슬라이드 1

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1 Hadoop Tutorial - 설치및실행 한재선 (NexR 대표이사 ) jshan0000@gmail.com H.P:

2 Brief History Hadoop 소개 2005년 Doug Cutting(Lucene & Nutch 개발자 ) 에의해시작 Nutch 오픈소스검색엔진의분산확장이슈에서출발 2006년 Yahoo의전폭적인지원 (Doug Cutting과전담팀고용 ) 2008년 Apache Top-level Project로승격현재 (2008년4월) release Hadoop Java 언어기반 Apache 라이선스 많은컴포넌트들 HDFS, HBase, MapReduce, Hadoop On Demand(HOD), Streaming, HQL, Hama, Mahout, etc

3 Hadoop 구조 Nutch: Open Source Search Engine MapReduce: 분산데이터처리시스템 HBase: 분산데이터베이스 HDFS: 분산파일시스템 Google Search MapReduce Bigtable GFS Commodity PC 서버클러스터

4 Hadoop Versions Version Release Release Release Release Release Not released yet Not released yet Not released yet Current stable version

5 HBase Versions Version Release Release Release Release Release Not released yet Current stable version

6 Hadoop Project Issue Tracking

7 Hadoop Project 상황 지난 100 일간생성된이슈와해결된이슈들누적분포

8 Hadoop 설치 1. hadoop tar.gz 다운및압축해제 2. conf/hadoop-env.sh 편집 export JAVA_HOME=/usr/java/jdk1.6.0_03 3. conf/hadoop-site.xml 편집 (conf/hadoop-default.xml 에서필요한내용가져와편집 ) <property> <name>hadoop.tmp.dir</name> <value>/home/${user.name}/tmp/hadoop ${user.name}</value> <description>a base for other temporary directories.</description> </property> <property> <name>fs.default.name</name> <value>hdfs:// :9000/</value> </property> <property> <name>mapred.job.tracker</name> <value> :9001</value> </property> <property> <name>dfs.replication</name> <value>3</value>  </property>

9 Hadoop 설치 4. conf/masters 편집 conf/slaves 편집 ssh pub-key 등록 $ ssh-keygen $ ssh-copy-id -i ~/.ssh/id_rsa.pub id@server // 접속하려는서버의 id/ 주소 7. HDFS format $ bin/hadoop namenode format 8. 실행 $ bin/start-all.sh 9. 정지 $ bin/stop-all.sh % HADOOP_HOME 경로는 master 와 slave 모두동일하게하자. master 에서실행시 slave 들과 rsync 를통해 sync 맞춤 % 문제있는경우 iptable 설정확인 iptable 설정을제거하거나 hadoop 이쓰는포트들을등록

10 Hadoop 실행 - DFS $ bin/hadoop dfs Usage: java FsShell [-ls <path>] [-lsr <path>] [-du <path>] [-dus <path>] [-count <path>] [-mv <src> <dst>] [-cp <src> <dst>] [-rm <path>] [-rmr <path>] [-expunge] [-put <localsrc>... <dst>] [-copyfromlocal <localsrc>... <dst>] [-movefromlocal <localsrc>... <dst>] [-get [-ignorecrc] [-crc] <src> <localdst>] [-getmerge <src> <localdst> [addnl]] [-cat <src>] [-text <src>] [-copytolocal [-ignorecrc] [-crc] <src> <localdst>] [-movetolocal [-crc] <src> <localdst>] [-mkdir <path>] [-setrep [-R] [-w] <rep> <path/file>] [-touchz <path>] [-test -[ezd] <path>] [-stat [format] <path>] [-tail [-f] <file>] [-chmod [-R] <MODE[,MODE]... OCTALMODE> PATH...] [-chown [-R] [OWNER][:[GROUP]] PATH...] [-chgrp [-R] GROUP PATH...] [-help [cmd]]

11 Hadoop 실행 - MapReduce 1. 작성한 MapReduce class 들을 jar 파일로묶기 $ jar -cvf wordcount.jar -C wordcount_classes/. 2. DFS에 input 디렉토리생성및 input file들복사 $ bin/hadoop dfs mkdir wordcount/input $ bin/hadoop dfs ls wordcount /user/jshan/wordcount/input <dir> $ bin/hadoop dfs put file01.txt wordcount/input // file01.txt는로컬파일 $ bin/hadoop dfs put file02.txt wordcount/input // file02.txt는로컬파일 $ bin/hadoop dfs ls wordcount/input /user/jshan/wordcount/input/file01.txt <r 1> /user/jshan/wordcount/input/file02.txt <r 1> $ bin/hadoop dfs cat wordcount/input/file01.txt Hello World Bye World $ bin/hadoop dfs cat wordcount/input/file02.txt Hello Hadoop Goodbye Hadoop 3. MapReduce 실행 $ bin/hadoop jar wordcount.jar org.myorg.wordcount /user/jshan/wordcount/input /user/jshan/wordcount/output $ bin/hadoop dfs -cat /user/jshan/wordcount/output/part Bye 1 Goodbye 1 Hadoop 2 Hello 2 World 2 Source:

12 Hadoop MapReduce 프로그래밍 1. package org.myorg; import java.io.ioexception; 4. import java.util.*; import org.apache.hadoop.fs.path; 7. import org.apache.hadoop.conf.*; 8. import org.apache.hadoop.io.*; 9. import org.apache.hadoop.mapred.*; 10. import org.apache.hadoop.util.*; public class WordCount { public static class Map extends MapReduceBase implements Mapper<LongWritable, Text, Text, IntWritable> { 15. private final static IntWritable one = new IntWritable(1); 16. private Text word = new Text(); public void map(longwritable key, Text value, OutputCollector<Text, IntWritable> output, Reporter reporter) throws IOException { 19. String line = value.tostring(); 20. StringTokenizer tokenizer = new StringTokenizer(line); 21. while (tokenizer.hasmoretokens()) { 22. word.set(tokenizer.nexttoken()); 23. output.collect(word, one); 24. } 25. } 26. }

13 Hadoop MapReduce 프로그래밍 28. public static class Reduce extends MapReduceBase implements Reducer<Text, IntWritable, Text, IntWritable> { 29. public void reduce(text key, Iterator<IntWritable> values, OutputCollector<Text, IntWritable> output, Reporter reporter) throws IOException { 30. int sum = 0; 31. while (values.hasnext()) { 32. sum += values.next().get(); 33. } 34. output.collect(key, new IntWritable(sum)); 35. } 36. } public static void main(string[] args) throws Exception { 39. JobConf conf = new JobConf(WordCount.class); 40. conf.setjobname("wordcount"); conf.setoutputkeyclass(text.class); 43. conf.setoutputvalueclass(intwritable.class); conf.setmapperclass(map.class); 46. conf.setcombinerclass(reduce.class); 47. conf.setreducerclass(reduce.class); conf.setinputformat(textinputformat.class); 50. conf.setoutputformat(textoutputformat.class); FileInputFormat.setInputPaths(conf, new Path(args[0])); 53. FileOutputFormat.setOutputPath(conf, new Path(args[1])); JobClient.runJob(conf); 57. } 58. }

14 Hadoop DFS 관리도구

15 Hadoop MapReduce 관리도구

슬라이드 1

슬라이드 1 Hadoop 기반 규모확장성있는패킷분석도구 충남대학교데이터네트워크연구실이연희 yhlee06@cnu.ac.kr Intro 목차 인터넷트래픽측정 Apache Hadoop Hadoop 기반트래픽분석시스템 Hadoop을이용한트래픽분석예제 - 2- Intro 트래픽이란 - 3- Intro Data Explosion - 4- Global Trend: Data Explosion