Chambers William Andrew - Spark: the definitive guide: big data processing made simple
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by Bill Chambers and Matei Zaharia
Copyright 2018 Databricks. All rights reserved.
Printed in the United States of America.
Published by OReilly Media, Inc. , 1005 Gravenstein Highway North, Sebastopol, CA 95472.
OReilly books may be purchased for educational, business, or sales promotional use. Online editions are also available for most titles ( .
- Editor: Nicole Tache
- Production Editor: Justin Billing
- Copyeditor: Octal Publishing, Inc., Chris Edwards, and Amanda Kersey
- Proofreader: Jasmine Kwityn
- Indexer: Judith McConville
- Interior Designer: David Futato
- Cover Designer: Karen Montgomery
- Illustrator: Rebecca Demarest
- February 2018: First Edition
- 2018-02-08: First Release
- 2018-03-16: Second Release
See http://oreilly.com/catalog/errata.csp?isbn=9781491912218 for release details.
The OReilly logo is a registered trademark of OReilly Media, Inc. Spark: The Definitive Guide, the cover image, and related trade dress are trademarks of OReilly Media, Inc. Apache, Spark and Apache Spark are trademarks of the Apache Software Foundation.
While the publisher and the authors have used good faith efforts to ensure that the information and instructions contained in this work are accurate, the publisher and the authors disclaim all responsibility for errors or omissions, including without limitation responsibility for damages resulting from the use of or reliance on this work. Use of the information and instructions contained in this work is at your own risk. If any code samples or other technology this work contains or describes is subject to open source licenses or the intellectual property rights of others, it is your responsibility to ensure that your use thereof complies with such licenses and/or rights.
978-1-491-91221-8
[LSI]
Welcome to this first edition of Spark: The Definitive Guide! We are excited to bring you the most complete resource on Apache Spark today, focusing especially on the new generation of Spark APIs introduced in Spark 2.0.
Apache Spark is currently one of the most popular systems for large-scale data processing, with APIs in multiple programming languages and a wealth of built-in and third-party libraries. Although the project has existed for multiple yearsfirst as a research project started at UC Berkeley in 2009, then at the Apache Software Foundation since 2013the open source community is continuing to build more powerful APIs and high-level libraries over Spark, so there is still a lot to write about the project. We decided to write this book for two reasons. First, we wanted to present the most comprehensive book on Apache Spark, covering all of the fundamental use cases with easy-to-run examples. Second, we especially wanted to explore the higher-level structured APIs that were finalized in Apache Spark 2.0namely DataFrames, Datasets, Spark SQL, and Structured Streamingwhich older books on Spark dont always include. We hope this book gives you a solid foundation to write modern Apache Spark applications using all the available tools in the project.
In this preface, well tell you a little bit about our background, and explain who this book is for and how we have organized the material. We also want to thank the numerous people who helped edit and review this book, without whom it would not have been possible.
Both of the books authors have been involved in Apache Spark for a long time, so we are very excited to be able to bring you this book.
Bill Chambers started using Spark in 2014 on several research projects. Currently, Bill is a Product Manager at Databricks where he focuses on enabling users to write various types of Apache Spark applications. Bill also regularly blogs about Spark and presents at conferences and meetups on the topic. Bill holds a Masters in Information Management and Systems from the UC Berkeley School of Information.
Matei Zaharia started the Spark project in 2009, during his time as a PhD student at UC Berkeley. Matei worked with other Berkeley researchers and external collaborators to design the core Spark APIs and grow the Spark community, and has continued to be involved in new initiatives such as the structured APIs and Structured Streaming. In 2013, Matei and other members of the Berkeley Spark team co-founded Databricks to further grow the open source project and provide commercial offerings around it. Today, Matei continues to work as Chief Technologist at Databricks, and also holds a position as an Assistant Professor of Computer Science at Stanford University, where he does research on large-scale systems and AI. Matei received his PhD in Computer Science from UC Berkeley in 2013.
We designed this book mainly for data scientists and data engineers looking to use Apache Spark. The two roles have slightly different needs, but in reality, most application development covers a bit of both, so we think the material will be useful in both cases. Specifically, in our minds, the data scientist workload focuses more on interactively querying data to answer questions and build statistical models, while the data engineer job focuses on writing maintainable, repeatable production applicationseither to use the data scientists models in practice, or just to prepare data for further analysis (e.g., building a data ingest pipeline). However, we often see with Spark that these roles blur. For instance, data scientists are able to package production applications without too much hassle and data engineers use interactive analysis to understand and inspect their data to build and maintain pipelines.
While we tried to provide everything data scientists and engineers need to get started, there are some things we didnt have space to focus on in this book. First, this book does not include in-depth introductions to some of the analytics techniques you can use in Apache Spark, such as machine learning. Instead, we show you how to invoke these techniques using libraries in Spark, assuming you already have a basic background in machine learning. Many full, standalone books exist to cover these techniques in formal detail, so we recommend starting with those if you want to learn about these areas. Second, this book focuses more on application development than on operations and administration (e.g., how to manage an Apache Spark cluster with dozens of users). Nonetheless, we have tried to include comprehensive material on monitoring, debugging, and configuration in Parts of the book to help engineers get their application running efficiently and tackle day-to-day maintenance. Finally, this book places less emphasis on the older, lower-level APIs in Sparkspecifically RDDs and DStreamsto introduce most of the concepts using the newer, higher-level structured APIs. Thus, the book may not be the best fit if you need to maintain an old RDD or DStream application, but should be a great introduction to writing new applications.
The following typographical conventions are used in this book:
ItalicIndicates new terms, URLs, email addresses, filenames, and file extensions.
Constant widthUsed 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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