REST Resource: projects.locations.featurestores

Resource: Featurestore

Vertex AI Feature Store provides a centralized repository for organizing, storing, and serving ML features. The Featurestore is a top-level container for your features and their values.

JSON representation
{
  "name": string,
  "createTime": string,
  "updateTime": string,
  "etag": string,
  "labels": {
    string: string,
    ...
  },
  "onlineServingConfig": {
    object (OnlineServingConfig)
  },
  "state": enum (State),
  "encryptionSpec": {
    object (EncryptionSpec)
  }
}
Fields
name

string

Output only. Name of the Featurestore. Format: projects/{project}/locations/{location}/featurestores/{featurestore}

createTime

string (Timestamp format)

Output only. Timestamp when this Featurestore was created.

A timestamp in RFC3339 UTC "Zulu" format, with nanosecond resolution and up to nine fractional digits. Examples: "2014-10-02T15:01:23Z" and "2014-10-02T15:01:23.045123456Z".

updateTime

string (Timestamp format)

Output only. Timestamp when this Featurestore was last updated.

A timestamp in RFC3339 UTC "Zulu" format, with nanosecond resolution and up to nine fractional digits. Examples: "2014-10-02T15:01:23Z" and "2014-10-02T15:01:23.045123456Z".

etag

string

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

labels

map (key: string, value: string)

Optional. The labels with user-defined metadata to organize your Featurestore.

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 Featurestore(System labels are excluded)." System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable.

onlineServingConfig

object (OnlineServingConfig)

Optional. Config for online storage resources. If unset, the featurestore will not have an online store and cannot be used for online serving.

state

enum (State)

Output only. State of the featurestore.

encryptionSpec

object (EncryptionSpec)

Optional. Customer-managed encryption key spec for data storage. If set, both of the online and offline data storage will be secured by this key.

OnlineServingConfig

OnlineServingConfig specifies the details for provisioning online serving resources.

JSON representation
{
  "fixedNodeCount": integer,
  "scaling": {
    object (Scaling)
  }
}
Fields
fixedNodeCount

integer

The number of nodes for the online store. The number of nodes doesn't scale automatically, but you can manually update the number of nodes. If set to 0, the featurestore will not have an online store and cannot be used for online serving.

scaling

object (Scaling)

Online serving scaling configuration. Only one of fixedNodeCount and scaling can be set. Setting one will reset the other.

Scaling

Online serving scaling configuration. If minNodeCount and maxNodeCount are set to the same value, the cluster will be configured with the fixed number of node (no auto-scaling).

JSON representation
{
  "minNodeCount": integer,
  "maxNodeCount": integer
}
Fields
minNodeCount

integer

Required. The minimum number of nodes to scale down to. Must be greater than or equal to 1.

maxNodeCount

integer

The maximum number of nodes to scale up to. Must be greater than minNodeCount, and less than or equal to 10 times of 'minNodeCount'.

State

Possible states a featurestore can have.

Enums
STATE_UNSPECIFIED Default value. This value is unused.
STABLE State when the featurestore configuration is not being updated and the fields reflect the current configuration of the featurestore. The featurestore is usable in this state.
UPDATING The state of the featurestore configuration when it is being updated. During an update, the fields reflect either the original configuration or the updated configuration of the featurestore. For example, onlineServingConfig.fixed_node_count can take minutes to update. While the update is in progress, the featurestore is in the UPDATING state, and the value of fixedNodeCount can be the original value or the updated value, depending on the progress of the operation. Until the update completes, the actual number of nodes can still be the original value of fixedNodeCount. The featurestore is still usable in this state.

Methods

batchReadFeatureValues

Batch reads Feature values from a Featurestore.

create

Creates a new Featurestore in a given project and location.

delete

Deletes a single Featurestore.

get

Gets details of a single Featurestore.

list

Lists Featurestores in a given project and location.

patch

Updates the parameters of a single Featurestore.

searchFeatures

Searches Features matching a query in a given project.