一、准备工作

 (1)Hadoop2.7.2 在linux部署完毕,成功启动dfs和yarn,通过jps查看,进程都存在

 (2)安装maven

二、最终效果

 在windows系统中,直接通过Run as Java Application运行wordcount,而不需要先打包成jar包,然后在linux终端运行

三,操作步骤

 1、启动dfs和yarn

  终端:${HADOOP_HOME}/sbin/start-dfs.sh

    ${HADOOP_HOME}/sbin/start-yarn.sh

  通过在namenode节点上jps查看显示:

  4852 NameNode
  5364 ResourceManager
  5141 SecondaryNameNode
  10335 Jps

  在datanode节点上使用jps查看显示:

  10369 Jps
  4852 NameNode
  5364 ResourceManager
  5141 SecondaryNameNode

 2、Eclipse基础配置

  (1)将hadoop-eclipse-plugin-2.7.2.jar插件下载,放在Eclipse的目录下的plugins目录下,启动Eclipse,然后点击查看Hadoop插件是否生效,点击windows——>preferences,如下图1

    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" alt="" />

  (2)将hadoop-2.7.2的解压包添加到2所示的目录,点击OK

 3、Eclipse创建maven工程

  (1)创建过程省略

  (2)添加dependency,POM.xml中的依赖项如下:

  hadoop-common

  hadoop-hdfs

  hadoop-mapreduce-client-core

      hadoop-mapreduce-client-common  

  <!-- https://mvnrepository.com/artifact/org.apache.hadoop/hadoop-common -->
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-common</artifactId>
<version>2.7.2</version>
</dependency>
<!-- https://mvnrepository.com/artifact/org.apache.hadoop/hadoop-hdfs -->
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-hdfs</artifactId>
<version>2.7.2</version>
</dependency>
<!-- https://mvnrepository.com/artifact/org.apache.hadoop/hadoop-mapreduce-client-core -->
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-mapreduce-client-core</artifactId>
<version>2.7.2</version>
</dependency>
<!-- https://mvnrepository.com/artifact/org.apache.hadoop/hadoop-mapreduce-client-common -->
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-mapreduce-client-common</artifactId>
<version>2.7.2</version>
</dependency>

   (3)此时可能会卡顿一段时间,Build workpath如果特别慢的话,请参考我前不久的一篇解决方法,等到maven中的依赖包下载install完毕即可

 4、编写mapreduce中的wordcount代码

  代码此处不在累述,,简单代码架构(红色框的那个包)和内容如下:

        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" alt="" />

  WCMapper类:  

package cn.edu.nupt.hadoop.mr.wordcount;

import java.io.IOException;

import org.apache.commons.lang.StringUtils;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper; // 4个泛型中,前两个是指定的mapper输入数据的类型
//map 和 reduce 的数据输入输出是以key-value的形式封装的
//默认情况下,框架传递给我们的mapper的输入数据中,key是要处理的文本中一行的其实偏移量,这一行的内容作为value
// JDK 中long string等使用jdk自带的序列化机制,序列化之后会携带很多附加信息,造成网络传输冗余,
// 所以Hadoop自己封装了一些序列化机制
public class WCMapper extends Mapper<LongWritable, Text, Text, LongWritable>{ // mapreduce框架每读一行就调用一次该方法
@Override
protected void map(LongWritable key, Text value,Context context)
throws IOException, InterruptedException {
//具体的业务写在这个方法中,而且我们业务要处理的数据已经被该框架传递进来
// key是这一行的其实偏移量,value是文本内容
String line = value.toString(); String[] words = StringUtils.split(line, " "); for(String word : words){ context.write(new Text(word), new LongWritable(1));
}
}
}

  WCReducer类: 

package cn.edu.nupt.hadoop.mr.wordcount;

import java.io.IOException;

import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer; public class WCReducer extends Reducer<Text, LongWritable, Text, LongWritable>{ // 框架在map处理完成之后,将所有的kv对缓存起来,进行分组,然后传递一个组
// <key,{value1,value2...valuen}>
//<hello,{1,1,1,1,1,1.....}>
@Override
protected void reduce(Text key, Iterable<LongWritable> values,Context context)
throws IOException, InterruptedException { long count = 0;
for(LongWritable value:values){ count += value.get();
}
context.write(key, new LongWritable(count));
}
}

  WCRunner类

package cn.edu.nupt.hadoop.mr.wordcount;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
/**
*
*<p> WCRunner.java
* Description:<br/>
* (1)用来描述一个作业<br/>
* (2)比如,该作业使用哪个类作为逻辑处理中的map,哪个作为reduce
* (3)还可以指定改作业要处理的数据所在的路径
* (4)还可以指定作业输出的路径
*<p>
* Company: cstor
*
* @author zhuxy
* 2016年8月4日 下午9:58:02
*/
public class WCRunner { public static void main(String[] args) throws Exception { Configuration conf = new Configuration(); Job wcjob = Job.getInstance(conf); // 找到Mapper和Reducer两个类所在的路径
//设置整个job所用的那些类在哪个jar下
wcjob.setJarByClass(WCRunner.class); //本job使用的mapper和reducer类
wcjob.setMapperClass(WCMapper.class);
wcjob.setReducerClass(WCReducer.class); //指定reduce的输出数据kv类型
wcjob.setOutputKeyClass(Text.class);
wcjob.setOutputValueClass(LongWritable.class); // 指定map的输出数据的kv类型
wcjob.setMapOutputKeyClass(Text.class);
wcjob.setMapOutputValueClass(LongWritable.class); //
// FileInputFormat.setInputPaths(wcjob, new Path("hdfs://master:9000/wc/input/testHdfs.txt"));
// FileOutputFormat.setOutputPath(wcjob, new Path("hdfs://master:9000/wc/output7/")); FileInputFormat.setInputPaths(wcjob, new Path("file:///E:/input/testwc.txt"));
FileOutputFormat.setOutputPath(wcjob, new Path("file:///E:/output3/")); wcjob.waitForCompletion(true);
}
}

  此时代码张贴完毕。

 5、在CentOS的本地创建一个文件,命名为testHdfs.txt(这个是我之前的测试文件,内容不重要,名字不重要,一致即可),内容如下:

    hello java

    hello Hadoop

    hello world

  创建好后,将文件上传到hdfs文件系统的/wc/input文件夹下面

  hadoop fs -put ./testHdfs.txt /wc/input

 6、在WCRunner类中,右击Run as -->Java Application,出现如下错误:

  log4j:WARN No appenders could be found for logger (org.apache.hadoop.metrics2.lib.MutableMetricsFactory).
  log4j:WARN Please initialize the log4j system properly.
  log4j:WARN See http://logging.apache.org/log4j/1.2/faq.html#noconfig for more info.
  Exception in thread "main" java.lang.NullPointerException
      at java.lang.ProcessBuilder.start(ProcessBuilder.java:1012)
      at org.apache.hadoop.util.Shell.runCommand(Shell.java:483)
      at org.apache.hadoop.util.Shell.run(Shell.java:456)
      at org.apache.hadoop.util.Shell$ShellCommandExecutor.execute(Shell.java:722)
      at org.apache.hadoop.util.Shell.execCommand(Shell.java:815)
      at org.apache.hadoop.util.Shell.execCommand(Shell.java:798)

    ……

   at org.apache.hadoop.mapreduce.Job.waitForCompletion(Job.java:1308)
     at cn.edu.nupt.hadoop.mr.wordcount.WCRunner.main(WCRunner.java:55)

  解决办法:参考:eclipse Run on Hadoop java.lang.NullPointerException

  方法:在Hadoop的bin目录下放winutils.exe,在环境变量中配置 HADOOP_HOME,把hadoop.dll拷贝到C:\Windows\System32下面即可

    注:此处最好将HADOOP_HOME/bin目录添加到path中,这样可以运行本地模式,即是上述代码中注释的部分

  两个文件的下载地址:win10下hadoo2.7.2的hadoop.dll和winutils.exe

 7、此时再次运行Run as -->Java Application,出现问题如下:  

  log4j:WARN No appenders could be found for logger (org.apache.hadoop.metrics2.lib.MutableMetricsFactory).
  log4j:WARN Please initialize the log4j system properly.
  log4j:WARN See http://logging.apache.org/log4j/1.2/faq.html#noconfig for more info.

  文件夹创建成功,但是文件夹下面没有success 和 运行结果part*文件,即/wc/output3下面没内容(输出结果)。

  解决办法:点击windows-->perspective-->open perspective-->other-->MapReduce,Eclipse界面效果如下:

    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eTh+j4PO5kHneSwZ/M500W8Cbp4kUCc6HAXDZiZmuxhdHNYrHY37v3dAsxWqkJGzVhpYxWyug+oDHSA5vLq3PSdd42dXYq13HWWcStmsfYNdhlcirF5lkcmofmofn5al6q0C+MurUsS/HSZTnNiz+NtEuNFEVhOBmYJ3zx4vCLF4cD8+wfkiiK2sWUvpgkfumSzJny0iXZiz+NrEwTvpIseClZQk++kDjE00zWyWx1Mlud1FYrtQ9qpLYaqa1WaquR2qqltmqprUZiq26q+fKGqlpirbzX8WUqt1JsvZiad7TXyhRbb4q5u+I70kXWsvrq965wS0csJULLDaGleNiSeDlv+6W8d8KS34ntSOFb8gWWfL4lj2/O5ZtzeZyQsKpYrjmba87mmbM55kyuOZNjzuKYM9vbP41uv8gxZ1ZVvbUv6asqcyZ3bH900qd5Y9kcczbPnMM153DNuTw7+XxzPt9SwLcw+JZCgaVEaBnUYQujbkkV+mf2sZmX5hGZzibTWWU6m0xnk+lt9sEUVrcAnVWmd4ZZZXqbfCrROpWut3mWdTkldy2u93IJuf3QKtNb5VPzXhuD5qF5aH5+mmePKBYeb1qaOfbrSzKa/0wSbSu1/5shnKTqBIY6gcFpQnHNY/95UfjrZKkz/teXZL9OlixOGUnt1nzDlP9nkoiefOE0h681V8vQajlaLXNBah9USdFKGVolQ6ukaEVTzcYb6gqp7WZf18ZUHlNiY4pt5WJbudhWdrfrixReichWUl/z3hXeDaG1WGgt7ukIDEt+Jyz5ncvcAoGVwer4c0zHJb41j2/N4Vly+JYcHjdkX1UM15LFs2TyLJlcSwbHkkG/tnd8GtNxwT6jCItOfmtv8qd5ikyOJZNryeJasriWbK4l217Hksu35PKt+QJrvsDKEFiLhdZBHbbweBN7RPnMPjbz0jxyd8TQKzLcHXEgMtwVuQ9GXGY8AjwmvaD3jHQE93rW1991a0PfS6eLfDTmmIfmoXlofl6av90vXnis8ffpo/+eJLXzk+RX54cvdaqm/8XypU7Vr84J/v0nyVQwzUVRYpuKoigbTvzqnICefP40d1hrrpRhHlRIp7gpxW5KsfNXk1fuT/7ihpopQcvvdX3h+Oraew5Cm9DEK8nvhCV/XqhOLcx/Jyz5nbCak8O2fKEtv67m7bDkt8OSt9baYi8lv7XPjTf3Jb+51/G6N/nNZG4a15re3rE+puMcx5rGtaZzrekcazrXGpWc/GYyN51nTedaM3jWDJ41k2fN5Fuz+NZsgTWbb8sW2HKHbbnDtiKh7Z4OX3is8Xa/+Jl9bOaleaSVo2nlqFs56laOpm1I3cbRtHLUbXY0bRy185COaeVo6FOtbgEa9xh1q2euPb51KoyO0Thzp+MoommbqjlV1uVa0Dw0D80/bPM1nYKFRxsXp8l/+ZNkioviX5wTfF0m6ZabUJxEcaJbbtpWKvnFOcEvL4rdImkujHzOEE2iRAXP8Itzw/Tkv57mCbWWShleKcNvyvAKGV4hxStkeIUEr5DiN6U4U4Iz6YEUL5fQYOUSrFSClYmxEjFWIsZuiLAbIqxYhBWPYIUjWNEIViBEC4RovhDNG0bzh9HcYTRXgOYK0Bw+msVHs/loFt+WybNlOEjn2tK4tutOOLZrHNs1jtvAGZDGtaVxbelcWwbPls6zZdLwbdl8NJuP5gjQHD5aJETv6fCFRxtrOoef2cdmXppH6vrG6vrGal1eXQceYw9q73rMKOr6xuo8J72nOK9Y6/sqHv3QwdMuCs1D89D8PDRf0sJdeLThd9fkL1yQuHFe/MI54fNnBM+f5j1/mvf8GcEL54QvnBd7hnkEnx1+4byInvyX03yR3nJTTtyUE0w5wZQ5kNoplxHlUqJcRpTJiDIJUSYhSiV4qQQvkeIlErxEjN+Q4DdEeLEILxbhRSN4oQgvHMELRjCGEMsfwfKFWN4wlivEcgRYzjCWLcCyBFgWH8vko5k8NIOHZvDRdB6azkPTeGgaF73ORa/z0Otcn9BhaVw0nYum89EMPprBRzN5aBYfzRJg2QIsexjLEWCFI9g9HbHwaENJC/eZfWzmpXmE2SVlsmTMLimzS8bsktHj8i4ps0tW3iWlz5bbz0rLu2TlXdLyLhmTJXWkOAeycroOS2afZ0ntwY5c5hRSl0g75VOnHKWmJt16cLkuNA/NQ/Pz03x+4+DCH+tfvSr71/Pi+eW503yJ3nJTRtB48SEHZVJ3H6JNSIwXi/FiEV4kwotEeKEILxjBC0ZwhhDLF2J5QixPiOXSPuQ0IQGWKcAyBWgGH03no+l8NI2PpvHQ6w6uzYgzLI2HpjkqZAjQTAGaKcDsViTAikTYPR2x8Mf6vIbBZ/axmZfmkeI7YgAAgKzagd8dr3vliujfLoh/flY0j/y/RL7MYKkeJavlZLWcrBolq0bJ6lGySm6nUmanQk5WyMgKGWl3LCnBlBDlUqJcQpRLiVIJUSohSiTEDRFxQ0zQzlQ4gheO4AVCnDGCM4R4/jCeN4znDuO5w3jOMJbNd/14hGXysUwelsnHMmh4WLqDDJ59hh5k8rBMnj0xi49l87EcAZYjwHKH8bxhPE+IF4vwdhXRIrP87nh9Vu2A37fvqQZ8CAAAcfEdcU4DO+inhldOtC+6PPLbFOk8siB5xGCyiI2k2EhKjKTYSIonPBEZSdGEGyMOhA6GacbJ4XFyeIIUjBM0fAPBNxA8A8E1EFwDwXEwpLPDdmFQRwxqiUGPgfNQOzU5laUnhvT2Uhw9wdHbL9QmM395vXfzTw05DWy/b99TDfgQAADi4jvi/Gb+NSZr4+mKV49WLzxSO4+8dKRuxYlb81vzSeDVH6u+PF15jcnKb+b7ffueasCHAAAQF98RF7YKcxuGrjFZF/ObzmRWJ6ZXJKTdBLySmF5xJrP6Yn7TNSYrt2GosFXo9+17qgEfAgBAXHxHXNQmKmwV5jfzcxrYWbUDmbX3gBnIqh3IaWDnN/MLW4VFbSK/b99TDfgQAAAA4E8QZvcoAAAAAPgL8CEAAADAn4APAQAAAP4EfAgAAADwJ+BDAAAAgD8BHwIAAAD8CfgQAAAA4E/AhwAAAAB/Aj4EAAAA+BPwIQAAAMCfIBNWitXLm7TgAAAAAPD4QarqO8dU437vAwAAAHg2QRgljSYzDgAAAAB+AXwIAAAA8CcIo6TROIkDAAAAgF9AGCWNE5M4AAAAAPgFhFHSOGHCAAAAgFk510kAD4yvVUUYJY3jJgwAAACYlXOdxK0RHHgAznUSvlYVYZQ0jhuxR0d0zMlJC/5ILwEAAPB4ONdJ3BLiwANwrpPwtaoIo6TRYMQeGI1aM26aKYD2obmWLTn51+Q9H5eknHqY3p4oTn273ev84WsNh681+L29Z4qGI//kxO/NAHPlYmSgaZRl4hX65epnO4kmIQ48AGc7CV+rijBKGvUT2ANgMGL8uxU5x18cHmwwTuIGo/ewqJiTJjM+p8oXt757L+d7Ufn+liOfXwxa8WDtPSGEf/Zx6AfLXAn/7GPXgKW7rizddcXvfTpZHbrF7z08Cp578wA9aDjyTxjnj/hkilkeOn77tYYj/+T33oD7x2lCmrbDfmngTAfRKMSns/sEtfsE9cM5tOK2eViCkSTlNexZ5kwH4WtVaR9C54rBiAr6KnKifq3TSHKjXhQPNZrMuMHoJTIq5oTJjN9/5ZrkIO2dQ5f3rSs+Ftx3fXfFd3/au/rlB+jwCSH0g2WC5guCW+dDP1jGv3Ve0Hwh9INlrgEOH/J/qzQfff0lPeCJxjYf3O/3fh6G5z4pfu6T4uc/jn/uzQM0+gm05uA/6xp+Z+J9qqxfNHLm/9Yc/Ge/9wlMR9CTtS6HFchgfZjREpjDWpfDwlETbUKC/ixN2+GLkYF+aexMB94oxKazPYbaFU9sOmjdeGDiQo5RpvQe9ixzpsOnESCMkkbdODoDl4tubT5VEZRQ9ffTNVvP1m270BCa1MQoTss+/uuaS+9YJ9W1V97LjXpxhF1vmPCSHhV9wjiJz3wJV8r2bBSk7NA1f3Vs8+LiY8GtsX+//vVHV2PC77/CE0XoB8t4DWe4DWdCP1jGrT/Dazgb+sEy59lhmYH2Ib/36eS19e/Tg+rWO2u3b57v+uzYpUhw4WO6l+c+KZ4av3mAHlR+93OLZCduTTXLQ8Zvv1b53c/9vubzRFkwsiK2d/4K9iYG+Co4w6n5YB2DtS6H1VJzESfxARWL2ckS6DSun4QuRgbO3+Xm9kye6cAbhjEPuoeoHXHUtihyzS5r/DVTUbUJxSiCpOQqSjRKURQ1PeUZ5EyHTyOY3Yc2xt/cVySKuCE+XC49ViGPqxk9f7M9I/IXNv2QK1lHfyUV9k1Pn9mHBkoWu6IbR0tSTuX9/fOhC0ETDZ/sXf1y7rYv2o5ujFzzlvcKhSHI0sTu+fwZYMcuRVwUwvA5OTun9mz3+KUczak92+mAIbFuJh+a/7ubhcLW3t9/+/0X0SfqWX3vfPnprc6eB1quWeIfmw89/3H89MnSPc9bJDtx61Wz/Jvx26+V7nne9SxjC+KpLWWPssmyYMd1HnpZ/kF8aF0Oi9nDGugs1KhYOInjJE6bkKA/awYTYmxx26nuuBWuPzseh+7Mgw8lMahdJ6jVO8bf/0p5NsNyvQS/kEcd+YnaHk1tPUpdL6UI4qmyoqshyBuJmY/Zh7QGdAb+eLDgi8SODWc6/3qWtekca/OFnq+S7pZmf192bnlfeQimauHW76u6vKoxZ6th3Do9/Xj0iYlJ3FfxO5lvkISGpj3zDXry6J+WZId+XBX+l7Ebr+549zcp36zZu/pl7xUKQpCliawZ+58j7JilSHDB/UzeF6EfLOsujesujbMPyuJDP1jmPNvNUy3ddWXNwazHdXfeWR265Q/fbP4o6PPF+w5l3irbcv7QB0eOv3vw6N7raTeb76z+asOnoVt6h4SzL1dBCILMvFAPvpJzxfm7OI/5gu2/dHwe2jF++7XC0F96Sy8LRkLyH3mTZcFTy8WOWfqQVywLRlbE9DyOtX10MHtYH2a04CSuaSuMjgzRqFiatkKcxC9GBjLLLtZVJFyMDMRRk5dctx8WdsxSBHFZjfwtSEAc28dF5/ZMnm7HGwSYK1Ep+Ortxoiz1NIvtZ98o9geZdtyBN0RS+2Ko/bEU6HRxOaDaEoRZhgn3BOHdr6BIMiKnfWY+3z5OgRB3kjMFHjM3zf1ia8gIXH2+g7NqSDtQw/cgA9Ot/s0AoRR0qgxoDOQGvdp25iNhh6zddioCb/NjGTEvYzLSkrPLG7M2WqatOkmsOnptA/5Kl5x7veYpppQ1xDqmopzv3fO71398rkv/3Dqj5+0X3hz+9u/KUk55b1CQQiyNLFzxv7nCDt6KRJUcD+Ts1PR1/n6jvdeO7Zuw8Vt3+/exGs8y286F/rBMo0BrewYTi7r/uPhnKW7rsTmtDyuu/POwLBsYFg2wJesCD/8xandK3/4tLrvTnlPU0TmlTfCDiXk5+fdrHrj849f//zj2ZerJzEACcmb2/I+Kp5788D0yZyt/2GR7MCtqWb57vHbr+eE/Ie33LKgme5inuhJDJjP/S0LQlZE9zymtX1EfJjRMqBqwUncNMpiMkKyOhNYbQkLVoVcjAykEeg0qUm7vC/m1O2XBSEhQVucTxo7eukMKzO3Z/J0O14vwJxUtVr/EjYel6v4/if0/a3qVV8pIs6QO2Kp3fEO4qgdseRXkVbmbYtrYr1gaMcbyCtvrHjl8JDrfMbhFbRtZLgFz4GMw3TNoR1vIGuv2ifjNiLIxvL7LXI15GEa8MXpdp9GgDBKGjV6dAaSov7EUqI0znGPCmVrsbaKyOKTr9RnbtZpdLpxVOst/XjUiQkT7qt4xpEAdOAH28AP6MAPGUcCXE+VpJzau/rlvatfLkk55bM9RggSkNhJj3sSAxzeH8RANXp2dAASEMumIztjV9gjPcM8YEcHTJ/3OjkLOW31i3HtOmYAAA9hSURBVI79MSz/QGrzuW0Z3y76cd3NmuIDn3+yZ/U7Gj2aXTe4dNeVTw5kRaY1yZSTj+vuZiGVWftG2KFOIYuv5Eh1I/fkXalNma/vPTgwovKRMn1lXGfKghzNOFqlz07N2yNp99JPG09VWBEd67IaD0F68EsT3ett2r9q77xmvfVSWvBL3sLKgqZ68HUvXle7LAhZEc2wzwcxUA0jxDPLveb01ZuKtD8AZUH07XvW8WjJLQzZUua54PbVm8pC3NZ52iUcG9EZu8LjF5UuezRrb3PbuEAGa0DVwhptwUlc08mM7txV15O6YFXIglUhCTnhAp1mUwTzcET4LI8iIwTZUtYZu8K+CN6fqNmeSR+cvoPX8zEniemmoCOGo2nGU5m6nXG6v0bo954kdzp8aFcctSuW2h1LBR22xV01uibW84d2LEbWHkp8BQmJc5tcseNQCLI4McMt+P4pX4us2FHnqJ/qmE8NQTaW32+R1IdpwCen7/g0AoRR0qjW22bg7I+f9WtQGtexYBzTjI9zO1LYvQ0S2ajGR/qxqBPjJsxX8XM7l53ftezczmXndi47v3PZzJ14gRGCBCR06G1qPTt6Cz2wqRkhCBKS53E2AAli+Ahzgx0d4PojV+p7chZej/r8eNmxir50mq3p34ZkRKqUWrFY6b+7m4nGzp7VIV++sutAt7iDPdpX3nOjXdhUN1Sy7XLMFyfP+chyXnr6TGkQsjy6x2OSXklHY84mexICnJNT49IgxFmcHR2AOO53dl54Y+u/BXzzq+U7fv323oUrv/uvDw+9+ofI19Yep89e27TQiY8KpUFuq+fjXrysdmkQ4uiTdiD6aXG9QY8tRhC3nZraVlveFudKutahOykNQpCAWLZ7z9PD3BfcI8ujbY/c6T07e3PfI6+9PdjGBeawLnayBlQtAyoWkxHC7Aln9oTX9aQKeHWHz+/alDQQGFYY+EW419yO2OV0J/Z160kIcG6E/Wd2Ls+kDxLvYHV81MnlAsu3ibbYS4qE+KHAPcJ9Jw3bo7DQGLsJ7YilQqOpXbHU1h+JyKRx18Q6PnvHYmRtKhq7EXnlENs+mRqCLE5Mp1/pmbrEVxxvPGtTXRPL1nrOu1Rwqe9MmboKfyrX6ySCIPYidYmvICGxU504xl4qeK3pRuIdn0aAMEoa1TrbDJw89BlHh9GcPPTZ+RPfOLkQs6WrLPLqqeDBgQFf6ceiTowbsZkvsXfTqzMH+IT+wXAcdsQun/qDns6m1pUGIcuju23q7oQA+4zXMFccT+fsk7PwiwPvb88I3ZG5g+ZvKV8FxH7B48g2rD+25+sLt29z/XF3PvnD9uB3gwIrb7V9GBl/rjKdJb5dPVAWfSMytyM1sfLU67sPvLrm7ZWbP+thD8+2MuzogOXR3c532CkFxLKnxTsOXW/BOXa/fc/DGXnuk+LV54VfZg/tL1WdqNOe77FVSvWvrT1+39tXGuS6et7vZcZNmWnsib1IcKl7pLMH11yX5fVcDW9hHgvumeU86y3X/bnyvkf309tcNi6QwaKt6GIPa10x8+LpQGZZ4MXzgeFhgYFhzE0RzMAI5uGIcO/p3QkBAQkdbve+PLrblhfs2K85PZM+Oky8g9XxUCf7E4ybD43vihn/6qDqm+OaiNPjf9o7uekg9u0J6pto6qsfqW+iqJ3R1NfHifirRtfEOp7DJ1JDECQk1mNmcWI6PbOBHniGOcau82gdD43dgKxNnarv0Iodtc7rlq2dOnRckVe2FnEmorEbEHsDtYmvOItPjcvW0mbDm7mmJ4l3fBoBwihpVOlsM3D8+8+dRB34c79k1AlHONAvlifGbhMIJb7Sj0adMBixmS+RHbfK9dDjS3QzJHbELkeCS1U6m6o7YQmCLIllO8YhuTqbSmfLDUaWxLI7Ype7nPIS5gI7KgAJYtzP5Cz8NnLdivi1H5/9E81rUZ98fulbuUxbkN114njRxk+PN9/mzlzhEdydT+7xpQKxSqWzJRVXLgoNz+3MqBjIv9SYuCV5Q+SNAy9u3a/S2Ypq6z/5etMsK+N405kazBTvOHTt1jn2qOC9oHemPg+9tXfhygjn56H73r7SINfV83pp76tdGoQsj+p2FvE69nVF+8rQGzq1+2657KiA5VHdXlvyFuax4J5ZzjBvuW6PkHv/3u/XR29z2ThmDyswhxXYwwrsYQWeHgjfsy48LDA8LPBwRHhgBHNTBDM8Yldq0q6Zdq07YYnjcrnBSBDDeY9zfCZ9dJhwB6vloU4SMizfHJ88cE69L8EQnmgKOqh9Z4tu/R5sdzy1JZIKDKe2HKFCjlJfH7NEJBpcE2t57NDFyNpU+2DRIXatw36mBjy0loemH3L+XjQkxi3Row5aW5u4yB7jNh+zAUE2lNnjUz3N2O3Szhj60LWgc+wR7Kum282itTw04Y5PI0AYJY1KrW0GjoR/LjXiNK5jsW5Szi6R6w0nokIFQpmv9KPHTxgmsJkvkR23yvXQ40t0vhPZUQHIkhi2UmtT5ocgAQntWptSa2uPWY4gIbl0DCthSUDI5oDlUSzbTGHuNTfn38/kLJypYfzXsTXvnVu/PjXw7TPrXzr8cbug/07zcMqF2ykXbh/en/Xt9p9mrPAo7m52lv75469PJ7+2++D+7Ohz9XFh2bsX7dz6Q1oRfZYzopxpZfJDEMTRjLZ0MzJ1KjeYboYdFYAgwaVKjyZZCUsciS6du1ZgRwUgzrublcSIVS06yoPEiFX3k+to3nX1vN2L99Uu3ey2Al7HDlgJSxxL4Rbgsa1uuewo+7xrS6VRMWwfYR6PbulmxPFQud2Ct1xWwhJkatfcnv+pU7P2NreNE/RkrWOwAhmsTcWsTWUDgeeZqf/nfwQerHN+Elq3PnyAzfSVnhuMbA4OcbvB4JDNU1efyzPpg4Q2rIaHOjmZbo1KMda2qG/3oFsO69bvmfz0W8PlQjz4MLX+W2r9t2RgOPVVJL49ynzo7IRrYg2PHboYWZOK1vDQmtQQZHFi6AZk0SG28zCNh9bUJi5CHJMOG3BLdD9MO7QC2VDmpT6vbA3idq00t06mTbo1QF/UZTy9gtea00ho82kEs/vQ93sDlWaChh6rJlGZfFgnuz1mtCnNRMzRUP6w3Ff6ffqQk72bXvX4Ep2vB47+VYZjhh3l+CucJcEhS6aeJLeHzHeYbXoAgiCOd1Wvk7Nzpibvv49/+nzEe++d3tI41HmvV3Zwb9GBPYUH9hSG78j9y9ofZ/hxejR3Nzu/++PKrgFBbTfvg4Mn/uXLPb8K3vfD9aKR0Qkf8e4r4/F2w0pY4jjj+q60Odj5p6epDttjlnvpfKrC8qiYkPt/O/tuz9oWHSU1u/HdnrX3vQ4ePuTzXqb1PBcfcrlrl7KOB8Dt3XP6e71rSzNYwvQ/QpVuntow5z3O5EO5wYibgktn86EH3zil1sZkJKxjsAJPszadZ21KGth0kLnpIG1Cu9atD2cWJ8yUnh/ivpKlm91+jub2THrFw4fOZGEXso1qnW3cSG47av4o1BJxbryscTKDSf7tIPZZGP7h11RkEr7nhPnHi0afPsQrW4MgiPMd3/m27vL+nnZoBeLiQ06/8Zj36k/2MHspF0/ioTEb6FzXSXboYsTFh1aE1npcyDW4LPQQ20fNOfqQQmOdgf17NmrNmN5K6q3kqWNfaSfMGlGTSqNRKiX05I8Ht/OFo77Sfzwer59AZ75Edtwq10OPL9HNnPuEo9Ra9RPohAmLPHA98A+nN65Npgn8w8ltQYl+b286OTcrP935FT1+68/r/N7PFHlbkYCE9vsL3v73rS06SmslXdn+963+v4v73IUgZEn0oN/bmB/msnE0dRUJgXsGAsMKA8MK6U9C4RG71q0Pr6tI8PvtnGrDarg2J/HXbOklxhqujaKobceI0Gjs8HldZYu5hmu7UoxHnCXe+TtZUEUeSbIcOGN0TazhskMXI2tS7IdpB5cjG0rtp1JCkMUJaY4YWos2hCxCQmKciRumjDOGzqpJWGTP8lLfXoq+RE3CIkfyVECKs+Dy0IPOBmxpB5dPa8C1wvQZ14u6caoN87WqCKOkcUxjnYHm2y0Hfwg9cmjnkUM7v9+78cihnZGHdtCHRw7tPPhD6M3K8hnSfzwer5tAZ75E1fWvXQ89vkQ3c+7TQk5288ZPo0K+TN69NXPrxqQv/nisuqbf711N5y5H8snXm+jxa+s+7Ojn+a+ZwWMBW7Pt49JNCLIkevA+c9f/ba9X/L6890VnwhLEeeNPIw++cU4EbObF87sOR+wK/CL8cER4atIuAZvp7/uyjmmsp9qwaq6NZpCPHjg72cdF6cPIJFP2zcmI03pnwA9nDe9uMZU04JcLzbvjxp3zDwd7+2JkTYrnfNQGZNFB9jxd4pFwqg3ztaoIo6RxTG2dGf6Icogv8wpXMDpz7o/H4nXj6KyXeBbIzrr118+i1n94IHhj3E1mj9/78YpMMXmPJ6fHP+XkrQzeIB6d8Fs/HVO/RVkSPej3xXkMZAchCIJsyvN/Jw/FP+7GnWrFqjk2mqNJpsuMCefhzhhdSpHx+KVx58xXkZp1u7SRP5lL6mwhP+qc8w8He/tiZM0Vj8nSNcjy7dXzUv9RcaoV87WqCKOkcVRtfXREHovXjaOP9BIAAACPh5OtWBXH5pWwBF1Vizksweyc2fajJnCf+i/7x9NKzJsOaHwlzhH2N4uR1VfmpdRj5WQr5mtVEUZJ46jK+uiIPBqvM6CP9BIAAACPh5MtWNWQDXgATrZgvlYVYZQ0ylVWAAAAYFZOtGDAA+NrVRFGSaNMaQEAAAAAv4AwShplCgsAAAAA+AWEUdIoVVgAAAAAwC8gReXN0jELAAAAAPgFZGhYKxmzAAAAAIBfQMSjZgAAAADwF+BDAAAAgD8BHwIAAAD8CVJRxwIAAACmc59vo+1dg48f6imUTx/qFxsAAACA6czqRiPisc7uoc7uoRHx2GOGvq6/neVBBD4EAAAwN3xZkUiiYPVyRRKFH2H1cv1tKw8i8CEAAIC54dWKevt5YqnC7/T28/xtKw8i8CEAAIA5MN2HpHJV34BAKlf5nb4Bgb895UHk5kN9IgMAAIDf8bvZOPHanocV3WML5aPqJ4R7bKG/beVBBD4EAMAThviJwVt7Hj40yBGNjmlcGZYoPZCPajxiHhGDHJG/PeVBBD4EAAAwBzx8aIgrVii1TkQy9c9DC36154aT53YUdHNlrjGPjiGu2N+e8iByLub/ByaL1JODFJXqAAAAAElFTkSuQmCC" alt="" />  

  并且在底部出现MapReduce Locations,效果如下:

    aaarticlea/png;base64,iVBORw0KGgoAAAANSUhEUgAAAmwAAABNCAIAAACUmK/IAAAWDklEQVR4nO2deXgUZZ7H69nFGWdEZWZEn8fZ53GeZ93ZZ2V8Ro3rOtGJM2Z1Ms64s7uDI8tweBAQJSgaFBFowIBBEAlyhKQJ4cwdckAScycEIuToXNySA3KRozsX6aS7k9o/urv6raq3qqs73XQ3/f0+34en+q16r6q3fp9+36oOTI1VAwMDrFVtnaOcucSBgYGAfXb+ZVm2p3jHKkKJTaxQTYlye/kaGxszbwTs8w8XsizLqhzcpeT8lLEsq2XnKmiDqpllm+UOIKtz+tIIMvr0JXZ34+1ekTugjzDso2aUQzTAHkHJEghe7iju4T6bCWsXnf4LUVUz21br8C7Xnp8pQlRG8rk8fvIdtbhfbrIHIcr1TrwBw3CAQxBVOBPlqymRZGZTIg+pCuRfEE1h21g2PsX+LruaSjPkQ7a5cJlKWQlYkgdQc7mk8bff8v1yiT07E+WueAAICsMiu3cm2lO8g6Sm4wz1L4jOrZWMlTK7AqRDm6qZfzb5y7lza+m7zCGb3KuSrktcNasAooJEymHmLw211rYVilpiTRE3vq2W13GVgl3CApv56Vp2rnk9nGVZli0rpPeX3nH57FKVCnYRewVAFfM1XmvLxFu6cLCDduXy8Q/Dvmi3zESJx6LCtdzEJuKJqIJ1Xb+CaJkoQCvZpTCoqZpFpCQoEq+17bVgppnY1cyrK8DeTFQ+7LKssPGC7gTss0CU1bJzuQ3yJKSwbQQGylg+YyQaL7PL/I2Bdza48q3sMS8DcEfKdDBAAFFadvlKBbtIUspDlDwVpJ3ooKAvLGaiMEyzm5+J9hTvsIHUjE/e4q7dmakfQXQKrxQpCXA8iKawbQIqE8vFwsmNtXZqXTIbUtvyiSzLbw/RVKlvEjKMIU+dzK54LWXSpiK2bWvsoiV3Ox2Xzi5TqaCnCiE6t1by3TGnOyh/fWEYdvczUXJFl/98lBWt9tLkPxAVhjllu6hgo1oMUcHDVy5wU2EjiKpUkQfIbFNzCXthD6LkoiXL2oGozJcD864yliKSMfQvN/xlAHrHpbNLVir6iqMQojKPTp3rICsxoqTSYdgP7eaZKI+cImYqmIr6C0QVv1JEmnVkljD1mShZhUyNMhJnEW+zCiBKLj4HuGImKrNarnwZgN4vWYgqXL13AKISM1HnOkgdbDAMk3bzTFSASd7HnuId9p+K+glEZeYQUrvkUUQvR/6ZqESYLuO/nyJgobg6uw2Tb61SiAoe6SlovMwu8YNAmyUYI+6OoxCVqZS8WJY5N+1xqeApr/lcURctnOugvKjDDIb9za6fiZIvDokhSf4pBiVv6voJRB19pYiVXVOlni7xNIX37i5BF95bu9Ivo0pVJHUA6zqIWl47MkvLqmr5EJVovMwuca8FkJaZqJH9ElwIu9klKyUWYNtqhe9mlxHHC1/bJs8MH6hOdzBA+gsTDMNufyY6RfkDRGXeB5HZRVrJ+XHVbw1ZPgvFV4dVAFFSTvTFuW666deWrLIvBz5qwTW6429GGHbUbn8mOkX5A0Sde6WItNT5KeMv9Ek+FXPELG1Swl0jakvIIRQgS9mpX2gPQvQOs8zluONvSRhWbsxE72gTv693CUG937cfojAM+7MxE4VhGIZhJ42ZKAzDMAw7acxEYRiGYdhJYyYKwzAMw04aM1EY9hEX4sUob7FK+j/3hf3NmIneCS5jFf2cNABvqLrEHnnnWfBn4mHP2nW/GYN93Q5AdMxDcj2ZIQiCIMgV8gGIDkAQBEGQVwoQhSAIgiAnBYhCEARBkJMCRCEIgiDISfkwREusuo2nC4IgCIJs8lWIgqAQBEGQx+WTEAVBIQiCIG+Q70EUBIUgCIK8RL4H0QFwFIIgCPIO+SREB8BRCIIgyAvkqxAdAEchCIIgT8uHIQpBEARBnhUgCkEQBEFOChCFIAiCICdlH6IcRwFRCIIgCCIFiEIQBEGQk1IEUXJRF4IgCIIgs5RCFByFIAiCIIEcgChQCkEQBEGkHIYoDMMwDMNmA6IwDMMw7KQBURiGYRh20oAoDMMwDDtphyGaU1gNwzAMw3BOYbUDEDVnaGgbgGFvM8uy5vHpphfwIJ8T4hV8e6wUohiOsNfaHDE9F64h7xUCF+xuK4IoBiLszQZBIRkhfMFuNSAK+7ZZTEMhWSF8wW61fYjmFFbXtw7AsHcaBIXsCkEMdp8dhuiR/UtOFe0pK9xblB+Tl7v/xIn4jMxDKelHE1ITDyWnxCWkH0rNO3uxy+Mduz2uutp3vOxSeumF9NIL2acu17foPN4kfzMgCtkVIAq7zw5DND76jaYB/dBQf/+Atler7ezX3ujRtdzUXu3SZZ+53tSuLTh7Yc+hE6WaNo/37TY4s+zikayzqTnVqTnVBzPPFtW0erxJ/mZAFLIrMURTznS8tOZ08JoKKf9JdTpPc9Pjwxv2fiuEqI5zfPQbjYNj4wbTUFdv86YvNc/89vyi99qv3GgfNBwvb7umHb/cN/Zt1ZXDGZVkrjvMNWcKzh1dXRSv2rIvOy75VHzq6fjU09HHSrfHHq+I/7QqaX1tzTmPN9JPLA9Ro9FYXlZ29Oie5JTZaekvZWVFmYxG9wdtyLskCGL1rbrnVpb/ZfG2/w7dKuW/LPkq6JMyjw9v2PvtPESvLFnW+tQzo79/8frTzzbNfZOE6HfX+nbGf+vahuZtCWYWZytPd6trjn7SU3d87dakyD05UQeKOW/albV1V0pnVWJdWsRt6PtUnKtp35V3/v0DpyPSNLma9tt8AutbdfWt2QuZ4PWnp1qOPESvXL64fdvS0VtfT5j+fXLisb7+f92vnqfV3jSMjxt9hqYl4UyIutXTrVCsVnUIE17ipsKXWmU3kZQYok8vL5y96HNNv1HKc5Zsfnp5oSfuC5s9EtxgR+08RKv+5Vfal/6gezlE+1JI1aO/ut41wEG0/ELH7iNFlPoSwxibHAugXgXRC1mbK0+ukHJN/prG1DXSHXe4+67tY+Xl3rf2lIbFVWhaeo2mid4h/cHSy5vTNbK5XAM8d5RJhejExMTp06d3Rm1PTPprVsaTY2NPdXfNGh75p4mJnw0MPrB69Svbt8+JjX2Hl6cknGEosCoJZ5ipMcEClZJw8vI7UqIbIOpOLrsbonY3xBJD9Kn3cl4LjdD0S36RmrNk81Pv5Ujej1YtTHTtTeHGGx92k52HaOP/vF771LMNv3tJ8/RvNP/5Z3ImmnWqMTFXOGrrW3X1iWHMCzvzuG0mbL/ihnoVRJsyIqoKNowbTOMG05Uh06jBZDCazB/1Y2OVJ1fWJ62mZty/mAnc0uBoda7t48ufn4jOP2++0HqDybyRX39jX8FF6Vy+AdHJycmGhvru7u6YfZtPVcTnnFxx9ep/6LT/ptP9YnxsZlrKw2kp91+/cf+7S2Y2t9z/5db7eFGzJJwJCRGG/1Z1iDjRMVl5RXKrJNwRjPoCRN1KTkIkKQWzT4cg+sSSjL+/+4Wm3/hxZO67a1IXrDj4v6F7X567jcuyYNmWJ5ZkiMZbw/oXGFsQa21Y/4IDQcyzNz7sJiuCaF2LjvPuHYvNEO3XNNU+89tzj86qe3V2W2UtCdF9x/JKalvJXBYnhDFBO3MtHxtUQcGqCtExEs6NDGZCs5Wnu9X1aRu/y9+oHzcVdU/87KThT0WDY2OGrw+W7ThcoR83lWWtrE1YRc2oDmUCIxscrc6FfVQl1fx+fRbLsnl1159fezwirWZRdOnTq1JZll2XVC2dMXsh48DFUmbXlElCtKgw+4PwV3fv3mAwRmQeX2I0zdbr/3li4pHS4ocNxh/f6PipbvCenq57Ks/8ZGDwrgMH7uUF2pJwJkSt5sOlVR0SolaHTwUPHK543HIIYoAoT1RYyhCUZVlBEKtr0f16UfIb72+VmYm+teKrXy9KptyJtgjmdnskuMGO2mGIbt2yLK28TXO5/8bNW8U1nVkFlzJOtWWUt2WUtyUVNh8vb0stbTmUraHXx4OoJYbmRgYzoTtVQQzDhKlbdHXmocOtliRY8prHkzrUks6hiBxn3F6uFn6uMHVLgyrIsppqCd8VOwMZfooCa5LXn86NKOiauDdT/8C7WfMONWoH9TduDnX0jozoTcUZn1Qd+YSaUQxRW5u5u0XUJFsfzbvIbQdb/vzajF15jY3X+6fN2bc+ucpomrh7bkxEWg3LsquOnpXOyAOesM0JYdy1s+wNzaZ3jUzkt5x60fnptipIkxDt6mydv/C5y5fDxg1PZqT/uV/7KDv5gGnivpMnfjI+fpd+7B9Hh6cND//DgG5av27a55t+youaZq4IUWdeh7XhwbYoa00z44NLF7DJlpkouVUdQh5nK5OaSOyQAnGrOkTQLGqZws7y04hCeDyUK9ycQLY1vITkKbVMwRmzpNsOtY938TNR+ePFEH38jSOLP9qu6Td+sbd03fa8jzdnLF+XFPrxwXnLY95ZdYBl2XdW7nj8jSP8XA2qIN7gJK0kalnSaXeuTHap4AZ7iR2G6IaNK1puGUy3+ibGRyYnWd2osXfE2DVk4GaimcXnjhfW0usjIMp9p8uNDBYOJm6UVOwMJEFiG17ZC63b3DjLjQwWk5XMpQ5leFgK2pkre1eIXXh1MPvqcKd2tDpx3a4Th6Yfv/Xg28nzDjVmFjWqvsnbsr/km2NnhkaNBemffndwJbUEIUQTwogemdtGaZK1O7Zey9/PMv7l8oTChvZtWXXMa3t7h/QlTR3Ma3srr3TrDaaVhyulMxIQtdNmayLlMP7FTQgTXQ7qRaezk7PgmWheToZe/+rE5COZGb+4eOHhCePdo2PTkxJm3Br9obb/R+fOzejrv6df+0NNw73q/Rt4gdbCFRsCrFsEREvCbVvWWG8O/kQ6QSCSJwRpRJPdVsHhPMqWhMtDtFUdIlgcppZJ6ywvC8PLQ/aOn592Eshq6L0QlkmcZP5pV6KpQ3TW/Lj3VkVp+o3agdF+3a1e7UhP//DN3qHunoGbfYMsy4at/mbW/Dh+LsmbTknUkok5ctmlgxvsJXYYop+uXTVmMnac+KC3InLwfNpIZ2NP5zUSonGJ2aWaFnp9CcT7NfzJovUY4QjjqCMYOtyosqZzU0xe+bxc5DzYum0e5QqXWB852P5Qlv7iMJtR03hP6qCZoPrxieb2wdbOobau4RvdIwMjxrzUNafjPqKWQFnOtX0tJW42fpNyI4OZ0LCFtCmao4vDf9x0Mr70Usa5lunz1V26W8+uTp82Zx/Lstuy6osau6Qz8pdeqW0Wn3DhYYKLK/m9wXqWFH1REEA0PT2hp+dZdnJmU+NDBfkz8/JmHDt8X43m7rrG+05kzfjjyz9+c8ETSUmrTp48ajAYeIGWZFKIupW3DkuEd9G8TBD+JQFGlsdnC08UothYRYOoGD/UMvkSQlRYCHkyKGwTT04pEJUqk7+DPFrJJNRFy7mz5qlXrN0ts5z7kWrvrHlqfi6pAaksalXsDGTC1JQ71252enBz6MaH3WeHIZqelTt0tfBGsaokaXVbWuiEXncz+e+d3R0cRKMPpVVduUmvj7eca7FzEKWOM/H4tgtRrjSGsR+vX4wsf/Dt5Iey9Pda56C3xkwrt2SujcrdFF341YFTuxO+6x82nEhedyp2BbUEPkQbVEHc/UDpOB+rwYFBFGQqbDnnL9Lr/isyV28wbcuqW59cpRsZeyemLK/uum5kXDZj9kLLjFCqzWYics+5qYe5HaIDOm1M9PMF3z5smrzbYJzW1XV/V9f0waG7hoennTo1/cMVf43atbShsZIeNQWoUpMfbbgkZ1NUiBJ44pOKv4MCH5tcAFE7szrnIUo/CVOHKHcMI/vOldMvFgmGzeML9q/cEP3ppthVETFUf7g++sk3D8jFE5sdg6jozlUEUSeWneDbY4UQ1XIeGRlpSXqrJmtl6O9m9lWu66s+aOj+riN/HQfRY5mFJTXXyCw2JyxjgqJy+YnWgUJ85I6piApkglUV1nTrtjA9NFuYMWHZwgRR4WTtlu16VaRlrzqUCYyspzeb8IuR5Q+8m/XQ28lvH20c0Zt6Bw1Xrg98f2PwWsdQc+dQS9dw76AhM3F9afT71Oz8WszLs9b2WLYpTbL2ol4VxGV3uOWc/7a9QJVUZTRNcJf7Rt/IN7mNcrlsp47aZmszgoLlD1OHMtwlIC+o3EW3HR+lprWNhOjorVt7dj8TEztTP/ajQd09O6Me3aia0fz9D3X9P9gfMyN636uSsZkVrU3yVmhtj/7Ip4JitPJWMAWgEgJQsJ5pSbbVxGsMQS1yTZQ3dTanqkskypTqLCtquLAaXuH0k+Dwcq4QouaGU5vGk9M/cREMm+jM+mcXxQeGHggMPRAYGvcbqwND4yyJi+OTSq+Ixlv2QoYh4lW9KmiZWn4AcwdXRAUyy9RSN7hsdmpwg73EiiCqadFy1l3M68h5p6tyVczHz41d3nT18OumseHe3BUcRAurL6oT82ub+8lcFicsY4KiTvITT0YGM6HZZEqs7d2T4HUV5GFR66zLGgsSKNljiTdZKIWTtXPbCcusKyTChkn5xcjy1zcdqa/OvTlgoDr92Mai3cupeWNDmcDIel7fLQ2OWhdk7ZSoSUQvshcwDMMsi3Wq5ZxjCi+9tbd0eVyFKrnqs4Sza5Oqzl7tox9sqYV/IcRtbtFqKqICBdeFclg9dwUDI6MWEMVSLzqZTp430oLl3O7urg9WvDKqn34i977S0ry4wz/v7vhBTOyDn67++cbPQ6SCrDB4C9dhRe++hKv5M1G1bU8JpTzxZ/JZo/hlJf5rNuQrw8QLTGr+1NaSKjyOPq2jvbYk8dBWWDj1JNiS+S8WUcukz0SlXs3iy+k/tiAeOaWNXfma9m9r21/6MPWxOd+Y/fJHafma9nxNe/n5LonbxzaGGXsDmBd/KqICpe9cu9nFwQ32EiuDaLOWc3P6spG61X01n6V9+Yrh6ubOb5f2X8gx6lo5iF7q0celFh7OOkvmusNctPej1CObstN2Up1yJKIk9hOPN9JPLP5jC5Vnig/FL9y86W9Go/Hwwc83RDzW1KQZHb1lMIxLB2fnRV88vY0/+YDsShDENM3a6u/7/hCe/tj/7ZJySPjxc1f6PD68Ye+3wxC9ED97qOYzw7VtTSfCJlq2d+SFdp09yLLs13uT5y/dNH9pBOeTFRc83j03uSIvPT9mdYF6rYQ/qzxV5vFG+onl/+zf5OSk8AUiV0vJU03IsxJDFIZdZYch2n+poCH21fPxs81u2D97fKibZdmWTm1h1feltddKappLapqLq1vOXb7p8e7Bd7w9/r+4gJfeL0AUdp8dhigMe5U9DlHI+4UgBrvPgCjs2wZEIbtCEIPdZ0AU9m0DopBdIYjB7rMiiMKwN9uD0RnyCXl8iMJ3sBVB1IOjH4IgCIK8VoAoBEEQBDkpQBSCfEMdHR2ebgLkG8JQkZHLTw4gCkG+IURGSKEwVGQEiEKQnwqREVIoDBUZAaIQ5KdCZIQUCkNFRoAoBPmpEBkhhcJQkREgCkF+KkRGSKEwVGTk8pPz/7CnJK/8rTAxAAAAAElFTkSuQmCC" alt="" />

  此时右击黄色的Map/Reduce Locations,选择New Had*,然后编辑如下,

    aaarticlea/png;base64,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" alt="" />

  编辑结束点击finish。再次运行Run as -->Java Application,出现想要的结果了,如图:

      aaarticlea/png;base64,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" alt="" />

  该图出现基本代表运行成功,没问题。但是发现MapReduce程序运行的计数器等信息没有打印在控制台,控制台只打印了log4j三行信息。解决方法见第8条

 8、解决将输出的信息打印到Console上。

  参考:Eclipse中运行MapReduce程序时控制台无法打印进度信息的问题

  这种情况一般是由于log4j这个日志信息打印模块的配置信息没有给出造成的,可以在项目的src目录下,新建一个文件,命名为“log4j.properties”,填入以下信息:

    log4j.rootLogger=INFO, stdout
log4j.appender.stdout=org.apache.log4j.ConsoleAppender
log4j.appender.stdout.layout=org.apache.log4j.PatternLayout
log4j.appender.stdout.layout.ConversionPattern=%d %p [%c] - %m%n
log4j.appender.logfile=org.apache.log4j.FileAppender
log4j.appender.logfile.File=target/spring.log
log4j.appender.logfile.layout=org.apache.log4j.PatternLayout
log4j.appender.logfile.layout.ConversionPattern=%d %p [%c] - %m%n

    aaarticlea/png;base64,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" alt="" />

 9、此时,所有的问题解决

 (1)控制台打印信息

 2016-08-05 00:56:45,209 INFO [org.apache.hadoop.conf.Configuration.deprecation] - session.id is deprecated. Instead, use dfs.metrics.session-id
  2016-08-05 00:56:45,211 INFO [org.apache.hadoop.metrics.jvm.JvmMetrics] - Initializing JVM Metrics with processName=JobTracker, sessionId=
  2016-08-05 00:56:45,856 WARN [org.apache.hadoop.mapreduce.JobResourceUploader] - Hadoop command-line option parsing not performed. Implement the Tool interface and execute your application with ToolRunner to remedy this.
  2016-08-05 00:56:45,918 WARN [org.apache.hadoop.mapreduce.JobResourceUploader] - No job jar file set.  User classes may not be found. See Job or Job#setJar(String).
  2016-08-05 00:56:45,976 INFO [org.apache.hadoop.mapreduce.lib.input.FileInputFormat] - Total input paths to process : 1

 (2)/wc/outputn/part*输出的信息

    Hadoop    1
    hello   3    

    java  1
    world    1

  至此成功实现。

四、日志分析

  分析执行日志会发现,发现Runjar启动的是本地的JVM,使用的是Local模式,从如下几处的日志可以得出:

   (1)启动的JVM processName=JobTracker,此处不是yarn

   (2)JobID是job_local……而非job_直接数字

  说明运行的本地运行,只是使用了HDFS文件系统,但是执行框架是本地的,而非集群的yarn框架。

五、MR程序的几种提交运行模式(本地、集群(windows、linux中eclipse))

(以下内容为传智播客上课笔记)

本地模型运行

1/在windows的eclipse里面直接运行main方法,就会将job提交给本地执行器localjobrunner执行
      ----输入输出数据可以放在本地路径下(c:/wc/srcdata/)
      ----输入输出数据也可以放在hdfs中(hdfs://weekend110:9000/wc/srcdata)
           
2/在linux的eclipse里面直接运行main方法,但是不要添加yarn相关的配置,也会提交给localjobrunner执行
      ----输入输出数据可以放在本地路径下(/home/hadoop/wc/srcdata/)
      ----输入输出数据也可以放在hdfs中(hdfs://weekend110:9000/wc/srcdata)

集群模式运行

1/将工程打成jar包,上传到服务器,然后用hadoop命令提交  hadoop jar wc.jar cn.itcast.hadoop.mr.wordcount.WCRunner
2/在linux的eclipse中直接运行main方法,也可以提交到集群中去运行,但是,必须采取以下措施:
      ----在工程src目录下加入 mapred-site.xml  和  yarn-site.xml
      ----将工程打成jar包(wc.jar),同时在main方法中添加一个conf的配置参数 conf.set("mapreduce.job.jar","wc.jar");

3/在windows的eclipse中直接运行main方法,也可以提交给集群中运行,但是因为平台不兼容,需要做很多的设置修改
        ----要在windows中存放一份hadoop的安装包(解压好的)
        ----要将其中的lib和bin目录替换成根据你的windows版本重新编译出的文件
        ----再要配置系统环境变量 HADOOP_HOME  和 PATH
        ----修改YarnRunner这个类的源码

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