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3. è¬›ç¾©å†…å®¹ ï® åºè«– - ä¸¦åˆ—ãƒ‡ãƒ¼ã‚¿ãƒ™ãƒ¼ã‚¹ã®å‰ã« ï® ä¸¦åˆ—å‡¦ç†ã®åŸºç¤Ž ï® ï® ä¸¦åˆ—å‡¦ç†ã®Terminology ä¸¦åˆ—è¨ˆç®—æ©Ÿã‚¢ãƒ¼ã‚ãƒ†ã‚¯ãƒãƒ£ ï® ä¸¦åˆ—ãƒ‡ãƒ¼ã‚¿ãƒ™ãƒ¼ã‚¹ã®ã‚¢ãƒ¼ã‚ãƒ†ã‚¯ãƒãƒ£ ï® ãƒ‡ãƒ¼ã‚¿ãƒ™ãƒ¼ã‚¹å‡¦ç†ã®ä¸¦åˆ—åŒ– ï® çµåˆå‡¦ç†ã®é«˜é€ŸåŒ– ï® ï® ï® ï® ä¸¦åˆ—ãƒãƒƒã‚·ãƒ¥çµåˆ ä¸¦åˆ—ã‚½ãƒ¼ãƒˆ ãƒ‘ãƒ¼ãƒ†ã‚£ã‚·ãƒ§ãƒ‹ãƒ³ã‚°æ‰‹æ³• å¤šé‡çµåˆã‚„è¨ˆç®—æ©Ÿé–“ã®ãƒ‡ãƒ¼ã‚¿äº¤æ›ã§ç™ºç”Ÿã™ã‚‹å•é¡Œ ï® MapReduceã«ã‚ˆã‚‹é–¢ä¿‚æ¼”ç®—ã®ä¸¦åˆ—å‡¦ç† 3 4. ãƒ‡ãƒ¼ã‚¿ãƒ™ãƒ¼ã‚¹é–‹ç™ºã®æµã‚Œ ï® Coddã®è«–æ–‡: 1970å¹´ ï® ï® ï® ï® System Rã‚„Ingres: 70å¹´ä»£ä¸ç›¤ Oracle, IBM DB2, Ingres: 80å¹´ä»£åºç›¤ ä¸¦åˆ—ãƒ‡ãƒ¼ã‚¿ãƒ™ãƒ¼ã‚¹ã®éš†ç››: 80å¹´ä»£å¾ŒåŠ ï® ï® A Relational Model of Data for Large Shared Data Banks, Communications of ACM å•†ç”¨
dann 2014/02/16
mapreduce
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DSN2012
dann 2012/03/26
mapreduce

performance
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VLDBâ€™11ã®èªã‚€äºˆå®šã®è«–æ–‡ãƒªã‚¹ãƒˆ - maropuã®ãƒ¡ãƒ¢å¢“å ´
VLDB'11ã‹ã‚‰èªã‚€äºˆå®šã®è«–æ–‡ãƒªã‚¹ãƒˆã‚’Pick-upï¼ˆciteulikeã«ç™»éŒ²äºˆå®šãƒªã‚¹ãƒˆï¼‰ DBç³»/ãƒ¢ãƒ€ãƒ³ãƒãƒ¼ãƒ‰ã‚¦ã‚§ã‚¢ç³»/åˆ†æ•£ã‚·ã‚¹ãƒ†ãƒ ç³»/ã‚°ãƒ©ãƒ•ã‚¢ãƒ«ã‚´ãƒªã‚ºãƒ ç³»ã‚’ä¸å¿ƒã« http://www.vldb.org/2011/?q=node/28 HYRISE - A Main Memory Hybrid Storage Engine Martin Grund (Hasso-Plattner-Institut), Jens Krueger (Hasso-Plattner-Institut), Hasso Plattner (Hasso-Plattner Institute), Alexander Zeier (Hasso-Plattner Institute), Philippe Cudre-Mauroux (MIT CSAIL), Samuel Madden (MIT) Fast Sparse
dann 2012/03/14
mapreduce

research
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MapReduceã®ãƒ‘ã‚¿ãƒ¼ãƒ³ã€ã‚¢ãƒ«ã‚´ãƒªã‚ºãƒ ã€ãã—ã¦ãƒ¦ãƒ¼ã‚¹ã‚±ãƒ¼ã‚¹ - ãã—ã ã®Hatena
Ilya Katsovæ°ã«ã‚ˆã‚‹ã€ŒMapReduce Patterns, Algorithms, and Use Casesã€ã®ç¿»è¨³ http://highlyscala ble.wordpress.com/2012/02/01/mapreduce-patterns/ (ä¸‹æ›¸ãã«å…¥ã‚Œã¦æŽ¨æ•²ã™ã‚‹ã¤ã‚‚ã‚ŠãŒã€ãªã‚“ã‹å…¬é–‹ã•ã‚Œã¦ã—ã¾ã£ã¦ã„ãŸã®ã§ã€ã‚ã¨ã§ã„ã‚ã„ã‚ä¿®æ£ã™ã‚‹ã¨æ€ã„ã¾ã™) February 1, 2012 ã“ã®è¨˜äº‹ã§ã¯ã€Webã‚„ç§‘å¦è«–æ–‡ã§è¦‹ã‚‰ã‚Œã‚‹ç•°ãªã‚‹ãƒ†ã‚¯ãƒ‹ãƒƒã‚¯ã®ä½“ç³»çš„ãªè¦–ç‚¹ã‚’ä¸Žãˆã‚‹ãŸã‚ã«ã€æ•°ã€…ã®MapReduceãƒ‘ã‚¿ãƒ¼ãƒ³ã¨ã‚¢ãƒ«ã‚´ãƒªã‚ºãƒ ã‚’ã¾ã¨ã‚ãŸã€‚ ã„ãã¤ã‹ã®å®Ÿç”¨çš„ãªã‚±ãƒ¼ã‚¹ã‚¹ã‚¿ãƒ‡ã‚£ã‚‚æä¾›ã—ã¦ã„ã‚‹ã€‚ ã™ã¹ã¦ã®èª¬æ˜Žã¨ã‚³ãƒ¼ãƒ‰ã‚¹ãƒ‹ãƒšãƒƒãƒˆã§ã¯ã€Mapperã€Reducerã€Combinerã€Partitionaerã€ã‚½ãƒ¼ãƒ†ã‚£ãƒ³ã‚°ã«ãŠã„ã¦Hadoopã®æ¨™æº–çš„ãªMapReduceãƒ¢ãƒ‡ãƒ«ã‚’åˆ©ç”¨ã—ã¾ã™ã€‚ã“ã®ãƒ•ãƒ¬ãƒ¼
dann 2012/02/27
mapreduce

hadoop
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MapReduce Patterns, Algorithms, and Use Cases
In this article IÂ digestedÂ a number of MapReduce patterns and algorithms to give a systematic view of the different techniques that can be found on the web or scientific articles. Several practical case studies are also provided. All descriptions and code snippets use the standard Hadoopâ€™s MapReduce model with Mappers, Reduces, Combiners, Partitioners, and sorting. This framework is depicted in th
dann 2012/02/10
designpattern

mapreduce
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dann 2011/08/05
mapreduce
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NUS Computing - Home
dann 2011/06/23
mapreduce

hadoop
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Mining of Massive Datasets
The book has a new Web site www.mmds.org. This page will no longer be maintained. Your browser should be automatically redirected to the new site in 10 seconds. The book has now been published by Cambridge University Press. The publisher is offering a 20% discount to anyone who buys the hardcopy Here. By agreement with the publisher, you can still download it free from this page. Cambridge Press d
dann 2011/01/26
mapreduce
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ç¬¬2å›ž,ç¬¬3å›žMapReduceæœ¬èªæ›¸ä¼š - ç§‘å¦ã¨éžç§‘å¦ã®è¿·å®®
ç¬¬1å›žã¯ã“ã¡ã‚‰ ç¬¬2å›ž æ—¥æ™‚ 2010/09/26 19:30 - 21:00ï¼Ÿ å ´æ‰€ éƒ½å†…æŸæ‰€ æŒ‘æˆ¦è€… marqs shiumachi æ¨™çš„ Data-Intensive Text Processing with MapReduce ç¯„å›² 3ç« æ®‹ã‚Š(marqs)4ç« é€”ä¸ã¾ã§(shiumachi) ç¬¬3å›ž(take1) 10/3ã«ã‚„ã‚‹ã¯ãšã ã£ãŸãŒã€marqs ãŒä¼šå ´ã«ç€ã„ãŸã¨ãŸã‚“ã«(ãƒ”ãƒ¼)ã—ãŸã®ã§ä¸æ¢ ç¬¬3å›ž(take2) æ—¥æ™‚ 2010/10/11 19:30 - 21:00ï¼Ÿ å ´æ‰€ éƒ½å†…æŸæ‰€ æŒ‘æˆ¦è€… marqs shiumachi æ¨™çš„ Data-Intensive Text Processing with MapReduce ç¯„å›² 4ç« æ®‹ã‚Š(shiumachi)5ç« é€”ä¸ã¾ã§(marqs) Data-Intensive Text Processing with MapReduce ch
dann 2010/12/20
mapreduce

cool
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MapReduce/Bigtable for Distributed Optimization
Philosophy We strive to create an environment conducive to many different types of research across many different time scales and levels of risk. Learn more about our Philosophy Learn more
dann 2010/12/17
mapreduce

distributed
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æ©Ÿæ¢°å¦ç¿’ Ã— MapReduce - ny23ã®æ—¥è¨˜
å€‹äººçš„ãªèˆˆå‘³ã¨ã„ã†ã‚ˆã‚Šï¼Œé›‘ç”¨çµ¡ã¿ã§çœºã‚ãŸè«–æ–‡ã®ç´¹ä»‹ï¼Žæ©Ÿæ¢°å¦ç¿’ã‚¢ãƒ«ã‚´ãƒªã‚ºãƒ ã‚’ä¸¦åˆ—åˆ†æ•£åŒ–ã™ã‚‹ã¨ã„ã†è©±ãŒæœ€è¿‘æµè¡Œã£ã¦ã„ã‚‹ã‚ˆã†ã ï¼Žå…¨ç„¶ç¶²ç¾…çš„ã§ã¯ãªã„ã‘ã©ï¼Œèª°ã‹ã®å½¹ã«ç«‹ã¤ã‹ã‚‚çŸ¥ã‚Œãªã„ã®ã§ï¼Œå¹¾ã¤ã‹ãƒ¡ãƒ¢ã—ã¦ãŠãï¼Žã¾ãšå¤å…¸çš„ã«ã¯ã“ã‚Œï¼Œ Map-reduce for machine learning on multicore (NIPS 2006) å¤å…¸çš„ãªæ©Ÿæ¢°å¦ç¿’ã‚¢ãƒ«ã‚´ãƒªã‚ºãƒ ï¼ˆãƒãƒƒãƒå¦ç¿’ï¼‰ã®å¤šãã¯ï¼ŒStatistical Query Model ã§è¨˜è¿°ã§ãã¦ï¼Œãã‚Œã‚‰ã¯ summation form ã§è¨˜è¿°ã§ãã‚‹ (ã‹ã‚‰ï¼ŒMapReduce ã§ä¸¦åˆ—åŒ–ã§ãã‚‹)ï¼Žå®Ÿè£…ã¯ Mahoutï¼ŽãŸã æœ€è¿‘ã¯ï¼Œãƒãƒƒãƒã‚¢ãƒ«ã‚´ãƒªã‚ºãƒ ã§è§£ã‘ã‚‹å•é¡Œã«ã¯å¤šãã®å ´åˆå¯¾å¿œã™ã‚‹ã‚ªãƒ³ãƒ©ã‚¤ãƒ³ã‚¢ãƒ«ã‚´ãƒªã‚ºãƒ ãŒææ¡ˆã•ã‚Œã¦ã„ã¦ï¼Œãƒãƒƒãƒã‚¢ãƒ«ã‚´ãƒªã‚ºãƒ ã‚’ä¸¦åˆ—åŒ–ã™ã‚‹ã“ã¨ã®ãƒ¡ãƒªãƒƒãƒˆã¯ã‚ã¾ã‚Šç„¡ã„ï¼Žã‚ªãƒ³ãƒ©ã‚¤ãƒ³ã‚¢ãƒ«ã‚´ãƒªã‚ºãƒ ã ã¨ãƒ‘ãƒ©ãƒ¡ã‚¿ãŒé€£ç¶šçš„ã«æ›´æ–°ã•ã‚Œã‚‹ã®ã§ï¼ŒMapR
dann 2010/11/30
mapreduce
ãƒªãƒ³ã‚¯
ã€ŽReal-Time MapReduceã€ã¸ã®ã‚³ãƒ¡ãƒ³ãƒˆ
ãƒ–ãƒƒã‚¯ãƒžãƒ¼ã‚¯ã—ã¾ã—ãŸ ã“ã“ã«ãƒ„ã‚¤ãƒ¼ãƒˆå†…å®¹ãŒè¨˜è¼‰ã•ã‚Œã¾ã™ https://b.hatena.ne.jp/URLã¯spanã§å›²ã‚“ã§ãã ã•ã„ Twitterã§å…±æœ‰
dann 2010/11/13
s4

mapreduce
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Designing algorithms for Map Reduce
Since the emerging of Hadoop implementation, I have been trying to morph existing algorithms from various areas into the map/reduce model. The result is pretty encouraging and I've found Map/Reduce is applicable in a wide spectrum of application scenarios. So I want to write down my findings but then found the scope is too broad and also I haven't spent enough time to explore different probl em dom
dann 2010/11/09
mapreduce
ãƒªãƒ³ã‚¯
é–‹ç™ºãƒ¡ãƒ¢: ãƒãƒ¼ã‚«ãƒ«MapReduceã®æ€§èƒ½
Kyoto Cabinetã«MapReduceã‚’å®Ÿè£…ã—ãŸã¨ã„ã†è©±ã¯å‰å›žæ›¸ã„ãŸãŒã€ãã®Luaãƒã‚¤ãƒ³ãƒ‡ã‚£ãƒ³ã‚°ã§ã‚‚MapReduceã‚’ã‚µãƒãƒ¼ãƒˆã—ãŸã€‚ã¾ãŸã€Kyoto Tycoonã¨ãã®ã‚¹ã‚¯ãƒªãƒ—ãƒˆè¨€èªžæ‹¡å¼µã§ã‚‚MapReduceã‚’ã‚µãƒãƒ¼ãƒˆã—ãŸã€‚ä»Šå›žã¯ãã®æ€§èƒ½ã«ã¤ã„ã¦è§£èª¬ã™ã‚‹ã€‚ ãƒãƒ¼ã‚«ãƒ«MapReduceã®ãƒ„ãƒœ ä¸–ã«è¨€ã†MapReduceã¯åˆ†æ•£å‡¦ç†ã®ãƒ•ãƒ¬ãƒ¼ãƒ ãƒ¯ãƒ¼ã‚¯ã ã‘ã‚Œã©ã‚‚ã€KC/KTã®ã€Œãƒãƒ¼ã‚«ãƒ«MapReduceã€ã¯åˆ†æ•£å‡¦ç†ã‚’è¡Œã‚ãªã„ã€‚åˆ†æ•£å‡¦ç†ã‚’ã—ãªã‹ã£ãŸã‚‰ãƒ‡ãƒ¼ã‚¿å‡¦ç†èƒ½åŠ›ãŒä¸ŠãŒã‚‰ãªã„ã˜ã‚ƒãªã„ã‹ã¨æ€ã†ã‹ã‚‚ã—ã‚Œãªã„ã‘ã‚Œã©ã‚‚ã€ãã†ã¨ã‚‚é™ã‚‰ãªã„ã®ã ã€‚å‰å›žã‚‚æ›¸ã„ãŸã‘ã©ã‚‚ã€MapReduceãƒ•ãƒ¬ãƒ¼ãƒ ãƒ¯ãƒ¼ã‚¯éƒ¨åˆ†ã‚’ã†ã¾ãå®Ÿè£…ã™ã‚‹ã¨ã€æ™‚é–“åŠ¹çŽ‡ã¨ç©ºé–“åŠ¹çŽ‡ã®åŒæ–¹ã‚’å‘ä¸Šã•ã›ã‚‹ã“ã¨ãŒã§ãã‚‹ã€‚ç‰¹ã«ã‚ãƒ£ãƒƒã‚·ãƒ¥ã¨ã‚½ãƒ¼ãƒˆã®éƒ¨åˆ†ã«å·¥å¤«ãŒã‚ã‚‹ã€‚ MapReduceã¯ã€ãƒªãƒã‚¸ãƒˆãƒªå†…ï¼ˆKCã§ã¯ãƒ‡ãƒ¼ã‚¿ãƒ™ãƒ¼ã‚¹ãƒ•ã‚¡ã‚¤ãƒ«å†…ï¼‰ã®å„ãƒ¬ã‚³ãƒ¼ãƒ‰ã‹ã‚‰ã‚ãƒ¼ã¨å€¤
dann 2010/11/08
mapreduce
ãƒªãƒ³ã‚¯
S4: Distributed Stream Computing Platform
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dann 2010/11/07
java

realtime

mapreduce
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Hadoopã‚’ä½¿ã„ã“ãªã™(1)
ã¾ãšã€ 1 ã®å…¥åŠ›ãƒ•ã‚¡ã‚¤ãƒ«ã‚’åˆ†å‰²ã™ã‚‹æ–¹æ³•ã¯ã€InputFormatã‚¯ãƒ©ã‚¹ã®ã€getSplitsé–¢æ•°ã‚’ä¸Šæ›¸ãã™ã‚‹ã“ã¨ã§ã€ã‚«ã‚¹ã‚¿ãƒžã‚¤ã‚ºã§ãã¾ã™ã€‚ ã¾ãŸã€ 3 ã®InputSplitã‹ã‚‰ã€Keyã¨Valueã‚’æŠ½å‡ºã™ã‚‹å‡¦ç†ã‚‚ã€InputFormatã‚¯ãƒ©ã‚¹ã‚’é€šã˜ã¦ã‚«ã‚¹ã‚¿ãƒžã‚¤ã‚ºã§ãã¾ã™ã€‚ InputFormatã®getRecordReaderé–¢æ•°ã‚’é€šã˜ã¦ã€RecordReaderã‚¯ãƒ©ã‚¹ã‚’ç”Ÿæˆã™ã‚‹ã®ã§ã™ãŒã€ã“ã‚Œã«ä»»æ„ã®RecordReaderã‚¯ãƒ©ã‚¹ã‚’æŒ‡å®šã™ã‚Œã°OKã§ã™ã€‚ 2 ã®Mapå‡¦ç†ã§ã™ãŒã€ãƒ¦ãƒ¼ã‚¶ãŒæŒ‡å®šã—ãŸMapperã‚¯ãƒ©ã‚¹ã®å‡¦ç†ã‚’å®Ÿè¡Œã—ã¾ã™ã€‚ Mapperã‚¯ãƒ©ã‚¹ã¯ã€MapRunnerã‚¯ãƒ©ã‚¹ã‚’é€šã˜ã¦ã€åˆæœŸåŒ–å‡¦ç†ã€mapé–¢æ•°ã‚’ç¹°ã‚Šè¿”ã™éŽç¨‹ã€çµ‚äº†å‡¦ç†ã¨ã„ã£ãŸä¸€é€£ã®æµã‚Œã‚’å®Ÿè¡Œã—ã¾ã™ã€‚ MapRunnerã‚¯ãƒ©ã‚¹ã‚’ã‚«ã‚¹ã‚¿ãƒžã‚¤ã‚ºã™ã‚Œã°ã€ã“ã†ã—ãŸæµã‚Œã‚’åˆ¶å¾¡ã™ã‚‹ã“ã¨ãŒã§ãã¾ã™ã€‚ 0.20.0ã‹ã‚‰ã®æ–°ã—ã„MapRed
dann 2010/03/01
hadoop

mapreduce
ãƒªãƒ³ã‚¯
Jimmy Lin Â» Data-Intensive Text Processing with MapReduce
dann 2010/03/01
mapreduce

algorithm

hadoop

cool
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Googleã®åŸºç›¤ã‚¯ãƒãƒ¼ãƒ³ Hadoopã«ã¤ã„ã¦
2. è‡ªå·±ç´¹ä»‹ å¤ªç”°ä¸€æ¨¹ æ±äº¬å¤§å¦æƒ…å ±ç†å·¥å¦ç³»ç ”ç©¶ç§‘ã‚³ãƒ³ãƒ”ãƒ¥ãƒ¼ã‚¿ãƒ¼ç§‘å¦å°‚æ”»çŸ³å·ç ”ç©¶å®¤ M1 HPC ç³»ã®è©± ( ä¸¦åˆ—ãƒ•ã‚¡ã‚¤ãƒ«ã‚·ã‚¹ãƒ†ãƒ ) å€‹äººã‚µã‚¤ãƒˆ http://kzk9.net/ http://kzk9.net/blog/ èˆˆå‘³ OS, ãƒãƒƒãƒˆãƒ¯ãƒ¼ã‚¯ , I/O, åˆ†æ•£ã‚·ã‚¹ãƒ†ãƒ OSS çš„æ´»å‹• I was a committer of KDE, uim, SigScheme copybench? Kernel Reporter 3. ã¨ã¯ï¼Ÿ Google ã®åŸºç›¤ã‚½ãƒ•ãƒˆã‚¦ã‚§ã‚¢ã®ã‚¯ãƒãƒ¼ãƒ³ Google File System, MapReduce Yahoo Research ã® Doug Cutting æ°ãŒé–‹ç™º å…ƒã€…ã¯ Lucene ã®ã‚µãƒ–ãƒ—ãƒã‚¸ã‚§ã‚¯ãƒˆ Doug ã®åä¾›ã®æŒã£ã¦ã„ã‚‹ã¬ã„ãã‚‹ã¿ã®åå‰ Java ã§è¨˜è¿° ! 4. Google é–¢é€£ å‚è€ƒè«–æ–‡ & ã‚¹ãƒ©ã‚¤ãƒ‰ The Goog
dann 2009/06/08
google

hadoop

mapreduce
ãƒªãƒ³ã‚¯
Introduction to "Cloud Computing" (Fall 2008)
What are the lab sessions? The lectures focus on concepts and theory, but there's often quite a gap between that and actually getting your code to run. There are a lot of details that are best practiced in a hands-on/tutorial environment with peers. Rem ember to bring your laptops! The lab sessions will be loosely structured: I will discuss algorithms, share tips and tricks, answer any questi
dann 2009/05/19
cloud

mapreduce
ãƒªãƒ³ã‚¯
Amazon Elastic MapReduceã§perlã‚’ä½¿ã£ãŸå‡¦ç†ã‚’ã—ã¦ã¿ã‚‹ï¼ˆãã®3ï¼‰
Amazon Elastic MapReduceã®ä¾‹ã§å‡ºã¦ãã‚‹ã®ã¯ä»Šã¾ã§è¦‹ãŸé™ã‚Šã§ã¯ã€ã¿ã‚“ãªs3n://ã§å§‹ã¾ã‚‹S3 Native FileSystemä¸Šã«ãƒ•ã‚¡ã‚¤ãƒ«ã‚’ç½®ã„ã¦ã„ã‚‹ã€‚ http://wiki.apache.org/hadoop/AmazonS3 ã«ã‚ã‚‹ã‚ˆã†ã«ã€ã‚‚ã†ä¸€ã¤ s3://ã§å§‹ã¾ã‚‹S3 Block FileSystemã¨ã„ã†ã®ãŒã‚ã‚‹ã€‚ ã“ã‚Œã¾ã§S3fsã£ã¦è¨€ã£ã¦ãŸã‘ã©ã“ã‚Œã¯s3-fuseã¨ç´›ã‚‰ã‚ã—ã„ã—ã€åå‰ã¨ã—ã¦æ£ã—ããªã„ã®ã§S3 Block FileSystemã¨å‘¼ã¶ã¹ãã§ã—ãŸã€‚ ã§ã€ã“ã‚Œã‚’ä½¿ã„ãŸã„ã€‚ ãƒ¡ãƒªãƒƒãƒˆã¯ã€ä»¥ä¸‹ã®ã‚ˆã†ã«ç†è§£ã—ã¦ã‚‹ã€‚ ãƒ•ã‚¡ã‚¤ãƒ«ãŒãƒ–ãƒãƒƒã‚¯ã«åˆ†å‰²ã•ã‚Œã‚‹ã®ã§ã€é€šå¸¸5GBã¾ã§ã¨ã„ã†S3ã®ãƒ•ã‚¡ã‚¤ãƒ«ã‚µã‚¤ã‚ºã®åˆ¶é™ã‚’è¶…ãˆã‚‰ã‚Œã‚‹ ãƒ•ã‚¡ã‚¤ãƒ«ãŒãƒ–ãƒãƒƒã‚¯ã«åˆ†å‰²ã•ã‚Œã‚‹ã®ã§ã€HDFSã¨åŒæ§˜Hadoopã®å„jobtaskã«å‡¦ç†ã‚’åŠ¹çŽ‡ã‚ˆãåˆ†æ•£ã§ãã‚‹ ãƒ‡ãƒ¡ãƒªãƒƒãƒˆã¯ã€ãŸã¶ã‚“ã“ã‚“ãªæ„Ÿã˜
dann 2009/04/11
perl

mapreduce

aws

amazon
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