Criar um cluster usando bibliotecas de cliente

Criar um cluster usando bibliotecas de cliente

O código de exemplo abaixo mostra como usar as bibliotecas de cliente do Cloud para criar um cluster do Serviço Gerenciado para Apache Spark, executar um job no cluster e excluir o cluster.

Também é possível realizar essas tarefas usando:

• Solicitações da API REST em Guia de início rápido: como usar o API Explorer
• o Google Cloud console em Criar um cluster usando o Google Cloud console
• a Google Cloud CLI em Criar um cluster usando a Google Cloud CLI

Antes de começar

Faça login na sua Google Cloud conta do. Se você começou a usar o Google Cloudagora, crie uma conta para avaliar o desempenho dos nossos produtos em situações reais. Clientes novos também recebem US $300 em créditos para executar, testar e implantar cargas de trabalho.

In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

Roles required to select or create a project

• Select a project: Selecting a project doesn't require a specific IAM role—you can select any project that you've been granted a role on.
• Create a project: To create a project, you need the Project Creator role (roles/resourcemanager.projectCreator), which contains the resourcemanager.projects.create permission. Learn how to grant roles.

Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

Go to project selector

Verify that billing is enabled for your Google Cloud project.

Enable the Dataproc API.

Roles required to enable APIs

To enable APIs, you need the Service Usage Admin IAM role (roles/serviceusage.serviceUsageAdmin), which contains the serviceusage.services.enable permission. Learn how to grant roles.

Enable the API

In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

Roles required to select or create a project

• Select a project: Selecting a project doesn't require a specific IAM role—you can select any project that you've been granted a role on.
• Create a project: To create a project, you need the Project Creator role (roles/resourcemanager.projectCreator), which contains the resourcemanager.projects.create permission. Learn how to grant roles.

Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

Go to project selector

Verify that billing is enabled for your Google Cloud project.

Enable the Dataproc API.

Roles required to enable APIs

To enable APIs, you need the Service Usage Admin IAM role (roles/serviceusage.serviceUsageAdmin), which contains the serviceusage.services.enable permission. Learn how to grant roles.

Enable the API

Executar o código

Faça o tutorial: clique em Abrir no Cloud Shell para executar um tutorial das bibliotecas de cliente do Cloud para Python que cria um cluster, executa um job do PySpark e exclui o cluster.

Abrir no Cloud Shell

Go

1. Instalar a biblioteca de cliente Para saber mais, consulte Como configurar seu ambiente de desenvolvimento.
2. Configurar a autenticação
3. Clone e execute o código de amostra do GitHub.
   Como selecionar um arquivo de entrada do PySpark:as amostras de código da biblioteca de cliente do Google, abaixo, executam um job PySpark que você especifica como parâmetro de entrada. É possível transmitir o simples "Hello World" aplicativo PySpark, localizado no Cloud Storage em gs://dataproc-examples/pyspark/hello-world/hello-world.py ou seu próprio aplicativo PySpark. Consulte Como fazer upload de objetos para saber mais sobre o upload de arquivos para o Cloud Storage.
   
   // This quickstart shows how you can use the Dataproc Client library to create a
   // Dataproc cluster, submit a PySpark job to the cluster, wait for the job to finish
   // and finally delete the cluster.
   //
   // Usage:
   //
   //	go build
   //	./quickstart --project_id <PROJECT_ID> --region <REGION> \
   //	    --cluster_name <CLUSTER_NAME> --job_file_path <GCS_JOB_FILE_PATH>
   package main
   
   import (
   	"context"
   	"flag"
   	"fmt"
   	"io"
   	"log"
   	"regexp"
   
   	dataproc "cloud.google.com/go/dataproc/apiv1"
   	"cloud.google.com/go/dataproc/apiv1/dataprocpb"
   	"cloud.google.com/go/storage"
   	"google.golang.org/api/option"
   )
   
   func main() {
   	var projectID, clusterName, region, jobFilePath string
   	flag.StringVar(&projectID, "project_id", "", "Cloud Project ID, used for creating resources.")
   	flag.StringVar(&region, "region", "", "Region that resources should be created in.")
   	flag.StringVar(&clusterName, "cluster_name", "", "Name of Cloud Dataproc cluster to create.")
   	flag.StringVar(&jobFilePath, "job_file_path", "", "Path to job file in GCS.")
   	flag.Parse()
   
   	ctx := context.Background()
   
   	// Create the cluster client.
   	endpoint := fmt.Sprintf("%s-dataproc.googleapis.com:443", region)
   	clusterClient, err := dataproc.NewClusterControllerClient(ctx, option.WithEndpoint(endpoint))
   	if err != nil {
   		log.Fatalf("error creating the cluster client: %s\n", err)
   	}
   
   	// Create the cluster config.
   	createReq := &dataprocpb.CreateClusterRequest{
   		ProjectId: projectID,
   		Region:    region,
   		Cluster: &dataprocpb.Cluster{
   			ProjectId:   projectID,
   			ClusterName: clusterName,
   			Config: &dataprocpb.ClusterConfig{
   				MasterConfig: &dataprocpb.InstanceGroupConfig{
   					NumInstances:   1,
   					MachineTypeUri: "n1-standard-2",
   				},
   				WorkerConfig: &dataprocpb.InstanceGroupConfig{
   					NumInstances:   2,
   					MachineTypeUri: "n1-standard-2",
   				},
   			},
   		},
   	}
   
   	// Create the cluster.
   	createOp, err := clusterClient.CreateCluster(ctx, createReq)
   	if err != nil {
   		log.Fatalf("error submitting the cluster creation request: %v\n", err)
   	}
   
   	createResp, err := createOp.Wait(ctx)
   	if err != nil {
   		log.Fatalf("error creating the cluster: %v\n", err)
   	}
   
   	// Defer cluster deletion.
   	defer func() {
   		dReq := &dataprocpb.DeleteClusterRequest{
   			ProjectId:   projectID,
   			Region:      region,
   			ClusterName: clusterName,
   		}
   		deleteOp, err := clusterClient.DeleteCluster(ctx, dReq)
   		deleteOp.Wait(ctx)
   		if err != nil {
   			fmt.Printf("error deleting cluster %q: %v\n", clusterName, err)
   			return
   		}
   		fmt.Printf("Cluster %q successfully deleted\n", clusterName)
   	}()
   
   	// Output a success message.
   	fmt.Printf("Cluster created successfully: %q\n", createResp.ClusterName)
   
   	// Create the job client.
   	jobClient, err := dataproc.NewJobControllerClient(ctx, option.WithEndpoint(endpoint))
   
   	// Create the job config.
   	submitJobReq := &dataprocpb.SubmitJobRequest{
   		ProjectId: projectID,
   		Region:    region,
   		Job: &dataprocpb.Job{
   			Placement: &dataprocpb.JobPlacement{
   				ClusterName: clusterName,
   			},
   			TypeJob: &dataprocpb.Job_PysparkJob{
   				PysparkJob: &dataprocpb.PySparkJob{
   					MainPythonFileUri: jobFilePath,
   				},
   			},
   		},
   	}
   
   	submitJobOp, err := jobClient.SubmitJobAsOperation(ctx, submitJobReq)
   	if err != nil {
   		fmt.Printf("error with request to submitting job: %v\n", err)
   		return
   	}
   
   	submitJobResp, err := submitJobOp.Wait(ctx)
   	if err != nil {
   		fmt.Printf("error submitting job: %v\n", err)
   		return
   	}
   
   	re := regexp.MustCompile("gs://(.+?)/(.+)")
   	matches := re.FindStringSubmatch(submitJobResp.DriverOutputResourceUri)
   
   	if len(matches) < 3 {
   		fmt.Printf("regex error: %s\n", submitJobResp.DriverOutputResourceUri)
   		return
   	}
   
   	// Dataproc job outget gets saved to a GCS bucket allocated to it.
   	storageClient, err := storage.NewClient(ctx)
   	if err != nil {
   		fmt.Printf("error creating storage client: %v\n", err)
   		return
   	}
   
   	obj := fmt.Sprintf("%s.000000000", matches[2])
   	reader, err := storageClient.Bucket(matches[1]).Object(obj).NewReader(ctx)
   	if err != nil {
   		fmt.Printf("error reading job output: %v\n", err)
   		return
   	}
   
   	defer reader.Close()
   
   	body, err := io.ReadAll(reader)
   	if err != nil {
   		fmt.Printf("could not read output from Dataproc Job: %v\n", err)
   		return
   	}
   
   	fmt.Printf("Job finished successfully: %s", body)
   }
   

Java

1. Instalar a biblioteca de cliente Para saber mais, consulte Como configurar um ambiente de desenvolvimento do Java.
2. Configurar a autenticação
3. Clone e execute o código de amostra do GitHub.
   Como selecionar um arquivo de entrada do PySpark:as amostras de código da biblioteca de cliente do Google, abaixo, executam um job PySpark que você especifica como parâmetro de entrada. É possível transmitir o simples "Hello World" aplicativo PySpark, localizado no Cloud Storage em gs://dataproc-examples/pyspark/hello-world/hello-world.py ou seu próprio aplicativo PySpark. Consulte Como fazer upload de objetos para saber mais sobre o upload de arquivos para o Cloud Storage.
   /* This quickstart sample walks a user through creating a Cloud Dataproc
    * cluster, submitting a PySpark job from Google Cloud Storage to the
    * cluster, reading the output of the job and deleting the cluster, all
    * using the Java client library.
    *
    * Usage:
    *     mvn clean package -DskipTests
    *
    *     mvn exec:java -Dexec.args="<PROJECT_ID> <REGION> <CLUSTER_NAME> <GCS_JOB_FILE_PATH>"
    *
    *     You can also set these arguments in the main function instead of providing them via the CLI.
    */
   
   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 com.google.cloud.dataproc.v1.Job;
   import com.google.cloud.dataproc.v1.JobControllerClient;
   import com.google.cloud.dataproc.v1.JobControllerSettings;
   import com.google.cloud.dataproc.v1.JobMetadata;
   import com.google.cloud.dataproc.v1.JobPlacement;
   import com.google.cloud.dataproc.v1.PySparkJob;
   import com.google.cloud.storage.Blob;
   import com.google.cloud.storage.Storage;
   import com.google.cloud.storage.StorageOptions;
   import com.google.protobuf.Empty;
   import java.io.IOException;
   import java.util.concurrent.ExecutionException;
   import java.util.regex.Matcher;
   import java.util.regex.Pattern;
   
   public class Quickstart {
   
     public static void quickstart(
         String projectId, String region, String clusterName, String jobFilePath)
         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();
   
       // Configure the settings for the job controller client.
       JobControllerSettings jobControllerSettings =
           JobControllerSettings.newBuilder().setEndpoint(myEndpoint).build();
   
       // Create both a cluster controller client and job 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);
           JobControllerClient jobControllerClient =
               JobControllerClient.create(jobControllerSettings)) {
         // Configure the settings for our cluster.
         InstanceGroupConfig masterConfig =
             InstanceGroupConfig.newBuilder()
                 .setMachineTypeUri("n1-standard-2")
                 .setNumInstances(1)
                 .build();
         InstanceGroupConfig workerConfig =
             InstanceGroupConfig.newBuilder()
                 .setMachineTypeUri("n1-standard-2")
                 .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 clusterResponse = createClusterAsyncRequest.get();
         System.out.println(
             String.format("Cluster created successfully: %s", clusterResponse.getClusterName()));
   
         // Configure the settings for our job.
         JobPlacement jobPlacement = JobPlacement.newBuilder().setClusterName(clusterName).build();
         PySparkJob pySparkJob = PySparkJob.newBuilder().setMainPythonFileUri(jobFilePath).build();
         Job job = Job.newBuilder().setPlacement(jobPlacement).setPysparkJob(pySparkJob).build();
   
         // Submit an asynchronous request to execute the job.
         OperationFuture<Job, JobMetadata> submitJobAsOperationAsyncRequest =
             jobControllerClient.submitJobAsOperationAsync(projectId, region, job);
         Job jobResponse = submitJobAsOperationAsyncRequest.get();
   
         // Print output from Google Cloud Storage.
         Matcher matches =
             Pattern.compile("gs://(.*?)/(.*)").matcher(jobResponse.getDriverOutputResourceUri());
         matches.matches();
   
         Storage storage = StorageOptions.getDefaultInstance().getService();
         Blob blob = storage.get(matches.group(1), String.format("%s.000000000", matches.group(2)));
   
         System.out.println(
             String.format("Job finished successfully: %s", new String(blob.getContent())));
   
         // Delete the cluster.
         OperationFuture<Empty, ClusterOperationMetadata> deleteClusterAsyncRequest =
             clusterControllerClient.deleteClusterAsync(projectId, region, clusterName);
         deleteClusterAsyncRequest.get();
         System.out.println(String.format("Cluster \"%s\" successfully deleted.", clusterName));
   
       } catch (ExecutionException e) {
         System.err.println(String.format("quickstart: %s ", e.getMessage()));
       }
     }
   
     public static void main(String... args) throws IOException, InterruptedException {
       if (args.length != 4) {
         System.err.println(
             "Insufficient number of parameters provided. Please make sure a "
                 + "PROJECT_ID, REGION, CLUSTER_NAME and JOB_FILE_PATH are provided, in this order.");
         return;
       }
   
       String projectId = args[0]; // project-id of project to create the cluster in
       String region = args[1]; // region to create the cluster
       String clusterName = args[2]; // name of the cluster
       String jobFilePath = args[3]; // location in GCS of the PySpark job
   
       quickstart(projectId, region, clusterName, jobFilePath);
     }
   }

Node.js

1. Instalar a biblioteca de cliente Para saber mais, consulte Como configurar um ambiente de desenvolvimento Node.js.
2. Configurar a autenticação
3. Clone e execute o código de amostra do GitHub.
   Como selecionar um arquivo de entrada do PySpark:as amostras de código da biblioteca de cliente do Google, abaixo, executam um job PySpark que você especifica como parâmetro de entrada. É possível transmitir o simples "Hello World" aplicativo PySpark, localizado no Cloud Storage em gs://dataproc-examples/pyspark/hello-world/hello-world.py ou seu próprio aplicativo PySpark. Consulte Como fazer upload de objetos para saber mais sobre o upload de arquivos para o Cloud Storage.
   // This quickstart sample walks a user through creating a Dataproc
   // cluster, submitting a PySpark job from Google Cloud Storage to the
   // cluster, reading the output of the job and deleting the cluster, all
   // using the Node.js client library.
   
   'use strict';
   
   function main(projectId, region, clusterName, jobFilePath) {
     const dataproc = require('@google-cloud/dataproc');
     const {Storage} = require('@google-cloud/storage');
   
     // Create a cluster client with the endpoint set to the desired cluster region
     const clusterClient = new dataproc.v1.ClusterControllerClient({
       apiEndpoint: `${region}-dataproc.googleapis.com`,
       projectId: projectId,
     });
   
     // Create a job client with the endpoint set to the desired cluster region
     const jobClient = new dataproc.v1.JobControllerClient({
       apiEndpoint: `${region}-dataproc.googleapis.com`,
       projectId: projectId,
     });
   
     async function quickstart() {
       // Create the cluster config
       const cluster = {
         projectId: projectId,
         region: region,
         cluster: {
           clusterName: clusterName,
           config: {
             masterConfig: {
               numInstances: 1,
               machineTypeUri: 'n1-standard-2',
             },
             workerConfig: {
               numInstances: 2,
               machineTypeUri: 'n1-standard-2',
             },
           },
         },
       };
   
       // Create the cluster
       const [operation] = await clusterClient.createCluster(cluster);
       const [response] = await operation.promise();
   
       // Output a success message
       console.log(`Cluster created successfully: ${response.clusterName}`);
   
       const job = {
         projectId: projectId,
         region: region,
         job: {
           placement: {
             clusterName: clusterName,
           },
           pysparkJob: {
             mainPythonFileUri: jobFilePath,
           },
         },
       };
   
       const [jobOperation] = await jobClient.submitJobAsOperation(job);
       const [jobResponse] = await jobOperation.promise();
   
       const matches =
         jobResponse.driverOutputResourceUri.match('gs://(.*?)/(.*)');
   
       const storage = new Storage();
   
       const output = await storage
         .bucket(matches[1])
         .file(`${matches[2]}.000000000`)
         .download();
   
       // Output a success message.
       console.log(`Job finished successfully: ${output}`);
   
       // Delete the cluster once the job has terminated.
       const deleteClusterReq = {
         projectId: projectId,
         region: region,
         clusterName: clusterName,
       };
   
       const [deleteOperation] =
         await clusterClient.deleteCluster(deleteClusterReq);
       await deleteOperation.promise();
   
       // Output a success message
       console.log(`Cluster ${clusterName} successfully deleted.`);
     }
   
     quickstart();
   }
   
   const args = process.argv.slice(2);
   
   if (args.length !== 4) {
     console.log(
       'Insufficient number of parameters provided. Please make sure a ' +
         'PROJECT_ID, REGION, CLUSTER_NAME and JOB_FILE_PATH are provided, in this order.'
     );
   }
   
   main(...args);

Python

1. Instalar a biblioteca de cliente Para saber mais, consulte Como configurar um ambiente de desenvolvimento do Python.
2. Configurar a autenticação
3. Clone e execute o código de amostra do GitHub.
   Como selecionar um arquivo de entrada do PySpark:as amostras de código da biblioteca de cliente do Google, abaixo, executam um job PySpark que você especifica como parâmetro de entrada. É possível transmitir o simples "Hello World" aplicativo PySpark, localizado no Cloud Storage em gs://dataproc-examples/pyspark/hello-world/hello-world.py ou seu próprio aplicativo PySpark. Consulte Como fazer upload de objetos para saber mais sobre o upload de arquivos para o Cloud Storage.
   """
   This quickstart sample walks a user through creating a Cloud Dataproc
   cluster, submitting a PySpark job from Google Cloud Storage to the
   cluster, reading the output of the job and deleting the cluster, all
   using the Python client library.
   
   Usage:
       python quickstart.py --project_id <PROJECT_ID> --region <REGION> \
           --cluster_name <CLUSTER_NAME> --job_file_path <GCS_JOB_FILE_PATH>
   """
   
   import argparse
   import re
   
   from google.cloud import dataproc_v1 as dataproc
   from google.cloud import storage
   
   
   def quickstart(project_id, region, cluster_name, job_file_path):
       # Create the cluster client.
       cluster_client = dataproc.ClusterControllerClient(
           client_options={"api_endpoint": "{}-dataproc.googleapis.com:443".format(region)}
       )
   
       # Create the cluster config.
       cluster = {
           "project_id": project_id,
           "cluster_name": cluster_name,
           "config": {
               "master_config": {
                   "num_instances": 1,
                   "machine_type_uri": "n1-standard-2",
                   "disk_config": {"boot_disk_size_gb": 100},
               },
               "worker_config": {
                   "num_instances": 2,
                   "machine_type_uri": "n1-standard-2",
                   "disk_config": {"boot_disk_size_gb": 100},
               },
           },
       }
   
       # Create the cluster.
       operation = cluster_client.create_cluster(
           request={"project_id": project_id, "region": region, "cluster": cluster}
       )
       result = operation.result()
   
       print("Cluster created successfully: {}".format(result.cluster_name))
   
       # Create the job client.
       job_client = dataproc.JobControllerClient(
           client_options={"api_endpoint": "{}-dataproc.googleapis.com:443".format(region)}
       )
   
       # Create the job config.
       job = {
           "placement": {"cluster_name": cluster_name},
           "pyspark_job": {"main_python_file_uri": job_file_path},
       }
   
       operation = job_client.submit_job_as_operation(
           request={"project_id": project_id, "region": region, "job": job}
       )
       response = operation.result()
   
       # Dataproc job output gets saved to the Google Cloud Storage bucket
       # allocated to the job. Use a regex to obtain the bucket and blob info.
       matches = re.match("gs://(.*?)/(.*)", response.driver_output_resource_uri)
   
       output = (
           storage.Client()
           .get_bucket(matches.group(1))
           .blob(f"{matches.group(2)}.000000000")
           .download_as_bytes()
           .decode("utf-8")
       )
   
       print(f"Job finished successfully: {output}")
   
       # Delete the cluster once the job has terminated.
       operation = cluster_client.delete_cluster(
           request={
               "project_id": project_id,
               "region": region,
               "cluster_name": cluster_name,
           }
       )
       operation.result()
   
       print("Cluster {} successfully deleted.".format(cluster_name))
   
   
   if __name__ == "__main__":
       parser = argparse.ArgumentParser(
           description=__doc__,
           formatter_class=argparse.RawDescriptionHelpFormatter,
       )
       parser.add_argument(
           "--project_id",
           type=str,
           required=True,
           help="Project to use for creating resources.",
       )
       parser.add_argument(
           "--region",
           type=str,
           required=True,
           help="Region where the resources should live.",
       )
       parser.add_argument(
           "--cluster_name",
           type=str,
           required=True,
           help="Name to use for creating a cluster.",
       )
       parser.add_argument(
           "--job_file_path",
           type=str,
           required=True,
           help="Job in GCS to execute against the cluster.",
       )
   
       args = parser.parse_args()
       quickstart(args.project_id, args.region, args.cluster_name, args.job_file_path)

Veja o resultado

O código gera o registro do driver do job para o bucket de preparo padrão do Serviço Gerenciado para Apache Spark no Cloud Storage. É possível conferir a saída do driver do job no Google Cloud console na página Jobs do Serviço Gerenciado para Apache Spark do seu projeto no Google Cloud console. Clique no código da tarefa para conferir a saída da tarefa na página Detalhes da tarefa.

A seguir

• Consulte Recursos adicionais da biblioteca de cliente do Cloud do Serviço Gerenciado para Apache Spark .