MapReduce计数程序（自主复习）

1.MyWordCount类

注意：

1.本机+测试，两个注释都放开

2.本机跑集群，要开异构平台为true

3.集群跑，把两个注释都注起来，然后在集群上面跑

package com.littlepage.wc;

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.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;

import java.io.IOException;

public class MyWordCount {
    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
        //1.读取配置
        Configuration conf=new Configuration(true);
        //设定本地环境运行，不进行集群运行
//        conf.set("mapreduce.framework.name","local");
        //设定异构平台
//        conf.set("mapreduce.app-submission.cross-platform","true");
        //2.设定Job
        Job job=Job.getInstance(conf);
        //3.设定Job执行的类
        job.setJarByClass(MyWordCount.class);
        //4.设定JobName
        job.setJobName("SteveYu's word count");
        //5.设定输入path
        Path infile=new Path("/data/wc/input");
        TextInputFormat.addInputPath(job,infile);
        //6.设定输出path
        Path outfile=new Path("/data/wc/loveloveOutput");
        if(outfile.getFileSystem(conf).exists(outfile)) outfile.getFileSystem(conf).delete(outfile,true);
        TextOutputFormat.setOutputPath(job,outfile);
        //7.设定MapperClass和ReduceClass
        job.setMapperClass(WordCountMapper.class);
        job.setReducerClass(WordCountReducer.class);
        //8.设定输出的Key，Value格式
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);
        //9.等待程序完成
        job.waitForCompletion(true);
    }
}

2.WordCountMapper类

作用：

定义一个拆分文本的功能，将Mapper进行拆分成key, value的形式

package com.littlepage.wc;

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

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

public class WordCountMapper extends Mapper<Object,Text,Text,IntWritable> {
    private final static IntWritable one=new IntWritable();
    private Text word=new Text();

    @Override
    protected 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);
        }
    }
}

3.WordCountReducer类

作用:

进行第二次映射计算

package com.littlepage.wc;

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

import java.io.IOException;

public class WordCountReducer extends Reducer<Text, IntWritable,Text,IntWritable> {
    private IntWritable result=new IntWritable();
    //相同的key为一组 ，这一组数据调用一次reduce
    //hello 1

    @Override
    protected void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
        int sum=;
        for (IntWritable val:values) {
            sum+=val.get();
        }
        result.set(sum);
        context.write(key,result);
    }
}

4.单机跑可能出现的问题

1.hadoop必须解压

2.hadoop必须配置HADOOP_HOME以及环境变量

3.hadoop必须将core-site.xml放进resources文件夹里面，并且文件夹得标识为source文件夹

4.hadoop的bin在windows必须粘贴为windows版本，并且，我们需要把hadoop.dll复制到system32文件夹内，因为system32是存放系统小工具的一个文件夹