Google Cloud Classic v7.29.0, Jun 26 24

Google Cloud Classic v7.29.0 published on Wednesday, Jun 26, 2024 by Pulumi

gcp.vertex.AiFeatureOnlineStoreFeatureview

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Google Cloud Classic v7.29.0 published on Wednesday, Jun 26, 2024 by Pulumi

pulumi/pulumi-gcp

Example Usage

Vertex Ai Featureonlinestore Featureview

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({});

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()

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
	})
}

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();

});

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();

    }
}

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

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],
        }],
    },
});

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],
        )],
    ))

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
	})
}

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,
                    },
                },
            },
        },
    });

});

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());

    }
}

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

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({});

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()

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
	})
}

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();

});

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();

    }
}

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: {}

Create AiFeatureOnlineStoreFeatureview Resource

Resources are created with functions called constructors. To learn more about declaring and configuring resources, see Resources.

Constructor syntax

new AiFeatureOnlineStoreFeatureview(name: string, args: AiFeatureOnlineStoreFeatureviewArgs, opts?: CustomResourceOptions);

@overload
def AiFeatureOnlineStoreFeatureview(resource_name: str,
                                    args: AiFeatureOnlineStoreFeatureviewArgs,
                                    opts: Optional[ResourceOptions] = None)

@overload
def AiFeatureOnlineStoreFeatureview(resource_name: str,
                                    opts: Optional[ResourceOptions] = None,
                                    feature_online_store: Optional[str] = None,
                                    region: Optional[str] = None,
                                    big_query_source: Optional[AiFeatureOnlineStoreFeatureviewBigQuerySourceArgs] = None,
                                    feature_registry_source: Optional[AiFeatureOnlineStoreFeatureviewFeatureRegistrySourceArgs] = None,
                                    labels: Optional[Mapping[str, str]] = None,
                                    name: Optional[str] = None,
                                    project: Optional[str] = None,
                                    sync_config: Optional[AiFeatureOnlineStoreFeatureviewSyncConfigArgs] = None,
                                    vector_search_config: Optional[AiFeatureOnlineStoreFeatureviewVectorSearchConfigArgs] = None)

func NewAiFeatureOnlineStoreFeatureview(ctx *Context, name string, args AiFeatureOnlineStoreFeatureviewArgs, opts ...ResourceOption) (*AiFeatureOnlineStoreFeatureview, error)

public AiFeatureOnlineStoreFeatureview(string name, AiFeatureOnlineStoreFeatureviewArgs args, CustomResourceOptions? opts = null)

public AiFeatureOnlineStoreFeatureview(String name, AiFeatureOnlineStoreFeatureviewArgs args)
public AiFeatureOnlineStoreFeatureview(String name, AiFeatureOnlineStoreFeatureviewArgs args, CustomResourceOptions options)

type: gcp:vertex:AiFeatureOnlineStoreFeatureview
properties: # The arguments to resource properties.
options: # Bag of options to control resource's behavior.

Parameters

name string: The unique name of the resource.
args AiFeatureOnlineStoreFeatureviewArgs: The arguments to resource properties.
opts CustomResourceOptions: Bag of options to control resource's behavior.

resource_name str: The unique name of the resource.
args AiFeatureOnlineStoreFeatureviewArgs: The arguments to resource properties.
opts ResourceOptions: Bag of options to control resource's behavior.

ctx Context: Context object for the current deployment.
name string: The unique name of the resource.
args AiFeatureOnlineStoreFeatureviewArgs: The arguments to resource properties.
opts ResourceOption: Bag of options to control resource's behavior.

name string: The unique name of the resource.
args AiFeatureOnlineStoreFeatureviewArgs: The arguments to resource properties.
opts CustomResourceOptions: Bag of options to control resource's behavior.

name String: The unique name of the resource.
args AiFeatureOnlineStoreFeatureviewArgs: The arguments to resource properties.
options CustomResourceOptions: Bag of options to control resource's behavior.

Constructor example

The following reference example uses placeholder values for all input properties.

var aiFeatureOnlineStoreFeatureviewResource = new Gcp.Vertex.AiFeatureOnlineStoreFeatureview("aiFeatureOnlineStoreFeatureviewResource", new()
{
    FeatureOnlineStore = "string",
    Region = "string",
    BigQuerySource = new Gcp.Vertex.Inputs.AiFeatureOnlineStoreFeatureviewBigQuerySourceArgs
    {
        EntityIdColumns = new[]
        {
            "string",
        },
        Uri = "string",
    },
    FeatureRegistrySource = new Gcp.Vertex.Inputs.AiFeatureOnlineStoreFeatureviewFeatureRegistrySourceArgs
    {
        FeatureGroups = new[]
        {
            new Gcp.Vertex.Inputs.AiFeatureOnlineStoreFeatureviewFeatureRegistrySourceFeatureGroupArgs
            {
                FeatureGroupId = "string",
                FeatureIds = new[]
                {
                    "string",
                },
            },
        },
    },
    Labels = 
    {
        { "string", "string" },
    },
    Name = "string",
    Project = "string",
    SyncConfig = new Gcp.Vertex.Inputs.AiFeatureOnlineStoreFeatureviewSyncConfigArgs
    {
        Cron = "string",
    },
    VectorSearchConfig = new Gcp.Vertex.Inputs.AiFeatureOnlineStoreFeatureviewVectorSearchConfigArgs
    {
        EmbeddingColumn = "string",
        BruteForceConfig = null,
        CrowdingColumn = "string",
        DistanceMeasureType = "string",
        EmbeddingDimension = 0,
        FilterColumns = new[]
        {
            "string",
        },
        TreeAhConfig = new Gcp.Vertex.Inputs.AiFeatureOnlineStoreFeatureviewVectorSearchConfigTreeAhConfigArgs
        {
            LeafNodeEmbeddingCount = "string",
        },
    },
});

example, err := vertex.NewAiFeatureOnlineStoreFeatureview(ctx, "aiFeatureOnlineStoreFeatureviewResource", &vertex.AiFeatureOnlineStoreFeatureviewArgs{
	FeatureOnlineStore: pulumi.String("string"),
	Region:             pulumi.String("string"),
	BigQuerySource: &vertex.AiFeatureOnlineStoreFeatureviewBigQuerySourceArgs{
		EntityIdColumns: pulumi.StringArray{
			pulumi.String("string"),
		},
		Uri: pulumi.String("string"),
	},
	FeatureRegistrySource: &vertex.AiFeatureOnlineStoreFeatureviewFeatureRegistrySourceArgs{
		FeatureGroups: vertex.AiFeatureOnlineStoreFeatureviewFeatureRegistrySourceFeatureGroupArray{
			&vertex.AiFeatureOnlineStoreFeatureviewFeatureRegistrySourceFeatureGroupArgs{
				FeatureGroupId: pulumi.String("string"),
				FeatureIds: pulumi.StringArray{
					pulumi.String("string"),
				},
			},
		},
	},
	Labels: pulumi.StringMap{
		"string": pulumi.String("string"),
	},
	Name:    pulumi.String("string"),
	Project: pulumi.String("string"),
	SyncConfig: &vertex.AiFeatureOnlineStoreFeatureviewSyncConfigArgs{
		Cron: pulumi.String("string"),
	},
	VectorSearchConfig: &vertex.AiFeatureOnlineStoreFeatureviewVectorSearchConfigArgs{
		EmbeddingColumn:     pulumi.String("string"),
		BruteForceConfig:    nil,
		CrowdingColumn:      pulumi.String("string"),
		DistanceMeasureType: pulumi.String("string"),
		EmbeddingDimension:  pulumi.Int(0),
		FilterColumns: pulumi.StringArray{
			pulumi.String("string"),
		},
		TreeAhConfig: &vertex.AiFeatureOnlineStoreFeatureviewVectorSearchConfigTreeAhConfigArgs{
			LeafNodeEmbeddingCount: pulumi.String("string"),
		},
	},
})

var aiFeatureOnlineStoreFeatureviewResource = new AiFeatureOnlineStoreFeatureview("aiFeatureOnlineStoreFeatureviewResource", AiFeatureOnlineStoreFeatureviewArgs.builder()
    .featureOnlineStore("string")
    .region("string")
    .bigQuerySource(AiFeatureOnlineStoreFeatureviewBigQuerySourceArgs.builder()
        .entityIdColumns("string")
        .uri("string")
        .build())
    .featureRegistrySource(AiFeatureOnlineStoreFeatureviewFeatureRegistrySourceArgs.builder()
        .featureGroups(AiFeatureOnlineStoreFeatureviewFeatureRegistrySourceFeatureGroupArgs.builder()
            .featureGroupId("string")
            .featureIds("string")
            .build())
        .build())
    .labels(Map.of("string", "string"))
    .name("string")
    .project("string")
    .syncConfig(AiFeatureOnlineStoreFeatureviewSyncConfigArgs.builder()
        .cron("string")
        .build())
    .vectorSearchConfig(AiFeatureOnlineStoreFeatureviewVectorSearchConfigArgs.builder()
        .embeddingColumn("string")
        .bruteForceConfig()
        .crowdingColumn("string")
        .distanceMeasureType("string")
        .embeddingDimension(0)
        .filterColumns("string")
        .treeAhConfig(AiFeatureOnlineStoreFeatureviewVectorSearchConfigTreeAhConfigArgs.builder()
            .leafNodeEmbeddingCount("string")
            .build())
        .build())
    .build());

ai_feature_online_store_featureview_resource = gcp.vertex.AiFeatureOnlineStoreFeatureview("aiFeatureOnlineStoreFeatureviewResource",
    feature_online_store="string",
    region="string",
    big_query_source=gcp.vertex.AiFeatureOnlineStoreFeatureviewBigQuerySourceArgs(
        entity_id_columns=["string"],
        uri="string",
    ),
    feature_registry_source=gcp.vertex.AiFeatureOnlineStoreFeatureviewFeatureRegistrySourceArgs(
        feature_groups=[gcp.vertex.AiFeatureOnlineStoreFeatureviewFeatureRegistrySourceFeatureGroupArgs(
            feature_group_id="string",
            feature_ids=["string"],
        )],
    ),
    labels={
        "string": "string",
    },
    name="string",
    project="string",
    sync_config=gcp.vertex.AiFeatureOnlineStoreFeatureviewSyncConfigArgs(
        cron="string",
    ),
    vector_search_config=gcp.vertex.AiFeatureOnlineStoreFeatureviewVectorSearchConfigArgs(
        embedding_column="string",
        brute_force_config=gcp.vertex.AiFeatureOnlineStoreFeatureviewVectorSearchConfigBruteForceConfigArgs(),
        crowding_column="string",
        distance_measure_type="string",
        embedding_dimension=0,
        filter_columns=["string"],
        tree_ah_config=gcp.vertex.AiFeatureOnlineStoreFeatureviewVectorSearchConfigTreeAhConfigArgs(
            leaf_node_embedding_count="string",
        ),
    ))

const aiFeatureOnlineStoreFeatureviewResource = new gcp.vertex.AiFeatureOnlineStoreFeatureview("aiFeatureOnlineStoreFeatureviewResource", {
    featureOnlineStore: "string",
    region: "string",
    bigQuerySource: {
        entityIdColumns: ["string"],
        uri: "string",
    },
    featureRegistrySource: {
        featureGroups: [{
            featureGroupId: "string",
            featureIds: ["string"],
        }],
    },
    labels: {
        string: "string",
    },
    name: "string",
    project: "string",
    syncConfig: {
        cron: "string",
    },
    vectorSearchConfig: {
        embeddingColumn: "string",
        bruteForceConfig: {},
        crowdingColumn: "string",
        distanceMeasureType: "string",
        embeddingDimension: 0,
        filterColumns: ["string"],
        treeAhConfig: {
            leafNodeEmbeddingCount: "string",
        },
    },
});

type: gcp:vertex:AiFeatureOnlineStoreFeatureview
properties:
    bigQuerySource:
        entityIdColumns:
            - string
        uri: string
    featureOnlineStore: string
    featureRegistrySource:
        featureGroups:
            - featureGroupId: string
              featureIds:
                - string
    labels:
        string: string
    name: string
    project: string
    region: string
    syncConfig:
        cron: string
    vectorSearchConfig:
        bruteForceConfig: {}
        crowdingColumn: string
        distanceMeasureType: string
        embeddingColumn: string
        embeddingDimension: 0
        filterColumns:
            - string
        treeAhConfig:
            leafNodeEmbeddingCount: string

AiFeatureOnlineStoreFeatureview Resource Properties

To learn more about resource properties and how to use them, see Inputs and Outputs in the Architecture and Concepts docs.

Inputs

The AiFeatureOnlineStoreFeatureview resource accepts the following input properties:

FeatureOnlineStore string

The name of the FeatureOnlineStore to use for the featureview.

Region string

The region for the resource. It should be the same as the featureonlinestore region.

BigQuerySource AiFeatureOnlineStoreFeatureviewBigQuerySource

Configures how data is supposed to be extracted from a BigQuery source to be loaded onto the FeatureOnlineStore. Structure is documented below.

FeatureRegistrySource AiFeatureOnlineStoreFeatureviewFeatureRegistrySource

Configures the features from a Feature Registry source that need to be loaded onto the FeatureOnlineStore. Structure is documented below.

Labels Dictionary<string, string>

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.

Name string

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.

Project string

The ID of the project in which the resource belongs. If it is not provided, the provider project is used.

SyncConfig AiFeatureOnlineStoreFeatureviewSyncConfig

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.

VectorSearchConfig AiFeatureOnlineStoreFeatureviewVectorSearchConfig

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.

FeatureOnlineStore string

The name of the FeatureOnlineStore to use for the featureview.

Region string

The region for the resource. It should be the same as the featureonlinestore region.

BigQuerySource AiFeatureOnlineStoreFeatureviewBigQuerySourceArgs

Configures how data is supposed to be extracted from a BigQuery source to be loaded onto the FeatureOnlineStore. Structure is documented below.

FeatureRegistrySource AiFeatureOnlineStoreFeatureviewFeatureRegistrySourceArgs

Configures the features from a Feature Registry source that need to be loaded onto the FeatureOnlineStore. Structure is documented below.

Labels map[string]string

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.

Name string

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.

Project string

The ID of the project in which the resource belongs. If it is not provided, the provider project is used.

SyncConfig AiFeatureOnlineStoreFeatureviewSyncConfigArgs

VectorSearchConfig AiFeatureOnlineStoreFeatureviewVectorSearchConfigArgs

featureOnlineStore String

The name of the FeatureOnlineStore to use for the featureview.

region String

The region for the resource. It should be the same as the featureonlinestore region.

bigQuerySource AiFeatureOnlineStoreFeatureviewBigQuerySource

Configures how data is supposed to be extracted from a BigQuery source to be loaded onto the FeatureOnlineStore. Structure is documented below.

featureRegistrySource AiFeatureOnlineStoreFeatureviewFeatureRegistrySource

Configures the features from a Feature Registry source that need to be loaded onto the FeatureOnlineStore. Structure is documented below.

labels Map<String,String>

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.

name String

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.

project String

The ID of the project in which the resource belongs. If it is not provided, the provider project is used.

syncConfig AiFeatureOnlineStoreFeatureviewSyncConfig

vectorSearchConfig AiFeatureOnlineStoreFeatureviewVectorSearchConfig

featureOnlineStore string

The name of the FeatureOnlineStore to use for the featureview.

region string

The region for the resource. It should be the same as the featureonlinestore region.

bigQuerySource AiFeatureOnlineStoreFeatureviewBigQuerySource

Configures how data is supposed to be extracted from a BigQuery source to be loaded onto the FeatureOnlineStore. Structure is documented below.

featureRegistrySource AiFeatureOnlineStoreFeatureviewFeatureRegistrySource

Configures the features from a Feature Registry source that need to be loaded onto the FeatureOnlineStore. Structure is documented below.

labels {[key: string]: string}

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.

name string

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.

project string

The ID of the project in which the resource belongs. If it is not provided, the provider project is used.

syncConfig AiFeatureOnlineStoreFeatureviewSyncConfig

vectorSearchConfig AiFeatureOnlineStoreFeatureviewVectorSearchConfig

feature_online_store str

The name of the FeatureOnlineStore to use for the featureview.

region str

The region for the resource. It should be the same as the featureonlinestore region.

big_query_source AiFeatureOnlineStoreFeatureviewBigQuerySourceArgs

Configures how data is supposed to be extracted from a BigQuery source to be loaded onto the FeatureOnlineStore. Structure is documented below.

feature_registry_source AiFeatureOnlineStoreFeatureviewFeatureRegistrySourceArgs

Configures the features from a Feature Registry source that need to be loaded onto the FeatureOnlineStore. Structure is documented below.

labels Mapping[str, str]

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.

name str

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.

project str

The ID of the project in which the resource belongs. If it is not provided, the provider project is used.

sync_config AiFeatureOnlineStoreFeatureviewSyncConfigArgs

vector_search_config AiFeatureOnlineStoreFeatureviewVectorSearchConfigArgs

featureOnlineStore String

The name of the FeatureOnlineStore to use for the featureview.

region String

The region for the resource. It should be the same as the featureonlinestore region.

bigQuerySource Property Map

Configures how data is supposed to be extracted from a BigQuery source to be loaded onto the FeatureOnlineStore. Structure is documented below.

featureRegistrySource Property Map

Configures the features from a Feature Registry source that need to be loaded onto the FeatureOnlineStore. Structure is documented below.

labels Map<String>

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.

name String

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.

project String

The ID of the project in which the resource belongs. If it is not provided, the provider project is used.

syncConfig Property Map

vectorSearchConfig Property Map

Outputs

All input properties are implicitly available as output properties. Additionally, the AiFeatureOnlineStoreFeatureview resource produces the following output properties:

CreateTime string: The timestamp of when the featureOnlinestore was created in RFC3339 UTC "Zulu" format, with nanosecond resolution and up to nine fractional digits.
EffectiveLabels Dictionary<string, string>: All of labels (key/value pairs) present on the resource in GCP, including the labels configured through Pulumi, other clients and services.
Id string: The provider-assigned unique ID for this managed resource.
PulumiLabels Dictionary<string, string>: The combination of labels configured directly on the resource and default labels configured on the provider.
UpdateTime string: The timestamp of when the featureOnlinestore was last updated in RFC3339 UTC "Zulu" format, with nanosecond resolution and up to nine fractional digits.

CreateTime string: The timestamp of when the featureOnlinestore was created in RFC3339 UTC "Zulu" format, with nanosecond resolution and up to nine fractional digits.
EffectiveLabels map[string]string: All of labels (key/value pairs) present on the resource in GCP, including the labels configured through Pulumi, other clients and services.
Id string: The provider-assigned unique ID for this managed resource.
PulumiLabels map[string]string: The combination of labels configured directly on the resource and default labels configured on the provider.
UpdateTime string: The timestamp of when the featureOnlinestore was last updated in RFC3339 UTC "Zulu" format, with nanosecond resolution and up to nine fractional digits.

createTime String: The timestamp of when the featureOnlinestore was created in RFC3339 UTC "Zulu" format, with nanosecond resolution and up to nine fractional digits.
effectiveLabels Map<String,String>: All of labels (key/value pairs) present on the resource in GCP, including the labels configured through Pulumi, other clients and services.
id String: The provider-assigned unique ID for this managed resource.
pulumiLabels Map<String,String>: The combination of labels configured directly on the resource and default labels configured on the provider.
updateTime String: The timestamp of when the featureOnlinestore was last updated in RFC3339 UTC "Zulu" format, with nanosecond resolution and up to nine fractional digits.

createTime string: The timestamp of when the featureOnlinestore was created in RFC3339 UTC "Zulu" format, with nanosecond resolution and up to nine fractional digits.
effectiveLabels {[key: string]: string}: All of labels (key/value pairs) present on the resource in GCP, including the labels configured through Pulumi, other clients and services.
id string: The provider-assigned unique ID for this managed resource.
pulumiLabels {[key: string]: string}: The combination of labels configured directly on the resource and default labels configured on the provider.
updateTime string: The timestamp of when the featureOnlinestore was last updated in RFC3339 UTC "Zulu" format, with nanosecond resolution and up to nine fractional digits.

create_time str: The timestamp of when the featureOnlinestore was created in RFC3339 UTC "Zulu" format, with nanosecond resolution and up to nine fractional digits.
effective_labels Mapping[str, str]: All of labels (key/value pairs) present on the resource in GCP, including the labels configured through Pulumi, other clients and services.
id str: The provider-assigned unique ID for this managed resource.
pulumi_labels Mapping[str, str]: The combination of labels configured directly on the resource and default labels configured on the provider.
update_time str: The timestamp of when the featureOnlinestore was last updated in RFC3339 UTC "Zulu" format, with nanosecond resolution and up to nine fractional digits.

createTime String: The timestamp of when the featureOnlinestore was created in RFC3339 UTC "Zulu" format, with nanosecond resolution and up to nine fractional digits.
effectiveLabels Map<String>: All of labels (key/value pairs) present on the resource in GCP, including the labels configured through Pulumi, other clients and services.
id String: The provider-assigned unique ID for this managed resource.
pulumiLabels Map<String>: The combination of labels configured directly on the resource and default labels configured on the provider.
updateTime String: The timestamp of when the featureOnlinestore was last updated in RFC3339 UTC "Zulu" format, with nanosecond resolution and up to nine fractional digits.

Look up Existing AiFeatureOnlineStoreFeatureview Resource

Get an existing AiFeatureOnlineStoreFeatureview resource’s state with the given name, ID, and optional extra properties used to qualify the lookup.

public static get(name: string, id: Input<ID>, state?: AiFeatureOnlineStoreFeatureviewState, opts?: CustomResourceOptions): AiFeatureOnlineStoreFeatureview

@staticmethod
def get(resource_name: str,
        id: str,
        opts: Optional[ResourceOptions] = None,
        big_query_source: Optional[AiFeatureOnlineStoreFeatureviewBigQuerySourceArgs] = None,
        create_time: Optional[str] = None,
        effective_labels: Optional[Mapping[str, str]] = None,
        feature_online_store: Optional[str] = None,
        feature_registry_source: Optional[AiFeatureOnlineStoreFeatureviewFeatureRegistrySourceArgs] = None,
        labels: Optional[Mapping[str, str]] = None,
        name: Optional[str] = None,
        project: Optional[str] = None,
        pulumi_labels: Optional[Mapping[str, str]] = None,
        region: Optional[str] = None,
        sync_config: Optional[AiFeatureOnlineStoreFeatureviewSyncConfigArgs] = None,
        update_time: Optional[str] = None,
        vector_search_config: Optional[AiFeatureOnlineStoreFeatureviewVectorSearchConfigArgs] = None) -> AiFeatureOnlineStoreFeatureview

func GetAiFeatureOnlineStoreFeatureview(ctx *Context, name string, id IDInput, state *AiFeatureOnlineStoreFeatureviewState, opts ...ResourceOption) (*AiFeatureOnlineStoreFeatureview, error)

public static AiFeatureOnlineStoreFeatureview Get(string name, Input<string> id, AiFeatureOnlineStoreFeatureviewState? state, CustomResourceOptions? opts = null)

public static AiFeatureOnlineStoreFeatureview get(String name, Output<String> id, AiFeatureOnlineStoreFeatureviewState state, CustomResourceOptions options)

Resource lookup is not supported in YAML

name: The unique name of the resulting resource.
id: The unique provider ID of the resource to lookup.
state: Any extra arguments used during the lookup.
opts: A bag of options that control this resource's behavior.

resource_name: The unique name of the resulting resource.
id: The unique provider ID of the resource to lookup.

name: The unique name of the resulting resource.
id: The unique provider ID of the resource to lookup.
state: Any extra arguments used during the lookup.
opts: A bag of options that control this resource's behavior.

name: The unique name of the resulting resource.
id: The unique provider ID of the resource to lookup.
state: Any extra arguments used during the lookup.
opts: A bag of options that control this resource's behavior.

name: The unique name of the resulting resource.
id: The unique provider ID of the resource to lookup.
state: Any extra arguments used during the lookup.
opts: A bag of options that control this resource's behavior.

The following state arguments are supported:

BigQuerySource AiFeatureOnlineStoreFeatureviewBigQuerySource

Configures how data is supposed to be extracted from a BigQuery source to be loaded onto the FeatureOnlineStore. Structure is documented below.

CreateTime string

The timestamp of when the featureOnlinestore was created in RFC3339 UTC "Zulu" format, with nanosecond resolution and up to nine fractional digits.

EffectiveLabels Dictionary<string, string>

All of labels (key/value pairs) present on the resource in GCP, including the labels configured through Pulumi, other clients and services.

FeatureOnlineStore string

The name of the FeatureOnlineStore to use for the featureview.

FeatureRegistrySource AiFeatureOnlineStoreFeatureviewFeatureRegistrySource

Configures the features from a Feature Registry source that need to be loaded onto the FeatureOnlineStore. Structure is documented below.

Labels Dictionary<string, string>

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.

Name string

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.

Project string

The ID of the project in which the resource belongs. If it is not provided, the provider project is used.

PulumiLabels Dictionary<string, string>

The combination of labels configured directly on the resource and default labels configured on the provider.

Region string

The region for the resource. It should be the same as the featureonlinestore region.

SyncConfig AiFeatureOnlineStoreFeatureviewSyncConfig

UpdateTime string

The timestamp of when the featureOnlinestore was last updated in RFC3339 UTC "Zulu" format, with nanosecond resolution and up to nine fractional digits.

VectorSearchConfig AiFeatureOnlineStoreFeatureviewVectorSearchConfig

BigQuerySource AiFeatureOnlineStoreFeatureviewBigQuerySourceArgs

Configures how data is supposed to be extracted from a BigQuery source to be loaded onto the FeatureOnlineStore. Structure is documented below.

CreateTime string

The timestamp of when the featureOnlinestore was created in RFC3339 UTC "Zulu" format, with nanosecond resolution and up to nine fractional digits.

EffectiveLabels map[string]string

All of labels (key/value pairs) present on the resource in GCP, including the labels configured through Pulumi, other clients and services.

FeatureOnlineStore string

The name of the FeatureOnlineStore to use for the featureview.

FeatureRegistrySource AiFeatureOnlineStoreFeatureviewFeatureRegistrySourceArgs

Configures the features from a Feature Registry source that need to be loaded onto the FeatureOnlineStore. Structure is documented below.

Labels map[string]string

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.

Name string

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.

Project string

The ID of the project in which the resource belongs. If it is not provided, the provider project is used.

PulumiLabels map[string]string

The combination of labels configured directly on the resource and default labels configured on the provider.

Region string

The region for the resource. It should be the same as the featureonlinestore region.

SyncConfig AiFeatureOnlineStoreFeatureviewSyncConfigArgs

UpdateTime string

The timestamp of when the featureOnlinestore was last updated in RFC3339 UTC "Zulu" format, with nanosecond resolution and up to nine fractional digits.

VectorSearchConfig AiFeatureOnlineStoreFeatureviewVectorSearchConfigArgs

bigQuerySource AiFeatureOnlineStoreFeatureviewBigQuerySource

Configures how data is supposed to be extracted from a BigQuery source to be loaded onto the FeatureOnlineStore. Structure is documented below.

createTime String

The timestamp of when the featureOnlinestore was created in RFC3339 UTC "Zulu" format, with nanosecond resolution and up to nine fractional digits.

effectiveLabels Map<String,String>

All of labels (key/value pairs) present on the resource in GCP, including the labels configured through Pulumi, other clients and services.

featureOnlineStore String

The name of the FeatureOnlineStore to use for the featureview.

featureRegistrySource AiFeatureOnlineStoreFeatureviewFeatureRegistrySource

Configures the features from a Feature Registry source that need to be loaded onto the FeatureOnlineStore. Structure is documented below.

labels Map<String,String>

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.

name String

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.

project String

The ID of the project in which the resource belongs. If it is not provided, the provider project is used.

pulumiLabels Map<String,String>

The combination of labels configured directly on the resource and default labels configured on the provider.

region String

The region for the resource. It should be the same as the featureonlinestore region.

syncConfig AiFeatureOnlineStoreFeatureviewSyncConfig

updateTime String

The timestamp of when the featureOnlinestore was last updated in RFC3339 UTC "Zulu" format, with nanosecond resolution and up to nine fractional digits.

vectorSearchConfig AiFeatureOnlineStoreFeatureviewVectorSearchConfig

bigQuerySource AiFeatureOnlineStoreFeatureviewBigQuerySource

Configures how data is supposed to be extracted from a BigQuery source to be loaded onto the FeatureOnlineStore. Structure is documented below.

createTime string

The timestamp of when the featureOnlinestore was created in RFC3339 UTC "Zulu" format, with nanosecond resolution and up to nine fractional digits.

effectiveLabels {[key: string]: string}

All of labels (key/value pairs) present on the resource in GCP, including the labels configured through Pulumi, other clients and services.

featureOnlineStore string

The name of the FeatureOnlineStore to use for the featureview.

featureRegistrySource AiFeatureOnlineStoreFeatureviewFeatureRegistrySource

Configures the features from a Feature Registry source that need to be loaded onto the FeatureOnlineStore. Structure is documented below.

labels {[key: string]: string}

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.

name string

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.

project string

The ID of the project in which the resource belongs. If it is not provided, the provider project is used.

pulumiLabels {[key: string]: string}

The combination of labels configured directly on the resource and default labels configured on the provider.

region string

The region for the resource. It should be the same as the featureonlinestore region.

syncConfig AiFeatureOnlineStoreFeatureviewSyncConfig

updateTime string

The timestamp of when the featureOnlinestore was last updated in RFC3339 UTC "Zulu" format, with nanosecond resolution and up to nine fractional digits.

vectorSearchConfig AiFeatureOnlineStoreFeatureviewVectorSearchConfig

big_query_source AiFeatureOnlineStoreFeatureviewBigQuerySourceArgs

Configures how data is supposed to be extracted from a BigQuery source to be loaded onto the FeatureOnlineStore. Structure is documented below.

create_time str

The timestamp of when the featureOnlinestore was created in RFC3339 UTC "Zulu" format, with nanosecond resolution and up to nine fractional digits.

effective_labels Mapping[str, str]

All of labels (key/value pairs) present on the resource in GCP, including the labels configured through Pulumi, other clients and services.

feature_online_store str

The name of the FeatureOnlineStore to use for the featureview.

feature_registry_source AiFeatureOnlineStoreFeatureviewFeatureRegistrySourceArgs

Configures the features from a Feature Registry source that need to be loaded onto the FeatureOnlineStore. Structure is documented below.

labels Mapping[str, str]

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.

name str

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.

project str

The ID of the project in which the resource belongs. If it is not provided, the provider project is used.

pulumi_labels Mapping[str, str]

The combination of labels configured directly on the resource and default labels configured on the provider.

region str

The region for the resource. It should be the same as the featureonlinestore region.

sync_config AiFeatureOnlineStoreFeatureviewSyncConfigArgs

update_time str

The timestamp of when the featureOnlinestore was last updated in RFC3339 UTC "Zulu" format, with nanosecond resolution and up to nine fractional digits.

vector_search_config AiFeatureOnlineStoreFeatureviewVectorSearchConfigArgs

bigQuerySource Property Map

Configures how data is supposed to be extracted from a BigQuery source to be loaded onto the FeatureOnlineStore. Structure is documented below.

createTime String

The timestamp of when the featureOnlinestore was created in RFC3339 UTC "Zulu" format, with nanosecond resolution and up to nine fractional digits.

effectiveLabels Map<String>

All of labels (key/value pairs) present on the resource in GCP, including the labels configured through Pulumi, other clients and services.

featureOnlineStore String

The name of the FeatureOnlineStore to use for the featureview.

featureRegistrySource Property Map

Configures the features from a Feature Registry source that need to be loaded onto the FeatureOnlineStore. Structure is documented below.

labels Map<String>

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.

name String

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.

project String

The ID of the project in which the resource belongs. If it is not provided, the provider project is used.

pulumiLabels Map<String>

The combination of labels configured directly on the resource and default labels configured on the provider.

region String

The region for the resource. It should be the same as the featureonlinestore region.

syncConfig Property Map

updateTime String

The timestamp of when the featureOnlinestore was last updated in RFC3339 UTC "Zulu" format, with nanosecond resolution and up to nine fractional digits.

vectorSearchConfig Property Map

Supporting Types

AiFeatureOnlineStoreFeatureviewBigQuerySource, AiFeatureOnlineStoreFeatureviewBigQuerySourceArgs

EntityIdColumns List<string>: Columns to construct entityId / row keys. Start by supporting 1 only.
Uri string: The BigQuery view URI that will be materialized on each sync trigger based on FeatureView.SyncConfig.

EntityIdColumns []string: Columns to construct entityId / row keys. Start by supporting 1 only.
Uri string: The BigQuery view URI that will be materialized on each sync trigger based on FeatureView.SyncConfig.

entityIdColumns List<String>: Columns to construct entityId / row keys. Start by supporting 1 only.
uri String: The BigQuery view URI that will be materialized on each sync trigger based on FeatureView.SyncConfig.

entityIdColumns string[]: Columns to construct entityId / row keys. Start by supporting 1 only.
uri string: The BigQuery view URI that will be materialized on each sync trigger based on FeatureView.SyncConfig.

entity_id_columns Sequence[str]: Columns to construct entityId / row keys. Start by supporting 1 only.
uri str: The BigQuery view URI that will be materialized on each sync trigger based on FeatureView.SyncConfig.

entityIdColumns List<String>: Columns to construct entityId / row keys. Start by supporting 1 only.
uri String: The BigQuery view URI that will be materialized on each sync trigger based on FeatureView.SyncConfig.

AiFeatureOnlineStoreFeatureviewFeatureRegistrySource, AiFeatureOnlineStoreFeatureviewFeatureRegistrySourceArgs

FeatureGroups List<AiFeatureOnlineStoreFeatureviewFeatureRegistrySourceFeatureGroup>: List of features that need to be synced to Online Store. Structure is documented below.

FeatureGroups []AiFeatureOnlineStoreFeatureviewFeatureRegistrySourceFeatureGroup: List of features that need to be synced to Online Store. Structure is documented below.

featureGroups List<AiFeatureOnlineStoreFeatureviewFeatureRegistrySourceFeatureGroup>: List of features that need to be synced to Online Store. Structure is documented below.

featureGroups AiFeatureOnlineStoreFeatureviewFeatureRegistrySourceFeatureGroup[]: List of features that need to be synced to Online Store. Structure is documented below.

feature_groups Sequence[AiFeatureOnlineStoreFeatureviewFeatureRegistrySourceFeatureGroup]: List of features that need to be synced to Online Store. Structure is documented below.

featureGroups List<Property Map>: List of features that need to be synced to Online Store. Structure is documented below.

AiFeatureOnlineStoreFeatureviewFeatureRegistrySourceFeatureGroup, AiFeatureOnlineStoreFeatureviewFeatureRegistrySourceFeatureGroupArgs

FeatureGroupId string: Identifier of the feature group.
FeatureIds List<string>: Identifiers of features under the feature group.

FeatureGroupId string: Identifier of the feature group.
FeatureIds []string: Identifiers of features under the feature group.

featureGroupId String: Identifier of the feature group.
featureIds List<String>: Identifiers of features under the feature group.

featureGroupId string: Identifier of the feature group.
featureIds string[]: Identifiers of features under the feature group.

feature_group_id str: Identifier of the feature group.
feature_ids Sequence[str]: Identifiers of features under the feature group.

featureGroupId String: Identifier of the feature group.
featureIds List<String>: Identifiers of features under the feature group.

AiFeatureOnlineStoreFeatureviewSyncConfig, AiFeatureOnlineStoreFeatureviewSyncConfigArgs

Cron string: Cron schedule (https://en.wikipedia.org/wiki/Cron) to launch scheduled runs. To explicitly set a timezone to the cron tab, apply a prefix in the cron tab: "CRON_TZ=${IANA_TIME_ZONE}" or "TZ=${IANA_TIME_ZONE}".

Cron string: Cron schedule (https://en.wikipedia.org/wiki/Cron) to launch scheduled runs. To explicitly set a timezone to the cron tab, apply a prefix in the cron tab: "CRON_TZ=${IANA_TIME_ZONE}" or "TZ=${IANA_TIME_ZONE}".

cron String: Cron schedule (https://en.wikipedia.org/wiki/Cron) to launch scheduled runs. To explicitly set a timezone to the cron tab, apply a prefix in the cron tab: "CRON_TZ=${IANA_TIME_ZONE}" or "TZ=${IANA_TIME_ZONE}".

cron string: Cron schedule (https://en.wikipedia.org/wiki/Cron) to launch scheduled runs. To explicitly set a timezone to the cron tab, apply a prefix in the cron tab: "CRON_TZ=${IANA_TIME_ZONE}" or "TZ=${IANA_TIME_ZONE}".

cron str: Cron schedule (https://en.wikipedia.org/wiki/Cron) to launch scheduled runs. To explicitly set a timezone to the cron tab, apply a prefix in the cron tab: "CRON_TZ=${IANA_TIME_ZONE}" or "TZ=${IANA_TIME_ZONE}".

cron String: Cron schedule (https://en.wikipedia.org/wiki/Cron) to launch scheduled runs. To explicitly set a timezone to the cron tab, apply a prefix in the cron tab: "CRON_TZ=${IANA_TIME_ZONE}" or "TZ=${IANA_TIME_ZONE}".

AiFeatureOnlineStoreFeatureviewVectorSearchConfig, AiFeatureOnlineStoreFeatureviewVectorSearchConfigArgs

EmbeddingColumn string: Column of embedding. This column contains the source data to create index for vector search.
BruteForceConfig AiFeatureOnlineStoreFeatureviewVectorSearchConfigBruteForceConfig: Configuration options for using brute force search, which simply implements the standard linear search in the database for each query. It is primarily meant for benchmarking and to generate the ground truth for approximate search.
CrowdingColumn string: Column of crowding. This column contains crowding attribute which is a constraint on a neighbor list produced by nearest neighbor search requiring that no more than some value k' of the k neighbors returned have the same value of crowdingAttribute.
DistanceMeasureType string: The distance measure used in nearest neighbor search. For details on allowed values, see the API documentation. Possible values are: SQUARED_L2_DISTANCE, COSINE_DISTANCE, DOT_PRODUCT_DISTANCE.
EmbeddingDimension int: The number of dimensions of the input embedding.
FilterColumns List<string>: Columns of features that are used to filter vector search results.
TreeAhConfig AiFeatureOnlineStoreFeatureviewVectorSearchConfigTreeAhConfig: Configuration options for the tree-AH algorithm (Shallow tree + Asymmetric Hashing). Please refer to this paper for more details: https://arxiv.org/abs/1908.10396 Structure is documented below.

EmbeddingColumn string: Column of embedding. This column contains the source data to create index for vector search.
BruteForceConfig AiFeatureOnlineStoreFeatureviewVectorSearchConfigBruteForceConfig: Configuration options for using brute force search, which simply implements the standard linear search in the database for each query. It is primarily meant for benchmarking and to generate the ground truth for approximate search.
CrowdingColumn string: Column of crowding. This column contains crowding attribute which is a constraint on a neighbor list produced by nearest neighbor search requiring that no more than some value k' of the k neighbors returned have the same value of crowdingAttribute.
DistanceMeasureType string: The distance measure used in nearest neighbor search. For details on allowed values, see the API documentation. Possible values are: SQUARED_L2_DISTANCE, COSINE_DISTANCE, DOT_PRODUCT_DISTANCE.
EmbeddingDimension int: The number of dimensions of the input embedding.
FilterColumns []string: Columns of features that are used to filter vector search results.
TreeAhConfig AiFeatureOnlineStoreFeatureviewVectorSearchConfigTreeAhConfig: Configuration options for the tree-AH algorithm (Shallow tree + Asymmetric Hashing). Please refer to this paper for more details: https://arxiv.org/abs/1908.10396 Structure is documented below.

embeddingColumn String: Column of embedding. This column contains the source data to create index for vector search.
bruteForceConfig AiFeatureOnlineStoreFeatureviewVectorSearchConfigBruteForceConfig: Configuration options for using brute force search, which simply implements the standard linear search in the database for each query. It is primarily meant for benchmarking and to generate the ground truth for approximate search.
crowdingColumn String: Column of crowding. This column contains crowding attribute which is a constraint on a neighbor list produced by nearest neighbor search requiring that no more than some value k' of the k neighbors returned have the same value of crowdingAttribute.
distanceMeasureType String: The distance measure used in nearest neighbor search. For details on allowed values, see the API documentation. Possible values are: SQUARED_L2_DISTANCE, COSINE_DISTANCE, DOT_PRODUCT_DISTANCE.
embeddingDimension Integer: The number of dimensions of the input embedding.
filterColumns List<String>: Columns of features that are used to filter vector search results.
treeAhConfig AiFeatureOnlineStoreFeatureviewVectorSearchConfigTreeAhConfig: Configuration options for the tree-AH algorithm (Shallow tree + Asymmetric Hashing). Please refer to this paper for more details: https://arxiv.org/abs/1908.10396 Structure is documented below.

embeddingColumn string: Column of embedding. This column contains the source data to create index for vector search.
bruteForceConfig AiFeatureOnlineStoreFeatureviewVectorSearchConfigBruteForceConfig: Configuration options for using brute force search, which simply implements the standard linear search in the database for each query. It is primarily meant for benchmarking and to generate the ground truth for approximate search.
crowdingColumn string: Column of crowding. This column contains crowding attribute which is a constraint on a neighbor list produced by nearest neighbor search requiring that no more than some value k' of the k neighbors returned have the same value of crowdingAttribute.
distanceMeasureType string: The distance measure used in nearest neighbor search. For details on allowed values, see the API documentation. Possible values are: SQUARED_L2_DISTANCE, COSINE_DISTANCE, DOT_PRODUCT_DISTANCE.
embeddingDimension number: The number of dimensions of the input embedding.
filterColumns string[]: Columns of features that are used to filter vector search results.
treeAhConfig AiFeatureOnlineStoreFeatureviewVectorSearchConfigTreeAhConfig: Configuration options for the tree-AH algorithm (Shallow tree + Asymmetric Hashing). Please refer to this paper for more details: https://arxiv.org/abs/1908.10396 Structure is documented below.

embedding_column str: Column of embedding. This column contains the source data to create index for vector search.
brute_force_config AiFeatureOnlineStoreFeatureviewVectorSearchConfigBruteForceConfig: Configuration options for using brute force search, which simply implements the standard linear search in the database for each query. It is primarily meant for benchmarking and to generate the ground truth for approximate search.
crowding_column str: Column of crowding. This column contains crowding attribute which is a constraint on a neighbor list produced by nearest neighbor search requiring that no more than some value k' of the k neighbors returned have the same value of crowdingAttribute.
distance_measure_type str: The distance measure used in nearest neighbor search. For details on allowed values, see the API documentation. Possible values are: SQUARED_L2_DISTANCE, COSINE_DISTANCE, DOT_PRODUCT_DISTANCE.
embedding_dimension int: The number of dimensions of the input embedding.
filter_columns Sequence[str]: Columns of features that are used to filter vector search results.
tree_ah_config AiFeatureOnlineStoreFeatureviewVectorSearchConfigTreeAhConfig: Configuration options for the tree-AH algorithm (Shallow tree + Asymmetric Hashing). Please refer to this paper for more details: https://arxiv.org/abs/1908.10396 Structure is documented below.

embeddingColumn String: Column of embedding. This column contains the source data to create index for vector search.
bruteForceConfig Property Map: Configuration options for using brute force search, which simply implements the standard linear search in the database for each query. It is primarily meant for benchmarking and to generate the ground truth for approximate search.
crowdingColumn String: Column of crowding. This column contains crowding attribute which is a constraint on a neighbor list produced by nearest neighbor search requiring that no more than some value k' of the k neighbors returned have the same value of crowdingAttribute.
distanceMeasureType String: The distance measure used in nearest neighbor search. For details on allowed values, see the API documentation. Possible values are: SQUARED_L2_DISTANCE, COSINE_DISTANCE, DOT_PRODUCT_DISTANCE.
embeddingDimension Number: The number of dimensions of the input embedding.
filterColumns List<String>: Columns of features that are used to filter vector search results.
treeAhConfig Property Map: Configuration options for the tree-AH algorithm (Shallow tree + Asymmetric Hashing). Please refer to this paper for more details: https://arxiv.org/abs/1908.10396 Structure is documented below.

AiFeatureOnlineStoreFeatureviewVectorSearchConfigTreeAhConfig, AiFeatureOnlineStoreFeatureviewVectorSearchConfigTreeAhConfigArgs

LeafNodeEmbeddingCount string: Number of embeddings on each leaf node. The default value is 1000 if not set.

LeafNodeEmbeddingCount string: Number of embeddings on each leaf node. The default value is 1000 if not set.

leafNodeEmbeddingCount String: Number of embeddings on each leaf node. The default value is 1000 if not set.

leafNodeEmbeddingCount string: Number of embeddings on each leaf node. The default value is 1000 if not set.

leaf_node_embedding_count str: Number of embeddings on each leaf node. The default value is 1000 if not set.

leafNodeEmbeddingCount String: Number of embeddings on each leaf node. The default value is 1000 if not set.

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:

$ pulumi import gcp:vertex/aiFeatureOnlineStoreFeatureview:AiFeatureOnlineStoreFeatureview default projects/{{project}}/locations/{{region}}/featureOnlineStores/{{feature_online_store}}/featureViews/{{name}}

$ pulumi import gcp:vertex/aiFeatureOnlineStoreFeatureview:AiFeatureOnlineStoreFeatureview default {{project}}/{{region}}/{{feature_online_store}}/{{name}}

$ pulumi import gcp:vertex/aiFeatureOnlineStoreFeatureview:AiFeatureOnlineStoreFeatureview default {{region}}/{{feature_online_store}}/{{name}}

$ pulumi import gcp:vertex/aiFeatureOnlineStoreFeatureview:AiFeatureOnlineStoreFeatureview default {{feature_online_store}}/{{name}}

To learn more about importing existing cloud resources, see Importing resources.

Package Details

Repository: Google Cloud (GCP) Classic pulumi/pulumi-gcp
License: Apache-2.0
Notes: This Pulumi package is based on the google-beta Terraform Provider.

Google Cloud Classic v7.29.0 published on Wednesday, Jun 26, 2024 by Pulumi

pulumi/pulumi-gcp

gcp.vertex.AiFeatureOnlineStoreFeatureview

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Example Usage

Vertex Ai Featureonlinestore Featureview

Vertex Ai Featureonlinestore Featureview Feature Registry

Vertex Ai Featureonlinestore Featureview With Vector Search

Create AiFeatureOnlineStoreFeatureview Resource

Constructor syntax

Parameters

Constructor example

AiFeatureOnlineStoreFeatureview Resource Properties

Inputs

Outputs

Look up Existing AiFeatureOnlineStoreFeatureview Resource

Supporting Types

AiFeatureOnlineStoreFeatureviewBigQuerySource, AiFeatureOnlineStoreFeatureviewBigQuerySourceArgs

AiFeatureOnlineStoreFeatureviewFeatureRegistrySource, AiFeatureOnlineStoreFeatureviewFeatureRegistrySourceArgs

AiFeatureOnlineStoreFeatureviewFeatureRegistrySourceFeatureGroup, AiFeatureOnlineStoreFeatureviewFeatureRegistrySourceFeatureGroupArgs

AiFeatureOnlineStoreFeatureviewSyncConfig, AiFeatureOnlineStoreFeatureviewSyncConfigArgs

AiFeatureOnlineStoreFeatureviewVectorSearchConfig, AiFeatureOnlineStoreFeatureviewVectorSearchConfigArgs

AiFeatureOnlineStoreFeatureviewVectorSearchConfigTreeAhConfig, AiFeatureOnlineStoreFeatureviewVectorSearchConfigTreeAhConfigArgs

Import

Package Details

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