Hadoop Mapreduce之WordCount实现

1.新建一个WCMapper继承Mapper

public class WCMapper extends Mapper<LongWritable, Text, Text, LongWritable> {

      @Override

      protected void map(LongWritable key, Text value, Context context)

                  throws IOException, InterruptedException {

            //接收数据V1

            String line = value.toString();

            //切分数据

            String[] wordsStrings = line.split(" ");

            //循环

            for (String w: wordsStrings) {

                  //出现一次，记一个一，输出

                  context.write(new Text(w), new LongWritable(1));

            }

      }

}

 

2.新建一个WCReducer继承Reducer

public class WCReducer extends Reducer<Text, LongWritable, Text, LongWritable> {

      @Override

      protected void reduce(Text key, Iterable<LongWritable> v2s, Context context)

                  throws IOException, InterruptedException {

            // TODO Auto-generated method stub

            //接收数据

            //Text k3 = k2;

            //定义一个计算器

            long counter = 0;

            //循环v2s

            for (LongWritable i : v2s)

            {

                  counter += i.get();

            }

            //输出

            context.write(key, new LongWritable(counter));

      }

}

3.WordCount类实现Main方法

/*

 * 1.分析具体的业力逻辑，确定输入输出数据样式

 * 2.自定义一个类，这个类要继承import org.apache.hadoop.mapreduce.Mapper;

 * 重写map方法，实现具体业务逻辑，将新的kv输出

 * 3.自定义一个类，这个类要继承import org.apache.hadoop.mapreduce.Reducer;

 * 重写reduce，实现具体业务逻辑

 * 4.将自定义的mapper和reducer通过job对象组装起来

 */

public class WordCount {

      public static void main(String[] args) throws Exception {

            // 构建Job对象

            Job job = Job.getInstance(new Configuration());

            

            // 注意：main方法所在的类

            job.setJarByClass(WordCount.class);

            

            // 设置Mapper相关属性

            job.setMapperClass(WCMapper.class);

            job.setMapOutputKeyClass(Text.class);

            job.setMapOutputValueClass(LongWritable.class);

            FileInputFormat.setInputPaths(job, new Path("/words.txt"));

            

            // 设置Reducer相关属性

            job.setReducerClass(WCReducer.class);

            job.setOutputKeyClass(Text.class);

            job.setOutputValueClass(LongWritable.class);

            FileOutputFormat.setOutputPath(job, new Path("/wcount619"));

            // 提交任务

            job.waitForCompletion(true);

      }     

}

 

 

4.打包为wc.jar，并上传到linux，并在Hadoop下运行

     hadoop jar /root/wc.jar