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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

Black box testing


During test-driven development, we retain an internal perspective of the system. We identify all possible paths and exercise them through test case inputs to validate the expected output. However, using only valid input is not sufficient, especially when implementing MapReduce applications that execute against possibly billions of lines of data. As we cannot generate all possible cases of invalid input, we look at techniques that increase the data coverage of tests.

Taking a step back, the development lifecycle begins with data exploration followed by the algorithm design. Having a data scientist performing these tasks in a non-scalable development language such as R or Python is the basis of black box testing. Data scientists use multiple tools to extract meaning, insights, and ultimately, value from data. These tools provide powerful capabilities and rich visualizations that enable them to quickly conclude into mathematical models. The drawback is that the resulting implementation...

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