Svelte框架：编译时优化的高性能前端框架

1.需求

2.思路分析

3.Idea代码

DefinePartitionJob

package demo7;

import demo5.DescIntWritable;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
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.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;

import java.io.IOException;

public class DefinePartitionJob {
    public static void main(String[] args) throws IOException, InterruptedException, ClassNotFoundException {

        Configuration conf = new Configuration();
        conf.set("fs.defaultFS","hdfs://hadoop10:8020");

        Job job = Job.getInstance(conf);
        job.setJarByClass(DefinePartitionJob.class);

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

        TextInputFormat.addInputPath(job,new Path("/mapreduce/demo10"));
        TextOutputFormat.setOutputPath(job,new Path("/mapreduce/demo10/out"));

        job.setMapperClass(DefinePartitonMapper.class);
        job.setReducerClass(DefinePartitonReducer.class);
        //map输出的键与值类型
        job.setMapOutputKeyClass(DescIntWritable.class);
        job.setMapOutputValueClass(Subject.class);
        //reducer输出的键与值类型
        job.setOutputKeyClass(Subject.class);
        job.setOutputValueClass(DescIntWritable.class);

        //设置reduceTask的个数
        job.setNumReduceTasks(4);
        //设置自定义分区
        job.setPartitionerClass(MyPartition.class);

        boolean b = job.waitForCompletion(true);
        System.out.println(b);

    }


    static class DefinePartitonMapper extends Mapper<LongWritable, Text, DescIntWritable,Subject> {
        @Override
        protected void map(LongWritable key, Text value,Context context) throws IOException, InterruptedException {
            String[] arr = value.toString().split("\t");
            context.write(new DescIntWritable(Integer.parseInt(arr[2])),new Subject(arr[0],arr[1]));
        }
    }
    static class DefinePartitonReducer extends Reducer<DescIntWritable,Subject,Subject,DescIntWritable> {
        @Override
        protected void reduce(DescIntWritable key, Iterable<Subject> values, Context context) throws IOException, InterruptedException {
            for (Subject subject : values) {
                context.write(subject, key);
            }
        }
    }}

MyPartition

package demo7;

import demo5.DescIntWritable;
import org.apache.hadoop.mapreduce.Partitioner;

public class MyPartition extends Partitioner<DescIntWritable,Subject> {
    @Override
    public int getPartition(DescIntWritable key, Subject value, int numPartitions) {
        if ("语文".equals(value.getKemu())){
            return 0;
        }else if ("数学".equals(value.getKemu())) {
            return 1;
        }else if ("英语".equals(value.getKemu())) {
            return 2;
        }
            return 3;


        }
}

 Subject

package demo7;

import org.apache.hadoop.io.Writable;

import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;

public class Subject implements Writable{
    private String name;
    private String kemu;

    public Subject() {
    }

    public Subject(String name, String kemu) {
        this.name = name;
        this.kemu = kemu;
    }

    public String getName() {
        return name;
    }

    public void setName(String name) {
        this.name = name;
    }

    public String getKemu() {
        return kemu;
    }

    public void setKemu(String kemu) {
        this.kemu = kemu;
    }

    @Override
    public void write(DataOutput out) throws IOException {
        out.writeUTF(name);
        out.writeUTF(kemu);
    }

    @Override
    public void readFields(DataInput in) throws IOException {
        this.name = in.readUTF();
        this.kemu = in.readUTF();

    }

    @Override
    public String toString() {
        return name + " " +kemu;
    }
}

 4.在hdfs查看结果

不要去争辩，多提升自己~