mapreduce案例：获取PI的值

package com.hadoop.Pi;

import java.io.IOException;
import java.util.Random;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.DoubleWritable;
import org.apache.hadoop.io.IntWritable;
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;

/*
 * @author:翟超科
 * time:2019.9.3
 * content:核心思想是向以(0,0),(0,1),(1,0),(1,1)为顶点的正方形中投掷随机点。
 * 统计(0.5,0.5)为圆心的单位圆中落点占总落点数的百分比，即可算出单位圆的面积Pi/4，
 * 然后乘以4即得到Pi的近似值。从输入文件中读入一行内容。每一行都是一个数字，
 * 代表随机投掷那么多点来估算Pi的值。在Mapper中则随机生成指定数量的随机点(x,y)。
 * x和y的范围在0-1之间。然后求出(x,y)与(0.5,0.5)的距离。
 * 如果超过0.5，则输出
 * */
public class Pi {
    public static class PiMapper
        extends Mapper<Object, Text, Text, IntWritable>{
        //生成一个0-1的随机数
        private static Random rd = new Random();
        //执行map
        public void map(Object key, Text value, Context context
                          ) throws IOException, InterruptedException {
            //实验进行pointNum次
            int pointNum = Integer.parseInt(value.toString());
            for(int i = 0; i < pointNum; i++){
                // 取随机数
                double x = rd.nextDouble();
                double y = rd.nextDouble();
                // 计算与(0.5,0.5)的距离，如果小于0.5就在单位圆里面
                x -= 0.5;
                y -= 0.5;
                double distance = Math.sqrt(x*x + y*y);
                //如果随机点在圆外，定义结果是0，否则为1
                IntWritable result = new IntWritable(0);
                if (distance <= 0.5){
                    result = new IntWritable(1);
                }
                //将键值对交给reduce处理
                context.write(value, result);
            }
        }
    }

    public static class PiReducer
                    extends Reducer<Text,IntWritable,Text,DoubleWritable> {
        //定义结果变量
        private DoubleWritable result = new DoubleWritable();
        public void reduce(Text key, Iterable<IntWritable> values,
                            Context context
                            ) throws IOException, InterruptedException {

            double pointNum = Double.parseDouble(key.toString());
            double sum = 0;
            //求结果等于1的和
            for (IntWritable val : values) {
                sum += val.get();
            }
            //结果*4即为PI值
            result.set(sum/pointNum*4);
            //输出结果到输出文件目录
            context.write(key, result);
        }
    }

    public static void main(String[] args) throws Exception {
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf,"calculate pi");
        job.setJarByClass(Pi.class);
        job.setMapperClass(PiMapper.class);
        job.setReducerClass(PiReducer.class);
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(IntWritable.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(DoubleWritable.class);
        FileInputFormat.addInputPath(job, new Path("hdfs://192.168.13.101:9000/pi"));//输入文件目录
        FileOutputFormat.setOutputPath(job, new Path("hdfs://192.168.13.101:9000/output2"));//输出文件目录
        System.exit(job.waitForCompletion(true) ? 0 : 1);
    }
}