boto EMR 添加步骤并自动终止

Question

Python 2.7.12

boto3==1.3.1

如何向运行 EMR 集群添加一个步骤并且在步骤完成后终止集群，无论它是失败还是成功？

创建集群

response = client.run_job_flow(
    Name=name,
    LogUri='s3://mybucket/emr/',
    ReleaseLabel='emr-5.9.0',
    Instances={
        'MasterInstanceType': instance_type,
        'SlaveInstanceType': instance_type,
        'InstanceCount': instance_count,
        'KeepJobFlowAliveWhenNoSteps': True,
        'Ec2KeyName': 'KeyPair',
        'EmrManagedSlaveSecurityGroup': 'sg-1234',
        'EmrManagedMasterSecurityGroup': 'sg-1234',
        'Ec2SubnetId': 'subnet-1q234',
    },
    Applications=[
        {'Name': 'Spark'},
        {'Name': 'Hadoop'}
    ],
    BootstrapActions=[
        {
            'Name': 'Install Python packages',
            'ScriptBootstrapAction': {
                'Path': 's3://mybucket/code/spark/bootstrap_spark_cluster.sh'
            }
        }
    ],
    VisibleToAllUsers=True,
    JobFlowRole='EMR_EC2_DefaultRole',
    ServiceRole='EMR_DefaultRole',
    Configurations=[
        {
            'Classification': 'spark',
            'Properties': {
                'maximizeResourceAllocation': 'true'
            }
        },
    ],
)

添加一个步骤

response = client.add_job_flow_steps(
    JobFlowId=cluster_id,
    Steps=[
        {
            'Name': 'Run Step',
            'ActionOnFailure': 'TERMINATE_CLUSTER',
            'HadoopJarStep': {
                'Args': [
                    'spark-submit',
                    '--deploy-mode', 'cluster',
                    '--py-files',
                    's3://mybucket/code/spark/spark_udfs.py',
                    's3://mybucket/code/spark/{}'.format(spark_script),
                    '--some-arg'
                ],
                'Jar': 'command-runner.jar'
            }
        }
    ]
)

这成功添加了一个步骤并运行，但是，当该步骤成功完成时，我希望集群自动终止，如 AWS CLI 中所述：http://docs.aws.amazon.com/cli/latest/reference/emr/create-cluster.html

Answer 1

在你的情况下（使用 boto3 创建集群）你可以添加这些标志 'TerminationProtected': False, 'AutoTerminate': True, 到您的集群创建。这样，在您完成运行的步骤后，集群将关闭。

另一个解决方案是在您想要运行的步骤之后立即添加另一个步骤来终止集群。所以基本上你需要运行这个命令作为步骤

aws emr terminate-clusters --cluster-ids your_cluster_id

棘手的部分是检索 cluster_id。您可以在这里找到一些解决方案：Does an EMR master node know it's cluster id?

Answer 2

建议的'AutoTerminate': True参数对我不起作用。但是，当我将参数 'KeepJobFlowAliveWhenNoSteps' 从 True 设置为 False 时，它起作用了。您的代码应如下所示：

response = client.run_job_flow(
    Name=name,
    LogUri='s3://mybucket/emr/',
    ReleaseLabel='emr-5.9.0',
    Instances={
        'MasterInstanceType': instance_type,
        'SlaveInstanceType': instance_type,
        'InstanceCount': instance_count,
        'KeepJobFlowAliveWhenNoSteps': False,
        'Ec2KeyName': 'KeyPair',
        'EmrManagedSlaveSecurityGroup': 'sg-1234',
        'EmrManagedMasterSecurityGroup': 'sg-1234',
        'Ec2SubnetId': 'subnet-1q234',
    },
    Applications=[
        {'Name': 'Spark'},
        {'Name': 'Hadoop'}
    ],
    BootstrapActions=[
        {
            'Name': 'Install Python packages',
            'ScriptBootstrapAction': {
                'Path': 's3://mybucket/code/spark/bootstrap_spark_cluster.sh'
            }
        }
    ],
    VisibleToAllUsers=True,
    JobFlowRole='EMR_EC2_DefaultRole',
    ServiceRole='EMR_DefaultRole',
    Configurations=[
        {
            'Classification': 'spark',
            'Properties': {
                'maximizeResourceAllocation': 'true'
            }
        },
    ],
)

Answer 3

您可以创建一个 short-lived 集群，该集群在所有步骤运行完成后自动终止，方法是在 Instances 参数中指定 'KeepJobFlowAliveWhenNoSteps': False。我在 GitHub 中添加了一个完整的示例，展示了如何执行此操作。

下面是演示中的一些代码：

def run_job_flow(
        name, log_uri, keep_alive, applications, job_flow_role, service_role,
        security_groups, steps, emr_client):
    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

下面是一些使用实际参数调用此函数的代码：

output_prefix = 'pi-calc-output'
pi_step = {
    'name': 'estimate-pi-step',
    'script_uri': f's3://{bucket_name}/{script_key}',
    'script_args':
        ['--partitions', '3', '--output_uri',
        f's3://{bucket_name}/{output_prefix}']
}
cluster_id = emr_basics.run_job_flow(
    f'{prefix}-cluster', f's3://{bucket_name}/logs',
    False, ['Hadoop', 'Hive', 'Spark'], job_flow_role, service_role,
    security_groups, [pi_step], emr_client)

boto EMR 添加步骤并自动终止

boto EMR add step and auto terminate

python

amazon-web-services

emr

boto3