1、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。
2、具体代码如下：
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
*/
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