pulumi-gcp-kotlin/com.pulumi.gcp.vertex.kotlin/AiFeatureStoreEntityTypeArgs

AiFeatureStoreEntityTypeArgs

data class AiFeatureStoreEntityTypeArgs(val description: Output<String>? = null, val featurestore: Output<String>? = null, val labels: Output<Map<String, String>>? = null, val monitoringConfig: Output<AiFeatureStoreEntityTypeMonitoringConfigArgs>? = null, val name: Output<String>? = null, val offlineStorageTtlDays: Output<Int>? = null) : ConvertibleToJava<AiFeatureStoreEntityTypeArgs>

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",
        },
    },
});
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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": 10,
        },
        "import_features_analysis": {
            "state": "ENABLED",
            "anomaly_detection_baseline": "PREVIOUS_IMPORT_FEATURES_STATS",
        },
    })
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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",
            },
        },
    });
});
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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
	})
}
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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());
    }
}
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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
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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,
});
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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)
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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,
    });
});
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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
	})
}
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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());
    }
}
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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
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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}}
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Constructors

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constructor(description: Output<String>? = null, featurestore: Output<String>? = null, labels: Output<Map<String, String>>? = null, monitoringConfig: Output<AiFeatureStoreEntityTypeMonitoringConfigArgs>? = null, name: Output<String>? = null, offlineStorageTtlDays: Output<Int>? = null)

Properties

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val description: Output<String>? = null

Optional. Description of the EntityType.

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val featurestore: Output<String>? = null

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

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val labels: Output<Map<String, String>>? = null

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.

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val monitoringConfig: Output<AiFeatureStoreEntityTypeMonitoringConfigArgs>? = null

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.

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val name: Output<String>? = null

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.

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val offlineStorageTtlDays: Output<Int>? = null

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.

Functions

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open override fun toJava(): AiFeatureStoreEntityTypeArgs