Instanciar modelo de fluxo de trabalho in-line

Instancia um modelo de fluxo de trabalho in-line usando as bibliotecas de cliente do Cloud.

Mais informações

Para ver a documentação detalhada que inclui este exemplo de código, consulte:

• Usar fluxos de trabalho inline do Dataproc

Exemplo de código

Go

Antes de testar esta amostra, siga as instruções de configuração do Go no Guia de início rápido do Dataproc usando bibliotecas de cliente. Para mais informações, consulte a documentação de referência da API Go do Dataproc.

Para autenticar no Dataproc, configure o Application Default Credentials. Para mais informações, consulte Configurar a autenticação para um ambiente de desenvolvimento local.

import (
	"context"
	"fmt"
	"io"

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

func instantiateInlineWorkflowTemplate(w io.Writer, projectID, region string) error {
	// projectID := "your-project-id"
	// region := "us-central1"

	ctx := context.Background()

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

	// Create jobs for the workflow.
	teragenJob := &dataprocpb.OrderedJob{
		JobType: &dataprocpb.OrderedJob_HadoopJob{
			HadoopJob: &dataprocpb.HadoopJob{
				Driver: &dataprocpb.HadoopJob_MainJarFileUri{
					MainJarFileUri: "file:///usr/lib/hadoop-mapreduce/hadoop-mapreduce-examples.jar",
				},
				Args: []string{
					"teragen",
					"1000",
					"hdfs:///gen/",
				},
			},
		},
		StepId: "teragen",
	}

	terasortJob := &dataprocpb.OrderedJob{
		JobType: &dataprocpb.OrderedJob_HadoopJob{
			HadoopJob: &dataprocpb.HadoopJob{
				Driver: &dataprocpb.HadoopJob_MainJarFileUri{
					MainJarFileUri: "file:///usr/lib/hadoop-mapreduce/hadoop-mapreduce-examples.jar",
				},
				Args: []string{
					"terasort",
					"hdfs:///gen/",
					"hdfs:///sort/",
				},
			},
		},
		StepId: "terasort",
		PrerequisiteStepIds: []string{
			"teragen",
		},
	}

	// Create the cluster placement.
	clusterPlacement := &dataprocpb.WorkflowTemplatePlacement{
		Placement: &dataprocpb.WorkflowTemplatePlacement_ManagedCluster{
			ManagedCluster: &dataprocpb.ManagedCluster{
				ClusterName: "my-managed-cluster",
				Config: &dataprocpb.ClusterConfig{
					GceClusterConfig: &dataprocpb.GceClusterConfig{
						// Leave "ZoneUri" empty for "Auto Zone Placement"
						// ZoneUri: ""
						ZoneUri: "us-central1-a",
					},
				},
			},
		},
	}

	// Create the Instantiate Inline Workflow Template Request.
	req := &dataprocpb.InstantiateInlineWorkflowTemplateRequest{
		Parent: fmt.Sprintf("projects/%s/regions/%s", projectID, region),
		Template: &dataprocpb.WorkflowTemplate{
			Jobs: []*dataprocpb.OrderedJob{
				teragenJob,
				terasortJob,
			},
			Placement: clusterPlacement,
		},
	}

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

	if err := op.Wait(ctx); err != nil {
		return fmt.Errorf("InstantiateInlineWorkflowTemplate.Wait: %w", err)
	}

	// Output a success message.
	fmt.Fprintf(w, "Workflow created successfully.")
	return nil
}

Java

Antes de testar esta amostra, siga as instruções de configuração do Java no Guia de início rápido do Dataproc usando bibliotecas de cliente. Para mais informações, consulte a documentação de referência da API Java do Dataproc.

Para autenticar no Dataproc, configure o Application Default Credentials. Para mais informações, consulte Configurar a autenticação para um ambiente de desenvolvimento local.

import com.google.api.gax.longrunning.OperationFuture;
import com.google.cloud.dataproc.v1.ClusterConfig;
import com.google.cloud.dataproc.v1.GceClusterConfig;
import com.google.cloud.dataproc.v1.HadoopJob;
import com.google.cloud.dataproc.v1.ManagedCluster;
import com.google.cloud.dataproc.v1.OrderedJob;
import com.google.cloud.dataproc.v1.RegionName;
import com.google.cloud.dataproc.v1.WorkflowMetadata;
import com.google.cloud.dataproc.v1.WorkflowTemplate;
import com.google.cloud.dataproc.v1.WorkflowTemplatePlacement;
import com.google.cloud.dataproc.v1.WorkflowTemplateServiceClient;
import com.google.cloud.dataproc.v1.WorkflowTemplateServiceSettings;
import com.google.protobuf.Empty;
import java.io.IOException;
import java.util.concurrent.ExecutionException;

public class InstantiateInlineWorkflowTemplate {

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

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

    // Configure the settings for the workflow template service client.
    WorkflowTemplateServiceSettings workflowTemplateServiceSettings =
        WorkflowTemplateServiceSettings.newBuilder().setEndpoint(myEndpoint).build();

    // Create a workflow template service 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 (WorkflowTemplateServiceClient workflowTemplateServiceClient =
        WorkflowTemplateServiceClient.create(workflowTemplateServiceSettings)) {

      // Configure the jobs within the workflow.
      HadoopJob teragenHadoopJob =
          HadoopJob.newBuilder()
              .setMainJarFileUri("file:///usr/lib/hadoop-mapreduce/hadoop-mapreduce-examples.jar")
              .addArgs("teragen")
              .addArgs("1000")
              .addArgs("hdfs:///gen/")
              .build();
      OrderedJob teragen =
          OrderedJob.newBuilder().setHadoopJob(teragenHadoopJob).setStepId("teragen").build();

      HadoopJob terasortHadoopJob =
          HadoopJob.newBuilder()
              .setMainJarFileUri("file:///usr/lib/hadoop-mapreduce/hadoop-mapreduce-examples.jar")
              .addArgs("terasort")
              .addArgs("hdfs:///gen/")
              .addArgs("hdfs:///sort/")
              .build();
      OrderedJob terasort =
          OrderedJob.newBuilder()
              .setHadoopJob(terasortHadoopJob)
              .addPrerequisiteStepIds("teragen")
              .setStepId("terasort")
              .build();

      // Configure the cluster placement for the workflow.
      // Leave "ZoneUri" empty for "Auto Zone Placement".
      // GceClusterConfig gceClusterConfig =
      //     GceClusterConfig.newBuilder().setZoneUri("").build();
      GceClusterConfig gceClusterConfig =
          GceClusterConfig.newBuilder().setZoneUri("us-central1-a").build();
      ClusterConfig clusterConfig =
          ClusterConfig.newBuilder().setGceClusterConfig(gceClusterConfig).build();
      ManagedCluster managedCluster =
          ManagedCluster.newBuilder()
              .setClusterName("my-managed-cluster")
              .setConfig(clusterConfig)
              .build();
      WorkflowTemplatePlacement workflowTemplatePlacement =
          WorkflowTemplatePlacement.newBuilder().setManagedCluster(managedCluster).build();

      // Create the inline workflow template.
      WorkflowTemplate workflowTemplate =
          WorkflowTemplate.newBuilder()
              .addJobs(teragen)
              .addJobs(terasort)
              .setPlacement(workflowTemplatePlacement)
              .build();

      // Submit the instantiated inline workflow template request.
      String parent = RegionName.format(projectId, region);
      OperationFuture<Empty, WorkflowMetadata> instantiateInlineWorkflowTemplateAsync =
          workflowTemplateServiceClient.instantiateInlineWorkflowTemplateAsync(
              parent, workflowTemplate);
      instantiateInlineWorkflowTemplateAsync.get();

      // Print out a success message.
      System.out.printf("Workflow ran successfully.");

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

Node.js

Antes de testar esta amostra, siga as instruções de configuração do Node.js no Guia de início rápido do Dataproc usando bibliotecas de cliente. Para mais informações, consulte a documentação de referência da API Node.js do Dataproc.

Para autenticar no Dataproc, configure o Application Default Credentials. Para mais informações, consulte Configurar a autenticação para um ambiente de desenvolvimento local.

const dataproc = require('@google-cloud/dataproc');

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

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

async function instantiateInlineWorkflowTemplate() {
  // Create the formatted parent.
  const parent = client.regionPath(projectId, region);

  // Create the template
  const template = {
    jobs: [
      {
        hadoopJob: {
          mainJarFileUri:
            'file:///usr/lib/hadoop-mapreduce/hadoop-mapreduce-examples.jar',
          args: ['teragen', '1000', 'hdfs:///gen/'],
        },
        stepId: 'teragen',
      },
      {
        hadoopJob: {
          mainJarFileUri:
            'file:///usr/lib/hadoop-mapreduce/hadoop-mapreduce-examples.jar',
          args: ['terasort', 'hdfs:///gen/', 'hdfs:///sort/'],
        },
        stepId: 'terasort',
        prerequisiteStepIds: ['teragen'],
      },
    ],
    placement: {
      managedCluster: {
        clusterName: 'my-managed-cluster',
        config: {
          gceClusterConfig: {
            // Leave 'zoneUri' empty for 'Auto Zone Placement'
            // zoneUri: ''
            zoneUri: 'us-central1-a',
          },
        },
      },
    },
  };

  const request = {
    parent: parent,
    template: template,
  };

  // Submit the request to instantiate the workflow from an inline template.
  const [operation] = await client.instantiateInlineWorkflowTemplate(request);
  await operation.promise();

  // Output a success message
  console.log('Workflow ran successfully.');

Python

Antes de testar esta amostra, siga as instruções de configuração do Python no Guia de início rápido do Dataproc usando bibliotecas de cliente. Para mais informações, consulte a documentação de referência da API Python do Dataproc.

Para autenticar no Dataproc, configure o Application Default Credentials. Para mais informações, consulte Configurar a autenticação para um ambiente de desenvolvimento local.

from google.cloud import dataproc_v1 as dataproc

def instantiate_inline_workflow_template(project_id, region):
    """This sample walks a user through submitting a workflow
    for a Cloud Dataproc using the Python client library.

    Args:
        project_id (string): Project to use for running the workflow.
        region (string): Region where the workflow resources should live.
    """

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

    parent = f"projects/{project_id}/regions/{region}"

    template = {
        "jobs": [
            {
                "hadoop_job": {
                    "main_jar_file_uri": "file:///usr/lib/hadoop-mapreduce/"
                    "hadoop-mapreduce-examples.jar",
                    "args": ["teragen", "1000", "hdfs:///gen/"],
                },
                "step_id": "teragen",
            },
            {
                "hadoop_job": {
                    "main_jar_file_uri": "file:///usr/lib/hadoop-mapreduce/"
                    "hadoop-mapreduce-examples.jar",
                    "args": ["terasort", "hdfs:///gen/", "hdfs:///sort/"],
                },
                "step_id": "terasort",
                "prerequisite_step_ids": ["teragen"],
            },
        ],
        "placement": {
            "managed_cluster": {
                "cluster_name": "my-managed-cluster",
                "config": {
                    "gce_cluster_config": {
                        # Leave 'zone_uri' empty for 'Auto Zone Placement'
                        # 'zone_uri': ''
                        "zone_uri": "us-central1-a"
                    }
                },
            }
        },
    }

    # Submit the request to instantiate the workflow from an inline template.
    operation = workflow_template_client.instantiate_inline_workflow_template(
        request={"parent": parent, "template": template}
    )
    operation.result()

    # Output a success message.
    print("Workflow ran successfully.")

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