Hadoop入门（二十一）Mapreduce的求和程序

一、简介

求和是统计中最常使用到的，现在使用Mapreduce在海量数据中统计数据的求和。

 

二、例子

（1）实例描述
给出三个文件，每个文件中都存储了若干个数值，求所有数值中的求和。

样例输入：                                            
1）file1：  

1
2
3
7
9
-99
2

2）file2：  

11
2
23
17
9
199
22

3）file3：  

21
12
3
17
2
39
12

 期望输出：

314

 

（2）问题分析
实现统计海量数据的求和，不能将所有的数据加载到内存，计算只能使用类似外部排序的方式，加载一部分数据统计求和，接着加载另一部分进行统计。

（3）实现步骤

1）Map过程 
    首先使用默认的TextInputFormat类对输入文件进行处理，得到文本中每行的偏移量及其内容。显然，Map过程首先必须分析输入的<key,value>对，得到数值，然后在mapper中统计单个分块的求和。

2）Reduce过程 
    经过map方法处理后，Reduce过程将获取每个mapper的求和进行统计，分行统计出总的求和。

 

（3）关键代码

package com.mk.mapreduce;


import org.apache.commons.lang.StringUtils;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.NullWritable;
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;
import java.net.URI;

public class SumValue {

    public static class SumValueMapper extends Mapper<LongWritable, Text, IntWritable, NullWritable> {

        private int sumValue = 0;

        @Override
        protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {

            if (StringUtils.isBlank(value.toString())) {
                System.out.println("空白行");
                return;
            }

            int v = Integer.parseInt(value.toString().trim());

            sumValue = sumValue + v;
        }

        @Override
        protected void cleanup(Context context) throws IOException, InterruptedException {
             context.write( new IntWritable(sumValue), NullWritable.get());
        }
    }


    public static class SumValueReducer extends Reducer< IntWritable, NullWritable,IntWritable, NullWritable> {

        private int sumValue = 0;
        @Override
        protected void reduce(IntWritable key, Iterable<NullWritable> values, Context context) throws IOException, InterruptedException {

            int v = key.get();
            sumValue = sumValue + v;

        }

        @Override
        protected void cleanup(Context context) throws IOException, InterruptedException {
             context.write( new IntWritable(sumValue), NullWritable.get());
        }
    }

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

        String uri = "hdfs://192.168.150.128:9000";
        String input = "/sumValue/input";
        String output = "/sumValue/output";
        Configuration conf = new Configuration();
        if (System.getProperty("os.name").toLowerCase().contains("win"))
            conf.set("mapreduce.app-submission.cross-platform", "true");

        FileSystem fileSystem = FileSystem.get(URI.create(uri), conf);
        Path path = new Path(output);
        fileSystem.delete(path, true);

        Job job = new Job(conf, "SumValue");
        job.setJar("./out/artifacts/hadoop_test_jar/hadoop-test.jar");
        job.setJarByClass(SumValue.class);
        job.setMapperClass(SumValueMapper.class);
        job.setReducerClass(SumValueReducer.class);
        job.setMapOutputKeyClass(IntWritable.class);
        job.setMapOutputValueClass(NullWritable.class);
        job.setOutputKeyClass(IntWritable.class);
        job.setOutputValueClass(NullWritable.class);
        FileInputFormat.addInputPaths(job, uri + input);
        FileOutputFormat.setOutputPath(job, new Path(uri + output));


        boolean ret = job.waitForCompletion(true);
        System.out.println(job.getJobName() + "-----" + ret);
    }
}