【Hadoop离线基础总结】MapReduce增强（上）

MapReduce增强

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MapReduce的分区与reduceTask的数量

• 概述
  MapReduce当中的分区：物以类聚，人以群分。相同key的数据，去往同一个reduce。
  ReduceTask的数量默认为一个，可以自己设定数量  job.setNumRudeceTasks(3)
  分区决定了我们的数据该去往哪一个ReduceTask里面去
• 用代码实现
  注意：分区的案例，只能打成jar包发布到集群上面去运行，本地模式已经不能正常运行了

定义一个map类

package cn.itcast.mr.demo1;

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

import java.io.IOException;

public class PartitionMapper extends Mapper<LongWritable, Text, Text, NullWritable> {
    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
        //输出k2,v2   k2是一行文本数据，v2为NullWritable
        context.write(value, NullWritable.get());
    }
}

定义一个reduce类

package cn.itcast.mr.demo1;

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

import java.io.IOException;

public class PartitionReducer extends Reducer<Text, NullWritable, Text, NullWritable> {
    @Override
    protected void reduce(Text key, Iterable<NullWritable> values, Context context) throws IOException, InterruptedException {
        context.write(key, NullWritable.get());
    }
}

自定义Partitioner

package cn.itcast.mr.demo1;

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

public class PartitionOwn extends Partitioner<Text, NullWritable> {
    /**
     * 这个方法决定了数据去往哪一个reduce
     *
     * @param text          k2
     * @param nullWritable  v2
     * @param numReduceTask
     * @return
     */
    @Override
    public int getPartition(Text text, NullWritable nullWritable, int numReduceTask) {
        //以"\t"切分k2的数据
        String result = text.toString().split("\t")[5];
        System.out.println(result);
        //判断结果值大于15去往一个分区，小于15去往一个分区
        if (Integer.parseInt(result) > 15) {
            return 1;
        } else {
            return 0;
        }
    }
}

程序main函数入口

package cn.itcast.mr.demo1;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
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.lib.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;

import java.io.IOException;

public class PartitionMain extends Configured implements Tool {


    public static void main(String[] args) throws Exception {
        int run = ToolRunner.run(new Configuration(), new PartitionMain(), args);
        System.exit(run);
    }


    @Override
    public int run(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
        //获取我们的job对象，封装我们的job任务
        Job job = Job.getInstance(super.getConf(), "myPartition");
        //打成jar包运行时必备
        job.setJarByClass(PartitionMain.class);

        //第一步：读取文件，解析成k1,v1
        job.setInputFormatClass(TextInputFormat.class);
        //设置输入类型
        TextInputFormat.setInputPaths(job, new Path("hdfs://node01:8020/partitionin"));

        //第二步：自定义map逻辑，接收k1,v1,转换成k2,v2
        job.setMapperClass(PartitionMapper.class);
        //设置k2,v2类型
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(NullWritable.class);

        //第三步：分区    相同key的数据发送到同一个reduce中去
        job.setPartitionerClass(PartitionOwn.class);

        //第四步到第六步省略

        //第七步：自定义reduce逻辑
        job.setReducerClass(PartitionReducer.class);
        //设置k3,v3类型
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(NullWritable.class);

        //设置reduceTask的数量
        //如果reduceTask的数量比分区多，就会有空文件
        //如果reduceTask的数量比分区少，就会有一个reduce处理更多的数据
        job.setNumReduceTasks(2);

        job.setOutputFormatClass(TextOutputFormat.class);
        TextOutputFormat.setOutputPath(job,new Path("hdfs://node01:8020/partition_out"));

        //提交任务
        boolean b = job.waitForCompletion(true);
        return b ? 0 : 1;
    }
}

---

排序以及序列化

• 概述
  序列化（Serialization）是指把结构化对象转化为字节流。
  反序列化（Deserialization）是序列化的逆过程，把字节流转为结构化对象。
  Mapreduce是按照字典顺序对k2的值进行排序。Hadoop没有沿用java的serialize方式实现序列化，可以用自己的writable接口实现序列化。
  实现Writable可以进行序列化，实现Comparable可以进行排序，如果想既实现序列话，又进行排序，可以同时实现Writable和Comparable，或者实现WritableComparable
  如果以一行文本内容作为k2，不能够实现二次排序的功能，这时可以这两个字段封装成一个JavaBean当做的k2

• 代码实现

a	1
a	9
b	3
a	7
b	8
b	10
a	5
将以上进行排序

自定义JavaBean并重写CompareTo

package cn.itcast.mr.demo2;

import org.apache.hadoop.io.WritableComparable;

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

public class K2Bean implements WritableComparable<K2Bean> {
    //将数据中字母封装到第一个，数字封装到第二个
    private String first;
    private int second;

    /**
     * compareTo方法，用于数据的比较排序
     *
     * @param o
     * @return
     */
    @Override
    public int compareTo(K2Bean o) {
        //首先比较第一个字段
        int i = this.first.compareTo(o.first);
        //如果第一个字段相同，就比较第二个字段
        if (i == 0) {
            int i1 = Integer.valueOf(this.second).compareTo(Integer.valueOf(o.second));
            return i1;      //如果改为-i1，则为按第二个字段的字典顺序的倒序排序
        } else {
            //如果第一个字段不同，直接返回结果
            return i;       //如果改为-i，则为按第一个字段的字典顺序的倒序排序
        }

    }

    /**
     * Writable 序列化的方法
     *
     * @param out
     * @throws IOException
     */
    @Override
    public void write(DataOutput out) throws IOException {
        out.writeUTF(first);
        out.writeInt(second);
    }

    /**
     * 反序列化的方法
     *
     * @param in
     * @throws IOException
     */
    @Override
    public void readFields(DataInput in) throws IOException {
        this.first = in.readUTF();
        this.second = in.readInt();
    }

	//toSting方法
    @Override
    public String toString() {
        return first + '\t' + second;
    }
	
	//get(),set()方法
    public String getFirst() {
        return first;
    }

    public void setFirst(String first) {
        this.first = first;
    }

    public int getSecond() {
        return second;
    }

    public void setSecond(int second) {
        this.second = second;
    }
}

定义一个map类

package cn.itcast.mr.demo2;

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

import java.io.IOException;

public class SortMapper extends Mapper<LongWritable, Text, K2Bean, NullWritable> {
    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
        //将接收到的数据切割为字符串集合
        String[] split = value.toString().split("\t");
        //将字符串转换为k2Bean
        K2Bean k2Bean = new K2Bean();
        k2Bean.setFirst(split[0]);
        k2Bean.setSecond(Integer.parseInt(split[1]));
        //输出k2,v2
        context.write(k2Bean, NullWritable.get());
    }
}

定义一个reduce类

package cn.itcast.mr.demo2;

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

import java.io.IOException;

public class SortReducer extends Reducer<K2Bean, NullWritable, K2Bean, NullWritable> {
    @Override
    protected void reduce(K2Bean key, Iterable<NullWritable> values, Context context) throws IOException, InterruptedException {
        //循环遍历会让重复的Key2都打印出来
        for (NullWritable value : values) {
            context.write(key, NullWritable.get());
        }
    }
}

程序main函数入口

package cn.itcast.mr.demo2;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;

public class SortMain extends Configured implements Tool {

    public static void main(String[] args) throws Exception {
        //创建Configuration对象
        Configuration configuration = new Configuration();
        //执行ToolRunner得到一个int类型的返回状态码
        int run = ToolRunner.run(configuration, new SortMain(), args);
        //程序退出
        System.exit(run);
    }

    @Override
    public int run(String[] args) throws Exception {
        //创建job对象
        Job job = Job.getInstance(super.getConf(), "sort");
        //输入数据，设置输入路径
        job.setInputFormatClass(TextInputFormat.class);
        TextInputFormat.setInputPaths(job, new Path("file:////Volumes/赵壮备份/大数据离线课程资料/4.大数据离线第四天/排序/input"));

        //自定义map逻辑
        job.setMapperClass(SortMapper.class);
        job.setMapOutputKeyClass(K2Bean.class);
        job.setMapOutputValueClass(NullWritable.class);

        //分区、排序、规约、分组省略

        //自定义reduce逻辑
        job.setReducerClass(SortReducer.class);
        job.setOutputKeyClass(K2Bean.class);
        job.setOutputValueClass(NullWritable.class);

        //输出数据，设置输出路径
        job.setOutputFormatClass(TextOutputFormat.class);
        TextOutputFormat.setOutputPath(job, new Path("file:////Volumes/赵壮备份/大数据离线课程资料/4.大数据离线第四天/排序/output"));

        //提交任务
        boolean b = job.waitForCompletion(true);
        return b ? 0 : 1;
    }
}

---

计数器

• 自带计数器
  
• 自定义计数器

第一种是通过context上下文对象获取计数器

package cn.itcast.mr.demo2;

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

import java.io.IOException;

public class SortMapper extends Mapper<LongWritable, Text, K2Bean, NullWritable> {
    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
    
		//自定义计数器，这里实现了统计map数据的条数
		Counter counter = context.getCounter("MR_COUNT", "MapRecordCounter");
        counter.increment(1L);
        
        //将接收到的数据切割为字符串集合
        String[] split = value.toString().split("\t");
        //将字符串转换为k2Bean
        K2Bean k2Bean = new K2Bean();
        k2Bean.setFirst(split[0]);
        k2Bean.setSecond(Integer.parseInt(split[1]));
        //输出k2,v2
        context.write(k2Bean, NullWritable.get());
    }
}

第二种是通过enum枚举类型定义计数器

package cn.itcast.mr.demo2;

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

import java.io.IOException;

public class SortReducer extends Reducer<K2Bean, NullWritable, K2Bean, NullWritable> {

	public static enum Counter{
		REDUCE_INPUT_RECORDS, REDUCE_INPUT_VAL_NUMS,
	}
	
    @Override
    protected void reduce(K2Bean key, Iterable<NullWritable> values, Context context) throws IOException, InterruptedException {
    	//统计输入的key有多少
    	context.getCounter(Counter.REDUCE_INPUT_RECORDS).increment(1L);
        //循环遍历会让重复的Key2都打印出来
        for (NullWritable value : values) {
        	//统计输出的value有多少
        	context.getCounter(Counter.REDUCE_INPUT_VAL_NUMS).increment(1L);
            context.write(key, NullWritable.get());
        }
    }
}

---

Combiner

• 概述
  combiner可以先对相同k2进行合并，减少发送到reduce阶段的k2的数量，这么做的好处是可以节约网络带宽。
  combiner其实就是一个reducer类，但是这个reducer类的输入和输出比较特殊，输入是k2,v2，输出还是k2,v2。
  combiner不能改变数据结果值，只是用于调用减少发送到reduce端的数据量。
  注意：求平均值不能用combiner。

---

MapReduce综合练习之上网流量统计

• 统计求和

自定义JavaBean(FlowNum)

package cn.itcast.mr.demo3;

import org.apache.hadoop.io.Writable;

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

public class FlowNum implements Writable {
    //定义上行、下行、上行总、下行总流量
    private Integer upload;
    private Integer download;
    private Integer uploadSum;
    private Integer downloadSum;

    /**
     * 序列化
     *
     * @param out
     * @throws IOException
     */
    @Override
    public void write(DataOutput out) throws IOException {
        out.writeInt(upload);
        out.writeInt(download);
        out.writeInt(uploadSum);
        out.writeInt(downloadSum);
    }

    /**
     * 反序列化
     *
     * @param in
     * @throws IOException
     */
    @Override
    public void readFields(DataInput in) throws IOException {
        this.upload = in.readInt();
        this.download = in.readInt();
        this.uploadSum = in.readInt();
        this.downloadSum = in.readInt();
    }

    public Integer getUpload() {
        return upload;
    }

    public void setUpload(Integer upload) {
        this.upload = upload;
    }

    public Integer getDownload() {
        return download;
    }

    public void setDownload(Integer download) {
        this.download = download;
    }

    public Integer getUploadSum() {
        return uploadSum;
    }

    public void setUploadSum(Integer uploadSum) {
        this.uploadSum = uploadSum;
    }

    public Integer getDownloadSum() {
        return downloadSum;
    }

    public void setDownloadSum(Integer downloadSum) {
        this.downloadSum = downloadSum;
    }

    @Override
    public String toString() {
        return upload + "\t" + download + "\t" + uploadSum + "\t" + downloadSum;
    }
}

定义一个map类

package cn.itcast.mr.demo3;

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

import java.io.IOException;

public class FlowNumMapper extends Mapper<LongWritable, Text, Text, FlowNum> {
    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
        //以"\t"切分Text数据
        String[] split = value.toString().split("\t");
        //手机号
        String phoneNum = split[1];
        //index 6、7、8、9分别对应上行流量、下行流量、上行总流量、下行总流量
        Integer upload = Integer.parseInt(split[6]);
        Integer download = Integer.parseInt(split[7]);
        Integer uploadSum = Integer.parseInt(split[8]);
        Integer downloadSum = Integer.parseInt(split[9]);
        FlowNum flowNum = new FlowNum();
        flowNum.setUpload(upload);
        flowNum.setDownload(download);
        flowNum.setUploadSum(uploadSum);
        flowNum.setDownloadSum(downloadSum);
        //输出k2,v2
        context.write(new Text(phoneNum), flowNum);
    }
}

定义reducer类

package cn.itcast.mr.demo3;

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

import java.io.IOException;

public class FlowNumReducer extends Reducer<Text, FlowNum, Text, FlowNum> {
    @Override
    protected void reduce(Text key, Iterable<FlowNum> values, Context context) throws IOException, InterruptedException {
        //定义上行、下行、上行总、下行总流量，并初始化
        int upload = 0;
        int download = 0;
        int uploadSum = 0;
        int downloadSum = 0;
        //遍历累加流量
        for (FlowNum value : values) {
            upload += value.getUpload();
            download += value.getDownload();
            uploadSum += value.getUploadSum();
            downloadSum += value.getDownloadSum();
        }
        FlowNum flowNum = new FlowNum();
        flowNum.setUpload(upload);
        flowNum.setDownload(download);
        flowNum.setUploadSum(uploadSum);
        flowNum.setDownloadSum(downloadSum);
        //输出k3,v3
        context.write(key, flowNum);
    }
}

程序main函数入口

package cn.itcast.mr.demo3;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
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.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;

public class FlowNumMain extends Configured implements Tool {
    @Override
    public int run(String[] args) throws Exception {
        //获取job对象
        Job job = Job.getInstance(super.getConf(), "flowSum");
        //输入数据，设置输入路径
        job.setInputFormatClass(TextInputFormat.class);
        TextInputFormat.setInputPaths(job, new Path("file:////Volumes/赵壮备份/大数据离线课程资料/4.大数据离线第四天/流量统计/input"));

        //自定义map逻辑
        job.setMapperClass(FlowNumMapper.class);
        //设置k2,v2类型
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(FlowNum.class);

        //自定义reduce逻辑
        job.setReducerClass(FlowNumReducer.class);
        //设置k3,v3类型
        job.setOutputValueClass(FlowNum.class);
        job.setOutputKeyClass(Text.class);

        //输出数据，设置输出路径
        job.setOutputFormatClass(TextOutputFormat.class);
        TextOutputFormat.setOutputPath(job, new Path("file:////Volumes/赵壮备份/大数据离线课程资料/4.大数据离线第四天/流量统计/output2"));

        //上传任务到集群
        boolean b = job.waitForCompletion(true);
        return b ? 0 : 1;
    }

    public static void main(String[] args) throws Exception {
        //获取Configuration对象
        Configuration configuration = new Configuration();
        //使用ToolRunner返回一个状态码
        int run = ToolRunner.run(configuration, new FlowNumMain(), args);
        //系统推出
        System.exit(run);
    }
}

 

• 上行流量倒序排序

自定义JavaBean并重写CompareTo

package cn.itcast.mr.demo4;

import org.apache.hadoop.io.WritableComparable;

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

public class FlowNumSort implements WritableComparable<FlowNumSort> {
    private Integer upFlow;
    private Integer downFlow;
    private Integer upCountFlow;
    private Integer downCountFlow;

	//因为是倒序，所以需要返回 "-i"
    @Override
    public int compareTo(FlowNumSort o) {
        int i = this.upFlow.compareTo(o.upFlow);
        return -i;
    }

    /**
     * 序列化
     *
     * @param out
     * @throws IOException
     */
    @Override
    public void write(DataOutput out) throws IOException {
        out.writeInt(upFlow);
        out.writeInt(downFlow);
        out.writeInt(upCountFlow);
        out.writeInt(downCountFlow);
    }

    /**
     * 反序列化
     *
     * @param in
     * @throws IOException
     */
    @Override
    public void readFields(DataInput in) throws IOException {
        this.upFlow = in.readInt();
        this.downFlow = in.readInt();
        this.upCountFlow = in.readInt();
        this.downCountFlow = in.readInt();
    }

    public Integer getUpFlow() {
        return upFlow;
    }

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

    public Integer getDownFlow() {
        return downFlow;
    }

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

    public Integer getUpCountFlow() {
        return upCountFlow;
    }

    public void setUpCountFlow(Integer upCountFlow) {
        this.upCountFlow = upCountFlow;
    }

    public Integer getDownCountFlow() {
        return downCountFlow;
    }

    public void setDownCountFlow(Integer downCountFlow) {
        this.downCountFlow = downCountFlow;
    }

    @Override
    public String toString() {
        return upFlow + "\t" + downFlow + "\t" + upCountFlow + "\t" + downCountFlow;
    }
}

定义一个Mapper类

package cn.itcast.mr.demo4;

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

import java.io.IOException;

public class FlowNumSortMapper extends Mapper<LongWritable, Text, FlowNumSort, Text> {
    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
        String[] split = value.toString().split("\t");
        FlowNumSort flowNumSort = new FlowNumSort();
        flowNumSort.setUpFlow(Integer.parseInt(split[1]));
        flowNumSort.setDownFlow(Integer.parseInt(split[2]));
        flowNumSort.setUpCountFlow(Integer.parseInt(split[3]));
        flowNumSort.setDownCountFlow(Integer.parseInt(split[4]));
        context.write(flowNumSort, new Text(split[0]));
    }
}

定义一个Reducer类

package cn.itcast.mr.demo4;

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

import java.io.IOException;

public class FlowNumSortReducer extends Reducer<FlowNumSort, Text, FlowNumSort, Text> {
    @Override
    protected void reduce(FlowNumSort key, Iterable<Text> values, Context context) throws IOException, InterruptedException {
        for (Text value : values) {
            context.write(key, value);
        }
    }
}

程序main函数入口

package cn.itcast.mr.demo4;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
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.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;

public class FlowNumSortMain extends Configured implements Tool {
    @Override
    public int run(String[] args) throws Exception {
        Job job = Job.getInstance(super.getConf(), "FlowSort");
        job.setInputFormatClass(TextInputFormat.class);
        TextInputFormat.setInputPaths(job, new Path("file:////Volumes/赵壮备份/大数据离线课程资料/4.大数据离线第四天/流量统计/output"));

        job.setMapperClass(FlowNumSortMapper.class);
        job.setMapOutputKeyClass(FlowNumSort.class);
        job.setMapOutputValueClass(Text.class);

        job.setReducerClass(FlowNumSortReducer.class);
        job.setOutputKeyClass(FlowNumSort.class);
        job.setOutputValueClass(Text.class);

        job.setOutputFormatClass(TextOutputFormat.class);
        TextOutputFormat.setOutputPath(job, new Path("file:////Volumes/赵壮备份/大数据离线课程资料/4.大数据离线第四天/流量统计/flowout_put"));

        boolean b = job.waitForCompletion(true);
        return b ? 0 : 1;
    }

    public static void main(String[] args) throws Exception {
        Configuration configuration = new Configuration();
        int run = ToolRunner.run(configuration, new FlowNumSortMain(), args);
        System.exit(run);
    }
}

 

• 手机号码分区
  在需求一的基础上，继续完善，将不同的手机号分到不同的数据文件的当中去

定义一个Partitioner类

package cn.itcast.mr.demo3;

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

public class PhonePartition extends Partitioner<Text,FlowNum> {
    @Override
    public int getPartition(Text text, FlowNum flowNum, int i) {
        String phoneNum = text.toString();
        if (phoneNum.startsWith("135")){
            return 0;
        } else if (phoneNum.startsWith("136")){
            return 1;
        } else if (phoneNum.startsWith("137")){
            return 2;
        } else if (phoneNum.startsWith("138")){
            return 3;
        } else if (phoneNum.startsWith("139")){
            return 4;
        } else {
            return 5;
        }
    }
}

Mapper类和Reducer类和需求一的相同
程序main函数要添加打包方式、调用分区类、设置reduce数，修改输入和输出数据路径

package cn.itcast.mr.demo3;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
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.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;

public class FlowNumMain extends Configured implements Tool {
    @Override
    public int run(String[] args) throws Exception {
        //获取job对象
        Job job = Job.getInstance(super.getConf(), "flowSum");
        //输入数据，设置输入路径
        job.setInputFormatClass(TextInputFormat.class);
        TextInputFormat.setInputPaths(job, new Path(args[0]));

        //打包
        job.setJarByClass(FlowNumMain.class);

        //自定义map逻辑
        job.setMapperClass(FlowNumMapper.class);
        //设置k2,v2类型
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(FlowNum.class);

        //分区
        job.setPartitionerClass(PhonePartition.class);

        //自定义reduce逻辑
        job.setReducerClass(FlowNumReducer.class);
        //设置k3,v3类型
        job.setOutputValueClass(FlowNum.class);
        job.setOutputKeyClass(Text.class);

        //指定reduce个数
        job.setNumReduceTasks(6);

        //输出数据，设置输出路径
        job.setOutputFormatClass(TextOutputFormat.class);
        TextOutputFormat.setOutputPath(job, new Path(args[1]));

        //上传任务到集群
        boolean b = job.waitForCompletion(true);
        return b ? 0 : 1;
    }

    public static void main(String[] args) throws Exception {
        //获取Configuration对象
        Configuration configuration = new Configuration();
        //使用ToolRunner返回一个状态码
        int run = ToolRunner.run(configuration, new FlowNumMain(), args);
        //系统退出
        System.exit(run);
    }
}