MapReduce的规约-->自定义Combiner

package combiner;

import java.io.IOException;
import java.net.URI;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
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;
/**
 * hello you
 * hello me
 * 
 * Map运行完成之后会进行 分区 排序 分组
 * 这里讲分组之后的规约
 * 
 * 自定义规约
 * 
 * 
 */
public class WordCountApp {
    
    //可以是文件夹或者文件
    private static final String inputPaths = "hdfs://hadoop:9000/hello";
    //只能是文件夹
    private static final String OUT_PATH = "hdfs://hadoop:9000/out";


    public static void main(String[] args) throws Exception {
        
        Configuration conf = new Configuration();
        
        FileSystem fileSystem = FileSystem.get(new URI(OUT_PATH), conf);
        boolean delete = fileSystem.delete(new Path(OUT_PATH), true);
        if (delete) {
            System.out.println("delete out_path ok");
        }
        
        
        Job job = new Job(conf, WordCountApp.class.getSimpleName());
        
        job.setJarByClass(WordCountApp.class);
        
        FileInputFormat.setInputPaths(job, inputPaths);
        job.setInputFormatClass(TextInputFormat.class);
        
        job.setMapperClass(MyMapper.class);
        
        //加入Combiner
        //map产生的输出在这个Combiner运行 运行完成交给myreduce
        job.setCombinerClass(MyReducer.class);  
        
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(LongWritable.class);
        
        job.setReducerClass(MyReducer.class);
        job.setOutputKeyClass(Text.class); 
        job.setOutputValueClass(LongWritable.class);
        
        FileOutputFormat.setOutputPath(job, new Path(OUT_PATH));
        
        //提交并等待运行结束
        job.waitForCompletion(true);
    }
    
    /**
     * 输入的key  偏移量 LongWritable 类型
     * 输入的value  行文本的内容
     * 输出的key        输出的key  单词
     * 输出的value    输出的value 次数
     * 
     */
    public static class MyMapper extends Mapper<LongWritable, Text, Text, LongWritable>{
        
        /**
         * 解析源文件会产生2个键值对，分别是<0,hello you><10,hello me>;所以map函数会被调用2次
         */
        @Override
        protected void map(LongWritable key, Text value,
                org.apache.hadoop.mapreduce.Mapper<LongWritable, Text, Text, LongWritable>.Context context)
                throws IOException, InterruptedException {
            
            final String line = value.toString();    
            String[] split = line.split("\t");
            for (String word : split) {
                context.write(new Text(word), new LongWritable(1));
            }
        }
    }

    //map函数执行结束后,map输出的<K,V>一共有4个，分别是<hello,1>,<you,1>,<hello,1>,<me,1>
    //分区（类似分部门），默认只有一个区
    //排序后的结果:<Hello,1>,<Hello,1>,<me,1>,<you,1>
    //分组（把相同key的value放在一起）:<Hello,{1,1}>,<me,{1}>,<you,{1}>
    //规约（可选）
    
    
    
    //map产生的<k,v>分发到reduce的过程称作shuffle
    /**
     * 输入部分和map的输出部分一致，reduce接收的数据来自于map的输出
     */
    public static class MyReducer extends Reducer<Text, LongWritable, Text, LongWritable>{
        
        /**
         * 相同key的value放在 Iterable<LongWritable> 中
         * 每一组调用一次reduce函数，一共调用了3次  第一次过来的是
         * 分组的数量与reduce函数的调用次数相等
         */
        @Override
        protected void reduce(Text key, Iterable<LongWritable> values,
                org.apache.hadoop.mapreduce.Reducer<Text, LongWritable, Text, LongWritable>.Context context)
                throws IOException, InterruptedException {
        
            Long count=0l;
            for (LongWritable times : values) {
                count+=times.get();
            }
            context.write(key, new LongWritable(count));
        }
    }
    
    

}