Using inline Dataproc workflows

Unlike standard workflows that instantiate a previously created workflow template resource, inline workflows use a YAML file or an embedded WorkflowTemplate definition to run a workflow.

.

Creating and running an inline workflow

gcloud

See Instantiate a workflow using a YAML file.

REST & CMD LINE

Before using any of the request data below, make the following replacements:

HTTP method and URL:

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

Request JSON body:

{
  "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"
        }
      }
    }
  }
}

To send your request, expand one of these options:

You should receive a JSON response similar to the following:

{
  "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

Currently, the creation of inline workflows is not supported in the Cloud Console. Workflow templates and instantiated workflows can be viewed from Dataproc Workflows page.

Go

  1. Install the client library
  2. Set up application default credentials
  3. Run the 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)
    	}
    
    	// 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. Install the client library
  2. Set up application default credentials
  3. Run the 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. Install the client library
  2. Set up application default credentials
  3. Run the 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. Install the client library
  2. Set up application default credentials
  3. Run the 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.")