MapReduce for Idiots
Photo by Stuart Pilbrow I'll admit it, I was intimidated by MapReduce. I'd tried to read explanations of it, but even the wonderful Joel Spolsky left me scratching my head. So I plowed ahead trying to build decent pipelines to process massive amounts of data without it. Finally my friend Andraz staged an intervention after I proudly described my latest setup: "Pete, that's Map Reduce". Sure enough
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AgentSpace ver.3 ä½è—¤ä¸€éƒŽ (å›½ç«‹æƒ…å ±å¦ç ”究所) ã¯ã˜ã‚㫠旧版AgentSpaceã¯1997年秋ã«é–‹ç™ºã•ã‚ŒãŸ Java 言語を利用ã—ãŸãƒ¢ãƒã‚¤ãƒ«ã‚¨ãƒ¼ã‚¸ã‚§ãƒ³ãƒˆã‚·ã‚¹ãƒ†ãƒ ã§ã™ã€‚ãã‚‚ãã‚‚Java言語ã®æ‰‹ç¿’ã„ã‚’å…¼ãã¦çŸæ™‚é–“ã§ä½œã£ãŸã‚·ã‚¹ãƒ†ãƒ ã§ã‚ã—ãŸãŒã€çµæžœã¨ã—ã¦å¤šãã®æ–¹ã€…ã«ã”åˆ©ç”¨é ‚ãã“ã¨ã«ãªã‚Šã¾ã—ãŸã€‚1999年以é™åŒã‚·ã‚¹ãƒ†ãƒ ã®æ›´æ–°ã¯è¡Œã£ã¦ã„ã¾ã›ã‚“ã§ã—ãŸãŒã€ã“ã“1,2å¹´ã¯å†ã³å¤šãã®å•ã„åˆã‚ã›ã‚’ã„ãŸã ã„ã¦ãŠã‚Šã¾ã—ãŸã€‚ãŸã ã€ãã®å•ã„åˆã‚ã›ã®å¤šããŒã€J2SE上ã§å‹•ä½œã™ã‚‹ã‚ˆã†ã«ã—ã¦æ¬²ã—ã„ã¨ã„ã†ã‚‚ã®ã§ã—ãŸã€‚å¤ã„システムã®ãƒ¡ãƒ³ãƒ†ãƒŠãƒ³ã‚¹ã¯ã—ãŸããªã„ã®ã§ã™ãŒã€2005å¹´5月ã«10日間ã»ã©æ¬§å·žå‡ºå¼µã‚’ã—ã¦ã„ãŸã¨ãã«æš‡ã‚’見ã¤ã‘ã¦æ–°è¦ã«æ›¸ãç›´ã—ã¾ã—ãŸã€‚ 2020年代ã«ãªã£ã¦ã‚‚利用者（å’論や修論å‘ã‘ãªã©ï¼‰ãŒãŠã‚‰ã‚Œã‚‹ã“ã¨ã‹ã‚‰ã€2020å¹´ã®å¹´æœ«ã«ãƒ¡ãƒ³ãƒ†ãƒŠãƒ³ã‚¹ã‚’è¡Œã„ã¾ã—ãŸã€‚ã¾ãŸã€UDPマルãƒã‚ャストを利用ã—ã¦ã€è¿‘å ´ã®A
Cloud9: A MapReduce Library for Hadoop
This page describes the code used to run experiments in the following paper: Jimmy Lin and Michael Schatz. Design Patterns for Efficient Graph Algorithms in MapReduce. Proceedings of the 2010 Workshop on Mining and Learning with Graphs Workshop (MLG-2010), July 2010, Washington, D.C. There's code in Cloud9 that illustrates three different design patterns for graph algorithms in MapReduce using Pag
An Introduction to Hadoop - O'Reilly Media Free, Live Events
An Introduction to Hadoop Date: This event took place live on July 16 2009 Presented by: Tom White, Christophe Bisciglia Duration:Â Approximately 60 minutes. Cost:Â Free Questions? Please send email to Tweet Description: In this webcast, Cloudera founder Christophe Bisciglia and O'Reilly author Tom White will provide an introduction to Hadoop/MapReduce, the open source project that allows organizati
blog.katsuma.tv
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Exploring Erlang with Map/Reduce
Under the category of "Concurrent Oriented Programming", Erlang has got some good attention recently due to some declared success from Facebook engineers of using Erlang in large scale applications. Tempted to figure out the underlying ingredients of Erlang, I decided to spent some time to learn the language. Multi-threading Problem Multiple threads of execution is a common programming model in mo
Kansai.pm ã§ã®ç™ºè¡¨è³‡æ–™ (Hadoop Streaming 㧠MapReduce) - naoyaã®ã¯ã¦ãªãƒ€ã‚¤ã‚¢ãƒªãƒ¼
Kansai.pm ã«å‚åŠ ã—ã¾ã—ãŸã€‚ã¨ã¦ã‚‚楽ã—ã‹ã£ãŸã§ã™ã€‚自分も "Hadoop Streaming 㧠MapReduce" ã¨ã„ã†é¡Œç›®ã§ç™ºè¡¨ã—ã¾ã—ãŸã€‚å–り急ãŽã€è³‡æ–™ã‚’以下ã«å…¬é–‹ã—ã¾ã™ã€‚ http://bloghackers.net/~naoya/ppt/080530kansaipm.ppt MapReduce 㯠Google ã®ãƒãƒƒã‚¯ã‚¨ãƒ³ãƒ‰ã§å‹•ã„ã¦ã„る分散並列ãƒãƒƒãƒå‡¦ç†ã‚·ã‚¹ãƒ†ãƒ ã§ã™ã€‚GFS 㯠Google ã®åˆ†æ•£ãƒ•ã‚¡ã‚¤ãƒ«ã‚·ã‚¹ãƒ†ãƒ ã§ã™ã€‚Google ウェアã®ã‚¯ãƒãƒ¼ãƒ³ã¨ã—ã¦ã‚ªãƒ¼ãƒ—ンソースã§é–‹ç™ºã•ã‚Œã¦ã„ã‚‹ã®ãŒ Hadoop。Hadoop 㯠Yahoo! Inc ã‚„ Facebook, Amazon.com ãªã©ã§ã‚‚利用ã•ã‚Œã¦ã„ã‚‹ã¨ã®ã“ã¨ã€‚Hadoop 㯠Java ã§ã™ãŒã€Hadoop Streaming を使ã†ã¨ã€€Java 以外ã§ã‚‚ MapReduce ã§ãã¾ã™ã€‚ 以下ã®ã‚¨ãƒ³ãƒˆãƒªã‚‚åˆ
MapReduce - naoyaã®ã¯ã¦ãªãƒ€ã‚¤ã‚¢ãƒªãƒ¼
"MapReduce" 㯠Google ã®ãƒãƒƒã‚¯ã‚¨ãƒ³ãƒ‰ã§åˆ©ç”¨ã•ã‚Œã¦ã„る並列計算システムã§ã™ã€‚検索エンジンã®ã‚¤ãƒ³ãƒ‡ãƒƒã‚¯ã‚¹ä½œæˆã‚’ã¯ã˜ã‚ã¨ã™ã‚‹ã€å¤§è¦æ¨¡ãªå…¥åŠ›ãƒ‡ãƒ¼ã‚¿ã«å¯¾ã™ã‚‹ãƒãƒƒãƒå‡¦ç†ã‚’想定ã—ã¦ä½œã‚‰ã‚ŒãŸã‚·ã‚¹ãƒ†ãƒ ã§ã™ã€‚ MapReduce ã®é¢ç™½ã„ã¨ã“ã‚ã¯ã€map() 㨠reduce() ã¨ã„ã†äºŒã¤ã®é–¢æ•°ã®çµ„ã¿åˆã‚ã›ã‚’定義ã™ã‚‹ã ã‘ã§ã€å¤§è¦æ¨¡ãƒ‡ãƒ¼ã‚¿ã«å¯¾ã™ã‚‹æ§˜ã€…ãªè¨ˆç®—å•é¡Œã‚’解決ã™ã‚‹ã“ã¨ãŒã§ãる点ã§ã™ã€‚ MapReduce ã®è¨ˆç®—モデル map() ã«ã¯ãã®è¨ˆç®—å•é¡Œã®ãƒ‡ãƒ¼ã‚¿ã¨ã—ã¦ã® key-value ペアãŒæ¬¡ã€…ã«æ¸¡ã£ã¦ãã¾ã™ã€‚map() ã§ã¯ key-value 値ã®ãƒšã‚¢ã‚’ç•°ãªã‚‹è¤‡æ•°ã® key-value ペアã«å¤‰æ›ã—ã¾ã™ã€‚reduce() ã«ã¯ã€map() ã§ä½œã£ãŸ key-value ペアをåŒä¸€ã® key ã§æŸããŸã‚‚ã®ãŒé †ç•ªã«æ¸¡ã£ã¦ãã¾ã™ã€‚ãã® key-values ペアを任æ„ã®å½¢å¼ã«å¤‰æ›ã™ã‚‹ã“ã¨
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