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: Optional[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: Optional[str] = None)[source]¶
Deprecated. Please see apply().
- apply_feature_table(feature_tables: Union[List[feast.feature_table.FeatureTable], feast.feature_table.FeatureTable], project: Optional[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: feast.config.Config¶
- property core_secure: bool¶
Retrieve Feast Core client-side SSL/TLS setting
- Returns
Whether client-side SSL/TLS is enabled
- property core_url: str¶
Retrieve Feast Core URL
- Returns
Feast Core URL string
- delete_feature_table(name: str, project: Optional[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: Optional[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: Optional[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: Optional[str] = None, chunk_size: int = 10000, max_workers: int = 1, 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: bool¶
Retrieve Feast Job Service client-side SSL/TLS setting
- Returns
Whether client-side SSL/TLS is enabled
- property job_service_url: str¶
Retrieve Feast Job Service URL
- Returns
Feast Job Service URL string
- list_entities(project: Optional[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: Optional[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: Optional[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)
- property project: str¶
Retrieve currently active project
- Returns
Project name
- property serving_secure: bool¶
Retrieve Feast Serving client-side SSL/TLS setting
- Returns
Whether client-side SSL/TLS is enabled
- property serving_url: str¶
Retrieve Feast Serving URL
- Returns
Feast Serving URL string
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
feast.constants module¶
- class feast.constants.AuthProvider(value)[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_SECTION: str = 'general'¶
Default section in Feast configuration file to specify options
- class feast.constants.ConfigMeta(name, bases, attrs)[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”
- 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_SERVER_SSL_CERT: str = 'core_server_ssl_cert'¶
Path to certificate(s) to secure connection to Feast Core
- 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
- 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_TEMPLATE_PATH: Optional[str] = 'emr_cluster_template_path'¶
Template path of EMR cluster
- 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
- SERVING_SERVER_SSL_CERT: str = 'serving_server_ssl_cert'¶
Path to certificate(s) to secure connection to Feast Serving
- 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_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/
- STENCIL_URL: str = 'stencil_url'¶
ProtoRegistry Address (currently only Stencil Server is supported as registry) https://github.com/gojekfarm/stencil
- USAGE = 'usage'¶
Usage enabled
- feast.constants.DATETIME_COLUMN: str = 'datetime'¶
Default datetime column name for point-in-time join
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
- class feast.data_format.FileFormat[source]¶
Bases:
abc.ABC
Defines an abtract file forma used to encode feature data in files
- class feast.data_format.ParquetFormat[source]¶
Bases:
feast.data_format.FileFormat
Defines the Parquet data format
- class feast.data_format.ProtoFormat(class_path: str)[source]¶
Bases:
feast.data_format.StreamFormat
Defines the Protobuf data format
feast.data_source module¶
- class feast.data_source.BigQueryOptions(table_ref: Optional[str], query: Optional[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 query¶
Returns the BigQuery SQL query referenced by this source
- property table_ref¶
Returns the table ref of this BQ table
- class feast.data_source.BigQuerySource(event_timestamp_column: Optional[str] = '', table_ref: Optional[str] = None, created_timestamp_column: Optional[str] = '', field_mapping: Optional[Dict[str, str]] = None, date_partition_column: Optional[str] = '', query: Optional[str] = None)[source]¶
Bases:
feast.data_source.DataSource
- property bigquery_options¶
Returns the bigquery options of this data source
- get_table_query_string() → str[source]¶
Returns a string that can directly be used to reference this table in SQL
- property query¶
- static source_datatype_to_feast_value_type() → Callable[[str], feast.value_type.ValueType][source]¶
- property table_ref¶
- class feast.data_source.DataSource(event_timestamp_column: Optional[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.
- class feast.data_source.FileOptions(file_format: Optional[feast.data_format.FileFormat], file_url: Optional[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
- class feast.data_source.FileSource(event_timestamp_column: Optional[str] = '', file_url: Optional[str] = None, path: Optional[str] = None, file_format: Optional[feast.data_format.FileFormat] = None, 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
- property path¶
Returns the file path of this feature data source
- static source_datatype_to_feast_value_type() → Callable[[str], feast.value_type.ValueType][source]¶
- 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
- 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
- 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
feast.driver_test_data module¶
- class feast.driver_test_data.EventTimestampType(value)[source]¶
Bases:
enum.Enum
An enumeration.
- TZ_AWARE_FIXED_OFFSET = 2¶
- TZ_AWARE_US_PACIFIC = 3¶
- TZ_AWARE_UTC = 1¶
- TZ_NAIVE = 0¶
- feast.driver_test_data.create_customer_daily_profile_df(customers, start_date, end_date) → pandas.core.frame.DataFrame[source]¶
Example df generated by this function:
datetime | customer_id | current_balance | avg_passenger_count | lifetime_trip_count | created ||------------------+-------------+-----------------+---------------------+---------------------+------------------| | 2021-03-17 19:31 | 1010 | 0.889188 | 0.049057 | 412 | 2021-03-24 19:38 | | 2021-03-18 19:31 | 1010 | 0.979273 | 0.212630 | 639 | 2021-03-24 19:38 | | 2021-03-19 19:31 | 1010 | 0.976549 | 0.176881 | 70 | 2021-03-24 19:38 | | 2021-03-20 19:31 | 1010 | 0.273697 | 0.325012 | 68 | 2021-03-24 19:38 | | 2021-03-21 19:31 | 1010 | 0.438262 | 0.313009 | 192 | 2021-03-24 19:38 | | | … | … | … | … | | | 2021-03-19 19:31 | 1001 | 0.738860 | 0.857422 | 344 | 2021-03-24 19:38 | | 2021-03-20 19:31 | 1001 | 0.848397 | 0.745989 | 106 | 2021-03-24 19:38 | | 2021-03-21 19:31 | 1001 | 0.301552 | 0.185873 | 812 | 2021-03-24 19:38 | | 2021-03-22 19:31 | 1001 | 0.943030 | 0.561219 | 322 | 2021-03-24 19:38 | | 2021-03-23 19:31 | 1001 | 0.354919 | 0.810093 | 273 | 2021-03-24 19:38 |
- feast.driver_test_data.create_driver_hourly_stats_df(drivers, start_date, end_date) → pandas.core.frame.DataFrame[source]¶
Example df generated by this function:
datetime | driver_id | conv_rate | acc_rate | avg_daily_trips | created ||------------------+-----------+-----------+----------+-----------------+------------------| | 2021-03-17 19:31 | 5010 | 0.229297 | 0.685843 | 861 | 2021-03-24 19:34 | | 2021-03-17 20:31 | 5010 | 0.781655 | 0.861280 | 769 | 2021-03-24 19:34 | | 2021-03-17 21:31 | 5010 | 0.150333 | 0.525581 | 778 | 2021-03-24 19:34 | | 2021-03-17 22:31 | 5010 | 0.951701 | 0.228883 | 570 | 2021-03-24 19:34 | | 2021-03-17 23:31 | 5010 | 0.819598 | 0.262503 | 473 | 2021-03-24 19:34 | | | … | … | … | … | | | 2021-03-24 16:31 | 5001 | 0.061585 | 0.658140 | 477 | 2021-03-24 19:34 | | 2021-03-24 17:31 | 5001 | 0.088949 | 0.303897 | 618 | 2021-03-24 19:34 | | 2021-03-24 18:31 | 5001 | 0.096652 | 0.747421 | 480 | 2021-03-24 19:34 | | 2021-03-17 19:31 | 5005 | 0.142936 | 0.707596 | 466 | 2021-03-24 19:34 | | 2021-03-17 19:31 | 5005 | 0.142936 | 0.707596 | 466 | 2021-03-24 19:34 |
feast.entity module¶
- class feast.entity.Entity(name: str, value_type: feast.value_type.ValueType = <ValueType.UNKNOWN: 0>, description: str = '', join_key: Optional[str] = None, 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 join_key¶
Returns the join key of this entity
- 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: feast.value_type.ValueType¶
Returns the type of this entity
feast.errors module¶
- exception feast.errors.FeastClassImportError(module_name, class_name, class_type='provider')[source]¶
Bases:
Exception
- exception feast.errors.FeastClassInvalidName(class_name: str, class_type: str)[source]¶
Bases:
Exception
- exception feast.errors.FeastEntityDFMissingColumnsError(expected, missing)[source]¶
Bases:
Exception
- exception feast.errors.FeastExtrasDependencyImportError(extras_type: str, nested_error: str)[source]¶
Bases:
Exception
- exception feast.errors.FeastJoinKeysDuringMaterialization(source: str, join_key_columns: Set[str], source_columns: Set[str])[source]¶
Bases:
Exception
- exception feast.errors.FeastModuleImportError(module_name: str, module_type: str)[source]¶
Bases:
Exception
- exception feast.errors.FeastOfflineStoreUnsupportedDataSource(offline_store_name: str, data_source_name: str)[source]¶
Bases:
Exception
- exception feast.errors.FeastOnlineStoreInvalidName(online_store_class_name: str)[source]¶
Bases:
Exception
- exception feast.errors.FeastOnlineStoreUnsupportedDataSource(online_store_name: str, data_source_name: str)[source]¶
Bases:
Exception
- exception feast.errors.FeastProviderLoginError[source]¶
Bases:
Exception
Error class that indicates a user has not authenticated with their provider.
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: feast.value_type.ValueType¶
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: MutableMapping[str, str]¶
Getter for labels of this field
- property name¶
Getter for name of this field
- class feast.feature.FeatureRef(name: str, feature_table: str)[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
- Parameters
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.
- Parameters
feature_ref_str – String representation of the feature reference
- Returns
FeatureRef that refers to the given feature
feast.feature_store module¶
- class feast.feature_store.FeatureStore(repo_path: Optional[str] = None, config: Optional[feast.repo_config.RepoConfig] = None)[source]¶
Bases:
object
A FeatureStore object is used to define, create, and retrieve features.
- Parameters
repo_path – Path to a feature_store.yaml used to configure the feature store
config (RepoConfig) – Configuration object used to configure the feature store
- apply(objects: Union[feast.entity.Entity, feast.feature_view.FeatureView, List[Union[feast.feature_view.FeatureView, feast.entity.Entity]]])[source]¶
Register objects to metadata store and update related infrastructure.
The apply method registers one or more definitions (e.g., Entity, FeatureView) and registers or updates these objects in the Feast registry. Once the registry has been updated, the apply method will update related infrastructure (e.g., create tables in an online store) in order to reflect these new definitions. All operations are idempotent, meaning they can safely be rerun.
- Parameters
objects (List[Union[FeatureView, Entity]]) – A list of FeatureView or Entity objects that should be registered
Examples
Register a single Entity and FeatureView.
>>> from feast.feature_store import FeatureStore >>> from feast import Entity, FeatureView, Feature, ValueType, FileSource >>> from datetime import timedelta >>> >>> fs = FeatureStore() >>> customer_entity = Entity(name="customer", value_type=ValueType.INT64, description="customer entity") >>> customer_feature_view = FeatureView( >>> name="customer_fv", >>> entities=["customer"], >>> features=[Feature(name="age", dtype=ValueType.INT64)], >>> input=FileSource(path="file.parquet", event_timestamp_column="timestamp"), >>> ttl=timedelta(days=1) >>> ) >>> fs.apply([customer_entity, customer_feature_view])
- config: feast.repo_config.RepoConfig¶
- delete_feature_view(name: str)[source]¶
Deletes a feature view or raises an exception if not found.
- Parameters
name – Name of feature view
- get_entity(name: str) → feast.entity.Entity[source]¶
Retrieves an entity.
- Parameters
name – Name of entity
- Returns
Returns either the specified entity, or raises an exception if none is found
- get_feature_view(name: str) → feast.feature_view.FeatureView[source]¶
Retrieves a feature view.
- Parameters
name – Name of feature view
- Returns
Returns either the specified feature view, or raises an exception if none is found
- get_historical_features(entity_df: Union[pandas.core.frame.DataFrame, str], feature_refs: List[str]) → feast.infra.offline_stores.offline_store.RetrievalJob[source]¶
Enrich an entity dataframe with historical feature values for either training or batch scoring.
This method joins historical feature data from one or more feature views to an entity dataframe by using a time travel join.
Each feature view is joined to the entity dataframe using all entities configured for the respective feature view. All configured entities must be available in the entity dataframe. Therefore, the entity dataframe must contain all entities found in all feature views, but the individual feature views can have different entities.
Time travel is based on the configured TTL for each feature view. A shorter TTL will limit the amount of scanning that will be done in order to find feature data for a specific entity key. Setting a short TTL may result in null values being returned.
- Parameters
entity_df (Union[pd.DataFrame, str]) – An entity dataframe is a collection of rows containing all entity columns (e.g., customer_id, driver_id) on which features need to be joined, as well as a event_timestamp column used to ensure point-in-time correctness. Either a Pandas DataFrame can be provided or a string SQL query. The query must be of a format supported by the configured offline store (e.g., BigQuery)
feature_refs – A list of features that should be retrieved from the offline store. Feature references are of the format “feature_view:feature”, e.g., “customer_fv:daily_transactions”.
- Returns
RetrievalJob which can be used to materialize the results.
Examples
Retrieve historical features using a BigQuery SQL entity dataframe
>>> from feast.feature_store import FeatureStore >>> >>> fs = FeatureStore(config=RepoConfig(provider="gcp")) >>> retrieval_job = fs.get_historical_features( >>> entity_df="SELECT event_timestamp, order_id, customer_id from gcp_project.my_ds.customer_orders", >>> feature_refs=["customer:age", "customer:avg_orders_1d", "customer:avg_orders_7d"] >>> ) >>> feature_data = retrieval_job.to_df() >>> model.fit(feature_data) # insert your modeling framework here.
- get_online_features(feature_refs: List[str], entity_rows: List[Dict[str, Any]]) → feast.online_response.OnlineResponse[source]¶
Retrieves the latest online feature data.
Note: This method will download the full feature registry the first time it is run. If you are using a remote registry like GCS or S3 then that may take a few seconds. The registry remains cached up to a TTL duration (which can be set to infinity). If the cached registry is stale (more time than the TTL has passed), then a new registry will be downloaded synchronously by this method. This download may introduce latency to online feature retrieval. In order to avoid synchronous downloads, please call refresh_registry() prior to the TTL being reached. Remember it is possible to set the cache TTL to infinity (cache forever).
- Parameters
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.
entity_rows – A list of dictionaries where each key-value is an entity-name, entity-value pair.
- Returns
OnlineResponse containing the feature data in records.
Examples
>>> from feast import FeatureStore >>> >>> store = FeatureStore(repo_path="...") >>> feature_refs = ["sales:daily_transactions"] >>> entity_rows = [{"customer_id": 0},{"customer_id": 1}] >>> >>> online_response = store.get_online_features( >>> feature_refs, entity_rows) >>> online_response_dict = online_response.to_dict() >>> print(online_response_dict) {'sales:daily_transactions': [1.1,1.2], 'sales:customer_id': [0,1]}
- list_entities(allow_cache: bool = False) → List[feast.entity.Entity][source]¶
Retrieve a list of entities from the registry
- Parameters
allow_cache (bool) – Whether to allow returning entities from a cached registry
- Returns
List of entities
- list_feature_views() → List[feast.feature_view.FeatureView][source]¶
Retrieve a list of feature views from the registry
- Returns
List of feature views
- materialize(start_date: datetime.datetime, end_date: datetime.datetime, feature_views: Optional[List[str]] = None) → None[source]¶
Materialize data from the offline store into the online store.
This method loads feature data in the specified interval from either the specified feature views, or all feature views if none are specified, into the online store where it is available for online serving.
- Parameters
start_date (datetime) – Start date for time range of data to materialize into the online store
end_date (datetime) – End date for time range of data to materialize into the online store
feature_views (List[str]) – Optional list of feature view names. If selected, will only run materialization for the specified feature views.
Examples
Materialize all features into the online store over the interval from 3 hours ago to 10 minutes ago.
>>> from datetime import datetime, timedelta >>> from feast.feature_store import FeatureStore >>> >>> fs = FeatureStore(config=RepoConfig(provider="gcp")) >>> fs.materialize( >>> start_date=datetime.utcnow() - timedelta(hours=3), end_date=datetime.utcnow() - timedelta(minutes=10) >>> )
- materialize_incremental(end_date: datetime.datetime, feature_views: Optional[List[str]] = None) → None[source]¶
Materialize incremental new data from the offline store into the online store.
This method loads incremental new feature data up to the specified end time from either the specified feature views, or all feature views if none are specified, into the online store where it is available for online serving. The start time of the interval materialized is either the most recent end time of a prior materialization or (now - ttl) if no such prior materialization exists.
- Parameters
end_date (datetime) – End date for time range of data to materialize into the online store
feature_views (List[str]) – Optional list of feature view names. If selected, will only run materialization for the specified feature views.
Examples
Materialize all features into the online store up to 5 minutes ago.
>>> from datetime import datetime, timedelta >>> from feast.feature_store import FeatureStore >>> >>> fs = FeatureStore(config=RepoConfig(provider="gcp", registry="gs://my-fs/", project="my_fs_proj")) >>> fs.materialize_incremental(end_date=datetime.utcnow() - timedelta(minutes=5))
- property project: str¶
- refresh_registry()[source]¶
Fetches and caches a copy of the feature registry in memory.
Explicitly calling this method allows for direct control of the state of the registry cache. Every time this method is called the complete registry state will be retrieved from the remote registry store backend (e.g., GCS, S3), and the cache timer will be reset. If refresh_registry() is run before get_online_features() is called, then get_online_feature() will use the cached registry instead of retrieving (and caching) the registry itself.
Additionally, the TTL for the registry cache can be set to infinity (by setting it to 0), which means that refresh_registry() will become the only way to update the cached registry. If the TTL is set to a value greater than 0, then once the cache becomes stale (more time than the TTL has passed), a new cache will be downloaded synchronously, which may increase latencies if the triggering method is get_online_features()
- repo_path: pathlib.Path¶
feast.feature_table module¶
- class feast.feature_table.FeatureTable(name: str, entities: List[str], features: List[feast.feature.Feature], batch_source: Optional[Union[feast.data_source.BigQuerySource, feast.data_source.FileSource]] = None, stream_source: Optional[Union[feast.data_source.KafkaSource, feast.data_source.KinesisSource]] = 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
feast.feature_view module¶
- class feast.feature_view.FeatureView(name: str, entities: List[str], ttl: Optional[Union[google.protobuf.duration_pb2.Duration, datetime.timedelta]], input: Union[feast.data_source.BigQuerySource, feast.data_source.FileSource], features: List[feast.feature.Feature] = [], tags: Optional[Dict[str, str]] = None, online: bool = True)[source]¶
Bases:
object
A FeatureView defines a logical grouping of serveable features.
- created_timestamp: Optional[google.protobuf.timestamp_pb2.Timestamp] = None¶
- features: List[feast.feature.Feature]¶
- 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: Union[feast.data_source.BigQuerySource, feast.data_source.FileSource]¶
- 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¶
- materialization_intervals: List[Tuple[datetime.datetime, datetime.datetime]]¶
- property most_recent_end_time: Optional[datetime.datetime]¶
- to_proto() → feast.core.FeatureView_pb2.FeatureView[source]¶
Converts an feature view object to its protobuf representation.
- Returns
FeatureViewProto protobuf
- ttl: Optional[datetime.timedelta]¶
feast.names module¶
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.
feast.registry module¶
- class feast.registry.GCSRegistryStore(uri: str)[source]¶
Bases:
feast.registry.RegistryStore
- get_registry_proto()[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_proto(updater: Optional[Callable[[feast.core.Registry_pb2.Registry], feast.core.Registry_pb2.Registry]] = None)[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(repo_path: pathlib.Path, registry_path_string: str)[source]¶
Bases:
feast.registry.RegistryStore
- get_registry_proto()[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_proto(updater: Optional[Callable[[feast.core.Registry_pb2.Registry], feast.core.Registry_pb2.Registry]] = None)[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, repo_path: pathlib.Path, cache_ttl: datetime.timedelta)[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
- apply_materialization(feature_view: feast.feature_view.FeatureView, project: str, start_date: datetime.datetime, end_date: datetime.datetime)[source]¶
Updates materialization intervals tracked for a single feature view in Feast
- Parameters
feature_view – Feature view that will be updated with an additional materialization interval tracked
project – Feast project that this feature view belongs to
start_date (datetime) – Start date of the materialization interval to track
end_date (datetime) – End date of the materialization interval to track
- cached_registry_proto: Optional[feast.core.Registry_pb2.Registry] = None¶
- cached_registry_proto_created: Optional[datetime.datetime] = None¶
- cached_registry_proto_ttl: datetime.timedelta¶
- 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, allow_cache: bool = False) → 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, allow_cache: bool = False) → List[feast.entity.Entity][source]¶
Retrieve a list of entities from the registry
- Parameters
allow_cache – Whether to allow returning entities from a cached registry
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, allow_cache: bool = False) → List[feast.feature_view.FeatureView][source]¶
Retrieve a list of feature views from the registry
- Parameters
allow_cache – Allow returning feature views from the cached registry
project – Filter feature tables based on project name
- Returns
List of feature views
- 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_proto()[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_proto(updater: Optional[Callable[[feast.core.Registry_pb2.Registry], feast.core.Registry_pb2.Registry]] = None)[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¶
- class feast.repo_config.FeastBaseModel(**extra_data: Any)[source]¶
Bases:
pydantic.main.BaseModel
Feast Pydantic Configuration Class
- class feast.repo_config.FeastConfigBaseModel[source]¶
Bases:
pydantic.main.BaseModel
Feast Pydantic Configuration Class
- class feast.repo_config.RegistryConfig(*, path: pydantic.types.StrictStr, cache_ttl_seconds: pydantic.types.StrictInt = 600, **extra_data: Any)[source]¶
Bases:
feast.repo_config.FeastBaseModel
Metadata Store Configuration. Configuration that relates to reading from and writing to the Feast registry.
- cache_ttl_seconds: pydantic.types.StrictInt¶
The cache TTL is the amount of time registry state will be cached in memory. If this TTL is exceeded then the registry will be refreshed when any feature store method asks for access to registry state. The TTL can be set to infinity by setting TTL to 0 seconds, which means the cache will only be loaded once and will never expire. Users can manually refresh the cache by calling feature_store.refresh_registry()
- Type
- class feast.repo_config.RepoConfig(*, registry: Union[pydantic.types.StrictStr, feast.repo_config.RegistryConfig] = 'data/registry.db', project: pydantic.types.StrictStr, provider: pydantic.types.StrictStr, online_store: Any = None, offline_store: Any = None, repo_path: pathlib.Path = None, **data: Any)[source]¶
Bases:
feast.repo_config.FeastBaseModel
Repo config. Typically loaded from feature_store.yaml
- offline_store: Any¶
Offline store configuration (optional depending on provider)
- Type
OfflineStoreConfig
- online_store: Any¶
Online store configuration (optional depending on provider)
- Type
OnlineStoreConfig
- project: pydantic.types.StrictStr¶
Feast project id. This can be any alphanumeric string up to 16 characters. You can have multiple independent feature repositories deployed to the same cloud provider account, as long as they have different project ids.
- Type
- registry: Union[pydantic.types.StrictStr, feast.repo_config.RegistryConfig]¶
Path to metadata store. Can be a local path, or remote object storage path, e.g. a GCS URI
- Type
- repo_path: Optional[pathlib.Path]¶
- feast.repo_config.load_repo_config(repo_path: pathlib.Path) → feast.repo_config.RepoConfig[source]¶
feast.repo_operations module¶
- class feast.repo_operations.ParsedRepo(feature_tables, feature_views, entities)[source]¶
Bases:
tuple
- property entities¶
Alias for field number 2
- property feature_tables¶
Alias for field number 0
- property feature_views¶
Alias for field number 1
- feast.repo_operations.apply_total(repo_config: feast.repo_config.RepoConfig, repo_path: pathlib.Path)[source]¶
- feast.repo_operations.cli_check_repo(repo_path: pathlib.Path)[source]¶
- feast.repo_operations.get_ignore_files(repo_root: pathlib.Path, ignore_paths: List[str]) → Set[pathlib.Path][source]¶
Get all ignore files that match any of the user-defined ignore paths
- feast.repo_operations.get_repo_files(repo_root: pathlib.Path) → List[pathlib.Path][source]¶
Get the list of all repo files, ignoring undesired files & directories specified in .feastignore
- feast.repo_operations.is_valid_name(name: str) → bool[source]¶
A name should be alphanumeric values and underscores but not start with an underscore
- feast.repo_operations.parse_repo(repo_root: pathlib.Path) → feast.repo_operations.ParsedRepo[source]¶
Collect feature table definitions from feature repo
- feast.repo_operations.py_path_to_module(path: pathlib.Path, repo_root: pathlib.Path) → str[source]¶
- feast.repo_operations.read_feastignore(repo_root: pathlib.Path) → List[str][source]¶
Read .feastignore in the repo root directory (if exists) and return the list of user-defined ignore paths
- feast.repo_operations.registry_dump(repo_config: feast.repo_config.RepoConfig, repo_path: pathlib.Path)[source]¶
For debugging only: output contents of the metadata registry
- feast.repo_operations.teardown(repo_config: feast.repo_config.RepoConfig, repo_path: pathlib.Path)[source]¶
feast.telemetry module¶
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.
- feast.type_map.pa_to_feast_value_type(value: Union[pyarrow.lib.ChunkedArray, str]) → 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.type_map.python_value_to_proto_value(value: Any, feature_type: Optional[feast.value_type.ValueType] = None) → feast.types.Value_pb2.Value[source]¶
feast.utils module¶
- feast.utils.make_tzaware(t: datetime.datetime) → datetime.datetime[source]¶
We assume tz-naive datetimes are UTC
feast.value_type module¶
- class feast.value_type.ValueType(value)[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¶
- UNIX_TIMESTAMP = 8¶
- UNIX_TIMESTAMP_LIST = 18¶
- UNKNOWN = 0¶
feast.version module¶
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.BigQuerySource(event_timestamp_column: Optional[str] = '', table_ref: Optional[str] = None, created_timestamp_column: Optional[str] = '', field_mapping: Optional[Dict[str, str]] = None, date_partition_column: Optional[str] = '', query: Optional[str] = None)[source]¶
Bases:
feast.data_source.DataSource
- property bigquery_options¶
Returns the bigquery options of this data source
- get_table_query_string() → str[source]¶
Returns a string that can directly be used to reference this table in SQL
- property query¶
- static source_datatype_to_feast_value_type() → Callable[[str], feast.value_type.ValueType][source]¶
- property table_ref¶
- 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: Optional[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: Optional[str] = None)[source]¶
Deprecated. Please see apply().
- apply_feature_table(feature_tables: Union[List[feast.feature_table.FeatureTable], feast.feature_table.FeatureTable], project: Optional[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: feast.config.Config¶
- property core_secure: bool¶
Retrieve Feast Core client-side SSL/TLS setting
- Returns
Whether client-side SSL/TLS is enabled
- property core_url: str¶
Retrieve Feast Core URL
- Returns
Feast Core URL string
- delete_feature_table(name: str, project: Optional[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: Optional[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: Optional[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: Optional[str] = None, chunk_size: int = 10000, max_workers: int = 1, 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: bool¶
Retrieve Feast Job Service client-side SSL/TLS setting
- Returns
Whether client-side SSL/TLS is enabled
- property job_service_url: str¶
Retrieve Feast Job Service URL
- Returns
Feast Job Service URL string
- list_entities(project: Optional[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: Optional[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: Optional[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)
- property project: str¶
Retrieve currently active project
- Returns
Project name
- property serving_secure: bool¶
Retrieve Feast Serving client-side SSL/TLS setting
- Returns
Whether client-side SSL/TLS is enabled
- property serving_url: str¶
Retrieve Feast Serving URL
- Returns
Feast Serving URL string
- class feast.Entity(name: str, value_type: feast.value_type.ValueType = <ValueType.UNKNOWN: 0>, description: str = '', join_key: Optional[str] = None, 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 join_key¶
Returns the join key of this entity
- 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: feast.value_type.ValueType¶
Returns the type of this entity
- class feast.Feature(name: str, dtype: feast.value_type.ValueType, labels: Optional[MutableMapping[str, str]] = None)[source]¶
Bases:
object
Feature field type
- property dtype: feast.value_type.ValueType¶
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: MutableMapping[str, str]¶
Getter for labels of this field
- property name¶
Getter for name of this field
- class feast.FeatureStore(repo_path: Optional[str] = None, config: Optional[feast.repo_config.RepoConfig] = None)[source]¶
Bases:
object
A FeatureStore object is used to define, create, and retrieve features.
- Parameters
repo_path – Path to a feature_store.yaml used to configure the feature store
config (RepoConfig) – Configuration object used to configure the feature store
- apply(objects: Union[feast.entity.Entity, feast.feature_view.FeatureView, List[Union[feast.feature_view.FeatureView, feast.entity.Entity]]])[source]¶
Register objects to metadata store and update related infrastructure.
The apply method registers one or more definitions (e.g., Entity, FeatureView) and registers or updates these objects in the Feast registry. Once the registry has been updated, the apply method will update related infrastructure (e.g., create tables in an online store) in order to reflect these new definitions. All operations are idempotent, meaning they can safely be rerun.
- Parameters
objects (List[Union[FeatureView, Entity]]) – A list of FeatureView or Entity objects that should be registered
Examples
Register a single Entity and FeatureView.
>>> from feast.feature_store import FeatureStore >>> from feast import Entity, FeatureView, Feature, ValueType, FileSource >>> from datetime import timedelta >>> >>> fs = FeatureStore() >>> customer_entity = Entity(name="customer", value_type=ValueType.INT64, description="customer entity") >>> customer_feature_view = FeatureView( >>> name="customer_fv", >>> entities=["customer"], >>> features=[Feature(name="age", dtype=ValueType.INT64)], >>> input=FileSource(path="file.parquet", event_timestamp_column="timestamp"), >>> ttl=timedelta(days=1) >>> ) >>> fs.apply([customer_entity, customer_feature_view])
- config: feast.repo_config.RepoConfig¶
- delete_feature_view(name: str)[source]¶
Deletes a feature view or raises an exception if not found.
- Parameters
name – Name of feature view
- get_entity(name: str) → feast.entity.Entity[source]¶
Retrieves an entity.
- Parameters
name – Name of entity
- Returns
Returns either the specified entity, or raises an exception if none is found
- get_feature_view(name: str) → feast.feature_view.FeatureView[source]¶
Retrieves a feature view.
- Parameters
name – Name of feature view
- Returns
Returns either the specified feature view, or raises an exception if none is found
- get_historical_features(entity_df: Union[pandas.core.frame.DataFrame, str], feature_refs: List[str]) → feast.infra.offline_stores.offline_store.RetrievalJob[source]¶
Enrich an entity dataframe with historical feature values for either training or batch scoring.
This method joins historical feature data from one or more feature views to an entity dataframe by using a time travel join.
Each feature view is joined to the entity dataframe using all entities configured for the respective feature view. All configured entities must be available in the entity dataframe. Therefore, the entity dataframe must contain all entities found in all feature views, but the individual feature views can have different entities.
Time travel is based on the configured TTL for each feature view. A shorter TTL will limit the amount of scanning that will be done in order to find feature data for a specific entity key. Setting a short TTL may result in null values being returned.
- Parameters
entity_df (Union[pd.DataFrame, str]) – An entity dataframe is a collection of rows containing all entity columns (e.g., customer_id, driver_id) on which features need to be joined, as well as a event_timestamp column used to ensure point-in-time correctness. Either a Pandas DataFrame can be provided or a string SQL query. The query must be of a format supported by the configured offline store (e.g., BigQuery)
feature_refs – A list of features that should be retrieved from the offline store. Feature references are of the format “feature_view:feature”, e.g., “customer_fv:daily_transactions”.
- Returns
RetrievalJob which can be used to materialize the results.
Examples
Retrieve historical features using a BigQuery SQL entity dataframe
>>> from feast.feature_store import FeatureStore >>> >>> fs = FeatureStore(config=RepoConfig(provider="gcp")) >>> retrieval_job = fs.get_historical_features( >>> entity_df="SELECT event_timestamp, order_id, customer_id from gcp_project.my_ds.customer_orders", >>> feature_refs=["customer:age", "customer:avg_orders_1d", "customer:avg_orders_7d"] >>> ) >>> feature_data = retrieval_job.to_df() >>> model.fit(feature_data) # insert your modeling framework here.
- get_online_features(feature_refs: List[str], entity_rows: List[Dict[str, Any]]) → feast.online_response.OnlineResponse[source]¶
Retrieves the latest online feature data.
Note: This method will download the full feature registry the first time it is run. If you are using a remote registry like GCS or S3 then that may take a few seconds. The registry remains cached up to a TTL duration (which can be set to infinity). If the cached registry is stale (more time than the TTL has passed), then a new registry will be downloaded synchronously by this method. This download may introduce latency to online feature retrieval. In order to avoid synchronous downloads, please call refresh_registry() prior to the TTL being reached. Remember it is possible to set the cache TTL to infinity (cache forever).
- Parameters
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.
entity_rows – A list of dictionaries where each key-value is an entity-name, entity-value pair.
- Returns
OnlineResponse containing the feature data in records.
Examples
>>> from feast import FeatureStore >>> >>> store = FeatureStore(repo_path="...") >>> feature_refs = ["sales:daily_transactions"] >>> entity_rows = [{"customer_id": 0},{"customer_id": 1}] >>> >>> online_response = store.get_online_features( >>> feature_refs, entity_rows) >>> online_response_dict = online_response.to_dict() >>> print(online_response_dict) {'sales:daily_transactions': [1.1,1.2], 'sales:customer_id': [0,1]}
- list_entities(allow_cache: bool = False) → List[feast.entity.Entity][source]¶
Retrieve a list of entities from the registry
- Parameters
allow_cache (bool) – Whether to allow returning entities from a cached registry
- Returns
List of entities
- list_feature_views() → List[feast.feature_view.FeatureView][source]¶
Retrieve a list of feature views from the registry
- Returns
List of feature views
- materialize(start_date: datetime.datetime, end_date: datetime.datetime, feature_views: Optional[List[str]] = None) → None[source]¶
Materialize data from the offline store into the online store.
This method loads feature data in the specified interval from either the specified feature views, or all feature views if none are specified, into the online store where it is available for online serving.
- Parameters
start_date (datetime) – Start date for time range of data to materialize into the online store
end_date (datetime) – End date for time range of data to materialize into the online store
feature_views (List[str]) – Optional list of feature view names. If selected, will only run materialization for the specified feature views.
Examples
Materialize all features into the online store over the interval from 3 hours ago to 10 minutes ago.
>>> from datetime import datetime, timedelta >>> from feast.feature_store import FeatureStore >>> >>> fs = FeatureStore(config=RepoConfig(provider="gcp")) >>> fs.materialize( >>> start_date=datetime.utcnow() - timedelta(hours=3), end_date=datetime.utcnow() - timedelta(minutes=10) >>> )
- materialize_incremental(end_date: datetime.datetime, feature_views: Optional[List[str]] = None) → None[source]¶
Materialize incremental new data from the offline store into the online store.
This method loads incremental new feature data up to the specified end time from either the specified feature views, or all feature views if none are specified, into the online store where it is available for online serving. The start time of the interval materialized is either the most recent end time of a prior materialization or (now - ttl) if no such prior materialization exists.
- Parameters
end_date (datetime) – End date for time range of data to materialize into the online store
feature_views (List[str]) – Optional list of feature view names. If selected, will only run materialization for the specified feature views.
Examples
Materialize all features into the online store up to 5 minutes ago.
>>> from datetime import datetime, timedelta >>> from feast.feature_store import FeatureStore >>> >>> fs = FeatureStore(config=RepoConfig(provider="gcp", registry="gs://my-fs/", project="my_fs_proj")) >>> fs.materialize_incremental(end_date=datetime.utcnow() - timedelta(minutes=5))
- property project: str¶
- refresh_registry()[source]¶
Fetches and caches a copy of the feature registry in memory.
Explicitly calling this method allows for direct control of the state of the registry cache. Every time this method is called the complete registry state will be retrieved from the remote registry store backend (e.g., GCS, S3), and the cache timer will be reset. If refresh_registry() is run before get_online_features() is called, then get_online_feature() will use the cached registry instead of retrieving (and caching) the registry itself.
Additionally, the TTL for the registry cache can be set to infinity (by setting it to 0), which means that refresh_registry() will become the only way to update the cached registry. If the TTL is set to a value greater than 0, then once the cache becomes stale (more time than the TTL has passed), a new cache will be downloaded synchronously, which may increase latencies if the triggering method is get_online_features()
- repo_path: pathlib.Path¶
- class feast.FeatureTable(name: str, entities: List[str], features: List[feast.feature.Feature], batch_source: Optional[Union[feast.data_source.BigQuerySource, feast.data_source.FileSource]] = None, stream_source: Optional[Union[feast.data_source.KafkaSource, feast.data_source.KinesisSource]] = 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
- class feast.FeatureView(name: str, entities: List[str], ttl: Optional[Union[google.protobuf.duration_pb2.Duration, datetime.timedelta]], input: Union[feast.data_source.BigQuerySource, feast.data_source.FileSource], features: List[feast.feature.Feature] = [], tags: Optional[Dict[str, str]] = None, online: bool = True)[source]¶
Bases:
object
A FeatureView defines a logical grouping of serveable features.
- created_timestamp: Optional[google.protobuf.timestamp_pb2.Timestamp] = None¶
- features: List[feast.feature.Feature]¶
- 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: Union[feast.data_source.BigQuerySource, feast.data_source.FileSource]¶
- 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¶
- materialization_intervals: List[Tuple[datetime.datetime, datetime.datetime]]¶
- property most_recent_end_time: Optional[datetime.datetime]¶
- to_proto() → feast.core.FeatureView_pb2.FeatureView[source]¶
Converts an feature view object to its protobuf representation.
- Returns
FeatureViewProto protobuf
- ttl: Optional[datetime.timedelta]¶
- class feast.FileSource(event_timestamp_column: Optional[str] = '', file_url: Optional[str] = None, path: Optional[str] = None, file_format: Optional[feast.data_format.FileFormat] = None, 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
- property path¶
Returns the file path of this feature data source
- static source_datatype_to_feast_value_type() → Callable[[str], feast.value_type.ValueType][source]¶
- 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
- 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
- class feast.RepoConfig(*, registry: Union[pydantic.types.StrictStr, feast.repo_config.RegistryConfig] = 'data/registry.db', project: pydantic.types.StrictStr, provider: pydantic.types.StrictStr, online_store: Any = None, offline_store: Any = None, repo_path: pathlib.Path = None, **data: Any)[source]¶
Bases:
feast.repo_config.FeastBaseModel
Repo config. Typically loaded from feature_store.yaml
- offline_store: Any¶
Offline store configuration (optional depending on provider)
- Type
OfflineStoreConfig
- online_store: Any¶
Online store configuration (optional depending on provider)
- Type
OnlineStoreConfig
- project: pydantic.types.StrictStr¶
Feast project id. This can be any alphanumeric string up to 16 characters. You can have multiple independent feature repositories deployed to the same cloud provider account, as long as they have different project ids.
- Type
- registry: Union[pydantic.types.StrictStr, feast.repo_config.RegistryConfig]¶
Path to metadata store. Can be a local path, or remote object storage path, e.g. a GCS URI
- Type
- repo_path: Optional[pathlib.Path]¶
- class feast.SourceType(value)[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(value)[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¶
- UNIX_TIMESTAMP = 8¶
- UNIX_TIMESTAMP_LIST = 18¶
- UNKNOWN = 0¶