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# Uso de AWS SDK para Java para crear un clúster de Amazon EMR
<a name="calling-emr-with-java-sdk"></a>

 AWS SDK para Java Incluye tres paquetes con la funcionalidad Amazon EMR:
+  [com.amazonaws.services.elasticmapreduce](https://docs.aws.amazon.com/AWSJavaSDK/latest/javadoc/com/amazonaws/services/elasticmapreduce/package-summary.html) 
+  [com.amazonaws.services.elasticmapreduce.model](https://docs.aws.amazon.com/AWSJavaSDK/latest/javadoc/com/amazonaws/services/elasticmapreduce/model/package-summary.html) 
+  [com.amazonaws.services.elasticmapreduce.util](https://docs.aws.amazon.com/AWSJavaSDK/latest/javadoc/com/amazonaws/services/elasticmapreduce/util/package-summary.html) 

Para obtener más información sobre estos paquetes, consulte la [referencia de la API de AWS SDK para Java](https://docs.aws.amazon.com/sdk-for-java/latest/reference/).

El siguiente ejemplo ilustra cómo SDKs se puede simplificar la programación con Amazon EMR. El código de muestra siguiente utiliza el objeto `StepFactory`, una clase helper para crear los tipos de pasos de Amazon EMR comunes, para crear un clúster de Hive interactivo con la depuración habilitada. 

```
import com.amazonaws.AmazonClientException;
import com.amazonaws.auth.AWSCredentials;
import com.amazonaws.auth.AWSStaticCredentialsProvider;
import com.amazonaws.auth.profile.ProfileCredentialsProvider;
import com.amazonaws.services.elasticmapreduce.AmazonElasticMapReduce;
import com.amazonaws.services.elasticmapreduce.AmazonElasticMapReduceClientBuilder;
import com.amazonaws.services.elasticmapreduce.model.*;
import com.amazonaws.services.elasticmapreduce.util.StepFactory;

public class Main {

	public static void main(String[] args) {
		AWSCredentialsProvider profile = null;
		try {
			credentials_profile = new ProfileCredentialsProvider("default"); // specifies any named profile in
																																				// .aws/credentials as the credentials provider
		} catch (Exception e) {
			throw new AmazonClientException(
					"Cannot load credentials from .aws/credentials file. " +
							"Make sure that the credentials file exists and that the profile name is defined within it.",
					e);
		}

		// create an EMR client using the credentials and region specified in order to
		// create the cluster
		AmazonElasticMapReduce emr = AmazonElasticMapReduceClientBuilder.standard()
				.withCredentials(credentials_profile)
				.withRegion(Regions.US_WEST_1)
				.build();

		// create a step to enable debugging in the AWS Management Console
		StepFactory stepFactory = new StepFactory();
		StepConfig enabledebugging = new StepConfig()
				.withName("Enable debugging")
				.withActionOnFailure("TERMINATE_JOB_FLOW")
				.withHadoopJarStep(stepFactory.newEnableDebuggingStep());

		// specify applications to be installed and configured when EMR creates the
		// cluster
		Application hive = new Application().withName("Hive");
		Application spark = new Application().withName("Spark");
		Application ganglia = new Application().withName("Ganglia");
		Application zeppelin = new Application().withName("Zeppelin");

		// create the cluster
		RunJobFlowRequest request = new RunJobFlowRequest()
				.withName("MyClusterCreatedFromJava")
				.withReleaseLabel("emr-5.20.0") // specifies the EMR release version label, we recommend the latest release
				.withSteps(enabledebugging)
				.withApplications(hive, spark, ganglia, zeppelin)
				.withLogUri("s3://path/to/my/emr/logs") // a URI in S3 for log files is required when debugging is enabled
				.withServiceRole("EMR_DefaultRole") // replace the default with a custom IAM service role if one is used
				.withJobFlowRole("EMR_EC2_DefaultRole") // replace the default with a custom EMR role for the EC2 instance
																								// profile if one is used
				.withInstances(new JobFlowInstancesConfig()
						.withEc2SubnetId("subnet-12ab34c56")
						.withEc2KeyName("myEc2Key")
						.withInstanceCount(3)
						.withKeepJobFlowAliveWhenNoSteps(true)
						.withMasterInstanceType("m4.large")
						.withSlaveInstanceType("m4.large"));

		RunJobFlowResult result = emr.runJobFlow(request);
		System.out.println("The cluster ID is " + result.toString());

	}

}
```

Como mínimo, debe pasar un rol de servicio y un rol de flujo de trabajo correspondientes a EMR\_ DefaultRole y EC2 EMR\_ \_, respectivamente. DefaultRole Puede hacerlo invocando este comando para la misma cuenta. AWS CLI En primer lugar, vea si ya existen los roles: 

```
aws iam list-roles | grep EMR
```

Se mostrarán tanto el perfil de la instancia (EC2EMR\_ \_DefaultRole) como el rol de servicio (EMR\_DefaultRole), si existen: 

```
"RoleName": "EMR_DefaultRole", 
            "Arn": "arn:aws:iam::{{AccountID}}:role/EMR_DefaultRole"
            "RoleName": "EMR_EC2_DefaultRole", 
            "Arn": "arn:aws:iam::{{AccountID}}:role/EMR_EC2_DefaultRole"
```

Si los roles predeterminados no existen, puede utilizar el siguiente comando para crearlos:

```
aws emr create-default-roles
```