[B! hadoop] takuma510ã®ãƒ–ãƒƒã‚¯ãƒžãƒ¼ã‚¯

takuma510 id:takuma510

hadoopã«é–¢ã™ã‚‹takuma510ã®ãƒ–ãƒƒã‚¯ãƒžãƒ¼ã‚¯ (18)

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1å°æ§‹æˆã®Hadoopã‚’30åˆ†ã§è©¦ã—ã¦ã¿ã‚‹(Ubuntu + Cloudera)
(å‚è€ƒ) Clouderaç¤¾ã®Hadoopãƒ‘ãƒƒã‚±ãƒ¼ã‚¸ã®æƒ…å ± http://archive.cloudera.com/docs/ å¿…è¦ãªã‚‚ã® ãƒ»Ubuntuã‚„debianã®Linuxç’°å¢ƒ1å°(æ‰‹å…ƒã§ã¯Ubuntu Server 11.04/10.04/9.10/8.04, debian 5ã‚ãŸã‚Šã§è©¦ã—ã¦ã„ã¾ã™ãŒã€ä»–ãƒãƒ¼ã‚¸ãƒ§ãƒ³ã§ã‚‚å¤§ä¸ˆå¤«ã ã¨æ€ã„ã¾ã™) ãƒ»ã‚¤ãƒ³ã‚¿ãƒ¼ãƒãƒƒãƒˆæŽ¥ç¶š ãƒ»Sun(Oracle)ã®Javaãƒ‘ãƒƒã‚±ãƒ¼ã‚¸(aptã§ã‚¤ãƒ³ã‚¿ãƒ¼ãƒãƒƒãƒˆã‹ã‚‰ã‚¤ãƒ³ã‚¹ãƒˆãƒ¼ãƒ«) ãƒ»Clouderaç¤¾ã®CDH3ã®Hadoopãƒ‘ãƒƒã‚±ãƒ¼ã‚¸(aptã§ã‚¤ãƒ³ã‚¿ãƒ¼ãƒãƒƒãƒˆã‹ã‚‰ã‚¤ãƒ³ã‚¹ãƒˆãƒ¼ãƒ«) ä½œæ¥æ‰‹é † 1. ã‚¤ãƒ³ã‚¹ãƒˆãƒ¼ãƒ«: Linuxç’°å¢ƒã«ã¦ã€rootã§ä½œæ¥ã—ã¾ã™ã€‚ sudo su 1-1. Sun(Oracle)ã®Javaã‚’å…¥ã‚Œã¾ã™ã€‚(Sun(Oracle)ã®ã‚‚ã®ãŒå¿…è¦ã§ã™ã€‚) â€» ã“ã“ã§ã€ubuntu 10ã‚„11ã®äººã¯/etc
takuma510 2014/02/28
hadoop

ubuntu
ãƒªãƒ³ã‚¯
Integrating R with Cloudera Impala for Real-Time Queries on Hadoop
Introduction Cloudera Impala supports low-latency, interactive queries on Hadoop data sets either stored in Hadoop Distributed File System (HDFS) or HBase, the distributed NoSQL database for Hadoop. Impalaâ€™s notion is to use Hadoop as a storage engine but move away from MapReduce algorithms. Instead, Impala uses distributed queries, a concept inherited from massive parallel processing databases. A
takuma510 2013/12/10
Rè¨€èªž

hadoop
ãƒªãƒ³ã‚¯
Cloudera | ãƒã‚¤ãƒ–ãƒªãƒƒãƒ‰ãƒ‡ãƒ¼ã‚¿ã‚«ãƒ³ãƒ‘ãƒ‹ãƒ¼
ãƒ‡ãƒ¼ã‚¿ã‚’ä¿¡é ¼ã—ã€AI ã‚’ä¿¡é ¼ã™ã‚‹ ä¿¡é ¼ã§ãã‚‹ãƒ‡ãƒ¼ã‚¿ã€ä¿¡é ¼ã§ãã‚‹ãƒ¢ãƒ‡ãƒ«ã€ä¿¡é ¼ã§ãã‚‹ AI ã‚’å®Ÿç¾ã™ã‚‹ãŸã‚ã«ã€ã“ã‚Œã»ã©å¤šãã®ã‚¯ãƒ©ã‚¦ãƒ‰ã®ã•ã¾ã–ã¾ãªãƒ‡ãƒ¼ã‚¿ã‚¿ã‚¤ãƒ—ã‚’ç®¡ç†ã§ãã€ã‚ªãƒ¼ãƒ—ãƒ³ãƒ‡ãƒ¼ã‚¿ã®ã‚¤ãƒŽãƒ™ãƒ¼ã‚·ãƒ§ãƒ³ã¨å¤§è¦æ¨¡å±•é–‹ã«å¯¾å¿œã§ãã‚‹ãƒ—ãƒ©ãƒƒãƒˆãƒ•ã‚©ãƒ¼ãƒ ã¯ä»–ã«ã‚ã‚Šã¾ã›ã‚“ã€‚
takuma510 2013/11/07
hadoop
ãƒªãƒ³ã‚¯
Batch and Stream processing with SQL
1. Batch and Stream processing with SQL 2013/11/07 Cloudera World Tokyo 2013 TAGOMORI Satoshi @tagomoris LINE Corp. 13å¹´11æœˆ7æ—¥æœ¨æ›œæ—¥
takuma510 2013/11/07
hadoop

hive
ãƒªãƒ³ã‚¯
OSXã«Apache Hadoopã‚’ã‚¤ãƒ³ã‚¹ãƒˆãƒ¼ãƒ« - memorandum
Macboook Airãªã©ã®ãƒãƒ¼ã‚«ãƒ«ç’°å¢ƒã§Hadoopã‚’æ°—è»½ã«è©¦ã—ãŸã„ã‚±ãƒ¼ã‚¹ã‚‚ã‚ã‚‹ã‹ã¨æ€ã„ã¾ã™ã€‚OSX 1å°ã ã‘ã§Hadoopã‚’ä½¿ã†å ´åˆã®ç’°å¢ƒæ§‹ç¯‰æ‰‹é †ã‚’ã¾ã¨ã‚ã¾ã—ãŸã€‚ å‚è€ƒ - å…¬å¼ã‚µã‚¤ãƒˆ:Single Node Setup ç’°å¢ƒ OSX 10.8.4 Apache Hadoop 1.1.2 Java 1.6 ã‚¤ãƒ³ã‚¹ãƒˆãƒ¼ãƒ«æ‰‹é † Homebrewã§Hadoopã‚’ã‚¤ãƒ³ã‚¹ãƒˆãƒ¼ãƒ«ã—ã¾ã™ã€‚ brew install hadoop sshèªè¨¼ç”¨ã®éµã‚’ä½œæˆã—ã¾ã™ã€‚ ssh-keygen -t rsa -P "" cat ~/.ssh/id_rsa.pub >> ~/.ssh/authorized_keys ã€Œã‚·ã‚¹ãƒ†ãƒ ç’°å¢ƒè¨å®šã€ã®ã€Œå…±æœ‰ã€ã§ã€ãƒªãƒ¢ãƒ¼ãƒˆãƒã‚°ã‚¤ãƒ³ã‚’ã‚ªãƒ³ã«ã—ã¾ã™ã€‚ localhostã«ãƒ‘ã‚¹ãƒ¯ãƒ¼ãƒ‰ãªã—ã§sshãƒã‚°ã‚¤ãƒ³ã§ãã‚‹ã“ã¨ã‚’ç¢ºèªã—ã¾ã™ã€‚ ssh localhost è¨å®šãƒ•ã‚¡ã‚¤ãƒ«ã‚’ä¿®æ£ã—ã¾ã™ã€‚
takuma510 2013/11/07
hadoop

OSX
ãƒªãƒ³ã‚¯
RHadoop ã®ç´¹ä»‹
10. > small.ints = to.dfs(1:10) > out = mapreduce(input = small.ints, map = function(k,v) keyval(k, k^2)) > res = from.dfs(out) > colres <- do.call('rbind', lapply(res,"[[",2)) > t(colres) [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [1,] 1 4 9 16 25 36 49 64 81 100 > groups = to.dfs(rbinom(32, n = 50, prob = 0.4)) > out = mapreduce(input = groups, reduce = function(k,vv) keyval(k, length(vv
takuma510 2013/11/05
Rè¨€èªž

hadoop
ãƒªãƒ³ã‚¯
Using R with Hadoop
R and Hadoop are changing the way organizations manage and utilize big data. Think Big Analytics and Revolution Analytics are helping clients plan, build, test and implement innovative solutions based on the two techno logies that allow clients to analyze data in new ways; exposing new insights for the business. Join us as Jeffrey Breen explains the core techno logy concepts and illustrates how to u
takuma510 2013/09/18
Rè¨€èªž

hadoop
ãƒªãƒ³ã‚¯
Mahoutã§ä½“æ„Ÿã™ã‚‹æ©Ÿæ¢°å¦ç¿’ã®å®Ÿè·µ è¨˜äº‹ä¸€è¦§ | gihyo.jp
ç¬¬2å›žã€Œã‚ã‚‹å•†å“ã¨ã„ã£ã—ã‚‡ã«ã‚ˆãå£²ã‚Œã‚‹å•†å“ã¯ä½•ã‹ï¼Ÿã€ã‚’è¦‹ã¤ã‘ã‚‹ã«ã¯ã€€ï½žãƒžãƒ¼ã‚±ãƒƒãƒˆãƒ»ãƒã‚¹ã‚±ãƒƒãƒˆåˆ†æžã®è€ƒãˆæ–¹ ã‚„ã¾ã‹ã¤ 2013-03-06
takuma510 2013/09/13
æ©Ÿæ¢°å¦ç¿’

mahout

Hadoop
ãƒªãƒ³ã‚¯
Storage infrastructure using HBase behind LINE messages
The document discusses NHN Japan's use of HBase for the LINE messaging platform's storage infrastructure. Some key points: - HBase is used to store tens of billions of message rows per day for LINE, achieving sub-10ms response times and high availability through dual clusters. - The presentation covers their experience migrating HBase clusters between data centers online, handling NameNode failure
takuma510 2013/01/27
hadoop

hbase
ãƒªãƒ³ã‚¯
The Netflix Tech Blog: Hadoop Platform as a Service in the Cloud
by Sriram Krishnan and Eva Tse, Cloud Big Data Platform Hadoop has become the de facto standard for managing and processing hundreds of terabytes to petabytes of data. At Netflix, our Hadoop-based data warehouse is petabyte-scale, and growing rapidly. However, with the big data explosion in recent times, even this is not very novel anymore. Our architecture, however, is unique as it enables us to
takuma510 2013/01/11
hadoop

aws
ãƒªãƒ³ã‚¯
Evernote Blog ï¿½ Blog Archive ï¿½ New updates to Web and Windows
ì—ë²„ë…¸íŠ¸ì— ëê°€ ìƒˆë¡œì›Œìš”?ì—ë²„ë…¸íŠ¸ì—ì„œ ë¬´ìŠ¨ ì¼ì´ ì¼ì–´ë‚˜ê³ ìžˆëŠ”ì§€ ê¶ê¸ˆí•˜ì‹ ê°€ìš”? ì•„ëž˜ì˜ ê¸°ì‚¬ë“¤ì„ í™•ì¸í•˜ì—¬ ìš°ë¦¬ê°€ ìž‘ì—… ì¤‘ì¸ í¥ë¯¸ë¡œìš´ ê²ƒë“¤ì„ ëª¨ë‘ ë³¼ ìˆ˜ ìžˆìŠµë‹ˆë‹¤. ìƒˆë¡œìš´ ì†Œì‹ë ˆê±°ì‹œ ë²„ì „ Evernote ì•± ì‚¬ìš© ì¤‘ì§€2024ë…„ 3ì›” 26ì¼, ì €í¬ëŠ” ë ˆê±°ì‹œ ë²„ì „ Evernote ì•±ì— ìž‘ë³„ì„ ê³ í•©ë‹ˆë‹¤. v10 ì´ì „ì˜ Evernote ê²½í—˜ì„ ë‹¨ì¼í™”í•˜ë©´ ë³´ì•ˆ ìˆ˜ì¤€ì„ í¬ê²Œ ë†’ì´ê³ ë” ë¹ ë¥¸ ê°œë°œì„ ìœ„í•´ ë” ë§Žì€ ìžì›ì„ íˆ¬ìž…í• ìˆ˜ ìžˆìŠµë‹ˆë‹¤. ë” ì½ê¸° 14ê°€ì§€ ì£¼ìš” ê¸°ëŠ¥ì´ ì´ì œ ëª¨ë“ ì‚¬ìš©ìžì—ê²Œ ì œê³µë©ë‹ˆë‹¤ì´ ì¤‘ìš”í•œ Evernote ê¸°ëŠ¥ë“¤ì€ ê²€ìƒ‰, ì²¨ë¶€ ê´€ë¦¬, ë…¸íŠ¸ ì•¡ì„¸ìŠ¤ ë“± í•µì‹¬ì ì¸ ì œí’ˆ ì„±ëŠ¥ì„ ë†’ì—¬ì¤ë‹ˆë‹¤. ì´ì œ ëˆ„êµ¬ë‚˜ ê·¸ ê¸°ëŠ¥ì„ ì‚¬ìš©í•´ Evernoteì˜ ìž ìž¬ì„±ì„ ìµœëŒ€í•œ í™œìš©í• ìˆ˜ ìžˆìŠµë‹ˆë‹¤.
takuma510 2012/12/13
Hadoop
ãƒªãƒ³ã‚¯
ãƒ‘ã‚¿ãƒ¼ãƒ³ã§ã‚ã‹ã‚‹Hadoop MapReduce ãƒ“ãƒƒã‚°ãƒ‡ãƒ¼ã‚¿ã®ãƒ‡ãƒ¼ã‚¿å‡¦ç†å…¥é–€ - wyukawa's diary
ãƒ‘ã‚¿ãƒ¼ãƒ³ã§ã‚ã‹ã‚‹Hadoop MapReduce ãƒ“ãƒƒã‚°ãƒ‡ãƒ¼ã‚¿ã®ãƒ‡ãƒ¼ã‚¿å‡¦ç†å…¥é–€ (NEXTâ€ONE) ä½œè€…: ä¸‰æœ¨å¤§çŸ¥å‡ºç‰ˆç¤¾/ãƒ¡ãƒ¼ã‚«ãƒ¼: ç¿”æ³³ç¤¾ç™ºå£²æ—¥: 2012/08/28ãƒ¡ãƒ‡ã‚£ã‚¢: å¤§åž‹æœ¬è³¼å…¥: 1äºº ã‚¯ãƒªãƒƒã‚¯: 49å›žã“ã®å•†å“ã‚’å«ã‚€ãƒ–ãƒã‚° (5ä»¶) ã‚’è¦‹ã‚‹ Amazonã®ãƒ¬ãƒ“ãƒ¥ãƒ¼ã§ã¯è‘—è€…ç´¹ä»‹ãŒã²ã©ã„ã¨ã„ã†ã“ã¨ã§æ˜Ÿã²ã¨ã¤ã«ãªã£ã¦ã„ãŸæœ¬ã€‚ å€‹äººçš„ã«ã¯å…¨ãæ°—ã«ãªã‚‰ãªã‹ã£ãŸãªã€‚ ã€Œãˆã€è‘—è€…ç´¹ä»‹ãŒãã‚“ãªã«ã²ã©ã„ã®ã‹ã€‚ã¦ã“ã¨ã¯å°‘ãªãã¨ã‚‚æŸæ°ã®jQuery Mobileæœ¬ã‚ˆã‚Šã™ã”ã„ã‚“ã ã‚ˆãªã€‚ãã‚Œãªã‚‰ã‚ã‚‹æ„å‘³æ¥½ã—ã¿ã ã€‚ã©ã‚Œã©ã‚Œè‘—è€…ç´¹ä»‹ã ã‘ã§ã‚‚ç«‹ã¡èªã¿ã—ã¦ã¿ã‚ˆã†ã‹ãªã€ã¨ã‹æ€ã£ã¦æœŸå¾…ã™ã‚‹ã¨è‚©é€ã‹ã—ã ã¨æ€ã„ã¾ã™ï½—ã€€å‰²ã¨æ™®é€šã ã¨æ€ã†ã—ã€‚ ã•ã¦å†…å®¹ã®ã»ã†ã§ã™ãŒåƒ•ãŒã±ã‚‰ã£ã¨è¦‹ãŸé™ã‚Šè‰¯ã„æœ¬ã ã¨æ€ã„ã¾ã™ã€‚ ãŸã ã—Hadoopã®å…¥é–€æ›¸ã§ã¯ç„¡ã„æ°—ãŒã—ã¾ã™ã€‚ã©ã¡ã‚‰ã‹ã¨ã„ã†ã¨2å†Šç›®ä»¥é™ã«èªã‚€æœ¬ã€‚ ãã—ã¦ã‚¿ã‚¤ãƒˆãƒ«ã‹ã‚‰ã¯ã‚ã‹ã‚Š
takuma510 2012/11/08
hadoop
ãƒªãƒ³ã‚¯
ãƒ“ãƒƒã‚°ãƒ‡ãƒ¼ã‚¿å‡¦ç†ã®å¸¸è˜ã‚’Javaã§èº«ã«ã¤ã‘ã‚‹
Hadoopã‚’ã¯ã˜ã‚ã€Javaè¨€èªžã‚’ä½¿ã£ã¦æ§‹ç¯‰ã•ã‚Œã‚‹ã“ã¨ãŒå¤šã„ã€Œãƒ“ãƒƒã‚°ãƒ‡ãƒ¼ã‚¿ã€å‡¦ç†ã®ãŸã‚ã®ãƒ•ãƒ¬ãƒ¼ãƒ ãƒ¯ãƒ¼ã‚¯ï¼ãƒ©ã‚¤ãƒ–ãƒ©ãƒªã‚’ç´¹ä»‹ã—ãªãŒã‚‰ã€å¤§é‡ãƒ‡ãƒ¼ã‚¿ã‚’æ´»ç”¨ã™ã‚‹ãŸã‚ã®æŠ€è¡“ã®å¸¸è˜ã‚’èº«ã«ä»˜ã‘ã¦ã„ãé€£è¼‰ æ¤œç´¢ã‚¨ãƒ³ã‚¸ãƒ³ã®å¸¸è˜ã‚’Apache Solrã§èº«ã«ã¤ã‘ã‚‹ ãƒ“ãƒƒã‚°ãƒ‡ãƒ¼ã‚¿å‡¦ç†ã®å¸¸è˜ã‚’Javaã§èº«ã«ã¤ã‘ã‚‹ï¼ˆ1ï¼‰ã€€æ¤œç´¢ã‚¨ãƒ³ã‚¸ãƒ³ã®æ¦‚è¦ã¨å‘¨è¾ºæŠ€è¡“ã«ã¤ã„ã¦è§£èª¬ã—ä¸»ãªåˆ©ç”¨æ–¹æ³•ã‚„å®Ÿè£…ã‚½ãƒ•ãƒˆã‚¦ã‚§ã‚¢ã€Solrã®åˆ©ç”¨æ–¹æ³•ãªã©ã‚’ç°¡å˜ã«ç´¹ä»‹ã—ã¾ã™
takuma510 2012/06/05
programming

hadoop

java
ãƒªãƒ³ã‚¯
å¹³æˆï¼’ï¼‘å¹´åº¦ç”£å¦é€£æºã‚½ãƒ•ãƒˆã‚¦ã‚§ã‚¢å·¥å¦å®Ÿè·µäº‹æ¥ï¼ˆé«˜ä¿¡é ¼ã‚¯ãƒ©ã‚¦ãƒ‰å®Ÿç¾ç”¨ã‚½ãƒ•ãƒˆã‚¦ã‚§ã‚¢é–‹ç™ºï¼ˆåˆ†æ•£åˆ¶å¾¡å‡¦ç†æŠ€è¡“ç‰ã«ä¿‚ã‚‹ãƒ‡ãƒ¼ã‚¿ã‚»ãƒ³ã‚¿ãƒ¼ã®é«˜ä¿¡é ¼åŒ–ã«å‘ã‘ãŸå®Ÿè¨¼äº‹æ¥ï¼‰ï¼‰äº‹æ¥æˆæžœå ±å‘Šæ›¸
takuma510 2012/03/10
hadoop
ãƒªãƒ³ã‚¯
NTTãƒ‡ãƒ¼ã‚¿ã®Hadoopå ±å‘Šæ›¸ãŒã™ã”ã‹ã£ãŸ - ç§‘å¦ã¨éžç§‘å¦ã®è¿·å®®
æ¥ç•Œãƒˆãƒƒãƒ— ã®ã‚¨ãƒ³ã‚¿ãƒ¼ãƒ—ãƒ©ã‚¤ã‚º Hadoop ä¼æ¥ Cloudera ã«å…¥ç¤¾ã—ã¾ã—ãŸ http://www.cloudera.co.jp/ ä»Šå¹´ã®6æœˆã«ã€ã€Œå¹³æˆï¼’ï¼‘å¹´åº¦ ç”£å¦é€£æºã‚½ãƒ•ãƒˆã‚¦ã‚§ã‚¢å·¥å¦å®Ÿè·µäº‹æ¥å ±å‘Šæ›¸ã€ã¨ã„ã†ãƒ‰ã‚ãƒ¥ãƒ¡ãƒ³ãƒˆç¾¤ãŒçµŒç”£çœã‹ã‚‰å…¬è¡¨ã•ã‚Œã¾ã—ãŸã€‚ ãã®ã†ã¡ã®ä¸€ã¤ã«ã€NTTãƒ‡ãƒ¼ã‚¿ã«å§”è¨—ã•ã‚ŒãŸHadoopã«é–¢ã™ã‚‹å®Ÿè¨¼å®Ÿé¨“ã®å ±å‘Šæ›¸ãŒã‚ã‚Šã¾ã—ãŸã®ã§ã€ä»Šæ›´ãªãŒã‚‰èªã‚“ã§ã¿ã‚‹ã“ã¨ã«ã—ã¾ã—ãŸã€‚ Hadoopç•Œéšˆã®äººã¯ã‚‚ã†ã¿ã‚“ãªã¨ã£ãã«èªã‚“ã§ã‚‹ã®ã‹ã‚‚ã—ã‚Œã¾ã›ã‚“ã‘ã©ã€‚ http://www.meti.go.jp/policy/mono_info_service/joho/downloadfiles/2010software_research/clou_dist_software.pdf ã€Œé«˜ä¿¡é ¼ã‚¯ãƒ©ã‚¦ãƒ‰å®Ÿç¾ç”¨ã‚½ãƒ•ãƒˆã‚¦ã‚§ã‚¢é–‹ç™ºï¼ˆåˆ†æ•£åˆ¶å¾¡å‡¦ç†æŠ€è¡“ç‰ã«ä¿‚ã‚‹ãƒ‡ãƒ¼ã‚¿ã‚»ãƒ³ã‚¿ãƒ¼é«˜ä¿¡é ¼åŒ–ã«å‘ã‘ãŸå®Ÿè¨¼äº‹æ¥ï¼‰ã€ã¨ã„ã†
takuma510 2012/03/03
hadoop
ãƒªãƒ³ã‚¯
GitHub - parthatalukdar/junto: This toolkit consists of implementations of various graph-based semi-supervised learning (SSL) algorithms. Currently, three algorithms are implemented: Gaussian Random Fields (GRF), Adsorption, and Modified Adsorption (MAD).
---------------------------------------------------------- The Junto Label Propagation Toolkit Author: Partha Pratim Talukdar (partha@talukdar.net) Contributors: Jason Baldridge (jbaldrid@mail.utexas.edu) ---------------------------------------------------------- Introduction ============ This package provides an implementation of the Adsorption and Modified Adsorption (MAD) algorithms described i
takuma510 2012/01/29
hadoop

machine learning
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ç©ºé–“æœ¨ã‚’åˆ©ç”¨ã—ãŸé–¢é€£äº‹ä¾‹ã®æŠ½å‡ºæŠ€è¡“ - Preferred Networks Research & Development
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takuma510 2012/01/27
machine learning

hadoop
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