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Google Cloud Classic v7.27.0 published on Thursday, Jun 13, 2024 by Pulumi

gcp.vertex.AiFeatureStoreEntityType

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Google Cloud Classic v7.27.0 published on Thursday, Jun 13, 2024 by Pulumi

    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=gcp.vertex.AiFeatureStoreOnlineServingConfigArgs(
            fixed_node_count=2,
        ),
        encryption_spec=gcp.vertex.AiFeatureStoreEncryptionSpecArgs(
            kms_key_name="kms-name",
        ))
    entity = gcp.vertex.AiFeatureStoreEntityType("entity",
        name="terraform",
        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",
            ),
        ))
    
    package main
    
    import (
    	"github.com/pulumi/pulumi-gcp/sdk/v7/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(),
    			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
    	})
    }
    
    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,
                },
                ImportFeaturesAnalysis = new Gcp.Vertex.Inputs.AiFeatureStoreEntityTypeMonitoringConfigImportFeaturesAnalysisArgs
                {
                    State = "ENABLED",
                    AnomalyDetectionBaseline = "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.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()
                .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)
                        .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=gcp.vertex.AiFeatureStoreOnlineServingConfigArgs(
            fixed_node_count=2,
        ),
        encryption_spec=gcp.vertex.AiFeatureStoreEncryptionSpecArgs(
            kms_key_name="kms-name",
        ))
    entity = gcp.vertex.AiFeatureStoreEntityType("entity",
        name="terraform2",
        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)
    
    package main
    
    import (
    	"github.com/pulumi/pulumi-gcp/sdk/v7/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(),
    			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
    	})
    }
    
    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 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.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()
                .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
    

    Create AiFeatureStoreEntityType Resource

    Resources are created with functions called constructors. To learn more about declaring and configuring resources, see Resources.

    Constructor syntax

    new AiFeatureStoreEntityType(name: string, args: AiFeatureStoreEntityTypeArgs, opts?: CustomResourceOptions);
    @overload
    def AiFeatureStoreEntityType(resource_name: str,
                                 args: AiFeatureStoreEntityTypeArgs,
                                 opts: Optional[ResourceOptions] = None)
    
    @overload
    def AiFeatureStoreEntityType(resource_name: str,
                                 opts: Optional[ResourceOptions] = None,
                                 featurestore: Optional[str] = None,
                                 description: 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)
    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.
    
    

    Parameters

    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.

    Example

    The following reference example uses placeholder values for all input properties.

    var aiFeatureStoreEntityTypeResource = new Gcp.Vertex.AiFeatureStoreEntityType("aiFeatureStoreEntityTypeResource", new()
    {
        Featurestore = "string",
        Description = "string",
        Labels = 
        {
            { "string", "string" },
        },
        MonitoringConfig = new Gcp.Vertex.Inputs.AiFeatureStoreEntityTypeMonitoringConfigArgs
        {
            CategoricalThresholdConfig = new Gcp.Vertex.Inputs.AiFeatureStoreEntityTypeMonitoringConfigCategoricalThresholdConfigArgs
            {
                Value = 0,
            },
            ImportFeaturesAnalysis = new Gcp.Vertex.Inputs.AiFeatureStoreEntityTypeMonitoringConfigImportFeaturesAnalysisArgs
            {
                AnomalyDetectionBaseline = "string",
                State = "string",
            },
            NumericalThresholdConfig = new Gcp.Vertex.Inputs.AiFeatureStoreEntityTypeMonitoringConfigNumericalThresholdConfigArgs
            {
                Value = 0,
            },
            SnapshotAnalysis = new Gcp.Vertex.Inputs.AiFeatureStoreEntityTypeMonitoringConfigSnapshotAnalysisArgs
            {
                Disabled = false,
                MonitoringIntervalDays = 0,
                StalenessDays = 0,
            },
        },
        Name = "string",
        OfflineStorageTtlDays = 0,
    });
    
    example, err := vertex.NewAiFeatureStoreEntityType(ctx, "aiFeatureStoreEntityTypeResource", &vertex.AiFeatureStoreEntityTypeArgs{
    	Featurestore: pulumi.String("string"),
    	Description:  pulumi.String("string"),
    	Labels: pulumi.StringMap{
    		"string": pulumi.String("string"),
    	},
    	MonitoringConfig: &vertex.AiFeatureStoreEntityTypeMonitoringConfigArgs{
    		CategoricalThresholdConfig: &vertex.AiFeatureStoreEntityTypeMonitoringConfigCategoricalThresholdConfigArgs{
    			Value: pulumi.Float64(0),
    		},
    		ImportFeaturesAnalysis: &vertex.AiFeatureStoreEntityTypeMonitoringConfigImportFeaturesAnalysisArgs{
    			AnomalyDetectionBaseline: pulumi.String("string"),
    			State:                    pulumi.String("string"),
    		},
    		NumericalThresholdConfig: &vertex.AiFeatureStoreEntityTypeMonitoringConfigNumericalThresholdConfigArgs{
    			Value: pulumi.Float64(0),
    		},
    		SnapshotAnalysis: &vertex.AiFeatureStoreEntityTypeMonitoringConfigSnapshotAnalysisArgs{
    			Disabled:               pulumi.Bool(false),
    			MonitoringIntervalDays: pulumi.Int(0),
    			StalenessDays:          pulumi.Int(0),
    		},
    	},
    	Name:                  pulumi.String("string"),
    	OfflineStorageTtlDays: pulumi.Int(0),
    })
    
    var aiFeatureStoreEntityTypeResource = new AiFeatureStoreEntityType("aiFeatureStoreEntityTypeResource", AiFeatureStoreEntityTypeArgs.builder()
        .featurestore("string")
        .description("string")
        .labels(Map.of("string", "string"))
        .monitoringConfig(AiFeatureStoreEntityTypeMonitoringConfigArgs.builder()
            .categoricalThresholdConfig(AiFeatureStoreEntityTypeMonitoringConfigCategoricalThresholdConfigArgs.builder()
                .value(0)
                .build())
            .importFeaturesAnalysis(AiFeatureStoreEntityTypeMonitoringConfigImportFeaturesAnalysisArgs.builder()
                .anomalyDetectionBaseline("string")
                .state("string")
                .build())
            .numericalThresholdConfig(AiFeatureStoreEntityTypeMonitoringConfigNumericalThresholdConfigArgs.builder()
                .value(0)
                .build())
            .snapshotAnalysis(AiFeatureStoreEntityTypeMonitoringConfigSnapshotAnalysisArgs.builder()
                .disabled(false)
                .monitoringIntervalDays(0)
                .stalenessDays(0)
                .build())
            .build())
        .name("string")
        .offlineStorageTtlDays(0)
        .build());
    
    ai_feature_store_entity_type_resource = gcp.vertex.AiFeatureStoreEntityType("aiFeatureStoreEntityTypeResource",
        featurestore="string",
        description="string",
        labels={
            "string": "string",
        },
        monitoring_config=gcp.vertex.AiFeatureStoreEntityTypeMonitoringConfigArgs(
            categorical_threshold_config=gcp.vertex.AiFeatureStoreEntityTypeMonitoringConfigCategoricalThresholdConfigArgs(
                value=0,
            ),
            import_features_analysis=gcp.vertex.AiFeatureStoreEntityTypeMonitoringConfigImportFeaturesAnalysisArgs(
                anomaly_detection_baseline="string",
                state="string",
            ),
            numerical_threshold_config=gcp.vertex.AiFeatureStoreEntityTypeMonitoringConfigNumericalThresholdConfigArgs(
                value=0,
            ),
            snapshot_analysis=gcp.vertex.AiFeatureStoreEntityTypeMonitoringConfigSnapshotAnalysisArgs(
                disabled=False,
                monitoring_interval_days=0,
                staleness_days=0,
            ),
        ),
        name="string",
        offline_storage_ttl_days=0)
    
    const aiFeatureStoreEntityTypeResource = new gcp.vertex.AiFeatureStoreEntityType("aiFeatureStoreEntityTypeResource", {
        featurestore: "string",
        description: "string",
        labels: {
            string: "string",
        },
        monitoringConfig: {
            categoricalThresholdConfig: {
                value: 0,
            },
            importFeaturesAnalysis: {
                anomalyDetectionBaseline: "string",
                state: "string",
            },
            numericalThresholdConfig: {
                value: 0,
            },
            snapshotAnalysis: {
                disabled: false,
                monitoringIntervalDays: 0,
                stalenessDays: 0,
            },
        },
        name: "string",
        offlineStorageTtlDays: 0,
    });
    
    type: gcp:vertex:AiFeatureStoreEntityType
    properties:
        description: string
        featurestore: string
        labels:
            string: string
        monitoringConfig:
            categoricalThresholdConfig:
                value: 0
            importFeaturesAnalysis:
                anomalyDetectionBaseline: string
                state: string
            numericalThresholdConfig:
                value: 0
            snapshotAnalysis:
                disabled: false
                monitoringIntervalDays: 0
                stalenessDays: 0
        name: string
        offlineStorageTtlDays: 0
    

    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.

    Note: This field is non-authoritative, and will only manage the labels present in your configuration. Please refer to the field effective_labels for all of the labels present on the resource.

    MonitoringConfig 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.
    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.

    Note: This field is non-authoritative, and will only manage the labels present in your configuration. Please refer to the field effective_labels for all of the labels present on the resource.

    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.

    Note: This field is non-authoritative, and will only manage the labels present in your configuration. Please refer to the field effective_labels for all of the labels present on the resource.

    monitoringConfig 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.
    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.

    Note: This field is non-authoritative, and will only manage the labels present in your configuration. Please refer to the field effective_labels for all of the labels present on the resource.

    monitoringConfig 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.
    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.

    Note: This field is non-authoritative, and will only manage the labels present in your configuration. Please refer to the field effective_labels for all of the labels present on the resource.

    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.

    Note: This field is non-authoritative, and will only manage the labels present in your configuration. Please refer to the field effective_labels for all of the labels present on the resource.

    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.
    EffectiveLabels Dictionary<string, string>
    All of labels (key/value pairs) present on the resource in GCP, including the labels configured through Pulumi, other clients and services.
    Etag string
    Used to perform consistent read-modify-write updates.
    Id string
    The provider-assigned unique ID for this managed resource.
    PulumiLabels Dictionary<string, string>
    The combination of labels configured directly on the resource and default labels configured on the provider.
    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.
    EffectiveLabels 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.
    Etag string
    Used to perform consistent read-modify-write updates.
    Id string
    The provider-assigned unique ID for this managed resource.
    PulumiLabels map[string]string
    The combination of labels configured directly on the resource and default labels configured on the provider.
    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.
    effectiveLabels 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.
    etag String
    Used to perform consistent read-modify-write updates.
    id String
    The provider-assigned unique ID for this managed resource.
    pulumiLabels Map<String,String>
    The combination of labels configured directly on the resource and default labels configured on the provider.
    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.
    effectiveLabels {[key: string]: string}
    All of labels (key/value pairs) present on the resource in GCP, including the labels configured through Pulumi, other clients and services.
    etag string
    Used to perform consistent read-modify-write updates.
    id string
    The provider-assigned unique ID for this managed resource.
    pulumiLabels {[key: string]: string}
    The combination of labels configured directly on the resource and default labels configured on the provider.
    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.
    effective_labels Mapping[str, str]
    All of labels (key/value pairs) present on the resource in GCP, including the labels configured through Pulumi, other clients and services.
    etag str
    Used to perform consistent read-modify-write updates.
    id str
    The provider-assigned unique ID for this managed resource.
    pulumi_labels Mapping[str, str]
    The combination of labels configured directly on the resource and default labels configured on the provider.
    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.
    effectiveLabels Map<String>
    All of labels (key/value pairs) present on the resource in GCP, including the labels configured through Pulumi, other clients and services.
    etag String
    Used to perform consistent read-modify-write updates.
    id String
    The provider-assigned unique ID for this managed resource.
    pulumiLabels Map<String>
    The combination of labels configured directly on the resource and default labels configured on the provider.
    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,
            effective_labels: Optional[Mapping[str, 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,
            pulumi_labels: Optional[Mapping[str, str]] = 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.
    EffectiveLabels Dictionary<string, string>
    All of labels (key/value pairs) present on the resource in GCP, including the labels configured through Pulumi, other clients and services.
    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.

    Note: This field is non-authoritative, and will only manage the labels present in your configuration. Please refer to the field effective_labels for all of the labels present on the resource.

    MonitoringConfig 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.
    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.
    PulumiLabels Dictionary<string, string>
    The combination of labels configured directly on the resource and default labels configured on the provider.
    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.
    EffectiveLabels 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.
    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.

    Note: This field is non-authoritative, and will only manage the labels present in your configuration. Please refer to the field effective_labels for all of the labels present on the resource.

    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.
    PulumiLabels map[string]string
    The combination of labels configured directly on the resource and default labels configured on the provider.
    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.
    effectiveLabels 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.
    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.

    Note: This field is non-authoritative, and will only manage the labels present in your configuration. Please refer to the field effective_labels for all of the labels present on the resource.

    monitoringConfig 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.
    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.
    pulumiLabels Map<String,String>
    The combination of labels configured directly on the resource and default labels configured on the provider.
    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.
    effectiveLabels {[key: string]: string}
    All of labels (key/value pairs) present on the resource in GCP, including the labels configured through Pulumi, other clients and services.
    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.

    Note: This field is non-authoritative, and will only manage the labels present in your configuration. Please refer to the field effective_labels for all of the labels present on the resource.

    monitoringConfig 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.
    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.
    pulumiLabels {[key: string]: string}
    The combination of labels configured directly on the resource and default labels configured on the provider.
    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.
    effective_labels Mapping[str, str]
    All of labels (key/value pairs) present on the resource in GCP, including the labels configured through Pulumi, other clients and services.
    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.

    Note: This field is non-authoritative, and will only manage the labels present in your configuration. Please refer to the field effective_labels for all of the labels present on the resource.

    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.
    pulumi_labels Mapping[str, str]
    The combination of labels configured directly on the resource and default labels configured on the provider.
    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.
    effectiveLabels Map<String>
    All of labels (key/value pairs) present on the resource in GCP, including the labels configured through Pulumi, other clients and services.
    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.

    Note: This field is non-authoritative, and will only manage the labels present in your configuration. Please refer to the field effective_labels for all of the labels present on the resource.

    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.
    pulumiLabels Map<String>
    The combination of labels configured directly on the resource and default labels configured on the provider.
    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, AiFeatureStoreEntityTypeMonitoringConfigArgs

    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, AiFeatureStoreEntityTypeMonitoringConfigCategoricalThresholdConfigArgs

    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, AiFeatureStoreEntityTypeMonitoringConfigImportFeaturesAnalysisArgs

    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, AiFeatureStoreEntityTypeMonitoringConfigNumericalThresholdConfigArgs

    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, AiFeatureStoreEntityTypeMonitoringConfigSnapshotAnalysisArgs

    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

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

    Warning: monitoring_interval is deprecated and will be removed in a future release.

    Deprecated: monitoring_interval is deprecated and will be removed 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

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

    Warning: monitoring_interval is deprecated and will be removed in a future release.

    Deprecated: monitoring_interval is deprecated and will be removed 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

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

    Warning: monitoring_interval is deprecated and will be removed in a future release.

    Deprecated: monitoring_interval is deprecated and will be removed 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

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

    Warning: monitoring_interval is deprecated and will be removed in a future release.

    Deprecated: monitoring_interval is deprecated and will be removed 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

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

    Warning: monitoring_interval is deprecated and will be removed in a future release.

    Deprecated: monitoring_interval is deprecated and will be removed 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

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

    Warning: monitoring_interval is deprecated and will be removed in a future release.

    Deprecated: monitoring_interval is deprecated and will be removed 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:

    • {{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}}
    

    To learn more about importing existing cloud resources, see Importing resources.

    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.
    gcp logo
    Google Cloud Classic v7.27.0 published on Thursday, Jun 13, 2024 by Pulumi