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  • 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 + "	" + 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 + "	" + 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("	");
    
            // 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("	");
    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

     



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  • 原文地址:https://www.cnblogs.com/TiePiHeTao/p/553245f6cfd1def30074f56d9719fc35.html
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