about云hadoop源码分析之mapredue字符串分解StringTokenizer用法【hadoop2.7.1】

问题导读


1.map中StringTokenizer字符串是如何分割的？
2.StringTokenizer如何判断是否还有字符串？
3.nextToken()的作用是什么？







在mapreduce中，我们经常会遇到字符串分割

如下面wordcount
[mw_shl_code=java,true]package wordcount;
import java.io.IOException;
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.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.util.GenericOptionsParser;

public class wordcount {
        public static class TokenizerMapper
    extends Mapper<Object, Text, Text, IntWritable>{

private final static IntWritable one = new IntWritable(1);
private Text word = new Text();
   
public void map(Object key, Text value, Context context
                 ) throws IOException, InterruptedException {
   StringTokenizer itr = new StringTokenizer(value.toString());
   while (itr.hasMoreTokens()) {
     word.set(itr.nextToken());
     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: wordcount <in> [<in>...] <out>");
   System.exit(2);
}
Job job = Job.getInstance(conf, "word count");
job.setJarByClass(wordcount.class);
job.setMapperClass(TokenizerMapper.class);
job.setCombinerClass(IntSumReducer.class);
job.setReducerClass(IntSumReducer.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
for (int i = 0; i < otherArgs.length - 1; ++i) {
   FileInputFormat.addInputPath(job, new Path(otherArgs));
}
FileOutputFormat.setOutputPath(job,
   new Path(otherArgs[otherArgs.length - 1]));
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}[/mw_shl_code]

中


[mw_shl_code=java,true] public void map(Object key, Text value, Context context
                 ) throws IOException, InterruptedException {
   StringTokenizer itr = new StringTokenizer(value.toString());
   while (itr.hasMoreTokens()) {
     word.set(itr.nextToken());
     context.write(word, one);
   }
}[/mw_shl_code]


StringTokenizer itr = new StringTokenizer(value.toString());的含义是什么？


我们看下面内容

StringTokenizer(String str)：构造一个用来解析str的StringTokenizer对象。
     java默认的分隔符是“空格”、“制表符(‘\t’)”、“换行符(‘\n’)”、“回车符(‘\r’)”。

从上面我们看出value被“空格”、“制表符(‘\t’)”、“换行符(‘\n’)”、“回车符(‘\r’)”分割。

itr.hasMoreTokens()表示返回是否还有分隔符。

String nextToken()：返回从当前位置到下一个分隔符的字符串。