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Optimizing Hadoop for MapReduce

You're reading from   Optimizing Hadoop for MapReduce This book is the perfect introduction to sophisticated concepts in MapReduce and will ensure you have the knowledge to optimize job performance. This is not an academic treatise; it's an example-driven tutorial for the real world.

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Product type Paperback
Published in Feb 2014
Publisher
ISBN-13 9781783285655
Length 120 pages
Edition Edition
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Author (1):
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Khaled Tannir Khaled Tannir
Author Profile Icon Khaled Tannir
Khaled Tannir
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Toc

Chapter 1. Understanding Hadoop MapReduce

MapReduce, the popular data-intensive distributed computing model is emerging as an important programming model for large-scale data-parallel applications such as web indexing, data mining, and scientific simulation.

Hadoop is the most popular open source Java implementation of the Google's MapReduce programming model. It is already being used for large-scale data analysis tasks by many companies and is often used for jobs where low response time is critical.

Before going deep into MapReduce programming and Hadoop performance tuning, we will review the MapReduce model basics and learn about factors that affect Hadoop's performance.

In this chapter, we will cover the following:

  • The MapReduce model

  • An overview of Hadoop MapReduce

  • How MapReduce works internally

  • Factors that affect MapReduce performance

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