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MapReduce Design Patterns

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MapReduce Design Patterns

Sách keo gáy. Bìa mềm
 
Thể loại:Computers - Programming
 
Năm:2012
 
Ngôn ngữ:english
 
Trang:230 / 251
 
Until now, design patterns for the MapReduce framework have been
scattered among various research papers, blogs, and books. This handy
guide brings together a unique collection of valuable MapReduce patterns
that will save you time and effort regardless of the domain, language,
or development framework you’re using.Each pattern is explained in
context, with pitfalls and caveats clearly identified to help you avoid
common design mistakes when modeling your big data architecture. This
book also provides a complete overview of MapReduce that explains its
origins and implementations, and why design patterns are so important.
All code examples are written for Hadoop.Summarization patterns: get a
top-level view by summarizing and grouping data Filtering patterns: view
data subsets such as records generated from one user Data organization
patterns: reorganize data to work with other systems, or to make
MapReduce analysis easier Join patterns: analyze different datasets
together to discover interesting relationships Metapatterns: piece
together several patterns to solve multi-stage problems, or to perform
several analytics in the same job Input and output patterns: customize
the way you use Hadoop to load or store data "A clear exposition of
MapReduce programs for common data processing patterns—this book is
indespensible for anyone using Hadoop." --Tom White, author of Hadoop:
The Definitive Guide