Crea un cluster Dataproc utilizzando le librerie client

Crea un cluster Dataproc utilizzando le librerie client

Il codice campione elencato di seguito mostra come utilizzare le librerie client di Cloud per creare un cluster Dataproc, eseguire un job sul cluster, quindi eliminare il cluster.

Puoi anche eseguire queste attività utilizzando:

• Richieste API REST in Guide rapide che utilizzano Explorer API
• la console Google Cloud in Creare un cluster Dataproc utilizzando la console Google Cloud
• Google Cloud CLI in Crea un cluster Dataproc utilizzando Google Cloud CLI

Prima di iniziare

Accedi al tuo account Google Cloud . Se non conosci Google Cloud, crea un account per valutare le prestazioni dei nostri prodotti in scenari reali. I nuovi clienti ricevono anche 300 $di crediti senza costi per l'esecuzione, il test e il deployment dei workload.

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

Esegui il codice

Prova la procedura dettagliata:fai clic su Apri in Cloud Shell per eseguire una procedura dettagliata delle librerie client di Cloud per Python che crea un cluster, esegue un job PySpark e poi elimina il cluster.

Apri in Cloud Shell

Go

1. Installa la libreria client Per saperne di più, vedi Configurazione dell'ambiente di sviluppo.
2. Configurare l'autenticazione
3. Clona ed esegui il codice GitHub di esempio.
   Selezione di un file di input PySpark: Gli esempi di codice della libreria client Google riportati di seguito eseguono un job PySpark che specifichi come parametro di input. Puoi inserire la semplice app PySpark "Hello World" che si trova in Cloud Storage all'indirizzo gs://dataproc-examples/pyspark/hello-world/hello-world.py o la tua app PySpark. Consulta Caricamento di oggetti per scoprire di più sul caricamento di file in 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. Installa la libreria client Per saperne di più, vedi Configurazione di un ambiente di sviluppo Java.
2. Configurare l'autenticazione
3. Clona ed esegui il codice GitHub di esempio.
   Selezione di un file di input PySpark: Gli esempi di codice della libreria client Google riportati di seguito eseguono un job PySpark che specifichi come parametro di input. Puoi inserire la semplice app PySpark "Hello World" che si trova in Cloud Storage all'indirizzo gs://dataproc-examples/pyspark/hello-world/hello-world.py o la tua app PySpark. Consulta Caricamento di oggetti per scoprire di più sul caricamento di file in 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. Installa la libreria client Per saperne di più, consulta Configurazione di un ambiente di sviluppo Node.js.
2. Configurare l'autenticazione
3. Clona ed esegui il codice GitHub di esempio.
   Selezione di un file di input PySpark: Gli esempi di codice della libreria client Google riportati di seguito eseguono un job PySpark che specifichi come parametro di input. Puoi inserire la semplice app PySpark "Hello World" che si trova in Cloud Storage all'indirizzo gs://dataproc-examples/pyspark/hello-world/hello-world.py o la tua app PySpark. Consulta Caricamento di oggetti per scoprire di più sul caricamento di file in 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. Installa la libreria client Per saperne di più, consulta Configurazione di un ambiente di sviluppo Python.
2. Configurare l'autenticazione
3. Clona ed esegui il codice GitHub di esempio.
   Selezione di un file di input PySpark: Gli esempi di codice della libreria client Google riportati di seguito eseguono un job PySpark che specifichi come parametro di input. Puoi inserire la semplice app PySpark "Hello World" che si trova in Cloud Storage all'indirizzo gs://dataproc-examples/pyspark/hello-world/hello-world.py o la tua app PySpark. Consulta Caricamento di oggetti per scoprire di più sul caricamento di file in 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)

Visualizzare l'output

Il codice restituisce il log del driver del job al bucket di staging Dataproc predefinito in Cloud Storage. Puoi visualizzare l'output del driver del job dalla console Google Cloud nella pagina Job di Dataproc del tuo progetto nella console Google Cloud . Fai clic sull'ID job per visualizzare l'output del job nella pagina Dettagli job.

Passaggi successivi

• Consulta la libreria client Cloud di Dataproc Risorse aggiuntive.