FeatureTable

tecton.FeatureTable

A Tecton Feature Table.

Feature Tables are used to batch push features into Tecton from external feature computation systems.

Attributes

Name	Data Type	Description
description	str	Returns the description of the Tecton object.
entities	List[specs.EntitySpec]The Entities for this Feature View.
id	str	Returns the unique id of the Tecton object.
info		A dataclass containing basic info about this Tecton object.
join_keys	List[str]	The join key column names.
name	str	Returns the name of the Tecton object.
online_serving_index	List[str]	The set of join keys that will be indexed and queryable during online serving.
owner	Optional[str]	Returns the owner of the Tecton object.
tags	Dict[str, str]	Returns the tags of the Tecton object.
url	str	Returns a link to the Tecton Web UI.
wildcard_join_key	Optional[set]	Returns a wildcard join key column name if it exists; Otherwise returns None.
workspace	Optional[str]	Returns the workspace that this Tecton object belongs to.

Methods

Name	Description
__init__(...)	Instantiate a new FeatureTable.
cancel_materialization_job(...)	Cancels the scheduled or running batch materialization job for this Feature View specified by the job identifier.
delete_keys(...)	Deletes any materialized data that matches the specified join keys from the FeatureTable.
deletion_status(...)	Displays information for deletion jobs created with the delete_keys() method,which may include past jobs, scheduled jobs, and job failures.
get_feature_columns()	The features produced by this FeatureView.
get_features_for_events(...)	Returns a TectonDataFrame of historical values for this feature view.
get_features_in_range(...)	Returns a TectonDataFrame with historical feature values for this Feature View within the input time range.
get_historical_features(...)	Returns a TectonDataFrame of historical values for this feature table.
get_materialization_job(...)	Retrieves data about the specified materialization job for this Feature View.
get_online_features(...)	Returns a single Tecton FeatureVector from the Online Store.
get_timestamp_field()	Returns the name of the timestamp field of this Feature Table.
ingest()	Ingests a Dataframe into the FeatureTable.
list_materialization_jobs()	Retrieves the list of all materialization jobs for this Feature View.
materialization_status(...)	Displays materialization information for the FeatureView, which may include past jobs, scheduled jobs, and job failures.
summary()	Displays a human readable summary.
validate()	Validate this Tecton object and its dependencies (if any).
with_join_key_map(...)	Rebind join keys for a Feature View used in a Feature Service.
with_name(...)	Rename a Feature View used in a Feature Service.

__init__(...)

Instantiate a new FeatureTable.

Parameters

• name (str) – Unique, human friendly name that identifies the FeatureTable.

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

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

• tags (Optional[Dict[str, str) – Tags associated with this Tecton Object (key-value pairs of arbitrary metadata). (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 first set to False via the same tecton apply or a separate tecton apply. prevent_destroy can be used to prevent accidental changes such as inadvertantly 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)

• entities (List[Entity]) – A list of Entity objects, used to organize features.

• schema (List[Field]) – A schema for the FeatureTable. Supported types are: Int64, Float64, String, Bool and Array with Int64, Float32, Float64 and String typed elements. Additionally you must have exactly one Timestamp typed column for the feature timestamp.

• ttl (timedelta) – The TTL (or “look back window”) for features defined by this feature table. This parameter determines how long features will live in the online store and how far to “look back” relative to a training example’s timestamp when generating offline training sets. Shorter TTLs improve performance and reduce costs.

• online (bool) – Enable writing to online feature store. (Default: False)

• offline (bool) – Enable writing to offline feature store. (Default: False)

• offline_store (Union[OfflineStoreConfig, DeltaConfig, None]) – Configuration for Tecton's Offline Store. Note that Feature Tables only support the Delta format and do not support publish_full_features=True. Default:

  OfflineStoreConfig(
      staging_table_format=DeltaConfig(datetime.timedelta(days=1), subdirectory_override=None),
  )

• online_store (Union[DynamoConfig, RedisConfig, None]) – Configuration for how data is written to the online feature store. (Default: None)

• batch_compute ([Union[DatabricksClusterConfig, EMRClusterConfig, DatabricksJsonClusterConfig, EMRJsonClusterConfig, None]) – Configuration for batch materialization clusters. Should be one of: [EMRClusterConfig, DatabricksClusterConfig, EMRJsonClusterConfig, DatabricksJsonClusterConfig] (Default: None)

• online_serving_index (Optional[List[str) – (Advanced) Defines the set of join keys that will be indexed and queryable during online serving. Defaults to the complete set of join keys. Up to one join key may be omitted. If one key is omitted, online requests to a Feature Service will return all feature vectors that match the specified join keys. (Default: None)

• alert_email (Optional[str]) – Email that alerts for this FeatureTable will be sent to. (Default: None)

• tecton_materialization_runtime (Optional[str]) - Version of the Tecton Materialization Runtime used for materialization jobs. Required on 0.8+ when materialization is enabled (with online=True or offline=True). Recommended to set to the SDK version the Feature Table was applied with (e.g. "0.8.0"). (Default: None)

Example

from tecton import Entity, FeatureTable
from tecton.types import Field, String, Timestamp, Int64
import datetime

# Declare your user Entity instance here or import it if defined elsewhere in
# your Tecton repo.

user = ...

schema = [
    Field("user_id", String),
    Field("timestamp", Timestamp),
    Field("user_login_count_7d", Int64),
    Field("user_login_count_30d", Int64),
]

user_login_counts = FeatureTable(
    name="user_login_counts",
    entities=[user],
    schema=schema,
    online=True,
    offline=True,
    ttl=datetime.timedelta(days=30),
    alert_email="xxx@yyy.com",
)

cancel_materialization_job(...)

Cancels the scheduled or running batch materialization job for this Feature View specified by the job identifier. Once cancelled, a job will not be retried further.

Job run state will be set to MANUAL_CANCELLATION_REQUESTED. Note that cancellation is asynchronous, so it may take some time for the cancellation to complete. If job run is already in MANUAL_CANCELLATION_REQUESTED or in a terminal state then it’ll return the job.

Parameters

• job_id (str) – ID string of the materialization job.

Returns

MaterializationJobData object for the cancelled job.

delete_keys(...)

Deletes any materialized data that matches the specified join keys from the FeatureTable.

This method kicks off a job to delete the data in the offline and online stores. If a FeatureTable has multiple entities, the full set of join keys must be specified. Only supports Dynamo online store. Maximum 500,000 keys can be deleted per request.

Parameters

• keys (Union[DataFrame, DataFrame]) – The Dataframe to be deleted. Must conform to the FeatureTable join keys.

• online (bool) – (Optional, default=True) Whether or not to delete from the online store. (Default: True)

• offline (bool) – (Optional, default=True) Whether or not to delete from the offline store. (Default: True)

Returns

List of ID strings of the created Entity Deletion jobs.

deletion_status(...)

warning

deletion_status is deprecated starting in Tecton 0.8. Instead, the call to delete_keys will return a list of job ids that can be passed into get_materialization_job to see the status of your entity deletion jobs.

Displays information for deletion jobs created with the delete_keys() method,which may include past jobs, scheduled jobs, and job failures.

Parameters

• verbose – If set to true, method will display additional low level deletion information, useful for debugging. (Default: False)

• limit – Maximum number of jobs to return. (Default: 1000)

• sort_columns – A comma-separated list of column names by which to sort the rows. (Default: None)

• errors_only: If set to true, method will only return jobs that failed with an error. (Default: False)

get_feature_columns()

The features produced by this FeatureView.

get_features_for_events(...)

Returns a TectonDataFrame of historical values for this feature table.

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.

If no arguments are passed in, all feature values for this feature view will be returned in a Tecton DataFrame.

info

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

Parameters

• events (Union[pyspark.sql.DataFrame,pandas.DataFrame,TectonDataFrame]) – A dataframe of all possible join keys and timestamps that specify which feature values to fetch. To distinguish between columns in the events dataframe and feature columns, feature columns are labeled as feature_view_name.feature_name in the returned DataFrame.

• timestamp_key (str) – Name of the time column in the events dataframe. This method will fetch the latest features computed before the specified timestamps in this column. If unspecified, will default to the time column of the events dataframe if there is only one present. If more than one time column is present in the events dataframe, you must specify which column you’d like to use. (Default: None)

• from_source (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. (Default: None)

• save (bool) – Whether to persist the DataFrame as a Dataset object. (Default: False)

• save_as (str) – Name to save the DataFrame as. If unspecified and save=True, a name will be generated. (Default: None)

• mock_inputs (Optional[Dict[str, Union[pandas.DataFrame, pyspark_dataframe.DataFrame]]]) – Dictionary for mock inputs that should be used instead of fetching directly from raw data sources. The keys should match the feature view’s function parameters. For feature views with multiple sources, mocking some data sources and using raw data for others is supported. Using mock_inputs is incompatible with from_source=False and save/save_as.

• compute_mode (Union[str, tecton.ComputeMode, None]) – Compute mode to use to produce the data frame. Valid string values are "spark", "snowflake", "athena", and "rift".

Returns

A TectonDataFrame

Examples

A FeatureTableft with join key user_id.

1. ft.get_features_for_events(events) where events=pandas.Dataframe({'user_id': [1,2,3], 'date': [datetime(...), datetime(...), datetime(...)]}) Fetch historical features from the offline store for users 1, 2, and 3 for the specified timestamps in the events dataframe.

2. ft.get_features_for_events(events, save_as='my_dataset) where events=pandas.Dataframe({'user_id': [1,2,3], 'date': [datetime(...), datetime(...), datetime(...)]}) Fetch historical features from the offline store for users 1, 2, and 3 for the specified timestamps in the events dataframe. Save the DataFrame as dataset with the name my_dataset.

3. ft.get_features_for_events(events, timestamp_key='date_1') where events=pandas.Dataframe({'user_id': [1,2,3], 'date_1': [datetime(...), datetime(...), datetime(...)], 'date_2': [datetime(...), datetime(...), datetime(...)]}) Fetch historical features from the offline store for users 1, 2, and 3 for the specified timestamps in the ‘date_1’ column in the events dataframe.

get_features_in_range(...)

Returns a TectonDataFrame of historical values for this Feature View which were valid within the input time range. A feature value is considered to be valid at a specific point in time if the Online Store would have returned that value if queried at that moment in time.

The DataFrame returned by this method contains the following:

• Entity Join Key Columns

• Feature Value Columns and

• The columns _valid_from and _valid_to that specify the time range for which the row of features values is valid. The time range defined by [_valid_from, _valid_to) will never intersect with any other rows for the same join keys.

  • _valid_from (Inclusive)

    The timestamp from which feature values were valid and returned from the Online Feature Store for the corresponding set of join keys.

    _valid_from will never be less than end_time. Values for which _valid_from is equal to start_time may have been valid prior to start_time.

  • _valid_to (Exclusive) The timestamp from which feature values are invalid and no longer returned from the Online Feature Store for the corresponding set of join keys.

    _valid_to will never be greater than end_time. Values for which _valid_to is equal to end_time may be valid beyond end_time.

Parameters

• start_time (datetime.datetime) – The inclusive start time of the time range to compute features for.

• end_time (datetime.datetime) – The exclusive end time of the time range to compute features for.

• max_lookback (datetime.timedelta) – [Non-Aggregate Feature Views Only] A performance optimization that configures how far back before start_time to look for events in the raw data. If set, get_features_in_range() may not include all entities with valid feature values in the specified time range, but get_features_in_range() will never return invalid values.

• entities (Union[pyspark.sql.DataFrame, pandas.DataFrame, TectonDataFrame]) – Filter feature data returned to a set of entity IDs. If specified, this DataFrame should only contain join key columns. (Default: None)

• save (bool) – Whether to persist the DataFrame as a Dataset object. Default is False. (Default: False)

• save_as (str) – Name to save the DataFrame as. If unspecified and save=True, a name will be generated. (Default: None)

• mock_inputs (Optional[Dict[str, Union[pandas.DataFrame, pyspark_dataframe.DataFrame]]]) – Dictionary for mock inputs that should be used instead of fetching directly from raw data sources. The keys should match the Feature View’s function parameters. For Feature Views with multiple sources, mocking some data sources and using raw data for others is supported. Using mock_inputs is incompatible with from_source=False and save/save_as.

• compute_mode (Union[str, tecton.ComputeMode, None]) – Compute mode to use to produce the DataFrame. Valid string values are "spark", "snowflake", "athena", and "rift".

Returns

A TectonDataFrame with feature values for the requested time range in the format specified above.

get_historical_features(...)

Returns a TectonDataFrame of historical values for this feature table.

If no arguments are passed in, all feature values for this feature table will be returned in a TectonDataFrame.

info

The timestamp_key parameter is only applicable when a spine is passed in. Parameters start_time, end_time, and entities are only applicable when a spine is not passed in.

Parameters

• spine (Union[pyspark.sql.DataFrame, pandas.DataFrame, TectonDataFrame]) – The spine to join against, as a dataframe. If present, the returned DataFrame will contain rollups for all (join key, temporal key) combinations that are required to compute a full frame from the spine. To distinguish between spine columns and feature columns, feature columns are labeled as feature_view_name.feature_name in the returned DataFrame. If spine is not specified, it’ll return a DataFrame of feature values in the specified time range. (Default: None)

• timestamp_key (str) – Name of the time column in spine. This method will fetch the latest features computed before the specified timestamps in this column. If unspecified, will default to the time column of the spine if there is only one present. (Default: None)

• entities (Union[pyspark.sql.DataFrame, pandas.DataFrame, TectonDataFrame]) – A DataFrame that is used to filter down feature values. If specified, this DataFrame should only contain join key columns. (Default: None)

• start_time (Union[pendulum.DateTime, datetime.datetime]) – The interval start time from when we want to retrieve features. If no timezone is specified, will default to using UTC. (Default: None)

• end_time (Union[pendulum.DateTime, datetime.datetime]) – The interval end time until when we want to retrieve features. If no timezone is specified, will default to using UTC. (Default: None)

• save (bool) – Whether to persist the DataFrame as a Dataset object. (Default: False).

• save_as (str) – name to save the DataFrame as. If unspecified and save=True, a name will be generated. (Default: None)

• compute_mode (Union[str, tecton.ComputeMode, None]) – Compute mode to use to produce the data frame. Valid string values are "spark", athena, and "rift".

Returns

A TectonDataFrame with features values.

Examples

A FeatureTable ft with join key user_id.

1. ft.get_historical_features(spine) where spine=pandas.Dataframe({'user_id': [1,2,3], 'date': [datetime(...), datetime(...), datetime(...)]}) Fetch historical features from the offline store for users 1, 2, and 3 for the specified timestamps in the spine.

2. ft.get_historical_features(spine, save_as='my_dataset) where spine=pandas.Dataframe({'user_id': [1,2,3], 'date': [datetime(...), datetime(...), datetime(...)]}) Fetch historical features from the offline store for users 1, 2, and 3 for the specified timestamps in the spine. Save the DataFrame as dataset with the name my_dataset.

3. ft.get_historical_features(spine, timestamp_key='date_1') where spine=pandas.Dataframe({'user_id': [1,2,3], 'date_1': [datetime(...), datetime(...), datetime(...)], 'date_2': [datetime(...), datetime(...), datetime(...)]}) Fetch historical features from the offline store for users 1, 2, and 3 for the specified timestamps in the ‘date_1’ column in the spine.

4. ft.get_historical_features(start_time=datetime(...), end_time=datetime(...)) Fetch all historical features from the offline store in the time range specified by start_time and end_time.

get_materialization_job(...)

Retrieves data about the specified materialization job for this Feature View.

This data includes information about job attempts.

Parameters

• job_id (str) – ID string of the materialization job.

Returns

MaterializationJobData object for the job.

get_online_features(...)

Returns a single Tecton FeatureVector from the Online Store.

Parameters

• join_keys (Mapping[str, Union[int, int64, str, bytes]]) – Join keys of the enclosed FeatureTable.

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

Returns

A FeatureVector of the results.

Examples

A FeatureTable ft with join key user_id.

1. ft.get_online_features(join_keys={'user_id': 1}) Fetch the latest features from the online store for user 1.

2. ft.get_online_features(join_keys={'user_id': 1}, include_join_keys_in_respone=True) Fetch the latest features from the online store for user 1 and include the join key information (user_id=1) in the returned FeatureVector.

get_timestamp_field()

Returns the name of the timestamp field of this Feature Table.

ingest()

Ingests a Dataframe into the FeatureTable.

This method kicks off a materialization job to write the data into the offline and online store, depending on the Feature Table configuration.

Parameters

• df (Union[DataFrame, DataFrame]) – The Dataframe to be ingested. Has to conform to the FeatureTable schema.

list_materialization_jobs()

Retrieves the list of all materialization jobs for this Feature View.

Returns

List of MaterializationJobData objects.

materialization_status(...)

Displays materialization information for the FeatureView, which may include past jobs, scheduled jobs, and job failures.

This method returns different information depending on the type of FeatureView.

Parameters

• verbose – If set to true, method will display additional low level materialization information, useful for debugging. (Default: False)
• limit – Maximum number of jobs to return. (Default: 1000)
• sort_columns – A comma-separated list of column names by which to sort the rows. (Default: None)
• errors_only – If set to true, method will only return jobs that failed with an error. (Default: False)

summary()

Displays a human readable summary of this data source.

validate()

Validate this Tecton object and its dependencies (if any).

Validation performs most of the same checks and operations as tecton plan.

1. Check for invalid object configurations, e.g. setting conflicting fields.

2. For Data Sources and Feature Views, test query code and derive schemas. e.g. test that a Data Source’s specified s3 path exists or that a Feature View’s SQL code executes and produces supported feature data types.

Objects already applied to Tecton do not need to be re-validated on retrieval (e.g. fv = tecton.get_workspace('prod').get_feature_view('my_fv')) since they have already been validated during tecton plan. Locally defined objects (e.g. my_ds = BatchSource(name="my_ds", ...)) may need to be validated before some of their methods can be called, e.g. my_feature_view.get_features_for_events().

with_join_key_map(...)

Rebind join keys for a Feature View used in a Feature Service.

The keys in join_key_map should be the feature view join keys, and the values should be the feature service overrides.

Parameters

• join_key_map

Example

from tecton import FeatureService

# The join key for this feature service will be "feature_service_user_id".
feature_service = FeatureService(
    name="feature_service",
    features=[
        my_feature_view.with_join_key_map({"user_id": "feature_service_user_id"}),
    ],
)

# Here is a more sophisticated example. The join keys for this feature service will be "transaction_id",
# "sender_id", and "recipient_id" and will contain three feature views named "transaction_features",
# "sender_features", and "recipient_features".
transaction_fraud_service = FeatureService(
    name="transaction_fraud_service",
    features=[
        # Select a subset of features from a feature view.
        transaction_features[["amount"]],
        # Rename a feature view and/or rebind its join keys. In this example, we want user features for both the
        # transaction sender and recipient, so include the feature view twice and bind it to two different feature
        # service join keys.
        user_features.with_name("sender_features").with_join_key_map({"user_id": "sender_id"}),
        user_features.with_name("recipient_features").with_join_key_map({"user_id": "recipient_id"}),
    ],
)

with_name(...)

Rename a Feature View used in a Feature Service.

Parameters

• namespace

Example

from tecton import FeatureService

# The feature view in this feature service will be named "new_named_feature_view" in training data dataframe
# columns and other metadata.
feature_service = FeatureService(
    name="feature_service",
    features=[my_feature_view.with_name("new_named_feature_view")],
)

# Here is a more sophisticated example. The join keys for this feature service will be "transaction_id",
# "sender_id", and "recipient_id" and will contain three feature views named "transaction_features",
# "sender_features", and "recipient_features".
transaction_fraud_service = FeatureService(
    name="transaction_fraud_service",
    features=[
        # Select a subset of features from a feature view.
        transaction_features[["amount"]],
        # Rename a feature view and/or rebind its join keys. In this example, we want user features for both the
        # transaction sender and recipient, so include the feature view twice and bind it to two different feature
        # service join keys.
        user_features.with_name("sender_features").with_join_key_map({"user_id": "sender_id"}),
        user_features.with_name("recipient_features").with_join_key_map({"user_id": "recipient_id"}),
    ],
)