Membuat instance template alur kerja inline

Membuat instance template alur kerja inline menggunakan Library Klien Cloud.

Mempelajari lebih lanjut

Untuk dokumentasi mendetail yang menyertakan contoh kode ini, lihat artikel berikut:

• Menggunakan alur kerja inline

Contoh kode

Go

Sebelum mencoba contoh ini, ikuti petunjuk penyiapan Go di Panduan memulai Managed Service for Apache Spark menggunakan library klien. Untuk mengetahui informasi selengkapnya, lihat dokumentasi referensi API Go Managed Service for Apache Spark.

Untuk melakukan autentikasi ke Managed Service for Apache Spark, siapkan Kredensial Default Aplikasi. Untuk mengetahui informasi selengkapnya, lihat Menyiapkan autentikasi untuk lingkungan pengembangan lokal.

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

Sebelum mencoba contoh ini, ikuti petunjuk penyiapan Java di Panduan memulai Managed Service for Apache Spark menggunakan library klien. Untuk mengetahui informasi selengkapnya, lihat dokumentasi referensi API Java Managed Service for Apache Spark.

Untuk melakukan autentikasi ke Managed Service for Apache Spark, siapkan Kredensial Default Aplikasi. Untuk mengetahui informasi selengkapnya, lihat Menyiapkan autentikasi untuk lingkungan pengembangan lokal.

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

Sebelum mencoba contoh ini, ikuti petunjuk penyiapan Node.js di Panduan memulai Managed Service for Apache Spark menggunakan library klien. Untuk mengetahui informasi selengkapnya, lihat dokumentasi referensi API Node.js Managed Service for Apache Spark.

Untuk melakukan autentikasi ke Managed Service for Apache Spark, siapkan Kredensial Default Aplikasi. Untuk mengetahui informasi selengkapnya, lihat Menyiapkan autentikasi untuk lingkungan pengembangan lokal.

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

Sebelum mencoba contoh ini, ikuti petunjuk penyiapan Python di Panduan memulai Managed Service for Apache Spark menggunakan library klien. Untuk mengetahui informasi selengkapnya, lihat dokumentasi referensi API Python Managed Service for Apache Spark.

Untuk melakukan autentikasi ke Managed Service for Apache Spark, siapkan Kredensial Default Aplikasi. Untuk mengetahui informasi selengkapnya, lihat Menyiapkan autentikasi untuk lingkungan pengembangan lokal.

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.")

Langkah berikutnya

Untuk menelusuri dan memfilter contoh kode untuk produk Google Cloud lainnya, lihat browser contohGoogle Cloud .