„Managed Service for Apache Spark“ ist der neue Name für das Produkt, das früher als „Dataproc on Compute Engine“ (Clusterbereitstellung) und „Google Cloud Serverless for Apache Spark“ (serverlose Bereitstellung) bekannt war.

Inline-Workflow-Vorlage instanziieren

Instanziiert eine Inline-Workflow-Vorlage mithilfe von Cloud-Clientbibliotheken.

Weitere Informationen

Eine ausführliche Dokumentation, die dieses Codebeispiel enthält, finden Sie hier:

Inline-Workflows verwenden

Codebeispiel

Go

Folgen Sie der Go Einrichtungsanleitung in der Kurzanleitung zu Managed Service for Apache Spark mit Clientbibliotheken, bevor Sie dieses Beispiel anwenden. Weitere Informationen finden Sie in der API-Referenzdokumentation zu Managed Service for Apache SparkGo.

Richten Sie zur Authentifizierung bei Managed Service for Apache Spark die Standardanmeldedaten für Anwendungen ein. Weitere Informationen finden Sie unter Authentifizierung für eine lokale Entwicklungsumgebung einrichten.

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

Folgen Sie der Java Einrichtungsanleitung in der Kurzanleitung zu Managed Service for Apache Spark mit Clientbibliotheken, bevor Sie dieses Beispiel anwenden. Weitere Informationen finden Sie in der API-Referenzdokumentation zu Managed Service for Apache SparkJava.

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

Folgen Sie der Node.js Einrichtungsanleitung in der Kurzanleitung zu Managed Service for Apache Spark mit Clientbibliotheken, bevor Sie dieses Beispiel anwenden. Weitere Informationen finden Sie in der API-Referenzdokumentation zu Managed Service for Apache SparkNode.js.

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

Folgen Sie der Python Einrichtungsanleitung in der Kurzanleitung zu Managed Service for Apache Spark mit Clientbibliotheken, bevor Sie dieses Beispiel anwenden. Weitere Informationen finden Sie in der API-Referenzdokumentation zu Managed Service for Apache SparkPython.

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.")

Weitere Informationen

Wenn Sie nach Codebeispielen für andere Produkte von Google Cloud suchen und filtern möchten, können Sie den Beispielbrowser fürGoogle Cloud verwenden.

Inline-Workflow-Vorlage instanziieren Mit Sammlungen den Überblick behalten Sie können Inhalte basierend auf Ihren Einstellungen speichern und kategorisieren.

Weitere Informationen

Codebeispiel

Go

Java

Node.js

Python

Weitere Informationen

Inline-Workflow-Vorlage instanziieren