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Programming MapReduce with Scalding

Programming MapReduce with Scalding

By : Antonios Chalkiopoulos
4.3 (6)
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Programming MapReduce with Scalding

Programming MapReduce with Scalding

4.3 (6)
By: Antonios Chalkiopoulos

Overview of this book

This book is an easy-to-understand, practical guide to designing, testing, and implementing complex MapReduce applications in Scala using the Scalding framework. It is packed with examples featuring log-processing, ad-targeting, and machine learning. This book is for developers who are willing to discover how to effectively develop MapReduce applications. Prior knowledge of Hadoop or Scala is not required; however, investing some time on those topics would certainly be beneficial.
Table of Contents (11 chapters)
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10
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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Programming MapReduce with Scalding
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