United States Patent: 7650331
Barroso, L.A., et al., "Web Search for a Planet: The Google Cluster Architecture," IEEE Micro, 23(2):22-28, Apr. 2003. cited by other . Ghemawat, S., et al., "The Google File System," 19th Symposium on Operating Systems Principles, pp. 29-43, Lake George, New York, 2003. cited by other . Rabin, M.O., "Efficient Dispersal of Information for Security, Load Balancing and Fault Tolerance," Journal of
MapReduce: A Flexible Data Processing Tool – Communications of the ACM
MapReduce advantages over parallel databases include storage-system independence and fine-grain fault tolerance for large jobs. Mapreduce is a programming model for processing and generating large data sets.4 Users specify a map function that processes a key/value pair to generate a set of intermediate key/value pairs and a reduce function that merges all intermediate values associated with the sa
MapReduce | Craig Henderson
MapReduce C++ Implementation Check out my Blog posts about MapReduce. This library implements a single-machine platform for programming using the the Google MapReduce idiom. Users specify a map function that processes a key/value pair to generate a set of intermediate key/value pairs, and a reduce function that merges all intermediate values associated with the same intermediate key. Many re
Three different implementations of MapReduce | DBMSÂ 2 : DataBase Management System Services
So far as I can see, there are three implementations of MapReduce that matter for enterprise analytic use – Hadoop, Greenplum’s, and Aster Data’s.* Hadoop has of course been available for a while, and used for a number of different things, while Greenplum’s and Aster Data’s versions of MapReduce – both in late-stage beta – have far fewer users. *Perhaps Nokia’s Disco or another implementation will
HBase MapReduce 101 - Part I
In this and the following posts I would like to take the opportunity to go into detail about the MapReduce process as provided by Hadoop but more importantly how it applies to HBase. MapReduce MapReduce as a process was designed to solve the problem of processing in excess of terabytes of data in a scalable way. There should be a way to design such a system that increases in performance linearly w
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大é‡ã®ãƒ‡ãƒ¼ã‚¿ã‚’処ç†ã™ã‚‹æ‰‹æ³•ã¨ã—ã¦ç™»å ´ã—ãŸMapReduce。クラウドã«å¯¾å¿œã—ãŸåˆ†æ•£å‡¦ç†ã®å®šç•ªã¨ã—ã¦è©±é¡Œã«ä¸Šã‚‹ã“ã¨ãŒå¢—ãˆã¦ãã¾ã—ãŸã€‚ MapReduceã¯ã€å¤§é‡ã®ãƒ‡ãƒ¼ã‚¿ã‚’分割ã—ã€åˆ†å‰²ã—ãŸãƒ‡ãƒ¼ã‚¿ã‚’分散ã—ãŸãƒŽãƒ¼ãƒ‰ã«æŠ•ã’ã¦ãƒŽãƒ¼ãƒ‰ã”ã¨ã«å‡¦ç†ã‚’実行ã€çµæžœã‚’集約ã—ã¦æœ€çµ‚çš„ãªç”ãˆã‚’求ã‚ã‚‹ã€ã¨ã„ã£ãŸæ‰‹æ³•ã§ã™ã€‚ ã—ã‹ã—MapReduceãŒç™»å ´ã™ã‚‹ä»¥å‰ã‹ã‚‰å•†ç”¨ãƒ¬ãƒ™ãƒ«ã§ä½¿ã‚ã‚Œã¦ã„ãŸåˆ†æ•£å‡¦ç†æ‰‹æ³•ãŒã‚ã‚Šã¾ã™ã€‚データを分散ã—ãŸãƒ‡ãƒ¼ã‚¿ãƒ™ãƒ¼ã‚¹ã«æ ¼ç´ã—処ç†ã‚’è¡Œã†ãƒ‘ラレル・リレーショナルデータベース（パラレルRDB）ãŒãã®1ã¤ã§ã™ã€‚ パラレルRDBã¯ã€ãƒ‡ãƒ¼ã‚¿ã‚’複数ã®ãƒ‡ãƒ¼ã‚¿ãƒ™ãƒ¼ã‚¹ã«åˆ†æ•£ã—ã¦é…ç½®ã€ãƒ‡ãƒ¼ã‚¿ãƒ™ãƒ¼ã‚¹ã”ã¨ã«å‡¦ç†ã‚’è¡Œã„ã€çµæžœã‚’求ã‚る手法ã§ã™ã€‚ä¸å¤®ã«å…±æœ‰ãƒ¡ãƒ¢ãƒªã‚’é…ç½®ã™ã‚‹ãªã©ã®æ–¹æ³•ã§åˆ†æ•£ã—ãŸãƒ‡ãƒ¼ã‚¿ãƒ™ãƒ¼ã‚¹åŒå£«ã®é€£æºã‚’è¡Œã†ã“ã¨ãŒä¸€èˆ¬çš„ã§ã™ã€‚ ã§ã¯ãƒ‘ラレル・リレーショナルデータベースã¯MapReduceよりé…ã„ã®ã‹ï¼Ÿ 劣るã®ã‹ï¼Ÿ 両者
Doug Hellmann: Implementing MapReduce with multiprocessing
Tuesday, April 28, 2009 Implementing MapReduce with multiprocessing Implementing MapReduce with multiprocessingWhile looking for example applications for Python's multiiprocessing module to use in this week's PyMOTW, someone suggested implementing MapReduce. Below is the simple implementation I came up with (the source is included in the PyMOTW tarball as of version 1.89).The Pool class can be use
MapReduceメモ - プãƒã‚°ãƒ©ãƒžã®æ€ç´¢
å°å· 明彦, 阪井 èª : ãƒã‚±ãƒƒãƒˆé§†å‹•é–‹ç™º 日本ã®ã‚½ãƒ•ãƒˆã‚¦ã‚§ã‚¢é–‹ç™ºã®ç¾å ´ã§ç”Ÿã¿å‡ºã•ã‚ŒãŸã€Œãƒã‚±ãƒƒãƒˆé§†å‹•é–‹ç™ºã€ã¨ã„ã†æ¦‚念をã€æ•°å¤šãã®å®Ÿä¾‹ã‚’å…ƒã«ãƒ¢ãƒ‡ãƒ«åŒ–・体系化を試ã¿ãŸæœ€åˆã®æœ¬ã€‚ å°å· 明彦, 阪井 èª : Redmineã«ã‚ˆã‚‹ã‚¿ã‚¹ã‚¯ãƒžãƒã‚¸ãƒ¡ãƒ³ãƒˆå®Ÿè·µæŠ€æ³• Redmineã«ã‚ˆã‚‹ãƒã‚±ãƒƒãƒˆé§†å‹•é–‹ç™ºã®å®Ÿè·µæŠ€æ³•ã«é–¢ã™ã‚‹æœ€åˆã®æœ¬ã€‚アジャイルãªã‚½ãƒ•ãƒˆã‚¦ã‚§ã‚¢é–‹ç™ºã¸ã®é©ç”¨æ–¹æ³•ã€TestLinkã«ã‚ˆã‚‹ãƒ†ã‚¹ãƒˆç®¡ç†æ‰‹æ³•ã«ã¤ã„ã¦ã‚‚言åŠã€‚ 清水 å‰ç”·: 「派生開発ã€ã‚’æˆåŠŸã•ã›ã‚‹ãƒ—ãƒã‚»ã‚¹æ”¹å–„ã®æŠ€è¡“ã¨æ¥µæ„ 組込システム開発をベースã¨ã—ã¦ã€ã‚½ãƒ•ãƒˆã‚¦ã‚§ã‚¢é–‹ç™ºç‰¹æœ‰ã®ã‚¹ã‚¿ã‚¤ãƒ«ã§ã‚る派生開発ã€ç‰¹ã«XDDPã«ã¤ã„ã¦è§£èª¬ã—ãŸä¸–ç•Œã§ã‚‚稀ãªæœ¬ã€‚æ—¢å˜è£½å“ã‚’ä¿å®ˆã™ã‚‹ã®ã§ã¯ãªã継続的ã«æ©Ÿèƒ½è¿½åŠ ã—ã¦ã„ã昨今ã®é–‹ç™ºã§ã¯ã€æ´¾ç”Ÿé–‹ç™ºç‰¹æœ‰ã®å•é¡Œã‚’æ„è˜ã—ãªã‘ã‚Œã°ãªã‚‰ãªã„。XDDPã¯ãƒ—ãƒã‚»ã‚¹è«–ã ã‘ã§ãªãã€è¦ä»¶å®šç¾©ãªã©ã®ä¸Šæµå·¥ç¨‹ã®å“質改善ã«ã‚‚役立ã¤ã®ã§æ³¨æ„。 Le
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GREEã•ã‚“ã§ä¸å®šæœŸã§ã‚„ã£ã¦ã‚‹ã€GREE Labsオープンソーステクノãƒã‚¸ãƒ¼å‹‰å¼·ä¼šã§ã€Hadoopã®è©±ã‚’èžã„ã¦ãã¾ã—ãŸã€‚Hadoopã¯ã€ã¤ã¾ã‚Šã¯Googleã®GFSã‚„MapReduceã®ã‚¯ãƒãƒ¼ãƒ³ã ãã†ã§ã€ã€ŒGoogleを支ãˆã‚‹æŠ€è¡“ã€ã«ãƒˆã‚メã„ãŸäººãªã‚‰å¿…見ã§ã™ã。 発表ã¯ã€æŠ€è¡“é¢ã‚’ç°¡æ½”ã«æŠ¼ã•ãˆãŸã†ãˆã§ã‚ã‹ã‚Šã‚„ã™ãã€ãã®ã†ãˆå®Ÿéš›ã®åˆ©ç”¨äº‹ä¾‹ã®è©±ã‚‚èžã‘ãŸã®ãŒé¢ç™½ã‹ã£ãŸã¨æ€ã„ã¾ã™ã€‚最近ã®Webç³»ã§ã¯ã€ã‚µãƒ¼ãƒ“スé¢ã§ã‚‚マãƒã‚¿ã‚¤ã‚ºé¢ã§ã‚‚ã€ãƒ‡ãƒ¼ã‚¿ãƒžã‚¤ãƒ‹ãƒ³ã‚°ã¨ã‹è¡Œå‹•ã‚¿ãƒ¼ã‚²ãƒ†ã‚£ãƒ³ã‚°ã¨ã‹ãŒã‚¢ãƒ„ã„ã‚“ã ãªãã¨æ€ã„ã¾ã—ãŸã€‚ プレゼン資料もust録画も公開ã•ã‚Œã¦ã„ã¾ã™ãŒã€ä»¥ä¸‹ã€è‡ªåˆ†ã®ãƒ¡ãƒ¢ã¨ã„ã†æ„味ã§è¨˜éŒ²ã—ã¦ãŠãã¾ã™ã€‚ Hadoopã«ã¤ã„ã¦ï¼ˆå¤ªç”°ä¸€æ¨¹ï¼‰ Preferred Infrastructureã®CTOã§ã€Sedueã®ä½œè€…。大é‡ã®ãƒ‡ãƒ¼ã‚¿ã®å‡¦ç†ãŒãƒ†ãƒ¼ãƒžã§ã€åŠåˆ†ã¯é…’ã§ã§ãã¦ã„ã‚‹w。ãã‚“ãªã“ã‚“ãªã§ã€ã¯ã¦ãƒ–検索ã§ã‚‚使ã‚ã‚Œã¦ã„
Write your first MapReduce program in 20 minutes – Michael Nielsen
The slow revolution Some revolutions are marked by a single, spectacular event: the storming of the Bastille during the French Revolution, or the destruction of the towers of the World Trade Center on September 11, 2001, which so changed the US’s relationship with the rest of the world. But often the most important revolutions aren’t announced with the blare of trumpets. They occur softly, too slo
Amazon Elastic MapReduceを使ã£ã¦ã¿ãŸ - moratorium
Amazon Elastic MapReduceを使ã£ã¦ã¿ãŸ 2009-04-03 (Fri) 3:06 Amazon EC2 連日ã®EC2ãƒã‚¿ã§ã™ã€‚本日ã€Amazonã‹ã‚‰Elastic MapReduceã¨ã„ã†ã‚µãƒ¼ãƒ“スãŒãƒªãƒªãƒ¼ã‚¹ã•ã‚Œã¾ã—ãŸã€‚大è¦æ¨¡ãƒ‡ãƒ¼ã‚¿å‡¦ç†æŠ€è¡“ãŒä¸€æ°—ã«æ°‘é–“ã®æ‰‹ã«ä¸‹ã‚Šã¦ãã‚‹ã€ã¾ã•ã«é©å‘½çš„ãªã‚µãƒ¼ãƒ“スã ã¨æ€ã„ã¾ã™ã€‚ Amazon Elastic MapReduce Amazon ElasticMapReduce 紹介ビデオ With Hadoop, Amazon Adds A Web-Scale Data Processing Engine To Its Cloud Computer by techcrunch.com Elastic MapReduceã¯ã€Googleã®åŸºç›¤æŠ€è¡“ã®ä¸€ã¤ã§ã‚ã‚‹MapReduceを時間å˜ä½èª²é‡‘ã§å®Ÿè¡Œã§ãるサービスã§ã™ã€‚MapReduceã«ã¤ã„ã¦ã¯ä»¥
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What are the differences between Crunch and Cascading in terms of design and usage?
https://docs.google.com/viewer?a=v&q=cache:qx8OedDuUgcJ:ppl.stanford.edu/cs315a/pub/Main/CS315a/paper.pdf+filetype:pdf+%22phoenix%22+%22mapreduce%22&hl=en&pid=bl&srcid=ADGEESi3RRnQC6iCRigMi3S82r2mUT9Zs0SZiaeSc3pAPxkyxnUnOHvRX3Jiu0tNXrCiG9FtnerwkLTyTh_8ephW
https://docs.google.com/viewer?a=v&q=cache:leFGRpWNb0IJ:csl.stanford.edu/~christos/publications/2011.phoenixplus.mapreduce.pdf+filetype:pdf+%22phoenix%22+%22mapreduce%22&hl=en&pid=bl&srcid=ADGEESh8yFlu4ysfF8xk8UDrn1rVfI0WFXQFcQlbgkkJ9nP1IevybV6KuFNNKv8Eyg3
Other
https://docs.google.com/viewer?a=v&q=cache:8DTllz1c5A0J:pages.cs.wisc.edu/~jignesh/publ/hadoopjoin.pdf+filetype:pdf+%22A+comparison+of+join+algorithms+for+log+processing+in+MaPreduce%22&hl=en&pid=bl&srcid=ADGEESgXjmGdMCDX5mmKDDVF7tr7rNbOzD_VvDBYko1yK9LMCcC
GitHub - ThinkBigAnalytics/colossal-pipe: The Colossal Pipe framework for map/reduce processing.
IBM Developer
https://portal.gigaom.com/blog/mapreduce-vs-sql-its-not-one-or-the-other
Amazon Web Services Developer Community : Finding Similar Items with Amazon Elastic MapReduce, Python, and Hadoop Streaming
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