Menggunakan alur kerja Dataproc inline

Tetap teratur dengan koleksi Simpan dan kategorikan konten berdasarkan preferensi Anda.

gcloud

Lihat Membuat instance alur kerja menggunakan file YAML.

REST

Sebelum menggunakan salah satu data permintaan, lakukan penggantian berikut:

• project-id: Google Cloud project ID
• region: region cluster, seperti "us-central1"
• zoneUri: Tentukan zone dalam region cluster, seperti "us-central1-b", atau biarkan kosong ("") untuk menggunakan Penempatan Zona Otomatis Dataproc
• clusterName: nama cluster

Metode HTTP dan URL:

POST https://dataproc.googleapis.com/v1/projects/project-id/regions/region/workflowTemplates:instantiateInline

Meminta isi JSON:

{
  "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": "cluster-name",
      "config": {
        "gceClusterConfig": {
          "zoneUri": "zone"
        }
      }
    }
  }
}

Untuk mengirim permintaan Anda, perluas salah satu opsi berikut:

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://dataproc.googleapis.com/v1/projects/project-id/regions/region/workflowTemplates:instantiateInline"

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://dataproc.googleapis.com/v1/projects/project-id/regions/region/workflowTemplates:instantiateInline" | Select-Object -Expand Content

Anda akan melihat respons JSON yang mirip seperti berikut:

{
  "name": "projects/project-id/regions/region/operations/2fbd0dad-...",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.dataproc.v1.WorkflowMetadata",
    "graph": {
      "nodes": [
        {
          "stepId": "teragen",
          "state": "RUNNABLE"
        },
        {
          "stepId": "terasort",
          "prerequisiteStepIds": [
            "teragen"
          ],
          "state": "BLOCKED"
        }
      ]
    },
    "state": "PENDING",
    "startTime": "2020-04-02T22:50:44.826Z"
  }
}

Konsol

Saat ini, pembuatan alur kerja inline tidak didukung di konsol Google Cloud. Template alur kerja dan alur kerja yang dibuat instance-nya dapat dilihat dari halaman Alur Kerja Dataproc.

Go

1. Menginstal library klien
2. Menyiapkan kredensial default aplikasi
3. Jalankan kode
   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

1. Menginstal library klien
2. Menyiapkan kredensial default aplikasi
3. Jalankan kode tersebut
   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

1. Menginstal library klien
2. Menyiapkan kredensial default aplikasi
3. Menjalankan kode

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

1. Menginstal library klien
2. Menyiapkan kredensial default aplikasi
3. Jalankan kode tersebut
   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.")