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.Partitioner;
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Partitioner组件应用
 * @author F嘉阳
 * @date 2018-04-20
 */
public class WordCountPartitionerApp {
    /**
     * 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();

            String[] words = line.split(" ");//以空格分隔

                //每一个空格是一个手机品牌，另一个是销售数量
                context.write(new Text(words[0]), new LongWritable(Long.parseLong(words[1])));

        }
    }

    /**
     * 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));

        }
    }

    /**
     * Partitioner处理类
     */
    public static class MyPartitioner extends Partitioner<Text,LongWritable>{
         @Override
         public int getPartition(Text key, LongWritable value, int i) {

             if ("xiaomi".equals(key.toString())) {
                 return 0;//若为xiaomi则交由0 ReduceTask处理
             }
             if ("huawei".equals(key.toString())) {
                 return 1;//若为huawei则交由1 ReduceTask处理
             }
             if ("iphone".equals(key.toString())) {
                 return 2;//若为iphone则交由2 ReduceTask处理
             }

             return 3;//若为其他，则交由3 ReduceTask处理
         }
     }

    /**
     * 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(WordCountPartitionerApp.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);

        //设置job的Partition
        job.setPartitionerClass(MyPartitioner.class);
        //设置四个reducer，每个分区一个，否则Partitioner配置不生效
        job.setNumReduceTasks(4);

        //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);

    }


}