结合案例讲解MapReduce重要知识点 ----------- 自定义MapReduce数据类型（1）重写Writable接口

重写Writable接口

如下代码就是自定义mr数据类型，在wordcount类使用它。

WordCountWritable

import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
import org.apache.hadoop.io.Writable;


/**
 * 自定义wc的数据类型：
 * @author lyd
 */
public class WordCountWritable implements Writable{
	public String word;
	public int counter;
	
	public WordCountWritable(){
	}
	
	public WordCountWritable(String word, int counter) {
		this.word = word;
		this.counter = counter;
	}

	/**
	 * 写
	 */
	@Override
	public void write(DataOutput out) throws IOException {
		out.writeUTF(word);
		out.writeInt(counter);
	}

	/**
	 * 读
	 */
	@Override
	public void readFields(DataInput in) throws IOException {
		this.word = in.readUTF();
		this.counter = in.readInt();
	}

	/**
	 * @return the word
	 */
	public String getWord() {
		return word;
	}

	/**
	 * @param word the word to set
	 */
	public void setWord(String word) {
		this.word = word;
	}

	/**
	 * @return the counter
	 */
	public int getCounter() {
		return counter;
	}

	/**
	 * @param counter the counter to set
	 */
	public void setCounter(int counter) {
		this.counter = counter;
	}

	/* (non-Javadoc)
	 * @see java.lang.Object#toString()
	 */
	@Override
	public String toString() {
		return word + ":" + counter;
	}
}

WordCount

import java.io.IOException;
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.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

import edu.qianfeng.mr.day01.WordCountWritable;

public class WordCount {
public static class MyMapper extends Mapper{
	
	Text word = new Text();
	IntWritable one = new IntWritable(1);
	
	@Override
	protected void map(LongWritable key, Text value,Context context)
			throws IOException, InterruptedException {
		//获取行数据
		String line = value.toString();
		//对数据进行拆分   [hello,qianfeng,hi,qianfeng] [hello,1603] [hi,hadoop,hi,spark]
		String []  words = line.split(" ");
		//循环数组
		for (String s : words) {
			word.set(s);
			context.write(word, one);
		}
		
	}
}

/**
 * 自定义reducer类
 * @author lyd
 *
 */
public static class MyReducer extends Reducer{
	
	@Override
	protected void reduce(Text key, Iterable value,Context context)
			throws IOException, InterruptedException {
		//定义一个计数器
		int counter = 0;
		//循环奇数
		for (IntWritable i : value) {
			counter += i.get();
		}
		//创建数据类型对象
		WordCountWritable wc = new WordCountWritable(key.toString(), counter);
		//reduce阶段的最终输出
		context.write(wc, null);
	}
}

/**
 * job的主入口
 * @param args
 */
public static void main(String[] args) {
	
	try {
		//获取配置对象
		Configuration conf = new Configuration();
		//创建job
		Job job = new Job(conf, "wordcount");
		//为job设置运行主类
		job.setJarByClass(WordCount.class);
		
		//设置map阶段的属性
		job.setMapperClass(MyMapper.class);
		job.setMapOutputKeyClass(Text.class);
		job.setMapOutputValueClass(IntWritable.class);
		FileInputFormat.addInputPath(job, new Path(args[0]));
		
		
		//设置reduce阶段的属性
		job.setReducerClass(MyReducer.class);
		job.setOutputKeyClass(WordCountWritable.class);
		job.setOutputValueClass(NullWritable.class);
		FileOutputFormat.setOutputPath(job, new Path(args[1]));
		
		//提交运行作业job 并打印信息
		int isok = job.waitForCompletion(true)?0:1;
		//退出job
		System.exit(isok);
		
	} catch (IOException | ClassNotFoundException | InterruptedException e) {
		e.printStackTrace();
	}
	}
}