6. MapReduce custom partition implementation

MapReduce The built-in partition is HashPartitioner
principle ： First pair map Output key seek hash value , On the mold reduce task Number , Based on the results , Determine this output kv Yes , Matched reduce Task removal .

Custom partitions need to be inherited Partitioner, make carbon copies getpariton() Method
Custom partition class ：

Be careful ：map The output of is <K,V> Key value pair
among int partitionIndex = dict.get(text.toString()),partitionIndex Is to obtain K Value

attach ： Computed text

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

Need to be in main Function , Specify a custom partition class

Custom partition class ：

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 Represents the map Stage output key,IntWritable Represents the output value 
    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;
    }
}

Be careful ：map The output of is a key value pair <K,V>,int partitionIndex = dict.get(text.toString()); Medium partitionIndex yes map Output the value of the key in the key value pair , That is to say K Value .
Maper class ：

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) {
            //  Every word appears １ Time , Output as intermediate result 
            context.write(new Text(word), new IntWritable(1));
        }
    }
}

Reducer class ：

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) {
            //  Every word appears １ Time , Output as intermediate result 
            context.write(new Text(word), new IntWritable(1));
        }
    }
}

main function ：

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());
        //  hit jar package 
        job.setJarByClass(WordCountMain.class);
        //  adopt job Set input / Output format 
        //job.setInputFormatClass(TextInputFormat.class);
        //job.setOutputFormatClass(TextOutputFormat.class);
        //  Set input / The output path 
        FileInputFormat.setInputPaths(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));
        //  Set up processing Map/Reduce Stage class 
        job.setMapperClass(WordCountMap.class);
        //map combine
        //job.setCombinerClass(WordCountReduce.class);
        job.setReducerClass(WordCountReduce.class);
        // If map、reduce Output. kv Same for type , Set up directly reduce Output. kv That's right ; If it's not the same , It needs to be set separately map, reduce Output. kv type 
        //job.setMapOutputKeyClass(.class)
        //  Set the final output key/value The type of m
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);
        job.setPartitionerClass(CustomPartitioner.class);
        job.setNumReduceTasks(4);
        //  Submit the assignment 
        job.waitForCompletion(true);

    }
}

main Function parameter setting ：