Three different resources help you manage your IAM policy for Vertex AI Featurestore. Each of these resources serves a different use case:
google_vertex_ai_featurestore_iam_policy
: Authoritative. Sets the IAM policy for the featurestore and replaces any existing policy already attached.google_vertex_ai_featurestore_iam_binding
: Authoritative for a given role. Updates the IAM policy to grant a role to a list of members. Other roles within the IAM policy for the featurestore are preserved.google_vertex_ai_featurestore_iam_member
: Non-authoritative. Updates the IAM policy to grant a role to a new member. Other members for the role for the featurestore are preserved.A data source can be used to retrieve policy data in advent you do not need creation
google_vertex_ai_featurestore_iam_policy
: Retrieves the IAM policy for the featurestoredata "google_iam_policy" "admin" {
binding {
role = "roles/viewer"
members = [
"user:jane@example.com",
]
}
}
resource "google_vertex_ai_featurestore_iam_policy" "policy" {
project = google_vertex_ai_featurestore.featurestore.project
region = google_vertex_ai_featurestore.featurestore.region
featurestore = google_vertex_ai_featurestore.featurestore.name
policy_data = data.google_iam_policy.admin.policy_data
}
resource "google_vertex_ai_featurestore_iam_binding" "binding" {
project = google_vertex_ai_featurestore.featurestore.project
region = google_vertex_ai_featurestore.featurestore.region
featurestore = google_vertex_ai_featurestore.featurestore.name
role = "roles/viewer"
members = [
"user:jane@example.com",
]
}
resource "google_vertex_ai_featurestore_iam_member" "member" {
project = google_vertex_ai_featurestore.featurestore.project
region = google_vertex_ai_featurestore.featurestore.region
featurestore = google_vertex_ai_featurestore.featurestore.name
role = "roles/viewer"
member = "user:jane@example.com"
}
The following arguments are supported:
featurestore
- (Required) Used to find the parent resource to bind the IAM policy toregion
- (Optional) The region of the dataset. eg us-central1 Used to find the parent resource to bind the IAM policy to. If not specified,
the value will be parsed from the identifier of the parent resource. If no region is provided in the parent identifier and no
region is specified, it is taken from the provider configuration.
project
- (Optional) The ID of the project in which the resource belongs.
If it is not provided, the project will be parsed from the identifier of the parent resource. If no project is provided in the parent identifier and no project is specified, the provider project is used.
member/members
- (Required) Identities that will be granted the privilege in role
.
Each entry can have one of the following values:
role
- (Required) The role that should be applied. Only one
google_vertex_ai_featurestore_iam_binding
can be used per role. Note that custom roles must be of the format
[projects|organizations]/{parent-name}/roles/{role-name}
.
policy_data
- (Required only by google_vertex_ai_featurestore_iam_policy
) The policy data generated by
a google_iam_policy
data source.
In addition to the arguments listed above, the following computed attributes are exported:
etag
- (Computed) The etag of the IAM policy.For all import syntaxes, the "resource in question" can take any of the following forms:
Any variables not passed in the import command will be taken from the provider configuration.
Vertex AI featurestore IAM resources can be imported using the resource identifiers, role, and member.
IAM member imports use space-delimited identifiers: the resource in question, the role, and the member identity, e.g.
$ terraform import google_vertex_ai_featurestore_iam_member.editor "projects/{{project}}/locations/{{region}}/featurestores/{{featurestore}} roles/viewer user:jane@example.com"
IAM binding imports use space-delimited identifiers: the resource in question and the role, e.g.
$ terraform import google_vertex_ai_featurestore_iam_binding.editor "projects/{{project}}/locations/{{region}}/featurestores/{{featurestore}} roles/viewer"
IAM policy imports use the identifier of the resource in question, e.g.
$ terraform import google_vertex_ai_featurestore_iam_policy.editor projects/{{project}}/locations/{{region}}/featurestores/{{featurestore}}
This resource supports User Project Overrides.