install hadoop 1.2.1 and hive 1.0.1

hadoop 2016. 3. 29. 14:34


hadoop 1.2.1 과 hive 1.0.1에 대한 설치 내용을 간략히 기술한다.



* hadoop 로컬 실행 모드 설치


1) ssh

hadoop을 로컬 실행 모드로 실행하기 위해서 ssh가 연결할 수 있는 환경이어야 한다.

./bin/start-all.sh실행시 ssh로 연결하니 ssh를 잘 연결해야 한다. 



$ ssh-keygen

$ cat ~/.ssh/id_rsa.pub | ssh localhost 'cat >> ~/.ssh/authorized_keys'

$ ssh -l 계정 localhost

또는 

$ ssh 계정@localhost

$ cat ~/.ssh/id_rsa.pub >>  ~/.ssh/known_hosts



맥 환경이면, 시스템 설정 -> 공유 -> 원격 로그인을 활성화하여 ssh접근 되게 함


2) 환경설정

export JAVA_HOME=/Library/Java/JavaVirtualMachines/jdk1.8.0_40.jdk/Contents/Home




3) 하둡 다운로드

curl http://apache.tt.co.kr/hadoop/common/hadoop-1.2.1/hadoop-1.2.1-bin.tar.gz 

설치 

mv 압축디렉토리 /usr/local/hadoop-1.2.1


4) 하둡 설정 수정 

$ vi /usr/local/hadoop-1.2.1/conf/mapred-site.xml

<configuration>

    <property>

        <name>mapred.job.tracker</name>

        <value>localhost:9001</value>

    </property>

</configuration>


$ vi /usr/local/hadoop-1.2.1/conf/hdfs-site.xml

<configuration>

    <property>

        <name>dfs.replication</name>

        <value>1</value>

    </property>

</configuration>


$ vi /usr/local/hadoop-1.2.1/conf/core-site.xml

<configuration>

    <property>

        <name>fs.default.name</name>

        <value>hdfs://localhost:9000</value>

   </property>

</configuration>



5) 환경 변수 추가

.bashrc에 다음을 추가

(만약 JAVA_HOME을 설정안했다는 로그가 출력하면, .profile에도 추가한다.)


export JAVA_HOME=/Library/Java/JavaVirtualMachines/jdk1.8.0_40.jdk/Contents/Home

export PATH=/usr/local/hadoop-1.2.1/bin:$PATH



6) 네임 노드를 포맷한다.


$ ./bin/hadoop namenode -format

만약 권한 이슈관련 에러가 발생한다면, 에러가 발생한 네임노드 디렉토리에 chmod 755 명령어를 이용한다.

(네임노드 포맷을 하지 않으면, http://localhost:50070/dfshealth.jsp 페이지를 열 수 없으며, 

logs 디렉토리의 로그 파일 에서 ipc.Client: Retrying connect to server: localhost/127.0.0.1:9000 이란 로그를 볼 수 있을 것이다.)


예) chmod 755 /tmp/hadoop/dfs/name




7) 실행


$ ./bin/start-all.sh


에러 없고, 데몬 잘 뜬 경우라면 잘된 경우이다. 




8) 확인


브라우져에서 http://localhost:50030/jobtracker.jsp를 열어서 jobtracker 페이지가 동작하는 지 확인한다.

브라우져에서 http://localhost:50070/dfshealth.jsp를 열어서 namenode 페이가 동작하는 지 확인한다.




* hive 설치 


1) hive 1.0.1 버전을 설치

http://apache.tt.co.kr/hive/hive-1.0.1/apache-hive-1.0.1-bin.tar.gz를 다운로드한다. 


압축을 풀고, /usr/local/hive-1.0.1에 복사한다. 


2) 권한 설정


hadoop fs -mkdir /tmp

hadoop fs -mkdir /user/hive/warehouse

hadoop fs -chmod go+w /tmp

hadoop fs -chmod go+w /user/hive/warehouse

hadoop fs -chmod go+w /tmp/hive



3) PATH 설정


bashrc에 PATH에 /usr/local/hive-1.0.1/bin를 추가한다. 


$ vi ~/.bashrc

export JAVA_HOME=/Library/Java/JavaVirtualMachines/jdk1.8.0_40.jdk/Contents/Home

export PATH=/usr/local/hadoop-1.2.1/bin:/usr/local/hive-1.0.1/bin:$PATH



정상적으로 동작하는지 확인한다.


hive> show tables;

OK

Time taken: 0.012 seconds

hive> select 1 + 1;

OK

2

Time taken: 0.342 seconds, Fetched: 1 row(s)





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[hive] 함수 설명 보기

hadoop 2016. 3. 28. 19:23


hive 함수를 보려면 다음 명령어를 사용한다.


SHOW FUNCTIONS; 

DESCRIBE FUNCTION 함수이름;

DESCRIBE FUNCTION EXTENDED 함수이름;

 


예)


> DESCRIBE FUNCTION  xpath_string;

OK

xpath_string(xml, xpath) - Returns the text contents of the first xml node that matches the xpath expression

Time taken: 0.006 seconds, Fetched: 1 row(s)



> DESCRIBE FUNCTION EXTENDED xpath_string;

OK

xpath_string(xml, xpath) - Returns the text contents of the first xml node that matches the xpath expression

Example:

  > SELECT xpath_string('<a><b>b</b><c>cc</c></a>','a/c') FROM src LIMIT 1;

  'cc'

  > SELECT xpath_string('<a><b>b1</b><b>b2</b></a>','a/b') FROM src LIMIT 1;

  'b1'

  > SELECT xpath_string('<a><b>b1</b><b>b2</b></a>','a/b[2]') FROM src LIMIT 1;

  'b2'

  > SELECT xpath_string('<a><b>b1</b><b>b2</b></a>','a') FROM src LIMIT 1;

  'b1b2'

Time taken: 0.01 seconds, Fetched: 10 row(s)



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[hive] 하이브는 등가 조인(equal join)만 지원한다.

hadoop 2016. 3. 25. 09:55


하이브는 현재 등가 조인(equal join)만 지원한다. 비등가 조인(non equal join)은 지원하지 않는다. 

즉 = 만 된다.(예, on a.id = b.id)


https://issues.apache.org/jira/browse/HIVE-3133



대신, 다양한 조인을 지원한다. 


JOIN, LEFT OUTER JOIN, RIGHT OUTER JOIN,FULL OUTER JOIN, CROSS JOIN






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[hive] 데이터를 하나로 합치기

hadoop 2016. 2. 29. 15:45


hive의 hql에서 하나의 테이블처럼 데이터를 하나로 합치는 방법은 다음과 같다. union all을 사용한다.



SELECT unioned.id, unioned.var1, unioned.var2

FROM (

  SELECT a.id, a.var1, a.var2

  FROM table_A a


  UNION ALL


  SELECT b.id, b.var1, b.var2

  from table_B b

) unioned;



주의할 점
1. FROM 절에 쓰는 테이블 명에 다른 alias 이름을 다르게 해줘야 한다.
2. 하위 질의의 필드명이 모두 동일해야 한다.
3. 특히 GROUP BY 절을 쓴다면, select에서 사용하는 as 절 뒷 부분이 아니라, 원래 select 원본 데이터를 기준으로 한다.


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[hive] 날짜 구하기

hadoop 2016. 2. 26. 19:21



hive에서 날짜를 구하는 방법을 작성한 예시 코드이다.


오늘 날짜를 구하려면 다음과 같이 unix_timestamp를 이용하거나 unix_timestamp와 from_unixtime() 함수를 이용한 방식이 있다. 사람이 읽을 수 있는 함수를 보려면 from_unixtime() 함수를 잘 활용한다.


> select unix_timestamp();

1456481927


> select from_unixtime(unix_timestamp());

2016-02-26 19:21:52

 

YYYYMMDD와 같은 형태로 날짜의 특정 항목만 얻고 싶다면, year(), month()등과 같은 함수를 사용한다. 


unix_timestamp을 사용하는 방식을 사용하면 에러가 발생한다. 


> select year(unix_timestamp()), month(unix_timestamp()), day(unix_timestamp()), hour(unix_timestamp()), minute(unix_timestamp()), second(unix_timestamp());

 


current_timestamp이나 current_date로 현재 날짜를 얻을 수 있다. 두 개의 큰 차이는 current_date는 hour, minute, second 함수를 함께 사용할 수 없다.



> select year(current_timestamp), month(current_timestamp), day(current_timestamp), hour(current_timestamp), minute(current_timestamp), second(current_timestamp);

 2016 2 26 19 21 3

 


> select year(current_date), month(current_date), day(current_date), hour(current_date), minute(current_date), second(current_date);

 2016 2 26 NULL NULL NULL


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[펌] hadoop streaming 기초 지식 쌓기

hadoop 2016. 2. 17. 20:50


hadoop streaming을 처음 하는 사람에게 추천하는 글



http://www.michael-noll.com/tutorials/writing-an-hadoop-mapreduce-program-in-python/




mapper.py

#!/usr/bin/env python import sys # input comes from STDIN (standard input) for line in sys.stdin: # remove leading and trailing whitespace line = line.strip() # split the line into words words = line.split() # increase counters for word in words: # write the results to STDOUT (standard output); # what we output here will be the input for the # Reduce step, i.e. the input for reducer.py # # tab-delimited; the trivial word count is 1 

print '%s\t%s' % (word, 1)




reducer.py


#!/usr/bin/env python from operator import itemgetter import sys current_word = None current_count = 0 word = None # input comes from STDIN for line in sys.stdin: # remove leading and trailing whitespace line = line.strip() # parse the input we got from mapper.py word, count = line.split('\t', 1) # convert count (currently a string) to int try: count = int(count) except ValueError: # count was not a number, so silently # ignore/discard this line continue # this IF-switch only works because Hadoop sorts map output # by key (here: word) before it is passed to the reducer if current_word == word: current_count += count else: if current_word: # write result to STDOUT print '%s\t%s' % (current_word, current_count) current_count = count current_word = word # do not forget to output the last word if needed! if current_word == word: 

print '%s\t%s' % (current_word, current_count)



실행


hadoop jar contrib/streaming/hadoop-*streaming*.jar \

-mapper ./mapper.py \

-reducer ./reducer.py \

-file ./mapper.py  \

-file ./reducer.py \ -input /user/hduser/gutenberg/* \

-output /user/hduser/gutenberg-output

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[hadoop] top n 소팅

hadoop 2016. 2. 16. 20:56


hadoop을 돌려서 키와 개수를 얻었고, 이에 대한 top n 소팅을 하고 싶다.


url별 개수별로 hadoop map-reduce를 돌려 다음과 같이 얻었다고 가정한다.


hadoop fs -text /user/google/count/2016/02/15/*


/search/test  15

/search/abc  10

/search/check  20

...





sort와 head를 그냥 사용하면 결과를 얻을 수 있다.

hadoop fs -text /user/google/count/2016/02/15/* | sort -n -k2 -r | head -n3


/search/check  20

/search/test  15

/search/abc  10





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[hadoop] scoop 쓸 때 유의사항

hadoop 2016. 2. 5. 14:58


1)


"AND \$CONDITIONS"를 WHERE 끝에 반드시 써야 한다!!! 아 삽질~


sqoop...

--query "SELECT id, name

                 FROM $db_table

                 WHERE id >=1  AND \$CONDITIONS " \

....



출처 : https://sqoop.apache.org/docs/1.4.2/SqoopUserGuide.html

If you want to import the results of a query in parallel, then each map task will need to execute a copy of the query, with results partitioned by bounding conditions inferred by Sqoop. Your query must include the token $CONDITIONS which each Sqoop process will replace with a unique condition expression. You must also select a splitting column with --split-by.

<Note>

If you are issuing the query wrapped with double quotes ("), you will have to use \$CONDITIONS instead of just $CONDITIONS to disallow your shell from treating it as a shell variable. For example, a double quoted query may look like: "SELECT * FROM x WHERE a='foo' AND \$CONDITIONS"





2) 


병렬로 돌리고 싶다면, num-mappers을 사용한다.

--num-mappers $num_mappers


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[hadoop] hadoop distcp

hadoop 2016. 2. 5. 11:59


hadoo2의 distcp를 사용하여 hdfs끼리 복사하는 예시이다.


(문서를 보면, distcp2를 설명하지만, 사실 지금은 distcp를 쓰면 자동으로 distcp2이다..)

/usr/lib/hadoop-mapreduce/hadoop-distcp-2.6.0-cdh5.5.1.jar)


$ hadoop distcp -m 12 hdfs://internal-hadoop1.google.com/user/www/score /user/www/score


m은 동시에 copy할 수 있는 map 개수인데, 12라고 적어도 12개의 mapper가 동작하지 않을 수 있다. 실제 보니까, 내부 hadoop에서 13개의 mapper가 동작했다. 


만약 특정 사용자를 owner로 하고 싶다면, HADOOP_USER_NAME를 사용한다.


$ HADOOP_USER_NAME=google hadoop distcp -m 12 hdfs://internal-hadoop1.google.com/user/www/score  /user/www/score


$ hadoop fs -ls  /user/www/score

drwxr-xr-x   - google   supergroup          0 2016-02-05 11:21 /user/www/score




만약 이미 존재하는 파일이 있으면, overwite가 되지 않는다. chown을 잘 쓰거나 -overwrite와 -delete 하려 super user 권한(hdfs)이 필요하다.


$ HADOOP_USER_NAME=hdfs hadoop fs -chown deploy  /user/www/score


$ HADOOP_USER_NAME=hdfs hadoop distcp -m 12 -overwrite -delete hdfs://internal-hadoop1.google.com/user/www/score  /user/www/score



권한이 없으면, 아래와 같이 에러가 발생한다.


Caused by: org.apache.hadoop.ipc.RemoteException(org.apache.hadoop.security.AccessControlException): Permission denied: user=deploy, access=WRITE, inode="/user/www/score":hdfs:supergroup:drwxr-xr-x



또한, 다중 복사가 가능하다.

hadoop fs -cp A디렉토리 B디렉토리 C디렉토리 하면,  C 디렉토리에 A디렉토리와 B디렉토리가 복사된다. 



출처 :

https://hadoop.apache.org/docs/r1.2.1/distcp.html

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[hadoop] 왜 hadoop2은 hadoop 요청시 mapreduce.Job: map 99% reduce 33%에서 잠깐 블럭되는 걸까?

hadoop 2016. 2. 3. 18:06


hadoop2 실행시, map 99%, reduce 33%에서 잠깐 10분을 블럭되었다가 그 이후에 처리된다.

hadoop이 왜 10분 정도 블럭되는지 조사한 내용이다.



16/02/03 17:49:35 INFO mapreduce.Job:  map 99% reduce 33%



hadoop 장비 로그는 다음과 같다. 뭔가 10분동안 블럭되는 데. spilling이라는 문구가 눈에 보인다.

중간 중간 spilling 처리 작업 로그가 있고, 그 다음에 finished spillin이 있다.


2016-02-03 16:01:46,175 INFO [Thread-13] org.apache.hadoop.mapred.MapTask: Spilling map output
2016-02-03 16:01:46,175 INFO [Thread-13] org.apache.hadoop.mapred.MapTask: bufstart = 103441438; bufend = 199707946; bufvoid = 268435456
2016-02-03 16:01:46,175 INFO [Thread-13] org.apache.hadoop.mapred.MapTask: kvstart = 25860352(103441408); kvend = 63348756(253395024); length = 29620461/16777216
2016-02-03 16:01:46,175 INFO [Thread-13] org.apache.hadoop.mapred.MapTask: (EQUATOR) 226551477 kvi 56637864(226551456)
2016-02-03 16:01:46,190 INFO [main] org.apache.hadoop.streaming.PipeMapRed: R/W/S=276100000/276093708/0 in:320301=276100000/862 [rec/s] out:320294=276093713/862 [rec/s]
2016-02-03 16:01:46,392 INFO [main] org.apache.hadoop.streaming.PipeMapRed: R/W/S=276200000/276190849/0 in:320417=276200000/862 [rec/s] out:320407=276190849/862 [rec/s]
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2016-02-03 16:02:32,332 INFO [Thread-13] org.apache.hadoop.mapred.MapTask: kvstart = 20306512(81226048); kvend = 57794916(231179664); length = 29620461/16777216
2016-02-03 16:02:32,332 INFO [Thread-13] org.apache.hadoop.mapred.MapTask: (EQUATOR) 204336112 kvi 51084024(204336096)
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2016-02-03 16:02:47,132 INFO [Thread-14] org.apache.hadoop.streaming.PipeMapRed: MRErrorThread done
2016-02-03 16:02:47,132 INFO [main] org.apache.hadoop.streaming.PipeMapRed: mapRedFinished
2016-02-03 16:02:47,133 INFO [main] org.apache.hadoop.mapred.MapTask: Starting flush of map output
2016-02-03 16:02:47,133 INFO [main] org.apache.hadoop.mapred.MapTask: Spilling map output
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2016-02-03 16:02:50,474 INFO [main] org.apache.hadoop.mapred.Merger: Merging 40 sorted segments
2016-02-03 16:02:50,479 INFO [main] org.apache.hadoop.io.compress.CodecPool: Got brand-new decompressor [.snappy]
2016-02-03 16:02:50,480 INFO [main] org.apache.hadoop.io.compress.CodecPool: Got brand-new decompressor [.snappy]
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2016-02-03 16:02:50,483 INFO [main] org.apache.hadoop.io.compress.CodecPool: Got brand-new decompressor [.snappy]
2016-02-03 16:02:50,484 INFO [main] org.apache.hadoop.io.compress.CodecPool: Got brand-new decompressor [.snappy]
2016-02-03 16:02:50,485 INFO [main] org.apache.hadoop.io.compress.CodecPool: Got brand-new decompressor [.snappy]
2016-02-03 16:02:50,486 INFO [main] org.apache.hadoop.io.compress.CodecPool: Got brand-new decompressor [.snappy]
2016-02-03 16:02:50,487 INFO [main] org.apache.hadoop.io.compress.CodecPool: Got brand-new decompressor [.snappy]
2016-02-03 16:02:50,487 INFO [main] org.apache.hadoop.io.compress.CodecPool: Got brand-new decompressor [.snappy]
2016-02-03 16:02:50,488 INFO [main] org.apache.hadoop.io.compress.CodecPool: Got brand-new decompressor [.snappy]
2016-02-03 16:02:50,489 INFO [main] org.apache.hadoop.io.compress.CodecPool: Got brand-new decompressor [.snappy]
2016-02-03 16:02:50,490 INFO [main] org.apache.hadoop.io.compress.CodecPool: Got brand-new decompressor [.snappy]
2016-02-03 16:02:50,491 INFO [main] org.apache.hadoop.io.compress.CodecPool: Got brand-new decompressor [.snappy]
2016-02-03 16:02:50,492 INFO [main] org.apache.hadoop.io.compress.CodecPool: Got brand-new decompressor [.snappy]
2016-02-03 16:02:50,493 INFO [main] org.apache.hadoop.io.compress.CodecPool: Got brand-new decompressor [.snappy]
2016-02-03 16:02:50,493 INFO [main] org.apache.hadoop.io.compress.CodecPool: Got brand-new decompressor [.snappy]
2016-02-03 16:02:50,494 INFO [main] org.apache.hadoop.io.compress.CodecPool: Got brand-new decompressor [.snappy]
2016-02-03 16:02:50,495 INFO [main] org.apache.hadoop.io.compress.CodecPool: Got brand-new decompressor [.snappy]
2016-02-03 16:02:50,496 INFO [main] org.apache.hadoop.io.compress.CodecPool: Got brand-new decompressor [.snappy]
2016-02-03 16:02:50,497 INFO [main] org.apache.hadoop.io.compress.CodecPool: Got brand-new decompressor [.snappy]
2016-02-03 16:02:50,498 INFO [main] org.apache.hadoop.io.compress.CodecPool: Got brand-new decompressor [.snappy]
2016-02-03 16:02:50,499 INFO [main] org.apache.hadoop.io.compress.CodecPool: Got brand-new decompressor [.snappy]
2016-02-03 16:02:50,500 INFO [main] org.apache.hadoop.io.compress.CodecPool: Got brand-new decompressor [.snappy]
2016-02-03 16:02:50,501 INFO [main] org.apache.hadoop.io.compress.CodecPool: Got brand-new decompressor [.snappy]
2016-02-03 16:02:50,501 INFO [main] org.apache.hadoop.io.compress.CodecPool: Got brand-new decompressor [.snappy]
2016-02-03 16:02:50,502 INFO [main] org.apache.hadoop.io.compress.CodecPool: Got brand-new decompressor [.snappy]
2016-02-03 16:02:50,503 INFO [main] org.apache.hadoop.io.compress.CodecPool: Got brand-new decompressor [.snappy]
2016-02-03 16:02:50,504 INFO [main] org.apache.hadoop.io.compress.CodecPool: Got brand-new decompressor [.snappy]
2016-02-03 16:02:50,505 INFO [main] org.apache.hadoop.io.compress.CodecPool: Got brand-new decompressor [.snappy]
2016-02-03 16:02:50,506 INFO [main] org.apache.hadoop.io.compress.CodecPool: Got brand-new decompressor [.snappy]
2016-02-03 16:02:50,507 INFO [main] org.apache.hadoop.io.compress.CodecPool: Got brand-new decompressor [.snappy]
2016-02-03 16:02:50,508 INFO [main] org.apache.hadoop.io.compress.CodecPool: Got brand-new decompressor [.snappy]
2016-02-03 16:02:50,509 INFO [main] org.apache.hadoop.io.compress.CodecPool: Got brand-new decompressor [.snappy]
2016-02-03 16:02:50,509 INFO [main] org.apache.hadoop.io.compress.CodecPool: Got brand-new decompressor [.snappy]
2016-02-03 16:02:50,510 INFO [main] org.apache.hadoop.io.compress.CodecPool: Got brand-new decompressor [.snappy]
2016-02-03 16:02:50,511 INFO [main] org.apache.hadoop.io.compress.CodecPool: Got brand-new decompressor [.snappy]
2016-02-03 16:02:50,512 INFO [main] org.apache.hadoop.io.compress.CodecPool: Got brand-new decompressor [.snappy]
2016-02-03 16:02:50,513 INFO [main] org.apache.hadoop.io.compress.CodecPool: Got brand-new decompressor [.snappy]
2016-02-03 16:02:50,514 INFO [main] org.apache.hadoop.io.compress.CodecPool: Got brand-new decompressor [.snappy]
2016-02-03 16:02:50,515 INFO [main] org.apache.hadoop.mapred.Merger: Down to the last merge-pass, with 40 segments left of total size: 11576265 bytes
2016-02-03 16:02:55,997 INFO [main] org.apache.hadoop.mapred.Merger: Merging 40 sorted segments
2016-02-03 16:02:56,017 INFO [main] org.apache.hadoop.mapred.Merger: Down to the last merge-pass, with 40 segments left of total size: 11524779 bytes
2016-02-03 16:03:01,378 INFO [main] org.apache.hadoop.mapred.Merger: Merging 40 sorted segments
2016-02-03 16:03:01,398 INFO [main] org.apache.hadoop.mapred.Merger: Down to the last merge-pass, with 40 segments left of total size: 11597690 bytes
2016-02-03 16:03:06,753 INFO [main] org.apache.hadoop.mapred.Merger: Merging 40 sorted segments
2016-02-03 16:03:06,772 INFO [main] org.apache.hadoop.mapred.Merger: Down to the last merge-pass, with 40 segments left of total size: 11584220 bytes
2016-02-03 16:03:12,195 INFO [main] org.apache.hadoop.mapred.Merger: Merging 40 sorted segments
2016-02-03 16:03:12,214 INFO [main] org.apache.hadoop.mapred.Merger: Down to the last merge-pass, with 40 segments left of total size: 11554950 bytes
2016-02-03 16:03:17,596 INFO [main] org.apache.hadoop.mapred.Merger: Merging 40 sorted segments
2016-02-03 16:03:17,615 INFO [main] org.apache.hadoop.mapred.Merger: Down to the last merge-pass, with 40 segments left of total size: 11527865 bytes
2016-02-03 16:03:22,982 INFO [main] org.apache.hadoop.mapred.Merger: Merging 40 sorted segments
2016-02-03 16:03:23,001 INFO [main] org.apache.hadoop.mapred.Merger: Down to the last merge-pass, with 40 segments left of total size: 11606423 bytes
2016-02-03 16:03:28,343 INFO [main] org.apache.hadoop.mapred.Merger: Merging 40 sorted segments
2016-02-03 16:03:28,366 INFO [main] org.apache.hadoop.mapred.Merger: Down to the last merge-pass, with 40 segments left of total size: 11530533 bytes
2016-02-03 16:03:33,597 INFO [main] org.apache.hadoop.mapred.Merger: Merging 40 sorted segments
2016-02-03 16:03:33,616 INFO [main] org.apache.hadoop.mapred.Merger: Down to the last merge-pass, with 40 segments left of total size: 11546832 bytes
2016-02-03 16:03:39,020 INFO [main] org.apache.hadoop.mapred.Merger: Merging 40 sorted segments
2016-02-03 16:03:39,043 INFO [main] org.apache.hadoop.mapred.Merger: Down to the last merge-pass, with 40 segments left of total size: 11504278 bytes
2016-02-03 16:03:44,336 INFO [main] org.apache.hadoop.mapred.Merger: Merging 40 sorted segments
2016-02-03 16:03:44,354 INFO [main] org.apache.hadoop.mapred.Merger: Down to the last merge-pass, with 40 segments left of total size: 11560124 bytes
2016-02-03 16:03:49,646 INFO [main] org.apache.hadoop.mapred.Merger: Merging 40 sorted segments
2016-02-03 16:03:49,664 INFO [main] org.apache.hadoop.mapred.Merger: Down to the last merge-pass, with 40 segments left of total size: 11507371 bytes
2016-02-03 16:03:54,891 INFO [main] org.apache.hadoop.mapred.Merger: Merging 40 sorted segments
2016-02-03 16:03:54,910 INFO [main] org.apache.hadoop.mapred.Merger: Down to the last merge-pass, with 40 segments left of total size: 11553016 bytes
2016-02-03 16:04:00,273 INFO [main] org.apache.hadoop.mapred.Merger: Merging 40 sorted segments
2016-02-03 16:04:00,291 INFO [main] org.apache.hadoop.mapred.Merger: Down to the last merge-pass, with 40 segments left of total size: 11584433 bytes
2016-02-03 16:04:05,615 INFO [main] org.apache.hadoop.mapred.Merger: Merging 40 sorted segments
2016-02-03 16:04:05,638 INFO [main] org.apache.hadoop.mapred.Merger: Down to the last merge-pass, with 40 segments left of total size: 11563511 bytes
2016-02-03 16:04:11,095 INFO [main] org.apache.hadoop.mapred.Merger: Merging 40 sorted segments
2016-02-03 16:04:11,113 INFO [main] org.apache.hadoop.mapred.Merger: Down to the last merge-pass, with 40 segments left of total size: 11543123 bytes
2016-02-03 16:04:16,364 INFO [main] org.apache.hadoop.mapred.Merger: Merging 40 sorted segments
2016-02-03 16:04:16,382 INFO [main] org.apache.hadoop.mapred.Merger: Down to the last merge-pass, with 40 segments left of total size: 11561494 bytes
2016-02-03 16:04:21,754 INFO [main] org.apache.hadoop.mapred.Merger: Merging 40 sorted segments
2016-02-03 16:04:21,772 INFO [main] org.apache.hadoop.mapred.Merger: Down to the last merge-pass, with 40 segments left of total size: 11533300 bytes
2016-02-03 16:04:27,097 INFO [main] org.apache.hadoop.mapred.Merger: Merging 40 sorted segments
2016-02-03 16:04:27,118 INFO [main] org.apache.hadoop.mapred.Merger: Down to the last merge-pass, with 40 segments left of total size: 11546776 bytes
2016-02-03 16:04:32,408 INFO [main] org.apache.hadoop.mapred.Merger: Merging 40 sorted segments
2016-02-03 16:04:32,425 INFO [main] org.apache.hadoop.mapred.Merger: Down to the last merge-pass, with 40 segments left of total size: 11524621 bytes
2016-02-03 16:04:37,744 INFO [main] org.apache.hadoop.mapred.Task: Task:attempt_1450233158783_28583_m_000000_0 is done. And is in the process of committing
2016-02-03 16:04:37,818 INFO [main] org.apache.hadoop.mapred.Task: Task 'attempt_1450233158783_28583_m_000000_0' done.
2016-02-03 16:04:37,918 INFO [main] org.apache.hadoop.metrics2.impl.MetricsSystemImpl: Stopping MapTask metrics system...
2016-02-03 16:04:37,919 INFO [main] org.apache.hadoop.metrics2.impl.MetricsSystemImpl: MapTask metrics system stopped.
2016-02-03 16:04:37,919 INFO [main] org.apache.hadoop.metrics2.impl.MetricsSystemImpl: MapTask metrics system shutdown complete.





reducer의 첫 번째 33%는 copy 단계이고, 다음 33%는 shuffle/sort 단계이고, 다음 (마지막) 33 %는 실제 reduce 작업이다.



map 태스크가 완료되면, map 태스크의 결과를 reduce 작업이 일어나는 곳으로 복사(copy)를 한다. map과 reduce는 동일 장비에서 일어나지 않기 때문에, map 태스트 작업이 완료되면, map 단계가 다 실행하기 전에 reduce 작업 단계에 일부 변경 내용을 알려 메모리를 복사시켜야 한다. 완료된 map 태스크 작업 내용이 있는 반면, 특정 map 태스크 작업 내용은 길어질 수 있다. 바로 이 부분 때문이다. 특정 map태스크가 길면 블럭이 되는 것이다.


모든 map 작업이 완료되어야 shuffle 작업을 시작한다. 또한 reduce의 전제 조건은 map에서 데이터를 다 받기 전까지 sort할 수 없기 때문이다. 



그림으로 설명하면, (출처 : http://ercoppa.github.io/HadoopInternals/AnatomyMapReduceJob.htm)

map 단계와 reduce 단계는 다음과 같다.






SPILLING의 단계는 다음과 같다.  (출처 : http://ercoppa.github.io/HadoopInternals/AnatomyMapReduceJob.htm)







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