PPT-MapReduce

Author : alida-meadow | Published Date : 2016-07-20

Concurrency for dataintensive applications 1 Dennis Kafura CS5204 Operating Systems MapReduce Dennis Kafura CS5204 Operating Systems 2 Jeff Dean Sanjay Ghemawat

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MapReduce: Transcript

Concurrency for dataintensive applications 1 Dennis Kafura CS5204 Operating Systems MapReduce Dennis Kafura CS5204 Operating Systems 2 Jeff Dean Sanjay Ghemawat Dennis Kafura CS5204 Operating Systems. ：. R. eusing . results . of . mapreduce. . jobs. Jun Fan. Outline. Introduction to . MapReduce. MapReduce. and its implementations such as . Hadoop. are common in . Facebook. , Yahoo, and Google as well as smaller companies. With . a heavy debt to:. Google Map Reduce OSDI 2004 slides. code.google.com. You are an engineer at:. Hare-brained-scheme.com. Your boss, comes to your office and says:. . “We’re going to be hog-nasty rich!. : Simplified Data Processing on Large Clusters. Jeffrey Dean & . Sanjay . Ghemawat. Appeared in:. OSDI '04: Sixth Symposium on Operating System Design and Implementation, San Francisco, CA, December, 2004. . Tsung. -Wei Huang. and Martin D. F. Wong. Department of Electrical and Computer Engineering (ECE). University of Illinois at Urbana-Champaign (UIUC), IL, USA. 2015 ACM International Symposium on Physical Design (ISPD). Cloud, Part II: Search => Cluster Apps => Scalable Machine Learning. David E. Culler. . CS162 – Operating Systems and Systems Programming. Lecture 40. December 3, . 2014. Proj. : CP 2 . today. and . Hadoop. Debapriyo Majumdar. Data Mining – Fall 2014. Indian Statistical Institute Kolkata. November 10, 2014. Let’s keep the intro short. Modern data mining: process immense amount of data quickly. , the Big Data Workhorse. Vyassa Baratham, Stony Brook University. April 20, 2013, 1:05-2:05pm. cSplash. 2013. Use several computers to process large amounts of data. Often significant . distribution overhead. 2.0 and YARN. Subash. D’Souza. Who am I?. Senior Specialist Engineer at . Shopzilla. Co-Organizer for the Los Angeles . Hadoop. User group. Organizer for Los Angeles . HBase. User Group. Reviewer on Apache Flume: Distributed Log Processing, . HPC(Exascale) . Systems. June 24 2012. HPC . 2012 . Cetraro. , Italy. Geoffrey Fox. gcf@indiana.edu. . Informatics, Computing and Physics. Indiana . University . Bloomington. Science Computing Environments. Yang . Ruan. , . Zhenhua . Guo. , . Yuduo. Zhou, Judy . Qiu. , Geoffrey Fox. Indiana . University, US. Outline. Introduction and Background. MapReduce. Iterative MapReduce. Distributed Workflow . Management Systems. By . Yufei. Tao, . Wenqing. . Lin, . Xiaokui. . Xia. Edited by Tuval Rotem. First topic:. What is MapReduce. But first…. Regular day at work. You’re doing your regular work while suddenly your boss, . . Shantenu Jha represented by . Geoffrey Fox. gcf@indiana.edu. . . http://www.infomall.org. . http://www.futuregrid.org. . . http://salsahpc.indiana.edu/. . Director, Digital Science Center, Pervasive Technology Institute. Source. MapReduce. : Simplified Data Processing in Large Clusters. . Jefferey. Dean and Sanjay . Ghemawat. . OSDI 2004. Example Scenario. 3. Genome data from roughly . one million users. 125 MB of data per user. MapReduce. Architecture. MapReduce. Internals. MapReduce. Examples. JobTracker. Interface. MapReduce. : A Real World Analogy. Coins Deposit. ?. MapReduce. : A Real World Analogy. Coins Deposit. Coins Counting Machine.

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