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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
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 |
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
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);
}
} |
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原文链接:https://blog.51cto.com/10312890/2462756
程序猿的技术大观园：www.javathinker.net
[这个贴子最后由 flybird 在 2020-03-12 13:55:02 重新编辑]
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Java技术网站 → 『 Java开发技术 』 → 『 大数据处理和分布式架构专题 』 → MapReduce自定义分区实现
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