使用Hadoop实现单词统计，hadoop实现单词

使用Hadoop实现单词统计，hadoop实现单词

本篇博客介绍使用Hadoop实现单词统计（来源：慕课网学习），下面是具体步骤：
1.创建目录

hadoop fs -mkdir input 
hadoop fs -mkdir word_count_class

2.复制博客末尾的代码到Java文件，命名WordCount.java，并运行如下命令编译java文件

javac -classpath /opt/hadoop-1.2.1/hadoop-core-1.2.1.jar:/opt/hadoop-1.2.1/lib/commons-cli-1.2.jar -d 编译后地址（自己制定，e.g.:wordcount_class） 编译后的文件名（自己指定，e.g.:WordCount）

3.打包指令

jar -cvf 打包后文件名.jar(自己指定） *.class

4.在当前目录的子目录input目录下创建两个输入文件(input1,input2)，里面加入需要统计的单词，用空格隔开（e.g.:word hadoop …），并输入文件提交输入文件给hadoop
hadoop fs -put 文件路径 提交后的路径

hadoop fs -put input/* input_wordcount/

5.提交jar给hadoop执行
hadoop jar jar包路径 执行的主函数名(主类名，main方法所在类名) 输入目录名 输出目录名

hadoop jar word_count_class/wordcount.jar WordCount input_wordcount output_wordcount

6.运行结果文件存在output_wordcount中，运行如下命令即可查看

hadoop fs -ls ouput_wordcount

7、查看output_wordcount目录下part-…文件即为统计结果

hadoop fs -cat ouput_wordcount/part-..

下面是代码：

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.LongWritable;
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.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;

public class WordCount {
	public static class WordCountMap extends
			Mapper<LongWritable, Text, Text, IntWritable> {
		private final IntWritable one = new IntWritable(1);
		private Text word = new Text();

		public void map(LongWritable key, Text value, Context context)
				throws IOException, InterruptedException {
			String line = value.toString();
			StringTokenizer token = new StringTokenizer(line);
			while (token.hasMoreTokens()) {
				word.set(token.nextToken());
				context.write(word, one);
			}
		}
	}

	public static class WordCountReduce extends
			Reducer<Text, IntWritable, Text, IntWritable> {
		public void reduce(Text key, Iterable<IntWritable> values,
				Context context) throws IOException, InterruptedException {
			int sum = 0;
			for (IntWritable val : values) {
				sum += val.get();
			}
			context.write(key, new IntWritable(sum));
		}
	}

	public static void main(String[] args) throws Exception {
		Configuration conf = new Configuration();
		Job job = new Job(conf);
		job.setJarByClass(WordCount.class);
		job.setJobName("wordcount");
		job.setOutputKeyClass(Text.class);
		job.setOutputValueClass(IntWritable.class);
		job.setMapperClass(WordCountMap.class);
		job.setReducerClass(WordCountReduce.class);
		job.setInputFormatClass(TextInputFormat.class);
		job.setOutputFormatClass(TextOutputFormat.class);
		FileInputFormat.addInputPath(job, new Path(args[0]));
		FileOutputFormat.setOutputPath(job, new Path(args[1]));
		job.waitForCompletion(true);
	}
}