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Mastering Hadoop

You're reading from   Mastering Hadoop Go beyond the basics and master the next generation of Hadoop data processing platforms

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Product type Paperback
Published in Dec 2014
Publisher
ISBN-13 9781783983643
Length 374 pages
Edition 1st Edition
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Author (1):
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Sandeep Karanth Sandeep Karanth
Author Profile Icon Sandeep Karanth
Sandeep Karanth
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Table of Contents (15) Chapters Close

Preface 1. Hadoop 2.X FREE CHAPTER 2. Advanced MapReduce 3. Advanced Pig 4. Advanced Hive 5. Serialization and Hadoop I/O 6. YARN – Bringing Other Paradigms to Hadoop 7. Storm on YARN – Low Latency Processing in Hadoop 8. Hadoop on the Cloud 9. HDFS Replacements 10. HDFS Federation 11. Hadoop Security 12. Analytics Using Hadoop A. Hadoop for Microsoft Windows Index

Chapter 2. Advanced MapReduce

MapReduce is a programming model for parallel and distributed processing of data. It consists of two steps: Map and Reduce. These steps are inspired from functional programming, a branch of computer science that deals with mathematical functions as computational units. Properties of functions such as immutability and statelessness are attractive for parallel and distributed processing. They provide a high degree of parallelism and fault tolerance at lower costs and semantic complexity.

In this chapter, we will look at advanced optimizations when running MapReduce jobs on Hadoop clusters. Every MapReduce job has input data and a Map task per split of this data. The Map task calls a map function repeatedly on every record, represented as a key-value pair. The map is a function that transforms data from one domain to another. The intermediate output records of each Map task are shuffled and sorted before transferring it downstream to the Reduce tasks...

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