使用命令行編譯打包運行自己的MapReduce程序 Hadoop2.4.1

網上的MapReduce WordCount教程對於如何編譯WordCount.java幾乎是一筆帶過… 而有寫到的，大多又是 0.20 等舊版本版本的做法，即 javac -classpath /usr/local/Hadoop/hadoop-1.0.1/hadoop-core-1.0.1.jar WordCount.java，但較新的 2.X 版本中，已經沒有 hadoop-core*.jar 這個文件，因此編輯和打包自己的MapReduce程序與舊版本有所不同。

本文以 Hadoop 2.4.1 環境下的WordCount實例來介紹 2.x 版本中如何編輯自己的MapReduce程序。

Hadoop 2.x 版本中的依賴 jar

Hadoop 2.x 版本中jar不再集中在一個 hadoop-core*.jar 中，而是分成多個 jar，如運行WordCount實例需要如下三個 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

編譯、打包 Hadoop MapReduce 程序

將上述 jar 添加至 classpath 路徑：

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"

接著就可以編譯 WordCount.java 了（使用的是 2.4.1 源碼中的 WordCount.java，源碼在文本最後面）：

javac WordCount.java

編譯時會有警告，可以忽略。編譯後可以看到生成了幾個.class文件。

使用Javac編譯自己的MapReduce程序

接著把 .class 文件打包成 jar，才能在 Hadoop 中運行：

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

打包完成後，運行試試，創建幾個輸入文件：

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

創建WordCount的輸入

開始運行：

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

不過這邊可能會遇到如下的提示 Exception in thread "main" java.lang.NoClassDefFoundError: WordCount ：

提示找不到 WordCount 類

因為程序中聲明了 package ，所以在命令中也要 org.apache.hadoop.examples 寫完整：

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

正確運行後的結果如下：

WordCount 運行結果

進階：使用Eclipse編譯運行MapReduce程序

使用命令行編譯運行MapReduce程序畢竟有些麻煩，修改一次就得手動編譯、打包一次，使用Eclipse編譯運行MapReduce程序會更加方便。

WordCount.java 源碼

文件位於 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 <in> <out>");
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. }

CentOS安裝和配置Hadoop2.2.0 http://www.linuxidc.com/Linux/2014-01/94685.htm

Ubuntu 13.04上搭建Hadoop環境 http://www.linuxidc.com/Linux/2013-06/86106.htm

Ubuntu 12.10 +Hadoop 1.2.1版本集群配置 http://www.linuxidc.com/Linux/2013-09/90600.htm

Ubuntu上搭建Hadoop環境（單機模式+偽分布模式） http://www.linuxidc.com/Linux/2013-01/77681.htm

Ubuntu下Hadoop環境的配置 http://www.linuxidc.com/Linux/2012-11/74539.htm

單機版搭建Hadoop環境圖文教程詳解 http://www.linuxidc.com/Linux/2012-02/53927.htm

搭建Hadoop環境（在Winodws環境下用虛擬機虛擬兩個Ubuntu系統進行搭建） http://www.linuxidc.com/Linux/2011-12/48894.htm

更多Hadoop相關信息見Hadoop 專題頁面 http://www.linuxidc.com/topicnews.aspx?tid=13