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Programming MapReduce with Scalding

You're reading from   Programming MapReduce with Scalding A practical guide to designing, testing, and implementing complex MapReduce applications in Scala

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Product type Paperback
Published in Jun 2014
Publisher
ISBN-13 9781783287017
Length 148 pages
Edition 1st Edition
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Author (1):
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Antonios Chalkiopoulos Antonios Chalkiopoulos
Author Profile Icon Antonios Chalkiopoulos
Antonios Chalkiopoulos
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Table of Contents (11) Chapters Close

Preface 1. Introduction to MapReduce FREE CHAPTER 2. Get Ready for Scalding 3. Scalding by Example 4. Intermediate Examples 5. Scalding Design Patterns 6. Testing and TDD 7. Running Scalding in Production 8. Using External Data Stores 9. Matrix Calculations and Machine Learning Index

Why Scala?

Development has evolved a lot since Java was originally invented 20 years ago. Java, as an imperative language, was designed for the Von-Neumann architecture, where a computer consists of a processor, a memory, and a bus that reads both instructions and data from the memory into the processor. In that architecture, it is safe to store values in variables, and then mutate them by assigning new values. Loop controls are thus normal to use, as shown in the following code:

  for ( int i=0; i < 1000000;  i++) {
    a=a+1;
  }

However, over the past decade, hardware engineers have been stressing that the Von-Neumann model is no longer sustainable. Since processors hit physical limitations at high frequencies, engineers look for evolution beyond the single-processor model. Nowadays, manufacturers integrate multiple cores onto a single integrated circuit die—a multiprocessor chip. Similarly, the emergence of cloud computing and Hadoop clusters bring into play another dimension...

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