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partioner是在MapTask阶段将数据写入环形缓冲区中进行的分区操作,其目的是为了划分出几个结果文件(ReduceTask,但是partioner必须小于ReduceTask个数),而是什么决定将一组数据发送给一次Reduce类中的reduce方法中呢?换句话说,Reduce类中的reduce方法中key一样,values有多个,是什么情况下的key是一样的,能不能自定义。其实这就是 GroupingComparator分组(辅助排序)的作用。
对Reduce阶段的数据根据某一个或几个字段进行分组。
分组排序步骤:
(1)自定义类继承WritableComparator
(2)重写compare()方法
@Overridepublic int compare(WritableComparable a, WritableComparable b) { // 比较的业务逻辑 return result;}
(3)创建一个构造将比较对象的类传给父类
protected OrderGroupingComparator() { super(OrderBean.class, true);}
有如下订单数据
表4-2 订单数据
订单id | 商品id | 成交金额 |
0000001 | Pdt_01 | 222.8 |
Pdt_02 | 33.8 | |
0000002 | Pdt_03 | 522.8 |
Pdt_04 | 122.4 | |
Pdt_05 | 722.4 | |
0000003 | Pdt_06 | 232.8 |
Pdt_02 | 33.8 |
现在需要求出每一个订单中最贵的商品。
1)输入数据
0000001 Pdt_01 222.8
0000002 Pdt_05 722.4 0000001 Pdt_02 33.8 0000003 Pdt_06 232.8 0000003 Pdt_02 33.8 0000002 Pdt_03 522.8 0000002 Pdt_04 122.4(2)期望输出数据
1 222.8
2 722.4
3 232.8
2.需求分析
(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升序排序,如果id相同再按照金额降序排序,发送到Reduce。
(2)在Reduce端利用groupingComparator将订单id相同的kv聚合成组,然后取第一个即是该订单中最贵商品,如图所示。
3.代码实现
(1)定义订单信息OrderBean类
package com.demo.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类
package com.demo.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OrderSortGroupingComparator类
package com.demo.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);
}
@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;
}
}
(4)编写OrderSortReducer类
package com.demo.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());
}
}
(5)编写OrderSortDriver类
package com.demo.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 {
// 输入输出路径需要根据自己电脑上实际的输入输出路径设置
args = new String[]{"e:/input/inputorder" , "e:/output1"};
// 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]));
// 8 设置reduce端的分组
job.setGroupingComparatorClass(OrderGroupingComparator.class);
// 7 提交
boolean result = job.waitForCompletion(true);
System.exit(result ? 0 : 1);
}
}
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