Submit a job

Console

Open the Dataproc Submit a job page in the Google Cloud console in your browser.

Spark job example

To submit a sample Spark job, fill in the fields on the Submit a job page, as follows:

1. Select your Cluster name from the cluster list.
2. Set Job type to Spark.
3. Set Main class or jar to org.apache.spark.examples.SparkPi.
4. Set Arguments to the single argument 1000.
5. Add file:///usr/lib/spark/examples/jars/spark-examples.jar to Jar files:
   1. file:/// denotes a Hadoop LocalFileSystem scheme. Dataproc installed /usr/lib/spark/examples/jars/spark-examples.jar on the cluster's master node when it created the cluster.
   2. Alternatively, you can specify a Cloud Storage path (gs://your-bucket/your-jarfile.jar) or a Hadoop Distributed File System path (hdfs://path-to-jar.jar) to one of your jars.

Click Submit to start the job. Once the job starts, it is added to the Jobs list.

Click the Job ID to open the Jobs page, where you can view the job's driver output. Since this job produces long output lines that exceed the width of the browser window, you can check the Line wrapping box to bring all output text within view in order to display the calculated result for pi.

You can view your job's driver output from the command line using the gcloud dataproc jobs wait command shown below (for more information, see View job output–GCLOUD COMMAND). Copy and paste your project ID as the value for the --project flag and your Job ID (shown on the Jobs list) as the final argument.

gcloud dataproc jobs wait job-id \
    --project=project-id \
    --region=region

Here are snippets from the driver output for the sample SparkPi job submitted above:

...
2015-06-25 23:27:23,810 INFO [dag-scheduler-event-loop]
scheduler.DAGScheduler (Logging.scala:logInfo(59)) - Stage 0 (reduce at
SparkPi.scala:35) finished in 21.169 s

2015-06-25 23:27:23,810 INFO [task-result-getter-3] cluster.YarnScheduler
(Logging.scala:logInfo(59)) - Removed TaskSet 0.0, whose tasks have all
completed, from pool

2015-06-25 23:27:23,819 INFO [main] scheduler.DAGScheduler
(Logging.scala:logInfo(59)) - Job 0 finished: reduce at SparkPi.scala:35,
took 21.674931 s

Pi is roughly 3.14189648
...
Job [c556b47a-4b46-4a94-9ba2-2dcee31167b2] finished successfully.

driverOutputUri:
gs://sample-staging-bucket/google-cloud-dataproc-metainfo/cfeaa033-749e-48b9-...
...

gcloud

To submit a job to a Dataproc cluster, run the gcloud CLI gcloud dataproc jobs submit command locally in a terminal window or in Cloud Shell.

gcloud dataproc jobs submit job-command \
    --cluster=cluster-name \
    --region=region \
    other dataproc-flags \
    -- job-args

1. List the publicly accessible hello-world.py located in Cloud Storage.

   gcloud storage cat gs://dataproc-examples/pyspark/hello-world/hello-world.py
   

   File Listing:

   #!/usr/bin/python
   import pyspark
   sc = pyspark.SparkContext()
   rdd = sc.parallelize(['Hello,', 'world!'])
   words = sorted(rdd.collect())
   print(words)

2. Submit the Pyspark job to Dataproc.

   gcloud dataproc jobs submit pyspark \
       gs://dataproc-examples/pyspark/hello-world/hello-world.py \
       --cluster=cluster-name  \
       --region=region
   

   Terminal output:

   Waiting for job output...
   …
   ['Hello,', 'world!']
   Job finished successfully.
   

1. Run the SparkPi example pre-installed on the Dataproc cluster's master node.

   gcloud dataproc jobs submit spark \
       --cluster=cluster-name \
       --region=region \
       --class=org.apache.spark.examples.SparkPi \
       --jars=file:///usr/lib/spark/examples/jars/spark-examples.jar \
       -- 1000
   

   Terminal output:

   Job [54825071-ae28-4c5b-85a5-58fae6a597d6] submitted.
   Waiting for job output…
   …
   Pi is roughly 3.14177148
   …
   Job finished successfully.
   …
   

REST

This section shows how to submit a Spark job to compute the approximate value of pi using the Dataproc jobs.submit API.

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

• project-id: Google Cloud project ID
• region: cluster region
• clusterName: cluster name

HTTP method and URL:

POST https://dataproc.googleapis.com/v1/projects/project-id/regions/region/jobs:submit

Request JSON body:

{
  "job": {
    "placement": {
      "clusterName": "cluster-name"
    },
    "sparkJob": {
      "args": [
        "1000"
      ],
      "mainClass": "org.apache.spark.examples.SparkPi",
      "jarFileUris": [
        "file:///usr/lib/spark/examples/jars/spark-examples.jar"
      ]
    }
  }
}

To send your request, expand one of these options:

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/jobs:submit"

$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/jobs:submit" | Select-Object -Expand Content

You should receive a JSON response similar to the following:

{
  "reference": {
    "projectId": "project-id",
    "jobId": "job-id"
  },
  "placement": {
    "clusterName": "cluster-name",
    "clusterUuid": "cluster-Uuid"
  },
  "sparkJob": {
    "mainClass": "org.apache.spark.examples.SparkPi",
    "args": [
      "1000"
    ],
    "jarFileUris": [
      "file:///usr/lib/spark/examples/jars/spark-examples.jar"
    ]
  },
  "status": {
    "state": "PENDING",
    "stateStartTime": "2020-10-07T20:16:21.759Z"
  },
  "jobUuid": "job-Uuid"
}

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.Job;
   import com.google.cloud.dataproc.v1.JobControllerClient;
   import com.google.cloud.dataproc.v1.JobControllerSettings;
   import com.google.cloud.dataproc.v1.JobMetadata;
   import com.google.cloud.dataproc.v1.JobPlacement;
   import com.google.cloud.dataproc.v1.SparkJob;
   import com.google.cloud.storage.Blob;
   import com.google.cloud.storage.Storage;
   import com.google.cloud.storage.StorageOptions;
   import java.io.IOException;
   import java.util.concurrent.ExecutionException;
   import java.util.regex.Matcher;
   import java.util.regex.Pattern;
   
   public class SubmitJob {
   
     public static void submitJob() throws IOException, InterruptedException {
       // TODO(developer): Replace these variables before running the sample.
       String projectId = "your-project-id";
       String region = "your-project-region";
       String clusterName = "your-cluster-name";
       submitJob(projectId, region, clusterName);
     }
   
     public static void submitJob(String projectId, String region, String clusterName)
         throws IOException, InterruptedException {
       String myEndpoint = String.format("%s-dataproc.googleapis.com:443", region);
   
       // Configure the settings for the job controller client.
       JobControllerSettings jobControllerSettings =
           JobControllerSettings.newBuilder().setEndpoint(myEndpoint).build();
   
       // Create a job controller client with the configured settings. Using a try-with-resources
       // closes the client,
       // but this can also be done manually with the .close() method.
       try (JobControllerClient jobControllerClient =
           JobControllerClient.create(jobControllerSettings)) {
   
         // Configure cluster placement for the job.
         JobPlacement jobPlacement = JobPlacement.newBuilder().setClusterName(clusterName).build();
   
         // Configure Spark job settings.
         SparkJob sparkJob =
             SparkJob.newBuilder()
                 .setMainClass("org.apache.spark.examples.SparkPi")
                 .addJarFileUris("file:///usr/lib/spark/examples/jars/spark-examples.jar")
                 .addArgs("1000")
                 .build();
   
         Job job = Job.newBuilder().setPlacement(jobPlacement).setSparkJob(sparkJob).build();
   
         // Submit an asynchronous request to execute the job.
         OperationFuture<Job, JobMetadata> submitJobAsOperationAsyncRequest =
             jobControllerClient.submitJobAsOperationAsync(projectId, region, job);
   
         Job response = submitJobAsOperationAsyncRequest.get();
   
         // Print output from Google Cloud Storage.
         Matcher matches =
             Pattern.compile("gs://(.*?)/(.*)").matcher(response.getDriverOutputResourceUri());
         matches.matches();
   
         Storage storage = StorageOptions.getDefaultInstance().getService();
         Blob blob = storage.get(matches.group(1), String.format("%s.000000000", matches.group(2)));
   
         System.out.println(
             String.format("Job finished successfully: %s", new String(blob.getContent())));
   
       } catch (ExecutionException e) {
         // If the job does not complete successfully, print the error message.
         System.err.println(String.format("submitJob: %s ", e.getMessage()));
       }
     }
   }

Python

1. Install the client library
2. Set up application default credentials
3. Run the code
   import re
   
   
   from google.cloud import dataproc_v1 as dataproc
   from google.cloud import storage
   
   
   def submit_job(project_id, region, cluster_name):
       # Create the job client.
       job_client = dataproc.JobControllerClient(
           client_options={"api_endpoint": f"{region}-dataproc.googleapis.com:443"}
       )
   
       # Create the job config. 'main_jar_file_uri' can also be a
       # Google Cloud Storage URL.
       job = {
           "placement": {"cluster_name": cluster_name},
           "spark_job": {
               "main_class": "org.apache.spark.examples.SparkPi",
               "jar_file_uris": ["file:///usr/lib/spark/examples/jars/spark-examples.jar"],
               "args": ["1000"],
           },
       }
   
       operation = job_client.submit_job_as_operation(
           request={"project_id": project_id, "region": region, "job": job}
       )
       response = operation.result()
   
       # Dataproc job output gets saved to the Google Cloud Storage bucket
       # allocated to the job. Use a regex to obtain the bucket and blob info.
       matches = re.match("gs://(.*?)/(.*)", response.driver_output_resource_uri)
   
       output = (
           storage.Client()
           .get_bucket(matches.group(1))
           .blob(f"{matches.group(2)}.000000000")
           .download_as_bytes()
           .decode("utf-8")
       )
   
       print(f"Job finished successfully: {output}")
   
   

Go

1. Install the client library
2. Set up application default credentials
3. Run the code
   import (
   	"context"
   	"fmt"
   	"io"
   	"io/ioutil"
   	"log"
   	"regexp"
   
   	dataproc "cloud.google.com/go/dataproc/apiv1"
   	"cloud.google.com/go/dataproc/apiv1/dataprocpb"
   	"cloud.google.com/go/storage"
   	"google.golang.org/api/option"
   )
   
   func submitJob(w io.Writer, projectID, region, clusterName string) error {
   	// projectID := "your-project-id"
   	// region := "us-central1"
   	// clusterName := "your-cluster"
   	ctx := context.Background()
   
   	// Create the job client.
   	endpoint := fmt.Sprintf("%s-dataproc.googleapis.com:443", region)
   	jobClient, err := dataproc.NewJobControllerClient(ctx, option.WithEndpoint(endpoint))
   	if err != nil {
   		log.Fatalf("error creating the job client: %s\n", err)
   	}
   
   	// Create the job config.
   	submitJobReq := &dataprocpb.SubmitJobRequest{
   		ProjectId: projectID,
   		Region:    region,
   		Job: &dataprocpb.Job{
   			Placement: &dataprocpb.JobPlacement{
   				ClusterName: clusterName,
   			},
   			TypeJob: &dataprocpb.Job_SparkJob{
   				SparkJob: &dataprocpb.SparkJob{
   					Driver: &dataprocpb.SparkJob_MainClass{
   						MainClass: "org.apache.spark.examples.SparkPi",
   					},
   					JarFileUris: []string{"file:///usr/lib/spark/examples/jars/spark-examples.jar"},
   					Args:        []string{"1000"},
   				},
   			},
   		},
   	}
   
   	submitJobOp, err := jobClient.SubmitJobAsOperation(ctx, submitJobReq)
   	if err != nil {
   		return fmt.Errorf("error with request to submitting job: %w", err)
   	}
   
   	submitJobResp, err := submitJobOp.Wait(ctx)
   	if err != nil {
   		return fmt.Errorf("error submitting job: %w", err)
   	}
   
   	re := regexp.MustCompile("gs://(.+?)/(.+)")
   	matches := re.FindStringSubmatch(submitJobResp.DriverOutputResourceUri)
   
   	if len(matches) < 3 {
   		return fmt.Errorf("regex error: %s", submitJobResp.DriverOutputResourceUri)
   	}
   
   	// Dataproc job output gets saved to a GCS bucket allocated to it.
   	storageClient, err := storage.NewClient(ctx)
   	if err != nil {
   		return fmt.Errorf("error creating storage client: %w", err)
   	}
   
   	obj := fmt.Sprintf("%s.000000000", matches[2])
   	reader, err := storageClient.Bucket(matches[1]).Object(obj).NewReader(ctx)
   	if err != nil {
   		return fmt.Errorf("error reading job output: %w", err)
   	}
   
   	defer reader.Close()
   
   	body, err := ioutil.ReadAll(reader)
   	if err != nil {
   		return fmt.Errorf("could not read output from Dataproc Job: %w", err)
   	}
   
   	fmt.Fprintf(w, "Job finished successfully: %s", body)
   
   	return nil
   }
   

Node.js

1. Install the client library
2. Set up application default credentials
3. Run the code
   const dataproc = require('@google-cloud/dataproc');
   const {Storage} = require('@google-cloud/storage');
   
   // TODO(developer): Uncomment and set the following variables
   // projectId = 'YOUR_PROJECT_ID'
   // region = 'YOUR_CLUSTER_REGION'
   // clusterName = 'YOUR_CLUSTER_NAME'
   
   // Create a client with the endpoint set to the desired cluster region
   const jobClient = new dataproc.v1.JobControllerClient({
     apiEndpoint: `${region}-dataproc.googleapis.com`,
     projectId: projectId,
   });
   
   async function submitJob() {
     const job = {
       projectId: projectId,
       region: region,
       job: {
         placement: {
           clusterName: clusterName,
         },
         sparkJob: {
           mainClass: 'org.apache.spark.examples.SparkPi',
           jarFileUris: [
             'file:///usr/lib/spark/examples/jars/spark-examples.jar',
           ],
           args: ['1000'],
         },
       },
     };
   
     const [jobOperation] = await jobClient.submitJobAsOperation(job);
     const [jobResponse] = await jobOperation.promise();
   
     const matches =
       jobResponse.driverOutputResourceUri.match('gs://(.*?)/(.*)');
   
     const storage = new Storage();
   
     const output = await storage
       .bucket(matches[1])
       .file(`${matches[2]}.000000000`)
       .download();
   
     // Output a success message.
     console.log(`Job finished successfully: ${output}`);