在Amazon EMR上运行Hadoop MapReduce作业

不久前,我发布了如何使用CLI设置EMR群集的信息。 在本文中,我将展示如何使用适用于AWS的Java SDK来设置集群。 展示如何使用Java AWS开发工具包执行此操作的最佳方法是展示完整的示例,因此,让我们开始吧。

    • 设置一个新的Maven项目

为此,我创建了一个新的默认Maven项目。 您可以运行该项目中的主类来启动EMR集群并执行我在本文中创建的MapReduce作业:

package net.pascalalma.aws.emr;

import com.amazonaws.AmazonServiceException;
import com.amazonaws.auth.AWSCredentials;
import com.amazonaws.auth.PropertiesCredentials;
import com.amazonaws.regions.Region;
import com.amazonaws.regions.Regions;
import com.amazonaws.services.ec2.model.InstanceType;
import com.amazonaws.services.elasticmapreduce.AmazonElasticMapReduceClient;
import com.amazonaws.services.elasticmapreduce.model.*;
import com.amazonaws.services.elasticmapreduce.util.StepFactory;
import com.amazonaws.services.s3.AmazonS3;
import com.amazonaws.services.s3.AmazonS3Client;

import java.util.Arrays;
import java.util.Date;
import java.util.List;
import java.util.UUID;

/**
 * Created with IntelliJ IDEA.
 * User: pascal
 * Date: 22-07-13
 * Time: 20:45
 */
public class MyClient {

    private static final String HADOOP_VERSION = "1.0.3";
    private static final int INSTANCE_COUNT = 1;
    private static final String INSTANCE_TYPE = InstanceType.M1Small.toString();
    private static final UUID RANDOM_UUID = UUID.randomUUID();
    private static final String FLOW_NAME = "dictionary-" + RANDOM_UUID.toString();
    private static final String BUCKET_NAME = "map-reduce-intro";
    private static final String S3N_HADOOP_JAR =
            "s3n://" + BUCKET_NAME + "/job/MapReduce-1.0-SNAPSHOT.jar";
    private static final String S3N_LOG_URI = "s3n://" + BUCKET_NAME + "/log/";
    private static final String[] JOB_ARGS =
            new String[]{"s3n://" + BUCKET_NAME + "/input/input.txt",
                    "s3n://" + BUCKET_NAME + "/result/" + FLOW_NAME};
    private static final List ARGS_AS_LIST = Arrays.asList(JOB_ARGS);
    private static final List DONE_STATES = Arrays
            .asList(new JobFlowExecutionState[]{JobFlowExecutionState.COMPLETED,
                    JobFlowExecutionState.FAILED,
                    JobFlowExecutionState.TERMINATED});
    static AmazonS3 s3;
    static AmazonElasticMapReduceClient emr;

    private static void init() throws Exception {
        AWSCredentials credentials = new PropertiesCredentials(
                MyClient.class.getClassLoader().getResourceAsStream("AwsCredentials.properties"));
        s3 = new AmazonS3Client(credentials);
        emr = new AmazonElasticMapReduceClient(credentials);
        emr.setRegion(Region.getRegion(Regions.EU_WEST_1));
    }

    private static JobFlowInstancesConfig configInstance() throws Exception {

        // Configure instances to use
        JobFlowInstancesConfig instance = new JobFlowInstancesConfig();
        instance.setHadoopVersion(HADOOP_VERSION);
        instance.setInstanceCount(INSTANCE_COUNT);
        instance.setMasterInstanceType(INSTANCE_TYPE);
        instance.setSlaveInstanceType(INSTANCE_TYPE);
        // instance.setKeepJobFlowAliveWhenNoSteps(true);
        // instance.setEc2KeyName("4synergy_palma");

        return instance;
    }

    private static void runCluster() throws Exception {
        // Configure the job flow
        RunJobFlowRequest request = new RunJobFlowRequest(FLOW_NAME, configInstance());
        request.setLogUri(S3N_LOG_URI);

        // Configure the Hadoop jar to use
        HadoopJarStepConfig jarConfig = new HadoopJarStepConfig(S3N_HADOOP_JAR);
        jarConfig.setArgs(ARGS_AS_LIST);

        try {

            StepConfig enableDebugging = new StepConfig()
                    .withName("Enable debugging")
                    .withActionOnFailure("TERMINATE_JOB_FLOW")
                    .withHadoopJarStep(new StepFactory().newEnableDebuggingStep());

            StepConfig runJar =
                    new StepConfig(S3N_HADOOP_JAR.substring(S3N_HADOOP_JAR.indexOf('/') + 1),
                            jarConfig);

            request.setSteps(Arrays.asList(new StepConfig[]{enableDebugging, runJar}));

            //Run the job flow
            RunJobFlowResult result = emr.runJobFlow(request);

            //Check the status of the running job
            String lastState = "";

            STATUS_LOOP:
            while (true) {
                DescribeJobFlowsRequest desc =
                        new DescribeJobFlowsRequest(
                                Arrays.asList(new String[]{result.getJobFlowId()}));
                DescribeJobFlowsResult descResult = emr.describeJobFlows(desc);
                for (JobFlowDetail detail : descResult.getJobFlows()) {
                    String state = detail.getExecutionStatusDetail().getState();
                    if (isDone(state)) {
                        System.out.println("Job " + state + ": " + detail.toString());
                        break STATUS_LOOP;
                    } else if (!lastState.equals(state)) {
                        lastState = state;
                        System.out.println("Job " + state + " at " + new Date().toString());
                    }
                }
                Thread.sleep(10000);
            }
        } catch (AmazonServiceException ase) {
            System.out.println("Caught Exception: " + ase.getMessage());
            System.out.println("Reponse Status Code: " + ase.getStatusCode());
            System.out.println("Error Code: " + ase.getErrorCode());
            System.out.println("Request ID: " + ase.getRequestId());
        }
    }

    public static boolean isDone(String value) {
        JobFlowExecutionState state = JobFlowExecutionState.fromValue(value);
        return DONE_STATES.contains(state);
    }

    public static void main(String[] args) {
        try {
            init();
            runCluster();
        } catch (Exception e) {
            e.printStackTrace();  
        }
    }
}

在此类中,我首先声明一些常量,我认为这些常量是显而易见的。 在init()方法中,我使用添加到项目中的凭据属性文件。 我将此文件添加到了Maven项目的'/ main / resources'文件夹中。 它包含我的访问密钥和秘密密钥。
我还将EMR客户的区域设置为“ EU-WEST”。
下一个方法是“ configInstance()”。 在这种方法中,我通过设置Hadoop版本,实例数,实例大小等来创建和配置JobFlowInstance。您还可以配置'keepAlive'设置,以在作业完成后使集群保持活动状态。 在某些情况下这可能会有所帮助。 如果要使用此选项,则还可以设置要用于访问集群的密钥对,这可能很有用,因为如果不设置此密钥就无法访问集群。 方法“ runCluster()”是集群实际运行的地方。 它创建启动集群的请求。 在此请求中,添加了必须执行的步骤。 在我们的例子中,其中一个步骤是运行在先前步骤中创建的JAR文件。 我还添加了一个调试步骤,以便在集群完成并终止后我们可以访问调试日志记录。 我们可以简单地访问我用常量'S3N_LOG_URI'设置的S3存储桶中的日志文件。 创建此请求后,我们将基于此请求启动集群。 然后,我们每隔10秒钟拉动一次,以查看作业是否完成,并在控制台上显示一条消息,指示作业的当前状态。 要执行第一次运行,我们必须准备输入。

    • 准备输入

作为作业的输入(有关此示例作业的更多信息,请参见此),我们必须使字典内容可用于EMR群集。 此外,我们必须使JAR文件可用,并确保输出和日志目录存在于我们的S3存储桶中。 有几种方法可以执行此操作:您还可以通过使用SDK以编程方式来执行此操作,也可以通过从命令行使用S3cmd来执行此操作,或者使用AWS管理控制台来执行此操作 。 只要最终得到类似的设置,就可以了:

  • s3:// map-reduce-intro
  • s3:// map-reduce-intro / input
  • s3://map-reduce-intro/input/input.txt
  • s3:// map-reduce-intro / job
  • s3://map-reduce-intro/job/MapReduce-1.0-SNAPSHOT.jar
  • s3:// map-reduce-intro / log
  • s3:// map-reduce-intro / result

或在使用S3cmd时如下所示:

s3cmd-1.5.0-alpha1$ s3cmd ls --recursive s3://map-reduce-intro/
2013-07-20 13:06    469941   s3://map-reduce-intro/input/input.txt
2013-07-20 14:12      5491   s3://map-reduce-intro/job/MapReduce-1.0-SNAPSHOT.jar
2013-08-06 14:30         0   s3://map-reduce-intro/log/
2013-08-06 14:27         0   s3://map-reduce-intro/result/

在上面的示例中,我已经在代码中引入了S3客户端。 您还可以使用它来准备输入或获取输出,作为客户工作的一部分。

    • 运行集群

一切就绪后,我们就可以运行作业。 我只是在IntelliJ中运行'MyClient'的主要方法,并在控制台中获得以下输出:

Job STARTING at Tue Aug 06 16:31:55 CEST 2013
Job RUNNING at Tue Aug 06 16:36:18 CEST 2013
Job SHUTTING_DOWN at Tue Aug 06 16:38:40 CEST 2013
Job COMPLETED: {
  JobFlowId: j-JDB14HVTRC1L
  ,Name: dictionary-8288df47-8aef-4ad3-badf-ee352a4a7c43
  ,LogUri: s3n://map-reduce-intro/log/,AmiVersion: 2.4.0
  ,ExecutionStatusDetail: {State: COMPLETED,CreationDateTime: Tue Aug 06 16:31:58 CEST 2013
    ,StartDateTime: Tue Aug 06 16:36:14 CEST 2013
    ,ReadyDateTime: Tue Aug 06 16:36:14 CEST 2013
    ,EndDateTime: Tue Aug 06 16:39:02 CEST 2013
    ,LastStateChangeReason: Steps completed}
  ,Instances: {MasterInstanceType: m1.small
    ,MasterPublicDnsName: ec2-54-216-104-11.eu-west-1.compute.amazonaws.com
    ,MasterInstanceId: i-93268ddf
    ,InstanceCount: 1
    ,InstanceGroups: [{InstanceGroupId: ig-2LURHNAK5NVKZ
      ,Name: master
      ,Market: ON_DEMAND
      ,InstanceRole: MASTER
      ,InstanceType: m1.small
      ,InstanceRequestCount: 1
      ,InstanceRunningCount: 0
      ,State: ENDED
      ,LastStateChangeReason: Job flow terminated
      ,CreationDateTime: Tue Aug 06 16:31:58 CEST 2013
      ,StartDateTime: Tue Aug 06 16:34:28 CEST 2013
      ,ReadyDateTime: Tue Aug 06 16:36:10 CEST 2013
      ,EndDateTime: Tue Aug 06 16:39:02 CEST 2013}]
    ,NormalizedInstanceHours: 1
    ,Ec2KeyName: 4synergy_palma
    ,Placement: {AvailabilityZone: eu-west-1a}
    ,KeepJobFlowAliveWhenNoSteps: false
    ,TerminationProtected: false
    ,HadoopVersion: 1.0.3}
  ,Steps: [
    {StepConfig: {Name: Enable debugging
      ,ActionOnFailure: TERMINATE_JOB_FLOW
      ,HadoopJarStep: {Properties: []
        ,Jar: s3://us-east-1.elasticmapreduce/libs/script-runner/script-runner.jar
        ,Args: [s3://us-east-1.elasticmapreduce/libs/state-pusher/0.1/fetch]}
    }
    ,ExecutionStatusDetail: {State: COMPLETED,CreationDateTime: Tue Aug 06 16:31:58 CEST 2013
      ,StartDateTime: Tue Aug 06 16:36:12 CEST 2013
      ,EndDateTime: Tue Aug 06 16:36:40 CEST 2013
      ,}
    }
  , {StepConfig: {Name: /map-reduce-intro/job/MapReduce-1.0-SNAPSHOT.jar
      ,ActionOnFailure: TERMINATE_JOB_FLOW
      ,HadoopJarStep: {Properties: []
        ,Jar: s3n://map-reduce-intro/job/MapReduce-1.0-SNAPSHOT.jar
        ,Args: [s3n://map-reduce-intro/input/input.txt
          , s3n://map-reduce-intro/result/dictionary-8288df47-8aef-4ad3-badf-ee352a4a7c43]}
    }
    ,ExecutionStatusDetail: {State: COMPLETED
      ,CreationDateTime: Tue Aug 06 16:31:58 CEST 2013
      ,StartDateTime: Tue Aug 06 16:36:40 CEST 2013
      ,EndDateTime: Tue Aug 06 16:38:10 CEST 2013
      ,}
    }]
  ,BootstrapActions: []
  ,SupportedProducts: []
  ,VisibleToAllUsers: false
,}
Process finished with exit code 0

当然,我们在S3存储桶中配置的“结果”文件夹中有一个结果:

屏幕截图-2013-08-06-at-19-39-15

我将结果转移到我的本地计算机上,并进行了查看:

屏幕截图-2013-08-06-at-19-41-44

这样就可以得出这个简单的结论,但我认为,这是创建Hadoop作业并在对它进行单元测试之后在群集上运行它的完整示例,就像对待所有软件一样。

以该设置为基础,可以轻松地提出更复杂的业务案例,并对其进行测试和配置以在AWS EMR上运行。

参考: The Pragmatic Integrator博客上的JCG合作伙伴 Pascal Alma在Amazon EMR上运行Hadoop MapReduce作业 。

翻译自: https://www.javacodegeeks.com/2013/09/run-your-hadoop-mapreduce-job-on-amazon-emr.html

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