Map Reduce 프로그래밍

강의

Map Reduce 프로그래밍

• 디스크 기반
• Key, Value 쌍의 집합 형태의 데이터셋 포맷
• 변경 불가 (immutable)
• map 혹은 reduce 오퍼레이션으로만 데이터 조작 가능
• 셔플링 : Map 결과 Reduce단에 적재

 

Map

• (k, v) → [(k', v')*] 형태
• 지정된 HDFS 파일로부터 시스템에 의해 입력

 

Reduce

• (k', [v1', v2', v3', ...]) → (k'', v'')
• Map의 출력 중 같은 키를 갖는 페어를 묶어서 시스템에 의해 입력
• HDFS에 출력 저장

 

Shuffling

• Mapper의 출력을 Reducer로 전송하는 프로세스
• 전송하는 데이터의 크기가 크면 네트워크 병목 초래 및 시간 효율 감소

 

Sorting

• Mapper의 출력을 Reducer가 받아 키 별로 정렬

 

Data Skew

• 각 태스크가 처리하는 데이터의 크기 간에 불균형 발생

 

문제점

• 낮은 생산성 
  • 융통성 부족
  • 튜닝 및 최적화 어려움
• 배치작업 중심
  • Low Latency가 아닌 Throughput 중심
• Shuffling 후에 Data Skew 발생 가능성 높음
• 디스크를 통한 입출력

 

대안

• 범용적인 대용량 데이터 처리 프레임워크 : YARN, Spark
• SQL 이용 : Hive, Presto

 

---

예제

WordCountMapper

cd hadoop-3.3.4/

bin/hdfs dfs -mkdir /user
bin/hdfs dfs -mkdir /user/hdoop
bin/hdfs dfs -mkdir input

vi words.txt	# 랜덤 단어 입력

bin/hdfs dfs -put words.txt input

bin/hadoop jar hadoop-*-examples.jar wordcount input output
bin/hdfs dfs -cat output/part-r-00000

bin/hadoop == bin/yarn

bin/hdfs dfs -ls input
bin/hdfs dfs -ls output

/**
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 * or more contributor license agreements.  See the NOTICE file
 * distributed with this work for additional information
 * regarding copyright ownership.  The ASF licenses this file
 * to you under the Apache License, Version 2.0 (the
 * "License"); you may not use this file except in compliance
 * with the License.  You may obtain a copy of the License at
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package org.apache.hadoop.examples;

import java.io.IOException;
import java.util.StringTokenizer;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
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;
import org.apache.hadoop.util.GenericOptionsParser;

public class WordCount {

  public static class TokenizerMapper 
       extends Mapper<Object, Text, Text, IntWritable>{
    
    private final static IntWritable one = new IntWritable(1);
    private Text word = new Text();
      
    public void map(Object key, Text value, Context context
                    ) throws IOException, InterruptedException {
      StringTokenizer itr = new StringTokenizer(value.toString());
      while (itr.hasMoreTokens()) {
        word.set(itr.nextToken());
        context.write(word, one);
      }
    }
  }
  
  public static class IntSumReducer 
       extends Reducer<Text,IntWritable,Text,IntWritable> {
    private IntWritable result = new IntWritable();

    public void reduce(Text key, Iterable<IntWritable> values, 
                       Context context
                       ) throws IOException, InterruptedException {
      int sum = 0;
      for (IntWritable val : values) {
        sum += val.get();
      }
      result.set(sum);
      context.write(key, result);
    }
  }

  public static void main(String[] args) throws Exception {
    Configuration conf = new Configuration();
    String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();
    if (otherArgs.length < 2) {
      System.err.println("Usage: wordcount <in> [<in>...] <out>");
      System.exit(2);
    }
    Job job = Job.getInstance(conf, "word count");
    job.setJarByClass(WordCount.class);
    job.setMapperClass(TokenizerMapper.class);
    job.setCombinerClass(IntSumReducer.class);
    job.setReducerClass(IntSumReducer.class);
    job.setOutputKeyClass(Text.class);
    job.setOutputValueClass(IntWritable.class);
    for (int i = 0; i < otherArgs.length - 1; ++i) {
      FileInputFormat.addInputPath(job, new Path(otherArgs[i]));
    }
    FileOutputFormat.setOutputPath(job,
      new Path(otherArgs[otherArgs.length - 1]));
    System.exit(job.waitForCompletion(true) ? 0 : 1);
  }
}