Hadoop读书笔记（十四）MapReduce中TopK算法（Top100算法）

标签：top k   top100   mapreduce topk算法   hadoop 1.x   

Hadoop读书笔记系列文章：http://blog.csdn.net/caicongyang/article/category/2166855 （系列文章会逐步修整完成，添加数据文件格式预计相关注释）

1.说明：

从给定的文件中的找到最大的100个值，给定的数据文件格式如下：

533
16565
17800
2929
11374
9826
6852
20679
18224
21222
8227
5336
912
29525
3382
2100
10673
12284
31634
27405
18015
...

2.下文代码中使用到TreeMap类，所以先写一个demo

TreeMapDemo.java

package suanfa;

import java.util.Map.Entry;
import java.util.TreeMap;

public class TreeMapDemo {
	public static void main(String[] args) {
		TreeMap<Long, Long> tree = new TreeMap<Long, Long>();
		tree.put(1333333L, 1333333L);
		tree.put(1222222L, 1222222L);
		tree.put(1555555L, 1555555L);
		tree.put(1444444L, 1444444L);
		for (Entry<Long, Long> entry : tree.entrySet()) {
			System.out.println(entry.getKey()+":"+entry.getValue());
		}
		System.out.println(tree.firstEntry().getValue()); //最小值
		System.out.println(tree.lastEntry().getValue()); //最大值
		System.out.println(tree.navigableKeySet());	//从小到大的正序key集合
		System.out.println(tree.descendingKeySet());//从大到小的倒序key集合
	}
}

3.MapReduce代码

TopKAapp.java

package suanfa;

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

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.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 org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.apache.hadoop.mapreduce.lib.partition.HashPartitioner;

/**
 * 
 * <p>
 * Title: TopKAapp.java Package suanfa
 * </p>
 * <p>
 * Description: 从算1000w个数据中找到最大的100个数
 * <p>
 * 
 * @author Tom.Cai
 * @created 2014-12-10 下午10:56:44
 * @version V1.0
 * 
 */
public class TopKAapp {
	private static final String INPUT_PATH = "hdfs://192.168.80.100:9000/topk_input";
	private static final String OUT_PATH = "hdfs://192.168.80.100:9000/topk_out";

	public static void main(String[] args) throws Exception {
		Configuration conf = new Configuration();
		final FileSystem fileSystem = FileSystem.get(new URI(INPUT_PATH), conf);
		final Path outPath = new Path(OUT_PATH);
		if (fileSystem.exists(outPath)) {
			fileSystem.delete(outPath, true);
		}

		final Job job = new Job(conf, TopKAapp.class.getSimpleName());
		FileInputFormat.setInputPaths(job, INPUT_PATH);
		job.setMapperClass(MyMapper.class);
		job.setPartitionerClass(HashPartitioner.class);
		job.setNumReduceTasks(1);
		job.setReducerClass(MyReducer.class);
		job.setOutputKeyClass(NullWritable.class);
		job.setOutputValueClass(LongWritable.class);
		FileOutputFormat.setOutputPath(job, new Path(OUT_PATH));
		job.setOutputFormatClass(TextOutputFormat.class);
		job.waitForCompletion(true);
	}

	static class MyMapper extends Mapper<LongWritable, Text, NullWritable, LongWritable> {
		public static final int K = 100;
		private TreeMap<Long, Long> tree = new TreeMap<Long, Long>();

		public void map(LongWritable key, Text text, Context context) throws IOException, InterruptedException {
			long temp = Long.parseLong(text.toString());
			tree.put(temp, temp);
			if (tree.size() > K)
				tree.remove(tree.firstKey());
		}

		@Override
		protected void cleanup(Context context) throws IOException, InterruptedException {
			for (Long text : tree.values()) {
				context.write(NullWritable.get(), new LongWritable(text));
			}
		}
	}

	static class MyReducer extends Reducer<NullWritable, LongWritable, NullWritable, LongWritable> {
		public static final int K = 100;
		private TreeMap<Long, Long> tree = new TreeMap<Long, Long>();

		@Override
		protected void cleanup(Context context) throws IOException, InterruptedException {
			for (Long val : tree.descendingKeySet()) {
				context.write(NullWritable.get(), new LongWritable(val));
			}
		}

		@Override
		protected void reduce(NullWritable key, Iterable<LongWritable> values, Context context) throws IOException, InterruptedException {
			for (LongWritable value : values) {
				tree.put(value.get(), value.get());
				if (tree.size() > K)
					tree.remove(tree.firstKey());
			}
		}
	}
}

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Hadoop读书笔记（十四）MapReduce中TopK算法（Top100算法）

标签：top k   top100   mapreduce topk算法   hadoop 1.x   

原文地址：http://blog.csdn.net/caicongyang/article/details/41875095