【hadoop学习项目】8. 多文件中的表合并处理

数据内容
chinese.txt

1 lisi 89
2 lisi 73
3 lisi 67
1 zhangyang 49
2 zhangyang 83
3 zhangyang 27
1 lixiao 77
2 lixiao 66
3 lixiao 89

english.txt

1 lisi 75
2 lisi 94
3 lisi 100
1 zhangyang 61
2 zhangyang 59
3 zhangyang 98
1 lixiao 25
2 lixiao 47
3 lixiao 48

math.txt

1 lisi 75
2 lisi 94
3 lisi 100
1 zhangyang 61
2 zhangyang 59
3 zhangyang 98
1 lixiao 25
2 lixiao 47
3 lixiao 48

package hadoop_test.mutil_files_09.domain;

import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;

import org.apache.hadoop.io.Writable;

public class Score implements Writable{
	
	private String name;
	private int chinese;
	private int english;
	private int math;

	@Override
	public void write(DataOutput out) throws IOException {
		out.writeUTF(name);
		out.writeInt(chinese);
		out.writeInt(english);
		out.writeInt(math);
		
	}

	@Override
	public void readFields(DataInput in) throws IOException {
		this.name=in.readUTF();
		this.chinese=in.readInt();
		this.english=in.readInt();
		this.math=in.readInt();
		
	}

	public String getName() {
		return name;
	}

	public void setName(String name) {
		this.name = name;
	}

	public int getChinese() {
		return chinese;
	}

	public void setChinese(int chinese) {
		this.chinese = chinese;
	}

	public int getEnglish() {
		return english;
	}

	public void setEnglish(int english) {
		this.english = english;
	}

	public int getMath() {
		return math;
	}

	public void setMath(int math) {
		this.math = math;
	}

	@Override
	public String toString() {
		return "Score [name=" + name + ", chinese=" + chinese + ", english=" + english + ", math=" + math + "]";
	}
	
	

}

package hadoop_test.mutil_files_09.score;

import hadoop_test.Utils_hadoop;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

import hadoop_test.mutil_files_09.domain.Score;
import org.apache.log4j.BasicConfigurator;


public class ScoreDriver {
	public static void main(String[] args) throws Exception {
		System.setProperty("HADOOP_USER_NAME", "root");

		BasicConfigurator.configure();
		Configuration conf=new Configuration();
		Job job=Job.getInstance(conf);

		job.setJarByClass(ScoreDriver.class);

		job.setMapperClass(ScoreMapper.class);
		job.setReducerClass(ScoreReducer.class);

		job.setMapOutputKeyClass(Text.class);
		job.setMapOutputValueClass(Score.class);
		job.setOutputKeyClass(Text.class);
		job.setOutputValueClass(Score.class);

		FileInputFormat.setInputPaths(job,new Path("/hadoop_test/m_file_test"));
		FileOutputFormat.setOutputPath(job,new Path("/hadoop_test/m_file_test/result"));
		
		job.waitForCompletion(true);

	}
}

package hadoop_test.mutil_files_09.score;

import java.io.IOException;

import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.lib.input.FileSplit;

import hadoop_test.mutil_files_09.domain.Score;

//split
public class ScoreMapper extends Mapper{
	//一行一行处理
	@Override
	protected void map(LongWritable key,  Text value, Context context) throws IOException, InterruptedException {
		//split切片类, FileSplit文件分割器
		FileSplit split = (FileSplit) context.getInputSplit();
		//split.getPath().getName();获得正在读取的这个split的文件名
		String filename = split.getPath().getName();
		
//		String filepath=split.getPath().toString();
//		System.out.println(filepath);
		//line===》 1 lisi 3
		String line = value.toString();
		//理解为join 的 key
		String name = line.split(" ")[1];
		//分数
		int score = Integer.parseInt(line.split(" ")[2]);
		Score s = new Score();
		s.setName(name);

//		System.out.println(filename);
		if(filename.equals("chinese.txt")){
			s.setChinese(score);
		}
		if(filename.equals("english.txt")){
			s.setEnglish(score);
		}
		if(filename.equals("math.txt")){
			s.setMath(score);
		}
		context.write(new Text(s.getName()), s);
	}

}

package hadoop_test.mutil_files_09.score;

import java.io.IOException;

import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;

import hadoop_test.mutil_files_09.domain.Score;

public class ScoreReducer extends Reducer{

	@Override
	protected void reduce(Text key, Iterable values, Context context) throws IOException, InterruptedException {
		//new 一个tmp方法
		Score tmp=new Score();
		tmp.setName(key.toString());

		for(Score value:values){
			tmp.setChinese(tmp.getChinese()+value.getChinese());
			tmp.setEnglish(tmp.getEnglish()+value.getEnglish());
			tmp.setMath(tmp.getMath()+value.getMath());
		}
//		System.out.println(key+":"+tmp);
		context.write(key, tmp);
	}

}

数据结果

Hadoop学习：MapReduce实现两张表合并
Hadoop-MapReduce(多表合并)
Hadoop案例：Mapper端多表合并