hadoop之hadoop-mapreduce-examples-2.7.0.jar

前2篇blog中测试hadoop代码的时候都用到了这个jar，那么很有必要去分析一下源码。

分析源码之前很有必要先写一个wordcount，代码如下

package mytest;
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;

public class WordCount {

  public static class TokenizerMapper
       extends Mapper{

    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 {
    private IntWritable result = new IntWritable();

    public void reduce(Text key, Iterable 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();
    Job job = Job.getInstance(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(args[0]));
    FileOutputFormat.setOutputPath(job, new Path(args[1]));
    System.exit(job.waitForCompletion(true) ? 0 : 1);
  }
}

检索相关源码，发现需要2个jar，分别为hadoop-common-2.7.0.jar和hadoop-mapreduce-client-core-2.7.0.jar

使用myeclipse导出为Runnable Jar后，执行

~/hadoop-2.7.0/bin/hadoop jar my.jar mytest.WordCount /user/hadoop/input /user/hadoop/output3

测试成功

因为有个“package mytest”所以执行的时候需要使用mytest.WorCount！

仔细回忆之前执行命令的时候并没有加上类似mytest.这类的东西就能执行成功。我们去检索源码看看。执行。

find ~/  -name   *hadoop-mapreduce-examples*

输出内容为

/home/hadoop/hadoop-2.7.0/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.7.0.jar
/home/hadoop/hadoop-2.7.0/share/hadoop/mapreduce/sources/hadoop-mapreduce-examples-2.7.0-sources.jar
/home/hadoop/hadoop-2.7.0/share/hadoop/mapreduce/sources/hadoop-mapreduce-examples-2.7.0-test-sources.jar
/home/hadoop/hadoop-2.7.0/share/doc/hadoop/hadoop-mapreduce-examples

解压缩hadoop-mapreduce-examples-2.7.0-sources.jar后导入myeclipse查看源码。

检索“grep”字段，发现出现在ExampleDriver.java中，看样这个文件是这个jar的入口。

那么Runnable Jar怎么确定这个文件的入口呢。解压缩Runnable Jar后发现META-INF 中有如下的描述

Main-Class: org.apache.hadoop.examples.ExampleDriver

原来Runnable Jar是可以配置默认入口的。可以通过myeclipse导出Jar的时候设置默认入口。

将ExampleDriver.java导入自己的工程，修修改改后，测试。执行

~/hadoop-2.7.0/bin/hadoop jar my.jar wordcount /user/hadoop/input /user/hadoop/output4

很多东西具体看源码比较详细，以后有特殊的地方可以细细分析。


tip：分析log日志可以发现。

map和reduce中的syso输出到log日志上。

Main中的syso输出到屏幕上。