使用groupingcomparator求同一订单中最大金额的订单
有如下订单数据,现在需要求出每一个订单中成交金额最大的一笔交易
订单id |
商品id |
成交金额 |
Order_0000001 |
Pdt_01 |
222.8 |
Order_0000001 |
Pdt_05 |
25.8 |
Order_0000002 |
Pdt_03 |
522.8 |
Order_0000002 |
Pdt_04 |
122.4 |
Order_0000002 |
Pdt_05 |
722.4 |
Order_0000003 |
Pdt_01 |
222.8 |
groupingcomparator作用:对mapTash的输出数据进行分组
测试数据
Order_0000001,Pdt_01,222.8
Order_0000001,Pdt_05,25.8
Order_0000002,Pdt_05,325.8
Order_0000002,Pdt_03,522.8
Order_0000002,Pdt_04,122.4
Order_0000003,Pdt_01,222.8
代码
package cn.feizhou.secondarysort;
import org.apache.hadoop.io.WritableComparable;
import org.apache.hadoop.io.WritableComparator;
/**
* 利用reduce端的GroupingComparator来实现将一组bean看成相同的key,本质是相同的Id分为一组
*
*/
public class ItemidGroupingComparator extends WritableComparator {
//传入作为key的bean的class类型,以及制定需要让框架做反射获取实例对象
protected ItemidGroupingComparator() {
super(OrderBean.class, true);
}
@Override
public int compare(WritableComparable a, WritableComparable b) {
OrderBean abean = (OrderBean) a;
OrderBean bbean = (OrderBean) b;
//比较两个bean时,指定只比较bean中的orderid
return abean.getItemid().compareTo(bbean.getItemid());
}
}
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/**
* 分区
*
*/
public class ItemIdPartitioner extends Partitioner<OrderBean, NullWritable>{
@Override
public int getPartition(OrderBean bean, NullWritable value, int numReduceTasks) {
//相同id的订单bean,会发往相同的partition
//而且,产生的分区数,是会跟用户设置的reduce task数保持一致
//假如numReduceTasks=2,那么ID是奇数的分为一区,偶数的分为一区
return (bean.getItemid().hashCode() & Integer.MAX_VALUE) % numReduceTasks;
}
}
----------------------------------------------
/**
* 订单类
*/
public class OrderBean implements WritableComparable<OrderBean>{
private Text itemid;//ID
private DoubleWritable amount;//价格
public OrderBean() {
}
public OrderBean(Text itemid, DoubleWritable amount) {
set(itemid, amount);
}
public void set(Text itemid, DoubleWritable amount) {
this.itemid = itemid;
this.amount = amount;
}
public Text getItemid() {
return itemid;
}
public DoubleWritable getAmount() {
return amount;
}
@Override
public int compareTo(OrderBean o) {
//如果ID相同,按价格降序
int cmp = this.itemid.compareTo(o.getItemid());
if (cmp == 0) {
cmp = -this.amount.compareTo(o.getAmount());
}
return cmp;
}
@Override
public void write(DataOutput out) throws IOException {
out.writeUTF(itemid.toString());
out.writeDouble(amount.get());
}
@Override
public void readFields(DataInput in) throws IOException {
String readUTF = in.readUTF();
double readDouble = in.readDouble();
this.itemid = new Text(readUTF);
this.amount= new DoubleWritable(readDouble);
}
@Override
public String toString() {
return itemid.toString() + "\t" + amount.get();
}
}
----------------------------------------------
/**
*
*
*/
public class SecondarySort {
static class SecondarySortMapper extends Mapper<LongWritable, Text, OrderBean, NullWritable>{
OrderBean bean = new OrderBean();
@Override
protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
String line = value.toString();
String[] fields = StringUtils.split(line, ",");
bean.set(new Text(fields[0]), new DoubleWritable(Double.parseDouble(fields[2])));
context.write(bean, NullWritable.get());
}
}
static class SecondarySortReducer extends Reducer<OrderBean, NullWritable, OrderBean, NullWritable>{
//到达reduce时,相同id的所有bean已经被看成一组,且金额最大的那个一排在第一位,这边只要第一个
@Override
protected void reduce(OrderBean key, Iterable<NullWritable> values, Context context) throws IOException, InterruptedException {
context.write(key, NullWritable.get());
}
}
public static void main(String[] args) throws Exception {
Configuration conf = new Configuration();
Job job = Job.getInstance(conf);
job.setJarByClass(SecondarySort.class);
job.setMapperClass(SecondarySortMapper.class);
job.setReducerClass(SecondarySortReducer.class);
job.setOutputKeyClass(OrderBean.class);
job.setOutputValueClass(NullWritable.class);
FileInputFormat.setInputPaths(job, new Path("H:/test"));
FileOutputFormat.setOutputPath(job, new Path("H:/out"));
//在此设置自定义的Groupingcomparator类
job.setGroupingComparatorClass(ItemidGroupingComparator.class);
//定义分区算法
job.setPartitionerClass(ItemIdPartitioner.class);
//定义分区参数
job.setNumReduceTasks(2);
job.waitForCompletion(true);
}
}
----------------------------------------------
测试结果