type given the raw column type.

"""

raise NotImplementedError

def get_table_column_names_and_types(

self, config: RepoConfig

) -> Iterable[Tuple[str, str]]:

"""

Returns the list of column names and raw column types.

Args:

config: Configuration object used to configure a feature store.

"""

raise NotImplementedError

def get_table_query_string(self) -> str:

"""

Returns a string that can directly be used to reference this table in SQL.

"""

raise NotImplementedError

def _extract_timestamps_from_proto(self, data_source_proto: DataSourceProto):

"""

Internal method to extract created_timestamp and last_updated_timestamp from protobuf.

Called automatically by the base from_proto method.

"""

if data_source_proto.HasField("meta"):

if data_source_proto.meta.HasField("created_timestamp"):

self.created_timestamp = (

data_source_proto.meta.created_timestamp.ToDatetime().replace(

tzinfo=timezone.utc

)

)

if data_source_proto.meta.HasField("last_updated_timestamp"):

self.last_updated_timestamp = (

data_source_proto.meta.last_updated_timestamp.ToDatetime().replace(

tzinfo=timezone.utc

)

)

def _set_timestamps_in_proto(self, data_source_proto: DataSourceProto):

"""

Internal method to set created_timestamp and last_updated_timestamp in protobuf.

Called automatically by the base to_proto method.

"""

if not data_source_proto.HasField("meta"):

data_source_proto.meta.CopyFrom(DataSourceProto.SourceMeta())

if self.created_timestamp:

data_source_proto.meta.created_timestamp.FromDatetime(

self.created_timestamp

)

if self.last_updated_timestamp:

data_source_proto.meta.last_updated_timestamp.FromDatetime(

self.last_updated_timestamp

)

@abstractmethod

def source_type(self) -> DataSourceProto.SourceType.ValueType: ...

@typechecked

class KafkaSource(DataSource):

"""A KafkaSource allow users to register Kafka streams as data sources."""

def __init__(

self,

*,

name: str,

timestamp_field: str,

message_format: StreamFormat,

bootstrap_servers: Optional[str] = None,

kafka_bootstrap_servers: Optional[str] = None,

topic: Optional[str] = None,

created_timestamp_column: Optional[str] = "",

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

description: Optional[str] = "",

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

owner: Optional[str] = "",

batch_source: Optional[DataSource] = None,

watermark_delay_threshold: Optional[timedelta] = None,

):

"""

Creates a KafkaSource object.

Args:

name: Name of data source, which should be unique within a project

timestamp_field: Event timestamp field used for point-in-time joins of feature values.

message_format: StreamFormat of serialized messages.

bootstrap_servers: (Deprecated) The servers of the kafka broker in the form "localhost:9092".

kafka_bootstrap_servers (optional): The servers of the kafka broker in the form "localhost:9092".

topic (optional): The name of the topic to read from in the kafka source.

created_timestamp_column (optional): Timestamp column indicating when the row

was created, used for deduplicating rows.

field_mapping (optional): A dictionary mapping of column names in this data

source to feature names in a feature table or view. Only used for feature

columns, not entity or timestamp columns.

description (optional): A human-readable description.

tags (optional): A dictionary of key-value pairs to store arbitrary metadata.

owner (optional): The owner of the data source, typically the email of the primary

maintainer.

batch_source (optional): The datasource that acts as a batch source.

watermark_delay_threshold (optional): The watermark delay threshold for stream data.

Specifically how late stream data can arrive without being discarded.

"""

if bootstrap_servers:

warnings.warn(

(

"The 'bootstrap_servers' parameter has been deprecated in favor of 'kafka_bootstrap_servers'. "

"Feast 0.25 and onwards will not support the 'bootstrap_servers' parameter."

),

DeprecationWarning,

)

super().__init__(

name=name,

timestamp_field=timestamp_field,

created_timestamp_column=created_timestamp_column,

field_mapping=field_mapping,

description=description,

tags=tags,

owner=owner,

)

self.batch_source = batch_source

kafka_bootstrap_servers = kafka_bootstrap_servers or bootstrap_servers or ""

topic = topic or ""

self.kafka_options = KafkaOptions(

kafka_bootstrap_servers=kafka_bootstrap_servers,

message_format=message_format,

topic=topic,

watermark_delay_threshold=watermark_delay_threshold,

)

def __eq__(self, other):

if not isinstance(other, KafkaSource):

return False

if not super().__eq__(other):

return False

if (

self.kafka_options.kafka_bootstrap_servers

!= other.kafka_options.kafka_bootstrap_servers

or self.kafka_options.message_format != other.kafka_options.message_format

or self.kafka_options.topic != other.kafka_options.topic

or self.kafka_options.watermark_delay_threshold

!= other.kafka_options.watermark_delay_threshold

):

return False

return True

def __hash__(self):

return super().__hash__()

@staticmethod

def from_proto(data_source: DataSourceProto):

watermark_delay_threshold = None

if data_source.kafka_options.HasField("watermark_delay_threshold"):

watermark_delay_threshold = (

data_source.kafka_options.watermark_delay_threshold.ToTimedelta()

)

return KafkaSource(

name=data_source.name,

field_mapping=dict(data_source.field_mapping),

kafka_bootstrap_servers=data_source.kafka_options.kafka_bootstrap_servers,

message_format=StreamFormat.from_proto(

data_source.kafka_options.message_format

),

watermark_delay_threshold=watermark_delay_threshold,

topic=data_source.kafka_options.topic,

created_timestamp_column=data_source.created_timestamp_column,

timestamp_field=data_source.timestamp_field,

description=data_source.description,

tags=dict(data_source.tags),

owner=data_source.owner,

batch_source=(

DataSource.from_proto(data_source.batch_source)

if data_source.HasField("batch_source")

else None

),

)

def _to_proto_impl(self) -> DataSourceProto:

data_source_proto = DataSourceProto(

name=self.name,

type=DataSourceProto.STREAM_KAFKA,

field_mapping=self.field_mapping,

kafka_options=self.kafka_options.to_proto(),

description=self.description,

tags=self.tags,

owner=self.owner,

)

data_source_proto.timestamp_field = self.timestamp_field

data_source_proto.created_timestamp_column = self.created_timestamp_column

if self.batch_source:

data_source_proto.batch_source.MergeFrom(self.batch_source.to_proto())

return data_source_proto

def validate(self, config: RepoConfig):

raise NotImplementedError

def get_table_column_names_and_types(

self, config: RepoConfig

) -> Iterable[Tuple[str, str]]:

raise NotImplementedError

@staticmethod

def source_datatype_to_feast_value_type() -> Callable[[str], ValueType]:

return type_map.redshift_to_feast_value_type

def get_table_query_string(self) -> str:

raise NotImplementedError

def source_type(self) -> DataSourceProto.SourceType.ValueType:

return DataSourceProto.STREAM_KAFKA

@typechecked

class RequestSource(DataSource):

"""

RequestSource that can be used to provide input features for on demand transforms

Attributes:

name: Name of the request data source

schema: Schema mapping from the input feature name to a ValueType

description: A human-readable description.

tags: A dictionary of key-value pairs to store arbitrary metadata.

owner: The owner of the request data source, typically the email of the primary

maintainer.

"""

name: str

schema: List[Field]

description: str

tags: Dict[str, str]

owner: str

def __init__(

self,

*,

name: str,

schema: List[Field],

timestamp_field: Optional[str] = None,

description: Optional[str] = "",

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

owner: Optional[str] = "",

):

"""Creates a RequestSource object."""

super().__init__(

name=name,

timestamp_field=timestamp_field,

description=description,

tags=tags,

owner=owner,

)

self.schema = schema

def validate(self, config: RepoConfig):

raise NotImplementedError

def get_table_column_names_and_types(

self, config: RepoConfig

) -> Iterable[Tuple[str, str]]:

raise NotImplementedError

def __eq__(self, other):

if not isinstance(other, RequestSource):

return False

if not super().__eq__(other):

return False

if isinstance(self.schema, List) and isinstance(other.schema, List):

for field1, field2 in zip(self.schema, other.schema):

if field1 != field2:

return False

return True

else:

return False

def __hash__(self):

return super().__hash__()

@staticmethod

def from_proto(data_source: DataSourceProto):

schema_pb = data_source.request_data_options.schema

list_schema = []

for field_proto in schema_pb:

list_schema.append(Field.from_proto(field_proto))

return RequestSource(

name=data_source.name,

schema=list_schema,

timestamp_field=data_source.timestamp_field,

description=data_source.description,

tags=dict(data_source.tags),

owner=data_source.owner,

)

def _to_proto_impl(self) -> DataSourceProto:

schema_pb = []

if isinstance(self.schema, Dict):

for key, value in self.schema.items():

schema_pb.append(

Field(name=key, dtype=from_value_type(value.value)).to_proto()

)

else:

for field in self.schema:

schema_pb.append(field.to_proto())

data_source_proto = DataSourceProto(

name=self.name,

type=DataSourceProto.REQUEST_SOURCE,

description=self.description,

tags=self.tags,

owner=self.owner,

)

data_source_proto.timestamp_field = self.timestamp_field

data_source_proto.request_data_options.schema.extend(schema_pb)

return data_source_proto

def get_table_query_string(self) -> str:

raise NotImplementedError

@staticmethod

def source_datatype_to_feast_value_type() -> Callable[[str], ValueType]:

raise NotImplementedError

def source_type(self) -> DataSourceProto.SourceType.ValueType:

return DataSourceProto.REQUEST_SOURCE

@typechecked

class KinesisSource(DataSource):

"""A KinesisSource allows users to register Kinesis streams as data sources."""

def validate(self, config: RepoConfig):

raise NotImplementedError

def get_table_column_names_and_types(

self, config: RepoConfig

) -> Iterable[Tuple[str, str]]:

raise NotImplementedError

@staticmethod

def from_proto(data_source: DataSourceProto):

return KinesisSource(

name=data_source.name,

timestamp_field=data_source.timestamp_field,

field_mapping=dict(data_source.field_mapping),

record_format=StreamFormat.from_proto(

data_source.kinesis_options.record_format

),

region=data_source.kinesis_options.region,

stream_name=data_source.kinesis_options.stream_name,

created_timestamp_column=data_source.created_timestamp_column,

description=data_source.description,

tags=dict(data_source.tags),

owner=data_source.owner,

batch_source=(

DataSource.from_proto(data_source.batch_source)

if data_source.HasField("batch_source")

else None

),

)

@staticmethod

def source_datatype_to_feast_value_type() -> Callable[[str], ValueType]:

raise NotImplementedError

def get_table_query_string(self) -> str:

raise NotImplementedError

def __init__(

self,

*,

name: str,

record_format: StreamFormat,

region: str,

stream_name: str,

timestamp_field: Optional[str] = "",

created_timestamp_column: Optional[str] = "",

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

description: Optional[str] = "",

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

owner: Optional[str] = "",

batch_source: Optional[DataSource] = None,

):

"""

Args:

name: The unique name of the Kinesis source.

record_format: The record format of the Kinesis stream.

region: The AWS region of the Kinesis stream.

stream_name: The name of the Kinesis stream.

timestamp_field: Event timestamp field used for point-in-time joins of

feature values.

created_timestamp_column: Timestamp column indicating when the row

was created, used for deduplicating rows.

field_mapping: A dictionary mapping of column names in this data

source to feature names in a feature table or view. Only used for feature

columns, not entity or timestamp columns.

description: A human-readable description.

tags: A dictionary of key-value pairs to store arbitrary metadata.

owner: The owner of the Kinesis source, typically the email of the primary

maintainer.

batch_source: A DataSource backing the Kinesis stream (used for retrieving historical features).

"""

if record_format is None:

raise ValueError("Record format must be specified for kinesis source")

super().__init__(

name=name,

timestamp_field=timestamp_field,

created_timestamp_column=created_timestamp_column,

field_mapping=field_mapping,

description=description,

tags=tags,

owner=owner,

)

self.batch_source = batch_source

self.kinesis_options = KinesisOptions(

record_format=record_format, region=region, stream_name=stream_name

)

def __eq__(self, other):

if not isinstance(other, KinesisSource):

return False

if not super().__eq__(other):

return False

if (

self.kinesis_options.record_format != other.kinesis_options.record_format

or self.kinesis_options.region != other.kinesis_options.region

or self.kinesis_options.stream_name != other.kinesis_options.stream_name

):

return False

return True

def __hash__(self):

return super().__hash__()

def _to_proto_impl(self) -> DataSourceProto:

data_source_proto = DataSourceProto(

name=self.name,

type=DataSourceProto.STREAM_KINESIS,

field_mapping=self.field_mapping,

kinesis_options=self.kinesis_options.to_proto(),

description=self.description,

tags=self.tags,

owner=self.owner,

)

data_source_proto.timestamp_field = self.timestamp_field

data_source_proto.created_timestamp_column = self.created_timestamp_column

if self.batch_source:

data_source_proto.batch_source.MergeFrom(self.batch_source.to_proto())

return data_source_proto

def source_type(self) -> DataSourceProto.SourceType.ValueType:

return DataSourceProto.STREAM_KINESIS

class PushMode(enum.Enum):

ONLINE = 1

OFFLINE = 2

ONLINE_AND_OFFLINE = 3

@typechecked

class PushSource(DataSource):

"""

A source that can be used to ingest features on request

"""

# TODO(adchia): consider adding schema here in case where Feast manages pushing events to the offline store

# TODO(adchia): consider a "mode" to support pushing raw vs transformed events

batch_source: Optional[DataSource] = None

upstream_feature_view_names: List[str] = []

def __init__(

self,

*,

name: str,

batch_source: Optional[DataSource] = None,

upstream_feature_view_names: Optional[Sequence[Union[Any, str]]] = None,

description: Optional[str] = "",

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

owner: Optional[str] = "",

):

"""

Creates a PushSource object.

Args:

name: Name of the push source.

batch_source (optional): The batch source that backs this push source. It's used when

materializing from the offline store to the online store, and when retrieving

historical features.

upstream_feature_view_names (optional): Sequence of upstream FeatureView/LabelView

objects or names consumed by the external process feeding this push source.

Represents external pipeline lineage metadata and does not trigger Feast-managed

DAG execution.

description (optional): A human-readable description.

tags (optional): A dictionary of key-value pairs to store arbitrary metadata.

owner (optional): The owner of the data source, typically the email of the primary

maintainer.

"""

super().__init__(name=name, description=description, tags=tags, owner=owner)

self.batch_source = batch_source

normalized_upstream_names: List[str] = []

if upstream_feature_view_names is not None:

for item in upstream_feature_view_names:

if hasattr(item, "name"):

name_val = item.name

if not isinstance(name_val, str) or not name_val.strip():

raise ValueError(

f"Upstream feature view object {item} has an invalid or empty name"

)

normalized_upstream_names.append(name_val.strip())

elif isinstance(item, str):

if not item.strip():

raise ValueError("Upstream feature view name cannot be empty")

normalized_upstream_names.append(item.strip())

else:

raise TypeError(

f"Unsupported type for upstream feature view: {type(item)}. "

"Expected str or object with a 'name' attribute (e.g. FeatureView, LabelView)."

)

self.upstream_feature_view_names = normalized_upstream_names

def __eq__(self, other):

if not isinstance(other, PushSource):

return False

if not super().__eq__(other):

return False

if self.batch_source != other.batch_source:

return False

if self.upstream_feature_view_names != other.upstream_feature_view_names:

return False

return True

def __hash__(self):

return super().__hash__()

def validate(self, config: RepoConfig):

raise NotImplementedError

def get_table_column_names_and_types(

self, config: RepoConfig

) -> Iterable[Tuple[str, str]]:

raise NotImplementedError

@staticmethod

def from_proto(data_source: DataSourceProto):

batch_source = (

DataSource.from_proto(data_source.batch_source)

if data_source.HasField("batch_source")

else None

)

upstream_feature_view_names = (

list(data_source.push_options.upstream_feature_view_names)

if data_source.HasField("push_options")

else []

)

return PushSource(

name=data_source.name,

batch_source=batch_source,

upstream_feature_view_names=upstream_feature_view_names,

description=data_source.description,

tags=dict(data_source.tags),

owner=data_source.owner,

)

def _to_proto_impl(self) -> DataSourceProto:

data_source_proto = DataSourceProto(

name=self.name,

type=DataSourceProto.PUSH_SOURCE,

description=self.description,

tags=self.tags,

owner=self.owner,

)

data_source_proto.push_options.upstream_feature_view_names.extend(

self.upstream_feature_view_names

)

# Only set timestamp fields if we have a batch source and this PushSource doesn't have its own fields

if self.batch_source and not (

self.timestamp_field or self.created_timestamp_column or self.field_mapping

):

data_source_proto.timestamp_field = self.batch_source.timestamp_field

data_source_proto.created_timestamp_column = (

self.batch_source.created_timestamp_column