Linux下打包运行MR程序

在配置好基本环境之后，linux下打包运行MR程序分为这么几步：

1.编写MR程序；

2.编译xx.java源文件【javac  wordcount.java】

3.打包jar 【jar  -cvf  WordCount.jar  ./WordCount*】

4.运行jar 【 hadoop  jar  WordCount.jar  org.apache.hadoop.examples.WordCount  /input  /output】注意WordCount前面要将包名写完整

转载自 使用命令行编译打包运行MR程序

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 2.6.0 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，如使用 Hadoop 2.6.0 运行 WordCount 实例至少需要如下三个 jar:
$HADOOP_HOME/share/hadoop/common/hadoop-common-2.6.0.jar
$HADOOP_HOME/share/hadoop/mapreduce/hadoop-mapreduce-client-core-2.6.0.jar
$HADOOP_HOME/share/hadoop/common/lib/commons-cli-1.2.jar

实际上，通过命令 

hadoop classpath

 我们可以得到运行 Hadoop 程序所需的全部 classpath 信息。

编译、打包 Hadoop MapReduce 程序

我们将 Hadoop 的 classhpath 信息添加到 CLASSPATH 变量中，在 ~/.bashrc 中增加如下几行：

export HADOOP_HOME=/usr/local/hadoop
export CLASSPATH=$($HADOOP_HOME/bin/hadoop classpath):$CLASSPATH

别忘了执行 

source ~/.bashrc

 使变量生效，接着就可以通过 

javac

 命令编译
WordCount.java 了（使用的是 Hadoop 源码中的 WordCount.java，源码在文本最后面）：

javac WordCount.java

Shell
命令

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

Shell
命令

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

Shell
命令


创建WordCount的输入

开始运行：

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

Shell
命令

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

Shell
命令

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-2.6.0-src\hadoop-mapreduce-project\hadoop-mapreduce-examples\src\main\java\org\apache\hadoop\examples 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;
 
public class WordCount {
 
public static class TokenizerMapper
extends Mapper<Object, Text, Text, IntWritable>{
 
private final static IntWritable one = new IntWritable(1);
private Text word = new Text();
 
public void map(Object key, Text value, Context context
) throws IOException, InterruptedException {
StringTokenizer itr = new StringTokenizer(value.toString());
while (itr.hasMoreTokens()) {
word.set(itr.nextToken());
context.write(word, one);
}
}
}
 
public static class IntSumReducer
extends Reducer<Text,IntWritable,Text,IntWritable> {
private IntWritable result = new IntWritable();
 
public void reduce(Text key, Iterable<IntWritable> values,
Context context
) throws IOException, InterruptedException {
int sum = 0;
for (IntWritable val : values) {
sum += val.get();
}
result.set(sum);
context.write(key, result);
}
}
 
public static void main(String[] args) throws Exception {
Configuration conf = new Configuration();
String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();
if (otherArgs.length != 2) {
System.err.println("Usage: wordcount <in> <out>");
System.exit(2);
}
Job job = new Job(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, new Path(otherArgs[0]));
FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}

Java

参考资料

http://blog.sina.com.cn/s/blog_68cceb610101r6tg.html
http://www.cppblog.com/humanchao/archive/2014/05/27/207118.aspx