Hadoop2.2.0下通过编译单词统计例子WordCount.java熟悉如何编译自己的程序

摘要：本文记录了如何在Hadoop 2.2.0环境下编译、启动运行和测试自己的程序。

关键词：hadoop2.2.0 编译例子 WordCount.java  打包 wordcount.jar

 

一、前提条件：部署好hadoop 2.2.0环境

部署hadoop 2.2.0 环境可以参考：Hadoop2.2.0单节点安装及测试

环境如下：

用户：hduser

hadoop 2.2.0 部署在目录： /home/hduser/hadoop-2.2.0

WordCount.java 通过winrar解压文件 /home/hduser/hadoop-2.2.0/share/hadoop/mapreduce/sources/hadoop-mapreduce-examples-2.2.0-sources.jar 可以得到。

设置环境变量PATH：

vim /home/hduser/.bash_profile ,执行命令 i ,并填入如下内容

export HADOOP_HOME=/home/hduser/hadoop-2.2.0
export HADOOP_LIB_HOME=/home/hduser/hadoop-2.2.0/share/hadoop

PATH=$HADOOP_HOME/bin:$PATH:$HOME/bin

export PATH

顺序执行命令 ESC  , :  , wq , enter

 然后 source /home/hduser/.bash_profile

二、单词统计例子（WordCount.java）是什么样子的

/**
 * 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
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 */
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);
  }
}

三、准备数据

cd /home/hduser
wget http://www.gutenberg.org/cache/epub/20417/pg20417.txt
cd hadoop-2.2.0
bin/hdfs dfs -mkdir /tmp
bin/hdfs dfs -copyFromLocal /home/hduser/pg20417.txt /tmp
bin/hdfs dfs -ls /tmp

四、编译hadoop2.2 例子程序WordCount.java

4.1 编译需要hadoop2.2的库文件：
/home/hduser/hadoop-2.2.0/share/hadoop/common/lib/commons-cli-1.2.jar
/home/hduser/hadoop-2.2.0/share/hadoop/common/hadoop-common-2.2.0.jar
/home/hduser/hadoop-2.2.0/share/hadoop/mapreduce/hadoop-mapreduce-client-core-2.2.0.jar

4.2 编译工具：

1) 在redhat或者ubuntu下开启Eclipse进行编译

step1:

创建工程，创建包org.apache.hadoop.examples，将WordCount.java加入包中，这时会看到许多错误，那是因为缺少hadoop的jar包。

step2: 

然后加入库文件commons-cli-1.2.jar, hadoop-common-2.2.0.jar, hadoop-mapreduce-client-core-2.2.0.jar。这时，之前的错误就消失了。

step3:

在Eclipse自动编译后，进入工程bin目录org.apache.hadoop.examples下可以看到三个class文件：WordCount.class，WordCount$IntSumReducer.class，WordCount$TokenizerMapper.class

step4: 

cd /home/hduser/workspace/hdpHelloWord/bin/org/apache/hadoop/examples

step5: 

用命令打包 jar -cvf wordcount.jar -C ./ .  可以得到 wordcount.jar

step6: 

执行命令 hadoop jar wordcount.jar org.apache.hadoop.examples.WordCount /tmp /tmp-output ，注意加上包名org.apache.hadoop.examples.WordCount才能正常运行。运行需要一点点时间，屏幕会输出中间结果。

step7: 

查看结果，执行命令 hdfs dfs -ls /tmp-output/tmp-output/_SUCCESS/tmp-output/part-r-00000

2) 用命令行编译

step1: 

javac -classpath /home/hduser/hadoop-2.2.0/share/hadoop/common/lib/commons-cli-1.2.jar:/home/hduser/hadoop-2.2.0/share/hadoop/common/hadoop-common-2.2.0.jar:/home/hduser/hadoop-2.2.0/share/hadoop/mapreduce/hadoop-mapreduce-client-core-2.2.0.jar WordCount.java

step2: 

用命令打包 jar -cvf wordcount.jar -C ./ .  可以得到 wordcount.jar

step3: 

执行命令 hadoop jar wordcount.jar org.apache.hadoop.examples.WordCount /tmp /tmp-output ，注意加上包名org.apache.hadoop.examples.WordCount才能正常运行。运行需要一点点时间，屏幕会输出中间结果。

step4: 

查看结果，执行命令 hdfs dfs -ls /tmp-output/tmp-output/_SUCCESS/tmp-output/part-r-00000

执行编译

执行打包

查看打包结果

五、总结

利用hadoop2.2.0编译自己工程或者代码时，注意一下几点：

1）搭建好hadoop2.2.0环境

2) 知道需要的hadoop库文件

3) 编译时引用hadoop的库文件

4) 执行时注意加入包名，或者打包时加入包名