Hadoop學習之路(6)MapReduce自定義分區實現

MapReduce自帶的分區器是HashPartitioner
原理：先對map輸出的key求hash值，再模上reduce task個數，根據結果，決定此輸出kv對，被匹配的reduce任務取走。

自定義分分區須要繼承Partitioner，複寫getpariton()方法
自定義分區類：

注意：map的輸出是<K,V>鍵值對
其中int partitionIndex = dict.get(text.toString())，partitionIndex是獲取K的值

附：被計算的的文本

Dear Dear Bear Bear River Car Dear Dear  Bear Rive
Dear Dear Bear Bear River Car Dear Dear  Bear Rive

須要在main函數中設置，指定自定義分區類

自定義分區類：

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Partitioner;
import java.util.HashMap;
public class CustomPartitioner extends Partitioner<Text, IntWritable> {
    public static HashMap<String, Integer> dict = new HashMap<String, Integer>();
    //Text表明着map階段輸出的key,IntWritable表明着輸出的值
    static{
        dict.put("Dear", 0);
        dict.put("Bear", 1);
        dict.put("River", 2);
        dict.put("Car", 3);
    }
    public int getPartition(Text text, IntWritable intWritable, int i) {
        //
        int partitionIndex = dict.get(text.toString());
        return partitionIndex;
    }
}

注意：map的輸出結果是鍵值對<K,V>,int partitionIndex = dict.get(text.toString());中的partitionIndex是map輸出鍵值對中的鍵的值，也就是K的值。
Maper類：

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;

import java.io.IOException;

public class WordCountMap extends Mapper<LongWritable, Text, Text, IntWritable> {
    public void map(LongWritable key, Text value, Context context)
            throws IOException, InterruptedException {
        String[] words = value.toString().split("\t");
        for (String word : words) {
            // 每一個單詞出現１次，做爲中間結果輸出
            context.write(new Text(word), new IntWritable(1));
        }
    }
}

Reducer類：

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
import java.io.IOException;
public class WordCountMap extends Mapper<LongWritable, Text, Text, IntWritable> {
    public void map(LongWritable key, Text value, Context context)
            throws IOException, InterruptedException {
        String[] words = value.toString().split("\t");
        for (String word : words) {
            // 每一個單詞出現１次，做爲中間結果輸出
            context.write(new Text(word), new IntWritable(1));
        }
    }
}

main函數：

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
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 java.io.IOException;
public class WordCountMain {
    public static void main(String[] args) throws IOException,
            ClassNotFoundException, InterruptedException {
        if (args.length != 2 || args == null) {
            System.out.println("please input Path!");
            System.exit(0);
        }
        Configuration configuration = new Configuration();
        configuration.set("mapreduce.job.jar","/home/bruce/project/kkbhdp01/target/com.kaikeba.hadoop-1.0-SNAPSHOT.jar");
        Job job = Job.getInstance(configuration, WordCountMain.class.getSimpleName());
        // 打jar包
        job.setJarByClass(WordCountMain.class);
        // 經過job設置輸入/輸出格式
        //job.setInputFormatClass(TextInputFormat.class);
        //job.setOutputFormatClass(TextOutputFormat.class);
        // 設置輸入/輸出路徑
        FileInputFormat.setInputPaths(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));
        // 設置處理Map/Reduce階段的類
        job.setMapperClass(WordCountMap.class);
        //map combine
        //job.setCombinerClass(WordCountReduce.class);
        job.setReducerClass(WordCountReduce.class);
        //若是map、reduce的輸出的kv對類型一致，直接設置reduce的輸出的kv對就行；若是不同，須要分別設置map, reduce的輸出的kv類型
        //job.setMapOutputKeyClass(.class)
        // 設置最終輸出key/value的類型m
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);
        job.setPartitionerClass(CustomPartitioner.class);
        job.setNumReduceTasks(4);
        // 提交做業
        job.waitForCompletion(true);

    }
}

main函數參數設置：