第一个map reduce程序

完成了第一个mapReduce例子，记录一下。

实验环境:

hadoop在三台ubuntu机器上部署
开发在window7上进行
hadoop版本2.2.0

下载了hadoop-eclipse-plugin-2.2.0.jar放入eclipse的plugin文件夹中，重启后有如下标识

下方右击: add hadoop location

此时，eclipse 左侧会有

上图即简单的实现了一个嵌于eclipse中的用于访问hdfs系统的client端，其中可以增删改查文件。

-------------------------------

下面研究一下编程吧...

1 新建一个map/Reduce Project，本例中由于eclpise与hadoop不在一台主机上，着实费了一些时间

如下图，此时应该选择第二项(specify hadoop library locaiton), 导入的应该是hadoop目录下的lib/native文件夹，这里如果选错了，project是建不起来的！

2. 取出\hadoop-2.2.0\share\hadoop\mapreduce\sources\hadoop-mapreduce-examples-2.2.0-sources.jar下，解压后导入eclipse，好了，可以开始研究代码了。.

基本流程如下

(input) <k1, v1> -> map -> <k2, v2> -> combine -> <k2, v2> -> reduce -> <k3, v3> (output)

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 * 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
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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 {
/*Mapper(14-26行)中的map方法(18-25行)通过指定的 TextInputFormat(49行)一次处理一行。然后，它通过StringTokenizer 以空格为分隔符将一行切分为若干tokens，之后，输出< <word>, 1> 形式的键值对。*/
  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);
      }
    }
  }
  
/*
做Combiner: 每次map运行之后，会对输出按照key进行排序，然后把输出传递给本地的combiner（按照作业的配置与Reducer一样），进行本地聚合
做Reducer中的reduce方法 仅是将每个key（本例中就是单词）出现的次数求和。
*/
  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); // combiner与reducer用的类一样，减轻reducer负担
    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);
  }
}

3. wordCount执行效果如下

root@kali:/data/hadoop/share/hadoop# hadoop jar ./mapreduce/hadoop-mapreduce-examples-2.2..jar  wordcount /abc/start-all.sh /abd/out
// :: INFO Configuration.deprecation: session.id is deprecated. Instead, use dfs.metrics.session-id
// :: INFO jvm.JvmMetrics: Initializing JVM Metrics with processName=JobTracker, sessionId=
// :: INFO input.FileInputFormat: Total input paths to process :
// :: INFO mapreduce.JobSubmitter: number of splits:
// :: INFO Configuration.deprecation: user.name is deprecated. Instead, use mapreduce.job.user.name
// :: INFO Configuration.deprecation: mapred.jar is deprecated. Instead, use mapreduce.job.jar
// :: INFO Configuration.deprecation: mapred.output.value.class is deprecated. Instead, use mapreduce.job.output.value.class
// :: INFO Configuration.deprecation: mapreduce.combine.class is deprecated. Instead, use mapreduce.job.combine.class
// :: INFO Configuration.deprecation: mapreduce.map.class is deprecated. Instead, use mapreduce.job.map.class

http://phz50.iteye.com/blog/932373

http://www.ibm.com/developerworks/cn/java/j-javadev2-15/

http://www.cnblogs.com/flyoung2008/archive/2011/12/09/2281400.html

http://f.dataguru.cn/thread-167597-1-1.html  hadoop在eclipse中上传文件size为0

http://cs.smith.edu/dftwiki/index.php/Hadoop_Tutorial_1_--_Running_WordCount  step by step!