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 inputecho "echo of the rainbow" > ./input/file0echo "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-srchadoop-mapreduce-projecthadoop-mapreduce-examplessrcmainjavaorgapachehadoopexamples 中：

/**
* Licensed to the Apache Software Foundation （ASF） under one
* or more contributor license agreements. See the NOTICE file
* distributed with this work for additional information
* regarding copyright ownership. The ASF licenses this file
* to you under the Apache License, Version 2.0 （the
* "License"）; you may not use this file except in compliance
* with the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.hadoop.examples;
import java.io.IOException;
import java.util.StringTokenizer;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.util.GenericOptionsParser;
publicclassWordCount{
publicstaticclassTokenizerMapper
extendsMapper<Object,Text,Text,IntWritable>{
privatefinalstaticIntWritable one =newIntWritable（1）;
privateText word =newText（）;
publicvoid map（Object key,Text value,Context context
）throwsIOException,InterruptedException{
StringTokenizer itr =newStringTokenizer（value.toString（））;
while（itr.hasMoreTokens（））{
word.set（itr.nextToken（））;
context.write（word, one）;
}
}
}
publicstaticclassIntSumReducer
extendsReducer<Text,IntWritable,Text,IntWritable>{
privateIntWritable result =newIntWritable（）;
publicvoid reduce（Text key,Iterable<IntWritable> values,
Context context
）throwsIOException,InterruptedException{
int sum =0;
for（IntWritable val : values）{
sum += val.get（）;
}
result.set（sum）;
context.write（key, result）;
}
}
publicstaticvoid main（String[] args）throwsException{
Configuration conf =newConfiguration（）;
String[] otherArgs =newGenericOptionsParser（conf, args）.getRemainingArgs（）;
if（otherArgs.length ！=2）{
System.err.println（"Usage: wordcount <in> <out>"）;
System.exit（2）;
}
Job job =newJob（conf,"word count"）;
job.setJarByClass（WordCount.class）;
job.setMapperClass（TokenizerMapper.class）;
job.setCombinerClass（IntSumReducer.class）;
job.setReducerClass（IntSumReducer.class）;
job.setOutputKeyClass（Text.class）;
job.setOutputValueClass（IntWritable.class）;
FileInputFormat.addInputPath（job,newPath（otherArgs[0]））;
FileOutputFormat.setOutputPath（job,newPath（otherArgs[1]））;
System.exit（job.waitForCompletion（true）？0:1）;
}
}

CentOS安装和配置Hadoop2.2.0 http://www.linuxidc.com/Linux/2014-01/94685.htmUbuntu 13.04上搭建Hadoop环境 http://www.linuxidc.com/Linux/2013-06/86106.htmUbuntu 12.10 +Hadoop 1.2.1版本集群配置 http://www.linuxidc.com/Linux/2013-09/90600.htmUbuntu上搭建Hadoop环境（单机模式+伪分布模式） http://www.linuxidc.com/Linux/2013-01/77681.htmUbuntu下Hadoop环境的配置 http://www.linuxidc.com/Linux/2012-11/74539.htm单机版搭建Hadoop环境图文教程详解 http://www.linuxidc.com/Linux/2012-02/53927.htm搭建Hadoop环境（在Winodws环境下用虚拟机虚拟两个Ubuntu系统进行搭建） http://www.linuxidc.com/Linux/2011-12/48894.htm更多Hadoop相关信息见Hadoop 专题页面 http://www.linuxidc.com/topicnews.aspx？tid=13本文永久更新链接地址：http://www.linuxidc.com/Linux/2015-02/113489.htm