MapReduce编程实例:
MapReduce编程实例(一),详细介绍在集成环境中运行第一个MapReduce程序 WordCount及代码分析
MapReduce编程实例(五),MapReduce实现单表关联
排序,比较简单,上代码,代码中有注释,欢迎交流。
总体是利用MapReduce本身对Key进行排序的特性和按key值有序的分配到不同的partition。Mapreduce默认会对每个reduce按text类型key按字母顺序排序,对intwritable类型按大小进行排序。
- package com.t.hadoop;
- import java.io.IOException;
- 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.Mapper;
- import org.apache.hadoop.mapreduce.Partitioner;
- import org.apache.hadoop.mapreduce.Reducer;
- import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
- import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
- import org.apache.hadoop.util.GenericOptionsParser;
- /**
- * 排序
- * 利用MapReduce默认的对Key进行排序
- * 继承Partitioner类,重写getPartition使Mapper结果整体有序分到相应的Partition,输入到Reduce分别排序。
- * 利用全局变量统计位置
- * @author daT dev.tao@gmail.com
- *
- */
- public class Sort {
- public static class SortMapper extends Mapper<Object, Text, IntWritable, IntWritable>{
- //直接输出key,value,key为需要排序的值,value任意
- @Override
- protected void map(Object key, Text value,
- Context context)throws IOException, InterruptedException {
- System.out.println("Key: "+key+" "+"Value: "+value);
- context.write(new IntWritable(Integer.valueOf(value.toString())),new IntWritable(1));
- }
- }
- public static class SortReducer extends Reducer<IntWritable, IntWritable, IntWritable, IntWritable>{
- public static IntWritable lineNum = new IntWritable(1);//记录该数据的位置
- //查询value的个数,有多少个就输出多少个Key值。
- @Override
- protected void reduce(IntWritable key, Iterable<IntWritable> value,
- Context context) throws IOException, InterruptedException {
- System.out.println("lineNum: "+lineNum);
- for(IntWritable i:value){
- context.write(lineNum, key);
- }
- lineNum = new IntWritable(lineNum.get()+1);
- }
- }
- public static class SortPartitioner extends Partitioner<IntWritable, IntWritable>{
- //根据key对数据进行分派
- @Override
- public int getPartition(IntWritable key, IntWritable value, int partitionNum) {
- System.out.println("partitionNum: "+partitionNum);
- int maxnum = 23492;//输入的最大值,自己定义的。mapreduce 自带的有采样算法和partition的实现可以用,此例没有用。
- int bound = maxnum/partitionNum;
- int keyNum = key.get();
- for(int i=0;i<partitionNum;i++){
- if(keyNum>bound*i&&keyNum<=bound*(i+1)){
- return i;
- }
- }
- return -1;
- }
- }
- public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException{
- Configuration conf = new Configuration();
- String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();
- if(otherArgs.length<2){
- System.out.println("input parameters errors");
- System.exit(2);
- }
- Job job= new Job(conf);
- job.setJarByClass(Sort.class);
- job.setMapperClass(SortMapper.class);
- job.setPartitionerClass(SortPartitioner.class);//此例不需要combiner,需要设置Partitioner
- job.setReducerClass(SortReducer.class);
- job.setOutputKeyClass(IntWritable.class);
- job.setOutputValueClass(IntWritable.class);
- FileInputFormat.addInputPath(job, new Path(otherArgs[0]));
- FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));
- System.exit(job.waitForCompletion(true)?0:1);
- }
- }