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@file:Suppress("NAME_SHADOWING", "DEPRECATION")
package com.pulumi.gcp.vertex.kotlin
import com.pulumi.core.Output
import com.pulumi.core.Output.of
import com.pulumi.gcp.vertex.AiFeatureGroupArgs.builder
import com.pulumi.gcp.vertex.kotlin.inputs.AiFeatureGroupBigQueryArgs
import com.pulumi.gcp.vertex.kotlin.inputs.AiFeatureGroupBigQueryArgsBuilder
import com.pulumi.kotlin.ConvertibleToJava
import com.pulumi.kotlin.PulumiTagMarker
import com.pulumi.kotlin.applySuspend
import kotlin.Pair
import kotlin.String
import kotlin.Suppress
import kotlin.Unit
import kotlin.collections.Map
import kotlin.jvm.JvmName
/**
* Vertex AI Feature Group.
* To get more information about FeatureGroup, see:
* * [API documentation](https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.featureGroups)
* * How-to Guides
* * [Creating a Feature Group](https://cloud.google.com/vertex-ai/docs/featurestore/latest/create-featuregroup)
* ## Example Usage
* ### Vertex Ai Feature Group
*
* ```typescript
* import * as pulumi from "@pulumi/pulumi";
* import * as gcp from "@pulumi/gcp";
* const sampleDataset = new gcp.bigquery.Dataset("sample_dataset", {
* datasetId: "job_load_dataset",
* friendlyName: "test",
* description: "This is a test description",
* location: "US",
* });
* const sampleTable = new gcp.bigquery.Table("sample_table", {
* deletionProtection: false,
* datasetId: sampleDataset.datasetId,
* tableId: "job_load_table",
* schema: `[
* {
* "name": "feature_id",
* "type": "STRING",
* "mode": "NULLABLE"
* },
* {
* "name": "feature_timestamp",
* "type": "TIMESTAMP",
* "mode": "NULLABLE"
* }
* ]
* `,
* });
* const featureGroup = new gcp.vertex.AiFeatureGroup("feature_group", {
* name: "example_feature_group",
* 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"],
* },
* });
* ```
* ```python
* import pulumi
* import pulumi_gcp as gcp
* sample_dataset = gcp.bigquery.Dataset("sample_dataset",
* dataset_id="job_load_dataset",
* 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="job_load_table",
* schema="""[
* {
* "name": "feature_id",
* "type": "STRING",
* "mode": "NULLABLE"
* },
* {
* "name": "feature_timestamp",
* "type": "TIMESTAMP",
* "mode": "NULLABLE"
* }
* ]
* """)
* feature_group = gcp.vertex.AiFeatureGroup("feature_group",
* name="example_feature_group",
* 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"],
* })
* ```
* ```csharp
* using System.Collections.Generic;
* using System.Linq;
* using Pulumi;
* using Gcp = Pulumi.Gcp;
* return await Deployment.RunAsync(() =>
* {
* var sampleDataset = new Gcp.BigQuery.Dataset("sample_dataset", new()
* {
* DatasetId = "job_load_dataset",
* 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 = "job_load_table",
* Schema = @"[
* {
* ""name"": ""feature_id"",
* ""type"": ""STRING"",
* ""mode"": ""NULLABLE""
* },
* {
* ""name"": ""feature_timestamp"",
* ""type"": ""TIMESTAMP"",
* ""mode"": ""NULLABLE""
* }
* ]
* ",
* });
* var featureGroup = new Gcp.Vertex.AiFeatureGroup("feature_group", new()
* {
* Name = "example_feature_group",
* 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",
* },
* },
* });
* });
* ```
* ```go
* package main
* import (
* "fmt"
* "github.com/pulumi/pulumi-gcp/sdk/v7/go/gcp/bigquery"
* "github.com/pulumi/pulumi-gcp/sdk/v7/go/gcp/vertex"
* "github.com/pulumi/pulumi/sdk/v3/go/pulumi"
* )
* func main() {
* pulumi.Run(func(ctx *pulumi.Context) error {
* sampleDataset, err := bigquery.NewDataset(ctx, "sample_dataset", &bigquery.DatasetArgs{
* DatasetId: pulumi.String("job_load_dataset"),
* 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("job_load_table"),
* Schema: pulumi.String(`[
* {
* "name": "feature_id",
* "type": "STRING",
* "mode": "NULLABLE"
* },
* {
* "name": "feature_timestamp",
* "type": "TIMESTAMP",
* "mode": "NULLABLE"
* }
* ]
* `),
* })
* if err != nil {
* return err
* }
* _, err = vertex.NewAiFeatureGroup(ctx, "feature_group", &vertex.AiFeatureGroupArgs{
* Name: pulumi.String("example_feature_group"),
* 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
* }
* return nil
* })
* }
* ```
* ```java
* package generated_program;
* import com.pulumi.Context;
* import com.pulumi.Pulumi;
* import com.pulumi.core.Output;
* 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 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 sampleDataset = new Dataset("sampleDataset", DatasetArgs.builder()
* .datasetId("job_load_dataset")
* .friendlyName("test")
* .description("This is a test description")
* .location("US")
* .build());
* var sampleTable = new Table("sampleTable", TableArgs.builder()
* .deletionProtection(false)
* .datasetId(sampleDataset.datasetId())
* .tableId("job_load_table")
* .schema("""
* [
* {
* "name": "feature_id",
* "type": "STRING",
* "mode": "NULLABLE"
* },
* {
* "name": "feature_timestamp",
* "type": "TIMESTAMP",
* "mode": "NULLABLE"
* }
* ]
* """)
* .build());
* var featureGroup = new AiFeatureGroup("featureGroup", AiFeatureGroupArgs.builder()
* .name("example_feature_group")
* .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());
* }
* }
* ```
* ```yaml
* resources:
* featureGroup:
* type: gcp:vertex:AiFeatureGroup
* name: feature_group
* properties:
* name: example_feature_group
* 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
* sampleDataset:
* type: gcp:bigquery:Dataset
* name: sample_dataset
* properties:
* datasetId: job_load_dataset
* 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: job_load_table
* schema: |
* [
* {
* "name": "feature_id",
* "type": "STRING",
* "mode": "NULLABLE"
* },
* {
* "name": "feature_timestamp",
* "type": "TIMESTAMP",
* "mode": "NULLABLE"
* }
* ]
* ```
*
* ## Import
* FeatureGroup can be imported using any of these accepted formats:
* * `projects/{{project}}/locations/{{region}}/featureGroups/{{name}}`
* * `{{project}}/{{region}}/{{name}}`
* * `{{region}}/{{name}}`
* * `{{name}}`
* When using the `pulumi import` command, FeatureGroup can be imported using one of the formats above. For example:
* ```sh
* $ pulumi import gcp:vertex/aiFeatureGroup:AiFeatureGroup default projects/{{project}}/locations/{{region}}/featureGroups/{{name}}
* ```
* ```sh
* $ pulumi import gcp:vertex/aiFeatureGroup:AiFeatureGroup default {{project}}/{{region}}/{{name}}
* ```
* ```sh
* $ pulumi import gcp:vertex/aiFeatureGroup:AiFeatureGroup default {{region}}/{{name}}
* ```
* ```sh
* $ pulumi import gcp:vertex/aiFeatureGroup:AiFeatureGroup default {{name}}
* ```
* @property bigQuery Indicates that features for this group come from BigQuery Table/View. By default treats the source as a sparse time series source, which is required to have an entityId and a feature_timestamp column in the source.
* Structure is documented below.
* @property description The description of the FeatureGroup.
* @property labels The labels with user-defined metadata to organize your FeatureGroup.
* **Note**: This field is non-authoritative, and will only manage the labels present in your configuration.
* Please refer to the field `effective_labels` for all of the labels present on the resource.
* @property name The resource name of the Feature Group.
* @property project The ID of the project in which the resource belongs.
* If it is not provided, the provider project is used.
* @property region The region of feature group. eg us-central1
*/
public data class AiFeatureGroupArgs(
public val bigQuery: Output? = null,
public val description: Output? = null,
public val labels: Output>? = null,
public val name: Output? = null,
public val project: Output? = null,
public val region: Output? = null,
) : ConvertibleToJava {
override fun toJava(): com.pulumi.gcp.vertex.AiFeatureGroupArgs =
com.pulumi.gcp.vertex.AiFeatureGroupArgs.builder()
.bigQuery(bigQuery?.applyValue({ args0 -> args0.let({ args0 -> args0.toJava() }) }))
.description(description?.applyValue({ args0 -> args0 }))
.labels(labels?.applyValue({ args0 -> args0.map({ args0 -> args0.key.to(args0.value) }).toMap() }))
.name(name?.applyValue({ args0 -> args0 }))
.project(project?.applyValue({ args0 -> args0 }))
.region(region?.applyValue({ args0 -> args0 })).build()
}
/**
* Builder for [AiFeatureGroupArgs].
*/
@PulumiTagMarker
public class AiFeatureGroupArgsBuilder internal constructor() {
private var bigQuery: Output? = null
private var description: Output? = null
private var labels: Output>? = null
private var name: Output? = null
private var project: Output? = null
private var region: Output? = null
/**
* @param value Indicates that features for this group come from BigQuery Table/View. By default treats the source as a sparse time series source, which is required to have an entityId and a feature_timestamp column in the source.
* Structure is documented below.
*/
@JvmName("xuymbrbalahtuwth")
public suspend fun bigQuery(`value`: Output) {
this.bigQuery = value
}
/**
* @param value The description of the FeatureGroup.
*/
@JvmName("qiwiiotqqrkppnve")
public suspend fun description(`value`: Output) {
this.description = value
}
/**
* @param value The labels with user-defined metadata to organize your FeatureGroup.
* **Note**: This field is non-authoritative, and will only manage the labels present in your configuration.
* Please refer to the field `effective_labels` for all of the labels present on the resource.
*/
@JvmName("oaimnhmxifpybqfu")
public suspend fun labels(`value`: Output>) {
this.labels = value
}
/**
* @param value The resource name of the Feature Group.
*/
@JvmName("rqsksvaffstbtabf")
public suspend fun name(`value`: Output) {
this.name = value
}
/**
* @param value The ID of the project in which the resource belongs.
* If it is not provided, the provider project is used.
*/
@JvmName("hgxjyytwvkqhljfp")
public suspend fun project(`value`: Output) {
this.project = value
}
/**
* @param value The region of feature group. eg us-central1
*/
@JvmName("cewvgbrlosqtxtpc")
public suspend fun region(`value`: Output) {
this.region = value
}
/**
* @param value Indicates that features for this group come from BigQuery Table/View. By default treats the source as a sparse time series source, which is required to have an entityId and a feature_timestamp column in the source.
* Structure is documented below.
*/
@JvmName("sqgqttrtkpvadbnq")
public suspend fun bigQuery(`value`: AiFeatureGroupBigQueryArgs?) {
val toBeMapped = value
val mapped = toBeMapped?.let({ args0 -> of(args0) })
this.bigQuery = mapped
}
/**
* @param argument Indicates that features for this group come from BigQuery Table/View. By default treats the source as a sparse time series source, which is required to have an entityId and a feature_timestamp column in the source.
* Structure is documented below.
*/
@JvmName("vckcnvhaxbijendc")
public suspend fun bigQuery(argument: suspend AiFeatureGroupBigQueryArgsBuilder.() -> Unit) {
val toBeMapped = AiFeatureGroupBigQueryArgsBuilder().applySuspend { argument() }.build()
val mapped = of(toBeMapped)
this.bigQuery = mapped
}
/**
* @param value The description of the FeatureGroup.
*/
@JvmName("ldjqjhsmpubebuqs")
public suspend fun description(`value`: String?) {
val toBeMapped = value
val mapped = toBeMapped?.let({ args0 -> of(args0) })
this.description = mapped
}
/**
* @param value The labels with user-defined metadata to organize your FeatureGroup.
* **Note**: This field is non-authoritative, and will only manage the labels present in your configuration.
* Please refer to the field `effective_labels` for all of the labels present on the resource.
*/
@JvmName("hciciilpgplpobnr")
public suspend fun labels(`value`: Map?) {
val toBeMapped = value
val mapped = toBeMapped?.let({ args0 -> of(args0) })
this.labels = mapped
}
/**
* @param values The labels with user-defined metadata to organize your FeatureGroup.
* **Note**: This field is non-authoritative, and will only manage the labels present in your configuration.
* Please refer to the field `effective_labels` for all of the labels present on the resource.
*/
@JvmName("fscykojlsidjfigi")
public fun labels(vararg values: Pair) {
val toBeMapped = values.toMap()
val mapped = toBeMapped.let({ args0 -> of(args0) })
this.labels = mapped
}
/**
* @param value The resource name of the Feature Group.
*/
@JvmName("qcaadeypmtutdauf")
public suspend fun name(`value`: String?) {
val toBeMapped = value
val mapped = toBeMapped?.let({ args0 -> of(args0) })
this.name = mapped
}
/**
* @param value The ID of the project in which the resource belongs.
* If it is not provided, the provider project is used.
*/
@JvmName("ldlfmuqkblieaivd")
public suspend fun project(`value`: String?) {
val toBeMapped = value
val mapped = toBeMapped?.let({ args0 -> of(args0) })
this.project = mapped
}
/**
* @param value The region of feature group. eg us-central1
*/
@JvmName("anghoeeovphdroxi")
public suspend fun region(`value`: String?) {
val toBeMapped = value
val mapped = toBeMapped?.let({ args0 -> of(args0) })
this.region = mapped
}
internal fun build(): AiFeatureGroupArgs = AiFeatureGroupArgs(
bigQuery = bigQuery,
description = description,
labels = labels,
name = name,
project = project,
region = region,
)
}
