GroupingComparator 自定义分组

图示说明：

 

有如下订单数据：

现在需要求出每一个订单中最贵的商品。

 需求分析实现

（1）利用“订单id和成交金额”作为key，可以将map阶段读取到的所有订单数据按照id分区，按照金额排序，发送到reduce。

（2）在reduce端利用groupingcomparator将订单id相同的kv聚合成组，然后取第一个即是最大值。

代码实现：

定义订单信息OrderBean

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

public class OrderBean implements WritableComparable<OrderBean> {
    private int order_id; // 订单id号
    private double price; // 价格

    public OrderBean() {
        super();
    }

    public OrderBean(int order_id, double price) {
        super();
        this.order_id = order_id;
        this.price = price;
    }

    @Override
    public void write(DataOutput out) throws IOException {
        out.writeInt(order_id);
        out.writeDouble(price);
    }

    @Override
    public void readFields(DataInput in) throws IOException {
        order_id = in.readInt();
        price = in.readDouble();
    }

    @Override
    public String toString() {
        return order_id + "\t" + price;
    }

    public int getOrder_id() {
        return order_id;
    }

    public void setOrder_id(int order_id) {
        this.order_id = order_id;
    }

    public double getPrice() {
        return price;
    }

    public void setPrice(double price) {
        this.price = price;
    }

    // todo 排序规则 根据订单号正序进行排序 如果订单号相同 则根据价格倒序排序
    @Override
    public int compareTo(OrderBean o) {

        int result ;

        if (order_id > o.getOrder_id()) {
            result = 1;
        } else if (order_id < o.getOrder_id()) {
            result = -1;
        } else {
            // 价格倒序排序
            result = price > o.getPrice() ? -1 : 1;
        }

        return result;
    }
}

 

 

 

69

 

 

 

1

import java.io.DataInput;

2

import java.io.DataOutput;

3

import java.io.IOException;

4

5

public class OrderBean implements WritableComparable<OrderBean> {

6

    private int order_id; // 订单id号

7

    private double price; // 价格

8

9

    public OrderBean() {

10

        super();

11

    }

12

13

    public OrderBean(int order_id, double price) {

14

        super();

15

        this.order_id = order_id;

16

        this.price = price;

17

    }

18

19

    @Override

20

    public void write(DataOutput out) throws IOException {

21

        out.writeInt(order_id);

22

        out.writeDouble(price);

23

    }

24

25

    @Override

26

    public void readFields(DataInput in) throws IOException {

27

        order_id = in.readInt();

28

        price = in.readDouble();

29

    }

30

31

    @Override

32

    public String toString() {

33

        return order_id + "\t" + price;

34

    }

35

36

    public int getOrder_id() {

37

        return order_id;

38

    }

39

40

    public void setOrder_id(int order_id) {

41

        this.order_id = order_id;

42

    }

43

44

    public double getPrice() {

45

        return price;

46

    }

47

48

    public void setPrice(double price) {

49

        this.price = price;

50

    }

51

52

    // todo 排序规则 根据订单号正序进行排序 如果订单号相同 则根据价格倒序排序

53

    @Override

54

    public int compareTo(OrderBean o) {

55

56

        int result ;

57

58

        if (order_id > o.getOrder_id()) {

59

            result = 1;

60

        } else if (order_id < o.getOrder_id()) {

61

            result = -1;

62

        } else {

63

            // 价格倒序排序

64

            result = price > o.getPrice() ? -1 : 1;

65

        }

66

67

        return result;

68

    }

69

}

 

 

编写OrderMapper处理流程

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 OrderMapper extends Mapper<LongWritable, Text, OrderBean, NullWritable> {
    OrderBean k = new OrderBean();

    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {

        // 1 获取一行
        String line = value.toString();

        // 2 截取
        String[] fields = line.split("\t");

        // 3 封装对象
        k.setOrder_id(Integer.parseInt(fields[0]));
        k.setPrice(Double.parseDouble(fields[2]));

        // 4 写出
        context.write(k, NullWritable.get());
    }
}

 

 

 

27

 

 

 

1

import org.apache.hadoop.io.LongWritable;

2

import org.apache.hadoop.io.NullWritable;

3

import org.apache.hadoop.io.Text;

4

import org.apache.hadoop.mapreduce.Mapper;

5

6

import java.io.IOException;

7

8

public class OrderMapper extends Mapper<LongWritable, Text, OrderBean, NullWritable> {

9

    OrderBean k = new OrderBean();

10

11

    @Override

12

    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {

13

14

        // 1 获取一行

15

        String line = value.toString();

16

17

        // 2 截取

18

        String[] fields = line.split("\t");

19

20

        // 3 封装对象

21

        k.setOrder_id(Integer.parseInt(fields[0]));

22

        k.setPrice(Double.parseDouble(fields[2]));

23

24

        // 4 写出

25

        context.write(k, NullWritable.get());

26

    }

27

}

 

 

编写OrderPartitioner处理流程

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

//todo 重新分区规则 订单号一样的 来到同一个分区中
public class OrderPartitioner extends Partitioner<OrderBean, NullWritable> {

    @Override
    public int getPartition(OrderBean key, NullWritable value, int numReduceTasks) {

        return (key.getOrder_id() & Integer.MAX_VALUE) % numReduceTasks;
    }
}

 

 

 

12

 

 

 

1

import org.apache.hadoop.io.NullWritable;

2

import org.apache.hadoop.mapreduce.Partitioner;

3

4

//todo 重新分区规则 订单号一样的 来到同一个分区中

5

public class OrderPartitioner extends Partitioner<OrderBean, NullWritable> {

6

7

    @Override

8

    public int getPartition(OrderBean key, NullWritable value, int numReduceTasks) {

9

10

        return (key.getOrder_id() & Integer.MAX_VALUE) % numReduceTasks;

11

    }

12

}

 

 

编写OrderGroupingComparator处理流程

import org.apache.hadoop.io.WritableComparable;
import org.apache.hadoop.io.WritableComparator;

//todo 自定义分组规则 订单号一样的来到同一个分组中
public class OrderGroupingComparator extends WritableComparator {

    protected OrderGroupingComparator() {
        super(OrderBean.class, true);
    }

    @Override
    public int compare(WritableComparable a, WritableComparable b) {

        OrderBean aBean = (OrderBean) a;
        OrderBean bBean = (OrderBean) b;

        int result;

        if (aBean.getOrder_id() > bBean.getOrder_id()) {
            result = 1;
        } else if (aBean.getOrder_id() < bBean.getOrder_id()) {
            result = -1;
        } else {
            result = 0;
        }

        return result;
    }
}

 

 

 

29

 

 

 

1

import org.apache.hadoop.io.WritableComparable;

2

import org.apache.hadoop.io.WritableComparator;

3

4

//todo 自定义分组规则 订单号一样的来到同一个分组中

5

public class OrderGroupingComparator extends WritableComparator {

6

7

    protected OrderGroupingComparator() {

8

        super(OrderBean.class, true);

9

    }

10

11

    @Override

12

    public int compare(WritableComparable a, WritableComparable b) {

13

14

        OrderBean aBean = (OrderBean) a;

15

        OrderBean bBean = (OrderBean) b;

16

17

        int result;

18

19

        if (aBean.getOrder_id() > bBean.getOrder_id()) {

20

            result = 1;

21

        } else if (aBean.getOrder_id() < bBean.getOrder_id()) {

22

            result = -1;

23

        } else {

24

            result = 0;

25

        }

26

27

        return result;

28

    }

29

}

 

 

编写OrderReducer处理流程

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

import java.io.IOException;

public class OrderReducer extends Reducer<OrderBean, NullWritable, OrderBean, NullWritable> {

    @Override
    protected void reduce(OrderBean key, Iterable<NullWritable> values, Context context)
            throws IOException, InterruptedException {
        System.out.println(key);
        for (NullWritable value : values) {
            System.out.println(value);
        }

        context.write(key, NullWritable.get());
    }
}

 

 

 

18

 

 

 

1

import org.apache.hadoop.io.NullWritable;

2

import org.apache.hadoop.mapreduce.Reducer;

3

4

import java.io.IOException;

5

6

public class OrderReducer extends Reducer<OrderBean, NullWritable, OrderBean, NullWritable> {

7

8

    @Override

9

    protected void reduce(OrderBean key, Iterable<NullWritable> values, Context context)

10

            throws IOException, InterruptedException {

11

        System.out.println(key);

12

        for (NullWritable value : values) {

13

            System.out.println(value);

14

        }

15

16

        context.write(key, NullWritable.get());

17

    }

18

}

 

 

编写OrderDriver处理流程

import org.apache.hadoop.conf.Configuration;
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.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import java.io.IOException;

public class OrderDriver {

    public static void main(String[] args) throws Exception, IOException {

        // 1 获取配置信息
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf);

        // 2 设置jar包加载路径
        job.setJarByClass(OrderDriver.class);

        // 3 加载map/reduce类
        job.setMapperClass(OrderMapper.class);
        job.setReducerClass(OrderReducer.class);

        // 4 设置map输出数据key和value类型
        job.setMapOutputKeyClass(OrderBean.class);
        job.setMapOutputValueClass(NullWritable.class);

        // 5 设置最终输出数据的key和value类型
        job.setOutputKeyClass(OrderBean.class);
        job.setOutputValueClass(NullWritable.class);

        // 6 设置输入数据和输出数据路径
        FileInputFormat.setInputPaths(job, new Path("D:\\TiePiHeTao\\input"));
        FileOutputFormat.setOutputPath(job, new Path("D:\\TiePiHeTao\\output"));

//		// 10 设置reduce端的分组
		job.setGroupingComparatorClass(OrderGroupingComparator.class);

        // 7 设置分区
        job.setPartitionerClass(OrderPartitioner.class);

        // 8 设置reduce个数
        job.setNumReduceTasks(3);

        // 9 提交
        boolean result = job.waitForCompletion(true);
        System.exit(result ? 0 : 1);
    }
}

 

 

 

49

 

 

 

1

import org.apache.hadoop.conf.Configuration;

2

import org.apache.hadoop.fs.Path;

3

import org.apache.hadoop.io.NullWritable;

4

import org.apache.hadoop.mapreduce.Job;

5

import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;

6

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

7

import java.io.IOException;

8

9

public class OrderDriver {

10

11

    public static void main(String[] args) throws Exception, IOException {

12

13

        // 1 获取配置信息

14

        Configuration conf = new Configuration();

15

        Job job = Job.getInstance(conf);

16

17

        // 2 设置jar包加载路径

18

        job.setJarByClass(OrderDriver.class);

19

20

        // 3 加载map/reduce类

21

        job.setMapperClass(OrderMapper.class);

22

        job.setReducerClass(OrderReducer.class);

23

24

        // 4 设置map输出数据key和value类型

25

        job.setMapOutputKeyClass(OrderBean.class);

26

        job.setMapOutputValueClass(NullWritable.class);

27

28

        // 5 设置最终输出数据的key和value类型

29

        job.setOutputKeyClass(OrderBean.class);

30

        job.setOutputValueClass(NullWritable.class);

31

32

        // 6 设置输入数据和输出数据路径

33

        FileInputFormat.setInputPaths(job, new Path("D:\\TiePiHeTao\\input"));

34

        FileOutputFormat.setOutputPath(job, new Path("D:\\TiePiHeTao\\output"));

35

36

//// 10 设置reduce端的分组

37

job.setGroupingComparatorClass(OrderGroupingComparator.class);

38

39

        // 7 设置分区

40

        job.setPartitionerClass(OrderPartitioner.class);

41

42

        // 8 设置reduce个数

43

        job.setNumReduceTasks(3);

44

45

        // 9 提交

46

        boolean result = job.waitForCompletion(true);

47

        System.exit(result ? 0 : 1);

48

    }

49

}

 

 

 

总结：

• 分组：发生在数据调用reduce方法之前 相同key的作为一组去调用
• 默认规则:key相同 为一组
• 自定义分组：

• 继承 WritableComparator
  重写compare方法  根据该方法返回的结果来判断是否相等 
  只要你指定返回为0  那么mr就认为相等

   

   

   

  3

   

   

   

  1

  继承 WritableComparator

  2

  重写compare方法  根据该方法返回的结果来判断是否相等 

  3

  只要你指定返回为0  那么mr就认为相等

   

   
• 自定义分组如何生效

• job.setGroupingComparatorClass(OrderGrouping.class);

   

   

   

  1

   

   

   

  1

  job.setGroupingComparatorClass(OrderGrouping.class);

   

   
• 自定义排序和自定义分组的梳理
  
  • 自定义排序 正数 大于 、负数小于、零等于
  • 自定义分组 零相等 、非零不相等