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

ButterflyFish id:ButterflyFish

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ButterflyFish 2017/09/16
nosql

hadoop

book
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Data Analytics Service Company and Its Ruby Usage
Treasure Data is a data analytics service company that makes heavy use of Ruby in its platform and services. It uses Ruby for components like Fluentd (log collection), Embulk (data loading), scheduling, and its Rails-based API and console. Java and JRuby are also used for components involving Hadoop and Presto processing. The company's architecture includes collectors that ingest data, a PlazmaDB
ButterflyFish 2015/12/12
ruby

hadoop

analytics
ãƒªãƒ³ã‚¯
ç¬¬12å›žã€€è¤‡æ•°ã®ãƒ—ãƒã‚»ã‚¹ã«ãŠã‘ã‚‹å”èª¿å‹•ä½œã®ãŸã‚ã®ä»•çµ„ã¿â”€ã‚³ãƒ¼ãƒ‡ã‚£ãƒãƒ¼ã‚·ãƒ§ãƒ³ | gihyo.jp
ã¯ã˜ã‚ã« å‰å›žã¯ã€åˆ†æ•£ã‚·ã‚¹ãƒ†ãƒ æŠ€è¡“ã‚’åŸºæœ¬ã¨ã™ã‚‹è€éšœå®³æ€§ã®ãŸã‚ã®ä»•çµ„ã¿ã¨ã—ã¦ã€ãƒ¬ãƒ—ãƒªã‚±ãƒ¼ã‚·ãƒ§ãƒ³ã¨ãƒã‚®ãƒ³ã‚°ã«ã¤ã„ã¦è¿°ã¹ã¾ã—ãŸã€‚ä»Šå›žã¯ã€åˆ†æ•£ã‚·ã‚¹ãƒ†ãƒ ã«ãŠã„ã¦è¤‡æ•°ã®ãƒ—ãƒã‚»ã‚¹ãŒå”èª¿ã—ã¦å‹•ä½œã™ã‚‹ãŸã‚ã®ä»•çµ„ã¿ã§ã‚ã‚‹ã‚³ãƒ¼ãƒ‡ã‚£ãƒãƒ¼ã‚·ãƒ§ãƒ³ã«ã¤ã„ã¦ã€ãã®æ¦‚è¦ã‚’èª¬æ˜Žã—ã¾ã™ã€‚ ã‚³ãƒ¼ãƒ‡ã‚£ãƒãƒ¼ã‚·ãƒ§ãƒ³ã¨ã¯ ä¸¦åˆ—ãƒ‡ãƒ¼ã‚¿å‡¦ç†ç³»ã«ãŠã‘ã‚‹ã‚³ãƒ¼ãƒ‡ã‚£ãƒãƒ¼ã‚·ãƒ§ãƒ³ã¯ã€è¤‡æ•°ã®ãƒ—ãƒã‚»ã‚¹é–“ã«ãŠã„ã¦ã€å”èª¿ã—ã¦å‹•ä½œã‚’ã™ã‚‹ã€ã¾ãŸã¯ã€åŒæ„ã‚’å–ã‚‹ãŸã‚ã®æŠ€è¡“ã§ã™ã€‚ã™ãªã‚ã¡ã€ã‚³ãƒ¼ãƒ‡ã‚£ãƒãƒ¼ã‚·ãƒ§ãƒ³ã‚’è¡Œã†ã“ã¨ã«ã‚ˆã‚Šã€ä¸¦åˆ—ãƒ‡ãƒ¼ã‚¿å‡¦ç†ç³»ã«ãŠã‘ã‚‹è¤‡æ•°ã®ãƒ—ãƒã‚»ã‚¹ãŒåŒã˜ç›®çš„ï¼ˆã‚‚ã—ãã¯å€¤ï¼‰ã‚’å…±æœ‰ã—ã€å„ã€…ãŒãã®ç›®çš„ã®ã‚‚ã¨ã§ä½•ã‚‰ã‹ã®å‡¦ç†ã‚’å®Ÿè¡Œã§ãã‚‹ã‚ˆã†ã«ãªã‚Šã¾ã™ã€‚å½“è©²æŠ€è¡“ã¯ã€ãŸã¨ãˆã°ã€è¤‡æ•°ã®ãƒ—ãƒã‚»ã‚¹ã«ãŠã‘ã‚‹çŠ¶æ…‹ã‚„ãƒ¬ãƒ—ãƒªã‚«é–“ã®å€¤ã®ä¸€è²«æ€§ã‚’ä¿ã¤ãŸã‚ã«ç”¨ã„ã‚‰ã‚Œã¾ã™ã€‚ ã‚³ãƒ¼ãƒ‡ã‚£ãƒãƒ¼ã‚·ãƒ§ãƒ³ã«ãŠã„ã¦ã¯ã€å¤šãã®å ´åˆã€æ¬¡ã®ã“ã¨ã‚’å‰æã¨ã—ã¦è°è«–ã•ã‚Œã‚‹ãŸã‚ã€ç‰¹ã«æ˜Žç¤ºçš„ã«è¨€åŠã—ãªã„é™ã‚Šã€æœ¬é€£è¼‰ã«ãŠã„
ButterflyFish 2015/10/21
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ã¾ã¨ã‚

architecture

hadoop
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ç¬¬1å›žã€€ãªãœã€Hadoopã¯ã©ã®ã‚ˆã†ã«å‹•ãã®ã‹ã€ã‚’å¦ã¶ã®ã‹ | gihyo.jp
ã¯ã˜ã‚ã« ãƒ“ãƒƒã‚°ãƒ‡ãƒ¼ã‚¿è§£æžã®ãŸã‚ã®ã‚·ã‚¹ãƒ†ãƒ åŸºç›¤ã¨ã—ã¦ã€Hadoopã‚’ã¯ã˜ã‚ã¨ã™ã‚‹ã‚ªãƒ¼ãƒ—ãƒ³ã‚½ãƒ¼ã‚¹ã®ãƒ‡ãƒ¼ã‚¿å‡¦ç†ã‚½ãƒ•ãƒˆã‚¦ã‚§ã‚¢ï¼ˆãƒ‡ãƒ¼ã‚¿å‡¦ç†ç³»ï¼‰ãŒåºƒãåˆ©ç”¨ã•ã‚Œã¤ã¤ã‚ã‚Šã¾ã™ãŒã€å½“è©²ãƒ‡ãƒ¼ã‚¿å‡¦ç†ç³»ã‚’ã™ã§ã«åˆ©ç”¨ã—ã¦ã„ã‚‹ã€ã‚‚ã—ãã¯åˆ©ç”¨ã®æ¤œè¨Žã‚’ã—ã¦ã„ã‚‹èªè€…ã®æ–¹ã€…ã®ä¸ã«ã¯ã€ãŸã¨ãˆã°ä»¥ä¸‹ã®ã‚ˆã†ãªå•é¡Œã‚’æŠ±ãˆã¦ã„ã‚‹æ–¹ãŒå°‘ãªã‹ã‚‰ãšã„ã‚‰ã£ã—ã‚ƒã‚‹ã®ã§ã¯ãªã„ã§ã—ã‚‡ã†ã‹ã€‚ ãƒ‡ãƒ¼ã‚¿å‡¦ç†ç³»ã®ä½¿ã„æ–¹ã¯ãªã‚“ã¨ãªãã‚ã‹ã‚‹ãŒã€ãã®å†…éƒ¨ã‚’ã‚ã¾ã‚Šç†è§£ã§ãã¦ã„ãªã„ã€‚ã¾ãŸã¯ã€å†…éƒ¨ã®å‹•ä½œåŽŸç†ãŒã‚ˆãã‚ã‹ã‚‰ãªã„ã®ã§ã€æœ¬æ ¼çš„ã«ä½¿ã†æ°—ã«ãªã‚‰ãªã„ã€‚ åŒæ§˜ã®ç›®çš„ã‚’é”æˆã™ã‚‹è¤‡æ•°ã®ãƒ‡ãƒ¼ã‚¿å‡¦ç†ç³»ã«ãŠã„ã¦ã€ã©ã‚Œã‚’ä½¿ã£ã¦è‰¯ã„ã‹ãŒã‚ˆãã‚ã‹ã‚‰ãªã„ã€‚ã¾ãŸã¯ã€é©åˆ‡ã«ä½¿ã„åˆ†ã‘ã‚‰ã‚Œã¦ã„ãªã„æ°—ãŒã™ã‚‹ã€‚ãŸã¨ãˆã°ã€ã©ã®ã‚ˆã†ãªå ´åˆã«Hadoopã‚’ç”¨ã„ã¦ã€ã©ã®ã‚ˆã†ãªå ´åˆã«åŒé¡žã®ãƒ‡ãƒ¼ã‚¿å‡¦ç†ç³»ã§ã‚ã‚‹Impalaã‚„Sparkã‚’ç”¨ã„ã‚Œã°è‰¯ã„ã‹ãŒâ€œâ æ˜Žç¢ºã«â â€ã‚ã‹ã‚‰ãªã„ã€‚ ã“ã®ã‚ˆã†ãªå•é¡Œã‚’è§£æ±ºã™ã‚‹ã«ã¯ã€
ButterflyFish 2015/04/01
ã‚ã¨ã§èªã‚€

hadoop

architecture
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ã‚·ãƒ³ãƒ—ãƒ«ãªæ§‹é€ ã§æ‹¡å¼µæ€§ã¨å¯ç”¨æ€§ã‚’ä¿æŒã™ã‚‹KVSã€ŒRiakã€--Hadoopã¨ã‚‚èžåˆ
å°åˆ·ã™ã‚‹ ãƒ¡ãƒ¼ãƒ«ã§é€ã‚‹ ãƒ†ã‚ã‚¹ãƒˆ HTML é›»åæ›¸ç± PDF ãƒ€ã‚¦ãƒ³ãƒãƒ¼ãƒ‰ ãƒ†ã‚ã‚¹ãƒˆ é›»åæ›¸ç± PDF ã‚¯ãƒªãƒƒãƒ—ã—ãŸè¨˜äº‹ã‚’Myãƒšãƒ¼ã‚¸ã‹ã‚‰èªã‚€ã“ã¨ãŒã§ãã¾ã™ ã€ŒRiakã€ã¯NoSQLã®ä¸ã§ã‚‚ã‚ãƒ¼ãƒãƒªãƒ¥ãƒ¼ã‚¹ãƒˆã‚¢ï¼ˆKVSï¼‰ã«åˆ†é¡žã•ã‚Œã€ãƒªãƒ¬ãƒ¼ã‚·ãƒ§ãƒŠãƒ«ãƒ‡ãƒ¼ã‚¿ãƒ™ãƒ¼ã‚¹ï¼ˆRDBï¼‰ã®ã‚ˆã†ãªè¤‡é›‘ãªãƒ‡ãƒ¼ã‚¿ã®å–ã‚Šæ‰±ã„ã‚„ãƒˆãƒ©ãƒ³ã‚¶ã‚¯ã‚·ãƒ§ãƒ³ã®ä»•çµ„ã¿ã‚’æŒãŸãªã„ä¸€æ–¹ã§ã€ãã®ã‚·ãƒ³ãƒ—ãƒ«ãªãƒ‡ãƒ¼ã‚¿æ§‹é€ ã‚’ãƒ™ãƒ¼ã‚¹ã«ã—ãŸé«˜ã„æ‹¡å¼µæ€§ã¨å¯ç”¨æ€§ã‚’ç‰¹å¾´ã¨ã—ã¦ã„ã¾ã™ã€‚ã“ã“ã§ã¯Riakã®ãƒ¡ãƒªãƒƒãƒˆã¨æŠ€è¡“çš„ãªæ¦‚è¦ã‚’èª¬æ˜Žã—ã€ã„ãã¤ã‹ä»£è¡¨çš„ãªãƒ¦ãƒ¼ã‚¹ã‚±ãƒ¼ã‚¹ã‚’å–ã‚Šä¸Šã’ã¦ã”ç´¹ä»‹ã—ã¾ã™ã€‚ ã‚³ãƒ³ã‚·ã‚¹ãƒ†ãƒ³ãƒˆãƒãƒƒã‚·ãƒ¥ã§åˆ†æ•£ã•ã›ã‚‹ Riakã¯ç±³Basho Techno logiesã«ã‚ˆã£ã¦2008å¹´ã«é–‹ç™ºãŒå§‹ã‚ã‚‰ã‚Œã€ã‚ªãƒ¼ãƒ—ãƒ³ã‚½ãƒ¼ã‚¹ã‚½ãƒ•ãƒˆã‚¦ã‚§ã‚¢ï¼ˆOSSï¼‰ã¨ã—ã¦å®Ÿè£…ãŒé€²ã‚ã‚‰ã‚Œã¦ãã¾ã—ãŸã€‚Riakã¯åŸºæœ¬çš„ãªã‚¢ãƒ¼ã‚ãƒ†ã‚¯ãƒãƒ£ã¨ã—ã¦Amazon Web Servicesã®ã€ŒDynamo
ButterflyFish 2015/03/23
ã‚ã¨ã§èªã‚€

Infrastructure

technology

hadoop
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ãƒã‚°åŽé›†ãƒ•ãƒ¬ãƒ¼ãƒ ãƒ¯ãƒ¼ã‚¯ã®æ–°ãƒãƒ¼ã‚¸ãƒ§ãƒ³ã€ŒFlumeNGã€
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ButterflyFish 2015/01/25
ã‚ã¨ã§èªã‚€

hadoop

architecture

technology
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Hadoop on docker å‰ç·¨ - White scenery @showyou, hatena
å¾ŒåŠæ›¸ãã¾ã—ãŸ(Â Hadoop on docker å¾Œç·¨ - White scenery @showyou, hatenaÂ ) æ›¸ãå¿˜ã‚Œã¦ã„ã¾ã—ãŸãŒã€ã“ã®è¨˜äº‹ Spark, SQL on Hadoop etc. Advent Calendar 2014 - QiitaÂ ã®23æ—¥ç›®ã«ãªã‚Šã¾ã™ã€‚ ã¯ã˜ã‚ã¯Pivotalç¤¾ã®å•†ç”¨SQL on Hadoopã§ã‚ã‚‹HAWQã®è©±ã§ã‚‚ã—ã‚ˆã†ã‹ã¨æ€ã„ã¾ã—ãŸãŒã€å…ˆæ—¥æŽ²è¼‰ã—ãŸã‚¹ãƒ©ã‚¤ãƒ‰ã«å¤§ä½“æ›¸ã„ã¦ã„ã‚‹ã®ã§èªã‚“ã§é ‚ã‘ã‚Œã°ã¨æ€ã„ã¾ã™ã€‚ ä»Šå›žã¯Hadoop on Dockerã«ã¤ã„ã¦ã§ã‚‚è©±ã—ã¾ã™ã€‚ æœ€è¿‘ã¯Hadoopã‚’å‹•ã‹ã™ã®ã«dockerã‚’ä½¿ã†å‹•ããŒå‡ºã¦ã„ã¾ã™ã€‚ dockerã§çµ„ã¿ä¸Šã’ã‚‹ãƒ¡ãƒªãƒƒãƒˆã¨ã—ã¦ã¯ã€ 1. VMã«æ¯”ã¹ã‚‹ã¨é€Ÿåº¦ãŒé€Ÿã„(ã¨è€ƒãˆã‚‰ã‚Œã‚‹) 2. å„ç¤¾é…å¸ƒã—ã¦ã„ã‚‹VMã¨é•ã„Dockerfileè¾ºã‚Šã‚’è¦‹ã‚Œã°ä½•ã‚„ã£ã¦ã‚‹ã‹ç†è§£ã§ãã‚‹ 3. imageã‚’ç®¡ç†ã™ã‚Œ
ButterflyFish 2014/12/24
hadoop

docker

linux

ã‚ã¨ã§èªã‚€
ãƒªãƒ³ã‚¯
http://agilecatcloud.com/2014/07/28/big-data-%E3%81%AE%E8%AA%BF%E6%9F%BB%EF%BC%9Agoogle-%E3%81%AE-dataflow-%E3%81%AF%E3%80%81mapreduce-%E3%81%AE%E6%AD%A3%E5%BD%93%E3%81%AA%E7%B6%99%E6%89%BF%E8%80%85%E3%81%AB%E3%81%AA%E3%82%8A%E5%BE%97/
ButterflyFish 2014/07/28
google

hadoop

ã‚ã¨ã§èªã‚€
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ButterflyFish 2014/07/15
hadoop

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ButterflyFish 2014/06/24
hadoop

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development
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Googleã®è™Žã®åã€ŒBigQueryã€ã‚’Fluentdãƒ¦ãƒ¼ã‚¶ãƒ¼ãŒä½¿ã‚ãªã„ç†ç”±ãŒãªããªã£ãŸç†ç”± #gcpja - Qiita
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ButterflyFish 2014/05/19
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fluentd

google

hadoop
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ButterflyFish 2012/02/14
hadoop
ãƒªãƒ³ã‚¯
Mapreduce2.0 - æ€¥ãŒã°å›žã‚Œã€é¸ã¶ãªã‚‰è¿‘é“
æ¬¡ä¸–ä»£Hadoopã®é–‹ç™ºãŒé€²ã‚“ã§ã„ã‚‹ã€‚ç¾çŠ¶ã®æŽ¨ç§»ã§ã¯ã€å°‘ãªãã¨ã‚‚åˆ†æ•£ã‚¯ãƒ©ã‚¦ãƒ‰ã§ã®ã€ŒOSSã‚¤ãƒ³ãƒ•ãƒ©ã€ã¨ã—ã¦ã¯HadoopãŒæœ€æœ‰åŠ›å€™è£œã§ã‚ã‚‹ã“ã¨ã¯é–“é•ã„ãªã„ã€‚ã‚¯ãƒ©ã‚¦ãƒ‰ä¸Šã§ã®åˆ†æ•£å‡¦ç†åŸºç›¤ã§ã®æŠ€è¡“ç«¶äº‰ã§ã¯Googleã‚„AmazonãŒç›¸å½“æŠœãã‚“ã§ã„ã‚‹ç¾åœ¨ã€ãã‚Œã«å¯¾æŠ—ã—ã†ã‚‹å¯èƒ½æ€§ãŒã‚ã‚‹OSSã¯Hadoopã®æ½®æµã®å»¶é•·ç·šä¸Šã«ã—ã‹è€ƒãˆã‚‰ã‚Œãªã„ã€‚ãã®å½¢ã¨ã—ã¦Hadoop-MapReduce2.0ãŒã‚ã‚‹ã‚ˆã†ã«è¦‹ãˆã‚‹ã€‚ç¾åœ¨ã®çŠ¶æ…‹ã§è‡ªåˆ†ãªã‚Šã®æ¬¡ä¸–ä»£Hadoopã®ç†è§£ã‚’ã¾ã¨ã‚ã¦ãŠãã€‚åŸºæœ¬çš„ã«å…¨éƒ¨ã¯è¦‹åˆ‡ã‚Œã¦ã„ãªã„ã®ã§ã€ãã®ã‚ãŸã‚Šã¯ã‚ã—ã‹ã‚‰ãšã€‚åŸºæœ¬çš„ã«æ¬¡ä¸–ä»£Hadoopã®ä»•çµ„ã¿ã¯å¤§ããäºŒã¤ã®è¦ç´ ã‹ã‚‰ãªã‚‹ ç¾åœ¨ã®ã¨ã“ã‚ã®æŸ±ã¯HDFSã¨Mapreduce2.0ã®äºŒã¤ã ã€‚ ã¾ãšMapReduceã€‚ã“ã‚Œã¯å¾“æ¥ã®ã€ŒMapReduceã€ã¨ã„ã†ã‚‚ã®ã‹ã‚‰ã¯ã»ã©é ã„ã€‚ã‚€ã—ã‚ã€Œä»»æ„ã€ã®åˆ†æ•£å‡¦ç†å®Ÿè¡Œãƒ•ãƒ¬ãƒ¼ãƒ ãƒ¯ãƒ¼ã‚¯ã«ãŸã„ã—ã¦ã€é©åˆ‡ãªãƒªã‚½ãƒ¼ã‚¹ã‚’
ButterflyFish 2011/10/21
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hadoop

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[ANNOUNCEMENT] Yahoo focusing on Apache Hadoop, discontinuing â€œThe Yahoo Distribution of Hadoopâ€ Â· Yahoo! Hadoop Blog
[ANNOUNCEMENT] Yahoo focusing on Apache Hadoop, discontinuing â€œThe Yahoo Distribution of Hadoopâ€ Hi Folks, I'm pleased to announce that after some reflection, Yahoo! has decided to discontinue the "The Yahoo Distribution of Hadoop" and focus on Apache Hadoop. We plan to remove all references to a Yahoo distribution from our website (developer.yahoo.com/hadoop), close our github repo (yahoo.github.
ButterflyFish 2011/02/22
yahoo

hadoop

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The Next Generation of Apache Hadoop MapReduce Â· Yahoo! Hadoop Blog
Overview In the Big Data business running fewer larger clusters is cheaper than running more small clusters. Larger clusters also process larger data sets and support more jobs and users. The Apache Hadoop MapReduce framework has hit a scalability limit around 4,000 machines. We are developing the next generation of Apache Hadoop MapReduce that factors the framework into a generic resource schedu
ButterflyFish 2011/02/22
hadoop

architecture

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Apache Nutchâ„¢
Nutch is a highly extensible, highly scala ble, matured, production-ready Web crawler which enables fine grained configuration and accomodates a wide variety of data acquisition tasks. Scala ble Relying on Apache Hadoopâ„¢ data structures, Nutch is great for batch processing large data volumes but can also be tailored to smaller jobs. Pluggable Out of the box Nutch offer powerful plugins i.e., parsing
ButterflyFish 2011/01/28
apache

hadoop
ãƒªãƒ³ã‚¯
ãã‚ãã‚Hadoopã«ã¤ã„ã¦ã²ã¨ã“ã¨è¨€ã£ã¦ãŠãã‹ - nokunoã®æ—¥è¨˜
ã‚‚ã†ã“ã®æ‰‹ã®è©±é¡Œã¯å‡ºå°½ãã—ãŸæ„ŸãŒã‚ã‚Šã¾ã™ãŒã€æœ€è¿‘Hadoopã«ã¤ã„ã¦è€ƒãˆã‚‹ã“ã¨ãŒå¤šã„ã®ã§ã€ã‚¨ãƒ³ãƒˆãƒªã«ã—ã¦ã¿ã¾ã™ã€‚ãªãŠã€ã“ã“ã§ã¯ãƒ™ãƒ¼ã‚·ãƒƒã‚¯ãªMapReduce+HDFSã®ã“ã¨ã‚’Hadoopã¨å‘¼ã¶ã“ã¨ã«ã—ã¾ã™ã€‚ Hadoopã¨ã¯Hadoopã¨ã¯è¨€ã‚ãšã¨çŸ¥ã‚ŒãŸGoogleã®MapReduce/GFSã®ã‚ªãƒ¼ãƒ—ãƒ³ã‚½ãƒ¼ã‚¹ã®ã‚¯ãƒãƒ¼ãƒ³ã§ã™ã€‚MapReduceã§ã¯ãƒ—ãƒã‚°ãƒ©ãƒžã¯Mapã¨Reduceã¨ã„ã†2ã¤ã®é–¢æ•°ã‚’æ›¸ãã ã‘ã§ã€ä¸¦åˆ—åˆ†æ•£å‡¦ç†ã‚’ã™ã‚‹ã“ã¨ãŒã§ãã¾ã™ã€‚ã“ã‚Œã¯(1) ãƒ‡ãƒ¼ã‚¿ã‚’å®Ÿéš›ã«æŒã¤ãƒžã‚·ãƒ³ã«ãƒ—ãƒã‚°ãƒ©ãƒ ã‚’é…å¸ƒã™ã‚‹ (2) Mapã¨Reduceã‚’ã¤ãªãShuffleãƒ•ã‚§ãƒ¼ã‚ºã§ã‚ãƒ¼ã‚’ã‚°ãƒ«ãƒ¼ãƒ—åŒ–ã—ã¦ã‚½ãƒ¼ãƒˆã™ã‚‹ã€(3) éšœå®³æ™‚ã®ãƒ•ã‚§ãƒ¼ãƒ«ã‚ªãƒ¼ãƒãƒ¼ã‚„ãƒ¬ãƒ—ãƒªã‚±ãƒ¼ã‚·ãƒ§ãƒ³ã€ã¨ã„ã£ãŸå‡¦ç†ã‚’ãƒ•ãƒ¬ãƒ¼ãƒ ãƒ¯ãƒ¼ã‚¯å´ãŒå—ã‘æŒã¤ã“ã¨ã«ã‚ˆã£ã¦ã€ãƒ—ãƒã‚°ãƒ©ãƒžå´ã®è² æ‹…ã‚’æ¸›ã‚‰ã™ã‚‚ã®ã§ã™ã€‚GFSã«å¯¾å¿œã™ã‚‹HDFSã«ã¯ãƒ•ã‚¡ã‚¤ãƒ«ã‚’ã‚¯ãƒ©ã‚¹ã‚¿ã«åˆ†æ•£ã—ã¦ä¿å˜
ButterflyFish 2011/01/05
hadoop

google
ãƒªãƒ³ã‚¯
Hadoopã‚’è¡¨è¨ˆç®—ã®ã‚ˆã†ã«ä½¿ãˆã‚‹ã€ŒInfoSphere BigInsightsã€ã€IBMãŒç™ºè¡¨
IBMãŒHadoopã‚’ãƒ™ãƒ¼ã‚¹ã«ã—ãŸãƒ‡ãƒ¼ã‚¿åˆ†æžã‚½ãƒ•ãƒˆã‚¦ã‚§ã‚¢ã€ŒInfoSphere BigInsightsã€ã‚’ã€å…ˆé€±ãƒãƒ¼ãƒžã§é–‹å‚¬ã•ã‚ŒãŸåŒç¤¾ã®ã‚¤ãƒ™ãƒ³ãƒˆã€ŒIBM Information On Demand Europe 2010ã€ã§ç™ºè¡¨ã—ã¾ã—ãŸã€‚ InfoSphere BigInsightsã¯ã€Apache Hadoopã‚’ãƒ™ãƒ¼ã‚¹ã«ã—ãŸåˆ†æžã‚¨ãƒ³ã‚¸ãƒ³ã®ã€ŒBigInsights Coreã€ã¨ã€ãã®ä¸Šã§å¤§è¦æ¨¡ãƒ‡ãƒ¼ã‚¿ã‚’Webãƒ–ãƒ©ã‚¦ã‚¶ã‚’åˆ©ç”¨ã—ã¦ã‚¹ãƒ—ãƒ¬ãƒƒãƒ‰ã‚·ãƒ¼ãƒˆã®ã‚ˆã†ãªãƒ¦ãƒ¼ã‚¶ãƒ¼ã‚¤ãƒ³ã‚¿ãƒ¼ãƒ•ã‚§ã‚¤ã‚¹ã§åˆ†æžã‚’è¡Œãˆã‚‹ã€ŒBigSheetsã€ã®2ã¤ã‹ã‚‰æ§‹æˆã•ã‚Œã¾ã™ã€‚ BigSheetsã§è¡¨è¨ˆç®—ã®ã‚ˆã†ã«ãƒ‡ãƒ¼ã‚¿åˆ†æž BigInsights Coreã®ãƒ™ãƒ¼ã‚¹ã¨ãªã£ã¦ã„ã‚‹Hadoopã¯ã€æ•°ç™¾ä¸‡ä»¶ãªã©å¤§è¦æ¨¡ãªãƒ‡ãƒ¼ã‚¿ã‚’å¤§é‡ã®ã‚µãƒ¼ãƒã§åˆ†æ•£å‡¦ç†ã™ã‚‹ãŸã‚ã®ã‚ªãƒ¼ãƒ—ãƒ³ã‚½ãƒ¼ã‚¹ã®ã‚½ãƒ•ãƒˆã‚¦ã‚§ã‚¢ã§ã™ã€‚Yahoo!ã‚„Facebookã€æ—¥æœ¬ã§ã¯Cookpad
ButterflyFish 2010/05/25
hadoop

cloud

ibm
ãƒªãƒ³ã‚¯
Hadoop AvatarNode High Availability
Our Use-Case The Hadoop Distributed File System's (HDFS) NameNode is a single point of falure. This has been a major stumbling block in using HDFS for a 24x7 type of deployment. It has been a topic of discussion among a wide circle of engineers. I am part of a team that is operating a cluster of 1200 nodes and a total size of 12 PB. This cluster is currently running hadoop 0.20. The NameNode is co
ButterflyFish 2010/04/24
hadoop

software

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The Economistã§ã®æƒ…å ±ãƒžãƒã‚¸ãƒ¡ãƒ³ãƒˆç‰¹é›† - ç§‘å¦ã¨éžç§‘å¦ã®è¿·å®®
ä»Šé€±ã®The Economistã¯æƒ…å ±ãƒžãƒã‚¸ãƒ¡ãƒ³ãƒˆç‰¹é›†ã§ã™ã€‚ ä¸–ã®ä¸ã«ã‚ãµã‚Œã‚‹è†¨å¤§ãªãƒ‡ãƒ¼ã‚¿ã‚„æƒ…å ±ã‚’æ‰±ã†ãŸã‚ã®ãƒˆãƒ”ãƒƒã‚¯ãŒã€ã‹ãªã‚Šã®åˆ†é‡ã§æ›¸ã‹ã‚Œã¦ã„ã¾ã™ã€‚ ç‰¹ã«å‰æçŸ¥è˜ãªãã¦ã‚‚èªã‚ã¾ã™ãŒã€ã‚³ãƒ³ãƒ”ãƒ¥ãƒ¼ã‚¿ã«é–¢ã™ã‚‹åŸºç¤ŽçŸ¥è˜ãŒã‚ã‚Œã°ã‚ˆã‚Šæ¥½ã—ã‚ã¾ã™ã€‚ ã¡ãªã¿ã«ç§ã®å‚åŠ ã—ã¦ã„ã‚‹The Economistèªã‚€éšŠ(é€šç§°ã‚¨ã‚³éšŠ)ã§ã¯ã€ä»Šé€±æœ¨æ›œæ—¥ã« New rules for big data ã¨ Show me ã‚’èªã‚€äºˆå®šã§ã™ã€‚ Data, data everywhere Information has gone from scarce to superabundant. That brings huge new benefits, says Kenneth Cukier (interviewed here)â€•but also big headaches http://www.economist.com/spe
ButterflyFish 2010/03/02
it

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hadoop
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