Gerard Maas - Stream Processing with Apache Spark: Mastering Structured Streaming and Spark Streaming
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by Gerard Maas and Franois Garillot
Copyright 2019 Franois Garillot and Gerard Maas Images. All rights reserved.
Printed in the United States of America.
Published by OReilly Media, Inc. , 1005 Gravenstein Highway North, Sebastopol, CA 95472.
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- June 2019: First Edition
- 2019-06-12: First Release
See http://oreilly.com/catalog/errata.csp?isbn=9781491944240 for release details.
The OReilly logo is a registered trademark of OReilly Media, Inc. Stream Processing with Apache Spark, the cover image, and related trade dress are trademarks of OReilly Media, Inc.
The views expressed in this work are those of the authors, and do not represent the publishers views. 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.
978-1-491-94424-0
[LSI]
Welcome to Stream Processing with Apache Spark!
Its very exciting to see how much both the Apache Spark project, as well as stream processing with Apache Spark have come along since it was first started by Matei Zaharia at University of California Berkeley in 2009. Apache Spark started off as the first unified engine for big data processing and has grown into the de-facto standard for all things big data.
Stream Processing with Apache Spark is an excellent introduction to the concepts, tools, and capabilities of Apache Spark as a stream processing engine. This book will first introduce you to the core Spark concepts necessary to understand modern distributed processing. Then it will explore different stream processing architectures and the fundamental architectural trade-offs between then. Finally, it will illustrate how Structured Streaming in Apache Spark makes it easy to implement distributed streaming applications. In addition, it will also cover the older Spark Streaming (aka, DStream) APIs for building streaming applications with legacy connectors.
In all, this book covers everything youll need to know to master building and operating streaming applications using Apache Spark! We look forward to hearing about what youll build!
Tathagata Das
Cocreator of Spark Streaming and Structured Streaming
Michael Armbrust
Cocreator of Spark SQL and Structured Streaming
Bill Chambers
Coauthor of Spark: The Definitive Guide
May 2019
We created this book for software professionals who have an affinity for data and who want to improve their knowledge and skills in the area of stream processing, and who are already familiar with or want to use Apache Spark for their streaming applications.
We have included a comprehensive introduction to the concepts behind stream processing. These concepts form the foundations to understand the two streaming APIs offered by Apache Spark: Structured Streaming and Spark Streaming.
We offer an in-depth exploration of these APIs and provide insights into their features, application, and practical advice derived from our experience.
Beyond the coverage of the APIs and their practical applications, we also discuss several advanced techniques that belong in the toolbox of every stream-processing practitioner.
Readers of all levels will benefit from the introductory parts of the book, whereas more experienced professionals will draw new insights from the advanced techniques covered and will receive guidance on how to learn more.
We have made no assumptions about your required knowledge of Spark, but readers who are not familiar with Sparks data-processing capabilities should be aware that in this book, we focus on its streaming capabilities and APIs. For a more general view of the Spark capabilities and ecosystem, we recommend Spark: The Definitive Guide by Bill Chambers and Matei Zaharia (OReilly).
The programming language used across the book is Scala. Although Spark provides bindings in Scala, Java, Python, and R, we think that Scala is the language of choice for streaming applications. Even though many of the code samples could be translated into other languages, some areas, such as complex stateful computations, are best approached using the Scala programming language.
Spark is an Apache open source project hosted officially by theApache Foundation, but which mostly uses GitHub for itsdevelopment. You can also download it as a binary, pre-compiled package at the following address: https://spark.apache.org/downloads.html.
From there, you can begin running Spark on one or moremachines, which we will explain later. Packages exist for all of themajor Linux distributions, which should help installation.
For the purposes of this book, we use examples and codecompatible with Spark 2.4.0, and except forminor output and formatting details, those examples should staycompatible with future Spark versions.
Note, however, that Spark is a program that runs on the JavaVirtual Machine (JVM), which you should install and make accessibleon every machine on which any Spark component will run.
To install a Java Development Kit (JDK), we recommend OpenJDK,which is packaged on many systems and architectures, as well.
You can also install the Oracle JDK.
Spark, as any Scala program, runs on any system on which a JDKversion 6 or later is present. The recommended Java runtime for Spark depends on the version:
For Spark versions below 2.0, Java 7 is the recommended version.
For Spark versions 2.0 and above, Java 8 is the recommended version.
The examples in this book are in Scala. This is theimplementation language of core Spark, but it is by far not the onlylanguage in which it can be used; as of this writing, Spark offers APIs inPython, Java, and R.
Scala is one of the most feature-complete programming languages today, inthat it offers both functional and object-oriented aspects. Yet, itsconcision and type inference makes the basic elements of its syntax easyto understand.
Scala as a beginner language has many advantages from a pedagogicalviewpoint, its regular syntax and semantics being one of the mostimportant.
Bjrn Regnell, Lund University
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