MapReduce: DistributedCache的使用例子

清疚 2023-06-30 11:00 6阅读 0赞

需求场景：   过滤无意义的单词后再进行文本词频统计。处理流程是：  
1）预定义要过滤的无意义单词保存成文件，保存到HDFS中；  
2）程序中将该文件定位为作业的缓存文件，使用DistributedCache类；  
3）Map中读入缓存文件，对文件中的单词不做词频统计。  
该场景主要解决文件在Hadoop各task之间共享的问题，用conf传递参数不能传输大文件，于是通过DistributedCache派发文件到各节点。

java 例子如下

package com.word;
     
    import java.io.BufferedReader;
    import java.io.FileReader;
    import java.io.IOException;
    import java.net.URI;
    import java.util.HashSet;
    import java.util.StringTokenizer;
     
    import org.apache.hadoop.conf.Configuration;
    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.filecache.DistributedCache;
    import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
    import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
    import org.apache.hadoop.util.GenericOptionsParser;
     
     
    public class FilterWordCount {
    	public static class TokenizerMapper extends Mapper<Object, Text, Text, IntWritable>{
    		private final static IntWritable one = new IntWritable(1);
    		private Text word = new Text();
    		private HashSet<String> keyWord;
    	    private Path[] localFiles;
    	    //setup函数在Map task启动之后立即执行
    	    public void setup(Context context) throws IOException,InterruptedException{
    	    	keyWord=new  HashSet<String>();
    	    	Configuration conf=context.getConfiguration();
    	    	localFiles=DistributedCache.getLocalCacheFiles(conf);
    	    	//将缓存文件内容读入到当前Map Task的全局变量中
    	    	for(int i=0;i<localFiles.length;i++){
    	    		String aKeyWord;
    	    		BufferedReader br=new BufferedReader(
                            new FileReader(localFiles[i].toString()));
    	    		while((aKeyWord=br.readLine())!=null){
    	    			keyWord.add(aKeyWord);
    	    		}
    	    		br.close();
    	    	}
    	    }
    	    //根据缓存文件中缓存的无意义单词对输入流进行过滤
    		public void map(Object key, Text value, Context context)throws IOException, InterruptedException {
    			StringTokenizer itr = new StringTokenizer(value.toString());
    			while (itr.hasMoreTokens()) {
    				String aword=itr.nextToken();//获取字符
    				if(!keyWord.contains(aword)){//不包含无意义单词
    					word.set(aword);
    					context.write(word, one);
    				}
    			}
    		}
    	}
    	
    	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);
    			context.write(key, result);
    		}
    	}
    	
    	public static void main(String[] args) throws Exception {
    	
    		Configuration conf = new Configuration();
    		String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();
    		if (otherArgs.length != 2) {
    			System.err.println("Usage: FilterWordCount <in> <out>");
    			System.exit(2);
    		}
    		//将HDFS上的文件设置成当前作业的缓存文件
    		DistributedCache.addCacheFile(new URI("/tmp/fjs/kw.txt"), conf);
    		
    		Job job = new Job(conf, "FilterWordCount");
    		job.setJarByClass(FilterWordCount.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);
    	}
    }
    /*
     * 统计的输入文件：hadoop fs -put /var/log/boot.log /tmp/fjs/
     * 无意义单词缓存文件：/tmp/fjs/kw.txt
     * 结果输出文件：/tmp/fjs/fwcout
     * 执行命令：hadoop jar /mnt/FilterWordCount.jar /tmp/fjs/boot.log /tmp/fjs/fwcout
     */