Categories

## Functional Programming in Scala, 1st Edition – 978-1617290657

See: Publisher ‏ : ‎ Manning Publications; 1st edition (September 14, 2014)

“Leads to deep insights into the nature of computation.”
From the Foreword by Martin Odersky, Creator of Scala

Functional Programming in Scala is a serious tutorial for programmers looking to learn FP and apply it to the everyday business of coding. This Video Editions book guides readers from basic techniques to advanced topics in a logical, concise, and clear progression. In it, you’ll find concrete examples that open up the world of functional programming.

Functional programming (FP) is a style of software development emphasizing functions that don’t depend on program state. Functional code is easier to test and reuse, simpler to parallelize, and less prone to bugs than other code. Scala is an emerging JVM language that offers strong support for FP. Its familiar syntax and transparent interoperability with Java make Scala a great place to start learning FP.Inside:

• Functional programming concepts
• The whys and hows of FP
• How to write multicore programs
• Checks for understanding

This title assumes no prior experience with functional programming. Some prior exposure to Scala or Java is helpful.

Paul Chiusano and Rúnar Bjarnason are recognized experts in functional programming with Scala and are core contributors to the Scalaz library.

The definitive guide to functional programming for Scala and Java 8 developers!
William E. Wheeler, TekSystems

Shows you the approach and mindset to raise your Scala way beyond ‘a better Java’.

NARRATED BY MARK THOMAS

## Resources

Publisher Website: https://www.manning.com/books/functional-programming-in-scala

Categories

## Hands-on Scala Programming: Learn Scala in a Practical, Project-Based Way – ISBN-13: 978-9811456930

See: Hands-on Scala Programming: Learn Scala in a Practical, Project-Based Way, Publisher ‏ : ‎ Li Haoyi (July 11, 2020)

Hands-on Scala teaches you how to use the Scala programming language in a practical, project-based fashion. This book is designed to quickly teach an existing programmer everything needed to go from “hello world” to building production applications like interactive websites, parallel web crawlers, and distributed systems in Scala. In the process you will learn how to use the Scala language to solve challenging problems in an elegant and intuitive manner.

Currently 50% off the normal price (US\$88) until the end of the month! E-Book versions of Hands-on Scala are available from the book’s website, https://handsonscala.com

### Review

“Hands-On Scala is the best way to learn about writing Scala in this simple and straightforward manner, and a great resource for getting things done using the Scala ecosystem.”

“I helped review some of this book – excellent work by Haoyi; if you’re looking to get into Scala, recommended!”

• Alex Allain, author of Jumping into C++

“Fantastic book! I got the privilege to review it, and I can honestly say it made me a better engineer! Tour de force by Haoyi”

• Dimitar Simeonov, entrepreneur

“This will be great! Haoyi asked me to read an early draft. Very pragmatic. Great examples.”

Li Haoyi graduated from MIT with a degree in Computer Science and Engineering, and since then has been a major contributor to the Scala community. His open source projects have over 10,000 stars on Github, and are downloaded over 7,000,000 times a month. Haoyi has used Scala professionally to build distributed backend systems, programming languages, high-performance web applications, and much more. Haoyi writes a blog about Scala and other technical topics at https://lihaoyi.com

Categories

## Scala for the Impatient, 2nd Edition – B01MR67YSO ISBN-13: 978-0134540566, 2016

See: Scala for the Impatient, 2nd Edition, by Cay Horstmann, Publisher ‏ : ‎ Addison-Wesley Professional; 2nd edition (December 28, 2016)

Interest in the Scala programming language continues to grow for many reasons. Scala embraces the functional programming style without abandoning the object-oriented paradigm, and it allows you to write programs more concisely than in Java. Because Scala runs on the JVM, it can access any Java library and is interoperable with familiar Java frameworks. Scala also makes it easier to leverage the full power of concurrency.

Written for experienced Java, C++, or C# programmers who are new to Scala or functional programming, Scala for the Impatient, Second Edition, introduces the key Scala concepts and techniques you need in order to be productive quickly. It is the perfect introduction to the language, particularly for impatient readers who want to learn the fundamentals of Scala so they can start coding quickly. It doesn’t attempt to exhaustively list all the features of the language or make you suffer through long and contrived examples. Instead, carefully crafted examples and hands-on activities guide you through well-defined stages of competency, from basic to expert.

This revised edition has been thoroughly updated for Scala 2.12 and reflects current Scala usage. It includes added coverage of recent Scala features, including string interpolation, dynamic invocation, implicit classes, and futures. Scala is a big language, but you can use it effectively without knowing all of its details intimately. This title provides precisely the information that you need to get started in compact, easy-to-understand chunks.

Get started quickly with Scala’s interpreter, syntax, tools, and unique idioms

Master core language features: functions, arrays, maps, tuples, packages, imports, exception handling, and more

Become familiar with object-oriented programming in Scala: classes, inheritance, and traits

Use Scala for real-world programming tasks: working with files, regular expressions, and XML

Work with higher-order functions and the powerful Scala collections library

Leverage Scala’s powerful pattern matching and case classes

Create concurrent programs with Scala futures

Implement domain-specific languages

Understand the Scala type system

Apply advanced “power tools,” such as annotations, implicits, and type classes

## Resources

Purchase book: http://www.informit.com/title/9780134540566

Cay S. Horstmann is principal author of Core Java®, Volumes I & II, Tenth Edition (Prentice Hall), as well as a dozen other books for professional programmers and computer science students. He is a professor of computer science at San Jose State University and a Java Champion.

Categories

## Programming in Scala, Fifth Edition, by Martin Odersky – B097KT5XNK ISBN-13: 978-0997148008, June 15, 2021

See: Programming in Scala Fifth Edition, by Martin Odersky, Publisher ‏ : ‎ Artima Press; 5th edition (June 15, 2021)

This book is the authoritative tutorial on the Scala programming language, co-written by the language’s designer, Martin Odersky. This fifth edition is a major rewrite of the entire book, adding new material to cover the many changes in Scala 3.0. In fact we have added so much new material that we split the book into two volumes. This volume is a tutorial of Scala and functional programming.

## Praise for the earlier editions of Programming in Scala

Programming in Scala is probably one of the best programming books I’ve ever read. I like the writing style, the brevity, and the thorough explanations. The book seems to answer every question as it enters my mind—it’s always one step ahead of me. The authors don’t just give you some code and take things for granted. They give you the meat so you really understand what’s going on. I really like that.

• Ken Egervari, Chief Software Architect

Programming in Scala is clearly written, thorough, and easy to follow. It has great examples and useful tips throughout. It has enabled our organization to ramp up on the Scala language quickly and efficiently. This book is great for any programmer who is trying to wrap their head around the flexibility and elegance of the Scala language.

• Larry Morroni, Owner, Morroni Technologies, Inc.

The Programming in Scala book serves as an excellent tutorial to the Scala language. Working through the book, it flows well with each chapter building on concepts and examples described in earlier ones. The book takes care to explain the language constructs in depth, often providing examples of how the language differs from Java. As well as the main language, there is also some coverage of libraries such as containers and actors.

I have found the book really easy to work through, and it is probably one of the better written technical books I have read recently. I really would recommend this book to any programmer wanting to find out more about the Scala language.

• Matthew Todd

I am amazed by the effort undertaken by the authors of Programming in Scala. This book is an invaluable guide to what I like to call Scala the Platform: a vehicle to better coding, a constant inspiration for scalable software design and implementation. If only I had Scala in its present mature state and this book on my desk back in 2003, when co-designing and implementing parts of the Athens 2004 Olympic Games Portal infrastructure!

To all readers: No matter what your programming background is, I feel you will find programming in Scala liberating and this book will be a loyal friend in the journey.

• Christos KK Loverdos, Software Consultant, Researcher

Programming in Scala is a superb in-depth introduction to Scala, and it’s also an excellent reference. I’d say that it occupies a prominent place on my bookshelf, except that I’m still carrying it around with me nearly everywhere I go.

• Brian Clapper, President, ArdenTex, Inc.

Great book, well written with thoughtful examples. I would recommend it to both seasoned programmers and newbies.

• Howard Lovatt

The book Programming in Scala is not only about how, but more importantly, why to develop programs in this new programming language. The book’s pragmatic approach in introducing the power of combining object-oriented and functional programming leaves the reader without any doubts as to what Scala really is.

• Dr. Ervin Varga, CEO/founder, EXPRO I.T. Consulting

This is a great introduction to functional programming for OO programmers. Learning about FP was my main goal, but I also got acquainted with some nice Scala surprises like case classes and pattern matching. Scala is an intriguing language and this book covers it well.

There’s always a fine line to walk in a language introduction book between giving too much or not enough information. I find Programming in Scala to achieve a perfect balance.

• Jeff Heon, Programmer Analyst

Read Programming in Scala for the content, but if you’re reading the electronic version, definitely take advantage of the digital features that the authors took the care to build in!

• Dianne Marsh, Founder/Software Consultant, SRT Solutions

Lucidity and technical completeness are hallmarks of any well-written book, and I congratulate Martin Odersky, Lex Spoon, and Bill Venners on a job indeed very well done! The Programming in Scala book starts by setting a strong foundation with the basic concepts and ramps up the user to an intermediate level & beyond. This book is certainly a must buy for anyone aspiring to learn Scala.

• Jagan Nambi, Enterprise Architecture, GMAC Financial Services

Programming in Scala is a pleasure to read. This is one of those well-written technical books that provide deep and comprehensive coverage of the subject in an exceptionally concise and elegant manner.

The book is organized in a very natural and logical way. It is equally well suited for a curious technologist who just wants to stay on top of the current trends and a professional seeking deep understanding of the language core features and its design rationales. I highly recommend it to all interested in functional programming in general. For Scala developers, this book is unconditionally a must-read.

• Igor Khlystov, Software Architect/Lead Programmer, Greystone Inc.

The book Programming in Scala outright oozes the huge amount of hard work that has gone into it. I’ve never read a tutorial-style book before that accomplishes to be introductory yet comprehensive: in their (misguided) attempt to be approachable and not “confuse” the reader, most tutorials silently ignore aspects of a subject that are too advanced for the current discussion. This leaves a very bad taste, as one can never be sure as to the understanding one has achieved. There is always some residual “magic” that hasn’t been explained and cannot be judged at all by the reader. This book never does that, it never takes anything for granted: every detail is either sufficiently explained or a reference to a later explanation is given. Indeed, the text is extensively cross-referenced and indexed, so that forming a complete picture of a complex topic is relatively easy.

• Gerald Loeffler, Enterprise Java Architect

Programming in Scala by Martin Odersky, Lex Spoon, and Bill Venners: in times where good programming books are rare, this excellent introduction for intermediate programmers really stands out. You’ll find everything here you need to learn this promising language.

• Christian Neukirchen

Programming in Scala, Fifth Edition

Martin Odersky, Lex Spoon, Bill Venners, and Frank Sommers

Artima Press, Walnut Creek, California

Martin Odersky is the creator of the Scala language and a professor at EPFL in Lausanne, Switzerland. Lex Spoon worked on Scala for two years as a post-doc with Martin Odersky. Bill Venners is president of Artima, Inc. Frank Sommers is president of Autospaces, Inc.

Artima Press is an imprint of Artima, Inc.

2070 N Broadway Unit 305, Walnut Creek, California 94597

First edition published as PrePrint™ eBook 2007

First edition published 2008

Second edition published 2010

Third edition published 2016

Fourth edition published 2019

Fifth edition published as PrePrint™ eBook 2021

Build date of this impression July 12, 2021

Produced in the United States of America

No part of this publication may be reproduced, modified, distributed, stored in a retrieval system, republished, displayed, or performed, for commercial or noncommercial purposes or for compensation of any kind without prior written permission from Artima, Inc.

All information and materials in this book are provided “as is” and without warranty of any kind.

The term “Artima” and the Artima logo are trademarks or registered trademarks of Artima, Inc. All other company and/or product names may be trademarks or registered trademarks of their owners.

to Nastaran – M.O.

to Fay – L.S.

to Siew – B.V.

to Jungwon – F.S.

—-

Foreword

Acknowledgments

Introduction

1. A Scalable Language
2. First Steps in Scala
3. Next Steps in Scala
4. Classes and Objects
5. Basic Types and Operations
6. Functional Objects
7. Built-in Control Structures
8. Functions and Closures
9. Control Abstraction
10. Composition and Inheritance
11. Traits
12. Packages, Imports, and Exports
13. Pattern Matching
14. Working with Lists
15. Working with Other Collections
16. Mutable Objects
17. Scala’s Hierarchy
18. Type Parameterization
19. Enums
20. Abstract Members
21. Givens
22. Extension Methods
23. Typeclasses
24. Collections in Depth
25. Assertions and Tests

Glossary

Bibliography

Index

Foreword

Watching the birth of a new programming language is a funny thing. To anyone who uses a programming language—whether you are dabbling with programming for the first time or are a grizzled career software engineer—programming languages just seem to exist. Like a hammer or axe, a programming language is a tool, already there, that enables us to perform our trade. We seldom think of how that tool came to be, what the process was to design it. We might have opinions about its design, but beyond that, we usually just put up with it and push forward.

Being there as a programming language is created, however, brings a totally different perspective. The possibilities for what could be seem endless. Yet at the same time, the programming language must satisfy what seems like an infinite list of constraints. It’s an odd tension.

New programming languages are created for many reasons: a personal desire to scratch a niggling itch, a profound academic insight, technical debt, or the benefit of hindsight of other compiler architectures—even politics. Scala 3 is a combination of some of these.

Whatever combination it may be, it all started when Martin Odersky disappeared one day, emerging a few days later to announce in a research group meeting that he had begun experimenting with bringing the DOT calculus to life by writing a new compiler from scratch.[1] Here we were, a group of PhD students and postdocs who had until recently been a big part of the development and maintenance of Scala 2. At the time, Scala was reaching what felt like unfathomable heights of success, especially for an esoteric and academic programming language from a school with a funny-sounding name in Switzerland. Scala had recently taken off among startups in the Bay Area, and Typesafe, later named Lightbend, had recently been formed to support, maintain, and manage releases of Scala 2. So why all of a sudden a new compiler and possibly a new and different programming language? Most were skeptical. Martin was undeterred.

Months passed. Like clockwork, at twelve noon every day, the entire lab would gather in the hallway connecting all of our offices. After a fair number of us and Martin had assembled, we’d venture together to one of EPFL’s many cafeterias, grab lunch, and later, an after-lunch coffee. Each day during this ritual, ideas for this new compiler were a recurrent theme. Discussions would ping pong about, anywhere from focusing on something “150%” compatible with Scala 2 (to avoid a Python 2 versus Python 3 debacle), to creating new language with full-spectrum dependent types.

One by one, the skeptics in the research group seemed to become sold by some appealing aspect of Scala 3, be it streamlining the implementation of the typechecker, the brand new compiler architecture, or the powerful additions to the type system. Over time, much of the community also came around to the idea of Scala 3 being a marked improvement over Scala 2. Different people had different reasons for this. For some, it was the improvements in readability via the decision to make braces and parentheses around conditions in conditionals optional. For others, it was improvements to the type system; for example, match types for improved type-level programming. The list went on.

Rather than blindly steaming ahead on the design of Scala 3 based on hunch alone, I can confidently assert that Scala 3 is the result of much learning from design decisions of the past, and years of conversation with the EPFL research group and the Scala community. And there was no way but to start from a clean slate, and build on clean foundations. With this from-scratch approach, what emerged is, at its core, a new programming language.

Scala 3 is a new programming language. Sure, it might be compatible with Scala 2, and it might sound like the third major release of an already-existing programming language. But don’t let that fool you. Scala 3 represents a substantial streamlining of many of the experimental ideas pioneered in Scala 2.

Perhaps what is most uniquely “Scala” about Scala 3 is what happened to implicits. Scala, since its inception, has been used by clever programmers to achieve functionality that few thought was even possible given Scala’s feature set, let alone something Scala was designed for. The feature previously-known as implicits is perhaps the most well-known feature of Scala that has been exploited to bend Scala 2 in unexpected ways. Example use cases of implicits include retroactively adding a method to a class, without extending and re-compiling that class. Or, given a type signature used in some context, automatically selecting the right implementation for that context. This is just the tip of the iceberg—we even wrote a research paper attempting to catalog the multitude of ways in which developers have used implicits.[2]

This is like providing a user with knobs and levers and leaving it to them to build a smoothly-functioning piece of machinery, like the mechanical calculator shown in Figure 2.0. But often, what comes out instead is something that looks more like a kinetic sculpture of Theo Jansen, such as the one shown in Figure 2.0, than anything with an obvious use.[3] Simply put, you give a programming community something as basic as a lever and a knob, and the intrepid will seek to find creative ways to use it. It’s human nature. But perhaps here, Scala 2’s mistake was the idea to provide something as general-purpose as something like knobs and levers in the first place.

What was intended.

What we got.

The point here is that in Scala 2, there was this endless array of possibilities for what implicits could be used for, which necessitated an entire research paper, and which the community generally couldn’t agree upon how to sanely use. No language feature should have such a murky purpose for being. And yet, here they were—implicits were seen by many as the unique and powerful feature of Scala that essentially no other language had, and by many others as a mysterious and often frustrating mechanism that would invasively rewrite code that you had written to be something else.

You may have heard the oft-repeated mantra that in many ways, Scala 3 represents a simplification of the Scala that came before it. The story of implicits is an excellent example. Cognizant of the back flips programmers were doing with implicits in an attempt to realize broader programming patterns like typeclass derivation, Martin, with the help of many others, came to the conclusion that we should not focus on implicits as a mechanism for people to use in the most general case. Rather, we should focus on what programmers want to do with implicits, and make that easier and more performant. This is where the mantra, “Scala 3 focuses on intent rather than mechanism,” comes from.

With Scala 3, rather than focus on the generality of implicits as a mechanism, the decision was made to focus on specific use-cases that developers had in mind when choosing to use implicits in the first place, and to make these patterns more direct to use. Examples include passing context or configuration information to methods implicitly, without the programmer having to explicitly thread through repetitive arguments; retroactively adding methods to classes; and converting between types, like Ints and Doubles during arithmetic. Now, Scala 3 makes these use cases available to programmers without needing to understand some “deep” intuition about how the Scala compiler resolves implicits. You can instead just focus on tasks like “add a method foo to class Bar without having to recompile it.” No PhD required. Just replace the previous notion of “implicit” with other more direct keywords that correspond to specific use cases, such as given and using. See Chapters 21 and 22 for more on this.

This story of “prioritizing intent over mechanism” doesn’t stop at the revamping of implicits. Rather, the philosophy goes on to touch upon most every aspect of the language. Examples include additions and streamlining of many aspects of Scala’s type system, from union types, to enums, to match types—or even the cleanups to Scala’s syntax: optional braces to improve readability, and more readable “quiet” syntax for ifs, elses, and whiles, resulting in conditionals that look much more English-like.

Don’t take my word for it. Whether you’re a newcomer to Scala, or an experienced Scala developer, I hope you find many of the new design ideas that have entered Scala 3 to be as refreshing and straightforward as I do!

Heather Miller

Lausanne, Switzerland

June 1, 2021

Footnotes:

[1] DOT, or dependent object types, calculus is a series of formalizations attempting to characterize the essence of Scala’s type system.

[2] Krikava, et. al., Scala implicits are everywhere. [Kri19]

[3] For a more dynamic depiction of the kinetic sculptures of Theo Jansen, which are collectively entitled Strandbeest, see https://www.youtube.com/watch?v=LewVEF2B_pM.

Acknowledgments

Many people have contributed to this book and to the material it covers. We are grateful to all of them.

Scala itself has been a collective effort of many people. The design and the implementation of version 1.0 was helped by Philippe Altherr, Vincent Cremet, Gilles Dubochet, Burak Emir, Stéphane Micheloud, Nikolay Mihaylov, Michel Schinz, Erik Stenman, and Matthias Zenger. Phil Bagwell, Antonio Cunei, Iulian Dragos, Gilles Dubochet, Miguel Garcia, Philipp Haller, Sean McDirmid, Ingo Maier, Donna Malayeri, Adriaan Moors, Hubert Plociniczak, Paul Phillips, Aleksandar Prokopec, Tiark Rompf, Lukas Rytz, and Geoffrey Washburn joined in the effort to develop the second and current version of the language and tools.

Gilad Bracha, Nathan Bronson, Caoyuan, Aemon Cannon, Craig Chambers, Chris Conrad, Erik Ernst, Matthias Felleisen, Mark Harrah, Shriram Krishnamurti, Gary Leavens, David MacIver, Sebastian Maneth, Rickard Nilsson, Erik Meijer, Lalit Pant, David Pollak, Jon Pretty, Klaus Ostermann, Jorge Ortiz, Didier Rémy, Miles Sabin, Vijay Saraswat, Daniel Spiewak, James Strachan, Don Syme, Erik Torreborre, Mads Torgersen, Philip Wadler, Jamie Webb, John Williams, Kevin Wright, and Jason Zaugg have shaped the design of the language by graciously sharing their ideas with us in lively and inspiring discussions, by contributing important pieces of code to the open source effort, as well as through comments on previous versions of this document. The contributors to the Scala mailing list have also given very useful feedback that helped us improve the language and its tools.

George Berger has worked tremendously to make the build process and the web presence for the book work smoothly. As a result this project has been delightfully free of technical snafus.

Many people gave us valuable feedback on early versions of the text. Thanks goes to Eric Armstrong, George Berger, Alex Blewitt, Gilad Bracha, William Cook, Bruce Eckel, Stéphane Micheloud, Todd Millstein, David Pollak, Frank Sommers, Philip Wadler, and Matthias Zenger. Thanks also to the Silicon Valley Patterns group for their very helpful review: Dave Astels, Tracy Bialik, John Brewer, Andrew Chase, Bradford Cross, Raoul Duke, John P. Eurich, Steven Ganz, Phil Goodwin, Ralph Jocham, Yan-Fa Li, Tao Ma, Jeffery Miller, Suresh Pai, Russ Rufer, Dave W. Smith, Scott Turnquest, Walter Vannini, Darlene Wallach, and Jonathan Andrew Wolter. And we’d like to thank Dewayne Johnson and Kim Leedy for their help with the cover art, and Frank Sommers for his work on the index.

We would also like to thank those who submitted comments and errata after the first two editions were published, including Felix Siegrist, Lothar Meyer-Lerbs, Diethard Michaelis, Roshan Dawrani, Donn Stephan, William Uther, Francisco Reverbel, Jim Balter, and Freek de Bruijn, Ambrose Laing, Sekhar Prabhala, Levon Saldamli, Andrew Bursavich, Hjalmar Peters, Thomas Fehr, Alain O’Dea, Rob Dickens, Tim Taylor, Christian Sternagel, Michel Parisien, Joel Neely, Brian McKeon, Thomas Fehr, Joseph Elliott, Gabriel da Silva Ribeiro, Thomas Fehr, Pablo Ripolles, Douglas Gaylor, Kevin Squire, Harry-Anton Talvik, Christopher Simpkins, Martin Witmann-Funk, Jim Balter, Peter Foster, Craig Bordelon, Heinz-Peter Gumm, Peter Chapin, Kevin Wright, Ananthan Srinivasan, Omar Kilani, Donn Stephan, Guenther Waffler.

Lex would like to thank Aaron Abrams, Jason Adams, Henry and Emily Crutcher, Joey Gibson, Gunnar Hillert, Matthew Link, Toby Reyelts, Jason Snape, John and Melinda Weathers, and all of the Atlanta Scala Enthusiasts for many helpful discussions about the language design, its mathematical underpinnings, and how to present Scala to working engineers.

A special thanks to Dave Briccetti and Adriaan Moors for reviewing the third edition, and to Marconi Lanna for not only reviewing, but providing motivation for the third edition by giving a talk entitled “What’s new since Programming in Scala.”

Bill would like to thank Gary Cornell, Greg Doench, Andy Hunt, Mike Leonard, Tyler Ortman, Bill Pollock, Dave Thomas, and Adam Wright for providing insight and advice on book publishing. Bill would also like to thank Dick Wall for collaborating on our Stairway to Scala course, which is in great part based on this book. Our many years of experience teaching Stairway to Scala helped make this book better. Lastly, Bill would like to thank Darlene Gruendl and Samantha Woolf for their help in getting the third edition completed.

Finally, we would like to thank Julien Richard-Foy for his work to bring the fourth edition of this book up to date with Scala 2.13, in particular the new collections redesign.

Introduction

This book is a tutorial for the Scala programming language, written by people directly involved in the development of Scala. Our goal is that by reading this book, you can learn everything you need to be a productive Scala programmer. All examples in this book compile with Scala version 3.0.0.

The main target audience for this book is programmers who want to learn to program in Scala. If you want to do your next software project in Scala, then this is the book for you. In addition, the book should be interesting to programmers wishing to expand their horizons by learning new concepts. If you’re a Java programmer, for example, reading this book will expose you to many concepts from functional programming as well as advanced object-oriented ideas. We believe learning about Scala, and the ideas behind it, can help you become a better programmer in general.

General programming knowledge is assumed. While Scala is a fine first programming language, this is not the book to use to learn programming.

On the other hand, no specific knowledge of programming languages is required. Even though most people use Scala on the Java platform, this book does not presume you know anything about Java. However, we expect many readers to be familiar with Java, and so we sometimes compare Scala to Java to help such readers understand the differences.

How to use this book

Because the main purpose of this book is to serve as a tutorial, the recommended way to read this book is in chapter order, from front to back. We have tried hard to introduce one topic at a time, and explain new topics only in terms of topics we’ve already introduced. Thus, if you skip to the back to get an early peek at something, you may find it explained in terms of concepts you don’t quite understand. To the extent you read the chapters in order, we think you’ll find it quite straightforward to gain competency in Scala, one step at a time.

If you see a term you do not know, be sure to check the glossary and the index. Many readers will skim parts of the book, and that is just fine. The glossary and index can help you backtrack whenever you skim over something too quickly.

After you have read the book once, it should also serve as a language reference. There is a formal specification of the Scala language, but the language specification tries for precision at the expense of readability. Although this book doesn’t cover every detail of Scala, it is quite comprehensive and should serve as an approachable language reference as you become more adept at programming in Scala.

## Bibliography:

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[Mor68] Morrison, Donald R. PATRICIA—Practical Algorithm To Retrieve Information Coded in Alphanumeric. J. ACM, 15(4):514–534, 1968. ISSN 0004-5411. http://doi.acm.org/10.1145/321479.321481. [ DOI ]

[DeR75] DeRemer, Frank and Hans Kron. Programming-in-the large versus programming-in-the-small. In Proceedings of the international conference on Reliable software, pages 114–121. ACM, New York, NY, USA, 1975. http://doi.acm.org/10.1145/800027.808431. [ DOI ]

[SPJ02] Simon Peyton Jones, et.al. Haskell 98 Language and Libraries, Revised Report. Technical report, http://www.haskell.org/onlinereport, 2002.

[Vaz07] Vaziri, Mandana, Frank Tip, Stephen Fink, and Julian Dolby. Declarative Object Identity Using Relation Types. In Proc. ECOOP 2007, pages 54–78. 2007.

[Mey91] Meyers, Scott. Effective C++. Addison-Wesley, 1991.

[Rum04] Rumbaugh, James, Ivar Jacobson, and Grady Booch. The Unified Modeling Language Reference Manual (2nd Edition). Addison-Wesley, 2004.

[Goe06] Goetz, Brian, Tim Peierls, Joshua Bloch, Joseph Bowbeer, David Homes, and Doug Lea. Java Concurrency in Practice. Addison Wesley, 2006.

[Mey00] Meyer, Bertrand. Object-Oriented Software Construction. Prentice Hall, 2000.

[Eck98] Eckel, Bruce. Thinking in Java. Prentice Hall, 1998.

[Eva03] Evans, Eric. Domain-Driven Design: Tackling Complexity in the Heart of Software. Addison-Wesley Professional, 2003.

[Aho86] Aho, Alfred V., Ravi Sethi, and Jeffrey D. Ullman. Compilers: Principles, Techniques, and Tools. Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA, 1986. ISBN 0-201-10088-6.

[Abe96] Abelson, Harold and Gerald Jay Sussman. Structure and Interpretation of Computer Programs. The MIT Press, second edition, 1996.

[Ode03] Odersky, Martin, Vincent Cremet, Christine Röckl, and Matthias Zenger. A Nominal Theory of Objects with Dependent Types. In Proc. ECOOP’03, Springer LNCS, pages 201–225. July 2003.

[Ode11] Odersky, Martin. The Scala Language Specification, Version 2.9. EPFL, May 2011. Available on the web at http://www.scala-lang.org/docu/manuals.html (accessed April 20, 2014).

[Ode05] Odersky, Martin and Matthias Zenger. Scalable Component Abstractions. In Proceedings of OOPSLA, pages 41–58. October 2005.

[Emi07] Emir, Burak, Martin Odersky, and John Williams. Matching Objects With Patterns. In Proc. ECOOP, Springer LNCS, pages 273–295. July 2007.

[Ste99] Steele, Jr., Guy L. Growing a Language. Higher-Order and Symbolic Computation, 12:221–223, 1999. Transcript of a talk given at OOPSLA 1998.

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[Jav] The Java Tutorials: Creating a GUI with JFC/Swing. Available on the web at http://java.sun.com/docs/books/tutorial/uiswing.

[Lan66] Landin, Peter J. The Next 700 Programming Languages. Communications of the ACM, 9(3):157–166, 1966.

[Fow04] Fowler, Martin. Inversion of Control Containers and the Dependency Injection pattern. January 2004. Available on the web at http://martinfowler.com/articles/injection.html (accesssed August 6, 2008).

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[Kay03] Kay, Alan C. An email to Stefan Ram on the meaning of the term “object-oriented programming”, July 2003. The email is published on the web at http://www.purl.org/stefan_ram/pub/doc_kay_oop_en (accesssed June 6, 2008).

[Dij70] Dijkstra, Edsger W. Notes on Structured Programming., April 1970. Circulated privately. Available at http://www.cs.utexas.edu /users/EWD/ewd02xx/EWD249.PDF as EWD249 (accessed June 6, 2008).

[Ste15] Steindorfer, Michael J and Jurgen J Vinju. Optimizing hash-array mapped tries for fast and lean immutable JVM collections. In ACM SIGPLAN Notices, volume 50, pages 783–800. ACM, 2015.

[Kri19] Krikava, Filip, Heather Miller, and Jan Vitek. Scala implicits are everywhere: a large-scale study of the use of Scala implicits in the wild. In Proceedings of the ACM on Programming Languages, volume 3. ACM, 2019. https://doi.org/10.1145/3360589. [ DOI ]

Martin Odersky is the creator of the Scala language. He is a professor at EPFL in Lausanne, Switzerland, where since 2001 he has led the team that developed the Scala language libraries, and compiler. He is a founder of Lightbend, Inc. Lex Spoon worked on Scala for two years at EPFL and is now a software engineer at Square, Inc. Bill Venners is president of Artima, Inc. He is a community representative on the Scala Center Advisory Board, and the designer of ScalaTest. Frank Sommers is president of Autospaces, Inc.

Martin works on programming languages and systems, more specifically on the topic of how to combine object-oriented and functional programming. Since 2001 he has concentrated on designing, implementing, and refining Scala. Previously, he has influenced the development of Java as a co-designer of Java generics and as the original author of the current javac reference compiler. He is a fellow of the ACM.

Lex Spoon is a software engineer at Square, Inc., which provides easy to use business software and mobile payments. In addition to Scala, he has helped develop a wide variety of programming languages, including the dynamic language Smalltalk, the scientific language X10, and the logic language CodeQL.

Bill Venners is president of Artima, Inc., publisher of the Artima website (www.artima.com) and provider of Scala consulting, training, books, and tools. He is author of the book, Inside the Java Virtual Machine, a programmer-oriented survey of the Java platform’s architecture and internals. His popular columns in JavaWorld magazine covered Java internals, object-oriented design, and Jini. Bill is a community representative on the Scala Center advisory board, and is the lead developer and designer of the ScalaTest test framework and the Scalactic library for functional, object-oriented programming.

Frank Sommers is founder and president of Autospaces, Inc, a company providing workflow automation solutions to the financial services industry. Frank has been an active Scala user for over twelve years, and has worked with the language daily ever since.

## Scala Glossary from the Book:

Glossary

algebraic data type A type defined by providing several alternatives, each of which comes with its own constructor. It usually comes with a way to decompose the type through pattern matching. The concept is found in specification languages and functional programming languages. Algebraic data types can be emulated in Scala with case classes.

alternative A branch of a match expression. It has the form “case pattern => expression.” Another name for alternative is case.

annotation An annotation appears in source code and is attached to some part of the syntax. Annotations are computer processable, so you can use them to effectively add an extension to Scala.

anonymous class An anonymous class is a synthetic subclass generated by the Scala compiler from a new expression in which the class or trait name is followed by curly braces. The curly braces contains the body of the anonymous subclass, which may be empty. However, if the name following new refers to a trait or class that contains abstract members, these must be made concrete inside the curly braces that define the body of the anonymous subclass.

anonymous function Another name for function literal.

apply You can apply a method, function, or closure to arguments, which means you invoke it on those arguments.

argument When a function is invoked, an argument is passed for each parameter of that function. The parameter is the variable that refers to the argument. The argument is the object passed at invocation time. In addition, applications can take (command line) arguments that show up in the Array[String] passed to main methods of singleton objects.

assign You can assign an object to a variable. Afterwards, the variable will refer to the object.

auxiliary constructor Extra constructors defined inside the curly braces of the class definition, which look like method definitions named this, but with no result type.

block One or more expressions and declarations surrounded by curly braces. When the block evaluates, all of its expressions and declarations are processed in order, and then the block returns the value of the last expression as its own value. Blocks are commonly used as the bodies of functions, for expressions, while loops, and any other place where you want to group a number of statements together. More formally, a block is an encapsulation construct for which you can only see side effects and a result value. The curly braces in which you define a class or object do not, therefore, form a block, because fields and methods (which are defined inside those curly braces) are visible from the outside. Such curly braces form a template.

bound variable A bound variable of an expression is a variable that’s both used and defined inside the expression. For instance, in the function literal expression (x: Int) => (x, y), both variables x and y are used, but only x is bound, because it is defined in the expression as an Int and the sole argument to the function described by the expression.

by-name parameter A parameter that is marked with a => in front of the parameter type, e.g., (x: => Int). The argument corresponding to a by-name parameter is evaluated not before the method is invoked, but each time the parameter is referenced by name inside the method. If a parameter is not by-name, it is by-value.

by-value parameter A parameter that is not marked with a => in front of the parameter type, e.g., (x: Int). The argument corresponding to a by-value parameter is evaluated before the method is invoked. By-value parameters contrast with by-name parameters.

class Defined with the class keyword, a class may either be abstract or concrete, and may be parameterized with types and values when instantiated. In “new ArrayString“, the class being instantiated is Array and the type of the value that results is Array[String]. A class that takes type parameters is called a type constructor. A type can be said to have a class as well, as in: the class of type Array[String] is Array.

closure A function object that captures free variables, and is said to be “closed” over the variables visible at the time it is created.

companion class A class that shares the same name with a singleton object defined in the same source file. The class is the singleton object’s companion class.

companion object A singleton object that shares the same name with a class defined in the same source file. Companion objects and classes have access to each other’s private members. In addition, any implicit conversions defined in the companion object will be in scope anywhere the class is used.

contravariant A contravariant annotation can be applied to a type parameter of a class or trait by putting a minus sign (-) before the type parameter. The class or trait then subtypes contravariantly with—in the opposite direction as—the type annotated parameter. For example, Function1 is contravariant in its first type parameter, and so Function1[Any, Any] is a subtype of Function1[String, Any].

covariant A covariant annotation can be applied to a type parameter of a class or trait by putting a plus sign (+) before the type parameter. The class or trait then subtypes covariantly with—in the same direction as—the type annotated parameter. For example, List is covariant in its type parameter, so List[String] is a subtype of List[Any].

currying A way to write functions with multiple parameter lists. For instance def f(x: Int)(y: Int) is a curried function with two parameter lists. A curried function is applied by passing several arguments lists, as in: f(3)(4). However, it is also possible to write a partial application of a curried function, such as f(3).

declare You can declare an abstract field, method, or type, which gives an entity a name but not an implementation. The key difference between declarations and definitions is that definitions establish an implementation for the named entity, declarations do not.

define To define something in a Scala program is to give it a name and an implementation. You can define classes, traits, singleton objects, fields, methods, local functions, local variables, etc. Because definitions always involve some kind of implementation, abstract members are declared not defined.

direct subclass A class is a direct subclass of its direct superclass.

direct superclass The class from which a class or trait is immediately derived, the nearest class above it in its inheritance hierarchy. If a class Parent is mentioned in a class Child’s optional extends clause, then Parent is the direct superclass of Child. If a trait is mentioned in Child’s extends clause, the trait’s direct superclass is the Child’s direct superclass. If Child has no extends clause, then AnyRef is the direct superclass of Child. If a class’s direct superclass takes type parameters, for example class Child extends Parent[String], the direct superclass of Child is still Parent, not Parent[String]. On the other hand, Parent[String] would be the direct supertype of Child. See supertype for more discussion of the distinction between class and type.

equality When used without qualification, equality is the relation between values expressed by ==’. See also reference equality.

expression Any bit of Scala code that yields a result. You can also say that an expression evaluates to a result or results in a value.

filter An if followed by a boolean expression in a for expression. In for(i <- 1 to 10; if i % 2 == 0), the filter is “if i % 2 == 0”. The value to the right of the if is the filter expression.

filter expression A filter expression is the boolean expression following an if in a for expression. In for(i <- 1 to 10; if i % 2 == 0), the filter expression is “i % 2 == 0”.

first-class function Scala supports first-class functions, which means you can express functions in function literal syntax, i.e., (x: Int) => x + 1, and that functions can be represented by objects, which are called function values.

for comprehension Another name for for expression.

free variable A free variable of an expression is a variable that’s used inside the expression but not defined inside the expression. For instance, in the function literal expression (x: Int) => (x, y), both variables x and y are used, but only y is a free variable, because it is not defined inside the expression.

function A function can be invoked with a list of arguments to produce a result. A function has a parameter list, a body, and a result type. Functions that are members of a class, trait, or singleton object are called methods. Functions defined inside other functions are called local functions. Functions with the result type of Unit are called procedures. Anonymous functions in source code are called function literals. At run time, function literals are instantiated into objects called function values.

function literal A function with no name in Scala source code, specified with function literal syntax. For example, (x: Int, y: Int) => x + y.

function value A function object that can be invoked just like any other function. A function value’s class extends one of the FunctionN traits (e.g., Function0, Function1) from package scala, and is usually expressed in source code via function literal syntax. A function value is “invoked” when its apply method is called. A function value that captures free variables is a closure.

functional style The functional style of programming emphasizes functions and evaluation results and deemphasizes the order in which operations occur. The style is characterized by passing function values into looping methods, immutable data, methods with no side effects. It is the dominant paradigm of languages such as Haskell and Erlang, and contrasts with the imperative style.

generator A generator defines a named val and assigns to it a series of values in a for expression. For example, in for(i <- 1 to 10), the generator is “i <- 1 to 10”. The value to the right of the <- is the generator expression.

generator expression A generator expression generates a series of values in a for expression. For example, in for(i <- 1 to 10), the generator expression is “1 to 10”.

generic class A class that takes type parameters. For example, because scala.List takes a type parameter, scala.List is a generic class.

generic trait A trait that takes type parameters. For example, because trait scala.collection.Set takes a type parameter, it is a generic trait.

helper function A function whose purpose is to provide a service to one or more other functions nearby. Helper functions are often implemented as local functions.

helper method A helper function that’s a member of a class. Helper methods are often private.

immutable An object is immutable if its value cannot be changed after it is created in any way visible to clients. Objects may or may not be immutable.

imperative style The imperative style of programming emphasizes careful sequencing of operations so that their effects happen in the right order. The style is characterized by iteration with loops, mutating data in place, and methods with side effects. It is the dominant paradigm of languages such as C, C++, C# and Java, and contrasts with the functional style.

initialize When a variable is defined in Scala source code, you must initialize it with an object.

instance An instance, or class instance, is an object, a concept that exists only at run time.

instantiate To instantiate a class is to make a new object from the class, an action that happens only at run time.

invariant Invariant is used in two ways. It can mean a property that always holds true when a data structure is well-formed. For example, it is an invariant of a sorted binary tree that each node is ordered before its right subnode, if it has a right subnode. Invariant is also sometimes used as a synonym for nonvariant: “class Array is invariant in its type parameter.”

invoke You can invoke a method, function, or closure on arguments, meaning its body will be executed with the specified arguments.

JVM The JVM is the Java Virtual Machine, or runtime, that hosts a running Scala program.

literal 1, “One”, and (x: Int) => x + 1 are examples of literals. A literal is a shorthand way to describe an object, where the shorthand exactly mirrors the structure of the created object.

local function A local function is a def defined inside a block. To contrast, a def defined as a member of a class, trait, or singleton object is called a method.

local variable A local variable is a val or var defined inside a block. Although similar to local variables, parameters to functions are not referred to as local variables, but simply as parameters or “variables” without the “local.”

member A member is any named element of the template of a class, trait, or singleton object. A member may be accessed with the name of its owner, a dot, and its simple name. For example, top-level fields and methods defined in a class are members of that class. A trait defined inside a class is a member of its enclosing class. A type defined with the type keyword in a class is a member of that class. A class is a member of the package in which is it defined. By contrast, a local variable or local function is not a member of its surrounding block.

meta-programming Meta-programming software is software whose input is itself software. Compilers are meta-programs, as are tools like scaladoc. Meta-programming software is required in order to do anything with an annotation.

method A method is a function that is a member of some class, trait, or singleton object.

mixin Mixin is what a trait is called when it is being used in a mixin composition. In other words, in “trait Hat,” Hat is just a trait, but in “new Cat extends AnyRef with Hat,” Hat can be called a mixin. When used as a verb, “mix in” is two words. For example, you can mix traits into classes or other traits.

mixin composition The process of mixing traits into classes or other traits. Mixin composition differs from traditional multiple inheritance in that the type of the super reference is not known at the point the trait is defined, but rather is determined anew each time the trait is mixed into a class or other trait.

modifier A keyword that qualifies a class, trait, field, or method definition in some way. For example, the private modifier indicates that a class, trait, field, or method being defined is private.

multiple definitions The same expression can be assigned in multiple definitions if you use the syntax val v1, v2, v3 = exp.

nonvariant A type parameter of a class or trait is by default nonvariant. The class or trait then does not subtype when that parameter changes. For example, because class Array is nonvariant in its type parameter, Array[String] is neither a subtype nor a supertype of Array[Any].

operation In Scala, every operation is a method call. Methods may be invoked in operator notation, such as b + 2, and when in that notation, + is an operator.

parameter Functions may take zero to many parameters. Each parameter has a name and a type. The distinction between parameters and arguments is that arguments refer to the actual objects passed when a function is invoked. Parameters are the variables that refer to those passed arguments.

parameterless function A function that takes no parameters, which is defined without any empty parentheses. Invocations of parameterless functions may not supply parentheses. This supports the uniform access principle, which enables the def to be changed into a val without requiring a change to client code.

parameterless method A parameterless method is a parameterless function that is a member of a class, trait, or singleton object.

parametric field A field defined as a class parameter.

partially applied function A function that’s used in an expression and that misses some of its arguments. For instance, if function f has type Int => Int => Int, then f and f(1) are partially applied functions.

path-dependent type A type like swiss.cow.Food. The swiss.cow part is a path that forms a reference to an object. The meaning of the type is sensitive to the path you use to access it. The types swiss.cow.Food and fish.Food, for example, are different types.

pattern In a match expression alternative, a pattern follows each case keyword and precedes either a pattern guard or the => symbol.

pattern guard In a match expression alternative, a pattern guard can follow a pattern. For example, in “case x if x % 2 == 0 => x + 1”, the pattern guard is “if x % 2 == 0”. A case with a pattern guard will only be selected if the pattern matches and the pattern guard yields true.

predicate A predicate is a function with a Boolean result type.

primary constructor The main constructor of a class, which invokes a superclass constructor, if necessary, initializes fields to passed values, and executes any top-level code defined between the curly braces of the class. Fields are initialized only for value parameters not passed to the superclass constructor, except for any that are not used in the body of the class and can therefore be optimized away.

procedure A procedure is a function with result type of Unit, which is therefore executed solely for its side effects.

reassignable A variable may or may not be reassignable. A var is reassignable while a val is not.

receiver The receiver of a method call is the variable, expression, or object on which the method is invoked.

recursive A function is recursive if it calls itself. If the only place the function calls itself is the last expression of the function, then the function is tail recursive.

reference A reference is the Java abstraction of a pointer, which uniquely identifies an object that resides on the JVM’s heap. Reference type variables hold references to objects, because reference types (instances of AnyRef) are implemented as Java objects that reside on the JVM’s heap. Value type variables, by contrast, may sometimes hold a reference (to a boxed wrapper type) and sometimes not (when the object is being represented as a primitive value). Speaking generally, a Scala variable refers to an object. The term “refers” is more abstract than “holds a reference.” If a variable of type scala.Int is currently represented as a primitive Java int value, then that variable still refers to the Int object, but no reference is involved.

reference equality Reference equality means that two references identify the very same Java object. Reference equality can be determined, for reference types only, by calling eq in AnyRef. (In Java programs, reference equality can be determined using == on Java reference types.)

reference type A reference type is a subclass of AnyRef. Instances of reference types always reside on the JVM’s heap at run time.

referential transparency A property of functions that are independent of temporal context and have no side effects. For a particular input, an invocation of a referentially transparent function can be replaced by its result without changing the program semantics.

refers A variable in a running Scala program always refers to some object. Even if that variable is assigned to null, it conceptually refers to the Null object. At runtime, an object may be implemented by a Java object or a value of a primitive type, but Scala allows programmers to think at a higher level of abstraction about their code as they imagine it running. See also reference.

refinement type A type formed by supplying a base type a number of members inside curly braces. The members in the curly braces refine the types that are present in the base type. For example, the type of “animal that eats grass” is Animal { type SuitableFood = Grass }.

result An expression in a Scala program yields a result. The result of every expression in Scala is an object.

result type A method’s result type is the type of the value that results from calling the method. (In Java, this concept is called the return type.)

return A function in a Scala program returns a value. You can call this value the result of the function. You can also say the function results in the value. The result of every function in Scala is an object.

runtime The Java Virtual Machine, or JVM, that hosts a running Scala program. Runtime encompasses both the virtual machine, as defined by the Java Virtual Machine Specification, and the runtime libraries of the Java API and the standard Scala API. The phrase at run time (with a space between run and time) means when the program is running, and contrasts with compile time.

runtime type The type of an object at run time. To contrast, a static type is the type of an expression at compile time. Most runtime types are simply bare classes with no type parameters. For example, the runtime type of “Hi” is String, and the runtime type of (x: Int) => x + 1 is Function1. Runtime types can be tested with isInstanceOf.

script A file containing top level definitions and statements, which can be run directly with scala without explicitly compiling. A script must end in an expression, not a definition.

selector The value being matched on in a match expression. For example, in “s match { case _ => }”, the selector is s.

self type A self type of a trait is the assumed type of this, the receiver, to be used within the trait. Any concrete class that mixes in the trait must ensure that its type conforms to the trait’s self type. The most common use of self types is for dividing a large class into several traits as described in Chapter 32.

semi-structured data XML data is semi-structured. It is more structured than a flat binary file or text file, but it does not have the full structure of a programming language’s data structures.

serialization You can serialize an object into a byte stream which can then be saved to files or transmitted over the network. You can later deserialize the byte stream, even on different computer, and obtain an object that is the same as the original serialized object.

shadow A new declaration of a local variable shadows one of the same name in an enclosing scope.

signature Signature is short for type signature.

singleton object An object defined with the object keyword. Each singleton object has one and only one instance. A singleton object that shares its name with a class, and is defined in the same source file as that class, is that class’s companion object. The class is its companion class. A singleton object that doesn’t have a companion class is a standalone object.

standalone object A singleton object that has no companion class.

statement An expression, definition, or import, i.e., things that can go into a template or a block in Scala source code.

static type See type.

subclass A class is a subclass of all of its superclasses and supertraits.

subtrait A trait is a subtrait of all of its supertraits.

subtype The Scala compiler will allow any of a type’s subtypes to be used as a substitute wherever that type is required. For classes and traits that take no type parameters, the subtype relationship mirrors the subclass relationship. For example, if class Cat is a subclass of abstract class Animal, and neither takes type parameters, type Cat is a subtype of type Animal. Likewise, if trait Apple is a subtrait of trait Fruit, and neither takes type parameters, type Apple is a subtype of type Fruit. For classes and traits that take type parameters, however, variance comes into play. For example, because abstract class List is declared to be covariant in its lone type parameter (i.e., List is declared List[+A]), List[Cat] is a subtype of List[Animal], and List[Apple] a subtype of List[Fruit]. These subtype relationships exist even though the class of each of these types is List. By contrast, because Set is not declared to be covariant in its type parameter (i.e., Set is declared Set[A] with no plus sign), Set[Cat] is not a subtype of Set[Animal]. A subtype should correctly implement the contracts of its supertypes, so that the Liskov Substitution Principle applies, but the compiler only verifies this property at the level of type checking.

superclass A class’s superclasses include its direct superclass, its direct superclass’s direct superclass, and so on, all the way up to Any.

supertrait A class’s or trait’s supertraits, if any, include all traits directly mixed into the class or trait or any of its superclasses, plus any supertraits of those traits.

supertype A type is a supertype of all of its subtypes.

synthetic class A synthetic class is generated automatically by the compiler rather than being written by hand by the programmer.

tail recursive A function is tail recursive if the only place the function calls itself is the last operation of the function.

target typing Target typing is a form of type inference that takes into account the type that’s expected. In nums.filter((x) => x > 0), for example, the Scala compiler infers type of x to be the element type of nums, because the filter method invokes the function on each element of nums.

template A template is the body of a class, trait, or singleton object definition. It defines the type signature, behavior, and initial state of the class, trait, or object.

trait A trait, which is defined with the trait keyword, is like an abstract class that cannot take any value parameters and can be “mixed into” classes or other traits via the process known as mixin composition. When a trait is being mixed into a class or trait, it is called a mixin. A trait may be parameterized with one or more types. When parameterized with types, the trait constructs a type. For example, Set is a trait that takes a single type parameter, whereas Set[Int] is a type. Also, Set is said to be “the trait of” type Set[Int].

type Every variable and expression in a Scala program has a type that is known at compile time. A type restricts the possible values to which a variable can refer, or an expression can produce, at run time. A variable or expression’s type can also be referred to as a static type if necessary to differentiate it from an object’s runtime type. In other words, “type” by itself means static type. Type is distinct from class because a class that takes type parameters can construct many types. For example, List is a class, but not a type. List[T] is a type with a free type parameter. List[Int] and List[String] are also types (called ground types because they have no free type parameters). A type can have a “class” or “trait.” For example, the class of type List[Int] is List. The trait of type Set[String] is Set.

type constraint Some annotations are type constraints, meaning that they add additional limits, or constraints, on what values the type includes. For example, @positive could be a type constraint on the type Int, limiting the type of 32-bit integers down to those that are positive. Type constraints are not checked by the standard Scala compiler, but must instead be checked by an extra tool or by a compiler plugin.

type constructor A class or trait that takes type parameters.

type parameter A parameter to a generic class or generic method that must be filled in by a type. For example, class List is defined as “class List[T] { …”, and method identity, a member of object Predef, is defined as “def identityT = x”. The T in both cases is a type parameter.

type signature A method’s type signature comprises its name, the number, order, and types of its parameters, if any, and its result type. The type signature of a class, trait, or singleton object comprises its name, the type signatures of all of its members and constructors, and its declared inheritance and mixin relations.

uniform access principle The uniform access principle states that variables and parameterless functions should be accessed using the same syntax. Scala supports this principle by not allowing parentheses to be placed at call sites of parameterless functions. As a result, a parameterless function definition can be changed to a val, or vice versa, without affecting client code.

unreachable At the Scala level, objects can become unreachable, at which point the memory they occupy may be reclaimed by the runtime. Unreachable does not necessarily mean unreferenced. Reference types (instances of AnyRef) are implemented as objects that reside on the JVM’s heap. When an instance of a reference type becomes unreachable, it indeed becomes unreferenced, and is available for garbage collection. Value types (instances of AnyVal) are implemented as both primitive type values and as instances of Java wrapper types (such as java.lang.Integer), which reside on the heap. Value type instances can be boxed (converted from a primitive value to a wrapper object) and unboxed (converted from a wrapper object to a primitive value) throughout the lifetime of the variables that refer to them. If a value type instance currently represented as a wrapper object on the JVM’s heap becomes unreachable, it indeed becomes unreferenced, and is available for garbage collection. But if a value type currently represented as a primitive value becomes unreachable, then it does not become unreferenced, because it does not exist as an object on the JVM’s heap at that point in time. The runtime may reclaim memory occupied by unreachable objects, but if an Int, for example, is implemented at run time by a primitive Java int that occupies some memory in the stack frame of an executing method, then the memory for that object is “reclaimed” when the stack frame is popped as the method completes. Memory for reference types, such as Strings, may be reclaimed by the JVM’s garbage collector after they become unreachable.

unreferenced See unreachable.

value The result of any computation or expression in Scala is a value, and in Scala, every value is an object. The term value essentially means the image of an object in memory (on the JVM’s heap or stack).

value type A value type is any subclass of AnyVal, such as Int, Double, or Unit. This term has meaning at the level of Scala source code. At runtime, instances of value types that correspond to Java primitive types may be implemented in terms of primitive type values or instances of wrapper types, such as java.lang.Integer. Over the lifetime of a value type instance, the runtime may transform it back and forth between primitive and wrapper types (i.e., to box and unbox it).

variable A named entity that refers to an object. A variable is either a val or a var. Both vals and vars must be initialized when defined, but only vars can be later reassigned to refer to a different object.

variance A type parameter of a class or trait can be marked with a variance annotation, either covariant (+) or contravariant (-). Such variance annotations indicate how subtyping works for a generic class or trait. For example, the generic class List is covariant in its type parameter, and thus List[String] is a subtype of List[Any]. By default, i.e., absent a + or – annotation, type parameters are nonvariant.

wildcard type A wildcard type includes references to type variables that are unknown. For example, Array[_] is a wildcard type. It is an array where the element type is completely unknown.

yield An expression can yield a result. The yield keyword designates the result of a for expression.

Categories

## Programming Scala, 3rd Edition – B095XLMMGC – ISBN-13: 978-1492077893

See: Programming Scala, 3rd Edition, by Dean Wampler, Publisher ‏ : ‎ O’Reilly Media; 3rd edition (May 26, 2021)

Get up to speed on Scala–the JVM, JavaScript, and natively compiled language that offers all the benefits of functional programming, a modern object model, and an advanced type system. Packed with code examples, this comprehensive book shows you how to be productive with the language and ecosystem right away. You’ll learn why Scala is ideal for building today’s highly scalable, data-centric applications while maximizing developer productivity.

While Java remains popular and Kotlin has become popular, Scala hasn’t been sitting still. This third edition covers the new features in Scala 3 with updates throughout the book. Programming Scala is ideal for beginning to advanced developers who want a complete understanding of Scala’s design philosophy and features with a thoroughly practical focus.

• Program faster with Scala’s succinct and flexible syntax
• Dive into basic and advanced functional programming techniques
• Build killer big data and distributed apps using Scala’s functional combinators and tools like Spark and Akka
• Create concise solutions to challenging design problems with the sophisticated type system, mixin composition with traits, pattern matching, and more

## Resources

Errata Page: http://oreilly.com/catalog/0636920365686/errata

Supplemental Content: https://github.com/deanwampler/programming-scala-book-code-examples

Dean Wampler is an expert in data engineering for scalable streaming data systems and applications of machine learning and artificial intelligence. He is a principal software engineer at Domino Data Lab. Previously he worked at Anyscale and Lightbend, where he worked on scalable machine learning with Ray and distributed streaming data systems with Apache Spark, Apache Kafka, Kubernetes, and other tools. Besides Programming Scala, Dean is also the author of What Is Ray?, Distributed Computing Made Simple, Fast Data Architectures for Streaming Applications, and Functional Programming for Java Developers, as well as the coauthor of Programming Hive, all from O’Reilly. He is a contributor to several open source projects and a frequent conference speaker, and he co-organizes several conferences around the world and several user groups in Chicago. Dean has a PhD in physics from the University of Washington. Find Dean on Twitter: @deanwampler.

Categories

## Scala Cookbook: Recipes for Object-Oriented and Functional Programming, 2nd Edition – ISBN-13: 978-1492051541, 2021

See: Scala Cookbook: Recipes for Object-Oriented and Functional Programming, 2nd Edition, Publisher ‏ : ‎ O’Reilly Media; 2nd edition (August 31, 2021)

Save time and trouble building object-oriented, functional, and concurrent applications with Scala 3. The latest edition of this comprehensive cookbook is packed with more than 250 ready-to-use recipes and 700 code examples to help you solve the most common problems when working with Scala and its popular libraries.

Whether you’re working on web, big data, or distributed applications, this cookbook provides recipes based on real-world scenarios for experienced Scala developers and for programmers just learning to use this JVM language. Author Alvin Alexander includes practical solutions from his experience using Scala for highly scalable applications that support concurrency and distribution.

Recipes cover:

• Strings, numbers, and control structures
• Classes, methods, objects, traits, packaging, and imports
• Functional programming in a variety of situations
• Building Scala applications with sbt
• Collections covering Scala’s wealth of classes and methods
• Actors and concurrency
• List, array, map, set, and more
• Files, processes, and command-line tasks
• Web services and interacting with Java
• Databases and persistence, data types and idioms

## Resources

Errata Page: http://oreilly.com/catalog/0636920252702/errata

Categories

## Functional Programming, Simplified: Scala Edition – B076J7CJKY ISBN-13: 978-1979788786, 2017

See: Functional Programming, Simplified: Scala Edition, by Alvin Alexander, Publisher ‏ : ‎ CreateSpace Independent Publishing Platform (December 7, 2017)

If you’ve had trouble trying to learn Functional Programming (FP), you’re not alone. In this book, Alvin Alexander — author of the Scala Cookbook and former teacher of Java and Object-Oriented Programming (OOP) classes — writes about his own problems in trying to understand FP, and how he finally conquered it.

What he originally learned is that experienced FP developers are driven by two goals: to use only immutable values, and write only pure functions. What he later learned is that they have these goals as the result of another larger goal: they want all of their code to look and work just like algebra.

While that sounds simple, it turns out that these goals require them to use many advanced Scala features — which they often use all at the same time. As a result, their code can look completely foreign to novice FP developers. As Mr. Alexander writes, “When you first see their code it’s easy to ask, ‘Why would anyone write code like this?’”

Mr. Alexander answers that “Why?” question by explaining the benefits of writing pure functional code. Once you understand those benefits — your motivation for learning FP — he shares five rules for programming in the book:

• All fields must be immutable (‘val’ fields).
• All functions must be pure functions.
• Null values are not allowed.
• Whenever you use an ‘if’ you must also use an ‘else’.
• You won’t create OOP classes that encapsulate data and behavior; instead you’ll design data structures using Scala ‘case’ classes, and write pure functions that operate on those data structures.

In the book you’ll see how those five, simple rules naturally lead you to write pure, functional code that reads like algebra. He also shares one more Golden Rule for learning:

Lessons in the book include:

• How and why to write only pure functions
• Why pure function signatures are much more important than OOP method signatures
• Why recursion is a natural tool for functional programming, and how to write recursive algorithms
• Because the Scala ‘for’ expression is so important to FP, dozens of pages explain the details of how it works
• In the end you’ll see that monads aren’t that difficult because they’re a natural extension of the Five Rules
• The book finishes with lessons on FP data modeling, and two main approaches for organizing your pure functions

As Mr. Alexander writes, “In this book I take the time to explain all of the concepts that are used to write FP code in Scala. As I learned from my own experience, once you understand the Five Rules and the small concepts, you can understand Scala/FP.”

Please note that because of the limits on how large a printed book can be, the paperback version does not include all of the chapters that are in the Kindle eBook. The following lessons are not in the paperback version:

• The ScalaCheck lessons
• The Type Classes lessons
• The appendices

Because those lessons didn’ fit in the print version, they have been made freely available online.

(Alvin Alexander (alvinalexander.com) wrote the popular Scala Cookbook for O’Reilly, and also self-published two other books, How I Sold My Business: A Personal Diary, and A Survival Guide for New Consultants.)

Alvin took the circuitous route to software development. He managed to get a degree in Aerospace Engineering from Texas A&M University, while all he was really trying to do was play baseball. Once he became a practicing engineer, he realized he liked software and programming more than engineering. So in approximate order he taught himself Fortran, C, Unix, network administration, sed, awk, Lisp, Perl, Java, JavaScript, Python, Ruby, JRuby, PHP, and Scala. During this process he started a software consulting firm, grew it to fifteen people, sold it, and moved to Alaska. After returning to the “Lower 48,” he self-published two books (“How I Sold My Business: A Personal Diary”, and “Zen and the Art of Consulting”), and then wrote the “Scala Cookbook” for O’Reilly. He also created alvinalexander.com, which receives millions of page views every year.

Categories

## Hello, Scala – B079WK8P1X ISBN-13: 978-1720790020, 2018

See: Hello, Scala: Learn Scala fast with small, easy lessons, by Alvin Alexander, Publisher ‏ : ‎ CreateSpace Independent Publishing Platform (September 8, 2018)

Update: In July, 2021, the price of this Kindle book has been drastically reduced so you can learn Scala as inexpensively as possible.

In his latest book, Alvin Alexander, author of the Scala Cookbook and Functional Programming, Simplified, brings you a swift introduction to the Scala programming language. In a little over 200 fast-paced pages, Mr. Alexander demonstrates that Scala is a beautiful, modern, expressive programming language. The book is broken down into 56 short chapters to help you easily find what you need. Lessons include:

• An introduction to Scala’s two types of variables, val and var
• Scala control structures, including powerful for expressions and match expressions
• An overview of Scala collections classes and methods
• Object-oriented programming (OOP), including features of Scala classes and methods
• Functional programming (FP), including pure functions, using functions as variables, case classes, match expressions, functional error handling, and more
• Modular programming with traits
• How to build Scala projects with SBT
• How to write TDD and BDD unit tests with ScalaTest
• Programming concurrency with Akka actors and Scala futures

To help get you started with Scala as fast as possible, the book shares many source code examples, including several open source Github projects that you can run immediately. All examples in the book have been written with Scala 2.12, and represent 2018’s “best practices” for Scala programming.

Alvin took the circuitous route to software development. He managed to get a degree in Aerospace Engineering from Texas A&M University, while all he was really trying to do was play baseball. Once he became a practicing engineer, he realized he liked software and programming more than engineering. So in approximate order he taught himself Fortran, C, Unix, network administration, sed, awk, Lisp, Perl, Java, JavaScript, Python, Ruby, JRuby, PHP, and Scala. During this process he started a software consulting firm, grew it to fifteen people, sold it, and moved to Alaska. After returning to the “Lower 48,” he self-published two books (“How I Sold My Business: A Personal Diary”, and “Zen and the Art of Consulting”), and then wrote the “Scala Cookbook” for O’Reilly. He also created alvinalexander.com, which receives millions of page views every year.

Preface

Prelude: A Taste of Scala

Getting started

The Scala Programming Language

Hello, World

Hello, World (Version 2)

The Scala REPL

Two Types of Variables

The Type is Optional

A Few Built-In Types

Command-Line I/O

Control Structures

The if/then/else Construct

for Loops

for Expressions

match Expressions

try/catch/finally Expressions

Classes

Auxiliary Class Constructors

Supplying Default Values for Constructor Parameters

A First Look at Methods

Enumerations (and a Complete Pizza Class)

Traits and Abstract Classes

Using Traits as Interfaces

Using Traits Like Abstract Classes

Abstract Classes

Collections Classes

ArrayBuffer Class

List Class

Vector Class

Map Class

Set Class

Anonymous Functions

Common Methods on Sequences

Common Map Methods

A Few Miscellaneous Items

Tuples

Scala and Swing

An OOP Example

A Scala + JavaFX Example

SBT and ScalaTest

The Scala Build Tool (SBT)

Using ScalaTest with SBT

Writing BDD-style tests with ScalaTest and SBT

Functional Programming

Pure Functions

Passing Functions Around

No Null Values

Companion Objects

Case Classes

Case Objects

Functional Error Handling

Concurrency

Akka Actors

Akka Actor Examples

Futures

Summary

## Preface:

1. Preface

Have you ever fallen in love with a programming language? I still remember when I first saw the book, The C Programming Language, and how I fell in love with its simple syntax and the ability to interact with a computer at a low level. In 1996 I loved Java because it made OOP simple. A few years later I found Ruby and loved its elegance.

Then in 2011 I was aimlessly wandering around Alaska and stumbled across the book, Programming in Scala, and I was stunned by its remarkable marriage of Ruby and Java:

The syntax was as elegant and concise as Ruby

It feels dynamic, but it’s statically typed

It compiles to class files that run on the JVM

You can use the thousands of Java libraries in existence with your Scala code

In the first edition of the book, Beginning Scala, David Pollak states that Scala will change the way you think about programming, and that’s a good thing. Learning Scala has not only been a joy, but it’s led me on a journey to appreciate concepts like modular programming, immutability, referential transparency, and functional programming, and most importantly, how those ideas help to dramatically reduce bugs in my code.

Is Scala DICEE?

DICEE is an acronym that was coined by Guy Kawasaki, who became famous as a developer evangelist for the original Apple Macintosh team. He says that great products are DICEE, meaning Deep, Indulgent, Complete, Elegant, and Emotive:

Deep: The product doesn’t run out of features and functionality after a few weeks of use. Its creators have anticipated what you’ll need once you come up to speed. As your demands get more sophisticated, you won’t need a different product.

Indulgent: A great product is a luxury. It makes you feel special when you buy it (and use it).

Complete: A great product is more than a physical thing. Documentation counts. Customer service counts. Tech support counts.

Elegant: A great product has an elegant user interface. Things work the way you’d think they would. A great product doesn’t fight you, it enhances you.

Emotive: A great product incites you to action. It is so deep, indulgent, complete, and elegant that it compels you to tell other people about it. You’re bringing the good news to help others, not yourself.

Two years after discovering Scala — way back in 2013 — I came to the conclusion that it meets the definition of DICEE, and I think it’s just as true today:

Scala is deep: After all these years I continue to learn new techniques to write better code.

Scala is indulgent: Just like Ruby, I feel special and fortunate to use a language that’s so well thought out.

Scala is complete: The documentation is excellent, terrific frameworks exist, and the support groups are terrific.

Scala is elegant: Once you grasp its main concepts you’ll fall in love with how it works just like you expect it to.

Scala is emotive: Everyone who works with it wants to tell you how special it is. Myself, I had never written a programming book in my life, but by 2012 I was eagerly mailing people at O’Reilly to tell them how much I wanted to write the Scala Cookbook.

As I write this book many years later I hope to share not just the nuts and bolts of the Scala language, but also its elegance and the joy of using it.

Alvin Alexander

alvinalexander.com

Categories

## B076J7CJKY

See: Functional Programming, Simplified: (Scala Edition)

Categories

## B00HUEG8KK

See: Functional Programming Patterns in Scala and Clojure: Write Lean Programs for the JVM 1st Edition, Kindle Edition

Categories

## 1617290653

See: Functional Programming in Scala 1st Edition

Categories

## B075879N76

See: Functional Programming: A PragPub Anthology: Exploring Clojure, Elixir, Haskell, Scala, and Swift 1st Edition

Categories

## Actor model – Actor-based concurrency

” (WP)

The actor model in computer science is a mathematical model of concurrent computation that treats actor as the universal primitive of concurrent computation. In response to a message it receives, an actor can: make local decisions, create more actors, send more messages, and determine how to respond to the next message received. Actors may modify their own private state, but can only affect each other indirectly through messaging (removing the need for lock-based synchronization).

The actor model originated in 1973.[1] It has been used both as a framework for a theoretical understanding of computation and as the theoretical basis for several practical implementations of concurrent systems. The relationship of the model to other work is discussed in actor model and process calculi.

## History

Main article: History of the Actor model

According to Carl Hewitt, unlike previous models of computation, the actor model was inspired by physics, including general relativity and quantum mechanics.[citation needed] It was also influenced by the programming languages LispSimula, early versions of Smalltalkcapability-based systems, and packet switching. Its development was “motivated by the prospect of highly parallel computing machines consisting of dozens, hundreds, or even thousands of independent microprocessors, each with its own local memory and communications processor, communicating via a high-performance communications network.”[2] Since that time, the advent of massive concurrency through multi-core and manycore computer architectures has revived interest in the actor model.

Following Hewitt, Bishop, and Steiger’s 1973 publication, Irene Greif developed an operational semantics for the actor model as part of her doctoral research.[3] Two years later, Henry Baker and Hewitt published a set of axiomatic laws for actor systems.[4][5] Other major milestones include William Clinger’s 1981 dissertation introducing a denotational semantics based on power domains[2] and Gul Agha‘s 1985 dissertation which further developed a transition-based semantic model complementary to Clinger’s.[6] This resulted in the full development of actor model theory.

Major software implementation work was done by Russ Atkinson, Giuseppe Attardi, Henry Baker, Gerry Barber, Peter Bishop, Peter de Jong, Ken Kahn, Henry Lieberman, Carl Manning, Tom Reinhardt, Richard Steiger and Dan Theriault in the Message Passing Semantics Group at Massachusetts Institute of Technology (MIT). Research groups led by Chuck Seitz at California Institute of Technology (Caltech) and Bill Dally at MIT constructed computer architectures that further developed the message passing in the model. See Actor model implementation.

Research on the actor model has been carried out at California Institute of TechnologyKyoto University Tokoro Laboratory, Microelectronics and Computer Technology Corporation (MCC), MIT Artificial Intelligence LaboratorySRIStanford UniversityUniversity of Illinois at Urbana–Champaign,[7] Pierre and Marie Curie University (University of Paris 6), University of PisaUniversity of Tokyo Yonezawa Laboratory, Centrum Wiskunde & Informatica (CWI) and elsewhere.

## Fundamental concepts

The actor model adopts the philosophy that everything is an actor. This is similar to the everything is an object philosophy used by some object-oriented programming languages.

An actor is a computational entity that, in response to a message it receives, can concurrently:

• send a finite number of messages to other actors;
• create a finite number of new actors;
• designate the behavior to be used for the next message it receives.

There is no assumed sequence to the above actions and they could be carried out in parallel.

Decoupling the sender from communications sent was a fundamental advance of the actor model enabling asynchronous communication and control structures as patterns of passing messages.[8]

Recipients of messages are identified by address, sometimes called “mailing address”. Thus an actor can only communicate with actors whose addresses it has. It can obtain those from a message it receives, or if the address is for an actor it has itself created.

The actor model is characterized by inherent concurrency of computation within and among actors, dynamic creation of actors, inclusion of actor addresses in messages, and interaction only through direct asynchronous message passing with no restriction on message arrival order.

## Formal systems

Over the years, several different formal systems have been developed which permit reasoning about systems in the actor model. These include:

There are also formalisms that are not fully faithful to the actor model in that they do not formalize the guaranteed delivery of messages including the following (See Attempts to relate actor semantics to algebra and linear logic):

## Applications

The actor model can be used as a framework for modeling, understanding, and reasoning about a wide range of concurrent systems. For example:

• Electronic mail (email) can be modeled as an actor system. Accounts are modeled as actors and email addresses as actor addresses.
• Web services can be modeled with Simple Object Access Protocol (SOAP) endpoints modeled as actor addresses.
• Objects with locks (e.g., as in Java and C#) can be modeled as a serializer, provided that their implementations are such that messages can continually arrive (perhaps by being stored in an internal queue). A serializer is an important kind of actor defined by the property that it is continually available to the arrival of new messages; every message sent to a serializer is guaranteed to arrive.
• Testing and Test Control Notation (TTCN), both TTCN-2 and TTCN-3, follows actor model rather closely. In TTCN actor is a test component: either parallel test component (PTC) or main test component (MTC). Test components can send and receive messages to and from remote partners (peer test components or test system interface), the latter being identified by its address. Each test component has a behaviour tree bound to it; test components run in parallel and can be dynamically created by parent test components. Built-in language constructs allow the definition of actions to be taken when an expected message is received from the internal message queue, like sending a message to another peer entity or creating new test components.

## Message-passing semantics

The actor model is about the semantics of message passing.

### Unbounded nondeterminism controversy

Arguably, the first concurrent programs were interrupt handlers. During the course of its normal operation a computer needed to be able to receive information from outside (characters from a keyboard, packets from a network, etc). So when the information arrived the execution of the computer was interrupted and special code (called an interrupt handler) was called to put the information in a data buffer where it could be subsequently retrieved.

In the early 1960s, interrupts began to be used to simulate the concurrent execution of several programs on one processor.[15] Having concurrency with shared memory gave rise to the problem of concurrency control. Originally, this problem was conceived as being one of mutual exclusion on a single computer. Edsger Dijkstra developed semaphores and later, between 1971 and 1973,[16] Tony Hoare[17] and Per Brinch Hansen[18] developed monitors to solve the mutual exclusion problem. However, neither of these solutions provided a programming language construct that encapsulated access to shared resources. This encapsulation was later accomplished by the serializer construct ([Hewitt and Atkinson 1977, 1979] and [Atkinson 1980]).

The first models of computation (e.g.Turing machines, Post productions, the lambda calculusetc.) were based on mathematics and made use of a global state to represent a computational step (later generalized in [McCarthy and Hayes 1969] and [Dijkstra 1976] see Event orderings versus global state). Each computational step was from one global state of the computation to the next global state. The global state approach was continued in automata theory for finite-state machines and push down stack machines, including their nondeterministic versions. Such nondeterministic automata have the property of bounded nondeterminism; that is, if a machine always halts when started in its initial state, then there is a bound on the number of states in which it halts.

Edsger Dijkstra further developed the nondeterministic global state approach. Dijkstra’s model gave rise to a controversy concerning unbounded nondeterminism (also called unbounded indeterminacy), a property of concurrency by which the amount of delay in servicing a request can become unbounded as a result of arbitration of contention for shared resources while still guaranteeing that the request will eventually be serviced. Hewitt argued that the actor model should provide the guarantee of service. In Dijkstra’s model, although there could be an unbounded amount of time between the execution of sequential instructions on a computer, a (parallel) program that started out in a well defined state could terminate in only a bounded number of states [Dijkstra 1976]. Consequently, his model could not provide the guarantee of service. Dijkstra argued that it was impossible to implement unbounded nondeterminism.

Hewitt argued otherwise: there is no bound that can be placed on how long it takes a computational circuit called an arbiter to settle (see metastability (electronics)).[19] Arbiters are used in computers to deal with the circumstance that computer clocks operate asynchronously with respect to input from outside, e.g., keyboard input, disk access, network input, etc. So it could take an unbounded time for a message sent to a computer to be received and in the meantime the computer could traverse an unbounded number of states.

The actor model features unbounded nondeterminism which was captured in a mathematical model by Will Clinger using domain theory.[2] In the actor model, there is no global state.[dubious – discuss]

### Direct communication and asynchrony

Messages in the actor model are not necessarily buffered. This was a sharp break with previous approaches to models of concurrent computation. The lack of buffering caused a great deal of misunderstanding at the time of the development of the actor model and is still a controversial issue. Some researchers argued that the messages are buffered in the “ether” or the “environment”. Also, messages in the actor model are simply sent (like packets in IP); there is no requirement for a synchronous handshake with the recipient.

### Actor creation plus addresses in messages means variable topology

A natural development of the actor model was to allow addresses in messages. Influenced by packet switched networks [1961 and 1964], Hewitt proposed the development of a new model of concurrent computation in which communications would not have any required fields at all: they could be empty. Of course, if the sender of a communication desired a recipient to have access to addresses which the recipient did not already have, the address would have to be sent in the communication.

For example, an actor might need to send a message to a recipient actor from which it later expects to receive a response, but the response will actually be handled by a third actor component that has been configured to receive and handle the response (for example, a different actor implementing the observer pattern). The original actor could accomplish this by sending a communication that includes the message it wishes to send, along with the address of the third actor that will handle the response. This third actor that will handle the response is called the resumption (sometimes also called a continuation or stack frame). When the recipient actor is ready to send a response, it sends the response message to the resumption actor address that was included in the original communication.

So, the ability of actors to create new actors with which they can exchange communications, along with the ability to include the addresses of other actors in messages, gives actors the ability to create and participate in arbitrarily variable topological relationships with one another, much as the objects in Simula and other object-oriented languages may also be relationally composed into variable topologies of message-exchanging objects.

### Inherently concurrent

As opposed to the previous approach based on composing sequential processes, the actor model was developed as an inherently concurrent model. In the actor model sequentiality was a special case that derived from concurrent computation as explained in actor model theory.

### No requirement on order of message arrival

Hewitt argued against adding the requirement that messages must arrive in the order in which they are sent to the actor. If output message ordering is desired, then it can be modeled by a queue actor that provides this functionality. Such a queue actor would queue the messages that arrived so that they could be retrieved in FIFO order. So if an actor X sent a message M1 to an actor Y, and later X sent another message M2 to Y, there is no requirement that M1 arrives at Y before M2.

In this respect the actor model mirrors packet switching systems which do not guarantee that packets must be received in the order sent. Not providing the order of delivery guarantee allows packet switching to buffer packets, use multiple paths to send packets, resend damaged packets, and to provide other optimizations.

For example, actors are allowed to pipeline the processing of messages. What this means is that in the course of processing a message M1, an actor can designate the behavior to be used to process the next message, and then in fact begin processing another message M2 before it has finished processing M1. Just because an actor is allowed to pipeline the processing of messages does not mean that it must pipeline the processing. Whether a message is pipelined is an engineering tradeoff. How would an external observer know whether the processing of a message by an actor has been pipelined? There is no ambiguity in the definition of an actor created by the possibility of pipelining. Of course, it is possible to perform the pipeline optimization incorrectly in some implementations, in which case unexpected behavior may occur.

### Locality

Another important characteristic of the actor model is locality.

Locality means that in processing a message, an actor can send messages only to addresses that it receives in the message, addresses that it already had before it received the message, and addresses for actors that it creates while processing the message. (But see Synthesizing addresses of actors.)

Also locality means that there is no simultaneous change in multiple locations. In this way it differs from some other models of concurrency, e.g., the Petri net model in which tokens are simultaneously removed from multiple locations and placed in other locations.

### Composing actor systems

The idea of composing actor systems into larger ones is an important aspect of modularity that was developed in Gul Agha’s doctoral dissertation,[6] developed later by Gul Agha, Ian Mason, Scott Smith, and Carolyn Talcott.[9]

### Behaviors

A key innovation was the introduction of behavior specified as a mathematical function to express what an actor does when it processes a message, including specifying a new behavior to process the next message that arrives. Behaviors provided a mechanism to mathematically model the sharing in concurrency.

Behaviors also freed the actor model from implementation details, e.g., the Smalltalk-72 token stream interpreter. However, it is critical to understand that the efficient implementation of systems described by the actor model require extensive optimization. See Actor model implementation for details.

### Modeling other concurrency systems

Other concurrency systems (e.g.process calculi) can be modeled in the actor model using a two-phase commit protocol.[20]

### Computational Representation Theorem

There is a Computational Representation Theorem in the actor model for systems which are closed in the sense that they do not receive communications from outside. The mathematical denotation denoted by a closed system {\displaystyle {\mathtt {S}}} is constructed from an initial behavior ⊥S and a behavior-approximating function progressionS. These obtain increasingly better approximations and construct a denotation (meaning) for {\displaystyle {\mathtt {S}}} as follows [Hewitt 2008; Clinger 1981]:{\displaystyle \mathbf {Denote} _{\mathtt {S}}\equiv \lim _{i\to \infty }\mathbf {progression} _{{\mathtt {S}}^{i}}(\bot _{\mathtt {S}})}

In this way, S` can be mathematically characterized in terms of all its possible behaviors (including those involving unbounded nondeterminism). Although {\displaystyle \mathbf {Denote} _{\mathtt {S}}} is not an implementation of {\displaystyle {\mathtt {S}}}, it can be used to prove a generalization of the Church-Turing-Rosser-Kleene thesis [Kleene 1943]:

A consequence of the above theorem is that a finite actor can nondeterministically respond with an uncountable[clarify] number of different outputs.

### Relationship to logic programming

One of the key motivations for the development of the actor model was to understand and deal with the control structure issues that arose in development of the Planner programming language.[citation needed] Once the actor model was initially defined, an important challenge was to understand the power of the model relative to Robert Kowalski‘s thesis that “computation can be subsumed by deduction”. Hewitt argued that Kowalski’s thesis turned out to be false for the concurrent computation in the actor model (see Indeterminacy in concurrent computation).

Nevertheless, attempts were made to extend logic programming to concurrent computation. However, Hewitt and Agha [1991] claimed that the resulting systems were not deductive in the following sense: computational steps of the concurrent logic programming systems do not follow deductively from previous steps (see Indeterminacy in concurrent computation). Recently, logic programming has been integrated into the actor model in a way that maintains logical semantics.[19]

### Migration

Migration in the actor model is the ability of actors to change locations. E.g., in his dissertation, Aki Yonezawa modeled a post office that customer actors could enter, change locations within while operating, and exit. An actor that can migrate can be modeled by having a location actor that changes when the actor migrates. However the faithfulness of this modeling is controversial and the subject of research.[citation needed]

### Security

The security of actors can be protected in the following ways:

A delicate point in the actor model is the ability to synthesize the address of an actor. In some cases security can be used to prevent the synthesis of addresses (see Security). However, if an actor address is simply a bit string then clearly it can be synthesized although it may be difficult or even infeasible to guess the address of an actor if the bit strings are long enough. SOAP uses a URL for the address of an endpoint where an actor can be reached. Since a URL is a character string, it can clearly be synthesized although encryption can make it virtually impossible to guess.

Synthesizing the addresses of actors is usually modeled using mapping. The idea is to use an actor system to perform the mapping to the actual actor addresses. For example, on a computer the memory structure of the computer can be modeled as an actor system that does the mapping. In the case of SOAP addresses, it’s modeling the DNS and the rest of the URL mapping.

### Contrast with other models of message-passing concurrency

Robin Milner‘s initial published work on concurrency[21] was also notable in that it was not based on composing sequential processes. His work differed from the actor model because it was based on a fixed number of processes of fixed topology communicating numbers and strings using synchronous communication. The original communicating sequential processes (CSP) model[22] published by Tony Hoare differed from the actor model because it was based on the parallel composition of a fixed number of sequential processes connected in a fixed topology, and communicating using synchronous message-passing based on process names (see Actor model and process calculi history). Later versions of CSP abandoned communication based on process names in favor of anonymous communication via channels, an approach also used in Milner’s work on the calculus of communicating systems and the π-calculus.

These early models by Milner and Hoare both had the property of bounded nondeterminism. Modern, theoretical CSP ([Hoare 1985] and [Roscoe 2005]) explicitly provides unbounded nondeterminism.

Petri nets and their extensions (e.g., coloured Petri nets) are like actors in that they are based on asynchronous message passing and unbounded nondeterminism, while they are like early CSP in that they define fixed topologies of elementary processing steps (transitions) and message repositories (places).

## Influence

The actor model has been influential on both theory development and practical software development.

### Theory

The actor model has influenced the development of the π-calculus and subsequent process calculi. In his Turing lecture, Robin Milner wrote:[23]

Now, the pure lambda-calculus is built with just two kinds of thing: terms and variables. Can we achieve the same economy for a process calculus? Carl Hewitt, with his actors model, responded to this challenge long ago; he declared that a value, an operator on values, and a process should all be the same kind of thing: an actor.

This goal impressed me, because it implies the homogeneity and completeness of expression … But it was long before I could see how to attain the goal in terms of an algebraic calculus…

So, in the spirit of Hewitt, our first step is to demand that all things denoted by terms or accessed by names—values, registers, operators, processes, objects—are all of the same kind of thing; they should all be processes.

### Practice

The actor model has had extensive influence on commercial practice. For example, Twitter has used actors for scalability.[24] Also, Microsoft has used the actor model in the development of its Asynchronous Agents Library.[25] There are many other actor libraries listed in the actor libraries and frameworks section below.

According to Hewitt [2006], the actor model addresses issues in computer and communications architecture, concurrent programming languages, and Web services including the following:

• Scalability: the challenge of scaling up concurrency both locally and nonlocally.
• Transparency: bridging the chasm between local and nonlocal concurrency. Transparency is currently a controversial issue. Some researchers[who?] have advocated a strict separation between local concurrency using concurrent programming languages (e.g., Java and C#) from nonlocal concurrency using SOAP for Web services. Strict separation produces a lack of transparency that causes problems when it is desirable/necessary to change between local and nonlocal access to Web services (see Distributed computing).
• Inconsistency: inconsistency is the norm because all very large knowledge systems about human information system interactions are inconsistent. This inconsistency extends to the documentation and specifications of very large systems (e.g., Microsoft Windows software, etc.), which are internally inconsistent.

Many of the ideas introduced in the actor model are now also finding application in multi-agent systems for these same reasons [Hewitt 2006b 2007b]. The key difference is that agent systems (in most definitions) impose extra constraints upon the actors, typically requiring that they make use of commitments and goals.

## Programming with actors

A number of different programming languages employ the actor model or some variation of it. These languages include:

### Actor libraries and frameworks

Actor libraries or frameworks have also been implemented to permit actor-style programming in languages that don’t have actors built-in. Some of these frameworks are:

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” (WP)

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## B00A32NZEI

See: Programming Concurrency on the JVM: Mastering Synchronization, STM, and Actors 1st Edition

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