If your job flow is long-running (such as a Hive data warehouse) or complex, you may require more than 256 steps to process your data. You can bypass the 256-step limitation in various ways, including using the SSH shell to connect to the master node and submitting queries directly to the software running on the master node, such as Hive and Hadoop. For more information on how to do this, go to Add More than 256 Steps to a Job Flow in the Amazon Elastic MapReduce Developer's Guide.

A step specifies the location of a JAR file stored either on the master node of the job flow or in Amazon S3. Each step is performed by the main function of the main class of the JAR file. The main class can be specified either in the manifest of the JAR or by using the MainFunction parameter of the step.

Elastic MapReduce executes each step in the order listed. For a step to be considered complete, the main function must exit with a zero exit code and all Hadoop jobs started while the step was running must have completed and run successfully.

You can only add steps to a job flow that is in one of the following states: STARTING, BOOTSTRAPPING, RUNNING, or WAITING.

• "mapr-m3" - launch the job flow using MapR M3 Edition.
• "mapr-m5" - launch the job flow using MapR M5 Edition.
• "mapr" with the user arguments specifying "--edition,m3" or "--edition,m5" - launch the job flow using MapR M3 or M5 Edition, respectively.

DescribeJobFlows returns a list of job flows that match all of the supplied parameters. The parameters can include a list of job flow IDs, job flow states, and restrictions on job flow creation date and time.

Regardless of supplied parameters, only job flows created within the last two months are returned.

If no parameters are supplied, then job flows matching either of the following criteria are returned:

• Job flows created and completed in the last two weeks
• Job flows created within the last two months that are in one of the following states:
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  RUNNING

  ,
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  WAITING

  ,
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  SHUTTING_DOWN

  ,
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  STARTING

Amazon Elastic MapReduce can return a maximum of 512 job flow descriptions.

Fail - Fail the step. Wait - Continue waiting until the instance group is no longer arrested (requires manual intervention). Continue - Proceed onto the next step.

The following example removes the stack tag with value Prod from a cluster:

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KeepJobFlowAliveWhenNoSteps

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TRUE

For additional protection, you can set the JobFlowInstancesConfig

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TerminationProtected

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TRUE

A maximum of 256 steps are allowed in each job flow.

If your job flow is long-running (such as a Hive data warehouse) or complex, you may require more than 256 steps to process your data. You can bypass the 256-step limitation in various ways, including using the SSH shell to connect to the master node and submitting queries directly to the software running on the master node, such as Hive and Hadoop. For more information on how to do this, go to Add More than 256 Steps to a Job Flow in the Amazon Elastic MapReduce Developer's Guide.

For long running job flows, we recommend that you periodically store your results.

SetTerminationProtection is used to prevent accidental termination of a job flow and to ensure that in the event of an error, the instances will persist so you can recover any data stored in their ephemeral instance storage.

To terminate a job flow that has been locked by setting SetTerminationProtection to

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true

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false

For more information, go to Protecting a Job Flow from Termination in the Amazon Elastic MapReduce Developer's Guide.

See also: Hadoop Streaming

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   AWSCredentials credentials = new BasicAWSCredentials(accessKey, secretKey);
   AmazonElasticMapReduce emr = new AmazonElasticMapReduceClient(credentials);

HadoopJarStepConfig config = new StreamingStep {
    Inputs = new List<string> { "s3://elasticmapreduce/samples/wordcount/input" },
    Output = "s3://my-bucket/output/",
    Mapper = "s3://elasticmapreduce/samples/wordcount/wordSplitter.py",
    Reducer = "aggregate"
}.ToHadoopJarStepConfig();

StepConfig wordCount = new StepConfig {
    Name = "Word Count",
    ActionOnFailure = "TERMINATE_JOB_FLOW",
    HadoopJarStep = config
};

RunJobFlowRequest request = new RunJobFlowRequest {
    Name = "Word Count",
    Steps = new List<StepConfig> { wordCount },
    LogUri = "s3://log-bucket/",
    Instances = new JobFlowInstancesConfig {
        Ec2KeyName = "keypair",
        HadoopVersion = "0.20",
        InstanceCount = 5,
        KeepJobFlowAliveWhenNoSteps = true,
        MasterInstanceType = "m1.small",
        SlaveInstanceType = "m1.small"
   }
};

   RunJobFlowResult result = emr.RunJobFlow(request).RunJobFlowResult;

The call to TerminateJobFlows is asynchronous. Depending on the configuration of the job flow, it may take up to 5-20 minutes for the job flow to completely terminate and release allocated resources, such as Amazon EC2 instances.