使用命令行编译打包运行自己的MapReduce程序 Hadoop2.4.1

网上的MapReduce WordCount教程对于如何编译WordCount.java几乎是一笔带过… 而有写到的，大多又是 0.20 等旧版本版本的做法，即 javac -classpath /usr/local/Hadoop/hadoop-1.0.1/hadoop-core-1.0.1.jar WordCount.java，但较新的 2.X 版本中，已经没有 hadoop-core*.jar 这个文件，因此编辑和打包自己的MapReduce程序与旧版本有所不同。

本文以 Hadoop 2.4.1 环境下的WordCount实例来介绍 2.x 版本中如何编辑自己的MapReduce程序。

Hadoop 2.x 版本中的依赖 jar

Hadoop 2.x 版本中jar不再集中在一个 hadoop-core*.jar 中，而是分成多个 jar，如运行WordCount实例需要如下三个 jar:

• $HADOOP_HOME/share/hadoop/common/hadoop-common-2.4.1.jar
• $HADOOP_HOME/share/hadoop/mapreduce/hadoop-mapreduce-client-core-2.4.1.jar
• $HADOOP_HOME/share/hadoop/common/lib/commons-cli-1.2.jar

编译、打包 Hadoop MapReduce 程序

将上述 jar 添加至 classpath 路径：

export CLASSPATH="$HADOOP_HOME/share/hadoop/common/hadoop-common-2.4.1.jar:$HADOOP_HOME/share/hadoop/mapreduce/hadoop-mapreduce-client-core-2.4.1.jar:$HADOOP_HOME/share/hadoop/common/lib/commons-cli-1.2.jar:$CLASSPATH"

 

接着就可以编译 WordCount.java 了（使用的是 2.4.1 源码中的 WordCount.java，源码在文本最后面）：

javac WordCount.java

编译时会有警告，可以忽略。编译后可以看到生成了几个.class文件。

使用Javac编译自己的MapReduce程序

接着把 .class 文件打包成 jar，才能在 Hadoop 中运行：

jar -cvf WordCount.jar ./WordCount*.class

打包完成后，运行试试，创建几个输入文件：

Mkdir input
echo "echo of the rainbow" > ./input/file0
echo "the waiting game" > ./input/file1

创建WordCount的输入

开始运行：

/usr/local/hadoop/bin/hadoop jar WordCount.jar WordCount input output

不过这边可能会遇到如下的提示 Exception in thread "main" java.lang.NoClassDefFoundError: WordCount ：

提示找不到 WordCount 类

因为程序中声明了 package ，所以在命令中也要 org.apache.hadoop.examples 写完整：

/usr/local/hadoop/bin/hadoop jar WordCount.jar org.apache.hadoop.examples.WordCount input output

正确运行后的结果如下：

WordCount 运行结果

进阶：使用Eclipse编译运行MapReduce程序

使用命令行编译运行MapReduce程序毕竟有些麻烦，修改一次就得手动编译、打包一次，使用Eclipse编译运行MapReduce程序会更加方便。

WordCount.java 源码

文件位于 hadoop-2.4.1-src\hadoop-mapreduce-project\hadoop-mapreduce-examples\src\main\java\org\apache\hadoop\examples 中：

1. /**
2. * Licensed to the Apache Software Foundation (ASF) under one
3. * or more contributor license agreements. See the NOTICE file
4. * distributed with this work for additional information
5. * regarding copyright ownership. The ASF licenses this file
6. * to you under the Apache License, Version 2.0 (the
7. * "License"); you may not use this file except in compliance
8. * with the License. You may obtain a copy of the License at
9. *
10. * http://www.apache.org/licenses/LICENSE-2.0
11. *
12. * Unless required by applicable law or agreed to in writing, software
13. * distributed under the License is distributed on an "AS IS" BASIS,
14. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
15. * See the License for the specific language governing permissions and
16. * limitations under the License.
17. */
18. package org.apache.hadoop.examples;
19. import java.io.IOException;
20. import java.util.StringTokenizer;
21. import org.apache.hadoop.conf.Configuration;
22. import org.apache.hadoop.fs.Path;
23. import org.apache.hadoop.io.IntWritable;
24. import org.apache.hadoop.io.Text;
25. import org.apache.hadoop.mapreduce.Job;
26. import org.apache.hadoop.mapreduce.Mapper;
27. import org.apache.hadoop.mapreduce.Reducer;
28. import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
29. import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
30. import org.apache.hadoop.util.GenericOptionsParser;
31. publicclassWordCount{
32. publicstaticclassTokenizerMapper
33. extendsMapper<Object,Text,Text,IntWritable>{
34. privatefinalstaticIntWritable one =newIntWritable(1);
35. privateText word =newText();
36. publicvoid map(Object key,Text value,Context context
37. )throwsIOException,InterruptedException{
38. StringTokenizer itr =newStringTokenizer(value.toString());
39. while(itr.hasMoreTokens()){
40. word.set(itr.nextToken());
41. context.write(word, one);
42. }
43. }
44. }
45. publicstaticclassIntSumReducer
46. extendsReducer<Text,IntWritable,Text,IntWritable>{
47. privateIntWritable result =newIntWritable();
48. publicvoid reduce(Text key,Iterable<IntWritable> values,
49. Context context
50. )throwsIOException,InterruptedException{
51. int sum =0;
52. for(IntWritable val : values){
53. sum += val.get();
54. }
55. result.set(sum);
56. context.write(key, result);
57. }
58. }
59. publicstaticvoid main(String[] args)throwsException{
60. Configuration conf =newConfiguration();
61. String[] otherArgs =newGenericOptionsParser(conf, args).getRemainingArgs();
62. if(otherArgs.length !=2){
63. System.err.println("Usage: wordcount ");
64. System.exit(2);
65. }
66. Job job =newJob(conf,"word count");
67. job.setJarByClass(WordCount.class);
68. job.setMapperClass(TokenizerMapper.class);
69. job.setCombinerClass(IntSumReducer.class);
70. job.setReducerClass(IntSumReducer.class);
71. job.setOutputKeyClass(Text.class);
72. job.setOutputValueClass(IntWritable.class);
73. FileInputFormat.addInputPath(job,newPath(otherArgs[0]));
74. FileOutputFormat.setOutputPath(job,newPath(otherArgs[1]));
75. System.exit(job.waitForCompletion(true)?0:1);
76. }
77. }