LifetimeWindow

Private Preview

This capability requires Data Compaction. Compaction and Lifetime Windows are in Private Preview and have limitations that will be resolved in future Tecton releases. See Limitations & Requirements for more details. This is currently available for Spark-based Feature Views -- support for Rift is coming soon.

Summary

This class describes a LifetimeWindow that is applied in an Aggregation within a Batch or Stream Feature View. Lifetime Windows are useful for expressing a feature like "user transaction average since Jan 1, 2000." See this page more information on Lifetime Windows and other Aggregation Windows.

Note that when using a LifetimeWindow, the Batch or Stream Feature View must have compaction_enabled=True and must set lifetime_start_time if materialization is enabled.

Description

Tecton Aggregations are applied over a specified time window using the time_window parameter. Use LifetimeWindow to create an Aggregation over a window that continuously expands.

Example

from tecton import batch_feature_view, FilteredSource, Aggregation, LifetimeWindow


@batch_feature_view(
    sources=[FilteredSource(transactions)],
    mode="spark_sql",
    entities=[user],
    aggregation_interval=timedelta(days=1),
    compaction_enabled=True,
    lifetime_start_time=datetime(2000, 1, 1),
    aggregations=[Aggregation(column="amount", function="mean", time_window=LifetimeWindow())],
)
def user_average_transaction_amount(transactions):
    return f"SELECT user_id, timestamp, amount FROM {transactions}"

The start time of this window is Jan 1, 2000 (as specified by lifetime_start_time). The end time of this window will be the most recent aggregation interval as of an incoming online feature request or training event timestamp (during offline feature retrieval).