PPT-MapReduce

Author : cheryl-pisano | Published Date : 2016-05-27

2 httpwwwgoogleorgflutrendsca 2012 Average Searches Per Day 5134000000 Motivation Process lots of data Google processed about 24 petabytes of data per day

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MapReduce: Transcript

2 httpwwwgoogleorgflutrendsca 2012 Average Searches Per Day 5134000000 Motivation Process lots of data Google processed about 24 petabytes of data per day in 2009 A single machine . ：. 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. : 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. . Based on the text by Jimmy Lin and Chris Dryer; and on the yahoo tutorial on . mapreduce. at . http://developer.yahoo.com/hadoop/tutorial/index.html. Namenode responsibilities:. Namespace management: file name, locations, access privileges etc.. Simplified Data Processing on Large . Clusters. by Jeffrey Dean and Sanjay . Ghemawa. Presented by Jon Logan. Outline. Problem Statement / Motivation. An Example Program. MapReduce. . vs. Hadoop. GFS / HDFS. Paradigms Activity at Data Intensive Workshop. . Shantenu Jha represented by . Geoffrey Fox. gcf@indiana.edu. . . http://www.infomall.org. . http://www.futuregrid.org. . . http://salsahpc.indiana.edu. Efficient and scalable architectures to perform pleasingly parallel, MapReduce and iterative data intensive computations on cloud environments. Thilina. . Gunarathne. (tgunarat@indiana.edu). Advisor : . , 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. 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. with MapReduce. Lars Kolb. , Hanna Köpcke, Andreas Thor, Erhard Rahm. Database . Group Leipzig. http://dbs.uni-leipzig.de. Glasgow, . CloudDB. 2011. Identification of semantically equivalent entities. Yang . Ruan. , . Zhenhua . Guo. , . Yuduo. Zhou, Judy . Qiu. , Geoffrey Fox. Indiana . University, US. Outline. Introduction and Background. MapReduce. Iterative MapReduce. Distributed Workflow . Management Systems. . 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. : Stopping Group Attacks by Spotting Lockstep Behavior in Social. . Networks (WWW2013). BEUTEL, ALEX, WANHONG XU, VENKATESAN GURUSWAMI, . CHRISTOPHER PALOW, AND CHRISTOS FALOUTSOS.. GROUP 20. Outline. 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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