Hadoop 学习笔记（十） MapReduce实现排序全局变量

一些疑问：
1 全排序的话，最后的应该sortJob.setNumReduceTasks(1);
2 如果多个reduce task都去修改 一个静态的 IntWritable ，IntWritable会乱序吧~
输入数据：
file1
2
32
654
32
15
756
65223
file2
5956
22
650
92
file3
26
54
6




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.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;


public class MySort {

    public static class IntSortMapper extends Mapper<Object, Text, IntWritable, NullWritable>{
        
        private IntWritable val = new IntWritable();
        
        public void map(Object key, Text value, Context context) throws IOException, InterruptedException{
            String line = value.toString().trim();
            val.set(Integer.parseInt(line));
            context.write(val, NullWritable.get());
        }
    }
    
    public static class IntSortReducer extends Reducer<IntWritable, NullWritable, IntWritable,IntWritable>{
        private IntWritable k = new IntWritable();
        public void reduce(IntWritable key, Iterable<NullWritable> values, Context context) throws IOException, InterruptedException{
            k.set(1);
            for (NullWritable value : values) {
                context.write(k, key);
            }
        }
    }
    
    
    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
        String dir_in = "hdfs://localhost:9000/in_sort";
        String dir_out = "hdfs://localhost:9000/out_sort";

        Path in = new Path(dir_in);
        Path out = new Path(dir_out);
        
        Configuration conf = new Configuration();
        Job sortJob = new Job(conf, "my_sort");

        sortJob.setJarByClass(MySort.class);

        sortJob.setInputFormatClass(TextInputFormat.class);
        sortJob.setMapperClass(IntSortMapper.class);
        //sortJob.setCombinerClass(SortReducer.class);
        //countJob.setPartitionerClass(HashPartitioner.class);
        sortJob.setMapOutputKeyClass(IntWritable.class);
        sortJob.setMapOutputValueClass(NullWritable.class);

        FileInputFormat.addInputPath(sortJob, in);

        sortJob.setReducerClass(IntSortReducer.class);
        sortJob.setNumReduceTasks(1);
        sortJob.setOutputKeyClass(IntWritable.class);
        sortJob.setOutputValueClass(IntWritable.class);
        //countJob.setOutputFormatClass(SequenceFileOutputFormat.class);

        FileOutputFormat.setOutputPath(sortJob, out);

        sortJob.waitForCompletion(true);

    }

}

修改reduce函数（不是用Iterable）
public static class IntSortReducer extends Reducer<IntWritable, NullWritable, IntWritable,IntWritable>{
        private IntWritable k = new IntWritable();
        public void reduce(IntWritable key, NullWritable value, Context context) throws IOException, InterruptedException{
            k.set(1);
            //for (NullWritable value : values) {
                context.write(k, key);
            //}
        }
    }

结果：（不是很理解，为啥去掉iterable后就只输出一个value  key哪去了呢）
2
6
15
22
26
32
32
54
92
650
654
756
5956
65223

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.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;


public class MySort {

    public static class IntSortMapper extends Mapper<Object, Text, IntWritable, NullWritable>{
        
        private IntWritable val = new IntWritable();
        
        public void map(Object key, Text value, Context context) throws IOException, InterruptedException{
            String line = value.toString().trim();
            val.set(Integer.parseInt(line));
            context.write(val, NullWritable.get());
        }
    }
    
    public static class IntSortReducer extends Reducer<IntWritable, NullWritable, IntWritable,IntWritable>{
        private static IntWritable num = new IntWritable(1);
        public void reduce(IntWritable key, Iterable<NullWritable> values, Context context) throws IOException, InterruptedException{
            
            for (NullWritable value : values) {
                context.write(num, key);
                num = new IntWritable(num.get() + 1);
            }
        }
    }
    
    
    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
        String dir_in = "hdfs://localhost:9000/in_sort";
        String dir_out = "hdfs://localhost:9000/out_sort";

        Path in = new Path(dir_in);
        Path out = new Path(dir_out);
        
        Configuration conf = new Configuration();
        Job sortJob = new Job(conf, "my_sort");

        sortJob.setJarByClass(MySort.class);

        sortJob.setInputFormatClass(TextInputFormat.class);
        sortJob.setMapperClass(IntSortMapper.class);
        //sortJob.setCombinerClass(SortReducer.class);
        //countJob.setPartitionerClass(HashPartitioner.class);
        sortJob.setMapOutputKeyClass(IntWritable.class);
        sortJob.setMapOutputValueClass(NullWritable.class);

        FileInputFormat.addInputPath(sortJob, in);

        sortJob.setReducerClass(IntSortReducer.class);
        sortJob.setNumReduceTasks(1);
        sortJob.setOutputKeyClass(IntWritable.class);
        sortJob.setOutputValueClass(IntWritable.class);
        //countJob.setOutputFormatClass(SequenceFileOutputFormat.class);

        FileOutputFormat.setOutputPath(sortJob, out);

        sortJob.waitForCompletion(true);

    }

}
1    2
2    6
3    15
4    22
5    26
6    32
7    32
8    54
9    92
10    650
11    654
12    756
13    5956
14    65223

Hadoop 学习笔记 （十） MapReduce实现排序 全局变量

Hadoop 学习笔记（十） MapReduce实现排序全局变量