boto EMR 添加步骤并自动终止

Question

Python 2.7.12

boto3==1.3.1

如何将步骤添加到正在运行的 EMR 集群并在步骤完成后终止集群，无论它失败还是成功？

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

在您的情况下（使用 boto3 创建集群），您可以添加这些标志 'TerminationProtected': False, 'AutoTerminate': True, 到您的集群创建。这样在你的步骤完成后运行集群就会被关闭。

另一种解决方案是在您要运行的步骤之后立即添加另一个步骤以终止集群。所以基本上你需要将此命令作为步骤运行

aws emr terminate-clusters --cluster-ids your_cluster_id

棘手的部分是检索 cluster_id。 在这里您可以找到一些解决方案：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

您可以通过在 Instances 参数中指定 'KeepJobFlowAliveWhenNoSteps': False 来创建一个在所有步骤运行后自动终止的短期集群。我已向 GitHub 添加了一个完整的示例，说明了如何执行此操作。

以下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

下面是一些使用一些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)