package com.fjy.hadoop.mapreduce;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
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;

/**
 * 使用MapReduce开发WordCount应用
 * @author F嘉阳
 * @date 2018-04-17
 */
public class WordCountApp {
    /**
     * Map：读取输入文件
     * Text:类似字符串
     */
    public static class MyMapper extends Mapper<LongWritable, Text, Text, LongWritable> {

        LongWritable one = new LongWritable(1);

        /**
         * @param key     偏移量
         * @param value   每行的字符串
         * @param context 上下文
         * @throws IOException
         * @throws InterruptedException
         */
        @Override
        protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
            /*super.map(key, value, context);*/

            //接收到每一行数据
            String line = value.toString();

            //按照指定分隔符进行拆分
            /*line.split("\t");//以Tab分隔*/
            String[] words = line.split(" ");//以空格分隔

            for (String word : words) {
                //通过上下文把map的处理结果输出
                context.write(new Text(word), one);
            }
        }
    }

    /**
     * Reduce 归并操作
     * LongWritable：文本出现的次数/求和后的次数
     */
    public static class MyReducer extends Reducer<Text, LongWritable, Text, LongWritable> {
        /**
         * @param key
         * @param values  相同偏移量的集合
         * @param context
         * @throws IOException
         * @throws InterruptedException
         */
        @Override
        protected void reduce(Text key, Iterable<LongWritable> values, Context context) throws IOException, InterruptedException {
            //super.reduce(key, values, context);

            long sum = 0;

            for (LongWritable value : values) {
                //求key出现的次数和总和
                sum += value.get();
            }
            //统计结果的输出
            context.write(key, new LongWritable(sum));

        }
    }

    /**
     * 定义Driver,封装MapReduce作业的所有信息
     */
    public static void main(String[] args) throws Exception {

        //创建Configuration
        Configuration configuration = new Configuration();

        //清理已存在的输出目录
        Path outputPath = new Path(args[1]);
        FileSystem fileSystem = FileSystem.get(configuration);
        if (fileSystem.exists(outputPath)){
            fileSystem.delete(outputPath);
            System.out.println("The exist files had been deleted!");
        }

        //创建作业
        Job job = Job.getInstance(configuration, "wordcount");

        //设置作业的主类
        job.setJarByClass(WordCountApp.class);

        //作业处理的输入路径
        FileInputFormat.setInputPaths(job, new Path(args[0]));

        //设置map相关参数
        job.setMapperClass(MyMapper.class);

        //设置map输出的key的类型
        job.setMapOutputKeyClass(Text.class);

        //设置map输出的value的类型
        job.setMapOutputValueClass(LongWritable.class);

        //设置Reduce相关参数
        job.setReducerClass(MyReducer.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(LongWritable.class);

        //通过job设置combiner处理类，逻辑上与reduce一致，注意，如果要计算平均数等不能使用Combiner!
        job.setCombinerClass(MyReducer.class);


        //设置作业处理输出结果的输出路径
        FileOutputFormat.setOutputPath(job, new Path(args[1]));

        //作业提交
        job.waitForCompletion(true);

        //作业完成后退出
        System.exit(job.waitForCompletion(true) ? 0 : 1);

    }


}