使用eclipse开发MapReduce

2023年8月4日 202次阅读 来源: MapReduce

1.增加插件
将插件hadoop-eclipse-plugin-1.0.4.jar放入/usr/lib/eclipse/plugins目录下
（完成后重新启动eclipse）[插件存放路径视eclipse存放位置而定]
2.配置hadoop的安装路径
eclipse中
window—preferences，在左边栏中找到Hadoop Map/Reduce，将hadoop的目录设置为hadoop的安装目录
3.建立MapRedece工程
创建一个MapReduce Project，点击eclipse主菜单上的File—New—Project，在弹出的对话框中选择MapReduce Project，之后输入Project的名
4.建立MapReduce程序
就和建立普通的java程序是一样的

package com.sun.mapreduce;
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.LongWritable;
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.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;

public class WordCount {
    public static class Map extends Mapper<LongWritable, Text, Text, IntWritable> {
        private final static IntWritable one = new IntWritable(1);
        private Text word = new Text();
        public void map(LongWritable key, Text value, Context context) 
            throws IOException, InterruptedException{
                String line = value.toString();
                StringTokenizer tokenizer = new StringTokenizer(line);
                while (tokenizer.hasMoreTokens()) {
                    word.set(tokenizer.nextToken());
                    context.write(word, one);
                }
            }
    }
    public static class Reduce extends Reducer<Text, IntWritable, Text, IntWritable> {

        @Override
            public void reduce(Text key, Iterable<IntWritable> values, Context context)
            throws IOException, InterruptedException {
                // TODO Auto-generated method stub
                int sum = 0;
                for (IntWritable val : values)
                    sum += val.get();
                context.write(key, new IntWritable(sum));
            }
    }
    public static void main(String[] args) throws Exception {
        Configuration conf = new Configuration();

        Job job = new Job(conf, "wordcount");
        job.setJarByClass(WordCount.class);

        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);

        job.setMapperClass(Map.class);
        job.setReducerClass(Reduce.class);

        job.setInputFormatClass(TextInputFormat.class);
        job.setOutputFormatClass(TextOutputFormat.class);

        FileInputFormat.addInputPath(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));

        job.waitForCompletion(true);

    }
}

5.执行程序
执行程序时要附加一定的参数
点击Run-run configurations ，在Arguments中填写参数，参数分别为输入文件的目录 输出文件的目录
例如/home/asheng/hadoop/in /home/asheng/hadoop/out(in目录下应该放置需要分析的文件，out目录不需要手工建立)
设置完成后点击Run即可，通过控制台可以观察运行状态，具体的运行结果在out目录下

    原文作者：MapReduce
    原文地址: https://segmentfault.com/a/1190000000355533
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