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<String> ARGS_AS_LIST = Arrays.asList(JOB_ARGS);private static final List<JobFlowExecutionState> 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 useJobFlowInstancesConfig 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 flowRunJobFlowRequest request = new RunJobFlowRequest(FLOW_NAME, configInstance());request.setLogUri(S3N_LOG_URI);// Configure the Hadoop jar to useHadoopJarStepConfig 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 flowRunJobFlowResult result = emr.runJobFlow(request);//Check the status of the running jobString 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：