MapReduce Design Patterns: Building Effective Algorithms and Analytics for Hadoop and Other Systems (BOK)

Donald Miner, Adam Shook

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Design patterns for the MapReduce framework, until now, 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 - so you can avoid some of the 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. Hadoop MapReduce code is provided to help you learn how to apply the design patterns by example. Topics include: Basic patterns, including map-only filter, group by, aggregation, distinct, and limit Joins: traditional reduce-side join, reduce-side join with Bloom filter, replicated join with distributed cache, merge join, Cartesian products, and intersections Binning, sharding for other systems, sorting, sampling, unions, and other patterns for organizing data Job optimization patterns, including multi-job map-only job folding, and overloading the key grouping to perform two jobs at once

Produktfakta

Språk Engelsk Engelsk Innbinding Heftet
Utgitt 2012 Forfatter Adam Shook, Donald Miner
Forlag
O'REILLY
ISBN 9781449327170
Antall sider 252 Dimensjoner 19,1cm x 23,5cm x 1,4cm
Vekt 399 gram Leverandør Bertram Trading Ltd
Emner og form Computer networking & communications, Computer programming / software development