管理特征存储区

了解如何创建、列出、描述、更新和删除特征存储区。特征存储区是实体类型、特征和特征值的顶层容器。

在线和离线存储空间

Vertex AI Feature Store 使用被归类为“在线存储空间”和“离线存储空间”的两种存储方法，这两种方法价格不同。所有特征存储区都具备离线存储空间，并且可以选用在线存储空间。

在线存储区保留特征的最新时间戳值，以便高效地处理在线传送请求。使用 API 运行导入作业时，如果数据写入在线存储区，您可以控制作业。跳过在线存储区可防止在线传送节点上产生任何负载。例如，运行回填作业时，您可以禁止写入在线存储区，并仅写入离线存储区。如需了解详情，请参阅 API 参考文档中的 disableOnlineServing 标志。

Vertex AI Feature Store（旧版）使用离线存储来存储数据，直到数据达到保留期限或直到您删除数据。您可以在离线存储区中存储无限量的数据。您可以通过管理保留的数据量来控制离线存储费用。您还可以替换特征存储区的默认在线存储数据保留限制和实体类型的离线数据保留限制。详细了解 Vertex AI Feature Store（旧版）配额和限制。

使用 Google Cloud 控制台查看您当前使用的在线和离线存储量。查看特征存储区的在线存储总量和离线存储总量监控指标以确定用量。

在线传送节点

在线传送节点可提供用于为低延迟在线传送存储和提供特征值的计算资源。即使这些节点不会传送数据，也始终会运行。您需要为每节点时付费。

在线传送节点的存储空间上限为每个节点 5 TB。详细了解 Vertex AI Feature Store（旧版）配额和限制。

您需要的在线传送务节点数与以下两个因素成正比：

  • 特征存储区收到的在线传送请求数（每秒查询次数）。
  • 写入在线存储的导入作业数量。

这两个因素都会影响节点的 CPU 利用率和性能。在 Google Cloud 控制台中，查看以下各项的指标：

  • 每秒查询次数：特征存储区的每秒查询次数。
  • 节点数：您的在线传送节点数。
  • CPU 利用率：节点的 CPU 利用率。

如果 CPU 利用率一直很高，请考虑增加特征存储区的在线传送节点数。

测试在线传送节点的性能

您可以测试在线传送节点的性能，以确保实时特征传送。这样，您可以确保特征存储区有足够的机器资源在预定的 QPS 或延迟时间阈值内执行。您可以基于各种基准参数（例如 QPS、延迟时间和 API）执行这些测试。如需了解测试在线传送节点性能的准则和最佳实践，请参阅Vertex AI Feature Store（旧版）最佳实践中的测试在线传送节点的性能以确保实时传送。

此外，您还可以使用 Vertex AI Benchmarker 开源工具对特征存储区资源的性能进行负载测试。Vertex AI Benchmarker 开源工具由 Python 命令行工具和 Java 工作器组成。

扩缩选项

您可以在以下选项之间切换，以配置在线传送节点的数量：

  • 自动扩缩

    如果您选择自动扩缩功能，特征存储区会根据 CPU 利用率自动更改节点数量。自动扩缩功能会审核流量模式以保持性能，并通过在流量增加时添加节点并在流量减少时移除节点来优化费用。

    自动扩缩非常适合流量逐渐增加和减少的流量模式。如果您将 Vertex AI Feature Store（旧版）广泛用于遇到频繁负载波动的流量模式，请使用自动扩缩来提高成本效益。

  • 分配固定的节点数

    如果您分配固定节点数，则无论流量模式如何，Vertex AI Feature Store（旧版）都会保持一致的节点数。固定的节点数将使费用保持可预测，并且当有足够的节点来处理流量时，节点应该性能良好。您可以手动更改固定的节点数，以处理流量模式的变化。

自动扩缩的其他注意事项

如果选择自动扩缩，还需要另外考虑以下四点：

  • 添加在线传送节点后，在线存储区需要时间来重新均衡数据。负载最多可能需要 20 分钟才能表现出性能显著提升。因此，扩缩节点数可能不适用于短暂的流量激增。此限制适用于手动扩缩和自动扩缩。

  • 如果向没有在线传送节点的特征存储区提交在线传送请求，则操作将返回错误。

关闭特征存储区中的在线传送

如果您不需要在线传送，并且想要防止在线传送节点发生更改，请将在线传送节点的数量设置为零。如需关闭特征存储区中的在线传送，请设置以下配置：

  1. 如果您使用自动扩缩功能，请移除 scaling 参数。

  2. 将在线传送节点的固定数量设置为 0。

如需详细了解如何创建特征存储区，请参阅创建特征存储区。如需详细了解如何修改现有特征存储区的配置，请参阅更新特征存储区。

如果将在线传送节点的数量设置为 0，则整个在线存储区（包括其数据）都会被删除。如果您希望暂时关闭在线存储区，然后将其恢复，则必须重新导入已删除的数据。

例如，如果您将特征存储区的在线传送节点数设置为 0，然后通过将节点数设置为 1 或更高来预配在线传送节点，Vertex AI Feature Store（旧版）不会将已删除的特征数据迁移到在线存储区。如需重新填充在线存储区，您必须重新导入数据。重新导入数据的一种方法是在停用在线传送节点之前导出历史数据，然后在预配节点后导入导出的数据。

预配在线传送节点时，您必须等待操作完成之后，才能导入新数据。只有在在线传送节点预配完成后，正在进行的导入作业才会恢复。

如果向没有在线传送节点的特征存储区提交在线传送请求，请求将返回错误。

创建特征存储区

创建特征存储区资源以包含实体类型和特征。特征存储区必须与源数据处于同一位置。例如，如果您的特征存储区位于 us-central,，则您可以从位于 us-central1 或位于美国多区域位置的 Cloud Storage 存储桶中的文件导入数据，但目前尚不支持双区域位置存储桶中的源数据。同样，对于 BigQuery，您可以从位于 us-central1 或位于美国多区域位置的表导入数据。如需了解详情，请参阅源数据要求。

Vertex AI Feature Store（旧版）的可用性可能因具体位置而异。如需了解详情，请参阅特征可用性。

网页界面

如果您尚未在 Google Cloud 项目中为所选区域创建特征存储区，则可以使用 Google Cloud 控制台创建特征存储区。如果项目和区域已有特征存储区，请使用其他方法。

如需使用 Google Cloud 控制台创建特征存储区，请执行以下操作：

  1. 在 Google Cloud 控制台的“Vertex AI”部分，转到特征页面。

    进入“特征”页面

  2. 点击创建特征存储区。
  3. 指定特征存储区的名称。
  4. 如果要为特征存储区启用在线传送，请点击开启在线传送切换开关并设置扩缩选项。
    如需详细了解在线传送和扩缩选项，请参阅在线传送节点
  5. 点击创建。

Terraform

以下示例使用 google_vertex_ai_featurestore Terraform 资源创建具有固定节点数的特征存储区。特征存储区的名称为 featurestore_xxxxxxxx，其中 xxxxxxxx 是随机生成的字母数字标识符。

如需了解如何应用或移除 Terraform 配置，请参阅基本 Terraform 命令。

# Featurestore name must be unique for the project
resource "random_id" "featurestore_name_suffix" {
  byte_length = 8
}

resource "google_vertex_ai_featurestore" "main" {
  name   = "featurestore_${random_id.featurestore_name_suffix.hex}"
  region = "us-central1"
  labels = {
    environment = "testing"
  }

  online_serving_config {
    fixed_node_count = 1
  }

  force_destroy = true
}

REST

如需创建特征存储区，请使用 featurestores.create 方法发送 POST 请求。

以下示例会创建固定节点数为 1 的特征存储区。 节点数用于指定在线传送节点的数量，这会影响特征存储区可以处理的在线传送请求的数量。如果节点数无法支持传入请求的数量，则延迟时间可能会增加。

在使用任何请求数据之前，请先进行以下替换：

  • LOCATION_ID：在其中创建特征存储区的区域。例如 us-central1。
  • PROJECT_ID：您的项目 ID。
  • FEATURESTORE_ID：特征存储区的 ID。

HTTP 方法和网址：

POST https://LOCATION_ID-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION_ID/featurestores?featurestoreId=FEATURESTORE_ID

请求 JSON 正文： 

{
  "online_serving_config": {
    "fixed_node_count": 1
  },
  "labels": {
    "environment": "testing"
  }
}

如需发送请求，请选择以下方式之一：

curl

将请求正文保存在名为 request.json 的文件中，然后执行以下命令： 

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://LOCATION_ID-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION_ID/featurestores?featurestoreId=FEATURESTORE_ID"

PowerShell

将请求正文保存在名为 request.json 的文件中，然后执行以下命令： 

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://LOCATION_ID-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION_ID/featurestores?featurestoreId=FEATURESTORE_ID" | Select-Object -Expand Content

您应该会看到类似如下所示的输出。您可以使用响应中的 OPERATION_ID 来获取操作的状态。

{
"name": "projects/PROJECT_NUMBER/locations/LOCATION_ID/featurestores/FEATURESTORE_ID/operations/OPERATION_ID",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.aiplatform.v1.CreateFeaturestoreOperationMetadata",
    "genericMetadata": {
      "createTime": "2021-02-26T00:44:40.722474Z",
      "updateTime": "2021-02-26T00:44:40.722474Z"
    }
  }
}

Python

如需了解如何安装或更新 Python 版 Vertex AI SDK，请参阅安装 Python 版 Vertex AI SDK。如需了解详情，请参阅 Python API 参考文档。 

from google.cloud import aiplatform

def create_featurestore_sample(
    project: str,
    location: str,
    featurestore_id: str,
    online_store_fixed_node_count: int = 1,
    sync: bool = True,
):

    aiplatform.init(project=project, location=location)

    fs = aiplatform.Featurestore.create(
        featurestore_id=featurestore_id,
        online_store_fixed_node_count=online_store_fixed_node_count,
        sync=sync,
    )

    fs.wait()

    return fs

Python

Python 版 Vertex AI SDK 的安装包含 Vertex AI 客户端库。如需了解如何安装 Python 版 Vertex AI SDK，请参阅安装 Python 版 Vertex AI SDK。如需了解详情，请参阅 Python 版 Vertex AI SDK API 参考文档。

from google.cloud import aiplatform

def create_featurestore_sample(
    project: str,
    featurestore_id: str,
    fixed_node_count: int = 1,
    location: str = "us-central1",
    api_endpoint: str = "us-central1-aiplatform.googleapis.com",
    timeout: int = 1200,
):
    # The AI Platform services require regional API endpoints, which need to be
    # in the same region or multi-region overlap with the Feature Store location.
    client_options = {"api_endpoint": api_endpoint}
    # Initialize client that will be used to create and send requests.
    # This client only needs to be created once, and can be reused for multiple requests.
    client = aiplatform.gapic.FeaturestoreServiceClient(client_options=client_options)
    parent = f"projects/{project}/locations/{location}"
    create_featurestore_request = aiplatform.gapic.CreateFeaturestoreRequest(
        parent=parent,
        featurestore_id=featurestore_id,
        featurestore=aiplatform.gapic.Featurestore(
            online_serving_config=aiplatform.gapic.Featurestore.OnlineServingConfig(
                fixed_node_count=fixed_node_count,
            ),
        ),
    )
    lro_response = client.create_featurestore(request=create_featurestore_request)
    print("Long running operation:", lro_response.operation.name)
    create_featurestore_response = lro_response.result(timeout=timeout)
    print("create_featurestore_response:", create_featurestore_response)

Java

在尝试此示例之前，请按照《Vertex AI 快速入门：使用客户端库》中的 Java 设置说明执行操作。如需了解详情，请参阅 Vertex AI Java API 参考文档。

如需向 Vertex AI 进行身份验证，请设置应用默认凭据。 如需了解详情，请参阅为本地开发环境设置身份验证。 


import com.google.api.gax.longrunning.OperationFuture;
import com.google.cloud.aiplatform.v1.CreateFeaturestoreOperationMetadata;
import com.google.cloud.aiplatform.v1.CreateFeaturestoreRequest;
import com.google.cloud.aiplatform.v1.Featurestore;
import com.google.cloud.aiplatform.v1.Featurestore.OnlineServingConfig;
import com.google.cloud.aiplatform.v1.Featurestore.OnlineServingConfig.Scaling;
import com.google.cloud.aiplatform.v1.FeaturestoreServiceClient;
import com.google.cloud.aiplatform.v1.FeaturestoreServiceSettings;
import com.google.cloud.aiplatform.v1.LocationName;
import java.io.IOException;
import java.util.concurrent.ExecutionException;
import java.util.concurrent.TimeUnit;
import java.util.concurrent.TimeoutException;

public class CreateFeaturestoreSample {

  public static void main(String[] args)
      throws IOException, InterruptedException, ExecutionException, TimeoutException {
    // TODO(developer): Replace these variables before running the sample.
    String project = "YOUR_PROJECT_ID";
    String featurestoreId = "YOUR_FEATURESTORE_ID";
    int minNodeCount = 1;
    int maxNodeCount = 5;
    String location = "us-central1";
    String endpoint = "us-central1-aiplatform.googleapis.com:443";
    int timeout = 900;
    createFeaturestoreSample(
        project, featurestoreId, minNodeCount, maxNodeCount, location, endpoint, timeout);
  }

  static void createFeaturestoreSample(
      String project,
      String featurestoreId,
      int minNodeCount,
      int maxNodeCount,
      String location,
      String endpoint,
      int timeout)
      throws IOException, InterruptedException, ExecutionException, TimeoutException {

    FeaturestoreServiceSettings featurestoreServiceSettings =
        FeaturestoreServiceSettings.newBuilder().setEndpoint(endpoint).build();

    // Initialize client that will be used to send requests. This client only needs to be created
    // once, and can be reused for multiple requests. After completing all of your requests, call
    // the "close" method on the client to safely clean up any remaining background resources.
    try (FeaturestoreServiceClient featurestoreServiceClient =
        FeaturestoreServiceClient.create(featurestoreServiceSettings)) {

      OnlineServingConfig.Builder builderValue =
          OnlineServingConfig.newBuilder()
              .setScaling(
                  Scaling.newBuilder().setMinNodeCount(minNodeCount).setMaxNodeCount(maxNodeCount));
      Featurestore featurestore =
          Featurestore.newBuilder().setOnlineServingConfig(builderValue).build();
      String parent = LocationName.of(project, location).toString();

      CreateFeaturestoreRequest createFeaturestoreRequest =
          CreateFeaturestoreRequest.newBuilder()
              .setParent(parent)
              .setFeaturestore(featurestore)
              .setFeaturestoreId(featurestoreId)
              .build();

      OperationFuture<Featurestore, CreateFeaturestoreOperationMetadata> featurestoreFuture =
          featurestoreServiceClient.createFeaturestoreAsync(createFeaturestoreRequest);
      System.out.format(
          "Operation name: %s%n", featurestoreFuture.getInitialFuture().get().getName());
      System.out.println("Waiting for operation to finish...");
      Featurestore featurestoreResponse = featurestoreFuture.get(timeout, TimeUnit.SECONDS);
      System.out.println("Create Featurestore Response");
      System.out.format("Name: %s%n", featurestoreResponse.getName());
    }
  }
}

Node.js

在尝试此示例之前，请按照《Vertex AI 快速入门：使用客户端库》中的 Node.js 设置说明执行操作。如需了解详情，请参阅 Vertex AI Node.js API 参考文档。

如需向 Vertex AI 进行身份验证，请设置应用默认凭据。 如需了解详情，请参阅为本地开发环境设置身份验证。 

/**
 * TODO(developer): Uncomment these variables before running the sample.\
 * (Not necessary if passing values as arguments)
 */

// const project = 'YOUR_PROJECT_ID';
// const featurestoreId = 'YOUR_FEATURESTORE_ID';
// const minNodeCount = <MINIMUM_NO_OF_NODES>;
// const maxNodeCount = <MAXIMUM_NO_OF_NODES>;
// const location = 'YOUR_PROJECT_LOCATION';
// const apiEndpoint = 'YOUR_API_ENDPOINT';
// const timeout = <TIMEOUT_IN_MILLI_SECONDS>;

// Imports the Google Cloud Featurestore Service Client library
const {FeaturestoreServiceClient} =
  require('@google-cloud/aiplatform').v1beta1;

// Specifies the location of the api endpoint
const clientOptions = {
  apiEndpoint: apiEndpoint,
};

// Instantiates a client
const featurestoreServiceClient = new FeaturestoreServiceClient(
  clientOptions
);

async function createFeaturestore() {
  // Configure the parent resource
  const parent = `projects/${project}/locations/${location}`;

  const featurestore = {
    onlineServingConfig: {
      scaling: {
        minNodeCount: minNodeCount,
        maxNodeCount: maxNodeCount,
      },
    },
  };

  const request = {
    parent: parent,
    featurestore: featurestore,
    featurestoreId: featurestoreId,
  };

  // Create Featurestore request
  const [operation] = await featurestoreServiceClient.createFeaturestore(
    request,
    {timeout: Number(timeout)}
  );
  const [response] = await operation.promise();

  console.log('Create featurestore response');
  console.log(`Name : ${response.name}`);
  console.log('Raw response:');
  console.log(JSON.stringify(response, null, 2));
}
createFeaturestore();

创建使用 CMEK 的特征存储区

在开始之前，如果您还没有 CMEK（客户管理的加密密钥），请使用 Cloud Key Management Service 配置客户管理的加密密钥并设置权限。以下示例会创建一个使用 CMEK 密钥的特征存储区。

如果 Vertex AI 失去对关联 CMEK 密钥的权限，则在 Vertex AI 可以再次使用该密钥之前，使用该密钥加密的特征存储区中的所有资源和值将无法访问。

30 天后，如果 Vertex AI 仍无权访问 CMEK 密钥，Vertex AI 会删除使用该密钥加密的所有特征存储区。创建新的特征存储区时，您无法重复使用这些特征存储区名称。

网页界面

请使用其他方法。您无法通过 Google Cloud 控制台创建特征存储区。

REST

在使用任何请求数据之前，请先进行以下替换：

  • LOCATION_ID：在其中创建特征存储区的区域。例如 us-central1。
  • PROJECT_ID：您的项目 ID。
  • FEATURESTORE_ID：特征存储区的 ID。
  • CMEK_PROJECT：包含 CMEK 的项目 ID 或项目编号。
  • KEY_RING：加密密钥所在的 Cloud Key Management Service 密钥环的名称。
  • KEY_NAME：要使用的加密密钥的名称。

HTTP 方法和网址：

POST https://LOCATION_ID-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION_ID/featurestores?featurestoreId=FEATURESTORE_ID

请求 JSON 正文： 

{
  "online_serving_config": {
    "fixed_node_count": 1
  },
  "encryption_spec":{
    "kms_key_name": "projects/CMEK_PROJECT/locations/LOCATION_ID/keyRings/KEY_RING/cryptoKeys/KEY_NAME"
  }
}

如需发送请求，请选择以下方式之一：

curl

将请求正文保存在名为 request.json 的文件中，然后执行以下命令： 

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://LOCATION_ID-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION_ID/featurestores?featurestoreId=FEATURESTORE_ID"

PowerShell

将请求正文保存在名为 request.json 的文件中，然后执行以下命令： 

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://LOCATION_ID-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION_ID/featurestores?featurestoreId=FEATURESTORE_ID" | Select-Object -Expand Content

您应该会看到类似如下所示的输出。您可以使用响应中的 OPERATION_ID 来获取操作的状态。

{
"name": "projects/PROJECT_NUMBER/locations/LOCATION_ID/featurestores/FEATURESTORE_ID/operations/OPERATION_ID",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.aiplatform.v1.CreateFeaturestoreOperationMetadata",
    "genericMetadata": {
      "createTime": "2021-02-26T00:44:40.722474Z",
      "updateTime": "2021-02-26T00:44:40.722474Z"
    }
  }
}

列出特征存储区

列出项目中的所有特征存储区。

网页界面

  1. 在 Google Cloud 控制台的“Vertex AI”部分，转到特征页面。

    转到“特征”页面

  2. 从区域下拉列表中选择一个区域。
  3. 在特征表中，查看特征存储区列，以查看您的项目中选定区域的特征存储区。

REST

如需列出项目中特定区域的特征存储区，请使用 featurestores.list 方法发送 GET 请求。

在使用任何请求数据之前，请先进行以下替换：

  • LOCATION_ID：特征存储区所在的区域，例如 us-central1。
  • PROJECT_ID：您的项目 ID。

HTTP 方法和网址：

GET https://LOCATION_ID-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION_ID/featurestores

如需发送请求，请选择以下方式之一：

curl

执行以下命令： 

curl -X GET \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     "https://LOCATION_ID-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION_ID/featurestores"

PowerShell

执行以下命令： 

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method GET `
    -Headers $headers `
    -Uri "https://LOCATION_ID-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION_ID/featurestores" | Select-Object -Expand Content

您应会收到如下所示的 JSON 响应：

{
  "featurestores": [
    {
      "name": "projects/PROJECT_NUMBER/locations/LOCATION_ID/featurestores/test",
      "createTime": "2021-02-26T00:44:44.216805Z",
      "updateTime": "2021-02-26T00:44:44.364916Z",
      "etag": "AMEw9yNL0s7qZh8lZVZ5T3BEuhoEgFR7JmjbbCSAkRZjeKDXkkIYnxxA4POe5BWT8cCn",
      "labels": {
        "environment": "testing"
      },
      "onlineServingConfig": {
        "fixedNodeCount": 2
      },
      "state": "STABLE"
    },
    {
      "name": "projects/PROJECT_NUMBER/locations/LOCATION_ID/featurestores/featurestore_demo",
      "createTime": "2021-02-25T00:39:40.598781Z",
      "updateTime": "2021-02-25T00:39:40.744038Z",
      "etag": "AMEw9yO_e0vm-9W_yeCz4rJm-XnnEMYQ-vQesevxya_sz-FckuysnDwo3cEXHdWWSeda",
      "labels": {
        "environment": "testing"
      },
      "onlineServingConfig": {
        "fixedNodeCount": 3
      },
      "state": "STABLE"
    }
  ]
}

Java

在尝试此示例之前，请按照《Vertex AI 快速入门：使用客户端库》中的 Java 设置说明执行操作。如需了解详情，请参阅 Vertex AI Java API 参考文档。

如需向 Vertex AI 进行身份验证，请设置应用默认凭据。 如需了解详情，请参阅为本地开发环境设置身份验证。 


import com.google.cloud.aiplatform.v1.Featurestore;
import com.google.cloud.aiplatform.v1.FeaturestoreServiceClient;
import com.google.cloud.aiplatform.v1.FeaturestoreServiceSettings;
import com.google.cloud.aiplatform.v1.ListFeaturestoresRequest;
import com.google.cloud.aiplatform.v1.LocationName;
import java.io.IOException;

public class ListFeaturestoresSample {

  public static void main(String[] args) throws IOException {
    // TODO(developer): Replace these variables before running the sample.
    String project = "YOUR_PROJECT_ID";
    String location = "us-central1";
    String endpoint = "us-central1-aiplatform.googleapis.com:443";
    listFeaturestoresSample(project, location, endpoint);
  }

  static void listFeaturestoresSample(String project, String location, String endpoint)
      throws IOException {
    FeaturestoreServiceSettings featurestoreServiceSettings =
        FeaturestoreServiceSettings.newBuilder().setEndpoint(endpoint).build();

    // Initialize client that will be used to send requests. This client only needs to be created
    // once, and can be reused for multiple requests. After completing all of your requests, call
    // the "close" method on the client to safely clean up any remaining background resources.
    try (FeaturestoreServiceClient featurestoreServiceClient =
        FeaturestoreServiceClient.create(featurestoreServiceSettings)) {

      ListFeaturestoresRequest listFeaturestoresRequest =
          ListFeaturestoresRequest.newBuilder()
              .setParent(LocationName.of(project, location).toString())
              .build();

      System.out.println("List Featurestores Response");
      for (Featurestore element :
          featurestoreServiceClient.listFeaturestores(listFeaturestoresRequest).iterateAll()) {
        System.out.println(element);
      }
    }
  }
}

Node.js

在尝试此示例之前，请按照《Vertex AI 快速入门：使用客户端库》中的 Node.js 设置说明执行操作。如需了解详情，请参阅 Vertex AI Node.js API 参考文档。

如需向 Vertex AI 进行身份验证，请设置应用默认凭据。 如需了解详情，请参阅为本地开发环境设置身份验证。 

/**
 * TODO(developer): Uncomment these variables before running the sample.\
 * (Not necessary if passing values as arguments)
 */

// const project = 'YOUR_PROJECT_ID';
// const location = 'YOUR_PROJECT_LOCATION';
// const apiEndpoint = 'YOUR_API_ENDPOINT';
// const timeout = <TIMEOUT_IN_MILLI_SECONDS>;

// Imports the Google Cloud Featurestore Service Client library
const {FeaturestoreServiceClient} = require('@google-cloud/aiplatform').v1;

// Specifies the location of the api endpoint
const clientOptions = {
  apiEndpoint: apiEndpoint,
};

// Instantiates a client
const featurestoreServiceClient = new FeaturestoreServiceClient(
  clientOptions
);

async function listFeaturestores() {
  // Configure the parent resource
  const parent = `projects/${project}/locations/${location}`;

  const request = {
    parent: parent,
  };

  // List featurestores request
  const [response] = await featurestoreServiceClient.listFeaturestores(
    request,
    {timeout: Number(timeout)}
  );

  console.log('List featurestores response');
  console.log('Raw response:');
  console.log(JSON.stringify(response, null, 2));
}
listFeaturestores();

其他语言

如需了解如何安装和使用 Python 版 Vertex AI SDK，请参阅使用 Python 版 Vertex AI SDK。如需了解详情，请参阅 Python 版 Vertex AI SDK API 参考文档。

查看特征存储区详情

获取有关特征存储区的详细信息，例如其名称和在线传送配置。如果使用 Google Cloud 控制台，您还可以查看特征存储区的 Cloud Monitoring 指标。

网页界面

  1. 在 Google Cloud 控制台的“Vertex AI”部分，转到特征页面。

    转到“特征”页面

  2. 从区域下拉列表中选择一个区域。
  3. 在特征表中，查看特征存储区列并找到要查看其信息的特征存储区。
  4. 点击特征存储区的名称以查看其 Monitoring 指标。
  5. 点击属性标签页查看特征存储区的在线传送配置。

REST

如需获取有关单个特征存储区的详细信息，请使用 featurestores.get 方法发送 GET 请求。

在使用任何请求数据之前，请先进行以下替换：

  • LOCATION_ID：特征存储区所在的区域，例如 us-central1。
  • PROJECT_ID：您的项目 ID。
  • FEATURESTORE_ID：特征存储区的 ID。

HTTP 方法和网址：

GET https://LOCATION_ID-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION_ID/featurestores/FEATURESTORE_ID

如需发送请求，请选择以下方式之一：

curl

执行以下命令： 

curl -X GET \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     "https://LOCATION_ID-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION_ID/featurestores/FEATURESTORE_ID"

PowerShell

执行以下命令： 

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method GET `
    -Headers $headers `
    -Uri "https://LOCATION_ID-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION_ID/featurestores/FEATURESTORE_ID" | Select-Object -Expand Content

您应会收到如下所示的 JSON 响应：

{
  "name": "projects/PROJECT_NUMBER/locations/LOCATION_ID/featurestores/FEATURESTORE_ID",
  "createTime": "2021-02-25T00:39:40.598781Z",
  "updateTime": "2021-02-25T00:39:40.744038Z",
  "etag": "AMEw9yNy_b4IaMIvw1803ZT38cpUtjfwlyLkR709oBCY6pQrm6dHophLcqhrvsNqkQQZ",
  "onlineServingConfig": {
    "fixedNodeCount": 3
  },
  "state": "STABLE"
}

Java

在尝试此示例之前，请按照《Vertex AI 快速入门：使用客户端库》中的 Java 设置说明执行操作。如需了解详情，请参阅 Vertex AI Java API 参考文档。

如需向 Vertex AI 进行身份验证，请设置应用默认凭据。 如需了解详情，请参阅为本地开发环境设置身份验证。 


import com.google.cloud.aiplatform.v1.Featurestore;
import com.google.cloud.aiplatform.v1.FeaturestoreName;
import com.google.cloud.aiplatform.v1.FeaturestoreServiceClient;
import com.google.cloud.aiplatform.v1.FeaturestoreServiceSettings;
import com.google.cloud.aiplatform.v1.GetFeaturestoreRequest;
import java.io.IOException;

public class GetFeaturestoreSample {

  public static void main(String[] args) throws IOException {
    // TODO(developer): Replace these variables before running the sample.
    String project = "YOUR_PROJECT_ID";
    String featurestoreId = "YOUR_FEATURESTORE_ID";
    String location = "us-central1";
    String endpoint = "us-central1-aiplatform.googleapis.com:443";
    getFeaturestoreSample(project, featurestoreId, location, endpoint);
  }

  static void getFeaturestoreSample(
      String project, String featurestoreId, String location, String endpoint) throws IOException {

    FeaturestoreServiceSettings featurestoreServiceSettings =
        FeaturestoreServiceSettings.newBuilder().setEndpoint(endpoint).build();

    // Initialize client that will be used to send requests. This client only needs to be created
    // once, and can be reused for multiple requests. After completing all of your requests, call
    // the "close" method on the client to safely clean up any remaining background resources.
    try (FeaturestoreServiceClient featurestoreServiceClient =
        FeaturestoreServiceClient.create(featurestoreServiceSettings)) {

      GetFeaturestoreRequest getFeaturestoreRequest =
          GetFeaturestoreRequest.newBuilder()
              .setName(FeaturestoreName.of(project, location, featurestoreId).toString())
              .build();

      Featurestore featurestore = featurestoreServiceClient.getFeaturestore(getFeaturestoreRequest);
      System.out.println("Get Featurestore Response");
      System.out.println(featurestore);
    }
  }
}

Node.js

在尝试此示例之前，请按照《Vertex AI 快速入门：使用客户端库》中的 Node.js 设置说明执行操作。如需了解详情，请参阅 Vertex AI Node.js API 参考文档。

如需向 Vertex AI 进行身份验证，请设置应用默认凭据。 如需了解详情，请参阅为本地开发环境设置身份验证。 

/**
 * TODO(developer): Uncomment these variables before running the sample.\
 * (Not necessary if passing values as arguments)
 */

// const project = 'YOUR_PROJECT_ID';
// const featurestoreId = 'YOUR_FEATURESTORE_ID';
// const location = 'YOUR_PROJECT_LOCATION';
// const apiEndpoint = 'YOUR_API_ENDPOINT';
// const timeout = <TIMEOUT_IN_MILLI_SECONDS>;

// Imports the Google Cloud Featurestore Service Client library
const {FeaturestoreServiceClient} = require('@google-cloud/aiplatform').v1;

// Specifies the location of the api endpoint
const clientOptions = {
  apiEndpoint: apiEndpoint,
};

// Instantiates a client
const featurestoreServiceClient = new FeaturestoreServiceClient(
  clientOptions
);

async function getFeaturestore() {
  // Configure the parent resource
  const name = `projects/${project}/locations/${location}/featurestores/${featurestoreId}`;

  const request = {
    name: name,
  };

  // Get Featurestore request
  const [response] = await featurestoreServiceClient.getFeaturestore(
    request,
    {timeout: Number(timeout)}
  );

  console.log('Get featurestore response');
  console.log(`Name : ${response.name}`);
  console.log('Raw response:');
  console.log(JSON.stringify(response, null, 2));
}
getFeaturestore();

其他语言

如需了解如何安装和使用 Python 版 Vertex AI SDK，请参阅使用 Python 版 Vertex AI SDK。如需了解详情，请参阅 Python 版 Vertex AI SDK API 参考文档。

更新特征存储区

更新特征存储区，例如，更改在线传送节点的数量或更新特征存储区的标签。

网页界面

您只能更新在线传送节点的数量。如要更新标签，请使用 API。

  1. 在 Google Cloud 控制台的“Vertex AI”部分，转到特征页面。

    转到“特征”页面

  2. 从区域下拉列表中选择一个区域。
  3. 在特征表中，查看特征存储区列，然后点击要更新的特征存储区的名称。
  4. 点击修改配置，打开修改特征存储区配置窗格。
  5. 修改特征存储区配置。
  6. 点击更新以应用更改。

REST

如需更新特征存储区，请使用 featurestores.patch 方法发送 PATCH 请求。

以下示例将特征存储区的在线传送节点数量更新为 2。其他所有设置都将保持不变。

在使用任何请求数据之前，请先进行以下替换：

  • LOCATION_ID：特征存储区所在的区域，例如 us-central1。
  • PROJECT_ID：您的项目 ID。
  • FEATURESTORE_ID：特征存储区的 ID。

HTTP 方法和网址：

PATCH https://LOCATION_ID-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION_ID/featurestores/FEATURESTORE_ID

请求 JSON 正文： 

{
  "online_serving_config": {
    "fixed_node_count": 2
  }
}

如需发送请求，请选择以下方式之一：

curl

将请求正文保存在名为 request.json 的文件中，然后执行以下命令： 

curl -X PATCH \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://LOCATION_ID-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION_ID/featurestores/FEATURESTORE_ID"

PowerShell

将请求正文保存在名为 request.json 的文件中，然后执行以下命令： 

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method PATCH `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://LOCATION_ID-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION_ID/featurestores/FEATURESTORE_ID" | Select-Object -Expand Content

您应该会看到类似如下所示的输出。您可以使用响应中的 OPERATION_ID 来获取操作的状态。

{
"name": "projects/PROJECT_NUMBER/locations/LOCATION_ID/featurestores/FEATURESTORE_ID/operations/OPERATION_ID",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.aiplatform.v1.UpdateFeaturestoreOperationMetadata",
    "genericMetadata": {
      "createTime": "2021-03-18T21:12:08.373664Z",
      "updateTime": "2021-03-18T21:12:08.373664Z"
    }
  }
}

Java

在尝试此示例之前，请按照《Vertex AI 快速入门：使用客户端库》中的 Java 设置说明执行操作。如需了解详情，请参阅 Vertex AI Java API 参考文档。

如需向 Vertex AI 进行身份验证，请设置应用默认凭据。 如需了解详情，请参阅为本地开发环境设置身份验证。 


import com.google.api.gax.longrunning.OperationFuture;
import com.google.cloud.aiplatform.v1.Featurestore;
import com.google.cloud.aiplatform.v1.Featurestore.OnlineServingConfig;
import com.google.cloud.aiplatform.v1.Featurestore.OnlineServingConfig.Scaling;
import com.google.cloud.aiplatform.v1.FeaturestoreName;
import com.google.cloud.aiplatform.v1.FeaturestoreServiceClient;
import com.google.cloud.aiplatform.v1.FeaturestoreServiceSettings;
import com.google.cloud.aiplatform.v1.UpdateFeaturestoreOperationMetadata;
import com.google.cloud.aiplatform.v1.UpdateFeaturestoreRequest;
import java.io.IOException;
import java.util.concurrent.ExecutionException;
import java.util.concurrent.TimeUnit;
import java.util.concurrent.TimeoutException;

public class UpdateFeaturestoreSample {

  public static void main(String[] args)
      throws IOException, InterruptedException, ExecutionException, TimeoutException {
    // TODO(developer): Replace these variables before running the sample.
    String project = "YOUR_PROJECT_ID";
    String featurestoreId = "YOUR_FEATURESTORE_ID";
    int minNodeCount = 2;
    int maxNodeCount = 4;
    String location = "us-central1";
    String endpoint = "us-central1-aiplatform.googleapis.com:443";
    int timeout = 300;
    updateFeaturestoreSample(
        project, featurestoreId, minNodeCount, maxNodeCount, location, endpoint, timeout);
  }

  static void updateFeaturestoreSample(
      String project,
      String featurestoreId,
      int minNodeCount,
      int maxNodeCount,
      String location,
      String endpoint,
      int timeout)
      throws IOException, InterruptedException, ExecutionException, TimeoutException {
    FeaturestoreServiceSettings featurestoreServiceSettings =
        FeaturestoreServiceSettings.newBuilder().setEndpoint(endpoint).build();

    // Initialize client that will be used to send requests. This client only needs to be created
    // once, and can be reused for multiple requests. After completing all of your requests, call
    // the "close" method on the client to safely clean up any remaining background resources.
    try (FeaturestoreServiceClient featurestoreServiceClient =
        FeaturestoreServiceClient.create(featurestoreServiceSettings)) {

      OnlineServingConfig.Builder builderValue =
          OnlineServingConfig.newBuilder()
              .setScaling(
                  Scaling.newBuilder().setMinNodeCount(minNodeCount).setMaxNodeCount(maxNodeCount));
      Featurestore featurestore =
          Featurestore.newBuilder()
              .setName(FeaturestoreName.of(project, location, featurestoreId).toString())
              .setOnlineServingConfig(builderValue)
              .build();

      UpdateFeaturestoreRequest request =
          UpdateFeaturestoreRequest.newBuilder().setFeaturestore(featurestore).build();

      OperationFuture<Featurestore, UpdateFeaturestoreOperationMetadata> updateFeaturestoreFuture =
          featurestoreServiceClient.updateFeaturestoreAsync(request);
      System.out.format(
          "Operation name: %s%n", updateFeaturestoreFuture.getInitialFuture().get().getName());
      System.out.println("Waiting for operation to finish...");
      Featurestore featurestoreResponse = updateFeaturestoreFuture.get(timeout, TimeUnit.SECONDS);
      System.out.println("Update Featurestore Response");
      System.out.format("Name: %s%n", featurestoreResponse.getName());
    }
  }
}

Node.js

在尝试此示例之前，请按照《Vertex AI 快速入门：使用客户端库》中的 Node.js 设置说明执行操作。如需了解详情，请参阅 Vertex AI Node.js API 参考文档。

如需向 Vertex AI 进行身份验证，请设置应用默认凭据。 如需了解详情，请参阅为本地开发环境设置身份验证。 

/**
 * TODO(developer): Uncomment these variables before running the sample.\
 * (Not necessary if passing values as arguments)
 */

// const project = 'YOUR_PROJECT_ID';
// const featurestoreId = 'YOUR_FEATURESTORE_ID';
// const minNodeCount = <MINIMUM_NO_OF_NODES>;
// const maxNodeCount = <MAXIMUM_NO_OF_NODES>;
// const location = 'YOUR_PROJECT_LOCATION';
// const apiEndpoint = 'YOUR_API_ENDPOINT';
// const timeout = <TIMEOUT_IN_MILLI_SECONDS>;

// Imports the Google Cloud Featurestore Service Client library
const {FeaturestoreServiceClient} =
  require('@google-cloud/aiplatform').v1beta1;

// Specifies the location of the api endpoint
const clientOptions = {
  apiEndpoint: apiEndpoint,
};

// Instantiates a client
const featurestoreServiceClient = new FeaturestoreServiceClient(
  clientOptions
);

async function updateFeaturestore() {
  // Configure the parent resource
  const parent = `projects/${project}/locations/${location}/featurestores/${featurestoreId}`;

  const featurestore = {
    name: parent,
    onlineServingConfig: {
      scaling: {
        minNodeCount: minNodeCount,
        maxNodeCount: maxNodeCount,
      },
    },
  };

  const request = {
    featurestore: featurestore,
  };

  // Update Featurestore request
  const [operation] = await featurestoreServiceClient.updateFeaturestore(
    request,
    {timeout: Number(timeout)}
  );
  const [response] = await operation.promise();

  console.log('Update featurestore response');
  console.log(`Name : ${response.name}`);
  console.log('Raw response:');
  console.log(JSON.stringify(response, null, 2));
}
updateFeaturestore();

其他语言

如需了解如何安装和使用 Python 版 Vertex AI SDK，请参阅使用 Python 版 Vertex AI SDK。如需了解详情，请参阅 Python 版 Vertex AI SDK API 参考文档。

删除特征存储区

删除特征存储区。如果特征存储区包含现有实体类型和特征，请启用 force 查询参数删除特征存储区及其所有内容。

网页界面

请使用其他方法。您无法通过 Google Cloud 控制台删除特征存储区。

REST

要删除特征存储区及其所有内容，请用 featurestores.delete 方法发送 DELETE 请求。

在使用任何请求数据之前，请先进行以下替换：

  • LOCATION_ID：特征存储区所在的区域，例如 us-central1。
  • PROJECT_ID：您的项目 ID。
  • FEATURESTORE_ID：特征存储区的 ID。
  • BOOLEAN：是否删除特征存储区，即使它包含实体类型和特征也是如此。force 查询参数是可选的，默认为 false。

HTTP 方法和网址：

DELETE https://LOCATION_ID-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION_ID/featurestores/FEATURESTORE_ID?force=BOOLEAN

如需发送请求，请选择以下方式之一：

curl

执行以下命令： 

curl -X DELETE \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     "https://LOCATION_ID-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION_ID/featurestores/FEATURESTORE_ID?force=BOOLEAN"

PowerShell

执行以下命令： 

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method DELETE `
    -Headers $headers `
    -Uri "https://LOCATION_ID-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION_ID/featurestores/FEATURESTORE_ID?force=BOOLEAN" | Select-Object -Expand Content

您应会收到如下所示的 JSON 响应：

{
  "name": "projects/PROJECT_NUMBER/locations/LOCATION_ID/operations/OPERATIONS_ID",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.aiplatform.v1.DeleteOperationMetadata",
    "genericMetadata": {
      "createTime": "2021-05-03T17:50:21.813112Z",
      "updateTime": "2021-05-03T17:50:21.813112Z"
    }
  },
  "done": true,
  "response": {
    "@type": "type.googleapis.com/google.protobuf.Empty"
  }
}

Python

如需了解如何安装或更新 Python 版 Vertex AI SDK，请参阅安装 Python 版 Vertex AI SDK。如需了解详情，请参阅 Python API 参考文档。 

from google.cloud import aiplatform

def delete_featurestore_sample(
    project: str,
    location: str,
    featurestore_name: str,
    sync: bool = True,
    force: bool = True,
):

    aiplatform.init(project=project, location=location)

    fs = aiplatform.featurestore.Featurestore(featurestore_name=featurestore_name)
    fs.delete(sync=sync, force=force)

Python

Python 版 Vertex AI SDK 的安装包含 Vertex AI 客户端库。如需了解如何安装 Python 版 Vertex AI SDK，请参阅安装 Python 版 Vertex AI SDK。如需了解详情，请参阅 Python 版 Vertex AI SDK API 参考文档。

from google.cloud import aiplatform

def delete_featurestore_sample(
    project: str,
    featurestore_id: str,
    location: str = "us-central1",
    api_endpoint: str = "us-central1-aiplatform.googleapis.com",
    timeout: int = 1200,
):
    # The AI Platform services require regional API endpoints, which need to be
    # in the same region or multi-region overlap with the Feature Store location.
    client_options = {"api_endpoint": api_endpoint}
    # Initialize client that will be used to create and send requests.
    # This client only needs to be created once, and can be reused for multiple requests.
    client = aiplatform.gapic.FeaturestoreServiceClient(client_options=client_options)
    name = client.featurestore_path(
        project=project, location=location, featurestore=featurestore_id
    )
    response = client.delete_featurestore(name=name)
    print("Long running operation:", response.operation.name)
    delete_featurestore_response = response.result(timeout=timeout)
    print("delete_featurestore_response:", delete_featurestore_response)

Java

在尝试此示例之前，请按照《Vertex AI 快速入门：使用客户端库》中的 Java 设置说明执行操作。如需了解详情，请参阅 Vertex AI Java API 参考文档。

如需向 Vertex AI 进行身份验证，请设置应用默认凭据。 如需了解详情，请参阅为本地开发环境设置身份验证。 


import com.google.api.gax.longrunning.OperationFuture;
import com.google.cloud.aiplatform.v1.DeleteFeaturestoreRequest;
import com.google.cloud.aiplatform.v1.DeleteOperationMetadata;
import com.google.cloud.aiplatform.v1.FeaturestoreName;
import com.google.cloud.aiplatform.v1.FeaturestoreServiceClient;
import com.google.cloud.aiplatform.v1.FeaturestoreServiceSettings;
import com.google.protobuf.Empty;
import java.io.IOException;
import java.util.concurrent.ExecutionException;
import java.util.concurrent.TimeUnit;
import java.util.concurrent.TimeoutException;

public class DeleteFeaturestoreSample {

  public static void main(String[] args)
      throws IOException, InterruptedException, ExecutionException, TimeoutException {
    // TODO(developer): Replace these variables before running the sample.
    String project = "YOUR_PROJECT_ID";
    String featurestoreId = "YOUR_FEATURESTORE_ID";
    boolean useForce = true;
    String location = "us-central1";
    String endpoint = "us-central1-aiplatform.googleapis.com:443";
    int timeout = 60;
    deleteFeaturestoreSample(project, featurestoreId, useForce, location, endpoint, timeout);
  }

  static void deleteFeaturestoreSample(
      String project,
      String featurestoreId,
      boolean useForce,
      String location,
      String endpoint,
      int timeout)
      throws IOException, InterruptedException, ExecutionException, TimeoutException {

    FeaturestoreServiceSettings featurestoreServiceSettings =
        FeaturestoreServiceSettings.newBuilder().setEndpoint(endpoint).build();

    // Initialize client that will be used to send requests. This client only needs to be created
    // once, and can be reused for multiple requests. After completing all of your requests, call
    // the "close" method on the client to safely clean up any remaining background resources.
    try (FeaturestoreServiceClient featurestoreServiceClient =
        FeaturestoreServiceClient.create(featurestoreServiceSettings)) {

      DeleteFeaturestoreRequest deleteFeaturestoreRequest =
          DeleteFeaturestoreRequest.newBuilder()
              .setName(FeaturestoreName.of(project, location, featurestoreId).toString())
              .setForce(useForce)
              .build();

      OperationFuture<Empty, DeleteOperationMetadata> operationFuture =
          featurestoreServiceClient.deleteFeaturestoreAsync(deleteFeaturestoreRequest);
      System.out.format("Operation name: %s%n", operationFuture.getInitialFuture().get().getName());
      System.out.println("Waiting for operation to finish...");
      operationFuture.get(timeout, TimeUnit.SECONDS);

      System.out.format("Deleted Featurestore.");
    }
  }
}

Node.js

在尝试此示例之前，请按照《Vertex AI 快速入门：使用客户端库》中的 Node.js 设置说明执行操作。如需了解详情，请参阅 Vertex AI Node.js API 参考文档。

如需向 Vertex AI 进行身份验证，请设置应用默认凭据。 如需了解详情，请参阅为本地开发环境设置身份验证。 

/**
 * TODO(developer): Uncomment these variables before running the sample.\
 * (Not necessary if passing values as arguments)
 */

// const project = 'YOUR_PROJECT_ID';
// const featurestoreId = 'YOUR_FEATURESTORE_ID';
// const force = <BOOLEAN>;
// const location = 'YOUR_PROJECT_LOCATION';
// const apiEndpoint = 'YOUR_API_ENDPOINT';
// const timeout = <TIMEOUT_IN_MILLI_SECONDS>;

// Imports the Google Cloud Featurestore Service Client library
const {FeaturestoreServiceClient} = require('@google-cloud/aiplatform').v1;

// Specifies the location of the api endpoint
const clientOptions = {
  apiEndpoint: apiEndpoint,
};

// Instantiates a client
const featurestoreServiceClient = new FeaturestoreServiceClient(
  clientOptions
);

async function deleteFeaturestore() {
  // Configure the name resource
  const name = `projects/${project}/locations/${location}/featurestores/${featurestoreId}`;

  const request = {
    name: name,
    force: Boolean(force),
  };

  // Delete Featurestore request
  const [operation] = await featurestoreServiceClient.deleteFeaturestore(
    request,
    {timeout: Number(timeout)}
  );
  const [response] = await operation.promise();

  console.log('Delete featurestore response');
  console.log('Raw response:');
  console.log(JSON.stringify(response, null, 2));
}
deleteFeaturestore();

后续步骤