gcp.vertex.AiFeatureStoreEntityType
Explore with Pulumi AI
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:
- API documentation
- How-to Guides
Example Usage
Vertex Ai Featurestore Entitytype
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()
{
Labels =
{
{ "foo", "bar" },
},
Region = "us-central1",
OnlineServingConfig = new Gcp.Vertex.Inputs.AiFeatureStoreOnlineServingConfigArgs
{
FixedNodeCount = 2,
},
EncryptionSpec = new Gcp.Vertex.Inputs.AiFeatureStoreEncryptionSpecArgs
{
KmsKeyName = "kms-name",
},
});
var entity = new Gcp.Vertex.AiFeatureStoreEntityType("entity", new()
{
Labels =
{
{ "foo", "bar" },
},
Description = "test description",
Featurestore = featurestore.Id,
MonitoringConfig = new Gcp.Vertex.Inputs.AiFeatureStoreEntityTypeMonitoringConfigArgs
{
SnapshotAnalysis = new Gcp.Vertex.Inputs.AiFeatureStoreEntityTypeMonitoringConfigSnapshotAnalysisArgs
{
Disabled = false,
MonitoringIntervalDays = 1,
StalenessDays = 21,
},
NumericalThresholdConfig = new Gcp.Vertex.Inputs.AiFeatureStoreEntityTypeMonitoringConfigNumericalThresholdConfigArgs
{
Value = 0.8,
},
CategoricalThresholdConfig = new Gcp.Vertex.Inputs.AiFeatureStoreEntityTypeMonitoringConfigCategoricalThresholdConfigArgs
{
Value = 10,
},
ImportFeaturesAnalysis = new Gcp.Vertex.Inputs.AiFeatureStoreEntityTypeMonitoringConfigImportFeaturesAnalysisArgs
{
State = "ENABLED",
AnomalyDetectionBaseline = "PREVIOUS_IMPORT_FEATURES_STATS",
},
},
});
});
package main
import (
"github.com/pulumi/pulumi-gcp/sdk/v6/go/gcp/vertex"
"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)
func main() {
pulumi.Run(func(ctx *pulumi.Context) error {
featurestore, err := vertex.NewAiFeatureStore(ctx, "featurestore", &vertex.AiFeatureStoreArgs{
Labels: pulumi.StringMap{
"foo": pulumi.String("bar"),
},
Region: pulumi.String("us-central1"),
OnlineServingConfig: &vertex.AiFeatureStoreOnlineServingConfigArgs{
FixedNodeCount: pulumi.Int(2),
},
EncryptionSpec: &vertex.AiFeatureStoreEncryptionSpecArgs{
KmsKeyName: pulumi.String("kms-name"),
},
})
if err != nil {
return err
}
_, err = vertex.NewAiFeatureStoreEntityType(ctx, "entity", &vertex.AiFeatureStoreEntityTypeArgs{
Labels: pulumi.StringMap{
"foo": pulumi.String("bar"),
},
Description: pulumi.String("test description"),
Featurestore: featurestore.ID(),
MonitoringConfig: &vertex.AiFeatureStoreEntityTypeMonitoringConfigArgs{
SnapshotAnalysis: &vertex.AiFeatureStoreEntityTypeMonitoringConfigSnapshotAnalysisArgs{
Disabled: pulumi.Bool(false),
MonitoringIntervalDays: pulumi.Int(1),
StalenessDays: pulumi.Int(21),
},
NumericalThresholdConfig: &vertex.AiFeatureStoreEntityTypeMonitoringConfigNumericalThresholdConfigArgs{
Value: pulumi.Float64(0.8),
},
CategoricalThresholdConfig: &vertex.AiFeatureStoreEntityTypeMonitoringConfigCategoricalThresholdConfigArgs{
Value: pulumi.Float64(10),
},
ImportFeaturesAnalysis: &vertex.AiFeatureStoreEntityTypeMonitoringConfigImportFeaturesAnalysisArgs{
State: pulumi.String("ENABLED"),
AnomalyDetectionBaseline: pulumi.String("PREVIOUS_IMPORT_FEATURES_STATS"),
},
},
})
if err != nil {
return err
}
return nil
})
}
package generated_program;
import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.gcp.vertex.AiFeatureStore;
import com.pulumi.gcp.vertex.AiFeatureStoreArgs;
import com.pulumi.gcp.vertex.inputs.AiFeatureStoreOnlineServingConfigArgs;
import com.pulumi.gcp.vertex.inputs.AiFeatureStoreEncryptionSpecArgs;
import com.pulumi.gcp.vertex.AiFeatureStoreEntityType;
import com.pulumi.gcp.vertex.AiFeatureStoreEntityTypeArgs;
import com.pulumi.gcp.vertex.inputs.AiFeatureStoreEntityTypeMonitoringConfigArgs;
import com.pulumi.gcp.vertex.inputs.AiFeatureStoreEntityTypeMonitoringConfigSnapshotAnalysisArgs;
import com.pulumi.gcp.vertex.inputs.AiFeatureStoreEntityTypeMonitoringConfigNumericalThresholdConfigArgs;
import com.pulumi.gcp.vertex.inputs.AiFeatureStoreEntityTypeMonitoringConfigCategoricalThresholdConfigArgs;
import com.pulumi.gcp.vertex.inputs.AiFeatureStoreEntityTypeMonitoringConfigImportFeaturesAnalysisArgs;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;
public class App {
public static void main(String[] args) {
Pulumi.run(App::stack);
}
public static void stack(Context ctx) {
var featurestore = new AiFeatureStore("featurestore", AiFeatureStoreArgs.builder()
.labels(Map.of("foo", "bar"))
.region("us-central1")
.onlineServingConfig(AiFeatureStoreOnlineServingConfigArgs.builder()
.fixedNodeCount(2)
.build())
.encryptionSpec(AiFeatureStoreEncryptionSpecArgs.builder()
.kmsKeyName("kms-name")
.build())
.build());
var entity = new AiFeatureStoreEntityType("entity", AiFeatureStoreEntityTypeArgs.builder()
.labels(Map.of("foo", "bar"))
.description("test description")
.featurestore(featurestore.id())
.monitoringConfig(AiFeatureStoreEntityTypeMonitoringConfigArgs.builder()
.snapshotAnalysis(AiFeatureStoreEntityTypeMonitoringConfigSnapshotAnalysisArgs.builder()
.disabled(false)
.monitoringIntervalDays(1)
.stalenessDays(21)
.build())
.numericalThresholdConfig(AiFeatureStoreEntityTypeMonitoringConfigNumericalThresholdConfigArgs.builder()
.value(0.8)
.build())
.categoricalThresholdConfig(AiFeatureStoreEntityTypeMonitoringConfigCategoricalThresholdConfigArgs.builder()
.value(10)
.build())
.importFeaturesAnalysis(AiFeatureStoreEntityTypeMonitoringConfigImportFeaturesAnalysisArgs.builder()
.state("ENABLED")
.anomalyDetectionBaseline("PREVIOUS_IMPORT_FEATURES_STATS")
.build())
.build())
.build());
}
}
import pulumi
import pulumi_gcp as gcp
featurestore = gcp.vertex.AiFeatureStore("featurestore",
labels={
"foo": "bar",
},
region="us-central1",
online_serving_config=gcp.vertex.AiFeatureStoreOnlineServingConfigArgs(
fixed_node_count=2,
),
encryption_spec=gcp.vertex.AiFeatureStoreEncryptionSpecArgs(
kms_key_name="kms-name",
))
entity = gcp.vertex.AiFeatureStoreEntityType("entity",
labels={
"foo": "bar",
},
description="test description",
featurestore=featurestore.id,
monitoring_config=gcp.vertex.AiFeatureStoreEntityTypeMonitoringConfigArgs(
snapshot_analysis=gcp.vertex.AiFeatureStoreEntityTypeMonitoringConfigSnapshotAnalysisArgs(
disabled=False,
monitoring_interval_days=1,
staleness_days=21,
),
numerical_threshold_config=gcp.vertex.AiFeatureStoreEntityTypeMonitoringConfigNumericalThresholdConfigArgs(
value=0.8,
),
categorical_threshold_config=gcp.vertex.AiFeatureStoreEntityTypeMonitoringConfigCategoricalThresholdConfigArgs(
value=10,
),
import_features_analysis=gcp.vertex.AiFeatureStoreEntityTypeMonitoringConfigImportFeaturesAnalysisArgs(
state="ENABLED",
anomaly_detection_baseline="PREVIOUS_IMPORT_FEATURES_STATS",
),
))
import * as pulumi from "@pulumi/pulumi";
import * as gcp from "@pulumi/gcp";
const featurestore = new gcp.vertex.AiFeatureStore("featurestore", {
labels: {
foo: "bar",
},
region: "us-central1",
onlineServingConfig: {
fixedNodeCount: 2,
},
encryptionSpec: {
kmsKeyName: "kms-name",
},
});
const entity = new gcp.vertex.AiFeatureStoreEntityType("entity", {
labels: {
foo: "bar",
},
description: "test description",
featurestore: featurestore.id,
monitoringConfig: {
snapshotAnalysis: {
disabled: false,
monitoringIntervalDays: 1,
stalenessDays: 21,
},
numericalThresholdConfig: {
value: 0.8,
},
categoricalThresholdConfig: {
value: 10,
},
importFeaturesAnalysis: {
state: "ENABLED",
anomalyDetectionBaseline: "PREVIOUS_IMPORT_FEATURES_STATS",
},
},
});
resources:
featurestore:
type: gcp:vertex:AiFeatureStore
properties:
labels:
foo: bar
region: us-central1
onlineServingConfig:
fixedNodeCount: 2
encryptionSpec:
kmsKeyName: kms-name
entity:
type: gcp:vertex:AiFeatureStoreEntityType
properties:
labels:
foo: bar
description: test description
featurestore: ${featurestore.id}
monitoringConfig:
snapshotAnalysis:
disabled: false
monitoringIntervalDays: 1
stalenessDays: 21
numericalThresholdConfig:
value: 0.8
categoricalThresholdConfig:
value: 10
importFeaturesAnalysis:
state: ENABLED
anomalyDetectionBaseline: PREVIOUS_IMPORT_FEATURES_STATS
Vertex Ai Featurestore Entitytype With Beta Fields
using System.Collections.Generic;
using System.Linq;
using Pulumi;
using Gcp = Pulumi.Gcp;
return await Deployment.RunAsync(() =>
{
var featurestore = new Gcp.Vertex.AiFeatureStore("featurestore", new()
{
Labels =
{
{ "foo", "bar" },
},
Region = "us-central1",
OnlineServingConfig = new Gcp.Vertex.Inputs.AiFeatureStoreOnlineServingConfigArgs
{
FixedNodeCount = 2,
},
EncryptionSpec = new Gcp.Vertex.Inputs.AiFeatureStoreEncryptionSpecArgs
{
KmsKeyName = "kms-name",
},
}, new CustomResourceOptions
{
Provider = google_beta,
});
var entity = new Gcp.Vertex.AiFeatureStoreEntityType("entity", new()
{
Labels =
{
{ "foo", "bar" },
},
Featurestore = featurestore.Id,
MonitoringConfig = new Gcp.Vertex.Inputs.AiFeatureStoreEntityTypeMonitoringConfigArgs
{
SnapshotAnalysis = new Gcp.Vertex.Inputs.AiFeatureStoreEntityTypeMonitoringConfigSnapshotAnalysisArgs
{
Disabled = false,
MonitoringInterval = "86400s",
},
CategoricalThresholdConfig = new Gcp.Vertex.Inputs.AiFeatureStoreEntityTypeMonitoringConfigCategoricalThresholdConfigArgs
{
Value = 0.3,
},
NumericalThresholdConfig = new Gcp.Vertex.Inputs.AiFeatureStoreEntityTypeMonitoringConfigNumericalThresholdConfigArgs
{
Value = 0.3,
},
},
OfflineStorageTtlDays = 30,
}, new CustomResourceOptions
{
Provider = google_beta,
});
});
package main
import (
"github.com/pulumi/pulumi-gcp/sdk/v6/go/gcp/vertex"
"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)
func main() {
pulumi.Run(func(ctx *pulumi.Context) error {
featurestore, err := vertex.NewAiFeatureStore(ctx, "featurestore", &vertex.AiFeatureStoreArgs{
Labels: pulumi.StringMap{
"foo": pulumi.String("bar"),
},
Region: pulumi.String("us-central1"),
OnlineServingConfig: &vertex.AiFeatureStoreOnlineServingConfigArgs{
FixedNodeCount: pulumi.Int(2),
},
EncryptionSpec: &vertex.AiFeatureStoreEncryptionSpecArgs{
KmsKeyName: pulumi.String("kms-name"),
},
}, pulumi.Provider(google_beta))
if err != nil {
return err
}
_, err = vertex.NewAiFeatureStoreEntityType(ctx, "entity", &vertex.AiFeatureStoreEntityTypeArgs{
Labels: pulumi.StringMap{
"foo": pulumi.String("bar"),
},
Featurestore: featurestore.ID(),
MonitoringConfig: &vertex.AiFeatureStoreEntityTypeMonitoringConfigArgs{
SnapshotAnalysis: &vertex.AiFeatureStoreEntityTypeMonitoringConfigSnapshotAnalysisArgs{
Disabled: pulumi.Bool(false),
MonitoringInterval: pulumi.String("86400s"),
},
CategoricalThresholdConfig: &vertex.AiFeatureStoreEntityTypeMonitoringConfigCategoricalThresholdConfigArgs{
Value: pulumi.Float64(0.3),
},
NumericalThresholdConfig: &vertex.AiFeatureStoreEntityTypeMonitoringConfigNumericalThresholdConfigArgs{
Value: pulumi.Float64(0.3),
},
},
OfflineStorageTtlDays: pulumi.Int(30),
}, pulumi.Provider(google_beta))
if err != nil {
return err
}
return nil
})
}
package generated_program;
import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.gcp.vertex.AiFeatureStore;
import com.pulumi.gcp.vertex.AiFeatureStoreArgs;
import com.pulumi.gcp.vertex.inputs.AiFeatureStoreOnlineServingConfigArgs;
import com.pulumi.gcp.vertex.inputs.AiFeatureStoreEncryptionSpecArgs;
import com.pulumi.gcp.vertex.AiFeatureStoreEntityType;
import com.pulumi.gcp.vertex.AiFeatureStoreEntityTypeArgs;
import com.pulumi.gcp.vertex.inputs.AiFeatureStoreEntityTypeMonitoringConfigArgs;
import com.pulumi.gcp.vertex.inputs.AiFeatureStoreEntityTypeMonitoringConfigSnapshotAnalysisArgs;
import com.pulumi.gcp.vertex.inputs.AiFeatureStoreEntityTypeMonitoringConfigCategoricalThresholdConfigArgs;
import com.pulumi.gcp.vertex.inputs.AiFeatureStoreEntityTypeMonitoringConfigNumericalThresholdConfigArgs;
import com.pulumi.resources.CustomResourceOptions;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;
public class App {
public static void main(String[] args) {
Pulumi.run(App::stack);
}
public static void stack(Context ctx) {
var featurestore = new AiFeatureStore("featurestore", AiFeatureStoreArgs.builder()
.labels(Map.of("foo", "bar"))
.region("us-central1")
.onlineServingConfig(AiFeatureStoreOnlineServingConfigArgs.builder()
.fixedNodeCount(2)
.build())
.encryptionSpec(AiFeatureStoreEncryptionSpecArgs.builder()
.kmsKeyName("kms-name")
.build())
.build(), CustomResourceOptions.builder()
.provider(google_beta)
.build());
var entity = new AiFeatureStoreEntityType("entity", AiFeatureStoreEntityTypeArgs.builder()
.labels(Map.of("foo", "bar"))
.featurestore(featurestore.id())
.monitoringConfig(AiFeatureStoreEntityTypeMonitoringConfigArgs.builder()
.snapshotAnalysis(AiFeatureStoreEntityTypeMonitoringConfigSnapshotAnalysisArgs.builder()
.disabled(false)
.monitoringInterval("86400s")
.build())
.categoricalThresholdConfig(AiFeatureStoreEntityTypeMonitoringConfigCategoricalThresholdConfigArgs.builder()
.value(0.3)
.build())
.numericalThresholdConfig(AiFeatureStoreEntityTypeMonitoringConfigNumericalThresholdConfigArgs.builder()
.value(0.3)
.build())
.build())
.offlineStorageTtlDays(30)
.build(), CustomResourceOptions.builder()
.provider(google_beta)
.build());
}
}
import pulumi
import pulumi_gcp as gcp
featurestore = gcp.vertex.AiFeatureStore("featurestore",
labels={
"foo": "bar",
},
region="us-central1",
online_serving_config=gcp.vertex.AiFeatureStoreOnlineServingConfigArgs(
fixed_node_count=2,
),
encryption_spec=gcp.vertex.AiFeatureStoreEncryptionSpecArgs(
kms_key_name="kms-name",
),
opts=pulumi.ResourceOptions(provider=google_beta))
entity = gcp.vertex.AiFeatureStoreEntityType("entity",
labels={
"foo": "bar",
},
featurestore=featurestore.id,
monitoring_config=gcp.vertex.AiFeatureStoreEntityTypeMonitoringConfigArgs(
snapshot_analysis=gcp.vertex.AiFeatureStoreEntityTypeMonitoringConfigSnapshotAnalysisArgs(
disabled=False,
monitoring_interval="86400s",
),
categorical_threshold_config=gcp.vertex.AiFeatureStoreEntityTypeMonitoringConfigCategoricalThresholdConfigArgs(
value=0.3,
),
numerical_threshold_config=gcp.vertex.AiFeatureStoreEntityTypeMonitoringConfigNumericalThresholdConfigArgs(
value=0.3,
),
),
offline_storage_ttl_days=30,
opts=pulumi.ResourceOptions(provider=google_beta))
import * as pulumi from "@pulumi/pulumi";
import * as gcp from "@pulumi/gcp";
const featurestore = new gcp.vertex.AiFeatureStore("featurestore", {
labels: {
foo: "bar",
},
region: "us-central1",
onlineServingConfig: {
fixedNodeCount: 2,
},
encryptionSpec: {
kmsKeyName: "kms-name",
},
}, {
provider: google_beta,
});
const entity = new gcp.vertex.AiFeatureStoreEntityType("entity", {
labels: {
foo: "bar",
},
featurestore: featurestore.id,
monitoringConfig: {
snapshotAnalysis: {
disabled: false,
monitoringInterval: "86400s",
},
categoricalThresholdConfig: {
value: 0.3,
},
numericalThresholdConfig: {
value: 0.3,
},
},
offlineStorageTtlDays: 30,
}, {
provider: google_beta,
});
resources:
featurestore:
type: gcp:vertex:AiFeatureStore
properties:
labels:
foo: bar
region: us-central1
onlineServingConfig:
fixedNodeCount: 2
encryptionSpec:
kmsKeyName: kms-name
options:
provider: ${["google-beta"]}
entity:
type: gcp:vertex:AiFeatureStoreEntityType
properties:
labels:
foo: bar
featurestore: ${featurestore.id}
monitoringConfig:
snapshotAnalysis:
disabled: false
monitoringInterval: 86400s
categoricalThresholdConfig:
value: 0.3
numericalThresholdConfig:
value: 0.3
offlineStorageTtlDays: 30
options:
provider: ${["google-beta"]}
Create AiFeatureStoreEntityType Resource
new AiFeatureStoreEntityType(name: string, args: AiFeatureStoreEntityTypeArgs, opts?: CustomResourceOptions);
@overload
def AiFeatureStoreEntityType(resource_name: str,
opts: Optional[ResourceOptions] = None,
description: Optional[str] = None,
featurestore: Optional[str] = None,
labels: Optional[Mapping[str, str]] = None,
monitoring_config: Optional[AiFeatureStoreEntityTypeMonitoringConfigArgs] = None,
name: Optional[str] = None,
offline_storage_ttl_days: Optional[int] = None)
@overload
def AiFeatureStoreEntityType(resource_name: str,
args: AiFeatureStoreEntityTypeArgs,
opts: Optional[ResourceOptions] = None)
func NewAiFeatureStoreEntityType(ctx *Context, name string, args AiFeatureStoreEntityTypeArgs, opts ...ResourceOption) (*AiFeatureStoreEntityType, error)
public AiFeatureStoreEntityType(string name, AiFeatureStoreEntityTypeArgs args, CustomResourceOptions? opts = null)
public AiFeatureStoreEntityType(String name, AiFeatureStoreEntityTypeArgs args)
public AiFeatureStoreEntityType(String name, AiFeatureStoreEntityTypeArgs args, CustomResourceOptions options)
type: gcp:vertex:AiFeatureStoreEntityType
properties: # The arguments to resource properties.
options: # Bag of options to control resource's behavior.
- name string
- The unique name of the resource.
- args AiFeatureStoreEntityTypeArgs
- The arguments to resource properties.
- opts CustomResourceOptions
- Bag of options to control resource's behavior.
- resource_name str
- The unique name of the resource.
- args AiFeatureStoreEntityTypeArgs
- The arguments to resource properties.
- opts ResourceOptions
- Bag of options to control resource's behavior.
- ctx Context
- Context object for the current deployment.
- name string
- The unique name of the resource.
- args AiFeatureStoreEntityTypeArgs
- The arguments to resource properties.
- opts ResourceOption
- Bag of options to control resource's behavior.
- name string
- The unique name of the resource.
- args AiFeatureStoreEntityTypeArgs
- The arguments to resource properties.
- opts CustomResourceOptions
- Bag of options to control resource's behavior.
- name String
- The unique name of the resource.
- args AiFeatureStoreEntityTypeArgs
- The arguments to resource properties.
- options CustomResourceOptions
- Bag of options to control resource's behavior.
AiFeatureStoreEntityType Resource Properties
To learn more about resource properties and how to use them, see Inputs and Outputs in the Architecture and Concepts docs.
Inputs
The AiFeatureStoreEntityType resource accepts the following input properties:
- Featurestore string
The name of the Featurestore to use, in the format projects/{project}/locations/{location}/featurestores/{featurestore}.
- Description string
Optional. Description of the EntityType.
- Labels Dictionary<string, string>
A set of key/value label pairs to assign to this EntityType.
- Monitoring
Config AiFeature Store Entity Type Monitoring Config 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.
- Offline
Storage intTtl Days 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.
- Monitoring
Config AiFeature Store Entity Type Monitoring Config Args 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.
- Offline
Storage intTtl Days 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.
- monitoring
Config AiFeature Store Entity Type Monitoring Config 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.
- offline
Storage IntegerTtl Days 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.
- monitoring
Config AiFeature Store Entity Type Monitoring Config 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.
- offline
Storage numberTtl Days Config for data retention policy in offline storage. TTL in days for feature values that will be stored in offline storage. The Feature Store offline storage periodically removes obsolete feature values older than offlineStorageTtlDays since the feature generation time. If unset (or explicitly set to 0), default to 4000 days TTL.
- featurestore str
The name of the Featurestore to use, in the format projects/{project}/locations/{location}/featurestores/{featurestore}.
- description str
Optional. Description of the EntityType.
- labels Mapping[str, str]
A set of key/value label pairs to assign to this EntityType.
- monitoring_
config AiFeature Store Entity Type Monitoring Config Args 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_ intttl_ days 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.
- monitoring
Config 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.
- offline
Storage NumberTtl Days 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:
- Create
Time string The timestamp of when the featurestore was created in RFC3339 UTC "Zulu" format, with nanosecond resolution and up to nine fractional digits.
- Etag string
Used to perform consistent read-modify-write updates.
- Id string
The provider-assigned unique ID for this managed resource.
- Region string
The region of the EntityType.
- Update
Time 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 string The timestamp of when the featurestore was created in RFC3339 UTC "Zulu" format, with nanosecond resolution and up to nine fractional digits.
- Etag string
Used to perform consistent read-modify-write updates.
- Id string
The provider-assigned unique ID for this managed resource.
- Region string
The region of the EntityType.
- Update
Time 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 String The timestamp of when the featurestore was created in RFC3339 UTC "Zulu" format, with nanosecond resolution and up to nine fractional digits.
- etag String
Used to perform consistent read-modify-write updates.
- id String
The provider-assigned unique ID for this managed resource.
- region String
The region of the EntityType.
- update
Time 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 string The timestamp of when the featurestore was created in RFC3339 UTC "Zulu" format, with nanosecond resolution and up to nine fractional digits.
- etag string
Used to perform consistent read-modify-write updates.
- id string
The provider-assigned unique ID for this managed resource.
- region string
The region of the EntityType.
- update
Time string The timestamp of when the featurestore was last updated in RFC3339 UTC "Zulu" format, with nanosecond resolution and up to nine fractional digits.
- create_
time str The timestamp of when the featurestore was created in RFC3339 UTC "Zulu" format, with nanosecond resolution and up to nine fractional digits.
- etag str
Used to perform consistent read-modify-write updates.
- id str
The provider-assigned unique ID for this managed resource.
- region str
The region of the EntityType.
- update_
time str The timestamp of when the featurestore was last updated in RFC3339 UTC "Zulu" format, with nanosecond resolution and up to nine fractional digits.
- create
Time String The timestamp of when the featurestore was created in RFC3339 UTC "Zulu" format, with nanosecond resolution and up to nine fractional digits.
- etag String
Used to perform consistent read-modify-write updates.
- id String
The provider-assigned unique ID for this managed resource.
- region String
The region of the EntityType.
- update
Time String The timestamp of when the featurestore was last updated in RFC3339 UTC "Zulu" format, with nanosecond resolution and up to nine fractional digits.
Look up Existing AiFeatureStoreEntityType Resource
Get an existing AiFeatureStoreEntityType resource’s state with the given name, ID, and optional extra properties used to qualify the lookup.
public static get(name: string, id: Input<ID>, state?: AiFeatureStoreEntityTypeState, opts?: CustomResourceOptions): AiFeatureStoreEntityType
@staticmethod
def get(resource_name: str,
id: str,
opts: Optional[ResourceOptions] = None,
create_time: Optional[str] = None,
description: Optional[str] = None,
etag: Optional[str] = None,
featurestore: Optional[str] = None,
labels: Optional[Mapping[str, str]] = None,
monitoring_config: Optional[AiFeatureStoreEntityTypeMonitoringConfigArgs] = None,
name: Optional[str] = None,
offline_storage_ttl_days: Optional[int] = None,
region: Optional[str] = None,
update_time: Optional[str] = None) -> AiFeatureStoreEntityType
func GetAiFeatureStoreEntityType(ctx *Context, name string, id IDInput, state *AiFeatureStoreEntityTypeState, opts ...ResourceOption) (*AiFeatureStoreEntityType, error)
public static AiFeatureStoreEntityType Get(string name, Input<string> id, AiFeatureStoreEntityTypeState? state, CustomResourceOptions? opts = null)
public static AiFeatureStoreEntityType get(String name, Output<String> id, AiFeatureStoreEntityTypeState state, CustomResourceOptions options)
Resource lookup is not supported in YAML
- name
- The unique name of the resulting resource.
- id
- The unique provider ID of the resource to lookup.
- state
- Any extra arguments used during the lookup.
- opts
- A bag of options that control this resource's behavior.
- resource_name
- The unique name of the resulting resource.
- id
- The unique provider ID of the resource to lookup.
- name
- The unique name of the resulting resource.
- id
- The unique provider ID of the resource to lookup.
- state
- Any extra arguments used during the lookup.
- opts
- A bag of options that control this resource's behavior.
- name
- The unique name of the resulting resource.
- id
- The unique provider ID of the resource to lookup.
- state
- Any extra arguments used during the lookup.
- opts
- A bag of options that control this resource's behavior.
- name
- The unique name of the resulting resource.
- id
- The unique provider ID of the resource to lookup.
- state
- Any extra arguments used during the lookup.
- opts
- A bag of options that control this resource's behavior.
- Create
Time string The timestamp of when the featurestore was created in RFC3339 UTC "Zulu" format, with nanosecond resolution and up to nine fractional digits.
- Description string
Optional. Description of the EntityType.
- Etag string
Used to perform consistent read-modify-write updates.
- Featurestore string
The name of the Featurestore to use, in the format projects/{project}/locations/{location}/featurestores/{featurestore}.
- Labels Dictionary<string, string>
A set of key/value label pairs to assign to this EntityType.
- Monitoring
Config AiFeature Store Entity Type Monitoring Config 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.
- Offline
Storage intTtl Days Config for data retention policy in offline storage. TTL in days for feature values that will be stored in offline storage. The Feature Store offline storage periodically removes obsolete feature values older than offlineStorageTtlDays since the feature generation time. If unset (or explicitly set to 0), default to 4000 days TTL.
- Region string
The region of the EntityType.
- Update
Time 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 string The timestamp of when the featurestore was created in RFC3339 UTC "Zulu" format, with nanosecond resolution and up to nine fractional digits.
- Description string
Optional. Description of the EntityType.
- Etag string
Used to perform consistent read-modify-write updates.
- Featurestore string
The name of the Featurestore to use, in the format projects/{project}/locations/{location}/featurestores/{featurestore}.
- Labels map[string]string
A set of key/value label pairs to assign to this EntityType.
- Monitoring
Config AiFeature Store Entity Type Monitoring Config Args 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.
- Offline
Storage intTtl Days Config for data retention policy in offline storage. TTL in days for feature values that will be stored in offline storage. The Feature Store offline storage periodically removes obsolete feature values older than offlineStorageTtlDays since the feature generation time. If unset (or explicitly set to 0), default to 4000 days TTL.
- Region string
The region of the EntityType.
- Update
Time 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 String The timestamp of when the featurestore was created in RFC3339 UTC "Zulu" format, with nanosecond resolution and up to nine fractional digits.
- description String
Optional. Description of the EntityType.
- etag String
Used to perform consistent read-modify-write updates.
- featurestore String
The name of the Featurestore to use, in the format projects/{project}/locations/{location}/featurestores/{featurestore}.
- labels Map<String,String>
A set of key/value label pairs to assign to this EntityType.
- monitoring
Config AiFeature Store Entity Type Monitoring Config 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.
- offline
Storage IntegerTtl Days Config for data retention policy in offline storage. TTL in days for feature values that will be stored in offline storage. The Feature Store offline storage periodically removes obsolete feature values older than offlineStorageTtlDays since the feature generation time. If unset (or explicitly set to 0), default to 4000 days TTL.
- region String
The region of the EntityType.
- update
Time 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 string The timestamp of when the featurestore was created in RFC3339 UTC "Zulu" format, with nanosecond resolution and up to nine fractional digits.
- description string
Optional. Description of the EntityType.
- etag string
Used to perform consistent read-modify-write updates.
- featurestore string
The name of the Featurestore to use, in the format projects/{project}/locations/{location}/featurestores/{featurestore}.
- labels {[key: string]: string}
A set of key/value label pairs to assign to this EntityType.
- monitoring
Config AiFeature Store Entity Type Monitoring Config 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.
- offline
Storage numberTtl Days Config for data retention policy in offline storage. TTL in days for feature values that will be stored in offline storage. The Feature Store offline storage periodically removes obsolete feature values older than offlineStorageTtlDays since the feature generation time. If unset (or explicitly set to 0), default to 4000 days TTL.
- region string
The region of the EntityType.
- update
Time string The timestamp of when the featurestore was last updated in RFC3339 UTC "Zulu" format, with nanosecond resolution and up to nine fractional digits.
- create_
time str The timestamp of when the featurestore was created in RFC3339 UTC "Zulu" format, with nanosecond resolution and up to nine fractional digits.
- description str
Optional. Description of the EntityType.
- etag str
Used to perform consistent read-modify-write updates.
- featurestore str
The name of the Featurestore to use, in the format projects/{project}/locations/{location}/featurestores/{featurestore}.
- labels Mapping[str, str]
A set of key/value label pairs to assign to this EntityType.
- monitoring_
config AiFeature Store Entity Type Monitoring Config Args 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_ intttl_ days Config for data retention policy in offline storage. TTL in days for feature values that will be stored in offline storage. The Feature Store offline storage periodically removes obsolete feature values older than offlineStorageTtlDays since the feature generation time. If unset (or explicitly set to 0), default to 4000 days TTL.
- region str
The region of the EntityType.
- update_
time str The timestamp of when the featurestore was last updated in RFC3339 UTC "Zulu" format, with nanosecond resolution and up to nine fractional digits.
- create
Time String The timestamp of when the featurestore was created in RFC3339 UTC "Zulu" format, with nanosecond resolution and up to nine fractional digits.
- description String
Optional. Description of the EntityType.
- etag String
Used to perform consistent read-modify-write updates.
- featurestore String
The name of the Featurestore to use, in the format projects/{project}/locations/{location}/featurestores/{featurestore}.
- labels Map<String>
A set of key/value label pairs to assign to this EntityType.
- monitoring
Config 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.
- offline
Storage NumberTtl Days Config for data retention policy in offline storage. TTL in days for feature values that will be stored in offline storage. The Feature Store offline storage periodically removes obsolete feature values older than offlineStorageTtlDays since the feature generation time. If unset (or explicitly set to 0), default to 4000 days TTL.
- region String
The region of the EntityType.
- update
Time 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
- Categorical
Threshold AiConfig Feature Store Entity Type Monitoring Config Categorical Threshold Config 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 AiAnalysis Feature Store Entity Type Monitoring Config Import Features Analysis The config for ImportFeatures Analysis Based Feature Monitoring. Structure is documented below.
- Numerical
Threshold AiConfig Feature Store Entity Type Monitoring Config Numerical Threshold Config 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 AiFeature Store Entity Type Monitoring Config Snapshot Analysis The config for Snapshot Analysis Based Feature Monitoring. Structure is documented below.
- Categorical
Threshold AiConfig Feature Store Entity Type Monitoring Config Categorical Threshold Config 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 AiAnalysis Feature Store Entity Type Monitoring Config Import Features Analysis The config for ImportFeatures Analysis Based Feature Monitoring. Structure is documented below.
- Numerical
Threshold AiConfig Feature Store Entity Type Monitoring Config Numerical Threshold Config 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 AiFeature Store Entity Type Monitoring Config Snapshot Analysis The config for Snapshot Analysis Based Feature Monitoring. Structure is documented below.
- categorical
Threshold AiConfig Feature Store Entity Type Monitoring Config Categorical Threshold Config 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 AiAnalysis Feature Store Entity Type Monitoring Config Import Features Analysis The config for ImportFeatures Analysis Based Feature Monitoring. Structure is documented below.
- numerical
Threshold AiConfig Feature Store Entity Type Monitoring Config Numerical Threshold Config 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 AiFeature Store Entity Type Monitoring Config Snapshot Analysis The config for Snapshot Analysis Based Feature Monitoring. Structure is documented below.
- categorical
Threshold AiConfig Feature Store Entity Type Monitoring Config Categorical Threshold Config 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 AiAnalysis Feature Store Entity Type Monitoring Config Import Features Analysis The config for ImportFeatures Analysis Based Feature Monitoring. Structure is documented below.
- numerical
Threshold AiConfig Feature Store Entity Type Monitoring Config Numerical Threshold Config 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 AiFeature Store Entity Type Monitoring Config Snapshot Analysis The config for Snapshot Analysis Based Feature Monitoring. Structure is documented below.
- categorical_
threshold_ Aiconfig Feature Store Entity Type Monitoring Config Categorical Threshold Config 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_ Aianalysis Feature Store Entity Type Monitoring Config Import Features Analysis The config for ImportFeatures Analysis Based Feature Monitoring. Structure is documented below.
- numerical_
threshold_ Aiconfig Feature Store Entity Type Monitoring Config Numerical Threshold Config 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 AiFeature Store Entity Type Monitoring Config Snapshot Analysis The config for Snapshot Analysis Based Feature Monitoring. Structure is documented below.
- categorical
Threshold Property MapConfig 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 Property MapAnalysis The config for ImportFeatures Analysis Based Feature Monitoring. Structure is documented below.
- numerical
Threshold Property MapConfig 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 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
- Anomaly
Detection stringBaseline 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 stringBaseline 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 StringBaseline 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 stringBaseline 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_ strbaseline 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.
- anomaly
Detection StringBaseline 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.
- Monitoring
Interval string monitoring_interval
is deprecated and will be removed in a future release.- Monitoring
Interval intDays 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 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 string monitoring_interval
is deprecated and will be removed in a future release.- Monitoring
Interval intDays 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.
- monitoring
Interval String monitoring_interval
is deprecated and will be removed in a future release.- monitoring
Interval IntegerDays 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 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.
- monitoring
Interval string monitoring_interval
is deprecated and will be removed in a future release.- monitoring
Interval numberDays 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 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 monitoring_interval
is deprecated and will be removed in a future release.- monitoring_
interval_ intdays 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.
- monitoring
Interval String monitoring_interval
is deprecated and will be removed in a future release.- monitoring
Interval NumberDays 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 Number Customized export features time window for snapshot analysis. Unit is one day. The default value is 21 days. Minimum value is 1 day. Maximum value is 4000 days.
Import
FeaturestoreEntitytype can be imported using any of these accepted formats:
$ pulumi import gcp:vertex/aiFeatureStoreEntityType:AiFeatureStoreEntityType default {{featurestore}}/entityTypes/{{name}}
Package Details
- Repository
- Google Cloud (GCP) Classic pulumi/pulumi-gcp
- License
- Apache-2.0
- Notes
This Pulumi package is based on the
google-beta
Terraform Provider.