

There are more AWS SDK examples available in the [AWS Doc SDK Examples](https://github.com/awsdocs/aws-doc-sdk-examples) GitHub repo.

# Basic examples for Amazon EMR using AWS SDKs
<a name="emr_code_examples_basics"></a>

The following code examples show how to use the basics of Amazon EMR with AWS SDKs. 

**Contents**
+ [Actions](emr_code_examples_actions.md)
  + [`AddJobFlowSteps`](emr_example_emr_AddJobFlowSteps_section.md)
  + [`DescribeCluster`](emr_example_emr_DescribeCluster_section.md)
  + [`DescribeStep`](emr_example_emr_DescribeStep_section.md)
  + [`ListSteps`](emr_example_emr_ListSteps_section.md)
  + [`RunJobFlow`](emr_example_emr_RunJobFlow_section.md)
  + [`TerminateJobFlows`](emr_example_emr_TerminateJobFlows_section.md)

# Actions for Amazon EMR using AWS SDKs
<a name="emr_code_examples_actions"></a>

The following code examples demonstrate how to perform individual Amazon EMR actions with AWS SDKs. Each example includes a link to GitHub, where you can find instructions for setting up and running the code. 

These excerpts call the Amazon EMR API and are code excerpts from larger programs that must be run in context. You can see actions in context in [Scenarios for Amazon EMR using AWS SDKs](emr_code_examples_scenarios.md). 

 The following examples include only the most commonly used actions. For a complete list, see the [Amazon EMR API Reference](https://docs.aws.amazon.com/emr/latest/APIReference/Welcome.html). 

**Topics**
+ [`AddJobFlowSteps`](emr_example_emr_AddJobFlowSteps_section.md)
+ [`DescribeCluster`](emr_example_emr_DescribeCluster_section.md)
+ [`DescribeStep`](emr_example_emr_DescribeStep_section.md)
+ [`ListSteps`](emr_example_emr_ListSteps_section.md)
+ [`RunJobFlow`](emr_example_emr_RunJobFlow_section.md)
+ [`TerminateJobFlows`](emr_example_emr_TerminateJobFlows_section.md)

# Use `AddJobFlowSteps` with an AWS SDK
<a name="emr_example_emr_AddJobFlowSteps_section"></a>

The following code examples show how to use `AddJobFlowSteps`.

------
#### [ Python ]

**SDK for Python (Boto3)**  
 There's more on GitHub. Find the complete example and learn how to set up and run in the [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/emr#code-examples). 
Add a Spark step, which is run by the cluster as soon as it is added.  

```
def add_step(cluster_id, name, script_uri, script_args, emr_client):
    """
    Adds a job step to the specified cluster. This example adds a Spark
    step, which is run by the cluster as soon as it is added.

    :param cluster_id: The ID of the cluster.
    :param name: The name of the step.
    :param script_uri: The URI where the Python script is stored.
    :param script_args: Arguments to pass to the Python script.
    :param emr_client: The Boto3 EMR client object.
    :return: The ID of the newly added step.
    """
    try:
        response = emr_client.add_job_flow_steps(
            JobFlowId=cluster_id,
            Steps=[
                {
                    "Name": name,
                    "ActionOnFailure": "CONTINUE",
                    "HadoopJarStep": {
                        "Jar": "command-runner.jar",
                        "Args": [
                            "spark-submit",
                            "--deploy-mode",
                            "cluster",
                            script_uri,
                            *script_args,
                        ],
                    },
                }
            ],
        )
        step_id = response["StepIds"][0]
        logger.info("Started step with ID %s", step_id)
    except ClientError:
        logger.exception("Couldn't start step %s with URI %s.", name, script_uri)
        raise
    else:
        return step_id
```
Run an Amazon EMR File System (EMRFS) command as a job step on a cluster. This can be used to automate EMRFS commands on a cluster instead of running commands manually through an SSH connection.  

```
import boto3
from botocore.exceptions import ClientError


def add_emrfs_step(command, bucket_url, cluster_id, emr_client):
    """
    Add an EMRFS command as a job flow step to an existing cluster.

    :param command: The EMRFS command to run.
    :param bucket_url: The URL of a bucket that contains tracking metadata.
    :param cluster_id: The ID of the cluster to update.
    :param emr_client: The Boto3 Amazon EMR client object.
    :return: The ID of the added job flow step. Status can be tracked by calling
             the emr_client.describe_step() function.
    """
    job_flow_step = {
        "Name": "Example EMRFS Command Step",
        "ActionOnFailure": "CONTINUE",
        "HadoopJarStep": {
            "Jar": "command-runner.jar",
            "Args": ["/usr/bin/emrfs", command, bucket_url],
        },
    }

    try:
        response = emr_client.add_job_flow_steps(
            JobFlowId=cluster_id, Steps=[job_flow_step]
        )
        step_id = response["StepIds"][0]
        print(f"Added step {step_id} to cluster {cluster_id}.")
    except ClientError:
        print(f"Couldn't add a step to cluster {cluster_id}.")
        raise
    else:
        return step_id


def usage_demo():
    emr_client = boto3.client("emr")
    # Assumes the first waiting cluster has EMRFS enabled and has created metadata
    # with the default name of 'EmrFSMetadata'.
    cluster = emr_client.list_clusters(ClusterStates=["WAITING"])["Clusters"][0]
    add_emrfs_step(
        "sync", "s3://elasticmapreduce/samples/cloudfront", cluster["Id"], emr_client
    )


if __name__ == "__main__":
    usage_demo()
```
+  For API details, see [AddJobFlowSteps](https://docs.aws.amazon.com/goto/boto3/elasticmapreduce-2009-03-31/AddJobFlowSteps) in *AWS SDK for Python (Boto3) API Reference*. 

------
#### [ SAP ABAP ]

**SDK for SAP ABAP**  
 There's more on GitHub. Find the complete example and learn how to set up and run in the [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/emr#code-examples). 

```
    TRY.
        " Build args list for Spark submit
        DATA lt_args TYPE /aws1/cl_emrxmlstringlist_w=>tt_xmlstringlist.
        APPEND NEW /aws1/cl_emrxmlstringlist_w( 'spark-submit' ) TO lt_args.
        APPEND NEW /aws1/cl_emrxmlstringlist_w( '--deploy-mode' ) TO lt_args.
        APPEND NEW /aws1/cl_emrxmlstringlist_w( 'cluster' ) TO lt_args.
        APPEND NEW /aws1/cl_emrxmlstringlist_w( iv_script_uri ) TO lt_args.
        APPEND LINES OF it_script_args TO lt_args.

        " Create step configuration
        DATA(lo_hadoop_jar_step) = NEW /aws1/cl_emrhadoopjarstepcfg(
          iv_jar = 'command-runner.jar'
          it_args = lt_args
        ).

        DATA(lo_step_config) = NEW /aws1/cl_emrstepconfig(
          iv_name = iv_name
          iv_actiononfailure = 'CONTINUE'
          io_hadoopjarstep = lo_hadoop_jar_step
        ).

        DATA lt_steps TYPE /aws1/cl_emrstepconfig=>tt_stepconfiglist.
        APPEND lo_step_config TO lt_steps.

        DATA(lo_result) = lo_emr->addjobflowsteps(
          iv_jobflowid = iv_cluster_id
          it_steps = lt_steps
        ).

        " Get first step ID
        DATA(lt_step_ids) = lo_result->get_stepids( ).
        READ TABLE lt_step_ids INDEX 1 INTO DATA(lo_step_id_obj).
        IF sy-subrc = 0.
          ov_step_id = lo_step_id_obj->get_value( ).
          MESSAGE |Step added with ID { ov_step_id }| TYPE 'I'.
        ENDIF.
      CATCH /aws1/cx_emrinternalservererr INTO DATA(lo_internal_error).
        DATA(lv_error) = lo_internal_error->if_message~get_text( ).
        MESSAGE lv_error TYPE 'E'.
    ENDTRY.
```
+  For API details, see [AddJobFlowSteps](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html) in *AWS SDK for SAP ABAP API reference*. 

------

# Use `DescribeCluster` with an AWS SDK or CLI
<a name="emr_example_emr_DescribeCluster_section"></a>

The following code examples show how to use `DescribeCluster`.

Action examples are code excerpts from larger programs and must be run in context. You can see this action in context in the following code example: 
+  [Getting started with Amazon EMR](emr_example_emr_GettingStarted_037_section.md) 

------
#### [ CLI ]

**AWS CLI**  
Command:  

```
aws emr describe-cluster --cluster-id j-XXXXXXXX
```
Output:  

```
For release-label based uniform instance groups cluster:

        {
            "Cluster": {
                "Status": {
                    "Timeline": {
                        "ReadyDateTime": 1436475075.199,
                        "CreationDateTime": 1436474656.563,
                    },
                    "State": "WAITING",
                    "StateChangeReason": {
                        "Message": "Waiting for steps to run"
                    }
                },
                "Ec2InstanceAttributes": {
                    "ServiceAccessSecurityGroup": "sg-xxxxxxxx",
                    "EmrManagedMasterSecurityGroup": "sg-xxxxxxxx",
                    "IamInstanceProfile": "EMR_EC2_DefaultRole",
                    "Ec2KeyName": "myKey",
                    "Ec2AvailabilityZone": "us-east-1c",
                    "EmrManagedSlaveSecurityGroup": "sg-yyyyyyyyy"
                },
                "Name": "My Cluster",
                "ServiceRole": "EMR_DefaultRole",
                "Tags": [],
                "TerminationProtected": true,
                "UnhealthyNodeReplacement": true,
                "ReleaseLabel": "emr-4.0.0",
                "NormalizedInstanceHours": 96,
                "InstanceGroups": [
                    {
                        "RequestedInstanceCount": 2,
                        "Status": {
                            "Timeline": {
                                "ReadyDateTime": 1436475074.245,
                                "CreationDateTime": 1436474656.564,
                                "EndDateTime": 1436638158.387
                            },
                            "State": "RUNNING",
                            "StateChangeReason": {
                                "Message": "",
                            }
                        },
                        "Name": "CORE",
                        "InstanceGroupType": "CORE",
                        "Id": "ig-YYYYYYY",
                        "Configurations": [],
                        "InstanceType": "m3.large",
                        "Market": "ON_DEMAND",
                        "RunningInstanceCount": 2
                    },
                    {
                        "RequestedInstanceCount": 1,
                        "Status": {
                            "Timeline": {
                                "ReadyDateTime": 1436475074.245,
                                "CreationDateTime": 1436474656.564,
                                "EndDateTime": 1436638158.387
                            },
                            "State": "RUNNING",
                            "StateChangeReason": {
                                "Message": "",
                            }
                        },
                        "Name": "MASTER",
                        "InstanceGroupType": "MASTER",
                        "Id": "ig-XXXXXXXXX",
                        "Configurations": [],
                        "InstanceType": "m3.large",
                        "Market": "ON_DEMAND",
                        "RunningInstanceCount": 1
                    }
                ],
                "Applications": [
                    {
                        "Name": "Hadoop"
                    }
                ],
                "VisibleToAllUsers": true,
                "BootstrapActions": [],
                "MasterPublicDnsName": "ec2-54-147-144-78.compute-1.amazonaws.com",
                "AutoTerminate": false,
                "Id": "j-XXXXXXXX",
                "Configurations": [
                    {
                        "Properties": {
                            "fs.s3.consistent.retryPeriodSeconds": "20",
                            "fs.s3.enableServerSideEncryption": "true",
                            "fs.s3.consistent": "false",
                            "fs.s3.consistent.retryCount": "2"
                        },
                        "Classification": "emrfs-site"
                    }
                ]
            }
        }


For release-label based instance fleet cluster:
{
    "Cluster": {
        "Status": {
            "Timeline": {
                "ReadyDateTime": 1487897289.705,
                "CreationDateTime": 1487896933.942
            },
            "State": "WAITING",
            "StateChangeReason": {
                "Message": "Waiting for steps to run"
            }
        },
        "Ec2InstanceAttributes": {
            "EmrManagedMasterSecurityGroup": "sg-xxxxx",
            "RequestedEc2AvailabilityZones": [],
            "RequestedEc2SubnetIds": [],
            "IamInstanceProfile": "EMR_EC2_DefaultRole",
            "Ec2AvailabilityZone": "us-east-1a",
            "EmrManagedSlaveSecurityGroup": "sg-xxxxx"
        },
        "Name": "My Cluster",
        "ServiceRole": "EMR_DefaultRole",
        "Tags": [],
        "TerminationProtected": false,
        "UnhealthyNodeReplacement": false,
        "ReleaseLabel": "emr-5.2.0",
        "NormalizedInstanceHours": 472,
        "InstanceCollectionType": "INSTANCE_FLEET",
        "InstanceFleets": [
            {
                "Status": {
                    "Timeline": {
                        "ReadyDateTime": 1487897212.74,
                        "CreationDateTime": 1487896933.948
                    },
                    "State": "RUNNING",
                    "StateChangeReason": {
                        "Message": ""
                    }
                },
                "ProvisionedSpotCapacity": 1,
                "Name": "MASTER",
                "InstanceFleetType": "MASTER",
                "LaunchSpecifications": {
                    "SpotSpecification": {
                        "TimeoutDurationMinutes": 60,
                        "TimeoutAction": "TERMINATE_CLUSTER"
                    }
                },
                "TargetSpotCapacity": 1,
                "ProvisionedOnDemandCapacity": 0,
                "InstanceTypeSpecifications": [
                    {
                        "BidPrice": "0.5",
                        "InstanceType": "m3.xlarge",
                        "WeightedCapacity": 1
                    }
                ],
                "Id": "if-xxxxxxx",
                "TargetOnDemandCapacity": 0
            }
        ],
        "Applications": [
            {
                "Version": "2.7.3",
                "Name": "Hadoop"
            }
        ],
        "ScaleDownBehavior": "TERMINATE_AT_INSTANCE_HOUR",
        "VisibleToAllUsers": true,
        "BootstrapActions": [],
        "MasterPublicDnsName": "ec2-xxx-xx-xxx-xx.compute-1.amazonaws.com",
        "AutoTerminate": false,
        "Id": "j-xxxxx",
        "Configurations": []
    }
}

For ami based uniform instance group cluster:

    {
        "Cluster": {
            "Status": {
                "Timeline": {
                    "ReadyDateTime": 1399400564.432,
                    "CreationDateTime": 1399400268.62
                },
                "State": "WAITING",
                "StateChangeReason": {
                    "Message": "Waiting for steps to run"
                }
            },
            "Ec2InstanceAttributes": {
                "IamInstanceProfile": "EMR_EC2_DefaultRole",
                "Ec2AvailabilityZone": "us-east-1c"
            },
            "Name": "My Cluster",
            "Tags": [],
            "TerminationProtected": true,
            "UnhealthyNodeReplacement": true,
            "RunningAmiVersion": "2.5.4",
            "InstanceGroups": [
                {
                    "RequestedInstanceCount": 1,
                    "Status": {
                        "Timeline": {
                            "ReadyDateTime": 1399400558.848,
                            "CreationDateTime": 1399400268.621
                        },
                        "State": "RUNNING",
                        "StateChangeReason": {
                            "Message": ""
                        }
                    },
                    "Name": "Master instance group",
                    "InstanceGroupType": "MASTER",
                    "InstanceType": "m1.small",
                    "Id": "ig-ABCD",
                    "Market": "ON_DEMAND",
                    "RunningInstanceCount": 1
                },
                {
                    "RequestedInstanceCount": 2,
                    "Status": {
                        "Timeline": {
                            "ReadyDateTime": 1399400564.439,
                            "CreationDateTime": 1399400268.621
                        },
                        "State": "RUNNING",
                        "StateChangeReason": {
                            "Message": ""
                        }
                    },
                    "Name": "Core instance group",
                    "InstanceGroupType": "CORE",
                    "InstanceType": "m1.small",
                    "Id": "ig-DEF",
                    "Market": "ON_DEMAND",
                    "RunningInstanceCount": 2
                }
            ],
            "Applications": [
                {
                    "Version": "1.0.3",
                    "Name": "hadoop"
                }
            ],
            "BootstrapActions": [],
            "VisibleToAllUsers": false,
            "RequestedAmiVersion": "2.4.2",
            "LogUri": "s3://myLogUri/",
            "AutoTerminate": false,
            "Id": "j-XXXXXXXX"
        }
    }
```
+  For API details, see [DescribeCluster](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/emr/describe-cluster.html) in *AWS CLI Command Reference*. 

------
#### [ Python ]

**SDK for Python (Boto3)**  
 There's more on GitHub. Find the complete example and learn how to set up and run in the [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/emr#code-examples). 

```
def describe_cluster(cluster_id, emr_client):
    """
    Gets detailed information about a cluster.

    :param cluster_id: The ID of the cluster to describe.
    :param emr_client: The Boto3 EMR client object.
    :return: The retrieved cluster information.
    """
    try:
        response = emr_client.describe_cluster(ClusterId=cluster_id)
        cluster = response["Cluster"]
        logger.info("Got data for cluster %s.", cluster["Name"])
    except ClientError:
        logger.exception("Couldn't get data for cluster %s.", cluster_id)
        raise
    else:
        return cluster
```
+  For API details, see [DescribeCluster](https://docs.aws.amazon.com/goto/boto3/elasticmapreduce-2009-03-31/DescribeCluster) in *AWS SDK for Python (Boto3) API Reference*. 

------
#### [ SAP ABAP ]

**SDK for SAP ABAP**  
 There's more on GitHub. Find the complete example and learn how to set up and run in the [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/emr#code-examples). 

```
    TRY.
        oo_result = lo_emr->describecluster(
          iv_clusterid = iv_cluster_id
        ).
        DATA(lo_cluster) = oo_result->get_cluster( ).
        DATA(lv_cluster_name) = lo_cluster->get_name( ).
        MESSAGE |Retrieved cluster information for { lv_cluster_name }| TYPE 'I'.
      CATCH /aws1/cx_emrinternalserverex INTO DATA(lo_internal_error).
        DATA(lv_error) = lo_internal_error->if_message~get_text( ).
        MESSAGE lv_error TYPE 'E'.
      CATCH /aws1/cx_emrinvalidrequestex INTO DATA(lo_invalid_error).
        lv_error = lo_invalid_error->if_message~get_text( ).
        MESSAGE lv_error TYPE 'E'.
    ENDTRY.
```
+  For API details, see [DescribeCluster](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html) in *AWS SDK for SAP ABAP API reference*. 

------

# Use `DescribeStep` with an AWS SDK or CLI
<a name="emr_example_emr_DescribeStep_section"></a>

The following code examples show how to use `DescribeStep`.

Action examples are code excerpts from larger programs and must be run in context. You can see this action in context in the following code example: 
+  [Getting started with Amazon EMR](emr_example_emr_GettingStarted_037_section.md) 

------
#### [ CLI ]

**AWS CLI**  
The following command describes a step with the step ID `s-3LZC0QUT43AM` in a cluster with the cluster ID `j-3SD91U2E1L2QX`:  

```
aws emr describe-step --cluster-id j-3SD91U2E1L2QX --step-id s-3LZC0QUT43AM
```
Output:  

```
{
    "Step": {
        "Status": {
            "Timeline": {
                "EndDateTime": 1433200470.481,
                "CreationDateTime": 1433199926.597,
                "StartDateTime": 1433200404.959
            },
            "State": "COMPLETED",
            "StateChangeReason": {}
        },
        "Config": {
            "Args": [
                "s3://us-west-2.elasticmapreduce/libs/hive/hive-script",
                "--base-path",
                "s3://us-west-2.elasticmapreduce/libs/hive/",
                "--install-hive",
                "--hive-versions",
                "0.13.1"
            ],
            "Jar": "s3://us-west-2.elasticmapreduce/libs/script-runner/script-runner.jar",
            "Properties": {}
        },
        "Id": "s-3LZC0QUT43AM",
        "ActionOnFailure": "TERMINATE_CLUSTER",
        "Name": "Setup hive"
    }
}
```
+  For API details, see [DescribeStep](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/emr/describe-step.html) in *AWS CLI Command Reference*. 

------
#### [ Python ]

**SDK for Python (Boto3)**  
 There's more on GitHub. Find the complete example and learn how to set up and run in the [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/emr#code-examples). 

```
def describe_step(cluster_id, step_id, emr_client):
    """
    Gets detailed information about the specified step, including the current state of
    the step.

    :param cluster_id: The ID of the cluster.
    :param step_id: The ID of the step.
    :param emr_client: The Boto3 EMR client object.
    :return: The retrieved information about the specified step.
    """
    try:
        response = emr_client.describe_step(ClusterId=cluster_id, StepId=step_id)
        step = response["Step"]
        logger.info("Got data for step %s.", step_id)
    except ClientError:
        logger.exception("Couldn't get data for step %s.", step_id)
        raise
    else:
        return step
```
+  For API details, see [DescribeStep](https://docs.aws.amazon.com/goto/boto3/elasticmapreduce-2009-03-31/DescribeStep) in *AWS SDK for Python (Boto3) API Reference*. 

------
#### [ SAP ABAP ]

**SDK for SAP ABAP**  
 There's more on GitHub. Find the complete example and learn how to set up and run in the [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/emr#code-examples). 

```
    TRY.
        oo_result = lo_emr->describestep(
          iv_clusterid = iv_cluster_id
          iv_stepid = iv_step_id
        ).
        DATA(lo_step) = oo_result->get_step( ).
        DATA(lv_step_name) = lo_step->get_name( ).
        MESSAGE |Retrieved step information for { lv_step_name }| TYPE 'I'.
      CATCH /aws1/cx_emrinternalserverex INTO DATA(lo_internal_error).
        DATA(lv_error) = lo_internal_error->if_message~get_text( ).
        MESSAGE lv_error TYPE 'E'.
      CATCH /aws1/cx_emrinvalidrequestex INTO DATA(lo_invalid_error).
        lv_error = lo_invalid_error->if_message~get_text( ).
        MESSAGE lv_error TYPE 'E'.
    ENDTRY.
```
+  For API details, see [DescribeStep](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html) in *AWS SDK for SAP ABAP API reference*. 

------

# Use `ListSteps` with an AWS SDK or CLI
<a name="emr_example_emr_ListSteps_section"></a>

The following code examples show how to use `ListSteps`.

------
#### [ CLI ]

**AWS CLI**  
The following command lists all of the steps in a cluster with the cluster ID `j-3SD91U2E1L2QX`:  

```
aws emr list-steps --cluster-id j-3SD91U2E1L2QX
```
+  For API details, see [ListSteps](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/emr/list-steps.html) in *AWS CLI Command Reference*. 

------
#### [ Python ]

**SDK for Python (Boto3)**  
 There's more on GitHub. Find the complete example and learn how to set up and run in the [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/emr#code-examples). 

```
def list_steps(cluster_id, emr_client):
    """
    Gets a list of steps for the specified cluster. In this example, all steps are
    returned, including completed and failed steps.

    :param cluster_id: The ID of the cluster.
    :param emr_client: The Boto3 EMR client object.
    :return: The list of steps for the specified cluster.
    """
    try:
        response = emr_client.list_steps(ClusterId=cluster_id)
        steps = response["Steps"]
        logger.info("Got %s steps for cluster %s.", len(steps), cluster_id)
    except ClientError:
        logger.exception("Couldn't get steps for cluster %s.", cluster_id)
        raise
    else:
        return steps
```
+  For API details, see [ListSteps](https://docs.aws.amazon.com/goto/boto3/elasticmapreduce-2009-03-31/ListSteps) in *AWS SDK for Python (Boto3) API Reference*. 

------
#### [ SAP ABAP ]

**SDK for SAP ABAP**  
 There's more on GitHub. Find the complete example and learn how to set up and run in the [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/emr#code-examples). 

```
    TRY.
        oo_result = lo_emr->liststeps(
          iv_clusterid = iv_cluster_id
        ).
        DATA(lt_steps) = oo_result->get_steps( ).
        DATA(lv_step_count) = lines( lt_steps ).
        MESSAGE |Retrieved { lv_step_count } steps for cluster| TYPE 'I'.
      CATCH /aws1/cx_emrinternalserverex INTO DATA(lo_internal_error).
        DATA(lv_error) = lo_internal_error->if_message~get_text( ).
        MESSAGE lv_error TYPE 'E'.
      CATCH /aws1/cx_emrinvalidrequestex INTO DATA(lo_invalid_error).
        lv_error = lo_invalid_error->if_message~get_text( ).
        MESSAGE lv_error TYPE 'E'.
    ENDTRY.
```
+  For API details, see [ListSteps](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html) in *AWS SDK for SAP ABAP API reference*. 

------

# Use `RunJobFlow` with an AWS SDK
<a name="emr_example_emr_RunJobFlow_section"></a>

The following code examples show how to use `RunJobFlow`.

------
#### [ Python ]

**SDK for Python (Boto3)**  
 There's more on GitHub. Find the complete example and learn how to set up and run in the [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/emr#code-examples). 

```
def run_job_flow(
    name,
    log_uri,
    keep_alive,
    applications,
    job_flow_role,
    service_role,
    security_groups,
    steps,
    emr_client,
):
    """
    Runs a job flow with the specified steps. A job flow creates a cluster of
    instances and adds steps to be run on the cluster. Steps added to the cluster
    are run as soon as the cluster is ready.

    This example uses the 'emr-5.30.1' release. A list of recent releases can be
    found here:
        https://docs.aws.amazon.com/emr/latest/ReleaseGuide/emr-release-components.html.

    :param name: The name of the cluster.
    :param log_uri: The URI where logs are stored. This can be an Amazon S3 bucket URL,
                    such as 's3://my-log-bucket'.
    :param keep_alive: When True, the cluster is put into a Waiting state after all
                       steps are run. When False, the cluster terminates itself when
                       the step queue is empty.
    :param applications: The applications to install on each instance in the cluster,
                         such as Hive or Spark.
    :param job_flow_role: The IAM role assumed by the cluster.
    :param service_role: The IAM role assumed by the service.
    :param security_groups: The security groups to assign to the cluster instances.
                            Amazon EMR adds all needed rules to these groups, so
                            they can be empty if you require only the default rules.
    :param steps: The job flow steps to add to the cluster. These are run in order
                  when the cluster is ready.
    :param emr_client: The Boto3 EMR client object.
    :return: The ID of the newly created cluster.
    """
    try:
        response = emr_client.run_job_flow(
            Name=name,
            LogUri=log_uri,
            ReleaseLabel="emr-5.30.1",
            Instances={
                "MasterInstanceType": "m5.xlarge",
                "SlaveInstanceType": "m5.xlarge",
                "InstanceCount": 3,
                "KeepJobFlowAliveWhenNoSteps": keep_alive,
                "EmrManagedMasterSecurityGroup": security_groups["manager"].id,
                "EmrManagedSlaveSecurityGroup": security_groups["worker"].id,
            },
            Steps=[
                {
                    "Name": step["name"],
                    "ActionOnFailure": "CONTINUE",
                    "HadoopJarStep": {
                        "Jar": "command-runner.jar",
                        "Args": [
                            "spark-submit",
                            "--deploy-mode",
                            "cluster",
                            step["script_uri"],
                            *step["script_args"],
                        ],
                    },
                }
                for step in steps
            ],
            Applications=[{"Name": app} for app in applications],
            JobFlowRole=job_flow_role.name,
            ServiceRole=service_role.name,
            EbsRootVolumeSize=10,
            VisibleToAllUsers=True,
        )
        cluster_id = response["JobFlowId"]
        logger.info("Created cluster %s.", cluster_id)
    except ClientError:
        logger.exception("Couldn't create cluster.")
        raise
    else:
        return cluster_id
```
+  For API details, see [RunJobFlow](https://docs.aws.amazon.com/goto/boto3/elasticmapreduce-2009-03-31/RunJobFlow) in *AWS SDK for Python (Boto3) API Reference*. 

------
#### [ SAP ABAP ]

**SDK for SAP ABAP**  
 There's more on GitHub. Find the complete example and learn how to set up and run in the [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/emr#code-examples). 

```
    TRY.
        " Create instances configuration
        DATA(lo_instances) = NEW /aws1/cl_emrjobflowinstsconfig(
          iv_masterinstancetype = 'm5.xlarge'
          iv_slaveinstancetype = 'm5.xlarge'
          iv_instancecount = 3
          iv_keepjobflowalivewhennos00 = iv_keep_alive
          iv_emrmanagedmastersecgroup = iv_primary_sec_grp
          iv_emrmanagedslavesecgroup = iv_secondary_sec_grp
        ).

        DATA(lo_result) = lo_emr->runjobflow(
          iv_name = iv_name
          iv_loguri = iv_log_uri
          iv_releaselabel = 'emr-5.30.1'
          io_instances = lo_instances
          it_steps = it_steps
          it_applications = it_applications
          iv_jobflowrole = iv_job_flow_role
          iv_servicerole = iv_service_role
          iv_ebsrootvolumesize = 10
          iv_visibletoallusers = abap_true
        ).

        ov_cluster_id = lo_result->get_jobflowid( ).
        MESSAGE 'EMR cluster created successfully.' TYPE 'I'.
      CATCH /aws1/cx_emrinternalservererr INTO DATA(lo_internal_error).
        DATA(lv_error) = lo_internal_error->if_message~get_text( ).
        MESSAGE lv_error TYPE 'E'.
      CATCH /aws1/cx_emrclientexc INTO DATA(lo_client_error).
        lv_error = lo_client_error->if_message~get_text( ).
        MESSAGE lv_error TYPE 'E'.
    ENDTRY.
```
+  For API details, see [RunJobFlow](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html) in *AWS SDK for SAP ABAP API reference*. 

------

# Use `TerminateJobFlows` with an AWS SDK
<a name="emr_example_emr_TerminateJobFlows_section"></a>

The following code examples show how to use `TerminateJobFlows`.

------
#### [ Python ]

**SDK for Python (Boto3)**  
 There's more on GitHub. Find the complete example and learn how to set up and run in the [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/emr#code-examples). 

```
def terminate_cluster(cluster_id, emr_client):
    """
    Terminates a cluster. This terminates all instances in the cluster and cannot
    be undone. Any data not saved elsewhere, such as in an Amazon S3 bucket, is lost.

    :param cluster_id: The ID of the cluster to terminate.
    :param emr_client: The Boto3 EMR client object.
    """
    try:
        emr_client.terminate_job_flows(JobFlowIds=[cluster_id])
        logger.info("Terminated cluster %s.", cluster_id)
    except ClientError:
        logger.exception("Couldn't terminate cluster %s.", cluster_id)
        raise
```
+  For API details, see [TerminateJobFlows](https://docs.aws.amazon.com/goto/boto3/elasticmapreduce-2009-03-31/TerminateJobFlows) in *AWS SDK for Python (Boto3) API Reference*. 

------
#### [ SAP ABAP ]

**SDK for SAP ABAP**  
 There's more on GitHub. Find the complete example and learn how to set up and run in the [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/emr#code-examples). 

```
    TRY.
        DATA lt_cluster_ids TYPE /aws1/cl_emrxmlstringlist_w=>tt_xmlstringlist.
        APPEND NEW /aws1/cl_emrxmlstringlist_w( iv_cluster_id ) TO lt_cluster_ids.

        lo_emr->terminatejobflows(
          it_jobflowids = lt_cluster_ids
        ).
        MESSAGE 'EMR cluster terminated successfully.' TYPE 'I'.
      CATCH /aws1/cx_emrinternalservererr INTO DATA(lo_internal_error).
        DATA(lv_error) = lo_internal_error->if_message~get_text( ).
        MESSAGE lv_error TYPE 'E'.
    ENDTRY.
```
+  For API details, see [TerminateJobFlows](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html) in *AWS SDK for SAP ABAP API reference*. 

------