辅助排序和二次排序案例
1）需求
有如下订单数据
订单id | 商品id | 成交金额 |
0000001 | Pdt_01 | 222.8 |
0000001 | Pdt_06 | 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 |
现在需要求出每一个订单中最贵的商品。
2）输入数据
0000001 Pdt_01 222.8
0000002 Pdt_06 722.4
0000001 Pdt_05 25.8
0000003 Pdt_01 222.8
0000003 Pdt_01 33.8
0000002 Pdt_03 522.8
0000002 Pdt_04 122.4
输出数据预期：
0：3 222.8
1：2 722.4
2：1 222.8
3）分析
（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。
4）代码实现
（1）定义订单信息OrderBean
package com.atguigu.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; // 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;
}
// 二次排序
@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;
}
} |
（2）编写OrderSortMapper
package com.atguigu.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 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());
}
} |
（3）编写OrderSortPartitioner
package com.atguigu.mapreduce.order;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.mapreduce.Partitioner;
publicclass OrderPartitioner extends Partitioner<OrderBean, NullWritable> {
@Override
publicint getPartition(OrderBean key, NullWritable value, int numReduceTasks) {
return (key.getOrder_id() & Integer.MAX_VALUE) % numReduceTasks;
}
} |
（4）编写OrderSortGroupingComparator
package com.atguigu.mapreduce.order;
import org.apache.hadoop.io.WritableComparable;
import org.apache.hadoop.io.WritableComparator;
publicclass OrderGroupingComparator extends WritableComparator {
protected OrderGroupingComparator() {
super(OrderBean.class, true);
}
@SuppressWarnings("rawtypes")
@Override
publicint compare(WritableComparable a, WritableComparable b) {
OrderBean aBean = (OrderBean) a;
OrderBean bBean = (OrderBean) b;
int result;
if (aBean.getOrder_id() > bBean.getOrder_id()) {
result = 1;
} elseif (aBean.getOrder_id() < bBean.getOrder_id()) {
result = -1;
} else {
result = 0;
}
return result;
}
} |
（5）编写OrderSortReducer
package com.atguigu.mapreduce.order;
import java.io.IOException;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.mapreduce.Reducer;
publicclass OrderReducer extends Reducer<OrderBean, NullWritable, OrderBean, NullWritable> {
@Override
protectedvoid reduce(OrderBean key, Iterable<NullWritable> values, Context context)
throws IOException, InterruptedException {
context.write(key, NullWritable.get());
}
} |
（6）编写OrderSortDriver
package com.atguigu.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;
publicclass OrderDriver {
publicstaticvoid 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(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
// 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);
}
} |
