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Learning Cascading
Learning Cascading

Learning Cascading: Build reliable, robust, and high-performance big data applications using the Cascading application development efficiently

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Profile Icon Covert Profile Icon Victoria Loewengart
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$48.99
Full star icon Full star icon Full star icon Full star icon Full star icon 5 (3 Ratings)
Paperback May 2015 276 pages 1st Edition
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Arrow left icon
Profile Icon Covert Profile Icon Victoria Loewengart
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$48.99
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Paperback May 2015 276 pages 1st Edition
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$35.99 $39.99
Paperback
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Renews at $19.99p/m
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Renews at $19.99p/m

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Learning Cascading

Chapter 2. Cascading Basics in Detail

In this chapter, we will begin our exploration of Cascading. We will look at its intended purpose, its structure, developing programs using it base classes, and managing the execution of these programs.

Understanding common Cascading themes

Cascading is an application development and a data processing framework that makes Hadoop development simpler and more durable over time. It does this by reducing the number of lines of code that are required to perform most tasks, and durability comes through its seamless support of Hadoop and other big data frameworks.

Hadoop application development with MapReduce and other frameworks is a difficult task with a very steep learning curve, as you have seen in the previous chapter. Cascading significantly simplifies this process with an intuitive high-level abstraction over Hadoop application development, job creation, and job processing. With Cascading, developers can create reusable components and pipe assemblies, which are reliable and scalable.

Data flows as processes

Imagine a plumbing system, a water filtration system, or a brewery—anything that uses a stream of some kind to be processed and is ultimately used. The system usually starts with...

Understanding how Cascading represents records

Now that you have gotten a glimpse of how you can implement a data processing system using Cascading, let's dive into the internals of it. In this section, we will learn how to define and structure data streams for Cascading processing.

Using tuples and defining fields

The idea of a tuple is very similar to that of a record in a database.

Tuples (cascading.tuple.Tuple) provide storage for a vector or a collection of values, addressed by offset that can be associated with specific object types and names. A tuple can have data objects of different types. A series of tuples make a tuple stream. A simple example of a tuple is [String name, Integer age]. A tuple has a large set of methods to get, set, append, and remove fields.

Strictly speaking, a tuple stores data in positional columns, such that each column is accessed by its ordinal position. Each element is assumed to represent a single data element, and all are assumed to be of a standard...

Understanding how Cascading controls data flow

Now, we know what the Cascading record looks like. How do we process these records? How do we move and manipulate data? Cascading provides us with the concept of pipes. Pipes control how data is managed during the processing segment.

Using pipes

Pipes are things that do stuff. The Cascading API allows the developer to assemble pipe assemblies that split, merge, group, or join streams. As data moves through pipes, streams may be separated or combined for various purposes:

Using pipes

Figure 2.3 – Pipe definition

Some pipes, such as Merge, GroupBy, and the Join classes, perform single actions on entire Tuple streams. Others, such as Each and Every require an operation to be attached to them. It is the operation code that performs the desired task. We will look at operations briefly in this chapter, though Chapter 3, Understanding Custom Operations will explore them in detail.

Creating and chaining

Pipes are created through both declaration and instantiation...

Putting it all together

This code illustrates how to put together a simple Cascading application, which takes inputs from two CSV (comma-separated value) files and joins them in common fields.

Here is the problem: in some organizations, employees are also published authors. We would like to compile a list of employees who are authors, and add department information to the list.

We have two CSV files: one file contains data on author's name, organization, journal title, and a keyword (the topic of their published work), and the second file contains department names and employee names. Our desired resultant list is the list of employees who are also authors, including their organization, department, journal title, and keyword. We will describe step by step how to build this application.

  1. Here are the important libraries to import in order for the program to work:
    import cascading.property.AppProps;
    import cascading.scheme.Scheme;
    import cascading.scheme.local.TextDelimited;
    import cascading...

Summary

In this chapter, you learned the basics of the Cascading API and the elements of a Cascading application. We started with the most basic building blocks of a Cascading application, such as tuples, fields, pipes, and simple pipe assemblies. We learned how to read and write data using taps and define data types using schemes. Later in the chapter, we learned how to create more complex pipe assemblies by joining separate datasets together, and showed you how to merge, spit, group, and sort data. We touched on operations as a lead-in into the next chapter. We introduced you to flows and learned how to assemble them into Cascades. Finally, we touched on data handling and how to use Cascading in both local and Hadoop modes. We concluded with our first fully functional Cascading application. This chapter gave us a foundation for a deeper immersion into Cascading in the consequent chapters.

In the next chapter, we will learn how to extend Cascading and make it transform data and also perform...

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Description

This book is intended for software developers, system architects and analysts, big data project managers, and data scientists who wish to deploy big data solutions using the Cascading framework. You must have a basic understanding of the big data paradigm and should be familiar with Java development techniques.

Who is this book for?

This book is intended for software developers, system architects and analysts, big data project managers, and data scientists who wish to deploy big data solutions using the Cascading framework. You must have a basic understanding of the big data paradigm and should be familiar with Java development techniques.

What you will learn

  • Familiarize yourself with tuples, pipes, taps, and flows and build your first Cascading application
  • Discover how to design, develop, and use custom operations
  • Design, develop, use, and reuse code with subassemblies and Cascades
  • Acquire the skills you need to integrate Cascading with external systems
  • Gain expertise in testing, QA, and performance tuning to run an efficient and successful Cascading project
  • Explore project management methodologies and steps to develop workable solutions
  • Discover the future of big data frameworks and understand how Cascading can help your software to evolve with it
  • Uncover sources of additional information and other tools that can make development tasks a lot easier
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Publication date : May 29, 2015
Length: 276 pages
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Language : English
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Table of Contents

11 Chapters
1. The Big Data Core Technology Stack Chevron down icon Chevron up icon
2. Cascading Basics in Detail Chevron down icon Chevron up icon
3. Understanding Custom Operations Chevron down icon Chevron up icon
4. Creating Custom Operations Chevron down icon Chevron up icon
5. Code Reuse and Integration Chevron down icon Chevron up icon
6. Testing a Cascading Application Chevron down icon Chevron up icon
7. Optimizing the Performance of a Cascading Application Chevron down icon Chevron up icon
8. Creating a Real-world Application in Cascading Chevron down icon Chevron up icon
9. Planning for Future Growth Chevron down icon Chevron up icon
A. Downloadable Software Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Full star icon Full star icon 5
(3 Ratings)
5 star 100%
4 star 0%
3 star 0%
2 star 0%
1 star 0%
John Farhat Aug 10, 2015
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I am new to "Big Data" so I enjoyed getting a functional understanding of Hadoop's overall structure in Chapter 1. Then each chapter helped build on the previous knowledge, and I found that a logical way to go. I was especially impressed when I got to chapter 8 which walked through a real life project from start to finish. The links to additional information, source code, utilities, libraries, and other content is very useful.This book is not a quick read! But it is clearly written from the perspective of practicing professionals. So it has a lot of helpful tips and suggestions to steer you away from pitfalls. Definitely has my vote.
Amazon Verified review Amazon
Krishnan Jul 27, 2015
Full star icon Full star icon Full star icon Full star icon Full star icon 5
A good book on Cascading. Ideal for novice on bigdata and data processing using Hadoop (Hadoop basics also explained well).Highly Recommended!
Amazon Verified review Amazon
Abhishek Srivastava Aug 04, 2015
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Its a great introduction to a awesome framework. I loved the book and I also loved the framework. This book provides good introduction and goes in depth as well.Only criticism I have is that this book did not talk about how to build Cascading apps using Maven, Gradle etc. This is a really important step when learning any jvm language technology these days.
Amazon Verified review Amazon
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