Python 使用 Boto3 API 创建 EMR 集群
EMR 指南
Amazon EMR
[PDF]Amazon EMR - 开发人员指南 - Amazon.com
使用API创建EMR
Python依赖包安装
pip install boto3API参考
EMR集群主要采用 boto3 EMR.Client.run_job_flow api 进行集群创建， 详细描述参考官网文档。
样例代码
# -*- encoding:utf8 -*-
""" author: quanbin_zhu time : 2017/11/13 15:19 """
import re
import boto3
class EmrClient(object):
def __init__(self):
self.emr_version = "emr-5.9.0"
self.emr_log_url = "s3://dev-xxx/borey-zhu/sparklog"
self.aws_ec2_key = "borey.zhu"
self.aws_access_key = "xxxxxxxxxxxxxxxxxxxxxxxx"
self.aws_secret_key = "xxxxxxxxxxxxxxxxxxxxxxxx+xxxxxxxxxx"
self.client = boto3.client('emr', region_name='cn-north-1', aws_access_key_id=self.aws_access_key,
aws_secret_access_key=self.aws_secret_key)
self.job_flow_id = None
def generate_step(self, step_name, step_command):
cmds = re.split('\\s+', step_command)
if not cmds:
raise ValueError
return {
'Name': step_name,
'ActionOnFailure': 'CANCEL_AND_WAIT',
'HadoopJarStep': {
'Jar': 'command-runner.jar',
'Args': cmds
}
}
def add_job_flow_steps(self, steps, flow_id=None):
if flow_id:
return self.client.add_job_flow_steps(JobFlowId=flow_id, Steps=steps)
elif self.job_flow_id:
return self.client.add_job_flow_steps(JobFlowId=self.job_flow_id, Steps=steps)
else:
return self.run_job_flow(steps)
def run_job_flow(self, steps=None):
response = self.client.run_job_flow(
# 集群名称
Name="SnifferAnalyse",
# 集群版本号 http://docs.aws.amazon.com/emr/latest/ReleaseGuide/emr-release-components.html
ReleaseLabel=self.emr_version,
# 集群日志存储Amazon S3的路径
LogUri=self.emr_log_url,
Instances={
# 实例类型
# https://amazonaws-china.com/cn/ec2/purchasing-options/dedicated-instances/
'MasterInstanceType': 'm3.xlarge',
'SlaveInstanceType': 'm3.xlarge',
# 实例数
'InstanceCount': 9,
# 保持EMR集群运行当没有步骤时
# True - 切换为 WAITING
# False - 关闭集群
'KeepJobFlowAliveWhenNoSteps': False,
# 终止保护
# True - 锁定集群和防止被API调用终止,用户干预,或在工作时流错误
'TerminationProtected': False,
# 使用现有 EC2 密钥对以 SSH 方式连接到 Amazon EMR 集群的主节点
'Ec2KeyName': self.aws_ec2_key,
# 指定子网 ID, 集群虚拟私有云 Amazon Virtual Private Cloud (亚马逊 VPC)
'Ec2SubnetId': 'subnet-xxxxxxx'
},
Applications=[
{
'Name': 'Hadoop'
},
{
'Name': 'Spark'
},
],
BootstrapActions=[
{
# YARN 资源最大化配置 for Spark
'Name': 'Spark Default Config',
'ScriptBootstrapAction': {
# Initialization shell for spark, you will find the doc in
# https://github.com/awslabs/emr-bootstrap-actions/tree/master/spark
'Path': 's3://support.elasticmapreduce/spark/maximize-spark-default-config',
}
},
],
Steps=steps,
# 对自己账号可见
VisibleToAllUsers=False,
# EC2 实例配置
JobFlowRole='EMR_EC2_DefaultRole',
# EMR 角色
ServiceRole='EMR_DefaultRole'
)
self.job_flow_id = response['JobFlowId']
return response
def describe_cluster(self, jobFlowId=None):
job_id = jobFlowId if jobFlowId else self.job_flow_id
if job_id:
return self.client.describe_cluster(
ClusterId=jobFlowId
)
if __name__ == "__main__":
emr = EmrClient()
steps = []
steps.append(
emr.generate_step("Load spark project from S3", "hadoop fs -get s3://dev-xxx/borey-zhu/spark/example.jar /home/hadoop/spark-example.jar")
)
spark_submit_cmd = """ spark-submit --master yarn --packages org.apache.hbase:hbase-hadoop-compat:1.3.0,org.apache.hbase:hbase-server:1.3.0,org.apache.hbase:hbase-common:1.3.0,org.apache.hbase:hbase-client:1.3.0 --repositories http://maven.aliyun.com/nexus/content/groups/public/ --driver-memory 2G --executor-cores 2 --num-executors 32 --executor-memory 2200M --conf spark.memory.fraction=0.75 --conf spark.memory.storageFraction=0.1 --conf spark.serializer=org.apache.spark.serializer.KryoSerializer --conf spark.driver.extraJavaOptions=-XX:+UseCompressedOops --conf spark.executor.extraJavaOptions=-XX:+UseCompressedOops --conf mapreduce.input.fileinputformat.list-status.num-threads=2 --class com.borey.spark.SparkTest /home/hadoop/spark-example.jar """
steps.append(
emr.generate_step("Spark Example", spark_submit_cmd),
)
response = emr.add_job_flow_steps(steps)
print response