如何使用boto启动和配置EMR集群

Question

我正在尝试启动集群并使用boto运行所有作业.我找到了许多创建job_flows的例子.但我不能为我的生活找到一个例子,表明:

1. 如何定义要使用的集群(通过clusted_id)
2. 如何配置启动集群(例如,如果我想为某些任务节点使用点实例)

我错过了什么吗？

Answer 1

Boto和底层EMR API目前正在混合术语集群和作业流程,并且不推荐使用作业流程.我认为他们是同义词.

您可以通过调用该boto.emr.connection.run_jobflow()函数来创建新集群.它将返回EMR为您生成的集群ID.

首先是所有强制性的东西:

#!/usr/bin/env python

import boto
import boto.emr
from boto.emr.instance_group import InstanceGroup

conn = boto.emr.connect_to_region('us-east-1')

然后我们指定实例组,包括我们要为TASK节点支付的现货价格:

instance_groups = []
instance_groups.append(InstanceGroup(
    num_instances=1,
    role="MASTER",
    type="m1.small",
    market="ON_DEMAND",
    name="Main node"))
instance_groups.append(InstanceGroup(
    num_instances=2,
    role="CORE",
    type="m1.small",
    market="ON_DEMAND",
    name="Worker nodes"))
instance_groups.append(InstanceGroup(
    num_instances=2,
    role="TASK",
    type="m1.small",
    market="SPOT",
    name="My cheap spot nodes",
    bidprice="0.002"))

最后我们开始一个新的集群:

cluster_id = conn.run_jobflow(
    "Name for my cluster",
    instance_groups=instance_groups,
    action_on_failure='TERMINATE_JOB_FLOW',
    keep_alive=True,
    enable_debugging=True,
    log_uri="s3://mybucket/logs/",
    hadoop_version=None,
    ami_version="2.4.9",
    steps=[],
    bootstrap_actions=[],
    ec2_keyname="my-ec2-key",
    visible_to_all_users=True,
    job_flow_role="EMR_EC2_DefaultRole",
    service_role="EMR_DefaultRole")

如果我们关心,我们也可以打印集群ID:

print "Starting cluster", cluster_id

Answer 2

我相信将使用boto3启动EMR集群的Python的最小数量为：

import boto3

client = boto3.client('emr', region_name='us-east-1')

response = client.run_job_flow(
    Name="Boto3 test cluster",
    ReleaseLabel='emr-5.12.0',
    Instances={
        'MasterInstanceType': 'm4.xlarge',
        'SlaveInstanceType': 'm4.xlarge',
        'InstanceCount': 3,
        'KeepJobFlowAliveWhenNoSteps': True,
        'TerminationProtected': False,
        'Ec2SubnetId': 'my-subnet-id',
        'Ec2KeyName': 'my-key',
    },
    VisibleToAllUsers=True,
    JobFlowRole='EMR_EC2_DefaultRole',
    ServiceRole='EMR_DefaultRole'
)

注意：您必须创建EMR_EC2_DefaultRole和EMR_DefaultRole。在亚马逊的文档声称，JobFlowRole和ServiceRole是可选的，但忽略他们并没有为我工作。那可能是因为我的子网是VPC子网，但是我不确定。

Answer 3

我使用以下代码创建安装了 flink 的 EMR，包括 3 个实例组。参考文档：https : //boto3.amazonaws.com/v1/documentation/api/latest/reference/services/emr.html#EMR.Client.run_job_flow

import boto3

masterInstanceType = 'm4.large'
coreInstanceType = 'c3.xlarge'
taskInstanceType = 'm4.large'
coreInstanceNum = 2
taskInstanceNum = 2
clusterName = 'my-emr-name'

emrClient = boto3.client('emr')

logUri = 's3://bucket/xxxxxx/'
releaseLabel = 'emr-5.17.0' #emr version
instances = {
    'Ec2KeyName': 'my_keyxxxxxx',
    'Ec2SubnetId': 'subnet-xxxxxx',
    'ServiceAccessSecurityGroup': 'sg-xxxxxx',
    'EmrManagedMasterSecurityGroup': 'sg-xxxxxx',
    'EmrManagedSlaveSecurityGroup': 'sg-xxxxxx',
    'KeepJobFlowAliveWhenNoSteps': True,
    'TerminationProtected': False,
    'InstanceGroups': [{
        'InstanceRole': 'MASTER',
        "InstanceCount": 1,
            "InstanceType": masterInstanceType,
            "Market": "SPOT",
            "Name": "Master"
        }, {
            'InstanceRole': 'CORE',
            "InstanceCount": coreInstanceNum,
            "InstanceType": coreInstanceType,
            "Market": "SPOT",
            "Name": "Core",
        }, {
            'InstanceRole': 'TASK',
            "InstanceCount": taskInstanceNum,
            "InstanceType": taskInstanceType,
            "Market": "SPOT",
            "Name": "Core",
        }
    ]
}
bootstrapActions = [{
    'Name': 'Log to Cloudwatch Logs',
    'ScriptBootstrapAction': {
        'Path': 's3://mybucket/bootstrap_cwl.sh'
    }
}, {
    'Name': 'Custom action',
    'ScriptBootstrapAction': {
        'Path': 's3://mybucket/install.sh'
    }
}]
applications = [{'Name': 'Flink'}]
serviceRole = 'EMR_DefaultRole'
jobFlowRole = 'EMR_EC2_DefaultRole'
tags = [{'Key': 'keyxxxxxx', 'Value': 'valuexxxxxx'},
        {'Key': 'key2xxxxxx', 'Value': 'value2xxxxxx'}
        ]
steps = [
    {
        'Name': 'Run Flink',
        'ActionOnFailure': 'TERMINATE_JOB_FLOW',
        'HadoopJarStep': {
            'Jar': 'command-runner.jar',
            'Args': ['flink', 'run',
                     '-m', 'yarn-cluster',
                     '-p', str(taskInstanceNum),
                     '-yjm', '1024',
                     '-ytm', '1024',
                     '/home/hadoop/test-1.0-SNAPSHOT.jar'
                     ]
        }
    },
]
response = emrClient.run_job_flow(
    Name=clusterName,
    LogUri=logUri,
    ReleaseLabel=releaseLabel,
    Instances=instances,
    Steps=steps,
    Configurations=configurations,
    BootstrapActions=bootstrapActions,
    Applications=applications,
    ServiceRole=serviceRole,
    JobFlowRole=jobFlowRole,
    Tags=tags
)