Utiliser des workflows Dataproc intégrés

Contrairement aux workflows standards qui instancient une ressource de modèle de workflow créée précédemment, les workflows intégrés utilisent un fichier YAML ou une définition WorkflowTemplate intégrée pour exécuter un workflow.

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Créer et exécuter un workflow intégré

gcloud

Consultez la page Instancier un workflow à l'aide d'un fichier YAML.

API REST et ligne de commande

Avant d'utiliser les données de requête ci-dessous, effectuez les remplacements suivants :

Méthode HTTP et URL :

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

Corps JSON de la requête :

{
  "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"
        }
      }
    }
  }
}

Pour envoyer votre requête, développez l'une des options suivantes :

Vous devriez recevoir une réponse JSON de ce type :

{
  "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"
  }
}

Console

Actuellement, Il n'est pas possible de créer des workflows intégrés dans Cloud Console. Vous pouvez afficher des modèles de workflow et des workflows instanciés à partir de la page Workflows de Dataproc.

Go

  1. Installer la bibliothèque cliente
  2. Configurer les identifiants par défaut de l'application
  3. Exécuter le code.
    import (
    	"context"
    	"fmt"
    	"io"
    
    	dataproc "cloud.google.com/go/dataproc/apiv1"
    	"google.golang.org/api/option"
    	dataprocpb "google.golang.org/genproto/googleapis/cloud/dataproc/v1"
    )
    
    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: %v", 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: %v", err)
    	}
    
    	if err := op.Wait(ctx); err != nil {
    		return fmt.Errorf("InstantiateInlineWorkflowTemplate.Wait: %v", err)
    	}
    
    	// Output a success message.
    	fmt.Fprintf(w, "Workflow created successfully.")
    	return nil
    }
    

Java

  1. Installer la bibliothèque cliente
  2. Configurer les identifiants par défaut de l'application
  3. Exécuter le code
    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. Installer la bibliothèque cliente
  2. Configurer les identifiants par défaut de l'application
  3. Exécuter le code
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. Installer la bibliothèque cliente
  2. Configurer les identifiants par défaut de l'application
  3. Exécuter le code
    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 = "projects/{}/regions/{}".format(project_id, 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.")