Usa flujos de trabajo intercalados de Dataproc

A diferencia de los flujos de trabajo estándar que crean una instancia de un recurso de plantilla de flujo de trabajo creado previamente, los flujos de trabajo intercalados usan un archivo YAML o una definición WorkflowTemplate incorporada para ejecutar un flujo de trabajo.

Crea y ejecuta un flujo de trabajo intercalado

gcloud

Consulta Crea una instancia de flujo de trabajo con un archivo YAML.

LÍNEA DE CMD Y REST

Antes de usar cualquiera de los datos de solicitud siguientes, realiza los siguientes reemplazos:

Método HTTP y URL:

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

Cuerpo JSON de la solicitud:

{
  "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"
        }
      }
    }
  }
}

Para enviar tu solicitud, expande una de estas opciones:

Deberías recibir una respuesta JSON similar a la que se muestra a continuación:

{
  "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

Por el momento, la creación de flujos de trabajo intercalados no es compatible con Cloud Console. Las plantillas de flujo de trabajo y los flujos de trabajo en los que se crearon instancias se pueden ver desde la página Flujos de trabajo de Dataproc.

Go

  1. Instala la biblioteca cliente
  2. Configura credenciales predeterminadas de la aplicación
  3. Ejecuta el código.
    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)
    	}
    
    	// 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. Instala la biblioteca cliente
  2. Configura credenciales predeterminadas de la aplicación
  3. Ejecuta el código.
    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. Instala la biblioteca cliente
  2. Configura credenciales predeterminadas de la aplicación
  3. Ejecuta el código
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. Instala la biblioteca cliente
  2. Configura credenciales predeterminadas de la aplicación
  3. Ejecuta el código.
    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.")