WordCount代码

package com.zhiyou.bd17.mr;


import java.io.IOException;


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.output.FileOutputFormat;


public class WordCount {


// 定义map
public static class WordCountMap extends Mapper<LongWritable, Text, Text, IntWritable>{


private String[] infos;
private Text oKey = new Text();
private IntWritable oValue = new IntWritable(1);
@Override
protected void map(LongWritable key, Text value, Mapper<LongWritable, Text, Text, IntWritable>.Context context)
throws IOException, InterruptedException {
//解析一行数据，转换成一个单词组成的数组
infos = value.toString().split("\\s");
for (String i : infos) {
// 把单词形成一个kv对发送给reducer（单词，1）
oKey.set(i);
context.write(oKey, oValue);
}
}
}

//定义reducer
public static class WordCountReducer extends Reducer<Text, IntWritable, Text, IntWritable> {


private int sum;
private IntWritable oValue = new IntWritable(0);
@Override
protected void reduce(Text key, Iterable<IntWritable> values,
Reducer<Text, IntWritable, Text, IntWritable>.Context context) throws IOException, InterruptedException {
sum = 0;
for (IntWritable value : values) {
sum += value.get();
}
// 输出kv（单词，单词的计数）
oValue.set(sum);
context.write(key, oValue);
}
}



//组装一个job到mr引擎上执行
public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException{

// 构建一个configuration，用来配置hdfs 的位置，和mr的各项参数
Configuration configuration = new Configuration();

// 创建job对象
Job job = Job.getInstance(configuration);
job.setJarByClass(WordCount.class);
job.setJobName("第一个mr作业：wordcount");

// 配置mr执行类
job.setMapperClass(WordCountMap.class);
//添加combinner，可以不写
// job.setCombinerClass(WordCountReducer.class);
job.setReducerClass(WordCountReducer.class);

// 设置输出kv类型(mapper和reduce输出类型一致时，可不写)
// job.setMapOutputKeyClass(Text.class);
// job.setMapOutputValueClass(IntWritable.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);

//设置数据源（待处理）
Path inputPath = new Path("/README.txt");
FileInputFormat.addInputPath(job, inputPath);

// 设置目标数据的存放位置
Path outputPath = new Path("/bd17/output/wordcount1");
outputPath.getFileSystem(configuration).delete(outputPath, true);
FileOutputFormat.setOutputPath(job, outputPath);

// 启动作业，分布式计算提交给mr引擎
boolean result = job.waitForCompletion(true);
//如果job运行成功了，我们的程序就会正常退出
System.exit(result?0:1);
}


}

执行结果：