Launch a Custom JAR Cluster

To create a cluster using a custom JAR file

1. Sign in to the AWS Management Console and open the Amazon Elastic MapReduce console at https://console.aws.amazon.com/elasticmapreduce/.

2. Click Create New Job Flow.

   

3. In the DEFINE JOB FLOW page, enter the following in the Define Job Flow section of the Create a New Job Flow dialog box:

   1. Enter a name in the Job Flow Name field.

      We recommended you use a descriptive name. It does not need to be unique.

   2. Select which version of Hadoop to run on your cluster in Hadoop Version. You can choose to run the Amazon distribution of Hadoop or one of two MapR distributions. For more information about MapR distributions for Hadoop, see Using the MapR Distribution for Hadoop.

   3. Select Run your own application.

   4. Select Custom JAR in the drop-down list.

   5. Click Continue.

   

4. In the SPECIFY PARAMETERS page, enter values in the boxes using the following table as a guide, and then click Continue.

   Field	Action
   JAR Location*	Specify the URI where your script resides in Amazon S3. The value must be in the form BucketName/path/ScriptName.
   JAR Arguments*	Enter a list of arguments (space-separated strings) to pass to the JAR file.

   * Required parameter

   

5. In the CONFIGURE EC2 INSTANCES page, select the type and number of instances, using the following table as a guide, and then click Continue.

   Note

   Twenty is the default maximum number of nodes per AWS account. For example, if you have two clusters running, the total number of nodes running for both clusters must be 20 or less. If you need more than 20 nodes, you must submit a request to increase your Amazon EC2 instance limit. For more information, go to the Request to Increase Amazon EC2 Instance Limit Form.

   Field	Action
   Instance Count	Specify the number of nodes to use in the Hadoop cluster. There is always one master node in each cluster. You can specify the number of core and tasks nodes.
   Instance Type	Specify the Amazon EC2 instance types to use as master, core, and task nodes. Valid types are m1.small (default), m1.large, m1.xlarge, c1.medium, c1.xlarge, m2.xlarge, m2.2xlarge, m2.4xlarge, cc1.4xlarge, hi1.4xlarge, hs1.8xlarge, and cg1.4xlarge. The cc2.8xlarge instance type is only supported in the US East (Northern Virginia), US West (Oregon), and EU (Ireland) regions. The cc1.4xlarge and hs1.8xlarge instance types are only supported in the US East (Northern Virginia) region. The hi1.4xlarge instance type is only supported in the US East (Northern Virginia) and EU (Ireland) regions.
   Request Spot Instances	Specify whether to run master, core, or task nodes on Spot Instances. For more information, see Lower Costs with Spot Instances (Optional)

   * Required parameter

   

6. In the ADVANCED OPTIONS page, set additional configuration options, using the following table as a guide, and then click Continue.

   Field	Action
   Amazon EC2 Key Pair	Optionally, specify a key pair that you created previously. For more information, see Create an Amazon EC2 Key Pair and PEM File. If you do not enter a value in this field, you cannot SSH into the master node.
   Amazon VPC Subnet Id	Optionally, specify a VPC subnet identifier to launch the cluster in an Amazon VPC. Set this only if you need to launch the cluster into a specific VPC subnet, otherwise you can leave this set to the default: No preference. For more information about how Amazon VPC integrates with Amazon EMR, see Select a Amazon VPC Subnet for the Cluster (Optional).
   Amazon S3 Log Path	Optionally, specify a path in Amazon S3 to store the Amazon EMR log files. The value must be in the form BucketName/path. If you do not supply a location, Amazon EMR does not log any files.
   Enable debugging	Select Yes to store Amazon Elastic MapReduce-generated log files. You must enable debugging at this level if you want to store the log files generated by Amazon EMR. If you select Yes, you must supply an Amazon S3 bucket name where Amazon Elastic MapReduce can upload your log files. For more information, see Troubleshoot a Cluster. Important You can enable debugging for a cluster only when you initially create the cluster.
   Keep Alive	Select Yes to cause the cluster to continue running when all processing is completed.
   Termination Protection	Select Yes to ensure the cluster is not shut down due to accident or error. For more information, see Protect a Cluster from Termination.
   Visible To All IAM Users	Select Yes to make the cluster visible and accessible to all IAM users on the AWS account. For more information, see Configure IAM User Permissions.

   

7. In the bootstrap ACTIONS page, select Proceed with no bootstrap Actions, and then click Continue.

   For more information about bootstrap actions, see Create Bootstrap Actions to Install Additional Software (Optional).

   

8. In the REVIEW page, review the information, edit as necessary to correct any of the values, and then click Create Job Flow when the information is correct.

   After you click Create Job Flow your request is processed; when it succeeds, a message appears.

   

9. Click Close.

   The Amazon EMR console shows the new cluster starting. Starting a new cluster may take several minutes, depending on the number and type of EC2 instances Amazon EMR is launching and configuring. Click the Refresh button for the latest view of the cluster's progress.

   

To create a cluster using a Custom JAR

• In the directory where you installed the Amazon EMR CLI, run the following from the command line. For more information, see the Command Line Interface Reference for Amazon EMR.

  • Linux, UNIX, and Mac OS X users:

    ./elastic-mapreduce --create --name "Test custom JAR" \
      --jar s3n://elasticmapreduce/samples/cloudburst/cloudburst.jar \
        --arg s3n://elasticmapreduce/samples/cloudburst/input/s_suis.br \
        --arg s3n://elasticmapreduce/samples/cloudburst/input/100k.br \
        --arg s3n://myawsbucket/cloud \
        --arg 36 --arg 3 --arg 0 --arg 1 --arg 240 --arg 48 --arg 24 \
        --arg 24 --arg 128 --arg 16

  • Windows users:

    ruby elastic-mapreduce --create --name "Test custom JAR" --jar s3n://elasticmapreduce/samples/cloudburst/cloudburst.jar --arg s3n://elasticmapreduce/samples/cloudburst/input/s_suis.br --arg s3n://elasticmapreduce/samples/cloudburst/input/100k.br --arg s3n://myawsbucket/cloud --arg 36 --arg 3 --arg 0 --arg 1 --arg 240 --arg 48 --arg 24--arg 24 --arg 128 --arg 16