大数据之倒序输出

package three;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.io.WritableComparator;
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.FileSplit;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

import java.io.IOException;

public class Daoxu extends WritableComparator {




    static {
        System.setProperty("hadoop.home.dir", "D:\\Studyingimportant\\hadoop-2.9.2");
    }

    public static class MyMapper extends Mapper<LongWritable,Text,Text,Text> {
        @Override
        protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {

            String s = value.toString();
            String FileName = ((FileSplit) context.getInputSplit()).getPath().getName();
            String[] split = s.split("\\s");
            if ("a.txt".equals(FileName)) {
                context.write(new Text(split[0]), new Text("a.txt"));
                context.write(new Text(split[1]), new Text("a.txt"));
            } else if ("b.txt".equals(FileName)) {
                context.write(new Text(split[0]), new Text("b.txt"));
                context.write(new Text(split[1]), new Text("b.txt"));
            } else if ("c.txt".equals(FileName)) {
                context.write(new Text(split[0]), new Text("c.txt"));
                context.write(new Text(split[1]), new Text("c.txt"));
            }
        }
    }
    public static class MyReducer extends Reducer<Text,Text,Text,Text> {
        @Override
        protected void reduce(Text key, Iterable<Text> values, Context context) throws IOException, InterruptedException {
            long a=0l;
            long b=0l;
            long c=0l;

            for (Text value:values){
                String s = value.toString();
                if ("a.txt".equals(s)){
                    a+=1;
                }else if ("b.txt".equals(s)){
                    b+=1;
                }else if ("c.txt".equals(s)){
                    c+=1;
                }
            }
            context.write(new Text(key),new Text("a.txt->"+a+"b.txt->"+b+"c.txt->"+c));
        }
    }

    public static void main(String[] args) throws Exception {
        //初始化一个作业
        Configuration conf = new Configuration();
        //给作业取个名字
        Job job = Job.getInstance(conf, "Three");
        //输入文件路径
        FileInputFormat.addInputPaths(job,args[0]);
        //map并行计算
        job.setMapperClass(MyMapper.class);
        //shuffle流程
        //reduce计算
        job.setReducerClass(MyReducer.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(Text.class);
        //输出文件路径
        FileOutputFormat.setOutputPath(job,new Path(args[1]));
        //判断文件是否存在
        FileSystem fs = FileSystem.get(conf);
        if (fs.exists(new Path(args[1]))){
            fs.delete(new Path(args[1]),true);
        }
        //判断是否成功
        boolean b = job.waitForCompletion(true);
        System.out.println(b ? 1 : 0);
    }

}

大数据之倒序输出

原文：https://www.cnblogs.com/whyuan/p/12876060.html