Preface
As parallel data analysis has grown common, practitioners in many fields have sought easier tools for this task. Apache Spark has quickly emerged as one of the most popular, extending and generalizing MapReduce. Spark offers three main benefits. First, it is easy to use—you can develop applications on your laptop, using a high-level API that lets you focus on the content of your computation. Second, Spark is fast, enabling interactive use and complex algorithms. And third, Spark is a general engine, letting you combine multiple types of computations (e.g., SQL queries, text processing, and machine learning) that might previously have required different engines. These features make Spark an excellent starting point to learn about Big Data in general.
This introductory book is meant to get you up and running with Spark quickly. You’ll learn how to download and run Spark on your laptop and use it interactively to learn the API. Once there, we’ll cover the details of available operations and distributed execution. Finally, you’ll get a tour of the higher-level libraries built into Spark, including libraries for machine learning, stream processing, and SQL. We hope that this book gives you the tools to quickly tackle data analysis problems, whether you do so on one machine or hundreds.
Audience
This book targets data scientists and engineers. We chose these two groups because they have the most to gain from using Spark to expand the scope of problems they can solve. Spark’s rich collection of data-focused libraries (like MLlib) makes it easy for data scientists to go beyond problems that fit on a single machine while using their statistical background. Engineers, meanwhile, will learn how to write general-purpose distributed programs in Spark and operate production applications. Engineers and data scientists will both learn different details from this book, but will both be able to apply Spark to solve large distributed problems in their respective fields.
Data scientists focus on answering questions or building models from data. They often have a statistical or math background and some familiarity with tools like Python, R, and SQL. We have made sure to include Python and, where relevant, SQL examples for all our material, as well as an overview of the machine learning and library in Spark. If you are a data scientist, we hope that after reading this book you will be able to use the same mathematical approaches to solve problems, except much faster and on a much larger scale.
The second group this book targets is software engineers who have some experience with Java, Python, or another programming language. If you are an engineer, we hope that this book will show you how to set up a Spark cluster, use the Spark shell, and write Spark applications to solve parallel processing problems. If you are familiar with Hadoop, you have a bit of a head start on figuring out how to interact with HDFS and how to manage a cluster, but either way, we will cover basic distributed execution concepts.
Regardless of whether you are a data scientist or engineer, to get the most out of this book you should have some familiarity with one of Python, Java, Scala, or a similar language. We assume that you already have a storage solution for your data and we cover how to load and save data from many common ones, but not how to set them up. If you don’t have experience with one of those languages, don’t worry: there are excellent resources available to learn these. We call out some of the books available in “Supporting Books”.
How This Book Is Organized
The chapters of this book are laid out in such a way that you should be able to go through the material front to back. At the start of each chapter, we will mention which sections we think are most relevant to data scientists and which sections we think are most relevant for engineers. That said, we hope that all the material is accessible to readers of either background.
The first two chapters will get you started with getting a basic Spark installation on your laptop and give you an idea of what you can accomplish with Spark. Once we’ve got the motivation and setup out of the way, we will dive into the Spark shell, a very useful tool for development and prototyping. Subsequent chapters then cover the Spark programming interface in detail, how applications execute on a cluster, and higher-level libraries available on Spark (such as Spark SQL and MLlib).
Supporting Books
If you are a data scientist and don’t have much experience with Python, the books Learning Python and Head First Python (both O’Reilly) are excellent introductions. If you have some Python experience and want more, Dive into Python (Apress) is a great book to help you get a deeper understanding of Python.
If you are an engineer and after reading this book you would like to expand your data analysis skills, Machine Learning for Hackers and Doing Data Science are excellent books (both O’Reilly).
This book is intended to be accessible to beginners. We do intend to release a deep-dive follow-up for those looking to gain a more thorough understanding of Spark’s internals.
Conventions Used in This Book
The following typographical conventions are used in this book:
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Indicates new terms, URLs, email addresses, filenames, and file extensions.
Constant width-
Used for program listings, as well as within paragraphs to refer to program elements such as variable or function names, databases, data types, environment variables, statements, and keywords.
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Shows commands or other text that should be typed literally by the user.
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Shows text that should be replaced with user-supplied values or by values determined by context.
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This element signifies a tip or suggestion.
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Code Examples
All of the code examples found in this book are on GitHub. You can examine them and check them out from https://github.com/databricks/learning-spark. Code examples are provided in Java, Scala, and Python.
Tip
Our Java examples are written to work with Java version 6 and higher. Java 8 introduces a new syntax called lambdas that makes writing inline functions much easier, which can simplify Spark code. We have chosen not to take advantage of this syntax in most of our examples, as most organizations are not yet using Java 8. If you would like to try Java 8 syntax, you can see the Databricks blog post on this topic. Some of the examples will also be ported to Java 8 and posted to the book’s GitHub site.
This book is here to help you get your job done. In general, if example code is offered with this book, you may use it in your programs and documentation. You do not need to contact us for permission unless you’re reproducing a significant portion of the code. For example, writing a program that uses several chunks of code from this book does not require permission. Selling or distributing a CD-ROM of examples from O’Reilly books does require permission. Answering a question by citing this book and quoting example code does not require permission. Incorporating a significant amount of example code from this book into your product’s documentation does require permission.
We appreciate, but do not require, attribution. An attribution usually includes the title, author, publisher, and ISBN. For example: “Learning Spark by Holden Karau, Andy Konwinski, Patrick Wendell, and Matei Zaharia (O’Reilly). Copyright 2015 Databricks, 978-1-449-35862-4.”
If you feel your use of code examples falls outside fair use or the permission given above, feel free to contact us at [email protected].
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Acknowledgments
The authors would like to thank the reviewers who offered feedback on this book: Joseph Bradley, Dave Bridgeland, Chaz Chandler, Mick Davies, Sam DeHority, Vida Ha, Andrew Gal, Michael Gregson, Jan Joeppen, Stephan Jou, Jeff Martinez, Josh Mahonin, Andrew Or, Mike Patterson, Josh Rosen, Bruce Szalwinski, Xiangrui Meng, and Reza Zadeh.
The authors would like to extend a special thanks to David Andrzejewski, David Buttler, Juliet Hougland, Marek Kolodziej, Taka Shinagawa, Deborah Siegel, Dr. Normen Müller, Ali Ghodsi, and Sameer Farooqui. They provided detailed feedback on the majority of the chapters and helped point out many significant improvements.
We would also like to thank the subject matter experts who took time to edit and write parts of their own chapters. Tathagata Das worked with us on a very tight schedule to finish Chapter 10. Tathagata went above and beyond with clarifying examples, answering many questions, and improving the flow of the text in addition to his technical contributions. Michael Armbrust helped us check the Spark SQL chapter for correctness. Joseph Bradley provided the introductory example for MLlib in Chapter 11. Reza Zadeh provided text and code examples for dimensionality reduction. Xiangrui Meng, Joseph Bradley, and Reza Zadeh also provided editing and technical feedback for the MLlib chapter.