Google Vertex AI Featurestore Entitytype

This page shows how to write Terraform for Vertex AI Featurestore Entitytype and write them securely.

google_vertex_ai_featurestore_entitytype (Terraform)

The Featurestore Entitytype in Vertex AI can be configured in Terraform with the resource name google_vertex_ai_featurestore_entitytype. The following sections describe how to use the resource and its parameters.

Example Usage from GitHub

An example could not be found in GitHub.

Review your Terraform file for Google best practices

Shisho Cloud, our free checker to make sure your Terraform configuration follows best practices, is available (beta).


The following arguments are supported:

  • featurestore - (Required) The name of the Featurestore to use, in the format projects/[project]/locations/[location]/featurestores/[featurestore].

  • name - (Optional) The name of the EntityType. This value may be up to 60 characters, and valid characters are [a-z0-9_]. The first character cannot be a number.

  • labels - (Optional) A set of key/value label pairs to assign to this EntityType.

  • monitoring_config - (Optional) The default monitoring configuration for all Features under this EntityType. If this is populated with [FeaturestoreMonitoringConfig.monitoring_interval] specified, snapshot analysis monitoring is enabled. Otherwise, snapshot analysis monitoring is disabled. Structure is documented below.

The monitoring_config block supports:

  • snapshot_analysis - (Optional) Configuration of how features in Featurestore are monitored. Structure is documented below.

The snapshot_analysis block supports:

  • disabled - (Optional) 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 monitoringInterval for Features under it.

  • monitoring_interval - (Optional) Configuration of the snapshot analysis based monitoring pipeline running interval. The value is rolled up to full day. A duration in seconds with up to nine fractional digits, terminated by 's'. Example: "3.5s".

In addition to the arguments listed above, the following computed attributes are exported:

  • id - an identifier for the resource with format [[featurestore]]/entityTypes/[[name]]

  • etag - Used to perform consistent read-modify-write updates.

  • create_time - The timestamp of when the featurestore was created in RFC3339 UTC "Zulu" format, with nanosecond resolution and up to nine fractional digits.

  • update_time - The timestamp of when the featurestore was last updated in RFC3339 UTC "Zulu" format, with nanosecond resolution and up to nine fractional digits.

Explanation in Terraform Registry

An entity type is a type of object in a system that needs to be modeled and have stored information about. For example, driver is an entity type, and driver0 is an instance of an entity type driver.

Warning: This resource is in beta, and should be used with the terraform-provider-google-beta provider. See Provider Versions for more details on beta resources. To get more information about FeaturestoreEntitytype, see:

Frequently asked questions

What is Google Vertex AI Featurestore Entitytype?

Google Vertex AI Featurestore Entitytype is a resource for Vertex AI of Google Cloud Platform. Settings can be wrote in Terraform.


Automate config file reviews on your commits

Fix issues in your infrastructure as code with auto-generated patches.