「Hadoop/Spark Conference Japan 2016ã€ã§å°æ²¢å¥å²PMCãŒèªžã£ãŸâ€œYARNã®ã„ã¾â€ | gihyo.jp
「Hadoop/Spark Conference Japan 2016ã€ã§å°æ²¢å¥å²PMCãŒèªžã£ãŸâ€œYARNã®ã„ã¾â€ 2013å¹´10月ã«Hadoop 2.2ãŒæ£å¼ãƒªãƒªãƒ¼ã‚¹ã•ã‚Œã¦ä»¥æ¥ã€Hadoopã®ä¸–ç•Œã¯å¤§ãã変ã‚ã‚Šã¾ã—ãŸã€‚ãã‚Œã¾ã§ã®æ¦‚念ã§ã‚る「Hadoopï¼ HDFS＋MapReduceã€ãŒåˆ·æ–°ã•ã‚Œã€MapReduceãŒå—ã‘æŒã£ã¦ã„ãŸMapデータ処ç†ã¨ã‚¯ãƒ©ã‚¹ã‚¿ãƒªã‚½ãƒ¼ã‚¹ç®¡ç†ãŒåˆ†é›¢ã§ãるよã†ã«ãªã‚Šã¾ã—ãŸã€‚ã“ã‚Œã«ã‚ˆã‚Šã€Sparkãªã©MapReduce以外ã®ãƒ‡ãƒ¼ã‚¿å‡¦ç†ã‚¨ãƒ³ã‚¸ãƒ³ãŒHadoop上ã§åˆ©ç”¨å¯èƒ½ã«ãªã‚Šã¾ã—ãŸã€‚ ãã—ã¦Hadoopã®ã‚¯ãƒ©ã‚¹ã‚¿ãƒªã‚½ãƒ¼ã‚¹ç®¡ç†ã‚’è¡Œã†ãƒŸãƒ‰ãƒ«ã‚¦ã‚§ã‚¢ã¨ã—ã¦ã€æ–°ãŸãªãƒ‡ãƒ•ã‚¡ã‚¯ãƒˆã‚¹ã‚¿ãƒ³ãƒ€ãƒ¼ãƒ‰ã®åœ°ä½ã‚’確立ã—よã†ã¨ã—ã¦ã„ã‚‹ã®ãŒã€ŒApache Hadoop YARN（Yet Another Resource Negotiator： YARNâ ）â ã€ã§ã™ã€‚YARNã®ç™»å ´ã¯ä¸¦åˆ—分散処ç†ã«ã©ã‚“
第13回　Hadoopã®è¨è¨ˆã¨å®Ÿè£…～並列データ処ç†ç³»Hadoop MapReduceï¼»1ï¼½ | gihyo.jp
ã¯ã˜ã‚㫠第一部ã§ã¯ã€Hadoopãªã©ã®ä¸¦åˆ—データ処ç†ç³»ã®åŸºç¤Žã§ã‚る並列データベース技術や分散システム技術を解説ã—ã¦ãã¾ã—ãŸã€‚第二部ã§ã¯ã€å®Ÿéš›ã®å‡¦ç†ç³»ã«ã‚ˆã‚Šç„¦ç‚¹ã‚’当ã¦ã€ãれらã®è¨è¨ˆã¨å®Ÿè£…を見ã¦ã„ãã¾ã™ã€‚ 第二部ã§ã¯ã€æœ€åˆã®4回を用ã„ã¦ã€Apache Hadoopã®ä¸¦åˆ—データ処ç†ç³»ã§ã‚ã‚‹Hadoop MapReduceを始ã‚ã¨ã—ã€å½“該処ç†ç³»ã®ãƒªã‚½ãƒ¼ã‚¹ç®¡ç†ã‚’è¡Œã†YARNãŠã‚ˆã³ã€æ±Žç”¨çš„ãªä¸¦åˆ—データ処ç†ç³»ã§ã‚ã‚‹Tezã«ã¤ã„ã¦è§£èª¬ã‚’è¡Œã†äºˆå®šã§ã™ã€‚ 今回ã¯ã€MapReduceã«ãŠã‘ã‚‹è¨è¨ˆæ–¹é‡ã‚„特徴ã«ã¤ã„ã¦è§£èª¬ã—ã¾ã™ã€‚ MapReduceã¨ã¯ MapReduceã¯ã€è¤‡æ•°ã®è¨ˆç®—機上ã§åŠ¹çŽ‡çš„ã«å‡¦ç†ã‚’è¡Œã†ãŸã‚ã®ãƒ‡ãƒ¼ã‚¿å‡¦ç†ç”¨ã®ãƒ—ãƒã‚°ãƒ©ãƒŸãƒ³ã‚°ãƒ¢ãƒ‡ãƒ«ã¨ã€ãã®ãƒ—ãƒã‚°ãƒ©ãƒŸãƒ³ã‚°ãƒ¢ãƒ‡ãƒ«ãŒå‹•ä½œã™ã‚‹å‡¦ç†ç³»ã®å®Ÿè£…ã§ã‚ã‚Šã€Googleã®Jeff Deanらã«ã‚ˆã‚Šé–‹ç™ºãŒå§‹ã‚られã¾ã—ãŸã€‚MapReduceã®ä»£è¡¨çš„ãªãƒ©ãƒ³ã‚¿ã‚¤ãƒ 処ç†ç³»ã«ã¯
GitHub - scalding-io/ProgrammingWithScalding: Programming MapReduce with Scalding
Source code for PACKT Book 'Programming MapReduce With Scalding' Find more information at http://scalding.io/ The book consists of 9 chapters Introduction to Map-Reduce - Introduction to Hadoop, Map Reduce, Pipelining, Cascading, Pig and Hive. Chapter presents benefits of higher level abstractions of Map Reduce (concepts and capabilities). Get ready for Scalding - Theory about Scalding - the Scala
New Instance Types for Amazon Elastic MapReduce | Amazon Web Services
AWS News Blog New Instance Types for Amazon Elastic MapReduce Thousands of AWS customers use Amazon Elastic MapReduce to process and store vast amounts of data. Because Elastic MapReduce is built around the Hadoop framework, it is easy to use hundreds or thousands of Amazon EC2 instances in parallel. Hot on the heels of the price reductions that we made last week, we are also adding support for 12
大è¦æ¨¡ã‚·ã‚¹ãƒ†ãƒ 管ç†ç ”究室　― Hadoop ã®æŠ•æ©Ÿçš„実行数ã¯ä½•æ•…ï¼”ãªã®ã‹ï¼Ÿâ€• – NAIST Edge
大è¦æ¨¡ã‚·ã‚¹ãƒ†ãƒ 管ç†ç ”究室ã§ã¯ï¼Œæœªæ¥ã®å¤§è¦æ¨¡è¶…並列分散コンピューティング環境ã®ãŸã‚ã®è¦ç´ 技術やビッグデータ解æžã«é–¢ã™ã‚‹ç ”究ã¨ï¼Œãれらã®å¿œç”¨ã¨ã—ã¦å¤§ç½å®³ã«å¯¾ã™ã‚‹ãƒªã‚¹ã‚¯ç®¡ç†ã‚„サービス・マãƒãƒ¼ã‚¸ãƒ¡ãƒ³ãƒˆã«é–¢ã™ã‚‹ç ”究を行ã£ã¦ã„ã¾ã™ï¼Žä»Šå›žã¯èŽ«å¤§ãªè¦æ¨¡ã®ã‚µãƒ¼ãƒãƒžã‚·ãƒ³ã‹ã‚‰æ§‹æˆã•ã‚Œã‚‹ã‚¯ãƒ©ã‚¦ãƒ‰ãƒ»ã‚³ãƒ³ãƒ”ューティングã«é–¢ã™ã‚‹ã‚¹ã‚±ã‚¸ãƒ¥ãƒ¼ãƒªãƒ³ã‚°å•é¡Œã‚’通ã—ã¦ç ”究æˆæžœã®ä¸€ç«¯ã‚’ã”紹介ã—ã¾ã™ï¼Ž クラウド・コンピューティング環境 クラウド・コンピューティングã¯ï¼ŒèŽ«å¤§ãªæ•°ã®ã‚µãƒ¼ãƒãƒžã‚·ãƒ³ã‚’接続ã—ã¦åˆ†æ•£ã‚³ãƒ³ãƒ”ューティングをæä¾›ã™ã‚‹è¨ˆç®—環境ã®ç·ç§°ã§ã™ï¼Žã‚µãƒ¼ãƒç’°å¢ƒã¯ãƒ‡ãƒ¼ã‚¿ã‚»ãƒ³ã‚¿ãƒ¼å†…ã«æ§‹ç¯‰ã•ã‚Œï¼Œãƒ‡ã‚¹ã‚¯ãƒˆãƒƒãƒ—PCã‹ã‚‰ã‚¿ãƒ–レットPC，モãƒã‚¤ãƒ«ãƒ•ã‚©ãƒ³ã¨ã„ã£ãŸå¤šæ§˜ãªç«¯æœ«ãŒã‚¯ãƒ©ã‚¤ã‚¢ãƒ³ãƒˆãƒžã‚·ãƒ³ã¨ã—ã¦ã‚¯ãƒ©ã‚¦ãƒ‰ãƒ»ã‚³ãƒ³ãƒ”ューティングサービスを利用ã—ã¦ã„ã¾ã™ï¼Ž クラウド・コンピューティングをæä¾›ã™ã‚‹ãƒ‡ãƒ¼ã‚¿ã‚»ãƒ³ã‚¿ãƒ¼ã§ã¯ï¼Œæ™®åŠå“レベルã®ã‚µãƒ¼ãƒã‚’増やã—ã¦å‡¦ç†ã‚’並列化
Pledged Patents – Open Patent Non-Assertion Pledge - Google
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Cloudera Blog
The ongoing progress in Artificial Intelligence is constantly expanding the realms of possibility, revolutionizing industries and societies on a global scale. The release of LLMs surged by 136% in 2023 compared to 2022, and this upward trend is projected to continue in 2024. Today, 44% of organizations are experimenting with generative AI, with 10% having […] Read blog post
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 problem dom
Informatics at Edinburgh: Informatics Theses
Informatics PhD Theses and MSc Dissertations Informatics Dissertations are made available as and when they are approved in their final form. Any relevant and published thesis can be found on the Edinburgh Research Archive. Informatics Forum, 10 Crichton Street, Edinburgh, EH8 9AB, Scotland, UK Tel: +44 131 651 5661, Fax: +44 131 651 1426, E-mail: [email protected] Please contact our webad
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 major step backwards | The Database Column
on Jan 17 in Database architecture, Database history, Database innovation posted by DeWitt [Note: Although the system attributes this post to a single author, it was written by David J. DeWitt and Michael Stonebraker] On January 8, a Database Column reader asked for our views on new distributed database research efforts, and we’ll begin here with our views on MapReduce. This is a good time to dis
S4
S4 - Presentation Transcript S4: the open-source distributed stream computing platformNachi Ueno 自己紹介 日本ã®ã©ã“ã‹ã®ç ”究所ã¨å‘¼ã°ã‚Œã¦ã„る組織ã§æ—¥ã€…åƒã„ã¦ã„ã¾ã™ 最近ã¯ã€Hadoop,OpenStack,スケールã™ã‚‹RDBMSã¨ã‹ã«èˆˆå‘³ãŒã‚ã‚Šã¾ã™ SimCloudã¨ã„ã†ã‚¯ãƒ©ã‚¦ãƒ‰ç®¡ç†ãƒ„ールを作ã£ã¦ã¾ã™ã€€->　OSS公開予定　 http://wiki.openstack.org/simcloud Twitter @nati OpenStackeré”ã¨@築地 S4 (Simple Scalable Streaming System) 　http://s4.io/ S4 is a general-purpose, distributed, scalable, partially fault-tole
Word Count with MapReduce on a GPU – A Python Example | atbrox
Atbrox is startup company providing technology and services for Search and Mapreduce/Hadoop. Our background is from Google, IBM and research. GPU – Graphical Processing Unit like the NVIDIA Tesla – is fascinating hardware, in particular regarding extreme parallelism (hundreds of cores) and memory bandwidth (tens of Gigabytes/second). The main programming languages for programming GPUs are C-based
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»¥
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
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[MAPREDUCE-2708] [MR-279] Design and implement MR Application Master recovery - ASF JIRA
http://infolab.stanford.edu/~ullman/pub/join-mr.pdf
http://www.mesosproject.org/
Iterativemapreduce.org
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