google_vertex_ai_featurestores_entity_type.md

title	platform
About the google_vertex_ai_featurestores_entity_type resource	gcp

Syntax

A google_vertex_ai_featurestores_entity_type is used to test a Google FeaturestoresEntityType resource

Examples

describe google_vertex_ai_featurestores_entity_type(name: "projects/#{gcp_project_id}/locations/#{featurestores_entity_type['region']}/featurestores/#{featurestores_entity_type['featurestore']}/entityTypes/#{featurestores_entity_type['name']}", region: ' value_region') do
	it { should exist }
	its('description') { should cmp 'value_description' }
	its('name') { should cmp 'value_name' }
	its('create_time') { should cmp 'value_createtime' }
	its('etag') { should cmp 'value_etag' }
	its('update_time') { should cmp 'value_updatetime' }

end

describe google_vertex_ai_featurestores_entity_type(name: "does_not_exit", region: ' value_region') do
	it { should_not exist }
end

Properties

Properties that can be accessed from the google_vertex_ai_featurestores_entity_type resource:

• labels: Optional. The labels with user-defined metadata to organize your EntityTypes. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information on and examples of labels. No more than 64 user labels can be associated with one EntityType (System labels are excluded)." System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable.

  • additional_properties:

• description: Optional. Description of the EntityType.

• name: Immutable. Name of the EntityType. Format: projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entity_type} The last part entity_type is assigned by the client. The entity_type can be up to 64 characters long and can consist only of ASCII Latin letters A-Z and a-z and underscore(_), and ASCII digits 0-9 starting with a letter. The value will be unique given a featurestore.

• create_time: Output only. Timestamp when this EntityType was created.

• monitoring_config: Configuration of how features in Featurestore are monitored.

  • import_features_analysis: Configuration of the Featurestore's ImportFeature Analysis Based Monitoring. This type of analysis generates statistics for values of each Feature imported by every ImportFeatureValues operation.

    • anomaly_detection_baseline: The baseline used to do anomaly detection for the statistics generated by import features analysis. Possible values:

      • BASELINE_UNSPECIFIED
      • LATEST_STATS
      • MOST_RECENT_SNAPSHOT_STATS
      • PREVIOUS_IMPORT_FEATURES_STATS

    • state: Whether to enable / disable / inherite default hebavior for import features analysis. Possible values:

      • STATE_UNSPECIFIED
      • DEFAULT
      • ENABLED
      • DISABLED

  • numerical_threshold_config: The config for Featurestore Monitoring threshold.

    • value: Specify a threshold value that can trigger the alert. 1. For categorical feature, the distribution distance is calculated by L-inifinity norm. 2. For numerical feature, the distribution distance is calculated by Jensen–Shannon divergence. Each feature must have a non-zero threshold if they need to be monitored. Otherwise no alert will be triggered for that feature.

  • categorical_threshold_config: The config for Featurestore Monitoring threshold.

    • value: Specify a threshold value that can trigger the alert. 1. For categorical feature, the distribution distance is calculated by L-inifinity norm. 2. For numerical feature, the distribution distance is calculated by Jensen–Shannon divergence. Each feature must have a non-zero threshold if they need to be monitored. Otherwise no alert will be triggered for that feature.

  • snapshot_analysis: Configuration of the Featurestore's Snapshot Analysis Based Monitoring. This type of analysis generates statistics for each Feature based on a snapshot of the latest feature value of each entities every monitoring_interval.

    • monitoring_interval_days: Configuration of the snapshot analysis based monitoring pipeline running interval. The value indicates number of days.

    • staleness_days: Customized export features time window for snapshot analysis. Unit is one day. Default value is 3 weeks. Minimum value is 1 day. Maximum value is 4000 days.

    • disabled: The monitoring schedule for snapshot analysis. For EntityType-level config: unset / disabled = true indicates disabled by default for Features under it; otherwise by default enable snapshot analysis monitoring with monitoring_interval for Features under it. Feature-level config: disabled = true indicates disabled regardless of the EntityType-level config; unset monitoring_interval indicates going with EntityType-level config; otherwise run snapshot analysis monitoring with monitoring_interval regardless of the EntityType-level config. Explicitly Disable the snapshot analysis based monitoring.

• etag: Optional. Used to perform a consistent read-modify-write updates. If not set, a blind "overwrite" update happens.

• update_time: Output only. Timestamp when this EntityType was most recently updated.

• offline_storage_ttl_days: Optional. Config for data retention policy in offline storage. TTL in days for feature values that will be stored in offline storage. The Feature Store offline storage periodically removes obsolete feature values older than offline_storage_ttl_days since the feature generation time. If unset (or explicitly set to 0), default to 4000 days TTL.

GCP Permissions