官网的wordcount:链接: link
https://hadoop.apache.org/docs/stable/hadoop-mapreduce-client/hadoop-mapreduce-client-core/MapReduceTutorial.html
Input and Output types of a MapReduce job:
(input)
==mapreduce:==以map
输入到reduce,经过自己的处理,继续以map的形式输入,但每次的map形式都是按照自己的目的走的。下面分析一下这段代码
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;
public class WordCount {
public static class TokenizerMapper
extends Mapper<Object, Text, Text, IntWritable>{
//Object:代表输入的map的key,默认是偏移量
//Text:代表输入的map的value,默认是读取的一行字符串
//Text:代表输出的map的key,是已经分割的每个单词
//IntWritable:代表输出的map的value,计数为1
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> {
//Text:代表输入的map的key,是每个单词
//IntWritable:代表输入的map的value,map以后的value
//Text:代表输出的map的key,展示的结果
//IntWritable:代表输出的map的value,每个单词出现的总和
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 {
//加载的配置文件的信息
Configuration conf = new Configuration();
//构建出一个job对象并且给这个job命名一个名字
Job job = Job.getInstance(conf, "word count");
//运行job加载的主类
job.setJarByClass(WordCount.class);
//运行job加载的map类
job.setMapperClass(TokenizerMapper.class);
job.setCombinerClass(IntSumReducer.class);
//运行job加载的map类
job.setReducerClass(IntSumReducer.class);
//最终输出map键的类型
job.setOutputKeyClass(Text.class);
//最终map输出value的类型
job.setOutputValueClass(IntWritable.class);
//分布式文件输入的路径
FileInputFormat.addInputPath(job, new Path(args[0]));
//分布式文件结果输出的路径
FileOutputFormat.setOutputPath(job, new Path(args[1]));
//三目运算符表示程序是否执行成功
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}
打包在客户端运行
Compile WordCount.java and create a jar:
$ bin/hadoop com.sun.tools.javac.Main WordCount.java
$ jar cf wc.jar WordCount*.class
Assuming that:
将文件上传到分布式文件系统
/user/joe/wordcount/input - input directory in HDFS
/user/joe/wordcount/output - output directory in HDFS
Sample text-files as input:
查看文件是否上传成功
$ bin/hadoop fs -ls /user/joe/wordcount/input/
/user/joe/wordcount/input/file01
/user/joe/wordcount/input/file02
查看文件类容
$ bin/hadoop fs -cat /user/joe/wordcount/input/file01
Hello World Bye World
$ bin/hadoop fs -cat /user/joe/wordcount/input/file02
Hello Hadoop Goodbye Hadoop
Run the application:
$ bin/hadoop jar wc.jar WordCount /user/joe/wordcount/input /user/joe/wordcount/output
Output:
$ bin/hadoop fs -cat /user/joe/wordcount/output/part-r-00000
Bye 1
Goodbye 1
Hadoop 2
Hello 2
World 2