6、MapReduce自定义分区实现

MapReduce自带的分区器是HashPartitioner
原理：先对map输出的key求hash值，再模上reduce task个数，根据结果，决定此输出kv对，被匹配的reduce任务取走。

自定义分分区需要继承Partitioner，复写getpariton()方法
自定义分区类：

注意：map的输出是键值对
其中int partitionIndex = dict.get(text.toString())，partitionIndex是获取K的值

附：被计算的的文本

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

需要在main函数中设置，指定自定义分区类

自定义分区类：

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 {    public static HashMap dict = new HashMap();    //Text代表着map阶段输出的key,IntWritable代表着输出的值    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的输出结果是键值对,int partitionIndex = dict.get(text.toString());中的partitionIndex是map输出键值对中的键的值，也就是K的值。
Maper类：

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 {    public void map(LongWritable key, Text value, Context context)            throws IOException, InterruptedException {        String[] words = value.toString().split("\t");        for (String word : words) {            // 每个单词出现１次，作为中间结果输出            context.write(new Text(word), new IntWritable(1));        }    }}

Reducer类：

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 {    public void map(LongWritable key, Text value, Context context)            throws IOException, InterruptedException {        String[] words = value.toString().split("\t");        for (String word : words) {            // 每个单词出现１次，作为中间结果输出            context.write(new Text(word), new IntWritable(1));        }    }}

main函数：

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);        // 通过job设置输入/输出格式        //job.setInputFormatClass(TextInputFormat.class);        //job.setOutputFormatClass(TextOutputFormat.class);        // 设置输入/输出路径        FileInputFormat.setInputPaths(job, new Path(args[0]));        FileOutputFormat.setOutputPath(job, new Path(args[1]));        // 设置处理Map/Reduce阶段的类        job.setMapperClass(WordCountMap.class);        //map combine        //job.setCombinerClass(WordCountReduce.class);        job.setReducerClass(WordCountReduce.class);        //如果map、reduce的输出的kv对类型一致，直接设置reduce的输出的kv对就行；如果不一样，需要分别设置map, reduce的输出的kv类型        //job.setMapOutputKeyClass(.class)        // 设置最终输出key/value的类型m        job.setOutputKeyClass(Text.class);        job.setOutputValueClass(IntWritable.class);        job.setPartitionerClass(CustomPartitioner.class);        job.setNumReduceTasks(4);        // 提交作业        job.waitForCompletion(true);    }}

main函数参数设置：