feast package¶

Subpackages¶

Submodules¶

feast.cli module¶

feast.client module¶

class feast.client.Client(options: Optional[Dict[str, str]] = None, **kwargs)[source]¶

Bases: object

Feast Client: Used for creating, managing, and retrieving features.

apply(objects: Union[List[Union[feast.entity.Entity, feast.feature_table.FeatureTable]], feast.entity.Entity, feast.feature_table.FeatureTable], project: str = None)[source]¶

Idempotently registers entities and feature tables with Feast Core. Either a single entity or feature table or a list can be provided.

Parameters: objects – List of entities and/or feature tables that will be registered

Examples

>>> from feast import Client
>>> from feast.entity import Entity
>>> from feast.value_type import ValueType
>>>
>>> feast_client = Client(core_url="localhost:6565")
>>> entity = Entity(
>>>     name="driver_entity",
>>>     description="Driver entity for car rides",
>>>     value_type=ValueType.STRING,
>>>     labels={
>>>         "key": "val"
>>>     }
>>> )
>>> feast_client.apply(entity)

apply_entity(entities: Union[List[feast.entity.Entity], feast.entity.Entity], project: str = None)[source]¶: Deprecated. Please see apply().

apply_feature_table(feature_tables: Union[List[feast.feature_table.FeatureTable], feast.feature_table.FeatureTable], project: str = None)[source]¶: Deprecated. Please see apply().

archive_project(project)[source]¶

Archives a project. Project will still continue to function for ingestion and retrieval, but will be in a read-only state. It will also not be visible from the Core API for management purposes.

Parameters: project – Name of project to archive

property config¶

property core_secure¶

Retrieve Feast Core client-side SSL/TLS setting

Returns: Whether client-side SSL/TLS is enabled

property core_url¶

Retrieve Feast Core URL

Returns: Feast Core URL string

create_project(project: str)[source]¶

Creates a Feast project

Parameters: project – Name of project

delete_feature_table(name: str, project: str = None) → None[source]¶

Deletes a feature table.

Parameters

project – Feast project that this feature table belongs to
name – Name of feature table

get_entity(name: str, project: str = None) → feast.entity.Entity[source]¶

Retrieves an entity.

Parameters

project – Feast project that this entity belongs to
name – Name of entity

Returns

Returns either the specified entity, or raises an exception if none is found

get_feature_table(name: str, project: str = None) → feast.feature_table.FeatureTable[source]¶

Retrieves a feature table.

Parameters

project – Feast project that this feature table belongs to
name – Name of feature table

Returns

Returns either the specified feature table, or raises an exception if none is found

get_online_features(feature_refs: List[str], entity_rows: List[Dict[str, Any]], project: Optional[str] = None) → feast.online_response.OnlineResponse[source]¶

Retrieves the latest online feature data from Feast Serving. :param feature_refs: List of feature references that will be returned for each entity.

Each feature reference should have the following format: “feature_table:feature” where “feature_table” & “feature” refer to the feature and feature table names respectively. Only the feature name is required.

Parameters

entity_rows – A list of dictionaries where each key-value is an entity-name, entity-value pair.
project – Optionally specify the the project override. If specified, uses given project for retrieval. Overrides the projects specified in Feature References if also are specified.

Returns

GetOnlineFeaturesResponse containing the feature data in records. Each EntityRow provided will yield one record, which contains data fields with data value and field status metadata (if included).

Examples

>>> from feast import Client
>>>
>>> feast_client = Client(core_url="localhost:6565", serving_url="localhost:6566")
>>> feature_refs = ["sales:daily_transactions"]
>>> entity_rows = [{"customer_id": 0},{"customer_id": 1}]
>>>
>>> online_response = feast_client.get_online_features(
>>>     feature_refs, entity_rows, project="my_project")
>>> online_response_dict = online_response.to_dict()
>>> print(online_response_dict)
{'sales:daily_transactions': [1.1,1.2], 'sales:customer_id': [0,1]}

ingest(feature_table: Union[str, feast.feature_table.FeatureTable], source: Union[pandas.core.frame.DataFrame, str], project: str = None, chunk_size: int = 10000, max_workers: int = 7, timeout: int = 120) → None[source]¶

Batch load feature data into a FeatureTable.

Parameters

feature_table (typing.Union[str, feast.feature_table.FeatureTable]) – FeatureTable object or the string name of the feature table
source (typing.Union[pd.DataFrame, str]) –
Either a file path or Pandas Dataframe to ingest into Feast Files that are currently supported:
- parquet
- csv
- json
project – Feast project to locate FeatureTable
chunk_size (int) – Amount of rows to load and ingest at a time.
max_workers (int) – Number of worker processes to use to encode values.
timeout (int) – Timeout in seconds to wait for completion.

Examples

>>> from feast import Client
>>>
>>> client = Client(core_url="localhost:6565")
>>> ft_df = pd.DataFrame(
>>>         {
>>>            "datetime": [pd.datetime.now()],
>>>            "driver": [1001],
>>>            "rating": [4.3],
>>>         }
>>>     )
>>> client.set_project("project1")
>>>
>>> driver_ft = client.get_feature_table("driver")
>>> client.ingest(driver_ft, ft_df)

property job_service_secure¶

Retrieve Feast Job Service client-side SSL/TLS setting

Returns: Whether client-side SSL/TLS is enabled

property job_service_url¶

Retrieve Feast Job Service URL

Returns: Feast Job Service URL string

list_entities(project: str = None, labels: Dict[str, str] = {}) → List[feast.entity.Entity][source]¶

Retrieve a list of entities from Feast Core

Parameters

project – Filter entities based on project name
labels – User-defined labels that these entities are associated with

Returns

List of entities

list_feature_tables(project: str = None, labels: Dict[str, str] = {}) → List[feast.feature_table.FeatureTable][source]¶

Retrieve a list of feature tables from Feast Core

Parameters: project – Filter feature tables based on project name
Returns: List of feature tables

list_features_by_ref(project: str = None, entities: List[str] = [], labels: Dict[str, str] = {}) → Dict[feast.feature.FeatureRef, feast.feature.Feature][source]¶

Retrieve a dictionary of feature reference to feature from Feast Core based on filters provided.

Parameters

project – Feast project that these features belongs to
entities – Feast entity that these features are associated with
labels – Feast labels that these features are associated with

Returns

features>

Return type

Dictionary of <feature references

Examples

>>> from feast import Client
>>>
>>> feast_client = Client(core_url="localhost:6565")
>>> features = feast_client.list_features(project="test_project", entities=["driver_id"], labels={"key1":"val1","key2":"val2"})
>>> print(features)

list_projects() → List[str][source]¶

List all active Feast projects

Returns: List of project names

property project¶

Retrieve currently active project

Returns: Project name

property serving_secure¶

Retrieve Feast Serving client-side SSL/TLS setting

Returns: Whether client-side SSL/TLS is enabled

property serving_url¶

Retrieve Feast Serving URL

Returns: Feast Serving URL string

set_project(project: Optional[str] = None)[source]¶

Set currently active Feast project

Parameters: project – Project to set as active. If unset, will reset to the default project.

version(sdk_only=False)[source]¶: Returns version information from Feast Core and Feast Serving

feast.config module¶

class feast.config.Config(options: Optional[Dict[str, str]] = None, path: Optional[str] = None)[source]¶

Bases: object

Maintains and provides access to Feast configuration

Configuration is stored as key/value pairs. The user can specify options through either input arguments to this class, environmental variables, or by setting the config in a configuration file

exists(option)[source]¶

Tests whether a specific option is available

Parameters: option – Name of the option to check

Returns: Boolean true/false whether the option is set

get(option, default=<object object>)[source]¶

Returns a single configuration option as a string

Parameters

option – Name of the option
default – Default value to return if option is not found

Returns: String option that is returned

getboolean(option, default=<object object>)[source]¶

Returns a single configuration option as a boolean

Parameters

option – Name of the option
default – Default value to return if option is not found

Returns: Boolean option value that is returned

getfloat(option, default=<object object>)[source]¶

Returns a single configuration option as an integer

Parameters

option – Name of the option
default – Default value to return if option is not found

Returns: Float option value that is returned

getint(option, default=<object object>)[source]¶

Returns a single configuration option as an integer

Parameters

option – Name of the option
default – Default value to return if option is not found

Returns: Integer option value that is returned

save()[source]¶: Save the current configuration to disk. This does not include environmental variables or initialized options

set(option, value)[source]¶: Sets a configuration option. Must be serializable to string :param option: Option name to use as key :param value: Value to store under option

feast.constants module¶

class feast.constants.AuthProvider[source]¶

Bases: enum.Enum

An enumeration.

GOOGLE = 'google'¶

OAUTH = 'oauth'¶

feast.constants.CONFIG_FEAST_ENV_VAR_PREFIX: str = 'FEAST_'¶: Default prefix to Feast environmental variables

feast.constants.CONFIG_FILE_DEFAULT_DIRECTORY: str = '.feast'¶: Default directory to Feast configuration file

feast.constants.CONFIG_FILE_NAME: str = 'config'¶: Default Feast configuration file name

feast.constants.CONFIG_FILE_SECTION: str = 'general'¶: Default section in Feast configuration file to specify options

class feast.constants.ConfigMeta[source]¶

Bases: type

Class factory which customizes ConfigOptions class instantiation. Specifically, setting configuration option’s name to lowercase of capitalized variable.

class feast.constants.ConfigOptions[source]¶

Bases: object

Feast Configuration Options

AUTH_PROVIDER: str = 'auth_provider'¶

Authentication Provider - Google OpenID/OAuth

Options: “google” / “oauth”

AUTH_TOKEN: Optional[str] = 'auth_token'¶: JWT Auth token for user authentication to Feast

AZURE_BLOB_ACCOUNT_ACCESS_KEY: Optional[str] = 'azure_blob_account_access_key'¶: Account access key for Azure blob storage_client

AZURE_BLOB_ACCOUNT_NAME: Optional[str] = 'azure_blob_account_name'¶: Account name for Azure blob storage_client

BATCH_FEATURE_REQUEST_WAIT_TIME_SECONDS: str = 'batch_feature_request_wait_time_seconds'¶: Time to wait for historical feature requests before timing out.

BATCH_INGESTION_PRODUCTION_TIMEOUT: str = 'batch_ingestion_production_timeout'¶: Default timeout when running batch ingestion

CORE_ENABLE_SSL: str = 'core_enable_ssl'¶: Enable or disable TLS/SSL to Feast Core

CORE_SERVER_SSL_CERT: str = 'core_server_ssl_cert'¶: Path to certificate(s) to secure connection to Feast Core

CORE_URL: str = 'core_url'¶: Default Feast Core URL

DATAPROC_CLUSTER_NAME: Optional[str] = 'dataproc_cluster_name'¶: Dataproc cluster to run Feast Spark Jobs in

DATAPROC_EXECUTOR_CORES = 'dataproc_executor_cores'¶: No. of executor cores for Dataproc cluster

DATAPROC_EXECUTOR_INSTANCES = 'dataproc_executor_instances'¶: No. of executor instances for Dataproc cluster

DATAPROC_EXECUTOR_MEMORY = 'dataproc_executor_memory'¶: No. of executor memory for Dataproc cluster

DATAPROC_PROJECT: Optional[str] = 'dataproc_project'¶: Project of Dataproc cluster

DATAPROC_REGION: Optional[str] = 'dataproc_region'¶: Region of Dataproc cluster

DEADLETTER_PATH: str = 'deadletter_path'¶

Ingestion Job DeadLetter Destination. The choice of storage is connected to the choice of SPARK_LAUNCHER.

Eg. gs://some-bucket/output/, s3://some-bucket/output/, file:///data/subfolder/

EMR_CLUSTER_ID: Optional[str] = 'emr_cluster_id'¶: EMR cluster to run Feast Spark Jobs in

EMR_CLUSTER_TEMPLATE_PATH: Optional[str] = 'emr_cluster_template_path'¶: Template path of EMR cluster

EMR_LOG_LOCATION: Optional[str] = 'emr_log_location'¶: Log path of EMR cluster

EMR_REGION: Optional[str] = 'emr_region'¶: Region of EMR cluster

ENABLE_AUTH: str = 'enable_auth'¶: Enable user authentication to Feast Core

GRPC_CONNECTION_TIMEOUT: str = 'grpc_connection_timeout'¶: Default connection timeout to Feast Serving, Feast Core, and Feast Job Service (in seconds)

GRPC_CONNECTION_TIMEOUT_APPLY: str = 'grpc_connection_timeout_apply'¶: Default gRPC connection timeout when sending an ApplyFeatureTable command to Feast Core (in seconds)

HISTORICAL_FEATURE_OUTPUT_FORMAT: str = 'historical_feature_output_format'¶: File format of historical retrieval features

HISTORICAL_FEATURE_OUTPUT_LOCATION: Optional[str] = 'historical_feature_output_location'¶: File location of historical retrieval features

INGESTION_DROP_INVALID_ROWS = 'ingestion_drop_invalid_rows'¶: If set to true rows that do not pass custom validation (see feast.contrib.validation) won’t be saved to Online Storage

JOB_SERVICE_ENABLE_CONTROL_LOOP: str = 'job_service_enable_control_loop'¶: Enable or disable control loop for Feast Job Service

JOB_SERVICE_ENABLE_SSL: str = 'job_service_enable_ssl'¶: Enable or disable TLS/SSL to Feast Job Service

JOB_SERVICE_SERVER_SSL_CERT: str = 'job_service_server_ssl_cert'¶: Path to certificate(s) to secure connection to Feast Job Service

JOB_SERVICE_URL: Optional[str] = 'job_service_url'¶: Default Feast Job Service URL

OAUTH_AUDIENCE: Optional[str] = 'oauth_audience'¶: Oauth intended recipients

OAUTH_CLIENT_ID: Optional[str] = 'oauth_client_id'¶: Oauth client ID

OAUTH_CLIENT_SECRET: Optional[str] = 'oauth_client_secret'¶: Oauth client secret

OAUTH_GRANT_TYPE: Optional[str] = 'oauth_grant_type'¶: Oauth grant type

OAUTH_TOKEN_REQUEST_URL: Optional[str] = 'oauth_token_request_url'¶: Oauth token request url

PROJECT: str = 'project'¶: Feast project namespace to use

REDIS_HOST: str = 'redis_host'¶: Default Redis host

REDIS_PORT: str = 'redis_port'¶: Default Redis port

REDIS_SSL: str = 'redis_ssl'¶: Enable or disable TLS/SSL to Redis

REGISTRY_PATH: Optional[str] = 'registry_path'¶: Object store registry

S3_ENDPOINT_URL: Optional[str] = 's3_endpoint_url'¶: Endpoint URL for S3 storage_client

SERVING_ENABLE_SSL: str = 'serving_enable_ssl'¶: Enable or disable TLS/SSL to Feast Serving

SERVING_SERVER_SSL_CERT: str = 'serving_server_ssl_cert'¶: Path to certificate(s) to secure connection to Feast Serving

SERVING_URL: str = 'serving_url'¶: Default Feast Serving URL

SPARK_BQ_MATERIALIZATION_DATASET: Optional[str] = 'spark_bq_materialization_dataset'¶: The dataset id where the materialized view of BigQuerySource is going to be created by default, use the same dataset where view is located

SPARK_BQ_MATERIALIZATION_PROJECT: Optional[str] = 'spark_bq_materialization_project'¶: The project id where the materialized view of BigQuerySource is going to be created by default, use the same project where view is located

SPARK_HOME: Optional[str] = 'spark_home'¶: Directory where Spark is installed

SPARK_INGESTION_JAR: str = 'spark_ingestion_jar'¶

Feast Spark Job ingestion jar file. The choice of storage is connected to the choice of SPARK_LAUNCHER.

Eg. “dataproc” (http and gs), “emr” (http and s3), “standalone” (http and file)

SPARK_K8S_JOB_TEMPLATE_PATH = 'spark_k8s_job_template_path'¶

SPARK_K8S_NAMESPACE = 'spark_k8s_namespace'¶

SPARK_K8S_USE_INCLUSTER_CONFIG = 'spark_k8s_use_incluster_config'¶

SPARK_LAUNCHER: Optional[str] = 'spark_launcher'¶

Spark Job launcher. The choice of storage is connected to the choice of SPARK_LAUNCHER.

Options: “standalone”, “dataproc”, “emr”

SPARK_STAGING_LOCATION: Optional[str] = 'spark_staging_location'¶

Feast Spark Job ingestion jobs staging location. The choice of storage is connected to the choice of SPARK_LAUNCHER.

Eg. gs://some-bucket/output/, s3://some-bucket/output/, file:///data/subfolder/

SPARK_STANDALONE_MASTER: str = 'spark_standalone_master'¶: Spark resource manager master url

STATSD_ENABLED: str = 'statsd_enabled'¶: Enable or disable StatsD

STATSD_HOST: Optional[str] = 'statsd_host'¶: Default StatsD port

STATSD_PORT: Optional[str] = 'statsd_port'¶: Default StatsD port

STENCIL_URL: str = 'stencil_url'¶: ProtoRegistry Address (currently only Stencil Server is supported as registry) https://github.com/gojekfarm/stencil

TELEMETRY = 'telemetry'¶: Telemetry enabled

defaults()[source]¶

feast.constants.DATETIME_COLUMN: str = 'datetime'¶: Default datetime column name for point-in-time join

feast.constants.FEAST_CONFIG_FILE_ENV: str = 'FEAST_CONFIG'¶: Environmental variable to specify Feast configuration file location

class feast.constants.Option(name, default)[source]¶: Bases: object

feast.data_format module¶

class feast.data_format.AvroFormat(schema_json: str)[source]¶

Bases: feast.data_format.StreamFormat

Defines the Avro streaming data format that encodes data in Avro format

to_proto()[source]¶: Convert this StreamFormat into its protobuf representation.

class feast.data_format.FileFormat[source]¶

Bases: abc.ABC

Defines an abtract file forma used to encode feature data in files

classmethod from_proto(proto)[source]¶: Construct this FileFormat from its protobuf representation. Raises NotImplementedError if FileFormat specified in given proto is not supported.

abstract to_proto()[source]¶: Convert this FileFormat into its protobuf representation.

class feast.data_format.ParquetFormat[source]¶

Bases: feast.data_format.FileFormat

Defines the Parquet data format

to_proto()[source]¶: Convert this FileFormat into its protobuf representation.

class feast.data_format.ProtoFormat(class_path: str)[source]¶

Bases: feast.data_format.StreamFormat

Defines the Protobuf data format

to_proto()[source]¶: Convert this StreamFormat into its protobuf representation.

class feast.data_format.StreamFormat[source]¶

Bases: abc.ABC

Defines an abtracts streaming data format used to encode feature data in streams

classmethod from_proto(proto)[source]¶: Construct this StreamFormat from its protobuf representation.

abstract to_proto()[source]¶: Convert this StreamFormat into its protobuf representation.

feast.data_source module¶

class feast.data_source.BigQueryOptions(table_ref: str)[source]¶

Bases: object

DataSource BigQuery options used to source features from BigQuery query

classmethod from_proto(bigquery_options_proto: feast.core.DataSource_pb2.BigQueryOptions)[source]¶

Creates a BigQueryOptions from a protobuf representation of a BigQuery option

Parameters: bigquery_options_proto – A protobuf representation of a DataSource
Returns: Returns a BigQueryOptions object based on the bigquery_options protobuf

property table_ref¶: Returns the table ref of this BQ table

to_proto() → feast.core.DataSource_pb2.BigQueryOptions[source]¶

Converts an BigQueryOptionsProto object to its protobuf representation.

Returns: BigQueryOptionsProto protobuf

class feast.data_source.BigQuerySource(event_timestamp_column: str, table_ref: str, created_timestamp_column: Optional[str] = '', field_mapping: Optional[Dict[str, str]] = None, date_partition_column: Optional[str] = '')[source]¶

Bases: feast.data_source.DataSource

property bigquery_options¶: Returns the bigquery options of this data source

property table_ref¶

to_proto() → feast.core.DataSource_pb2.DataSource[source]¶: Converts an DataSourceProto object to its protobuf representation.

class feast.data_source.DataSource(event_timestamp_column: str, created_timestamp_column: Optional[str] = '', field_mapping: Optional[Dict[str, str]] = None, date_partition_column: Optional[str] = '')[source]¶

Bases: object

DataSource that can be used source features

property created_timestamp_column¶: Returns the created timestamp column of this data source

property date_partition_column¶: Returns the date partition column of this data source

property event_timestamp_column¶: Returns the event timestamp column of this data source

property field_mapping¶: Returns the field mapping of this data source

static from_proto(data_source)[source]¶: Convert data source config in FeatureTable spec to a DataSource class object.

to_proto() → feast.core.DataSource_pb2.DataSource[source]¶: Converts an DataSourceProto object to its protobuf representation.

class feast.data_source.FileOptions(file_format: feast.data_format.FileFormat, file_url: str)[source]¶

Bases: object

DataSource File options used to source features from a file

property file_format¶: Returns the file format of this file

property file_url¶: Returns the file url of this file

classmethod from_proto(file_options_proto: feast.core.DataSource_pb2.FileOptions)[source]¶

Creates a FileOptions from a protobuf representation of a file option

Parameters: file_options_proto – a protobuf representation of a datasource
Returns: Returns a FileOptions object based on the file_options protobuf

to_proto() → feast.core.DataSource_pb2.FileOptions[source]¶

Converts an FileOptionsProto object to its protobuf representation.

Returns: FileOptionsProto protobuf

class feast.data_source.FileSource(event_timestamp_column: str, file_format: feast.data_format.FileFormat, file_url: str, created_timestamp_column: Optional[str] = '', field_mapping: Optional[Dict[str, str]] = None, date_partition_column: Optional[str] = '')[source]¶

Bases: feast.data_source.DataSource

property file_options¶: Returns the file options of this data source

to_proto() → feast.core.DataSource_pb2.DataSource[source]¶: Converts an DataSourceProto object to its protobuf representation.

class feast.data_source.KafkaOptions(bootstrap_servers: str, message_format: feast.data_format.StreamFormat, topic: str)[source]¶

Bases: object

DataSource Kafka options used to source features from Kafka messages

property bootstrap_servers¶: Returns a comma-separated list of Kafka bootstrap servers

classmethod from_proto(kafka_options_proto: feast.core.DataSource_pb2.KafkaOptions)[source]¶

Creates a KafkaOptions from a protobuf representation of a kafka option

Parameters: kafka_options_proto – A protobuf representation of a DataSource
Returns: Returns a BigQueryOptions object based on the kafka_options protobuf

property message_format¶: Returns the data format that is used to encode the feature data in Kafka messages

to_proto() → feast.core.DataSource_pb2.KafkaOptions[source]¶

Converts an KafkaOptionsProto object to its protobuf representation.

Returns: KafkaOptionsProto protobuf

property topic¶: Returns the Kafka topic to collect feature data from

class feast.data_source.KafkaSource(event_timestamp_column: str, bootstrap_servers: str, message_format: feast.data_format.StreamFormat, topic: str, created_timestamp_column: Optional[str] = '', field_mapping: Optional[Dict[str, str]] = {}, date_partition_column: Optional[str] = '')[source]¶

Bases: feast.data_source.DataSource

property kafka_options¶: Returns the kafka options of this data source

to_proto() → feast.core.DataSource_pb2.DataSource[source]¶: Converts an DataSourceProto object to its protobuf representation.

class feast.data_source.KinesisOptions(record_format: feast.data_format.StreamFormat, region: str, stream_name: str)[source]¶

Bases: object

DataSource Kinesis options used to source features from Kinesis records

classmethod from_proto(kinesis_options_proto: feast.core.DataSource_pb2.KinesisOptions)[source]¶

Creates a KinesisOptions from a protobuf representation of a kinesis option

Parameters: kinesis_options_proto – A protobuf representation of a DataSource
Returns: Returns a KinesisOptions object based on the kinesis_options protobuf

property record_format¶: Returns the data format used to encode the feature data in the Kinesis records.

property region¶: Returns the AWS region of Kinesis stream

property stream_name¶: Returns the Kinesis stream name to obtain feature data from

to_proto() → feast.core.DataSource_pb2.KinesisOptions[source]¶

Converts an KinesisOptionsProto object to its protobuf representation.

Returns: KinesisOptionsProto protobuf

class feast.data_source.KinesisSource(event_timestamp_column: str, created_timestamp_column: str, record_format: feast.data_format.StreamFormat, region: str, stream_name: str, field_mapping: Optional[Dict[str, str]] = {}, date_partition_column: Optional[str] = '')[source]¶

Bases: feast.data_source.DataSource

property kinesis_options¶: Returns the kinesis options of this data source

to_proto() → feast.core.DataSource_pb2.DataSource[source]¶: Converts an DataSourceProto object to its protobuf representation.

class feast.data_source.SourceType[source]¶

Bases: enum.Enum

DataSource value type. Used to define source types in DataSource.

BATCH_BIGQUERY = 2¶

BATCH_FILE = 1¶

STREAM_KAFKA = 3¶

STREAM_KINESIS = 4¶

UNKNOWN = 0¶

feast.entity module¶

class feast.entity.Entity(name: str, description: str, value_type: feast.value_type.ValueType, labels: Optional[MutableMapping[str, str]] = None)[source]¶

Bases: object

Represents a collection of entities and associated metadata.

property created_timestamp¶: Returns the created_timestamp of this entity

property description¶: Returns the description of this entity

classmethod from_dict(entity_dict)[source]¶

Creates an entity from a dict

Parameters: entity_dict – A dict representation of an entity
Returns: Returns a EntityV2 object based on the entity dict

classmethod from_proto(entity_proto: feast.core.Entity_pb2.Entity)[source]¶

Creates an entity from a protobuf representation of an entity

Parameters: entity_proto – A protobuf representation of an entity
Returns: Returns a EntityV2 object based on the entity protobuf

classmethod from_yaml(yml: str)[source]¶

Creates an entity from a YAML string body or a file path

Parameters: yml – Either a file path containing a yaml file or a YAML string
Returns: Returns a EntityV2 object based on the YAML file

is_valid()[source]¶: Validates the state of a entity locally. Raises an exception if entity is invalid.

property labels¶: Returns the labels of this entity. This is the user defined metadata defined as a dictionary.

property last_updated_timestamp¶: Returns the last_updated_timestamp of this entity

property name¶: Returns the name of this entity

to_dict() → Dict[source]¶

Converts entity to dict

Returns: Dictionary object representation of entity

to_proto() → feast.core.Entity_pb2.Entity[source]¶

Converts an entity object to its protobuf representation

Returns: EntityV2Proto protobuf

to_spec_proto() → feast.core.Entity_pb2.EntitySpecV2[source]¶

Converts an EntityV2 object to its protobuf representation. Used when passing EntitySpecV2 object to Feast request.

Returns: EntitySpecV2 protobuf

to_yaml()[source]¶

Converts a entity to a YAML string.

Returns: Entity string returned in YAML format

property value_type¶: Returns the type of this entity

feast.feature module¶

class feast.feature.Feature(name: str, dtype: feast.value_type.ValueType, labels: Optional[MutableMapping[str, str]] = None)[source]¶

Bases: object

Feature field type

property dtype¶: Getter for data type of this field

classmethod from_proto(feature_proto: feast.core.Feature_pb2.FeatureSpecV2)[source]¶

Parameters: feature_proto – FeatureSpecV2 protobuf object
Returns: Feature object

property labels¶: Getter for labels of this field

property name¶: Getter for name of this field

to_proto() → feast.core.Feature_pb2.FeatureSpecV2[source]¶: Converts Feature object to its Protocol Buffer representation

class feast.feature.FeatureRef(name: str, feature_table: str = None)[source]¶

Bases: object

Feature Reference represents a reference to a specific feature.

classmethod from_proto(proto: feast.serving.ServingService_pb2.FeatureReferenceV2)[source]¶

Construct a feature reference from the given FeatureReference proto Arg:

proto: Protobuf FeatureReference to construct from

Returns: FeatureRef that refers to the given feature

classmethod from_str(feature_ref_str: str)[source]¶

Parse the given string feature reference into FeatureRef model String feature reference should be in the format feature_table:feature. Where “feature_table” and “name” are the feature_table name and feature name respectively. :param feature_ref_str: String representation of the feature reference

Returns: FeatureRef that refers to the given feature

to_proto() → feast.serving.ServingService_pb2.FeatureReferenceV2[source]¶: Convert and return this feature table reference to protobuf. :returns: Protobuf respresentation of this feature table reference.

feast.feature_store module¶

feast.feature_table module¶

class feast.feature_table.FeatureTable(name: str, entities: List[str], features: List[feast.feature.Feature], batch_source: Union[feast.data_source.BigQuerySource, feast.data_source.FileSource] = None, stream_source: Union[feast.data_source.KafkaSource, feast.data_source.KinesisSource, None] = None, max_age: Optional[google.protobuf.duration_pb2.Duration] = None, labels: Optional[MutableMapping[str, str]] = None)[source]¶

Bases: object

Represents a collection of features and associated metadata.

add_feature(feature: feast.feature.Feature)[source]¶: Adds a new feature to the feature table.

property batch_source¶: Returns the batch source of this feature table

property created_timestamp¶: Returns the created_timestamp of this feature table

property entities¶: Returns the entities of this feature table

property features¶: Returns the features of this feature table

classmethod from_dict(ft_dict)[source]¶

Creates a feature table from a dict

Parameters: ft_dict – A dict representation of a feature table
Returns: Returns a FeatureTable object based on the feature table dict

classmethod from_proto(feature_table_proto: feast.core.FeatureTable_pb2.FeatureTable)[source]¶

Creates a feature table from a protobuf representation of a feature table

Parameters: feature_table_proto – A protobuf representation of a feature table
Returns: Returns a FeatureTableProto object based on the feature table protobuf

classmethod from_yaml(yml: str)[source]¶

Creates a feature table from a YAML string body or a file path

Parameters: yml – Either a file path containing a yaml file or a YAML string
Returns: Returns a FeatureTable object based on the YAML file

is_valid()[source]¶: Validates the state of a feature table locally. Raises an exception if feature table is invalid.

property labels¶: Returns the labels of this feature table. This is the user defined metadata defined as a dictionary.

property last_updated_timestamp¶: Returns the last_updated_timestamp of this feature table

property max_age¶: Returns the maximum age of this feature table. This is the total maximum amount of staleness that will be allowed during feature retrieval for each specific feature that is looked up.

property name¶: Returns the name of this feature table

property stream_source¶: Returns the stream source of this feature table

to_dict() → Dict[source]¶

Converts feature table to dict

Returns: Dictionary object representation of feature table

to_proto() → feast.core.FeatureTable_pb2.FeatureTable[source]¶

Converts an feature table object to its protobuf representation

Returns: FeatureTableProto protobuf

to_spec_proto() → feast.core.FeatureTable_pb2.FeatureTableSpec[source]¶

Converts an FeatureTableProto object to its protobuf representation. Used when passing FeatureTableSpecProto object to Feast request.

Returns: FeatureTableSpecProto protobuf

to_yaml()[source]¶

Converts a feature table to a YAML string.

Returns: Feature table string returned in YAML format

feast.feature_view module¶

class feast.feature_view.FeatureView(name: str, entities: List[str], features: List[feast.feature.Feature], tags: Dict[str, str], ttl: Union[google.protobuf.duration_pb2.Duration, datetime.timedelta, None], online: bool, input: feast.data_source.BigQuerySource)[source]¶

Bases: object

A FeatureView defines a logical grouping of servable features.

created_timestamp: Optional[google.protobuf.timestamp_pb2.Timestamp] = None¶

entities: List[str] = None¶

features: List[Feature] = None¶

classmethod from_proto(feature_view_proto: feast.core.FeatureView_pb2.FeatureView)[source]¶

Creates a feature view from a protobuf representation of a feature view

Parameters: feature_view_proto – A protobuf representation of a feature view
Returns: Returns a FeatureViewProto object based on the feature view protobuf

input: BigQuerySource = None¶

is_valid()[source]¶: Validates the state of a feature view locally. Raises an exception if feature view is invalid.

last_updated_timestamp: Optional[google.protobuf.timestamp_pb2.Timestamp] = None¶

name: str = None¶

online: bool = None¶

tags: Dict[str, str] = None¶

to_proto() → feast.core.FeatureView_pb2.FeatureView[source]¶

Converts an feature view object to its protobuf representation.

Returns: FeatureViewProto protobuf

ttl: Optional[Duration] = None¶

feast.offline_store module¶

class feast.offline_store.BigQueryOfflineStore[source]¶

Bases: feast.offline_store.OfflineStore

static pull_latest_from_table(feature_view: feast.feature_view.FeatureView, start_date: datetime.datetime, end_date: datetime.datetime) → pyarrow.lib.Table[source]¶

class feast.offline_store.OfflineStore[source]¶

Bases: abc.ABC

OfflineStore is an object used for all interaction between Feast and the service used for offline storage of features. Currently BigQuery is supported.

abstract static pull_latest_from_table(feature_view: feast.feature_view.FeatureView, start_date: datetime.datetime, end_date: datetime.datetime) → Optional[pyarrow.lib.Table][source]¶

feast.offline_store.run_forward_field_mapping(table: pyarrow.lib.Table, feature_view: feast.feature_view.FeatureView) → pyarrow.lib.Table[source]¶

feast.offline_store.run_reverse_field_mapping(feature_view: feast.feature_view.FeatureView) → Tuple[List[str], List[str], str, Optional[str]][source]¶

If a field mapping exists, run it in reverse on the entity names, feature names, event timestamp column, and created timestamp column to get the names of the relevant columns in the BigQuery table.

Parameters: feature_view – FeatureView object containing the field mapping as well as the names to reverse-map.
Returns: Tuple containing the list of reverse-mapped entity names, reverse-mapped feature names, reverse-mapped event timestamp column, and reverse-mapped created timestamp column that will be passed into the query to the offline store.

feast.online_response module¶

class feast.online_response.OnlineResponse(online_response_proto: feast.serving.ServingService_pb2.GetOnlineFeaturesResponse)[source]¶

Bases: object

Defines a online response in feast.

property field_values¶: Getter for GetOnlineResponse’s field_values.

to_dict() → Dict[str, Any][source]¶: Converts GetOnlineFeaturesResponse features into a dictionary form.

feast.registry module¶

class feast.registry.GCSRegistryStore(uri: str)[source]¶

Bases: feast.registry.RegistryStore

get_registry()[source]¶

Retrieves the registry proto from the registry path. If there is no file at that path, returns an empty registry proto.

Returns: Returns either the registry proto stored at the registry path, or an empty registry proto.

update_registry(updater: Callable[[feast.core.Registry_pb2.Registry], feast.core.Registry_pb2.Registry])[source]¶

Updates the registry using the function passed in. If the registry proto has not been created yet this method will create it. This method writes to the registry path.

Parameters: updater – function that takes in the current registry proto and outputs the desired registry proto

class feast.registry.LocalRegistryStore(filepath: str)[source]¶

Bases: feast.registry.RegistryStore

get_registry()[source]¶

Retrieves the registry proto from the registry path. If there is no file at that path, returns an empty registry proto.

Returns: Returns either the registry proto stored at the registry path, or an empty registry proto.

update_registry(updater: Callable[[feast.core.Registry_pb2.Registry], feast.core.Registry_pb2.Registry])[source]¶

Updates the registry using the function passed in. If the registry proto has not been created yet this method will create it. This method writes to the registry path.

Parameters: updater – function that takes in the current registry proto and outputs the desired registry proto

class feast.registry.Registry(registry_path: str)[source]¶

Bases: object

Registry: A registry allows for the management and persistence of feature definitions and related metadata.

apply_entity(entity: feast.entity.Entity, project: str)[source]¶

Registers a single entity with Feast

Parameters

entity – Entity that will be registered
project – Feast project that this entity belongs to

apply_feature_table(feature_table: feast.feature_table.FeatureTable, project: str)[source]¶

Registers a single feature table with Feast

Parameters

feature_table – Feature table that will be registered
project – Feast project that this feature table belongs to

apply_feature_view(feature_view: feast.feature_view.FeatureView, project: str)[source]¶

Registers a single feature view with Feast

Parameters

feature_view – Feature view that will be registered
project – Feast project that this feature view belongs to

delete_feature_table(name: str, project: str)[source]¶

Deletes a feature table or raises an exception if not found.

Parameters

name – Name of feature table
project – Feast project that this feature table belongs to

delete_feature_view(name: str, project: str)[source]¶

Deletes a feature view or raises an exception if not found.

Parameters

name – Name of feature view
project – Feast project that this feature view belongs to

get_entity(name: str, project: str) → feast.entity.Entity[source]¶

Retrieves an entity.

Parameters

name – Name of entity
project – Feast project that this entity belongs to

Returns

Returns either the specified entity, or raises an exception if none is found

get_feature_table(name: str, project: str) → feast.feature_table.FeatureTable[source]¶

Retrieves a feature table.

Parameters

name – Name of feature table
project – Feast project that this feature table belongs to

Returns

Returns either the specified feature table, or raises an exception if none is found

get_feature_view(name: str, project: str) → feast.feature_view.FeatureView[source]¶

Retrieves a feature view.

Parameters

name – Name of feature view
project – Feast project that this feature view belongs to

Returns

Returns either the specified feature view, or raises an exception if none is found

list_entities(project: str) → List[feast.entity.Entity][source]¶

Retrieve a list of entities from the registry

Parameters: project – Filter entities based on project name
Returns: List of entities

list_feature_tables(project: str) → List[feast.feature_table.FeatureTable][source]¶

Retrieve a list of feature tables from the registry

Parameters: project – Filter feature tables based on project name
Returns: List of feature tables

list_feature_views(project: str) → List[feast.feature_view.FeatureView][source]¶

Retrieve a list of feature views from the registry

Parameters: project – Filter feature tables based on project name
Returns: List of feature tables

class feast.registry.RegistryStore[source]¶

Bases: abc.ABC

RegistryStore: abstract base class implemented by specific backends (local file system, GCS) containing lower level methods used by the Registry class that are backend-specific.

abstract get_registry()[source]¶

Retrieves the registry proto from the registry path. If there is no file at that path, returns an empty registry proto.

Returns: Returns either the registry proto stored at the registry path, or an empty registry proto.

abstract update_registry(updater: Callable[[feast.core.Registry_pb2.Registry], feast.core.Registry_pb2.Registry])[source]¶

Updates the registry using the function passed in. If the registry proto has not been created yet this method will create it. This method writes to the registry path.

Parameters: updater – function that takes in the current registry proto and outputs the desired registry proto

feast.repo_config module¶

feast.repo_operations module¶

feast.telemetry module¶

feast.telemetry.log_usage(function_name: str, telemetry_id: str, timestamp: datetime.datetime, version: Dict[str, Dict[str, str]])[source]¶

feast.type_map module¶

feast.type_map.feast_value_type_to_python_type(field_value_proto: feast.types.Value_pb2.Value) → Any[source]¶

Converts field value Proto to Dict and returns each field’s Feast Value Type value in their respective Python value.

Parameters: field_value_proto – Field value Proto
Returns: Python native type representation/version of the given field_value_proto

feast.type_map.pa_column_to_proto_column(feast_value_type: feast.value_type.ValueType, column: pyarrow.lib.ChunkedArray) → List[feast.types.Value_pb2.Value][source]¶

feast.type_map.pa_column_to_timestamp_proto_column(column: pyarrow.lib.ChunkedArray) → List[google.protobuf.timestamp_pb2.Timestamp][source]¶

feast.type_map.pa_to_feast_value_attr(pa_type: object)[source]¶

Returns the equivalent Feast ValueType string for the given pa.lib type.

Parameters: pa_type (object) – PyArrow type.
Returns: Feast attribute name in Feast ValueType string-ed representation.
Return type: str

feast.type_map.pa_to_feast_value_type(value: pyarrow.lib.ChunkedArray) → feast.value_type.ValueType[source]¶

feast.type_map.pa_to_value_type(pa_type: object)[source]¶

Returns the equivalent Feast ValueType for the given pa.lib type.

Parameters: pa_type (object) – PyArrow type.
Returns: Feast ValueType.
Return type: feast.types.Value_pb2.ValueType

feast.type_map.python_type_to_feast_value_type(name: str, value, recurse: bool = True) → feast.value_type.ValueType[source]¶

Finds the equivalent Feast Value Type for a Python value. Both native and Pandas types are supported. This function will recursively look for nested types when arrays are detected. All types must be homogenous.

Parameters

name – Name of the value or field
value – Value that will be inspected
recurse – Whether to recursively look for nested types in arrays

Returns

Feast Value Type

feast.value_type module¶

class feast.value_type.ValueType[source]¶

Bases: enum.Enum

Feature value type. Used to define data types in Feature Tables.

BOOL = 7¶

BOOL_LIST = 17¶

BYTES = 1¶

BYTES_LIST = 11¶

DOUBLE = 5¶

DOUBLE_LIST = 15¶

FLOAT = 6¶

FLOAT_LIST = 16¶

INT32 = 3¶

INT32_LIST = 13¶

INT64 = 4¶

INT64_LIST = 14¶

STRING = 2¶

STRING_LIST = 12¶

UNKNOWN = 0¶

to_tfx_schema_feature_type()[source]¶

feast.wait module¶

feast.wait.wait_retry_backoff(retry_fn: Callable[[], Tuple[Any, bool]], timeout_secs: int = 0, timeout_msg: Optional[str] = 'Timeout while waiting for retry_fn() to return True', max_interval_secs: int = 60) → Any[source]¶

Repeatedly try calling given retry_fn until it returns a True boolean success flag. Waits with a exponential backoff between retries until timeout when it throws TimeoutError. :param retry_fn: Callable that returns a result and a boolean success flag. :param timeout_secs: timeout in seconds to give up retrying and throw TimeoutError,

or 0 to retry perpetually.

Parameters

timeout_msg – Message to use when throwing TimeoutError.
max_interval_secs – max wait in seconds to wait between retries.

Returns

Returned Result from retry_fn() if success flag is True.

Module contents¶

class feast.Client(options: Optional[Dict[str, str]] = None, **kwargs)[source]¶

Bases: object

Feast Client: Used for creating, managing, and retrieving features.

apply(objects: Union[List[Union[feast.entity.Entity, feast.feature_table.FeatureTable]], feast.entity.Entity, feast.feature_table.FeatureTable], project: str = None)[source]¶

Idempotently registers entities and feature tables with Feast Core. Either a single entity or feature table or a list can be provided.

Parameters: objects – List of entities and/or feature tables that will be registered

Examples

>>> from feast import Client
>>> from feast.entity import Entity
>>> from feast.value_type import ValueType
>>>
>>> feast_client = Client(core_url="localhost:6565")
>>> entity = Entity(
>>>     name="driver_entity",
>>>     description="Driver entity for car rides",
>>>     value_type=ValueType.STRING,
>>>     labels={
>>>         "key": "val"
>>>     }
>>> )
>>> feast_client.apply(entity)

apply_entity(entities: Union[List[feast.entity.Entity], feast.entity.Entity], project: str = None)[source]¶: Deprecated. Please see apply().

apply_feature_table(feature_tables: Union[List[feast.feature_table.FeatureTable], feast.feature_table.FeatureTable], project: str = None)[source]¶: Deprecated. Please see apply().

archive_project(project)[source]¶

Archives a project. Project will still continue to function for ingestion and retrieval, but will be in a read-only state. It will also not be visible from the Core API for management purposes.

Parameters: project – Name of project to archive

property config¶

property core_secure¶

Retrieve Feast Core client-side SSL/TLS setting

Returns: Whether client-side SSL/TLS is enabled

property core_url¶

Retrieve Feast Core URL

Returns: Feast Core URL string

create_project(project: str)[source]¶

Creates a Feast project

Parameters: project – Name of project

delete_feature_table(name: str, project: str = None) → None[source]¶

Deletes a feature table.

Parameters

project – Feast project that this feature table belongs to
name – Name of feature table

get_entity(name: str, project: str = None) → feast.entity.Entity[source]¶

Retrieves an entity.

Parameters

project – Feast project that this entity belongs to
name – Name of entity

Returns

Returns either the specified entity, or raises an exception if none is found

get_feature_table(name: str, project: str = None) → feast.feature_table.FeatureTable[source]¶

Retrieves a feature table.

Parameters

project – Feast project that this feature table belongs to
name – Name of feature table

Returns

Returns either the specified feature table, or raises an exception if none is found

get_online_features(feature_refs: List[str], entity_rows: List[Dict[str, Any]], project: Optional[str] = None) → feast.online_response.OnlineResponse[source]¶

Retrieves the latest online feature data from Feast Serving. :param feature_refs: List of feature references that will be returned for each entity.

Each feature reference should have the following format: “feature_table:feature” where “feature_table” & “feature” refer to the feature and feature table names respectively. Only the feature name is required.

Parameters

entity_rows – A list of dictionaries where each key-value is an entity-name, entity-value pair.
project – Optionally specify the the project override. If specified, uses given project for retrieval. Overrides the projects specified in Feature References if also are specified.

Returns

GetOnlineFeaturesResponse containing the feature data in records. Each EntityRow provided will yield one record, which contains data fields with data value and field status metadata (if included).

Examples

>>> from feast import Client
>>>
>>> feast_client = Client(core_url="localhost:6565", serving_url="localhost:6566")
>>> feature_refs = ["sales:daily_transactions"]
>>> entity_rows = [{"customer_id": 0},{"customer_id": 1}]
>>>
>>> online_response = feast_client.get_online_features(
>>>     feature_refs, entity_rows, project="my_project")
>>> online_response_dict = online_response.to_dict()
>>> print(online_response_dict)
{'sales:daily_transactions': [1.1,1.2], 'sales:customer_id': [0,1]}

ingest(feature_table: Union[str, feast.feature_table.FeatureTable], source: Union[pandas.core.frame.DataFrame, str], project: str = None, chunk_size: int = 10000, max_workers: int = 7, timeout: int = 120) → None[source]¶

Batch load feature data into a FeatureTable.

Parameters

feature_table (typing.Union[str, feast.feature_table.FeatureTable]) – FeatureTable object or the string name of the feature table
source (typing.Union[pd.DataFrame, str]) –
Either a file path or Pandas Dataframe to ingest into Feast Files that are currently supported:
- parquet
- csv
- json
project – Feast project to locate FeatureTable
chunk_size (int) – Amount of rows to load and ingest at a time.
max_workers (int) – Number of worker processes to use to encode values.
timeout (int) – Timeout in seconds to wait for completion.

Examples

>>> from feast import Client
>>>
>>> client = Client(core_url="localhost:6565")
>>> ft_df = pd.DataFrame(
>>>         {
>>>            "datetime": [pd.datetime.now()],
>>>            "driver": [1001],
>>>            "rating": [4.3],
>>>         }
>>>     )
>>> client.set_project("project1")
>>>
>>> driver_ft = client.get_feature_table("driver")
>>> client.ingest(driver_ft, ft_df)

property job_service_secure¶

Retrieve Feast Job Service client-side SSL/TLS setting

Returns: Whether client-side SSL/TLS is enabled

property job_service_url¶

Retrieve Feast Job Service URL

Returns: Feast Job Service URL string

list_entities(project: str = None, labels: Dict[str, str] = {}) → List[feast.entity.Entity][source]¶

Retrieve a list of entities from Feast Core

Parameters

project – Filter entities based on project name
labels – User-defined labels that these entities are associated with

Returns

List of entities

list_feature_tables(project: str = None, labels: Dict[str, str] = {}) → List[feast.feature_table.FeatureTable][source]¶

Retrieve a list of feature tables from Feast Core

Parameters: project – Filter feature tables based on project name
Returns: List of feature tables

list_features_by_ref(project: str = None, entities: List[str] = [], labels: Dict[str, str] = {}) → Dict[feast.feature.FeatureRef, feast.feature.Feature][source]¶

Retrieve a dictionary of feature reference to feature from Feast Core based on filters provided.

Parameters

project – Feast project that these features belongs to
entities – Feast entity that these features are associated with
labels – Feast labels that these features are associated with

Returns

features>

Return type

Dictionary of <feature references

Examples

>>> from feast import Client
>>>
>>> feast_client = Client(core_url="localhost:6565")
>>> features = feast_client.list_features(project="test_project", entities=["driver_id"], labels={"key1":"val1","key2":"val2"})
>>> print(features)

list_projects() → List[str][source]¶

List all active Feast projects

Returns: List of project names

property project¶

Retrieve currently active project

Returns: Project name

property serving_secure¶

Retrieve Feast Serving client-side SSL/TLS setting

Returns: Whether client-side SSL/TLS is enabled

property serving_url¶

Retrieve Feast Serving URL

Returns: Feast Serving URL string

set_project(project: Optional[str] = None)[source]¶

Set currently active Feast project

Parameters: project – Project to set as active. If unset, will reset to the default project.

version(sdk_only=False)[source]¶: Returns version information from Feast Core and Feast Serving

class feast.Entity(name: str, description: str, value_type: feast.value_type.ValueType, labels: Optional[MutableMapping[str, str]] = None)[source]¶

Bases: object

Represents a collection of entities and associated metadata.

property created_timestamp¶: Returns the created_timestamp of this entity

property description¶: Returns the description of this entity

classmethod from_dict(entity_dict)[source]¶

Creates an entity from a dict

Parameters: entity_dict – A dict representation of an entity
Returns: Returns a EntityV2 object based on the entity dict

classmethod from_proto(entity_proto: feast.core.Entity_pb2.Entity)[source]¶

Creates an entity from a protobuf representation of an entity

Parameters: entity_proto – A protobuf representation of an entity
Returns: Returns a EntityV2 object based on the entity protobuf

classmethod from_yaml(yml: str)[source]¶

Creates an entity from a YAML string body or a file path

Parameters: yml – Either a file path containing a yaml file or a YAML string
Returns: Returns a EntityV2 object based on the YAML file

is_valid()[source]¶: Validates the state of a entity locally. Raises an exception if entity is invalid.

property labels¶: Returns the labels of this entity. This is the user defined metadata defined as a dictionary.

property last_updated_timestamp¶: Returns the last_updated_timestamp of this entity

property name¶: Returns the name of this entity

to_dict() → Dict[source]¶

Converts entity to dict

Returns: Dictionary object representation of entity

to_proto() → feast.core.Entity_pb2.Entity[source]¶

Converts an entity object to its protobuf representation

Returns: EntityV2Proto protobuf

to_spec_proto() → feast.core.Entity_pb2.EntitySpecV2[source]¶

Converts an EntityV2 object to its protobuf representation. Used when passing EntitySpecV2 object to Feast request.

Returns: EntitySpecV2 protobuf

to_yaml()[source]¶

Converts a entity to a YAML string.

Returns: Entity string returned in YAML format

property value_type¶: Returns the type of this entity

class feast.BigQuerySource(event_timestamp_column: str, table_ref: str, created_timestamp_column: Optional[str] = '', field_mapping: Optional[Dict[str, str]] = None, date_partition_column: Optional[str] = '')[source]¶

Bases: feast.data_source.DataSource

property bigquery_options¶: Returns the bigquery options of this data source

property table_ref¶

to_proto() → feast.core.DataSource_pb2.DataSource[source]¶: Converts an DataSourceProto object to its protobuf representation.

class feast.FileSource(event_timestamp_column: str, file_format: feast.data_format.FileFormat, file_url: str, created_timestamp_column: Optional[str] = '', field_mapping: Optional[Dict[str, str]] = None, date_partition_column: Optional[str] = '')[source]¶

Bases: feast.data_source.DataSource

property file_options¶: Returns the file options of this data source

to_proto() → feast.core.DataSource_pb2.DataSource[source]¶: Converts an DataSourceProto object to its protobuf representation.

class feast.KafkaSource(event_timestamp_column: str, bootstrap_servers: str, message_format: feast.data_format.StreamFormat, topic: str, created_timestamp_column: Optional[str] = '', field_mapping: Optional[Dict[str, str]] = {}, date_partition_column: Optional[str] = '')[source]¶

Bases: feast.data_source.DataSource

property kafka_options¶: Returns the kafka options of this data source

to_proto() → feast.core.DataSource_pb2.DataSource[source]¶: Converts an DataSourceProto object to its protobuf representation.

class feast.KinesisSource(event_timestamp_column: str, created_timestamp_column: str, record_format: feast.data_format.StreamFormat, region: str, stream_name: str, field_mapping: Optional[Dict[str, str]] = {}, date_partition_column: Optional[str] = '')[source]¶

Bases: feast.data_source.DataSource

property kinesis_options¶: Returns the kinesis options of this data source

to_proto() → feast.core.DataSource_pb2.DataSource[source]¶: Converts an DataSourceProto object to its protobuf representation.

class feast.Feature(name: str, dtype: feast.value_type.ValueType, labels: Optional[MutableMapping[str, str]] = None)[source]¶

Bases: object

Feature field type

property dtype¶: Getter for data type of this field

classmethod from_proto(feature_proto: feast.core.Feature_pb2.FeatureSpecV2)[source]¶

Parameters: feature_proto – FeatureSpecV2 protobuf object
Returns: Feature object

property labels¶: Getter for labels of this field

property name¶: Getter for name of this field

to_proto() → feast.core.Feature_pb2.FeatureSpecV2[source]¶: Converts Feature object to its Protocol Buffer representation

class feast.FeatureTable(name: str, entities: List[str], features: List[feast.feature.Feature], batch_source: Union[feast.data_source.BigQuerySource, feast.data_source.FileSource] = None, stream_source: Union[feast.data_source.KafkaSource, feast.data_source.KinesisSource, None] = None, max_age: Optional[google.protobuf.duration_pb2.Duration] = None, labels: Optional[MutableMapping[str, str]] = None)[source]¶

Bases: object

Represents a collection of features and associated metadata.

add_feature(feature: feast.feature.Feature)[source]¶: Adds a new feature to the feature table.

property batch_source¶: Returns the batch source of this feature table

property created_timestamp¶: Returns the created_timestamp of this feature table

property entities¶: Returns the entities of this feature table

property features¶: Returns the features of this feature table

classmethod from_dict(ft_dict)[source]¶

Creates a feature table from a dict

Parameters: ft_dict – A dict representation of a feature table
Returns: Returns a FeatureTable object based on the feature table dict

classmethod from_proto(feature_table_proto: feast.core.FeatureTable_pb2.FeatureTable)[source]¶

Creates a feature table from a protobuf representation of a feature table

Parameters: feature_table_proto – A protobuf representation of a feature table
Returns: Returns a FeatureTableProto object based on the feature table protobuf

classmethod from_yaml(yml: str)[source]¶

Creates a feature table from a YAML string body or a file path

Parameters: yml – Either a file path containing a yaml file or a YAML string
Returns: Returns a FeatureTable object based on the YAML file

is_valid()[source]¶: Validates the state of a feature table locally. Raises an exception if feature table is invalid.

property labels¶: Returns the labels of this feature table. This is the user defined metadata defined as a dictionary.

property last_updated_timestamp¶: Returns the last_updated_timestamp of this feature table

property max_age¶: Returns the maximum age of this feature table. This is the total maximum amount of staleness that will be allowed during feature retrieval for each specific feature that is looked up.

property name¶: Returns the name of this feature table

property stream_source¶: Returns the stream source of this feature table

to_dict() → Dict[source]¶

Converts feature table to dict

Returns: Dictionary object representation of feature table

to_proto() → feast.core.FeatureTable_pb2.FeatureTable[source]¶

Converts an feature table object to its protobuf representation

Returns: FeatureTableProto protobuf

to_spec_proto() → feast.core.FeatureTable_pb2.FeatureTableSpec[source]¶

Converts an FeatureTableProto object to its protobuf representation. Used when passing FeatureTableSpecProto object to Feast request.

Returns: FeatureTableSpecProto protobuf

to_yaml()[source]¶

Converts a feature table to a YAML string.

Returns: Feature table string returned in YAML format

class feast.FeatureView(name: str, entities: List[str], features: List[feast.feature.Feature], tags: Dict[str, str], ttl: Union[google.protobuf.duration_pb2.Duration, datetime.timedelta, None], online: bool, input: feast.data_source.BigQuerySource)[source]¶

Bases: object

A FeatureView defines a logical grouping of servable features.

created_timestamp: Optional[google.protobuf.timestamp_pb2.Timestamp] = None¶

entities: List[str] = None¶

features: List[Feature] = None¶

classmethod from_proto(feature_view_proto: feast.core.FeatureView_pb2.FeatureView)[source]¶

Creates a feature view from a protobuf representation of a feature view

Parameters: feature_view_proto – A protobuf representation of a feature view
Returns: Returns a FeatureViewProto object based on the feature view protobuf

input: BigQuerySource = None¶

is_valid()[source]¶: Validates the state of a feature view locally. Raises an exception if feature view is invalid.

last_updated_timestamp: Optional[google.protobuf.timestamp_pb2.Timestamp] = None¶

name: str = None¶

online: bool = None¶

tags: Dict[str, str] = None¶

to_proto() → feast.core.FeatureView_pb2.FeatureView[source]¶

Converts an feature view object to its protobuf representation.

Returns: FeatureViewProto protobuf

ttl: Optional[Duration] = None¶

class feast.SourceType[source]¶

Bases: enum.Enum

DataSource value type. Used to define source types in DataSource.

BATCH_BIGQUERY = 2¶

BATCH_FILE = 1¶

STREAM_KAFKA = 3¶

STREAM_KINESIS = 4¶

UNKNOWN = 0¶

class feast.ValueType[source]¶

Bases: enum.Enum

Feature value type. Used to define data types in Feature Tables.

BOOL = 7¶

BOOL_LIST = 17¶

BYTES = 1¶

BYTES_LIST = 11¶

DOUBLE = 5¶

DOUBLE_LIST = 15¶

FLOAT = 6¶

FLOAT_LIST = 16¶

INT32 = 3¶

INT32_LIST = 13¶

INT64 = 4¶

INT64_LIST = 14¶

STRING = 2¶

STRING_LIST = 12¶

UNKNOWN = 0¶

to_tfx_schema_feature_type()[source]¶