IDEA本地编写mapreducer的序列化案例分析时，在本地测试无报错，无输出，代码是跟着视频写的，如何解决？

运行后结果：
2022-01-27 17:02:12,548 INFO [org.apache.hadoop.mapreduce.Job] - Job job_local156721860_0001 running in uber mode : false
2022-01-27 17:02:12,548 INFO [org.apache.hadoop.mapreduce.Job] - map 0% reduce 0%
2022-01-27 17:02:12,548 INFO [org.apache.hadoop.mapreduce.Job] - Job job_local156721860_0001 failed with state FAILED due to: NA
2022-01-27 17:02:12,548 INFO [org.apache.hadoop.mapreduce.Job] - Counters: 0

Process finished with exit code 1

程序代码如下：


FlowBean代码：
package com.atguigu.mapreduce.writable2;

import org.apache.hadoop.io.Writable;

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

public class FlowBean implements Writable {
    
    private long upFlow;//上行流量
    private long downFlow;//下行流量
    private long sumFlow;//总流量
    
        public FlowBean() {}
    
        public long getUpFlow() {
            return upFlow;
        }

        public void setUpFlow(long upFlow) {
            this.upFlow = upFlow;
        }

        public long getDownFlow() {
            return downFlow;
        }

        public void setDownFlow(long downFlow) {
            this.downFlow = downFlow;
        }

        public long getSumFlow() {
            return sumFlow;
        }

        public void setSumFlow(long sumFlow) {
            this.sumFlow = sumFlow;
        }
        
        public void setSumFlow() {
            this.sumFlow = this.upFlow+this.downFlow;
        }
        
    @Override
    public void write(DataOutput out) throws IOException {
        out.writeLong(upFlow);
        out.writeLong(downFlow);
        out.writeLong(sumFlow);
    }
    
    @Override
    public void readFields(DataInput in) throws IOException {
        this.upFlow = in.readLong();
        this.downFlow = in.readLong();
        this.sumFlow = in.readLong();
    }
    
        @Override
        public String toString() {
            return upFlow + "\t" + downFlow + "\t" + sumFlow;//格式与需求格式对应
        }
    }



Mapper代码：
package com.atguigu.mapreduce.writable2;

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

import java.io.IOException;

public class FlowMapper extends Mapper<LongWritable, Text, Text, FlowBean> {
    
    private Text outk = new Text();
    private FlowBean outv = new FlowBean();
    
    @Override
    protected void map(LongWritable key, Text value, Mapper<LongWritable, Text, Text, FlowBean>.Context context) throws IOException, InterruptedException {
        
        String line = value.toString();
        
        String[] split = line.split("\t");
        
        String intkphone = split[1];//输入的key:电话号码
        String intvup = split[split.length - 3];//输入的value1:上行流量
        String intvdown = split[split.length - 2];//输入的value2:下行流量

        outk.set(intkphone);//将输入的key值封装成输出的key值。
        outv.setUpFlow(Long.parseLong(intvup));//将输入的value1值转换为Long类型的数据后封装成outv。
        outv.setDownFlow(Long.parseLong(intvdown));//将输入的value2值转换为Long类型的数据后封装成outv。
        outv.setSumFlow();//将输入的value1值和value2值转换为Long类型的数据后相加再封装成outv。实现intvup+intvdown部分在FlowBean中重写的setSumFlow方法中。

        //写出
        context.write(outk, outv);
    }
}



Reducer代码：
package com.atguigu.mapreduce.writable2;

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

import java.io.IOException;

public class FlowReducer extends Reducer<Text, FlowBean, Text, FlowBean> {

    //new一个outv
    private FlowBean outv = new FlowBean();

    @Override
    protected void reduce(Text key, Iterable<FlowBean> values, Reducer<Text, FlowBean, Text, FlowBean>.Context context) throws IOException, InterruptedException {

        //初始化累加变量
        long tatalup = 0;
        long tataldown = 0;
        for (FlowBean value : values) {
            tatalup += value.getUpFlow();
            tataldown += value.getDownFlow();
        }

        //封装：封装输出的outk和outv,因为传进来的Text类型的电话号码没有发生改变，outk可以直接调用，所以不需要重新创建一个outk;而outv需要重新创建并封装起来。
        outv.setUpFlow(tatalup);
        outv.setDownFlow(tataldown);
        outv.setSumFlow();

        //写出:key为传进来的key，所以直接引用即可；value则是新建的outv，所以需要改变它的值。
        context.write(key, outv);
    }
}


Driver代码：
package com.atguigu.mapreduce.writable2;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
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;

public class FlowDriver {
    
    public static void main(String[] args) throws IOException, InterruptedException, ClassNotFoundException {
        
        Configuration confg = new Configuration();
        Job job = Job.getInstance(confg);

        job.setJarByClass(FlowDriver.class);

        job.setMapperClass(FlowMapper.class);
        job.setReducerClass(FlowReducer.class);

        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputKeyClass(FlowBean.class);
        
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(FlowBean.class);
        
        FileInputFormat.setInputPaths(job, new Path("C:\\bigdata\\大数据之Hadoop 3.x\\资料\\11_input\\inflow\\phone_data.txt"));
        FileOutputFormat.setOutputPath(job, new Path("C:\\bigdata\\maven\\flow1344"));

        boolean result = job.waitForCompletion(true);
        System.exit(result?0:1);
    }
}