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

package generated_program;
import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.gcp.vertex.AiFeatureStore;
import com.pulumi.gcp.vertex.AiFeatureStoreArgs;
import com.pulumi.gcp.vertex.inputs.AiFeatureStoreOnlineServingConfigArgs;
import com.pulumi.gcp.vertex.inputs.AiFeatureStoreEncryptionSpecArgs;
import com.pulumi.gcp.vertex.AiFeatureStoreEntityType;
import com.pulumi.gcp.vertex.AiFeatureStoreEntityTypeArgs;
import com.pulumi.gcp.vertex.inputs.AiFeatureStoreEntityTypeMonitoringConfigArgs;
import com.pulumi.gcp.vertex.inputs.AiFeatureStoreEntityTypeMonitoringConfigSnapshotAnalysisArgs;
import com.pulumi.gcp.vertex.inputs.AiFeatureStoreEntityTypeMonitoringConfigNumericalThresholdConfigArgs;
import com.pulumi.gcp.vertex.inputs.AiFeatureStoreEntityTypeMonitoringConfigCategoricalThresholdConfigArgs;
import com.pulumi.gcp.vertex.inputs.AiFeatureStoreEntityTypeMonitoringConfigImportFeaturesAnalysisArgs;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;
public class App {
    public static void main(String[] args) {
        Pulumi.run(App::stack);
    }
    public static void stack(Context ctx) {
        var featurestore = new AiFeatureStore("featurestore", AiFeatureStoreArgs.builder()
            .labels(Map.of("foo", "bar"))
            .region("us-central1")
            .onlineServingConfig(AiFeatureStoreOnlineServingConfigArgs.builder()
                .fixedNodeCount(2)
                .build())
            .encryptionSpec(AiFeatureStoreEncryptionSpecArgs.builder()
                .kmsKeyName("kms-name")
                .build())
            .build());
        var entity = new AiFeatureStoreEntityType("entity", AiFeatureStoreEntityTypeArgs.builder()
            .labels(Map.of("foo", "bar"))
            .description("test description")
            .featurestore(featurestore.id())
            .monitoringConfig(AiFeatureStoreEntityTypeMonitoringConfigArgs.builder()
                .snapshotAnalysis(AiFeatureStoreEntityTypeMonitoringConfigSnapshotAnalysisArgs.builder()
                    .disabled(false)
                    .monitoringIntervalDays(1)
                    .stalenessDays(21)
                    .build())
                .numericalThresholdConfig(AiFeatureStoreEntityTypeMonitoringConfigNumericalThresholdConfigArgs.builder()
                    .value(0.8)
                    .build())
                .categoricalThresholdConfig(AiFeatureStoreEntityTypeMonitoringConfigCategoricalThresholdConfigArgs.builder()
                    .value(10)
                    .build())
                .importFeaturesAnalysis(AiFeatureStoreEntityTypeMonitoringConfigImportFeaturesAnalysisArgs.builder()
                    .state("ENABLED")
                    .anomalyDetectionBaseline("PREVIOUS_IMPORT_FEATURES_STATS")
                    .build())
                .build())
            .build());
    }
}
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Vertex Ai Featurestore Entitytype With Beta Fields

package generated_program;
import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.gcp.vertex.AiFeatureStore;
import com.pulumi.gcp.vertex.AiFeatureStoreArgs;
import com.pulumi.gcp.vertex.inputs.AiFeatureStoreOnlineServingConfigArgs;
import com.pulumi.gcp.vertex.inputs.AiFeatureStoreEncryptionSpecArgs;
import com.pulumi.gcp.vertex.AiFeatureStoreEntityType;
import com.pulumi.gcp.vertex.AiFeatureStoreEntityTypeArgs;
import com.pulumi.gcp.vertex.inputs.AiFeatureStoreEntityTypeMonitoringConfigArgs;
import com.pulumi.gcp.vertex.inputs.AiFeatureStoreEntityTypeMonitoringConfigSnapshotAnalysisArgs;
import com.pulumi.gcp.vertex.inputs.AiFeatureStoreEntityTypeMonitoringConfigCategoricalThresholdConfigArgs;
import com.pulumi.gcp.vertex.inputs.AiFeatureStoreEntityTypeMonitoringConfigNumericalThresholdConfigArgs;
import com.pulumi.resources.CustomResourceOptions;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;
public class App {
    public static void main(String[] args) {
        Pulumi.run(App::stack);
    }
    public static void stack(Context ctx) {
        var featurestore = new AiFeatureStore("featurestore", AiFeatureStoreArgs.builder()
            .labels(Map.of("foo", "bar"))
            .region("us-central1")
            .onlineServingConfig(AiFeatureStoreOnlineServingConfigArgs.builder()
                .fixedNodeCount(2)
                .build())
            .encryptionSpec(AiFeatureStoreEncryptionSpecArgs.builder()
                .kmsKeyName("kms-name")
                .build())
            .build(), CustomResourceOptions.builder()
                .provider(google_beta)
                .build());
        var entity = new AiFeatureStoreEntityType("entity", AiFeatureStoreEntityTypeArgs.builder()
            .labels(Map.of("foo", "bar"))
            .featurestore(featurestore.id())
            .monitoringConfig(AiFeatureStoreEntityTypeMonitoringConfigArgs.builder()
                .snapshotAnalysis(AiFeatureStoreEntityTypeMonitoringConfigSnapshotAnalysisArgs.builder()
                    .disabled(false)
                    .monitoringInterval("86400s")
                    .build())
                .categoricalThresholdConfig(AiFeatureStoreEntityTypeMonitoringConfigCategoricalThresholdConfigArgs.builder()
                    .value(0.3)
                    .build())
                .numericalThresholdConfig(AiFeatureStoreEntityTypeMonitoringConfigNumericalThresholdConfigArgs.builder()
                    .value(0.3)
                    .build())
                .build())
            .offlineStorageTtlDays(30)
            .build(), CustomResourceOptions.builder()
                .provider(google_beta)
                .build());
    }
}
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Import

FeaturestoreEntitytype can be imported using any of these accepted formats:

$ 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.

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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