详述执行map reduce 程序的步骤（本地执行MR、服务器上执行MR）

MR程序的执行环境有两种：本地测试环境、服务器环境。    1、本地环境执行MR程序的步骤：      （1）在windows下配置hadoop的环境变量      （2）拷贝debug工具(winutils)到HADOOP_HOME/bin      （3）从源码中拷贝org.apache.hadoop.io.nativeio.NativeIO.java到我们的mr的src目录下，修改NativeIO.java。（大家可去http://download.csdn.net/detail/u013226462/9516657下载。）注意：确保项目的lib需要真实安装的jdk的lib      （4）MR程序调用配置文件的代码需要改变：          a、src不能有服务器的hadoop配置文件          b、在调用时使用：

Configuration config = new  Configuration();
config.set("fs.defaultFS", "hdfs://lida1:8020");
config.set("yarn.resourcemanager.hostname", "lida1");

下面详细介绍如何在本地直接调用MR，在服务器上执行MR。说明：输入文件、输出路径、项目结构如下图所示    1)、编写MR代码

/**2016年4月29日
*auther:lida
*wordCount
*com.dada.wordCount
*RunTest.java
*  */
package com.dada.mr;

/**
* @author lida
*
*/
import java.io.IOException;
import java.util.StringTokenizer;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

public class WC {
public static Text k = new Text();
public static IntWritable v = new IntWritable(1);
public static void main(String[] args) {
Configuration config = new Configuration();
config.set("fs.defaultFS", "hdfs://lida1:8020");
config.set("yarn.resourcemanager.hostname", "lida1");
try {
FileSystem fs = FileSystem.get(config);
Job job = Job.getInstance(config);
job.setJarByClass(WC.class);

job.setJobName("wc");
job.setMapperClass(WordCountMapper.class);
job.setReducerClass(WordCountReducer.class);
job.setCombinerClass(WordCountReducer.class);
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(IntWritable.class);

// 设置MR执行的输入文件
FileInputFormat.addInputPath(job, new Path("/lida-hdfs_1/input_files/wordCount"));
// 该目录表示MR执行之后的结果数据所在目录，必须不能存在
Path outputPath = new Path("/lida-hdfs_1/output_files");
// 注意：输入文件的绝对路径为：/lida-hdfs_1/input_files/wordCount/wordCount.txt，但是，命令行里不用写其绝对路径。

if (fs.exists(outputPath)) {
fs.delete(outputPath, true);
}
FileOutputFormat.setOutputPath(job, outputPath);
boolean f = job.waitForCompletion(true);
if (f) {
System.out.println("job 成功执行");
}
} catch (Exception e) {
e.printStackTrace();
}
}

public static class WordCountMapper extends Mapper<LongWritable, Text, Text, IntWritable> {
// map task执行过程中调用的方法，计算框架会循环调用该方法，默认每行数据调用一次
protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
String line = value.toString();
StringTokenizer st = new StringTokenizer(line);
while (st.hasMoreTokens()) {
String word = st.nextToken();
k.set(word);
context.write(k, v);
}
}
}

public static class WordCountReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
protected void reduce(Text arg0, Iterable<IntWritable> arg1, Context arg2)
throws IOException, InterruptedException {
int sum = 0;
for (IntWritable i : arg1) {
sum = sum + i.get();
}
v.set(sum);
arg2.write(arg0, v);
}
}
}

    2）、配置。从源码中拷贝org.apache.hadoop.io.nativeio.NativeIO.java到我们的mr的src目录下，然后修改NativeIO.java。     注意：确保项目的lib是本地安装的jdk的lib    3）、执行。本地直接Run on hadoop。    如果eclipse控制台没报错，那么该MR程序是没问题的。可从下图看到MR的执行过程。当MR执行完毕后，可从下面两图中看到执行结果。然后在DFS Location就能看到MR程序的执行结果。如下图所示：    2、服务器环境执行MR程序的步骤：      2.1、在本地直接调用MR，执行MR的过程在服务器上（真正企业运行环境）        （1）在src下放置服务器上的hadoop配置文件        （2）从源码中拷贝 org.apache.hadoop.mapred.YRANRunner.java到我们的mr的src目录下，修改YARNRunner.java。注意：确保项目的lib需要真实安装的jdk的lib        （3）把MR程序打包（jar），直接放到本地        （4）本地执行main方法，servlet调用MR。下面详细介绍如何在本地直接调用MR，在服务器上执行MR。说明：输入文件、输出路径、项目结构如下图所示    1）、编写代码

/**2016年4月29日
*auther:lida
*wordCount
*com.dada.wordCount
*RunTest.java
*  */
package com.dada.mr;

/**
* @author lida
*
*/
import java.io.IOException;
import java.util.StringTokenizer;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

public class WC {
public static Text k = new Text();
public static IntWritable v = new IntWritable(1);

public static void main(String[] args) {
Configuration config = new Configuration();
try {
FileSystem fs = FileSystem.get(config);
Job job = Job.getInstance(config);
job.setJar("C:\\Users\\lida\\Desktop\\wc.jar"); //此处的写法和其他人的写法（job.setJarByClass(XX.class) ）可能不同，原因是这样写会报错，下面的错误总结会讲到。
job.setJobName("wc");
job.setMapperClass(WordCountMapper.class);
job.setReducerClass(WordCountReducer.class);
job.setCombinerClass(WordCountReducer.class);
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(IntWritable.class);

// 设置MR执行的输入文件
FileInputFormat.addInputPath(job, new Path("/lida-hdfs_1/input_files/wordCount"));
// 该目录表示MR执行之后的结果数据所在目录，必须不能存在
Path outputPath = new Path("/lida-hdfs_1/output_files");
// 注意：输入文件的绝对路径为：/lida-hdfs_1/input_files/wordCount/wordCount.txt，但是，命令行里不用写其绝对路径。

if (fs.exists(outputPath)) {
fs.delete(outputPath, true);
}
FileOutputFormat.setOutputPath(job, outputPath);
boolean f = job.waitForCompletion(true);
if (f) {
System.out.println("job 成功执行");
}
} catch (Exception e) {
e.printStackTrace();
}
}

public static class WordCountMapper extends Mapper<LongWritable, Text, Text, IntWritable> {
// map task执行过程中调用的方法，计算框架会循环调用该方法，默认每行数据调用一次
protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
String line = value.toString();
StringTokenizer st = new StringTokenizer(line);
while (st.hasMoreTokens()) {
String word = st.nextToken();
k.set(word);
context.write(k, v);
}
}
}

public static class WordCountReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
protected void reduce(Text arg0, Iterable<IntWritable> arg1, Context arg2)
throws IOException, InterruptedException {
int sum = 0;
for (IntWritable i : arg1) {
sum = sum + i.get();
}
v.set(sum);
arg2.write(arg0, v);
}
}
}

    2）、配置      （1）在src下放置服务器上的hadoop配置文件。core-site.xml、hdfs-site.xml、mapred-site.xml、yarn-site.xml。      （2）从源码中拷贝org.apache.hadoop.mapred.YARNRunner.java到我们的mr的src目录下，然后修改YARNRunner.java。       修改哪些代码，请看http://www.360doc.com/content/14/0728/11/597197_397616444.shtml       注意：确保项目的lib是本地安装的jdk的lib    3）、打包。把MR程序打包（jar），直接放到本地。和普通java程序打包的方式相同。如下图：       4）、执行。本地执行main方法，servlet调用MR。       5）、监控。监控方式同上。    2.2、直接在服务器上使用命令的方式调用MR程序，执行过程也在服务器上      （1）把MR程序打包（jar），传送到服务器上      （2）通过命令：hadoop jar jar路径  类的全限定名 例如：hadoop jar /examples/wc.jar com.dada.mr.WordCount（wc.jar为打包好的jar包名；com.dada.mr为man方法所在的包名；WordCount为man方法所在的类）执行MR程序。下面详细介绍如何在服务器上执行MR程序。说明：输入文件、输出路径、项目结构如下图所示    1）、编写MR代码

/**
*
*/
package com.dada.mr;

/**
* @author 李达
*
*  2016年5月5日
*   wordCount
*   com.dada.wc
*/

import java.io.IOException;
import java.util.StringTokenizer;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

public class WC {
public static Text k = new Text();
public static IntWritable v = new IntWritable(1);
public static void main(String[] args) {
Configuration config = new Configuration();
try {
FileSystem fs = FileSystem.get(config);
Job job = Job.getInstance(config);
job.setJarByClass(WC.class);
job.setJobName("wc");
job.setMapperClass(WordCountMapper.class);
job.setReducerClass(WordCountReducer.class);
job.setCombinerClass(WordCountReducer.class);
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(IntWritable.class);

// 有两种方式解决输入、输出路径的问题：
// 1、代码中写死
// 设置MR执行的输入文件
// FileInputFormat.addInputPath(job, new
//Path("/lida-hdfs_1/input_files/wordCount"));
// 该目录表示MR执行之后的结果数据所在目录，必须不能存在
// Path outputPath = new Path("/lida-hdfs_1/output_files");
// 2、代码中不写死，写个参数，当在命令行执行MR时，再传参（官网是这么干的）。
FileInputFormat.addInputPath(job, new Path(args[0]));
Path outputPath = new Path(args[1]);
// 命令行脚本如下：
// ./hadoop jar /examplesByLiDa/wc.jar com.dada.mr.WC /lida-hdfs_1/input_files/wordCount /lida-hdfs_1/output_files
// 注意：输入文件的绝对路径为：/lida-hdfs_1/input_files/wordCount/wordCount.txt，但是，命令行里不用写其绝对路径。

if (fs.exists(outputPath)) {
fs.delete(outputPath, true);
}
FileOutputFormat.setOutputPath(job, outputPath);
boolean f = job.waitForCompletion(true);
if (f) {
System.out.println("job 成功执行");
}
} catch (Exception e) {
e.printStackTrace();
}
}

static class WordCountMapper extends Mapper<LongWritable, Text, Text, IntWritable> {
// map task执行过程中调用的方法，计算框架会循环调用该方法，默认每行数据调用一次
protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
String line = value.toString();
StringTokenizer st = new StringTokenizer(line);
while (st.hasMoreTokens()) {
String word = st.nextToken();
k.set(word);
context.write(k, v);
}
}
}

static class WordCountReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
protected void reduce(Text arg0, Iterable<IntWritable> arg1, Context arg2)
throws IOException, InterruptedException {
int sum = 0;
for (IntWritable i : arg1) {
sum = sum + i.get();
}
v.set(sum);
arg2.write(arg0, v);
}
}
}

    2）、打包。和普通java程序的打包方式相同。    3）、执行      （1）把打好的jar包传到hdfs（actvice）所在的服务器上      （2）进入hadoop/bin/下，执行脚本

./hadoop jar /examplesByLiDa/wc.jar com.dada.mr.WC /lida-hdfs_1/input_files/wordCount /lida-hdfs_1/output_files

    4）、监控。监控方式同上。到此，全剧终！附：测试过程中遇到的问题及解决方法问题1：

org.apache.hadoop.security.AccessControlException: Permission denied: user=aolei, access=EXECUTE, inode="/tmp":root:supergroup:drwx------
at org.apache.hadoop.hdfs.server.namenode.FSPermissionChecker.checkFsPermission(FSPermissionChecker.java:271)
at org.apache.hadoop.hdfs.server.namenode.FSPermissionChecker.check(FSPermissionChecker.java:257)
at org.apache.hadoop.hdfs.server.namenode.FSPermissionChecker.checkTraverse(FSPermissionChecker.java:208)
at org.apache.hadoop.hdfs.server.namenode.FSPermissionChecker.checkPermission(FSPermissionChecker.java:171)
at org.apache.hadoop.hdfs.server.namenode.FSNamesystem.checkPermission(FSNamesystem.java:5904)
at org.apache.hadoop.hdfs.server.namenode.FSNamesystem.checkPermission(FSNamesystem.java:5886)
at org.apache.hadoop.hdfs.server.namenode.FSNamesystem.checkOwner(FSNamesystem.java:5842)
at org.apache.hadoop.hdfs.server.namenode.FSNamesystem.setPermissionInt(FSNamesystem.java:1626)
at org.apache.hadoop.hdfs.server.namenode.FSNamesystem.setPermission(FSNamesystem.java:1607)
at org.apache.hadoop.hdfs.server.namenode.NameNodeRpcServer.setPermission(NameNodeRpcServer.java:579)
at org.apache.hadoop.hdfs.protocolPB.ClientNamenodeProtocolServerSideTranslatorPB.setPermission(ClientNamenodeProtocolServerSideTranslatorPB.java:416)
at org.apache.hadoop.hdfs.protocol.proto.ClientNamenodeProtocolProtos$ClientNamenodeProtocol$2.callBlockingMethod(ClientNamenodeProtocolProtos.java)
at org.apache.hadoop.ipc.ProtobufRpcEngine$Server$ProtoBufRpcInvoker.call(ProtobufRpcEngine.java:585)
at org.apache.hadoop.ipc.RPC$Server.call(RPC.java:928)
at org.apache.hadoop.ipc.Server$Handler$1.run(Server.java:2013)
at org.apache.hadoop.ipc.Server$Handler$1.run(Server.java:2009)
at java.security.AccessController.doPrivileged(Native Method)
at javax.security.auth.Subject.doAs(Subject.java:415)
at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1614)
at org.apache.hadoop.ipc.Server$Handler.run(Server.java:2007)

at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)
at sun.reflect.NativeConstructorAccessorImpl.newInstance(Unknown Source)
at sun.reflect.DelegatingConstructorAccessorImpl.newInstance(Unknown Source)
at java.lang.reflect.Constructor.newInstance(Unknown Source)
at org.apache.hadoop.ipc.RemoteException.instantiateException(RemoteException.java:106)
at org.apache.hadoop.ipc.RemoteException.unwrapRemoteException(RemoteException.java:73)
at org.apache.hadoop.hdfs.DFSClient.setPermission(DFSClient.java:2165)
at org.apache.hadoop.hdfs.DistributedFileSystem$23.doCall(DistributedFileSystem.java:1236)
at org.apache.hadoop.hdfs.DistributedFileSystem$23.doCall(DistributedFileSystem.java:1232)
at org.apache.hadoop.fs.FileSystemLinkResolver.resolve(FileSystemLinkResolver.java:81)
at org.apache.hadoop.hdfs.DistributedFileSystem.setPermission(DistributedFileSystem.java:1232)
at org.apache.hadoop.fs.FileSystem.mkdirs(FileSystem.java:597)
at org.apache.hadoop.mapreduce.JobSubmitter.copyAndConfigureFiles(JobSubmitter.java:179)
at org.apache.hadoop.mapreduce.JobSubmitter.copyAndConfigureFiles(JobSubmitter.java:301)
at org.apache.hadoop.mapreduce.JobSubmitter.submitJobInternal(JobSubmitter.java:389)
at org.apache.hadoop.mapreduce.Job$10.run(Job.java:1285)
at org.apache.hadoop.mapreduce.Job$10.run(Job.java:1282)
at java.security.AccessController.doPrivileged(Native Method)
at javax.security.auth.Subject.doAs(Unknown Source)
at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1614)
at org.apache.hadoop.mapreduce.Job.submit(Job.java:1282)
at org.apache.hadoop.mapreduce.Job.waitForCompletion(Job.java:1303)
at com.dada.mr.WC.main(WC.java:66)
Caused by: org.apache.hadoop.ipc.RemoteException(org.apache.hadoop.security.AccessControlException): Permission denied: user=aolei, access=EXECUTE, inode="/tmp":root:supergroup:drwx------
at org.apache.hadoop.hdfs.server.namenode.FSPermissionChecker.checkFsPermission(FSPermissionChecker.java:271)
at org.apache.hadoop.hdfs.server.namenode.FSPermissionChecker.check(FSPermissionChecker.java:257)
at org.apache.hadoop.hdfs.server.namenode.FSPermissionChecker.checkTraverse(FSPermissionChecker.java:208)
at org.apache.hadoop.hdfs.server.namenode.FSPermissionChecker.checkPermission(FSPermissionChecker.java:171)
at org.apache.hadoop.hdfs.server.namenode.FSNamesystem.checkPermission(FSNamesystem.java:5904)
at org.apache.hadoop.hdfs.server.namenode.FSNamesystem.checkPermission(FSNamesystem.java:5886)
at org.apache.hadoop.hdfs.server.namenode.FSNamesystem.checkOwner(FSNamesystem.java:5842)
at org.apache.hadoop.hdfs.server.namenode.FSNamesystem.setPermissionInt(FSNamesystem.java:1626)
at org.apache.hadoop.hdfs.server.namenode.FSNamesystem.setPermission(FSNamesystem.java:1607)
at org.apache.hadoop.hdfs.server.namenode.NameNodeRpcServer.setPermission(NameNodeRpcServer.java:579)
at org.apache.hadoop.hdfs.protocolPB.ClientNamenodeProtocolServerSideTranslatorPB.setPermission(ClientNamenodeProtocolServerSideTranslatorPB.java:416)
at org.apache.hadoop.hdfs.protocol.proto.ClientNamenodeProtocolProtos$ClientNamenodeProtocol$2.callBlockingMethod(ClientNamenodeProtocolProtos.java)
at org.apache.hadoop.ipc.ProtobufRpcEngine$Server$ProtoBufRpcInvoker.call(ProtobufRpcEngine.java:585)
at org.apache.hadoop.ipc.RPC$Server.call(RPC.java:928)
at org.apache.hadoop.ipc.Server$Handler$1.run(Server.java:2013)
at org.apache.hadoop.ipc.Server$Handler$1.run(Server.java:2009)
at java.security.AccessController.doPrivileged(Native Method)
at javax.security.auth.Subject.doAs(Subject.java:415)
at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1614)
at org.apache.hadoop.ipc.Server$Handler.run(Server.java:2007)

at org.apache.hadoop.ipc.Client.call(Client.java:1411)
at org.apache.hadoop.ipc.Client.call(Client.java:1364)
at org.apache.hadoop.ipc.ProtobufRpcEngine$Invoker.invoke(ProtobufRpcEngine.java:206)
at com.sun.proxy.$Proxy14.setPermission(Unknown Source)
at org.apache.hadoop.hdfs.protocolPB.ClientNamenodeProtocolTranslatorPB.setPermission(ClientNamenodeProtocolTranslatorPB.java:314)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(Unknown Source)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(Unknown Source)
at java.lang.reflect.Method.invoke(Unknown Source)
at org.apache.hadoop.io.retry.RetryInvocationHandler.invokeMethod(RetryInvocationHandler.java:187)
at org.apache.hadoop.io.retry.RetryInvocationHandler.invoke(RetryInvocationHandler.java:102)
at com.sun.proxy.$Proxy15.setPermission(Unknown Source)
at org.apache.hadoop.hdfs.DFSClient.setPermission(DFSClient.java:2163)
... 16 more

    解决方法：

<property>
<name>dfs.permissions</name>
<value>false</value>
</property>

hadoop dfsadmin -refreshNodes

或者

hadoop fs -chmod 777 /tmp

问题2：

ExitCodeException exitCode=1: /bin/bash: line 0: fg: no job control
at org.apache.hadoop.util.Shell.runCommand(Shell.java:538)
at org.apache.hadoop.util.Shell.run(Shell.java:455)
at org.apache.hadoop.util.Shell$ShellCommandExecutor.execute(Shell.java:702)
at org.apache.hadoop.yarn.server.nodemanager.DefaultContainerExecutor.launchContainer(DefaultContainerExecutor.java:195)
at org.apache.hadoop.yarn.server.nodemanager.containermanager.launcher.ContainerLaunch.call(ContainerLaunch.java:300)
at org.apache.hadoop.yarn.server.nodemanager.containermanager.launcher.ContainerLaunch.call(ContainerLaunch.java:81)
at java.util.concurrent.FutureTask.run(FutureTask.java:262)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:745)
Container exited with a non-zero exit code 1
.Failing this attempt.. Failing the application.
2016-05-09 16:43:11,531 INFO  [main] mapreduce.Job (Job.java:monitorAndPrintJob(1380)) - Counters: 0

    解决方法：http://www.360doc.com/content/14/0728/11/597197_397616444.shtml问题3：

2016-05-09 17:15:03,121 WARN  [main] util.NativeCodeLoader (NativeCodeLoader.java:<clinit>(62)) - Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
2016-05-09 17:15:06,883 WARN  [main] mapreduce.JobSubmitter (JobSubmitter.java:copyAndConfigureFiles(150)) - Hadoop command-line option parsing not performed. Implement the Tool interface and execute your application with ToolRunner to remedy this.
2016-05-09 17:15:06,909 WARN  [main] mapreduce.JobSubmitter (JobSubmitter.java:copyAndConfigureFiles(259)) - No job jar file set.  User classes may not be found. See Job or Job#setJar(String).
2016-05-09 17:15:06,947 INFO  [main] input.FileInputFormat (FileInputFormat.java:listStatus(281)) - Total input paths to process : 1
2016-05-09 17:15:07,237 INFO  [main] mapreduce.JobSubmitter (JobSubmitter.java:submitJobInternal(396)) - number of splits:1
2016-05-09 17:15:07,419 INFO  [main] mapreduce.JobSubmitter (JobSubmitter.java:printTokens(479)) - Submitting tokens for job: job_1462791130009_0014
2016-05-09 17:15:07,535 INFO  [main] mapred.YARNRunner (YARNRunner.java:createApplicationSubmissionContext(374)) - Job jar is not present. Not adding any jar to the list of resources.
2016-05-09 17:15:07,575 INFO  [main] impl.YarnClientImpl (YarnClientImpl.java:submitApplication(236)) - Submitted application application_1462791130009_0014
2016-05-09 17:15:07,605 INFO  [main] mapreduce.Job (Job.java:submit(1289)) - The url to track the job: http://lida1:8088/proxy/application_1462791130009_0014/ 2016-05-09 17:15:07,606 INFO  [main] mapreduce.Job (Job.java:monitorAndPrintJob(1334)) - Running job: job_1462791130009_0014
2016-05-09 17:15:11,665 INFO  [main] mapreduce.Job (Job.java:monitorAndPrintJob(1355)) - Job job_1462791130009_0014 running in uber mode : false
2016-05-09 17:15:11,668 INFO  [main] mapreduce.Job (Job.java:monitorAndPrintJob(1362)) -  map 0% reduce 0%
2016-05-09 17:15:11,700 INFO  [main] mapreduce.Job (Job.java:monitorAndPrintJob(1375)) - Job job_1462791130009_0014 failed with state FAILED due to: Application application_1462791130009_0014 failed 2 times due to AM Container for appattempt_1462791130009_0014_000002 exited with  exitCode: 1 due to: Exception from container-launch: ExitCodeException exitCode=1:
ExitCodeException exitCode=1:
at org.apache.hadoop.util.Shell.runCommand(Shell.java:538)
at org.apache.hadoop.util.Shell.run(Shell.java:455)
at org.apache.hadoop.util.Shell$ShellCommandExecutor.execute(Shell.java:702)
at org.apache.hadoop.yarn.server.nodemanager.DefaultContainerExecutor.launchContainer(DefaultContainerExecutor.java:195)
at org.apache.hadoop.yarn.server.nodemanager.containermanager.launcher.ContainerLaunch.call(ContainerLaunch.java:300)
at org.apache.hadoop.yarn.server.nodemanager.containermanager.launcher.ContainerLaunch.call(ContainerLaunch.java:81)
at java.util.concurrent.FutureTask.run(FutureTask.java:262)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:745)
Container exited with a non-zero exit code 1
.Failing this attempt.. Failing the application.
2016-05-09 17:15:11,723 INFO  [main] mapreduce.Job (Job.java:monitorAndPrintJob(1380)) - Counters: 0

另外，从图形化界面上可看到还有一个报错信息，如下图：

解决方法：http://www.cnblogs.com/gaoxing/p/4466924.html 在mapred-site.xml中添加进hadoopjar包即可。

问题4：

2016-05-09 17:49:30,572 INFO  [main] mapreduce.Job (Job.java:printTaskEvents(1441)) - Task Id : attempt_1462791130009_0020_m_000000_2, Status : FAILEDError: java.lang.RuntimeException: java.lang.ClassNotFoundException: Class com.dada.mr.WC$WordCountMapper not foundat org.apache.hadoop.conf.Configuration.getClass(Configuration.java:1905)at org.apache.hadoop.mapreduce.task.JobContextImpl.getMapperClass(JobContextImpl.java:186)at org.apache.hadoop.mapred.MapTask.runNewMapper(MapTask.java:722)at org.apache.hadoop.mapred.MapTask.run(MapTask.java:340)at org.apache.hadoop.mapred.YarnChild$2.run(YarnChild.java:168)at java.security.AccessController.doPrivileged(Native Method)at javax.security.auth.Subject.doAs(Subject.java:415)at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1614)at org.apache.hadoop.mapred.YarnChild.main(YarnChild.java:163)Caused by: java.lang.ClassNotFoundException: Class com.dada.mr.WC$WordCountMapper not foundat org.apache.hadoop.conf.Configuration.getClassByName(Configuration.java:1811)at org.apache.hadoop.conf.Configuration.getClass(Configuration.java:1903)... 8 more2016-05-09 17:49:37,635 INFO  [main] mapreduce.Job (Job.java:monitorAndPrintJob(1362)) -  map 100% reduce 100%2016-05-09 17:49:37,670 INFO  [main] mapreduce.Job (Job.java:monitorAndPrintJob(1375)) - Job job_1462791130009_0020 failed with state FAILED due to: Task failed task_1462791130009_0020_m_000000Job failed as tasks failed. failedMaps:1 failedReduces:02016-05-09 17:49:37,761 INFO  [main] mapreduce.Job (Job.java:monitorAndPrintJob(1380)) - Counters: 9Job CountersFailed map tasks=4Launched map tasks=4Other local map tasks=3Data-local map tasks=1Total time spent by all maps in occupied slots (ms)=16275Total time spent by all reduces in occupied slots (ms)=0Total time spent by all map tasks (ms)=16275Total vcore-seconds taken by all map tasks=16275Total megabyte-seconds taken by all map tasks=16665600

参考链接：http://stackoverflow.com/questions/21373550/class-not-found-exception-in-mapreduce-wordcount-job  

解决方法：

job.setJar("wordcount.jar");