Hadoop1.0 Eclipse Plugin-作业提交

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mb5ff2f31984205 2012-05-24 13:01:00

文章标签 hadoop apache java eclipse mapreduce 文章分类 虚拟化云计算

1.环境

Jdk：1.6.0_10-rc2

Hadoop：hadoop-1.0.0.tar.gz

Eclipse 版本：3.4.0

Hadoop Eclipse 插件：hadoop-eclipse-plugin-1.0.0.jar

操作系统：Windows7 32位旗舰版

2.Eclipse插件配置

2.1 把"hadoop-eclipse-plugin-1.0.0.jar"放到Eclipse的目录的"plugins"中(eclipse/plugins)，重新启动Eclipse生效

2.2 选择Elipse Window菜单下的"Preference"，配置"Hadoop Map/Reduce"选项，选择Hadoop的安装根目录

2.3 配置Hadoop Location

在配置Hadoop Location之前确定hadoop 已启动起来

Eclipse 切换到“Map/Reduce Locations” 视图，在"Map/Reduce Locations"视图右击选择"New Hadoop Location"，

* Map/Reduce Master与mapred-site.xml配置文件对应

* DFS Mast 与core-site.xml配置对应

创建完成后 ,切换到JavaEE视图刷新右边的DFS Locations 就会看到dfs文件结构

可以在节点上右键创建删除目录做测试

3.运行wordCount例子程序

创建一个 Map/Reduce Project项目

创建成功后 WordCount报名对应(源码在hadoop\src\examples\org\apache\hadoop\examples目录下)

WordCount.java

package org.apache.hadoop.examples;

import java.io.IOException;
import java.net.URI;
import java.util.StringTokenizer;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FSDataInputStream;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.FileUtil;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IOUtils;
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);
    }
    //删除输出目录
    FileSystem fileSystem = FileSystem.get(URI.create(args[1]),conf);
    fileSystem.delete(new Path("/user/admin/output"), true);

    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]));
    boolean flag = job.waitForCompletion(true);
    FSDataInputStream  fDataInputStream = null;
    try {
      fDataInputStream = fileSystem.open(new Path("/user/admin/output/part-r-00000"));
      String line = null;
      while ((line = fDataInputStream.readLine()) != null) {
        System.out.println(line);;
    }
  } catch (Exception e) {
    e.printStackTrace();
  } finally {
    IOUtils.closeStream(fDataInputStream);
    IOUtils.closeStream(fileSystem);
  }
    System.exit( flag ? 0 : 1);
  }
}