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Google Cloud Classic v8.9.3 published on Monday, Nov 18, 2024 by Pulumi
gcp.vertex.AiFeatureOnlineStoreFeatureview
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FeatureView is representation of values that the FeatureOnlineStore will serve based on its syncConfig.
To get more information about FeatureOnlineStoreFeatureview, see:
- API documentation
- How-to Guides
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={
"auto_scaling": {
"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={
"cron": "0 0 * * *",
},
big_query_source={
"uri": pulumi.Output.all(
project=tf_test_table.project,
dataset_id=tf_test_table.dataset_id,
table_id=tf_test_table.table_id
).apply(lambda resolved_outputs: f"bq://{resolved_outputs['project']}.{resolved_outputs['dataset_id']}.{resolved_outputs['table_id']}")
,
"entity_id_columns": ["test_entity_column"],
})
project = gcp.organizations.get_project()
package main
import (
"fmt"
"github.com/pulumi/pulumi-gcp/sdk/v8/go/gcp/bigquery"
"github.com/pulumi/pulumi-gcp/sdk/v8/go/gcp/organizations"
"github.com/pulumi/pulumi-gcp/sdk/v8/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, &organizations.LookupProjectArgs{}, 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={
"auto_scaling": {
"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={
"big_query_source": {
"input_uri": pulumi.Output.all(
project=sample_table.project,
dataset_id=sample_table.dataset_id,
table_id=sample_table.table_id
).apply(lambda resolved_outputs: f"bq://{resolved_outputs['project']}.{resolved_outputs['dataset_id']}.{resolved_outputs['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={
"cron": "0 0 * * *",
},
feature_registry_source={
"feature_groups": [{
"feature_group_id": sample_feature_group.name,
"feature_ids": [sample_feature.name],
}],
})
package main
import (
"fmt"
"github.com/pulumi/pulumi-gcp/sdk/v8/go/gcp/bigquery"
"github.com/pulumi/pulumi-gcp/sdk/v8/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:
- ${sampleFeature.name}
Vertex Ai Featureonlinestore Featureview Cross Project
import * as pulumi from "@pulumi/pulumi";
import * as gcp from "@pulumi/gcp";
import * as time from "@pulumi/time";
const testProject = gcp.organizations.getProject({});
const project = new gcp.organizations.Project("project", {
projectId: "tf-test_41150",
name: "tf-test_89313",
orgId: "123456789",
billingAccount: "000000-0000000-0000000-000000",
deletionPolicy: "DELETE",
});
const wait60Seconds = new time.index.Sleep("wait_60_seconds", {createDuration: "60s"}, {
dependsOn: [project],
});
const vertexai = new gcp.projects.Service("vertexai", {
service: "aiplatform.googleapis.com",
project: project.projectId,
disableOnDestroy: false,
}, {
dependsOn: [wait60Seconds],
});
const featureonlinestore = new gcp.vertex.AiFeatureOnlineStore("featureonlinestore", {
name: "example_cross_project_featureview",
project: project.projectId,
labels: {
foo: "bar",
},
region: "us-central1",
bigtable: {
autoScaling: {
minNodeCount: 1,
maxNodeCount: 2,
cpuUtilizationTarget: 80,
},
},
}, {
dependsOn: [vertexai],
});
const sampleDataset = new gcp.bigquery.Dataset("sample_dataset", {
datasetId: "example_cross_project_featureview",
friendlyName: "test",
description: "This is a test description",
location: "US",
});
const viewer = new gcp.bigquery.DatasetIamMember("viewer", {
project: testProject.then(testProject => testProject.projectId),
datasetId: sampleDataset.datasetId,
role: "roles/bigquery.dataViewer",
member: pulumi.interpolate`serviceAccount:service-${project.number}@gcp-sa-aiplatform.iam.gserviceaccount.com`,
}, {
dependsOn: [featureonlinestore],
});
const wait30Seconds = new time.index.Sleep("wait_30_seconds", {createDuration: "30s"}, {
dependsOn: [viewer],
});
const sampleTable = new gcp.bigquery.Table("sample_table", {
deletionProtection: false,
datasetId: sampleDataset.datasetId,
tableId: "example_cross_project_featureview",
schema: `[
{
"name": "feature_id",
"type": "STRING",
"mode": "NULLABLE"
},
{
"name": "example_cross_project_featureview",
"type": "STRING",
"mode": "NULLABLE"
},
{
"name": "feature_timestamp",
"type": "TIMESTAMP",
"mode": "NULLABLE"
}
]
`,
});
const sampleFeatureGroup = new gcp.vertex.AiFeatureGroup("sample_feature_group", {
name: "example_cross_project_featureview",
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_cross_project_featureview",
region: "us-central1",
featureGroup: sampleFeatureGroup.name,
description: "A sample feature",
labels: {
"label-one": "value-one",
},
});
const crossProjectFeatureview = new gcp.vertex.AiFeatureOnlineStoreFeatureview("cross_project_featureview", {
name: "example_cross_project_featureview",
project: project.projectId,
region: "us-central1",
featureOnlineStore: featureonlinestore.name,
syncConfig: {
cron: "0 0 * * *",
},
featureRegistrySource: {
featureGroups: [{
featureGroupId: sampleFeatureGroup.name,
featureIds: [sampleFeature.name],
}],
projectNumber: testProject.then(testProject => testProject.number),
},
}, {
dependsOn: [
vertexai,
wait30Seconds,
],
});
import pulumi
import pulumi_gcp as gcp
import pulumi_time as time
test_project = gcp.organizations.get_project()
project = gcp.organizations.Project("project",
project_id="tf-test_41150",
name="tf-test_89313",
org_id="123456789",
billing_account="000000-0000000-0000000-000000",
deletion_policy="DELETE")
wait60_seconds = time.index.Sleep("wait_60_seconds", create_duration=60s,
opts = pulumi.ResourceOptions(depends_on=[project]))
vertexai = gcp.projects.Service("vertexai",
service="aiplatform.googleapis.com",
project=project.project_id,
disable_on_destroy=False,
opts = pulumi.ResourceOptions(depends_on=[wait60_seconds]))
featureonlinestore = gcp.vertex.AiFeatureOnlineStore("featureonlinestore",
name="example_cross_project_featureview",
project=project.project_id,
labels={
"foo": "bar",
},
region="us-central1",
bigtable={
"auto_scaling": {
"min_node_count": 1,
"max_node_count": 2,
"cpu_utilization_target": 80,
},
},
opts = pulumi.ResourceOptions(depends_on=[vertexai]))
sample_dataset = gcp.bigquery.Dataset("sample_dataset",
dataset_id="example_cross_project_featureview",
friendly_name="test",
description="This is a test description",
location="US")
viewer = gcp.bigquery.DatasetIamMember("viewer",
project=test_project.project_id,
dataset_id=sample_dataset.dataset_id,
role="roles/bigquery.dataViewer",
member=project.number.apply(lambda number: f"serviceAccount:service-{number}@gcp-sa-aiplatform.iam.gserviceaccount.com"),
opts = pulumi.ResourceOptions(depends_on=[featureonlinestore]))
wait30_seconds = time.index.Sleep("wait_30_seconds", create_duration=30s,
opts = pulumi.ResourceOptions(depends_on=[viewer]))
sample_table = gcp.bigquery.Table("sample_table",
deletion_protection=False,
dataset_id=sample_dataset.dataset_id,
table_id="example_cross_project_featureview",
schema="""[
{
"name": "feature_id",
"type": "STRING",
"mode": "NULLABLE"
},
{
"name": "example_cross_project_featureview",
"type": "STRING",
"mode": "NULLABLE"
},
{
"name": "feature_timestamp",
"type": "TIMESTAMP",
"mode": "NULLABLE"
}
]
""")
sample_feature_group = gcp.vertex.AiFeatureGroup("sample_feature_group",
name="example_cross_project_featureview",
description="A sample feature group",
region="us-central1",
labels={
"label-one": "value-one",
},
big_query={
"big_query_source": {
"input_uri": pulumi.Output.all(
project=sample_table.project,
dataset_id=sample_table.dataset_id,
table_id=sample_table.table_id
).apply(lambda resolved_outputs: f"bq://{resolved_outputs['project']}.{resolved_outputs['dataset_id']}.{resolved_outputs['table_id']}")
,
},
"entity_id_columns": ["feature_id"],
})
sample_feature = gcp.vertex.AiFeatureGroupFeature("sample_feature",
name="example_cross_project_featureview",
region="us-central1",
feature_group=sample_feature_group.name,
description="A sample feature",
labels={
"label-one": "value-one",
})
cross_project_featureview = gcp.vertex.AiFeatureOnlineStoreFeatureview("cross_project_featureview",
name="example_cross_project_featureview",
project=project.project_id,
region="us-central1",
feature_online_store=featureonlinestore.name,
sync_config={
"cron": "0 0 * * *",
},
feature_registry_source={
"feature_groups": [{
"feature_group_id": sample_feature_group.name,
"feature_ids": [sample_feature.name],
}],
"project_number": test_project.number,
},
opts = pulumi.ResourceOptions(depends_on=[
vertexai,
wait30_seconds,
]))
package main
import (
"fmt"
"github.com/pulumi/pulumi-gcp/sdk/v8/go/gcp/bigquery"
"github.com/pulumi/pulumi-gcp/sdk/v8/go/gcp/organizations"
"github.com/pulumi/pulumi-gcp/sdk/v8/go/gcp/projects"
"github.com/pulumi/pulumi-gcp/sdk/v8/go/gcp/vertex"
"github.com/pulumi/pulumi-time/sdk/go/time"
"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)
func main() {
pulumi.Run(func(ctx *pulumi.Context) error {
testProject, err := organizations.LookupProject(ctx, &organizations.LookupProjectArgs{}, nil)
if err != nil {
return err
}
project, err := organizations.NewProject(ctx, "project", &organizations.ProjectArgs{
ProjectId: pulumi.String("tf-test_41150"),
Name: pulumi.String("tf-test_89313"),
OrgId: pulumi.String("123456789"),
BillingAccount: pulumi.String("000000-0000000-0000000-000000"),
DeletionPolicy: pulumi.String("DELETE"),
})
if err != nil {
return err
}
wait60Seconds, err := time.NewSleep(ctx, "wait_60_seconds", &time.SleepArgs{
CreateDuration: "60s",
}, pulumi.DependsOn([]pulumi.Resource{
project,
}))
if err != nil {
return err
}
vertexai, err := projects.NewService(ctx, "vertexai", &projects.ServiceArgs{
Service: pulumi.String("aiplatform.googleapis.com"),
Project: project.ProjectId,
DisableOnDestroy: pulumi.Bool(false),
}, pulumi.DependsOn([]pulumi.Resource{
wait60Seconds,
}))
if err != nil {
return err
}
featureonlinestore, err := vertex.NewAiFeatureOnlineStore(ctx, "featureonlinestore", &vertex.AiFeatureOnlineStoreArgs{
Name: pulumi.String("example_cross_project_featureview"),
Project: project.ProjectId,
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),
},
},
}, pulumi.DependsOn([]pulumi.Resource{
vertexai,
}))
if err != nil {
return err
}
sampleDataset, err := bigquery.NewDataset(ctx, "sample_dataset", &bigquery.DatasetArgs{
DatasetId: pulumi.String("example_cross_project_featureview"),
FriendlyName: pulumi.String("test"),
Description: pulumi.String("This is a test description"),
Location: pulumi.String("US"),
})
if err != nil {
return err
}
viewer, err := bigquery.NewDatasetIamMember(ctx, "viewer", &bigquery.DatasetIamMemberArgs{
Project: pulumi.String(testProject.ProjectId),
DatasetId: sampleDataset.DatasetId,
Role: pulumi.String("roles/bigquery.dataViewer"),
Member: project.Number.ApplyT(func(number string) (string, error) {
return fmt.Sprintf("serviceAccount:service-%v@gcp-sa-aiplatform.iam.gserviceaccount.com", number), nil
}).(pulumi.StringOutput),
}, pulumi.DependsOn([]pulumi.Resource{
featureonlinestore,
}))
if err != nil {
return err
}
wait30Seconds, err := time.NewSleep(ctx, "wait_30_seconds", &time.SleepArgs{
CreateDuration: "30s",
}, pulumi.DependsOn([]pulumi.Resource{
viewer,
}))
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_cross_project_featureview"),
Schema: pulumi.String(`[
{
"name": "feature_id",
"type": "STRING",
"mode": "NULLABLE"
},
{
"name": "example_cross_project_featureview",
"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_cross_project_featureview"),
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_cross_project_featureview"),
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, "cross_project_featureview", &vertex.AiFeatureOnlineStoreFeatureviewArgs{
Name: pulumi.String("example_cross_project_featureview"),
Project: project.ProjectId,
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,
},
},
},
ProjectNumber: pulumi.String(testProject.Number),
},
}, pulumi.DependsOn([]pulumi.Resource{
vertexai,
wait30Seconds,
}))
if err != nil {
return err
}
return nil
})
}
using System.Collections.Generic;
using System.Linq;
using Pulumi;
using Gcp = Pulumi.Gcp;
using Time = Pulumi.Time;
return await Deployment.RunAsync(() =>
{
var testProject = Gcp.Organizations.GetProject.Invoke();
var project = new Gcp.Organizations.Project("project", new()
{
ProjectId = "tf-test_41150",
Name = "tf-test_89313",
OrgId = "123456789",
BillingAccount = "000000-0000000-0000000-000000",
DeletionPolicy = "DELETE",
});
var wait60Seconds = new Time.Index.Sleep("wait_60_seconds", new()
{
CreateDuration = "60s",
}, new CustomResourceOptions
{
DependsOn =
{
project,
},
});
var vertexai = new Gcp.Projects.Service("vertexai", new()
{
ServiceName = "aiplatform.googleapis.com",
Project = project.ProjectId,
DisableOnDestroy = false,
}, new CustomResourceOptions
{
DependsOn =
{
wait60Seconds,
},
});
var featureonlinestore = new Gcp.Vertex.AiFeatureOnlineStore("featureonlinestore", new()
{
Name = "example_cross_project_featureview",
Project = project.ProjectId,
Labels =
{
{ "foo", "bar" },
},
Region = "us-central1",
Bigtable = new Gcp.Vertex.Inputs.AiFeatureOnlineStoreBigtableArgs
{
AutoScaling = new Gcp.Vertex.Inputs.AiFeatureOnlineStoreBigtableAutoScalingArgs
{
MinNodeCount = 1,
MaxNodeCount = 2,
CpuUtilizationTarget = 80,
},
},
}, new CustomResourceOptions
{
DependsOn =
{
vertexai,
},
});
var sampleDataset = new Gcp.BigQuery.Dataset("sample_dataset", new()
{
DatasetId = "example_cross_project_featureview",
FriendlyName = "test",
Description = "This is a test description",
Location = "US",
});
var viewer = new Gcp.BigQuery.DatasetIamMember("viewer", new()
{
Project = testProject.Apply(getProjectResult => getProjectResult.ProjectId),
DatasetId = sampleDataset.DatasetId,
Role = "roles/bigquery.dataViewer",
Member = project.Number.Apply(number => $"serviceAccount:service-{number}@gcp-sa-aiplatform.iam.gserviceaccount.com"),
}, new CustomResourceOptions
{
DependsOn =
{
featureonlinestore,
},
});
var wait30Seconds = new Time.Index.Sleep("wait_30_seconds", new()
{
CreateDuration = "30s",
}, new CustomResourceOptions
{
DependsOn =
{
viewer,
},
});
var sampleTable = new Gcp.BigQuery.Table("sample_table", new()
{
DeletionProtection = false,
DatasetId = sampleDataset.DatasetId,
TableId = "example_cross_project_featureview",
Schema = @"[
{
""name"": ""feature_id"",
""type"": ""STRING"",
""mode"": ""NULLABLE""
},
{
""name"": ""example_cross_project_featureview"",
""type"": ""STRING"",
""mode"": ""NULLABLE""
},
{
""name"": ""feature_timestamp"",
""type"": ""TIMESTAMP"",
""mode"": ""NULLABLE""
}
]
",
});
var sampleFeatureGroup = new Gcp.Vertex.AiFeatureGroup("sample_feature_group", new()
{
Name = "example_cross_project_featureview",
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_cross_project_featureview",
Region = "us-central1",
FeatureGroup = sampleFeatureGroup.Name,
Description = "A sample feature",
Labels =
{
{ "label-one", "value-one" },
},
});
var crossProjectFeatureview = new Gcp.Vertex.AiFeatureOnlineStoreFeatureview("cross_project_featureview", new()
{
Name = "example_cross_project_featureview",
Project = project.ProjectId,
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,
},
},
},
ProjectNumber = testProject.Apply(getProjectResult => getProjectResult.Number),
},
}, new CustomResourceOptions
{
DependsOn =
{
vertexai,
wait30Seconds,
},
});
});
package generated_program;
import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.gcp.organizations.OrganizationsFunctions;
import com.pulumi.gcp.organizations.inputs.GetProjectArgs;
import com.pulumi.gcp.organizations.Project;
import com.pulumi.gcp.organizations.ProjectArgs;
import com.pulumi.time.sleep;
import com.pulumi.time.SleepArgs;
import com.pulumi.gcp.projects.Service;
import com.pulumi.gcp.projects.ServiceArgs;
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.DatasetIamMember;
import com.pulumi.gcp.bigquery.DatasetIamMemberArgs;
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 com.pulumi.resources.CustomResourceOptions;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;
public class App {
public static void main(String[] args) {
Pulumi.run(App::stack);
}
public static void stack(Context ctx) {
final var testProject = OrganizationsFunctions.getProject();
var project = new Project("project", ProjectArgs.builder()
.projectId("tf-test_41150")
.name("tf-test_89313")
.orgId("123456789")
.billingAccount("000000-0000000-0000000-000000")
.deletionPolicy("DELETE")
.build());
var wait60Seconds = new Sleep("wait60Seconds", SleepArgs.builder()
.createDuration("60s")
.build(), CustomResourceOptions.builder()
.dependsOn(project)
.build());
var vertexai = new Service("vertexai", ServiceArgs.builder()
.service("aiplatform.googleapis.com")
.project(project.projectId())
.disableOnDestroy(false)
.build(), CustomResourceOptions.builder()
.dependsOn(wait60Seconds)
.build());
var featureonlinestore = new AiFeatureOnlineStore("featureonlinestore", AiFeatureOnlineStoreArgs.builder()
.name("example_cross_project_featureview")
.project(project.projectId())
.labels(Map.of("foo", "bar"))
.region("us-central1")
.bigtable(AiFeatureOnlineStoreBigtableArgs.builder()
.autoScaling(AiFeatureOnlineStoreBigtableAutoScalingArgs.builder()
.minNodeCount(1)
.maxNodeCount(2)
.cpuUtilizationTarget(80)
.build())
.build())
.build(), CustomResourceOptions.builder()
.dependsOn(vertexai)
.build());
var sampleDataset = new Dataset("sampleDataset", DatasetArgs.builder()
.datasetId("example_cross_project_featureview")
.friendlyName("test")
.description("This is a test description")
.location("US")
.build());
var viewer = new DatasetIamMember("viewer", DatasetIamMemberArgs.builder()
.project(testProject.applyValue(getProjectResult -> getProjectResult.projectId()))
.datasetId(sampleDataset.datasetId())
.role("roles/bigquery.dataViewer")
.member(project.number().applyValue(number -> String.format("serviceAccount:service-%s@gcp-sa-aiplatform.iam.gserviceaccount.com", number)))
.build(), CustomResourceOptions.builder()
.dependsOn(featureonlinestore)
.build());
var wait30Seconds = new Sleep("wait30Seconds", SleepArgs.builder()
.createDuration("30s")
.build(), CustomResourceOptions.builder()
.dependsOn(viewer)
.build());
var sampleTable = new Table("sampleTable", TableArgs.builder()
.deletionProtection(false)
.datasetId(sampleDataset.datasetId())
.tableId("example_cross_project_featureview")
.schema("""
[
{
"name": "feature_id",
"type": "STRING",
"mode": "NULLABLE"
},
{
"name": "example_cross_project_featureview",
"type": "STRING",
"mode": "NULLABLE"
},
{
"name": "feature_timestamp",
"type": "TIMESTAMP",
"mode": "NULLABLE"
}
]
""")
.build());
var sampleFeatureGroup = new AiFeatureGroup("sampleFeatureGroup", AiFeatureGroupArgs.builder()
.name("example_cross_project_featureview")
.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_cross_project_featureview")
.region("us-central1")
.featureGroup(sampleFeatureGroup.name())
.description("A sample feature")
.labels(Map.of("label-one", "value-one"))
.build());
var crossProjectFeatureview = new AiFeatureOnlineStoreFeatureview("crossProjectFeatureview", AiFeatureOnlineStoreFeatureviewArgs.builder()
.name("example_cross_project_featureview")
.project(project.projectId())
.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())
.projectNumber(testProject.applyValue(getProjectResult -> getProjectResult.number()))
.build())
.build(), CustomResourceOptions.builder()
.dependsOn(
vertexai,
wait30Seconds)
.build());
}
}
resources:
project:
type: gcp:organizations:Project
properties:
projectId: tf-test_41150
name: tf-test_89313
orgId: '123456789'
billingAccount: 000000-0000000-0000000-000000
deletionPolicy: DELETE
wait60Seconds:
type: time:sleep
name: wait_60_seconds
properties:
createDuration: 60s
options:
dependson:
- ${project}
wait30Seconds:
type: time:sleep
name: wait_30_seconds
properties:
createDuration: 30s
options:
dependson:
- ${viewer}
vertexai:
type: gcp:projects:Service
properties:
service: aiplatform.googleapis.com
project: ${project.projectId}
disableOnDestroy: false # Needed for CI tests for permissions to propagate, should not be needed for actual usage
options:
dependson:
- ${wait60Seconds}
viewer:
type: gcp:bigquery:DatasetIamMember
properties:
project: ${testProject.projectId}
datasetId: ${sampleDataset.datasetId}
role: roles/bigquery.dataViewer
member: serviceAccount:service-${project.number}@gcp-sa-aiplatform.iam.gserviceaccount.com
options:
dependson:
- ${featureonlinestore}
featureonlinestore:
type: gcp:vertex:AiFeatureOnlineStore
properties:
name: example_cross_project_featureview
project: ${project.projectId}
labels:
foo: bar
region: us-central1
bigtable:
autoScaling:
minNodeCount: 1
maxNodeCount: 2
cpuUtilizationTarget: 80
options:
dependson:
- ${vertexai}
sampleDataset:
type: gcp:bigquery:Dataset
name: sample_dataset
properties:
datasetId: example_cross_project_featureview
friendlyName: test
description: This is a test description
location: US
sampleTable:
type: gcp:bigquery:Table
name: sample_table
properties:
deletionProtection: false
datasetId: ${sampleDataset.datasetId}
tableId: example_cross_project_featureview
schema: |
[
{
"name": "feature_id",
"type": "STRING",
"mode": "NULLABLE"
},
{
"name": "example_cross_project_featureview",
"type": "STRING",
"mode": "NULLABLE"
},
{
"name": "feature_timestamp",
"type": "TIMESTAMP",
"mode": "NULLABLE"
}
]
sampleFeatureGroup:
type: gcp:vertex:AiFeatureGroup
name: sample_feature_group
properties:
name: example_cross_project_featureview
description: A sample feature group
region: us-central1
labels:
label-one: value-one
bigQuery:
bigQuerySource:
inputUri: bq://${sampleTable.project}.${sampleTable.datasetId}.${sampleTable.tableId}
entityIdColumns:
- feature_id
sampleFeature:
type: gcp:vertex:AiFeatureGroupFeature
name: sample_feature
properties:
name: example_cross_project_featureview
region: us-central1
featureGroup: ${sampleFeatureGroup.name}
description: A sample feature
labels:
label-one: value-one
crossProjectFeatureview:
type: gcp:vertex:AiFeatureOnlineStoreFeatureview
name: cross_project_featureview
properties:
name: example_cross_project_featureview
project: ${project.projectId}
region: us-central1
featureOnlineStore: ${featureonlinestore.name}
syncConfig:
cron: 0 0 * * *
featureRegistrySource:
featureGroups:
- featureGroupId: ${sampleFeatureGroup.name}
featureIds:
- ${sampleFeature.name}
projectNumber: ${testProject.number}
options:
dependson:
- ${vertexai}
- ${wait30Seconds}
variables:
testProject:
fn::invoke:
Function: gcp:organizations:getProject
Arguments: {}
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={
"auto_scaling": {
"min_node_count": 1,
"max_node_count": 2,
"cpu_utilization_target": 80,
},
},
embedding_management={
"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={
"cron": "0 0 * * *",
},
big_query_source={
"uri": pulumi.Output.all(
project=tf_test_table.project,
dataset_id=tf_test_table.dataset_id,
table_id=tf_test_table.table_id
).apply(lambda resolved_outputs: f"bq://{resolved_outputs['project']}.{resolved_outputs['dataset_id']}.{resolved_outputs['table_id']}")
,
"entity_id_columns": ["test_entity_column"],
},
vector_search_config={
"embedding_column": "embedding",
"filter_columns": ["country"],
"crowding_column": "test_crowding_column",
"distance_measure_type": "DOT_PRODUCT_DISTANCE",
"tree_ah_config": {
"leaf_node_embedding_count": "1000",
},
"embedding_dimension": 2,
})
project = gcp.organizations.get_project()
package main
import (
"fmt"
"github.com/pulumi/pulumi-gcp/sdk/v8/go/gcp/bigquery"
"github.com/pulumi/pulumi-gcp/sdk/v8/go/gcp/organizations"
"github.com/pulumi/pulumi-gcp/sdk/v8/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, &organizations.LookupProjectArgs{}, 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",
},
},
},
ProjectNumber = "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"),
},
},
},
ProjectNumber: 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: &vertex.AiFeatureOnlineStoreFeatureviewVectorSearchConfigBruteForceConfigArgs{},
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())
.projectNumber("string")
.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={
"entity_id_columns": ["string"],
"uri": "string",
},
feature_registry_source={
"feature_groups": [{
"feature_group_id": "string",
"feature_ids": ["string"],
}],
"project_number": "string",
},
labels={
"string": "string",
},
name="string",
project="string",
sync_config={
"cron": "string",
},
vector_search_config={
"embedding_column": "string",
"brute_force_config": {},
"crowding_column": "string",
"distance_measure_type": "string",
"embedding_dimension": 0,
"filter_columns": ["string"],
"tree_ah_config": {
"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"],
}],
projectNumber: "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
projectNumber: 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
In Python, inputs that are objects can be passed either as argument classes or as dictionary literals.
The AiFeatureOnlineStoreFeatureview resource accepts the following input properties:
- Feature
Online stringStore - 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.
- Big
Query AiSource Feature Online Store Featureview Big Query Source - Configures how data is supposed to be extracted from a BigQuery source to be loaded onto the FeatureOnlineStore. Structure is documented below.
- Feature
Registry AiSource Feature Online Store Featureview Feature Registry Source - 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_labelsfor 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.
- Sync
Config AiFeature Online Store Featureview Sync Config - 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.
- Vector
Search AiConfig Feature Online Store Featureview Vector Search Config - 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.
- Feature
Online stringStore - 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.
- Big
Query AiSource Feature Online Store Featureview Big Query Source Args - Configures how data is supposed to be extracted from a BigQuery source to be loaded onto the FeatureOnlineStore. Structure is documented below.
- Feature
Registry AiSource Feature Online Store Featureview Feature Registry Source Args - 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_labelsfor 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.
- Sync
Config AiFeature Online Store Featureview Sync Config Args - 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.
- Vector
Search AiConfig Feature Online Store Featureview Vector Search Config Args - 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.
- feature
Online StringStore - 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.
- big
Query AiSource Feature Online Store Featureview Big Query Source - Configures how data is supposed to be extracted from a BigQuery source to be loaded onto the FeatureOnlineStore. Structure is documented below.
- feature
Registry AiSource Feature Online Store Featureview Feature Registry Source - 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_labelsfor 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.
- sync
Config AiFeature Online Store Featureview Sync Config - 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.
- vector
Search AiConfig Feature Online Store Featureview Vector Search Config - 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.
- feature
Online stringStore - 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.
- big
Query AiSource Feature Online Store Featureview Big Query Source - Configures how data is supposed to be extracted from a BigQuery source to be loaded onto the FeatureOnlineStore. Structure is documented below.
- feature
Registry AiSource Feature Online Store Featureview Feature Registry Source - 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_labelsfor 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.
- sync
Config AiFeature Online Store Featureview Sync Config - 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.
- vector
Search AiConfig Feature Online Store Featureview Vector Search Config - 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.
- feature_
online_ strstore - 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_ Aisource Feature Online Store Featureview Big Query Source Args - Configures how data is supposed to be extracted from a BigQuery source to be loaded onto the FeatureOnlineStore. Structure is documented below.
- feature_
registry_ Aisource Feature Online Store Featureview Feature Registry Source Args - 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_labelsfor 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 AiFeature Online Store Featureview Sync Config Args - 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.
- vector_
search_ Aiconfig Feature Online Store Featureview Vector Search Config Args - 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.
- feature
Online StringStore - 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.
- big
Query Property MapSource - Configures how data is supposed to be extracted from a BigQuery source to be loaded onto the FeatureOnlineStore. Structure is documented below.
- feature
Registry Property MapSource - 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_labelsfor 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.
- sync
Config Property Map - 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.
- vector
Search Property MapConfig - 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.
Outputs
All input properties are implicitly available as output properties. Additionally, the AiFeatureOnlineStoreFeatureview resource produces the following output properties:
- Create
Time string - The timestamp of when the featureOnlinestore was created in RFC3339 UTC "Zulu" format, with nanosecond resolution and up to nine fractional digits.
- Effective
Labels 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.
- Pulumi
Labels Dictionary<string, string> - The combination of labels configured directly on the resource and default labels configured on the provider.
- Update
Time 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 string - The timestamp of when the featureOnlinestore was created in RFC3339 UTC "Zulu" format, with nanosecond resolution and up to nine fractional digits.
- Effective
Labels 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.
- Pulumi
Labels map[string]string - The combination of labels configured directly on the resource and default labels configured on the provider.
- Update
Time 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 String - The timestamp of when the featureOnlinestore was created in RFC3339 UTC "Zulu" format, with nanosecond resolution and up to nine fractional digits.
- effective
Labels 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.
- pulumi
Labels Map<String,String> - The combination of labels configured directly on the resource and default labels configured on the provider.
- update
Time 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 string - The timestamp of when the featureOnlinestore was created in RFC3339 UTC "Zulu" format, with nanosecond resolution and up to nine fractional digits.
- effective
Labels {[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.
- pulumi
Labels {[key: string]: string} - The combination of labels configured directly on the resource and default labels configured on the provider.
- update
Time 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.
- create
Time String - The timestamp of when the featureOnlinestore was created in RFC3339 UTC "Zulu" format, with nanosecond resolution and up to nine fractional digits.
- effective
Labels 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.
- pulumi
Labels Map<String> - The combination of labels configured directly on the resource and default labels configured on the provider.
- update
Time 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) -> AiFeatureOnlineStoreFeatureviewfunc 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.
- Big
Query AiSource Feature Online Store Featureview Big Query Source - Configures how data is supposed to be extracted from a BigQuery source to be loaded onto the FeatureOnlineStore. Structure is documented below.
- Create
Time string - The timestamp of when the featureOnlinestore was created in RFC3339 UTC "Zulu" format, with nanosecond resolution and up to nine fractional digits.
- Effective
Labels 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.
- Feature
Online stringStore - The name of the FeatureOnlineStore to use for the featureview.
- Feature
Registry AiSource Feature Online Store Featureview Feature Registry Source - 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_labelsfor 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.
- Pulumi
Labels 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.
- Sync
Config AiFeature Online Store Featureview Sync Config - 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.
- Update
Time string - 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 AiConfig Feature Online Store Featureview Vector Search Config - 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.
- Big
Query AiSource Feature Online Store Featureview Big Query Source Args - Configures how data is supposed to be extracted from a BigQuery source to be loaded onto the FeatureOnlineStore. Structure is documented below.
- Create
Time string - The timestamp of when the featureOnlinestore was created in RFC3339 UTC "Zulu" format, with nanosecond resolution and up to nine fractional digits.
- Effective
Labels 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.
- Feature
Online stringStore - The name of the FeatureOnlineStore to use for the featureview.
- Feature
Registry AiSource Feature Online Store Featureview Feature Registry Source Args - 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_labelsfor 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.
- Pulumi
Labels 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.
- Sync
Config AiFeature Online Store Featureview Sync Config Args - 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.
- Update
Time string - 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 AiConfig Feature Online Store Featureview Vector Search Config Args - 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.
- big
Query AiSource Feature Online Store Featureview Big Query Source - Configures how data is supposed to be extracted from a BigQuery source to be loaded onto the FeatureOnlineStore. Structure is documented below.
- create
Time String - The timestamp of when the featureOnlinestore was created in RFC3339 UTC "Zulu" format, with nanosecond resolution and up to nine fractional digits.
- effective
Labels 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.
- feature
Online StringStore - The name of the FeatureOnlineStore to use for the featureview.
- feature
Registry AiSource Feature Online Store Featureview Feature Registry Source - 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_labelsfor 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.
- pulumi
Labels 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.
- sync
Config AiFeature Online Store Featureview Sync Config - 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.
- update
Time String - 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 AiConfig Feature Online Store Featureview Vector Search Config - 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.
- big
Query AiSource Feature Online Store Featureview Big Query Source - Configures how data is supposed to be extracted from a BigQuery source to be loaded onto the FeatureOnlineStore. Structure is documented below.
- create
Time string - The timestamp of when the featureOnlinestore was created in RFC3339 UTC "Zulu" format, with nanosecond resolution and up to nine fractional digits.
- effective
Labels {[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.
- feature
Online stringStore - The name of the FeatureOnlineStore to use for the featureview.
- feature
Registry AiSource Feature Online Store Featureview Feature Registry Source - 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_labelsfor 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.
- pulumi
Labels {[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.
- sync
Config AiFeature Online Store Featureview Sync Config - 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.
- update
Time string - 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 AiConfig Feature Online Store Featureview Vector Search Config - 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.
- big_
query_ Aisource Feature Online Store Featureview Big Query Source Args - 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_ strstore - The name of the FeatureOnlineStore to use for the featureview.
- feature_
registry_ Aisource Feature Online Store Featureview Feature Registry Source Args - 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_labelsfor 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 AiFeature Online Store Featureview Sync Config Args - 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.
- 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_ Aiconfig Feature Online Store Featureview Vector Search Config Args - 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.
- big
Query Property MapSource - Configures how data is supposed to be extracted from a BigQuery source to be loaded onto the FeatureOnlineStore. Structure is documented below.
- create
Time String - The timestamp of when the featureOnlinestore was created in RFC3339 UTC "Zulu" format, with nanosecond resolution and up to nine fractional digits.
- effective
Labels Map<String> - All of labels (key/value pairs) present on the resource in GCP, including the labels configured through Pulumi, other clients and services.
- feature
Online StringStore - The name of the FeatureOnlineStore to use for the featureview.
- feature
Registry Property MapSource - 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_labelsfor 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.
- pulumi
Labels 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.
- sync
Config Property Map - 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.
- update
Time String - 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 Property MapConfig - 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.
Supporting Types
AiFeatureOnlineStoreFeatureviewBigQuerySource, AiFeatureOnlineStoreFeatureviewBigQuerySourceArgs
- Entity
Id List<string>Columns - 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 []stringColumns - 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 List<String>Columns - 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 string[]Columns - 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_ Sequence[str]columns - 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.
- entity
Id List<String>Columns - 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
- Feature
Groups List<AiFeature Online Store Featureview Feature Registry Source Feature Group> - List of features that need to be synced to Online Store. Structure is documented below.
- Project
Number string - The project number of the parent project of the feature Groups.
- Feature
Groups []AiFeature Online Store Featureview Feature Registry Source Feature Group - List of features that need to be synced to Online Store. Structure is documented below.
- Project
Number string - The project number of the parent project of the feature Groups.
- feature
Groups List<AiFeature Online Store Featureview Feature Registry Source Feature Group> - List of features that need to be synced to Online Store. Structure is documented below.
- project
Number String - The project number of the parent project of the feature Groups.
- feature
Groups AiFeature Online Store Featureview Feature Registry Source Feature Group[] - List of features that need to be synced to Online Store. Structure is documented below.
- project
Number string - The project number of the parent project of the feature Groups.
- feature_
groups Sequence[AiFeature Online Store Featureview Feature Registry Source Feature Group] - List of features that need to be synced to Online Store. Structure is documented below.
- project_
number str - The project number of the parent project of the feature Groups.
- feature
Groups List<Property Map> - List of features that need to be synced to Online Store. Structure is documented below.
- project
Number String - The project number of the parent project of the feature Groups.
AiFeatureOnlineStoreFeatureviewFeatureRegistrySourceFeatureGroup, AiFeatureOnlineStoreFeatureviewFeatureRegistrySourceFeatureGroupArgs
- Feature
Group stringId - Identifier of the feature group.
- Feature
Ids List<string> - Identifiers of features under the feature group.
- Feature
Group stringId - Identifier of the feature group.
- Feature
Ids []string - Identifiers of features under the feature group.
- feature
Group StringId - Identifier of the feature group.
- feature
Ids List<String> - Identifiers of features under the feature group.
- feature
Group stringId - Identifier of the feature group.
- feature
Ids string[] - Identifiers of features under the feature group.
- feature_
group_ strid - Identifier of the feature group.
- feature_
ids Sequence[str] - Identifiers of features under the feature group.
- feature
Group StringId - Identifier of the feature group.
- feature
Ids 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
- Embedding
Column string - Column of embedding. This column contains the source data to create index for vector search.
- Brute
Force AiConfig Feature Online Store Featureview Vector Search Config Brute Force Config - 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 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.
- Distance
Measure stringType - 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 List<string> - Columns of features that are used to filter vector search results.
- Tree
Ah AiConfig Feature Online Store Featureview Vector Search Config Tree Ah Config - 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 string - Column of embedding. This column contains the source data to create index for vector search.
- Brute
Force AiConfig Feature Online Store Featureview Vector Search Config Brute Force Config - 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 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.
- Distance
Measure stringType - 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 []string - Columns of features that are used to filter vector search results.
- Tree
Ah AiConfig Feature Online Store Featureview Vector Search Config Tree Ah Config - 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 String - Column of embedding. This column contains the source data to create index for vector search.
- brute
Force AiConfig Feature Online Store Featureview Vector Search Config Brute Force Config - 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 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.
- distance
Measure StringType - 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 Integer - The number of dimensions of the input embedding.
- filter
Columns List<String> - Columns of features that are used to filter vector search results.
- tree
Ah AiConfig Feature Online Store Featureview Vector Search Config Tree Ah Config - 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 string - Column of embedding. This column contains the source data to create index for vector search.
- brute
Force AiConfig Feature Online Store Featureview Vector Search Config Brute Force Config - 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 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.
- distance
Measure stringType - 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 number - The number of dimensions of the input embedding.
- filter
Columns string[] - Columns of features that are used to filter vector search results.
- tree
Ah AiConfig Feature Online Store Featureview Vector Search Config Tree Ah Config - 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_ Aiconfig Feature Online Store Featureview Vector Search Config Brute Force Config - 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_ strtype - 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_ Aiconfig Feature Online Store Featureview Vector Search Config Tree Ah Config - 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 String - Column of embedding. This column contains the source data to create index for vector search.
- brute
Force Property MapConfig - 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 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.
- distance
Measure StringType - 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 Number - The number of dimensions of the input embedding.
- filter
Columns List<String> - Columns of features that are used to filter vector search results.
- tree
Ah Property MapConfig - 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
- Leaf
Node stringEmbedding Count - Number of embeddings on each leaf node. The default value is 1000 if not set.
- Leaf
Node stringEmbedding Count - Number of embeddings on each leaf node. The default value is 1000 if not set.
- leaf
Node StringEmbedding Count - Number of embeddings on each leaf node. The default value is 1000 if not set.
- leaf
Node stringEmbedding Count - Number of embeddings on each leaf node. The default value is 1000 if not set.
- leaf_
node_ strembedding_ count - Number of embeddings on each leaf node. The default value is 1000 if not set.
- leaf
Node StringEmbedding Count - 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-betaTerraform Provider.