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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

Operations on groups

Operations groupAll and groupBy are essential building blocks of Scalding applications, and they generate groups. groupAll generates a single group containing all the available tuples. groupBy generates m number of groups, where m is the number of unique keys in the data.

For example, if groupBy('color) is executed and three unique colors exist in the data, then three groups will be generated. Once grouping is achieved, a number of group operations can be applied to them.

The first seven group operations average, count, min, max, sum, size, and sizeAveStdev are useful to extract statistics from data, and their syntax is as follows:

group.average(field -> newField)
group.count(field -> newField) { function }
group.min(field -> newField)
group.max(field -> newField)
group.sum(field -> newField)
group.size(newField)
group.sizeAveStdev(field -> sizeField,averageField, stdField)

We can also apply multiple group operations on the same group. To calculate...

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