单词统计

标签：缓冲区   iterable   targe   split   单词   pac   main   nap   inpu   

package hdfs;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
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 java.io.IOException;

/**
 * 单词统计
 */
public class WordCount {
        //本地运行和远程调用方式需要
        static {
            System.setProperty("hadoop.home.dir", "D:\\Studyingimportant\\hadoop-2.9.2");
        }
    public static class MyMapper extends Mapper<LongWritable, Text, Text, LongWritable> {
        /**
         * mapper任务(task)
         * @param key  行索引
         * @param value 每行数据
         * @param context mr中的上下文环境
         * @throws IOException
         * @throws InterruptedException
         */
        @Override
        protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
            //①将每行数据拆分成单词数组
            String line = value.toString();
            String[] words = line.split(" ");
            //②将各个单词映射成<k,v>
            for (String word : words) {
                //③写到内存缓冲区
                context.write(new Text(word), new LongWritable(1));
            }
        }
    }


    public static class MyReducer extends Reducer<Text, LongWritable, Text, LongWritable> {
        /**
         * reducer任务(task)
         * @param key 每个单词
         * @param values 单词个数的集合
         * @param context 上下文环境
         * @throws IOException
         * @throws InterruptedException
         */
        @Override
        protected void reduce(Text key, Iterable<LongWritable> values, Context context) throws IOException, InterruptedException {
            Long sum = 0L;
            //①遍历指定key单词对应的个数集合[1,1,1]
            for (LongWritable value : values) {
                //②累加个数
                 sum += value.get();
            }
            //③输出
            context.write(key, new LongWritable(sum));
        }
    }

    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
        //0.初始化一个job
        Configuration conf = new Configuration();
        /*************************2.远程调用方式start******************************/
//        conf.set("yarn.resourcemanager.hostname", "hadoop-senior.test.com");
//        conf.set("mapreduce.framework.name", "yarn");
//        conf.set("yarn.nodemanager.aux-services", "mapreduce_shuffle");
//        conf.set("mapreduce.app-submission.cross-platform","true");
//        conf.set("fs.defaultFS", "hdfs://hadoop-senior.test.com:8020");
        /*************************2.远程调用方式end******************************/
        Job job = Job.getInstance(conf, "word-count");
        /*************************2.远程调用方式start******************************/
        //job.setJar("D:\\IdeaProjects\\hadoop_demo\\target\\hadoop_demo-1.0-SNAPSHOT.jar");
        /*************************2.远程调用方式end******************************/
        /*************************3.打jar集群方式start******************************/
        job.setJarByClass(WordCount.class);
        /*************************3.打jar集群方式start******************************/
        //1.输入文件
        FileInputFormat.addInputPath(job, new Path(args[0]));
        //2.map并行计算
        job.setMapperClass(MyMapper.class);
        //如果map输出的k和v类型  与 reduce输出的k和v类型一致的话，可省略
//        job.setMapOutputKeyClass(Text.class);
//        job.setMapOutputValueClass(LongWritable.class);
        //3.shuffle流程(内部实现)
        //4.reduce计算
        job.setReducerClass(MyReducer.class);
        job.setOutputKeyClass(Text.class);
        job.setMapOutputValueClass(LongWritable.class);
        //5.输出文件
        FileOutputFormat.setOutputPath(job, new Path(args[1]));

        //6.提交作业(总入口)
        boolean result = job.waitForCompletion(true);
        System.out.println(result ? 1 : 0);
    }
}

单词统计

标签：缓冲区   iterable   targe   split   单词   pac   main   nap   inpu   

原文地址：https://www.cnblogs.com/whyuan/p/12784276.html