Hadoop之MapReduce改进的计数单词（八）

前面写的那个是老版本的写法，现在更新下，现代人的写法。
精简了许多代码，需要注意的是，在执行job时，要注意将
key和value的值进行下转换，否则map与reduce方法中的值
类型不对应就不能达到预期效果。
1，文件存放的路径与结果路径

2，文件的具体内容

3，参数路径

4具体结果

5，详细代码

    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;    import org.apache.hadoop.util.GenericOptionsParser;    public class WordCount {        public static final IntWritable ONE = new IntWritable(1);        public static class WordCountMapper extends Mapper<LongWritable, Text, Text, IntWritable>{            @Override            protected void map(LongWritable key, Text value, Mapper<LongWritable, Text, Text, IntWritable>.Context context)                    throws IOException, InterruptedException {                String[] vs = value.toString().split("\\s");//正则表达式，表示通过空格分隔                for (String v : vs) {                    context.write(new Text(v), ONE);                }            }        }        public static  class WordCountReducer extends Reducer<Text, IntWritable, Text, IntWritable>{            @Override            protected void reduce(Text key, Iterable<IntWritable> values,                    Reducer<Text, IntWritable, Text, IntWritable>.Context context) throws IOException, InterruptedException {                int count = 0;                for (IntWritable v : values) {                    count += v.get();                }                context.write(key, new IntWritable(count));            }        }        public static void main(String[] args) {            try {                Configuration conf = new Configuration();                String[] paths = new GenericOptionsParser(conf, args).getRemainingArgs();                if(paths.length < 2){                    throw new RuntimeException("usage <input> <output>");                }                Job job = Job.getInstance(conf, "wordcount");                job.setJarByClass(WordCount.class);                //job.setCombinerClass(WordCountReducer.class);//有多个从机时需要指定reducer类，但是我这里是伪分布的只有一个所以不需要                job.setMapperClass(WordCountMapper.class);                job.setMapOutputKeyClass(Text.class);  //因为map中返回的多了个long型的数据，在reduce接受的时候必须要转下字符类型                job.setMapOutputValueClass(IntWritable.class);                job.setReducerClass(WordCountReducer.class);                FileInputFormat.addInputPaths(job, paths[0]);//同时写入两个文件的内容                FileOutputFormat.setOutputPath(job, new Path(paths[1]));//整合好结果后输出的位置                System.exit(job.waitForCompletion(true) ? 0 : 1);//执行job            } catch (IOException e) {                e.printStackTrace();            } catch (ClassNotFoundException e) {                e.printStackTrace();            } catch (InterruptedException e) {                e.printStackTrace();            }        }    }