FeatureService

Summary

In Tecton, a Feature Service exposes an API for accessing a set of FeatureViews.
 
Once deployed in production, each model has one associated Feature Service that serves the model its features. A Feature Service contains a list of the Feature Views associated with a model. It also includes user-provided metadata such as name, description, and owner that Tecton uses to organize feature data.

Example

from tecton import FeatureService, LoggingConfig
# Import your feature views declared in your feature repo directory
from feature_repo.features.feature_views import last_transaction_amount_sql, transaction_amount_is_high
...

# Declare Feature Service
fraud_detection_feature_service = FeatureService(
    name='fraud_detection_feature_service',
    description='A FeatureService providing features for a model that predicts if a transaction is fraudulent.',
    features=[
        last_transaction_amount_sql,
        transaction_amount_is_high,
        ...
    ]
    logging=LoggingConfig(
        sample_rate=0.5,
        log_effective_times=False,
    )
)

Attributes

Name	Data Type	Description
created_at	Optional[datetime.datetime]	Returns the time that this Tecton object was created or last updated. None for locally defined objects.
defined_in	Optional[str]	The repo filename where this object was declared. None for locally defined objects.
description	str	Description of the Feature Service.
enable_online_caching	bool	Whether Online Caching is enabled for this Feature Service
feature_metadata	List[FeatureMetadata]	Returns the list of all feature columns included in this feature service as well as associated metadata including data type of the feature, user-defined descriptions, and user-defined tags.
feature_views	Set[FeatureView]	Returns the set of Feature Views directly depended on by this Feature Service.   A single Feature View may be included multiple times in a Feature Service under different namespaces. See the FeatureReference documentation. This method dedupes those Feature Views.
features	List[FeatureReference]	Returns the list of feature references included in this Feature Service.   FeatureReferences are references to Feature Views/Tables that may select a subset of features, override the Feature View/Table namespace, or re-map join-keys.
id	str	Returns the unique id of the Tecton object.
info
name	str	Name of the Feature Service.
online_serving_enabled	bool	Whether Online Serving is enabled for this Feature Service
owner	str	Owner of the Feature Service.
prevent_destroy	bool	Whether this Feature Service will be blocked from being deleted or re-created during tecton plan/apply.
tags	Dict[str, str]	Returns the tags of the Tecton object.
workspace	Optional[str]	Returns the workspace that this Tecton object belongs to. None for locally defined objects.

Methods

Name	Description
__init__(...)	Instantiates a new FeatureService.
get_feature_columns()	Returns the list of all feature columns included in this feature service.
get_features_for_events(...)	Fetch a TectonDataFrame of feature values from this FeatureService.
get_historical_features(...)	Fetch a TectonDataFrame of feature values from this FeatureService.
get_job(...)	Retrieves data about the specified job.
get_online_features(...)	Returns a single tecton.FeatureVector from the Online Store.
list_jobs()	Retrieves the list of dataset jobs created for this feature service.
query_features(...)	[Advanced Feature] Queries the FeatureService with a partial set of join_keys defined in the online_serving_index
summary()	Displays a human-readable summary
validate()	Method is deprecated and will be removed in a future version. As of Tecton version 1.0, objects are validated upon object creation, so validation is unnecessary.

__init__(...)

Instantiates a new FeatureService.

Parameters

• name (str) - A unique name for the Feature Service.


• description (Optional[str]) - A human-readable description. Default: None


• tags (Optional[Dict[str, str]]) - Tags associated with this Tecton Object (key-value pairs of arbitrary metadata). Default: None


• owner (Optional[str]) - Owner name (typically the email of the primary maintainer). Default: None


• prevent_destroy (bool) - If True, this Tecton object will be blocked from being deleted or re-created (i.e. a destructive update) during tecton plan/apply. To remove or update this object, prevent_destroy must be set to False via the same tecton apply or a separate tecton apply. prevent_destroy can be used to prevent accidental changes such as inadvertently deleting a Feature Service used in production or recreating a Feature View that triggers expensive rematerialization jobs. prevent_destroy also blocks changes to dependent Tecton objects that would trigger a recreate of the tagged object, e.g. if prevent_destroy is set on a Feature Service, that will also prevent deletions or re-creates of Feature Views used in that service. prevent_destroy is only enforced in live (i.e. non-dev) workspaces. Default: false


• online_serving_enabled (bool) - If True, users can send realtime requests to this FeatureService, and only FeatureViews with online materialization enabled can be added to this FeatureService. Default: true


• features (Optional[List[Union[FeatureReference, FeatureView]]]) - The list of FeatureView or FeatureReference that this FeatureService will serve. Default: None


• logging (Optional[LoggingConfig]) - A configuration for logging feature requests sent to this Feature Service. Default: None


• on_demand_environment (Optional[str]) - (Deprecated) Renamed to realtime_environment Default: None


• realtime_environment (Optional[str]) - The environment in which all the Realtime Feature Views for this feature service should be executed. Defaults to None, which means the Realtime Feature Views are executed in the same environment as the feature service, without any resource isolation. This may be preferred for low-latency feature services which do not have dependencies. Learn more about environments at https://docs.tecton.ai/docs/defining-features/feature-views/realtime-feature-view/realtime-feature-view-environments. Default: None


• options (Optional[Dict[str, str]]) - Additional options to configure the Feature Service. Used for advanced use cases and beta features. Default: None


• enable_online_caching (bool) - If True, the feature server will read and write feature values to the online serving cache for feature views and tables that have caching enabled (have cache_config set). Default: false


• transform_server_group (Optional[TransformServerGroup]) - Optional, the Transform Server Group used for executing all Realtime Feature Views in the Feature Service. Default: None


• feature_server_group (FeatureServerGroup) - Optional, the Feature Server Group used for online feature serving. Default: None

get_feature_columns()

Returns the list of all feature columns included in this feature service.

Returns

List[str]

get_features_for_events(...)

info

This method is functionally equivalent to get_historical_features(spine) and has been renamed in Tecton 0.8+ for clarity. get_historical_features() will be deprecated in a future release.

Fetch a TectonDataFrame of feature values from this FeatureService.
 
This method will return feature values for each row provided in the spine DataFrame. The feature values returned by this method will respect the timestamp provided in the timestamp column of the spine Data Frame.
 
By default (i.e. from_source=None), this method fetches feature values from the Offline Store for Feature Views that have offline materialization enabled and otherwise computes feature values on the fly from raw data.

Parameters

• events (Union[pyspark.sql.dataframe.DataFrame, pandas.core.frame.DataFrame, TectonDataFrame, str]) - A dataframe of possible join keys, request data keys, and timestamps that specify which feature values to fetch.To distinguish between the event dataframe columns and feature columns, feature columns are labeled as feature_view_name__feature_name in the returned DataFrame.


• timestamp_key (Optional[str]) - Name of the time column in the spine DataFrame. This method will fetch the latest features computed before the specified timestamps in this column. Not applicable if the FeatureService strictly contains RealtimeFeatureViews with no feature view dependencies. Default: None


• from_source (Optional[bool]) - Whether feature values should be recomputed from the original data source. If None, feature values will be fetched from the Offline Store for Feature Views that have offline materialization enabled and otherwise computes feature values on the fly from raw data. Use from_source=True to force computing from raw data and from_source=False to error if any Feature Views are not materialized. Defaults to None. Default: None


• compute_mode (Union[tecton_core.compute_mode.ComputeMode, str, NoneType]) - Compute mode to use to produce the data frame. Default: None

Returns

TectonDataFrame

Examples

A Feature Service fs that contains a Batch Feature View and Stream Feature View with join keys user_id and ad_id.

1. fs.get_features_for_events(events) where events=pandas.Dataframe({'user_id': [1,2,3], 'ad_id': ['a234', 'b256', 'c9102'], 'date': [datetime(...), datetime(...), datetime(...)]})

Fetches historical features from the offline store for users 1, 2, and 3 for the specified timestamps and ad ids in the events DataFrame.

2. fs.get_features_for_events(events, save_as='my_dataset) where events=pandas.Dataframe({'user_id': [1,2,3], 'ad_id': ['a234', 'b256', 'c9102'], 'date': [datetime(...), datetime(...), datetime(...)]})

Fetches historical features from the offline store for users 1, 2, and 3 for the specified timestamps and ad ids in the events DataFrame. Save the DataFrame as dataset with the name my_dataset.

3. fv.get_features_for_events(events, timestamp_key='date_1') where events=pandas.Dataframe({'user_id': [1,2,3], 'ad_id': ['a234', 'b256', 'c9102'], 'date_1': [datetime(...), ...], 'date_2': [datetime(...), ...]})

Fetches historical features from the offline store for users 1, 2, and 3 for the ad ids and specified timestamps in the date_1 column in the events DataFrame.

A Feature Service fs_on_demand that contains only RealtimeFeatureViews and expects request time data for the key amount.

The request time data is defined in the feature definition as such:

request_schema = StructType()
request_schema.add(StructField("amount", DoubleType()))
transaction_request = RequestDataSource(request_schema=request_schema)

1. fs_on_demand.get_features_for_events(events) where events=pandas.Dataframe({'amount': [30, 50, 10000]})

Fetches historical features from the offline store with request data inputs 30, 50, and 10000.

A Feature Service fs_all that contains feature views of all types with join key ‘user_id’ and expects request time data for the key amount.

1. fs_all.get_features_for_events(events) where events=pandas.Dataframe({'user_id': [1,2,3], 'amount': [30, 50, 10000], 'date': [datetime(...), datetime(...), datetime(...)]})

Fetches historical features from the offline store for users 1, 2, and 3 for the specified timestamps and request data inputs in the events data.

get_historical_features(...)

Fetch a TectonDataFrame of feature values from this FeatureService.
 
This method will return feature values for each row provided in the spine DataFrame. The feature values returned by this method will respect the timestamp provided in the timestamp column of the spine DataFrame.
 
By default (i.e. from_source=None), this method fetches feature values from the Offline Store for Feature Views that have offline materialization enabled and otherwise computes feature values on the fly from raw data.

Parameters

• spine (Union[pyspark.sql.dataframe.DataFrame, pandas.core.frame.DataFrame, TectonDataFrame, str]) - A dataframe of possible join keys, request data keys, and timestamps that specify which feature values to fetch.To distinguish between spine columns and feature columns, feature columns are labeled as feature_view_name__feature_name in the returned DataFrame.


• timestamp_key (Optional[str]) - Name of the time column in the spine DataFrame. This method will fetch the latest features computed before the specified timestamps in this column. Not applicable if the FeatureService strictly contains RealtimeFeatureViews with no feature view dependencies. Default: None


• include_feature_view_timestamp_columns (bool) - Whether to include timestamp columns for each FeatureView in the FeatureService. Default is False. Default: false


• from_source (Optional[bool]) - Whether feature values should be recomputed from the original data source. If None, feature values will be fetched from the Offline Store for Feature Views that have offline materialization enabled and otherwise computes feature values on the fly from raw data. Use from_source=True to force computing from raw data and from_source=False to error if any Feature Views are not materialized. Defaults to None. Default: None


• compute_mode (Union[tecton_core.compute_mode.ComputeMode, str, NoneType]) - Compute mode to use to produce the data frame. Default: None

Returns

TectonDataFrame

Examples

A FeatureService fs that contains a BatchFeatureView and StreamFeatureView with join keys user_id and ad_id.

1. fs.get_historical_features(spine) where spine=pandas.Dataframe({'user_id': [1,2,3], 'ad_id': ['a234', 'b256', 'c9102'], 'date': [datetime(...), datetime(...), datetime(...)]})

Fetch historical features from the offline store for users 1, 2, and 3 for the specified timestamps and ad ids in the spine.

2. fs.get_historical_features(spine, save_as='my_dataset) where spine=pandas.Dataframe({'user_id': [1,2,3], 'ad_id': ['a234', 'b256', 'c9102'], 'date': [datetime(...), datetime(...), datetime(...)]})

Fetch historical features from the offline store for users 1, 2, and 3 for the specified timestamps and ad ids in the spine. Save the DataFrame as dataset with the name my_dataset.

3. fv.get_historical_features(spine, timestamp_key='date_1') where spine=pandas.Dataframe({'user_id': [1,2,3], 'ad_id': ['a234', 'b256', 'c9102'], 'date_1': [datetime(...), ...], 'date_2': [datetime(...), ...]})

Fetch historical features from the offline store for users 1, 2, and 3 for the ad ids and specified timestamps in the date_1 column in the spine.

A FeatureService fs_on_demand that contains only RealtimeFeatureViews and expects request time data for the key amount.

The request time data is defined in the feature definition as such:

request_schema = StructType()
request_schema.add(StructField("amount", DoubleType()))
transaction_request = RequestDataSource(request_schema=request_schema)

1. fs_on_demand.get_historical_features(spine) where spine=pandas.Dataframe({'amount': [30, 50, 10000]})

Fetch historical features from the offline store with request data inputs 30, 50, and 10000.

A FeatureService fs_all that contains feature views of all types with join key ‘user_id’ and expects request time data for the key amount.

1. fs_all.get_historical_features(spine) where spine=pandas.Dataframe({'user_id': [1,2,3], 'amount': [30, 50, 10000], 'date': [datetime(...), datetime(...), datetime(...)]})

Fetch historical features from the offline store for users 1, 2, and 3 for the specified timestamps and request data inputs in the spine.

get_job(...)

Retrieves data about the specified job.

Parameters

• job_id (str) - ID string of the job.

Returns

TectonJob: JobData object for the job.

get_online_features(...)

Returns a single tecton.FeatureVector from the Online Store.

Parameters

• join_keys (Optional[Mapping[str,Union[int, numpy.int64, str, bytes]]]) - Join keys of the enclosed FeatureViews. Default: None


• include_join_keys_in_response (bool) - Whether to include join keys as part of the response FeatureVector. Default: false


• request_data (Optional[Mapping[str,Union[int, numpy.int64, str, bytes, float]]]) - Dictionary of request context values. Only applicable when the FeatureService contains RealtimeFeatureViews. Default: None

Returns

FeatureVector: tecton.FeatureVector of the results.

Examples

A FeatureService fs that contains a BatchFeatureView and StreamFeatureView with join keys user_id and ad_id.

1. fs.get_online_features(join_keys={'user_id': 1, 'ad_id': 'c234'}) Fetch the latest features from the online store for user 1 and ad ‘c234’.

2. fv.get_online_features(join_keys={'user_id': 1, 'ad_id': 'c234'}, include_join_keys_in_response=True) Fetch the latest features from the online store for user 1 and ad id ‘c234’. Include the join key information (user_id=1, ad_id=’c234’) in the returned FeatureVector.

A FeatureService fs_on_demand that contains only RealtimeFeatureViews and expects request time data for key amount.

The request time data is defined in the feature definition as such:

request_schema = StructType()
request_schema.add(StructField("amount", DoubleType()))
transaction_request = RequestDataSource(request_schema=request_schema)

1. fs_on_demand.get_online_features(request_data={'amount': 30}) Fetch the latest features from the online store with amount = 30.

A FeatureService fs_all that contains feature views of all types with join key user_id and expects request time data for key amount.

1. fs_all.get_online_features(join_keys={'user_id': 1}, request_data={'amount': 30}) Fetch the latest features from the online store for user 1 with amount = 30.

list_jobs()

Retrieves the list of dataset jobs created for this feature service.

Returns

List[TectonJob]: List of JobData objects.

query_features(...)

[Advanced Feature] Queries the FeatureService with a partial set of join_keys defined in the online_serving_index of the included FeatureViews. Returns TectonDataFrame of all matched records.

Parameters

• join_keys (Mapping[str,Union[int, numpy.int64, str, bytes]]) - Query join keys, i.e., a union of join keys in the online_serving_index of all enclosed FeatureViews.

Returns

TectonDataFrame

summary()

Displays a human-readable summary

(Deprecated) validate()

Method is deprecated and will be removed in a future version. As of Tecton version 1.0, objects are validated upon object creation, so validation is unnecessary.

Returns

None