tecton.MonitoringConfig¶
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class
tecton.MonitoringConfig(monitor_freshness, expected_feature_freshness=None, alert_email=None)¶ Configuration used to specify monitoring options.
This class describes the FeatureView materialization freshness and alerting configurations. Requires materialization to be enabled. Freshness monitoring requires online materialization to be enabled. See Monitoring Materialization for more details.
- Parameters
monitor_freshness (bool) – Defines the enabled/disabled state of monitoring when feature data is materialized to the online feature store.
expected_feature_freshness (str, optional) – Threshold used to determine if recently materialized feature data is stale. Data is stale if
now - anchor_time(most_recent_feature_value) > expected_feature_freshness. Value must be at least 2 times the feature tile length. If not specified, a value determined by the Tecton backend is usedalert_email (str, optional) – Email that alerts for this FeatureView will be sent to.
An example declaration of a MonitorConfig
from tecton import batch_feature_view, Input, MonitoringConfig # For all named arguments to the batch feature view, see docs for details and types. @batch_feature_view( inputs={'credit_scores': Input(credit_scores_batch)}, # Can be an argument instance to a batch feature view decorator monitoring = MonitoringConfig( monitor_freshness=True, expected_feature_freshness="1w", alert_email="jules@tecton.ai" ), # Other named arguments ... ) # Your batch feature view function def credit_batch_feature_view(credit_scores): ...
Methods
Initialize self.
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__init__(monitor_freshness, expected_feature_freshness=None, alert_email=None)¶ Initialize self. See help(type(self)) for accurate signature.