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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.core.Output.of
import com.pulumi.gcp.vertex.AiFeatureOnlineStoreFeatureviewArgs.builder
import com.pulumi.gcp.vertex.kotlin.inputs.AiFeatureOnlineStoreFeatureviewBigQuerySourceArgs
import com.pulumi.gcp.vertex.kotlin.inputs.AiFeatureOnlineStoreFeatureviewBigQuerySourceArgsBuilder
import com.pulumi.gcp.vertex.kotlin.inputs.AiFeatureOnlineStoreFeatureviewFeatureRegistrySourceArgs
import com.pulumi.gcp.vertex.kotlin.inputs.AiFeatureOnlineStoreFeatureviewFeatureRegistrySourceArgsBuilder
import com.pulumi.gcp.vertex.kotlin.inputs.AiFeatureOnlineStoreFeatureviewSyncConfigArgs
import com.pulumi.gcp.vertex.kotlin.inputs.AiFeatureOnlineStoreFeatureviewSyncConfigArgsBuilder
import com.pulumi.gcp.vertex.kotlin.inputs.AiFeatureOnlineStoreFeatureviewVectorSearchConfigArgs
import com.pulumi.gcp.vertex.kotlin.inputs.AiFeatureOnlineStoreFeatureviewVectorSearchConfigArgsBuilder
import com.pulumi.kotlin.ConvertibleToJava
import com.pulumi.kotlin.PulumiTagMarker
import com.pulumi.kotlin.applySuspend
import kotlin.Pair
import kotlin.String
import kotlin.Suppress
import kotlin.Unit
import kotlin.collections.Map
import kotlin.jvm.JvmName
/**
* 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}}
* ```
* @property bigQuerySource Configures how data is supposed to be extracted from a BigQuery source to be loaded onto the FeatureOnlineStore.
* Structure is documented below.
* @property featureOnlineStore The name of the FeatureOnlineStore to use for the featureview.
* @property featureRegistrySource Configures the features from a Feature Registry source that need to be loaded onto the FeatureOnlineStore.
* Structure is documented below.
* @property labels 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.
* @property name 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.
* @property project The ID of the project in which the resource belongs.
* If it is not provided, the provider project is used.
* @property region The region for the resource. It should be the same as the featureonlinestore region.
* - - -
* @property syncConfig 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.
* @property vectorSearchConfig 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 data class AiFeatureOnlineStoreFeatureviewArgs(
public val bigQuerySource: Output? = null,
public val featureOnlineStore: Output? = null,
public val featureRegistrySource: Output? = null,
public val labels: Output>? = null,
public val name: Output? = null,
public val project: Output? = null,
public val region: Output? = null,
public val syncConfig: Output? = null,
public val vectorSearchConfig: Output? =
null,
) : ConvertibleToJava {
override fun toJava(): com.pulumi.gcp.vertex.AiFeatureOnlineStoreFeatureviewArgs =
com.pulumi.gcp.vertex.AiFeatureOnlineStoreFeatureviewArgs.builder()
.bigQuerySource(bigQuerySource?.applyValue({ args0 -> args0.let({ args0 -> args0.toJava() }) }))
.featureOnlineStore(featureOnlineStore?.applyValue({ args0 -> args0 }))
.featureRegistrySource(
featureRegistrySource?.applyValue({ args0 ->
args0.let({ args0 ->
args0.toJava()
})
}),
)
.labels(labels?.applyValue({ args0 -> args0.map({ args0 -> args0.key.to(args0.value) }).toMap() }))
.name(name?.applyValue({ args0 -> args0 }))
.project(project?.applyValue({ args0 -> args0 }))
.region(region?.applyValue({ args0 -> args0 }))
.syncConfig(syncConfig?.applyValue({ args0 -> args0.let({ args0 -> args0.toJava() }) }))
.vectorSearchConfig(
vectorSearchConfig?.applyValue({ args0 ->
args0.let({ args0 ->
args0.toJava()
})
}),
).build()
}
/**
* Builder for [AiFeatureOnlineStoreFeatureviewArgs].
*/
@PulumiTagMarker
public class AiFeatureOnlineStoreFeatureviewArgsBuilder internal constructor() {
private var bigQuerySource: Output? = null
private var featureOnlineStore: Output? = null
private var featureRegistrySource:
Output? = null
private var labels: Output>? = null
private var name: Output? = null
private var project: Output? = null
private var region: Output? = null
private var syncConfig: Output? = null
private var vectorSearchConfig: Output? =
null
/**
* @param value Configures how data is supposed to be extracted from a BigQuery source to be loaded onto the FeatureOnlineStore.
* Structure is documented below.
*/
@JvmName("yeflfsieaypnakmh")
public suspend fun bigQuerySource(`value`: Output) {
this.bigQuerySource = value
}
/**
* @param value The name of the FeatureOnlineStore to use for the featureview.
*/
@JvmName("tpshnbexlhhxqqgg")
public suspend fun featureOnlineStore(`value`: Output) {
this.featureOnlineStore = value
}
/**
* @param value Configures the features from a Feature Registry source that need to be loaded onto the FeatureOnlineStore.
* Structure is documented below.
*/
@JvmName("jwjqbsctvihhgekx")
public suspend fun featureRegistrySource(`value`: Output) {
this.featureRegistrySource = value
}
/**
* @param value 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.
*/
@JvmName("tmbybnglgoaxjcnd")
public suspend fun labels(`value`: Output>) {
this.labels = value
}
/**
* @param value 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.
*/
@JvmName("erqktydxpxaxkmyj")
public suspend fun name(`value`: Output) {
this.name = value
}
/**
* @param value The ID of the project in which the resource belongs.
* If it is not provided, the provider project is used.
*/
@JvmName("unxgbghcnhlabqhq")
public suspend fun project(`value`: Output) {
this.project = value
}
/**
* @param value The region for the resource. It should be the same as the featureonlinestore region.
* - - -
*/
@JvmName("sytjqqfmyptdacst")
public suspend fun region(`value`: Output) {
this.region = value
}
/**
* @param value 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.
*/
@JvmName("glfuxmiuaxbanouc")
public suspend fun syncConfig(`value`: Output) {
this.syncConfig = value
}
/**
* @param value 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.
*/
@JvmName("gfkuidvhfykuingj")
public suspend fun vectorSearchConfig(`value`: Output) {
this.vectorSearchConfig = value
}
/**
* @param value Configures how data is supposed to be extracted from a BigQuery source to be loaded onto the FeatureOnlineStore.
* Structure is documented below.
*/
@JvmName("rhvltuvnpnfkchqk")
public suspend fun bigQuerySource(`value`: AiFeatureOnlineStoreFeatureviewBigQuerySourceArgs?) {
val toBeMapped = value
val mapped = toBeMapped?.let({ args0 -> of(args0) })
this.bigQuerySource = mapped
}
/**
* @param argument Configures how data is supposed to be extracted from a BigQuery source to be loaded onto the FeatureOnlineStore.
* Structure is documented below.
*/
@JvmName("yvlkrxhhuckyhuqg")
public suspend fun bigQuerySource(argument: suspend AiFeatureOnlineStoreFeatureviewBigQuerySourceArgsBuilder.() -> Unit) {
val toBeMapped = AiFeatureOnlineStoreFeatureviewBigQuerySourceArgsBuilder().applySuspend {
argument()
}.build()
val mapped = of(toBeMapped)
this.bigQuerySource = mapped
}
/**
* @param value The name of the FeatureOnlineStore to use for the featureview.
*/
@JvmName("tpwsdhahnasginhb")
public suspend fun featureOnlineStore(`value`: String?) {
val toBeMapped = value
val mapped = toBeMapped?.let({ args0 -> of(args0) })
this.featureOnlineStore = mapped
}
/**
* @param value Configures the features from a Feature Registry source that need to be loaded onto the FeatureOnlineStore.
* Structure is documented below.
*/
@JvmName("jsrisntxkanfhboh")
public suspend fun featureRegistrySource(`value`: AiFeatureOnlineStoreFeatureviewFeatureRegistrySourceArgs?) {
val toBeMapped = value
val mapped = toBeMapped?.let({ args0 -> of(args0) })
this.featureRegistrySource = mapped
}
/**
* @param argument Configures the features from a Feature Registry source that need to be loaded onto the FeatureOnlineStore.
* Structure is documented below.
*/
@JvmName("lbrjornxuvflopej")
public suspend fun featureRegistrySource(argument: suspend AiFeatureOnlineStoreFeatureviewFeatureRegistrySourceArgsBuilder.() -> Unit) {
val toBeMapped = AiFeatureOnlineStoreFeatureviewFeatureRegistrySourceArgsBuilder().applySuspend {
argument()
}.build()
val mapped = of(toBeMapped)
this.featureRegistrySource = mapped
}
/**
* @param value 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.
*/
@JvmName("yqvvuahhshmaxluf")
public suspend fun labels(`value`: Map?) {
val toBeMapped = value
val mapped = toBeMapped?.let({ args0 -> of(args0) })
this.labels = mapped
}
/**
* @param values 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.
*/
@JvmName("jvqswgxkylbtpkvo")
public fun labels(vararg values: Pair) {
val toBeMapped = values.toMap()
val mapped = toBeMapped.let({ args0 -> of(args0) })
this.labels = mapped
}
/**
* @param value 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.
*/
@JvmName("ytilscpswnogylxx")
public suspend fun name(`value`: String?) {
val toBeMapped = value
val mapped = toBeMapped?.let({ args0 -> of(args0) })
this.name = mapped
}
/**
* @param value The ID of the project in which the resource belongs.
* If it is not provided, the provider project is used.
*/
@JvmName("usfjxdwmkbkfftxp")
public suspend fun project(`value`: String?) {
val toBeMapped = value
val mapped = toBeMapped?.let({ args0 -> of(args0) })
this.project = mapped
}
/**
* @param value The region for the resource. It should be the same as the featureonlinestore region.
* - - -
*/
@JvmName("mkqbutuhxxbuvkdr")
public suspend fun region(`value`: String?) {
val toBeMapped = value
val mapped = toBeMapped?.let({ args0 -> of(args0) })
this.region = mapped
}
/**
* @param value 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.
*/
@JvmName("fdhodvcweewybjwg")
public suspend fun syncConfig(`value`: AiFeatureOnlineStoreFeatureviewSyncConfigArgs?) {
val toBeMapped = value
val mapped = toBeMapped?.let({ args0 -> of(args0) })
this.syncConfig = mapped
}
/**
* @param argument 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.
*/
@JvmName("ohgmrlhnlghxhdhc")
public suspend fun syncConfig(argument: suspend AiFeatureOnlineStoreFeatureviewSyncConfigArgsBuilder.() -> Unit) {
val toBeMapped = AiFeatureOnlineStoreFeatureviewSyncConfigArgsBuilder().applySuspend {
argument()
}.build()
val mapped = of(toBeMapped)
this.syncConfig = mapped
}
/**
* @param value 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.
*/
@JvmName("mbkkohjfomgdwcof")
public suspend fun vectorSearchConfig(`value`: AiFeatureOnlineStoreFeatureviewVectorSearchConfigArgs?) {
val toBeMapped = value
val mapped = toBeMapped?.let({ args0 -> of(args0) })
this.vectorSearchConfig = mapped
}
/**
* @param argument 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.
*/
@JvmName("itdrkibptiqjyqik")
public suspend fun vectorSearchConfig(argument: suspend AiFeatureOnlineStoreFeatureviewVectorSearchConfigArgsBuilder.() -> Unit) {
val toBeMapped = AiFeatureOnlineStoreFeatureviewVectorSearchConfigArgsBuilder().applySuspend {
argument()
}.build()
val mapped = of(toBeMapped)
this.vectorSearchConfig = mapped
}
internal fun build(): AiFeatureOnlineStoreFeatureviewArgs = AiFeatureOnlineStoreFeatureviewArgs(
bigQuerySource = bigQuerySource,
featureOnlineStore = featureOnlineStore,
featureRegistrySource = featureRegistrySource,
labels = labels,
name = name,
project = project,
region = region,
syncConfig = syncConfig,
vectorSearchConfig = vectorSearchConfig,
)
}
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