The ttl (time-to-live) Parameter in Feature Views

The value of ttl affects the availability of feature data in the online store and the generation of training feature data.

ttl specifies the amount of time, prior to the current time, for which feature data should be available in the online store. At materialization time, feature data with timestamps earlier than the current time minus the ttl value are not written to the online store. Tecton never writes feature data with timestamps earlier than feature_start_time to the online store. So Tecton writes features as far back as the max(feature_start_time, current time - ttl) to the online store

ttl is a Batch and Stream Feature View parameter, as well as a Feature Table parameter.

note

For a Feature View that contains one or more Aggregations, the Feature View's ttl value is implicity set to the aggregation_interval value.

Effect of ttl on the materialization of feature data into the online store

Increasing the ttl value can increase the amount and throughput of data written to the online store. and may affect availability for reads from the online store.

Effect of ttl on the availability of feature data in the online store

ttl specifies the amount of time, prior to the current time, that feature data is available in the online store. Feature data with timestamps earlier than the current time minus the ttl value will expire.

Effect of ttl on the generation of training feature data

ttl specifies the maximum amount of time prior to the timestamp of a training event, that data in a Feature View's data source is available for generating feature data for the training event.

note

Lower ttl values will allow get_historical_features() to run more efficiently in some cases, because the amount of training data generated will be reduced.

note

ttl has no effect on deletion of feature values from the offline store. To delete values from the offline store, use the delete_keys() method.