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Programming MapReduce with Scalding
Programming MapReduce with Scalding

Programming MapReduce with Scalding: A practical guide to designing, testing, and implementing complex MapReduce applications in Scala

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Profile Icon Antonios Chalkiopoulos
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Full star icon Full star icon Full star icon Full star icon Half star icon 4.3 (6 Ratings)
Paperback Jun 2014 148 pages 1st Edition
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Arrow left icon
Profile Icon Antonios Chalkiopoulos
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€18.99 per month
Full star icon Full star icon Full star icon Full star icon Half star icon 4.3 (6 Ratings)
Paperback Jun 2014 148 pages 1st Edition
eBook
€11.99 €17.99
Paperback
€22.99
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Free Trial
Renews at €18.99p/m
eBook
€11.99 €17.99
Paperback
€22.99
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Programming MapReduce with Scalding

Chapter 2. Get Ready for Scalding

Scalding is a domain-specific language built on top of the capabilities provided by Cascading. It was developed and open-sourced in Twitter and offers a higher level of abstraction by leveraging the power of Scala. In this chapter, we will:

  • Get familiar with Scala
  • Set up Hadoop and our development environment
  • Execute our first Scalding application

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...

Scala basics

Scala modernizes Java's object-oriented approach while adding in the mix functional programming. It compiles into byte-code, and it can be executed on any Java virtual machine; thus, libraries and classes of Java and Scala communicate seamlessly.

Scala, similar to Java, is a statically typed programming language but can infer type information. It can infer that t is a String type in the following example:

val t = "Text"

Semicolons are not required when terminating commands. Variables are declared, with var and constants with val, and Scala favors immutability, which means that we should try to minimize the usage of variables.

Scala is fully object-oriented and functional. There are no primitives, like float or int only objects such as Int, Long, Double, String, Boolean, Float. Also there is no null.

The Scala equivalent of Java interfaces is called trait. Scala allows traits to be partially implemented, that is, it is possible to define default implementations for...

Scala build tools

There are many build tools we can use to compile and build Scala or Scalding applications. They provide support for mixed Java/Scala projects, dependency management and useful plugins.

We can use Simple Build Tool (sbt) that allows incremental compilation and is itself implemented in Scala, or Maven, which is popular among developers, is mature, and provides a large range of plugins.

There are other build tools, such as Gradle and buildr, that support Scala, but in this book, we will use Maven and a number of plugins for project dependencies and assembly descriptors due to its high compatibility with continuous integration systems and most developers being familiar with this tool.

Hello World in Scala

To execute a Hello World application in Scala, we will use the latest version of Scala 2.10, the recommended JDK for Hadoop (Oracle JDK 1.6) and Maven. All we need is to add in our build tool a Scala library as a dependency and a plugin that compiles Scala:

<dependency>
  <groupId>org.scala-lang</groupId>
  <artifactId>scala-library</artifactId>
  <version>2.10.3</version>
</dependency>
...
<plugin>
  <groupId>net.alchim31.maven</groupId>
  <artifactId>scala-maven-plugin</artifactId>
  <version>3.1.6</version>
</plugin>

And the Scala source code:

object HelloWorld {
  def main(args: Array[String]) {
    println("Hello, world!")
  }
}

The preceding code can be executed with the following:

$ mvn package exec:java -Dexec.mainClass=HelloWorld

Development editors

Popular IDEs support Scala development through plugins. We should use them to enjoy autocompletion, error highlighting, code refactoring, navigation capabilities, integrated debugger, and much more.

The Scala IDE provides Eclipse prebundled with required plugins and is available at http://scala-ide.org. In a project, we need to add the Scala nature or facet in order to be able to execute Scala and Scalding applications.

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 in computing...

Scala basics


Scala modernizes Java's object-oriented approach while adding in the mix functional programming. It compiles into byte-code, and it can be executed on any Java virtual machine; thus, libraries and classes of Java and Scala communicate seamlessly.

Scala, similar to Java, is a statically typed programming language but can infer type information. It can infer that t is a String type in the following example:

val t = "Text"

Semicolons are not required when terminating commands. Variables are declared, with var and constants with val, and Scala favors immutability, which means that we should try to minimize the usage of variables.

Scala is fully object-oriented and functional. There are no primitives, like float or int only objects such as Int, Long, Double, String, Boolean, Float. Also there is no null.

The Scala equivalent of Java interfaces is called trait. Scala allows traits to be partially implemented, that is, it is possible to define default implementations for some methods. A...

Scala build tools


There are many build tools we can use to compile and build Scala or Scalding applications. They provide support for mixed Java/Scala projects, dependency management and useful plugins.

We can use Simple Build Tool (sbt) that allows incremental compilation and is itself implemented in Scala, or Maven, which is popular among developers, is mature, and provides a large range of plugins.

There are other build tools, such as Gradle and buildr, that support Scala, but in this book, we will use Maven and a number of plugins for project dependencies and assembly descriptors due to its high compatibility with continuous integration systems and most developers being familiar with this tool.

Hello World in Scala


To execute a Hello World application in Scala, we will use the latest version of Scala 2.10, the recommended JDK for Hadoop (Oracle JDK 1.6) and Maven. All we need is to add in our build tool a Scala library as a dependency and a plugin that compiles Scala:

<dependency>
  <groupId>org.scala-lang</groupId>
  <artifactId>scala-library</artifactId>
  <version>2.10.3</version>
</dependency>
...
<plugin>
  <groupId>net.alchim31.maven</groupId>
  <artifactId>scala-maven-plugin</artifactId>
  <version>3.1.6</version>
</plugin>

And the Scala source code:

object HelloWorld {
  def main(args: Array[String]) {
    println("Hello, world!")
  }
}

The preceding code can be executed with the following:

$ mvn package exec:java -Dexec.mainClass=HelloWorld
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Description

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.

What you will learn

  • Set up an environment to execute jobs in local and Hadoop mode
  • Preview the complete Scalding API through examples and illustrations
  • Learn about Scalding capabilities, testing, and pipelining jobs
  • Understand the concepts of MapReduce patterns and the applications of its ecosystem
  • Implement logfile analysis and adtargeting applications using best practices
  • Apply a testdriven development (TDD) methodology and structure Scalding applications in a modular and testable way
  • Interact with external NoSQL and SQL data stores from Scalding
  • Deploy, schedule, monitor, and maintain production systems

Product Details

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Publication date : Jun 25, 2014
Length: 148 pages
Edition : 1st
Language : English
ISBN-13 : 9781783287017
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Publication date : Jun 25, 2014
Length: 148 pages
Edition : 1st
Language : English
ISBN-13 : 9781783287017
Languages :
Tools :

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Table of Contents

10 Chapters
1. Introduction to MapReduce Chevron down icon Chevron up icon
2. Get Ready for Scalding Chevron down icon Chevron up icon
3. Scalding by Example Chevron down icon Chevron up icon
4. Intermediate Examples Chevron down icon Chevron up icon
5. Scalding Design Patterns Chevron down icon Chevron up icon
6. Testing and TDD Chevron down icon Chevron up icon
7. Running Scalding in Production Chevron down icon Chevron up icon
8. Using External Data Stores Chevron down icon Chevron up icon
9. Matrix Calculations and Machine Learning Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon

Customer reviews

Top Reviews
Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.3
(6 Ratings)
5 star 50%
4 star 33.3%
3 star 16.7%
2 star 0%
1 star 0%
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soulmachine Feb 23, 2015
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book is very easy to understand because it has many tiny examples that are very detailed to let you understand core APIs quickly.Chapter 4 "Intermediate Examples" elaborate two complete examples.Chapter 5 "Scalding Design Patterns" introduces three kind of desing paterns that are very pratical and insightful.The only weakness of this book is that it uses fields based APIs, but in my opinion, type safe APIs are more modern and elegant, however, all the knowlege of fiedls based APIs can apply to type safe APIs seeminglessly.
Amazon Verified review Amazon
Sujit Pal Jul 17, 2014
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Scalding is a small but very powerful and expressive Scala DSL built on top of Cascading, itself a Java API that exposes relational algebra constructs that expand to Map and Reduce operators in the backend. Scalding was developed at Twitter and open sourced - it has reasonably good documentation on GitHub and support is available on their Google Groups mailing list. There is also Paco Nathan's Cascading book where Scalding gets a chapter. However, this is the first book devoted completely to Scalding, and it does a great job making the Scalding API accessible to a broader audience.The target audience for this book is someone who is somewhat familiar with Scala and Hadoop, though not necessarily an expert at either. The author describes the behavior of various Scalding operations using before and after diagrams on small datasets which I thought was very helpful in understanding the API. The book covers the original fields based API and the typed API, and finally the Matrix API, all through case examples that increase in complexity as more advanced features are explained. There is also some coverage on making Scalding work with various NoSQL databases using custom Taps rather than just files.I am not an expert at Scalding, but I have used it in the past so I was quite familiar with some features of the DSL. But having read this book, I have a much better idea of Scalding's capabilities and how I can use them.DISCLAIMER - I did not buy this book, I requested a copy from a PackT representative because I was interested in learning more about Scalding and thought this book may help (I was right), and I thought my perspective as someone somewhat familiar with Scalding would be useful for other readers.
Amazon Verified review Amazon
Si Dunn Jul 29, 2014
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Programming MapReduce with Scalding offers clear, well-illustrated, smoothly paced how-to steps, as well as easy-to-digest definitions and descriptions. It takes the reader from setting up and running a Hadoop mini-cluster and local-development environment to applying Scalding to real-use cases, as well as developing good test and test-driven development methodologies, running Scalding in production, using external data stores, and applying matrix calculations and machine learning.The book is written for developers who have at least "a basic understanding" of Hadoop and MapReduce, but is also intended for experienced Hadoop developers who may be "enlightened by this alternative methodology of developing MapReduce applications with Scalding."It does help to be somewhat familiar with MapReduce, Scalding, Scala, Hadoop, Maven, Eclipse and the Linux environment. But Antonio Chalkiopoulo does a good job of keeping the examples accessible even when readers are new to some of the packages.
Amazon Verified review Amazon
Tushar Kapila Oct 28, 2015
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
few more examples with mixed join, how to refer to the elements in a fold, would be usful for everyday work
Amazon Verified review Amazon
tom Oct 22, 2015
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
It's got good coverage and examples to get you descent with fields API, but missing a bit on explaining how the code would translate into mappers and reducers and what you should watch for to optimize your code. It's also out dated since people are using the typed API now.
Amazon Verified review Amazon
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