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<K,V>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 Rive Dear 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<Text, IntWritable> { public static HashMap<String, Integer> dict = new HashMap<String, Integer>(); //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<K,V>,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<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<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：