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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 2. Get Ready for Scalding FREE CHAPTER 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

A simple example

To make clear that Scalding operations can be chained together to implement a complete pipeline look at the following example:

Tsv(args("input"), ('kid,'age,'fruits))
.read
.flatMap('fruits -> 'fruit) { text : String => text.split(",") }
.project('kid, 'fruit)
.write(Tsv("results.tsv"))

The same example can be expressed in a number of pipes, where we assemble and control how each pipe connects to another:

val logs = Tsv(args("logfiles"), LogsOperations.schema )
  .read
  .extractSomeUserInfo

val customers = Tsv(args("cust_log"),COperations.schema).read
  .extractSomeCustomerInfo

val joined = logs.joinWithSmaller(customers, 'user)

val result = joined.filter(
  .write(Tsv(args("output")))

The preceding code allows us to whiteboard our designs for processing data before implementing a data processing flow. Refer to the pipeline definition of the first chapter to see how...

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