大資料技術之輔助排序和二次排序案例（GroupingComparator）

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大資料技術之輔助排序和二次排序案例（GroupingComparator）

1）需求

有如下訂單資料

訂單id	商品id	成交金額
0000001	Pdt_01	222.8
0000001	Pdt_05	25.8
0000002	Pdt_03	522.8
0000002	Pdt_04	122.4
0000002	Pdt_05	722.4
0000003	Pdt_01	222.8
0000003	Pdt_02	33.8

現在需要求出每一個訂單中最貴的商品。

2）輸入資料 GroupingComparator.txt

   Pdt_01    222.8   Pdt_05    722.4   Pdt_05    25.8   Pdt_01    222.8   Pdt_01    33.8   Pdt_03    522.8   Pdt_04    122.4

輸出資料預期：

3    222.8

part-r-00000.txt

2    722.4

part-r-00001.txt

1    222.8

part-r-00002.txt

3）分析

（1）利用“訂單id和成交金額”作為key，可以將map階段讀取到的所有訂單資料按照id分區，按照金額排序，發送到reduce。

（2）在reduce端利用groupingcomparator將訂單id相同的kv彙總成組，然後取第一個即是最大值。

 

4）實現

定義訂單資訊OrderBean

package com.xyg.mapreduce.order;import java.io.DataInput;import java.io.DataOutput;import java.io.IOException;import org.apache.hadoop.io.WritableComparable;public class OrderBean implements WritableComparable<OrderBean> {    private int order_id; // 訂單id號    private double price; // 價格    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;    }    // 二次排序    @Override    public int compareTo(OrderBean o) {        int result = order_id > o.getOrder_id() ? 1 : -1;        if (order_id > o.getOrder_id()) {            result = 1;        } else if (order_id < o.getOrder_id()) {            result = -1;        } else {            // 價格倒序排序            result = price > o.getPrice() ? -1 : 1;        }        return result;    }}

編寫OrderSortMapper處理流程

package com.xyg.mapreduce.order;
import java.io.IOException;import org.apache.hadoop.io.LongWritable;import org.apache.hadoop.io.NullWritable;import org.apache.hadoop.io.Text;import org.apache.hadoop.mapreduce.Mapper;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 擷取一行        String line = value.toString();        // 2 截取        String[] fields = line.split("\t");                // 3 封裝對象        k.setOrder_id(Integer.parseInt(fields[0]));        k.setPrice(Double.parseDouble(fields[2]));                // 4 寫出        context.write(k, NullWritable.get());    }}

編寫OrderSortReducer處理流程

package com.xyg.mapreduce.order;
import java.io.IOException;import org.apache.hadoop.io.NullWritable;import org.apache.hadoop.mapreduce.Reducer;public class OrderReducer extends Reducer<OrderBean, NullWritable, OrderBean, NullWritable> {    @Override    protected void reduce(OrderBean key, Iterable<NullWritable> values, Context context) throws IOException, InterruptedException {           context.write(key, NullWritable.get());    }}

編寫OrderSortDriver處理流程

package com.xyg.mapreduce.order;import java.io.IOException;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;public class OrderDriver {    public static void main(String[] args) throws Exception, IOException {        // 1 擷取配置資訊        Configuration conf = new Configuration();        Job job = Job.getInstance(conf);        // 2 設定jar包載入路徑        job.setJarByClass(OrderDriver.class);        // 3 載入map/reduce類        job.setMapperClass(OrderMapper.class);        job.setReducerClass(OrderReducer.class);        // 4 設定map輸出資料key和value類型        job.setMapOutputKeyClass(OrderBean.class);        job.setMapOutputValueClass(NullWritable.class);        // 5 設定最終輸出資料的key和value類型        job.setOutputKeyClass(OrderBean.class);        job.setOutputValueClass(NullWritable.class);        // 6 設定輸入資料和輸出資料路徑        FileInputFormat.setInputPaths(job, new Path(args[0]));        FileOutputFormat.setOutputPath(job, new Path(args[1]));        // 10 設定reduce端的分組        job.setGroupingComparatorClass(OrderGroupingComparator.class);        // 7 設定分區        job.setPartitionerClass(OrderPartitioner.class);        // 8 設定reduce個數        job.setNumReduceTasks(3);        // 9 提交        boolean result = job.waitForCompletion(true);        System.exit(result ? 0 : 1);    }}OrderSortDriver

編寫OrderSortPartitioner處理流程

package com.xyg.mapreduce.order;
import org.apache.hadoop.io.NullWritable;import org.apache.hadoop.mapreduce.Partitioner;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;    }}

編寫OrderSortGroupingComparator處理流程

package com.xyg.mapreduce.order;
import org.apache.hadoop.io.WritableComparable;import org.apache.hadoop.io.WritableComparator;public class OrderGroupingComparator extends WritableComparator {    protected OrderGroupingComparator() {        super(OrderBean.class, true);    }    @SuppressWarnings("rawtypes")    @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;    }}

大資料技術之輔助排序和二次排序案例（GroupingComparator）