Welcome to MapReduce Design Patterns! This book will be unique in some ways and familiar in others. First and foremost, this book is obviously about design patterns, which are templates or general guides to solving problems. We took a look at other design patterns books that have been written in the past as inspiration, particularly Design Patterns: Elements of Reusable Object-Oriented Software, by Gamma et al. (1995), which is commonly referred to as “The Gang of Four” book. For each pattern, you’ll see a template that we reuse over and over that we loosely based off of their book. Repeatedly seeing a similar template will help you get to the specific information you need. This will be especially useful in the future when using this book as a reference.

This book is a bit more open-ended than a book in the “cookbook” series of texts as we don’t call out specific problems. However, similarly to the cookbooks, the lessons in this book are short and categorized. You’ll have to go a bit further than just copying and pasting our code to solve your problems, but we hope that you will find a pattern to get you at least 90% of the way for just about all of your challenges.

This book is mostly about the analytics side of Hadoop or MapReduce. We intentionally try not to dive into too much detail on how Hadoop or MapReduce works or talk too long about the APIs that we are using. These topics have been written about quite a few times, both online and in print, so we decided to focus on analytics.

In this preface, we’ll talk about how to read this book since its format might be a bit different than most books you’ve read.

Until now, design patterns for the MapReduce framework have been scattered among various research papers, blogs, and books. This handy guide brings together a unique collection of valuable MapReduce patterns that will save you time and effort regardless of the domain, language, or development framework you’re using.

Each pattern is explained in context, with pitfalls and caveats clearly identified to help you avoid common design mistakes when modeling your big data architecture. This book also provides a complete overview of MapReduce that explains its origins and implementations, and why design patterns are so important. All code examples are written for Hadoop.

• Summarization patterns: get a top-level view by summarizing and grouping data
• Filtering patterns: view data subsets such as records generated from one user
• Data organization patterns: reorganize data to work with other systems, or to make MapReduce analysis easier
• Join patterns: analyze different datasets together to discover interesting relationships
• Metapatterns: piece together several patterns to solve multi-stage problems, or to perform several analytics in the same job
• Input and output patterns: customize the way you use Hadoop to load or store data

"A clear exposition of MapReduce programs for common data processing patterns—this book is indespensible for anyone using Hadoop."

--Tom White, author of Hadoop: The Definitive Guide