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Build cloud applications and infrastructure by combining the safety and reliability of infrastructure as code with the power of the Kotlin programming language.
@file:Suppress("NAME_SHADOWING", "DEPRECATION")
package com.pulumi.gcp.vertex.kotlin
import com.pulumi.core.Output
import com.pulumi.gcp.vertex.kotlin.outputs.AiFeatureOnlineStoreFeatureviewBigQuerySource
import com.pulumi.gcp.vertex.kotlin.outputs.AiFeatureOnlineStoreFeatureviewFeatureRegistrySource
import com.pulumi.gcp.vertex.kotlin.outputs.AiFeatureOnlineStoreFeatureviewSyncConfig
import com.pulumi.gcp.vertex.kotlin.outputs.AiFeatureOnlineStoreFeatureviewVectorSearchConfig
import com.pulumi.kotlin.KotlinCustomResource
import com.pulumi.kotlin.PulumiTagMarker
import com.pulumi.kotlin.ResourceMapper
import com.pulumi.kotlin.options.CustomResourceOptions
import com.pulumi.kotlin.options.CustomResourceOptionsBuilder
import com.pulumi.resources.Resource
import kotlin.Boolean
import kotlin.String
import kotlin.Suppress
import kotlin.Unit
import kotlin.collections.Map
import com.pulumi.gcp.vertex.kotlin.outputs.AiFeatureOnlineStoreFeatureviewBigQuerySource.Companion.toKotlin as aiFeatureOnlineStoreFeatureviewBigQuerySourceToKotlin
import com.pulumi.gcp.vertex.kotlin.outputs.AiFeatureOnlineStoreFeatureviewFeatureRegistrySource.Companion.toKotlin as aiFeatureOnlineStoreFeatureviewFeatureRegistrySourceToKotlin
import com.pulumi.gcp.vertex.kotlin.outputs.AiFeatureOnlineStoreFeatureviewSyncConfig.Companion.toKotlin as aiFeatureOnlineStoreFeatureviewSyncConfigToKotlin
import com.pulumi.gcp.vertex.kotlin.outputs.AiFeatureOnlineStoreFeatureviewVectorSearchConfig.Companion.toKotlin as aiFeatureOnlineStoreFeatureviewVectorSearchConfigToKotlin
/**
* Builder for [AiFeatureOnlineStoreFeatureview].
*/
@PulumiTagMarker
public class AiFeatureOnlineStoreFeatureviewResourceBuilder internal constructor() {
public var name: String? = null
public var args: AiFeatureOnlineStoreFeatureviewArgs = AiFeatureOnlineStoreFeatureviewArgs()
public var opts: CustomResourceOptions = CustomResourceOptions()
/**
* @param name The _unique_ name of the resulting resource.
*/
public fun name(`value`: String) {
this.name = value
}
/**
* @param block The arguments to use to populate this resource's properties.
*/
public suspend fun args(block: suspend AiFeatureOnlineStoreFeatureviewArgsBuilder.() -> Unit) {
val builder = AiFeatureOnlineStoreFeatureviewArgsBuilder()
block(builder)
this.args = builder.build()
}
/**
* @param block A bag of options that control this resource's behavior.
*/
public suspend fun opts(block: suspend CustomResourceOptionsBuilder.() -> Unit) {
this.opts = com.pulumi.kotlin.options.CustomResourceOptions.opts(block)
}
internal fun build(): AiFeatureOnlineStoreFeatureview {
val builtJavaResource =
com.pulumi.gcp.vertex.AiFeatureOnlineStoreFeatureview(
this.name,
this.args.toJava(),
this.opts.toJava(),
)
return AiFeatureOnlineStoreFeatureview(builtJavaResource)
}
}
/**
* FeatureView is representation of values that the FeatureOnlineStore will serve based on its syncConfig.
* To get more information about FeatureOnlineStoreFeatureview, see:
* * [API documentation](https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.featureOnlineStores.featureViews)
* * How-to Guides
* * [Official Documentation](https://cloud.google.com/vertex-ai/docs)
* ## Example Usage
* ### Vertex Ai Featureonlinestore Featureview
*
* ```typescript
* import * as pulumi from "@pulumi/pulumi";
* import * as gcp from "@pulumi/gcp";
* const featureonlinestore = new gcp.vertex.AiFeatureOnlineStore("featureonlinestore", {
* name: "example_feature_view",
* labels: {
* foo: "bar",
* },
* region: "us-central1",
* bigtable: {
* autoScaling: {
* minNodeCount: 1,
* maxNodeCount: 2,
* cpuUtilizationTarget: 80,
* },
* },
* });
* const tf_test_dataset = new gcp.bigquery.Dataset("tf-test-dataset", {
* datasetId: "example_feature_view",
* friendlyName: "test",
* description: "This is a test description",
* location: "US",
* });
* const tf_test_table = new gcp.bigquery.Table("tf-test-table", {
* deletionProtection: false,
* datasetId: tf_test_dataset.datasetId,
* tableId: "example_feature_view",
* schema: ` [
* {
* "name": "entity_id",
* "mode": "NULLABLE",
* "type": "STRING",
* "description": "Test default entity_id"
* },
* {
* "name": "test_entity_column",
* "mode": "NULLABLE",
* "type": "STRING",
* "description": "test secondary entity column"
* },
* {
* "name": "feature_timestamp",
* "mode": "NULLABLE",
* "type": "TIMESTAMP",
* "description": "Default timestamp value"
* }
* ]
* `,
* });
* const featureview = new gcp.vertex.AiFeatureOnlineStoreFeatureview("featureview", {
* name: "example_feature_view",
* region: "us-central1",
* featureOnlineStore: featureonlinestore.name,
* syncConfig: {
* cron: "0 0 * * *",
* },
* bigQuerySource: {
* uri: pulumi.interpolate`bq://${tf_test_table.project}.${tf_test_table.datasetId}.${tf_test_table.tableId}`,
* entityIdColumns: ["test_entity_column"],
* },
* });
* const project = gcp.organizations.getProject({});
* ```
* ```python
* import pulumi
* import pulumi_gcp as gcp
* featureonlinestore = gcp.vertex.AiFeatureOnlineStore("featureonlinestore",
* name="example_feature_view",
* labels={
* "foo": "bar",
* },
* region="us-central1",
* bigtable=gcp.vertex.AiFeatureOnlineStoreBigtableArgs(
* auto_scaling=gcp.vertex.AiFeatureOnlineStoreBigtableAutoScalingArgs(
* min_node_count=1,
* max_node_count=2,
* cpu_utilization_target=80,
* ),
* ))
* tf_test_dataset = gcp.bigquery.Dataset("tf-test-dataset",
* dataset_id="example_feature_view",
* friendly_name="test",
* description="This is a test description",
* location="US")
* tf_test_table = gcp.bigquery.Table("tf-test-table",
* deletion_protection=False,
* dataset_id=tf_test_dataset.dataset_id,
* table_id="example_feature_view",
* schema=""" [
* {
* "name": "entity_id",
* "mode": "NULLABLE",
* "type": "STRING",
* "description": "Test default entity_id"
* },
* {
* "name": "test_entity_column",
* "mode": "NULLABLE",
* "type": "STRING",
* "description": "test secondary entity column"
* },
* {
* "name": "feature_timestamp",
* "mode": "NULLABLE",
* "type": "TIMESTAMP",
* "description": "Default timestamp value"
* }
* ]
* """)
* featureview = gcp.vertex.AiFeatureOnlineStoreFeatureview("featureview",
* name="example_feature_view",
* region="us-central1",
* feature_online_store=featureonlinestore.name,
* sync_config=gcp.vertex.AiFeatureOnlineStoreFeatureviewSyncConfigArgs(
* cron="0 0 * * *",
* ),
* big_query_source=gcp.vertex.AiFeatureOnlineStoreFeatureviewBigQuerySourceArgs(
* uri=pulumi.Output.all(tf_test_table.project, tf_test_table.dataset_id, tf_test_table.table_id).apply(lambda project, dataset_id, table_id: f"bq://{project}.{dataset_id}.{table_id}"),
* entity_id_columns=["test_entity_column"],
* ))
* project = gcp.organizations.get_project()
* ```
* ```csharp
* using System.Collections.Generic;
* using System.Linq;
* using Pulumi;
* using Gcp = Pulumi.Gcp;
* return await Deployment.RunAsync(() =>
* {
* var featureonlinestore = new Gcp.Vertex.AiFeatureOnlineStore("featureonlinestore", new()
* {
* Name = "example_feature_view",
* Labels =
* {
* { "foo", "bar" },
* },
* Region = "us-central1",
* Bigtable = new Gcp.Vertex.Inputs.AiFeatureOnlineStoreBigtableArgs
* {
* AutoScaling = new Gcp.Vertex.Inputs.AiFeatureOnlineStoreBigtableAutoScalingArgs
* {
* MinNodeCount = 1,
* MaxNodeCount = 2,
* CpuUtilizationTarget = 80,
* },
* },
* });
* var tf_test_dataset = new Gcp.BigQuery.Dataset("tf-test-dataset", new()
* {
* DatasetId = "example_feature_view",
* FriendlyName = "test",
* Description = "This is a test description",
* Location = "US",
* });
* var tf_test_table = new Gcp.BigQuery.Table("tf-test-table", new()
* {
* DeletionProtection = false,
* DatasetId = tf_test_dataset.DatasetId,
* TableId = "example_feature_view",
* Schema = @" [
* {
* ""name"": ""entity_id"",
* ""mode"": ""NULLABLE"",
* ""type"": ""STRING"",
* ""description"": ""Test default entity_id""
* },
* {
* ""name"": ""test_entity_column"",
* ""mode"": ""NULLABLE"",
* ""type"": ""STRING"",
* ""description"": ""test secondary entity column""
* },
* {
* ""name"": ""feature_timestamp"",
* ""mode"": ""NULLABLE"",
* ""type"": ""TIMESTAMP"",
* ""description"": ""Default timestamp value""
* }
* ]
* ",
* });
* var featureview = new Gcp.Vertex.AiFeatureOnlineStoreFeatureview("featureview", new()
* {
* Name = "example_feature_view",
* Region = "us-central1",
* FeatureOnlineStore = featureonlinestore.Name,
* SyncConfig = new Gcp.Vertex.Inputs.AiFeatureOnlineStoreFeatureviewSyncConfigArgs
* {
* Cron = "0 0 * * *",
* },
* BigQuerySource = new Gcp.Vertex.Inputs.AiFeatureOnlineStoreFeatureviewBigQuerySourceArgs
* {
* Uri = Output.Tuple(tf_test_table.Project, tf_test_table.DatasetId, tf_test_table.TableId).Apply(values =>
* {
* var project = values.Item1;
* var datasetId = values.Item2;
* var tableId = values.Item3;
* return $"bq://{project}.{datasetId}.{tableId}";
* }),
* EntityIdColumns = new[]
* {
* "test_entity_column",
* },
* },
* });
* var project = Gcp.Organizations.GetProject.Invoke();
* });
* ```
* ```go
* package main
* import (
* "fmt"
* "github.com/pulumi/pulumi-gcp/sdk/v7/go/gcp/bigquery"
* "github.com/pulumi/pulumi-gcp/sdk/v7/go/gcp/organizations"
* "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 {
* featureonlinestore, err := vertex.NewAiFeatureOnlineStore(ctx, "featureonlinestore", &vertex.AiFeatureOnlineStoreArgs{
* Name: pulumi.String("example_feature_view"),
* Labels: pulumi.StringMap{
* "foo": pulumi.String("bar"),
* },
* Region: pulumi.String("us-central1"),
* Bigtable: &vertex.AiFeatureOnlineStoreBigtableArgs{
* AutoScaling: &vertex.AiFeatureOnlineStoreBigtableAutoScalingArgs{
* MinNodeCount: pulumi.Int(1),
* MaxNodeCount: pulumi.Int(2),
* CpuUtilizationTarget: pulumi.Int(80),
* },
* },
* })
* if err != nil {
* return err
* }
* _, err = bigquery.NewDataset(ctx, "tf-test-dataset", &bigquery.DatasetArgs{
* DatasetId: pulumi.String("example_feature_view"),
* FriendlyName: pulumi.String("test"),
* Description: pulumi.String("This is a test description"),
* Location: pulumi.String("US"),
* })
* if err != nil {
* return err
* }
* _, err = bigquery.NewTable(ctx, "tf-test-table", &bigquery.TableArgs{
* DeletionProtection: pulumi.Bool(false),
* DatasetId: tf_test_dataset.DatasetId,
* TableId: pulumi.String("example_feature_view"),
* Schema: pulumi.String(` [
* {
* "name": "entity_id",
* "mode": "NULLABLE",
* "type": "STRING",
* "description": "Test default entity_id"
* },
* {
* "name": "test_entity_column",
* "mode": "NULLABLE",
* "type": "STRING",
* "description": "test secondary entity column"
* },
* {
* "name": "feature_timestamp",
* "mode": "NULLABLE",
* "type": "TIMESTAMP",
* "description": "Default timestamp value"
* }
* ]
* `),
* })
* if err != nil {
* return err
* }
* _, err = vertex.NewAiFeatureOnlineStoreFeatureview(ctx, "featureview", &vertex.AiFeatureOnlineStoreFeatureviewArgs{
* Name: pulumi.String("example_feature_view"),
* Region: pulumi.String("us-central1"),
* FeatureOnlineStore: featureonlinestore.Name,
* SyncConfig: &vertex.AiFeatureOnlineStoreFeatureviewSyncConfigArgs{
* Cron: pulumi.String("0 0 * * *"),
* },
* BigQuerySource: &vertex.AiFeatureOnlineStoreFeatureviewBigQuerySourceArgs{
* Uri: pulumi.All(tf_test_table.Project, tf_test_table.DatasetId, tf_test_table.TableId).ApplyT(func(_args []interface{}) (string, error) {
* project := _args[0].(string)
* datasetId := _args[1].(string)
* tableId := _args[2].(string)
* return fmt.Sprintf("bq://%v.%v.%v", project, datasetId, tableId), nil
* }).(pulumi.StringOutput),
* EntityIdColumns: pulumi.StringArray{
* pulumi.String("test_entity_column"),
* },
* },
* })
* if err != nil {
* return err
* }
* _, err = organizations.LookupProject(ctx, nil, nil)
* if err != nil {
* return err
* }
* return nil
* })
* }
* ```
* ```java
* package generated_program;
* import com.pulumi.Context;
* import com.pulumi.Pulumi;
* import com.pulumi.core.Output;
* import com.pulumi.gcp.vertex.AiFeatureOnlineStore;
* import com.pulumi.gcp.vertex.AiFeatureOnlineStoreArgs;
* import com.pulumi.gcp.vertex.inputs.AiFeatureOnlineStoreBigtableArgs;
* import com.pulumi.gcp.vertex.inputs.AiFeatureOnlineStoreBigtableAutoScalingArgs;
* import com.pulumi.gcp.bigquery.Dataset;
* import com.pulumi.gcp.bigquery.DatasetArgs;
* import com.pulumi.gcp.bigquery.Table;
* import com.pulumi.gcp.bigquery.TableArgs;
* import com.pulumi.gcp.vertex.AiFeatureOnlineStoreFeatureview;
* import com.pulumi.gcp.vertex.AiFeatureOnlineStoreFeatureviewArgs;
* import com.pulumi.gcp.vertex.inputs.AiFeatureOnlineStoreFeatureviewSyncConfigArgs;
* import com.pulumi.gcp.vertex.inputs.AiFeatureOnlineStoreFeatureviewBigQuerySourceArgs;
* import com.pulumi.gcp.organizations.OrganizationsFunctions;
* import com.pulumi.gcp.organizations.inputs.GetProjectArgs;
* 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 featureonlinestore = new AiFeatureOnlineStore("featureonlinestore", AiFeatureOnlineStoreArgs.builder()
* .name("example_feature_view")
* .labels(Map.of("foo", "bar"))
* .region("us-central1")
* .bigtable(AiFeatureOnlineStoreBigtableArgs.builder()
* .autoScaling(AiFeatureOnlineStoreBigtableAutoScalingArgs.builder()
* .minNodeCount(1)
* .maxNodeCount(2)
* .cpuUtilizationTarget(80)
* .build())
* .build())
* .build());
* var tf_test_dataset = new Dataset("tf-test-dataset", DatasetArgs.builder()
* .datasetId("example_feature_view")
* .friendlyName("test")
* .description("This is a test description")
* .location("US")
* .build());
* var tf_test_table = new Table("tf-test-table", TableArgs.builder()
* .deletionProtection(false)
* .datasetId(tf_test_dataset.datasetId())
* .tableId("example_feature_view")
* .schema("""
* [
* {
* "name": "entity_id",
* "mode": "NULLABLE",
* "type": "STRING",
* "description": "Test default entity_id"
* },
* {
* "name": "test_entity_column",
* "mode": "NULLABLE",
* "type": "STRING",
* "description": "test secondary entity column"
* },
* {
* "name": "feature_timestamp",
* "mode": "NULLABLE",
* "type": "TIMESTAMP",
* "description": "Default timestamp value"
* }
* ]
* """)
* .build());
* var featureview = new AiFeatureOnlineStoreFeatureview("featureview", AiFeatureOnlineStoreFeatureviewArgs.builder()
* .name("example_feature_view")
* .region("us-central1")
* .featureOnlineStore(featureonlinestore.name())
* .syncConfig(AiFeatureOnlineStoreFeatureviewSyncConfigArgs.builder()
* .cron("0 0 * * *")
* .build())
* .bigQuerySource(AiFeatureOnlineStoreFeatureviewBigQuerySourceArgs.builder()
* .uri(Output.tuple(tf_test_table.project(), tf_test_table.datasetId(), tf_test_table.tableId()).applyValue(values -> {
* var project = values.t1;
* var datasetId = values.t2;
* var tableId = values.t3;
* return String.format("bq://%s.%s.%s", project,datasetId,tableId);
* }))
* .entityIdColumns("test_entity_column")
* .build())
* .build());
* final var project = OrganizationsFunctions.getProject();
* }
* }
* ```
* ```yaml
* resources:
* featureonlinestore:
* type: gcp:vertex:AiFeatureOnlineStore
* properties:
* name: example_feature_view
* labels:
* foo: bar
* region: us-central1
* bigtable:
* autoScaling:
* minNodeCount: 1
* maxNodeCount: 2
* cpuUtilizationTarget: 80
* tf-test-dataset:
* type: gcp:bigquery:Dataset
* properties:
* datasetId: example_feature_view
* friendlyName: test
* description: This is a test description
* location: US
* tf-test-table:
* type: gcp:bigquery:Table
* properties:
* deletionProtection: false
* datasetId: ${["tf-test-dataset"].datasetId}
* tableId: example_feature_view
* schema: |2
* [
* {
* "name": "entity_id",
* "mode": "NULLABLE",
* "type": "STRING",
* "description": "Test default entity_id"
* },
* {
* "name": "test_entity_column",
* "mode": "NULLABLE",
* "type": "STRING",
* "description": "test secondary entity column"
* },
* {
* "name": "feature_timestamp",
* "mode": "NULLABLE",
* "type": "TIMESTAMP",
* "description": "Default timestamp value"
* }
* ]
* featureview:
* type: gcp:vertex:AiFeatureOnlineStoreFeatureview
* properties:
* name: example_feature_view
* region: us-central1
* featureOnlineStore: ${featureonlinestore.name}
* syncConfig:
* cron: 0 0 * * *
* bigQuerySource:
* uri: bq://${["tf-test-table"].project}.${["tf-test-table"].datasetId}.${["tf-test-table"].tableId}
* entityIdColumns:
* - test_entity_column
* variables:
* project:
* fn::invoke:
* Function: gcp:organizations:getProject
* Arguments: {}
* ```
*
* ### Vertex Ai Featureonlinestore Featureview Feature Registry
*
* ```typescript
* import * as pulumi from "@pulumi/pulumi";
* import * as gcp from "@pulumi/gcp";
* const featureonlinestore = new gcp.vertex.AiFeatureOnlineStore("featureonlinestore", {
* name: "example_feature_view_feature_registry",
* labels: {
* foo: "bar",
* },
* region: "us-central1",
* bigtable: {
* autoScaling: {
* minNodeCount: 1,
* maxNodeCount: 2,
* cpuUtilizationTarget: 80,
* },
* },
* });
* const sampleDataset = new gcp.bigquery.Dataset("sample_dataset", {
* datasetId: "example_feature_view_feature_registry",
* friendlyName: "test",
* description: "This is a test description",
* location: "US",
* });
* const sampleTable = new gcp.bigquery.Table("sample_table", {
* deletionProtection: false,
* datasetId: sampleDataset.datasetId,
* tableId: "example_feature_view_feature_registry",
* schema: `[
* {
* "name": "feature_id",
* "type": "STRING",
* "mode": "NULLABLE"
* },
* {
* "name": "example_feature_view_feature_registry",
* "type": "STRING",
* "mode": "NULLABLE"
* },
* {
* "name": "feature_timestamp",
* "type": "TIMESTAMP",
* "mode": "NULLABLE"
* }
* ]
* `,
* });
* const sampleFeatureGroup = new gcp.vertex.AiFeatureGroup("sample_feature_group", {
* name: "example_feature_view_feature_registry",
* description: "A sample feature group",
* region: "us-central1",
* labels: {
* "label-one": "value-one",
* },
* bigQuery: {
* bigQuerySource: {
* inputUri: pulumi.interpolate`bq://${sampleTable.project}.${sampleTable.datasetId}.${sampleTable.tableId}`,
* },
* entityIdColumns: ["feature_id"],
* },
* });
* const sampleFeature = new gcp.vertex.AiFeatureGroupFeature("sample_feature", {
* name: "example_feature_view_feature_registry",
* region: "us-central1",
* featureGroup: sampleFeatureGroup.name,
* description: "A sample feature",
* labels: {
* "label-one": "value-one",
* },
* });
* const featureviewFeatureregistry = new gcp.vertex.AiFeatureOnlineStoreFeatureview("featureview_featureregistry", {
* name: "example_feature_view_feature_registry",
* region: "us-central1",
* featureOnlineStore: featureonlinestore.name,
* syncConfig: {
* cron: "0 0 * * *",
* },
* featureRegistrySource: {
* featureGroups: [{
* featureGroupId: sampleFeatureGroup.name,
* featureIds: [sampleFeature.name],
* }],
* },
* });
* ```
* ```python
* import pulumi
* import pulumi_gcp as gcp
* featureonlinestore = gcp.vertex.AiFeatureOnlineStore("featureonlinestore",
* name="example_feature_view_feature_registry",
* labels={
* "foo": "bar",
* },
* region="us-central1",
* bigtable=gcp.vertex.AiFeatureOnlineStoreBigtableArgs(
* auto_scaling=gcp.vertex.AiFeatureOnlineStoreBigtableAutoScalingArgs(
* min_node_count=1,
* max_node_count=2,
* cpu_utilization_target=80,
* ),
* ))
* sample_dataset = gcp.bigquery.Dataset("sample_dataset",
* dataset_id="example_feature_view_feature_registry",
* friendly_name="test",
* description="This is a test description",
* location="US")
* sample_table = gcp.bigquery.Table("sample_table",
* deletion_protection=False,
* dataset_id=sample_dataset.dataset_id,
* table_id="example_feature_view_feature_registry",
* schema="""[
* {
* "name": "feature_id",
* "type": "STRING",
* "mode": "NULLABLE"
* },
* {
* "name": "example_feature_view_feature_registry",
* "type": "STRING",
* "mode": "NULLABLE"
* },
* {
* "name": "feature_timestamp",
* "type": "TIMESTAMP",
* "mode": "NULLABLE"
* }
* ]
* """)
* sample_feature_group = gcp.vertex.AiFeatureGroup("sample_feature_group",
* name="example_feature_view_feature_registry",
* description="A sample feature group",
* region="us-central1",
* labels={
* "label-one": "value-one",
* },
* big_query=gcp.vertex.AiFeatureGroupBigQueryArgs(
* big_query_source=gcp.vertex.AiFeatureGroupBigQueryBigQuerySourceArgs(
* input_uri=pulumi.Output.all(sample_table.project, sample_table.dataset_id, sample_table.table_id).apply(lambda project, dataset_id, table_id: f"bq://{project}.{dataset_id}.{table_id}"),
* ),
* entity_id_columns=["feature_id"],
* ))
* sample_feature = gcp.vertex.AiFeatureGroupFeature("sample_feature",
* name="example_feature_view_feature_registry",
* region="us-central1",
* feature_group=sample_feature_group.name,
* description="A sample feature",
* labels={
* "label-one": "value-one",
* })
* featureview_featureregistry = gcp.vertex.AiFeatureOnlineStoreFeatureview("featureview_featureregistry",
* name="example_feature_view_feature_registry",
* region="us-central1",
* feature_online_store=featureonlinestore.name,
* sync_config=gcp.vertex.AiFeatureOnlineStoreFeatureviewSyncConfigArgs(
* cron="0 0 * * *",
* ),
* feature_registry_source=gcp.vertex.AiFeatureOnlineStoreFeatureviewFeatureRegistrySourceArgs(
* feature_groups=[gcp.vertex.AiFeatureOnlineStoreFeatureviewFeatureRegistrySourceFeatureGroupArgs(
* feature_group_id=sample_feature_group.name,
* feature_ids=[sample_feature.name],
* )],
* ))
* ```
* ```csharp
* using System.Collections.Generic;
* using System.Linq;
* using Pulumi;
* using Gcp = Pulumi.Gcp;
* return await Deployment.RunAsync(() =>
* {
* var featureonlinestore = new Gcp.Vertex.AiFeatureOnlineStore("featureonlinestore", new()
* {
* Name = "example_feature_view_feature_registry",
* Labels =
* {
* { "foo", "bar" },
* },
* Region = "us-central1",
* Bigtable = new Gcp.Vertex.Inputs.AiFeatureOnlineStoreBigtableArgs
* {
* AutoScaling = new Gcp.Vertex.Inputs.AiFeatureOnlineStoreBigtableAutoScalingArgs
* {
* MinNodeCount = 1,
* MaxNodeCount = 2,
* CpuUtilizationTarget = 80,
* },
* },
* });
* var sampleDataset = new Gcp.BigQuery.Dataset("sample_dataset", new()
* {
* DatasetId = "example_feature_view_feature_registry",
* FriendlyName = "test",
* Description = "This is a test description",
* Location = "US",
* });
* var sampleTable = new Gcp.BigQuery.Table("sample_table", new()
* {
* DeletionProtection = false,
* DatasetId = sampleDataset.DatasetId,
* TableId = "example_feature_view_feature_registry",
* Schema = @"[
* {
* ""name"": ""feature_id"",
* ""type"": ""STRING"",
* ""mode"": ""NULLABLE""
* },
* {
* ""name"": ""example_feature_view_feature_registry"",
* ""type"": ""STRING"",
* ""mode"": ""NULLABLE""
* },
* {
* ""name"": ""feature_timestamp"",
* ""type"": ""TIMESTAMP"",
* ""mode"": ""NULLABLE""
* }
* ]
* ",
* });
* var sampleFeatureGroup = new Gcp.Vertex.AiFeatureGroup("sample_feature_group", new()
* {
* Name = "example_feature_view_feature_registry",
* Description = "A sample feature group",
* Region = "us-central1",
* Labels =
* {
* { "label-one", "value-one" },
* },
* BigQuery = new Gcp.Vertex.Inputs.AiFeatureGroupBigQueryArgs
* {
* BigQuerySource = new Gcp.Vertex.Inputs.AiFeatureGroupBigQueryBigQuerySourceArgs
* {
* InputUri = Output.Tuple(sampleTable.Project, sampleTable.DatasetId, sampleTable.TableId).Apply(values =>
* {
* var project = values.Item1;
* var datasetId = values.Item2;
* var tableId = values.Item3;
* return $"bq://{project}.{datasetId}.{tableId}";
* }),
* },
* EntityIdColumns = new[]
* {
* "feature_id",
* },
* },
* });
* var sampleFeature = new Gcp.Vertex.AiFeatureGroupFeature("sample_feature", new()
* {
* Name = "example_feature_view_feature_registry",
* Region = "us-central1",
* FeatureGroup = sampleFeatureGroup.Name,
* Description = "A sample feature",
* Labels =
* {
* { "label-one", "value-one" },
* },
* });
* var featureviewFeatureregistry = new Gcp.Vertex.AiFeatureOnlineStoreFeatureview("featureview_featureregistry", new()
* {
* Name = "example_feature_view_feature_registry",
* Region = "us-central1",
* FeatureOnlineStore = featureonlinestore.Name,
* SyncConfig = new Gcp.Vertex.Inputs.AiFeatureOnlineStoreFeatureviewSyncConfigArgs
* {
* Cron = "0 0 * * *",
* },
* FeatureRegistrySource = new Gcp.Vertex.Inputs.AiFeatureOnlineStoreFeatureviewFeatureRegistrySourceArgs
* {
* FeatureGroups = new[]
* {
* new Gcp.Vertex.Inputs.AiFeatureOnlineStoreFeatureviewFeatureRegistrySourceFeatureGroupArgs
* {
* FeatureGroupId = sampleFeatureGroup.Name,
* FeatureIds = new[]
* {
* sampleFeature.Name,
* },
* },
* },
* },
* });
* });
* ```
* ```go
* package main
* import (
* "fmt"
* "github.com/pulumi/pulumi-gcp/sdk/v7/go/gcp/bigquery"
* "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 {
* featureonlinestore, err := vertex.NewAiFeatureOnlineStore(ctx, "featureonlinestore", &vertex.AiFeatureOnlineStoreArgs{
* Name: pulumi.String("example_feature_view_feature_registry"),
* Labels: pulumi.StringMap{
* "foo": pulumi.String("bar"),
* },
* Region: pulumi.String("us-central1"),
* Bigtable: &vertex.AiFeatureOnlineStoreBigtableArgs{
* AutoScaling: &vertex.AiFeatureOnlineStoreBigtableAutoScalingArgs{
* MinNodeCount: pulumi.Int(1),
* MaxNodeCount: pulumi.Int(2),
* CpuUtilizationTarget: pulumi.Int(80),
* },
* },
* })
* if err != nil {
* return err
* }
* sampleDataset, err := bigquery.NewDataset(ctx, "sample_dataset", &bigquery.DatasetArgs{
* DatasetId: pulumi.String("example_feature_view_feature_registry"),
* FriendlyName: pulumi.String("test"),
* Description: pulumi.String("This is a test description"),
* Location: pulumi.String("US"),
* })
* if err != nil {
* return err
* }
* sampleTable, err := bigquery.NewTable(ctx, "sample_table", &bigquery.TableArgs{
* DeletionProtection: pulumi.Bool(false),
* DatasetId: sampleDataset.DatasetId,
* TableId: pulumi.String("example_feature_view_feature_registry"),
* Schema: pulumi.String(`[
* {
* "name": "feature_id",
* "type": "STRING",
* "mode": "NULLABLE"
* },
* {
* "name": "example_feature_view_feature_registry",
* "type": "STRING",
* "mode": "NULLABLE"
* },
* {
* "name": "feature_timestamp",
* "type": "TIMESTAMP",
* "mode": "NULLABLE"
* }
* ]
* `),
* })
* if err != nil {
* return err
* }
* sampleFeatureGroup, err := vertex.NewAiFeatureGroup(ctx, "sample_feature_group", &vertex.AiFeatureGroupArgs{
* Name: pulumi.String("example_feature_view_feature_registry"),
* Description: pulumi.String("A sample feature group"),
* Region: pulumi.String("us-central1"),
* Labels: pulumi.StringMap{
* "label-one": pulumi.String("value-one"),
* },
* BigQuery: &vertex.AiFeatureGroupBigQueryArgs{
* BigQuerySource: &vertex.AiFeatureGroupBigQueryBigQuerySourceArgs{
* InputUri: pulumi.All(sampleTable.Project, sampleTable.DatasetId, sampleTable.TableId).ApplyT(func(_args []interface{}) (string, error) {
* project := _args[0].(string)
* datasetId := _args[1].(string)
* tableId := _args[2].(string)
* return fmt.Sprintf("bq://%v.%v.%v", project, datasetId, tableId), nil
* }).(pulumi.StringOutput),
* },
* EntityIdColumns: pulumi.StringArray{
* pulumi.String("feature_id"),
* },
* },
* })
* if err != nil {
* return err
* }
* sampleFeature, err := vertex.NewAiFeatureGroupFeature(ctx, "sample_feature", &vertex.AiFeatureGroupFeatureArgs{
* Name: pulumi.String("example_feature_view_feature_registry"),
* Region: pulumi.String("us-central1"),
* FeatureGroup: sampleFeatureGroup.Name,
* Description: pulumi.String("A sample feature"),
* Labels: pulumi.StringMap{
* "label-one": pulumi.String("value-one"),
* },
* })
* if err != nil {
* return err
* }
* _, err = vertex.NewAiFeatureOnlineStoreFeatureview(ctx, "featureview_featureregistry", &vertex.AiFeatureOnlineStoreFeatureviewArgs{
* Name: pulumi.String("example_feature_view_feature_registry"),
* Region: pulumi.String("us-central1"),
* FeatureOnlineStore: featureonlinestore.Name,
* SyncConfig: &vertex.AiFeatureOnlineStoreFeatureviewSyncConfigArgs{
* Cron: pulumi.String("0 0 * * *"),
* },
* FeatureRegistrySource: &vertex.AiFeatureOnlineStoreFeatureviewFeatureRegistrySourceArgs{
* FeatureGroups: vertex.AiFeatureOnlineStoreFeatureviewFeatureRegistrySourceFeatureGroupArray{
* &vertex.AiFeatureOnlineStoreFeatureviewFeatureRegistrySourceFeatureGroupArgs{
* FeatureGroupId: sampleFeatureGroup.Name,
* FeatureIds: pulumi.StringArray{
* sampleFeature.Name,
* },
* },
* },
* },
* })
* if err != nil {
* return err
* }
* return nil
* })
* }
* ```
* ```java
* package generated_program;
* import com.pulumi.Context;
* import com.pulumi.Pulumi;
* import com.pulumi.core.Output;
* import com.pulumi.gcp.vertex.AiFeatureOnlineStore;
* import com.pulumi.gcp.vertex.AiFeatureOnlineStoreArgs;
* import com.pulumi.gcp.vertex.inputs.AiFeatureOnlineStoreBigtableArgs;
* import com.pulumi.gcp.vertex.inputs.AiFeatureOnlineStoreBigtableAutoScalingArgs;
* import com.pulumi.gcp.bigquery.Dataset;
* import com.pulumi.gcp.bigquery.DatasetArgs;
* import com.pulumi.gcp.bigquery.Table;
* import com.pulumi.gcp.bigquery.TableArgs;
* import com.pulumi.gcp.vertex.AiFeatureGroup;
* import com.pulumi.gcp.vertex.AiFeatureGroupArgs;
* import com.pulumi.gcp.vertex.inputs.AiFeatureGroupBigQueryArgs;
* import com.pulumi.gcp.vertex.inputs.AiFeatureGroupBigQueryBigQuerySourceArgs;
* import com.pulumi.gcp.vertex.AiFeatureGroupFeature;
* import com.pulumi.gcp.vertex.AiFeatureGroupFeatureArgs;
* import com.pulumi.gcp.vertex.AiFeatureOnlineStoreFeatureview;
* import com.pulumi.gcp.vertex.AiFeatureOnlineStoreFeatureviewArgs;
* import com.pulumi.gcp.vertex.inputs.AiFeatureOnlineStoreFeatureviewSyncConfigArgs;
* import com.pulumi.gcp.vertex.inputs.AiFeatureOnlineStoreFeatureviewFeatureRegistrySourceArgs;
* 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 featureonlinestore = new AiFeatureOnlineStore("featureonlinestore", AiFeatureOnlineStoreArgs.builder()
* .name("example_feature_view_feature_registry")
* .labels(Map.of("foo", "bar"))
* .region("us-central1")
* .bigtable(AiFeatureOnlineStoreBigtableArgs.builder()
* .autoScaling(AiFeatureOnlineStoreBigtableAutoScalingArgs.builder()
* .minNodeCount(1)
* .maxNodeCount(2)
* .cpuUtilizationTarget(80)
* .build())
* .build())
* .build());
* var sampleDataset = new Dataset("sampleDataset", DatasetArgs.builder()
* .datasetId("example_feature_view_feature_registry")
* .friendlyName("test")
* .description("This is a test description")
* .location("US")
* .build());
* var sampleTable = new Table("sampleTable", TableArgs.builder()
* .deletionProtection(false)
* .datasetId(sampleDataset.datasetId())
* .tableId("example_feature_view_feature_registry")
* .schema("""
* [
* {
* "name": "feature_id",
* "type": "STRING",
* "mode": "NULLABLE"
* },
* {
* "name": "example_feature_view_feature_registry",
* "type": "STRING",
* "mode": "NULLABLE"
* },
* {
* "name": "feature_timestamp",
* "type": "TIMESTAMP",
* "mode": "NULLABLE"
* }
* ]
* """)
* .build());
* var sampleFeatureGroup = new AiFeatureGroup("sampleFeatureGroup", AiFeatureGroupArgs.builder()
* .name("example_feature_view_feature_registry")
* .description("A sample feature group")
* .region("us-central1")
* .labels(Map.of("label-one", "value-one"))
* .bigQuery(AiFeatureGroupBigQueryArgs.builder()
* .bigQuerySource(AiFeatureGroupBigQueryBigQuerySourceArgs.builder()
* .inputUri(Output.tuple(sampleTable.project(), sampleTable.datasetId(), sampleTable.tableId()).applyValue(values -> {
* var project = values.t1;
* var datasetId = values.t2;
* var tableId = values.t3;
* return String.format("bq://%s.%s.%s", project,datasetId,tableId);
* }))
* .build())
* .entityIdColumns("feature_id")
* .build())
* .build());
* var sampleFeature = new AiFeatureGroupFeature("sampleFeature", AiFeatureGroupFeatureArgs.builder()
* .name("example_feature_view_feature_registry")
* .region("us-central1")
* .featureGroup(sampleFeatureGroup.name())
* .description("A sample feature")
* .labels(Map.of("label-one", "value-one"))
* .build());
* var featureviewFeatureregistry = new AiFeatureOnlineStoreFeatureview("featureviewFeatureregistry", AiFeatureOnlineStoreFeatureviewArgs.builder()
* .name("example_feature_view_feature_registry")
* .region("us-central1")
* .featureOnlineStore(featureonlinestore.name())
* .syncConfig(AiFeatureOnlineStoreFeatureviewSyncConfigArgs.builder()
* .cron("0 0 * * *")
* .build())
* .featureRegistrySource(AiFeatureOnlineStoreFeatureviewFeatureRegistrySourceArgs.builder()
* .featureGroups(AiFeatureOnlineStoreFeatureviewFeatureRegistrySourceFeatureGroupArgs.builder()
* .featureGroupId(sampleFeatureGroup.name())
* .featureIds(sampleFeature.name())
* .build())
* .build())
* .build());
* }
* }
* ```
* ```yaml
* resources:
* featureonlinestore:
* type: gcp:vertex:AiFeatureOnlineStore
* properties:
* name: example_feature_view_feature_registry
* labels:
* foo: bar
* region: us-central1
* bigtable:
* autoScaling:
* minNodeCount: 1
* maxNodeCount: 2
* cpuUtilizationTarget: 80
* sampleDataset:
* type: gcp:bigquery:Dataset
* name: sample_dataset
* properties:
* datasetId: example_feature_view_feature_registry
* friendlyName: test
* description: This is a test description
* location: US
* sampleTable:
* type: gcp:bigquery:Table
* name: sample_table
* properties:
* deletionProtection: false
* datasetId: ${sampleDataset.datasetId}
* tableId: example_feature_view_feature_registry
* schema: |
* [
* {
* "name": "feature_id",
* "type": "STRING",
* "mode": "NULLABLE"
* },
* {
* "name": "example_feature_view_feature_registry",
* "type": "STRING",
* "mode": "NULLABLE"
* },
* {
* "name": "feature_timestamp",
* "type": "TIMESTAMP",
* "mode": "NULLABLE"
* }
* ]
* sampleFeatureGroup:
* type: gcp:vertex:AiFeatureGroup
* name: sample_feature_group
* properties:
* name: example_feature_view_feature_registry
* description: A sample feature group
* region: us-central1
* labels:
* label-one: value-one
* bigQuery:
* bigQuerySource:
* inputUri: bq://${sampleTable.project}.${sampleTable.datasetId}.${sampleTable.tableId}
* entityIdColumns:
* - feature_id
* sampleFeature:
* type: gcp:vertex:AiFeatureGroupFeature
* name: sample_feature
* properties:
* name: example_feature_view_feature_registry
* region: us-central1
* featureGroup: ${sampleFeatureGroup.name}
* description: A sample feature
* labels:
* label-one: value-one
* featureviewFeatureregistry:
* type: gcp:vertex:AiFeatureOnlineStoreFeatureview
* name: featureview_featureregistry
* properties:
* name: example_feature_view_feature_registry
* region: us-central1
* featureOnlineStore: ${featureonlinestore.name}
* syncConfig:
* cron: 0 0 * * *
* featureRegistrySource:
* featureGroups:
* - featureGroupId: ${sampleFeatureGroup.name}
* featureIds:
* - ${sampleFeature.name}
* ```
*
* ### Vertex Ai Featureonlinestore Featureview With Vector Search
*
* ```typescript
* import * as pulumi from "@pulumi/pulumi";
* import * as gcp from "@pulumi/gcp";
* const featureonlinestore = new gcp.vertex.AiFeatureOnlineStore("featureonlinestore", {
* name: "example_feature_view_vector_search",
* labels: {
* foo: "bar",
* },
* region: "us-central1",
* bigtable: {
* autoScaling: {
* minNodeCount: 1,
* maxNodeCount: 2,
* cpuUtilizationTarget: 80,
* },
* },
* embeddingManagement: {
* enabled: true,
* },
* });
* const tf_test_dataset = new gcp.bigquery.Dataset("tf-test-dataset", {
* datasetId: "example_feature_view_vector_search",
* friendlyName: "test",
* description: "This is a test description",
* location: "US",
* });
* const tf_test_table = new gcp.bigquery.Table("tf-test-table", {
* deletionProtection: false,
* datasetId: tf_test_dataset.datasetId,
* tableId: "example_feature_view_vector_search",
* schema: `[
* {
* "name": "test_primary_id",
* "mode": "NULLABLE",
* "type": "STRING",
* "description": "primary test id"
* },
* {
* "name": "embedding",
* "mode": "REPEATED",
* "type": "FLOAT",
* "description": "embedding column for primary_id column"
* },
* {
* "name": "country",
* "mode": "NULLABLE",
* "type": "STRING",
* "description": "country"
* },
* {
* "name": "test_crowding_column",
* "mode": "NULLABLE",
* "type": "INTEGER",
* "description": "test crowding column"
* },
* {
* "name": "entity_id",
* "mode": "NULLABLE",
* "type": "STRING",
* "description": "Test default entity_id"
* },
* {
* "name": "test_entity_column",
* "mode": "NULLABLE",
* "type": "STRING",
* "description": "test secondary entity column"
* },
* {
* "name": "feature_timestamp",
* "mode": "NULLABLE",
* "type": "TIMESTAMP",
* "description": "Default timestamp value"
* }
* ]
* `,
* });
* const featureviewVectorSearch = new gcp.vertex.AiFeatureOnlineStoreFeatureview("featureview_vector_search", {
* name: "example_feature_view_vector_search",
* region: "us-central1",
* featureOnlineStore: featureonlinestore.name,
* syncConfig: {
* cron: "0 0 * * *",
* },
* bigQuerySource: {
* uri: pulumi.interpolate`bq://${tf_test_table.project}.${tf_test_table.datasetId}.${tf_test_table.tableId}`,
* entityIdColumns: ["test_entity_column"],
* },
* vectorSearchConfig: {
* embeddingColumn: "embedding",
* filterColumns: ["country"],
* crowdingColumn: "test_crowding_column",
* distanceMeasureType: "DOT_PRODUCT_DISTANCE",
* treeAhConfig: {
* leafNodeEmbeddingCount: "1000",
* },
* embeddingDimension: 2,
* },
* });
* const project = gcp.organizations.getProject({});
* ```
* ```python
* import pulumi
* import pulumi_gcp as gcp
* featureonlinestore = gcp.vertex.AiFeatureOnlineStore("featureonlinestore",
* name="example_feature_view_vector_search",
* labels={
* "foo": "bar",
* },
* region="us-central1",
* bigtable=gcp.vertex.AiFeatureOnlineStoreBigtableArgs(
* auto_scaling=gcp.vertex.AiFeatureOnlineStoreBigtableAutoScalingArgs(
* min_node_count=1,
* max_node_count=2,
* cpu_utilization_target=80,
* ),
* ),
* embedding_management=gcp.vertex.AiFeatureOnlineStoreEmbeddingManagementArgs(
* enabled=True,
* ))
* tf_test_dataset = gcp.bigquery.Dataset("tf-test-dataset",
* dataset_id="example_feature_view_vector_search",
* friendly_name="test",
* description="This is a test description",
* location="US")
* tf_test_table = gcp.bigquery.Table("tf-test-table",
* deletion_protection=False,
* dataset_id=tf_test_dataset.dataset_id,
* table_id="example_feature_view_vector_search",
* schema="""[
* {
* "name": "test_primary_id",
* "mode": "NULLABLE",
* "type": "STRING",
* "description": "primary test id"
* },
* {
* "name": "embedding",
* "mode": "REPEATED",
* "type": "FLOAT",
* "description": "embedding column for primary_id column"
* },
* {
* "name": "country",
* "mode": "NULLABLE",
* "type": "STRING",
* "description": "country"
* },
* {
* "name": "test_crowding_column",
* "mode": "NULLABLE",
* "type": "INTEGER",
* "description": "test crowding column"
* },
* {
* "name": "entity_id",
* "mode": "NULLABLE",
* "type": "STRING",
* "description": "Test default entity_id"
* },
* {
* "name": "test_entity_column",
* "mode": "NULLABLE",
* "type": "STRING",
* "description": "test secondary entity column"
* },
* {
* "name": "feature_timestamp",
* "mode": "NULLABLE",
* "type": "TIMESTAMP",
* "description": "Default timestamp value"
* }
* ]
* """)
* featureview_vector_search = gcp.vertex.AiFeatureOnlineStoreFeatureview("featureview_vector_search",
* name="example_feature_view_vector_search",
* region="us-central1",
* feature_online_store=featureonlinestore.name,
* sync_config=gcp.vertex.AiFeatureOnlineStoreFeatureviewSyncConfigArgs(
* cron="0 0 * * *",
* ),
* big_query_source=gcp.vertex.AiFeatureOnlineStoreFeatureviewBigQuerySourceArgs(
* uri=pulumi.Output.all(tf_test_table.project, tf_test_table.dataset_id, tf_test_table.table_id).apply(lambda project, dataset_id, table_id: f"bq://{project}.{dataset_id}.{table_id}"),
* entity_id_columns=["test_entity_column"],
* ),
* vector_search_config=gcp.vertex.AiFeatureOnlineStoreFeatureviewVectorSearchConfigArgs(
* embedding_column="embedding",
* filter_columns=["country"],
* crowding_column="test_crowding_column",
* distance_measure_type="DOT_PRODUCT_DISTANCE",
* tree_ah_config=gcp.vertex.AiFeatureOnlineStoreFeatureviewVectorSearchConfigTreeAhConfigArgs(
* leaf_node_embedding_count="1000",
* ),
* embedding_dimension=2,
* ))
* project = gcp.organizations.get_project()
* ```
* ```csharp
* using System.Collections.Generic;
* using System.Linq;
* using Pulumi;
* using Gcp = Pulumi.Gcp;
* return await Deployment.RunAsync(() =>
* {
* var featureonlinestore = new Gcp.Vertex.AiFeatureOnlineStore("featureonlinestore", new()
* {
* Name = "example_feature_view_vector_search",
* Labels =
* {
* { "foo", "bar" },
* },
* Region = "us-central1",
* Bigtable = new Gcp.Vertex.Inputs.AiFeatureOnlineStoreBigtableArgs
* {
* AutoScaling = new Gcp.Vertex.Inputs.AiFeatureOnlineStoreBigtableAutoScalingArgs
* {
* MinNodeCount = 1,
* MaxNodeCount = 2,
* CpuUtilizationTarget = 80,
* },
* },
* EmbeddingManagement = new Gcp.Vertex.Inputs.AiFeatureOnlineStoreEmbeddingManagementArgs
* {
* Enabled = true,
* },
* });
* var tf_test_dataset = new Gcp.BigQuery.Dataset("tf-test-dataset", new()
* {
* DatasetId = "example_feature_view_vector_search",
* FriendlyName = "test",
* Description = "This is a test description",
* Location = "US",
* });
* var tf_test_table = new Gcp.BigQuery.Table("tf-test-table", new()
* {
* DeletionProtection = false,
* DatasetId = tf_test_dataset.DatasetId,
* TableId = "example_feature_view_vector_search",
* Schema = @"[
* {
* ""name"": ""test_primary_id"",
* ""mode"": ""NULLABLE"",
* ""type"": ""STRING"",
* ""description"": ""primary test id""
* },
* {
* ""name"": ""embedding"",
* ""mode"": ""REPEATED"",
* ""type"": ""FLOAT"",
* ""description"": ""embedding column for primary_id column""
* },
* {
* ""name"": ""country"",
* ""mode"": ""NULLABLE"",
* ""type"": ""STRING"",
* ""description"": ""country""
* },
* {
* ""name"": ""test_crowding_column"",
* ""mode"": ""NULLABLE"",
* ""type"": ""INTEGER"",
* ""description"": ""test crowding column""
* },
* {
* ""name"": ""entity_id"",
* ""mode"": ""NULLABLE"",
* ""type"": ""STRING"",
* ""description"": ""Test default entity_id""
* },
* {
* ""name"": ""test_entity_column"",
* ""mode"": ""NULLABLE"",
* ""type"": ""STRING"",
* ""description"": ""test secondary entity column""
* },
* {
* ""name"": ""feature_timestamp"",
* ""mode"": ""NULLABLE"",
* ""type"": ""TIMESTAMP"",
* ""description"": ""Default timestamp value""
* }
* ]
* ",
* });
* var featureviewVectorSearch = new Gcp.Vertex.AiFeatureOnlineStoreFeatureview("featureview_vector_search", new()
* {
* Name = "example_feature_view_vector_search",
* Region = "us-central1",
* FeatureOnlineStore = featureonlinestore.Name,
* SyncConfig = new Gcp.Vertex.Inputs.AiFeatureOnlineStoreFeatureviewSyncConfigArgs
* {
* Cron = "0 0 * * *",
* },
* BigQuerySource = new Gcp.Vertex.Inputs.AiFeatureOnlineStoreFeatureviewBigQuerySourceArgs
* {
* Uri = Output.Tuple(tf_test_table.Project, tf_test_table.DatasetId, tf_test_table.TableId).Apply(values =>
* {
* var project = values.Item1;
* var datasetId = values.Item2;
* var tableId = values.Item3;
* return $"bq://{project}.{datasetId}.{tableId}";
* }),
* EntityIdColumns = new[]
* {
* "test_entity_column",
* },
* },
* VectorSearchConfig = new Gcp.Vertex.Inputs.AiFeatureOnlineStoreFeatureviewVectorSearchConfigArgs
* {
* EmbeddingColumn = "embedding",
* FilterColumns = new[]
* {
* "country",
* },
* CrowdingColumn = "test_crowding_column",
* DistanceMeasureType = "DOT_PRODUCT_DISTANCE",
* TreeAhConfig = new Gcp.Vertex.Inputs.AiFeatureOnlineStoreFeatureviewVectorSearchConfigTreeAhConfigArgs
* {
* LeafNodeEmbeddingCount = "1000",
* },
* EmbeddingDimension = 2,
* },
* });
* var project = Gcp.Organizations.GetProject.Invoke();
* });
* ```
* ```go
* package main
* import (
* "fmt"
* "github.com/pulumi/pulumi-gcp/sdk/v7/go/gcp/bigquery"
* "github.com/pulumi/pulumi-gcp/sdk/v7/go/gcp/organizations"
* "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 {
* featureonlinestore, err := vertex.NewAiFeatureOnlineStore(ctx, "featureonlinestore", &vertex.AiFeatureOnlineStoreArgs{
* Name: pulumi.String("example_feature_view_vector_search"),
* Labels: pulumi.StringMap{
* "foo": pulumi.String("bar"),
* },
* Region: pulumi.String("us-central1"),
* Bigtable: &vertex.AiFeatureOnlineStoreBigtableArgs{
* AutoScaling: &vertex.AiFeatureOnlineStoreBigtableAutoScalingArgs{
* MinNodeCount: pulumi.Int(1),
* MaxNodeCount: pulumi.Int(2),
* CpuUtilizationTarget: pulumi.Int(80),
* },
* },
* EmbeddingManagement: &vertex.AiFeatureOnlineStoreEmbeddingManagementArgs{
* Enabled: pulumi.Bool(true),
* },
* })
* if err != nil {
* return err
* }
* _, err = bigquery.NewDataset(ctx, "tf-test-dataset", &bigquery.DatasetArgs{
* DatasetId: pulumi.String("example_feature_view_vector_search"),
* FriendlyName: pulumi.String("test"),
* Description: pulumi.String("This is a test description"),
* Location: pulumi.String("US"),
* })
* if err != nil {
* return err
* }
* _, err = bigquery.NewTable(ctx, "tf-test-table", &bigquery.TableArgs{
* DeletionProtection: pulumi.Bool(false),
* DatasetId: tf_test_dataset.DatasetId,
* TableId: pulumi.String("example_feature_view_vector_search"),
* Schema: pulumi.String(`[
* {
* "name": "test_primary_id",
* "mode": "NULLABLE",
* "type": "STRING",
* "description": "primary test id"
* },
* {
* "name": "embedding",
* "mode": "REPEATED",
* "type": "FLOAT",
* "description": "embedding column for primary_id column"
* },
* {
* "name": "country",
* "mode": "NULLABLE",
* "type": "STRING",
* "description": "country"
* },
* {
* "name": "test_crowding_column",
* "mode": "NULLABLE",
* "type": "INTEGER",
* "description": "test crowding column"
* },
* {
* "name": "entity_id",
* "mode": "NULLABLE",
* "type": "STRING",
* "description": "Test default entity_id"
* },
* {
* "name": "test_entity_column",
* "mode": "NULLABLE",
* "type": "STRING",
* "description": "test secondary entity column"
* },
* {
* "name": "feature_timestamp",
* "mode": "NULLABLE",
* "type": "TIMESTAMP",
* "description": "Default timestamp value"
* }
* ]
* `),
* })
* if err != nil {
* return err
* }
* _, err = vertex.NewAiFeatureOnlineStoreFeatureview(ctx, "featureview_vector_search", &vertex.AiFeatureOnlineStoreFeatureviewArgs{
* Name: pulumi.String("example_feature_view_vector_search"),
* Region: pulumi.String("us-central1"),
* FeatureOnlineStore: featureonlinestore.Name,
* SyncConfig: &vertex.AiFeatureOnlineStoreFeatureviewSyncConfigArgs{
* Cron: pulumi.String("0 0 * * *"),
* },
* BigQuerySource: &vertex.AiFeatureOnlineStoreFeatureviewBigQuerySourceArgs{
* Uri: pulumi.All(tf_test_table.Project, tf_test_table.DatasetId, tf_test_table.TableId).ApplyT(func(_args []interface{}) (string, error) {
* project := _args[0].(string)
* datasetId := _args[1].(string)
* tableId := _args[2].(string)
* return fmt.Sprintf("bq://%v.%v.%v", project, datasetId, tableId), nil
* }).(pulumi.StringOutput),
* EntityIdColumns: pulumi.StringArray{
* pulumi.String("test_entity_column"),
* },
* },
* VectorSearchConfig: &vertex.AiFeatureOnlineStoreFeatureviewVectorSearchConfigArgs{
* EmbeddingColumn: pulumi.String("embedding"),
* FilterColumns: pulumi.StringArray{
* pulumi.String("country"),
* },
* CrowdingColumn: pulumi.String("test_crowding_column"),
* DistanceMeasureType: pulumi.String("DOT_PRODUCT_DISTANCE"),
* TreeAhConfig: &vertex.AiFeatureOnlineStoreFeatureviewVectorSearchConfigTreeAhConfigArgs{
* LeafNodeEmbeddingCount: pulumi.String("1000"),
* },
* EmbeddingDimension: pulumi.Int(2),
* },
* })
* if err != nil {
* return err
* }
* _, err = organizations.LookupProject(ctx, nil, nil)
* if err != nil {
* return err
* }
* return nil
* })
* }
* ```
* ```java
* package generated_program;
* import com.pulumi.Context;
* import com.pulumi.Pulumi;
* import com.pulumi.core.Output;
* import com.pulumi.gcp.vertex.AiFeatureOnlineStore;
* import com.pulumi.gcp.vertex.AiFeatureOnlineStoreArgs;
* import com.pulumi.gcp.vertex.inputs.AiFeatureOnlineStoreBigtableArgs;
* import com.pulumi.gcp.vertex.inputs.AiFeatureOnlineStoreBigtableAutoScalingArgs;
* import com.pulumi.gcp.vertex.inputs.AiFeatureOnlineStoreEmbeddingManagementArgs;
* import com.pulumi.gcp.bigquery.Dataset;
* import com.pulumi.gcp.bigquery.DatasetArgs;
* import com.pulumi.gcp.bigquery.Table;
* import com.pulumi.gcp.bigquery.TableArgs;
* import com.pulumi.gcp.vertex.AiFeatureOnlineStoreFeatureview;
* import com.pulumi.gcp.vertex.AiFeatureOnlineStoreFeatureviewArgs;
* import com.pulumi.gcp.vertex.inputs.AiFeatureOnlineStoreFeatureviewSyncConfigArgs;
* import com.pulumi.gcp.vertex.inputs.AiFeatureOnlineStoreFeatureviewBigQuerySourceArgs;
* import com.pulumi.gcp.vertex.inputs.AiFeatureOnlineStoreFeatureviewVectorSearchConfigArgs;
* import com.pulumi.gcp.vertex.inputs.AiFeatureOnlineStoreFeatureviewVectorSearchConfigTreeAhConfigArgs;
* import com.pulumi.gcp.organizations.OrganizationsFunctions;
* import com.pulumi.gcp.organizations.inputs.GetProjectArgs;
* 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 featureonlinestore = new AiFeatureOnlineStore("featureonlinestore", AiFeatureOnlineStoreArgs.builder()
* .name("example_feature_view_vector_search")
* .labels(Map.of("foo", "bar"))
* .region("us-central1")
* .bigtable(AiFeatureOnlineStoreBigtableArgs.builder()
* .autoScaling(AiFeatureOnlineStoreBigtableAutoScalingArgs.builder()
* .minNodeCount(1)
* .maxNodeCount(2)
* .cpuUtilizationTarget(80)
* .build())
* .build())
* .embeddingManagement(AiFeatureOnlineStoreEmbeddingManagementArgs.builder()
* .enabled(true)
* .build())
* .build());
* var tf_test_dataset = new Dataset("tf-test-dataset", DatasetArgs.builder()
* .datasetId("example_feature_view_vector_search")
* .friendlyName("test")
* .description("This is a test description")
* .location("US")
* .build());
* var tf_test_table = new Table("tf-test-table", TableArgs.builder()
* .deletionProtection(false)
* .datasetId(tf_test_dataset.datasetId())
* .tableId("example_feature_view_vector_search")
* .schema("""
* [
* {
* "name": "test_primary_id",
* "mode": "NULLABLE",
* "type": "STRING",
* "description": "primary test id"
* },
* {
* "name": "embedding",
* "mode": "REPEATED",
* "type": "FLOAT",
* "description": "embedding column for primary_id column"
* },
* {
* "name": "country",
* "mode": "NULLABLE",
* "type": "STRING",
* "description": "country"
* },
* {
* "name": "test_crowding_column",
* "mode": "NULLABLE",
* "type": "INTEGER",
* "description": "test crowding column"
* },
* {
* "name": "entity_id",
* "mode": "NULLABLE",
* "type": "STRING",
* "description": "Test default entity_id"
* },
* {
* "name": "test_entity_column",
* "mode": "NULLABLE",
* "type": "STRING",
* "description": "test secondary entity column"
* },
* {
* "name": "feature_timestamp",
* "mode": "NULLABLE",
* "type": "TIMESTAMP",
* "description": "Default timestamp value"
* }
* ]
* """)
* .build());
* var featureviewVectorSearch = new AiFeatureOnlineStoreFeatureview("featureviewVectorSearch", AiFeatureOnlineStoreFeatureviewArgs.builder()
* .name("example_feature_view_vector_search")
* .region("us-central1")
* .featureOnlineStore(featureonlinestore.name())
* .syncConfig(AiFeatureOnlineStoreFeatureviewSyncConfigArgs.builder()
* .cron("0 0 * * *")
* .build())
* .bigQuerySource(AiFeatureOnlineStoreFeatureviewBigQuerySourceArgs.builder()
* .uri(Output.tuple(tf_test_table.project(), tf_test_table.datasetId(), tf_test_table.tableId()).applyValue(values -> {
* var project = values.t1;
* var datasetId = values.t2;
* var tableId = values.t3;
* return String.format("bq://%s.%s.%s", project,datasetId,tableId);
* }))
* .entityIdColumns("test_entity_column")
* .build())
* .vectorSearchConfig(AiFeatureOnlineStoreFeatureviewVectorSearchConfigArgs.builder()
* .embeddingColumn("embedding")
* .filterColumns("country")
* .crowdingColumn("test_crowding_column")
* .distanceMeasureType("DOT_PRODUCT_DISTANCE")
* .treeAhConfig(AiFeatureOnlineStoreFeatureviewVectorSearchConfigTreeAhConfigArgs.builder()
* .leafNodeEmbeddingCount("1000")
* .build())
* .embeddingDimension("2")
* .build())
* .build());
* final var project = OrganizationsFunctions.getProject();
* }
* }
* ```
* ```yaml
* resources:
* featureonlinestore:
* type: gcp:vertex:AiFeatureOnlineStore
* properties:
* name: example_feature_view_vector_search
* labels:
* foo: bar
* region: us-central1
* bigtable:
* autoScaling:
* minNodeCount: 1
* maxNodeCount: 2
* cpuUtilizationTarget: 80
* embeddingManagement:
* enabled: true
* tf-test-dataset:
* type: gcp:bigquery:Dataset
* properties:
* datasetId: example_feature_view_vector_search
* friendlyName: test
* description: This is a test description
* location: US
* tf-test-table:
* type: gcp:bigquery:Table
* properties:
* deletionProtection: false
* datasetId: ${["tf-test-dataset"].datasetId}
* tableId: example_feature_view_vector_search
* schema: |
* [
* {
* "name": "test_primary_id",
* "mode": "NULLABLE",
* "type": "STRING",
* "description": "primary test id"
* },
* {
* "name": "embedding",
* "mode": "REPEATED",
* "type": "FLOAT",
* "description": "embedding column for primary_id column"
* },
* {
* "name": "country",
* "mode": "NULLABLE",
* "type": "STRING",
* "description": "country"
* },
* {
* "name": "test_crowding_column",
* "mode": "NULLABLE",
* "type": "INTEGER",
* "description": "test crowding column"
* },
* {
* "name": "entity_id",
* "mode": "NULLABLE",
* "type": "STRING",
* "description": "Test default entity_id"
* },
* {
* "name": "test_entity_column",
* "mode": "NULLABLE",
* "type": "STRING",
* "description": "test secondary entity column"
* },
* {
* "name": "feature_timestamp",
* "mode": "NULLABLE",
* "type": "TIMESTAMP",
* "description": "Default timestamp value"
* }
* ]
* featureviewVectorSearch:
* type: gcp:vertex:AiFeatureOnlineStoreFeatureview
* name: featureview_vector_search
* properties:
* name: example_feature_view_vector_search
* region: us-central1
* featureOnlineStore: ${featureonlinestore.name}
* syncConfig:
* cron: 0 0 * * *
* bigQuerySource:
* uri: bq://${["tf-test-table"].project}.${["tf-test-table"].datasetId}.${["tf-test-table"].tableId}
* entityIdColumns:
* - test_entity_column
* vectorSearchConfig:
* embeddingColumn: embedding
* filterColumns:
* - country
* crowdingColumn: test_crowding_column
* distanceMeasureType: DOT_PRODUCT_DISTANCE
* treeAhConfig:
* leafNodeEmbeddingCount: '1000'
* embeddingDimension: '2'
* variables:
* project:
* fn::invoke:
* Function: gcp:organizations:getProject
* Arguments: {}
* ```
*
* ## Import
* FeatureOnlineStoreFeatureview can be imported using any of these accepted formats:
* * `projects/{{project}}/locations/{{region}}/featureOnlineStores/{{feature_online_store}}/featureViews/{{name}}`
* * `{{project}}/{{region}}/{{feature_online_store}}/{{name}}`
* * `{{region}}/{{feature_online_store}}/{{name}}`
* * `{{feature_online_store}}/{{name}}`
* When using the `pulumi import` command, FeatureOnlineStoreFeatureview can be imported using one of the formats above. For example:
* ```sh
* $ pulumi import gcp:vertex/aiFeatureOnlineStoreFeatureview:AiFeatureOnlineStoreFeatureview default projects/{{project}}/locations/{{region}}/featureOnlineStores/{{feature_online_store}}/featureViews/{{name}}
* ```
* ```sh
* $ pulumi import gcp:vertex/aiFeatureOnlineStoreFeatureview:AiFeatureOnlineStoreFeatureview default {{project}}/{{region}}/{{feature_online_store}}/{{name}}
* ```
* ```sh
* $ pulumi import gcp:vertex/aiFeatureOnlineStoreFeatureview:AiFeatureOnlineStoreFeatureview default {{region}}/{{feature_online_store}}/{{name}}
* ```
* ```sh
* $ pulumi import gcp:vertex/aiFeatureOnlineStoreFeatureview:AiFeatureOnlineStoreFeatureview default {{feature_online_store}}/{{name}}
* ```
*/
public class AiFeatureOnlineStoreFeatureview internal constructor(
override val javaResource: com.pulumi.gcp.vertex.AiFeatureOnlineStoreFeatureview,
) : KotlinCustomResource(javaResource, AiFeatureOnlineStoreFeatureviewMapper) {
/**
* Configures how data is supposed to be extracted from a BigQuery source to be loaded onto the FeatureOnlineStore.
* Structure is documented below.
*/
public val bigQuerySource: Output?
get() = javaResource.bigQuerySource().applyValue({ args0 ->
args0.map({ args0 ->
args0.let({ args0 ->
aiFeatureOnlineStoreFeatureviewBigQuerySourceToKotlin(args0)
})
}).orElse(null)
})
/**
* The timestamp of when the featureOnlinestore was created in RFC3339 UTC "Zulu" format, with nanosecond resolution and up to nine fractional digits.
*/
public val createTime: Output
get() = javaResource.createTime().applyValue({ args0 -> args0 })
/**
* All of labels (key/value pairs) present on the resource in GCP, including the labels configured through Pulumi, other clients and services.
*/
public val effectiveLabels: Output>
get() = javaResource.effectiveLabels().applyValue({ args0 ->
args0.map({ args0 ->
args0.key.to(args0.value)
}).toMap()
})
/**
* The name of the FeatureOnlineStore to use for the featureview.
*/
public val featureOnlineStore: Output
get() = javaResource.featureOnlineStore().applyValue({ args0 -> args0 })
/**
* Configures the features from a Feature Registry source that need to be loaded onto the FeatureOnlineStore.
* Structure is documented below.
*/
public val featureRegistrySource: Output?
get() = javaResource.featureRegistrySource().applyValue({ args0 ->
args0.map({ args0 ->
args0.let({ args0 ->
aiFeatureOnlineStoreFeatureviewFeatureRegistrySourceToKotlin(args0)
})
}).orElse(null)
})
/**
* A set of key/value label pairs to assign to this FeatureView.
* **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.
*/
public val labels: Output>?
get() = javaResource.labels().applyValue({ args0 ->
args0.map({ args0 ->
args0.map({ args0 ->
args0.key.to(args0.value)
}).toMap()
}).orElse(null)
})
/**
* Name of the FeatureView. This value may be up to 60 characters, and valid characters are [a-z0-9_]. The first character cannot be a number.
*/
public val name: Output
get() = javaResource.name().applyValue({ args0 -> args0 })
/**
* The ID of the project in which the resource belongs.
* If it is not provided, the provider project is used.
*/
public val project: Output
get() = javaResource.project().applyValue({ args0 -> args0 })
/**
* The combination of labels configured directly on the resource
* and default labels configured on the provider.
*/
public val pulumiLabels: Output>
get() = javaResource.pulumiLabels().applyValue({ args0 ->
args0.map({ args0 ->
args0.key.to(args0.value)
}).toMap()
})
/**
* The region for the resource. It should be the same as the featureonlinestore region.
* - - -
*/
public val region: Output
get() = javaResource.region().applyValue({ args0 -> args0 })
/**
* Configures when data is to be synced/updated for this FeatureView. At the end of the sync the latest featureValues for each entityId of this FeatureView are made ready for online serving.
* Structure is documented below.
*/
public val syncConfig: Output?
get() = javaResource.syncConfig().applyValue({ args0 ->
args0.map({ args0 ->
args0.let({ args0 ->
aiFeatureOnlineStoreFeatureviewSyncConfigToKotlin(args0)
})
}).orElse(null)
})
/**
* The timestamp of when the featureOnlinestore was last updated in RFC3339 UTC "Zulu" format, with nanosecond resolution and up to nine fractional digits.
*/
public val updateTime: Output
get() = javaResource.updateTime().applyValue({ args0 -> args0 })
/**
* Configuration for vector search. It contains the required configurations to create an index from source data, so that approximate nearest neighbor (a.k.a ANN) algorithms search can be performed during online serving.
* Structure is documented below.
*/
public val vectorSearchConfig: Output?
get() = javaResource.vectorSearchConfig().applyValue({ args0 ->
args0.map({ args0 ->
args0.let({ args0 ->
aiFeatureOnlineStoreFeatureviewVectorSearchConfigToKotlin(args0)
})
}).orElse(null)
})
}
public object AiFeatureOnlineStoreFeatureviewMapper :
ResourceMapper {
override fun supportsMappingOfType(javaResource: Resource): Boolean =
com.pulumi.gcp.vertex.AiFeatureOnlineStoreFeatureview::class == javaResource::class
override fun map(javaResource: Resource): AiFeatureOnlineStoreFeatureview =
AiFeatureOnlineStoreFeatureview(
javaResource as
com.pulumi.gcp.vertex.AiFeatureOnlineStoreFeatureview,
)
}
/**
* @see [AiFeatureOnlineStoreFeatureview].
* @param name The _unique_ name of the resulting resource.
* @param block Builder for [AiFeatureOnlineStoreFeatureview].
*/
public suspend fun aiFeatureOnlineStoreFeatureview(
name: String,
block: suspend AiFeatureOnlineStoreFeatureviewResourceBuilder.() -> Unit,
): AiFeatureOnlineStoreFeatureview {
val builder = AiFeatureOnlineStoreFeatureviewResourceBuilder()
builder.name(name)
block(builder)
return builder.build()
}
/**
* @see [AiFeatureOnlineStoreFeatureview].
* @param name The _unique_ name of the resulting resource.
*/
public fun aiFeatureOnlineStoreFeatureview(name: String): AiFeatureOnlineStoreFeatureview {
val builder = AiFeatureOnlineStoreFeatureviewResourceBuilder()
builder.name(name)
return builder.build()
}
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