MapReduce手机流量统计

package com.yuejiesong.PhoneFlowCount;

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.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

import java.io.IOException;
import java.util.Calendar;

public class FlowCount {
    static class FlowCountMapper extends Mapper<LongWritable,Text,Text,FlowBean>{
        @Override
        protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
           //将一行内容转换成string
            String line = value.toString();
            //切分字段
            String[] fields = line.split("	");
            //取出手机号
            String phoneNbr = fields[0];
            //取出上行流量下行流量
            long upFlow = Long.parseLong(fields[1]);
            long dFlow = Long.parseLong(fields[2]);
            context.write(new Text(phoneNbr),new FlowBean(upFlow,dFlow));

        }
    }
    static class FlowCountReducer extends Reducer<Text, FlowBean, Text, FlowBean>{
        @Override
        protected void reduce(Text key, Iterable<FlowBean> values, Context context) throws IOException, InterruptedException {
            long sum_upFlow = 0;
            long sum_dFlow = 0;
            for(FlowBean bean:values){
                sum_upFlow += bean.getUpFlow();
                sum_dFlow += bean.getdFlow();
            }
            FlowBean resultBean = new FlowBean(sum_upFlow,sum_dFlow);
            context.write(key,resultBean);
        }
    }

    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf);
        //指定本程序的jar包所在的本地路径
        job.setJarByClass(FlowCount.class);
        //指定本业务job要使用的maper/reducer业务类
        job.setMapperClass(FlowCountMapper.class);
        job.setReducerClass(FlowCountReducer.class);
        //指定mapper输出数据的kv类型
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(FlowBean.class);
        //指定最终输出的数据的kv类型
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(FlowBean.class);
        //指定job的输入原始文件所在目录
        FileInputFormat.setInputPaths(job,new Path(args[0]));
        //指定job的输出结果所在目录
        FileOutputFormat.setOutputPath(job,new Path(args[1]));
        //将job中配置的相关参数，以及job所用的java类所在的jar包，提交给yarn去运行
        /* job.submit()*/
        boolean b = job.waitForCompletion(true);
        System.exit(b?0:1);

    }
}

(base) [root@pyspark flowcount]# hdfs dfs -text /flowcount20200410input/*
13726230501 200 1100
13396230502 300 1200
13897230503 400 1300
13897230503 100 300
13597230534 500 1400
13597230534 300 1200

hadoop jar HiveCountToolsUDF-1.0-SNAPSHOT.jar com.yuejiesong.PhoneFlowCount.FlowCount /flowcount20200410input/ /flowcount20200410output/

(base) [root@pyspark flowcount]# hadoop fs -ls /flowcount20200410output/ Found 2 items
-rw-r--r-- 1 root supergroup 0 2020-04-11 14:29 /flowcount20200410output/_SUCCESS
-rw-r--r-- 1 root supergroup 104 2020-04-11 14:29 /flowcount20200410output/part-r-00000
(base) [root@pyspark flowcount]# hadoop fs -text /flowcount20200410output/part-r-00000
13396230502 300 1200 1500
13597230534 800 2600 3400
13726230501 200 1100 1300
13897230503 500 1600 2100