MapReduce - A - 分区 - Partitioner

题目：
求每个通信商的上行、下行、总流量 (输出：通信商 上行 下行 总的)

思路：
通过手机号的前三位区分通信运营商
按照运营商分区后，在每个Reduce里计算各运营商总和即可

代码：

//分区比较器

package Partitioner02;

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

public class MyPartitioner extends Partitioner<Text, RecordWritable> {
    public int getPartition(Text text, RecordWritable recordWritable, int numPartitions) {
        String st = recordWritable.getProduct();
        if(st.equals("移动")){
            return 0;
        }
        else if(st.equals("联通")){
            return 1;
        }
        else if(st.equals("电信")){
            return 2;
        }
        else {
            return 3;
        }
    }
}

//实体类

package Partitioner02;

import org.apache.hadoop.io.Writable;

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

public class RecordWritable implements Writable {
    private String product;//运营商
    private String phone;//手机号
    private long upFlow;//上行流量
    private long downFlow;//下行流量
    private long sumFlow;//总流量
    public void write(DataOutput dataOutput) throws IOException {
        dataOutput.writeUTF(this.product);
        dataOutput.writeUTF(this.phone);
        dataOutput.writeLong(this.upFlow);
        dataOutput.writeLong(this.downFlow);
        dataOutput.writeLong(this.sumFlow);
    }

    public void readFields(DataInput dataInput) throws IOException {
        this.product = dataInput.readUTF();
        this.phone = dataInput.readUTF();
        this.upFlow = dataInput.readLong();
        this.downFlow = dataInput.readLong();
        this.sumFlow = dataInput.readLong();
    }

    @Override
    public String toString() {
        return "RecordWritable{" +
                "product=" + product +
                ", phone='" + phone + '\'' +
                ", upFlow=" + upFlow +
                ", downFlow=" + downFlow +
                ", sumFlow=" + sumFlow +
                '}';
    }

    public String getProduct() {
        return product;
    }

    public void setProduct(String product) {
        this.product = product;
    }

    public String getPhone() {
        return phone;
    }

    public void setPhone(String phone) {
        this.phone = phone;
    }

    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;
    }
}

//MapReduce

package Partitioner02;

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

public class MainDemo {
    public static void main(String[] args){
        try{
            Configuration conf = new Configuration();

            Job job = Job.getInstance(conf, "Partitioner02-MainDemo");

            job.setJarByClass(MainDemo.class);

            job.setMapperClass(MyMapper.class);
            job.setMapOutputKeyClass(Text.class);
            job.setMapOutputValueClass(RecordWritable.class);

            job.setReducerClass(MyReducer.class);
            job.setOutputKeyClass(Text.class);
            job.setOutputValueClass(NullWritable.class);

            //设置分区
            job.setPartitionerClass(MyPartitioner.class);
            job.setNumReduceTasks(4);

            FileInputFormat.addInputPath(job, new Path("C:/Users/Administrator/Desktop/Data/input/flow.log"));
            FileOutputFormat.setOutputPath(job, new Path("C:/Users/Administrator/Desktop/Data/output/01/00"));

            int success = job.waitForCompletion(true) ? 0: 1;

            System.exit(success);
        }
        catch (Exception e){
            e.printStackTrace();
        }
    }
    //自定义的Mapper类
    public static class MyMapper extends Mapper<Object, Text, Text, RecordWritable>{
        Text k = new Text("1");
        RecordWritable v = new RecordWritable();
        @Override
        protected void map(Object key, Text value, Context context) throws IOException, InterruptedException {
            String row = value.toString();
            String[] words = row.split("\t");
            int len = words.length;
            v.setUpFlow(Integer.parseInt(words[len-3]));
            v.setDownFlow(Integer.parseInt(words[len-2]));
            v.setSumFlow(v.getUpFlow()+v.getDownFlow());
            String[] st = words[1].split(" ");
            v.setPhone(st[0]);
            String st2 = st[0].substring(0,3);
            if(st2.matches("(139)|(138)|(137)|(136)|(135)|(134)|(159)|(158)|(157)|(150)|(151)|(152)|(147)|(188)|(187)|(182)|(183)|(184)|(178)")){
                v.setProduct("移动");
            }
            else if(st2.matches("(130)|(131)|(132)|(156)|(155)|(186)|(185)|(145)|(176)")){
                v.setProduct("联通");
            }
            else if(st2.matches("(133)|(153)|(189)|(180)|(181)|(177)|(173)")){
                v.setProduct("电信");
            }
            else {
                v.setProduct("其它");
            }
            context.write(k, v);
        }
    }
    //自定义的Reducer类
    public static class MyReducer extends Reducer<Text, RecordWritable, Text, NullWritable>{
        public Text k = new Text();
        public Long sum;
        @Override
        protected void reduce(Text key, Iterable<RecordWritable> values, Context context) throws IOException, InterruptedException {
            String st = "";
            sum = 0L;
            for(RecordWritable rw: values){
                st = rw.getProduct();
                sum += rw.getSumFlow();
            }
            st += " " + String.valueOf(sum);
            k.set(st);
            context.write(k, NullWritable.get());
        }
    }
}