Download e-book for kindle: MapReduce Design Patterns: Building Effective Algorithms and by Donald Miner, Adam Shook

By Donald Miner, Adam Shook

ISBN-10: 1449327176

ISBN-13: 9781449327170

Until now, layout styles for the MapReduce framework were scattered between quite a few learn papers, blogs, and books. this useful advisor brings jointly a different selection of necessary MapReduce styles that may prevent effort and time whatever the area, language, or improvement framework you’re using.

Each development is defined in context, with pitfalls and caveats truly pointed out that will help you steer clear of universal layout errors while modeling your great information structure. This e-book additionally presents a whole evaluation of MapReduce that explains its origins and implementations, and why layout styles are so very important. All code examples are written for Hadoop.
* Summarization patterns: get a top-level view through summarizing and grouping facts
* Filtering patterns: view information subsets equivalent to documents generated from one person
* Data association patterns: reorganize info to paintings with different platforms, or to make MapReduce research more straightforward
* Join patterns: research varied datasets jointly to find fascinating relationships
* Metapatterns: piece jointly numerous styles to unravel multi-stage difficulties, or to accomplish a number of analytics within the comparable activity
* Input and output patterns: customise how you use Hadoop to load or shop facts

"A transparent exposition of MapReduce courses for universal facts processing patterns—this e-book is indespensible for somebody utilizing Hadoop."

--Tom White, writer of Hadoop: The Definitive Guide

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Read or Download MapReduce Design Patterns: Building Effective Algorithms and Analytics for Hadoop and Other Systems PDF

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Extra info for MapReduce Design Patterns: Building Effective Algorithms and Analytics for Hadoop and Other Systems

Example text

1 Effects of Layout on Frequency and Leakage . . . . . . . . . . . . . .. . . . . . . . 2 D2D Variations . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 WID Variations . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Inferring Process Parameters .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Conclusion ..

1 INTRODUCTION CMOS device scaling has increased the impact of process variability to the point where it is now regarded as a major roadblock to further scaling [1]. The control of process fluctuations has not kept pace with rapidly shrinking device dimensions. Furthermore, the drive to improve performance has enticed device and circuit designers to operate at conditions that are more sensitive to variability. Oxide thickness, threshold voltage, and gate length variations have become more significant as the manufacturing process has entered sub-100 nm technology nodes.

1 Effects of Layout on Frequency and Leakage . . . . . . . . . . . . . .. . . . . . . . 2 D2D Variations . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 WID Variations . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Inferring Process Parameters .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Conclusion .. . . .

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MapReduce Design Patterns: Building Effective Algorithms and Analytics for Hadoop and Other Systems by Donald Miner, Adam Shook


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