Apache Hadoop YARN: Moving beyond MapReduce and Batch Processing with Apache Hadoop 2 (2014)
Foreword by Raymie Stata
William Gibson was fond of saying: “The future is already here—it’s just not very evenly distributed.” Those of us who have been in the web search industry have had the privilege—and the curse—of living in the future of Big Data when it wasn’t distributed at all. What did we learn? We learned to measure everything. We learned to experiment. We learned to mine signals out of unstructured data. We learned to drive business value through data science. And we learned that, to do these things, we needed a new data-processing platform fundamentally different from the business intelligence systems being developed at the time.
The future of Big Data is rapidly arriving for almost all industries. This is driven in part by widespread instrumentation of the physical world—vehicles, buildings, and even people are spitting out log streams not unlike the weblogs we know and love in cyberspace. Less obviously, digital records—such as digitized government records, digitized insurance policies, and digital medical records—are creating a trove of information not unlike the webpages crawled and parsed by search engines. It’s no surprise, then, that the tools and techniques pioneered first in the world of web search are finding currency in more and more industries. And the leading such tool, of course, is Apache Hadoop.
But Hadoop is close to ten years old. Computing infrastructure has advanced significantly in this decade. If Hadoop was to maintain its relevance in the modern Big Data world, it needed to advance as well. YARN represents just the advancement needed to keep Hadoop relevant.
As described in the historical overview provided in this book, for the majority of Hadoop’s existence, it supported a single computing paradigm: MapReduce. On the compute servers we had at the time, horizontal scaling—throwing more server nodes at a problem—was the only way the web search industry could hope to keep pace with the growth of the web. The MapReduce paradigm is particularly well suited for horizontal scaling, so it was the natural paradigm to keep investing in.
With faster networks, higher core counts, solid-state storage, and (especially) larger memories, new paradigms of parallel computing are becoming practical at large scales. YARN will allow Hadoop users to move beyond MapReduce and adopt these emerging paradigms. MapReduce will not go away—it’s a good fit for many problems, and it still scales better than anything else currently developed. But, increasingly, MapReduce will be just one tool in a much larger tool chest—a tool chest named “YARN.”
In short, the era of Big Data is just starting. Thanks to YARN, Hadoop will continue to play a pivotal role in Big Data processing across all industries. Given this, I was pleased to learn that YARN project founder Arun Murthy and project lead Vinod Kumar Vavilapalli have teamed up with Doug Eadline, Joseph Niemiec, and Jeff Markham to write a volume sharing the history and goals of the YARN project, describing how to deploy and operate YARN, and providing a tutorial on how to get the most out of it at the application level.
This book is a critically needed resource for the newly released Apache Hadoop 2.0, highlighting YARN as the significant breakthrough that broadens Hadoop beyond the MapReduce paradigm.
—Raymie Stata, CEO of Altiscale
Foreword by Paul Dix
No series on data and analytics would be complete without coverage of Hadoop and the different parts of the Hadoop ecosystem. Hadoop 2 introduced YARN, or “Yet Another Resource Negotiator,” which represents a major change in the internals of how data processing works in Hadoop. With YARN, Hadoop has moved beyond the MapReduce paradigm to expose a framework for building applications for data processing at scale. MapReduce has become just an application implemented on the YARN framework. This book provides detailed coverage of how YARN works and explains how you can take advantage of it to work with data at scale in Hadoop outside of MapReduce.
No one is more qualified to bring this material to you than the authors of this book. They’re the team at Hortonworks responsible for the creation and development of YARN. Arun, a co-founder of Hortonworks, has been working on Hadoop since its creation in 2006. Vinod has been contributing to the Apache Hadoop project full-time since mid-2007. Jeff and Joseph are solutions engineers with Hortonworks. Doug is the trainer for the popular Hadoop Fundamentals LiveLessons and has years of experience building Hadoop and clustered systems. Together, these authors bring a breadth of knowledge and experience with Hadoop and YARN that can’t be found elsewhere.
This book provides you with a brief history of Hadoop and MapReduce to set the stage for why YARN was a necessary next step in the evolution of the platform. You get a walk-through on installation and administration and then dive into the internals of YARN and the Capacity scheduler. You see how existing MapReduce applications now run as an applications framework on top of YARN. Finally, you learn how to implement your own YARN applications and look at some of the new YARN-based frameworks. This book gives you a comprehensive dive into the next generation Hadoop platform.
—Paul Dix, Series Editor