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Mapreduce程序实例并部署在Hadoop2.2.0上运行

w123aw 发表于 2014-1-12 01:15:55 [显示全部楼层] 回帖奖励 阅读模式 关闭右栏 1 61442
本帖最后由 pig2 于 2014-2-20 00:18 编辑

在Hadoop2.2.0伪分布式上面运行我们写好的Mapreduce程序。先给出这个程序所依赖的Maven包：

  1. <dependencies>

  2.     <dependency>

  3.         <groupId>org.apache.hadoop</groupId>

  4.         <artifactId>hadoop-mapreduce-client-core</artifactId>

  5.         <version>2.1.1-beta</version>

  6.     </dependency>

  7.     <dependency>

  8.         <groupId>org.apache.hadoop</groupId>

  9.         <artifactId>hadoop-common</artifactId>

  10.         <version>2.1.1-beta</version>

  11.     </dependency>

  12.     <dependency>

  13.         <groupId>org.apache.hadoop</groupId>

  14.         <artifactId>hadoop-mapreduce-client-common</artifactId>

  15.         <version>2.1.1-beta</version>

  16.     </dependency>

  17.     <dependency>

  18.         <groupId>org.apache.hadoop</groupId>

  19.         <artifactId>hadoop-mapreduce-client-jobclient</artifactId>

  20.         <version>2.1.1-beta</version>

  21.     </dependency>

  22. </dependencies>
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记得加上

  1. <dependency>

  2.         <groupId>org.apache.hadoop</groupId>

  3.         <artifactId>hadoop-mapreduce-client-common</artifactId>

  4.         <version>2.1.1-beta</version>

  5. </dependency>

  6. <dependency>

  7.         <groupId>org.apache.hadoop</groupId>

  8.         <artifactId>hadoop-mapreduce-client-jobclient</artifactId>

  9.         <version>2.1.1-beta</version>

  10. </dependency>
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否则运行程序的时候将会出现一下的异常：

  1. Exception in thread "main" java.io.IOException: Cannot initialize Cluster. 

  2.     Please check your configuration for mapreduce.framework.name and the 

  3.     correspond server addresses.

  4.         at org.apache.hadoop.mapreduce.Cluster.initialize(Cluster.java:120)

  5.         at org.apache.hadoop.mapreduce.Cluster.<init>(Cluster.java:82)

  6.         at org.apache.hadoop.mapreduce.Cluster.<init>(Cluster.java:75)

  7.         at org.apache.hadoop.mapred.JobClient.init(JobClient.java:465)

  8.         at org.apache.hadoop.mapred.JobClient.<init>(JobClient.java:444)

  9.         at org.apache.hadoop.mapred.JobClient.runJob(JobClient.java:826)

  10.         at com.wyp.hadoop.MaxTemperature.main(MaxTemperature.java:41)

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

  12.         at sun.reflect.NativeMethodAccessorImpl.invoke

  13.                            (NativeMethodAccessorImpl.java:57)

  14.         at sun.reflect.DelegatingMethodAccessorImpl.invoke

  15.                            (DelegatingMethodAccessorImpl.java:43)

  16.         at java.lang.reflect.Method.invoke(Method.java:606)

  17.         at com.intellij.rt.execution.application.AppMain.main(AppMain.java:120)
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好了，现在给出程序，代码如下：

  1. package com.wyp.hadoop;

  2. 

  3. import org.apache.hadoop.io.IntWritable;

  4. import org.apache.hadoop.io.LongWritable;

  5. import org.apache.hadoop.io.Text;

  6. import org.apache.hadoop.mapred.*;

  7. 

  8. import java.io.IOException;

  9. 

  10. /**

  11.  * User: wyp

  12.  * Date: 13-10-25

  13.  * Time: 下午3:26

  14.  * Email:wyphao.2007@163.com

  15.  */

  16. public class MaxTemperatureMapper extends MapReduceBase 

  17.                       implements Mapper<LongWritable, Text, 

  18.                       Text,IntWritable>{

  19.     private static final int MISSING = 9999;

  20. 

  21.     @Override

  22.     public void map(LongWritable key, Text value, 

  23.                       OutputCollector<Text, IntWritable> output, 

  24.                       Reporter reporter) throws IOException {

  25. 

  26.         String line = value.toString();

  27.         String year = line.substring(15, 19);

  28.         int airTemperature;

  29.         if(line.charAt(87) == '+'){

  30.             airTemperature = Integer.parseInt(line.substring(88, 92));

  31.         }else{

  32.             airTemperature = Integer.parseInt(line.substring(87, 92));

  33.         }

  34. 

  35.         String quality = line.substring(92, 93);

  36.         if(airTemperature != MISSING && quality.matches("[01459]")){

  37.             output.collect(new Text(year), new IntWritable(airTemperature));

  38.         }

  39.     }

  40. }

  41. 

  42. package com.wyp.hadoop;

  43. 

  44. import org.apache.hadoop.io.IntWritable;

  45. import org.apache.hadoop.io.Text;

  46. import org.apache.hadoop.mapred.MapReduceBase;

  47. import org.apache.hadoop.mapred.OutputCollector;

  48. import org.apache.hadoop.mapred.Reducer;

  49. import org.apache.hadoop.mapred.Reporter;

  50. 

  51. import java.io.IOException;

  52. import java.util.Iterator;

  53. 

  54. /**

  55.  * User: wyp

  56.  * Date: 13-10-25

  57.  * Time: 下午3:36

  58.  * Email:wyphao.2007@163.com

  59.  */

  60. public class MaxTemperatureReducer extends MapReduceBase 

  61.                     implements Reducer<Text, IntWritable, 

  62.                     Text, IntWritable> {

  63.     @Override

  64.     public void reduce(Text key, Iterator<IntWritable> values, 

  65.                     OutputCollector<Text, IntWritable> output, 

  66.                     Reporter reporter) throws IOException {

  67.         int maxValue = Integer.MIN_VALUE;

  68.         while (values.hasNext()){

  69.             maxValue = Math.max(maxValue, values.next().get());

  70.         }

  71. 

  72.         output.collect(key, new IntWritable(maxValue));

  73.     }

  74. }

  75. 

  76. package com.wyp.hadoop;

  77. 

  78. import org.apache.hadoop.fs.Path;

  79. import org.apache.hadoop.io.IntWritable;

  80. import org.apache.hadoop.io.Text;

  81. import org.apache.hadoop.mapred.FileInputFormat;

  82. import org.apache.hadoop.mapred.FileOutputFormat;

  83. import org.apache.hadoop.mapred.JobClient;

  84. import org.apache.hadoop.mapred.JobConf;

  85. 

  86. import java.io.IOException;

  87. 

  88. /**

  89.  * User: wyp

  90.  * Date: 13-10-25

  91.  * Time: 下午3:40

  92.  * Email:wyphao.2007@163.com

  93.  */

  94. public class MaxTemperature {

  95. 

  96.     public static void main(String[] args) throws IOException {

  97.         if(args.length != 2){

  98.             System.err.println("Error!");

  99.             System.exit(1);

  100.         }

  101. 

  102.         JobConf conf = new JobConf(MaxTemperature.class);

  103.         conf.setJobName("Max Temperature");

  104. 

  105.         FileInputFormat.addInputPath(conf, new Path(args[0]));

  106.         FileOutputFormat.setOutputPath(conf, new Path(args[1]));

  107.         conf.setMapperClass(MaxTemperatureMapper.class);

  108.         conf.setReducerClass(MaxTemperatureReducer.class);

  109.         conf.setOutputKeyClass(Text.class);

  110.         conf.setOutputValueClass(IntWritable.class);

  111. 

  112.         JobClient.runJob(conf);

  113. 

  114.     }

  115. }
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　将上面的程序编译和打包成jar文件，然后开始在Hadoop2.2.0（本文假定用户都部署好了Hadoop2.2.0）上面部署了。下面主要讲讲如何去部署：

　　首先，启动Hadoop2.2.0，命令如下：

  1. [wyp@wyp hadoop]$ sbin/start-dfs.sh 

  2. [wyp@wyp hadoop]$ sbin/start-yarn.sh 
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　如果你想看看Hadoop2.2.0是否运行成功，运行下面的命令去查看

  1. [wyp@wyp hadoop]$ jps

  2. 9582 Main

  3. 9684 RemoteMavenServer

  4. 16082 Jps

  5. 7011 DataNode

  6. 7412 ResourceManager

  7. 7528 NodeManager

  8. 7222 SecondaryNameNode

  9. 6832 NameNode
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　其中jps是jdk自带的一个命令，在jdk/bin目录下。如果你电脑上面出现了以上的几个进程（NameNode、SecondaryNameNode、NodeManager、ResourceManager、DataNode这五个进程必须出现！）说明你的Hadoop服务器启动成功了！现在来运行上面打包好的jar文件（这里为Hadoop.jar，其中/home/wyp/IdeaProjects/Hadoop/out/artifacts/Hadoop_jar/Hadoop.jar是它的绝对路径，不知道绝对路径是什么？那你好好去学学吧！），运行下面的命令：

  1. [wyp@wyp Hadoop_jar]$ /home/wyp/Downloads/hadoop/bin/hadoop jar \

  2.            /home/wyp/IdeaProjects/Hadoop/out/artifacts/Hadoop_jar/Hadoop.jar  \

  3.            com/wyp/hadoop/MaxTemperature \

  4.            /user/wyp/data.txt \

  5.            /user/wyp/result
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（上面是一条命令，由于太长了，所以我分行写，在实际情况中，请写一行！）其中，/home/wyp/Downloads/hadoop/bin/hadoop是hadoop的绝对路径，如果你在环境变量中配置好hadoop命令的路径就不需要这样写；com/wyp/hadoop/MaxTemperature是上面程序的main函数的入口；/user/wyp/data.txt是Hadoop文件系统（HDFS）中的绝对路径（注意：这里不是你Linux系统中的绝对路径！），为需要分析文件的路径（也就是input）；/user/wyp/result是分析结果输出的绝对路径（注意：这里不是你Linux系统中的绝对路径！而是HDFS上面的路径！而且/user/wyp/result一定不能存在，否则会抛出异常！这是Hadoop的保护机制，你总不想你以前运行好几天的程序突然被你不小心给覆盖掉了吧？所以，如果/user/wyp/result存在，程序会抛出异常，很不错啊）。好了。输入上面的命令，应该会得到下面类似的输出：

  1. 13/10/28 15:20:44 INFO client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8032

  2. 13/10/28 15:20:44 INFO client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8032

  3. 13/10/28 15:20:45 WARN mapreduce.JobSubmitter: Hadoop command-line option parsing not performed. Implement the Tool interface and execute your application with ToolRunner to remedy this.

  4. 13/10/28 15:20:45 WARN mapreduce.JobSubmitter: No job jar file set.  User classes may not be found. See Job or Job#setJar(String).

  5. 13/10/28 15:20:45 INFO mapred.FileInputFormat: Total input paths to process : 1

  6. 13/10/28 15:20:46 INFO mapreduce.JobSubmitter: number of splits:2

  7. 13/10/28 15:20:46 INFO Configuration.deprecation: user.name is deprecated. Instead, use mapreduce.job.user.name

  8. 13/10/28 15:20:46 INFO Configuration.deprecation: mapred.output.value.class is deprecated. Instead, use mapreduce.job.output.value.class

  9. 13/10/28 15:20:46 INFO Configuration.deprecation: mapred.job.name is deprecated. Instead, use mapreduce.job.name

  10. 13/10/28 15:20:46 INFO Configuration.deprecation: mapred.input.dir is deprecated. Instead, use mapreduce.input.fileinputformat.inputdir

  11. 13/10/28 15:20:46 INFO Configuration.deprecation: mapred.output.dir is deprecated. Instead, use mapreduce.output.fileoutputformat.outputdir

  12. 13/10/28 15:20:46 INFO Configuration.deprecation: mapred.map.tasks is deprecated. Instead, use mapreduce.job.maps

  13. 13/10/28 15:20:46 INFO Configuration.deprecation: mapred.output.key.class is deprecated. Instead, use mapreduce.job.output.key.class

  14. 13/10/28 15:20:46 INFO Configuration.deprecation: mapred.working.dir is deprecated. Instead, use mapreduce.job.working.dir

  15. 13/10/28 15:20:46 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1382942307976_0008

  16. 13/10/28 15:20:47 INFO mapred.YARNRunner: Job jar is not present. Not adding any jar to the list of resources.

  17. 13/10/28 15:20:49 INFO impl.YarnClientImpl: Submitted application application_1382942307976_0008 to ResourceManager at /0.0.0.0:8032

  18. 13/10/28 15:20:49 INFO mapreduce.Job: The url to track the job: http://wyp:8088/proxy/application_1382942307976_0008/

  19. 13/10/28 15:20:49 INFO mapreduce.Job: Running job: job_1382942307976_0008

  20. 13/10/28 15:20:59 INFO mapreduce.Job: Job job_1382942307976_0008 running in uber mode : false

  21. 13/10/28 15:20:59 INFO mapreduce.Job:  map 0% reduce 0%

  22. 13/10/28 15:21:35 INFO mapreduce.Job:  map 100% reduce 0%

  23. 13/10/28 15:21:38 INFO mapreduce.Job:  map 0% reduce 0%

  24. 13/10/28 15:21:38 INFO mapreduce.Job: Task Id : attempt_1382942307976_0008_m_000000_0, Status : FAILED

  25. Error: java.lang.RuntimeException: Error in configuring object

  26.         at org.apache.hadoop.util.ReflectionUtils.setJobConf(ReflectionUtils.java:109)

  27.         at org.apache.hadoop.util.ReflectionUtils.setConf(ReflectionUtils.java:75)

  28.         at org.apache.hadoop.util.ReflectionUtils.newInstance(ReflectionUtils.java:133)

  29.         at org.apache.hadoop.mapred.MapTask.runOldMapper(MapTask.java:425)

  30.         at org.apache.hadoop.mapred.MapTask.run(MapTask.java:341)

  31.         at org.apache.hadoop.mapred.YarnChild$2.run(YarnChild.java:162)

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

  33.         at javax.security.auth.Subject.doAs(Subject.java:415)

  34.         at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1491)

  35.         at org.apache.hadoop.mapred.YarnChild.main(YarnChild.java:157)

  36. Caused by: java.lang.reflect.InvocationTargetException

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

  38.         at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)

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

  40.         at java.lang.reflect.Method.invoke(Method.java:606)

  41.         at org.apache.hadoop.util.ReflectionUtils.setJobConf(ReflectionUtils.java:106)

  42.         ... 9 more

  43. Caused by: java.lang.RuntimeException: java.lang.RuntimeException: java.lang.ClassNotFoundException: Class com.wyp.hadoop.MaxTemperatureMapper1 not found

  44.         at org.apache.hadoop.conf.Configuration.getClass(Configuration.java:1752)

  45.         at org.apache.hadoop.mapred.JobConf.getMapperClass(JobConf.java:1058)

  46.         at org.apache.hadoop.mapred.MapRunner.configure(MapRunner.java:38)

  47.         ... 14 more

  48. Caused by: java.lang.RuntimeException: java.lang.ClassNotFoundException: Class com.wyp.hadoop.MaxTemperatureMapper1 not found

  49.         at org.apache.hadoop.conf.Configuration.getClass(Configuration.java:1720)

  50.         at org.apache.hadoop.conf.Configuration.getClass(Configuration.java:1744)

  51.         ... 16 more

  52. Caused by: java.lang.ClassNotFoundException: Class com.wyp.hadoop.MaxTemperatureMapper1 not found

  53.         at org.apache.hadoop.conf.Configuration.getClassByName(Configuration.java:1626)

  54.         at org.apache.hadoop.conf.Configuration.getClass(Configuration.java:1718)

  55.         ... 17 more

  56. 

  57. Container killed by the ApplicationMaster.

  58. Container killed on request. Exit code is 143
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程序居然抛出异常（ClassNotFoundException）

经个人总结，这通常是由于以下几种原因造成的：
（1）你编写了一个java lib，封装成了jar，然后再写了一个Hadoop程序，调用这个jar完成mapper和reducer的编写
（2）你编写了一个Hadoop程序，期间调用了一个第三方java lib。
之后，你将自己的jar包或者第三方java包分发到各个TaskTracker的HADOOP_HOME目录下，运行你的JAVA程序，报了以上错误。
那怎么解决呢？一个笨重的方法是，在运行Hadoop作业的时候，先运行下面的命令：
  1. [wyp@wyp Hadoop_jar]$ export \

  2.     HADOOP_CLASSPATH=/home/wyp/IdeaProjects/Hadoop/out/artifacts/Hadoop_jar/
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其中，/home/wyp/IdeaProjects/Hadoop/out/artifacts/Hadoop_jar/是上面Hadoop.jar文件所在的目录。好了，现在再运行一下Hadoop作业命令：
有一个比较推荐的方法，就是在提交作业的时候加上-libjars参数，后面跟着需要的类库的绝对路径。
  1. [wyp@wyp Hadoop_jar]$ hadoop jar /home/wyp/IdeaProjects/Hadoop/out/artifacts/Hadoop_jar/Hadoop.jar  com/wyp/hadoop/MaxTemperature /user/wyp/data.txt /user/wyp/result

  2. 13/10/28 15:34:16 INFO client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8032

  3. 13/10/28 15:34:16 INFO client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8032

  4. 13/10/28 15:34:17 WARN mapreduce.JobSubmitter: Hadoop command-line option parsing not performed. Implement the Tool interface and execute your application with ToolRunner to remedy this.

  5. 13/10/28 15:34:17 INFO mapred.FileInputFormat: Total input paths to process : 1

  6. 13/10/28 15:34:17 INFO mapreduce.JobSubmitter: number of splits:2

  7. 13/10/28 15:34:17 INFO Configuration.deprecation: user.name is deprecated. Instead, use mapreduce.job.user.name

  8. 13/10/28 15:34:17 INFO Configuration.deprecation: mapred.jar is deprecated. Instead, use mapreduce.job.jar

  9. 13/10/28 15:34:17 INFO Configuration.deprecation: mapred.output.value.class is deprecated. Instead, use mapreduce.job.output.value.class

  10. 13/10/28 15:34:17 INFO Configuration.deprecation: mapred.job.name is deprecated. Instead, use mapreduce.job.name

  11. 13/10/28 15:34:17 INFO Configuration.deprecation: mapred.input.dir is deprecated. Instead, use mapreduce.input.fileinputformat.inputdir

  12. 13/10/28 15:34:17 INFO Configuration.deprecation: mapred.output.dir is deprecated. Instead, use mapreduce.output.fileoutputformat.outputdir

  13. 13/10/28 15:34:17 INFO Configuration.deprecation: mapred.map.tasks is deprecated. Instead, use mapreduce.job.maps

  14. 13/10/28 15:34:17 INFO Configuration.deprecation: mapred.output.key.class is deprecated. Instead, use mapreduce.job.output.key.class

  15. 13/10/28 15:34:17 INFO Configuration.deprecation: mapred.working.dir is deprecated. Instead, use mapreduce.job.working.dir

  16. 13/10/28 15:34:18 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1382942307976_0009

  17. 13/10/28 15:34:18 INFO impl.YarnClientImpl: Submitted application application_1382942307976_0009 to ResourceManager at /0.0.0.0:8032

  18. 13/10/28 15:34:18 INFO mapreduce.Job: The url to track the job: http://wyp:8088/proxy/application_1382942307976_0009/

  19. 13/10/28 15:34:18 INFO mapreduce.Job: Running job: job_1382942307976_0009

  20. 13/10/28 15:34:26 INFO mapreduce.Job: Job job_1382942307976_0009 running in uber mode : false

  21. 13/10/28 15:34:26 INFO mapreduce.Job:  map 0% reduce 0%

  22. 13/10/28 15:34:41 INFO mapreduce.Job:  map 50% reduce 0%

  23. 13/10/28 15:34:53 INFO mapreduce.Job:  map 100% reduce 0%

  24. 13/10/28 15:35:17 INFO mapreduce.Job:  map 100% reduce 100%

  25. 13/10/28 15:35:18 INFO mapreduce.Job: Job job_1382942307976_0009 completed successfully

  26. 13/10/28 15:35:18 INFO mapreduce.Job: Counters: 43

  27.         File System Counters

  28.                 FILE: Number of bytes read=144425

  29.                 FILE: Number of bytes written=524725

  30.                 FILE: Number of read operations=0

  31.                 FILE: Number of large read operations=0

  32.                 FILE: Number of write operations=0

  33.                 HDFS: Number of bytes read=1777598

  34.                 HDFS: Number of bytes written=18

  35.                 HDFS: Number of read operations=9

  36.                 HDFS: Number of large read operations=0

  37.                 HDFS: Number of write operations=2

  38.         Job Counters 

  39.                 Launched map tasks=2

  40.                 Launched reduce tasks=1

  41.                 Data-local map tasks=2

  42.                 Total time spent by all maps in occupied slots (ms)=38057

  43.                 Total time spent by all reduces in occupied slots (ms)=24800

  44.         Map-Reduce Framework

  45.                 Map input records=13130

  46.                 Map output records=13129

  47.                 Map output bytes=118161

  48.                 Map output materialized bytes=144431

  49.                 Input split bytes=182

  50.                 Combine input records=0

  51.                 Combine output records=0

  52.                 Reduce input groups=2

  53.                 Reduce shuffle bytes=144431

  54.                 Reduce input records=13129

  55.                 Reduce output records=2

  56.                 Spilled Records=26258

  57.                 Shuffled Maps =2

  58.                 Failed Shuffles=0

  59.                 Merged Map outputs=2

  60.                 GC time elapsed (ms)=321

  61.                 CPU time spent (ms)=5110

  62.                 Physical memory (bytes) snapshot=552824832

  63.                 Virtual memory (bytes) snapshot=1228738560

  64.                 Total committed heap usage (bytes)=459800576

  65.         Shuffle Errors

  66.                 BAD_ID=0

  67.                 CONNECTION=0

  68.                 IO_ERROR=0

  69.                 WRONG_LENGTH=0

  70.                 WRONG_MAP=0

  71.                 WRONG_REDUCE=0

  72.         File Input Format Counters 

  73.                 Bytes Read=1777416

  74.         File Output Format Counters 

  75.                 Bytes Written=18
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到这里，程序就成功运行了？怎么查看刚刚程序运行的结果呢？运行下面命令：
  1. [wyp@wyp Hadoop_jar]$ hadoop fs -ls /user/wyp

  2. Found 2 items

  3. -rw-r--r--   1 wyp supergroup    1777168 2013-10-25 17:44 /user/wyp/data.txt

  4. drwxr-xr-x   - wyp supergroup          0 2013-10-28 15:35 /user/wyp/result

  5. [wyp@wyp Hadoop_jar]$ hadoop fs -ls /user/wyp/result

  6. Found 2 items

  7. -rw-r--r--   1 wyp supergroup    0 2013-10-28 15:35 /user/wyp/result/_SUCCESS

  8. -rw-r--r--   1 wyp supergroup  18 2013-10-28 15:35 /user/wyp/result/part-00000

  9. [wyp@wyp Hadoop_jar]$ hadoop fs -cat  /user/wyp/result/part-00000

  10. 1901        317

  11. 1902        244
复制代码
到此，你自己写好的一个Mapreduce程序终于成功运行了！
　　附程序测试的数据的下载地址： data.zip (144.46 KB, 下载次数: 47)
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