Linux下打包运行MR程序

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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/hadoopexport 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

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-2.6.0-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);
  }
}

参考资料

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

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