AiFeatureStoreEntityType class

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

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

const featurestore = new gcp.vertex.AiFeatureStore("featurestore", {
    name: "terraform",
    labels: {
        foo: "bar",
    },
    region: "us-central1",
    onlineServingConfig: {
        fixedNodeCount: 2,
    },
    encryptionSpec: {
        kmsKeyName: "kms-name",
    },
});
const entity = new gcp.vertex.AiFeatureStoreEntityType("entity", {
    name: "terraform",
    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",
        },
    },
});

import pulumi
import pulumi_gcp as gcp

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

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

return await Deployment.RunAsync(() =>
{
    var featurestore = new Gcp.Vertex.AiFeatureStore("featurestore", new()
    {
        Name = "terraform",
        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()
    {
        Name = "terraform",
        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 = 10.0,
            },
            ImportFeaturesAnalysis = new Gcp.Vertex.Inputs.AiFeatureStoreEntityTypeMonitoringConfigImportFeaturesAnalysisArgs
            {
                State = "ENABLED",
                AnomalyDetectionBaseline = "PREVIOUS_IMPORT_FEATURES_STATS",
            },
        },
    });

});

package main

import (
	"github.com/pulumi/pulumi-gcp/sdk/v9/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{
			Name: pulumi.String("terraform"),
			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{
			Name: pulumi.String("terraform"),
			Labels: pulumi.StringMap{
				"foo": pulumi.String("bar"),
			},
			Description:  pulumi.String("test description"),
			Featurestore: featurestore.ID().ToIDOutput().ToStringOutput(),
			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
	})
}

pulumi {
  required_providers {
    gcp = {
      source = "pulumi/gcp"
    }
  }
}

resource "gcp_vertex_aifeaturestore" "featurestore" {
  name = "terraform"
  labels = {
    "foo" = "bar"
  }
  region = "us-central1"
  online_serving_config = {
    fixed_node_count = 2
  }
  encryption_spec = {
    kms_key_name = "kms-name"
  }
}
resource "gcp_vertex_aifeaturestoreentitytype" "entity" {
  name = "terraform"
  labels = {
    "foo" = "bar"
  }
  description  = "test description"
  featurestore = gcp_vertex_aifeaturestore.featurestore.id
  monitoring_config = {
    snapshot_analysis = {
      disabled                 = false
      monitoring_interval_days = 1
      staleness_days           = 21
    }
    numerical_threshold_config = {
      value = 0.8
    }
    categorical_threshold_config = {
      value = 10
    }
    import_features_analysis = {
      state                      = "ENABLED"
      anomaly_detection_baseline = "PREVIOUS_IMPORT_FEATURES_STATS"
    }
  }
}

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.ArrayList;
import java.util.Arrays;
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()
            .name("terraform")
            .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()
            .name("terraform")
            .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(10.0)
                    .build())
                .importFeaturesAnalysis(AiFeatureStoreEntityTypeMonitoringConfigImportFeaturesAnalysisArgs.builder()
                    .state("ENABLED")
                    .anomalyDetectionBaseline("PREVIOUS_IMPORT_FEATURES_STATS")
                    .build())
                .build())
            .build());

    }
}

resources:
  featurestore:
    type: gcp:vertex:AiFeatureStore
    properties:
      name: terraform
      labels:
        foo: bar
      region: us-central1
      onlineServingConfig:
        fixedNodeCount: 2
      encryptionSpec:
        kmsKeyName: kms-name
  entity:
    type: gcp:vertex:AiFeatureStoreEntityType
    properties:
      name: terraform
      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

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

const featurestore = new gcp.vertex.AiFeatureStore("featurestore", {
    name: "terraform2",
    labels: {
        foo: "bar",
    },
    region: "us-central1",
    onlineServingConfig: {
        fixedNodeCount: 2,
    },
    encryptionSpec: {
        kmsKeyName: "kms-name",
    },
});
const entity = new gcp.vertex.AiFeatureStoreEntityType("entity", {
    name: "terraform2",
    labels: {
        foo: "bar",
    },
    featurestore: featurestore.id,
    monitoringConfig: {
        snapshotAnalysis: {
            disabled: false,
            monitoringInterval: "86400s",
        },
        categoricalThresholdConfig: {
            value: 0.3,
        },
        numericalThresholdConfig: {
            value: 0.3,
        },
    },
    offlineStorageTtlDays: 30,
});

import pulumi
import pulumi_gcp as gcp

featurestore = gcp.vertex.AiFeatureStore("featurestore",
    name="terraform2",
    labels={
        "foo": "bar",
    },
    region="us-central1",
    online_serving_config={
        "fixed_node_count": 2,
    },
    encryption_spec={
        "kms_key_name": "kms-name",
    })
entity = gcp.vertex.AiFeatureStoreEntityType("entity",
    name="terraform2",
    labels={
        "foo": "bar",
    },
    featurestore=featurestore.id,
    monitoring_config={
        "snapshot_analysis": {
            "disabled": False,
            "monitoring_interval": "86400s",
        },
        "categorical_threshold_config": {
            "value": 0.3,
        },
        "numerical_threshold_config": {
            "value": 0.3,
        },
    },
    offline_storage_ttl_days=30)

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

return await Deployment.RunAsync(() =>
{
    var featurestore = new Gcp.Vertex.AiFeatureStore("featurestore", new()
    {
        Name = "terraform2",
        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()
    {
        Name = "terraform2",
        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,
    });

});

package main

import (
	"github.com/pulumi/pulumi-gcp/sdk/v9/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{
			Name: pulumi.String("terraform2"),
			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{
			Name: pulumi.String("terraform2"),
			Labels: pulumi.StringMap{
				"foo": pulumi.String("bar"),
			},
			Featurestore: featurestore.ID().ToIDOutput().ToStringOutput(),
			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),
		})
		if err != nil {
			return err
		}
		return nil
	})
}

pulumi {
  required_providers {
    gcp = {
      source = "pulumi/gcp"
    }
  }
}

resource "gcp_vertex_aifeaturestore" "featurestore" {
  name = "terraform2"
  labels = {
    "foo" = "bar"
  }
  region = "us-central1"
  online_serving_config = {
    fixed_node_count = 2
  }
  encryption_spec = {
    kms_key_name = "kms-name"
  }
}
resource "gcp_vertex_aifeaturestoreentitytype" "entity" {
  name = "terraform2"
  labels = {
    "foo" = "bar"
  }
  featurestore = gcp_vertex_aifeaturestore.featurestore.id
  monitoring_config = {
    snapshot_analysis = {
      disabled            = false
      monitoring_interval = "86400s"
    }
    categorical_threshold_config = {
      value = 0.3
    }
    numerical_threshold_config = {
      value = 0.3
    }
  }
  offline_storage_ttl_days = 30
}

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 java.util.ArrayList;
import java.util.Arrays;
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()
            .name("terraform2")
            .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()
            .name("terraform2")
            .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());

    }
}

resources:
  featurestore:
    type: gcp:vertex:AiFeatureStore
    properties:
      name: terraform2
      labels:
        foo: bar
      region: us-central1
      onlineServingConfig:
        fixedNodeCount: 2
      encryptionSpec:
        kmsKeyName: kms-name
  entity:
    type: gcp:vertex:AiFeatureStoreEntityType
    properties:
      name: terraform2
      labels:
        foo: bar
      featurestore: ${featurestore.id}
      monitoringConfig:
        snapshotAnalysis:
          disabled: false
          monitoringInterval: 86400s
        categoricalThresholdConfig:
          value: 0.3
        numericalThresholdConfig:
          value: 0.3
      offlineStorageTtlDays: 30

Import

FeaturestoreEntitytype can be imported using any of these accepted formats:

{{featurestore}}/entityTypes/{{name}}

When using the pulumi import command, FeaturestoreEntitytype can be imported using one of the formats above. For example:

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

Constructors

AiFeatureStoreEntityType(String name, {AiFeatureStoreEntityTypeArgs? args, CustomResourceOptions? options}): Creates a new AiFeatureStoreEntityType. name The Pulumi resource name. args Arguments used to configure this AiFeatureStoreEntityType. The set of arguments for AiFeatureStoreEntityType. options Resource options controlling this resource's behavior.
AiFeatureStoreEntityType.reference(String urn): Creates a typed reference to an existing AiFeatureStoreEntityType resource.

Properties

childResources → Set<Resource>: finalinherited
completionSources ↔ Map<String, IOutputCompletionSource>: latefinalinherited
createTime ↔ Output<String>: The timestamp of when the featurestore was created in RFC3339 UTC "Zulu" format, with nanosecond resolution and up to nine fractional digits.
latefinal
deletionPolicy ↔ Output<String>: Whether Terraform will be prevented from destroying the resource. Defaults to DELETE. When a 'terraform destroy' or 'pulumi up' would delete the resource, the command will fail if this field is set to "PREVENT" in Terraform state. When set to "ABANDON", the command will remove the resource from Terraform management without updating or deleting the resource in the API. When set to "DELETE", deleting the resource is allowed.
latefinal
description ↔ Output<String?>: Optional. Description of the EntityType.
latefinal
effectiveLabels ↔ Output<Map<String, String>>: All of labels (key/value pairs) present on the resource in GCP, including the labels configured through Pulumi, other clients and services.
latefinal
etag ↔ Output<String>: Used to perform consistent read-modify-write updates.
latefinal
featurestore ↔ Output<String>: The name of the Featurestore to use, in the format projects/{project}/locations/{location}/featurestores/{featurestore}.
latefinal
hashCode → int: The hash code for this object.
no setterinherited
id ↔ Output<String>: getter/setter pairinherited
isCustom → bool: Returns whether this resource is provider-managed.
no setterinherited
isProtected → bool: Returns whether this resource is protected from deletion.
no setterinherited
isRemote → bool: Whether this resource is registered as remote.
no setterinherited
isResourceReference → bool: Whether this instance represents a resource value returned over RPC.
finalinherited
labels ↔ Output<Map<String, String>?>: A set of key/value label pairs to assign to this EntityType.
latefinal
monitoringConfig ↔ Output<AiFeatureStoreEntityTypeMonitoringConfig?>: 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.
latefinal
name ↔ Output<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.
latefinal
offlineStorageTtlDays ↔ Output<int?>: (Optional, Beta) 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.
latefinal
pulumiLabels ↔ Output<Map<String, String>>: The combination of labels configured directly on the resource and default labels configured on the provider.
latefinal
region ↔ Output<String>: The region of the EntityType.
latefinal
resourceTransforms → List<ResourceTransform>: Inherited/explicit async transforms.
no setterinherited
runtimeType → Type: A representation of the runtime type of the object.
no setterinherited
transformations → List<ResourceTransformation>: Inherited/explicit legacy transformations.
no setterinherited
updateTime ↔ Output<String>: The timestamp of when the featurestore was last updated in RFC3339 UTC "Zulu" format, with nanosecond resolution and up to nine fractional digits.
latefinal
urn ↔ Output<String>: latefinalinherited

Methods

failId(Object error) → void: Completes this resource ID with an error when registration fails.
inherited
failOutputs(Object error) → void: Completes all output properties with error.
inherited
failUrn(Object error) → void: Completes this resource URN with an error when registration fails.
inherited
getProvider(String moduleMember) → ProviderResource?: Returns provider for moduleMember's package, if configured.
inherited
getResourceName() → String: Returns this resource's logical name.
inherited
getResourceType() → String: Returns this resource's Pulumi type token.
inherited
noSuchMethod(Invocation invocation) → dynamic: Invoked when a nonexistent method or property is accessed.
inherited
registerOutput<T>(String propertyName, {Object? decoder(Object?)?, bool isSecret = false}) → Output<T>: Registers a dynamic output property for this resource.
inherited
resolveId(String? value, {required bool isKnown}) → void: Resolves the provider-assigned ID for this resource.
inherited
resolveOutputs(Struct outputs) → void: Resolves all output properties from a monitor response payload.
inherited
resolveUrn(String value) → void: Resolves this resource's URN once assigned by the engine.
inherited
serializeProperties(Map<String, dynamic> properties) → Future<Struct>: Serializes resource properties for RPC transmission.
inherited
toString() → String: A string representation of this object.
inherited

Operators

operator ==(Object other) → bool: The equality operator.
inherited

Static Methods

get(String name, Input<String> id, {AiFeatureStoreEntityTypeState? state, CustomResourceOptions? options}) → AiFeatureStoreEntityType: Gets an existing AiFeatureStoreEntityType resource's state with the given name and id.