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MapReduce and Hadoop Distributed File System

MapReduce and Hadoop Distributed File System

MapReduce and Hadoop Distributed File System. B . Ramamurthy. Contact: Dr. Bina Ramamurthy CSE Department University at Buffalo (SUNY) bina@buffalo.edu http://www.cse.buffalo.edu/faculty/bina Partially Supported by NSF DUE Grant: 0737243. The Context: Big-data.

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487 views • 35 slides
MapReduce and Hadoop Distributed File System

MapReduce and Hadoop Distributed File System

MapReduce and Hadoop Distributed File System. B . Ramamurthy. Contact: Dr. Bina Ramamurthy CSE Department University at Buffalo (SUNY) bina@buffalo.edu http://www.cse.buffalo.edu/faculty/bina Partially Supported by NSF DUE Grant: 0737243. The Context: Big-data.

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355 views • 35 slides
Investigation of Data Locality and Fairness in MapReduce

Investigation of Data Locality and Fairness in MapReduce

Investigation of Data Locality and Fairness in MapReduce . Zhenhua Guo , Geoffrey Fox, Mo Zhou. Outline. Introduction Data Locality and Fairness Experiments Conclusions. MapReduce Execution Overview. Google File System. Read input data Data locality. Input file. block 0. 1. 2.

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429 views • 29 slides
Joins in mapreduce

Joins in mapreduce

Joins in mapreduce. Shamik bose. motivation. MapReduce is the framework of choice for big data analytics Legacy systems do not perform very well for very large amounts of data Joins are computationally expensive, yet unavoidable in applications. Types of joins. Reduce-side Join

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371 views • 19 slides
Bottom Up Filtering

Bottom Up Filtering

Bottom Up Filtering. We know the current input word must serve as the first word in the derivation of the unexpanded node the parser is currently processing. Therefore the parser should not consider grammar rule for which the current word cannot serve as the " left corner "

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214 views • 15 slides

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