hadoop mapreduce入门实例

最新推荐文章于 2025-12-08 22:51:06 发布

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原文链接：https://www.cnblogs.com/liuwei6/p/6708116.html

文章标签：

#mapreduce #wordcount #入门

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本文详细介绍了一个基于Hadoop的简单WordCount MapReduce实例，包括自定义Mapper和Reducer类的实现，以及如何设置Job参数来运行任务。

转自：https://www.cnblogs.com/liuwei6/p/6708116.html

自己写的一个简单的wordcount mapreduce实例

三个类

自定义的mapper类

继承 org.apache.hadoop.mapreduce.Mapper类，并需要填写四个泛型类，分别代表key-in的数据类型，value-in的数据类型，key-out的数据类型，value-out的数据类型，key-in为偏移量，所以为long类型，采用hadoop序列化框架中的数据类型

Long--->LongWritable

String-->Text

Int-->IntWritable

Null-->NullWritable

方法中自定义切分规则

package com.zjxt.demo.map;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;

import java.io.IOException;

/**
 * @Author: heiheihaxi
 * @Date: 2019/9/23 17:29
 */
public class MyWordCountMapper extends Mapper<LongWritable, Text, Text, IntWritable>{
    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {

        String[] split = value.toString().split("-");
        for (String val : split){
            context.write(new Text(val), new IntWritable(1));
        }

    }
}

Reduce类

package com.zjxt.demo.reduce;

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;

import java.io.IOException;

/**
 * @Author: heiheihaxi
 * @Date: 2019/9/23 17:39
 */
public class MyWordCountReduce extends Reducer<Text, IntWritable, Text, IntWritable> {
    @Override
    protected void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
        int sum = 0;

        for (IntWritable value : values){
            sum += value.get();
        }

        context.write(key, new IntWritable(sum));

    }
}

Job类

package com.zjxt.demo.job;

import com.zjxt.demo.map.MyWordCountMapper;
import com.zjxt.demo.reduce.MyWordCountReduce;
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.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

import java.io.IOException;

/**
 * @Author: heiheihaxi
 * @Date: 2019/9/23 17:42
 */
public class MyWrodCountJob {
    public static void main(String[] args) {

        Configuration conf = new Configuration();

        try {
            Job job = Job.getInstance(conf);

            job.setJarByClass(MyWrodCountJob.class);

            job.setMapperClass(MyWordCountMapper.class);
            job.setReducerClass(MyWordCountReduce.class);

            job.setMapOutputKeyClass(Text.class);
            job.setMapOutputValueClass(IntWritable.class);

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

            Path input = new Path("hdfs://namenodecluster/tmp/test/input/test.txt");
            Path output = new Path("hdfs://namenodecluster/tmp/test/output");

            FileInputFormat.setInputPaths(job, input);
            FileOutputFormat.setOutputPath(job, output);

            boolean b = job.waitForCompletion(true);

            System.out.println("successs");
            System.exit(b ? 0 : 1);


        } catch (IOException | ClassNotFoundException | InterruptedException e) {
            e.printStackTrace();
        }

    }
}

最后，我的只能打包在hdfs的客户端上运行，不能在idea本机运行，本机运行虽然不报错，并没有结果文件输出。