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  5. AiFeatureStoreEntityType
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Google Cloud Classic v6.52.0, Mar 22 23

gcp.vertex.AiFeatureStoreEntityType

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.

To get more information about FeaturestoreEntitytype, see:

Example Usage

Vertex Ai Featurestore Entitytype

using System.Collections.Generic;
using Pulumi;
using Gcp = Pulumi.Gcp;

return await Deployment.RunAsync(() => 
{
    var featurestore = new Gcp.Vertex.AiFeatureStore("featurestore", new()
    {
        Labels = 
        {
            { "foo", "bar" },
        },
        Region = "us-central1",
        OnlineServingConfig = new Gcp.Vertex.Inputs.AiFeatureStoreOnlineServingConfigArgs
        {
            FixedNodeCount = 2,
        },
        EncryptionSpec = new Gcp.Vertex.Inputs.AiFeatureStoreEncryptionSpecArgs
        {
            KmsKeyName = "kms-name",
        },
    });

    var entity = new Gcp.Vertex.AiFeatureStoreEntityType("entity", new()
    {
        Labels = 
        {
            { "foo", "bar" },
        },
        Description = "test description",
        Featurestore = featurestore.Id,
        MonitoringConfig = new Gcp.Vertex.Inputs.AiFeatureStoreEntityTypeMonitoringConfigArgs
        {
            SnapshotAnalysis = new Gcp.Vertex.Inputs.AiFeatureStoreEntityTypeMonitoringConfigSnapshotAnalysisArgs
            {
                Disabled = false,
                MonitoringIntervalDays = 1,
                StalenessDays = 21,
            },
            NumericalThresholdConfig = new Gcp.Vertex.Inputs.AiFeatureStoreEntityTypeMonitoringConfigNumericalThresholdConfigArgs
            {
                Value = 0.8,
            },
            CategoricalThresholdConfig = new Gcp.Vertex.Inputs.AiFeatureStoreEntityTypeMonitoringConfigCategoricalThresholdConfigArgs
            {
                Value = 10,
            },
            ImportFeaturesAnalysis = new Gcp.Vertex.Inputs.AiFeatureStoreEntityTypeMonitoringConfigImportFeaturesAnalysisArgs
            {
                State = "ENABLED",
                AnomalyDetectionBaseline = "PREVIOUS_IMPORT_FEATURES_STATS",
            },
        },
    });

});

package main

import (
	"github.com/pulumi/pulumi-gcp/sdk/v6/go/gcp/vertex"
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)

func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		featurestore, err := vertex.NewAiFeatureStore(ctx, "featurestore", &vertex.AiFeatureStoreArgs{
			Labels: pulumi.StringMap{
				"foo": pulumi.String("bar"),
			},
			Region: pulumi.String("us-central1"),
			OnlineServingConfig: &vertex.AiFeatureStoreOnlineServingConfigArgs{
				FixedNodeCount: pulumi.Int(2),
			},
			EncryptionSpec: &vertex.AiFeatureStoreEncryptionSpecArgs{
				KmsKeyName: pulumi.String("kms-name"),
			},
		})
		if err != nil {
			return err
		}
		_, err = vertex.NewAiFeatureStoreEntityType(ctx, "entity", &vertex.AiFeatureStoreEntityTypeArgs{
			Labels: pulumi.StringMap{
				"foo": pulumi.String("bar"),
			},
			Description:  pulumi.String("test description"),
			Featurestore: featurestore.ID(),
			MonitoringConfig: &vertex.AiFeatureStoreEntityTypeMonitoringConfigArgs{
				SnapshotAnalysis: &vertex.AiFeatureStoreEntityTypeMonitoringConfigSnapshotAnalysisArgs{
					Disabled:               pulumi.Bool(false),
					MonitoringIntervalDays: pulumi.Int(1),
					StalenessDays:          pulumi.Int(21),
				},
				NumericalThresholdConfig: &vertex.AiFeatureStoreEntityTypeMonitoringConfigNumericalThresholdConfigArgs{
					Value: pulumi.Float64(0.8),
				},
				CategoricalThresholdConfig: &vertex.AiFeatureStoreEntityTypeMonitoringConfigCategoricalThresholdConfigArgs{
					Value: pulumi.Float64(10),
				},
				ImportFeaturesAnalysis: &vertex.AiFeatureStoreEntityTypeMonitoringConfigImportFeaturesAnalysisArgs{
					State:                    pulumi.String("ENABLED"),
					AnomalyDetectionBaseline: pulumi.String("PREVIOUS_IMPORT_FEATURES_STATS"),
				},
			},
		})
		if err != nil {
			return err
		}
		return nil
	})
}

package generated_program;

import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.gcp.vertex.AiFeatureStore;
import com.pulumi.gcp.vertex.AiFeatureStoreArgs;
import com.pulumi.gcp.vertex.inputs.AiFeatureStoreOnlineServingConfigArgs;
import com.pulumi.gcp.vertex.inputs.AiFeatureStoreEncryptionSpecArgs;
import com.pulumi.gcp.vertex.AiFeatureStoreEntityType;
import com.pulumi.gcp.vertex.AiFeatureStoreEntityTypeArgs;
import com.pulumi.gcp.vertex.inputs.AiFeatureStoreEntityTypeMonitoringConfigArgs;
import com.pulumi.gcp.vertex.inputs.AiFeatureStoreEntityTypeMonitoringConfigSnapshotAnalysisArgs;
import com.pulumi.gcp.vertex.inputs.AiFeatureStoreEntityTypeMonitoringConfigNumericalThresholdConfigArgs;
import com.pulumi.gcp.vertex.inputs.AiFeatureStoreEntityTypeMonitoringConfigCategoricalThresholdConfigArgs;
import com.pulumi.gcp.vertex.inputs.AiFeatureStoreEntityTypeMonitoringConfigImportFeaturesAnalysisArgs;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;

public class App {
    public static void main(String[] args) {
        Pulumi.run(App::stack);
    }

    public static void stack(Context ctx) {
        var featurestore = new AiFeatureStore("featurestore", AiFeatureStoreArgs.builder()        
            .labels(Map.of("foo", "bar"))
            .region("us-central1")
            .onlineServingConfig(AiFeatureStoreOnlineServingConfigArgs.builder()
                .fixedNodeCount(2)
                .build())
            .encryptionSpec(AiFeatureStoreEncryptionSpecArgs.builder()
                .kmsKeyName("kms-name")
                .build())
            .build());

        var entity = new AiFeatureStoreEntityType("entity", AiFeatureStoreEntityTypeArgs.builder()        
            .labels(Map.of("foo", "bar"))
            .description("test description")
            .featurestore(featurestore.id())
            .monitoringConfig(AiFeatureStoreEntityTypeMonitoringConfigArgs.builder()
                .snapshotAnalysis(AiFeatureStoreEntityTypeMonitoringConfigSnapshotAnalysisArgs.builder()
                    .disabled(false)
                    .monitoringIntervalDays(1)
                    .stalenessDays(21)
                    .build())
                .numericalThresholdConfig(AiFeatureStoreEntityTypeMonitoringConfigNumericalThresholdConfigArgs.builder()
                    .value(0.8)
                    .build())
                .categoricalThresholdConfig(AiFeatureStoreEntityTypeMonitoringConfigCategoricalThresholdConfigArgs.builder()
                    .value(10)
                    .build())
                .importFeaturesAnalysis(AiFeatureStoreEntityTypeMonitoringConfigImportFeaturesAnalysisArgs.builder()
                    .state("ENABLED")
                    .anomalyDetectionBaseline("PREVIOUS_IMPORT_FEATURES_STATS")
                    .build())
                .build())
            .build());

    }
}

import pulumi
import pulumi_gcp as gcp

featurestore = gcp.vertex.AiFeatureStore("featurestore",
    labels={
        "foo": "bar",
    },
    region="us-central1",
    online_serving_config=gcp.vertex.AiFeatureStoreOnlineServingConfigArgs(
        fixed_node_count=2,
    ),
    encryption_spec=gcp.vertex.AiFeatureStoreEncryptionSpecArgs(
        kms_key_name="kms-name",
    ))
entity = gcp.vertex.AiFeatureStoreEntityType("entity",
    labels={
        "foo": "bar",
    },
    description="test description",
    featurestore=featurestore.id,
    monitoring_config=gcp.vertex.AiFeatureStoreEntityTypeMonitoringConfigArgs(
        snapshot_analysis=gcp.vertex.AiFeatureStoreEntityTypeMonitoringConfigSnapshotAnalysisArgs(
            disabled=False,
            monitoring_interval_days=1,
            staleness_days=21,
        ),
        numerical_threshold_config=gcp.vertex.AiFeatureStoreEntityTypeMonitoringConfigNumericalThresholdConfigArgs(
            value=0.8,
        ),
        categorical_threshold_config=gcp.vertex.AiFeatureStoreEntityTypeMonitoringConfigCategoricalThresholdConfigArgs(
            value=10,
        ),
        import_features_analysis=gcp.vertex.AiFeatureStoreEntityTypeMonitoringConfigImportFeaturesAnalysisArgs(
            state="ENABLED",
            anomaly_detection_baseline="PREVIOUS_IMPORT_FEATURES_STATS",
        ),
    ))

import * as pulumi from "@pulumi/pulumi";
import * as gcp from "@pulumi/gcp";

const featurestore = new gcp.vertex.AiFeatureStore("featurestore", {
    labels: {
        foo: "bar",
    },
    region: "us-central1",
    onlineServingConfig: {
        fixedNodeCount: 2,
    },
    encryptionSpec: {
        kmsKeyName: "kms-name",
    },
});
const entity = new gcp.vertex.AiFeatureStoreEntityType("entity", {
    labels: {
        foo: "bar",
    },
    description: "test description",
    featurestore: featurestore.id,
    monitoringConfig: {
        snapshotAnalysis: {
            disabled: false,
            monitoringIntervalDays: 1,
            stalenessDays: 21,
        },
        numericalThresholdConfig: {
            value: 0.8,
        },
        categoricalThresholdConfig: {
            value: 10,
        },
        importFeaturesAnalysis: {
            state: "ENABLED",
            anomalyDetectionBaseline: "PREVIOUS_IMPORT_FEATURES_STATS",
        },
    },
});

resources:
  featurestore:
    type: gcp:vertex:AiFeatureStore
    properties:
      labels:
        foo: bar
      region: us-central1
      onlineServingConfig:
        fixedNodeCount: 2
      encryptionSpec:
        kmsKeyName: kms-name
  entity:
    type: gcp:vertex:AiFeatureStoreEntityType
    properties:
      labels:
        foo: bar
      description: test description
      featurestore: ${featurestore.id}
      monitoringConfig:
        snapshotAnalysis:
          disabled: false
          monitoringIntervalDays: 1
          stalenessDays: 21
        numericalThresholdConfig:
          value: 0.8
        categoricalThresholdConfig:
          value: 10
        importFeaturesAnalysis:
          state: ENABLED
          anomalyDetectionBaseline: PREVIOUS_IMPORT_FEATURES_STATS

Vertex Ai Featurestore Entitytype With Beta Fields

using System.Collections.Generic;
using Pulumi;
using Gcp = Pulumi.Gcp;

return await Deployment.RunAsync(() => 
{
    var featurestore = new Gcp.Vertex.AiFeatureStore("featurestore", new()
    {
        Labels = 
        {
            { "foo", "bar" },
        },
        Region = "us-central1",
        OnlineServingConfig = new Gcp.Vertex.Inputs.AiFeatureStoreOnlineServingConfigArgs
        {
            FixedNodeCount = 2,
        },
        EncryptionSpec = new Gcp.Vertex.Inputs.AiFeatureStoreEncryptionSpecArgs
        {
            KmsKeyName = "kms-name",
        },
    }, new CustomResourceOptions
    {
        Provider = google_beta,
    });

    var entity = new Gcp.Vertex.AiFeatureStoreEntityType("entity", new()
    {
        Labels = 
        {
            { "foo", "bar" },
        },
        Featurestore = featurestore.Id,
        MonitoringConfig = new Gcp.Vertex.Inputs.AiFeatureStoreEntityTypeMonitoringConfigArgs
        {
            SnapshotAnalysis = new Gcp.Vertex.Inputs.AiFeatureStoreEntityTypeMonitoringConfigSnapshotAnalysisArgs
            {
                Disabled = false,
                MonitoringInterval = "86400s",
            },
            CategoricalThresholdConfig = new Gcp.Vertex.Inputs.AiFeatureStoreEntityTypeMonitoringConfigCategoricalThresholdConfigArgs
            {
                Value = 0.3,
            },
            NumericalThresholdConfig = new Gcp.Vertex.Inputs.AiFeatureStoreEntityTypeMonitoringConfigNumericalThresholdConfigArgs
            {
                Value = 0.3,
            },
        },
        OfflineStorageTtlDays = 30,
    }, new CustomResourceOptions
    {
        Provider = google_beta,
    });

});

package main

import (
	"github.com/pulumi/pulumi-gcp/sdk/v6/go/gcp/vertex"
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)

func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		featurestore, err := vertex.NewAiFeatureStore(ctx, "featurestore", &vertex.AiFeatureStoreArgs{
			Labels: pulumi.StringMap{
				"foo": pulumi.String("bar"),
			},
			Region: pulumi.String("us-central1"),
			OnlineServingConfig: &vertex.AiFeatureStoreOnlineServingConfigArgs{
				FixedNodeCount: pulumi.Int(2),
			},
			EncryptionSpec: &vertex.AiFeatureStoreEncryptionSpecArgs{
				KmsKeyName: pulumi.String("kms-name"),
			},
		}, pulumi.Provider(google_beta))
		if err != nil {
			return err
		}
		_, err = vertex.NewAiFeatureStoreEntityType(ctx, "entity", &vertex.AiFeatureStoreEntityTypeArgs{
			Labels: pulumi.StringMap{
				"foo": pulumi.String("bar"),
			},
			Featurestore: featurestore.ID(),
			MonitoringConfig: &vertex.AiFeatureStoreEntityTypeMonitoringConfigArgs{
				SnapshotAnalysis: &vertex.AiFeatureStoreEntityTypeMonitoringConfigSnapshotAnalysisArgs{
					Disabled:           pulumi.Bool(false),
					MonitoringInterval: pulumi.String("86400s"),
				},
				CategoricalThresholdConfig: &vertex.AiFeatureStoreEntityTypeMonitoringConfigCategoricalThresholdConfigArgs{
					Value: pulumi.Float64(0.3),
				},
				NumericalThresholdConfig: &vertex.AiFeatureStoreEntityTypeMonitoringConfigNumericalThresholdConfigArgs{
					Value: pulumi.Float64(0.3),
				},
			},
			OfflineStorageTtlDays: pulumi.Int(30),
		}, pulumi.Provider(google_beta))
		if err != nil {
			return err
		}
		return nil
	})
}

package generated_program;

import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.gcp.vertex.AiFeatureStore;
import com.pulumi.gcp.vertex.AiFeatureStoreArgs;
import com.pulumi.gcp.vertex.inputs.AiFeatureStoreOnlineServingConfigArgs;
import com.pulumi.gcp.vertex.inputs.AiFeatureStoreEncryptionSpecArgs;
import com.pulumi.gcp.vertex.AiFeatureStoreEntityType;
import com.pulumi.gcp.vertex.AiFeatureStoreEntityTypeArgs;
import com.pulumi.gcp.vertex.inputs.AiFeatureStoreEntityTypeMonitoringConfigArgs;
import com.pulumi.gcp.vertex.inputs.AiFeatureStoreEntityTypeMonitoringConfigSnapshotAnalysisArgs;
import com.pulumi.gcp.vertex.inputs.AiFeatureStoreEntityTypeMonitoringConfigCategoricalThresholdConfigArgs;
import com.pulumi.gcp.vertex.inputs.AiFeatureStoreEntityTypeMonitoringConfigNumericalThresholdConfigArgs;
import com.pulumi.resources.CustomResourceOptions;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;

public class App {
    public static void main(String[] args) {
        Pulumi.run(App::stack);
    }

    public static void stack(Context ctx) {
        var featurestore = new AiFeatureStore("featurestore", AiFeatureStoreArgs.builder()        
            .labels(Map.of("foo", "bar"))
            .region("us-central1")
            .onlineServingConfig(AiFeatureStoreOnlineServingConfigArgs.builder()
                .fixedNodeCount(2)
                .build())
            .encryptionSpec(AiFeatureStoreEncryptionSpecArgs.builder()
                .kmsKeyName("kms-name")
                .build())
            .build(), CustomResourceOptions.builder()
                .provider(google_beta)
                .build());

        var entity = new AiFeatureStoreEntityType("entity", AiFeatureStoreEntityTypeArgs.builder()        
            .labels(Map.of("foo", "bar"))
            .featurestore(featurestore.id())
            .monitoringConfig(AiFeatureStoreEntityTypeMonitoringConfigArgs.builder()
                .snapshotAnalysis(AiFeatureStoreEntityTypeMonitoringConfigSnapshotAnalysisArgs.builder()
                    .disabled(false)
                    .monitoringInterval("86400s")
                    .build())
                .categoricalThresholdConfig(AiFeatureStoreEntityTypeMonitoringConfigCategoricalThresholdConfigArgs.builder()
                    .value(0.3)
                    .build())
                .numericalThresholdConfig(AiFeatureStoreEntityTypeMonitoringConfigNumericalThresholdConfigArgs.builder()
                    .value(0.3)
                    .build())
                .build())
            .offlineStorageTtlDays(30)
            .build(), CustomResourceOptions.builder()
                .provider(google_beta)
                .build());

    }
}

import pulumi
import pulumi_gcp as gcp

featurestore = gcp.vertex.AiFeatureStore("featurestore",
    labels={
        "foo": "bar",
    },
    region="us-central1",
    online_serving_config=gcp.vertex.AiFeatureStoreOnlineServingConfigArgs(
        fixed_node_count=2,
    ),
    encryption_spec=gcp.vertex.AiFeatureStoreEncryptionSpecArgs(
        kms_key_name="kms-name",
    ),
    opts=pulumi.ResourceOptions(provider=google_beta))
entity = gcp.vertex.AiFeatureStoreEntityType("entity",
    labels={
        "foo": "bar",
    },
    featurestore=featurestore.id,
    monitoring_config=gcp.vertex.AiFeatureStoreEntityTypeMonitoringConfigArgs(
        snapshot_analysis=gcp.vertex.AiFeatureStoreEntityTypeMonitoringConfigSnapshotAnalysisArgs(
            disabled=False,
            monitoring_interval="86400s",
        ),
        categorical_threshold_config=gcp.vertex.AiFeatureStoreEntityTypeMonitoringConfigCategoricalThresholdConfigArgs(
            value=0.3,
        ),
        numerical_threshold_config=gcp.vertex.AiFeatureStoreEntityTypeMonitoringConfigNumericalThresholdConfigArgs(
            value=0.3,
        ),
    ),
    offline_storage_ttl_days=30,
    opts=pulumi.ResourceOptions(provider=google_beta))

import * as pulumi from "@pulumi/pulumi";
import * as gcp from "@pulumi/gcp";

const featurestore = new gcp.vertex.AiFeatureStore("featurestore", {
    labels: {
        foo: "bar",
    },
    region: "us-central1",
    onlineServingConfig: {
        fixedNodeCount: 2,
    },
    encryptionSpec: {
        kmsKeyName: "kms-name",
    },
}, {
    provider: google_beta,
});
const entity = new gcp.vertex.AiFeatureStoreEntityType("entity", {
    labels: {
        foo: "bar",
    },
    featurestore: featurestore.id,
    monitoringConfig: {
        snapshotAnalysis: {
            disabled: false,
            monitoringInterval: "86400s",
        },
        categoricalThresholdConfig: {
            value: 0.3,
        },
        numericalThresholdConfig: {
            value: 0.3,
        },
    },
    offlineStorageTtlDays: 30,
}, {
    provider: google_beta,
});

resources:
  featurestore:
    type: gcp:vertex:AiFeatureStore
    properties:
      labels:
        foo: bar
      region: us-central1
      onlineServingConfig:
        fixedNodeCount: 2
      encryptionSpec:
        kmsKeyName: kms-name
    options:
      provider: ${["google-beta"]}
  entity:
    type: gcp:vertex:AiFeatureStoreEntityType
    properties:
      labels:
        foo: bar
      featurestore: ${featurestore.id}
      monitoringConfig:
        snapshotAnalysis:
          disabled: false
          monitoringInterval: 86400s
        categoricalThresholdConfig:
          value: 0.3
        numericalThresholdConfig:
          value: 0.3
      offlineStorageTtlDays: 30
    options:
      provider: ${["google-beta"]}

Create AiFeatureStoreEntityType Resource

new AiFeatureStoreEntityType(name: string, args: AiFeatureStoreEntityTypeArgs, opts?: CustomResourceOptions);
@overload
def AiFeatureStoreEntityType(resource_name: str,
                             opts: Optional[ResourceOptions] = None,
                             description: Optional[str] = None,
                             featurestore: Optional[str] = None,
                             labels: Optional[Mapping[str, str]] = None,
                             monitoring_config: Optional[AiFeatureStoreEntityTypeMonitoringConfigArgs] = None,
                             name: Optional[str] = None,
                             offline_storage_ttl_days: Optional[int] = None)
@overload
def AiFeatureStoreEntityType(resource_name: str,
                             args: AiFeatureStoreEntityTypeArgs,
                             opts: Optional[ResourceOptions] = None)
func NewAiFeatureStoreEntityType(ctx *Context, name string, args AiFeatureStoreEntityTypeArgs, opts ...ResourceOption) (*AiFeatureStoreEntityType, error)
public AiFeatureStoreEntityType(string name, AiFeatureStoreEntityTypeArgs args, CustomResourceOptions? opts = null)
public AiFeatureStoreEntityType(String name, AiFeatureStoreEntityTypeArgs args)
public AiFeatureStoreEntityType(String name, AiFeatureStoreEntityTypeArgs args, CustomResourceOptions options)

type: gcp:vertex:AiFeatureStoreEntityType
properties: # The arguments to resource properties.
options: # Bag of options to control resource's behavior.
name string
The unique name of the resource.
args AiFeatureStoreEntityTypeArgs
The arguments to resource properties.
opts CustomResourceOptions
Bag of options to control resource's behavior.
resource_name str
The unique name of the resource.
args AiFeatureStoreEntityTypeArgs
The arguments to resource properties.
opts ResourceOptions
Bag of options to control resource's behavior.
ctx Context
Context object for the current deployment.
name string
The unique name of the resource.
args AiFeatureStoreEntityTypeArgs
The arguments to resource properties.
opts ResourceOption
Bag of options to control resource's behavior.
name string
The unique name of the resource.
args AiFeatureStoreEntityTypeArgs
The arguments to resource properties.
opts CustomResourceOptions
Bag of options to control resource's behavior.
name String
The unique name of the resource.
args AiFeatureStoreEntityTypeArgs
The arguments to resource properties.
options CustomResourceOptions
Bag of options to control resource's behavior.

AiFeatureStoreEntityType Resource Properties

To learn more about resource properties and how to use them, see Inputs and Outputs in the Architecture and Concepts docs.

Inputs

The AiFeatureStoreEntityType resource accepts the following input properties:

Featurestore string

The name of the Featurestore to use, in the format projects/{project}/locations/{location}/featurestores/{featurestore}.

Description string

Optional. Description of the EntityType.

Labels Dictionary<string, string>

A set of key/value label pairs to assign to this EntityType.

MonitoringConfig AiFeatureStoreEntityTypeMonitoringConfigArgs

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.

Name string

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.

OfflineStorageTtlDays int

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 offlineStorageTtlDays since the feature generation time. If unset (or explicitly set to 0), default to 4000 days TTL.

Featurestore string

The name of the Featurestore to use, in the format projects/{project}/locations/{location}/featurestores/{featurestore}.

Description string

Optional. Description of the EntityType.

Labels map[string]string

A set of key/value label pairs to assign to this EntityType.

MonitoringConfig AiFeatureStoreEntityTypeMonitoringConfigArgs

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.

Name string

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.

OfflineStorageTtlDays int

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 offlineStorageTtlDays since the feature generation time. If unset (or explicitly set to 0), default to 4000 days TTL.

featurestore String

The name of the Featurestore to use, in the format projects/{project}/locations/{location}/featurestores/{featurestore}.

description String

Optional. Description of the EntityType.

labels Map<String,String>

A set of key/value label pairs to assign to this EntityType.

monitoringConfig AiFeatureStoreEntityTypeMonitoringConfigArgs

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.

name String

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.

offlineStorageTtlDays Integer

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 offlineStorageTtlDays since the feature generation time. If unset (or explicitly set to 0), default to 4000 days TTL.

featurestore string

The name of the Featurestore to use, in the format projects/{project}/locations/{location}/featurestores/{featurestore}.

description string

Optional. Description of the EntityType.

labels {[key: string]: string}

A set of key/value label pairs to assign to this EntityType.

monitoringConfig AiFeatureStoreEntityTypeMonitoringConfigArgs

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.

name string

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.

offlineStorageTtlDays number

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 offlineStorageTtlDays since the feature generation time. If unset (or explicitly set to 0), default to 4000 days TTL.

featurestore str

The name of the Featurestore to use, in the format projects/{project}/locations/{location}/featurestores/{featurestore}.

description str

Optional. Description of the EntityType.

labels Mapping[str, str]

A set of key/value label pairs to assign to this EntityType.

monitoring_config AiFeatureStoreEntityTypeMonitoringConfigArgs

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.

name str

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.

offline_storage_ttl_days int

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 offlineStorageTtlDays since the feature generation time. If unset (or explicitly set to 0), default to 4000 days TTL.

featurestore String

The name of the Featurestore to use, in the format projects/{project}/locations/{location}/featurestores/{featurestore}.

description String

Optional. Description of the EntityType.

labels Map<String>

A set of key/value label pairs to assign to this EntityType.

monitoringConfig Property Map

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.

name String

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.

offlineStorageTtlDays Number

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 offlineStorageTtlDays since the feature generation time. If unset (or explicitly set to 0), default to 4000 days TTL.

Outputs

All input properties are implicitly available as output properties. Additionally, the AiFeatureStoreEntityType resource produces the following output properties:

CreateTime string

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

Etag string

Used to perform consistent read-modify-write updates.

Id string

The provider-assigned unique ID for this managed resource.

Region string

The region of the EntityType.

UpdateTime string

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

CreateTime string

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

Etag string

Used to perform consistent read-modify-write updates.

Id string

The provider-assigned unique ID for this managed resource.

Region string

The region of the EntityType.

UpdateTime string

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

createTime String

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

etag String

Used to perform consistent read-modify-write updates.

id String

The provider-assigned unique ID for this managed resource.

region String

The region of the EntityType.

updateTime String

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

createTime string

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

etag string

Used to perform consistent read-modify-write updates.

id string

The provider-assigned unique ID for this managed resource.

region string

The region of the EntityType.

updateTime string

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

create_time str

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

etag str

Used to perform consistent read-modify-write updates.

id str

The provider-assigned unique ID for this managed resource.

region str

The region of the EntityType.

update_time str

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

createTime String

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

etag String

Used to perform consistent read-modify-write updates.

id String

The provider-assigned unique ID for this managed resource.

region String

The region of the EntityType.

updateTime String

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

Look up Existing AiFeatureStoreEntityType Resource

Get an existing AiFeatureStoreEntityType resource’s state with the given name, ID, and optional extra properties used to qualify the lookup.

public static get(name: string, id: Input<ID>, state?: AiFeatureStoreEntityTypeState, opts?: CustomResourceOptions): AiFeatureStoreEntityType
@staticmethod
def get(resource_name: str,
        id: str,
        opts: Optional[ResourceOptions] = None,
        create_time: Optional[str] = None,
        description: Optional[str] = None,
        etag: Optional[str] = None,
        featurestore: Optional[str] = None,
        labels: Optional[Mapping[str, str]] = None,
        monitoring_config: Optional[AiFeatureStoreEntityTypeMonitoringConfigArgs] = None,
        name: Optional[str] = None,
        offline_storage_ttl_days: Optional[int] = None,
        region: Optional[str] = None,
        update_time: Optional[str] = None) -> AiFeatureStoreEntityType
func GetAiFeatureStoreEntityType(ctx *Context, name string, id IDInput, state *AiFeatureStoreEntityTypeState, opts ...ResourceOption) (*AiFeatureStoreEntityType, error)
public static AiFeatureStoreEntityType Get(string name, Input<string> id, AiFeatureStoreEntityTypeState? state, CustomResourceOptions? opts = null)
public static AiFeatureStoreEntityType get(String name, Output<String> id, AiFeatureStoreEntityTypeState state, CustomResourceOptions options)
Resource lookup is not supported in YAML
name
The unique name of the resulting resource.
id
The unique provider ID of the resource to lookup.
state
Any extra arguments used during the lookup.
opts
A bag of options that control this resource's behavior.
resource_name
The unique name of the resulting resource.
id
The unique provider ID of the resource to lookup.
name
The unique name of the resulting resource.
id
The unique provider ID of the resource to lookup.
state
Any extra arguments used during the lookup.
opts
A bag of options that control this resource's behavior.
name
The unique name of the resulting resource.
id
The unique provider ID of the resource to lookup.
state
Any extra arguments used during the lookup.
opts
A bag of options that control this resource's behavior.
name
The unique name of the resulting resource.
id
The unique provider ID of the resource to lookup.
state
Any extra arguments used during the lookup.
opts
A bag of options that control this resource's behavior.
The following state arguments are supported:
CreateTime string

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

Description string

Optional. Description of the EntityType.

Etag string

Used to perform consistent read-modify-write updates.

Featurestore string

The name of the Featurestore to use, in the format projects/{project}/locations/{location}/featurestores/{featurestore}.

Labels Dictionary<string, string>

A set of key/value label pairs to assign to this EntityType.

MonitoringConfig AiFeatureStoreEntityTypeMonitoringConfigArgs

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.

Name string

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.

OfflineStorageTtlDays int

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 offlineStorageTtlDays since the feature generation time. If unset (or explicitly set to 0), default to 4000 days TTL.

Region string

The region of the EntityType.

UpdateTime string

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

CreateTime string

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

Description string

Optional. Description of the EntityType.

Etag string

Used to perform consistent read-modify-write updates.

Featurestore string

The name of the Featurestore to use, in the format projects/{project}/locations/{location}/featurestores/{featurestore}.

Labels map[string]string

A set of key/value label pairs to assign to this EntityType.

MonitoringConfig AiFeatureStoreEntityTypeMonitoringConfigArgs

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.

Name string

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.

OfflineStorageTtlDays int

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 offlineStorageTtlDays since the feature generation time. If unset (or explicitly set to 0), default to 4000 days TTL.

Region string

The region of the EntityType.

UpdateTime string

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

createTime String

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

description String

Optional. Description of the EntityType.

etag String

Used to perform consistent read-modify-write updates.

featurestore String

The name of the Featurestore to use, in the format projects/{project}/locations/{location}/featurestores/{featurestore}.

labels Map<String,String>

A set of key/value label pairs to assign to this EntityType.

monitoringConfig AiFeatureStoreEntityTypeMonitoringConfigArgs

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.

name String

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.

offlineStorageTtlDays Integer

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 offlineStorageTtlDays since the feature generation time. If unset (or explicitly set to 0), default to 4000 days TTL.

region String

The region of the EntityType.

updateTime String

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

createTime string

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

description string

Optional. Description of the EntityType.

etag string

Used to perform consistent read-modify-write updates.

featurestore string

The name of the Featurestore to use, in the format projects/{project}/locations/{location}/featurestores/{featurestore}.

labels {[key: string]: string}

A set of key/value label pairs to assign to this EntityType.

monitoringConfig AiFeatureStoreEntityTypeMonitoringConfigArgs

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.

name string

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.

offlineStorageTtlDays number

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 offlineStorageTtlDays since the feature generation time. If unset (or explicitly set to 0), default to 4000 days TTL.

region string

The region of the EntityType.

updateTime string

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

create_time str

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

description str

Optional. Description of the EntityType.

etag str

Used to perform consistent read-modify-write updates.

featurestore str

The name of the Featurestore to use, in the format projects/{project}/locations/{location}/featurestores/{featurestore}.

labels Mapping[str, str]

A set of key/value label pairs to assign to this EntityType.

monitoring_config AiFeatureStoreEntityTypeMonitoringConfigArgs

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.

name str

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.

offline_storage_ttl_days int

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 offlineStorageTtlDays since the feature generation time. If unset (or explicitly set to 0), default to 4000 days TTL.

region str

The region of the EntityType.

update_time str

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

createTime String

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

description String

Optional. Description of the EntityType.

etag String

Used to perform consistent read-modify-write updates.

featurestore String

The name of the Featurestore to use, in the format projects/{project}/locations/{location}/featurestores/{featurestore}.

labels Map<String>

A set of key/value label pairs to assign to this EntityType.

monitoringConfig Property Map

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.

name String

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.

offlineStorageTtlDays Number

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 offlineStorageTtlDays since the feature generation time. If unset (or explicitly set to 0), default to 4000 days TTL.

region String

The region of the EntityType.

updateTime String

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

Supporting Types

AiFeatureStoreEntityTypeMonitoringConfig

CategoricalThresholdConfig AiFeatureStoreEntityTypeMonitoringConfigCategoricalThresholdConfig

Threshold for categorical features of anomaly detection. This is shared by all types of Featurestore Monitoring for categorical features (i.e. Features with type (Feature.ValueType) BOOL or STRING). Structure is documented below.

ImportFeaturesAnalysis AiFeatureStoreEntityTypeMonitoringConfigImportFeaturesAnalysis

The config for ImportFeatures Analysis Based Feature Monitoring. Structure is documented below.

NumericalThresholdConfig AiFeatureStoreEntityTypeMonitoringConfigNumericalThresholdConfig

Threshold for numerical features of anomaly detection. This is shared by all objectives of Featurestore Monitoring for numerical features (i.e. Features with type (Feature.ValueType) DOUBLE or INT64). Structure is documented below.

SnapshotAnalysis AiFeatureStoreEntityTypeMonitoringConfigSnapshotAnalysis

The config for Snapshot Analysis Based Feature Monitoring. Structure is documented below.

CategoricalThresholdConfig AiFeatureStoreEntityTypeMonitoringConfigCategoricalThresholdConfig

Threshold for categorical features of anomaly detection. This is shared by all types of Featurestore Monitoring for categorical features (i.e. Features with type (Feature.ValueType) BOOL or STRING). Structure is documented below.

ImportFeaturesAnalysis AiFeatureStoreEntityTypeMonitoringConfigImportFeaturesAnalysis

The config for ImportFeatures Analysis Based Feature Monitoring. Structure is documented below.

NumericalThresholdConfig AiFeatureStoreEntityTypeMonitoringConfigNumericalThresholdConfig

Threshold for numerical features of anomaly detection. This is shared by all objectives of Featurestore Monitoring for numerical features (i.e. Features with type (Feature.ValueType) DOUBLE or INT64). Structure is documented below.

SnapshotAnalysis AiFeatureStoreEntityTypeMonitoringConfigSnapshotAnalysis

The config for Snapshot Analysis Based Feature Monitoring. Structure is documented below.

categoricalThresholdConfig AiFeatureStoreEntityTypeMonitoringConfigCategoricalThresholdConfig

Threshold for categorical features of anomaly detection. This is shared by all types of Featurestore Monitoring for categorical features (i.e. Features with type (Feature.ValueType) BOOL or STRING). Structure is documented below.

importFeaturesAnalysis AiFeatureStoreEntityTypeMonitoringConfigImportFeaturesAnalysis

The config for ImportFeatures Analysis Based Feature Monitoring. Structure is documented below.

numericalThresholdConfig AiFeatureStoreEntityTypeMonitoringConfigNumericalThresholdConfig

Threshold for numerical features of anomaly detection. This is shared by all objectives of Featurestore Monitoring for numerical features (i.e. Features with type (Feature.ValueType) DOUBLE or INT64). Structure is documented below.

snapshotAnalysis AiFeatureStoreEntityTypeMonitoringConfigSnapshotAnalysis

The config for Snapshot Analysis Based Feature Monitoring. Structure is documented below.

categoricalThresholdConfig AiFeatureStoreEntityTypeMonitoringConfigCategoricalThresholdConfig

Threshold for categorical features of anomaly detection. This is shared by all types of Featurestore Monitoring for categorical features (i.e. Features with type (Feature.ValueType) BOOL or STRING). Structure is documented below.

importFeaturesAnalysis AiFeatureStoreEntityTypeMonitoringConfigImportFeaturesAnalysis

The config for ImportFeatures Analysis Based Feature Monitoring. Structure is documented below.

numericalThresholdConfig AiFeatureStoreEntityTypeMonitoringConfigNumericalThresholdConfig

Threshold for numerical features of anomaly detection. This is shared by all objectives of Featurestore Monitoring for numerical features (i.e. Features with type (Feature.ValueType) DOUBLE or INT64). Structure is documented below.

snapshotAnalysis AiFeatureStoreEntityTypeMonitoringConfigSnapshotAnalysis

The config for Snapshot Analysis Based Feature Monitoring. Structure is documented below.

categorical_threshold_config AiFeatureStoreEntityTypeMonitoringConfigCategoricalThresholdConfig

Threshold for categorical features of anomaly detection. This is shared by all types of Featurestore Monitoring for categorical features (i.e. Features with type (Feature.ValueType) BOOL or STRING). Structure is documented below.

import_features_analysis AiFeatureStoreEntityTypeMonitoringConfigImportFeaturesAnalysis

The config for ImportFeatures Analysis Based Feature Monitoring. Structure is documented below.

numerical_threshold_config AiFeatureStoreEntityTypeMonitoringConfigNumericalThresholdConfig

Threshold for numerical features of anomaly detection. This is shared by all objectives of Featurestore Monitoring for numerical features (i.e. Features with type (Feature.ValueType) DOUBLE or INT64). Structure is documented below.

snapshot_analysis AiFeatureStoreEntityTypeMonitoringConfigSnapshotAnalysis

The config for Snapshot Analysis Based Feature Monitoring. Structure is documented below.

categoricalThresholdConfig Property Map

Threshold for categorical features of anomaly detection. This is shared by all types of Featurestore Monitoring for categorical features (i.e. Features with type (Feature.ValueType) BOOL or STRING). Structure is documented below.

importFeaturesAnalysis Property Map

The config for ImportFeatures Analysis Based Feature Monitoring. Structure is documented below.

numericalThresholdConfig Property Map

Threshold for numerical features of anomaly detection. This is shared by all objectives of Featurestore Monitoring for numerical features (i.e. Features with type (Feature.ValueType) DOUBLE or INT64). Structure is documented below.

snapshotAnalysis Property Map

The config for Snapshot Analysis Based Feature Monitoring. Structure is documented below.

AiFeatureStoreEntityTypeMonitoringConfigCategoricalThresholdConfig

Value double

Specify a threshold value that can trigger the alert. For categorical feature, the distribution distance is calculated by L-inifinity norm. Each feature must have a non-zero threshold if they need to be monitored. Otherwise no alert will be triggered for that feature. The default value is 0.3.

Value float64

Specify a threshold value that can trigger the alert. For categorical feature, the distribution distance is calculated by L-inifinity norm. Each feature must have a non-zero threshold if they need to be monitored. Otherwise no alert will be triggered for that feature. The default value is 0.3.

value Double

Specify a threshold value that can trigger the alert. For categorical feature, the distribution distance is calculated by L-inifinity norm. Each feature must have a non-zero threshold if they need to be monitored. Otherwise no alert will be triggered for that feature. The default value is 0.3.

value number

Specify a threshold value that can trigger the alert. For categorical feature, the distribution distance is calculated by L-inifinity norm. Each feature must have a non-zero threshold if they need to be monitored. Otherwise no alert will be triggered for that feature. The default value is 0.3.

value float

Specify a threshold value that can trigger the alert. For categorical feature, the distribution distance is calculated by L-inifinity norm. Each feature must have a non-zero threshold if they need to be monitored. Otherwise no alert will be triggered for that feature. The default value is 0.3.

value Number

Specify a threshold value that can trigger the alert. For categorical feature, the distribution distance is calculated by L-inifinity norm. Each feature must have a non-zero threshold if they need to be monitored. Otherwise no alert will be triggered for that feature. The default value is 0.3.

AiFeatureStoreEntityTypeMonitoringConfigImportFeaturesAnalysis

AnomalyDetectionBaseline string

Defines the baseline to do anomaly detection for feature values imported by each [entityTypes.importFeatureValues][] operation. The value must be one of the values below:

  • LATEST_STATS: Choose the later one statistics generated by either most recent snapshot analysis or previous import features analysis. If non of them exists, skip anomaly detection and only generate a statistics.
  • MOST_RECENT_SNAPSHOT_STATS: Use the statistics generated by the most recent snapshot analysis if exists.
  • PREVIOUS_IMPORT_FEATURES_STATS: Use the statistics generated by the previous import features analysis if exists.
State string

Whether to enable / disable / inherite default hebavior for import features analysis. The value must be one of the values below:

  • DEFAULT: The default behavior of whether to enable the monitoring. EntityType-level config: disabled.
  • ENABLED: Explicitly enables import features analysis. EntityType-level config: by default enables import features analysis for all Features under it.
  • DISABLED: Explicitly disables import features analysis. EntityType-level config: by default disables import features analysis for all Features under it.
AnomalyDetectionBaseline string

Defines the baseline to do anomaly detection for feature values imported by each [entityTypes.importFeatureValues][] operation. The value must be one of the values below:

  • LATEST_STATS: Choose the later one statistics generated by either most recent snapshot analysis or previous import features analysis. If non of them exists, skip anomaly detection and only generate a statistics.
  • MOST_RECENT_SNAPSHOT_STATS: Use the statistics generated by the most recent snapshot analysis if exists.
  • PREVIOUS_IMPORT_FEATURES_STATS: Use the statistics generated by the previous import features analysis if exists.
State string

Whether to enable / disable / inherite default hebavior for import features analysis. The value must be one of the values below:

  • DEFAULT: The default behavior of whether to enable the monitoring. EntityType-level config: disabled.
  • ENABLED: Explicitly enables import features analysis. EntityType-level config: by default enables import features analysis for all Features under it.
  • DISABLED: Explicitly disables import features analysis. EntityType-level config: by default disables import features analysis for all Features under it.
anomalyDetectionBaseline String

Defines the baseline to do anomaly detection for feature values imported by each [entityTypes.importFeatureValues][] operation. The value must be one of the values below:

  • LATEST_STATS: Choose the later one statistics generated by either most recent snapshot analysis or previous import features analysis. If non of them exists, skip anomaly detection and only generate a statistics.
  • MOST_RECENT_SNAPSHOT_STATS: Use the statistics generated by the most recent snapshot analysis if exists.
  • PREVIOUS_IMPORT_FEATURES_STATS: Use the statistics generated by the previous import features analysis if exists.
state String

Whether to enable / disable / inherite default hebavior for import features analysis. The value must be one of the values below:

  • DEFAULT: The default behavior of whether to enable the monitoring. EntityType-level config: disabled.
  • ENABLED: Explicitly enables import features analysis. EntityType-level config: by default enables import features analysis for all Features under it.
  • DISABLED: Explicitly disables import features analysis. EntityType-level config: by default disables import features analysis for all Features under it.
anomalyDetectionBaseline string

Defines the baseline to do anomaly detection for feature values imported by each [entityTypes.importFeatureValues][] operation. The value must be one of the values below:

  • LATEST_STATS: Choose the later one statistics generated by either most recent snapshot analysis or previous import features analysis. If non of them exists, skip anomaly detection and only generate a statistics.
  • MOST_RECENT_SNAPSHOT_STATS: Use the statistics generated by the most recent snapshot analysis if exists.
  • PREVIOUS_IMPORT_FEATURES_STATS: Use the statistics generated by the previous import features analysis if exists.
state string

Whether to enable / disable / inherite default hebavior for import features analysis. The value must be one of the values below:

  • DEFAULT: The default behavior of whether to enable the monitoring. EntityType-level config: disabled.
  • ENABLED: Explicitly enables import features analysis. EntityType-level config: by default enables import features analysis for all Features under it.
  • DISABLED: Explicitly disables import features analysis. EntityType-level config: by default disables import features analysis for all Features under it.
anomaly_detection_baseline str

Defines the baseline to do anomaly detection for feature values imported by each [entityTypes.importFeatureValues][] operation. The value must be one of the values below:

  • LATEST_STATS: Choose the later one statistics generated by either most recent snapshot analysis or previous import features analysis. If non of them exists, skip anomaly detection and only generate a statistics.
  • MOST_RECENT_SNAPSHOT_STATS: Use the statistics generated by the most recent snapshot analysis if exists.
  • PREVIOUS_IMPORT_FEATURES_STATS: Use the statistics generated by the previous import features analysis if exists.
state str

Whether to enable / disable / inherite default hebavior for import features analysis. The value must be one of the values below:

  • DEFAULT: The default behavior of whether to enable the monitoring. EntityType-level config: disabled.
  • ENABLED: Explicitly enables import features analysis. EntityType-level config: by default enables import features analysis for all Features under it.
  • DISABLED: Explicitly disables import features analysis. EntityType-level config: by default disables import features analysis for all Features under it.
anomalyDetectionBaseline String

Defines the baseline to do anomaly detection for feature values imported by each [entityTypes.importFeatureValues][] operation. The value must be one of the values below:

  • LATEST_STATS: Choose the later one statistics generated by either most recent snapshot analysis or previous import features analysis. If non of them exists, skip anomaly detection and only generate a statistics.
  • MOST_RECENT_SNAPSHOT_STATS: Use the statistics generated by the most recent snapshot analysis if exists.
  • PREVIOUS_IMPORT_FEATURES_STATS: Use the statistics generated by the previous import features analysis if exists.
state String

Whether to enable / disable / inherite default hebavior for import features analysis. The value must be one of the values below:

  • DEFAULT: The default behavior of whether to enable the monitoring. EntityType-level config: disabled.
  • ENABLED: Explicitly enables import features analysis. EntityType-level config: by default enables import features analysis for all Features under it.
  • DISABLED: Explicitly disables import features analysis. EntityType-level config: by default disables import features analysis for all Features under it.

AiFeatureStoreEntityTypeMonitoringConfigNumericalThresholdConfig

Value double

Specify a threshold value that can trigger the alert. 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. The default value is 0.3.

Value float64

Specify a threshold value that can trigger the alert. 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. The default value is 0.3.

value Double

Specify a threshold value that can trigger the alert. 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. The default value is 0.3.

value number

Specify a threshold value that can trigger the alert. 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. The default value is 0.3.

value float

Specify a threshold value that can trigger the alert. 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. The default value is 0.3.

value Number

Specify a threshold value that can trigger the alert. 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. The default value is 0.3.

AiFeatureStoreEntityTypeMonitoringConfigSnapshotAnalysis

Disabled bool

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.

MonitoringInterval string

Deprecated:

This field is unavailable in the GA provider and will be removed from the beta provider in a future release.

MonitoringIntervalDays int

Configuration of the snapshot analysis based monitoring pipeline running interval. The value indicates number of days. The default value is 1. If both FeaturestoreMonitoringConfig.SnapshotAnalysis.monitoring_interval_days and [FeaturestoreMonitoringConfig.SnapshotAnalysis.monitoring_interval][] are set when creating/updating EntityTypes/Features, FeaturestoreMonitoringConfig.SnapshotAnalysis.monitoring_interval_days will be used.

StalenessDays int

Customized export features time window for snapshot analysis. Unit is one day. The default value is 21 days. Minimum value is 1 day. Maximum value is 4000 days.

Disabled bool

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.

MonitoringInterval string

Deprecated:

This field is unavailable in the GA provider and will be removed from the beta provider in a future release.

MonitoringIntervalDays int

Configuration of the snapshot analysis based monitoring pipeline running interval. The value indicates number of days. The default value is 1. If both FeaturestoreMonitoringConfig.SnapshotAnalysis.monitoring_interval_days and [FeaturestoreMonitoringConfig.SnapshotAnalysis.monitoring_interval][] are set when creating/updating EntityTypes/Features, FeaturestoreMonitoringConfig.SnapshotAnalysis.monitoring_interval_days will be used.

StalenessDays int

Customized export features time window for snapshot analysis. Unit is one day. The default value is 21 days. Minimum value is 1 day. Maximum value is 4000 days.

disabled Boolean

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.

monitoringInterval String

Deprecated:

This field is unavailable in the GA provider and will be removed from the beta provider in a future release.

monitoringIntervalDays Integer

Configuration of the snapshot analysis based monitoring pipeline running interval. The value indicates number of days. The default value is 1. If both FeaturestoreMonitoringConfig.SnapshotAnalysis.monitoring_interval_days and [FeaturestoreMonitoringConfig.SnapshotAnalysis.monitoring_interval][] are set when creating/updating EntityTypes/Features, FeaturestoreMonitoringConfig.SnapshotAnalysis.monitoring_interval_days will be used.

stalenessDays Integer

Customized export features time window for snapshot analysis. Unit is one day. The default value is 21 days. Minimum value is 1 day. Maximum value is 4000 days.

disabled boolean

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.

monitoringInterval string

Deprecated:

This field is unavailable in the GA provider and will be removed from the beta provider in a future release.

monitoringIntervalDays number

Configuration of the snapshot analysis based monitoring pipeline running interval. The value indicates number of days. The default value is 1. If both FeaturestoreMonitoringConfig.SnapshotAnalysis.monitoring_interval_days and [FeaturestoreMonitoringConfig.SnapshotAnalysis.monitoring_interval][] are set when creating/updating EntityTypes/Features, FeaturestoreMonitoringConfig.SnapshotAnalysis.monitoring_interval_days will be used.

stalenessDays number

Customized export features time window for snapshot analysis. Unit is one day. The default value is 21 days. Minimum value is 1 day. Maximum value is 4000 days.

disabled bool

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 str

Deprecated:

This field is unavailable in the GA provider and will be removed from the beta provider in a future release.

monitoring_interval_days int

Configuration of the snapshot analysis based monitoring pipeline running interval. The value indicates number of days. The default value is 1. If both FeaturestoreMonitoringConfig.SnapshotAnalysis.monitoring_interval_days and [FeaturestoreMonitoringConfig.SnapshotAnalysis.monitoring_interval][] are set when creating/updating EntityTypes/Features, FeaturestoreMonitoringConfig.SnapshotAnalysis.monitoring_interval_days will be used.

staleness_days int

Customized export features time window for snapshot analysis. Unit is one day. The default value is 21 days. Minimum value is 1 day. Maximum value is 4000 days.

disabled Boolean

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.

monitoringInterval String

Deprecated:

This field is unavailable in the GA provider and will be removed from the beta provider in a future release.

monitoringIntervalDays Number

Configuration of the snapshot analysis based monitoring pipeline running interval. The value indicates number of days. The default value is 1. If both FeaturestoreMonitoringConfig.SnapshotAnalysis.monitoring_interval_days and [FeaturestoreMonitoringConfig.SnapshotAnalysis.monitoring_interval][] are set when creating/updating EntityTypes/Features, FeaturestoreMonitoringConfig.SnapshotAnalysis.monitoring_interval_days will be used.

stalenessDays Number

Customized export features time window for snapshot analysis. Unit is one day. The default value is 21 days. Minimum value is 1 day. Maximum value is 4000 days.

Import

FeaturestoreEntitytype can be imported using any of these accepted formats

 $ pulumi import gcp:vertex/aiFeatureStoreEntityType:AiFeatureStoreEntityType default {{featurestore}}/entityTypes/{{name}}

Package Details

Repository
Google Cloud (GCP) Classic pulumi/pulumi-gcp
License
Apache-2.0
Notes

This Pulumi package is based on the google-beta Terraform Provider.