Windows8.1+Eclipse搭建Hadoop2.7.2本地模式开发环境

下面介绍如何在Windows8.1上搭建hadoop2.7.2的本地模式开发环境，为后期做mapreduce的开发做准备。

在搭建开发环境之前，首先选择开发工具，就是大家都很熟悉的Eclipse（本人这次使用的是eclipse4.4.2版本），Eclipse提供了hadoop的插件，我们通过这个插件，就可以在eclipse中编写mapreduce。但是，这个插件可能会随着hadoop的版本升级或者eclipse的版本升级，而需要相应的去进行编译。所以，在我们开发之前，学会编译这个eclipse的hadoop插件至关重要，编译eclipse插件使用ant工具，ant工具不在本次的介绍范围内。

1、首先通过sourcetree获取hadoop2x-eclipse-plugin插件。

1.1、插件地址在github上:https://github.com/winghc/hadoop2x-eclipse-plugin.git上下载

1.2、将下载的插件hadoop2x-eclipse-plugin-master.zip在本地磁盘进行解压，解压之后的目录结构如下：

1.3、接着修改F:\Hadoop\eclipsechajian\hadoop2x-eclipse-plugin-master\src\contrib\eclipse-plugin目录下的build.xml文件

由于网站上都是基于hadoop2.6版本进行的编译，2.7.2版本对于build.xml需要修改如下：

找到<target name="jar" depends="compile" unless="skip.contrib">标签，在这个element下有一堆<copy file=....>的sub-element，将其中这段<copy file="${hadoop.home}/share/hadoop/common/lib/htrace-core-${htrace.version}.jar"  todir="${build.dir}/lib" verbose="true"/>更改为

<copy file="${hadoop.home}/share/hadoop/common/lib/htrace-core-${htrace.version}-incubating.jar"  todir="${build.dir}/lib" verbose="true"/>

 并添加两个新的element：

    <copy file="${hadoop.home}/share/hadoop/common/lib/servlet-api-${servlet-api.version}.jar"  todir="${build.dir}/lib" verbose="true"/>
    <copy file="${hadoop.home}/share/hadoop/common/lib/commons-io-${commons-io.version}.jar"  todir="${build.dir}/lib" verbose="true"/>

以上这些jar包在编译hadoop2.7.2  eclipse插件的时候需要用到，如果不添加就会报错，所以，我们在ant编译之前先添加进来。

1.4、然后再找到<jar arfile="${build.dir}/hadoop-${name}-${hadoop.version}.jar" manifest="${root}/META-INF/MANIFEST.MF">标签，把刚刚copy的包，在ant构建的时候写到mainfest.mf文件的Bundle-ClassPath中：

lib/servlet-api-${servlet-api.version}.jar,
 lib/commons-io-${commons-io.version}.jar,

并将lib/htrace-core-${htrace.version}.jar替换为lib/htrace-core-${htrace.version}-incubating.jar

1.5、再修改\hadoop2x-eclipse-plugin\src\ivy\libraries.properties文件，这个文件配置了ant构建需要用到各个jar包的版本，以及构建hadoop的版本，由于下载的这个插件是编译hadoop2.6.0的，所以我们需要修改以下配置，更改下列属性和其值使其对应hadoop2.7.2和当前环境的jar包版本：

     hadoop.version=2.7.2
     apacheant.version=1.9.7
     commons-collections.version=3.2.2
     commons-httpclient.version=3.1
     commons-logging.version=1.1.3
     commons-io.version=2.4
     slf4j-api.version=1.7.10
     slf4j-log4j12.version=1.7.10

其实在ant构建的时候，会选择本地hadoop2.7.2目录中的jar包版本(\hadoop-2.7.2\share\hadoop\common)，所以只要将版本号改成对应的版本号即可如下图：

1.6、最后修改\hadoop2x-eclipse-plugin\ivy\libraries.properties文件，文件的的版本如上图版本修改一样，但是还有一个版本需要修改的就是

htrace.version的版本要改成3.1.0，htrace.version=3.1.0

1.7、然后cd到F:\Hadoop\eclipsechajian\hadoop2x-eclipse-plugin-master\src\contrib\eclipse-plugin目录

执行以下命令：

ant jar -Dversion=2.7.2 -Declipse.home=D:\eclipse_hadoop -Dhadoop.home=F:\Hadoop\hadoop-2.7.2

解释下这个命令：-Dversion是指这个插件的版本，Declipse.home是指eclipse的安装目录，-Dhadoop.home指本地文件中hadoop-2.7.2的安装目录。

命令执行成功之后就可以在\hadoop2x-eclipse-plugin\build\contrib\eclipse-plugin目录下面找到

hadoop-eclipse-plugin-2.7.2.jar 包，这个包就是编译好的eclipse hadoop2.7.2插件，把这个插件放到eclipse安装目录的plugins目录下，我们就可以进入eclipse然后找到一个叫mapreduce的视图，就可以开始尝试编写mapreduce程序了。

1.8、下载eclipse并配置JDK

去http://www.eclipse.org/downloads/ 下载你需要的版本，我们这里下载的是win64位版。直接解压到目录中。进行简单设置，根据你的开发需要，选择jdk的版本

1.9、设置hadoop插件

在eclipse菜单中选择，window - preferences，打开设置菜单

至此Eclipse开发环境搭建完成，下面将搭建hadoop的运行环境，hadoop项目是需要将程序提交到hadoop运行环境上面运行的。

2、Eclipse插件编译好之后，就需要安装Hadoop2.7.2

hadoop环境搭建相对麻烦，需要安装虚拟机或者着cygwin什么的，但是通过查官方资料和摸索，在window上搭建了本地模式，可以不需要虚拟机和cygwin依赖，而且官网明确指出cygwin已经不支持hadoop2.x。

Windows下搭建Hadoop本地模式运行环境参考：http://wiki.apache.org/hadoop/Hadoop2OnWindows

下面配置windows环境：

2.1、Java JDK ：我采用的是1.8的，配置JAVA_HOME，如果默认安装，会安装在C:\Program Files\Java\jdk1.8.0_51。此目录存在空格，启动hadoop时将报错，JAVA_HOME is incorrect ...此时需要将环境变量JAVA_HOME值修改为：C:\Progra~1\Java\jdk1.8.0_51，Program Files可以有Progra~代替。

2.2、Hadoop 环境变量： 新建HADOOP_HOME，指向hadoop解压目录，如：F:\Hadoop\hadoop-2.7.2。然后在path环境变量中增加：%HADOOP_HOME%\bin;。

2.3、Hadoop 依赖库：winutils相关，hadoop在windows上运行需要winutils支持和hadoop.dll等文件，下载地址：http://download.csdn.net/detail/fly_leopard/9503059

注意hadoop.dll等文件不要与hadoop冲突。为了不出现依赖性错误可以将hadoop.dll放到c:/windows/System32下一份，然后重启计算机。

2.4、hadoop环境测试：

起一个cmd窗口，切换到hadoop-2.7.2\bin下，执行hadoop version命令，显示如下：

2.5、hadoop基本文件配置：hadoop配置文件位于：hadoop-2.7.2\etc\hadoop下

core-site.xml、hdfs-site.xml、mapred-site.xml、yarn-site.xml

core-site.xml:

<configuration>

<property>

    <name>fs.default.name</name>

    <value>hdfs://0.0.0.0:19000</value>

  </property>

</configuration>

hdfs-site.xml: 

<configuration>

       <property>

               <name>dfs.replication</name>

                <value>1</value>

       </property>

       <property>

                <name>dfs.namenode.name.dir</name>

               <value>file:/Hadoop/hadoop-2.7.2/data/dfs/namenode</value>

       </property>

       <property>

               <name>dfs.datanode.data.dir</name>

               <value>file:/Hadoop/hadoop-2.7.2/data/dfs/datanode</value>

       </property>

</configuration>

mapred-site.xml:

<configuration>

<property>

     <name>mapreduce.job.user.name</name>

     <value>%USERNAME%</value>

   </property>

   <property>

     <name>mapreduce.framework.name</name>

     <value>yarn</value>

   </property>

  <property>

    <name>yarn.apps.stagingDir</name>

    <value>/user/%USERNAME%/staging</value>

  </property>

  <property>

    <name>mapreduce.jobtracker.address</name>

    <value>local</value>

  </property>

</configuration>

其中%USERNAME%为你计算机执行hadoop的用户名。

yarn-site.xml:

<configuration>

<property>

    <name>yarn.server.resourcemanager.address</name>

    <value>0.0.0.0:8020</value>

  </property>

  <property>

    <name>yarn.server.resourcemanager.application.expiry.interval</name>

    <value>60000</value>

  </property>

  <property>

    <name>yarn.server.nodemanager.address</name>

    <value>0.0.0.0:45454</value>

  </property>

  <property>

    <name>yarn.nodemanager.aux-services</name>

    <value>mapreduce_shuffle</value>

  </property>

  <property>

    <name>yarn.nodemanager.aux-services.mapreduce.shuffle.class</name>

    <value>org.apache.hadoop.mapred.ShuffleHandler</value>

  </property>

  <property>

    <name>yarn.server.nodemanager.remote-app-log-dir</name>

    <value>/app-logs</value>

  </property>

  <property>

    <name>yarn.nodemanager.log-dirs</name>

    <value>/dep/logs/userlogs</value>

  </property>

  <property>

    <name>yarn.server.mapreduce-appmanager.attempt-listener.bindAddress</name>

    <value>0.0.0.0</value>

  </property>

  <property>

    <name>yarn.server.mapreduce-appmanager.client-service.bindAddress</name>

    <value>0.0.0.0</value>

  </property>

  <property>

    <name>yarn.log-aggregation-enable</name>

    <value>true</value>

  </property>

  <property>

    <name>yarn.log-aggregation.retain-seconds</name>

    <value>-1</value>

  </property>

  <property>

    <name>yarn.application.classpath</name>

    <value>%HADOOP_CONF_DIR%,%HADOOP_HOME%/share/hadoop/common/*,%HADOOP_HOME%/share/hadoop/common/lib/*,%HADOOP_HOME%/share/hadoop/hdfs/*,%HADOOP_HOME%/share/hadoop/hdfs/lib/*,%HADOOP_HOME%/share/hadoop/mapreduce/*,%HADOOP_HOME%/share/hadoop/mapreduce/lib/*,%HADOOP_HOME%/share/hadoop/yarn/*,%HADOOP_HOME%/share/hadoop/yarn/lib/*</value>

  </property>

</configuration>

其中：%HADOOP_CONF_DIR%为hadoop的安装路径；yarn.nodemanager.log-dirs配置项的路径是在你hadoop安装路径的跟目录创建，例如我的hadoop是在F盘，所以该配置项的目录就在F盘创建。

2.6、格式化系统文件：

hadoop-2.7.2/bin下执行 hdfs namenode -format

待执行完毕即可，不要重复format容易出现异常。

2.7、格式化完成后到hadoop-2.7.2/sbin下执行 start-dfs.cmd启动hadoop

访问：http://localhost:50070

2.8、在hadoop-2.7.2/sbin下执行start-yarn.cmd启动yarn，访问http://localhost:8088可查看 资源、节点管理

至此表示hadoop2.7.2运行环境搭建完成。

3、结合Eclipse创建MR项目并使用本地系统进行hadoop本地模式开发

我在者使用Eclipse开发使用的是本地文件系统，没有使用HDFS，HDFS在完全分布式下介绍的多，在这就不用介绍，另外使用Eclipse开发并不是很多文章介绍一定要配置DFS Locations（这个不影响开发），这个是用来查看集群上的HDFS文件系统的（我目前是这样理解），反正我使用这个连接本地windows8.1上启动的hadoop（本地模式），一直没练成功过，报下面的错误：

java.lang.NoClassDefFoundError: org/apache/htrace/SamplerBuilder

at org.apache.hadoop.hdfs.DFSClient.<init>(DFSClient.java:635)

at org.apache.hadoop.hdfs.DFSClient.<init>(DFSClient.java:619)

at org.apache.hadoop.hdfs.DistributedFileSystem.initialize(DistributedFileSystem.java:149)

at org.apache.hadoop.fs.FileSystem.createFileSystem(FileSystem.java:2653)

at org.apache.hadoop.fs.FileSystem.access$200(FileSystem.java:92)

at org.apache.hadoop.fs.FileSystem$Cache.getInternal(FileSystem.java:2687)

at org.apache.hadoop.fs.FileSystem$Cache.get(FileSystem.java:2669)

at org.apache.hadoop.fs.FileSystem.get(FileSystem.java:371)

at org.apache.hadoop.fs.FileSystem.get(FileSystem.java:170)

at org.apache.hadoop.eclipse.server.HadoopServer.getDFS(HadoopServer.java:478)

at org.apache.hadoop.eclipse.dfs.DFSPath.getDFS(DFSPath.java:146)

at org.apache.hadoop.eclipse.dfs.DFSFolder.loadDFSFolderChildren(DFSFolder.java:61)

at org.apache.hadoop.eclipse.dfs.DFSFolder$1.run(DFSFolder.java:178)

at org.eclipse.core.internal.jobs.Worker.run(Worker.java:54)

这个问题目前已解决，是因为缺少相应的插件jar包；需要将下面3个插件放入到$eclipse_home\plugins\目录下。

好了，下面进入使用Eclipse开发hadoop的介绍

3.1、上面环境搭建完成之后，下面开始讲如何进行开发，我们使用hadoop的wordcount来做测试。

创建mr项目

设置项目名称

创建类

设置类属性

创建完成后，将hadoop-2.7.2-src\hadoop-mapreduce-project\hadoop-mapreduce-examples\src\main\java\org\apache\hadoop\examples目录下的WordCount.java文件内容，copy到刚创建的文件中。

3.2接下来创建配置环境

在项目中创建一个名为resources的Source Floder，然后将F:\Hadoop\hadoop-2.7.2\etc\hadoop下的配置文件全部copy到该目录下。

3.3、运行WordCount程序

以上完成后，即完成开发环境配置，接下来试试运行是否成功。

上图中红圈标注的是重点，配置的是wordcount的输入输出路径，因为本地模式我使用的是本地文件系统而不是HDFS，所以该地方是使用的file:///而不是hdfs://（需要特别注意）。

然后点击Run按钮，hadoop就可运行了。

当出现下面情况，则表示运行成功：

16/09/15 22:18:37 INFO client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8032

16/09/15 22:18:39 WARN mapreduce.JobResourceUploader: No job jar file set.  User classes may not be found. See Job or Job#setJar(String).

16/09/15 22:18:39 INFO input.FileInputFormat: Total input paths to process : 2

16/09/15 22:18:40 INFO mapreduce.JobSubmitter: number of splits:2

16/09/15 22:18:41 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1473949101198_0001

16/09/15 22:18:41 INFO mapred.YARNRunner: Job jar is not present. Not adding any jar to the list of resources.

16/09/15 22:18:41 INFO impl.YarnClientImpl: Submitted application application_1473949101198_0001

16/09/15 22:18:41 INFO mapreduce.Job: The url to track the job: http://Lenovo-PC:8088/proxy/application_1473949101198_0001/

16/09/15 22:18:41 INFO mapreduce.Job: Running job: job_1473949101198_0001

16/09/15 22:18:53 INFO mapreduce.Job: Job job_1473949101198_0001 running in uber mode : false

16/09/15 22:18:53 INFO mapreduce.Job:  map 0% reduce 0%

16/09/15 22:19:03 INFO mapreduce.Job:  map 100% reduce 0%

16/09/15 22:19:10 INFO mapreduce.Job:  map 100% reduce 100%

16/09/15 22:19:11 INFO mapreduce.Job: Job job_1473949101198_0001 completed successfully

16/09/15 22:19:12 INFO mapreduce.Job: Counters: 50

File System Counters

FILE: Number of bytes read=119

FILE: Number of bytes written=359444

FILE: Number of read operations=0

FILE: Number of large read operations=0

FILE: Number of write operations=0

HDFS: Number of bytes read=194

HDFS: Number of bytes written=0

HDFS: Number of read operations=2

HDFS: Number of large read operations=0

HDFS: Number of write operations=0

Job Counters 

Killed map tasks=1

Launched map tasks=2

Launched reduce tasks=1

Rack-local map tasks=2

Total time spent by all maps in occupied slots (ms)=12156

Total time spent by all reduces in occupied slots (ms)=4734

Total time spent by all map tasks (ms)=12156

Total time spent by all reduce tasks (ms)=4734

Total vcore-milliseconds taken by all map tasks=12156

Total vcore-milliseconds taken by all reduce tasks=4734

Total megabyte-milliseconds taken by all map tasks=12447744

Total megabyte-milliseconds taken by all reduce tasks=4847616

Map-Reduce Framework

Map input records=2

Map output records=8

Map output bytes=78

Map output materialized bytes=81

Input split bytes=194

Combine input records=8

Combine output records=6

Reduce input groups=4

Reduce shuffle bytes=81

Reduce input records=6

Reduce output records=4

Spilled Records=12

Shuffled Maps =2

Failed Shuffles=0

Merged Map outputs=2

GC time elapsed (ms)=187

CPU time spent (ms)=1733

Physical memory (bytes) snapshot=630702080

Virtual memory (bytes) snapshot=834060288

Total committed heap usage (bytes)=484966400

Shuffle Errors

BAD_ID=0

CONNECTION=0

IO_ERROR=0

WRONG_LENGTH=0

WRONG_MAP=0

WRONG_REDUCE=0

File Input Format Counters 

Bytes Read=44

File Output Format Counters 

Bytes Written=43

然后在输出路径（运行中配置的输出路径）中查看运行结果：

运行当中可能出现如下问题：

1）、问题1：

16/09/15 22:12:08 INFO client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8032

Exception in thread "main" java.net.ConnectException: Call From Lenovo-PC/192.168.1.105 to 0.0.0.0:9000 failed on connection exception: java.net.ConnectException: Connection refused: no further information; For more details see:  http://wiki.apache.org/hadoop/ConnectionRefused

at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)

at sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)

at sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)

at java.lang.reflect.Constructor.newInstance(Constructor.java:423)

at org.apache.hadoop.net.NetUtils.wrapWithMessage(NetUtils.java:792)

at org.apache.hadoop.net.NetUtils.wrapException(NetUtils.java:732)

at org.apache.hadoop.ipc.Client.call(Client.java:1479)

at org.apache.hadoop.ipc.Client.call(Client.java:1412)

at org.apache.hadoop.ipc.ProtobufRpcEngine$Invoker.invoke(ProtobufRpcEngine.java:229)

at com.sun.proxy.$Proxy12.getFileInfo(Unknown Source)

at org.apache.hadoop.hdfs.protocolPB.ClientNamenodeProtocolTranslatorPB.getFileInfo(ClientNamenodeProtocolTranslatorPB.java:771)

at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)

at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)

at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)

at java.lang.reflect.Method.invoke(Method.java:498)

at org.apache.hadoop.io.retry.RetryInvocationHandler.invokeMethod(RetryInvocationHandler.java:191)

at org.apache.hadoop.io.retry.RetryInvocationHandler.invoke(RetryInvocationHandler.java:102)

at com.sun.proxy.$Proxy13.getFileInfo(Unknown Source)

at org.apache.hadoop.hdfs.DFSClient.getFileInfo(DFSClient.java:2108)

at org.apache.hadoop.hdfs.DistributedFileSystem$22.doCall(DistributedFileSystem.java:1305)

at org.apache.hadoop.hdfs.DistributedFileSystem$22.doCall(DistributedFileSystem.java:1301)

at org.apache.hadoop.fs.FileSystemLinkResolver.resolve(FileSystemLinkResolver.java:81)

at org.apache.hadoop.hdfs.DistributedFileSystem.getFileStatus(DistributedFileSystem.java:1301)

at org.apache.hadoop.fs.FileSystem.exists(FileSystem.java:1424)

at org.apache.hadoop.mapreduce.JobSubmissionFiles.getStagingDir(JobSubmissionFiles.java:116)

at org.apache.hadoop.mapreduce.JobSubmitter.submitJobInternal(JobSubmitter.java:144)

at org.apache.hadoop.mapreduce.Job$10.run(Job.java:1290)

at org.apache.hadoop.mapreduce.Job$10.run(Job.java:1287)

at java.security.AccessController.doPrivileged(Native Method)

at javax.security.auth.Subject.doAs(Subject.java:422)

at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1657)

at org.apache.hadoop.mapreduce.Job.submit(Job.java:1287)

at org.apache.hadoop.mapreduce.Job.waitForCompletion(Job.java:1308)

at org.apache.hadoop.examples.WordCount.main(WordCount.java:87)

Caused by: java.net.ConnectException: Connection refused: no further information

at sun.nio.ch.SocketChannelImpl.checkConnect(Native Method)

at sun.nio.ch.SocketChannelImpl.finishConnect(SocketChannelImpl.java:717)

at org.apache.hadoop.net.SocketIOWithTimeout.connect(SocketIOWithTimeout.java:206)

at org.apache.hadoop.net.NetUtils.connect(NetUtils.java:531)

at org.apache.hadoop.net.NetUtils.connect(NetUtils.java:495)

at org.apache.hadoop.ipc.Client$Connection.setupConnection(Client.java:614)

at org.apache.hadoop.ipc.Client$Connection.setupIOstreams(Client.java:712)

at org.apache.hadoop.ipc.Client$Connection.access$2900(Client.java:375)

at org.apache.hadoop.ipc.Client.getConnection(Client.java:1528)

at org.apache.hadoop.ipc.Client.call(Client.java:1451)

... 27 more

出现上述问题是由于项目中的core-site.xml中和本地安装的hadoop配置文件core-site.xml中的端口不一致，请修改成一致。

2）、问题2：

16/09/15 22:14:45 INFO client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8032

16/09/15 22:14:48 INFO ipc.Client: Retrying connect to server: 0.0.0.0/0.0.0.0:8032. Already tried 0 time(s); retry policy is RetryUpToMaximumCountWithFixedSleep(maxRetries=10, sleepTime=1000 MILLISECONDS)

16/09/15 22:14:50 INFO ipc.Client: Retrying connect to server: 0.0.0.0/0.0.0.0:8032. Already tried 1 time(s); retry policy is RetryUpToMaximumCountWithFixedSleep(maxRetries=10, sleepTime=1000 MILLISECONDS)

16/09/15 22:14:52 INFO ipc.Client: Retrying connect to server: 0.0.0.0/0.0.0.0:8032. Already tried 2 time(s); retry policy is RetryUpToMaximumCountWithFixedSleep(maxRetries=10, sleepTime=1000 MILLISECONDS)

16/09/15 22:14:54 INFO ipc.Client: Retrying connect to server: 0.0.0.0/0.0.0.0:8032. Already tried 3 time(s); retry policy is RetryUpToMaximumCountWithFixedSleep(maxRetries=10, sleepTime=1000 MILLISECONDS)

如果出现上述问题表示yarn没有启动，请启动yarn。

3）、问题3：

16/09/15 22:16:00 INFO client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8032

16/09/15 22:16:02 WARN mapreduce.JobResourceUploader: No job jar file set.  User classes may not be found. See Job or Job#setJar(String).

16/09/15 22:16:02 INFO input.FileInputFormat: Total input paths to process : 2

16/09/15 22:16:03 INFO mapreduce.JobSubmitter: number of splits:2

16/09/15 22:16:03 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1473948945298_0001

16/09/15 22:16:04 INFO mapred.YARNRunner: Job jar is not present. Not adding any jar to the list of resources.

16/09/15 22:16:04 INFO impl.YarnClientImpl: Submitted application application_1473948945298_0001

16/09/15 22:16:04 INFO mapreduce.Job: The url to track the job: http://Lenovo-PC:8088/proxy/application_1473948945298_0001/

16/09/15 22:16:04 INFO mapreduce.Job: Running job: job_1473948945298_0001

16/09/15 22:16:08 INFO mapreduce.Job: Job job_1473948945298_0001 running in uber mode : false

16/09/15 22:16:08 INFO mapreduce.Job:  map 0% reduce 0%

16/09/15 22:16:08 INFO mapreduce.Job: Job job_1473948945298_0001 failed with state FAILED due to: Application application_1473948945298_0001 failed 2 times due to AM Container for appattempt_1473948945298_0001_000002 exited with  exitCode: -1000

For more detailed output, check application tracking page:http://Lenovo-PC:8088/cluster/app/application_1473948945298_0001Then, click on links to logs of each attempt.

Diagnostics: Could not find any valid local directory for nmPrivate/container_1473948945298_0001_02_000001.tokens

Failing this attempt. Failing the application.

16/09/15 22:16:08 INFO mapreduce.Job: Counters: 0

如果出现上述问题，表示你没有使用管理员权限启动hadoop，请使用管理员权限启动hadoop。