Hadoop入门（二十一）Mapreduce的求和程序

你的名字 2023-06-09 10:28 77阅读 0赞

### 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);
        }
    }