Mengelola featurestore

# 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
}

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

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)

import com.google.api.gax.longrunning.OperationFuture;
import com.google.cloud.aiplatform.v1beta1.CreateFeaturestoreOperationMetadata;
import com.google.cloud.aiplatform.v1beta1.CreateFeaturestoreRequest;
import com.google.cloud.aiplatform.v1beta1.Featurestore;
import com.google.cloud.aiplatform.v1beta1.Featurestore.OnlineServingConfig;
import com.google.cloud.aiplatform.v1beta1.Featurestore.OnlineServingConfig.Scaling;
import com.google.cloud.aiplatform.v1beta1.FeaturestoreServiceClient;
import com.google.cloud.aiplatform.v1beta1.FeaturestoreServiceSettings;
import com.google.cloud.aiplatform.v1beta1.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());
    }
  }
}

/**
 * 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();

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);
      }
    }
  }
}

/**
 * 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();

import com.google.cloud.aiplatform.v1beta1.Featurestore;
import com.google.cloud.aiplatform.v1beta1.FeaturestoreName;
import com.google.cloud.aiplatform.v1beta1.FeaturestoreServiceClient;
import com.google.cloud.aiplatform.v1beta1.FeaturestoreServiceSettings;
import com.google.cloud.aiplatform.v1beta1.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);
    }
  }
}

/**
 * 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();

import com.google.api.gax.longrunning.OperationFuture;
import com.google.cloud.aiplatform.v1beta1.Featurestore;
import com.google.cloud.aiplatform.v1beta1.Featurestore.OnlineServingConfig;
import com.google.cloud.aiplatform.v1beta1.Featurestore.OnlineServingConfig.Scaling;
import com.google.cloud.aiplatform.v1beta1.FeaturestoreName;
import com.google.cloud.aiplatform.v1beta1.FeaturestoreServiceClient;
import com.google.cloud.aiplatform.v1beta1.FeaturestoreServiceSettings;
import com.google.cloud.aiplatform.v1beta1.UpdateFeaturestoreOperationMetadata;
import com.google.cloud.aiplatform.v1beta1.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());
    }
  }
}

/**
 * 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();

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)

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)

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.");
    }
  }
}

/**
 * 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();