Version: Beta 🚧

BatchFeatureView

Summary

A Tecton Batch Feature View, used for materializing features on a batch schedule from a BatchSource.

The BatchFeatureView should not be instantiated directly, the @batch_feature_view decorator is recommended instead.

Attributes

Name	Data Type	Description
`aggregation_interval`	`Optional[datetime.timedelta]`	How frequently the feature values are updated
`aggregation_secondary_key`	`Optional[str]`	Configures secondary key aggregates using the set column.
`aggregations`	`List[configs.Aggregation]`	List of `Aggregation` configs used by this Feature View.
`alert_email`	`Optional[str]`	Email that alerts for this FeatureView will be sent to.
`batch_schedule`	`Optional[datetime.timedelta]`	The batch schedule of this Feature View.
`batch_trigger`	`BatchTriggerType`	The `BatchTriggerType` for this FeatureView.
`cache_config`	`Optional[configs.CacheConfig]`	Uses cache for Feature View if online caching is enabled.
`compaction_enabled`	`bool`	(Private preview) Runs compaction job post-materialization; requires Dynamo and ImportTable API.
`context_parameter_name`	`Optional[str]`	Name of the function parameter that Tecton injects MaterializationContext object to.
`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`	`Optional[str]`	Returns the description of the Tecton object.
`entities`
`environment`	`Optional[str]`	The custom environment in which materialization jobs will be run. Defaults to `None`, which means jobs will execute in the default Tecton environment.
`expected_feature_freshness`	`Optional[pendulum.Duration]`	Threshold used to determine if recently materialized feature data is stale.
`feature_metadata`	`List[FeatureMetadata]`	Metadata of the Features generated by this Feature View. Includes data type, descriptions, and tags.
`feature_start_time`	`Optional[datetime.datetime]`
`id`	`str`	Returns the unique id of the Tecton object.
`incremental_backfills`	`bool`	Backfills incrementally from feature_start_time to current time, one interval at a time
`info`
`is_batch_trigger_manual`	`bool`	Whether this Feature View's batch trigger is `BatchTriggerType.Manual`.
`join_keys`	`List[str]`	The join key column names.
`manual_trigger_backfill_end_time`	`Optional[pendulum.datetime]`	If set, Tecton will schedule backfill materialization jobs for this Feature View up to this time.
`max_backfill_interval`	`Optional[pendulum.Duration]`	(Advanced) The time interval for which each backfill job will run to materialize feature data. This affects the number of backfill jobs that will run, which is (`<feature registration time>` - `feature_start_time`) / `max_backfill_interval`. Configuring the `max_backfill_interval` parameter appropriately will help to optimize large backfill jobs. If this parameter is not specified, then 10 backfill jobs will run (the default).
`max_source_data_delay`	`datetime.timedelta`	Returns the maximum data delay of input sources for this feature view.
`monitor_freshness`	`bool`	If true, enables monitoring when feature data is materialized to the online feature store.
`name`	`str`	Returns the name of the Tecton object.
`offline`	`bool`	Whether the Feature View is materialized to the offline feature store.
`offline_store`	`Optional[Union[configs.DeltaConfig, configs.ParquetConfig]]`	Configuration for the Offline Store of this Feature View.
`online`	`bool`	Whether the Feature View is materialized to the online feature store.
`online_serving_index`	`List[str]`	The set of join keys that will be indexed and queryable during online serving. defaults to the complete set of join keys.
`owner`	`Optional[str]`	Returns the owner of the Tecton object.
`prevent_destroy`	`bool`	If set to True, Tecton will block destructive actions taken on this Feature View or Feature Table.
`published_features_path`	`Optional[str]`	The location of published features in the offline store.
`sources`
`tags`	`Dict[str, str]`	Returns the tags of the Tecton object.
`tecton_materialization_runtime`	`Optional[str]`	Version of `tecton` package used by your job cluster.
`timestamp_field`	`Optional[str]`	timestamp column name for records from the feature view.
`transformations`
`ttl`	`Duration`	The TTL (or look back window) for features defined by this Feature View. 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.
`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. `None` for locally defined objects.

Methods

Name	Description
`cancel_materialization_job(...)`	Cancels the scheduled or running job by the job identifier.
`delete_keys(...)`	Deletes any materialized data that matches the specified join keys from the FeatureView.
`get_feature_columns()`	Retrieves the list of feature columns 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` of historical values for this feature view which were valid within the input time range.
`get_historical_features(...)`	Returns a `TectonDataFrame` of historical values for this feature view.
`get_job(...)`	Retrieves data about the specified job (materialization or dataset generation).
`get_materialization_job(...)`	Retrieves data about the specified materialization job.
`get_online_features(...)`	Returns a single Tecton `tecton.FeatureVector` from the Online Store.
`get_partial_aggregates(...)`	Returns the partially aggregated tiles in between `start_time` and `end_time` for a Feature View that uses the Tecton Aggregation Engine.
`get_timestamp_field()`	Returns the name of the timestamp field for this Feature View.
`list_jobs()`	Retrieves the list of all jobs (materialization and dataset generation) for this Feature View or Feature Table.
`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.
`print_transformation_schema()`	Prints the schema of the output of the transformation.
`run(...)`	Run the FeatureView. Supports transforming data directly from raw data sources or using mock data.
`run_transformation(...)`	Run the FeatureView Transformation as is without any aggregations or joins. Supports transforming data directly from raw data sources or using mock data.
`summary()`	Displays a human-readable summary.
`test_run(...)`	Run the FeatureView using mock data sources. This requires a local spark session.
`transformation_schema()`	Returns the schema of the output of the transformation.
`trigger_materialization_job(...)`	Starts a batch materialization job for this Feature View.
`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.
`wait_for_materialization_job(...)`	Blocks until the specified job has been completed.
`with_join_key_map(...)`	Rebind join keys for a Feature View or Feature Table used in a Feature Service.
`with_name(...)`	Rename a Feature View or Feature Table used in a Feature Service.

cancel_materialization_job(...)

Cancels the scheduled or running job 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

MaterializationJob: JobData object for the cancelled job.

delete_keys(...)

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

This method kicks off a job to delete the data in the offline and online stores. If a FeatureView has multiple entities, the full set of join keys must be specified. Only supports Delta as the offline store (offline_store=DeltaConfig()). Maximum 500,000 keys can be deleted per request.

Parameters

keys (Union[pyspark_dataframe.DataFrame, pandas.DataFrame]) - The Dataframe to be deleted. Must conform to the FeatureView join keys.

online (bool) - Whether or not to delete from the online store. Default: true

offline (bool) - Whether or not to delete from the offline store. Default: true

Returns

List[str]: List of job ids for jobs created for entity deletion.

get_feature_columns(...)

Retrieves the list of feature columns produced by this FeatureView.

Returns

List[str]: The features produced by this FeatureView.

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() is planned to be deprecated in a future release.

Parameters

events (Union[pyspark_dataframe.DataFrame, pandas.DataFrame, TectonDataFrame, str]) - A DataFrame containing all possible join key combinations and timestamps specifying which feature values to fetch. The returned DataFrame includes rollups for all (join key, timestamp) combinations necessary to compute a complete dataset. To differentiate between event 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 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 would like to use. 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

mock_inputs (Optional[Dict[str, Union[pandas.DataFrame, pyspark_dataframe.DataFrame]]]) - Mock sources that should be used instead of fetching directly from raw data sources. The keys of the dictionary 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. Default: None

compute_mode (Optional[Union[ComputeMode, str]]) - Compute mode to use to produce the data frame. Valid examples include spark and rift. Default: None

Returns

TectonDataFrame: A TectonDataFrame.

Examples

A FeatureView fv with join key user_id.

fv.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.
fv.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.
fv.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
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) is 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) is 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.
By default, (i.e. from_source=None), this method fetches feature values from the Offline Store for Feature Views that have offline materialization enabled. Otherwise, this method computes feature values directly from the original data source.

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.

entities (Optional[Union[pyspark_dataframe.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

max_lookback (Optional[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. 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. Default: None

mock_inputs (Optional[Dict[str, Union[pandas.DataFrame, pyspark_dataframe.DataFrame]]]) - Mock sources that should be used instead of fetching directly from raw data sources. The keys of the dictionary 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. Default: None

compute_mode (Optional[Union[ComputeMode, str]]) - Compute mode to use to produce the data frame. Examples include spark and rift. Default: None

Returns

TectonDataFrame: A TectonDataFrame with Feature Values for the requested time range in the format specified above.

get_historical_features(...)

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.

info

The ability to run get_historical_features as part of a unit test was added in SDK 0.7. To utilize this, provide the mocked data sources in the mock_inputs parameter in a test that is run via tecton test or pytest.

Returns a TectonDataFrame of historical values for this feature view.

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.

Note: 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 (Optional[Union[pyspark_dataframe.DataFrame, pandas.DataFrame, TectonDataFrame, str]]) - 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 (Optional[str]) - Name of the time column in the 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. If more than one time column is present in the spine, you must specify which column you'd like to use. Default: None

start_time (Optional[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 (Optional[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

entities (Optional[Union[pyspark_dataframe.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

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

mock_inputs (Optional[Dict[str, Union[pandas.DataFrame, pyspark_dataframe.DataFrame]]]) - Mock sources that should be used instead of fetching directly from raw data sources. The keys of the dictionary 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. Default: None

compute_mode (Optional[Union[ComputeMode, str]]) - Compute mode to use to produce the data frame. Valid examples include spark and rift. Default: None

Returns

TectonDataFrame: A TectonDataFrame.

Examples

A FeatureView fv with join key user_id.

fv.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.
fv.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.
fv.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.
fv.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_job(...)

Retrieves data about the specified job (materialization or dataset generation).

Parameters

job_id (str) - ID string of the job.

Returns

TectonJob: JobData object for the job.

get_materialization_job(...)

Retrieves data about the specified materialization job.

Parameters

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

Returns

MaterializationJob: MaterializationJobData object for the job.

get_online_features(...)

Returns a single Tecton tecton.FeatureVector from the Online Store.

Parameters

join_keys (Mapping[str, Union[int, numpy.int_, str, bytes]]) - The join keys to fetch from the online store.

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

Returns

FeatureVector: A tecton.FeatureVector of the results.

Examples

A FeatureView fv with join key user_id.

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

Returns the partially aggregated tiles in between start_time and end_time for a Feature View that uses the Tecton Aggregation Engine.

Parameters

start_time (datetime.datetime) - The start time of the time window to fetch partial aggregates for. The start_time will fall in the time interval of the first tile that is returned.

end_time (datetime.datetime) - The end time of the time window to fetch partial aggregates for. The end_time will fall in the time interval of the last tile that is returned.

mock_inputs (Optional[Dict[str, Union[pandas.DataFrame, pyspark_dataframe.DataFrame]]]) - Mock sources that should be used instead of fetching directly from raw data sources. The keys of the dictionary 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. Default: None

entities (Optional[Union[pyspark_dataframe.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

from_source (Optional[bool]) - Whether feature values should be recomputed from the original data source. If None, input 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 input Feature Views are not materialized. Defaults to None. Default: None

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

Returns

TectonDataFrame: A Tecton DataFrame with partially aggregated feature values and the _interval_start_time and _interval_end_time columns for each partial aggregation.

get_timestamp_field()

Returns the name of the timestamp field for this Feature View.

Returns

str

list_jobs()

Retrieves the list of all jobs (materialization and dataset generation) for this Feature View or Feature Table.

Returns

List[TectonJob]: List of JobData objects.

list_materialization_jobs()

Retrieves the list of all materialization jobs for this feature view.

Returns

List[MaterializationJob]: 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 (bool) - If set to true, method will display additional low level materialization information, useful for debugging. Default: false

limit (int) - Maximum number of jobs to return. Default: 1000

sort_columns (Optional[str]) - A comma-separated list of column names by which to sort the rows. Default: None

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

print_transformation_schema()

Prints the schema of the output of the transformation.

Returns

None

run(...)

info

The ability to call run as part of a unit test was added in SDK 0.7. To utilize this, provide the mocked data sources as part of the mock_sources kwargs in a test that is run via tecton test or pytest.

Run the FeatureView. Supports transforming data directly from raw data sources or using mock data.

To run the feature view with data from raw data sources, the environment must have access to the data sources.

Parameters

start_time (datetime.datetime) - The start time of the time window to materialize.

end_time (datetime.datetime) - The end time of the time window to materialize.

aggregation_level (Optional[str]) - For feature views with aggregations, aggregation_level configures which stage of the aggregation to run up to. The query for Aggregate Feature Views operates in three steps:
1. The feature view query is run over the provided time range. The user defined transformations are applied over the data source.
2. The result of #1 is aggregated into tiles the size of the aggregation_interval.
3. The tiles from #2 are combined to form the final feature values. The number of tiles that are combined is based off of the time_window of the aggregation.
  
  For testing and debugging purposes, to see the output of #1, use aggregation_level="disabled". For #2, use aggregation_level="partial". For #3, use aggregation_level="full". aggregation_level="full" is the default behavior if aggregation_level is not explicitly specified.
Default: None

mock_inputs (Optional[Dict[str, Union[pandas.DataFrame, pyspark_dataframe.DataFrame]]]) - Mock sources that should be used instead of fetching directly from raw data sources. The keys of the dictionary 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. Default: None

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

mock_inputs_kwargs (Union[pandas.DataFrame, pyspark_dataframe.DataFrame]) - Keyword arguments for mock sources 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.

Returns

TectonDataFrame: A tecton DataFrame of the results.

Example

import tecton
import pandas
from datetime import datetime

# Example running a non-aggregate feature view with mock data.
feature_view = tecton.get_workspace("my_workspace").get_feature_view("my_feature_view")

mock_fraud_user_data = pandas.DataFrame(
    {
        "user_id": ["user_1", "user_2", "user_3"],
        "timestamp": [datetime(2022, 5, 1, 0), datetime(2022, 5, 1, 2), datetime(2022, 5, 1, 5)],
        "credit_card_number": [1000, 4000, 5000],
    }
)

result = feature_view.run(
    start_time=datetime(2022, 5, 1), end_time=datetime(2022, 5, 2), fraud_users_batch=mock_fraud_user_data
)  # `fraud_users_batch` is the name of this FeatureView's data source parameter.

# Example running an aggregate feature view with real data.
aggregate_feature_view = tecton.get_workspace("my_workspace").get_feature_view("my_aggregate_feature_view")

result = aggregate_feature_view.run(
    start_time=datetime(2022, 5, 1), end_time=datetime(2022, 5, 2), aggregation_level="full"
)  # or "partial" or "disabled"

run_transformation(...)

info

In 0.8+, this method is a intended to be a replacement for the .run() method. The .run() method will be deprecated in a future release.

Run the FeatureView Transformation as is without any aggregations or joins. Supports transforming data directly from raw data sources or using mock data.

To run the feature view transformation with data from raw data sources, the environment must have access to the data sources.

Parameters

start_time (datetime.datetime) - The start time of the time window to materialize.

end_time (datetime.datetime) - The end time of the time window to materialize.

mock_inputs (Optional[Dict[str, Union[pandas.DataFrame, pyspark_dataframe.DataFrame]]]) - Mock sources that should be used instead of fetching directly from raw data sources. The keys of the dictionary 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. Default: None

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

Returns

TectonDataFrame: A tecton DataFrame of the results.

Example

import tecton
import pandas
from datetime import datetime

# Example running a non-aggregate Feature View with mock data.
feature_view = tecton.get_workspace("my_workspace").get_feature_view("my_feature_view")

mock_fraud_user_data = pandas.DataFrame(
    {
        "user_id": ["user_1", "user_2", "user_3"],
        "timestamp": [datetime(2022, 5, 1, 0), datetime(2022, 5, 1, 2), datetime(2022, 5, 1, 5)],
        "credit_card_number": [1000, 4000, 5000],
    }
)

result = feature_view.run_transformation(
    start_time=datetime(2022, 5, 1),
    end_time=datetime(2022, 5, 2),
    mock_inputs={"fraud_users_batch": mock_fraud_user_data},
)  # `fraud_users_batch` is the name of this Feature View's data source parameter.

# Example running an aggregate Feature View with real data.
aggregate_feature_view = tecton.get_workspace("my_workspace").get_feature_view("my_aggregate_feature_view")

result = aggregate_feature_view.run_transformation(start_time=datetime(2022, 5, 1), end_time=datetime(2022, 5, 2))

summary()

Displays a human-readable summary.

test_run(...)

Run the FeatureView using mock data sources. This requires a local spark session.

Unlike run, test_run is intended for unit testing. It will not make calls to your connected Tecton cluster or attempt to read real data.

Parameters

start_time (datetime.datetime) - The start time of the time window to materialize.

end_time (datetime.datetime) - The end time of the time window to materialize.

aggregation_level (Optional[str]) - For feature views with aggregations, aggregation_level configures what stage of the aggregation to run up to. The query for Aggregate Feature Views operates in three logical steps:
1. The feature view query is run over the provided time range. The user defined transformations are applied over the data source.
2. The result of #1 is aggregated into tiles the size of the aggregation_interval.
3. The tiles from #2 are combined to form the final feature values. The number of tiles that are combined is based off of the time_window of the aggregation.
  
  For testing and debugging purposes, to see the output of #1, use aggregation_level="disabled". For #2, use
  
  aggregation_level="partial". For #3, use aggregation_level="full".
Default: None

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

mock_inputs (Union[pandas.DataFrame, pyspark_dataframe.DataFrame]) - Keyword arguments with expected same keys as the FeatureView's inputs parameter. Each input name maps to a Spark DataFrame that should be evaluated for that node in the pipeline.

Returns

TectonDataFrame: A tecton.TectonDataFrame object.

transformation_schema()

Returns the schema of the output of the transformation.

Returns

List[types.Field]

trigger_materialization_job(...)

Starts a batch materialization job for this Feature View.

Parameters

start_time (datetime.datetime) - The job will materialize feature values between the start_time and end_time.

end_time (datetime.datetime) - The job will materialize feature values between the start_time and end_time.

online (bool) - Whether the job will materialize features to the online store. The Feature View must be configured with online=True in order to materialize features online.

offline (bool) - Whether the job will materialize features to the offline store. The Feature View must be configured with offline=True in order to materialize features offline.

use_tecton_managed_retries (bool) - If enabled, Tecton will automatically retry failed attempts. Disable to manage your own retry behavior. Default: true

overwrite (bool) - If enabled, you will be able to run materialization jobs for periods that previously have materialized data. This operation can be sensitive for feature views with existing materialized online data. For the offline store, all previously materialized data between the start time and end time will be dropped. For the online store, all previous data will remain, but may be overwritten by this job. Default: false

Returns

str: ID string of the created materialization job.

Raises

TectonValidationError: If job params are not valid.

wait_for_materialization_job()

Blocks until the specified job has been completed.

Parameters

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

timeout (Optional[datetime.timedelta]) - (Optional) timeout for this function. An exception is raised if the job does not complete within the specified time. Default: None

Returns

MaterializationJob: JobData object for the successful job.

Raises

JobTimeoutException: If timeout param is specified and job does not complete within the specified time.
MaterializationJobFailedException: If materialization job did not reach a successful state.

with_join_key_map()

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

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

Parameters

join_key_map (Dict[str, str]) - Dictionary remapping the join key names. Dictionary keys are join keys, values are the feature service override values.

Returns

FeatureReference

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 or Feature Table used in a Feature Service.

Parameters

namespace (str) - The namespace used to prefix the features joined from this FeatureView. By default, namespace is set to the FeatureView name.

Returns

FeatureReference

Examples

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"}),
    ],
)

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

BatchFeatureView

Summary​

Attributes​

Methods​

cancel_materialization_job(...)​

Parameters

Returns

delete_keys(...)​

Parameters

Returns

get_feature_columns(...)​

Returns

get_features_for_events(...)​

Parameters

Returns

Examples​

get_features_in_range(...)​

Parameters

Returns

get_historical_features(...)​

Parameters

Returns

Examples​

get_job(...)​

Parameters

Returns

get_materialization_job(...)​

Parameters

Returns

get_online_features(...)​

Parameters

Returns

Examples​

get_partial_aggregates(...)​

Parameters

Returns

get_timestamp_field()​

Returns

list_jobs()​

Returns

list_materialization_jobs()​

Returns

materialization_status()​

Parameters

print_transformation_schema()​

Returns

run(...)​

Parameters

Returns

Example​

run_transformation(...)​

Parameters

Returns

Example​

summary()​

test_run(...)​

Parameters

Returns

transformation_schema()​

Returns

trigger_materialization_job(...)​

Parameters

Returns

Raises

wait_for_materialization_job()​

Parameters

Returns

Raises

with_join_key_map()​

Parameters

Returns

Example

with_name()​

Parameters

Returns

Examples

validate()​

Returns

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Summary

Attributes

Methods

cancel_materialization_job(...)

delete_keys(...)

get_feature_columns(...)

get_features_for_events(...)

Examples

get_features_in_range(...)

get_historical_features(...)

Examples

get_job(...)

get_materialization_job(...)

get_online_features(...)

Examples

get_partial_aggregates(...)

get_timestamp_field()

list_jobs()

list_materialization_jobs()

materialization_status()

print_transformation_schema()

run(...)

Example

run_transformation(...)

Example

summary()

test_run(...)

transformation_schema()

trigger_materialization_job(...)

wait_for_materialization_job()

with_join_key_map()

with_name()

validate()