hadoop 二次排序 插入数据库
二次排序:根据自定义对象的compareTo 方法排序由下面的代码实现可以看出 二次排序的实质是 先根据第一个字段排完序后再排第二个字段
若还有第三个字段参与进来是否可以叫作三次排序呢 (？_ ？)
另：根据程序断点初步判断
设置job的sort 会在mapper 至combiner阶段执行
设置job的group会在combiner至reduce 阶段执行
不过在从combiner到reduce的时候若传递的key为自定义的对象即使重写了hashcode 和equals 方法也不会当成相同的key来处理 不得已在本程序中传输key为一个空Text()
不知是否有别的方法可以实现 ?
插入数据库的操作在 附件中有详细的实现.
package hdfs.demo2.final_indb;
import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
import java.sql.PreparedStatement;
import java.sql.ResultSet;
import java.sql.SQLException;
import java.util.ArrayList;
import java.util.Collections;
import java.util.List;
import java.util.StringTokenizer;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.io.Writable;
import org.apache.hadoop.io.WritableComparable;
import org.apache.hadoop.io.WritableComparator;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Partitioner;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.db.DBWritable;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
public class Demo2_3Mapp {
/**
* 用户自定义对象 保存
* @author Administrator
*
*/
public static class TopTenPair implements WritableComparable<TopTenPair>, DBWritable, Writable{
int prodid; //商品编码
int price;//商品价格
int count;//商品销售数量
@Override
public void write(PreparedStatement statement) throws SQLException {
statement.setInt(1, prodid);
statement.setInt(2, price);
statement.setInt(3, count);
}
@Override
public void readFields(ResultSet resultSet) throws SQLException {
this.prodid = resultSet.getInt(1);
this.price = resultSet.getInt(2);
this.count = resultSet.getInt(3);
}
/**
* Set the prodId and price and countvalues.
*/
public void set(int prodid, int price, int count) {
this.prodid = prodid;
this.price = price;
this.count = count;
}
public int getProdid() {
return prodid;
}
public int getPrice() {
return price;
}
public int getCount() {
return count;
}
@Override
// 反序列化，从流中的二进制转换成IntPair
public void readFields(DataInput in) throws IOException {
prodid = in.readInt();
price = in.readInt();
count= in.readInt();
}
@Override
// 序列化，将IntPair转化成使用流传送的二进制
public void write(DataOutput out) throws IOException {
out.writeInt(prodid);
out.writeInt(price);
out.writeInt(count);
}
@Override
// key的比较
public int compareTo(TopTenPair o) {
if ( o.count ==count) {
if( o.count==0){
returno.prodid - prodid;
}
returno.price-price;
}
return o.count-count;
}
// 新定义类应该重写的两个方法
@Override
public int hashCode() {
return count+prodid*3 ;
}
@Override
public boolean equals(Object right) {
if (right == null)
return false;
if (this == right)
return true;
if (right instanceof TopTenPair) {
TopTenPair r = (TopTenPair) right;
return r.prodid == prodid && r.price == price&& r.count == count;
} else {
return false;
}
}
@Override
public String toString(){
return getProdid()+"\t"+getPrice()+"\t"+getCount();
}
}
public static class TopTenPairS extends TopTenPair{
public TopTenPairS(){
}
// key的比较
@Override
public int compareTo(TopTenPair o) {
returno.price-price;
}
}
/**
* 分区函数类。根据first确定Partition。
*/
public static class FirstPartitioner extends
Partitioner<TopTenPair, Text> {
@Override
public int getPartition(TopTenPair key, Text value,
int numPartitions) {
return Math.abs(key.getProdid()) % numPartitions;
}
}
/**
* 分组函数类。只要first相同就属于同一个组。
*/
public static class GroupingComparator extends WritableComparator {
protected GroupingComparator() {
super(TopTenPair.class, true);
}
@Override
// Compare two WritableComparables.
public int compare(WritableComparable w1, WritableComparable w2) {
TopTenPair ip1 = (TopTenPair) w1;
TopTenPair ip2 = (TopTenPair) w2;
if (ip1.count == ip2.count) {
if(ip1.count==0){
returnip1.prodid - ip2.prodid;
}
returnip1.price - ip2.price ;
}
return ip1.count-ip2.count;
}
}
public static class Map extends
Mapper<LongWritable, Text, Text, Text> {
private final Text intkey= new Text();
private final Text intvalue = new Text();
//商品ID售价 数量
public void map(LongWritable key, Text value, Context context)
throws IOException, InterruptedException {
String line = value.toString();
StringTokenizer tokenizer = new StringTokenizer(line);
int prodid = 0;
int price = 0;
if (tokenizer.hasMoreTokens())
prodid = Integer.parseInt(tokenizer.nextToken());
if (tokenizer.hasMoreTokens())
price = Integer.parseInt(tokenizer.nextToken());
intkey.set(prodid+"");
intvalue.set(price+"");
//intvalue.set(0, price, 0);
context.write(intkey, intvalue);
}
}
public static class Demo2_3Combiner extends Reducer<Text, Text, Text, Text> {
public void reduce(Text key, Iterable<Text> values, Context context)
throws IOException, InterruptedException {
int count=0;
int maxPrice=0;
for (Text value : values) {
int v=Integer.parseInt(value.toString());
maxPrice=v<maxPrice?maxPrice:v;
count++;
}
//key :prodId
context.write(new Text(),new Text(key+"-"+maxPrice+"-"+count));
}
}
public static class Reduce extends
Reducer<Text, Text, TopTenPairS, Text> {
TopTenPair pair = new TopTenPair();
public void reduce(Text key, Iterable<Text> values,
Context context) throws IOException, InterruptedException {
String [] strs=null;
TopTenPair pair ;
List<TopTenPair> list=new ArrayList<Demo2_3Mapp.TopTenPair>();
for (Text val : values) {
pair = new TopTenPair();
strs=val.toString().split("-");
pair.set(Integer.parseInt(strs),
Integer.parseInt(strs),
Integer.parseInt(strs));
list.add(pair);
}
//按 count属性排序
Collections.sort(list);
List<TopTenPairS> lists=new ArrayList<Demo2_3Mapp.TopTenPairS>();
//取前4个对象
for(int i =0;i<4&& i<list.size();i++){
TopTenPair ttp=list.get(i);
TopTenPairS ttps=new TopTenPairS();
ttps.set(ttp.getProdid(), ttp.getPrice(), ttp.getCount());
lists.add(ttps);
}
//按 price 属性排序
Collections.sort(lists);
for(TopTenPairS ttps:lists){
System.out.println(ttps);
//参考 DBRecordWriter
//key 为数据类型, value：无用
context.write( ttps , new Text()); //输出到数据中
}
}
}
}
页:
[1]