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  • Book Overview & Buying Programming MapReduce with Scalding
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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
Buy this Book

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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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
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1
1. Introduction to MapReduce
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1. Introduction to MapReduce
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The Hadoop platform
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MapReduce
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MapReduce abstractions
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Introducing Cascading
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Summary
2
2. Get Ready for Scalding
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2. Get Ready for Scalding
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Why Scala?
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Scala basics
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Scala build tools
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Hello World in Scala
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Development editors
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Installing Hadoop in five minutes
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Running our first Scalding job
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Submitting a Scalding job in Hadoop
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Summary
3
3. Scalding by Example
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3. Scalding by Example
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Reading and writing files
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Understanding the core capabilities of Scalding
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Operations on groups
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A simple example
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Typed API
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Summary
4
4. Intermediate Examples
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4. Intermediate Examples
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Logfile analysis
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Exploring ad targeting
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Summary
5
5. Scalding Design Patterns
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5. Scalding Design Patterns
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The external operations pattern
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The dependency injection pattern
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The late bound dependency pattern
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Summary
6
6. Testing and TDD
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6. Testing and TDD
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Introduction to testing
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MapReduce testing challenges
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Development lifecycle with testing strategy
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TDD for Scalding developers
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Black box testing
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Summary
7
7. Running Scalding in Production
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7. Running Scalding in Production
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Executing Scalding in a Hadoop cluster
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Scheduling execution
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Coordinating job execution
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Configuring using a property file
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Configuring using Hadoop parameters
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Monitoring Scalding jobs
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Using slim JAR files
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Scalding execution throttling
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Summary
8
8. Using External Data Stores
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8. Using External Data Stores
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Interacting with external systems
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SQL databases
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NoSQL databases
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Search platforms
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Summary
9
9. Matrix Calculations and Machine Learning
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9. Matrix Calculations and Machine Learning
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Text similarity using TF-IDF
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Setting a similarity using the Jaccard index
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K-Means using Mahout
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Other libraries
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Summary
10
Index
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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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Programming MapReduce with Scalding
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