hadoop emr
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我认为完整的示例,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,则也可以设置要用于访问集群的密钥对,这可能会很有用,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:您也可以通过使用SDK以编程方式执行此操作,也可以使用S3cmd从命令行执行此操作,也可以使用AWS管理控制台进行此操作 。 只要您得到类似的设置,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
当一切就绪后,我们就可以运行工作。 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:
我将结果传输到我的本地计算机上,并进行了查看:
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,就像我们对所有软件所做的那样。
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。
翻译自: https://www.javacodegeeks.com/2013/09/run-your-hadoop-mapreduce-job-on-amazon-emr.html
hadoop emr