Hadoop入门案例-实现WordCount


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;

public class WordCount {


    /**
     * 默认MapReduce是通过TextInputFormat进行切片并交给Mapper类进行处理
     * TextInputFormat:
     *      key->当前前行的首字母的索引
     *      value->当前行的数据
     * Mapper类参数:
     *  进行了包装：
     *      输入key类型:Long类型
     *      输入value类型:String
     *      输出key类型:String（单词）
     *      输出value类型：Long（个数）
     *MapReduce为了在传输时需要进行序列化，通过包装，对基本类型进行包装，实现自己的序列化方法
     */
    public static class WordCountMapper extends Mapper<LongWritable,Text,Text,LongWritable>{

        LongWritable one = new LongWritable(1);

        /**
         *将每行数据拆分
         */
        @Override
        protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {

            String words = value.toString();
            //将每行数据拆分成各个单词
            String[] wordArr = words.split("\\s+");
            for (String word : wordArr) {
                //将单词输出 每个单词作为一个
                context.write(new Text(word),one);

            }


        }
    }


    /**
     *
     * 进行全局聚合
     * Reducer参数：
     * 同样对参数进行了包装
     *      输入：
     *          key：String（单词），value：Long（个数）
     *      输出：
     *          key：String，value：Long
     *
     */
    public static class WordCountReducer extends Reducer<Text,LongWritable,Text,LongWritable>{


        /**
         *将map输出结果进行全局聚合
         * key：单词，values：单词个数集合[1,1,1]，context:上下文
         */
        @Override
        protected void reduce(Text key, Iterable<LongWritable> values, Context context) throws IOException, InterruptedException {

            Long sum = 0L;
            for (LongWritable value:values) {
                //累加单词的个数
                sum += value.get();
            }
            //以 单词，个数输出最终结果
            context.write(key,new LongWritable(sum));
        }
    }





    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {

        //创建一个Job
        Configuration conf = new Configuration();
        conf.set("fs.defaultFS","hdfs://192.168.43.20:9000");
        Job job = Job.getInstance(conf,"word-count");
        //需要打成jar包才能运行
        job.setJarByClass(WordCount.class);

        //1.指定输入文件
            //通过参数传递一个path进来
        FileInputFormat.addInputPath(job,new Path(args[0]));

        //2.编写mapper处理逻辑
        job.setMapperClass(WordCountMapper.class);
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(LongWritable.class);
        //3.shuffle流程

        //  todo

        //4.reducer处理逻辑
        job.setReducerClass(WordCountReducer.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(LongWritable.class);
        //5.输出文件 并指定输出目录
        FileOutputFormat.setOutputPath(job,new Path(args[1]));

        //6.运行Job
        boolean result = job.waitForCompletion(true);
        System.out.println(result ? 1 : 0);
    }



}
使用maven打成jar包，上传到服务器
执行：bin/hadoop jar share/hadoop/mapreduce/jar包名称.jar 包名xxx.xxx.xxx.WordCount 输入文件目录输出文件目录
Hadoop入门案例-实现WordCount

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