MapReduce 实现分片取TopN 再Reduce取TopN

ZX: 在Hadoop的map阶段 , 每个map任务都可以执行特殊的任务 , 当然 , 也包括取每个分片中最大的几个值. 然后汇总 , 再取TopN

　　这样的好处是 , 如果有2万个分片 , 2亿条数据 ,在Map完之后 

　　假如是取Top10 , 那么经过Map阶段的Top10,最后只用处理2万X10 , 共计20万条数据 , 大大减少了带宽的消耗.

　　具体实现

　　

package TopN;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
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.output.FileOutputFormat;


import java.io.IOException;
import java.util.SortedMap;
import java.util.TreeMap;

/**
 * Created by Administrator on 2019/4/3.
 */
public class TopnRunner {

    public static class TopnMapper extends
            Mapper<LongWritable, Text, NullWritable, Text> {
        //定义一个排序集合，用来收集每个分片中topn值
        //xxx,12
        private SortedMap<Long, String> topN = new TreeMap<Long, String>();
        @Override
        protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
            //遍历整个分片数据集，把新的数据插入到SortedMap

            Long number = Long.valueOf(value.toString());

            //aa,10   (10, "aa,10")
            //找到每个分片中 最大5个数据
            topN.put(number, value.toString());

            //取出最大5个值
            if(topN.size() > 5) {
                //始终移除最小的那个
                topN.remove(topN.firstKey());
            }

            //只有 遍历完整个分片数据 才能调用context.write方法
            //context.write();
        }

        @Override
        protected void cleanup(Context context) throws IOException, InterruptedException {

            Text mapValue = new Text();

            //切记 该算法  采集数据 不多
            for (String tmp:topN.values()) {
                mapValue.set(tmp);
                context.write(NullWritable.get(), mapValue);
            }
            System.out.println("-----------------------------");
        }
    }

    public static class TopnReducer extends
            Reducer<NullWritable, Text, Text, NullWritable> {

        private SortedMap<Long, String> topN = new TreeMap<Long, String>();
        private Text reduceKey = new Text();

        @Override
        protected void reduce(NullWritable key, Iterable<Text> values, Context context) throws IOException, InterruptedException {
            //super.reduce(key, values, context);
            for (Text tmp:values) {
                System.out.println(tmp);
                topN.put(Long.valueOf(tmp.toString()), tmp.toString());


                if (topN.size() > 5) {
                    topN.remove(topN.firstKey());
                }
            }

            //获取整个数据集 topn
            for (String tmp:topN.values()) {
                reduceKey.set(tmp);
                context.write(reduceKey, NullWritable.get());
            }
        }
    }

    public static void main(String[] args) throws InterruptedException, IOException, ClassNotFoundException {
        System.setProperty("hadoop.home.dir",
                "E:\\hadoop-2.6.0-cdh5.15.0");

        Configuration conf = new Configuration();
        conf.set("fs.defaultFS", "hdfs://node1:8020");

        Job job = Job.getInstance(conf);
        job.setJobName("优化后的topn");
        job.setJarByClass(TopnRunner.class);

        Path inputPath = new Path("/topn.txt");
        FileInputFormat.addInputPath(job, inputPath);

        job.setMapperClass(TopnMapper.class);
        job.setMapOutputKeyClass(NullWritable.class);
        job.setMapOutputValueClass(Text.class);


        job.setReducerClass(TopnReducer.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(NullWritable.class);

        Path outputPath = new Path("/AASD");
        FileOutputFormat.setOutputPath(job, outputPath);

        job.waitForCompletion(true);

    }
}