WordCount 源码分析
WordCount 的源码一般是在下载的Hadoop安装包下的hadoop-1.2.1/src/examples/org/apache/hadoop/examples 里面有WordCount.java文件,你可以使用UltraEdit或者记事本打开。内容如下:
package org.apache.hadoop.examples;
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();
conf.set("mapred.job.tracker", "172.16.10.15:9001");//自己额外加的代码
String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();
if (otherArgs.length != 2) {
System.err.println("Usage: wordcount <in> <out>");
System.exit(2);
}
Job job = new Job(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);
FileInputFormat.addInputPath(job, new Path(otherArgs[0]));
FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}
我们开始一段一段分析代码,代码由map()
函数和reduce()
函数两个大部分组成,并被封装在了两个类中。
先来看Mapper部分:
代码使用了StringTokenizer
类,其作用类似String.split(),不过其默认的分隔符是“空格”、“制表符(‘\t’)”、“换行符(‘\n’)”、“回车符(‘\r’)”。
不鼓励使用StringTokenizer。
而这里使用一个键值对作为写入,写入的格式:<word,1>
context.write(word, one);
也就是说,map()
函数的作用就是分割文本,并且另其出现次数为1。
再来看看Reducer部分:
我们知道,在shuffle部分,已经处理过键值对了。输出的格式诸如:<hello,{1,1}>
所以这里使用的是Iterable<IntWritable> values
,并使用这个迭代器迭代出最后的值。
再次输出为键值对,context.write(key, result);