hadoop 2 cachefile 分布式缓存和 Configuration set get 的用法

最新推荐文章于 2024-06-05 14:41:12 发布

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本文详细介绍了使用Hadoop和MapReduce实现单词计数的方法，包括配置、设置、映射和归约过程。

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package com.wc;

import java.io.BufferedReader;
import java.io.FileReader;
import java.io.IOException;
import java.net.URI;
import java.util.HashSet;
import java.util.Set;
import java.util.StringTokenizer;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
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.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.util.GenericOptionsParser;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

public class WordCount {
	public static class TokenizerMapper extends
			Mapper<Object, Text, Text, IntWritable> {
		Logger logger=LoggerFactory.getLogger(TokenizerMapper.class);
		private final static IntWritable one = new IntWritable(1);
		private Text word = new Text();
		private Set<String> set;
		 private FileSystem hdfs;
		@Override
		protected void setup(org.apache.hadoop.mapreduce.Mapper.Context context)
				throws IOException, InterruptedException {
			
			super.setup(context);
			set=new HashSet<String>();
 
			Configuration conf = context.getConfiguration();
			logger.warn("welcome :"+conf.get("welcome"));
			
			URI[] patternsURIs = Job.getInstance(conf).getCacheFiles();
			
		    Path patternsPath = new Path(patternsURIs[0].getPath());
		    String patternsFileName = patternsPath.getName().toString();
		    logger.warn("patternsFileName :"+patternsFileName);
			
			BufferedReader br=new BufferedReader(new FileReader(patternsFileName));
			String line=null;
			while((line=br.readLine())!=null)
			{
				logger.warn("line :"+line);
				line=line.trim();
				set.add(line);
			}
		}
		
		  
		// value已经是文件内容的一行
		public void map(Object key, Text value, Context context)
				throws IOException, InterruptedException {
			StringTokenizer itr = new StringTokenizer(value.toString());
			while (itr.hasMoreTokens()) {
				String token = itr.nextToken();
				
				if(set.contains(word.toString()))
				{
					token+="ThisIsACachedWord";
				}
				word.set(token);
				context.write(word, one);
				logger.warn("the word is :"+word);
			}
		}
	}

	public static class IntSumReducer extends
			Reducer<Text, IntWritable, Text, IntWritable> {
		private IntWritable result = new IntWritable();

		public void reduce(Text key, Iterable<IntWritable> values,
				Context context) throws IOException, InterruptedException {
			int sum = 0;
			for (IntWritable val : values) {
				sum += val.get();
			}
			result.set(sum);
			
			Logger logger=LoggerFactory.getLogger(TokenizerMapper.class);
			logger.warn("the sum is :" + sum);
			context.write(key, result);
		}
	}

	public static void main(String[] args) throws Exception {
		Configuration conf = new Configuration();
		
		String[] otherArgs = new GenericOptionsParser(conf, args)
				.getRemainingArgs();
//		Job job = new Job(conf, "word count");
		conf.set("welcome", "hello HI");
		Job job = Job.getInstance(conf, WordCount.class.getName());
		job.addCacheFile(new Path(args[2]).toUri());
		job.setJarByClass(WordCount.class);
		job.setMapperClass(TokenizerMapper.class);
		job.setCombinerClass(IntSumReducer.class);
		job.setReducerClass(IntSumReducer.class);
		job.setOutputKeyClass(Text.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);
	}
}