如何从 Lambda 函数在亚马逊 EMR 上执行 spark 提交? [英] How to execute spark submit on amazon EMR from Lambda function?

问题描述

我想根据 S3 上的文件上传事件在 AWS EMR 集群上执行 spark 提交作业.我正在使用 AWS Lambda 函数来捕获事件，但我不知道如何通过 Lambda 函数在 EMR 集群上提交 spark 提交作业.

I want to execute spark submit job on AWS EMR cluster based on the file upload event on S3. I am using AWS Lambda function to capture the event but I have no idea how to submit spark submit job on EMR cluster from Lambda function.

我搜索的大多数答案都谈到在 EMR 集群中添加一个步骤.但我不知道我是否可以在添加的步骤中添加添加任何步骤来触发spark submit --with args".

Most of the answers that i searched talked about adding a step in the EMR cluster. But I do not know if I can add add any step to fire "spark submit --with args" in the added step.

推荐答案

你可以，我上周不得不做同样的事情！

You can, I had to same thing last week!

将 boto3 用于 Python(其他语言肯定有类似的解决方案)，您可以使用定义的步骤启动集群，也可以将步骤附加到已经启动的集群.

Using boto3 for Python (other languages would definitely have a similar solution) you can either start a cluster with the defined step, or attach a step to an already up cluster.

def lambda_handler(event, context):
    conn = boto3.client("emr")        
    cluster_id = conn.run_job_flow(
        Name='ClusterName',
        ServiceRole='EMR_DefaultRole',
        JobFlowRole='EMR_EC2_DefaultRole',
        VisibleToAllUsers=True,
        LogUri='s3n://some-log-uri/elasticmapreduce/',
        ReleaseLabel='emr-5.8.0',
        Instances={
            'InstanceGroups': [
                {
                    'Name': 'Master nodes',
                    'Market': 'ON_DEMAND',
                    'InstanceRole': 'MASTER',
                    'InstanceType': 'm3.xlarge',
                    'InstanceCount': 1,
                },
                {
                    'Name': 'Slave nodes',
                    'Market': 'ON_DEMAND',
                    'InstanceRole': 'CORE',
                    'InstanceType': 'm3.xlarge',
                    'InstanceCount': 2,
                }
            ],
            'Ec2KeyName': 'key-name',
            'KeepJobFlowAliveWhenNoSteps': False,
            'TerminationProtected': False
        },
        Applications=[{
            'Name': 'Spark'
        }],
        Configurations=[{
            "Classification":"spark-env",
            "Properties":{},
            "Configurations":[{
                "Classification":"export",
                "Properties":{
                    "PYSPARK_PYTHON":"python35",
                    "PYSPARK_DRIVER_PYTHON":"python35"
                }
            }]
        }],
        BootstrapActions=[{
            'Name': 'Install',
            'ScriptBootstrapAction': {
                'Path': 's3://path/to/bootstrap.script'
            }
        }],
        Steps=[{
            'Name': 'StepName',
            'ActionOnFailure': 'TERMINATE_CLUSTER',
            'HadoopJarStep': {
                'Jar': 's3n://elasticmapreduce/libs/script-runner/script-runner.jar',
                'Args': [
                    "/usr/bin/spark-submit", "--deploy-mode", "cluster",
                    's3://path/to/code.file', '-i', 'input_arg', 
                    '-o', 'output_arg'
                ]
            }
        }],
    )
    return "Started cluster {}".format(cluster_id)

将一个步骤附加到一个已经运行的集群

根据此处

def lambda_handler(event, context):
    conn = boto3.client("emr")
    # chooses the first cluster which is Running or Waiting
    # possibly can also choose by name or already have the cluster id
    clusters = conn.list_clusters()
    # choose the correct cluster
    clusters = [c["Id"] for c in clusters["Clusters"] 
                if c["Status"]["State"] in ["RUNNING", "WAITING"]]
    if not clusters:
        sys.stderr.write("No valid clusters
")
        sys.stderr.exit()
    # take the first relevant cluster
    cluster_id = clusters[0]
    # code location on your emr master node
    CODE_DIR = "/home/hadoop/code/"

    # spark configuration example
    step_args = ["/usr/bin/spark-submit", "--spark-conf", "your-configuration",
                 CODE_DIR + "your_file.py", '--your-parameters', 'parameters']

    step = {"Name": "what_you_do-" + time.strftime("%Y%m%d-%H:%M"),
            'ActionOnFailure': 'CONTINUE',
            'HadoopJarStep': {
                'Jar': 's3n://elasticmapreduce/libs/script-runner/script-runner.jar',
                'Args': step_args
            }
        }
    action = conn.add_job_flow_steps(JobFlowId=cluster_id, Steps=[step])
    return "Added step: %s"%(action)

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