创建集群

您可以使用 Cloud SDK gcloud 命令行工具、Dataproc API 或 Google Cloud Console 创建 Dataproc 集群。您还可以使用 Cloud 客户端库以编程方式创建集群。

集群名称：集群必须以小写字母开头，后面最多可跟 54 个小写字母、数字或连字符，但不能以连字符结尾。

集群地区：您可以为集群指定 global 地区或特定地区。全局区域为特殊的多地区端点，能够将实例部署到任何用户指定的 Compute Engine 区域。您还可以指定不同的地区（例如 us-east1 或 europe-west1），以存储 Dataproc 在用户指定地区内使用的隔离资源（包括虚拟机实例和 Cloud Storage）和元数据存储位置。如需详细了解全球和地区端点之间的差异，请参阅地区端点。如需了解如何选择地区，请参阅可用的地区和区域。您还可以运行 gcloud compute regions list 命令查看可用地区的列表。

Dataproc 集群中的 Compute Engine 虚拟机实例（虚拟机）包含主虚拟机和工作器虚拟机，该实例需要具有完整的内部 IP 网络访问权限。可用于创建集群的 default 网络有助于确保此访问。如需了解如何为 Dataproc 集群创建您自己的网络，请参阅Dataproc 集群网络配置。

创建 Dataproc 集群

gcloud

如需在命令行上创建 Dataproc 集群，请在终端窗口或 Cloud Shell 中本地运行 Cloud SDK gcloud dataproc 集群创建命令。

gcloud dataproc clusters create cluster-name \
    --region=region

上述命令会利用适用于您主虚拟机实例和工作器虚拟机实例、磁盘大小和类型、网络类型、部署集群的区域和地区的默认 Dataproc 服务设置以及其他集群设置创建一个集群。要了解如何使用命令行标记自定义集群设置，请参阅 gcloud dataproc clusters create 命令。

使用 YAML 文件创建集群

  1. 请运行以下 gcloud 命令将现有的 Dataproc 集群的配置导出到 YAML 文件中。
    gcloud dataproc clusters export my-existing-cluster --destination cluster.yaml
  2. 通过导入 YAML 文件配置来创建新集群。 
    gcloud dataproc clusters import my-new-cluster --source cluster.yaml

注意：在执行导出操作的过程中，特定于集群的字段（例如集群名称）、仅限输出的字段和自动应用的标签会被过滤掉。在用于创建集群的导入的 YAML 文件中，不允许使用这些字段。

REST 和命令行

本部分介绍如何创建采用所需值和默认配置（1 个主节点，2 个工作器节点）的集群。

在使用下面的请求数据之前，请先进行以下替换：

  • project-id：GCP 项目 ID
  • region：集群地区
  • clusterName：集群名称

HTTP 方法和网址：

POST https://dataproc.googleapis.com/v1/projects/project-id/regions/region/clusters

请求 JSON 正文： 

{
  "clusterName": "cluster-name",
  "config": {}
}

如需发送您的请求，请展开以下选项之一：

您应该收到类似以下内容的 JSON 响应：

{
"name": "projects/project-id/regions/region/operations/b5706e31......",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.dataproc.v1.ClusterOperationMetadata",
    "clusterName": "cluster-name",
    "clusterUuid": "5fe882b2-...",
    "status": {
      "state": "PENDING",
      "innerState": "PENDING",
      "stateStartTime": "2019-11-21T00:37:56.220Z"
    },
    "operationType": "CREATE",
    "description": "Create cluster with 2 workers",
    "warnings": [
      "For PD-Standard without local SSDs, we strongly recommend provisioning 1TB ...""
    ]
  }
}

控制台

在浏览器中，打开 Cloud Console 中的 Dataproc 创建集群页面。选择“设置集群”面板，其中的字段填充默认值。您可以选择每个面板，然后确认或更改默认值以自定义您的集群。

单击创建以创建集群。集群名称显示在集群页面中，预配集群后，其状态会更新为“正在运行”。点击集群名称以打开集群详情页面，您可以在其中检查集群的作业、实例和配置设置，还可以连接到集群上运行的网页界面。

Go

  1. 安装客户端库

  2. 设置应用默认凭据

  3. 运行代码，
    dataproc/create_cluster.go 
    import (
	"context"
	"fmt"
	"io"

	dataproc "cloud.google.com/go/dataproc/apiv1"
	"google.golang.org/api/option"
	dataprocpb "google.golang.org/genproto/googleapis/cloud/dataproc/v1"
)

func createCluster(w io.Writer, projectID, region, clusterName string) error {
	// projectID := "your-project-id"
	// region := "us-central1"
	// clusterName := "your-cluster"
	ctx := context.Background()

	// Create the cluster client.
	endpoint := region + "-dataproc.googleapis.com:443"
	clusterClient, err := dataproc.NewClusterControllerClient(ctx, option.WithEndpoint(endpoint))
	if err != nil {
		return fmt.Errorf("dataproc.NewClusterControllerClient: %v", err)
	}

	// Create the cluster config.
	req := &dataprocpb.CreateClusterRequest{
		ProjectId: projectID,
		Region:    region,
		Cluster: &dataprocpb.Cluster{
			ProjectId:   projectID,
			ClusterName: clusterName,
			Config: &dataprocpb.ClusterConfig{
				MasterConfig: &dataprocpb.InstanceGroupConfig{
					NumInstances:   1,
					MachineTypeUri: "n1-standard-1",
				},
				WorkerConfig: &dataprocpb.InstanceGroupConfig{
					NumInstances:   2,
					MachineTypeUri: "n1-standard-1",
				},
			},
		},
	}

	// Create the cluster.
	op, err := clusterClient.CreateCluster(ctx, req)
	if err != nil {
		return fmt.Errorf("CreateCluster: %v", err)
	}

	resp, err := op.Wait(ctx)
	if err != nil {
		return fmt.Errorf("CreateCluster.Wait: %v", err)
	}

	// Output a success message.
	fmt.Fprintf(w, "Cluster created successfully: %s", resp.ClusterName)
	return nil
}

Java

  1. 安装客户端库

  2. 设置应用默认凭据

  3. 运行代码
    samples/snippets/src/main/java/CreateCluster.java 
    import com.google.api.gax.longrunning.OperationFuture;
import com.google.cloud.dataproc.v1.Cluster;
import com.google.cloud.dataproc.v1.ClusterConfig;
import com.google.cloud.dataproc.v1.ClusterControllerClient;
import com.google.cloud.dataproc.v1.ClusterControllerSettings;
import com.google.cloud.dataproc.v1.ClusterOperationMetadata;
import com.google.cloud.dataproc.v1.InstanceGroupConfig;
import java.io.IOException;
import java.util.concurrent.ExecutionException;

public class CreateCluster {

  public static void createCluster() throws IOException, InterruptedException {
    // TODO(developer): Replace these variables before running the sample.
    String projectId = "your-project-id";
    String region = "your-project-region";
    String clusterName = "your-cluster-name";
    createCluster(projectId, region, clusterName);
  }

  public static void createCluster(String projectId, String region, String clusterName)
      throws IOException, InterruptedException {
    String myEndpoint = String.format("%s-dataproc.googleapis.com:443", region);

    // Configure the settings for the cluster controller client.
    ClusterControllerSettings clusterControllerSettings =
        ClusterControllerSettings.newBuilder().setEndpoint(myEndpoint).build();

    // Create a cluster controller client with the configured settings. The client only needs to be
    // created once and can be reused for multiple requests. Using a try-with-resources
    // closes the client, but this can also be done manually with the .close() method.
    try (ClusterControllerClient clusterControllerClient =
        ClusterControllerClient.create(clusterControllerSettings)) {
      // Configure the settings for our cluster.
      InstanceGroupConfig masterConfig =
          InstanceGroupConfig.newBuilder()
              .setMachineTypeUri("n1-standard-1")
              .setNumInstances(1)
              .build();
      InstanceGroupConfig workerConfig =
          InstanceGroupConfig.newBuilder()
              .setMachineTypeUri("n1-standard-1")
              .setNumInstances(2)
              .build();
      ClusterConfig clusterConfig =
          ClusterConfig.newBuilder()
              .setMasterConfig(masterConfig)
              .setWorkerConfig(workerConfig)
              .build();
      // Create the cluster object with the desired cluster config.
      Cluster cluster =
          Cluster.newBuilder().setClusterName(clusterName).setConfig(clusterConfig).build();

      // Create the Cloud Dataproc cluster.
      OperationFuture<Cluster, ClusterOperationMetadata> createClusterAsyncRequest =
          clusterControllerClient.createClusterAsync(projectId, region, cluster);
      Cluster response = createClusterAsyncRequest.get();

      // Print out a success message.
      System.out.printf("Cluster created successfully: %s", response.getClusterName());

    } catch (ExecutionException e) {
      System.err.println(String.format("Error executing createCluster: %s ", e.getMessage()));
    }
  }
}

Node.js

  1. 安装客户端库

  2. 设置应用默认凭据

  3. 运行代码
samples/createCluster.js 
const dataproc = require('@google-cloud/dataproc');

// TODO(developer): Uncomment and set the following variables
// projectId = 'YOUR_PROJECT_ID'
// region = 'YOUR_CLUSTER_REGION'
// clusterName = 'YOUR_CLUSTER_NAME'

// Create a client with the endpoint set to the desired cluster region
const client = new dataproc.v1.ClusterControllerClient({
  apiEndpoint: `${region}-dataproc.googleapis.com`,
  projectId: projectId,
});

async function createCluster() {
  // Create the cluster config
  const request = {
    projectId: projectId,
    region: region,
    cluster: {
      clusterName: clusterName,
      config: {
        masterConfig: {
          numInstances: 1,
          machineTypeUri: 'n1-standard-1',
        },
        workerConfig: {
          numInstances: 2,
          machineTypeUri: 'n1-standard-1',
        },
      },
    },
  };

  // Create the cluster
  const [operation] = await client.createCluster(request);
  const [response] = await operation.promise();

  // Output a success message
  console.log(`Cluster created successfully: ${response.clusterName}`);

Python

  1. 安装客户端库

  2. 设置应用默认凭据

  3. 运行代码
    samples/snippets/create_cluster.py 
    from google.cloud import dataproc_v1 as dataproc

def create_cluster(project_id, region, cluster_name):
    """This sample walks a user through creating a Cloud Dataproc cluster
       using the Python client library.

       Args:
           project_id (string): Project to use for creating resources.
           region (string): Region where the resources should live.
           cluster_name (string): Name to use for creating a cluster.
    """

    # Create a client with the endpoint set to the desired cluster region.
    cluster_client = dataproc.ClusterControllerClient(
        client_options={"api_endpoint": f"{region}-dataproc.googleapis.com:443"}
    )

    # Create the cluster config.
    cluster = {
        "project_id": project_id,
        "cluster_name": cluster_name,
        "config": {
            "master_config": {"num_instances": 1, "machine_type_uri": "n1-standard-1"},
            "worker_config": {"num_instances": 2, "machine_type_uri": "n1-standard-1"},
        },
    }

    # Create the cluster.
    operation = cluster_client.create_cluster(
        request={"project_id": project_id, "region": region, "cluster": cluster}
    )
    result = operation.result()

    # Output a success message.
    print(f"Cluster created successfully: {result.cluster_name}")